Obesity Prevention: The Role of Brain and Society on Individual Behavior

Obesity Prevention The Role of Brain and Society on Individual Behavior

Obesity Prevention The Role of Brain and Society on Individual Behavior Editorial Team Laurette Dubé (Lead Editor)

Professor, James McGill Chair, Consumer Psychology and Marketing, Desautels Faculty of Management; McGill University, Montreal, Canada

Antoine Bechara

Department of Psychology, University of Southern California, Los Angeles, CA, USA

Alain Dagher

Montreal Neurological Institute, McGill University, Montreal, Canada

Adam Drewnowski

Epidemiology, School of Public Health and Community Medicine; Director, Center for Public Health Nutrition, University of Washington, Washington, DC, USA

Jordan Lebel

John Molson School of Business, Concordia University, Montreal, Canada

Philip James

London School of Hygiene and Tropical Medicine, President International Association for the Study of Obesity (IASO)

Rickey Y. Yada

Advanced Foods and Materials Network, Networks of Centers of Excellence, University of Guelph, Ontario, Canada

Marie-Claire Laflamme-Sanders (Editorial Coordinator) McGill World Platform for Health and Economic Convergence, McGill University, Montreal, Canada

Amsterdam • Boston • Heidelberg • London • New York • Oxford • Paris San Diego • San Francisco • Singapore • Sydney • Tokyo Academic Press is an imprint of Elsevier

Academic Press is an imprint of Elsevier 32 Jamestown Road, London NW1 7BY, UK 30 Corporate Drive, Suite 400, Burlington, MA 01803, USA 525 B Street, Suite 1900, San Diego, CA 92101-4495, USA First edition 2010 Copyright © 2010 Elsevier Inc. All rights reserved Except chapter 5 which is in the public domain No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the publisher Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone (44) (0) 1865 843830; fax (44) (0) 1865 853333; email: [email protected]. Alternatively, visit the Science and Technology Books website at www.elsevierdirect.com/rights for further information Notice No responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material herein. Because of rapid advances in the medical sciences, in particular, independent verification of diagnoses and drug dosages should be made British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress ISBN: 978-0-12-374387-9 For information on all Academic Press publications visit our web site at www.elsevierdirect.com Typeset by MPS Limited, a Macmillan Company, Chennai, India. www.macmillansolutions.com Printed and bound in United States of America 10  11  12  13  14  15  10  9  8  7  6  5  4  3  2  1

Preface Over the years, the focus of obesity prevention and treatment has shifted from genetic and biological factors to various behavioral change interventions, to, more recently, re-designing physical, social and economic environments. As the increasing prevalence of obesity and its related chronic diseases indicate, these methods have clearly not sufficed. This two-part handbook provides the scientific foundations of a bolder “Brain-toSociety” approach to obesity prevention. In this approach, biology, the individual and the environment cannot as independent factors account for individual and collective lifestyle choices. Rather, to stop the progression of the obesity pandemic, we need an integrative approach rooted in an in-depth understanding of the pathways of the motives, antecedents, actions and consequences within each level of influence on obesity, and at their interface. We must develop a scientific basis that can guide the changes in policy and action that are needed to realign biology and the environment with what the individual and society can sustain.

The approach taken in the handbook is crossdisciplinary, multi-level and multi-sector. It aims at catalyzing the development of a body of scientific knowledge that can better conceive, articulate, measure and model the interfaces of health, biological, behavioral, physical, social and economic factors that drive individual, behavioral and societal behaviors. This will help public health scientists, professionals and organizations to act more effectively as leaders in galvanizing action and policy change to shift the dynamics underlying obesity and chronic disease prevention in a sustainable manner. It will also inspire scientists, professionals and organizations in food, agriculture, business, economics, politics, media, education, engineering and other non-health domains to develop novel ways to achieve their respective objectives while simultaneously contributing to individual and societal health. Ultimately, this new frontier of science will transcend boundaries across disciplines, bridge theories and data on gene, brain, behavior and environment, and provide the basis of a bolder approach to obesity prevention and treatment.

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List of Contributors Jennie Brand-Miller, School of Molecular and Microbial Biosciences, University of Sydney, Sydney, NSW, Australia

Alfonso Abizaid, Institute of Neuroscience, Carleton University, Ottawa, Canada Johan Alsiö, Department of Neuroscience, Uppsala University, Uppsala, Sweden

Eleanor Bryant, Centre for Psychology Studies, University of Bradford, Bradford, UK

Ross Andersen, Department of Kinesiology and Physical Education, McGill University, Montreal, Canada

Benjamin Caballero, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

Hymie Anisman, Institute of Neuroscience, Carleton University, Ottawa, Canada

Katherine G. Carman, Department of Economics, Tilburg University, Tilburg, The Netherlands

Narendra K. Arora, International Clinical Epidemiology Network, New Delhi, India

Kenneth D. Carr, Departments of Psychiatry and Pharmacology, New York University School of Medicine, New York, NY, USA

Livia S. A. Augustin, Unilever Health Institute, Unilever Research and Development, Vlaardingen, The Netherlands

Jean-Philippe Chaput, Department of Social and Preventive Medicine, Laval University, Quebec City, Canada

Marica Bakovic, Department of Human Health and Nutritional Sciences, University of Guelph, Guelph, Canada

Xiaoye Chen, Marketing Department, Desautels Faculty of Management, McGill University, Montreal, Canada

Ruth Bell, Department of Epidemiology and Public Health, University College London, London, UK

Laura Chiavaroli, Clinical Nutrition & Risk Factor Modification Center, St Michael’s Hospital, Toronto, Canada; Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Canada

Gary K. Beauchamp, Monell Chemical Senses Center, Philadelphia, PA, USA Janet Beauvais, McGill World Platform for Health and Economic Convergence, Desautels Faculty of Management, McGill University, Montreal, Canada

Stephen Colagiuri, Sydney Medical School, Boden, Institute of Obesity, Nutrition & Exercise, University of Sydney, Sydney, New South Wales, Australia

Antoine Bechara, Brain and Creativity Institute, Department of Psychology, University of Southern California, Los Angeles, CA, USA

Alain Dagher, Montreal Neurological Institute, McGill University, Montreal, Canada

William Bernstein, efficientfrontier.com, North Bend, OR, USA

Manoja Kumar Das, International Clinical Epidemiology Network, New Delhi, India

Lalita Bhattacharjee, National Food Policy Capacity Strengthening Programme, Food and Agriculture Organization of the United Nations, Bangladesh

John M. de Castro, College of Humanities and Social Sciences, Sam Houston State University, Huntsville, TX, USA

John Blundell, Institute of Psychological Sciences, Faculty of Medicine and Health, University of Leeds, Leeds, UK

Angelo Del Parigi, Senior Medical Director, External Medical Affairs, Pfizer Inc., New York, NY, USA

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List of contributors

Branden R. Deschambault, Department of Human Health and Nutritional Sciences, University of Guelph, Guelph, Canada

Ross A. Hammond, Center on Social and Economic Dynamics, Economic Studies Program, The Brookings Institution, Washington, DC, USA

Scott Dickinson, Sydney Medical School, Boden, Institute of Obesity, Nutrition & Exercise, University of Sydney, Sydney, New South Wales, Australia

Corinna Hawkes, School of Public Health, University of São Paulo, São Paulo, Brazil

Tanya L. Ditschun, Food Science and Technology Group, Senomyx, Inc., San Diego, CA, USA Adam Drewnowski, Center for Public Health Nutrition, School of Public Health, University of Washington, Seattle, WA, USA Laurette Dubé, The McGill World Platform for Health and Economic Convergence, Desautels Faculty of Management, McGill University, Montreal, Canada Petra Eichelsdoerfer, Bastyr University Research Institute, Bastyr University, Kenmore, WA, USA Ahmed El-Sohemy, Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, Canada Karen M. Eny, Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, Canada Brian G. Essex, Department of Psychology, Vanderbilt University, Nashville, TN, USA Gary W. Evans, College of Human Ecology, Cornell University, New York, NY, USA Graham Finlayson, Institute of Sciences, Faculty of Medicine University of Leeds, Leeds, UK

Psychological and Health,

Ayelet Fishbach, Booth School of Business, University of Chicago, Chicago, IL, USA Robert J. Fisher, Department of Marketing, Business Economics & Law, University of Alberta, Edmonton, Canada

C. Peter Herman, Department of Psychology, University of Toronto, Toronto, Canada William B. Irvine, Department of Philosophy, Wright State University, Dayton, OH, USA Philip James, International Association for the Study of Obesity, and International Obesity Task Force, London, UK David J. A. Jenkins, Clinical Nutrition & Risk Factor Modification Center, and Division of Endocrinology and Metabolism, St Michael’s Hospital, Toronto, Canada; Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Canada Philip J. Johnson, Department of Psychology, McGill University, Montreal, Canada Peter J. H. Jones, Richardson Center for Functional Foods and Nutraceuticals, Department of Food Science, University of Manitoba, Manitoba, Canada Andrea R. Josse, Clinical Nutrition & Risk Factor Modification Center, St Michael’s Hospital, Toronto, Canada; Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Canada Daniel Kahneman, Center for Health and WellBeing, Princeton University, Princeton, NJ, USA Cyril W. C. Kendall, Clinical Nutrition & Risk Factor Modification Center, St Michael’s Hospital, Toronto, Canada; Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Canada William D. S. Killgore, Cognitive Neuroimaging Laboratory, McLean Hospital, Harvard Medical School, Belmont, MA, USA

Amsterdam,

Neil King, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia

Amy A. Gorin, Department of Psychology, University of Connecticut, Storrs, CT, USA

Bärbel Knäuper, Department of Psychology, McGill University, Montreal, Canada

Jason Halford, Psychology Department, University of Liverpool, Liverpool, UK

Peter Kooreman, Department of Economics, Tilburg University, Tilburg, The Netherlands

Louise Fresco, Universiteit Amsterdam, The Netherlands

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List of contributors

Shiriki Kumanyika, Department of Biostatistics and Epidemiology and Department of Pediatrics (Gastroenterology; Section on Nutrition), University of Pennsylvania School of Medicine, Philadelphia, PA, USA Nicole Larson, Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA

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Carlos A. Monteiro, Department of Nutrition, School of Public Health, University of São Paulo, São Paulo, Brazil Karl J. Moore, Strategy and Organization Department, Desautels Faculty of Management, and Dept. of Neurology & Neurosurgery, McGill University, Montreal, Canada

Clare Lawton, Institute of Psychological Sciences, Faculty of Medicine and Health, University of Leeds, Leeds, UK

Spencer Moore, School of Kinesiology and Health Studies, Queen’s University; Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Canada

Kathleen E. Leahy, Department of Nutritional Sciences, Pennsylvania State University, PA, USA

Howard R. Moskowitz, Moskowitz Jacobs Inc., White Plains, NY, USA

Jordan LeBel, Marketing Department, John Molson School of Business, Concordia University, Montreal, Canada

David M. Mutch, Department of Human Health and Nutritional Sciences, University of Guelph, Guelph, Canada

Catherine Le Galès, CERMES3, National Institute for Health and Medical Research, U988, Paris, France

Kristian Ove R. Myrseth, ESMT European School of Management and Technology, Berlin, Germany

Allen S. Levine, Minnesota Obesity Center; Department of Food Science and Nutrition, University of Minnesota, Saint Paul, MN, USA

Erik Naslund, Clinical Sciences, Danderyd Hospital, Karolinska Istitutet, Stockholm, Sweden

Shanling Li, Desautels Faculty of Management, McGill University, Montreal, Canada Alexandra. W. Logue, City University of New York, New York, NY, USA Dylan MacKay, Richardson Center for Functional Foods and Nutraceuticals, University of Manitoba, Manitoba, Canada Michael Marmot, International Institute for Health and Society, University College London, London, UK John C. Mathers, Human Nutrition Research Centre, Institute for Ageing and Health, Newcastle University, Newcastle on Tyne, UK

Pawel K. Olszewski, Minnesota Obesity Center; Department of Neuroscience, Uppsala University, Uppsala, Sweden Jaak Panksepp, Department of VCAPP, College of Veterinary Medicine, Washington State University, Pullman, WA, USA Heather Patrick, Department of Medicine and of Clinical and Social Psychology, University of Rochester, Rochester, NY, USA Prabhu Pingali, Deputy Director, Agricultural Development, The Bill and Melinda Gates Foundation, USA Patricia P. Pliner, Department of Psychology, University of Toronto at Mississauga, Canada

John J. Medina, Department of Bioengineering, University of Washington, Seattle, WA, USA

Janet Polivy, Department of Psychology, University of Toronto at Mississauga, Mississauga, Canada

Julie A. Mennella, Monell Chemical Senses Center, Philadelphia, PA, USA

Michele Reisner, Moskowitz Jacobs Inc., White Plains, NY, USA

Lyne Mongeau, Department of Social and Preventive Medicine, University of Montreal, Montreal, Quebec, Canada

Lise Renaud, Social and Health Communication, Université du Québec à Montréal (UQAM), Montreal, Canada

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Denis Richard, Centre de Recherche de l’Institut universitaire de Cardiologie et de Pneumologie de Québec, Canada Thomas N. Robinson, Division of General Pediatrics, Department of Pediatrics and Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine; Center for Healthy Weight, Stanford University School of Medicine and Lucile Packard Children’s Hospital at Stanford, Stanford, CA, USA Barbara J. Rolls, Department of Nutritional Sciences, Pennsylvania State University, PA, USA Edmund T. Rolls, Oxford Centre for Computational Neuroscience, Oxford, UK Catherine Sabiston, Department of Kinesiology and Physical Education, McGill University, Montreal, Canada Sarah-Jeanne Salvy, Division of Behavioral Medicine, Department of Pediatrics, University at Buffalo, State University of New York, USA Nishta Saxena, Clinical Nutrition & Risk Factor Modification Center, St Michael’s Hospital, Toronto, Canada; Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Canada Michelle A. Schamberg, Cornell University, New York, NY, USA Helgi B. Schiöth, Department of Neuroscience, Uppsala University, Uppsala, Sweden T. N. Srinivasan, Samuel C. Park Jr Professor of Economics, Yale University, New Haven, CT, USA; Stanford Center for International Development, Stanford University, Stanford, CA, USA Christine Stich, Population Health, Prevention and Screening Unit, Cancer Care Ontario, Toronto, Canada Mary Story, Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA Beth M. Tannenbaum, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada

Louise Thibault, School of Dietetics and Human Nutrition, McGill University, Montreal, Canada Elena Timofeeva, Centre de Recherche de l’Institut universitaire de Cardiologie et de Pneumologie de Québec, Canada Kraisid Tontisirin, Institute of Nutrition, Mahidol University, Thailand and Former Director, Food and Nutrition Division, Food and Agriculture Organization of the United Nations, Italy Angelo Tremblay, Department of Social and Preventive Medicine, Laval University, Quebec City, Canada Josh van Loon, School of Community and Regional Planning, University of British Columbia, Vancouver, Canada Patrick Webb, Friedman School of Nutrition Science and Policy, Tufts University, Medford, MA, USA Nancy M. Wells, Design and Environmental Analysis, College of Human Ecology, Cornell University, New York, NY, USA Geoffrey C. Williams, Departments of Medicine and of Clinical and Social Psychology, University of Rochester, Rochester, NY, USA Julia M. W. Wong, Clinical Nutrition & Risk Factor Modification Center, St Michael’s Hospital, Toronto, Canada; Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Canada Lin Xiao, Brain and Creativity Institute, Department of Psychology, University of Southern California, Los Angeles, CA, USA Martin R Yeomans, School of Psychology, University of Sussex, Brighton, UK David H. Zald, Departments of Psychology, Psychiatry and Integrative Neuroscience Program, Vanderbilt University, Nashville, TN, USA Dan Zhang, Desautels Faculty of Management, McGill University, Montreal, Canada Wenqing Zhang, Desautels Faculty of Management, McGill University, Montreal, Canada.

Acknowledgments The editors would first like to thank all the authors for their dedication and hard work in producing and refining these chapters. By pushing the boundaries of their thinking and knowledge, they lay the foundation of a bolder approach to obesity prevention. The editors give their warmest thanks to Marie-Claire Laflamme-Sanders, editorial coordinator, for her brilliance and her perseverance in bringing this book to completion. The chapters assembled build upon a cycle of events on obesity, hosted by the McGill World Platform for Health and Economic Convergence from 2005 until 2008. This cycle progressively tapped into the “brain” and the “society” side of obesity prevention to develop a new way of thinking of and acting upon this problem. In this effort, we are grateful for the continued financial and substantive support of our partner organizations, which are committed to ensuring the health of all individuals around the world. These are: McGill University, the Global Alliance for the Prevention of Obesity and Related Chronic Diseases, the Fondation Lucie et André Chagnon, the Public Health Agency of Canada, the Ministère de la santé et des services sociaux, the Bill and Melinda Gates Foundation, the Dr. Robert C. and Veronica Atkins Foundation, the Robert Wood Johnson Foundation, the Institut national de la santé publique du Québec – National Collaborating Center on Public Health, the Direction de la santé publique de Montréal-Centre, Health Canada, the Agence de la santé et des services sociaux de Montréal, the Canadian Institutes of Health Research – Institute of Nutrition, Metabolism and Diabetes, the Canadian Institute for Health Information, the Canadian Institutes of Health

Research – Institute of Human Development, Child and Youth Health, the Canadian Institutes of Health Research – Institute of Neurosciences and Mental Health Addiction, the Heart and Stroke Foundation of Canada, the Centre hospitalier de l’Université de Montreal, the Fond de recherche en santé du Québec, the Réseau de recherche en santé des populations du Québec, the National Institute of Child Health and Human Development, the National Cancer Institute, the National Heart, Lung, and Blood Institute, the National Institute of Diabetes and Digestive and Kidney Diseases, the Office of Behavioral and Social Sciences Research, the Health and Learning Knowledge Center, the Ministère de l’agriculture, des pêcheries, et de l’alimentation du Québec, the Montreal Neurological Institute, the Advanced Foods and Materials Network, the Canadian AgriFood Policy Institute, the International Clinical Epidemiology Network, Advertising Standards Canada, Agriculture and Agri-Food Canada, the Alliance for Innovation in Agri-Food, the American Heart Association, the Canadian Association of Principles, the Canadian Council of Food and Nutrition, the Canadian Obesity Network, the Canadian Produce Marketing Association, the Centers for Disease Control and Prevention, the Chronic Disease Prevention Alliance of Canada, Concerned Children’s Advertisers, the Culinary Institute of America, Food and Consumer Products of Canada, the International Economic Forum of the Americas/Conférence of Montréal, the Joint Consortium for School Health, MobilizeYouth, the National Obesity Observatory, ParticipACTION, the Pennington Biomedical Research Center, and the University of Washington – Center for Public Health Nutrition.

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Introduction: On the Brain-to-Society Model of Motivated Choice and the Whole-of-Society Approach to Obesity Prevention Laurette Dubé, on behalf of the Editorial Team James McGill Chair, Consumer Psychology and Marketing, Desautels Faculty of Management, McGill University, Montreal, Canada

O U T L I N E Introduction The Brain-to-Society Model of Motivated Choice

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A Whole-of-Society Approach to Obesity Prevention

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Handbook Overview

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The Choice Architecture and what it Means for Obesity Prevention xxvi

Introduction The spread of childhood and adult obesity and other lifestyle-related diseases continues unabated in Canada, the USA, Europe, and other industrialized countries around the world.

Obesity Prevention: The Role of Brain and Society on Individual Behavior

The WHO estimates that over 1 billion people globally are overweight, and more than 400 million are obese. The number of obese is expected to grow by 75 percent by 2015 (James, 2006). In developing countries, such as India and China, the increased prevalence of overnutrition occurs

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2010 Elsevier Inc. © 2010,

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while a large proportion of the population still suffers from undernutrition (see Chapter 37 of this volume). This is in spite of a rich diversity of medical and behavioral individual- and communitybased interventions and wide-ranging policy change. This pandemic poses serious challenges not only to the health community, but also to society as a whole: the personal, societal and economic costs tied to it are extremely high. It is increasingly recognized that the patterns of food overconsumption and physical inactivity driving the obesity pandemic are rooted in the way modern industrialized society operates. It has created environmental conditions that overwhelm a biology evolved for dramatically different conditions. These challenges have been compounded by the radical transformational force of globalization, which has created a world where modern affluent economies, developing economies and emerging markets, and the least developed countries are all part of the same global system, where tradition and modernity intersect as never before (Dubé et al., 2008a). Globalization has accelerated the spread of ideas, information and cultural changes, and unleashed tremendous potential for individual, economic and social growth and developments. It has also significantly impacted health, as economic development and industrial progress are tied to increases in overweight and obesity rates (Cutler et al., 2003). This poor alignment between biology, markets and society is reflected not only in the rise of obesity and chronic diseases, but also in issues regarding food and nutrition security, poverty and health inequity, as well as child development and mental health. Our current environment presents an important challenge to human biology – one that will only continue to grow, unless we revisit some of the fundamental ways in which our society operates. These include: 1. The ways in which we – as individuals, families and communities – live, consume, invest, and take care of our children

2. The ways in which we – as educational, health, media and business organizations – produce, promote, trade and provide goods and services to individuals, families and communities 3. The ways in which we – as trade institutions, investment markets and governments – maintain the present health and economic divide that shapes the arena where individuals, families, communities and organizations evolve. Cutting-edge science from both the biology and the society sides of the equation is crucial to this effort, as are creative thinking and sustained commitment and action from all stakeholders around the world, at the local, national and global levels. An unprecedented convergence of interests, in the wake of the financial downturn, can yield breakthrough novel and more effective pathways for individual behavioral change as well as social and business innov­ation. Challenges and opportunities lie at new frontiers of transdisciplinary and cross-sectoral science; in novel behavioral change interventions; in new mind-sets and methods for organiz­ational decision-making; in public and private investment in business and social innovation; and in breakthrough institutional entrepreneurship for better balanced policy, governance, and government. Only this can pave the way toward a vision of present and future economy and society that biology can more sustainably withstand. More concretely, it means that: 1. Health and public health professionals, organizations and systems must galvanize individual and societal action by all actors in society. They must develop the necessary expertise and capabilities to provide their counterparts in education, agriculture, business, media, urban planning, and transportation, with guiding principles, frameworks for action and the best available evidence regarding the health impacts of policies and actions.

The Brain-to-Society model of motivated choice

2. Professionals, organizations and systems from all sectors that shape the current environment must mainstream health into their respective everyday and strategic activities, in a manner that is compatible and sustainable from the perspective of their primary sectors of activities. 3. Professionals, organizations and systems in the whole of society must singly and jointly engage in breakthrough and integrative innovation in science, policy and action to make healthy choices the default option for individuals. This handbook is based on the conviction that it is possible to reap the many benefits of modern economic development worldwide, without paying the high toll of obesity and its chronic disease consequences. In the rest of this Introduction, we present the Brain-to-Society model of motivated choice as the overarching conceptual framework of this handbook. We then provide an overview of the book. All in all, this collection assembles the scientific foundations for the proposed model as well as the multi-level and multi-sector components of the Whole-of-Society changes needed to curb the obesity pandemic.

The Brain-to-Society model of motivated choice The Brain-to-Society model of motivated choice (Daniel et al., 2008; Dubé et al., 2008b) is a broad integrative approach to understanding, mapping, modeling, and ultimately guiding in a more adaptive direction, the pathways by which brain systems (considering the genetic background and psychological predispositions) and society systems (through the familial, organizational and collective choices and policies in health, social and economic domains that shape environments) singly and jointly determine

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individual choices in domains of motivated adaptive behaviors. Motivated adaptive behaviors are cue-induced processes, shaped by human evolution and tied to biological drivers, that span a wide range of choices – for example, from food and companionship to strategies for minimizing physical and psychological discomfort (Kalivas and Volkow, 2005; Dubé et al., 2008b). At a basic level, sensory and emotional systems all have single and combined roles in food choice and eating. These signals interact with environmental cues in complex ways to define the motivational or “reward” value of food. Some of these biologically-driven processes are also involved in the less adaptive case of addiction (Volkow and O’Brien, 2007). Yet, on another level, humans have the capacity to regulate behavior in a flexible and goal-directed manner through deliberate and effortful acts of will power and self-control. They can overcome maladaptive cue-induced impulses and allow more adaptive choice alternatives. This capacity is linked to executive control systems, which include inhibition, decision-making, goal selection and planning, and are enabled by more recently evolved brain systems. These are also sensitive to environmental conditions (Diamond, 2009). The BtS model of motivated choice views individual choice as the outcome of the complex and dynamic relationships between biology and psychology, shaping choice and behavior, in response to environmental cues and taking into account both the immediate context and internalized life-course information called upon by the immediate context. These cue-induced processes operate on different timescales, and through a diversity of mechanisms that all together define how the brain acts as a command center for choice and behavior. Thus, the brain systems and society systems, which underlie the organizational and collective choices that in turn shape environment, are all part of the same system guiding individual choice (Figure 1).

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Figure 1.  The Brain-to-Society model of motivational choice.

The choice architecture and what it means for obesity prevention The BtS model of motivated choice also assumes that the default value of individual choice – i.e., the easiest and most natural option – is modulated by organizational and collective choices, which form the choice architecture within which individual choice occurs. The choice architecture emerges from the single and combined consequences of the choices made by governments, businesses, civil society, and community organizations that operate in health and non-health domains at local, national and global levels. Accounts of the biologically challenging qualities of the present choice

architecture are manifold (Wansink, 2006; Dubé et al., 2007): 1. There is excessive reliance on information and education in individual-level intervention 2. Nutrition and health are not sufficiently integrated into school, work and community activities and environments 3. Nutrition and health have not sufficiently penetrated innovation, value-chain and strategic activities in agriculture, food and other business sectors 4. The power of commercial and social marketing and media has not been shifted toward the promotion of healthy eating 5. Rural, industrial, economic and social development has thus far not paid sufficient

A Whole-of-Society approach to obesity prevention

attention to the challenges imposed to biology by environment 6. Policy changes that could lead to progress lie outside of the health jurisdiction 7. There is a lack of convergence in policy and action between developed and developing countries, between health and economic activities, and between the local, national and global levels of decisions 8. The political will as well as the health and development budgets devoted to the promotion of healthy eating are insufficient. In such a context, where the needed changes to create a protective choice architecture lie outside the traditional purview of health, health and public health professionals, organizations and systems, armed with insufficient means and limited power, cannot continue to promote healthy lifestyles if all other social and economic actors and individuals passively maintain a relative status quo. Conversely, health cannot continue to be managed from the outside, without a sophisticated understanding of the complex mechanisms, motives and success criteria that guide the decision-making and action of these non-health actors. As such, to transform the choice architecture into one that supports healthy lifestyles, both health and non-health actors must converge to lead effective changes. The research program underlying the BtS model deploys cutting-edge concepts and methods from neuroscience and systems sciences with the latest advances in behavioral and social sciences to better understand individual food choices in the contexts of biology and environment. While the brain suffers from decision-making shortcomings and self-control challenges in the face of a plentiful environment, it also possesses a unique capacity for selfpreservation, empathy and creativity, with the power to foster innovation, entrepreneurship and leadership. The approach that emerges from

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this book relies on the assumption that society systems, just like brain systems, are more amen­ able to changes than previously thought. Both systems can be changed in order to better sustain healthy lifestyle behaviors.

A Whole-of-Society approach to obesity prevention The BtS model of motivated choice calls for a dramatically different approach to population health that permeates its traditional boundaries and builds novel competencies and capacities in both health and non-health domains of activities. The Whole-of-Society (WoS) approach to obesity prevention reflects the fact that the neces­sary changes are woven into the everyday lives of individuals, communities, organizations, markets and societies. It goes beyond current “whole-of-government” approaches, which have called for the integration of healthy public policies within all sectors that contribute to lifestyle (education, agriculture, and industry and trade). As comprehensive as they may be, traditional governmental policies and programs alone cannot reach the scale, scope and speed of changes needed to reverse current obesity and chronic disease trends. The BtS model is motivated by the need to go beyond crossgovernmental efforts to harness the power of individuals themselves, communities and businesses, and of other social and economic actors. This approach brings together recent developments in science and the best models and practices from the fields of population health and global health, with breakthrough advances from the key sectors that shape the environment in which individual lifestyle choices are made: food and agriculture, education, media, finance, management, law, politics and economics. The aim is to better equip the population health and healthcare community to serve as catalysts

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and leaders in promoting policy convergence in economic domains, and, conversely, to better equip non-health policy-makers and strategists to place health on their agenda. Therefore, the WoS approach is: 1. Transdisciplinary – scientists, researchers, decision-makers and strategists from all fields work together to develop shared conceptual and methodological frameworks and strategies for policy change and innovation, which not only integrate but also transcend their respective disciplinary perspectives 2. Multi-sectoral – actors from public agencies, communities and the private sector, from all social and economic domains that contribute to lifestyles, are mobilized to place health on their strategic agenda 3. Multi-level – individuals, policy-makers and strategists whose decisions at the community, municipal, provincial/state, national, transnational and global levels influence the environment in which individual choice is made are involved.

Handbook overview Part 1 of this two-part handbook, “From Brain to Behavior”, provides the scientific foundations of the Brain-to-Society model of motivated choice. Part 2, “From Society to Behavior: Policy and Action”, then lays down the foundations of a Whole-of-Society approach to population health. In moving “From Brain to Behavior”, Section A of Part 1 examines sensory, reward, and other biological systems that explain how energy has become “delight” for living species. Section B shifts to executive control systems, which are unique to mankind, and also addresses selfcontrol challenges in the modern world of plenty, in particular when wired-in, non-adaptive

predispositions are culturally reinforced. The contributions in Section C move beyond brain systems driving behavior to examine more broadly other biological systems that impact energy balance and body weight, including genetics and epigenetics. Section D offers integrative and multi-level perspectives on eating, energy balance and body-weight regulation. Finally, in Section E, existing approaches to individual behavior changes are revisited in light of this more sophisticated understanding of the biological, motiv­ ational and rational bases of individual food choice and its relationship to energy balance and body weight. In Part 2, “From Society to Behavior: Policy and Action”, the emphasis shifts to the organizational and collective choices that shape the environment in which individual choice is made. Section A begins by laying out the needs and challenges in policy and action to prevent obesity, in both developed and developing countries. Section B focuses on the economy as a core agent shaping policy and action. The set of contributions in Section C covers policy and action to shift the drivers of food supply and demand in a healthy direction. In Section D, we then look to scaling up policies and actions to create family, school, community and social networks that support healthy individual choices. The socio-economic health gradient is examined in Section E, and finally, in Section F, existing broad societal approaches to obesity prevention are analyzed and the potential of systems science is introduced. The concluding chapter sets new frontiers in science, policy and action, introduces the Whole-of-Society approach to obesity prevention, and highlights the new models of capitalism and society that can support it.

References Cutler, D. M., Glaeser, D. L., & Shapiro, J. M. (2003). Why have Americans become more obese? Journal of Economic Perspectives, 17(3), 93–118.

References

Diamond, A. (2009). The interplay of biology and the environment broadly defined. Developmental Psychology, 45(1), 1–8. Dubé, L., Kouri, D., Fafard, K., & Sipos, I. (2007). Childhood obesity: A societal challenge in need of health public policy. Report on Policy Implication of the Health Challenge 2007. Think Tank for Canada. Dubé, L., Shetty, P., Webb, P., Fresco, L., McKnight, W., & Hawkes, C. (2008a). Framing Paper. Prepared for the Gates Foundation Workshop: From Crisis to a New Convergence of Agriculture, Agri-Food and Health. Held in Montreal, Quebec, November 8–9, 2008. Dubé, L., Bechara, A., Böckenholt, U., Ansari, A., Dagher, A., Daniel, M., De Sarbo, W. S., Fellows, L. K., Hammond, Ross, A., Huang, T. T.-K., Huettel, S., Kestens, Y.,

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Knäuper, B., Kooreman, P., Moore, D. S., & Smidts, A. (2008b). Towards a brain-to-society systems model of individual choice. Marketing Letters, 19, 323–336. James, P. (2006). Presentation offered during the 2006 Mcgill Health Challenge Think Tank. Held in Montreal, Quebec, October 25–27, 2006. Kalivas, P. W., & Volkow, N. D. (2005). The neural basis of addiction: A pathology of motivation and choice. American Journal of Psychiatry, 162(8), 1403–1413. Volkow, N. D., & O’Brien, C. P. (2007). Issues for DSM-V: Should obesity be included as a brain disorder? American Journal of Psychiatry, 164(5), 708–710. Wansink, B. (2006). Mindless eating: Why we eat more than we think. New York, NY: Bantam Books.

C H A P T E R

1 The Pleasures and Pains of Brain Regulatory Systems for Eating Jaak Panksepp Department of VCAPP, College of Veterinary Medicine, Washington State University, Pullman, WA, USA

o u t l i n e 1.1 Introduction

5

1.2 Satiety Agents versus Aversion-Inducing Agents 6 1.3 Various Methodologies to Evaluate Affective Change in Pre-Clinical Appetite Research

12

1.5 Conclusion

13

7

1.1  Introduction

Diverse neuropeptidergic details of this circuitry have now been clarified (Horvath and Diano, 2004; Broeberger, 2005; Konturek et al., 2005; Gao and Horvath, 2007; Coll et al., 2008). In brief, there are complex neuropeptide-based neural networks that are able to gauge the energy status of the organism, and to adjust foraging and eating behavior accordingly. This network is constructed of hypothalamic neuropeptides, such as hypocretin/orexin, neuropeptide Y and agouti-related peptide, -melanocyte-stimulating hormone, and melanin-concentrating hormone;

All basic survival needs of the body are represented in genetically ingrained circuits concentrated in subcortical visceral regions of the brain. Energy balance is regulated by a strict equation (Figure 1.1) that has recently been illuminated in great detail. For many decades, abundant evidence has indicated that medial hypothalamic regions, concentrated especially in the arcuate nucleus, contain major detectors for long-term homeostatic energy balance (Panksepp, 1974).

Obesity Prevention: The Role of Brain and Society on Individual Behavior

1.4 Conditioned Taste Aversions – From Animal Models to Human Brain Analysis?



2010 Elsevier Inc. © 2010,



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+

5.5 kg.

750 kcal + 20,000 kcal CHOW RAT

=

+

10,000 kcal + 19,750 kcal HEAT RAT Daily intake error <0.7 kcal

Figure 1.1  A female rat’s approximate yearly energy balance equation. A great deal is eaten without much change in body weight. With the small increase in body weight, the daily intake error was less than a kilocalorie. The remaining energy was dissipated as heat. Source: Figure 9.4 of Panksepp (1998: 172), reprinted with permission of Oxford University Press.

regulatory circuits that are controlled by peripheral signals of lipid status, such as leptin; gastro­ intestinal hunger hormones, such as ghrelin; as well as more direct metabolic effects on the hypothalamus that are not as well understood. As noted above, this knowledge has been summarized superbly many times. Often missing from the discussion of energy balance dynamics are the evolved psychological processes that mediate achieved/achieving homeostasis – the nature of the feelings of hunger in the brain, and the large variety of ways the pleasures and displeasures of taste can promote or hinder appetite. This is in addition to the many ways feeding behavior can be disrupted which have no relevance for the normal mechanisms of energy balance regulation. For instance, hunger makes sweetness taste more pleasant, and satiety makes the same sensation feel less pleasant (Cabanac, 1992).

This chapter will briefly focus on the latter factors, since they need to be considered more closely as investigators search for medicinal agents that may help humans better regulate their weight. It should be noted that considerable progress is being made in understanding how the brain codes taste qualities in both animals (Berridge, 2003; Peciña et al., 2006) and humans (Rolls, 2008; Rolls and Grabenhorst, 2009), but little of that work has yet been related to our understanding of appetite control agents.

1.2  Satiety agents versus aversion-inducing agents Presumably, the feeling that accompanies excessive depletion of energy leads to distressing feelings of hunger, while satisfaction of

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1.3  Various methodologies to evaluate affective change in pre-clinical appetite research

energy needs promotes a mood of contentment. Of course, few investigators of animal models of energy regulation are willing to use such psychological concepts; most restrict their discussions to measurable entities – changes in food intake, body weight, and various body energy distribution parameters. This is understandable, since we have no direct way of monitoring the psychological states of other animals. Yet this is also shortsighted if such states do exist and if they are of first-rate importance for how animals distribute their food-seeking and consummatory behaviors. We would be wise to reconsider our reluctance to envision the affective controls of animal behavior, using all the indirect methodological approaches at our disposal. Philosophically, to not consider such issues is tantamount to failing to deal with the real complexities of the brain. Practically, the failure to consider such issues may impair our capacity to sift real satietyproducing neural pathways and neurochemical agents from the many other ways that food intake could be disrupted. Since most pre-clinical investigators in the field have been trained in rigorous behavioristic approaches, where any discussion of mental changes in animals is considered to be inappropriate, a full and open discussion of such issues is rare in the literature. However, considering what we now know about brain evolution and the subcortical sources of affective processes in all mammals (Panksepp, 1998, 2005, 2008), wisdom dictates that we begin to evaluate such issues with more intensive methodologies. If adequate empirical approaches for monitoring affective changes did not exist, it would make no sense to suggest that such issues should be considered. Yet adequate comprehensive methodologies are available, albeit rarely used. Among the best measures are positive and negative affective states as can be measured with conditioned place preference (CPP) and conditioned place aversion (CPA) measures (Tzechentke, 2007). As will be noted later, there are also other, more direct behavioral measures,



such as the willingness of animals to play. Such affective measures, when used in pre-clinical animal models, would allow us to better ferret out those brain neurochemical pathways that need our most focused attention for the development of optimal appetite-reducing agents. Why do we need to consider such issues? Any of a large variety of negative affects can reduce feeding in animals, from anger to being “zonked-out” by drugs, with disgust, fearfulness, separation anxiety, and stomach cramps in between. It is important also not to forget the negative feelings arising from a variety of stressors, including fatigue and sickness promoting neurochemical changes in the brain and various painful bodily feelings. If we do not sift such appetite-reducing affects from the normal pathways of satiety at the outset of intensive research programs seeking new satietypromoting agents, we will be mismanaging our budgets and the efforts of our researchers. Since practically all agents off our pharmaceutical shelves, in high enough doses, can reduce food intake in animals, such affective issues need to be considered at the front end of research programs. Unfortunately, few investigators focus on them as fully as they deserve; usually a conditioned taste-aversion (CTA) paradigm is as far as most are prone to go. This is a good start, but, as will be summarized here, there are many more subtle ways to address such issues.

1.3  Various methodologies to evaluate affective change in pre-clinical appetite research There are many easy ways to reduce feeding pharmacologically, but only a few of these tell us much about the normal mechanisms of energy regulation. As noted, fearful animals eat less than normal. So do angry and sick ones. There are many affective changes beside satiety

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1.  Brain regulatory systems

that can reduce feeding, and investigators interested in seeking satiety agents have not spent enough time sifting those agents that produce the normal, good feeling of satiety after a satisfying meal from all the many other affective changes that can reduce feeding. With the increasing number of neuropeptidergic “satiety agents” that have been discovered in the brain (Table 1.1), we must be increasingly wary that many of them are reducing feeding by changing non-homeostatic affective feelings of animals rather than a feeling of satisfaction that emerges from no longer being hungry. In short, establishing affective criteria whereby one has discovered a “real” satiety agent is critical for future progress in developing medicines that will help people to regulate body weight optimally. When the body is out of homeostatic Table 1.1  Partial list of neuropeptides and other neuromodulators that have been found to reduce feeding and body weight Various amino acids

Glucagon-like peptide

AgRP

Interleukin-1

Amylin

Interleukin-6

-MSH

Insulin (central)

Beta-endorphin

Leptin

BDNF

Norepinephrine

Bombesin

Neurotensin

CART

NPY

CCK

Oxytocin

Corticosterone

PrRP

CRH

Peptide YY

Dynorphin

Serotonin

Galanin

Tumor necrosis factor 

Galanin-like peptide

Urocortin

Abbreviations: -MSH, -Melanocyte Stimulating Hormone; BDNF, Brain Derived Neutrophic Factor; CART, Cocaineand Amphetamine-Regulated Transcript CorticotropinReleasing Hormone; PrRP, Proline-Releasing Peptide. Sources: Horvath and Diano, 2004; Broeberger, 2005; Konturek et al., 2005; Gao and Horvath, 2007; Coll et al., 2008.

balance in terms of available energy, one feels hunger pangs and generalized distress that can be easily erased by a restoration of energy homeostasis. When one has eaten a satisfying meal, the stomach is distended, blood sugar levels rise, and one commonly feels rather sleepy. These physiological manifestations of satiety are accompanied by a shift from negative to positive affective states that are commonly called satisfaction or contentment. As noted, since it is admittedly hard to peer into the minds of our experimental animals, we need to find a variety of indirect measures that can help us gauge whether agents that reduce objectively monitored body weight and feeding behavior are also accompanied by feelings of satisfaction. If agents produce less desirable affective states, it would be good to know about them early in any research program. The most useful appetite control agents will need to facilitate appropriate affective changes, namely feelings of appetite satisfaction. Ever since the discovery that the neuropeptides cholecystokinin and bombesin could reduce appetite, followed soon after by the body-fat regulator leptin, the search for neuropeptide modulators of food intake has been a booming growth industry. The outstanding neurobehavioral science that has been fostered has had one enormous missing link – a meaningful discussion of how the various agents modify affective change. Without this critical linchpin, which will ultimately be a key to patient satisfaction and hence long-term compliance and efficacy, acceptable appetite control agents are unlikely to be discovered. Hence, affective issues should be evaluated soon after a substance is thought to be a natural satiety-producing agent. Affective change must be the gold standard that allows us to sift true value from empty promissory notes if we are to regulate appetite through a growing knowledge of feeding control and long-term energy balance regulatory networks in the brain (Panksepp, 1974, 1975; Panksepp et al., 1979). To re-emphasize, it is important to note that injecting animals with practically anything off

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1.3  Various methodologies to evaluate affective change in pre-clinical appetite research

the pharmaceutical shelf, at random, yields many agents that can reduce intake. Most do not reduce appetite normally; appetite is reduced because the animals are feeling anything from mildly unwell, fatigued, or simply very ill to emotionally distraught. Few investigators carry the discussion of their results toward such affective issues, except for the well-accepted fact that aversion can be monitored by CTA (Conditioned Taste Aversions); for the massive literature on CTAs (see http://www.CTALearning.com for an archival resource of the available literature). For instance, Panksepp et al., (1977) provided this service to Hoffman-LaRoche in the 1970s for one of their prime appetite control agents, which dissuaded them from proceeding further. The problem with just using the CTA measure is that agents which also produce excessive normal satiety can lead to a metabolically mediated conditioned reduction in meal size. Hence, other measures are essential. The one proposed early on, namely to follow the post-prandial “satiety sequence” of post-meal grooming, exploration, “house-keeping” activities and nap (Antin et al., 1995), is fine as a starter, yet it is missing a few critical keys to solving the affective issue – namely, did the “satiety agent” in fact produce a good feeling of satiety? Now that we have a host of peptides that reduce feeding (Table 1.1 provides a partial list), the above issue should be foremost in investigators’ minds. However, it is not. For instance, a stress peptide such as corticotrophin releasing factor (CRF), which reduces appetite, is not a sensible candidate for clinical use in a feedingregulation clinic. It simply makes animals emotionally aroused in various negative affective ways, including increasing signs of separation distress. It was also believed that the CRF2 receptor, reacting to urocortin, might be effective, but it has been shown only to produce emotional distress (Panksepp and Bekkedal, 1997). So what should investigators do? Take affect seriously. There are abundant good ways to monitor whether investigators could realistically



consider their favorite peptide to be a realistic satiety agent as opposed to an emotionally disruptive agent. The best measures would reflect increases in behavior rather than reductions (as with the above described “satiety sequence”, which is largely a reduction of behavior that also occurs when animals are simply tired). The following half-dozen gold-standard criteria would allow us to sift the most promising (i.e., real appetite control agents) from the less realistic candidates (after routine CTA studies have been completed). 1. Hunger dramatically reduces the motivation of young animals to indulge in roughand-tumble play. This amotivational state is immediately reversed by a single meal (Siviy and Panksepp, 1985). In this study, we evaluated the capacity of CCK and bombesin to simulate that effect. CCK had no such capacity, while bombesin did marginally yield some reversal. The effect, however, was not even close to the complete reversal of play suppression that was produced by a single meal. We proceeded to evaluate several other “promising” neuropeptides, but none proved to be promising. By fulfilling this criterion substantially, a researcher will have identified a truly promising neuropeptide for further study. 2. As discovered in the late 1960s, rats will not show a hunger-induced elevation of feeding with a rarely provided highincentive treat. Such an effect is routinely seen when monitoring feeding with normal maintenance chow (Figure 1.2). Thus, it could be argued that for normal satiety, an agent should reduce hunger-induced intake of maintenance food much more than intake of a rarely provided treat. Sickness would be expected to produce more comparable effects on each. 3. If a neuropeptide agent truly simulates a good feeling of satiety following hunger, it should produce a clear conditioned place

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1.  Brain regulatory systems

18

Intake as a function of level of deprivation with two incentives

16

Hamburger treat

Food intake (g)

14 12 10 8

Maintenance chow

6 4 2 0

Ad Lib.

24 h. Deprivation Level of food deprivation

Figure 1.2  A summary of the effects of high-incentive (raw hamburger) and much lower-incentive food (the rat’s normal maintenance chow) on intake as a function of degree of prior food deprivation. Source: Figure 9.3 of Panksepp (1998: 173), reprinted with permission of Oxford University Press.

preference (CPP) in hungry animals, but not in fully satiated animals. Indeed, in fully satiated animals, as could be insured by gavage of part of the next meal a short while before testing, the agent should either produce no CPP or perhaps even a mild conditioned place avoidance (CPA). In this regard, it should be noted that early on CCK produced no CPP response in hungry animals; indeed, it generated a clear aversion (Swerdlow et al., 1983). 4. Along the same lines, if a neuropeptide really produces feelings of satiety, hungry animals should work for intraventricular administration of the peptide much more under a state of hunger than in a state of satiety. 5. If an animal’s set-point for regulation has been truly shifted downward (i.e., to a leaner body mass), then the long-term hedonic equation should not have been shifted.

One way to monitor this is to give common laboratory animals, such as rats, continuous daily access to two concentrations of sugar. Animals normally systematically shift their intake from the more concentrated to the less concentrated sugar solution (Figure 1.3). Lean animals in a chronic state of hunger, such as those with experimental type 1 diabetes, sustain their preference for the more concentrated solution. If this were to happen with a putative long-term appetite control agent, then the inference should be that the body-weight set-point has not been shifted by the manipulation. If rats shift away from the sweeter solution more rapidly, the inference is that they are, in fact, internally experiencing excess energy repletion. 6. Finally, in line with our main thesis that the very best way to monitor affective change in animals is via their emotional vocalizations, we would suggest that if one paired a conditioned stimulus (CS) with infusion of satiety peptides in hungry animals, then gradually the CS would come to evoke appetitive 50-kHZ ultrasonic vocalizations (USVs) in anticipation of obtaining relief from the hunger. We have already observed this with a single 2-hour feeding period each day (Burgdorf and Panksepp, 2000), as well as a conditioned appetitive response to drugs of reward (Knutson et al., 1999; Burgdorf et al., 2001). In this case, this maneuver does not work for repeated short CS pairing with small bits of food typically used in operant conditioning. This suggests that the response has to be within the context of ecological validity (animals typically anticipate and take meals). If the CS were to evoke 22-kHz aversion-indicative USVs – a response seen with aversive drugs (Burgdorf et al., 2001) – then it would be highly unlikely that the peptide was reducing food intake by evoking feelings of satiety.

1.  From Brain to Behavior

1.3  Various methodologies to evaluate affective change in pre-clinical appetite research

Glucose consumed (ml)

50

11

Glucose crossover Top: Raw data Bottom: Relative data

Raw data Dilute 10% solution

40 30 20

Relative intakes of dilute solution under various metabolic conditions

Concentrated 35% solution

10

Diabetic rats

Percent glucose intake as dilute solution

35%

10%

Percent glucose taken as dilute solution

80 60 40 20

Percent preference 1

Normals recovering from obesity

80

Glucose

4

70

Normal untreated

60 50

Hypothalamic hyperphagic Normals getting insulin Genetically obese

40 30 20 10

7

Days

Days

Figure 1.3  Summary of the patterns of sugar water consumption in animals given continuous daily access to two solutions of different concentrations. Animals initially take most of their sugar from the concentrated solution, but gradually shift over to the less sweet dilute source. The right-hand graph summarizes the changes in glucose intake crossover patterns of various groups of rats with distinct energy regulatory problems. Source: Figure 9.10 of Panksepp (1998: 183), reprinted with permission of Oxford University Press.

The remarkable aspect of current feeding research, with such a cornucopia of appetite control agents, is that essentially none of these criteria have been studied. If it were demonstrated that a presumptive “satiety peptide” fulfilled all of these criteria, then this could be the recipe for reducing feeding with the desired positive affective consequences. Without at least fulfilling some of these criteria, claims simply from reductions of amount of food consumed are premature, and reflect hubris rather than sound affective neuroscientific thinking. Such tests are not often conducted because they are more difficult than the mere measurement of food intake. A more troublesome reason for neglect is that the above analysis

also requires investigators to openly consider a variety of affects as real functional properties of mammalian brains (Panksepp, 1998, 2005). Affects are real brain functions that allow animals to anticipate life-sustaining and life-detracting events. There has been one affective measure, conditioned taste aversion (CTA), that has been superbly developed, and it would be worthwhile providing an overview of this work as a trail-marker for what needs to be done with some of the other measures described above. The CTA procedure is now developed to a point where it could be used as one of the most rigorous ways to study appetite-related adverse affective changes in the human brain.

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1.  Brain regulatory systems

1.4  Conditioned taste aversions – from animal models to human brain analysis? In general, it is difficult to bring human emotional feelings under tight experimental control in research settings. This is not the case for other powerful affects, such as homeostatic feelings (e.g., hunger and thirst, which can easily be evoked hormonally). Likewise, certain sensory affects, such as nausea, can be modeled in animals using the straightforward CTA procedure, for which there is now a massive database (Riley and Freeman, 2004) – a rich pre-clinical animal literature (for recent reviews, see Sandner, 2004; Sewards, 2004; Mediavilla et al., 2005), including a fairly precise understanding of the underlying neuroanatomies in other mammals (Yamamoto et al., 1994; Reilly, 1999; Jiménez and Tapia, 2004; Reilly and Bornovalova, 2005; de la Torre-Vacas and Agüero-Zapata, 2006; Ramírez-Lugo et al., 2007). The CTA measure and the associated negative affects have widespread implications for human nutritional habits (Gietzen and Magrum, 2001; Scalera, 2002). This model has immediate implications for medical treatments and development of new and more precise therapeutics. CTA is a highly replicable and simple learning paradigm where novel tastes that are not intrinsically nauseating can be imbued with that aversive affect through simple classical conditioning principles (i.e., the pairing of a new taste with a nausea-producing manipulation). Lithium chloride is most commonly used, even though there are now many more precise brain manipulations, such as stimulation of 5HT3 and Substance P receptors (see below). Indeed, the human brain consequences of such conditioning could be evaluated with human brain-imaging. The conditioning could be done off-line (i.e., outside the scanner), which prevents people from being confronted with

unconditional nausea-promoting stimuli in the scanner. Such procedures may be also useful for delineating the circuitry for the associated fixed action patterns, such as gaping in rats (Limebeer et al., 2006). Beside the ability to control this powerful affect experimentally with a large number of distinct manipulations, the CTA paradigm provides a variety of controls that would be desirable to pursue both raw affective as well as learned-cognitive interactions (see, for example, Welzl et al., 2001; Hall and Symonds, 2006) that reflect true life experiences but can also be submitted to tight experimental control. Indeed, there are two distinct types of taste conditioning that transpire (Parker, 2003), one related to nausea (aversion) and one related to fear (avoidance), which can allow investigators to study two very distinct affects under almost identical conditions. A great strength of this model is the abundance of neurochemical manipulations currently available to directly modify specific neurochemical aspects of the underlying affect-generating circuitry. This has arisen largely because of the medical importance of controlling nausea and malaise following radiation and chemotherapies for cancers. Among the most commonly used anti-nausea agents are prochlorperazine, ondansetron and aprepitant. Their mechanisms of action are distinct and well-characterized, pharmacologically, neurochemically and functionally – especially for the latter two agents. Ondansetron is a specific serotonin 5-HT3 receptor antagonist, and aprepitant selectively blocks the NK1 tachykinin (i.e., Substance P) receptor. The former generally has a more restricted therapeutic profile (McAllister and Pratt, 1998). Although ondansetron can reverse classic lithium chloride-induced CTAs (Balleine et al., 1995) as well as aversions induced by imbalanced amino acid diets (Terry-Nathan et al., 1995), many other nausea-provoking emetics are not effectively reversed by ondansetron (Rudd et al., 1998).

1.  From Brain to Behavior

References

The large number of neurochemical manipulations for generating nausea, from apomorphine to 5-HT3 and Substance P receptor agonists (Landauer et al., 1995; Ciccocioppo et al., 1998) provides an armamentarium of convergent manipulations for actually taking the analysis of this affect to a fine circuit level in both animals and humans. At present, there is to our know­ ledge not a single brain-imaging study that has sought to study this as a model system – one that has all the desired advantages for a thorough scientific analysis, and perhaps none of the disadvantages of weak and ephemeral affects that are commonly used in human brain imaging of affective processes. The disadvantage is that these are experiments that one would not want to impose on non-medically sophisticated volunteer subjects. This may also be a blessing for obtaining the highest quality data from pro­ fessionally qualified individuals.

1.5  Conclusion Affective changes in energy regulatory studies have been neglected because it is widely assumed that qualities of animal minds cannot be systematically studied. That is wrong. Affects are ancient solutions for living, and primaryprocess variants appear to be a shared heritage in all mammals. Thus, we are finally in a position empirically to evaluate such issues in animals. Once we begin to do this with the wide array of objective measures that are available, we may be able to identify useful appetite regulating agents more readily than if we just continue traditional behavior-only analyses.

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Reilly, S. (1999). The parabrachial nucleus and conditioned taste aversion. Brain Research Bulletin, 48(3), 239–254. Reilly, S., & Bornovalova, M. A. (2005). Conditioned taste aversion and amygdala lesions in the rat: A critical. Neuroscience and Biobehavioral Reviews, 29(7), 1067–1088. Riley, A. L., & Freeman, K. B. (2004). Conditioned taste aversion: a database. Pharmacology, Biochemistry, and Behavior, 77, 655–656. Available online at: http://www. CTALearning.com. Rolls, E. T. (2009). Functional neuroimaging of umami taste: What makes umami pleasant? The American Journal of Clinical Nutrition, 90(3), 8045–8135. Rolls, E. T., & Grabenhorst, F. (2008). The orbitofrontal cortex and beyond: From affect to decision making. Progress in Neurobiology, 86(3), 216–244. Rudd, J. A., Ngan, M. P., & Wai, M. K. (1998). 5-HT3 receptors are not involved in conditioned taste aversions induced by 5-hydroxytryptamine, ipecacuanha or cisplatin. European Journal of Pharmacology, 352(2–3), 143–149. Sandner, G. (2004). Lower animal conditioning studies help in the understanding of human memory and its disorders: The merits of conditioned taste, odor, and flavor aversion research. American Journal of Physiology. Regulatory, Integrative and Comparative Physiology, 286(2), R251–R253. Scalera, G. (2002). Effects of conditioned food aversions on nutritional behavior in humans. Nutritional Neuroscience, 5(3), 159–188. Sewards, TV. (2004). Dual separate pathways for sensory and hedonic aspects of taste. Brain Research Bulletin, 62(4), 271–283. Siviy, S., & Panksepp, J. (1985). Energy balance and play in juvenile rats. Physiology & Behavior, 35, 435–441. Swerdlow, N. R., van der Kooy, D., Koob, G. F., & Wenger, J. R. (1983). Cholecystokinin produces conditioned placeaversions, not place-preferences, in food-deprived rats: Evidence against involvement in satiety. Life Sciences, 32, 2087–2093. Terry-Nathan, V. R., Gietzen, D. W., & Rogers, Q. R. (1995). Serotonin3 antagonists block aversion to saccharin in an amino acid-imbalanced diet. The American Journal of Physiology, 268, R1203–R1208. Tzechentke, T. M. (2007). Measuring reward with the conditioned place preference (CPP) paradigm: update of the last decade. Addiction Biology, 12, 227–252. Welzl, H., D’Adamo, P., & Lipp, H. P. (2001). Conditioned taste aversion as a learning and memory paradigm. Behavioural Brain Research, 125(1–2), 205–213. Yamamoto, T., Shimura, T., Sako, N., Yasoshima, Y., & Sakai, N. (1994). Neural substrates for conditioned taste aversion in the rat. Behavioural Brain Research, 65(2), 123–137.

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C H A P T E R

2 The Neurobiology of Appetite: Hunger as Addiction Alain Dagher Montreal Neurological Institute, McGill University, Montreal, Canada

o u t l i ne 2.1 Introduction

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2.2 Hunger as Addiction

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2.1  Introduction Obesity is caused by the consumption of excess calories. As such, it can be viewed as a failure of homeostatic systems that control body weight or, more appropriately, energy balance. The obese individual consumes excess calories in a non-homeostatic manner, as a result of excessive motivation or drive. A similar model has been proposed to explain drug addiction, in which hedonic homoestatic systems are dysregulated (Koob, 2008). There is considerable overlap between brain systems and neurotransmitters implicated in drug addiction and those known to control feeding behavior. Another way of looking at excess consumption of calories is that it is driven by pleasure,

Obesity Prevention: The Role of Brain and Society on Individual Behavior

2.4 Functional Brain Imaging of Cue Reactivity

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2.5 Conclusion

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or the anticipation of the palatable qualities of food. It has been suggested that there are two parallel systems for driving food intake: a homeostatic one that responds to energy signals from the body, transmitted mainly via the circulation and the vagus nerve, and acting via the hypothalamus; and a hedonic one in which food cues (odors, thoughts, the sight of food) have the ability to stimulate appetite in the absence of metabolic need (Figure 2.1). Note, however, that the addiction model provides a slightly different explanation of the effect of food cues, by viewing them as conditioned stimuli predictive of reward. In this chapter, we review the evidence linking drug addiction and obesity, and a recent study that suggests homeostatic signals interact with hedonic and incentive signals to trigger food intake.

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2010 Elsevier Inc. © 2010,

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2.  Neurobiology of appetite Homeostatic system Blood nutrients

Hedonic system Leptin

Sensory cues

Food thoughts

Vagus n.

Gut peptides

Hypothalamus

Cerebral cortex

Feeding

Feeding

Figure 2.1  A model of feeding behavior. According to this model, there are two kinds of eating: that triggered by energy deficit; and that triggered by hedonic factors, such as the anticipation of a delicious meal.

2.2  Hunger as addiction Donald Hebb suggested that hunger could be viewed as an addiction (Hebb, 1949). He proposed that hunger was a learned behavior, in which food is initially reinforcing because it reverses unpleasant sensations caused by lack of nutrients or stomach contractions. With time the behavior becomes organized and food cues, such as the sight and smell of food, become conditioned and develop the ability to induce craving, approach and consumption, much as drug-associated cues do. Conditioned cues are known to exert their incentive properties in part via the mesolimbic dopamine system (Phillips et al., 2003). Initially, unexpected food rewards trigger dopamine release; however, with time, conditioned cues paired with reward eventually promote dopamine release (Schultz, 2006). Drugs of abuse also promote dopamine release, as do their conditioned cues. The link between dopamine and feeding was first established when a dopamine receptor blockade attenuated the reinforcing effects of food, as it did for stimulant drugs and electrical brain stimulation, leading to the conclusion that addictive drugs act on the brain circuitry that controls feeding

(Wise et al., 1978). The addiction model, however, leads to the paradox of ascribing energy intake, the most basic survival behavior, to aberrant function of neurobiological systems. Nonetheless, evidence of parallels between addiction and feeding behavior continue to accumulate at the neurobiological and behavioral levels (Grigson, 2002), and we therefore have much to learn about obesity from the neuroscience of addiction (Volkow and Wise, 2005). Further support for the addiction model comes from recent findings in two domains: obesity genes and circulating energy balance hormones. Many obesity genes appear to act on reward circuitry (Farooqi and O’Rahilly, 2007). The FTO gene is expressed throughout the body and brain, but is particularly abundant in feeding-related areas in the hypothalamus, including the arcuate nucleus (Fredriksson et al., 2008). The arcuate has a direct projection to the lateral hypothalamic area (LHA), a structure long implicated in reward. Indeed, the LHA, through its outputs to the striatum and brainstem autonomic and motor nuclei, is the main hypothalamic output nucleus for the control of feeding behavior. It is also one of the sites where electrical brain stimulation is most rewarding (Wise, 2002). The product of the MC4-R gene, which has the strongest association with

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2.3  Response to conditioned cues

obesity, is also abundantly expressed in the arcuate nucleus and LHA (Adan et al., 2006). LHA neurons contain the neurotransmitter orexin, named because of its role in controlling feeding behavior, but which has also recently been implicated in drug addiction (Borgland et al., 2006). Indeed, in animals conditioned to associate a certain environment with food or drugs such as morphine or cocaine, orexin neurons in the LHA play a critical role in the expression of the preference and in its reinstatement after extinction (Harris et al., 2005). This finding further supports the idea of overlapping brain systems that mediate drug addiction and feeding. More compellingly, perhaps, an allele of the Taq1A polymorphism (the A1 allele) has been associated with both addiction and obesity (Barnard et al., 2009). This polymorphism appears to regulate dopamine D2 receptor expression, suggesting that it plays a role in the function of the reward system. Feeding is controlled in part by peripheral signals that convey information about the energy state of the individual. Four such metabolic signals have been shown to act on brain reward centers, indirectly via the hypothalamus, but also through direct effects on the mesolimbic dopamine system. Leptin, ghrelin and insulin all modulate food intake and act directly, though not exclusively, on dopamine neurons (Figlewicz et al., 2003; Abizaid et al., 2006; Fulton et al., 2006). Functional magnetic resonance imaging studies in humans have confirmed that the appetite stimulating hormone ghrelin enhances the response of the reward system to food cues (Malik et al., 2008) while the anorexigenic peptides PYY and leptin also act on reward-related brain areas (Batterham et al., 2007; Farooqi et al., 2007), presumably in an inhibitory fashion. One often mentioned difference between food and drugs is that drugs of abuse act directly on the brain, whereas the effects of food are indirect, since they must be digested before their components can enter the bloodstream. For example, after a puff of a cigarette, nicotine enters the brain within seconds, where it directly increases

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dopamine concentration by acting on dopamine neurons. Note, however, that there are multiple parallel neuronal and humoral avenues of communication between the gut and brain (Berthoud and Morrison, 2008). Appetite regulating peptides such as ghrelin (Abizaid et al., 2006) and PYY (Batterham et al., 2007), whose secretion is directly influenced by gut contents, can act on the dopamine system, providing a mechanism for food to act on reward systems almost as rapidly as some abused drugs. Also, ingested nutrients such as glucose enter the bloodstream and cross the blood–brain barrier. Indeed, experimental evidence suggests that sucrose has addictive properties very similar to those of cocaine and amphetamine (Avena et al., 2008). Finally, another similarity between feeding and addiction is the important role of stress in both behaviors. Stress is a major cause of relapse amongst abstinent drug users, and also a significant cause of failure in dieters (Adam and Epel, 2007). During stressful periods, most individuals increase their caloric intake (in particular, of saturated fats and carbohydrates). The brain areas that make up the appetitive network depicted in Figure 2.2 are all stress-sensitive.

2.3  Response to conditioned cues Previously neutral cues paired repeatedly with rewards acquire incentive properties. This phenomenon depends on the neural systems depicted in Figure 2.2. Four interconnected structures (shown in gray), the amygdala/hippocampus, insula, orbitofrontal cortex (OFC) and striatum are central elements in the control of appetitive behavior. Although each struc­ture depicted in this figure has a different role, the network as a whole is involved in learn­ ing about rewards (foods and drugs), allocating attention and effort towards them, assigning incentive value to stimuli in the environment

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PFC

ACC

Sensation (taste, olfaction, vision)

interoception (hunger, nausea) Insula

Amygdala hippocampus

OFC

Striatum

VTA (DA) Hypothalamus

Figure 2.2  The appetitive network. Brain regions involved in assigning incentive value to food- and drug-related stimuli and actions. Abbreviations: PC, prefrontal cortex; ACC, anterior cingulate cortex; OFC, orbitofrontal cortex; VTA, ventral tegmental area; DA, dopamine.

(e.g., conditioned cues) and integrating homeostatic information with information about the outside world (such as the availability of food). Homeostatic information is conveyed to the brain by circulating nutrients and hormones such as leptin, PYY and ghrelin acting primarily on the hypothalamus, and by the vagus nerve. The amygdala, insula, OFC and striatum all respond to conditioned stimuli predictive of reward (food- or drug-associated cues), as assessed in animals using electrical recordings, and in humans using functional magnetic resonance imaging (fMRI). Moreover, lesions of this network impair feeding or drug-seeking. For example, lesions of the amygdala or OFC (or disconnection of the two structures) abolish a behavior known as sensory specific satiety, in which a cue associated with a food fed to satiety loses its incentive properties (Holland and Gallagher, 2004). More generally, this network assigns incentive value to food (or drug) cues, and to the associated actions that lead to the consumption of the food (or drug). Lesions of any of these four regions impair feeding behavior in

some way. Cognitive influences on appetite are mediated by the prefrontal cortex, which exerts modulatory control over appetitive regions. A key component of the reward system is the ensemble of dopamine neurons that originate in the midbrain (especially the ventral tegmental area) and project to the striatum, amygdala, OFC, insula and prefrontal cortex. Dopamine has long been implicated in addiction, as it is released by all drugs of abuse as well as food and food cues (Di Chiara and Imperato, 1988), and dopamine blockade abolishes responding for food or drugs (Wise and Rompre, 1989). The insula has an important role in the multi­ modal processing of food information. The anterior insula is the first cortical relay of information from taste receptors in the oral cavity, but neurons there also respond to other properties of foods, such as texture, temperature, and olfactory and visual properties. The insula is also the sensory cortex for visceral information from the gut (Craig, 2002). Insula activity is modulated by cognitive and emotional factors, including hunger and attention, and by gut peptides such as

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2.3  Response to conditioned cues

ghrelin (Malik et al., 2008). The multimodal sensory features of foods in the mouth are encoded by neuronal ensembles within the insula, and this activity is modulated by hunger and satiety (de Araujo et al., 2006). The insula plays a crucial role in learning about the nutritional effects of ingested foods (de Araujo et al., 2008) and therefore aids in the ability of food cues to become conditioned. Insular lesions disrupt this phenomenon; animals with insula lesions fail to attribute incentive value to calorie-rich foods, for example (Balleine and Dickinson, 2000). Interestingly, cigarette smokers who developed insula damage (e.g., from stroke) found it easy to quit smoking (Naqvi et al., 2007). The ability of conditioned cues stimuli to trigger incentive states is a feature of both drug addiction and eating. Abstinent drug addicts report that drug cues or thoughts cause them to crave the drug, and everyone knows the feeling of seeing a dessert tray at the end of a meal, or walking past a bakery. The ability of cues to trigger appetite may be a component of the obesogenic environment, which bombards us with foods, odors, advertising, brand names and logos. Cue reactivity has been extensively studied in animals, and is starting to be used as a paradigm in human functional neuro­ imaging. A neutral stimulus or environment paired repeatedly with food or drug acquires the ability to trigger consumption of the food or drug. Kelley and colleagues have mapped gene expression changes in animals exposed to an environment previously paired with rewards (Kelley et al., 2005). For both food and addictive drugs, the paired environments caused activation in the prefrontal cortex, anterior cingulate cortex, insula, striatum and amygdala. A similar phenomenon is cue-potentiated feeding, where a conditioned cue triggers feeding, even in sated animals. This is thought to reflect craving rather than a non-specific increase in appetite, since it is only the food that was paired with the conditioned stimulus that is consumed (by analogy with drug cues, which do not cause a

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non-specific incentive state, but a specific craving for the drug itself). A similar phenomenon is described in humans, where food odors or thoughts only cause increased consumption of the target food (Fedoroff et al., 2003). Petrovich, Gallagher and Holland have, in a series of animal experiments, delineated the neural network for cue-potentiated feeding (see, for example, Petrovich and Gallagher, 2007). Key components include the basolateral amygdala, LHA, and medial prefrontal cortex including the OFC. In particular, a projection from the basolateral amygdala to LHA is crucial, since disconnecting these regions abolishes cue-potentiated feeding. Conditioned cues do more than inform an individual about available rewards; such cues also energize individuals by creating an incentive state, motivating them to approach and consume food or other rewards with great vigor – a phenomenon that appears to be mediated in large part by dopamine (Phillips et al., 2003). It appears that certain individuals react to appetitive signals with greater drive, and this may be a risk factor for developing addiction and obesity. Although considerable evidence exists for this model with respect to drug addiction, evidence is less abundant in the field of obesity. Some measures of reward sensitivity appear to predict appetitive behavior and obesity (Franken and Muris, 2005; Davis and Fox, 2008). Moreover, the personality variable of impulsivity, defined as a tendency to act without due consideration of long-term consequences, has been shown to confer vulnerability to drug addiction in humans (Verdejo-Garcia et al., 2008) and animals (Belin et al., 2008). Impulsivity may be in part a consequence of enhanced reward drive, where immediately available rewards acquire great saliency and incentive properties, and in our obesogenic environment it may promote excess calorie intake and obesity (Davis, 2009). Interestingly, humans who develop drug addiction also display an enhanced preference for sweet foods (Grigson, 2002), suggesting a common substrate for both.

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2.4  Functional brain imaging of cue reactivity Functional magnetic resonance imaging measures changes in regional cerebral blood flow, which indicates changes in synaptic activity. Conditioned cues were first combined with fMRI in drug addicts. Cigarette cues (pictures, videos, stories) typically activate areas of the appetitive network (Figure 2.2), namely the prefrontal cortex, amygdala, insula, striatum and OFC (Due et al., 2002; Wilson et al., 2004; McBride et al., 2006). Interestingly, very similar areas are activated by food cues (LaBar et al., 2001; Small et al., 2001, 2005; Arana et al., 2003; Simmons et al., 2005; Malik et al., 2008; Stice et al., 2008), including pictures, odors and tastes. Thus, as in animals (Kelley et al., 2005), conditioned cues activate an appetitive network, whether the cues are food- or drug-related. The fMRI response in these regions appears to be a measure of the appetitive impact of the cues. For example, the appetite stimulating peptide ghrelin enhanced the brain response to food pictures in the amygdala, insula, OFC and striatum (Malik et al., 2008). The response in amygdala and OFC correlated with subjective hunger. Interestingly, a personality measure of reward sensitivity also predicted the appetitive impact of food cues in humans (Beaver et al., 2006). The fMRI response to appetizing versus bland food pictures in the amygdala, OFC and ventral striatum was proportional to the score on the Behavioral Activation Scale. This measure also predicts weight gain and obesity (Franken and Muris, 2005). The foregoing suggests that fMRI responses to food or food cues could be a biomarker of vulnerability to obesity. Recent human brain-imaging studies appear to support this idea. Stice and colleagues have suggested that an enhanced insula response to the anticipation of food is predictive of obesity (Stice et al., 2009). Conversely, obesityprone individuals may display reduced activation

to actual food consumption in the striatum (Stice et al., 2008). Interestingly, the relationship between BMI and the fMRI signal was modulated by the Taq1A polymorphism.

2.5  Conclusion Obesity has been described as a neurobehavioral disorder caused by an interaction between a vulnerable brain and an obesogenic environment (O’Rahilly and Farooqi, 2008). Conditioned cues such as the sight and smell of food, or food advertising, have the ability to trigger an incentive state that is very similar to phenomena seen in drug addicts. The neural structures activated by conditioned cues, whether drug- or food-related, appear to overlap considerably. On the other hand, hormonal energy balance signals, such as ghrelin (Malik et al., 2008), also appear to act on the appetitive network, to increase feeding. This suggests an alternate model where there are not two brain systems for feeding (Figure 2.1), but a single appetitive system that can be modulated by both homeostatic and hedonic signals (Figure 2.3). Homeostatic signals

Hedonic signals

Vagus n.

Sensory cues

Leptin Food thoughts Blood nutrients

Appetitive system

Gut peptides

Arlousal

Attention

Motivation

Feeding

Figure 2.3  A new model of feeding behavior. Here, energy balance signals and conditioned cues act on the same brain systems to promote food intake. The shaded structure represents the appetitive system depicted in Figure 2.2. Not shown are cognitive factors, such as restraint (e.g., in dieters), that can down-regulate responses in this system.

1.  From Brain to Behavior

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Schultz, W. (2006). Behavioral theories and the neurophysiology of reward. Annual Review of Psychology, 57, 87–115. Simmons, W. K., Martin, A., & Barsalou, L. W. (2005). Pictures of appetizing foods activate gustatory cortices for taste and reward. Cerebral Cortex, 15, 1602–1608. Small, D. M., Zatorre, R. J., Dagher, A., Evans, A. C., & JonesGotman, M. (2001). Changes in brain activity related to eating chocolate: From pleasure to aversion. Brain, 124, 1720–1733. Small, D. M., Gerber, J. C., Mak, Y. E., & Hummel, T. (2005). Differential neural responses evoked by orthonasal versus retronasal odorant perception in humans. Neuron, 47, 593–605. Stice, E., Spoor, S., Bohon, C., & Small, D. M. (2008). Relation between obesity and blunted striatal response to food is moderated by TaqIA A1 allele. Science, 322, 449–452. Stice, E., Spoor, S., Ng, J., & Zald, D. H. (2009). Relation of obesity to consummatory and anticipatory food reward. Physiology & Behavior, 97, 551–560. Verdejo-Garcia, A., Lawrence, A. J., & Clark, L. (2008). Impulsivity as a vulnerability marker for substance-use disorders: Review of findings from high-risk research, problem gamblers and genetic association studies. Neuroscience and Biobehavioral Reviews, 32, 777–810. Volkow, N. D., & Wise, R. A. (2005). How can drug addiction help us understand obesity? Nature Neuroscience, 8, 555–560. Wilson, S. J., Sayette, M. A., & Fiez, J. A. (2004). Prefrontal responses to drug cues: A neurocognitive analysis. Nature Neuroscience, 7, 211–214. Wise, R. A. (2002). Brain reward circuitry: Insights from unsensed incentives. Neuron, 36, 229–240. Wise, R. A., & Rompre, P. P. (1989). Brain dopamine and reward. Annual Review of Psychology, 40, 191–225. Wise, R. A., Spindler, J., deWit, H., & Gerberg, G. J. (1978). Neuroleptic-induced “anhedonia” in rats: Pimozide blocks reward quality of food. Science, 201, 262–264.

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C H A P T E R

3 Opioids: Culprits for Overconsumption of Palatable Foods? 1

Pawel K. Olszewski1, 2, Johan Alsiö2, Helgi B. Schiöth2 and Allen S. Levine1

Minnesota Obesity Center, Department of Food Science and Nutrition, University of Minnesota, Saint Paul, MN, USA 2 Department of Neuroscience, Uppsala University, Uppsala, Sweden o u t l i n e

3.1 Introduction 3.2 Opioids and Feeding Behavior in Rodent Models 3.2.1 Opioids Promote the Intake of Palatable Foods 3.2.2 Opioids Within the Central Feeding-related Reward Network 3.2.3 Palatability of Ingested Tastants Affects Endogenous Opioid Tone 3.2.4 Opioids: in Search of Palatability or a Specific Macronutrient?

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3.3 Opioids and Dysregulation of Eating Patterns and Body Weight in Human Beings 3.3.1 Excessive Eating and Body Weight 3.3.2 Underweight Individuals

31 31 33

3.4 Conclusions and Perspectives

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3.1  Introduction A traditional dietary questionnaire probes eating habits of an individual, and it routinely assesses what foods are most palatable and

Obesity Prevention: The Role of Brain and Society on Individual Behavior

3.2.5 Opioids: Feeding for Calories or for Pleasure?

what amounts of such tastants are ingested with regularity. Preferred diet profiles differ a lot between people, yet one distinctive charac­ teristic emerges, and that is that fat and/or sugar constitute a significant percentage of the favored foods. Importantly, palatability serves

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© 2010, 2010 Elsevier Inc.

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3.  Opioids and Overconsumption

as a key factor promoting excessive intake of calories, which consequently leads to excess weight and obesity. Among endogenous substances involved in food-intake regulation, opioid peptides are per­ haps the prime suspects driving excess intake of palatable food. This notion is based on the effects of opioid agents on the consumption of preferred tastants, especially those rich in fat and sugar. Here, we discuss the role of opioids in overeating palatable foods as elucidated in animal studies, and present applicability of the basic research findings to understanding and treating obesity in humans.

3.2  Opioids and feeding behavior in rodent models 3.2.1  Opioids promote the intake of palatable foods Although opioid receptor antagonists reduce food intake in general, their effects are more pronounced when palatable foods are pro­ vided. Chronic naltrexone infusions via osmotic minipumps suppressed food intake and bodyweight gain more efficiently in animals with free access to a sucrose solution than in chow con­ trols (Marks-Kaufman et al., 1984). Naltrexone decreased intake of both sweet and oily mash in non-deprived rats (Kirkham et al., 1987). Furthermore, naloxone reduced the intake of sweet chow more than of standard chow in ad libitum- and schedule-fed animals, as well as after deprivation and chronic restriction (Levine et al., 1995). In fact, when rats were schedulefed and received sweetened chow for just 20 minutes per day, naloxone decreased con­ sumption of the palatable diet, but only to the level of the minimum daily calorie requirement. In that experiment, only high doses were effec­ tive in lowering the intake of standard pellets. Naloxone also reduced the intake of sucrose

and polycose in food-restricted rats, but not the intake of a cornstarch diet (Weldon et al., 1996). Anorexigenic effects of naloxone were most pronounced when chocolate chip cookies were presented, while naloxone did not modify the intake of high-fiber bland food (Giraudo et al., 1993). These results show that rats eating sugar diets are more sensitive to naloxone than rats fed plain food, and the ability to fulfill energy needs is not impaired by the antagonism of opi­ oid receptors. The relationship between the opioid sys­ tem and palatability was supported by studies employing non-caloric palatable substances. Opioid-receptor deficient mice displayed lower saccharin preference than wild-type mice (Yirmiya et al., 1988). The effects of naloxone on the consumption of artificially sweetened solu­ tions are similar to the effects on sucrose intake. Naloxone reduced the intake of saccharin solu­ tions at lower doses than it reduced the con­ sumption of NaCl or water (Turkish and Cooper, 1983). Naloxone pre-treatment reduced saccharin intake in non-deprived animals, blocked ­normal acquisition of saccharin preference (Lynch, 1986), and suppressed imbibition of fluid with saccharin, sucrose, NaCl or HCl, but it did not influence the intake of aversive quinine (Levine et al., 1982). Similar conclusions were reached in experi­ ments utilizing sham-feeding and sham-drinking; in this paradigm, the tastant consumed by rodents is immediately removed from the stom­ ach via a gastric fistula, preventing post-ingestive or post-absorptive feedback. In sham-drinking, naloxone reduced sucrose intake in non-deprived and water-deprived rats (Rockwood and Reid, 1982). Furthermore, while sucrose sham-drinking increases proportionally to sugar concentration (Kirkham and Cooper, 1988a), naloxone shifts the concentration-intake curve – that is, it reduces the intake of 10% sucrose to the amounts consumed by saline-injected animals drinking a less pre­ ferred 5% sucrose solution (Kirkham and Cooper, 1988a, 1988b). Importantly, naloxone does not

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3.2  Opioids And Feeding Behavior In Rodent Models

influence the ability to discriminate between sucrose concentrations, indicating that process­ ing of taste (i.e., “interpreting” flavor on a rela­ tive reward scale) is affected by opioid sig­naling, not the ability to discriminate a given taste (O’Hare et al., 1997). These results suggest that the palatability of solutions – that is, their orosen­ sory reinforcing value – is reduced by naloxone. Consequently, the acquisition and expression of preference for the orange odor, induced by concomitant intraoral infusions of sucrose, was blocked by systemic naltrexone applied prior to pairing the exposure to this odor with sucrose (Shide and Blass, 1991). In contrast, flavor prefer­ ences induced by intragastric infusions of sucrose were not affected by naltrexone (Azzara et al., 2000). In operant experiments, the progressive ratio schedule measures motivation for a reinfor­ cer; the effort needed for successive reward increases exponentially over sessions. When responding for sucrose, motivation increases with the concentration of sugar (Sclafani and Ackroff, 2003); naloxone, however, reduces this motivation (Cleary et al., 1996). A genetic dele­ tion of the mu opioid receptor (MOR) leads to a decreased motivation to consume food (Papaleo et al., 2007). In operant behavior stud­ ies, this diminished motivation has been asso­ ciated with the consumption of palatable and bland diets, which suggests that the MOR sup­ ports a drive to ingest foods regardless of their attractiveness. In addition, the MOR-null ani­ mals display a decreased feeding anticipatory activity (Kas et al., 2004). It does not negate the involvement of this receptor in hedonics of feeding, because any diet – with the exception of aversive tastants – is associated with pleasure and a positive “incentive” of feeding. Tabarin et al. (2005) reported that mice lacking the MOR were resistant to diet-induced obesity, and they did not exhibit impaired glucose tolerance. It was particularly the case when high-fat food was offered. Resistance to obesity did not stem only from a diminished propensity to overeat,

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but was also linked with a higher expression of mitochondrial enzymes involved in fatty acid oxidation in skeletal muscles.

3.2.2  Opioids within the central feeding-related reward network Initial studies elucidating the role of opi­oids in feeding focused on peripheral injections. However, a crucial question was later asked as to whether the action is mediated via peripheral and/or central receptors. Early experiments compared the effects of regular versus quarternary naltrexone on food intake. The latter, a charged molecule which does not cross the blood–brain barrier, has no effect on feeding, while the uncharged naltrex­ one reduces food intake (Carr and Simon, 1983; Marks-Kaufman et al., 1985). Hence, the targets for opioid agents are present at the central level. Subsequent studies showed that intracerebro­ ventricular and site-specific microinjections of opioid ligands affected consummatory behav­ ior (Levine and Billington, 2004; Olszewski and Levine, 2007). Importantly, the reward network seems to have a close relationship with opioid mechan­ isms driving intake of palatable foods. The nucleus accumbens (NAcc) is one of the major players mediating hedonics of food. Peripheral morphine increases the intake of sucrose and saccharin, and this effect can be reversed by naloxone. This was observed also following morphine injections into the NAcc and ventro­ medial striatum, but not into the caudate puta­ men (Evans and Vaccarino, 1990; Bakshi and Kelley, 1993a). In addition, systemic morphine induced Fos immunoreactivity, which serves as the marker of neuronal activation, in the NAcc (Bontempi and Sharp, 1997); it suggests that neu­ rons in this site respond to opiate stimulation. Although all types of opioid receptors are present in the NAcc, the MOR appears to be of greatest importance for food intake; the delta

1.  From Brain to Behavior

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3.  Opioids and Overconsumption

receptor (DOR) plays a relatively limited role, while activation of the kappa receptor (KOR) has rarely been shown to stimulate feeding. NAcc injections of agonists, such as morphine, [DAla2, D-Leu5]-enkephalin and beta-endorphin, elevated chow intake in rats by acting at the MOR and DOR. KOR stimulation had less or no effect; dynorphin produced an increase in food intake only at a very high dose, whereas the selective KOR agonist, U50488, was inef­ ficient (Majeed et al., 1986). Consumption of sucrose was increased by NAcc morphine, [DAla2, N-MePhe4, Gly-ol]-enkephalin (DAMGO) and [D-Pen2,5]-enkephalin, but not dynorphin or U50488 (Zhang and Kelley, 1997). DAMGO produced robust increases in food intake when injected into the NAcc (Bakshi and Kelley, 1993b). A smaller effect was seen with the DOR stimulation, while KOR agonists had no influ­ ence (Bakshi and Kelley, 1993b). The selective blockade of the MOR by administration of a non-reversible antagonist, beta-FNA, decreased consumption of sucrose by 40 percent during 4-h tests conducted 2, 3 and 4 days after infu­ sion (Ward et al., 2006). In the operant behav­ ior setting, NAcc DAMGO increased correct lever presses for sucrose and resulted in higher break-points on the progressive ratio schedule (Zhang et al., 2003). DAMGO also increased the intake of high-fat and high-carbohydrate diets in non-deprived rats, and elevated consumption of a high-fat diet in deprived rats (Zhang et al., 1998). The feeding effects of morphine at the NAcc are subject to sensitization: with repeated injections, consumption is further increased (Bakshi and Kelley, 1994). Such sensitization may implicate cues (environmental or proce­ dural) that over time become associated with hyperphagia and, through conditioning, induce feeding on their own (Wardle, 1990). The effect of morphine may stem from the compound’s acute stimulation of feeding as well as from facilitation of conditioning. The ventral tegmental area (VTA) is another component of the reward network that mediates

orexigenic action of opioids. Morphine injected in the VTA increases food intake, and this effect is reversible by naloxone (Mucha and Iversen, 1986). [D-Ala, Met]-enkephalin (DALA) infused into the VTA induces feeding in deprived and non-deprived rats (Cador et al., 1986). Not only is the amount of food eaten increased; so too is the time spent eating (Hamilton and Bozarth, 1988). VTA morphine increased the speed of consump­ tion upon refeeding, but it did not affect the latency to begin a meal (Noel and Wise, 1993). Deprivation-induced feeding was accelerated by VTA injections of DAMGO or [D-Pen2,D-Pen5]enkephalin (Noel and Wise, 1995). VTA DAMGO affected latency to feed and the number of active feeding episodes, and facilitated food-related behaviors (including those that did not result in ingesting calories); however, the magnitude of the observed effects was dependent on the context – for example, the presence of the ani­ mal in or outside the cage (Badiani et al., 1995). Finally, operant behavior experiments showed that the VTA MOR was the major mediator of reward (Devine and Wise, 1994). Since opiates are associated with the intake of palatable foods, endogenous opioids have been proposed to mediate hedonics of eating. In humans, experiments assessing the hedonic impact of food are performed with the aid of visual analog scale ratings. To measure hedonic impact in animals, the taste reactivity test was developed (Grill and Norgren, 1978); this can be used to assess the affective influence of tastants (Berridge, 2000). The test is based on stereo­ typic behaviors: positive (hedonic) and nega­ tive (aversive) reactions to palatable and bitter tastes, respectively. These responses are homol­ ogous across species, and they include both positive reactions (such as rapid tongue protru­ sions) and negative ones, including gaping and head shakes (Steiner et al., 2001). The magnitude of these responses is used to define the hedonic aspects of a tastant (Grill and Norgren, 1978). It was hypothesized that if opioids affect the hedonic processing of a gustatory stimulus,

1.  From Brain to Behavior

3.2  Opioids And Feeding Behavior In Rodent Models

these behaviors would be altered by opioid manipulations. Morphine increased the hedonic response to sucrose and attenuated aversive reactions to quinine, while naltrexone decreased the number of hedonic reactions to sucrose (Parker et al., 1992; Doyle et al., 1993; Clarke and Parker, 1995; Pecina and Berridge, 1995; Rideout and Parker, 1996). Changes in hedonic responses were coupled to concomitant effects on food intake, showing that the higher the per­ ceived palatability, the greater the amount of consumed food. Subregions of the NAcc and ventral pallidum have been dubbed “hedonic hot spots” likely to be implicated in this palat­ ability-induced hedonic state. Opioid activity within these regions contributes to reward ­mot­ivation: the notion that discrete sites medi­ ate hedonic reactions to opioids has been assessed by the taste reactivity test (Pecina and Berridge, 2000, 2005; Smith and Berridge, 2005). DAMGO elicits hedonic reactions only when administered in a small rostrodorsal subregion of the medial NAcc shell, while food intake is stimulated regardless of injection site in the medial shell area (Pecina and Berridge, 2005). Subdivisions of the ventral pallidum are even more distinct: DAMGO in the posterior part stimulates hedonic reactions and eating, while anterior infusions reduce the hedonic aspect of feeding and inhibit consumption (Smith and Berridge, 2005). These studies corroborate the involvement of opioid signaling within reward sites in hedonic reactions to palatable food.

3.2.3  Palatability of ingested tastants affects endogenous opioid tone Numerous studies have indicated that the opioid system’s activity is affected by palatable foods. Initial observations revealed that consump­ tion of such tastants modified certain behaviors or perception of stimuli in a similar way to that of administration of opioid agonists. For example sucrose relieves the sensation of pain or increases

27

the thresholds for pain reactions – for example, in the hot-plate and tail-flick tests (Anseloni et al., 2005; Segato et al., 2005). This effect was depend­ ent on the concentration rather than the total amount of the ingested macronutrient (hence, it paralleled the palatability scale). Rats provided with intermittent access to 25% glucose or 10% sucrose for 12 hours per day increased their daily sugar intake over time and doubled it within 10 days (Colantuoni et al., 2001). Interestingly, when deprived of sugar the animals displayed a range of behaviors (anxietylike behavior in the plus-maze test, teeth chat­ tering, forepaw tremor, head shakes) typical for rats undergoing opiate withdrawal (Colantuoni et al., 2002; Avena et al., 2008a, 2008b). In line with those findings, a similar array of behaviors was induced through the blockade of opioid signaling with naloxone in rats given access to sugar. The data indicate that intermittent access to sugar produces a state analogous to opioid dependence (Avena et al., 2005). Thirty days of intermittent sugar intake led to the elevation of MOR1 binding in the NAcc shell, cingulate cortex, hippocampus and locus coeru­ leus (Colantuoni et al., 2001). It is noteworthy that a similar pattern of MOR binding was detected as a result of opiate infusions (Vigano et al., 2003). In addition, enkephalin expression in the striatum and the NAcc was decreased in animals subjected to intermittent sucrose access (Spangler et al., 2004) or with limited daily access to a highfat/high-sugar diet (Kelley et al., 2003); analogous effects have been induced by repeated morphine injections. Thus, the influence of intermittent sucrose access is equivalent to the development of opiate dependence also at the neurochemical level. In addition, it has been hypothesized that down-regulation of enkephalin gene expression causes a compensatory increase in MOR presen­ tation (Avena et al., 2008b). Pomonis and collaborators studied neuro­ nal activation in rats given access to sucrose for 3 weeks. Sugar consumption induced an increase in Fos-IR in areas associated with

1.  From Brain to Behavior

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3.  Opioids and Overconsumption

opioid-mediated reward, including the NAcc, bed nucleus of the stria terminalis and the amygdala (Pomonis et al., 2000). Sucrose shamdrinking for 10 days produced a similar pattern of c-Fos expression in reward sites (Mungarndee et al., 2008). In rats consuming sugar for sev­ eral weeks, naltrexone injection not only pre­ cipitates withdrawal-like symptoms, but also changes Fos IR in the reward circuitry that con­ tains opioid receptors and responds to sucrose intake (Pomonis et al., 2000). It should be noted that a change in opioid tone within one site is capable of affecting the activity of other areas, which illustrates the complexity of the neural network governing feeding reward. For exam­ ple, injecting an orexigenic dose of DAMGO in the amygdala generates changes in c-Fos in the NAcc shell (Levine et al., 2004). Opioid-driven changes in neuronal activity have also been shown within the amygdala–hypothalamic para­ ventricular nucleus (PVN) pathway (Pomonis et al., 1997). Molecular studies have also provided a link between palatability and opioids. Intake of a high-fat/high-sucrose diet increased expression of dynorphin in the arcuate nucleus (ARC) of rats, simultaneously increasing dynorphin pep­ tide levels in the PVN (Welch et al., 1996). There were no effects on the mRNA levels of enkepha­ lin or proopiomelanocortin (POMC) in the ARC, or PVN protein levels of met-enkephalin or betaendorphin. When the intake of the palatable diet was matched through pair-feeding to the caloric intake of control animals maintained on a standard diet, no change in dynorphin levels was detected; this indicates that the diet per se does not affect dynorphin, but that such effects are seen only when palatable food is ingested in excessive amounts. Interestingly, restricted access to palatable food decreased the ARC expression of enkephalin and POMC. These effects indicate that limiting access to palatable food is similar to food restriction with regard to effects on opi­ oid signaling (see, for example, Kim et al., 1996). Accordingly, rats switched from palatable food

to a standard diet had decreased mRNA levels of POMC in the ARC, and decreased dynorphin expression in the PVN, ARC and lateral hypo­ thalamus (LH) (Levin and Dunn-Meynell, 2002). Restricted access to preferred food also affects the striatal regions: rats with a 3-h daily access to a palatable diet showed reduced expression of enkephalin in the NAcc shell and dorsal stria­ tum (Kelley et al., 2003); acute exposure had no effect. Overall, palatable diets, regardless of whether high in fat or sugar, promote increased activity within opioidergic circuits. Together with the data showing that opioids acting in these same central areas promote intake of palatable foods, feeding reward-driven increase in opioid tone may function as positive feedback, propelling consummatory behavior even further.

3.2.4  Opioids: in search of palatability or a specific macronutrient? The conclusion that opioids stimulate con­ sumption of palatable tastants was based on the outcome of experiments utilizing diets rich in fat and sugar. Hence, a concern was raised that opioids may not be involved in palat­ ability- but in macronutrient-driven feeding. In fact, neuropeptides affect consumption of individual macronutrients in a different man­ ner. For example, neuropeptide Y (NPY) pref­ erentially stimulates carbohydrate intake, while ghrelin enhances fat ingestion (Shimbara et al., 2004). Oxytocin seems implicated in sucrose consumption: oxytocin knockout mice overeat sucrose but not lipid emulsions (Miedlar et al., 2007; Sclafani et al., 2007). The orexigenic effects of galanin are stronger when high-fat diets are offered (Tempel et al., 1988; Odorizzi et al., 1999). The initial studies indicated that morphine increased preference for dietary fat in rats (Marks-Kaufman, 1982; Marks-Kaufman and Kanarek, 1980, 1990), whereas naloxone (MarksKaufman and Kanarek, 1981) and naltrexone

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3.2.  Opioids And Feeding Behavior In Rodent Models

(Marks-Kaufman et al., 1985) decreased fat pref­ erence. In addition, KOR agonists increased intake of a high-fat diet to a greater extent than carbohydrate (Romsos et al., 1987). Other stud­ ies linked opioids with elevated preferences toward protein and fat (Bhakthavatsalam and Leibowitz, 1986; Shor-Posner et al., 1986). Subsequently, preference studies confirmed that – just as in humans – food preferences vary between individuals in any strain of outbred rodents, and that accounting for such varia­ tions provides more accurate results. Correcting for baseline preference allowed investigators to show that morphine selectively increases intake of the macronutrient a given animal prefers: carbohydrate-preferring rats increased carbohydrate intake, while fat consumption was increased in fat-preferrers (Gosnell et al., 1990). It supported the conclusion that since opioids specifically increase intake of palatable food, the individual animal treated with opioid agonists is driven to consume the most palat­ able tastant. Accordingly, naloxone reduced the intake of only the preferred diet in animals with a simultaneous access to high-carbohydrate and high-fat food (Glass et al., 1996). It should be noted that conflicting data show that even after correcting for baseline preference, morphine increases primarily fat intake (Welch et al., 1994); this may still be a matter of palatability, since the preference for lipids was increased by mor­ phine only when these lipids were preferred by the animals at baseline (Glass et al., 1999a). Fat preference is thus affected only when the fat diet is preferred or palatable. These findings are in line with the role of opioids in palatability, and they do not provide a link between macro­ nutrients and opioid activity.

3.2.5  Opioids: feeding for calories or for pleasure? Another dilemma was whether opioids’ effect on the intake of palatable diets was associated

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with reward, or with a drive to ingest calories. This uncertainty was based on the fact that opi­ oid antagonists suppressed the consumption of diets whose hedonic value was very limited, such as standard laboratory chow. In addition, these antagonists were also capable of diminish­ ing orexigenic properties of peptides involved in the development of hunger, such as NPY or Agouti-related protein (AgRP); in those stud­ ies, regular chow pellets were offered (Pomonis et al., 1997; Olszewski et al., 2001). In order to evaluate the possible link between opioids and hunger, opioid gene expression and peptide levels were assessed during food restric­ tion and food deprivation. Hunger-­signaling genes and molecules such as NPY (Brady et al., 1990; Bi et al., 2003; de Rijke et al., 2005; Johansson et al., 2008) and AgRP (de Rijke et al., 2005) are typically up-regulated during catabolic states, while anorectic agents such as cocaineand amphetamine-regulated transcript (CART) (de Rijke et al., 2005) are down-regulated. Gene expression levels of dynorphin, enkephalin and POMC were decreased following different lev­ els of food restriction in a “dose (thus, hunger-)dependent” manner (Kim et al., 1996). The over­ all hypothalamic expression of dynorphin was also reduced by chronic food restriction and by acute deprivation (Johansson et al., 2008). Food deprivation lasting 48 hours was associ­ ated with decreased dynorphin and POMC expression, whereas 24-h deprivation decreased only POMC mRNA levels; enkephalin was not affected (Kim et al., 1996). Also, ARC POMC expression was lower upon deprivation (Brady et al., 1990; Bi et al., 2003; Johansson et al., 2008), although one should not forget that POMC gives rise to several molecules of orexigenic (beta-endorphin) as well as anorexigenic (alphamelanocyte stimulating hormone; -MSH) properties, which can make the POMC expres­ sion analysis difficult. Overall, deprivation and restriction studies have shown that opioid gene expression tends to be opposite to changes in the expression profile of hunger-related genes.

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3.  Opioids and Overconsumption

It suggests that opioids do not play a major role in stimulating consummatory activity per se even under the conditions of depleted energy stores of the organism (Haberny and Carr, 2005). In fact, a diminished activity of genes coding opioids in low-energy states seems to reflect a low level of hedonic stimulation in underfed animals. The aforementioned data were also in con­ cert with the hypothesis that opioids affected maintenance of meals, rather than initiation of feeding. Using a two-phase operant technique, Rudski and colleagues distinguished initiation and maintenance of meals; the animals had to press a lever 80 times to receive the first food pellet (initiation), but 3 times to receive each consecutive pellet (maintenance) (Rudski et al., 1994). Naloxone did not modify the time to receive the first pellet, but reduced the total number of pellets consumed. Hence, initiation of the meal was not affected, but maintenance of the meal was disrupted. Similarly, Kirkham and Blundell used a runway test to monitor both food motivation and food consumption (Kirkham and Blundell, 1986). Naloxone and naltrexone reduced food intake, while having no effect on running speed or latency to reach the food. Naloxone reduced sucrose consumption at a dose which had no effect on conditioned place preference for sucrose (Agmo et al., 1995). This finding also indicates that opioids are impli­ cated in the rewarding effect of sucrose, rather than the reinforcing effects. Although naloxone red­uced break-points for both sucrose and grain pellets on an operant progressive ratio schedule, the effects of the compound were stronger with free access to food (Glass et al., 1999b). One plausible role for opioids in the regula­ tion of calorie intake is through interference with satiety signals, which occur in response to a plethora of cues indicating that a suffi­ cient amount of food has been consumed. They include stomach distension and changes in gastrointestinal tract peristalsis, and increased blood levels of salt, glucose and nutrients

(Olszewski and Levine, 2007). Neuroactive agents released due to the presence of these cues – such as -MSH, cholecystokinin, oxy­ tocin and insulin – support termination of con­ summatory activity. It is noteworthy that these feeding inhibitory mechanisms do not seem to act with similar efficiency when palatable ver­ sus bland diets are offered. When “rewarding” ingestants are available, food intake is increased in terms of both calories and volume. For exam­ ple, short-term food-deprived rats given corn­ starch eat 50 percent less than animals presented with a diet high in sugar (Levine and Billington, 1989). Even rats fed for only 2 hours per day eat circa 30 percent more when sucrose is added to the diet, despite the fact that the amount of consumed sweet food approaches the volume capacity of the stomach. As mentioned above, opioid release seems to coincide with the consumption of preferred tastants; hence, the relationship between opi­ oids and satiety has been studied in the quest to elucidate mechanisms responsible for over­ consumption of palatable foods. Two such sys­ tems, oxytocin and -MSH, appear to be affected by the activity of the opioidergic pathways. Naltrexone at anorexigenic doses induced Fos IR in ARC -MSH neurons (Olszewski et al., 2001). Conversely, an opioid-like hypherphagic peptide, nociceptin/orphanin FQ, decreased the percentage of Fos-IR -MSH cells at refeed­ ing (Bomberg et al., 2006). -MSH levels were lower in the medial basal hypothalami of rats equipped with peripheral morphine pumps com­ pared to controls (Wardlaw et al., 1996). Chronic morphine infusions led to a time-dependent down-­regulation of the melanocortin-4 ­ receptor (MC4R) gene expression in the striatum (Alvaro et al., 1996). Morphine also reduced the expres­ sion of MC4R mRNA in the NAcc (Alvaro et al., 1996). The link between oxytocin and opi­ oids in feeding has been less extensively stud­ ied. Butorphanol tartrate, an opioid agonist, increases consumption of high-sugar food. The effective dose of butorphanol decreased sucrose

1.  From Brain to Behavior

3.3  Opioids and dysregulation of eating patterns and body weight in human beings

intake-driven activity of PVN oxytocin neurons (Olszewski and Levine, 2007). Interestingly, aside from affecting satiety, administration of opiates alleviates aversive consequences of toxin-tainted foods; therefore, activity of the opioid system prevents termination of feeding even under the conditions of jeopardized homeostasis. In line with these findings, naloxone potentiated aver­ sion-related hypophagia (Flanagan et al., 1988). It should be noted that oxytocin is a media­ tor of aversive responsiveness: opioids dimin­ ished activity of the oxytocin system, whereas naloxone stimulated oxytocin release in toxintreated rats. Overall, the relationship between opioids and energy consumption seems to be based on facilitating excessive intake of palatable food. Opioids, the release of which is stimulated by eating preferred foods, maintain consummatory behavior by silencing mechanisms that signal satiety. These peptides also contribute to “ignor­ ing” cues that suggest a danger to the homeo­ stasis brought on by ingesting “risky” foods.

3.3  Opioids and dysregulation of eating patterns and body weight in human beings One of the key issues in studies utilizing ani­ mals is applicability of the outcome of these studies to human conditions. Data obtained in basic research experiments pertaining to opi­ oids and overconsumption of palatable foods seem of particular importance, considering the growing number of overweight and obese indi­ viduals in the industrialized world. Hence, the discovery of the relationship between opioids and feeding reward in animals led to attempts to confirm it in humans, and subsequently to administer opioid ligands in people in order to alter eating patterns and, in consequence, mod­ ify body weight.

31

3.3.1  Excessive eating and body weight A study performed on morbidly obese patients revealed a significant positive correla­ tion between beta-endorphin and body weight, and with a degree of body-weight increase (Karayiannakis et al., 1998a). A higher plasma beta-endorphin level has been found in female subjects exhibiting moderate obesity (BMI: 31–39) compared to lean women (Baranowska et al., 2000). Interestingly, some cases of phar­ macotherapy-induced decreases in body weight in obese people lead to a decline in the betaendorphin profile (Baranowska et al., 2005). In non-diabetic obese women, higher plasma concentrations of this opioid correlate with lower insulin sensitivity (Percheron et al., 1998). Increased opioid activity is associated with the abdominal-type body fat distribution in obese women (Pasquali et al., 1993). Bulimia is charac­ terized by the dysregulation of beta-endorphin signaling; changes in plasma levels of this pep­ tide are mitigated by body weight (Fullerton et al., 1986; Waller et al., 1986). Also, craving for sweet tastants has been often associated with addiction to opiates, while opiate withdrawal can be relieved by consumption of sweets (Morabia et al., 1989; Willenbring et al., 1989). As early as in 1971, Zaks and co-workers reported a case of a detoxified drug addict treated daily with 1500 mg oral naloxone who experienced reduced appetite in response to this antagonist (Zaks et al., 1971). The use of lower doses of naltrexone – which is more potent than naloxone and has a half-life of about 4–10 hours – in similar patients for 14–42 days caused a decrease in body weight (Sternbach et al., 1982). Kyriakides and colleagues showed that naloxone reduced hyperphagia in two of three studied obese Prader-Willi patients (Kyriakides et al., 1980); however, subsequent studies produced conflicting results (Zlotkin et al., 1986; Zipf and Berntson, 1987; Benjamin and Buot-Smith, 1993). The first study on opioids and overweight in people without any underlying pathology

1.  From Brain to Behavior

32

3.  Opioids and Overconsumption

was performed by Theodore Schwartz, who used himself as the subject (Schwartz, 1981). He was 120 percent of his ideal body weight. Over 48 days, which included 39 days of self­administering various doses (up to 9.6 mg per day orally) of naloxone, a weight loss of 9 kg was achieved. There was no evident effect on hunger or eating stimulated by conditioning or social interactions; instead, naloxone seemed helpful in making a decision to discontinue consumption. Trenchard and Silverstone (1983) conducted a follow-up study employing double-blind con­ ditions in 12 volunteers given 0.8 and 1.6 mg naloxone intravenously (i.v.). The antagonist dose-dependently suppressed consumption, and the maximum effect was observed 2.5 h post-injection. No changes in subjective hunger ratings were noted. Cohen and colleagues stud­ ied a group of adults whose BMI did not exceed normal values by more than 20 percent, and found that i.v. naloxone reduced energy intake from 1918 kcal to 1372 kcal per day, including reducing intake of dietary fat by 30 percent (Cohen et al., 1985). To date, many studies have examined the effect of opioid receptor antagonism on eating. These trials have utilized naltrexone, naloxone and nalmefene. In the majority of experiments, a significant decrease in food consumption was achieved in subjects with normal and elevated body weight (Atkinson, 1982; Thompson et al., 1982; Trenchard and Silverstone, 1983; Cohen et al., 1985; Malcolm et al., 1985; Wolkowitz et al., 1985, 1988; Fantino et al., 1986; Spiegel et al., 1987; Melchior et al., 1989; Yeomans et al., 1990; Drewnowski et al., 1992; Yeomans and Gray, 1996). The reduction in ingested energy oscil­ lated around 20 percent, and did not seem affected by the body weight. Hunger at the onset of consumption did not differ between drug- and placebo-treated normal patients. Interestingly, some reports indicate that opioid antagonists may be effective at reducing hunger only in obese people; this is still under debate, as the results are inconclusive (Atkinson, 1982;

Malcolm et al., 1985; Wolkowitz et al., 1985, 1988; Spiegel et al., 1987; Drewnowski et al., 1992). Human experiments confirm the validity of the opioid-eating reward hypothesis. Naloxone was more effective at decreasing the intake of preferred food than of a less attractive diet. Antagonists did not affect perception of vari­ ous flavors, yet they reduced pleasantness of tastants containing sugar and fat (Yeomans, 2000). This diminished rewarding value of a meal led to a decrease in calorie consumption. Vehicle-treated subjects displayed a gradual increase in appetite during the initial stages of the meal, once the palatable nature of the tastants was discovered. Subjects belonging to the naloxone group did not exhibit this increase in appetite (Yeomans and Gray, 1997). Aside from their effects on consumption, opi­ oid ligands have been linked to other aspects of energy metabolism. Naloxone inhibits the insu­ lin and C-peptide response to an oral glucose load in obese but not in lean subjects (Giugliano et al., 1987). Metabolic and hormonal effects of beta-endorphin differ in obese versus normalweight individuals: the infusion of the opioid increased glucose, insulin and C-peptide lev­ els and suppressed circulating free fatty acid concentration in overweight subjects, whereas in lean subjects an opposite effect was seen in relation to the free fatty acids (Giugliano et al., 1992). Undergoing a successful dieting program, which causes a return to normal BMI values, does not alter metabolic responses to betaendorphin compared to obese controls (Giug­ liano et al., 1991). Patients subjected to vertical banded gastroplasty which resulted in a weight loss retained the altered beta-endorphin and insulin responses compared to lean controls, although these values were slightly lower than the pre-operative measurements (Karayiannakis et al., 1998b). It suggests that malfunctioning of this opioid system may predispose individuals to abnormal weight gain. This notion is sup­ ported by the fact that first-degree relatives of

1.  From Brain to Behavior

3.4  Conclusions and perspectives

obese people present a similarly altered meta­ bolic response to elevated beta-endorphin levels (Cozzolino et al., 1996). While the benefits stemming from the use of opioid antagonists in obesity still remain con­ troversial, these ligands have shown promise in the alleviation of symptoms of the binge-eating disorder characterized by overconsumption of palatable foods. Drewnowski and colleagues tested the effect of i.v. naloxone on the con­ sumption of snacks in obese binge eaters and normal-weight controls (Drewnowski et al., 1992). The opioid receptor antagonist was effective at reducing taste preferences in both groups, but it suppressed caloric intake only in overweight binge eaters, which supports the view that opioids regulate reward- rather than energy-driven eating. These results were corroborated by the study which showed that naloxone decreased hedonic responses to a vari­ ety of palatable high-fat and -sugar tastants in binge eaters and healthy controls, but reduced calorie intake only in individuals with the eating disorder (Drewnowski et al., 1995). It has been suggested that antagonism of opioid receptors suppresses episodic overconsumption; hence, the use of opioid ligands may be feasible only as a pre-meal and “acute” treatment (Drewnowski, 1995).

3.3.2  Underweight individuals Malfunctioning of opioid pathways may con­ tribute to the development of disorders resulting in self-starvation, such as anorexia nervosa and malnutrition related to aging. Beta-endorphin levels in the cerebrospinal fluid (CSF) are reduced in the elderly experiencing idiopathic senile anorexia (Martinez et al., 1993). Subjects at an advanced age display a trend indicating a higher appetite-related sensitivity to naloxone (MacIntosh et al., 2001). Females suffering from anorexia exhibit decreased CSF beta-endorphin; a successful

33

recovery is associated with returning betaendorphin levels to normal values. Anorexia patients exhibit a decline in plasma betaendorphin in response to a gastric load of just 300 kcal; a similar change is seen following a load of 700 kcal (Rigaud et al., 2007). In addition, morning levels of this peptide are also lower in the affected females compared to healthy con­ trols (Baranowska et al., 2000). These findings support the hypothesis that an impaired opioid tone may serve as a key element in the development of the condition dubbed the reward-deficiency syndrome. Drug and alcohol abuse, gambling, compulsive sex­ ual activity and undertaking risky sports are associated with it. It is characterized by general or specific anhedonia in response to the nor­ mal level of stimulation, and it includes a lack of pleasure derived from consumption of regu­ lar food at typical quantities and a necessity to ingest a greater amount of palatable food to perceive eating activity as rewarding (Comings and Blum, 2000). There have been attempts to use opioid receptor agonists in the eating disorder context. Intravenous butorphanol in women of normal body weight slightly increased the pleasantness of a subsequent meal, yet it did not translate into a higher caloric intake or elevated hunger levels (Drewnowski et al., 1992). However, the possibility that the consummatory response to opioid stimulation in underweight patients might be different cannot be excluded.

3.4  Conclusions and perspectives Opioid peptides drive consumption of palat­ able foods. Simultaneously, ingestion of such diets enhances the activity of the central reward circuitry, which incorporates endogenous opi­ oids. These peptides suppress brain mechanisms responsible for termination of eating behavior;

1.  From Brain to Behavior

34

3.  Opioids and Overconsumption

hence, they allow more calories to be ingested when preferred tastants are available. Altogether, this creates the self-propelling machinery of overconsumption of palatable foods, in which excessive intake of attractive ingestants acti­ vates opioid circuitry, which in turn causes fur­ ther increases in consumption. Although opioid receptor ligands have not been used extensively in obesity-related clinical trials, primarily due to these drugs’ toxicity and side effects, the opioid system should be considered at least as a viable target for prospective therapies aimed at decreas­ ing food intake and, in consequence, combating overweight and obesity. Importantly, interac­ tions with other components of the reward cir­ cuitry, such as dopamine, should be utilized in the development of advanced pharmacological interventions based on affecting several neural systems. This is particularly important in light of the recent findings showing the interdepen­ dent manner in which opioids and dopamine facilitate consummatory behavior. In addition, opioid gene expression and peptide profile can be used as a potential diagnostic marker in indi­ viduals predisposed to develop obesity or eating disorders, hence allowing preventative meas­ ures to be undertaken and certain therapeutic approaches to be initiated prior to the onset of the condition, or at its early stage.

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C H A P T E R

4 Taste, Olfactory and Food-texture Processing in the Brain and the Control of Appetite Edmund T. Rolls Oxford Centre for Computational Neuroscience, Oxford, UK o u t l i n e 4.1    Introduction

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4.2    Taste-Processing in the Primate Brain 42 4.2.1   Pathways 42 4.2.2   The Primary Taste Cortex 42 4.2.3   The Secondary Taste Cortex 42 4.2.4  The Pleasantness of the Taste of Food, Sensory-Specific Satiety, and the Effects of Variety on Food Intake 43 4.3    The Representation of Flavor: Convergence of Olfactory, Taste and Visual Inputs in the Orbitofrontal Cortex 44 4.4    The Texture of Food, Including Fat Texture 44 4.5    Imaging Studies in Humans 44 4.5.1   Taste 44 4.5.2   Odor 45 4.5.3  Olfactory–Taste Convergence to Represent Flavor and the Influence of Satiety 46 4.5.4   Oral Viscosity and Fat Texture 46 4.5.5   The Sight of Food 46

4.8    Implications for Understanding,   Preventing and Treating Obesity 47 4.8.1    Brain Processing of the Sensory Properties and Pleasantness of Food 49 4.8.2    Genetic Factors 49 4.8.3    Endocrine Factors and their Interaction with Brain Systems 50 4.8.4    Food Palatability 50 4.8.5    Sensory-specific Satiety and the Effects of Variety on Food Intake 51 4.8.6    Fixed Meal-times and the Availability of Food 51 4.8.7    Food Saliency and Portion Size 51 4.8.8    Energy Density of Food 51 4.8.9    Eating Rate 51 4.8.10  Stress 52 4.8.11  Food Craving 52 4.8.12  Energy Output 52 4.8.13  Cognitive Factors 52 4.8.14  The Psychology of Compliance with Information about Risk Factors for Obesity 52

4.6    Cognitive Effects on   Representations of Food

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4.9    Concluding Remarks

52

4.7    Synthesis

47

Acknowledgments

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Obesity Prevention: The Role of Brain and Society on Individual Behavior

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© 2010, 2010 Elsevier Inc.

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4.  Food, Taste, smell and texture

4.1  Introduction The aims of this chapter are to describe the rules of the cortical processing of taste and smell, how the pleasantness or affective value of taste and smell are represented in the brain, how cognitive factors modulate these affective representations, and how these affective representations play an important role in the control of appetite, food intake and obesity. To make the results relevant to understanding the control of human food intake, complementary evidence is provided by neurophysiological studies in nonhuman primates, and by functional neuroimaging studies in humans. A broad perspective of the brain processing involved in emotion and in hedonic aspects of the control of food intake is provided by Rolls (2005a).

4.2  Taste-processing in the primate brain 4.2.1  Pathways A diagram of the taste and related olfactory, somatosensory and visual pathways in primates is shown in Figure 4.1. The multimodal convergence that enables single neurons to respond to different combinations of taste, olfactory, texture, temperature and visual inputs to represent different flavors produced by often new combinations of sensory inputs is a theme of recent research that will be described.

4.2.2  The primary taste cortex The primary taste cortex in the primate anterior insula and adjoining frontal operculum contains not only taste neurons tuned to sweet, salt, bitter, sour (Scott et al., 1986; Yaxley et al., 1990; Rolls and Scott, 2003) and umami as exemplified by monosodium glutamate (Baylis and Rolls, 1991; Rolls

et al., 1996a), but also other neurons that encode oral somatosensory stimuli, including viscosity, fat texture, temperature and capsaicin (Verhagen et al., 2004). Some neurons in the primary taste cortex respond to particular combinations of taste and oral texture stimuli, but do not respond to olfactory stimuli or visual stimuli (Verhagen  et al., 2004). Neurons in the primary taste cortex do not represent the reward value of taste – that is, the appetite for a food – in that their firing is not decreased to zero by feeding the taste to satiety (Rolls et al., 1988; Yaxley et al., 1988).

4.2.3  The secondary taste cortex A secondary cortical taste area in primates was discovered by Rolls, Yaxley and Sienkiewicz in the caudolateral orbitofrontal cortex, extending several millimeters in front of the primary taste cortex (Rolls et al., 1990). Neurons in this region respond not only to each of the four classical prototypical tastes sweet, salt, bitter and sour (Rolls, 1997; Rolls and Scott, 2003); many also respond best to umami tastants such as glutamate (which is present in many natural foods, such as tomatoes, mushrooms and milk) (Baylis and Rolls, 1991), and inosine monophosphate (which is present in meat and some fish, such as tuna) (Rolls et al., 1996a). This evidence, taken together with the identification of glutamate taste receptors (Zhao et al., 2003; Maruyama et al., 2006), leads to the view that there are five prototypical types of taste information channels, with umami contributing, often in combination with corresponding olfactory inputs (Rolls et al., 1998; McCabe and Rolls, 2007; Rolls, 2009), to the flavor of protein. In addition, other neurons respond to water and some to somatosensory stimuli, including astringency as exemplified by tannic acid (Critchley and Rolls, 1996a), and capsaicin (Rolls et al., 2003a; Kadohisa et al., 2004). Taste responses are found in a large mediolateral extent of the orbitofrontal cortex (Pritchard et al., 2005; Rolls, 2008; Rolls and Grabenhorst, 2008).

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43

4.2  Taste-processing in the primate brain

VISION V1

V2

V4

Inferior temporal visual cortex Cingulate cortex Behavior Amygdala

Striatum Behavior

TASTE Taste receptors

Nucleus of the Thalamus solitary tract VPMpc nucleus

Lateral hypothalamus

Frontal operculum/Insula (Primary taste cortex)

Gate Orbitofrontal cortex

Gate function

Autonomic responses

Hunger neuron controlled by e.g., glucose utilization, stomach distension or body weight

OLFACTION

Olfactory bulb

Olfactory (Pyriform) cortex

Insula TOUCH

Thalamus VPL

Primary somatosensory cortex (1.2.3)

Figure 4.1  Schematic diagram of the taste and olfactory pathways in primates including humans showing how they converge with each other and with visual pathways. Hunger modulates the responsiveness of the representations in the orbitofrontal cortex of the taste, smell, texture and sight of food (indicated by the gate function), and the orbitofrontal cortex is where the palatability and pleasantness of food is represented. VPMpc, ventralposteromedial thalamic nucleus; V1, V2, V4, visual cortical areas.

4.2.4  The pleasantness of the taste of food, sensory-specific satiety, and the effects of variety on food intake The modulation of the reward value of a sensory stimulus such as the taste of food by motivational state – for example, hunger – is one important way in which motivational behavior is controlled (Rolls, 2005a, 2007). The subjective

correlate of this modulation is that food tastes pleasant when hungry, and tastes hedonically neutral when it has been eaten to satiety. The discovery of sensory-specific satiety was revealed by the selective reduction in the responses of lateral hypothalamic neurons to a food eaten to satiety (Rolls, 1981; Rolls et al., 1986). It has been shown that this is implemented in a region that projects to the hypothalamus, the orbitofrontal

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4.  Food, Taste, smell and texture

cortex (secondary taste), for the taste, odor, sight and texture of food (Rolls et al., 1989; Critchley and Rolls, 1996b; Rolls et al., 1999). This evidence shows that the reduced acceptance of food that occurs when food is eaten to satiety, the reduction in the pleasantness of its taste and flavor, and the effects of variety to increase food intake (Cabanac, 1971; Rolls and Rolls, 1977, 1982, 1997; Rolls et al., 1981a, 1981b, 1982, 1983a, 1983b, 1984; Rolls and Hetherington, 1989; Hetherington, 2007) are produced in the orbitofrontal cortex, but not at earlier stages of processing where the responses reflect factors such as the intensity of the taste, which is little affected by satiety (Rolls et al., 1983c; Rolls and Grabenhorst, 2008). In addition to providing an implementation of sensory-specific satiety (probably by habituation of the synaptic afferents to orbitofrontal neurons with a time-course of the order of length of a meal course), it is likely that visceral and other satiety-related signals reach the orbitofrontal cortex (as indicated in Figure 4.1) (from the nucleus of the solitary tract, via thalamic and possibly hypothalamic nuclei), and there modulate the representation of food, resulting in an output that reflects the reward (or appetitive) value of each food (Rolls, 2005a).

4.3  The representation of flavor: convergence of olfactory, taste and visual inputs in the orbitofrontal cortex Taste and olfactory pathways are brought together in the orbitofrontal cortex, where flavor is formed by learned associations at the neuronal level between these inputs (see Figure 4.1) (Thorpe et al., 1983; Rolls and Baylis, 1994; Critchley and Rolls, 1996c; Rolls, 1996; Rolls et al., 1996b; Verhagen et al., 2004). The visual and olfactory as well as the taste inputs represent the reward value

of the food, as shown by sensory-specific satiety effects (Critchley and Rolls, 1996b).

4.4  The texture of food, including fat texture Some orbitofrontal cortex neurons have oral texture-related responses that encode parametrically the viscosity of food in the mouth (shown using a methyl cellulose series in the range 1–10,000 centiPoise). Others independently encode the particulate quality of food in the mouth, produced quantitatively, for example, by adding 20- to 100-m microspheres to methyl cellulose (Rolls et al., 2003a). Others, finally, encode the oral texture of fat (Rolls  et al., 1999; Verhagen et al., 2003), as illustrated in Figure 4.2. In addition, some neurons in the orbitofrontal cortex reflect the temperature of substances in the mouth (Kadohisa et al., 2004, 2005). This temperature information is represented independently of other sensory inputs by some neurons, and in combination with taste or texture by other neurons.

4.5  Imaging studies in humans 4.5.1  Taste In humans, it has been shown in neuroimaging studies using functional magnetic resonance imaging (fMRI) that taste activates an area of the anterior insula/frontal operculum, which is probably the primary taste cortex, and part of the orbitofrontal cortex, which is probably the secondary taste cortex (Francis et al., 1999; O’Doherty et al., 2001; de Araujo et al., 2003a). Within individual subjects, separate areas of the orbitofrontal cortex are activated by sweet (pleasant) and by salt (unpleasant) tastes (O’Doherty  et al., 2001).

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4.5 Imaging studies in humans

Fat responsive neurons respond independently of viscosity e.g.

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Figure 4.2  A neuron in the primate orbitofrontal cortex responding to the texture of fat in the mouth independently of viscosity. The cell (bk265) increased its firing rate to a range of fats and oils (the viscosity of which is shown in centiPoise). The information that reaches this type of neuron is independent of a viscosity-sensing channel, in that the neuron did not respond to the methyl cellulose (CMC) viscosity series. The neuron responded to the texture rather than the chemical structure of the fat in that it also responded to silicone oil (Si(CH3)2O)n) and paraffin (mineral) oil (hydrocarbon). Some of these neurons have taste inputs. Source: Adapted from Verhagen and colleagues (2003).

We also found activation of the human amyg­ dala by the taste of glucose (Francis et al., 1999). Extending this study, O’Doherty and colleagues (2001) showed that the human amygdala was as much activated by the affectively pleasant taste of glucose as by the affectively negative taste of salt, and thus provided evidence that the human amygdala is not especially involved in processing aversive as compared to rewarding stimuli. Zald et al. (1998) had shown earlier that the amygdala, as well as the orbitofrontal cortex, responds to aversive (saline) taste stimuli. Umami taste stimuli activate the insular (­primary), orbitofrontal (secondary) and anter­ ior cin­gulate (tertiary; Rolls, 2008) taste cortical areas (de Araujo et al., 2003b). When the nucleotide 0.005-M inosine 5-monophosphate (IMP) was added to MSG (0.05 M), the BOLD (blood oxygenation-level dependent) signal in an anterior part of the orbitofrontal cortex ­sho­wed suprali­near additivity. This may reflect the subjective enhancement of umami taste that has been des­cribed when IMP is added to MSG

(Rolls, 2009). Overall, these results illustrate that the responses of the brain can reflect inputs produced by particular combinations of sen­sory stimuli with supralinear activations. The combination of sensory stimuli may be especially represented in particular brain regions, and may help to make the food pleasant.

4.5.2  Odor In humans, in addition to activation of the pyriform (olfactory) cortex (Zald and Pardo, 1997; Sobel et al., 2000; Poellinger et al., 2001), there is strong and consistent activation of the orbito­ frontal cortex by olfactory stimuli (Zatorre et al., 1992; Francis et al., 1999). This region appears to represent the pleasantness of odor, as shown by a sensory-specific satiety experiment with banana versus vanilla odor (O’Doherty et al., 2000). Further, pleasant odors tend to activate the medial, and unpleasant odors the more lateral, orbitofrontal cortex (Rolls et al., 2003b), adding to the evidence that there is a hedonic map in the

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4.  Food, Taste, smell and texture

orbitofrontal cortex and in the anterior cingulate cortex, which receives inputs from the orbitofrontal cortex (Rolls and Grabenhorst, 2008).

4.5.4  Oral viscosity and fat texture

The viscosity of food in the mouth is represented in the human primary taste cortex (in the anterior insula), and also in a mid-insular area that is not taste cortex but which represents oral som4.5.3  Olfactory–taste convergence to atosensory stimuli (de Araujo and Rolls, 2004). represent flavor and the influence of satiety Oral viscosity is also represented in the human Supra-additive effects indicating convergence orbitofrontal and perigenual cingulate cortices. It and interactions were found for taste (sucrose) is notable that the pregenual cingulate cortex, an and odor (strawberry) in the orbito­frontal and area in which many pleasant stimuli are repreanterior cingulate cortex. Activations in these sented, is strongly activated by the texture of fat regions were correlated with the pleasantness in the mouth and by oral sucrose (de Araujo and ratings given by the participants (de Araujo  Rolls, 2004). The pleasantness of fat texture may et al., 2003c; Small et al., 2004; Small and Prescott, be represented in the orbitofrontal and anterior 2005). These results provide evidence on the cingulate cortex, for activations in these regions neural substrate for the convergence of taste and are correlated with the subjective pleasantness of olfactory stimuli to produce flavor in humans,  fat (Grabenhorst et al., 2009). and on where the pleasantness of flavor is represented in the human brain. McCabe and Rolls (2007) have shown that 4.5.5  The sight of food the convergence of taste and olfactory informaO’Doherty et al. (2002) showed that visual tion appears to be important for the pleasantness stimuli associated with the taste of glucose of umami. They showed that when glutamate is activated the orbitofrontal cortex and some given in combination with a consonant savory connected areas, consistent with the priodor (vegetable), the resulting flavor can be much mate neurophysiology. Simmons, Martin and more pleasant than the glutamate taste or vege­ Barsalou found that showing pictures of foods, table odor alone. This reflected activations in the compared to pictures of locations, can also actipregenual cingulate cortex and medial orbito­ vate the orbitofrontal cortex (Simmons et al., frontal cortex. Certain sensory combinations, 2005). Similarly, the orbitofrontal cortex and therefore, can produce very pleasant food stimuli, connected areas were also found to be actiwhich may be important in driving food intake. vated after presentation of food stimuli to foodTo assess how satiety influences the brain acti- deprived subjects (Wang et al., 2004). vations to a whole food which produces taste, olfactory and texture stimulation, we measured brain activation by whole foods before and after 4.6  Cognitive effects on the food is eaten to satiety (de Araujo et al., 2003b). representations of food The foods eaten to satiety were either chocolate milk or tomato juice. A decrease in activation by To what extent does cognition influence the the food eaten to satiety relative to the other food was found in the orbitofrontal cortex (Kringelbach hedonics of food-related stimuli, and how far et al., 2003) but not in the primary taste cortex. down into the sensory system does cognitive This study provided evidence that the pleasant- influence reach? To address this, we performed ness of the flavor of food and sensory-specific an fMRI investigation in which the delivery of a standard test odor (isovaleric acid combined with satiety are represented in the orbitofrontal cortex.

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4.8 Implications for understanding, preventing and treating obesity

Cheddar cheese flavor, presented orthonasally using an olfactometer) was paired with a descriptor word on a screen, which on different trials was “Cheddar Cheese” or “Body Odor”. Participants rated the affective value of the test odor as sig­ni­ ficantly more pleasant when labeled “Cheddar Cheese” than when labeled “Body Odor”. These effects reflected activations in the medial orbito­ frontal cortex (OFC)/rostral anterior cingulate cortex (ACC) that had correlations with the pleasantness ratings (de Araujo et al., 2005) (see  Figure 4.3). The implication is that cognitive factors can have profound effects on our responses to the hedonic and sensory properties of food: these effects are manifest quite far down into sensory processing, so that hedonic representations of odors are affected (de Araujos et al., 2005). Similar cognitive effects and mechanisms have now been found for the taste and flavor of food (Grabenhorst et al., 2008). In addition, it has been found that with taste, flavor and olfactory foodrelated stimuli, attention to pleasantness modulates representations in the orbitofrontal cortex, whereas attention to intensity modulates activations in areas such as the primary taste cortex (Grabenhorst and Rolls, 2008; Rolls et al., 2008).

4.7  Synthesis These investigations show that representations of the reward/hedonic value and pleasantness of sensory, including food-related, stimuli in the brain are formed separately from representations of what the stimuli are. The pleasantness/reward value is represented in areas such as the orbito­ frontal cortex and pregenual cingulate cortex. It is here that satiety signals modulate the representations of food to make them implement reward so that they only occur when hunger is present. The satiety signals that help in this modulation may reach the orbitofrontal cortex from the hypothalamus. In turn, the orbitofrontal cortex

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projects to the hypothalamus, where neurons are found that respond to the sight, smell and taste of food if hunger is present (Rolls, 2007; Rolls and Grabenhorst, 2008). We have seen above some of the principles that help to make the food pleasant, including particular combinations of taste, olfactory, texture, visual and cognitive inputs. Below is developed a hypothesis that obesity is associated with overstimulation of these reward systems by very rewarding combinations of taste, odor, texture, visual and cognitive inputs.

4.8  Implications for understanding, preventing and treating obesity Understanding the mechanisms that control appetite is becoming an increasingly important issue, given the growing incidence of obesity (a three-fold increase in the UK since 1980 to a figure of 20 percent, as defined by a BMI 30) and its association with major health risks (with 1000 deaths each week in the UK attributable to obesity). It is important to understand and thereby be able to minimize and treat obesity, because many diseases are associated with a body weight that is much above normal. These diseases include diabetes, hypertension, cardi­o­vascular disease, hypercholesterolemia and gall bladder disease; in addition, obesity is ass­o­ciated with some deficits in reproductive function (e.g., ovulatory failure) and an excess mortality from certain types of cancer (Garrow, 1988; Barsh and Schwartz, 2002; Cummings and Schwartz, 2003; Schwartz and Porte, 2005). There are many factors that can cause or contribute to obesity in humans (Brownell and Fairburn, 1995; Morton et al., 2006; O’Rahilly and Farooqi, 2006) that are investigated with approaches within or related to neuroscience and psychology (Rolls, 2005a, 2005b, 2006, 2007). Rapid progress is being made in understanding  these, with the aim of leading to ­ better ways

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4.  Food, Taste, smell and texture

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Figure 4.3  Cognitive influences on olfactory representations in the human brain. Group (random) effects analysis showing the brain regions where the BOLD signal was correlated with pleasantness ­ratings given to the test odor. The pleasantness ratings were being modulated by the word labels. (a) Activations in the rostral anterior cingulate cortex, in the region adjoining the medial OFC, shown in a sagittal slice. (b) The same activation shown coronally. (c) Bilateral activations in the amygdala. (d) These activations extended anteriorly to the primary olfactory cortex. The image was thresheld at P  0.0001 uncorrected in order to show the extent of the activation. (e) Parametric plots of the data averaged across all subjects showing that the percentage BOLD change (fitted) correlates with the pleasantness ratings in the region shown in (a) and (b). The parametric plots were very similar for the primary olfactory region shown in (d). PST, post-stimulus time(s). (f) Parametric plots for the amygdala region shown in (c). Source: Adapted from DeAraujo et al., 2005.

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4.8 Implications for understanding, preventing and treating obesity

to minimize and treat obesity. These factors include the following:

4.8.1  Brain processing of the sensory properties and pleasantness of food The way in which the sensory factors produced by the taste, smell, texture and sight of food interact in the brain with satiety signals (such as gastric distension and satiety-related hormones) to determine the pleasantness and palatability of food, and therefore whether and how much food will be eaten, is described above and shown in Figures 4.1 and 4.4. The concept is that convergence of sensory inputs occurs in the orbitofrontal cortex and builds a representation of food flavor. The orbitofrontal cortex is where the pleasantness and palatability of food are represented, as demonstrated by the discoveries that these representations of food are only activated if hunger is present, and correlate with the subjective pleasantness of the food flavor (Rolls, 2005a, 2005b, 2006, 2007; Rolls and Grabenhorst, 2008). The orbito­frontal cortex representation of whether food is pleasant (given any satiety signals present) then drives brain areas such as the striatum and cingulate cortex that then lead to eating behavior. In the context of the obesity crisis, the past 30 years have seen a dramatic increase of the sensory stimulation produced by the taste, smell, texture and appearance of food, as well as its availability. Conversely, the satiety signals produced by stomach distension, satiety hormones, etc., have remained essentially unchanged. The effect on the brain’s control system for appetite (shown in Figures 4.1 and 4.4) is to lead to a net average increase in the reward value and palatability of food which overrides the satiety signals, contributes to the tendency to be over-stimulated by food, and therefore leads to overeating. In this scenario, it is important to better understand the rules used by the brain to produce the representation of the pleasantness of

49

food and how the system is modulated by eating and satiety. This understanding, and how the sensory factors can be designed and controlled so as not to override satiety signals, are important research areas in the understanding, prevention and treatment of obesity. Advances in understanding the receptors that encode the taste and olfactory properties of food (Buck, 2000; Zhao et al., 2003), and the processing in the brain of these properties (Rolls, 2004, 2005a, 2005b), are also important in providing the potential to produce highly palatable food that is at the same time nutritious and healthy. An important aspect of this hypothesis is that different humans may have reward systems that are strongly driven by the sensory and cognitive factors that make food highly palatable. In a test of this, we showed that activation to the sight and flavor of chocolate in the orbitofrontal and pregenual cingulate cortex was much higher in chocolate cravers than non-cravers (Rolls and McCabe, 2007). The concept that individual differences in responsiveness to food reward are reflected in brain activations in regions related to the control food intake (Beaver et al., 2006; Rolls and McCabe, 2007; Lowe et al., 2009; Van den Eynde and Treasure, 2009) may provide a way for understanding and helping to control food intake.

4.8.2  Genetic factors Genetic factors are of some importance, with some of the variance in weight and resting metabolic rate in a population of humans attribut­ able to inheritance (Morton et al., 2006; O’Rahilly and Farooqi, 2006, 2008). However the “obesity epidemic” that has occurred since the 1990s cannot be solely attributed to genetic changes, for which the timescale is far too short. Factors such as the increased palatability, variety and availability of food (as well as less exercise), crucial drivers of food intake, and the amount of food that is eaten (Rolls, 2005a, 2005b, 2006, 2007) are

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Obesity: sensory factors that make food increasingly palatable may override existing satiety signals

Cognitive factors: Conscious rational control Beliefs about the food Advertising Sensory factors: Taste Smell Texture Sight

Brain mechanisms: Sensory factors modulated by satiety signals produce reward value and appetite

Effects of: Variety Sensory-specific satiety Palatability Food concentration Portion size Ready availability

Eating: Autonomic, and endocrine effects

Satiety / hunger signals: Adipose signals Gut hormones Gastric distension

Figure 4.4  Obesity: sensory factors that make food increasingly palatable may override existing satiety signals. Schematic diagram to show how sensory factors interact in the orbitofrontal cortex with satiety signals to produce the hedonic, rewarding value of food, which leads to appetite and eating. Cognitive factors directly modulate this system in the brain.

more likely to be responsible for the upsurge in the incidence of obesity.

4.8.3  Endocrine factors and their interaction with brain systems

2006; Farooqi and O’Rahilly, 2009). Further, obese people generally have high levels of lep­ tin, so leptin production is not the problem. Instead, leptin resistance (i.e., insensitivity) may be somewhat related to obesity, with the resistance perhaps related in part to smaller effects of leptin on arcuate nucleus NPY/AGRP neurons (Munzberg and Myers, 2005).

A small proportion of cases of obesity can be related to gene-related dysfunctions of the peptide systems in the hypothalamus, with, for  example, 4 percent of obese people having defi4.8.4  Food palatability cient (MC4) receptors for melanocyte stimulating hormone (Morton et al., 2006; O’Rahilly A factor in obesity is food palatability, which, and Farooqi, 2006). Cases of obesity that can be with modern methods of food production, can related to changes in the leptin hormone satiety now be greater than would have been the case dursystem are very rare (O’Rahilly and Farooqi, ing the evolution of our feeding control systems. 

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4.8 Implications for understanding, preventing and treating obesity

These brain systems evolved so that internal signals from, for example, gastric distension and glucose utilization could act to decrease the pleasantness of the sensory sensations produced by feeding sufficiently by the end of a meal to stop further eating (Rolls, 2004, 2005a, 2005b). However, the greater palatability of modern food may mean that this balance is altered, so that there is a tendency for the greater palatability of food to be insufficiently decreased by a standard amount of food eaten (see Figure 4.4).

4.8.5  Sensory-specific satiety and the effects of variety on food intake Sensory-specific satiety is the decrease in the appetite for a particular food as it is eaten in a meal, without a decrease in the appetite for different foods (Rolls, 2004, 2005a, 2005b), as shown above. It is an important factor influencing how much of each food is eaten in a meal. Its evolutionary significance may be to encourage eating of a range of different foods, and thus obtaining a range of nutrients. As a result of sensory-­specific satiety, if a wide variety of foods is available, overeating in a meal can occur. Given that it is now possible to make available a wide range of food flavors, textures and appearances, and that such foods are readily available, this variety effect may be a factor in promoting excess food intake.

4.8.6  Fixed meal-times and the availability of food Another factor that could contribute to obesity is fixed meal-times, in that the normal control of food intake by alterations in inter-meal interval is not readily available in humans. Therefore, food may be eaten at a meal-time even if hunger is not present (Rolls, 2005a). Even more than this, because of the high and easy availability of food (in the home and workplace) and stimulation by advertising, there is a tendency to start eating

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again when satiety signals after a previous meal have decreased only a little, and the consequence is that the system again becomes overloaded.

4.8.7  Food saliency and portion size Making food salient, for example by placing it on display, may increase food selection, particularly in the obese (Schachter, 1971; Rodin, 1976). Portion size is a factor, since more is eaten if a large portion of food is presented (Kral and Rolls, 2004). Whether it can lead to obesity has not yet been demonstrated. The driving effects of visual and other stimuli (including the effects of advertising) on the brain systems that are activated by food reward may be different in different individuals, and contribute to obesity.

4.8.8  Energy density of food Although the gastric emptying rate is slower for high energy-density foods, this does not fully compensate for the energy density of the food (Hunt and Stubbs, 1975; Hunt, 1980). The implication is that eating energy-dense foods (e.g., highfat foods) may not allow gastric distension to contribute sufficiently to satiety. Because of this, the energy density of foods may be an important factor that influences how much energy is consumed in a meal (Kral and Rolls, 2004). Indeed, it is thought that obese people tend to eat foods with high energy-density, and to visit restaurants with high-energy density (e.g., high-fat) foods. It is also a matter of clinical experience that gastric emptying is faster in obese than in normal-weight individuals, meaning that gastric distension may play a less effective role in contributing to satiety in the obese.

4.8.9  Eating rate A factor related to the effects described above is the eating rate, which is typically fast in the

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obese (Otsuka et al., 2006) and may provide insufficient time for the full effect of satiety signals as food reaches the intestine to operate.

4.8.10  Stress Another potential factor in obesity is stress, which can induce eating and contribute to obesity. In a rat model of this, mild stress in the presence of food can lead to overeating and obesity (Torres and Nowson, 2007). This overeating is reduced by anti-anxiety drugs.

4.8.11  Food craving Binge eating has some parallels to addiction. In one rodent model of binge eating, access to sucrose for several hours each day can lead to binge-like consumption of the sucrose over a period of days (Colantuoni et al., 2002; Avena and Hoebel, 2003a, 2003b; Spangler et al., 2004). The binge eating is associated with the release of dopamine. In this model, binge eating resembles an addictive process, in that after binge eating has become a habit, sucrose withdrawal decreases dopamine release in the ventral striatum (a part of the brain involved in addiction to drugs such as amphetamine), altered binding of dopamine to its receptors in the ventral striatum is produced, and signs of withdrawal from an addiction occur. In withdrawal, the animals are also hypersensitive to the effects of amphetamine. Another rat model is being used to investigate the binge eating of fat, and whether the reinforcing cues associated with it can be reduced by the GABA-B receptor agonist baclofen (Corwin and Buda-Levin, 2004).

4.8.12  Energy output If energy intake is greater than energy output, body weight increases. Energy output is thus an important factor in the equation. A lack of exercise

tends to limit energy output, and thus contributes to obesity. It should be noted, though, that obese people do not generally suffer from a very low metabolic rate: in fact, as a population, in line with their elevated body weight, obese people have higher metabolic rates than normal-weight humans (Garrow, 1988).

4.8.13  Cognitive factors As shown above, cognitive factors, such as preconceptions about the nature of a particular food or odor, can reach down into the olfactory system in the orbitofrontal cortex which controls the palatability of food to influence how pleasant an olfactory stimulus is (de Araujo  et al., 2005). This has implications for further ways in which food intake can be controlled, and needs more investigation.

4.8.14  The psychology of compliance with information about risk factors   for obesity It is important to develop better ways to provide information that will be effective in the long term in decreasing food intake while maintaining a healthy diet, and in promoting an increase in energy expenditure by, for example, encouraging exercise.

4.9  Concluding remarks Recent advances are showing how the reward value of food is represented in the brain as a combination of taste, oral texture, olfactory and visual attributes of food; and how this reward representation which drives appetite and food intake is modulated by internal satiety signals, by sensory-specific satiety, by cognition and by attention.

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references

In this context, it is argued that the factors that contribute to driving people towards obesity include the greater stimulation in the past 30 years of the brain by sensory stimuli that make food palatable and pleasant, relative to internal satiety signals, which have remained unchanged in this short time. In this situation, it is important to understand much better the rules used by the brain to produce the representation of the pleasantness of food, and how the system is modulated by eating and satiety. This understanding, and how the sensory factors can be designed and controlled so as not to override satiety signals, are important research areas in the understanding, prevention and treatment of obesity. In this context, it may be important to better understand individual differences in the sensitivity of this food reward system. The factors that contribute to overstimulating the brain’s food reward systems relative to satiety signals include food palatability and appearance, sensory-specific satiety, food variety, food availability, the effects of visual stimulation and advertising, the energy density and nutritional content of food, portion size, and cognitive states. All these factors may need to be taken into account in the prevention of obesity.

Acknowledgments This research was supported by the Medical Research Council. The participation of many colleagues in the studies cited is sincerely acknowledged.

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Barsh, G. S., & Schwartz, M. W. (2002). Genetic approaches to studying energy balance: perception and integration. Nature Reviews Genetics, 3, 589–600. Baylis, L. L., & Rolls, E. T. (1991). Responses of neurons in the primate taste cortex to glutamate. Physiology and Behavior, 49, 973–979. Beaver, J. D., Lawrence, A. D., van Ditzhuijzen, J., Davis, M. H., Woods, A., & Calder, A. J. (2006). Individual  differences in reward drive predict neural responses to images of food. Journal of Neuroscience, 26, 5160–5166. Brownell, K. D., & Fairburn, C. (1995). Eating disorders and obesity: A comprehensive handbook. New York, NY: Guildford Press. Buck, L. (2000). Smell and taste: The chemical senses. In E. R. Kandel, J. H. Schwartz, & T. M. Jessell (Eds.), Principles of neural science (pp. 625–647). New York, NY: McGraw-Hill. Cabanac, M. (1971). Physiological role of pleasure. Science, 173, 1103–1107. Colantuoni, C., Rada, P., McCarthy, J., Patten, C., Avena, N. M., Chadeayne, A., & Hoebel, B. G. (2002). Evidence that intermittent, excessive sugar intake causes endogenous opioid dependence. Obesity Research, 10, 478–488. Corwin, R. L., & Buda-Levin, A. (2004). Behavioral models of binge-type eating. Physiology and Behavior, 82, 123–130. Critchley, H. D., & Rolls, E. T. (1996a). Responses of primate taste cortex neurons to the astringent tastant tannic acid. Chemical Senses, 21, 135–145. Critchley, H. D., & Rolls, E. T. (1996b). Hunger and satiety modify the responses of olfactory and visual neurons in the primate orbitofrontal cortex. Journal of Neurophysiology, 75, 1673–1686. Critchley, H. D., & Rolls, E. T. (1996c). Olfactory neuronal responses in the primate orbitofrontal cortex: Analysis  in an olfactory discrimination task. Journal of Neuro­phy­si­ ology, 75, 1659–1672. Cummings, D. E., & Schwartz, M. W. (2003). Genetics and pathophysiology of human obesity. Annual Reviews of Medicine, 54, 453–471. de Araujo, I. E. T., & Rolls, E. T. (2004). The representation in the human brain of food texture and oral fat. Journal of Neuroscience, 24, 3086–3093. de Araujo, I. E. T., Kringelbach, M. L., Rolls, E. T., & McGlone, F. (2003a). Human cortical responses to water in the mouth, and the effects of thirst. Journal of Neurophysiology, 90, 1865–1876. de Araujo, I. E. T., Kringelbach, M. L., Rolls, E. T., & Hobden, P. (2003b). The representation of umami taste in the human brain. Journal of Neurophysiology, 90, 313–319. de Araujo, I. E. T., Rolls, E. T., Kringelbach, M. L.,  McGlone, F., & Phillips, N. (2003c). Taste-olfactory convergence, and the representation of the pleasantness of flavour, in the human brain. European Journal of Neuroscience, 18, 2374–2390.

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de Araujo, I. E. T., Rolls, E. T., Velazco, M. I., Margot, C., & Cayeux, I. (2005). Cognitive modulation of olfactory processing. Neuron, 46, 671–679. Farooqi, I. S., & O’Rahilly, S. (2009). Leptin: A pivotal regulator of human energy homeostasis. The American Journal of Clinical Nutrition, 89, 980S–984S. Francis, S., Rolls, E. T., Bowtell, R., McGlone, F., O’Doherty, J., Browning, A., Clare, S., & Smith, E. (1999). The representation of pleasant touch in the brain and its relationship with taste and olfactory areas. NeuroReport, 10, 453–459. Garrow, J. S. (1988). Obesity and related diseases. London: Churchill Livingstone. Grabenhorst, F., & Rolls, E. T. (2008). Selective attention to affective value alters how the brain processes taste stimuli. European Journal of Neuroscience, 27, 723–729. Grabenhorst, F., Rolls, E. T., & Bilderbeck, A. (2008). How cognition modulates affective responses to taste and flavor: Top down influences on the orbitofrontal and pregenual cingulate cortices. Cerebral Cortex, 18, 1549–1559. Grabenhorst, F., Rolls, E. T., Parris, B. A., & D’Souza, A. (2009). How the brain represents the reward value of fat in the mouth. Aug 2009 14 [epub ahead of print]. Cerebral Cortex. Hetherington, M. M. (2007). Cues to overeat: psychological factors influencing overconsumption. The Proceedings of the Nutrition Society, 66, 113–123. Hunt, J. N. (1980). A possible relation between the regulation of gastric emptying and food intake. American Journal of Physiology, 239, G1–G4. Hunt, J. N., & Stubbs, D. F. (1975). The volume and energy content of meals as determinants of gastric emptying. Journal of Physiology, 245, 209–225. Kadohisa, M., Rolls, E. T., & Verhagen, J. V. (2004). Orbitofron­ tal cortex neuronal representation of temperature and cap­ saicin in the mouth. Neuroscience, 127, 207–221. Kadohisa, M., Rolls, E. T., & Verhagen, J. V. (2005). Neuronal representations of stimuli in the mouth: The primate insular taste cortex, orbitofrontal cortex, and amygdala. Chemical Senses, 30, 401–419. Kral, T. V., & Rolls, B. J. (2004). Energy density and portion size: Their independent and combined effects on energy intake. Physiology and Behavior, 82, 131–138. Kringelbach, M. L., O’Doherty, J., Rolls, E. T., & Andrews, C. (2003). Activation of the human orbitofrontal cortex to a liquid food stimulus is correlated with its subjective pleasantness. Cerebral Cortex, 13, 1064–1071. Lowe, M. R., van Steenburgh, J., Ochner, C., & Coletta, M. (2009). Neural correlates of individual differences related to appetite. Physiology Behavior, 97(5), 561–571. Maruyama, Y., Pereira, E., Margolskee, R. F., Chaudhari, N., &   Roper, S. D. (2006). Umami responses in mouse taste cells indicate more than one receptor. The Journal of Neuroscience, 26, 2227–2234.

McCabe, C., & Rolls, E. T. (2007). Umami: a delicious flavor formed by convergence of taste and olfactory pathways in the human brain. European Journal of Neuroscience, 25, 1855–1864. Morton, G. J., Cummings, D. E., Baskin, D. G., Barsh, G. S., &   Schwartz, M. W. (2006). Central nervous system control of food intake and body weight. Nature, 443,  289–295. Munzberg, H., & Myers, M. G. (2005). Molecular and anatomical determinants of central leptin resistance. Nature Neuroscience, 8, 566–570. O’Doherty, J., Rolls, E. T., Francis, S., Bowtell, R., McGlone, F., Kobal, G., Renner, B., & Ahne, G. (2000). Sensory-specific satiety related olfactory activation of the human orbito­ frontal cortex. NeuroReport, 11, 893–897. O’Doherty, J., Rolls, E. T., Francis, S., Bowtell, R., & McGlone, F. (2001). The representation of pleasant and aversive taste in the human brain. Journal of Neurophysiology, 85, 1315–1321. O’Doherty, J. P., Deichmann, R., Critchley, H. D., & Dolan, R. J. (2002). Neural responses during anticipation of a primary taste reward. Neuron, 33, 815–826. O’Rahilly, S., & Farooqi, I. S. (2006). Genetics of obesity. Philosophical Transactions of the Royal Society B, 361, 1095–1105. O’Rahilly, S., & Farooqi, I. S. (2008). Human obesity: a heritable neurobehavioral disorder that is highly sensitive to environmental conditions. Diabetes, 57, 2905–2910. Otsuka, R., Tamakoshi, K., Yatsuya, H., Murata, C., Sekiya, A.,  Wada, K., Zhang, H. M., Matsushita, K., Sugiura, K., Takefuji, S., OuYang, P., Nagasawa, N., Kondo, T., Sasaki, S.,  & Toyoshima, H. (2006). Eating fast leads to obesity: Find­ ings based on self-administered questionnaires among middle-aged Japanese men and women. Journal of Epide­mi­ ology, 16, 117–124. Poellinger, A., Thomas, R., Lio, P., Lee, A., Makris, N.,  Rosen, B. R., & Kwong, K. K. (2001). Activation and habituation in olfaction – an fMRI study. NeuroImage, 13, 547–560. Pritchard, T. C., Edwards, E. M., Smith, C. A., Hilgert, K. G., Gavlick, A. M., Maryniak, T. D., Schwartz, G. J., & Scott, T. R. (2005). Gustatory neural responses in the medial orbitofrontal cortex of the old world monkey. The Journal of Neuroscience, 25, 6047–6056. Rodin, J. (1976). The role of perception of internal and external signals in the regulation of feeding in overweight and non-obese individuals. Dahlem Konferenzen: Life Sciences Research Report, 2, 265–281. Rolls, B. J., & Hetherington, M. (1989). The role of variety in eating and body weight regulation. In R. Shepherd (Ed.), Handbook of the psychophysiology of human eating (pp. 57–84). Chichester: Wiley. Rolls, B. J., Rolls, E. T., Rowe, E. A., & Sweeney, K. (1981a). Sensory specific satiety in man. Physiology and Behavior, 27, 137–142.

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C H A P T E R

5 Cortical and Limbic Activation in Response to Low- and High-calorie Food William D.S. Killgore Cognitive Neuroimaging Laboratory, McLean Hospital, Harvard Medical School, Belmont, MA, USA

o u tl i n e 5.1 Introduction

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5.2 Brain Responses to Food Stimuli in Healthy Adults

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5.3  Modulating Factors   5.3.1  Body Mass

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5.1  Introduction Despite the marvelous advances of modern medicine and the undeniable evidence linking obesity to poor health, disease, and reduced life span, the industrialized world’s population is increasingly overweight/obese. In particular, in the United States obesity has reached epidemic proportions: some estimates suggest that nearly two-thirds of adults are overweight, and a

Obesity Prevention: The Role of Brain and Society on Individual Behavior

5.3.2  Mood

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5.4 Cortical and Limbic Activation to Food Images During Adolescent Development 65 5.5  Conclusion

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quarter or more of American adults are obese (Flegal et al., 2002; Hill and Wyatt, 2005). Inadequate education and lack of awareness cannot alone account for the increasing weight of Americans: Americans are reasonably welleducated in matters pertaining to health and medical issues, and popular media, including television and magazines, inundate the average person with messages about weight loss, nutrition and health on a daily basis. Thus,

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The Contents of this Chapter are in the Public Domain

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despite widespread awareness of the problem and a general comprehension of and appreciation for the importance of maintaining a healthy diet and exercise routine, a majority of individuals continue to have difficulty maintaining their caloric intake within a healthy range. Clearly, other factors must also be at work. For this reason, researchers have begun studying the various behavioral, neurobiological and genetic factors that may contribute to the types of food choices individuals make (Karhunen et al., 2000). The complex decisions about what and how much to eat are ultimately guided by the interplay of multiple neural systems within the brain. Recent advances in neuroimaging technologies have made it possible to observe directly the responses of the human brain to a variety of stimuli and cues associated with food, hunger, taste, smell and other influences on eating-related behavior. Techniques such as positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) have proven useful in mapping the regions of the human brain that are activated in response to food-related stimuli. They are providing important clues that may help explain why many people may find it difficult to resist certain foods, particularly those that are most implicated in weight-related problems. While the neuro­ imaging literature on brain responses to food and eating related stimuli has expanded rapidly in recent years (see, for example, Simmons et al., 2005; Beaver et al., 2006; Geliebter et al., 2006; Santel et al., 2006; Verhagen and Engelen, 2006), the present chapter will focus primarily on a circumscribed set of fMRI studies that have explored how the brain responds to visual images of foods that differ in their caloric content. This review is not meant to be exhaustive, but rather highlights some of the specific neural systems that may be particularly relevant to the initial responses people have when first seeing something edible.

5.2  Brain responses to food stimuli in healthy adults Because food is so essential to life sustenance, it makes sense for the brain to possess specialized systems for identifying and responding to potential sources of nutrients and energy. Studies with primates have shown that there are neurons in the brain that fire only in response to visual presentations of food (Rolls, 1994). These populations of neurons are found primarily in the brain systems important for motivation and emotion, including the orbitofrontal cortex, the hypothalamus and the amygdala. These brain structures are key nodes for identifying stimuli or environmental contingencies that are likely to affect the survival or wellbeing of an organism, and therefore evoke corresponding emotional states and behavioral responses. In contrast to the large body of knowledge on primates (Rolls, 1994, 1999, 2000), relatively little research has examined the neural responses of healthy humans to visual presentations of food stimuli. The primary body of evidence comes from neuro­imaging techniques. Some early studies using neuroimaging techniques such as single photon emission computed tomography (SPECT) and PET suggested that visually presented food stimuli only produced minor changes in regional cerebral blood flow (rCBF) in normal-weight women (Karhunen et al., 1997, 1999, 2000; Gordon et al., 2000). However, greater changes were observed in women with eating or appetite-related problems such as obesity or binge eating, particularly within frontal, prefrontal, temporal and parietal cortices (Karhunen et al., 1997, 2000). This suggested that cortical responses to visual presentations of food might be moderated by the motivational status of the individual, with these overweight individuals’ brain regions responding more to food stimuli compared to individuals of normal weight. LaBar and colleagues used fMRI

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5.2 Brain responses to food stimuli in healthy adults

to study responses to food stimuli when participants were hungry and when satiated (LaBar et al., 2001). They found that the amygdala was more responsive to food stimuli when a person is hungry relative to when satiated, suggesting that motivational status can significantly affect the responses of critical emotion-related brain structures to the same food stimuli. Although hunger is clearly the most salient motivator guiding human appetitive behavior, there are numerous other factors that can influence whether a person will eat, and what foods they will choose. In particular, the motivation to eat can be influenced by qualities intrinsic to the food itself, such as its flavor, texture and potential energy value – attributes which often correlate with higher caloric and fat content. Our research team, under the direction of Deborah Yurgelun-Todd, at McLean Hospital, was interested in whether there were specific neural systems in the brain that respond differently to visual images of foods according to their energy value (i.e., caloric content). To examine this question, we conducted a neuroimaging study in which healthy young women underwent fMRI scanning while viewing color photographs of food stimuli (Killgore et al., 2003). Two categories of foods were shown: (1) high-calorie foods such as cakes, cookies, ice cream, hot dogs, hamburgers and spaghetti dinners; and (2) low-calorie foods such as green leafy salads, raw vegetables, fruits, and whole-grain cereals. A third condition also presented subjects with non-edible food-related utensils such as forks, spoons, plates and cups, with no food present in the image. Some of the main findings from that study are summarized in Figure 5.1, with white areas indicating regions that were significantly activated by high-calorie foods, gray areas by low-calorie foods, and black areas by non-edible dining-related utensils. Most importantly, these three types of stimuli activated very different regions of the cortex and limbic system. Overall, when we looked for regions that were

Front

Back

Right hemisphere

Left hemisphere

Figure

5.1  Regions of significant activation in response to images of high-calorie foods (white), low-calorie foods (gray) and non-edible dining-related utensils (black) in healthy adult females. High-calorie foods activated large regions of dorsolateral and dorsomedial prefrontal cortex, while low-calorie foods activated only a few discrete regions of cortex, particularly in the temporal lobe. Non-edible utensils activated regions typically involved in visual processing of common objects and tools. All regions are significant at P  0.0005 (uncorrected), k  20. Source: Adapted from Killgore et al. (2003).

activated in common for both categories of food, we found that all foods shared activity within many limbic system structures, including the amygdala, hippocampus, posterior cingulate gyrus, calcarine cortex and medial prefrontal cortex – regions heavily involved in primitive emotional and motivational behavior (see Figure 5.2). When the food categories were considered separately, however, there were notable differences between the brain activity patterns of high- and low-calorie foods. The presentation of high-calorie foods, which were rated as most appealing by volunteers, clearly activated a distributed network of cerebral structures involved in emotion, self-reflection, inhibition, response selection and behavioral regulation, including the medial and dorsolateral prefrontal cortex,

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5.  Cortical - Limbic Activation

Medial prefrontal cortex

Calcarine cortex

Figure 5.2  Regions of common activation (i.e., conjunction analysis) for both high- and low-calorie foods in healthy adult females). Significant common activation was observed in limbic regions, including the amygdala, hippo­ campus and parahippocampal gyrus (upper left, upper right and lower left panels), the medial prefrontal cortex (upper right panel), and calcarine cortex (upper right and lower left panels). All regions are significant at P  0.005 (uncorrected), k  20. Source: Adapted from Killgore et al. (2003).

Amygdala/ hippocampus

Amygdala/ hippocampus

SPM {t} 3.0 2.5

Calcarine cortex

2.0 1.5 1.0 0.5 0

middle temporal gyri, amygdala, parahippo­ campal gyrus, thalamus, hypothalamus and cere­ bellum (see Figure 5.3). The activity within the prefrontal cortex was particularly interesting, as the medial aspects show heightened activation during evaluative judgments such as deciding whether an object is liked or disliked (Zysset et al., 2002), self-reflective thought processes (Lane et al., 1997; Gusnard et al., 2001; Johnson et al., 2002) and conflict resolution (Ridderinkhof et al., 2004; Etkin et al., 2006). This suggests that appealing high-calorie foods may activate prefrontal regions important for evaluating and relating stimuli to one’s own preferences and self-perceptions. The strong activity within the dorsolateral regions is also intriguing, as this area is often associated with inhibitory suppression and control of behavior (Liddle et al., 2001; Pliszka et al., 2006; Rubia et al., 2006). Perhaps, for most normal-weight eaters, visual perception of high-calorie foods initiates a complex series of processes involving evaluative,

self-reflective and inhibitory activities within the prefrontal cortex (Del Parigi et al., 2002). In contrast to the robust findings for visual presentations of high-calorie foods, we found that low-calorie foods were associated with considerably less brain activation overall, which was localized to a few small regions including the middle and superior temporal gyrus, the somatosensory cortex and the medial orbito­ frontal cortex (Killgore et al., 2003). It is interesting to note that presentation of low-calorie foods produced very little additional activation above and beyond that produced by the “resting” control condition, which involved viewing similarly colorful and visually complex images of non-edible shrubs, trees, rocks and flowers. We interpreted these findings to suggest that visual perception of low-calorie foods did little to arouse the self-evaluative, conflict monitoring or inhibitory regions of the prefrontal cortex, while alternatively activating small regions of a visceral/gustatory/sensory processing system.

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5.3  Modulating factors

High calorie

Low calorie

Medial prefrontal cortex

Medial orbitofrontal cortex SPM {t} 6

4

5 3 4 3

2

2 1 1

Thalamus

Amygdala/ parahippocampal gyrus

0

0

Hippocampus

Figure 5.3  Regions of activation specific to images of high- and low-calorie foods. High-calorie food images (left panel) significantly activated large regions of the dorsomedial prefrontal cortex, thalamus and limbic structures, including the amygdala and parahippocampal gyrus (P  0.0005, k  20). Low-calorie food images (right panel) produced limited activation of a few small regions, including the medial orbitofrontal cortex, hippocampus and super­ ior temporal gyrus (P  0.005, k  20). Source: Adapted from Killgore et al. (2003).

Finally, as expected, non-edible utensils simply activated visual object processing regions within the occipito-temporal junction that are known to activate in response to common objects (GrillSpector et al., 2001) and tools (Martin et al., 1996; Tranel et al., 1997), but not in the evaluative, self-referential and inhibitory regions of the prefrontal cortex seen for high-calorie foods. In conclusion, the brain responds very differently to foods that promise high energy and high palatability relative to those that may be healthier but less tantalizing choices. If “energy is delight”, then this is clearly evident in cortical responses to high-calorie foods.

5.3  Modulating factors While viewing burgers, activates

the previous findings suggested that high-calorie foods such as cheeseFrench fries and creamy milkshakes the prefrontal cortex more than less

enticing images of carrot sticks, celery stalks and whole-grain cereals, there are several factors that may modulate these effects. For instance, it is possible that individual differences lead some people to have a more pronounced brain response to particular classes of foods than others. Such individual differences in brain responses could conceivably lead to a pattern of food choices that over a lifetime could significantly affect health and weight status. Two factors that we believed might be particularly relevant to issues of food choices included body mass and mood state.

5.3.1  Body mass An obvious question is to ask whether people with weight problems respond differently to images of food than do normal-weight individuals. Differences in brain activity between normal-weight and obese individuals in response to images of food might provide clues about the

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underlying neurobiology of weight gain and obesity. Several studies have directly contrasted brain activity of lean and obese women during visual exposure to food (Karhunen et al., 1997, 1999, 2000; Geliebter et al., 2006). Generally, these studies have shown that, compared to women of normal weight, several cortical regions of obese women responded more to images of food, with frontal and prefrontal regions showing prominent activity in obese women with a history of binge eating (Karhunen et al., 2000), particularly for food stimuli typically involved in binge eating (Geliebter et al., 2006). We were interested to see whether body mass index (BMI) might be related to patterns of brain activity even among individuals within the normal weight spectrum (Killgore and Yurgelun-Todd, 2005a). If present, such patterns of responsiveness might provide biological markers for the predisposition toward eventual weight gain, or provide clues as to why some people eventually gain more weight than others. To examine this possibility, we again looked at the brain activity of a sample of normal-weight women described in the previous section. We focused on the orbitofrontal cortex, a region of the brain important for appetite-related responses, behavioral inhibition, and learning reward and punishment contingencies. This part of the cortex is critical for the ability to alter learned patterns of behavior in favor of new ones when the old behaviors no longer prove rewarding (Rolls, 2000). We asked whether differences in orbitofrontal activity in response to high-calorie and low-calorie foods might be related to subtle differences in body mass in these normal-weight women (Killgore and Yurgelun-Todd, 2005a). The results for that study are summarized in Figure 5.4. As expected, activity within the orbitofrontal cortex and anterior cingulate gyrus was significantly correlated with the women’s BMI scores, but the specific regions of correlated activity differed for high- and lowcalorie foods. When high-calorie food images

were presented, the leanest participants showed the greatest activity in these regions, while those with higher BMI scores tended to have less activity in these inhibitory and emotion processing regions (Killgore and Yurgelun-Todd, 2005a). Low-calorie food images evoked a similar negative relationship between BMI and brain activity, but only in a small region located at the juncture of the right inferior orbitofrontal cortex and the superior temporal pole (see Figure 5.4). These negative relationships were not observed when participants viewed images of diningrelated utensils. Thus, even within the normal weight range, individuals with the greatest body mass tended to show the least activity within the orbitofrontal and anterior cingulate regions of the brain in response to images of highly rewarding foods. The findings suggest that the magnitude of responsiveness of the orbitofrontal cortex to food is correlated with body mass. The orbito­ frontal cortex is important for representing the punishment or reward value of primary re­inforcers, and for ascribing reward value to stimuli. It also plays a critical role in the ability to adapt to complex and changing environments by reversing previously learned stimulus­reinforcement associations in response to changing reinforcement contingencies (Rolls, 2000). The orbitofrontal cortex allows the organism to adapt flexibly to new environments and to modify behavior as necessary (Rolls, 1984), and is important for behavioral inhibition (Bokura et al., 2001; Altshuler et al., 2005). Even for women within the normal range of weight, our findings showed that individuals with larger body mass were likely to have significantly less activity in response to images of food within brain regions that are vital for the ability to inhibit or change behavior as appropriate to meet long-term goals. Although these results were correlational in nature and limited by small sample size, they raise the speculative possibility that subtle differences in the responsiveness of the orbitofrontal cortex to high-calorie

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5.3 Modulating factors

Adjusted fMRI response

Body mass index R

A

L

0.00 –0.20 R2= 0.57 –0.40 18

20

22

24

26

28

30

Body mass index (kg/m2) Adjusted fMRI response

P

0.20

0.20 0.00 –0.20 R2= 0.75 –0.40

18

20

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24

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28

30

Body mass index (kg/m2)

Figure 5.4  Body mass index (BMI) was significantly correlated with activity in the orbitofrontal cortex during visual presentations of food images. The image on the left shows the ventral surface of the brain with regions of significant activity superimposed. During presentations of low-calorie foods (white voxels), BMI was negatively related to activity in a small region of the right posterior orbitofrontal cortex. The top graph shows the scatterplot for the maximally correlated voxel for the low-calorie food condition. During presentations of high-calorie foods (black voxels), BMI was negatively correlated with activity within the anter­ ior cingulate gyrus and orbitofrontal cortex. The bottom graph shows the scatterplot for the maximally correlated voxel for the high-calorie food condition. All regions are significant at P  0.005, k  10. A, anterior; P, posterior; R, right; L, left. Source: Adapted from Killgore and Yurgelun-Todd (2005a).

foods could affect long-term preferences for certain foods, and the choices people make regarding the short-term rewards of high-calorie foods versus the long-term health benefits of lowercalorie foods. Even a slight difference in such choices beginning early in life could potentially affect long-term health and weight status if such patterns persist for years or decades. Clearly, more research will be needed to evaluate these possibilities adequately.

5.3.2  Mood Another factor that may affect human diet­ ary choice is a person’s emotional state (Macht, 1999). Many people find that they have cravings

for certain types of foods when they are experi­ encing an emotionally stressful situation or a period of “the blues,” leading to the phenomenon known as “emotional eating” (Hill et al., 1991). Some individuals are carbohydrate cravers, who tend to experience these cravings more intensely when experiencing strong negative moods (Christensen and Pettijohn, 2001). Depression and other severe mood disturbances are often accompanied by changes in appetite and carbohydrate cravings (Fernstrom et al., 1987; Krauchi et al., 1990; Wardle, 1990; Kazes et al., 1993; Christensen and Somers, 1994). It is well known that mood states, particularly extreme negative states such as clinical depression, can alter the normal functioning of the prefrontal cortex, anterior cingulate, and

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5.  Cortical - Limbic Activation

limbic system (Baxter et al., 1989; Drevets et al., 1997; Mayberg et al., 1999). Mood-related alterations in the functioning of the prefrontal cortex and associated systems might also change the way these systems evaluate the motivational value of food, as well as the ability to inhibit behavior associated with cravings. As discussed earlier, several of these same prefrontal and limbic regions, the orbitofrontal and insular cortices in particular, appear to be critically involved in processing food-related stimuli and guiding the dietary choices that humans make (Wang et al., 2004). First, evidence suggests that the orbito­ frontal cortex plays an important role in appetitive behavior through an interaction between its medial and lateral aspects. Functionally, greater activity in the medial aspect of the orbitofrontal cortex has been associated with increased hunger and enhanced motivation to eat (Tataranni et al., 1999; Morris and Dolan, 2001; Small et al., 2001). However, once a person has eaten to satiety and no longer finds the taste of food to be pleasurable these same medial regions of the orbitofrontal cortex show reduced blood-flow activity, whereas regions of the lateral orbito­ frontal cortex show increased activation (Gautier et al., 2001; Small et al., 2001). Together, these findings suggest that activity within the medial orbitofrontal cortex may be associated with increased appetite and food-seeking, whereas the lateral regions of the orbitofrontal cortex may act to inhibit eating once the individual is satiated. A second important region for appetitive behavior appears to be the insula, a region of cortex that is wrapped within the inner folds of the lateral fissure. The insula may function as primary gustatory cortex in humans (Faurion et al., 1998; Pritchard et al., 1999; Del Parigi et al., 2005), and is activated directly when subjects taste salty or sweet flavors (Kobayakawa et al., 1996; Murayama et al., 1996). The insula also shows increased activity in response to the smell of food (O’Doherty et al., 2000), and is believed to monitor the ongoing status of internal somatic states (Reiman, 1997; Reiman

et al., 1997; Craig, 2003; Critchley et al., 2004). Moreover, the insula shows heightened activation during hunger (Tataranni et al., 1999; Small et al., 2001) and decreased activation following satiation (Gautier et al., 2000, 2001; Small et al., 2001; Del Parigi et al., 2002), suggesting that it is directly related to the desire to seek out or abstain from food. Because activity in these two regions can be affected by hunger as well as mood state, we hypothesized that state affect might modulate appetite via its influence on brain activity in the orbitofrontal and insular cortices (Killgore and Yurgelun-Todd, 2006). To test this possibility, we correlated the brain activity within the orbito­ frontal and insular cortices during high- and low-calorie food perception with self-reported mood state on a well-established scale known as the Positive and Negative Affect Schedule (PANAS) (Watson et al., 1988). This scale provides scores on two independent dimensions of mood state, known as positive affect (PA) and negative affect (NA). PA is a state of feeling enthusiastic, alert and active, while NA is a state of heightened unpleasant feelings and subjective distress (Watson and Tellegen, 1985). As shown in Figure 5.5, we found that PA and NA were linearly-related to fMRI signal intensity in specific regions of the orbitofrontal and insular cortices. Moreover, the pattern of activation suggested an interaction between mood state and the calorie content of the food images. People with the highest scores on PA showed greater activity in the medial orbitofrontal cortex and posterior insula (i.e., the “start eating” or “hunger” regions) in response to healthy low-calorie food images. When presented with less healthy high-calorie food images, PA scores correlated with greater activity in the lateral regions of the orbitofrontal cortex (i.e., the “stop eating” or “satiation” regions). In contrast, for those with higher scores on NA, perception of high-calorie foods was associated with greater activity in the medial orbitofrontal and posterior insular cortex (i.e., the “start eating” or “hunger”

1.  From Brain to Behavior

5.4  Cortical and limbic activation to food images during adolescent development

Positive affect (PA) High-calorie foods Lateral prefrontal cortex “Satiation” regions

P

A Low-calorie foods Medial prefrontal cortex “Hunger” regions

L Negative affect (NA) Low-calorie foods Lateral prefrontal cortex “Satiation” regions

P

A High-calorie foods Medial prefrontal cortex “Hunger” regions

L

Figure 5.5  Positive affect (PA) and negative affect (NA) correlated significantly with activity in discrete regions of the orbitofrontal cortex (OFC) during presentation of high-calorie (black) and low-calorie (white) food images. Higher scores on PA (top) correlated with greater activity in lateral OFC during high-calorie food presentations (black) and with activity in the medial OFC during low-calorie food presentations (white). In contrast, higher scores on NA (bottom) correlated with activity in the lateral OFC for low-calorie food presentations (white) and with activity in the medial OFC for high-calorie food images (black). Areas showing positive correlations with affect scores for both types of foods are shown in medium gray. All regions are significant at P  0.05, k  10. A, anterior; P, posterior; L, left. Source: Adapted from Killgore and Yurgelun-Todd (2006).

regions), whereas for healthier low-calorie food images high NA scores were related to greater activity in the lateral orbitofrontal cortex (i.e., the “stop eating” or “satiation” regions). In short, the findings from that analysis suggest that mood state may affect appetite via the relative changes in brain activity between lateral and medial orbitofrontal cortices. These findings suggest that a good or energetically positive mood (i.e., high PA) is associated with a pattern of activity in brain regions that might

65

increase the desire to consume healthier lowcalorie foods and reduce the desire to consume unhealthy high-calorie/high-fat foods. Similarly, a bad or subjectively distressed mood (i.e., high NA), was associated with patterns of brain activity that would lead to a greater desire to consume less healthy high-calorie/high-fat foods and lower the preference for healthier food choices. Although these findings are consistent with the reported cravings for unhealthy foods during negative mood states (Hill et al., 1991; Christensen and Pettijohn, 2001), they remain speculative, as the data are correlational in nature and we did not collect any ratings of cravings or preferences for the types of foods presented in the study. Recent evidence also suggests that there are individual differences in reward sensitivity that may contribute to the responsiveness of prefrontal and subcortical regions to highly rewarding foods (Beaver et al., 2006).

5.4  Cortical and limbic activation to food images during adolescent development Although long-term patterns of food consumption in adulthood can have dramatic consequences on health status, problems with overweight and obesity are increasingly common among children and adolescents (Flegal et al., 2002). Because eating and exercising patterns, established early in life, are likely to guide subsequent lifestyle choices and health-related behaviors, it is important to understand how the brains of children respond to images of food in ways that are similar to and different from those of adults. We know that the brain continues to develop throughout adolescence and into early adulthood, and that these changes affect how children process information (Casey et al., 2000; Killgore et al., 2001; Yurgelun-Todd et al., 2002, 2003; Yurgelun-Todd and Killgore, 2006).

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Because the regions of the brain most critical to the ability to form good judgments, delay gratification and inhibit inappropriate behavior are also the same ones that develop latest during adolescence (Casey et al., 2000; Giedd et al., 1999; Giedd, 2004), it may be more difficult for children to effectively regulate their dietary needs. For example, it has been shown that by about 7 years of age healthy children generally exhibit some understanding of when it is appropriate to inhibit certain responses, but their ability to successfully inhibit such responses may not be fully developed until years later (Dowsett and Livesey, 2000). These inhibitory capacities are believed to be mediated predominantly by the prefrontal cortex (Watanabe et al., 2002), a region that develops rapidly during the early adolescent years (Giedd et al., 1999; Kanemura et al., 2003), although the dorsolateral regions, which are particularly important for inhibition and judgment, may not reach full maturity until the early twenties (Giedd, 2004; Lenroot and Giedd, 2006). Functional neuroimaging studies suggest that the prefrontal cortex becomes progressively more active during self-control (Marsh et al., 2006) and emotional tasks as children mature (Killgore et al., 2001; Killgore and YurgelunTodd, 2004; Yurgelun-Todd and Killgore, 2006), and this activity correlates with the ability to inhibit behavior (Rubia et al., 2000). As described earlier, many of these same prefrontal regions are highly active when adults view images of high-calorie foods but not when viewing visually similar low-calorie foods, suggesting that the perception of highly appealing yet unhealthy foods stimulates much activity in brain regions important for inhibition and self-control (Killgore et al., 2003). Therefore, in a subsequent study, we decided to study the brain activation of children and adolescents as they viewed these same sets of food images and compare the results to our adult sample (Killgore and Yurgelun-Todd, 2005b). In that study, we presented the food images to eight healthy

female children and adolescents ranging in age from 9 to 15 years old as they underwent fMRI scanning. To gain a better understanding of the development of cortical and limbic responses to food, the data from the children and adolescents were compared statistically to the data from the adults reported earlier. Several important findings emerged. First, as with the adults in the previous study, the adolescent children showed significant activation of limbic regions, especially the hippocampus and parahippocampal gyri, in response to images of food, regardless of caloric content (see Figure 5.6a). Secondly, the children showed significantly different cortical responses to the highand low-calorie foods compared to adults (see Figure 5.6b). Whereas the adults showed large regions of activity within the dorsolateral and medial prefrontal cortex, the adolescent sample did not. A direct statistical comparison between the adolescents and adults showed greater activity in the medial prefrontal cortex of adults during the perception of high-calorie foods. The fact that the adult sample showed significant activation within inhibitory and self-reflective prefrontal regions whereas adolescents failed to show such activation suggests that adolescent brain development may not have reached the level of maturity necessary to consistently engage these important self-regulatory regions when viewing appetizing but unhealthy high-calorie foods. The adolescents, in contrast, tended to respond to food images with greater activation than adults in posterior brain regions that are generally associated with visual processing of objects such as tools and other inanimate objects. Finally, within the adolescent sample, we correlated the high-calorie brain responses with age to identify regions that might show developmental changes in brain activity. Interestingly, we found that activity within the orbitofrontal cortex increased with age in response to high-calorie but not lowcalorie foods (see Figure 5.6c). Together, these findings suggest that subcortical and limbic

1.  From Brain to Behavior

5.4  Cortical and limbic activation to food images during adolescent development

(a) Adolescent High–low calorie conjunction

Hippocampus

(b) Adults vs adolescents (high calorie) Dorsal medial prefrontal cortex Anterior cingulate

(c) Correlation with age (high calorie)

Medial orbitofrontal cortex

Figure 5.6  Developmental findings for responses of healthy adolescent children to images of food. (a) Regions of common activation (i.e., conjunction analysis) for both high- and low-calorie foods in healthy adolescents. Similar to the findings reported previously for adults, significant common activation was observed in the hippocampus for high- and low-calorie foods, P  0.005 (uncorrected), k  20. (b) When viewing images of high-calorie foods, adults show significantly greater activation of the dorsomedial prefrontal cortex, anter­ ior cingulate, precuneus and posterior cingulate gyrus than adolescents, while adolescents show greater activation in posterior visual processing regions than adults, P  0.005 (uncorrected), k  20. (c) A significant positive correlation (r  0.97) was observed between adolescent age and functional activity in the medial orbitofrontal cortex during visual perception of high-calorie food images, P  0.01, k  10. Source: Adapted from Killgore and Yurgelun-Todd (2005b).

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responses to rewarding food stimuli remain relatively constant over development, without much difference between children and adults, whereas regions of the brain that are particularly important for self-reflection, inhibition and modification of behavior show progressive age-related increases in functional activation. This may be due to experience, neural development through adolescence or, most likely, some combination of the two. The findings are in accord with an emerging neurodevelopmental model that suggests that acquisition of the greater self-control and inhibition capacities that normally emerge with adolescent development occurs in conjunction with progressive structural and functional changes within the prefrontal cortex and its interactions with subcortical structures (Luna et al., 2001; Fuster, 2002; Gomez-Perez et al., 2003; Marsh et al., 2006). While the behavioral correlates of these patterns of activation have yet to be determined, the developmental neuroimaging findings are important because they suggest that (1) some of the most primitive emotional regions of the brain, such as the limbic system, show very consistent activation among children and adults when presented with images of enticing highcalorie foods, but that (2) adult brains may respond to such foods with greater activation of inhibitory, self-monitoring and self-regulatory regions of the prefrontal cortex, perhaps allowing mature individuals greater voluntary control over their dietary intake. While speculative, this difference in frontal activity may partly explain why children, even if they have been educated about proper nutrition, may find it difficult (or at times impossible) to resist the temptations presented by the vast cornucopia of easily accessible high-calorie junk foods. At present, caution is warranted in extrapolating these imaging data to actual behavior, but future studies will undoubtedly clarify the extent to which differences in childhood and adult brain responses may contribute to food choices and health outcomes.

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5.5  Conclusion From this limited review, it is clear that the human brain responds to visual images of food differently according their energy value and immediate reward potential. For adults and children alike, several of the more primitive regions of the brain involved in motivation, emotion and memory appear to share a common activation to both high-and lowcalorie foods. Cortical regions, on the other hand, responded differently depending on the caloric content of the foods. When confronted with images of high-calorie food images such as Big Macs and French fries, the adult brain appears to show widespread cortical activation, particularly in prefrontal regions. Similar responses appear to be virtually absent for images of healthier low-calorie foods such as salads and whole-grain cereals. Because the activated regions are frequently implicated in tasks of judgment, decision-making, inhibition, self-reflection and behavioral control, one interpretation of these data is that high-calorie foods arouse considerable approach-avoidance conflict in healthy adults, but much less so in children and adolescents because of the immature development of their prefrontal inhibitory systems. The lack of arousal of such conflict and inhibition may account for some of the difficulties children have in moderating unhealthy food choices, and may help explain why the epidemic of obesity is growing so rapidly among young people. Similarly, the findings suggest that the responsiveness of these prefrontal systems to images of food may also be modulated by other individual factors, with higher body mass and negative mood states associated with patterns of brain activity that are similar to those associated with reduced inhibitory control and greater appetite in some studies. Clearly, these data are very preliminary and generate more new questions than they answer. Future research that links food-related brain responses

with actual eating behavior will be necessary to clarify the full implications of these findings. The emerging picture, however, suggests that some mostly subcortical and limbic aspects of our brains are hard-wired to respond to images of food in general, while with maturation other cortical regions appear to develop particularly strong responses to foods that have been associated with energy and pleasure.

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Killgore, W. D. S., Oki, M., & Yurgelun-Todd, D. A. (2001). Sex-specific developmental changes in amygdala responses to affective faces. Neuroreport, 12(2), 427–433. Killgore, W. D. S., Young, A. D., Femia, L. A., Bogorodzki, P., Rogowska, J., & Yurgelun-Todd, D. A. (2003). Cortical and limbic activation during viewing of high- versus low-calorie foods. Neuroimage, 19(4), 1381–1394. Kobayakawa, T., Endo, H., Ayabe-Kanamura, S., Kumagai, T., Yamaguchi, Y., Kikuchi, Y., et al. (1996). The primary gustatory area in human cerebral cortex studied by magnetoencephalography. Neuroscience Letters, 212(3), 155–158. Krauchi, K., Wirz-Justice, A., & Graw, P. (1990). The relationship of affective state to dietary preference: Winter depression and light therapy as a model. Journal of Affective Disorders, 20(1), 43–53. LaBar, K. S., Gitelman, D. R., Parrish, T. B., Kim, Y. H., Nobre, A. C., & Mesulam, M. M. (2001). Hunger selectively modulates corticolimbic activation to food stimuli in humans. Behavioral Neuroscience, 115(2), 493–500. Lane, R. D., Fink, G. R., Chau, P. M., & Dolan, R. J. (1997). Neural activation during selective attention to subjective emotional responses. Neuroreport, 8(18), 3969–3972. Lenroot, R. K., & Giedd, J. N. (2006). Brain development in children and adolescents: Insights from anatomical magnetic resonance imaging. Neuroscience and Biobehavioral Reviews, 30(6), 718–729. Liddle, P. F., Kiehl, K. A., & Smith, A. M. (2001). Eventrelated fMRI study of response inhibition. Human Brain Mapping, 12(2), 100–109. Luna, B., Thulborn, K. R., Munoz, D. P., Merriam, E. P., Garver, K. E., Minshew, N. J., et al. (2001). Maturation of widely distributed brain function subserves cognitive development. Neuroimage, 13(5), 786–793. Macht, M. (1999). Characteristics of eating in anger, fear, sadness and joy. Appetite, 33(1), 129–139. Marsh, R., Zhu, H., Schultz, R. T., Quackenbush, G., Royal, J., Skudlarski, P., et al. (2006). A developmental fMRI study of self-regulatory control. Human Brain Mapping, 27, 848–863. Martin, A., Wiggs, C. L., Ungerleider, L. G., & Haxby, J. V. (1996). Neural correlates of category-specific know­ ledge. Nature, 379(6566), 649–652. Mayberg, H. S., Liotti, M., Brannan, S. K., McGinnis, S., Mahurin, R. K., Jerabek, P. A., et al. (1999). Reciprocal limbic-cortical function and negative mood: Converging PET findings in depression and normal sadness. The American Journal of Psychiatry, 156(5), 675–682. Morris, J. S., & Dolan, R. J. (2001). Involvement of human amygdala and orbitofrontal cortex in hunger-enhanced memory for food stimuli. Journal of Neuroscience, 21(14), 5304–5310. Murayama, N., Nakasato, N., Hatanaka, K., Fujita, S., Igasaki, T., Kanno, A., et al. (1996). Gustatory evoked

magnetic fields in humans. Neuroscience Letters, 210(2), 121–123. O’Doherty, J., Rolls, E. T., Francis, S., Bowtell, R., McGlone, F., Kobal, G., et al. (2000). Sensory-specific satiety-related olfactory activation of the human orbitofrontal cortex. Neuroreport, 11(2), 399–403. Pliszka, S. R., Glahn, D. C., Semrud-Clikeman, M., Franklin, C., Perez, R., III, Xiong, J., et al. (2006). Neuroimaging of inhibitory control areas in children with attention deficit hyperactivity disorder who were treatment naive or in long-term treatment. The American Journal of Psychiatry, 163(6), 1052–1060. Pritchard, T. C., Macaluso, D. A., & Eslinger, P. J. (1999). Taste perception in patients with insular cortex lesions. Behavioral Neuroscience, 113(4), 663–671. Reiman, E. M. (1997). The application of positron emission tomography to the study of normal and pathologic emotions. Journal of Clinical Psychiatry, 58(Suppl. 16), 4–12. Reiman, E. M., Lane, R. D., Ahern, G. L., Schwartz, G. E., Davidson, R. J., Friston, K. J., et al. (1997). Neuroanatomical correlates of externally and internally generated human emotion. The American Journal of Psychiatry, 154(7), 918–925. Ridderinkhof, K. R., Ullsperger, M., Crone, E. A., & Nieuwenhuis, S. (2004). The role of the medial frontal cortex in cognitive control. Science, 306(5695), 443–447. Rolls, E. T. (1984). The neurophysiology of feeding. International Journal of Obesity, 8(Suppl. 1), 139–150. Rolls, E. T. (1994). Neural processing related to feeding in primates. In C. R. Legg & D. A. Booth (Eds.), Appetite: Neural and behavioral bases (pp. 11–53). Oxford: Oxford University Press. Rolls, E. T. (1999). The brain and emotion. New York, NY: Oxford University Press. Rolls, E. T. (2000). The orbitofrontal cortex and reward. Cerebral Cortex, 10(3), 284–294. Rubia, K., Overmeyer, S., Taylor, E., Brammer, M., Williams, S. C., Simmons, A., et al. (2000). Functional frontalisation with age: Mapping neurodevelopmental trajectories with fMRI. Neuroscience and Biobehavioral Reviews, 24(1), 13–19. Rubia, K., Smith, A. B., Woolley, J., Nosarti, C., Heyman, I., Taylor, E., et al. (2006). Progressive increase of frontostriatal brain activation from childhood to adulthood during event-related tasks of cognitive control. Human Brain Mapping, 27(12), 973–993. Santel, S., Baving, L., Krauel, K., Munte, T. F., & Rotte, M. (2006). Hunger and satiety in anorexia nervosa: fMRI during cognitive processing of food pictures. Brain Research, 1114(1), 138–148. Simmons, W. K., Martin, A., & Barsalou, L. W. (2005). Pictures of appetizing foods activate gustatory cortices for taste and reward. Cerebral Cortex, 15(10), 1602–1608. Small, D. M., Zatorre, R. J., Dagher, A., Evans, A. C., & Jones-Gotman, M. (2001). Changes in brain activity

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C H A P T E R

6 Reward-related Neuroadaptations Induced by Food Restriction: Pathogenic Potential of a Survival Mechanism Kenneth D. Carr Departments of Psychiatry and Pharmacology, New York University School of Medicine, New York, NY, USA

o u t l ine 6.1 Introduction

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6.2 Food Restriction may Augment Neurobiological Responses to Palatable Food in a way that Promotes Addictive Behavior 75 6.3 Food Restriction Enhances CNS and Behavioral Responses to Drugs of Abuse and Dopamine Receptor Agonists 76 6.4 Food Restriction Up-regulates D1 Dopamine Receptor-mediated Phosphorylation of Ionotropic

6.1  Introduction The increasing prevalence of obesity in Wester­n­ized societies has been partly attributed to the increasing abundance of inexpensive, energydense, highly palatable foods (Centers for Disease

Obesity Prevention: The Role of Brain and Society on Individual Behavior

Glutamate Receptors and Signaling Proteins that Underlie Synaptic Plasticity 77 6.5 Striatal Neuroadaptations Induced by Food Restriction may be Secondary to changes in Pre-synaptic Dopamine Neuronal Function

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6.6 A Schema to Consider as Research Continues

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Acknowledgments

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Control and Prevention (CDC), 2004; Cordain et al., 2005). It is not entirely understood why homeostatic regulatory systems are superseded in the “obesogenic” environment; it has been pointed out, however, that human evolution in an ecology of scarcity will have favored rigorous

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homeostatic controls selected to defend against starvation but not obesity (Chakravarthy and Booth, 2004; Schwartz and Niswender, 2004; Polivy and Herman, 2006). An additional significant factor may be the recent introduction of refined sugars to the human diet (Cordain et al., 2005), which are proposed to generate a supra­ normal reward signal in brain (Lenoir et al., 2007), not unlike that linked to drugs of abuse, and which can potentially lead to addictive behavior. The plausibility of this suggestion is highlighted by the recent pre-clinical finding that rats trained in a two-lever task invariably self-administered oral sucrose solution rather than concurrently available intravenous cocaine (Lenoir et al., 2007). Sucrose, by way of orosensory (Yu et al., 2000; Smith, 2004) and post-ingestive (de Araujo et al., 2008) signaling, leads to increased extracellular dopamine (DA) concentrations in the nucleus accumbens (Hajnal et al., 2004; Norgren et al., 2006) – an effect shared with virtually all drugs of abuse, and key to their rewarding and addictive properties (Pontieri et al., 1995; Bassareo and Di Chiara, 1999a). Nucleus accumbens dopamine concentrations also increase in response to cues signaling cocaine or sucrose availability, and coincide with the initiation of a response to obtain these rewards (Carelli, 2004). Commercial formulations of high-fat and high-sugar “snack” foods possess similarly potent incentive properties (Jarosz et al., 2006), and high-fat corn oil induces nucleus accumbens dopamine release similar to that observed during sucrose intake (Liang et al., 2006). While addiction has been conceptualized as a process through which drugs usurp and produce synaptic plasticity in dopaminergic and related neuronal circuits that normally mediate adaptive goal-directed behaviors such as food-seeking and procurement (Kelley and Berridge, 2002; Cardinal and Everitt, 2004; Di Chiara, 2005; Volkow and Wise, 2005), food itself, under some conditions, may commandeer this circuitry in a way that sustains maladaptive

behavior. The proposed phenomenon of sugar “addiction” is supported by animal models in which (1) intermittent access to sucrose produces behavioral cross-sensitization to psychostimulant drugs of abuse (Avena and Hoebel, 2003; Gosnell, 2005); (2) alternating 12-hour periods of sucrose access and food deprivation lead to binge-like intake of sucrose followed by withdrawal signs when access is terminated (Avena et al., 2008); and (3) a period of abstinence from sucrose is followed by powerful cue-induced reinstatement of sucrose-seeking similar to that observed in animal subjects abstaining from cocaine (Grimm et al., 2005). Several studies have assessed whether neuroadaptations that accompany cocaine addiction are present in rats with a history of sucrose self-administration and seeking. To date, neuroplastic changes induced by cocaine – including molecular and structural changes in the cell body and terminal regions of the mesoaccumbens dopamine pathway – have not been observed in rodents self-administering or withdrawn from sucrose (Robinson et al., 2001; Lu et al., 2003; Jones et al., 2007; Chen et al., 2008). An exception may be the bingepromoting protocol in which 12-hour periods of food deprivation are alternated with 12-hour periods of sucrose access (Avena et al., 2008). At sacrifice, these animals displayed decreased D2 dopamine receptor binding in the terminal field of the nigrostriatal pathway (i.e., caudateputamen) – a finding also obtained in neuro­ imaging studies of human cocaine addicts and obese subjects (Wang et al., 2004) – and increased D1 DA receptor binding in the nucleus accumbens. Interestingly, increased D1 DA receptor function in the nucleus accumbens appears to be a fundamental underpinning of the enhanced rewarding, cell signaling and transcriptional responses of food-restricted rats to drug challenge (Carr, 2007). As will be outlined below, food restriction produces reward-related neuroadaptations that likely promote foraging, food acquisition and ingestive behavior in

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6.2  Food restriction may augment neurobiological responses to palatable food

the energy-deficient subject. However, during self-imposed food restriction, induction of these neuroadaptations in an obesogenic environment may contribute to the ultimate failure of many weight-loss diets, the emergence of binge eating in chronic dieters, and the high co-morbidity of eating disorders and substance abuse.

6.2  Food restriction may augment neurobiological responses to palatable food in a way that promotes addictive behavior Self-imposed calorie restriction is a frequent response to the fear or attainment of overweight and obesity, yet the majority of diets fail (Polivy et al., 2008). Restrained eating oftentimes leads to loss of control, poor food choices, a disposition to binge, and the regain or surpassing of baseline body weight (Vitousek, 2004; Vitousek et al., 2004a; Polivy and Herman, 2006; Polivy et al., 2008). In fact, it has been suggested that dieting contributes to the obesity epidemic (Polivy and Herman, 2006). In the context of abundant palatable foods and associated cues, a common response to dietary restraint is binge eating, as observed in the Minnesota Study of calorie restriction among World War II conscientious objectors (Keys et al., 1950), studies of chronic dieters (Polivy and Herman, 2006) and protocols of food restriction or dieting in non-clinical populations (Laessle et al., 1996; Stice et al., 2000). These observations are not surprising, given the enhanced motivationalaffective responses to food and cues that have been observed in food-restricted subjects in the laboratory. Cabanac and LaFrance (1991) demonstrated in human and rodent subjects that several weeks of food restriction with weight loss prevents negative alliesthesia – i.e., the phenomenon whereby a preload of glucose causes

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a previously attractive sweet taste to become unpleasant. Another, though neurochemical, example of a persistent positive response in food-deprived relative to free-feeding subjects is dopamine release in the nucleus accumbens during ingestion of palatable food. Normally, an important difference between the dopaminergic response to food and abused drugs, believed to contribute to the addictive properties of the latter, is that contact with palatable food releases dopamine in the nucleus accumbens shell subdivision only when it is novel (as a learning signal), while drugs of abuse repeatedly increase extracellular dopamine in this region (Pontieri et al., 1995; Bassareo and Di Chiara, 1999a). When subjects are food-deprived, palatable food retains its ability to release dopamine in the nucleus accumbens shell despite the subject’s familiarity with it (Bassareo and Di Chiara, 1999b), thus rendering food more “drug-like” in this regard. In another example, when restrained eaters were exposed to food odors for 10–12 minutes prior to an opportunity to consume, craving and intake were strongly increased, while the same pre-exposure either had no effect or actually decreased intake in control subjects (Jansen and van den Hout, 1991; Fedoroff et al., 1997, 2003). In a rodent neuro­ chemical parallel to this observation, anticipation of access to highly palatable food was accompanied by nucleus accumbens dopa­mine release in food-deprived rats but not ad libitumfed rats, despite the fact that both groups consumed the food (Wilson et al., 1995). Polivy and Herman (2006) have discussed evolutionary explanations of the up-regulated incentive response, proclivity to binge, and low success rate among voluntary dieters. They point out that “gorging”, whenever possible, confers selective advantage upon a species that evolved to survive alternating cycles of scarcity and abundance. Consequently, in the physiological context of dieting or significant dieting history, the prepotent response to abundance is

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self-indulgence. Binge eating is common in the obese population, and is considered a contributing factor to the obese condition (Yanovski, 1993). It is estimated that up to 30 percent of obese patients display binge-eating disorder (Yanovski, 1993; Striegel-Moore and Franko, 2003) and, importantly, among obese individuals who binge eat there is a greater prevalence of cyclic dieting than among those who do not (Howard and Porzelius, 1999). In two welldeveloped pre-clinical models of binge-eating disorder, repeated cycles of food restriction or deprivation combined with periodic access to highly palatable food are necessary conditions for the emergence of binge-eating behavior (Hagan and Moss, 1997; Hagan et al., 2003; Avena et al., 2008). A variant form of maladaptive reward-directed behavior accompanying food restriction is the enhancement of drugseeking and -taking in animal models (Carroll and Meisch, 1984), and the association between dietary restraint and substance abuse in clinical and non-clinical populations (Herzog et al., 1992; Krahn et al., 1992; Wilson, 1993; Wiederman and Pryor, 1996; Corwin, 2006). Our laboratory has investigated CNS and behavioral responsiveness of food-restricted animals to drugs of abuse and other pharmacological probes. It is expected that some of the findings obtained will be applicable to understanding the enhanced incentive effects of food and cues among restrained eaters, and neurobiological factors contributing to the genesis of binge eating.

6.3  Food restriction enhances CNS and behavioral responses to drugs of abuse and dopamine receptor agonists Initial studies of the laboratory were aimed at assessing whether the enhancement of drug

self-administration behavior by food restriction (Carroll et al., 1979; Carroll and Meisch, 1984) reflects an increase in drug-reward magnitude. To conduct these studies, mature male rats were food-restricted until body weight decreased by 20 percent; testing occurred during the ensuing 1–3 weeks during which body weight was clamped at this value by titrating daily food allotment. A curve-shift method of electrical brain stimulation reward testing was used in conjunction with passive drug administration to quantify the reward-potentiating effects of drugs, as reflected in the leftward shift produced in the curve that relates animals’ rate of lever-pressing to the pulse frequency of contingent brain stimulation. This approach provides a measure of reward-related drug effects that is not conflated with the effects of food restriction on learning and performance capacity, or the possible negative reinforcing effects of drugs in the hungry subject. Consequently, effects of d-amphetamine, phencyclidine, cocaine and several other drugs were shown to be greater in food-restricted than ad libitum-fed rats (Cabeza de Vaca and Carr, 1998; Carr et al., 2000). To assess whether these enhanced drug effects were more likely a result of increased CNS sensitivity than changes in drug pharmacokinetics and bioavailability, intraperitoneal and intracerebroventricular (i.c.v.) routes of administration were compared. Consistent and pronounced differences between feeding groups in response to i.c.v. drug administration supported the involvement of a sensitized neural substrate. As follow-up to the behavioral studies, immuno­ staining for the protein product of the immediate early gene, c-fos, revealed that d-amphetamine and the D1 dopamine receptor agonist SKF82958 produced greater cellular activating effects in several dopamine terminal areas of foodrestricted relative to ad libitum-fed rat brain, including the dorsal and ventral striatum – i.e., caudate-putamen and nucleus accumbens (Carr and Kutchukhidze, 2000; Carr et al., 2003). Further, the D1 agonist was shown to induce

1.  From Brain to Behavior

6.4  Food restriction up-regulates D1 dopamine receptor-mediated phosphorylation

greater activation of the ERK 1/2 MAP kinase signaling cascade, the downstream nuclear tran­scription factor CREB, and neuropeptide gene expression (preprodynorphin and preprotachykinin) in the nucleus accumbens (Haberny et al., 2004; Haberny and Carr, 2005a, 2005b). These findings, and the abundant literature identifying the nucleus accumbens as a locus in which psychostimulants exert positive reinfor­cing effects and enhance responding for non-drug reinforcers (e.g., Hoebel et al., 1983; Pontieri et al., 1995; Carlezon et al., 1995; Carlezon and Wise, 1996; McBride et al., 1999; Parkinson et al., 1999; Wyvell and Berridge, 2000; Rodd-Henricks et al., 2002), suggested that, among the neuro­ adaptations associated with food restriction, increased D1 dopamine receptor function may be critically involved in the enhanced behavioral responsiveness to drugs of abuse. Support for this hypothesis was obtained in a study demonstrating that direct microinjections of d-amphetamine or SKF-82958 into the nucleus accumbens produced reward-potentiating and locomotor-activating effects that were markedly greater in food-restricted than ad libitum-fed subjects (Carr et al., 2009a). These findings almost certainly have implications for fooddirected behavior in as much as (1) persistent elevation of extracellular dopa­mine levels in the nucleus accumbens via dop­amine transporter knockdown increases food intake and incentive motivation to obtain a sweet food reward (Pecina et al., 2003); (2) amphetamine micro­ injected into the nucleus accumbens increases the incentive salience of sucrose reward (Wyvell and Berridge, 2000) and sucrose consumption (Sills and Vaccarino, 1996); (3) inactivation of dopamine input to the nucleus accumbens blocks local neuronal responses to a sucrose-paired cue and abolishes sucrose-directed behavior (Yun et al., 2004); and (4) a D1 dopamine receptor antagonist microinjected into the nucleus accumbens decreases sham drinking of sucrose solution (Smith, 2004; see, however, Hajnal and Norgren, 2001).

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6.4  Food restriction up-regulates D1 dopamine receptor-mediated phosphorylation of ionotropic glutamate receptors and signaling proteins that underlie synaptic plasticity The dopamine innervation of striatum is convergent with glutamate inputs from amygdala, hippocampus and prefrontal cortex (Groenewegen et al., 1999; Kalivas et al., 2005), and the integration of dopamine- and glutamate-coded signals is fundamentally involved in the regulation of accumbens neuronal activity (e.g., Surmeier et al., 2007), appetitive goal-directed behavior, and reward-related learning (Berke and Hyman, 2000; Kelley, 2004; Malenka and Bear, 2004; Dalley et al., 2005; Hyman et al., 2006). Acquisition of instrumental and Pavlovian food-directed responses is dependent upon coincident activation of D1 dopamine and NMDA glutamate receptors in the nucleus accumbens (Kelley, 2004; Dalley et al., 2005), and this mechanism appears to be up-regulated by food restriction. Upon D1 dopamine receptor stimulation, phosphorylation of the NMDA receptor NR1 subunit in the nucleus accumbens is greater in food-restricted than ad libitum-fed rats (Haberny and Carr, 2005a). NR1 phosphorylation increases NMDA receptor function (cation channel conductance) and recruits NMDA receptor-linked signal transduction pathways, including ERK 1/2 and CaMK II (Leonard et al., 1999; Vanhoutte et al., 1999; Dudman et al., 2003). Thus, we observed that D1 agonist administration induced an enhanced activation of ERK 1/2, CaMK II and CREB in food-restricted rats, and these effects were blocked by pre-treatment with the NMDA receptor antagonist MK-801. Among the ionotropic glutamate receptor types that are co-expressed with dopamine receptors in striatal neurons (Bernard et al., 1997;

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Wang et al., 2006), AMPA receptors mediate fast excitatory synaptic transmission (Barry and Ziff, 2002). The GluR2-lacking GluR1 subtype of this receptor undergoes activity-dependent trafficking, is Ca2 permeable, and its insertion and removal from the neuronal membrane underlie changes in synaptic strength (Barry and Ziff, 2002). Phosphorylation of GluR1 on Ser845 by the D1 dopamine receptor → cAMP and/or NMDA receptor → cGMP pathways enhances AMPA currents and facilitates rapid insertion into the post-synapse (Roche et al., 1996; Snyder et al., 2000; Man et al., 2007; Serulle et al., 2007). A single injection of cocaine or amphetamine, or a brief bout of sugar consumption, all lead to phosphorylation of GluR1 on Ser845 in a D1 receptor-dependent manner (Snyder et al., 2000; Rauggi et al., 2005). Recently, we observed that a brief intake of sucrose solution rapidly increased GluR1 protein levels in the synaptosomal fraction of the nucleus accumbens, suggesting increased trafficking to the synaptic membrane (Tukey et al., 2007). Moreover, phosphorylation of GluR1 on Ser845 in response to both D1 receptor stimulation and intake of sucrose solution is greater in food-restricted than ad libitum-fed subjects (Carr et al., 2010). Considering that increased surface expression of GluR1 in the nucleus accumbens has been identified as an enduring consequence of chronic cocaine that is necessary for behavioral sensitization, “craving” and vulnerability to relapse (Boudreau and Wolf, 2005; Conrad et al., 2008), the phosphorylation and trafficking of GluR1 during sucrose intake, and the up-regulation of D1 receptor-mediated phosphorylation of GluR1 by chronic food restriction, suggest a mechanism that may play a role in the facilitatory effect of food restriction on both adaptive and maladaptive forms of reward-directed behavior. Confirming a role of GluR1 phosphorylation in the invigoration of food-directed behavior, it was recently demonstrated that a conditioned stimulus associated with a sweet solution loses its ability to

increase instrumental responding for sucrose reward during extinction when food-restricted mice have mutations on the Ser831 and Ser845 phosphorylation sites of the GluR1 receptor (Crombag et al., 2008). The up-regulation of striatal ERK 1/2 MAP kinase signaling by food restriction may represent a mechanism, secondary to increased phosphorylation of NR1 and GluR1, that mediates enhanced reward-related learning under conditions of food scarcity, with the potential to be subverted by drugs and highly palatable foods during dietary restraint. Drugs of abuse activate ERK throughout the striatum in a D1 dopamine receptor-dependent manner (Valjent et al., 2004). The ERK cascade activates downstream transcription factors (CREB, Elk-1) and gene expression (Thomas and Huganir, 2004), and mediates synaptic plasticity, learning and memory (Sweatt, 2001; Thomas and Huganir, 2004). Importantly, several enduring addiction-related behavioral changes induced by drugs of abuse, including conditioned place preference (Valjent et al., 2000, 2001; Salzmann et al., 2003; Gerdjikov et al., 2004; Miller and Marshall, 2005), locomotor sensitization (Valjent et al., 2006) and cueinduced reinstatement of drug-seeking (Lu et al., 2005), are dependent upon ERK signaling. In this laboratory, it has been determined that ERK signaling induced by D1 dopamine receptor stimulation, though markedly greater in the nucleus accumbens and caudate-putamen of foodrestricted than ad libitum-fed subjects, does not contribute to the unlearned rewarding or motoractivating effects of drugs (Carr et al., 2009b). However, the up-regulated ERK signaling was shown to be necessary for the increased activation of the nuclear transcription factor CREB (Haberny and Carr, 2005a), and the immediateearly gene c-fos (Carr et al., 2009b). It therefore seems reasonable to hypothesize that the functional consequences of increa­sed ERK sig­naling will be evident in behavioral processes that are dependent on network strengthening, such as drug-mediated associative learning, which is

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6.5  Striatal neuroadaptations induced by food restriction may be secondary to changes

known to be increased by food restriction (Bell et al., 1997; Cabib et al., 2000). Given that drugs of abuse act on brain reward circuitry as proxies for natural rewards, up-regulation of a cell signaling cascade that has been implicated in drug addiction-related processes suggests that the natural function of this neuroadaptation is to facilitate synaptic plasticity and associative learning that promote food acquisition and ingestive behavior. This expectation is supported by a recent study in which training of foodrestricted rats to associate a cue with sucrose delivery was accompanied by activation of ERK in the nucleus accumbens (Shiflett et al., 2008). Moreover, the ability of this cue to subsequently invigorate instrumental res­ponding for sucrose pellets was blocked by pharmacological inhibition of nucleus accumbens ERK signaling. Thus, the learned incentive effects of cues associated with drugs of abuse and palatable food, as well as their ability to potentiate reward-directed instrumental behavior, appear to be reliant upon nucleus accumbens ERK signaling – a mechanism that is up-regulated by food restriction. As a result, dieting in the context of abundance may have pathogenic potential in the sense that neuroadaptations increase vulnera­bility to incentive properties of food and cues, and consequent episodes of binge intake will have increased capacity to produce synaptic plasticity and engender enduring augmentation of those incentive effects and their control over behavior.

6.5  Striatal neuroadaptations induced by food restriction may be secondary to changes in pre-synaptic dopamine neuronal function The increases in striatal dopamine and glutamate receptor function described above may be secondary, and compensatory, to a decrease in

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basal dopamine neuronal activity. Pothos and colleagues (1995a, 1995b) observed that basal extracellular dopamine concentrations in the nucleus accumbens of food-restricted rats are substantially lower than in ad libitum-fed subjects. Consistent with this finding, our laboratory obtained evidence of a decreased rate of striatal tyrosine hydroxylation (Pan et al., 2006) – suggestive of decreased dopamine synthesis and utilization – and, in a recent preliminary study, decreased membrane excitability of presumed ventral tegmental area (VTA) dopamine neurons in midbrain slice preparations from food-restricted subjects. Food restriction was also found to decrease striatal dopamine transporter function (i.e., Vmax; Zhen et al., 2006) which, according to the present schema, could be a pre-synaptic compensatory response to a persistent decrease in basal dopamine release. A dampening of basal dopaminergic activity, as suggested above, may conserve energy by decreasing spontaneous motor activity – as is known to be the case in food-restricted animals when in familiar environments devoid of food and related cues (Duffy et al., 1990; Hart and Turturro, 1998; Vitousek et al., 2004b). On the other hand, the potentiated response to food and cues – proposed to be based partly in striatal neuroadaptations – may begin with a positive gating of food-related signals to the mesoaccumbens dopamine pathway (Tobler et al., 2005; Yamamoto, 2006). Signals relating to less urgent biological drives, such as reproductive behavior, may be inhibited upstream of the dopamine pathway, as in the suppression of estrus (Campbell et al., 1977; Jones and Wade, 2002) and circulating testosterone (Sirotkin et al., 2008) by food restriction in female and male rats, respectively. Using immediate-early gene expression (c-fos) as a marker of neural activity, our laboratory observed that contact with a small palatable meal or environmental cues associated with that meal activated neurons in the dopamine cell body-rich ventral tegmental area (Park and Carr, 1998). More recently, we

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observed that transfer of rats to a fresh cage containing a morsel of lab chow activated VTA neurons in food-restricted but not ad libitumfed subjects, suggesting along with foregoing examples that food restriction may positively gate access of food-related signals to the meso­ accumbens dopamine pathway. A reordering of the hierarchy of survival needs during food restriction, in conjunction with episodic binges on supranormally rewarding food, may contribute to a narrowing of the behavioral repertoire of subjects by weakening accumbal neuronal ensembles (Pennartz et al., 1994) dedicated to competing forms of goal-directed behavior and strengthening those dedicated to food-directed behavior, paralleling a process that is hypothesized to occur during development of drug addiction (Kalivas and Hu, 2006). Studies of mRNA and protein levels of tyrosine hydroxylase, the rate-limiting enzyme in dopamine synthesis, have not yielded simple results that are consistent with the hypothesis of decreased dopamine neuronal activity in foodrestricted subjects. Although we have observed a decreased rate of tyrosine hydroxylation in the nucleus accumbens of food-restricted subjects – suggestive of decreased dopamine synthesis and utilization – food-restricted subjects displayed elevated tyrosine hydroxylase protein levels (Pan et al., 2006). The two findings are difficult to reco­n­cile unless food restriction slows tyrosine hydro­xy­lase degradation or decreases concentrations of co-factor tetrahydrobiopterin. Measurement of tyrosine hydroxylase mRNA in VTA has not helped to clarify the situation. Lindblom and collea­gues (2006) have observed that 12 days of food restriction leading to 8 percent body-weight loss in adolescent rats increased levels of tyrosine hydroxylase mRNA in the VTA. This result is suggestive of increased dopamine synthesis and utilization. Yet, in the protocol of our laboratory, which involves at least 21 days of food restriction and a 20 percent body-weight loss in mature rats, no changes in VTA tyrosine hydroxylase mRNA levels were detected (Pan et al., 2006). The changes

in dopamine neuronal function during food restriction may be complex and not reflected in measures of tyrosine hydroxylase levels and synthetic activity, particularly when they are measured at a single moment in time. What does seem likely, based on existing evidence, is that mesoaccumbens dopamine activity during food restriction alternates between two extremes, with hypoactivity prevailing when the prospect of food acquisition is nil and hyperactivity when food is anticipated, procured or ingested. The periods of low dopamine neuronal activity may lead to increased accumulation of cytoplasmic dopamine, and thereby increase the amount of dopamine released per storage vesicle in exocytosis (Pothos, 2002). Such an increase in quantal size could underlie the elevated extracellular dopamine concentrations seen in the nucleus accumbens of food-restricted rats injected with cocaine or amphetamine (Pothos et al., 1995a; Rouge-Pont et al., 1995; Cadoni et al., 2003). However, the high extracellular dopamine concentrations that occur during phasic release (e.g., in response to palatable food or psychostimulant challenge) may in turn trigger exceptional dopamine conservation responses in the foodrestricted subject. We have observed that following administration of the dopamine-releasing psychostimulant d-amphetamine, only food-restricted subjects show evidence of feedback inhibition of dopamine synthesis in nucleus accumbens, as inferred from decreased phosphorylation of tyrosine hydroxylase at Ser40 (a requirement for tyrosine hydroxylase activation and consequent dopamine synthesis) (Pan et al., 2006).

6.6  A schema to consider as research continues While the pre-clinical research is still in an early stage, evidence exists to support a schema in which an increased dopamine signal-to-noise ratio characterizes the episodic anticipation and ingestion of palatable food, which in turn exploits

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post-synaptic neuroadaptations, involving upregulated D1 and NMDA receptor-dependent MAP kinase and CaM kinase II signaling, and AMPA receptor trafficking, to induce synaptic plasticity that is less readily expressed in freefeeding subjects. Synaptic plasticity induced by repeated episodes of palatable food bingeing during dietary restraint, and perhaps across multiple episodes of dietary restraint, may lay the neurobiological foundation for a persistent proclivity to binge, paralleling processes that are believed to underlie the development of drug addiction. Recurrent pathogenic events and associated neuroplastic changes have been suggested to play a role in the development and progression of numerous behavioral disorders, including affective illness, panic disorder, functional psychoses and drug addiction (Klein and Gorman, 1987; Hyman and Malenka, 2001; Post, 2004; Kalivas, 2005). Though a variety of behavioral and neuro­ biological effects of a single episode of food restriction have been described above, little is known about their persistence or the effects of cyclic food restriction on brain reward mechanisms and behavioral sensitivity to drugs or sucrose. Considering the frequency with which individuals in Western societies cycle between dieting and relapse to unrestrained eating and weight gain, and the documented contribution of dieting history to drug abuse and binge eating, coordinated investigation of synaptic plasticity and behavioral sequelae of drug or palatable food exposure across multiple episodes of food restriction may provide additional insights into the underpinnings of maladaptive eating behavior that contribute to obesity.

Acknowledgments Research conducted in the author’s laboratory and discussed in this chapter is supported by grant DA03956 from NIDA/NIH.

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C H A P T E R

7 The Neuroeconomics of Food Selection and Purchase Brian G.Essex and David H.Zald Department of Psychology, Vanderbilt University, Nashville, TN, USA

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7.1  Introduction From a behavioral economics perspective, a food purchase (or any other type of purchase) can be broken down into processes involved in appraising the value and costs of available items, and selecting between items with competing valuations and associated costs. On the surface, this is a simple formulation that can

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be used to understand factors influencing food choice. This formulation is also amenable to examination at the neural level. In the past decade, electrophysiological studies in non-human primates and functional neuroimaging studies in humans have made significant inroads into elucidating the neural substrates involved in valuation and selection processes. In this chapter, we review the key neural substrates

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involved in positive valuation, negative valuation (i.e., costs, potential losses) and selection. An understanding of factors that influence these three processes helps reveal some of the difficulties (and perhaps some solutions) in directing individuals towards healthier food purchases. We particularly focus on processing within the orbitofrontal cortex (OFC), nucleus accumbens, anterior cingulate and insula, as these regions appear critical for aspects of positive and negative valuation, and selection.

7.2  Positive valuations Individuals ultimately decide to purchase particular foods because they want them and value them positively. The positive valuation may incorporate several different features, including the expected pleasant perceptual experiences of flavor, temperature and texture, as well as more abstract valuations such as the food’s long-term health benefits. Ultimately, these different valuation components are integrated to provide a net valuation of the food. This integration is likely to be dynamic, with the relative weighting of the different components changing over time depending upon the person’s current internal state and long-term goals. Changes in the relative weighting of different valuation components may be particularly critical in cases where there is disagreement between the different valuation components. For instance, some foods, such as ice cream and French fries, may be valued positively in the short run because they have a pleasant flavor and texture. However, these same foods may not be valued positively in terms of nutritional value and long-term cardiovascular health. In contrast, foods such as carrots and broccoli are likely to be valued less positively in the short run but more so in the long run, because they provoke weaker expectations of a positive flavor perception but provide longer-term health benefits.

Recent research has elucidated key neural systems involved in positive valuations of items that will be attained in the near future. These neural systems traverse midbrain, striatal and cortical levels of processing, and are marked by the presence of cells (or cell populations) whose activity scales with the magnitude of the reward (either in an absolute sense, or more often relative to a range of potentially available rewards). Evidence of scaled responses has been observed in studies on monkeys that vary either the type or the amount of food reward at the level of the dopamine (DA) midbrain, the striatum and the OFC (Tremblay and Schultz, 1999; PadoaSchioppa and Assad, 2006). Functional neuro­ imaging studies in humans have confirmed the sensitivity of these regions to rewards, and, depending upon the specific paradigm and region, the responses appear sensitive to the degree of pleasantness or the magnitude of the anticipated or received reward (Anderson et al., 2003; Small et al., 2003; Knutson et al., 2005).

7.3  Influences on positive valuations 7.3.1  Temporal discounting Individuals tend to discount the future valuation of money when making choices. The declining positive valuation of future reward is well described by a hyperbolic discounting function (Rachlin et al., 1991; Kirby and Marakovic, 1995). This type of discounting function implies that as one moves farther away from the present, differences in time-points provide progressively less impact on valuation. Critically, it also indicates that there will be a substantial decrement in value associated with a delayed reward relative to an immediate reward (see Figure 7.1). Applied to decisions involving foods, this implies that individuals will give a much greater valuation to immediate benefits

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B

Value

A

t2

t1

tA

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Figure 7.1  Subjective value of rewards increases as the time of reward nears. At time t1, the reward with the larger positive objective value (B) is preferred. At time t2, however, the reward with the smaller positive objective value (A) is preferred. The benefits of reward A are obtained at time tA and the benefits of reward B are obtained at time tB. The objective valuation of each reward is indicated by the height of the reward curve at the time point of reward receipt (e.g., at time tA for reward A); heights of the curves at times before this are subjective values. This model can help explain why foods with greater but more distant benefits are chosen when food consumption will be in the distant future, but rewards with smaller but immediate benefits are chosen when food consumption will be in the near future. Source: Adapted from Sozou, 1998.

than to benefits obtained in the future, leading individuals to ascribe little weight to long-term health benefits. In other words, in the face of an immediate perceptual reward, long-term benefits related to health will often fail to receive a valuation that can compete with the immediate perceptual components of reward. However, since the discount rate between two future timepoints is smaller than that between the present time-point and a future time-point, long-term health benefits may be better able to compete with the perceptual expectations of food when

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the decision is being made about what to eat at a future time-point. In other words, long-term meal planning may provide an advantage when attempting to emphasize health benefits over the perceptual experience associated with different foods. While long-term planning may be beneficial for promoting healthy eating, temporal discounting also helps to explain why individuals often fail to stick to their commitments to eat a healthy diet. Take, for example, a New Year’s resolution to stop frequenting a favorite fast-food establishment in order to establish a healthier diet. When deciding to forgo French fries in the next year in favor of healthier choices, an individual does not have to forgo any immediate benefits obtained from appealing fried foods, since all benefits are delayed (i.e., in the future). In such cases, he or she may decide to eat healthy foods, since the delayed (or long-run) discounted benefit of eating healthy foods is likely to be valued more highly in relation to the delayed discounted benefits of the less healthy French fries. However, when an individual is actually confronted with a decision to eat at a fast food restaurant versus buying food at a health food store in the present moment, it is harder to choose the healthy choice since the immediate non-discounted benefits from the French fries will be valued more highly than the delayed discounted benefits from the healthier food. There is increasing evidence that specific neural circuits may influence the slope of the temporal discounting functions, leading to more or less weighting of immediate over long-term rewards. Lesions of the basolateral amygdala and nucleus accumbens in rodents caused increased selection of smaller immediate rewards over larger delayed rewards (Cardinal et al., 2001; Winstanley et al., 2004). Conversely, the OFC may be involved in valuing immediate benefits more than larger future benefits, since lesions of this area in rodents lead to increased choosing of larger delayed over smaller immed­ iate rewards (Winstanley et al., 2004) and more

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neurons in this region respond to rewards delivered after a short delay than after a long delay (Roesch et al., 2006).

7.3.2  Satiety Whereas temporal discounting reflects a decline in value for future rewards, satiety and habituation cause declines in current valuations. Specifically, satiety produces a decline in the valuation of food rewards. This can occur at a global level, where any food is devalued, or at the sensory specific level, where a specific food is devalued. Many OFC cells that show activity in response to foods when an animal is hungry become unresponsive or show substantially reduced responsiveness to food when the animal is sated (Scott et al., 1995; Pritchard et al., 2008), with a more medial OFC area showing more general declines in responsiveness, and a more lateral area showing more selective declines. Selective satiety effects have also been observed when the foods are presented visually or as a smell only (Critchley and Rolls, 1996). In their natural environment, animals do not purchase food rewards. However, valuation can be expressed in terms of an animal’s willingness to perform operant responses to obtain a reward. In normal animals, the readiness to perform operant responses (e.g., voluntary leverpresses that have been previously reinforced) for a specific food reward inversely relates to the animal’s level of satiety (Baxter et al., 2000; Izquierdo et al., 2004). However, despite the OFC’s role in selective satiety, OFC lesions do not completely abolish satiety effects. In formal tests, monkeys with OFC lesions show a normal decrement in responses following selective satiation procedures (Baxter et al., 2000; Izquierdo et al., 2004). At the same time, damage to areas of the ventral frontal lobe (including the OFC) often result in a discontrol of eating. For instance, Bachevalier and Mishkin (1986) describe monkeys with hyperorality following

ventromedial lesions who would “grab objects, which they voraciously ate or destroyed”. Similarly, a number of case reports have noted the presence of voracious appetites in humans following OFC lesions or degeneration (Erb et al., 1989; Kirschbaum, 1951). More recent studies of dementia of the frontal-lobe type have examined correlations between atrophy and ratings of hyperphagia or “sweet tooth”, and have observed specific associations with OFC atrophy (Ikeda et al., 2002; Whitwell et al., 2007; Woolley et al., 2007). It remains to be seen, however, if these symptoms reflect a lack of satiety or more general disinhibition. The application of behavioral procedures similar to those used in animal studies may be needed to address this question directly. The effects of satiety are apparent at multiple levels of processing, including both cortical and subcortical areas involved in value. For instance, food satiety attenuates operant responses for intracranial self-stimulation both in the OFC and the lateral hypothalamus (Mora et al., 1979). Critically, the devaluation of stimuli based on satiety is also represented in the DA system, as food-associated DA efflux in both the nucleus accumbens and medial frontal cortex of rodents shows sensory-specific declines when rodents become sated on a specific food (Ahn and Phillips, 1999). Satiety may provide a particularly useful tool for reducing food intake, either at the specific or at the general level. This may be accomplished in several ways. At the most invasive level, bariatric surgery, vagal nerve stimulation or hormonal manipulations can increase signals of satiety (Sobocki et al., 2005; Chaudhri et al., 2008; Xanthakos, 2008). However, less invasive techniques may also increase satiety. For instance, slowing down the consumption of food may allow temporally lagged perceptions of satiety to impact reward valuation mechanisms prior to the conclusion of the meal. Similarly, asking individuals to slow down and concentrate on the perception of every bite may allow enough

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sensory exposure to induce sensory-specific satiety that might not develop if the person ate at a normal rate due to decreased exposure time (Proulx, 2008).

7.3.3  Recent availability While satiety affects valuations in the short term, there are more lingering effects of recent exposure that may last for days, weeks or even months. These have generally not been studied at the neural level, but almost certainly affect valuation, with items that one is frequently exposed to being generally devalued and items that are more rarely available gaining heightened valuation. Such heightened valuation occurs with items that are only seasonally available. The modern global distribution of many foods has removed the seasonal nature of some fruits and vegetables, but some retain clear seasons. Other items that are associated with specific holidays, or which are only marketed at certain times of the year (such as Girl Scout cookies in the United States) provide examples in which limited availability heightens the valuation when they first become available again. Most diets do not specifically incorporate seasonal or rarity features, but attention to such details could aid in increasing the valuation of certain foods.

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alternatively an ability to produce relative valuations, such that food valuations are made relative to the other available foods. Although some food-sensitive neurons in regions such as the OFC have been found to have responses that are invariant in the presence of other potential foods (Padoa-Schioppa and Assad, 2008), studies examining reward responses at the level of the DA midbrain, striatum and OFC have more frequently observed evidence of responses that are consistent with relative rather than absolute coding of rewards (Tremblay and Schultz, 1999; Bayer and Glimcher, 2005; Nieuwenhuis et al., 2005; Tobler et al., 2005; Elliott et al., 2008). Such responses are not limited to the actual receipt of the reward, but also to the expectation of the reward. This is important, given that food purchases are usually made based on the expectation of reward rather than on an actual sampling of the currently available foods. Additionally, such data suggest that during selection of rewards there may be an anchoring or rescaling based on other currently available rewards. For instance, in a study of DA neuron firing, Tobler and colleagues (2005) demonstrated that DA firing was adaptively rescaled depending upon the range of possible rewards in the current context. Given such adaptive coding, promotion of healthy food selection must be considered in relation to other available food options, since the other food options will dramatically influence the scale range involved in the valuation process.

7.3.4  Relative reward In many cases, potential rewards do not occur in isolation but amongst a set of other potential rewards. Indeed, any trip to a modern grocery store or a shopping mall food court makes apparent the abundance of potential food rewards that could be obtained. In order to select among a large group of positively valued foods, we need either to have very precise absolute valuations of each food, with many gradations in order to produce a clear hierarchy, or

7.4  Negative valuations When deciding whether or not to purchase particular foods, individuals will also weigh costs associated with a purchase. Every food purchase is associated with an immediate cost – the money that must be given up to receive it. The time and effort necessary to obtain or prepare the food may also be tallied as an immediate

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or short-term cost. When monkeys make choices to receive a juice reward, neurons in the anterior cingulate cortex, OFC and lateral prefrontal cortex are responsive to the amount of effort (number of lever-presses) that will be required to obtain it (Kennerley et al., 2009). Unhealthy foods are also associated with long-term costs, such as potential weight gain and other health problems (e.g., high blood pressure and cholesterol). fMRI research with humans has shown that many of the regions involved in the anticipation of reward (i.e., monetary gain) are also involved in the anticipation of aversive events (i.e., monetary loss). Anticipation of potential monetary gains and losses on the Monetary Incentive Delay Task are both consistently associated more with activation than anticipation of a neutral outcome in the caudate, insula, thalamus and ventral striatum (Knutson et al., 2001, 2003, 2008; Bjork et al., 2004; Juckel et al., 2006; Samanez-Larkin et al., 2007; Wrase et al., 2007a, 2007b; Dillon et al., 2008). In these studies, the anticipation of losses and gains occurs in the context of preparing to make a speeded response in order to gain money or avoid losing money. Thus, this may not represent a passive tallying of value, as much as the extent to which brain regions necessary to make appropriate goal-oriented responses are modulated by the value of stimuli. As such, the common ability of both positive and negative valuations to motivate responses appears to be executed through a shared neural circuitry. Interestingly, of the above-mentioned areas showing modulation based on anticipated gain and loss, only the ventral striatum shows a truly preferential pattern of modulations of positive more than negative valuations. This may provide a critical bias that may make it harder for negative valuation associated with health consequences to compete with positive valuations of foods. The neuroeconomics literature equates negative valuation with monetary loss, but in any ecologically valid system these valuations must

also include physical harm, pain, and other aversive events. Although multiple brain regions respond to aversive sensations, recent data have suggested that the insula plays a particularly important role in response to negative valuations across different modalities. Both the anticipation of painful heat and the anticipation of painful electrical stimulation that cannot be avoided are associated with increased activation in the anterior insula in humans (Ploghaus et al., 1999; Jensen et al., 2003). Since Jensen and colleagues (2003) also observed increased activity in the anterior insula following anticipation of painful electrical shock that could be avoided, this region appears to be important regardless of whether a response is required.

7.4.1  Effort One aspect related to food choice is how much work or effort may be required to obtain and prepare different foods. Research reveals that both the ventral striatum and anterior cingulate cortex are particularly important for these processes. When placed in a T-maze, rats usually prefer to engage in an effortful response (i.e., scale a barrier) to receive a large reward than to engage in a less effortful response to receive a smaller reward. However, rats with depleted levels of DA in the nucleus accumbens decrease their choice of the high-effort, high-reward arm of the T-maze and increase their choice of the low-effort, low-reward arm (Salamone et al., 1994; Cousins et al., 1996). Similarly, rats with lesions to the anterior cingulate cortex, those with severed connections between the anterior cingulate cortex and the basolateral amygdala, and those treated with a D1 dopamine receptor antagonist in the anterior cingulate cortex do not choose the high-effort, high-reward option in the T-maze more frequently than the loweffort, low-reward option (Walton et al., 2003; Schweimer and Hauber, 2005, 2006; Floresco and Ghods-Sharifi, 2007). These effects do not

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7.5 Influences on negative valuations

appear to reflect an inability to perform effortful motor responses or a preference for low reward, since rats with lesions to the medial frontal cortex, including the cingulate cortex, will more frequently choose the high-reward option if both options require a high level of effort (i.e., scaling a barrier) (Walton et al., 2002). Likewise, rats with depleted levels of DA in the nucleus accumbens do not choose to scale the barrier for reward any less than control rats if the other arm of the T-maze does not contain any reward (Cousins et al., 1996). DA-depleted rats also choose the high-reward option as much as control rats if neither arm of the T-maze contains a barrier (Salamone et al., 1994). These data indicate that DA depletions do not prevent the selection of appropriate actions when an individual just has to choose between no effort and effort, or no reward and reward. Rather, DA in the cingulate and accumbens impacts the integration of the negative valuations associated with effort with the positive valuations associated with obtaining a food reward, such that relative weighting of the positive and negative valuations is shifted in the positive direction.

7.5  Influences on negative valuations Like positive valuations, valuations of immediate (and short-term) negative value are likely to be computed more automatically than valuations of long-term negative value. Similarly, a variant of temporal discounting will apply, such that potential future aversive events will be viewed as less aversive than potential immediate aversive events. Temporal discount rates for monetary losses appear lower than those for monetary gains (Frederick et al., 2004), indicating that future aversive events will likely be less strongly impacted by discounting than future positive events. Nevertheless, the presence of any discounting may still weaken the impact of

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knowledge of future health consequences relative to the strong valuation of an immediately available food. Interestingly, some individuals actually prefer to suffer a loss immediately than delay it until the future (Yates and Watts, 1975; MacKeigan et al., 1993; Redelmeier and Heller, 1993), indicating that there may be a negative discount rate for some future aversive events (i.e., over time the valuation is augmented rather than decreased). This may arise because the emotional effects of waiting for a negative event may themselves be negative, thus providing an additional contribution to the negative valuation. There may be significant advantages to a negative discount rate for aversive events. Specifically, the presence of such an effect may help explain why some individuals prefer to incur present costs, such as the time, effort or financial costs associated with preparing healthy foods, in order to avoid more delayed costs, such as the health problems resulting from unhealthy eating habits. The issue of risk and ambiguity will also impact negative expected value. In behavioral economics, risk refers to the known probability of a negative event; as the probability of the negative event increases, the level of risk also increases. Ambiguity refers to the degree of uncertainty about the probability of the event. A situation is completely ambiguous if an individual has no knowledge of the probabilities of potential events, and it is partially ambiguous if an individual has a subjective idea of what the probabilities might be but does not know the objective probabilities. In considering risk, economists often examine expected value rather than a simple estimation of value, in that expected value takes into account both the probability of the risk and the value. For instance, if you flip a coin (50 percent probability of heads), with heads resulting in a $1.00 loss and tails resulting in no change, the expected value is 50 cents (because the average change is predicted to be a loss of 50 cents). An important implication of expected value is that the risk of developing a health

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problem from eating a food will influence the level of negative valuation in proportion to the perceived risk. However, when faced with the decision to select a food, a person is usually in a partially ambiguous situation because he or she is generally unable to provide a specific estimate of the risk of an adverse outcome from any given consumption of a food. In other words, negative valuation will be impacted by the uncertainty of risks. Whereas the person knows with 100 percent probability that he or she will lose money in buying the food, and often with very high probability that the food will be enjoyable, the expected negative valuation associated with health issues is often low, because the impact of any single food purchase on long-term health is perceived to be low and knowledge of the actual risk uncertain. The risk associated with repeated purchases of unhealthy foods will be more consequential, but (1) food selections are often made one at a time; and (2) even taken with respect to repeated purchases, temporal discounting and ambiguity regarding future outcomes will usually lower the strength and impact of the negative valuations.

7.6  Selection When purchasing a food, an individual is often confronted with many possible choices. Each potential choice has certain aspects that are valued positively and others that are valued negatively. To make a good choice, a person must somehow weigh the costs and benefits of each choice and then select the item that has the highest overall valuation. Like the valuation process, integration of values can be done automatically or more deliberately (e.g., rational weighing of costs and benefits). Research shows that individuals are more likely to choose an unhealthy food (i.e., chocolate cake) over a healthy food (i.e., fruit salad) under conditions of high cognitive load, suggesting that optimal

long-term valuations may require deliberate processing (Shiv and Fedorikhin, 1999). Different neural systems may be involved in different aspects of the integration process. To date, studies examining these integrative processes have primarily utilized explicit decision-making paradigms, in which a person has to select between potential rewarding stimuli. These types of studies can be divided into two categories: (1) those in which the selection process is strictly between two or more positively valued stimuli, with no explicit consideration of negative valuations, and (2) those that include an element of negative valuation, specifically monetary cost or effort.

7.6.1  Positive vs positive decisions There exist numerous human neuroimaging studies in which individuals have to choose between two or more stimuli, but typically such studies examine perceptual decisions or learning rather than the selection of one stimulus over another based on inherently differential reward value. As we have already seen, a number of brain regions show responses that scale with reward value. Such regions are natural candidates for the decision-making process of selecting between different rewards. However, the presence of valuation-linked responses also causes an empirical confound in that, unless properly accounted for, activations in a task might reflect the general process of valuation rather than specifically assaying the selection process. Although several of the studies described below attempt to assess the selection process, the extent to which they reflect selection vs valuation often remains unclear. One of the most directly pertinent reports regarding food selection is a study by Arana and colleagues (2003), who asked participants to imagine that they were in a restaurant looking at different menus while measuring brain activity with PET. All of the menus were individually

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tailored according to the subject’s preferences. In some trials subjects simply viewed the menu, while in others they were required to select items. The authors found that the rostral gyrus rectus (anterior-medial OFC) showed a greater response to high-incentive menus, and responded more when a choice was required. Although using a more restricted set of stimuli, Paulus and Frank (2003) reported both a medial frontopolar and an anterior cingulate activation when participants had to select their preference between two different soft-drink brands, relative to a perceptual judgment between the stimuli. Somewhat more complex experimental designs have analyzed preference for items other than food. For example, Kim and colleagues (2007) asked people to make preference decisions about faces vs making a perceptual judgment about the faces. Activations in the preference condition localized to the right ventral striatum and left medial OFC, with ventral striatal activations occurring earlier than OFC activations. The authors suggest that the ventral striatal activation provides a signal that the OFC uses when making an explicit choice. As noted above, a question can be raised as to whether these studies capture the selection process or a valuation process, since a simple liking or pleasantness assessment without choice is rarely used as a control condition. In other words, if we wish to understand selection without it being confounded by the valuation process on its own, the ideal contrast for a selection condition is one that explicitly involves valuations of single items without requiring the participant to draw contrasts, or make an actual selection. Such studies are missing from the literature. An alternative approach is to avoid the issue of separate conditions, and instead vary the difficulty of making the selection. This type of design is based on the premise that a harder selection process will require a greater engagement of areas involved in selection, and since both an easy and a hard decision still require valuation, the study avoids many of the

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limitations of contrasting a preference selection with another perceptual task. In their menu selection study, Arana and colleagues (2003) observed that the lateral OFC responded more when subjects had to choose between stimuli with similar prior pleasantness ratings. The authors suggest that this lateral OFC engagement arises because of a need to suppress responses to alternative desirable items. However, it is possible that lateral OFC areas become preferentially involved during hard decisions because a more fine-tuned valuation is necessary. If activations do indeed reflect the difficulty of the decision, they should vary parametrically in an inverse manner to the difference between the independent valuation of food stimuli. To date, no studies have taken this approach with food; however, Blair and colleagues (2006) examined the effect of reward distance when selecting objects that had been arbitrarily given different reward values (points). As in other selection tasks, ventromedial prefrontal activity was associated with decisions for rewarding stimuli, with increasing activations as the value went up. In contrast, the dorsal anterior cingulate appeared more responsive as the decision became more difficult (i.e., the closer the point value of the individual stimuli). This pattern of activity is consistent with a well-recognized role of the cingulate in conflict monitoring (Botvinick et al., 1999). However, it is important to note that the conflict here is not simply reflective of a need to control or inhibit motor responses, as is the case in many ­conflictmonitoring situations. Rather, the activity appears to be associated with conflict during the decision process (see also Pochon et al., 2008). Lesion studies also support the involvement of OFC/ventromedial regions in selecting between different food rewards. Monkeys and humans with OFC impairment show alterations in food selection (Baylis and Gaffan, 1991; Ikeda et al., 2002). In monkeys, there may be an important distinction between novel versus familiar items. Baylis and Gaffan (1991) studied

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responses primarily to novel foods, observing that monkeys with OFC lesions had more erratic establishment of preferences, and a willingness to eat foods that would normally be avoided (raw meat). In contrast, monkeys with OFC lesions have been observed to show stable food preferences in selecting among palatable foods that they were already familiar with (Izquierdo et al., 2004), indicating that, at a minimum, some form of preferential food selection is maintained. This suggests that relatively stable selection preferences can occur even in the absence of the OFC, when valuations and selection preferences are already established. This suggests that the OFC may play a rather specific role in situations where new valuations or new selection contrasts must be made. It may be similarly critical when valuations are dynamic. For instance, monkeys with OFC lesions fail to alter their selection when an item should have been devalued based on selective satiation (Baxter et al., 2000; Izquierdo et al., 2004). Because the monkeys in these studies still show evidence of satiation, Izquierdo and colleagues suggest this reflects an inability to use information about the devaluation of the items in making their selection, rather than a failure of the satiation process. Fellows and Farah (2007) examined the stability of preference judgments by examining the transitivity of preference ratings. That is, if a person has a stable preference hierarchy, such that they prefer item a over item b, and item b over item c, then they should also prefer item a over item c. A failure to show this transitivity suggests a core problem in establishing or accessing a preference hierarchy. To test for such errors, Fellows and Farah asked patients with ventromedial frontal damage and controls to select their preferences from various pairings of items from three categories of stimuli: food, famous people, and colors. The patients with ventromedial prefrontal damage showed substantially more errors of transitivity than control subjects. The patients clearly are able to

make selections, but appear unable to utilize a consistent scaling of item valuations when making their selections. Fellows and Farah suggest that this may lead to the appearance of capricious decision-making. Studies in both monkeys and humans with OFC impairment have been observed to show alterations in food selection (Baylis and Gaffan, 1991; Ikeda et al., 2002). However, it is unclear whether these changes reflect actual disruption of the evaluation of the relative value of foods, or are simply a consequence of perceptual deficits or impairments in associative learning. Recent data indicate that monkeys with OFC lesions show stable food preferences in selecting among palatable foods that they are familiar with (Izquierdo et al., 2004), indicating that, at a minimum, some form of preferential food selection is maintained, even though such animals may lose the ability to adaptively use information about food preferences to guide behavioral choices, especially when the value of one of the foods changes (Baxter et al., 2000). In summary, the neuroimaging and lesion data highlight the involvement of the OFC and anterior cingulate in explicit tasks involving the selection of competing rewarding stimuli. Studies of individuals with OFC lesions (particularly those including the medial OFC) ­support a role for this area in food selection processes, but suggest that it is not essential for selections per se. Rather, the OFC may be critical for making consistent decisions, especially in the face of changing valuations or potential comparisons. The anterior cingulate and the lateral OFC also appear to contribute to the selection pro­ cess, and to be particularly important when the choices are difficult and there is a need to reject an otherwise positive choice or resolve the relative valuation of two positively valued items, although the exact contribution (comparison of valuations, selection, inhibition of alternate items, conflict in making a selection, monitoring) remains to be detailed. The OFC’s contribution to these processes in regards to feeding

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is particularly notable in that this region is known to possess sensory representations of food, and alterations in food selection occur following OFC lesions. In contrast, the cingulate’s contribution during difficult decisions appears consistent with its role in broader executive functions in situations with conflicting potential responses.

7.6.2  Integration of positive and negative As already noted, we must make a distinction between situations in which individuals simply choose between different rewards vs when they have to also incorporate negative valuations into the decision process. A recent fMRI study by Knutson and colleagues (2007) is consistent with the conjecture that different regions of the brain compute positive and negative valuations of purchases. Subjects in this study performed a SHOP (“Save Holdings Or Purchase”) task in which they could purchase various products. For each trial, they first saw a picture of a labeled product (product period), then saw the price of the product (price period), and finally chose whether or not they would purchase the product. Following each of the two scanning sessions, the result of one random purchase decision was counted for real, and the subject was shipped the product and charged the price listed during the task if they had chosen to purchase it. Knutson and colleagues observed that activations in the nucleus accumbens (ventral striatum) during the product period and in the medial prefrontal cortex during the price period increased the probability that an individual decided to purchase a product, while activations in the insula during the price period decreased the probability. These data appear consistent with a parcellation of valuations, such that the nucleus accumbens and medial prefrontal cortex activity are associated with positive valuations of purchases, while the insula activity is associated with negative valuations.

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However, it should be noted that other variables (e.g., self-report variables, task variables) also predicted individuals’ choices in the study by Knutson and colleagues, and although activity in brain regions added to the predictions beyond what was predicted from these other variables, the prediction rate of the brain variables alone was only 60 percent. While this may suggest that activity in these brain regions does not accurately predict an individual’s selection, newer prediction methods have led to stronger predictions. Applying a different analytic approach to prediction on the data from the study of neural predictors of purchases, Grosenick and colleagues (2008) were able to predict subsequent choices from hemodynamic data alone at a prediction rate as high as 67 percent. While the work by Knutson and colleagues demonstrates a predictive relationship between activity in areas with positive and negative valuations and purchasing, it does not address how these valuations are actually integrated during a purchase decision. This question has yet to be fully answered, but there is a small set of candidate regions. Montague and Berns (2002) have proposed that a circuit containing the OFC and striatum, both of which are innervated by DA neurons, generates a common internal currency (a common valuation scale) for different rewards and aversive events and their predictors. This hypothesis rests on evidence that in both regions neurons can be found with responses to positive and negative stimuli, suggesting a potential for integration of both positive and negative valuations. Kennerley and colleagues (2009) further indicate that some OFC cells respond to a combination of both reward value and the amount of effort that will be needed to obtain the reward. However, the OFC’s role in coding costs is relatively abstract in that the OFC (at least in primates) does not appear to process much information about the specific motor actions necessary to obtain a reward (Wallis and Miller, 2003; Kennerley et al., 2009). Consistent with this interpretation, lesions of

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the OFC in monkeys lead to deficits in stimulus selection but not action selection for rewards (Rudebeck et al., 2008). In contrast, the anterior cingulate cortex appears to integrate valuations with information about the effort or responses needed to obtain the reward. Monkeys with lesions of the anterior cingulate cortex have deficits in action selection but not stimulus selection for rewards, a pattern of deficits opposite to that of monkeys with OFC lesions (Rudebeck et al., 2008). Value integration in the anterior cingulate cortex is elegantly demonstrated in the recent study by Kennerley and colleagues (2009), described earlier. Kennerley determined the percentage of cells in the anterior cingulate, OFC and lateral prefrontal cortex that were responsive to the probability of obtaining juice, the reward magnitude (amount of juice) and the cost (number of lever-presses) in two monkeys. Significantly more neurons in the anterior cingulate cortex responded to all three variables than did neurons in the other two regions. These findings support the idea that both the cingulate and the OFC are involved in integrating different aspects of positive and negative value, but suggest that the anterior cingulate neurons code a more complete set of variables impacting expected value and cost. To date, no studies that we are aware of have attempted to look at the integration of different aspects of costs. For instance, we would argue that three distinct types of costs may come into play for food purchases: one involving the monetary costs, one involving the amount of effort necessary to obtain or prepare the food, and one involving expected long-term health risk. Given the multiplexed nature of anterior cingulate coding, it is a leading candidate for this type of integration. However, this hypothesis is as yet untested.

7.7  Habits A criticism of the above analysis may be raised, in that the process of valuation and selection

ignores the habit-based nature that often directs the selection of food. Specifically, selection of the type of food, brand of food, or even which stores or restaurants to frequent, may arise not through an elaborate decision process but through a relatively automatic habit-based decision process. We often buy the same type or brand of food items without processing what alternatives are available. In such cases, there may be little in the way of a true selection process. However, even in the face of habit-based purchasing there is typically variability from day-to-day and week-to-week in what we choose to eat, because processes of long-term habituation limit the appeal of repeatedly eating the same thing at every meal. Stated another way, our natural need for variety in our diet (which promotes balanced nutritional intake) works in opposition to habit-based decisionmaking. The key question, then, is how do habit-based and more active selection processes interact? There are several possibilities. One possibility may function like a logistic decision tree, in which as long as the available items are not currently valued at too low a level, no active selection process is utilized. However, this simple decision rule would mean that individuals would turn down more valued options simply because a more familiar and acceptable item was present. A more likely decision tree would therefore require some element of evaluation of the reward magnitude of available food rewards, with the habit system being used when there is not a major discrepancy between available rewards (thus allowing high-value stimuli to trump habit). Both of these models rely on the idea that there is a dichotomy between a habit-based system and an active selection process, with some rule determining which system is utilized. However, it is possible that we do not need a two-system model to explain purchase behavior. Rather, the appearance of habitlike responses may be an artifact of the role of familiarity in valuation. Familiarity provides confidence that a food will induce pleasant

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sensations, and hence increases the expected value of the food item over other items for which there is lower confidence (and hence lower expected value). This familiarity would work in opposition to the devaluing habituation process, although both processes may have very different temporal properties, with habituation being strongest at short intervals since the last exposures, and familiarity’s positive influence acting over longer time intervals. This approach views habit-like food purchases not so much as an alternative to active selection, but rather as an expression of the strength of familiarity in the valuation process. These two formulations have different implications when attempting to change purchase behavior, in that if the purchase is related to habits there is a need to stop the automaticity of the purchasing behavior and potentially establish new habits. In contrast, if familiarity is driving the selection by enhancing the expected value of the foods, the emphasis should be placed on familiarizing the person with new, healthier options that are pleasant enough to provide a competitive alternative to the less healthy options.

7.8  Conclusions In this chapter, we have emphasized the processes of valuation, factors that influence valuation, and mechanisms for integrating different valuation components. Based on the emerging field of neuroeconomics, we have argued that there are several specific brain circuits that are involved in the process of positive valuation, negative valuation and choice behaviors based on these valuations. As a relatively young field, much remains to be elucidated about precisely how these brain areas accomplish their tasks. However, as our understanding of the specific biases within these systems becomes clearer it may be increasingly possible to utilize these insights in

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building appropriate strategies to address problematic eating behaviors. From the behavioral economics perspective, public policy programs aimed at helping people make healthier food choices should focus on understanding and altering how individuals perceive the positive and negative valuation of different food options. If people believe that there is a cost for choosing the healthier items, either the perception of cost or the actual cost should be addressed. For instance, if people perceive that unhealthy foods are easier to obtain, encouraging the development of convenient healthy foods (such as drive-through “fast health food” restaurants) would help to alleviate this negative valuation of the healthy items. Similarly, information about risks and benefits of different foods must attend to issues of ambiguity, uncertainty, and short-term versus long-term effects. The move towards having published dietary information on food labels and in restaurants is a good first step, but these data often provide almost no information about actual risks or benefits, especially short-term risks or benefits, which may greatly limit their impact. At the individual level, programs aimed at establishing healthier eating behaviors may also benefit from attending to temporal discounting factors in that forcing earlier decisions about where or what to eat will lead to greater relative weighting of the long-term benefits of healthy foods than when immediate decisions about food selection are required. None of these suggestions represent “silver bullets” for curing the public health problems associated with obesity; however, taken together they may provide enough of a bias on eating decisions to aid attempts to foster healthy eating behaviors.

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Whitwell, J. L., Sampson, E. L., Loy, C. T., Warren, J. E., Rossor, M. N., Fox, N. C., et al. (2007). VBM signatures of abnormal eating behaviours in frontotemporal lobar degeneration. Neuroimage, 35(1), 207–213. Winstanley, C. A., Theobald, D. E. H., Cardinal, R. N., & Robbins, T. W. (2004). Contrasting roles of basolateral amygdala and orbitofrontal cortex in impulsive choice. Journal of Neuroscience, 24(20), 4718–4722. Woolley, J. D., Gorno-Tempini, M. L., Seeley, W. W., Rankin, K., Lee, S. S., Matthews, B. R., et al. (2007). Binge eating is associated with right orbitofrontal-insular-striatal atrophy in frontotemporal dementia. Neurology, 69(14), 1424–1433. Wrase, J., Kahnt, T., Schiagenhauf, F., Beck, A., Cohen, M. X., Knutson, B., et al. (2007a). Different neural systems adjust motor behavior in response to reward and punishment. Neuroimage, 36(4), 1253–1262. Wrase, J., Schlagenhauf, F., Kienast, T., Wustenberg, T., Bermpohl, F., Kahnt, T., et al. (2007b). Dysfunction of reward processing correlates with alcohol craving in detoxified alcoholics. Neuroimage, 35(2), 787–794. Xanthakos, S. A. (2008). Bariatric surgery for extreme adolescent obesity: Indications, outcomes, and physiologic effects on the gut-brain axis. Pathophysiology, 15(2), 135–146. Yates, J. F., & Watts, R. A. (1975). Preferences for deferred losses. Organizational Behavior and Human Performance, 13(2), 294–306.

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C H A P T E R

8 Resisting Temptation: Impulse Control and Trade-offs between Immediate Rewards and Long-term Consequences 1

Lin Xiao1, Laurette Dubé 2 and Antoine Bechara3

Brain and Creativity Institute, Department of Psychology, University of Southern California, Los Angeles, CA, USA 2 Faculty of Management, McGill University, Montreal, Canada 3 Department of Psychiatry and Faculty of Management, McGill University, Montreal, Canada

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8.2 A Neural System for Decision-making and Will-power: The Somatic Marker Hypothesis 106 8.3 Empirical Evidence for Deficits of Decision-making Underlying Obesity 108

8.1  Introduction Food is a primary reward, with high-calorie foods typically being highly rewarding. The over-­ consumption of appetizing high-calorie food has contributed to the dramatic increase in obesity in modern society. More and more

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evidence show that there are similarities between drug addiction and obesity (Kelley and Berridge, 2002; Rolls, 2007a; Trinko et al., 2007; Volkow et al., 2008a). Indeed, obese individuals demonstrate the loss of control and compulsive eating that drug addicts demonstrate with respect to drugs. Moreover, both drug addicts

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and obese individuals tend towards the immediate gratification behaviors, such as taking drugs or consuming foods, and they neglect the negative future consequences of such actions. Therefore, we argue that dysfunction in decision-making, one of the core characteristics underlying drug addiction, also contributes to overeating and obesity. Specifically, we propose that the neurobiological mechanisms underlying obesity might result from the imbalance of two separate but interacting processes. One is related to hyperactivity in the impulsive mesolimbic systems, thereby resulting in exaggerated processing of the incentive values of food and food-related stimuli. The other is related to hypoactivity of the reflective prefrontal cortical (PFC) system, critical in inhibitory control over behavior associated with immediate reward. An imbalance in the interactions of these two systems leads to a loss of willpower in resisting drugs and food.

8.2  A neural system for decision-making and willpower: the somatic marker hypothesis The somatic marker hypothesis is a systemslevel neuroanatomical and cognitive framework for decision-making and for choosing according to long-term, rather than short-term, outcomes (Damasio, 1994). The key idea of this hypothesis is that the process of decision-making depends in many important ways on neural substrates that regulate homeostasis, emotion and feeling. The term “somatic” refers to the collection of bodyand brain-related responses that hallmark affective and emotional responses. Somatic states can be induced by both primary inducers and secondary inducers. Primary inducers are innate or learned stimuli that cause pleasurable or aversive states. Once present in the immediate environment, they

automatically and obligatorily elicit a somatic response. Food in the immediate environment is an example of a primary inducer. After a somatic state has been triggered by a primary inducer and experienced at least once, a pattern for this somatic state is formed. The subsequent presentation of a stimulus will evoke memories about a specific primary inducer. The entities generated by the recall of a personal or hypothetical emotional event (i.e., “thoughts” and “memories” of food taken) are called secondary inducers. Secondary inducers are presumed to reactivate the pattern of somatic state belonging to specific primary inducers. For example, recalling or imagining the experience of food taken reactivates the pattern of somatic state belonging to actual previous encounters of that food. Both the amygdala and ventromedial prefrontal cortex/orbital prefrontal cortex (VMPC/ OFC) are critical for triggering somatic states (Figure 8.1). However, their specific roles most likely differ. The amygdala is a critical substrate in the neural system necessary for triggering somatic states from primary inducers. According to the somatic marker framework, the amygdala links the feature of the stimulus to its affective/emotional attributes. The res­ ponses triggered through the amygdala are short-lived and habituate very quickly. The affective/emotional response is evoked through visceral motor structures such as the hypothalamus and autonomic brainstem nuclei that produce changes in internal milieu and visceral structures, as well as through behavior-related structures such as the striatum, periaqueductal gray (PAG) and other brainstem nuclei that produce changes in facial expression and specific approach or withdrawal behaviors. The primary inducers can be processed subliminally via the thalamus or explicitly via early sensory and high-order association cortices such as the insular and somatosensory cortex (Damasio, 1994; Bechara, 2004) (see Figure 8.1). Given the automatic and fast properties of this system for processing the affective/emotional attributes of

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DLPC

AC Striatum Insula

VMPC

DA A

Hyp 5–HT

Figure 8.1  A schematic diagram illustrating key structures belonging to the impulsive and the reflective systems. These include regions involved in (1) representing patterns of affective states (e.g., the insular and somatosensory cortices); (2) triggering of affective states (e.g., amygdala (A) and VMPC); (3) memory, impulse and attention control (e.g., lateral orbitofrontal, inferior frontal gyrus and dorsolateral prefrontal cortex (DLPC), hippocampus (Hip) and anterior cingulated (AC); and (4) behavioral actions (e.g., striatum and supplementary motor area). 5-HT, serotonin; DA, dopamine.

a stimulus, we have referred to it as the “impulsive” system, in which the amygdala is a key neural substrate. By contrast, the VMPC/OFC is a critical substrate in the neural system necessary for triggering somatic states from secondary inducers, although it can be involved in the emotions triggered by some primary inducers as well. Unlike the amygdala response, which is sudden and habituates quickly, the VMPC/OFC response is deliberate, slow, and lasts for a long time. The VMPC/OFC is a key structure in the reflective system, and dependent on the integrity of three sets of neural systems: the first is critical for working memory and its executive processes (inhibition, planning, cognitive flexibility), in which the dorsolateral prefrontal cortex (DLPC) is a critical neural substrate; the second is critical for processing emotions related to the

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non-conscious (e.g., in the brainstem) or conscious (e.g., in the insular/somatosensory cortex); and the third is critical for executing the emotional response, in which the anterior cingulate/supplementary motor area (SMA) are key structures (see Figure 8.1). The VMPC/ OFC serves the role of coupling these systems together. Damage or dysfunction of any of these systems, including the DLPC and anterior cingulate/SMA, can indirectly alter the normal function of the VMPC/OFC. Given the cognitive and slow nature of this system for processing the affective/emotional attributes of a stimulus, we have referred to it as the “reflective” system, in which the VMPC/OFC is a key neural substrate. When confronted with a choice, both the impulsive and reflective systems (or both primary and secondary induction) may be stimulated at the same time. The decisions are the product of a complex cognitive process subserved by these two separate, but interacting, neural systems: (1) an impulsive, amygdaladependent, neural system for signaling the pain or pleasure of the immediate prospects of an option; and (2) a reflective, prefrontal-dependent, neural system for signaling the pain or pleasure of the future prospects of an option (Bechara, 2005). While the amygdala is engaged in emotional situations requiring a rapid response (i.e., “low-order” emotional reactions arising from relatively automatic processes), the VMPC/OFC is engaged in emotional situations driven by thoughts and reflection. Once this initial amygdala emotional response is over, “high-order” emotional reactions begin to arise from relatively more controlled, higher-order processes involved in thinking, reasoning and consciousness. The final decision is determined by the relative strengths of the pain or pleasure signals associated with immediate or future prospects: when the immediate prospect is unpleasant but the future is more pleasant, then the positive signal of future prospects forms the basis for enduring the unpleasantness of immediate prospect. This also occurs when the future prospect

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is even more pleasant than the immediate one. Otherwise, immediate prospects predominate, and decisions shift towards short-term horizons. Therefore, there are at least two underlying types of dysfunction where this overall signal turns in favor of immediate outcomes: (1) hyperactivity in the amygdala or impulsive system, which exaggerates the rewarding impact of available incentives such as food; and (2) hypoactivity in the prefrontal cortex or reflective system, which forecasts the long-term consequences of a given action. Obese individuals may be afflicted with either one or both of those dysfunctions. We will review the evidence supporting this notion in the next section.

8.3  Empirical evidence for deficits of decision-making underlying obesity 8.3.1  Hypersensitivity to reward of food and food-related cues The hedonic effects of food are central to understanding food intake (Rolls, 2007b, 2007c). The most clearly established commonality of the mechanisms of food and drug intake is that they both exert their reinforcing effects partly by increasing dopamine (DA) in the brain reward circuitry including the ventral striatum, amygdala, midbrain and VMPC/OFC (Kelley et al., 2005). Animal studies also show that direct pharmacological activation of the ventral striatum amygdalo-hypothalamic circuit produces hyperphagia and increases preferentially the intake of foods high in fat and sugar, even in animals fed beyond apparent satiety (Petrovich et al., 2002; Kelley, 2004). Several lines of evidence suggest that food may induce greater incentive value in obese individuals compared to normal controls. Behavioral studies show that overweight children indicate food (pizza and snack food) as more

reinforcing, and consumed more energy than their leaner peers. The relative reinforcing value of food versus two non-food alternatives (time spent playing a hand-held video game, or time spent reading magazines or completing word searches or mazes) is also higher in overweight children and lower in non-overweight children (Temple et al., 2008). Eating food is also found to be more reinforcing than selected alternative activities for obese in comparison to nonobese young women (Epstein et al., 1996). The results of functional magnetic resonance imaging (fMRI) studies corroborate these behavioral data. One recent fMRI study reports that compared to lean adolescent girls, obese girls show greater activation in the gustatory cortex (anterior and mid-insular frontal operculum) and in somatosensory regions (parietal operculum and Rolandic operculum) in response to the anticipated intake of chocolate milkshake (versus a tasteless solution) and to actual consumption of milkshake (versus a tasteless solution) (Stice et al., 2008). Interestingly, one previous study shows that even during non-stimulation conditions (resting state), morbidly obese individuals had significantly greater glucose metabolism in the vicinity of the post-­central gyrus in the left and right parietal cortex (Brodmann’s area 1). Such enhanced activation is consistent with an enhanced sensitivity to food palatability in obese subjects (Wang et al., 2002). This area of the parietal cortex is where the somato-sensory maps of the mouth, lips and tongue are located, and is involved with taste perception (Urasaki et al., 1994). Indeed, it has also been suggested that the insular and somatosensory (SII, SI) cortex plays a key role in translating the raw physiological signals that are the hallmark of a somatic state into what one subjectively experiences as a feeling of desire or anticipation, or an urge (Damasio, 1994; Bechara and Damasio, 2005). Evidence shows that the insula is implicated in food craving (Pelchat et al., 2004). Recent evidence also shows that strokes that damage

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8.3  Empirical evidence for deficits of decision-making underlying obesity

the insula tend literally to wipe out the urge to smoke in individuals previously addicted to cigarette smoking (Naqvi et al., 2007). Similarly, other studies report changes in activity in the insular and somatosensory cortex in association with “high” or euphoric experience of acute doses of drugs (Verdejo-Garcia and Bechara, 2009). Taken together, these studies suggest that enhanced sensitivity in regions involved in the sensory processing of food may make food more rewarding and generate greater craving for high-fat, high-sugar food, and thus contribute to excess food consumption in these obese individuals. One proposed mechanism for how this may take place is that activation of interoceptive representations through the insula can, on the one hand, sensitize the impulsive system by increasing the desire, urge, or motivation to seek the rewarding food item (this action also includes engagement of the nucleus accumbens and associated mesolimbic dopamine system). On the other hand, insula activation may impact the prefrontal cortex functions, so that it can subvert attention, reasoning, planning and decision-making processes to formulate plans for action to seek and procure food. Put differently, these interoceptive representations have the capacity to “hijack” the cognitive resources necessary for exerting inhibitory control to resist calorie-rich food items (Naqvi and Bechara, 2009). This neural formulation can explain many of the neuroimaging results associated with brain activities induced by food-related items. Evidence suggests that in a normal brain, primary and secondary inducer processing can be elicited by the same stimulus and at the same time. For instance, looking at a picture of palatable food (chocolate or ice cream) may quickly and automatically trigger an emotional response (serving as a primary inducer), but at the same time it may generate thoughts (e.g., picturing oneself eating this food) that operate as a secondary inducer. Consistent with this notion, some neuroimaging studies show

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that food versus non-food pictures activate the amygdala (LaBar et al., 2001), the ventral stratum (Beaver et al., 2006), the insula (Wang et al., 2004; Porubska et al., 2006) and the orbitofrontal cortex (Holsen et al., 2005; Simmons et al., 2005) in healthy individuals. Moreover, the increased activity induced by food presentation in the caudate, insula and right OFC is significantly correlated with self-reports of hunger and desire for food in normal-weight subjects (Pelchat et al., 2004; Wang et al., 2004). Behavioral studies indicate that obese individuals are hyper-responsive to food cues in a wide range of assessments (Braet and Crombez, 2003; Halford et al., 2004). Current models of addiction have proposed that drug-related cues may trigger drug-seeking behaviors by eliciting hyperactivity in a brain network of reward areas (Robinson and Berridge, 2003; Volkow et al., 2003). Recent studies suggest that the same network of structures showing exaggerated responsiveness to drug cues in addiction is also hyper-reactive to visual food cues in obese individuals. For example, compared to normalweight controls, obese women exhibit greater activation in response to pictures of high-calorie foods in the medial and lateral OFC, amygdala, nucleus accumbens/ventral striatum, insula, anterior cingulate cortex, ventral pallidum, caudate, putamen and hippocampus (Stoeckel et al., 2008). In another fMRI study, relative to controls, obese women also show enhanced activity in the caudate, putamen, anterior insula, hippocampus and parietal lobule when they viewed high-calorie foods (Rothemund et al., 2007). The observed enhanced responsiveness of these regions could contribute to exaggerated appetitive motivation in obese individuals in response to food cues. Moreover, enactment of secondary inducers (recalling or imagining the experience of eating), which activates the VMPC/OFC and cingulate cortex, may produce an increase of the craving sensation and possibly a decrease in inhibitory control in obese individuals.

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Other studies have investigated the relationship between some personality traits, such as reward sensitivity, and overeating. Reward sensitivity and other constructs, such as behavioral activity and novelty/sensation, are conceptualized as a biologically-based personality trait regulated by the meso-cortico-limbic dopamine system (Cohen et al., 2005; Evans et al., 2006). Behavioral research in both healthy and overweight populations has shown that a person­a­lity trait of reward drive and related constructs predicts food craving, overeating, and relative body weight (Davis et al., 2002, 2004a; Bulik et al., 2003). Recent studies also show that sensi­ tivity to reward positively predicts the tend­ ency to overeat beyond caloric need and in the absence of hunger. It also predicts a heightened preference for foods high in fat and sugar. These two behaviors, in turn, predict a higher body mass index (BMI) (Franken and Muris, 2005; Davis et al., 2007). Using fMRI, Beaver and colleagues (2006) reported that individual variation in trait reward sensitivity is highly correlated with activation to images of highly palatable, appetizing foods (e.g., chocolate, ice cream) in a fronto-striatal-amygdala-midbrain network in healthy volunteers (Beaver et al., 2006). One recent study has provided the first evidence for a link between neural activity and reward sensi­ tivity in patients with a binge-eating disorder (Schienle et al., 2008). It shows that the bingeeating disorder patients report enhanced reward sensitivity and display stronger medial OFC responses while viewing high-calorie food pictures than other groups, and, as in the bingeeating disorder patients, medial OFC activity was positively correlated with self-reported reward responsiveness (Schienle et al., 2008). Taken together, these studies suggest that sensitivity to reward may underlie individual differences in preference for highly palatable and high-calorie food, thereby providing one with a behavioral predisposition to obesity (Davis et al., 2004a). However, some studies suggest that obese individuals may experience less food reward

and may use food to increase DA stimulation to a more desirable level. Wang and colleagues (2001) found that obese individuals had a significant reduction in DA D2 receptor availability in the striatum relative to lean individuals (Wang et al., 2001). The Taq1A allele (thought to be linked with lower receptor levels) is also more prevalent in obese individuals compared to normal controls (Noble et al., 1994). However, these studies generally used morbidly obese subjects, typically recruited from obesity treatment clinics – for example, in the study by Wang and colleagues (2001), the obese adults all had a BMI of over 40 (Class III obesity). In other studies, though, which show that sensitivity to reward positively predicts food craving and body weight, most of the samples were normalweight people (Franken and Muris, 2005; Beaver et al., 2006; Davis et al., 2007). Therefore, it is possible that high sensitivity to reward may foster the overeating of high-fat and high-sugar foods, which leads to down-regulation of D2 receptors to compensate for its overstimulation (Davis et al., 2004a). Interestingly, one recent study found that binge-eating disorder patients and obese subjects reported greater reward sensitivity than normal-weight controls, but only among those carrying the Taq1A allele with low DA D2 receptor (Davis et al., 2008). The authors suggest that one explanation for their findi­ngs could be that there is another genetic variant that interacts with the A1 allele to produce higher dopamine activity in the binge-eating disorder patients and obese participants (Davis et al., 2008).

8.3.2  Hypoactivity in the reflective system A critical neural region in the reflective system is the VMPC/OFC, but other neural components, including the DLPC and cingulate cortex outlined earlier, are also important. Several recent studies suggest that cognitive or

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8.3  Empirical evidence for deficits of decision-making underlying obesity

regulatory control of food intake is mediated by brain regions that we have hypothesized to be components of the so-called “reflective system”. For example, one study shows that word-level cognitive labels can change the subjective ratings of the affective value of the taste and flavor of a food when the taste or flavor stimulus is identical; this cognitive modulation is expressed in the OFC and anterior cingulate cortex (Grabenhorst et al., 2008). Evidence also shows that lean individuals preferentially increased neuronal activity in the prefrontal cortex to inhibit food consumption due to satiation (Del Parigi et al., 2002). Moreover, a recent study examined the responsiveness of the brain to images of food that differed in caloric content among normal-weight adolescent females. These authors found significant age-related increases in the activation of the OFC in response to high-calorie food images, but not to low-calorie images, suggesting a progressive engagement of reward evaluation and response inhibition in reaction to fattening and unhealthy food images during development (Killgore and Yurgelun-Todd, 2005). As has been proposed with regard to addiction, abnormalities in the reflective system involved in inhibitory control may also contribute to obesity. Using neuropsychological measures, one study shows that obese individuals make less advantageous choices in the Iowa Gambling Task – a paradigm that relies on the integrity of the VMPC/OFC for execution (Davis et al., 2004b). Decrements in other higher brain functions, including memory, abstract reasoning and attention, are also associated with an increased body weight in adults (Gunstad et al., 2006, 2007). One recent study further demonstrates that this association may exist as early as in childhood: overweight children and children at risk of overweight have decreased visuospatial organization and general mental ability compared to normalweight children (Li et al., 2008). In addition to these behavioral studies, other lines of research also provide evidence for impairment in the reflective system leading to

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uncontrolled eating behaviors. Two case studies report that damage to the right frontal cortex can result in the “Gourmand syndrome”, characterized by the passion for eating and a specific preference for fine food (Regard and Landis, 1997; Uher and Treasure, 2005). Indeed, AlonsoAlonso and Pascual-Leone (2007) proposed that hypoactivity in the right frontal cortex of obese individuals can lead to a general disregard for the long-term adverse consequences of behavioral choices, such as increased risk-taking and excessive food intake. In fMRI studies, obese men and women also have less activation of the left DLPC in response to a mean than do their lean counterparts (Le et al., 2006, 2007). Moreover, obese women showed the decreased left DLPC response to a mean compared to formerly obese women who successfully achieved weight loss by diet and exercise and maintained their weight loss for more than 3 months before the study (Le et al., 2007). These studies are consistent with other reports which show that the dorsal prefrontal cortex is particularly activated in successful weight-loss maintainers in response to meal consumption (Del Parigi et al., 2007), and stimulation of the left DLPC by using repetitive transcranial magnetic stimulation inhibited the development of food cravings (Uher et al., 2005). Although the mechanisms by which low D2 receptor availability would increase the risk of overeating are poorly understood, one recent study shows that low dopamine striatal D2 receptors are positively associated with metabolism in the prefrontal cortex, including the DLPC, medial OFC and anterior cingulate gyrus (Volkow et al., 2008b). These results suggest that decreased D2 receptors in obese subjects contribute to overeating in part through deregulation of prefrontal regions implicated in inhibitory control, emotion regulation and decision-making (Volkow et al., 2008b). Moreover, it appears that reduced D2 receptor density is associated with reduced capacity to learn negative characteristics of a stimulus from negative feedback. In a

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probabilistic learning task, individuals with the Taq1A allele associated with lower D2 receptor showed lower activity in the posterior medial frontal cortex (pMFC), involved in feedback monitoring, in response to negative feedback, than others did. This parallels the behavioral data that these A1-allele carriers are less efficient at avoiding actions with negative consequences (Klein et al., 2007). Taken together, these studies suggest that obese individuals have a downregulated reflective system. As a result, they are less likely to succeed in inhibiting the proponent responses, such as an intense desire to eat highcalorie food, and are also insensitive to future adverse consequences (e.g., gaining weight, diabetes) of their over-eating behaviors.

8.4  Conclusion Modern societies, where widely available, highly palatable and energy-dense food coexists with a continuous flow of food-related promotion and advertising through mass media, challenge individuals’ ability to inhibit desire, resist temptation and make advantageous decisions (Wardle, 2007). Here, we propose that addiction to anything, even food, is a condition in which the person becomes unable to choose according to long-term outcomes, which requires that the pain/pleasure signals triggered by the reflective system dominate those from the impulsive system. Two broad types of conditions could alter this relationship and lead to loss of willpower: (1) a dysfunctional reflective system that has lost its ability to process and trigger affective signals, which forecast the affect/emotion of future prospects; and (2) a hyperactive impulsive system that exaggerates the affective signals from immediate prospects. This increased strength of the impulsive system can alter the balance of power in favor of an overall affective state congruent with that of the amygdala. The triggering of these bottom-up, automatic and involuntary

affective signals through the amygdala will then modulate, bias or even “hijack” the topdown cognitive mechanisms needed for the normal operation of the reflective system. This is why, from the perspective of someone who is dieting and has lost the willpower to resist a tempting food, the decision to eat that food becomes very reasonable and logical at the time of consumption.

Acknowledgments The studies described in this chapter were supported by NIDA grant R01 DA023051.

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Noble, E. P., Noble, R. E., Ritchie, T., Syndulko, K., Bohlman, M. C., Noble, L. A., Zhang, Y., Sparkes, R. S., & Grandy, D. K. (1994). D2 dopamine receptor gene and obesity. The International Journal of Eating Disorders, 15(3), 205–217. Pelchat, M. L., Johnson, A., Chan, R., Valdez, J., & Ragland, J. D. (2004). Images of desire: Food-craving activation during fMRI. Neuroimage, 23(4), 1486–1493. Petrovich, G. D., Setlow, B., Holland, P. C., & Gallagher, M. (2002). Amygdalo-hypothalamic circuit allows learned cues to override satiety and promote eating. The Journal of Neuroscience, 22(19), 8748–8753. Porubska, K., Veit, R., Preissl, H., Fritsche, A., & Birbaumer, N. (2006). Subjective feeling of appetite modulates brain activity: An fMRI study. Neuroimage, 32(3), 1273–1280. Regard, M., & Landis, T. (1997). “Gourmand syndrome”: Eating passion associated with right anterior lesions. Neurology, 48(5), 1185–1190. Robinson, T. E., & Berridge, K. C. (2003). Addiction. Annual Review of Psychology, 54, 25–53. Rolls, E. T. (2007a). Understanding the mechanisms of food intake and obesity. Obesity Reviews, 8(Suppl. 1), 67–72. Rolls, E. T. (2007b). Sensory processing in the brain related to the control of food intake. The Proceedings of the Nutrition Society, 66(1), 96–112. Rolls, E. T. (2007c). Understanding the mechanisms of food intake and obesity. Obesity Reviews, 8(Suppl 1), 67–72. Rothemund, Y., Preuschhof, C., Bohner, G., Bauknecht, H. C., Klingebiel, R., Flor, H., & Klapp, B. F. (2007). Differential activation of the dorsal striatum by high-calorie visual food stimuli in obese individuals. Neuroimage, 37(2), 410–421. Schienle, A., Schafer, A., Hermann, A., & Vaitl, D. (2008). Binge-eating disorder: Reward sensitivity and brain activation to images of food. Biological Psychiatry, 65(8), 651–661. Simmons, W. K., Martin, A., & Barsalou, L. W. (2005). Pictures of appetizing foods activate gustatory cortices for taste and reward. Cerebral Cortex, 15(10), 1602–1608. Stice, E., Spoor, S., Bohon, C., Veldhuizen, M. G., & Small, D. M. (2008). Relation of reward from food intake and anticipated food intake to obesity: A functional magnetic resonance imaging study. Journal of Abnormal Psychology, 117(4), 924–935. Stoeckel, L. E., Weller, R. E., Cook, E. W., III, Twieg, D. B., Knowlton, R. C., & Cox, J. E. (2008). Widespread rewardsystem activation in obese women in response to pictures of high-calorie foods. Neuroimage, 41(2), 636–647. Temple, J. L., Legierski, C. M., Giacomelli, A. M., Salvy, S. J., & Epstein, L. H. (2008). Overweight children find food

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C H A P T E R

9 Hunger, Satiety, and Food Preferences: Effects of the Brain and the Body on the Self-control of Eating Alexandra W. Logue City University of New York, New York, NY, USA

o u t l i n e 9.1  Introduction

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9.5  Conclusions

9.1  Introduction Many individuals in developed countries must constantly choose between an energy intake that is not so pleasurable in the short term but is healthy in the long term (selfcontrol), and energy intake that is pleasurable in the short term but is unhealthy in the long term (impulsiveness) (Logue, 1988, 1995). They

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must choose between eating full-fat ice cream and sitting on the couch now, versus having clear arteries and normal insulin levels 30 years from now. In what ways do our bodies influence these choices? The present chapter will describe some of the ways in which our physiology affects the self-control of eating. The influence of our physiology on impulsive eating has much more than a theoretical

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effect. Body fatness is generally measured using the body mass index, or BMI (which is weight measured in kilograms divided by the square of height measured in meters). Using a definition of overweight as a BMI between 25.0 and 29.9, and of obese as a BMI of 30.0 or more, the percentage of adults aged 20–74 years in the United States classified as overweight or obese rose from 47 percent in 1976–1980 to over 66 percent in 2003–2004. By 2007, only Colorado had an obesity rate lower than 20 percent. In 30 states, over 30 percent of the population was obese (Centers for Disease Control and Prevention, n.d.). Such changes are not limited to the United States. What is eaten and how much, as well as activity levels, are rapidly changing in Asia, Africa, the Middle East and Latin America, resulting in rapidly rising obesity rates (Popkin, 2004). Energy expenditure has decreased due to increased use of elevators, washing machines, power lawn mowers, remote controls, cars, etc., and energy intake has increased as high-­calorie foods (ice cream, fast food, soda, chips, etc.) have become easier and cheaper to purchase. The resulting growing obesity rates have been described as an epidemic (Abelson and Kennedy, 2004), with terrible medical consequences, such as cardiovascular disease, diabetes, and cancer (Calle et al., 2003; Hill et al., 2003). This epidemic can be explained based on what we know about human evolution. Humans evolved in an environment in which food was scarce, physical activity was generally necessary in order to survive, and the foods that were available tended to be low in fat, sugar, and salt, and often high in fiber. Given that insufficient food results in weakness and then death, it was adaptive for evolving humans, similar to other animals, to eat as much as possible, and for the food consumed to be conserved within the body as much as possible. It was also adaptive to discount delayed food and other delayed reinforcers. Delayed reinforcers are uncertain reinforcers: events during a delay,

such as death due to starvation while waiting for food, can prevent someone from ever receiving the delayed reinforcer. Our bodies evolved to behave in exactly these ways – eating as much as possible, conserving the energy consumed within the body as much as possible, and discounting delayed reinforcers. However, what helped us in the environment in which we evolved does not help us now. Our evolved physiological mechanisms are maladaptive for the world in which we live today (Logue, 2004). There is a vast research literature on topics related to how our bodies influence our choices between healthy and non-healthy energy intake. Therefore, this chapter will touch only on some of the most prominent topics.

9.2  The components of self-control In comprehending the effects of different factors on self-control, it is helpful first to understand self-control’s constituent components, reinforcer amount and reinforcer delay. Choices of certain combinations of relatively smaller and larger reinforcer amounts, and of relatively smaller and larger reinforcer delays, constitute self-control and impulsiveness. Much research indicates that reinforcer amounts are valued less – are discounted – if they are delayed. Further, that discounting occurs according to a hyperbolic function (Figure 9.1a). It then follows that, under certain conditions, if someone is choosing between two reinforcers of different amounts and different delays, the value functions for the two reinfor­ cers will cross (Figure 9.1b). Under these circumstances, during the time period from x to y the larger, more delayed reinforcer is worth more and the individual will demonstrate self-control, and during the time period from y to z the smaller, less delayed reinforcer is valued more

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Reinforcer value

Reinforcer value

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Now (a)

x

Now

Later Time of choice

z

(b)

y

Later Time of choice

Much later

Figure 9.1  Reinforcer value as a function of time. (a) One reinforcer. The vertical bar shows the value of the reinforcer at the time it is received in the future; that value decreases as the current time is approached. (b) A larger, more delayed reinforcer, and a smaller, less delayed reinforcer. The letters x and y denote the time period during which choices will result in self-control, and the letters y and z denote the time period during which choices will result in impulsiveness.

and the individual will demonstrate impulsiveness (Logue, 1988, 1995). Defined in this way, it becomes clear that self-control can be increased or decreased by changing the perceived relative sizes of the reinforcers.

9.3  Physiological influences on self-control The physiological factors that influence selfcontrol for food affect both preferences for certain types of foods as well as the total amount consumed. These behaviors are not indepen­ dent, because people eat more of highly preferred foods and thus are likely to overconsume highly preferred foods to an unhealthy degree.

9.3.1  Preferences for salty, sweet, and other calorically dense food Humans, as well as many other species, are genetically predisposed to prefer salty foods, sweet foods, and other calorically dense food.

At birth humans do not show a preference for salt, apparently because the taste mechanism for salt is not yet mature. However, after about 4 months of age, babies and older humans show a preference for salty tastes (Bartoshuk and Beauchamp, 1994). The preference for sweet is stronger than for any other taste, and is present at birth (Pfaffmann, 1977; Maone et al., 1990). People also learn, with experience, to prefer foods that are calorically dense, such as high-fat foods (Logue, 2004). These preferences were adaptive during human evolution. Our bodies need salt for a variety of physiological functions, but salt can be difficult to obtain in the natural environment. Therefore, it would have been adaptive for humans to prefer salt whenever it was tasted. Similarly, in nature the taste of sweet is usually associated with ripe fruit. Ripe fruit not only contains essential vitamins, but also tends to have significant numbers of calories – two food characteristics that were difficult to obtain in the environment in which our ancestors evolved, but that were critical to their survival. Thus, once again, it would have been adaptive

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for humans to prefer sweet foods whenever they were encountered (Logue, 2004). In our current environment not only are foods with these characteristics much more easily available, but manufacturers have also learned how to generate maximally preferred foods by combining several preferred characteristics in one food – such as honey-roasted peanuts, which are not only sweet but also salty and high in fat. The result is that in a choice between a smaller, less delayed reinforcer (unneeded, immediate food) and a larger, more delayed reinforcer (future health), the smaller, less delayed reinforcers (the foods) are not so small; the difference in amount between the smaller, less delayed reinforcers and the larger, more delayed reinforcers is not so great. Therefore self-control, choice of the larger, more delayed reinforcer of good health, is less likely to occur. In other words, the characteristics of the food available can affect whether or not we overconsume and are impulsive (Forzano and Logue, 1995, Forzano et al., 1997).

9.3.2  Food cues and physiological responses The effects of food preference and food type on self-control for food can be observed simply by examining food choice. However, how strongly food preferences influence self-­control is a function of the differing strength of the physiological responses to certain foods. One example of such a physiological response is the pancreas’s release of insulin. Tasting food, smelling food, or even just thinking about food can cause this release. The insulin lowers blood sugar, and can thus induce hunger. In addition, the presence of insulin can also make it more likely that what is eaten will be stored as fat. Just as the salivation reflex can be conditioned to occur to the sound of a bell, so too can insulin release be conditioned to occur in response to the stimuli associated with food consumption.

Different foods can cause different amounts of insulin to be released (Powley, 1977; Vasselli, 1985; Tordoff and Friedman, 1989; Le Magnen, 1992). Foods that result in higher levels of blood glucose (i.e., foods that are characterized as having a high glycemic index) will also result in larger amounts of insulin being released. Such foods (white bread, as opposed to whole-wheat bread, for instance) are less satiating than are foods that result in less glucose being released (Holt et al., 1992; Lavin and Read, 1995). To further complicate this picture, some people are physiologically more responsive than others to food cues (Rodin, 1981). For all of these reasons, food cues can result in physiological changes that make it difficult to eat moderately, and for some people eating moderately is extremely difficult. Insulin is just one of many substances that our bodies release in response to food stimuli. Additional examples are cholecystokinin (CCK) and glucagon. These substances, similar to insulin, aid digestion. CCK is produced in the small intestine, and glucagon, again similar to insulin, is produced in the pancreas. Experiments suggest that increased levels of both CCK and glucagon tend to decrease feeding – these substances may be physiological indicators of satiety (Logue, 2004). They help to ensure that, once eating has begun, it does not continue indefinitely. Food manufacturers, grocery stores, and restaurateurs appear to take deliberate steps to increase the amount people eat through amplifying the food cues that cause appetite-inducing physiological responses. In addition to combining many inherently appealing characteristics in one food (such as honey-roasted peanuts), the food industry uses advertising that contains many appealing, mouth-watering stimuli, it supersizes portions, and it parades in front of us a huge variety of foods (Lieberman, 2006; Wansink, 2006). Similar to many other species, if humans are given the choice between novel foods, familiar foods eaten recently, and

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9.3  Physiological influences on self-control

familiar foods not eaten recently, they will tend to choose familiar foods not eaten recently. Therefore, humans will consume more when they are presented with varying sets of familiar foods then if they are presented with the same set of foods repeatedly (Logue, 2004). The food industry plays upon our physiological responses to food in order to make us more likely not only to choose certain foods, but also to eat larger amounts of those foods.

9.3.3  Adipose cells and the set point Fat is stored in the body in adipose cells. When these cells are full, people are less hungry, and vice versa (Sjöström, 1978, 1980). Thus, these cells contribute to the regulation of the body’s set point, the particular weight at which a body is approximately maintained, and to the regulation of how much is eaten. Heredity contributes to the number and distribution of adipose cells. When 12 pairs of identical adult male twins were overfed under highly controlled conditions, although individuals had gained between 10 and 29 pounds, the amounts of weight gained by the members of a twin pair were much more similar than the amounts gained by unrelated men. Similarly, the amounts of body fat and the locations of that body fat were more similar for the members of a twin pair than for unrelated men (Bouchard et al., 1990). Although the number of adipose cells can increase when someone gains weight, the number of adipose cells can never decrease (Sjöström, 1978, 1980). When someone loses weight that person’s total amount of body fat decreases. This decrease occurs by means of a reduction in the amount of fat stored in the adipose cells, not by a decrease in the number of cells. Thus, an individual whose weight is below his or her highest lifetime weight will always be hungry. Once again, such physiological characteristics were adaptive for the food-scarce environments in which

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humans evolved, but not for the environment in which most of us live now.

9.3.4  Metabolic rate and energy expenditure Body weight is a function not only of the amount and type of food consumed but also of the body’s energy expenditure. Total energy expenditure consists of three components: (1) basal metabolic rate – the energy expended for basic metabolic functions such as respiration and circulation, (2) the energy used by the body for voluntary and non-voluntary physical activity such as walking and fidgeting, and (3) the energy used following eating (Jéquier, 1987; Ravussin and Danforth, 1999). Different people have different metabolic rates, and there are many reasons for these individual differences. One is that fat supports a lower metabolic rate than muscle or bone (Jéquier, 1987). Therefore, two people can weigh the same, but the one with a higher percentage of body fat will have a lower metabolic rate. Metabolic rate is also affected by the amount eaten. If someone’s food intake decreases so that weight is lost, that person’s metabolic rate decreases, possibly for months after the level of food intake has returned to normal (Keesey and Corbett, 1984; Steen et al., 1988; Elliot et al., 1989). Metabolic rate can also be increased by certain types of exercise, such as a strenuous game of football, and that increase can last for hours beyond when the exercise ends (Edwards et al., 1935; Poehlman and Horton, 1989). However, there are individual differences in the degree to which exercise affects weight. In addition, the energy used following eating varies according to the amount and type of food eaten – more energy is used if more is eaten, and more energy is used following a high-carbohydrate than a high-fat meal (Keesey and Corbett, 1984; Jéquier, 1987). There appear to be links between adipose tissue, energy usage, and the body-weight set

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point. If rats’ weights are increased by continued free access to a high-fat diet, their adipose cells increase in number, and eventually the increased energy usage seen after eating disappears; a new set point has been reached (Keesey and Corbett, 1984). The links between adipose tissue, energy usage and the body-weight set point may be related to the level of leptin in the body. Leptin is a hormone produced by adipose cells; the greater the amount of adipose tissue the more leptin, and vice versa. When body weight decreases and adipose tissue shrinks, both the energy expended by the body and the amount of leptin in the body decrease. However, if someone who has lost weight is given injections of leptin sufficient to raise his or her leptin levels to those prior to the weight loss, energy expenditure can be maintained at pre-weightloss levels (Rosenbaum et al., 2005).

9.3.5  The hypothalamus Several brain structures have been found to affect how much and what is eaten. The structure that has been most extensively investigated, and that has been found to have a number of effects on eating, is the hypothalamus. Data collected in the 1940s and 1950s indicated that the ventromedial hypothalamus is important in satiation, and that the lateral hypothalamus is important in hunger (Logue, 2004). Subsequently, the hypothalamus was seen to be a major integrator of information regarding food consumption and energy storage and availability, both when that information is obtained from different brain structures and when it is obtained from elsewhere in the body. The hypothalamus integrates information from central (brain) areas, as well as from peripheral areas such as the gastrointestinal tract. The hypothalamus also plays a critical role in influencing the body’s responses that maintain homeostasis and the body-weight set point based on all of the collected information (Stellar and Stellar, 1985; Badman and Flier, 2005).

Recent research indicates that there may be a very long-lasting effect of leptin on the set point maintained by the hypothalamus. It appears that leptin actually affects the plasticity of synapses in the hypothalamus, and guides axon growth and location during hypothalamus development. Mice that are deficient in leptin develop with abnormally low numbers of certain kinds of excitatory and inhibitory synapses in the hypothalamus, and lack certain neural projection pathways. These brain structures are all related to feeding. Treatment with exogenous leptin can, under certain conditions, reverse these effects (Bouret and Draper, 2004; Elmquist and Flier, 2004; Pinto et al., 2004). These findings with leptin could help to explain the effects of food deprivation during development on later obesity. It is well known that food deprivation early in a woman’s pregnancy results in an increased probability of obesity in the offspring. This was demonstrated in men who had been conceived but not yet born at the time of the Dutch famine of 1944– 1945 (Ravelli et al., 1976). Not only are such offspring more likely to be obese, but they also show various physiological abnormalities such as lower fat oxidation (Sawaya et al., 2004).

9.3.6  Genetic contributions Some genetic contributions to the type and quantity of food eaten have already been described. In recent years, researchers have been successful at identifying a number of the specific genes that contribute to obesity. Such identifications have included a single rare gene whose presence results in morbid obesity, as well as a genetic variant associated with obesity and present in about 10 percent of the European-American and African-American populations. It is believed that these genes result in obesity by means of, in the first case, inhibiting the synthesis of fatty acids, and in the second case, by promoting overeating (Farooqi et al., 2003; Herbert et al., 2006).

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9.4  Promoting self-control for a healthy body weight

At the same time that research has been increasingly successful at identifying the genes that contribute to obesity, recent discoveries have also highlighted the dramatic influence on obesity of interactions between the genes and the environment. Certain environments can affect a fetus’s genes, and these effects can be passed on to the next generation. Such effects have been found in situations, described previously, in which food deprivation during pregnancy is associated with obesity in adulthood. This food deprivation can directly affect the fetus’s DNA, suppressing the expression of certain genes, perhaps permanently. Individuals who as fetuses suffered food deprivation are more likely to accumulate fat and to gain weight, as well as to suffer from type 2 diabetes and cardiovascular disease. Because the gene expression suppression can be permanent, it can be passed on to future generations. Such effects are now thought to explain the sudden huge increases in obesity rates in developing countries – countries in which, not too long ago, pregnant women often did not have enough to eat, but who now have more food available as well as less need to expend energy (Marabou, 2006).

9.4  Promoting self-control for a healthy body weight This chapter has outlined an extensive set of physiological characteristics that contribute to people overeating, acquiring body fat, and retaining body fat. When self-control is defined as eating and exercising so as to maintain a healthy weight, as opposed to an excessive, unhealthy weight, it is clear that the physiological characteristics described above make self-control extremely difficult to achieve. However, self-control is not impossible. Even though human physiology makes it likely that we will overeat and gain weight, this does not mean that these outcomes are inevitable. Just a few decades ago, when

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we had the same physiology that we have now, overweight and obesity were much less frequent in the United States. Despite humans’ physiological predispositions, the environment can have a strong influence on weight. Manipulating the environment to help control weight is an example of self-control. More specifically, the environment can be manipulated and self-control increased by the use of pre-commitment devices. These are actions that we take in the time period between x and y in Figure 9.1b to ensure that we cannot make the impulsive choice that would occur in the time period between y and z. Specific examples of pre-commitment devices that can help control weight include parking the car further away from the office to increase walking, keeping only healthy food in the house, and making pacts with friends to go bowling every Saturday. Precommitment devices can also be used to increase satiation and decrease calorie consumption by guaranteeing the presence at meals of only noncaloric beverages, small portion sizes, and highfiber breads and cereals (Ludwig et al., 1999; DellaValle et al., 2005; Kral, 2006). These self-control strategies may be useful in helping people to maintain their weight. However, if someone’s weight is sufficiently large as to cause significant health problems, behavioral self-control strategies may be insufficient in achieving a long-lasting weight loss. As described earlier in this chapter, physiological factors such as metabolic rate and adipose cells may make it extremely difficult for some individuals to lose weight permanently unless they are prepared to eat fewer calories and be hungry, perhaps for the rest of their life. There is some evidence that large amounts of exercise, at least an hour of aerobic exercise per day, may assist such a person in maintaining a weight loss (Jakicic and Otto, 2006). However, maintaining this level of exercise over perhaps decades is not easy for today’s Americans. An alternative in such cases, although it is not without risks, is surgery to bypass parts of the gastrointestinal tract. Such operations decrease how much food

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is absorbed from the gastrointestinal tract and help the patient to feel fuller faster. Although generally safe, such operations are not without surgical and post-surgical risk, and should therefore only be entertained for people who are at least 100 pounds overweight and for whom other weight-loss strategies have been unsuccessful (Bray and Gray, 1988; Kral, 1995).

9.5  Conclusions Although the research described in this chapter may seem quite daunting – outlining the great many physiological factors that contribute to overeating and weight gain in our current environment – the situation is not hopeless. No longer than a couple of decades ago, Americans were less obese and less likely to suffer from diseases caused or exacerbated by being overweight. Therefore, it is not impossible to change our current environment to promote healthier weights. Already, some schools and communities are regulating the types of foods available and the amount of exercise required (Winderman, 2004). Such actions are society’s versions of pre-commitment devices to ensure that students and citizens have healthy lifestyles. It is possible to create healthpromoting environments. The question becomes one of how much regulation is appropriate in our society in order to promote health. Armed with the information in this chapter, it is possible for us to escape our long-term unhealthy fate, and to avoid eating excessive amounts of highly caloric food. There are strategies that we can follow to ameliorate the effects of our evolutionary history and resulting physiology.

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Bartoshuk, L. M., & Beauchamp, G. K. (1994). Chemical senses. Annual Reviews of Psychology, 45, 419–449. Bouchard, C., Tremblay, A., Després, J. P., Nadeau, A., Lupien, P. J., Thériault, G., Dussault, J., Moorjani, S., Pinault, S., & Fournier, G. (1990). The response to longterm overfeeding in identical twins. New England Journal of Medicine, 322, 1477–1482. Bouret, S. G., Draper, S. J., & Simerly, R. B. (2004). Trophic action of leptin on hypothalamic neurons that regulate feeding. Science, 304, 108–110. Bray, G. A., & Gray, D. S. (1988). Obesity. Part II – Treatment. West Journal of Medicine, 149, 555–571. Calle, E. E., Rodriguez, C., Walker-Thurmond, K., & Thun, M. J. (2003). Overweight, obesity, and mortality from cancer in a prospectively studied cohort of US adults. New England Journal of Medicine, 348, 1625–1638. Centers for Disease Control and Prevention (n.d.). Over­weight and obesity trends among adults. Online. Available: http://www.cdc.gov/nccdphp/dnpa/obesity/trend/ index.htm (accessed 04. 11. 09). DellaValle, D. M., Roe, L. S., & Rolls, B. J. (2005). Does the consumption of caloric and noncaloric beverages with a meal affect energy intake? Appetite, 44, 187–193. Edwards, H. T., Thorndike, A., & Dill, D. B. (1935). The energy requirements in strenuous muscular exercise. New England Journal of Medicine, 213, 532–535. Elliot, D. L., Goldberg, L., Kuehl, K. S., & Bennett, W. M. (1989). Sustained depression of the resting metabolic rate after massive weight loss. American Journal of Clinical Nutrition, 49, 93–96. Elmquist, J. K., & Flier, J. S. (2004). The fat-brain axis enters a new dimension. Science, 304, 63–64. Farooqi, I. S., Keogh, J. M., Yeo, G. S. H., Lank, E. J., Cheetham, T., & O’Rahilly, S. (2003). Clinical spectrum of obesity and mutations in the melanocortin 4 receptor gene. New England Journal of Medicine, 348, 1085–1095. Forzano, L. B., & Logue, A. W. (1995). Self-control and impulsiveness in children and adults: Effects of food preferences. Journal of the Experimental Analysis of Behavior, 64, 33–46. Forzano, L. B., Porter, J. M., & Mitchell, T. S. (1997). Self-control and impulsiveness in adult human females: Effects of food preferences. Learning and Motivation, 28, 622–641. Herbert, A., Gerry, N. P., McQueen, M. B., Heid, I. M., Pfeufer, A., Illig, T., Wichmann, H. E., Meitinger, T., Hunter, D., Hu, F. B., Colditz, G., Hinney, A., Hebebrand, J., Koberwitz, K., Zhu, X., Cooper, R., Ardlie, K., Lyon, H., Hirschhorn, J. N., Laird, N. M., Lenburg, M. E., Lange, C., & Christman, M. F. (2006). A common genetic variant is associated with adult and childhood obesity. Science, 312, 279–283. Hill, J. O., Wyatt, H. R., Reed, G. W., & Peters, J. C. (2003). Obesity and the environment: Where do we go from here? Science, 299, 853–855.

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Pinto, S., Roseberry, A. G., Liu, H., Diano, S., Shanabrough, M., Cai, X., Friedman, J. M., & Horvath, T. L. (2004). Rapid rewiring of arcuate nucleus feeding circuits by leptin. Science, 304, 110–115. Poehlman, E. T., & Horton, E. S. (1989). The impact of food intake and exercise on energy expenditure. Nutrition Reviews, 47, 129–137. Popkin, B. M. (2004). The nutrition transition: an overview of world patterns of changes. Nutrition Reviews, 62, S140–S143. Powley, T. L. (1977). The ventromedial hypothalamic syndrome, satiety, and a cephalic phase hypothesis. Psychological Review, 84, 89–126. Ravelli, G. P, Stein, Z. A., & Susser, M. W. (1976). Obesity in young men after famine exposure in utero and early infancy. New England Journal of Medicine, 295, 349–353. Ravussin, E., & Danforth, E. (1999). Beyond sloth – physical activity and weight gain. Science, 283, 184–185. Rodin, J. (1981). Current status of the internal–external hypothesis for obesity. American Psychologist, 36, 361–372. Rosenbaum, M., Goldsmith, R., Bloomfield, D., Magnano, A., Weimer, L., Heymsfield, S., Gallagher, D., Mayer, L., Murphy, E., & Leibel, R. L. (2005). Low-dose leptin reverses skeletal muscle, autonomic, and neuroendocrine adaptations to maintenance of reduced weight. Journal of Clinical Investigation, 115, 3579–3586. Sawaya, A. L., Martins, P. A., Grillo, L. P., & Florêncio, T. T. (2004). Long-term effects of early malnutrition on body weight regulation. Nutrition Reviews, 62, S127–S133. Sjöström, L. (1978). The contribution of fat cells to the determination of body weight. In A. J. Stunkard (Ed.), Symposium on obesity: Basic mechanisms and treatment (pp. 493–521). Philadelphia, PA: W.B. Saunders. Sjöström, L. (1980). Fat cells and body weight. In A. J. Stunkard (Ed.), Obesity (pp. 72–100). Philadelphia, PA: W.B. Saunders. Steen, S. N., Oppliger, R. A., & Brownell, K. D. (1988). Metabolic effects of repeated weight loss and regain in adolescent wrestlers. Journal of the American Medical Association, 260, 1–50. Stellar, J. R., & Stellar, E. (1985). The neurobiology of motivation and reward. New York, NY: Springer-Verlag. Tordoff, M. G., & Friedman, M. I. (1989). Drinking saccharin increases food intake and preference – IV. Cephalic phase and metabolic factors. Appetite, 12, 37–56. Vasselli, J. R. (1985). Carbohydrate ingestion, hypoglycemia, and obesity. Appetite, 6, 53–59. Wansink, B. (2006). Mindless eating: Why we eat more than we think. New York, NY: Bantam. Winderman, L. (2004 July/August). Building a healthier country. Monitor on Psychology, 28–29.

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C H A P T E R

10 Associative Learning and the Control of Food Intake Louise Thibault School of Dietetics and Human Nutrition, McGill University, Montreal, Canada

o u tli n e 10.1  A Behavioral Reporting of Eating

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10.6  Nutrients and Cognition

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10.2  Eating is a Learned Behavior

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10.7  Dietary Fats and Learning

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10.8  Our Primitive Brain

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10.3  Forms of Learned Ingestive Response 126 10.4  Sensory-specific Anticipatory Eating 127 10.5  D  iurnal Rhythms and the Learned Response

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10.1  A behavioral reporting of eating Food intake from laboratory animals or human participants is generally reported as ingested weight (g), energy (kcal or kJ) or percent of total energy (%E) of carbohydrate, protein and fat. Such gravimetric estimates of caloric and macronutrient intake measures are often used as dependent variables in the elucidation of physiological events controlling eating and

Obesity Prevention: The Role of Brain and Society on Individual Behavior

drinking in experiments designed to provide evidence of the mechanisms involved. However, a measured intake of a food or a drink is not ingestive behavior, and tells us nothing about the neural and mental processes controlling the behavior of eating and drinking, let alone hunger and satiety. It was postulated that caloric and nutrientspecific control of intake are regulatory functions and not observable mechanisms (Booth et al., 1970; Booth, 1972a, 1972b; Peck, 1976; Pudel, 1976; Rolls, 1976; Wooley et al., 1976), and the existence

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of regulation has to be established by observing compensation to a challenge. The compensation of intake to such a challenge could then be used to work out the mechanisms involved at the behavioral level – for example, the input/output transformations in ingestion. Such a behavioral understanding of eating would require the observation of mental processes underlying immediate choices to accept or reject food or foods offered at a meal or over a fixed period. There is no recognized mechanism for the ingestion of a given food being controlled by its macronutrient content or even energy content without having some unlearned or learned (pre-ingestional) cues that predict post-ingestional effects specific to these. In this regard, reporting intake in units of volume rather than in energy units is more appropriate. Choices of whether or not to ingest foods are learned behaviors. Associate conditioning through sensory attributes from foods and drinks and their post-ingestional effects directly controls these choices. This behavioral understanding of eating and drinking is important in research on food intake.

less when sensory cues predicted lower energy supply (conditioned aversion). However, it was reported later that some of the food’s energy substrates may be metabolized before the end of the meal (within 1–2 minutes), which might also be predictive of subsequent absorption (Pilcher et al., 1974). The control of food intake based on the sensory properties of foods, such as flavors or textures, has been tested. It has been demonstrated that choices of foods and drinks are achieved by learning relative preferences/aversions for foods and drinks and appetite/satiety for nutrients, regardless of the source of energy (Booth, 1985; Booth and Thibault, 2000). Learning is a change in the organization of an individual’s behavior so that performance represents an adaptation to the external and internal environments (Booth, 1987). When sensory cues from foods (conditioned stimuli) are paired with prompt nutritional after-effects of eating (unconditioned stimuli), conditioned responses of food choice and intake can be induced (Le Magnen, 1999a).

10.2  Eating is a learned behavior

10.3  Forms of learned ingestive response

Since 1955, it has been claimed that the control of dietary intake according to the foods’ energy content is not immediate but rather an indirect response (Le Magnen, 1955). Le Magnen’s argument was based on the fact that there is not enough time before the end of a meal for energy to metabolize following the absorption of the digested macronutrients. Therefore, the post-ingestive effects of macro­ nutrients must be predicted through receptors in the oronasal cavity and the stomach. He indeed showed that laboratory rats could learn to eat greater amounts of foods when added sensory cues were predictive of energy supply to the tissue (conditioned preference), or learn to eat

In learned satiety, laboratory rats acquire the ability to predict the energy-related effects of eating a specific food from its oral sensory attributes and its distension of the stomach. This results in learned satiety: the rat responds to this configuration of stimuli, ends the meal, and so controls the amount eaten on that occasion (Booth, 1972c; Booth and Davis, 1973). Learned satiety pertaining to meal size (conditioned satiety) has been shown in human subjects (Booth et al., 1976), in rats (Booth, 1972c; Booth and Davis, 1973) and monkeys (Booth and Grinker, 1993). In contrast, learned appetite occurs in the state of nutritional deficiency. It pairs sensory cues from food and visceral state with the

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10.4  Sensory-specific anticipatory eating

post-absorptive effects of the lacking nutrient, which leads to a learned appetite for the nutrient (Gibson et al., 1995). Both these forms of learned ingestive res­ ponse are directly controlled in part by the phy­ siological negative-feedback signals of depletion or repletion. For example, rats given a choice between a protein-free (but complete) diet and a diluted protein diet increased their intake of the diluted one. When given a choice between a protein-free and a concentrated protein diet, rats decreased their intake of the concentrated one (Booth, 1974a). When diets were flavored, protein-deprived rats acquired a preference for the flavor of the protein-rich diet (Booth, 1974b). This protein-conditioned sensory preference depends upon an internal state set up by a recent protein deficit. The flavor preference was not expressed after gastric administration of hydrolyzed protein, but reappeared after an equi­ caloric dose of carbohydrate (Gibson and Booth, 1986; Baker et al., 1987). Classical conditioning and instrumental conditioning are two types of associative control of eating, in which presented relations between responses and/or stimuli result in a persisting change in behavior. In classical conditioning, first identified by Ivan Petrovich Pavlov (1927), a relatively neutral stimulus (CS) is paired with a stimulus of significant biological and behavioral change, such as food (US). Classical conditioning occurs when associations are syn­ t­hesized between two neutral stimuli, meaning that the predictive relation between the CS and the US leads to a conditioned response (CR) to the CS. Most of our likes and dislikes are learned through classical conditioning (Rozin and Millman, 1987). In discriminative instrumental eating, the divergent sensory characteristics of the food serve as discriminative stimuli for greater or lesser reinforcement (from subsequent lack of food) of eating as an operant

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response. Instrumental learning results from the association between action and consequence (outcome) (Dickinson and Balleine, 1994).

10.4  Sensory-specific anticipatory eating Le Magnen (1999b) applied the term “anticipatory satiety” to the process by which a rat learns to eat a smaller meal when it is followed by a short fast; conversely, it eats more of a differently flavored meal when it is followed by a long fast. It is also conceivable that the animal acquires an “anticipatory hunger” for the food preceding a long fast, with the increased intake overcoming the effects of prolonged deprivation. This contrasts with conditioned satiety (Le Magnen, 1955), which has been defined as learning to eat less as a result of immediate effects of the food (Booth and Davis, 1973). The difference is whether the lack of energy results from food deprivation (reinforcing anticipatory hunger) or from the consumption of foods that are calorically diluted (reinforcing conditioned appetite). This experimental approach has been reexamined in only a few studies (White et al., 2001; Thibault and Booth, 2006; Jarvandi et al., 2007, 2009a, 2009b). Each of these studies showed that rats could learn to eat more of a specifically textured/smelling food when it was followed by a long fast. This was compared to a test food paired with a subsequent short fast. Improving upon Le Magnen’s design of cycles of training and testing1, White and colleagues (2001) evaluated the impact of the texture of high-fat test foods on the amounts eaten before a short fast (3 hours) and a long fast (12.5 hours). Le Magnen (1967) used different textures and odors as cues to be conditioned by different

1

At the start of dark phase, maintenance food is removed for 3 hours and then test food is presented for 90 minutes. Subsequently, food deprivation of a duration specified to a distinctive sensory cue in the test food is induced.

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e­ ating after-effects. However, odorants, tastants and colors (for visual species) are easier to control than textural attributes (Thibault and Booth, 1999). Most work on learned preferences has indeed used flavoring as a conditioned or discriminative stimulus. Nevertheless, the tactile sense is as important in the liking of food and drink; research should be undertaken to examine mouthfeel as a cue. Booth and Baker (1990) showed that the size of food crumbs could serve as a cue in conditioned appetites. White and colleagues (2001) built upon this and prepared high- and low-fat diets as either a coarse powder or as small pellets. They also explored the possibility that learning an anticipatory satiety response depended upon the energy density of the test meals. Sufficient caloric intake may be crucial in preventing hunger; thus, this study better tested learning in relation with a highfat diet. Poppitt and Prentice (1996) too argued that diets high in energy density contribute to obesity. The limited experimental work on this issue points, however, to some ways in which concentrated energy can also moderate intake (Booth and Davis, 1973; Gibson and Booth, 2000). White and colleagues (2001) found a learned texture-cued increase in intake (from either a high- or low-fat diet) before a long fast relative to a shorter fast in rats that had the largest intakes in the first days of training. It was concluded that anticipatory hunger/satiety is observed only if enough calories are consumed before the short fast to prevent hunger from returning before the next access to food. That is, anticipatory hunger/satiety is an instrumental response that avoids some or all of the physiological effects of food deprivation. The effect of the macronutrient composition of meals on anticipatory eating was also tested, with flavor as the cuing stimulus, where the energy nutrient was entirely either carbohydrate or protein. The long fast was of 10 hours instead of 12.5 hours (Thibault and Booth, 2006). Both Le Magnen (1999a) and White and colleagues (2001) used conventional diets containing both

carbohydrate and protein. Nevertheless, there is considerable evidence in humans that the protein content of a meal is important in slowing the rise of hunger that leads to the next meal. We then tested a carbohydrate-free protein diet and a protein-free carbohydrate diet to see if protein was more effective in negatively reinforcing the acquisition of anticipatory hunger in rats. It has been suggested that glucose circulating to the brain is capable of enhancing memory in rats and humans (Gold, 1986; McNay et al., 2000). The hypothesis that glucose may improve memory is a basis for predicting that the carbohydrate diet would support learning better than the protein diet in the difficult task of anticipating a deficit in the supply of energy to tissues after a delay of some hours. Carbohydrate meals were therefore also compared to protein meals, to see if the diet yielding abundant glucose enhanced the memory of the different consequences of the deprivation periods which followed various flavors and thereby facilitated the learning of anti­ cipatory hunger. Both the carbohydrate-diet and the protein-diet groups switched from an initial greater intake of test food having an odor predictive of the short fast (conditioned preference) to a larger meal of the identical diet with a long-fast odor (anticipatory eating). However, the learned response declined in the last cycles of training. It was suggested that this phenomenon arose from the extra food’s effect on the negative reinforcement from physiological effects of lack of food. Indeed, the learned increase in intake before a long fast removes its own reinforcer (Booth and Davis, 1973). The phenomenon was also tested in rats of different genders (Thibault and Booth, 2006). Nance and colleagues (1976) suggested that male rats are similar to animals with ventromedial hypothalamus lesions, and adjust to post-ingestional effects of food slower than female rats do. It is possible that male rats may not learn anticipatory hunger as well as female rats, or may show some differences in temporal pattern of instrumental acquisition, its

1.  From Brain to Behavior

10.4.  Sensory-specific anticipatory eating

self-extinction and/or counteractive preference conditioning. Since female rats have a lower body weight and thus require less energy than male rats, they may be better able to ingest sufficient amounts of food to avoid hunger during a short postprandial fast. In addition, differences between sexes, at least in rats, may be involved in the regulation of body weight and control of food intake – for example, through sex hormones. In this experiment, diets were composed of a combination of carbohydrate and protein (Le Magnen, 1999a; White et al., 2001). If carbohydrate and protein individually have different effects, then the question is posed as to the extent to which either of these effects survive in a combination. A strong and rapid learning of anticipatory hunger was observed in male rats, which may relate to their greater size, in comparison to female rats. Large rats may be more responsive to the reinforcing and conditioning stimuli than the modest-sized females. In addition, large rats’ greater intake at experimental meals may generate more effective stimulation of the flavor cues and/or by the physiological consequences of the period of food deprivation. Jarvandi and colleagues (2007) examined anticipatory hunger/satiety in rats given a choice of nutrients before the fasts. Both White and colleagues (2001) and Thibault and Booth (2006) used a single food (i.e., animals could not change the composition of their test food). Allowing the subject to select its own food permits it to optimize the proportions of the differing components. A greater number of food containers encourages greater intake (Tordoff, 2002), which could be important for rats to discriminate a lack of hunger throughout the shorter fast from the hunger that develops during the longer fast (White et al., 2001). Moreover, previous experiments on the roles of macro­ nutrients in anticipatory eating showed that rats learn to avoid hunger by consuming either protein-rich or carbohydrate-rich food (Thibault and Booth, 2006). Therefore, Thibault and Booth (2006) looked to see if a simultaneous choice

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between two test foods, one protein-rich and the other carbohydrate-rich, affected anticipatory eating relative to access to a single balanced food. It was found that anticipatory hunger is learned when a choice is given between protein-rich and carbohydrate-rich foods, as well as on a single food. Also, anticipatory hunger extinguished itself, which again indicates that such learning improves on negativefeedback homeostasis with a feed-forward “hyper-homeostatic” mechanism. In a recent study, Jarvandi and colleagues (2009a) used a novel conditioning paradigm in rats and submitted them only to a long fast. They then examined whether the restoration of hunger as a result of extinction causes the re-learning of deficit-avoidance eating by continuing cycles beyond the expected extinction of the learned response. The results confirm previous observations, and are consistent with deficit-avoidance being acquired and partly self-extinguishing. The learned extra intake of food is instrumental to preventing the return of hunger (the negative reinforcer), whose removal extinguished the learned response (Jarvandi, 2008; Figure 10.1). This extinction (self-extinction) of the learned extra intake results in the return of hunger which as a negative reinforcement should emit new learned response (relearning). As expected, when training is continued, the resulting return of hunger induced re-learning of anticipatory eating. Because of the absence of any contrast with trials followed by short fasts, these findings provided

Figure 10.1  Evidence for instrumental behavior. Source: Jarvandi (2008).

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robust evidence that eating in rats can be controlled by instrumental learning reinforced by hunger. The use of this novel conditioning paradigm warrants more attention.

of anticipatory learning. Indeed, the parametric limits on anticipatory eating remain to be defined, with their potential implications about mechanisms.

10.5  Diurnal rhythms and the learned response

10.6  Nutrients and cognition

Meal patterns in rats have a strong nycthemeral rhythm (Selmaoui and Thibault, 2006), and the endogenous rhythm of melatonin is responsible for the increased intake of carbohydrates at the beginning of the activity period (Angers et al., 2003). The diurnal rhythms of intake and secretion of hormones can be synchronized to the availability of food (Woods et al., 2000). In rats fed ad libitum, the 24-hour rhythm of intake was statistically reliable from day to day for water, carbohydrate and protein; however, the rhythm was not predictable for fat (Selmaoui et al., 2004). This raises questions about the specific influence of fatty acids. The intake of carbohydrate and protein is more precisely controlled as a result of the effects of glucose and amino acids, respectively. The lack of control in regards to fat could account for the over-consumption of certain high-fat foods; the underlying behavioral mechanisms are worth investigating further. There are also no data available regarding the learned control of intake at different stages of the nycthemeral cycle. Sensory-specific anticipatory eating studies have thus far measured intake of test food over a fixed period. The effects of anticipatory learning on the sizes of the first and any subsequent meals and/or on the interval(s) between them are not known. The effects of anticipatory eating in meal size adjustment and/or in frequency of intake during an access period before deprivation could be measured by recording intakes with a computerized system. Testing the effect of circadian time in the light/dark cycle is also important, and would account for the metabolic conditions

Increasing attention has been focused in recent years on the effects of energy nutrients on cognition. Administering glucose solutions has been shown to facilitate cognitive performance such as memory in several studies. The mechanisms underlying this have yet to be elucidated. The administered glucose has both peripheral and central effects (Booth, 1979). Unlike glucose, fructose does not cross the blood–brain barrier and is metabolized only by the peripheral organs (mainly the liver). Therefore, a similar memory-enhancing effect with fructose in some cognitive tasks implies peripheral mechanisms. Hence, the systemic roles of dietary carbohydrate and glucogenic amino acids in the memory for the flavor-­hunger contingency should be investigated.

10.7  Dietary fats and learning Another issue pertains to the role dietary lipids may have in the central nervous system as it relates to the learning of anticipatory eating. A diet rich in saturated fats impairs the performance of rats in learning tasks (Greenwood and Winocur, 1996). In addition, (fat-rich) dietinduced obese (DIO) rats are more prone to disruptive effects of dietary fats on brain processes involved in motivation (Chambers et al., 2006). Lindqvist and colleagues (2006) reported that male rats fed a high-fat diet had fewer nerve cells in the hippocampus, even before they developed

1.  From Brain to Behavior

references

obesity. Feeding with fat, independently of its obesity-inducing effect, can impair working memory (Granholm et al., 2004). In a preliminary study of the role of high-fat maintenance diet in learned anticipatory eating, Jarvanti and colleagues maintained eight rats on a high-fat diet for 28 days and then evaluated their anticipatory eating of flavored chow. Intake of fat attenuated the overall pattern of learning eating in the animals, independently of body weight gain (Jarvandi et al., 2009b). Comparing the strength of learned response from previous experiments conducted in animals maintained on standard laboratory chow showed a weaker learned response of rats fed a high-fat diet (Jarvandi et al., 2009b). To the author’s knowledge, this is the first report regarding the effect of high-fat feeding on anti­ cipatory eating. Measuring the extent to which learned anticipatory eating is weakened following chronic intake of fat, replication of these results using dietary fats with different fatty acid profiles would be important. Mechanisms such as reduced or reversed intake-motivating effect of the longer food deprivation by the high-fat maintenance, satiating effects of fats in meals, and altered metabolic response of highfat feeding to fasting through fat oxidation should be investigated.

10.8  Our primitive brain Humans clearly can evaluate the delay between eating opportunities, and so can deliberately adjust their intake accordingly in order to manage hunger (Dibsdall et al., 1996). However, we may share with rats a more primitive capa­ city to acquire anticipatory hunger and satiety, according to the circumstances in which we experience hunger. We tend to eat meals according to social prescriptions, rather than when we choose. To prevent obesity, we ought to eat just enough to carry us through to the next meal.

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Even the tiny brain of a laboratory rat enables it to eat more of a food that prevents hunger, and to eat less if it is presented with a food before it is hungry again. It is therefore likely that, deep inside our brains, there are automatic mechanisms that help us (maybe unconsciously) to eat more if we tend to be hungry after a meal, or to eat less when there is more than enough to carry us through. The literature on learned appetite and satiety supports the concept that short-term intake is influenced by learning, addressing both current and future physiological requirements. Cognitive processes contribute to the control of food intake, as evidenced both in people and in laboratory rats. The improved understanding of the behavioral mechanisms of this underresearched type of learning is highly significant in approaching the development of diet-induced obesity. Poor performance of a basic regulatory mechanism with a high-fat diet could prove to be an extremely important phenomenon.

References Angers, K., Haddad, N., Selmaoui, B., & Thibault, L. (2003). Effect of melatonin on total food intake and macronutrient choice in rats. Physiology & Behavior, 80(1), 9–18. Baker, B. J., Booth, D. A., Duggan, J. P., & Gibson, E. L. (1987). Protein appetite demonstrated: Learned specificity of protein-cue preference to protein in adult rats. Nutrition Research, 7, 481–487. Booth, D. A. (1972a). Caloric compensation in rats with continuous or intermittent access to food. Physiology & Behavior, 8, 891–899. Booth, D. A. (1972b). Taste reactivity in starved, ready to eat and recently fed rats. Physiology & Behavior, 8, 908–910. Booth, D. A. (1972c). Conditioned satiety in the rat. Journal of Comparative and Physiological Psychology, 81, 457–471. Booth, D. A. (1974a). Food intake compensation for increase or decrease protein content of the diet. Behavioral Biology, 12, 31–40. Booth, D. A. (1974b). Acquired sensory preferences for protein in diabetic and normal rats. Physiological Psychology, 2, 344–348. Booth, D. A. (1985). Food-conditioned eating preferences and aversions with interoceptive elements: Learnt appetites and satieties. Annals of the New York Academy of Sciences, 443, 22–37.

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Booth, D. A. (1987). How to measure learned control of food and water intake. In F. M. Toates & N. E. Rowland (Eds.), Feeding and drinking (pp. 111–149). Amsterdam: Elsevier. Booth, D. A. (1979). Metabolism and the control of feeding in man and animals. In K. Brown & S. J. Cooper (Eds.), Chemical influences on behaviour (pp. 79–133). London: Academic Press. Booth, D. A., & Baker, B. J. (1990). Dl-fenfluramine challenge to nutrient-specific textural preference conditioned by concurrent presentation of two diets. Behavioral Neuroscience, 104, 226–229. Booth, D. A., Campbell, A. T., & Chase, A. (1970). Temporal bounds of postingestive glucose-induced satiety in man. Nature, 228, 1104–1105. Booth, D. A., & Davis, J. D. (1973). Gastrointestinal factors in the acquisition of oral sensory control of satiation. Physiology & Behavior, 11, 23–29. Booth, D. A., & Grinker, J. A. (1993). Learned control of meal size in spontaneously obese and nonobese bonnet macaque monkeys. Physiology & Behavior, 53(1), 51–57. Booth, D. A., Lee, M., & McAleavey, C. (1976). Acquired sensory control of satiation in man. British Journal of Psychology, 67(2), 137–147. Booth, D. A., & Thibault, L. (2000). Macronutrient-specific hungers and satieties and their neural bases, learnt from pre- and postingestional effects of eating particular foodstuffs. In H. R. Berthoud & R. J. Seeley (Eds.), Neural and metabolic control of macronutrient intake (pp. 61–81). Boca Raton, FL: CRC Press. Chambers, J. B., Heiman, J. U., Clegg, D. J., Benoit, S. (2006). Sex and strain differences impact learning and motivation in DR and DIO rats. Paper presented at the society for the study of ingestive behavior (SSIB). Naples, FL, USA. Appetite, 46, 343. Dibsdall, L. A., Wainwright, C. J., Read, N. W., & Booth, D. A. (1996). How fats and carbohydrates in familiar foods contribute to everyday satiety by their sensory and physiological actions. Nutrition and Food Science, 5, 37–43. Dickinson, A., & Balleine, B. (1994). Motivational control of goal-directed action. Animal Learning & Behavior, 22(1), 1–18. Gibson, E. L., & Booth, D.A. (1986). Acquired protein appetite in rats: Dependence on a protein-specific need state. Experientia, 42, 1003–1004. Gibson, E. L., & Booth, D.A. (2000). Food-conditioned odour rejection in the late stages of the meal, mediating learnt control of meal volume by after-effects of food consumption. Appetite, 34, 295–303. Gibson, E. L., Wainwright, C. J., & Booth, D.A. (1995). Disguised protein in lunch after low-protein breakfast conditions food-flavor preferences dependent on recent lack of protein intake. Physiology & Behavior, 58, 363–371.

Gold, PE. (1986). Glucose modulation of memory storage processing. Behavioral and Neural Biology, 45(3), 342–349. Granholm, A., Moore, A. B., Bimonte-Nelson, H. A., Singl­e­ ton, R. S., Haugabook, S.J., Amisial, L. D., et al. (2004). A high-fat, high cholesterol diet impairs working memory in middle-aged rats. Paper presented at the society for neuroscience, 34th Annual Meeting. Abstract 323.4., San Diego, CA, USA. Greenwood, C. E., & Winocur, G. (1996). Cognitive impairment in rats fed high-fat diets: A specific effect of saturated fattyacid intake. Behavioral Neuroscience, 110(3), 451–459. Jarvandi, S. (2008). Learning processes in food intake. PhD Thesis, Montreal: McGill University. Jarvandi, S., Booth, D. A., & Thibault, L. (2007). Hyperhomeostatic learning of anticipatory hunger in rats. Physiology & Behavior, 92(4), 541–547. Jarvandi, S., Thibault, L., & Booth, D. A. (2009a). Rats learn to eat more to avoid hunger. The Quarterly Journal of Experimental Psychology, 62(4), 663–672. Jarvandi, S., Booth, D. A., & Thibault, L. (2009b). Effects of high-fat maintenance diet on the motivation of eating after deprivation and on the learning of anticipatory eating and sensory preference in rats. Appetite, submitted. Le Magnen, J. (1955). Sur le mécanisme d’établissement des appétits caloriques. Comptes Rendus des Séances de l’Académie des Sciences, 240, 2436–2438. Le Magnen, J. (1967). Food intake. In C. F. Code, & W. Heidel, (Eds.). Handbook of physiology: Alimentary canal: Vol. 1. Washington, DC: American Physiological Society. Le Magnen, J. (1999a). Effects of the duration of a postprandial fast on the acquisition of appetites in the white rat [first published in French in 1957]. Appetite, 33, 27–29. Le Magnen, J. (1999b). Effects of the duration of pre- and postprandial fasting on the acquisition of appetite [first published in French in 1957]. Appetite, 33, 21–26. Lindqvist, A., Mohapel, P., Bouter, B., Frielingsdorf, H., Pizzo, D., Brundin, P., et al. (2006). High-fat diet impairs hippocampal neurogenesis in male rats. European Journal of Neurology, 13(12), 1385–1388. McNay, E. C., Fries, T. M., & Gold, P. E. (2000). Decreases in rat extracellular hippocampal glucose concentration associated with cognitive demand during a spatial task. Proceedings of The National Academy of Sciences USA, 97(6), 2881–2885. Nance, D. M., Garski, R. A., & Panksepp, J. (1976). Neural and hormonal determinants of sex differences in food intake and body weight. In D. Novin, W. Wyrwicka, & G. A. Bray (Eds.), Hunger: basic mechanisms and clinical implications (pp. 257–271). New York, NY: Raven Press. Pavlov, I. P. (1927). Les réflexes conditionnés trad. Franç. N and G Gricouroff. Paris: Alcan 1927; 1932 (Les réflexes conditionnels), Paris: PUF. Peck, J. W. (1976). Situational determinants of the body weights defended by normal rats and rats with ­ hypo­thalamic

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lesions. In D. Novin, W. Wyrwicka, & G. A. Bray (Eds.), Hunger: Basic mechanisms and clinical implications (pp. 297– 311). New York, NY: Raven Press. Pilcher, C. W. T., Jarman, S. P., & Booth, D. A. (1974). The route of glucose to the brain from food in the mouth of the rat. Journal of Comparative and Physiological Psychology, 87, 56–61. Poppitt, S. D., & Prentice, A. M. (1996). Energy density and its role in the control of food intake: Evidence from metabolic and community studies. Appetite, 26, 153–174. Pudel, V. E. (1976). Experimental feeding in man. In T. Silverstone (Ed.), Appetite and food intake (pp. 245–264). Berlin: Dahlem. Rolls, E. T. (1976). Neurophysiology and feeding. In T. Silverstone (Ed.), Appetite and food intake (pp. 22–42). Berlin: Dahlem. Rozin, P., & Millman, L. (1987). Family environment, not heredity, accounts for family resemblances in food preferences and attitudes: A twin study. Appetite, 8(2), 125–134. Selmaoui, B., & Thibault, L. (2006). Food ingestion and circadian rhythmicity. Biological Rhythm Research, 37(3), 179–189. Selmaoui, B., Paquet, J., & Thibault, L. (2004). Reliability of the circadian rhythm of water and macronutrient-rich

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diets intake in dietary choice. Chronic International, 21(3), 385–392. Thibault, L., & Booth, D. A. (1999). Macronutrient-­specific dietary selection in rodents and its neural bases. Neuroscience & Biobehavioral Reviews, 23, 457–528. Thibault, L., & Booth, D. A. (2006). Flavour-specific anticipatory hunger reinforced by either carbohydrate or protein. Physiology & Behavior, 88, 201–210. Tordoff, M. G. (2002). Obesity by choice: The powerful influence of nutrient availability on nutrient intake. American Journal of Physiology. Regulatory, Integrative and Comparative Physiology, 282, R1536–R1539. White, J. A., Mok, E., Thibault, L., & Booth, D. A. (2001). Acquisition of texture-cued fasting-anticipatory mealsize change in rats with adequate energy intake. Appetite, 37, 103–109. Woods, S. C., Schwartz, M. W., Baskin, D. G., & Seeley, TJ. (2000). Food intake and the regulation of body weight. Annual Review of Psychology, 51, 255–277. Wooley, S. C., Wooley, O. W., Bartoshuk, L. M., & Cabanac, M. J. C. (1976). Psychological aspects of feeding. In T. Silverstone (Ed.), Appetite and food intake (pp. 331–354). Berlin: Dahlem.

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C H A P T E R

11 Restrained Eating in a World of Plenty Janet Polivy1 and C. Peter Herman2

1

Department of Psychology, University of Toronto at Mississauga, Mississuaga, Canada 2 Department of Psychology, University of Toronto, Toronto, Ontario, Canada

o u t l i n e 11.1  Introduction

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11.6  The Removal of Food Cues

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11.2  T  he Effects of Having Food Cues   Present

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11.7  C  aloric Restriction in Animals   and Humans

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11.3  R  esponse to Food Cues in Restrained   and Unrestrained Eaters 136

11.8  I s CR Likely to be Effective for   Humans?

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11.4  F  ood Photographs and/or   Words – Indirect Food Cues

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11.9  C  aloric Restriction in the Presence   of Food Cues 142

11.5  Portion Size as Food Cue

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11.10  Dieting in a World of Food Cues

11.1  Introduction Western society now has the highest rates of obesity in history, at the same time as it has the highest rates of restrictive eating disorders. Dieting and body dissatisfaction have become the norm for young females (see, for example, Vartanian et al., 2005) as attitudes toward the overweight and obese have become less tolerant and accepting (Puhl and Brownell, 2001). Motivation to restrict eating is supported not only by society’s preference for thin physiques,

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but also by experimental evidence derived from animal studies (Pinel et al., 2000) that suggest that food restriction extends life and improves health. This evidence may apply to humans as well (see, for example, Walford, 2000; Delaney and Walford, 2005). As dieting has become almost ubiquitous among young women, the food environment has become “toxic” (Brownell and Horgen, 2004) in promoting overeating and obesity. Portion sizes in the Unites States have become huge, healthy food is more expensive than unhealthy, high-fat and high-sugar

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counterparts, and food and food cues are everywhere. How, then, does this environment of plenty affect those who are trying to restrain their eating (i.e., diet)? The present chapter will discuss the ubiquity of food cues in our environment, and review the literature on people’s responses to them. Such cues make it particularly difficult for humans to reduce, let alone control, their food intake and, particularly, to restrict their intake to the levels required for life-extension. We will discuss differences between animals in caloric-restriction experiments in laboratories, on the one hand, and free-living humans in our society, on the other. We will argue that the super-abundance of food cues in our society makes it particularly difficult for people to restrain their eating and diet successfully or achieve real caloric restriction.

11.2  The effects of having food cues present In nature, food cues customarily indicate what one can (and should) eat, and also specify what can be eaten (Weingarten, 1985). Thus, the presence of food cues may stimulate a desire to eat, specify what should be eaten, and increase the amount consumed (Woods, 1991; Weingarten, 1985). Exposure to palatable foods increases self-reported appetite, and consuming a little of the food increases the desire to eat it (Yeomans et al., 2004). Cornell and colleagues (1989) fed male subjects until they were sated, and then gave them a taste of ice cream or pizza, or nothing. All participants were then given ice cream and pizza to eat ad libitum. Those who had tasted ice cream ate more ice cream, and those who had tasted pizza ate more pizza, leading to the conclusion that specific food cues increase desire for the same food rather than stimulate a broader appetite for food in general. Fedoroff and colleagues (1997) exposed participants to the smell of pizza baking, and

observed increased pizza craving and pizza consumption for both restrained and unrestrained eaters (although this effect was particularly strong in restrained eaters, as will be discussed later). Fedoroff and colleagues (2003) replicated and extended these findings: participants sat in a room with an oven baking either cookies or pizza, and were then offered one of these. As in the study by Cornell and colleagues (1989), those who had smelled cookies ate more cookies but not pizza, whereas those who had smelled pizza ate more pizza but not cookies. Thus, the food cues elicited a food-specific response, increasing consumption only of the food to which participants had olfactory pre-exposure. Painter and colleagues (2002) placed containers of 30 chocolate candy kisses in offices every day for 15 consecutive working days. The candy containers were positioned on the desk for 5 days (making the candies both visible and convenient), in a desk drawer for 5 days (so that they were convenient but not visible) and on a shelf 2 meters away from the desk for 5 days (so they were visible but not convenient). When the container of candies was visible and convenient, more candies were eaten; when the container was inconveniently placed on the shelf, fewer candies were eaten. Having food both visually salient and easily attainable increased intake.

11.3  Response to food cues in restrained and unrestrained eaters Several studies have focused on how the pre­ sence of food cues influences restrained and unrestrained eaters (chronic dieters and nondieters). Restrained eaters attempt to control their food intake in order to lose weight. They are also susceptible to disinhibition of this rest­raint, so that they often overeat when their inhibitions are violated or even merely threatened (for reviews, see, for example, Polivy,

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11.3  Response to food cues in restrained and unrestrained eaters

1996; Herman and Polivy, 2004). Legoff and Spiegelman (1987) found increased salivation in response to attractive food cues in restrained eaters, but not in unrestrained ones. Rogers and Hill (1989) showed that the sight and smell of attractive foods increased hunger, salivation and eating in restrained eaters more than in unrestr­ained eaters exposed to the cues, restrained eaters not exposed to food cues, and restrained eaters exposed to unattractive, non-preferred foods. However, the changes in hunger ratings and/or salivation did not predict the amount of food eaten by individual participants: the amount eaten following exposure to food cues is thus not mediated by hunger. It appears that the mere sight and smell of palatable food, however, can overwhelm dieters’ motivation to restrain their eating. Restrained eaters ate more than unrestrained eaters after merely smelling attractive foods (Jansen and Van den Hout, 1991) and after smelling and thinking about palatable foods (Fedoroff et al., 1997, 2003). Fedoroff and colleagues (1997) had restrained and unrestrained eaters think about pizza while they smelled pizza baking, and then allowed them to eat pizza ad libitum. Pizza intake was higher in all food-cues conditions, but particularly so for restrained eaters. In addition, restrained and unrestrained eaters rated several foods, including pizza, similarly before they were exposed to the food cues. Following exposure, restrained eaters reported more cravings, liking and desire for the cued food than did unrestrained eaters (and more than did restrained eaters not exposed to food cues). Pre-exposure to the food cues induced a desire to eat in restrained eaters, but not in unrestrained eaters. Fedoroff and colleagues (2003) replicated and extended their 1997 study in order to determine whether the increased intake they had observed reflects a general desire to eat or a specific desire or craving for the particular cued food. Participants were assigned to one of three conditions: pizza cues (the smell of pizza

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­ aking while they wrote down their thoughts b about pizza), cookie cues (the smell of chocolate­chip cookies baking while they wrote down their thoughts about cookies) or no cue (no food baking while they wrote down their current thoughts). After pre-exposure (or not) to food cues, participants were presented with either pizza or cookies to eat ad libitum. Restrained eaters were again more responsive to food cues, eating more after exposure to them. However, as reported above, restrained eaters ate more only of the particular food to which they had previously been exposed, demonstrating the specificity of the response to food cues. Additionally, reports of craving, liking and desire to eat the foods were elevated only for the cued food. Restrained eaters presented with the non-cued food ate the same amount as did restrained eaters who had not been exposed to any food cues. Food cues thus appear to have a greater effect on restrained eaters than on unrestrained eaters for both the desire to eat a food and how much is actually eaten. Lowe and Butryn (2007) attribute increased motivation to eat palatable, attractive foods to the presence of attractive food in the environment, especially in chronic dieters. As in Weingarten’s (1985) two-factor theory of hunger and appetite, Lowe and Butryn’s (2007) hypothesis suggests that the mere presence of food may stimulate psychological hunger or appetite in dieters. Restrained eaters have a tendency to binge eat in a variety of situations when their diets become irrelevant or less important to them (for a review, see Polivy, 1996). Propensity for bingeeating behavior rather than restrained eating per se was studied in two experiments that examined psychophysiological responses to the presence of food (Karhunen et al., 1997; Vogele and Florin, 1997). In one study (Vogele and Florin, 1997), 30 female binge eaters and 30 non-bingers were assessed on measures of heart rate, blood pressure, electrodermal activity, and respiration rate at baseline, during exposure to their favorite (binge) food(s) and while ­ eating the

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food(s). Self-reported restrained eating, nervousness, distress, desire to binge and hunger were also measured. Psychophysiological arousal was elevated in binge eaters during the food-exposure period. Heart rate during foodcue exposure predicted the amount of food eaten by all types of participants, but particularly by binge eaters and normal restrained eaters. Karhunen and colleagues (1997) measured cephalic-phase responses repeatedly in 11 obese female binge eaters and 10 obese non-bingers during a baseline phase, followed by periods of anticipation of food, food exposure, and ad libitum eating. They examined indices of cephalicphase responses such as serum insulin, free fatty acids, plasma glucose and salivation, as well as hunger and the desire to eat. There were no differences between groups in the amount of food eaten or in cephalic-phase responses, but binge eaters exhibited a greater desire to eat following exposure to food cues than did non-bingers, indicating greater subjective reactivity to food in binge eaters, despite a lack of increased physical responsiveness.

11.4  Food photographs   and/or words – indirect   food cues It is not terribly surprising that prominent, sensory food cues (e.g., looking at and smelling food) influence hunger and eating, but recent research indicates that more remote, abstract, and indirect food cues also appear to influence appetite and eating. Female students who studied lists of food-related words (high- and lowcalorie foods) reported thoughts about eating, particularly if they were restrained eaters (Boon et al., 1998). Restrained eaters reported more thoughts about eating control, weight and body shape after reading such lists or after an actual eating episode. In fact, these dieters claimed that they ate somewhat less when thinking

about diet-related matters, but only if they were currently dieting; restrained eaters not currently dieting actually reported eating more when they experienced diet-related thoughts. Unrestrained eaters, on the other hand, were not affected by their thoughts either way. Similarly, Fishbach and colleagues (2003) showed that priming dieters with tempting food words made their diet goals more salient. Other remote food cues such as a magazine about chocolate or candy bars and other fattening foods that were inaccessible to the participants – the foods were presumably there for a later meeting in the same room – also increased dieters’ resistance to temptation; they were more likely to select a non-fattening food reward (an apple) than a fattening candy bar. Oakes and Slotterback (2000, 2001) assessed reactions to a list of foods to be rated for nutritional value (i.e., verbal food cues). Even such indirect, remote food cues led to lower fullness ratings and increased ratings of general hunger, desire to eat, and desire to eat a greater number of specific foods. This effect was stronger for non-dieters than for restrained eaters. On the other hand, Braet and Crombez (2003) found that obese children, like restrained eaters, were hyperresponsive to food words compared to non-food words and to non-obese children. These authors speculated that hyper-reactivity to food-related stimuli might initiate or maintain excessive eating which causes these ­children’s obesity. When restrained and unrestrained eaters were asked to imagine eating chocolate cake or drinking water while performing a simple reactiontime task, the reaction-time performance of the restrained eaters was slower while they imagined eating the cake, relative to imagining drinking the water. The reaction times of unrestrained eaters were not affected by cue exposure, suggesting again that restrained eaters are more reactive to food cues (Higgs, 2007). The evidence thus indicates that subtle, indirect food cues affect hunger and eating, especially in chronic dieters. Schachter’s (1971)

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11.5  Portion size as food cue

externality theory of obese/normal differences in behavior suggested that differences in responsiveness to food cues are based on body weight. Rodin (1981) argued that people who are more externally responsive are particularly sensitive to food cues, and thus have to exercise restraint to avoid eating and gaining weight whenever attractive food cues are present. Herman and Polivy (1980) extended the externality theory to include restrained and unrestrained eaters, positing that obese people and chronic dieters are more responsive to salient external food cues than are non-obese individuals, possibly because of their focus on dieting and weight control. They argued that the correlation between external responsiveness and restraint could reflect the effects of restraining one’s intake rather than being the cause of the restraint. Whichever way the causal connection works, the findings we have just reviewed support the hypothesis that restrained eaters should be more responsive to salient, external food cues than are unrestrained eaters.

11.5  Portion size as   food cue Portion sizes in North America have grown larger in recent years (Young and Nestle, 2002; Nielsen and Popkin, 2003). Increased amounts of food mean increased food cues present during an eating episode, making it more difficult for people to restrict their eating because people tend to eat all of the food served to them (Herman, 2005). Experimental manipulations of portion size (see, for example, DiLiberti et al., 2004; Levitsky and Youn, 2004; Wansink et al., 2005) indicate that larger portions lead to increased eating. DiLiberti and colleagues (2004) manipulated the sizes of pasta portions served in a cafeteria, presenting a standard portion of 248 grams and a large portion of 377 grams (for the same price). The patrons who purchased the meal

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(which was surreptitiously weighed before and after the person ate it) answered questions about their perceptions of how appropriate the meal size was and how much they ate compared to their usual intake. The larger meal increased consumption of the pasta by 172 calories, which was 43 percent more than the intake of customers served the regular-sized portion. Regardless of meal size, perceived appropriateness was the same for both groups. Levitsky and Young (2004) gave students a normal portion size, 25 percent more or 50 percent more than they ate at a baseline buffet lunch, and found that the more they were served, the more they ate (of each of the four foods that comprised the lunch meal). Similarly, Wansink and colleagues (2005) manipulated soup portions by fashioning bowls of soup that refilled themselves imperceptibly while the participants were eating. Participants given the self-refilling bowls ate 73 percent more soup than did students who ate from normal bowls of the same size. Despite their increased intake, these participants felt no more sated, and believed that they had eaten the same amount as did those eating from the normal bowls. Wansink and Kim (2005) served popcorn in large versus medium-sized packages; the large portions increased consumption by 45.3 percent. Using stale and bad-tasting popcorn produced the same pattern; large portions increased consumption by 33.6 percent more than did medium-sized portions. The effects of portion sizes on consumption may reflect what has been called “unit bias” (Geier et al., 2006). People appear to believe that a unit of food represents the appropriate portion of that food, so if the unit is larger, people think that it is appropriate to eat more. For example, when serving themselves M&Ms with a large spoon, people take more than if using a small spoon; they will eat greater amounts of a large Tootsie Roll than of a small one. The unit bias may be a cultural norm, learned in childhood (Rolls et al., 2000). Regardless of the source of the bias, it appears that whatever the portion

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they are given, people see it as the appropriate amount to eat. So if the portion is larger, the perception of what is appropriate to eat is also larger. The size of the portion is thus a food cue telling the individual how much to eat.

11.6  The removal of   food cues The presence of food cues of various kinds clearly has a major influence on hunger and eating behaviors. Limiting or removing food cues should also affect eating. When only a limited range of foods is available over an extended period of time, liking for a given food decreases over repeated presentation of it (Siegel and Pilgrim, 1958); this effect is referred to as “monotony”. Raynor and colleagues (2006) allowed overweight adults access to only one snack food for 8 weeks, inducing monotony and lowering liking ratings for the initially wellliked snack. The participants also lost weight over the 8 weeks. Monotony thus acts in a manner opposite to the presence of abundant food cues, which stimulate hunger and increased eating. A large literature demonstrates that when only limited types of foods are available and food cues are thus restricted, eating declines (either in the short-term, in a single meal due to sensory-specific satiety, whereby the palatability of the food decreases as the food is being consumed, or over the long-term, due to the effects of monotony; see review by Raynor and Epstein, 2001). One example of the effects of limiting food cues involves US soldiers who received a repetitious diet of prepared food rations. The soldiers lost more than 10 pounds in a single month because they ate so little (Hirsch, 1995). When food is forbidden, avoided voluntarily or simply not available, this reduces or eliminates food cues in a different manner and leads to increased thoughts of food (see, for example, Keys et al., 1950; Mann and Ward, 2001). When

people are undergoing starvation and food cues are drastically limited, not only do hunger and the urge to eat increase, but thoughts of food and even fantasies about food (producing imagined food cues) also increase (Keys et al., 1950). The desire to eat a forbidden food also increases, although increased consumption of the forbidden foods does not necessarily occur (Karhunen et al., 1997; Mann and Ward, 2001). It has been argued, however, that when the prohibited food is eaten, disinhibition often occurs and leads to increased consumption (see, for example, Polivy, 1998; Herman and Polivy, 2004). In fact, dieters who restrict their intake of particular favored foods appear to be especially likely to overeat when their diets are broken (Polivy and Herman, 1985, 1987). A relative absence of food cues thus has an effect opposite to the presence of abundant food cues, reducing rather than increasing consumption. It has been proposed that the development of sensory-specific satiety and monotony effects may even be an evolutionary adaptation to allow humans to stretch out meager food supplies during periodic food shortages (Polivy and Herman, 2006). On the other hand, when the reduction in food cues is a voluntary restriction (as in dieting) or when the restriction results in real hunger or malnutrition, when food becomes available there is often a rebound and food is overeaten (Polivy and Herman, 1985, 1987).

11.7  Caloric restriction in animals and humans A growing body of research suggests that underfeeding, or caloric restriction (CR), leads to better health and greatly increases longevity in calorically-restricted animals (for a review, see Pinel et al., 2000). Animals whose caloric intake is severely restricted (i.e., consuming only 60–70 percent of ad libitum [AL] intake) exhibit a variety of physiological benefits. In addition to

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11.8 Is CR likely to be effective for humans?

greater longevity and improved general health, these animals have been shown to have delayed onset of disorders such as cancer, heart disease and diabetes (Pinel et al., 2000), and slowed agerelated declines in cognitive functioning (Patel and Finch, 2002). Such advantages of CR have been demonstrated in animals from earthworms to rodents to primates (monkeys) (Pinel et al., 2000). The many benefits of CR seen in animal studies have encouraged speculation that longer, healthier lives are possible for humans if they too severely restrict their food intake (see, for example, Walford, 2000; Delaney and Walford, 2005). Despite the promised benefits of CR, some researchers have pointed out that it is difficult to maintain a diet as spartan as is required for CR, and have concluded that “for most people, quality of life seems to be preferred to quantity of life” (Olshansky et al., 2002: 9). One question is whether physiological outcomes of practicing CR are truly superior to those of healthy controls who do not restrict their food intake. Unfortunately, such research on human CR is limited. Although the research that has been done does suggest that CR can have physiological benefits (see, for example, Fontana et al., 2004; Meyer et al., 2006), these studies are short-term, and are constrained by self-selection biases with respect to the CR participants (i.e., people who choose to undertake CR may be healthier to begin with). The main claim for CR, that it greatly increases longevity, has yet to be demonstrated in humans (Polivy et al., 2008).

11.8  Is CR likely to be effective for humans? Several researchers have raised questions about the likelihood that CR will prove to be beneficial for humans, especially for humans living outside of a laboratory environment (see, for example, Vitousek, 2004; Le Bourg and Rattan,

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2006). In a thorough review of the literature on CR, Vitousek and colleagues (2004a) observed that animals subjected to CR suffer some impairment in physical functions. For example, growth, reproductive development, and resistance to some stressors all show evidence of problems in response to CR. In addition, other deleterious effects of CR include cold intolerance, higher levels of stress hormones, lower levels of sex hormones, and postural hypotension. There are also psychological side effects, such as (not surprisingly) hunger, accompanied by obsessive thoughts about food and eating, emotionality/irritability, social withdrawal, and a loss of interest in sex. For those conversant with the literature on eating, these side effects may sound familiar, as they closely resemble the problems reported by Keys and colleagues in the famous World War II Minnesota human starvation experiment (Keys et al., 1950), which was effectively a CR experiment, performed on a group of conscientious objectors. The participants were asked to lose 25 percent of their body weight so that the effects of caloric deficits, such as those being experienced in wartorn Europe and Asia, could be studied. The participants had great difficulty achieving the desired weight loss – the experimenters ultimately had to accept a loss of only 24 percent of the initial weight – and exhibited many of the same negative symptoms described above with respect to current CR experiments. Moreover, when weight was restored and food was again freely available, these participants exhibited the behavioral symptom of binge eating. As Vitousek and colleagues (2004a) point out, people experiencing so many disturbing symptoms would normally be seen as unhealthy. This point is not mentioned in CR research, however, because the outcomes of interest in CR studies are only longevity and freedom from disease. The fact that CR animals excel on these two main outcomes allows proponents to see CR as advantageous and to ignore the negative (side) effects. Laboratory animals are not in a

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­ osition to complain, but humans may prove to p be more vocal in their objections. Vitousek and colleagues object to CR researchers’ focus on one set of benefits while ignoring a large set of negative effects (Vitousek et al., 2004a). Another problem pointed out by Vitousek and colleagues (2004a) in regard to CR in animals is that those undergoing food restriction live in cages with no other food available, leaving them no opportunity to abandon their restriction and eat normally, let alone overeat. Moreover, these animals are protected from the stresses of daily life; they do not need to find food or shelter, are not exposed to germs or variations in temperature or other meteorological conditions, and avoid all of the stresses of social and family life. It is unlikely that humans undergoing CR would be able to enjoy such an ability to focus only on their diets. Other work by Vitousek and colleagues (2004b) criticizes CR advocates for failing to even examine behavioral and psychological effects of CR while advocating this treatment for use in humans, whose psychological distress will be more difficult to ignore. As with most natural phenomena, there are individual differences in response to CR. Not all animals tolerate CR equally well, especially among primates. Some animals become seriously ill on the same restriction that is beneficial for others. Vitousek and colleagues (2004a, 2004b) draw a parallel between CR animals and patients suffering from anorexia nervosa (AN); as with primates undergoing CR, some AN patients tolerate the caloric deficit (inherent in their disorder) better than do others. Those less able to tolerate severe caloric restriction may be the patients who subsequently become bulimic, reminiscent of the starved Minnesota conscientious objectors (see, for example, Bulik et al., 2005) or starved prisoners of war in World War II (Polivy et al., 1994) who began binge eating once food was freely available. Other negative side effects of CR have been reported in humans. Some people who attempt CR on their own

develop serious cardiac irregularities, as has also been observed in some AN patients (Vitousek et al., 2004a). The health effects of CR in humans are thus not uniformly positive at all. It must also be remembered that CR was developed in the rarified and protected environment of the animal laboratory; applying the same restrictions to humans requires careful consideration of the very different environment of free-living people. The stresses of daily life are likely to be magnified when added to the stress of CR, which would probably lead to serious negative effects in humans. This concern may be irrelevant, however, as the evidence suggests that very few humans will be able to maintain CR for long enough periods to do much damage to themselves (Vitousek et al., 2004a). Not even the most ardent advocate of CR, Roy Walford, was able to sustain CR. He and seven of his colleagues in the Biosphere II project were forced drastically to reduce their intake because their rations were inadequate, leading them all to undergo CR for 2 years. Although these researchers believed they had benefited from their restriction and intended to continue to maintain a restricted diet, all eight quickly regained the weight they had lost while in the Biosphere as soon as the project terminated (Vitousek et al., 2004a).

11.9  Caloric restriction in the presence of food cues Living in a biosphere (like an animal living in a laboratory) may allow humans to eat minimal amounts while minimizing discomfort. Most humans, however, do not live in a biosphere. Our environment is filled with attractive food cues in all areas of our lives. Store windows, food carts on the street, coffee and pastries in the lobby of every building, television and other media advertisements – food cues are ubiquitous in developed societies. Unlike

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11.9  Caloric restriction in the presence of food cues

CR ­ animals in the laboratory, who experience almost no food cues at all in their foodless cages and are housed with equally deprived fellow animals, humans are virtually unable to escape food cues. As we have seen, food cues produce increased food consumption in animals (see, for example, Woods, 1991) and humans (Fedoroff et al., 1997, 2003), even when they are not food deprived and have already eaten (see, for example, Cornell et al., 1989). A study of two severely amnesic patients who were unable to remember events for more than a minute (Rozin et al., 1998) demonstrated quite graphically that food cues are more powerful than internal signals of satiety. These patients ate a normal lunch, and after everything was cleared away were given a second lunch 10–30 minutes later. They proceeded to eat the second meal, and after another 10–30 minutes were happy to begin eating a third meal (until it was taken away out of fear that it might make them ill). The presence of food cues induces eating (even after a full meal or meals), especially if one does not remember having already eaten. A weaker version of this effect has been demonstrated by Higgs, who has shown that people reminded of their lunch eaten a couple of hours earlier eat less of a snack than do people who have not been reminded of their recent lunch (Higgs, 2002). Most people do not eat multiple full meals one after another, despite the ever-present food cues, but humans today are eating more than they ever did (Brownell and Horgen, 2004). Thus, caloric restriction is a much greater challenge for a freeliving humans living in a world of ever-present food cues than it is for animals undergoing a CR regimen in a food-free laboratory environment (Polivy et al., 2008). Even for animals, the presence of food cues adds to the stress and difficulty of CR. We deliberately manipulated the presence of food cues during a 14-week CR study of laboratory rats (Coelho et al., 2009) to determine whether the presence or absence of food cues affected physiological and behavioral responses to CR. Two

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groups of rats (CR and ad libitum-fed) were tested; half of each group was exposed to attractive, inaccessible food cues, i.e. Fruit Loops cereal that could be seen and smelled (but not reached or eaten) in wire-mesh baskets attached to the top of their cages. The food-cue-exposed rats were found to have higher levels of corticosterone (a stress hormone), increased food consumption over 24 hours during the re-feeding period, and weighed more after the ad libitum feeding period than did the non-cued rats. During the deprivation period, however, the CR cued rats weighed less than the non-cued CR rats did, possibly reflecting their greater stress in response to the presence of food cues. These animals went on to gain weight much more rapidly after the deprivation period, when food was again available on an ad libitum basis, and soon weighed as much as (or slightly more than) their non-cued peers. The presence of food cues during caloric restriction is thus an added stressor. In addition, it appears that those who try CR and then give up are likely to overeat and gain weight once they are re-exposed to food cues, which, in our society, is pretty much inevitable. The effect of food deprivation on responses to food cues has been investigated in humans as well as rats. College students in a series of two studies were deprived of food for 0, 6 or 24 hours, and were shown emotion-inducing or food-related pictures (Drobes et al., 2001). Although the emotional pictures had no effect on the students, food deprivation influenced both self-reports and physiological reactions in both studies. Heart-rate responses to food pictures were elevated in acutely food-deprived and chronically binge-eating students, who also rated the pictures as pleasanter than did controls and habitual food-restricting students. These results were seen as indicative of heightened appetitive motivation in response to food cues in deprived and binge-eating participants. A review of the research on food cravings and “food addictions” came to a similar conclusion;

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attempts to restrict consumption of ­ preferred foods produce increased desire to eat the food when it is present and available for eating (Rogers and Smit, 2000).

11.10  Dieting in a world of food cues As we have seen, chronic dieters or restrained eaters are more susceptible than are non-dieters to increased appetite and overeating when confronted with food cues (Polivy et al., 2008). Even though restrained eaters are not attempting the prodigious caloric restriction advocated for increased longevity, they are still trying to eat less than they would like, and to avoid palatable but high-calorie foods that would make it harder for them to lose weight (Herman and Polivy, 1980). The literature over the past three decades indicates that restraining one’s eating in order to diet and lose weight is a difficult enterprise. Stunkard’s famous conclusion that “most obese persons will not enter treatment for obesity. Of those who enter treatment, most will not lose much weight and of those who do lose weight, most will regain it” (Stunkard, 1975: 196) is as apt today as it was over 30 years ago, except that many obese, overweight and even normal-­weight individuals try repeatedly to lose wei­ght, but are unable to achieve lasting success (Polivy and Herman, 2002). Even when people are successful at losing weight, the long-term outcome for the vast majority is that they regain the weight that they lost (Wilson, 2002). In fact, the literature on restrained eating makes it clear that restrained eaters are particularly susceptible to the lure of attractive food cues, which not only impairs their ability to diet successfully, but also actually contributes to their tendency to overeat (for a review, see Polivy, 1996). It is not readily apparent whether those who decide to diet are inherently more vulnerable to the temptation posed by attractive food cues

(Rodin, 1981), or whether dieting makes them more receptive to such cues (Heatherton and Polivy, 1992). Attempting to restrain one’s intake in the face of ever-present reminders of the food that one is sacrificing clearly contributes to the well-documented difficulty of dieting. Trying to restrain one’s eating in the presence of ubiquitous attractive food cues makes dieting not only difficult but stressful; the attractive food cues surrounding us make caloric restriction both impossible and unbearable. It is thus not surprising that in a society of plentiful food, dieters are generally unable to lose weight and obesity is on the rise. As Brownell has pointed out (Brownell and Horgen, 2004), the super­abundance of food in our society has created a “toxic environment” that encourages overeating and overweight, not caloric restriction and weight loss. Thus, although many people acknowledge the need for moderation, environmental conditions conduce toward excess.

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C H A P T E R

12 The Genetic Determinants of Ingestive Behavior: Sensory, Energy Homeostasis and Food Reward Aspects of Ingestive Behavior Karen M. Eny and Ahmed El-Sohemy Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, Canada

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12.1  Introduction The prevalent obesogenic environment has been blamed in fostering the spread of the obes­ ity epidemic over the past three decades (Hill et al., 2003). However, not all individuals res­ pond physiologically and behaviorally the same way to the over-abundant food supply and sed­ entary lifestyle (Speakman, 2004). Indeed, obes­ ity is a polygenic disorder, which results from imbalances between energy input and energy

Obesity Prevention: The Role of Brain and Society on Individual Behavior

expenditure (Speakman, 2004). Although envi­ ronmental factors may affect either side of the energy balance equation, understanding the genetic determinants of food intake may help in developing appropriate prevention and treat­ ment strategies to address this growing problem. Early studies examining the genetic contri­ bution to food intake phenotypes such as total energy and macronutrient intake, macronutrient selection and meal patterns, to more recent stud­ ies examining food neophobia, have measured

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heritability using family units or the comparison of monozygotic to dizygotic twins (Wade et al., 1981; Heller et al., 1988; Perusse et al., 1988; de Castro, 1993; Knaapila et al., 2007). Overall, the genetic component contributed between 11 and 70 percent of the variance, with this range of results possibly due to differences in the pheno­ type that was measured and how it was meas­ ured, as well as how shared environment was accounted for across studies (Wade et al., 1981; Heller et al., 1988; Perusse et al., 1988; de Castro, 1993; Rankinen and Bouchard, 2006). An alter­ native method to measuring the genetic com­ ponent of food intake is by using the candidate gene approach. Candidate genes are selected based on knowledge of underlying mechanisms related to food intake, which can occur at multi­ ple points along the entire food intake process, even before food is consumed. Genetic varia­ tions such as single nucleotide polymorphisms (SNPs) or copy number variants (CNVs) can be examined to determine the role of the gene in various food intake phenotypes (Kowalski, 2004; Martinez-Hernandez et al., 2007). There has been considerable progress in examining genes involved in appetite regulation pathways, predisposing individuals to both polygenic obesity as well as severe early-onset mono­ genic and Mendelian forms of obesity (Loos and Bouchard, 2003; Rankinen et al., 2006; Cecil et al., 2007; Martinez-Hernandez et al., 2007), in addition to studies examining individuals with eating disorders such as anorexia nervosa and bulimia (Bulik et al., 2007). These studies, which examined obesity and anorexia as outcome vari­ ables, have shed light on genetic variants likely involved in affecting ingestive behaviors. The present review will focus on the recent discoveries of genes associated with ingestive behavior phenotypes as well as other candi­ date genes to examine in the future. It will also underline the methodological considerations needed to progress our current understanding of how common genetic variations affect food intake.

Given that ingestive behavior is a product of both environmental and genetic interactions, examining genetic determinants of food intake should account for all stages of food intake, from the pre-consummatory stage to termination and satiety, and consider both the external environ­ mental and internal biological signals contribut­ ing to food intake (Berthoud, 2002, 2004). Watts proposed a comprehensive model which breaks down ingestive behavior into several stages: ini­ tiation, procurement, consummatory, termina­ tion and satiation (Watts, 2000; Berthoud, 2002). First, the “initiation” phase can result from exter­ nal factors such as the sight and smell of food when it is directly available. Alternatively, inter­ nal factors can stimulate food intake, which can be signals associated with the incentive value of a food or other energy homeostatic factors regulating food intake. Next, in the “procure­ ment” phase, reward systems including learn­ ing and memory processes direct the individual to acquire the food. This phase is likely not as prominent in today’s over-abundant environ­ ment, characterized by ubiquitous fast-food out­ lets and high consumer demand for ready-to-eat foods. The “consummatory” phase spans from the cephalic to the gastrointestinal stages of eval­ uating the sensory properties of the food as well as sensing the ingested food, which together form memories of either reward or aversion. Finally, ingestive behavior ends with “termina­ tion”, where circulating nutrients and hormones continue to be sensed in the absorptive and post-absorptive states. Termination lasts as long as satiety signals prevail over other competing external factors that will initiate the next cycle of food intake (Watts, 2000; Berthoud, 2002). Accordingly, the biological determinants of ingestive behavior can be categorized into sen­ sory, energy homeostatic and reward aspects of food intake. It is therefore important to measure ingestive behavior phenotypes using different tools and study designs that can com­ plement traditional dietary intake methods. In addition to measuring total energy intake and

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12.2  Sensory determinants of food intake

the breakdown of macronutrients consumed using dietary records, recalls or question­ naires, food-intake behavior inventory ques­ tionnaires and food-preference checklists can be used to measure behaviors and food prefer­ ences. Furthermore, acute studies that measure preference and those that measure food intake initiation versus termination are important for determining genetic factors involved in influ­ encing ingestive behaviors on the level of a sin­ gle meal or snack. A number of studies, which used different methodological approaches, have identified genetic variants involved in sensory perception, energy homeostasis and reward cir­ cuits as they affect ingestive behaviors. These studies will be reviewed below in order to offer examples of strategies to be used to help iden­ tify new candidate genes affecting food intake.

12.2  Sensory determinants   of food intake Sensory factors including sense of smell and taste lie at the interface between the biological and environmental determinants of food intake. They can therefore play an important role in initiating food intake as well as influen­cing the reward circuits involved in learning and mem­ ory which can drive the procurement phase of ingestive behavior (Berthoud, 2002). It has been determined that sensory receptor genes, in addi­ tion to immune response genes, are significantly over-represented among the genes shown to vary in copy numbers, with olfactory recep­ tor genes particularly variable in copy num­ bers among the sensory genes (Nozawa et al., 2007). The mean difference in copy numbers of olfactory receptor genes between two individu­ als was 10.9, with the most extreme difference between two individuals being of 49 more genes (Nozawa et al., 2007). Thus, differences in copy numbers among olfactory receptor genes may explain individual variations in perception of

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smell. In addition to CNVs, genetic variations resulting from SNPs may also contribute to interindividual differences in olfaction and, there­ fore, dietary behaviors. A recent study examined a common variation in two positions in the human odorant receptor gene OR7D4, result­ ing in amino acid changes, R88W and T133M, which are in complete linkage disequilibrium (Keller et al., 2007). In comparison to individuals homozygous for the major allele (RT/RT), those heterozygous for the variants (RT/WM) rated androstenone, an odorous steroid compound, to be less intense (Keller et al., 2007). In addition, when subjects were exposed to four different odorants, the heterozygous individuals were 42 percent less likely to describe androstenone as “sickening” from a list of 146 semantic descrip­ tors in comparison to the RT/RT homozygous individuals. Individuals who were heterozygous for the variant were also more likely to describe the smell of vanillin as “honey”, “sweet” and “vanilla” from the list of 146 semantic descrip­ tors (Keller et al., 2007). Another olfactory recep­ tor, OR13G1, has been associated with risk of myocardial infarction (MI), and has been hypothesized to predispose individuals to MI by affecting food preferences (Shiffman et al., 2005). Future studies such as the one by Keller and col­ leagues will be important to continue to iden­ tify the odorants that stimulate each of the 437 putative human odorant receptors (Zhang et al., 2007). Subsequent studies can then examine how variants in olfactory receptor genes involved in detecting palatable food-related aromas affect food preferences and intake. Early studies involving humans have docu­ mented wide individual differences in taste perception between individuals (Blakeslee and Salmon, 1935). Taste perception, which is influ­ enced by both genes and environment, may be the most important determinant shaping food preferences (Glanz et al., 1998; El-Sohemy et al., 2007; Garcia-Bailo et al., 2009). Facial responses from newborns in response to sweet and unpleasantly salty solutions (Berridge, 1996)

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suggest an innate genetic contribution to taste perception. As individuals age, other competing factors such as diminished taste ­acuity, and envi­ ronmental factors such as socio-­cultural influ­ ences, may contribute to overall taste perception and, therefore, food preferences (Mennella et al., 2005; Navarro-Allende et al., 2008). Thus, exam­ ining differences in taste perception may be important in understanding obesity risk in chil­ dren and young adults. Over the past decade there have been con­ siderable advances in identifying putative taste receptors involved in detecting the five tradi­ tional taste modalities; bitter, sweet, sour, salty and umami (Garcia-Bailo et al., 2009). In addi­ tion, evidence from CD36 knockout mice sug­ gests a sixth modality for “fat taste” (Laugerette et al., 2005). Examining genetic variation in taste receptors may identify individuals predisposed to obesity because of differences in food prefer­ ences. Thus far in humans, genetic variations in bitter taste have been the most extensively studied. Bitter taste receptors are encoded by the family of T2R taste receptors, consisting of approximately 25 members (Behrens and Meyerhof, 2006). The TAS2R38 gene is char­ acterized by three SNPs (A49P, V262A, and I296V) which make up two common haplo­ types, AVI and PAV, and TAS2R38 detects two bitter compounds called phenylthiocarbamide (PTC) and 6-n-propylthiouracil (PROP) (Kim et al., 2003; Wooding et al., 2004; Bufe et al., 2005; Drayna, 2005; El-Sohemy et al., 2007; Mennella et al., 2005). Carriers of the PAV haplotype have been classified as “tasters”, since they have a higher sensitivity to PTC or PROP in compari­ son to individuals homozygous for AVI (Kim et al., 2003; Wooding et al., 2004; Bufe et al., 2005; Drayna, 2005; El-Sohemy et al., 2007; Mennella et al., 2005). A study involving children aged 5–10 years reported that carriers of the PAV haplotype preferred sweeter-tasting foods as measured both by a forced-choice, paired com­ parison of sucrose solutions and by asking participants about their favorite cereals and

beverages (Mennella et al., 2005). However, there were no associations found between geno­ type and sweet food preferences among the mothers of the children (Mennella et al., 2005). The discrepancy between the parents and off­ spring may be due to diminishing taste acuity with increasing age, as well as other competing cultural or environmental influences overriding taste preferences (Mennella et al., 2005). Like the olfactory receptor gene OR13G1, TAS2R50 was also associated with risk of MI, which was also hypothesized to be due to differences in dietary preferences (Shiffman et al., 2005). Future stud­ ies investigating genetic variations in the T1R family encoding sweet and umami taste recep­ tors and CD36 may help identify individuals at risk of developing poor dietary food preferences and a predisposition to obesity.

12.3  Energy homeostasis pathways and food intake Multiple hormonal, metabolic and ­neural inputs converge in the hypothalamus and brain­ stem to regulate the energy homeostasis path­ ways which control several phases of ingestive behavior (Schwartz et al., 2000; Berthoud, 2002; Morton et al., 2006). Both short-term and longterm signals are involved in orchestrating ­ingestive behaviors, with gastrointestinal hor­ mones largely regulating food intake acutely, whereas insulin and leptin, which reflect adi­ pose tissue stores, provide long-term regula­ tion (Woods et al., 1998). Several gastrointestinal hormones have been identified over the past 40 years (Chaudhri et al., 2006). Polymorphisms in the genes that encode each of these are good candidates that may impact food intake and that have yet to be examined. Recently, the European Prospective Investigation into Cancer and Nutrition (EPIC) study identified four genetic variants in cholecystokinin (CCK), a gastrointes­ tinal hormone signaling satiety, to be associated

1.  From Brain to Behavior

12.3  Energy homeostasis pathways and food intake

with extreme meal size in a population of Dutch women that compared obese women, classified as extreme meal-size consumers, to randomly selected controls (de Krom et al., 2007). Leptin, which is also involved in reducing food intake, was also examined in this cohort of women. Variants in the leptin and leptin receptor genes were associated with extreme snacking (de Krom et al., 2007). Previous studies have been mainly successful at identifying rare varia­ tions in genes involved in the leptin pathway, associated with monogenic forms of obesity (Montague et al., 1997; Ravussin and Bouchard, 2000; Loos and Bouchard, 2003), while those examining polygenic forms of obesity as an out­ come variable have been equivocal (Paracchini et al., 2005). Although a more recent study that also examined the association between leptin, leptin receptor genes, and extreme snacking and meal size reported no association, the definition used to classify individuals as extreme consum­ ers was not clear (Bienertova-Vasku et al., 2008). The use of an extreme discordant phenotype approach in the EPIC study, which defined extreme meal size and snacking by examin­ ing the top fifth percentile of subjects display­ ing each phenotype, was effective as it had the power to detect common genetic variations underlying these extreme phenotypes (Nebert, 2000; de Krom et al., 2007). Thus, the extreme discordant phenotype approach offers a prelim­ inary step in identifying genetic variants that may contribute to risk of a more complex phe­ notype such as polygenic obesity (Nebert, 2000; de Krom et al., 2007). Examining genetic variations in transcription factors regulating the expression of the hormones involved in regulating food intake are also important candidates to consider. A genetic var­ iant resulting in a C to T substitution (C1431T) in the peroxisome proliferator-activated recep­ tor- (PPAR-), which is involved in regulating leptin gene transcription, was associated with poor caloric compensation at lunch, 90 min­ utes after exposure to three calorically different

153

mid-morning snacks, among children aged between 4 and 10 years who were carriers of the T allele (Cecil et al., 2007). Thus, using a pre-load study design approach offers a valuable way to quantify food intake regulation phenotypes in young children, which may be a more effective way of measuring food intake compared to other methods such as dietary recall or questionnaires in children. Similar to leptin, insulin signaling in the brain decreases food intake (Schwartz et al., 2000). Among Dutch women from the EPIC study which examined a variation in the TUB gene, a downstream transcription factor and/or an adaptor molecule involved in insulin signal­ ing in the hypothalamus was associated with a lower consumption of energy from fat and a higher consumption of energy from mono- and disaccharides (van Vliet-Ostaptchouk et al., 2008). Glycemic load was also higher among individuals with the minor allele at the same locus as well as at a second locus, which are both in non-coding regions of the gene (van VlietOstaptchouk et al., 2008). In addition to hormonal signaling in the brain, nutrient sensing pathways may play a role in regulating food intake (Cota et al., 2007). A genetic variation in the glucose transporter type 2 (GLUT2) was found to be associated with a higher consumption of sugars in a cohort of obese individuals with early type 2 diabetes, as well as in a lean, diabetes-free cohort of young adults (Eny et al., 2008). This observation was reproduced both within the first cohort and between the two cohorts using two sets of 3-day food records completed 2 weeks apart in the first cohort, and a 1-month food frequency ques­ tionnaire (FFQ) in the second cohort. Since no other macronutrients were consumed in higher amounts, results from this study suggested that GLUT2 is involved in glucose sensing to affect habitual sugar consumption (Eny et al., 2008). In another study, the 1291C  G polymor­ phism in the 2a-adrenoreceptor (ADRA2A) gene, which is known to affect fasting glucose levels and insulin ­secretion, was associated with

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c­ onsumption of sweet food and sour milk prod­ ucts among children in grades 3 and 9 in Estonia (Maestu et al., 2007). Furthermore, children homozygous for the G allele had lower fast­ ing glucose concentrations (Maestu et al., 2007). Therefore, the higher consumption of sweet food products observed among those with the GG genotype may be in response to sensing low fasting blood glucose. There has been growing evidence from animal models that fatty-acid sensing also plays a role in energy homeostasis (He et al., 2006). The carnitine palmitoyltrans­ ferase I (CPT1) gene is an interesting candidate to determine whether differences in dietary fat consumption exist, since CPT1 is the rate lim­ iting enzyme for the entry of long-chain fatty acyl-CoAs in the mitochondria (He et al., 2006). Genetic variants of CPT1 have previously been implicated in modifying indices of obesity in response to dietary fat consumption (Robitaille et al., 2007). However, it is not known whether variants of this gene affect fat intake. Specialized neurons in the brain respond to the multiple inputs from hormones, nutrients and nerves. This results in either increased or decreased expression of neuropeptides affecting food intake (Schwartz et al., 2000). In animals, neuropeptide Y (NPY) acts as a potent inducer of food intake (Stanley and Leibowitz, 1985), but the effect in humans is unclear. A genetic variant in prepro-NPY was not associated with increased food intake in children aged between 1 and 9 years, as measured by parents and daycare staff recording consumption for 4 days twice per year, yet was associated with increased fasting triglycerides in boys aged 5, 7 and 9 years (Karvonen et al., 2006). Thus, the Leu7Pro variant examined (Karvonen et al., 2006) may represent a functional locus to be examined in future studies measuring actual food intake with a pre-load study approach in children as described previously (Cecil et al., 2007). Like NPY, the agouti-related protein (AGRP), which is another orexigenic neuropeptide, carries two ethnic-specific polymorphisms, one found

only in Caucasians (Ala67Thr) and one found only among African-Americans (38C  T) (Loos et al., 2005). The Ala67Thr polymorphism was associated with consuming a diet that was low in fat and high in carbohydrates as a percent­ age of total energy intake in Caucasians (Loos et al., 2005). Among African-Americans, the 38C  T polymorphism was observed to be associated with a lower percent of energy from protein consumed (Loos et al., 2005). The obser­ vation for differences in percentage of energy from macronutrients suggests that AGRP affects macro­nutrient selection preference rather than absolute intake, which is hypothesized to be mediated by the interaction of AGRP with the opioid system (Loos et al., 2005). It is possible that the discrepancy in macronutrient selection observed between the ethnic-specific genotypes may be due to other cultural or genetic differ­ ences in taste preference between the two eth­ nicities (Loos et al., 2005). Thus far, there has been extensive research investigating the role of rare variants of the ano­ rexigenic neuropeptide pro-opiomelano­cortin (POMC) and its related melanocortin recep­ tor genes and risk of severe obesity (Loos and Bouchard, 2003; Adan et al., 2006; Oswal and Yeo, 2007). Using a genome-wide linkage map­ ping approach was useful in detecting a gene locus associated with dietary intake using an FFQ among 816 participants of the San Antonio Heart Study (Cai et al., 2004). The locus identi­ fied was found on chromosome 2p22, which harbors the POMC gene, and was associated with total calories, protein, total fat, saturated fat, polyunsaturated fat and monounsaturated fat intake, with saturated fat having the highest linkage score (Cai et al., 2004). Yet, upon geno­ typing for two variants in the POMC gene, no association was reported with saturated fat intake, which was the only macronutrient exam­ ined in this follow-up analysis (Cai et al., 2004). Similarly, a genome-wide linkage study involv­ ing Hispanic children aged between 4 and 19 years identified a marker on chromosome 18 to

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12.4  Reward circuits and food intake

be associated with time participating in physical activity, carbohydrate intake, and percentage of energy from carbohydrate intake, as measured using two multiple-pass 24-hour recalls admin­ istered by dietitians and assisted by mothers when children were under 7 (Cai et al., 2006). The region identified on chromosome 18 har­ bors the melanocortin 4 receptor (MC4R) gene as well as another positional candidate gene, gastrin-releasing peptide, which is released by the gastrointestinal tract and also inhibits food intake by signaling in the brain (Cai et al., 2006). Consistent with Cai et al. (2006), a recent study examining the most common variant in the MC4R gene (V103I) reported that individu­ als carrying the 103I allele were more likely to be high carbohydrate consumers (P  0.06) as measured using a short qualitative FFQ among 7888 adults (Heid et al., 2008). This higher carbo­ hydrate consumption was examined as a poten­ tial factor in mediating the protective effect of this polymorphism on features of the metabolic syndrome, because the same variant was asso­ ciated with higher HDL-C, and lower waist cir­ cumference and HbA1c (Heid et al., 2008).

12.4  Reward circuits   and food intake Given that the drive to eat has been described to be one of the most powerful urges of human behavior (Del Parigi et al., 2003), coupled with the ubiquitous exposure to palatable foods, neural circuits involved in food reward and addiction have been proposed to possibly override energy homeostatic controls of food intake (ErlansonAlbertsson, 2005; Palmiter, 2007). Dopamine, serotonin and opiates have all been implicated in mediating the rewarding effect of palatable food (Erlanson-Albertsson, 2005). A Taq1 A1 variation which resides in a gene downstream from the 3 end of the dopamine D2 receptor (DRD2*A1) has been widely examined as a marker for genetic

155

variation in the DRD2 gene as it relates to addic­ tive behaviors (Neville et al., 2004), including studies investigating food reward. The DRD2*A1 was associated with food reinforcement among smokers undergoing smoking cessation treated with placebo versus those treated with bupro­ pion, a dopamine reuptake inhibitor (Lerman et al., 2004). Food reinforcement was assessed by having each subject choose either 100 g of their favorite snack food over a $1 alternative as a reward for completing a task, consisting of pushing a button 20 times, with increasing difficulty as the subject progressed (Lerman et al., 2004). A similar association was observed between DRD2 genotype and food reinforcement behavior among non-smoking obese subjects (Epstein et al., 2007). Furthermore, acute food intake was found to be higher among carriers of the DRD2*A1 allele who were classified as being high in food reinforcement as measured over a taste-test panel period in comparison to those without the DRD2*A1 allele or those low in food reinforcement with or without the DRD2*A1 allele (Epstein et al., 2004, 2007). The serotonergic pathway has also been examined in gene association studies relat­ ing to ingestive behaviors, and is thought to act as a satiety signal involved in food reward (Erlanson-Albertsson, 2005). Among overweight individuals a 1438G  A polymorphism in the serotonin receptor 5-HT2A (5-hydroxytryptamine) was associated with lower energy intake (Aubert et al., 2000). The same variant was associated with a lower intake of energy, total fat, monounsaturated fat, saturated fat and percent of energy from fat among children and adolescents aged 10–20 years (Herbeth et al., 2005). Diet was assessed using 3-day food records, which were checked and completed by a dietitian using photographs with three differ­ ent portion sizes (Herbeth et al., 2005). Finally, a third study involving elderly individuals reported that the T102C polymorphism in the 5-HT2A receptor gene was associated with a higher consumption of all essential amino acids

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and beef among individuals with the TT geno­ type (Prado-Lima et al., 2006). Since the essen­ tial amino acid tryptophan acts as a precursor for serotonin production (Young, 1996), this study suggests that TT individuals may have lower receptor activity and, therefore, signal for greater serotonin production, increasing the demand for tryptophan in the body. These observations correspond with the studies asso­ ciated with lower energy intake among indi­ viduals homozygous for the minor allele for the 1438G  A variant (Aubert et al., 2000; Herbeth et al., 2005), which is in linkage with the T102C variant (Prado-Lima et al., 2006).

12.5  Conclusions Using the candidate gene approach has been useful for identifying genes involved in inges­ tive behaviors. Genome-wide linkage scans among family pedigrees offer another useful strategy, as shown by studies that have identi­ fied POMC, melanocortin 4 receptor and ­gastrinreleasing peptide genes as being associated with dietary intake (Cai et al., 2004, 2006). This approach has the ability to identify new genetic loci involved in food intake phenotypes such as eating behaviors, or energy and nutrient intake or preference (Steinle et al., 2002; Bouchard et al., 2004; Collaku et al., 2004; Keskitalo et al., 2007). Recently, frequent use of sweet foods has been mapped to chromosome 16, which is currently not known to harbor any genes related to sugar consumption, yet contains three genes that have an unidentified function (Keskitalo et al., 2007). Genome-wide linkage studies as well as genome-wide association studies (GWAS) are beneficial in that no prior information regard­ ing gene function is required, and therefore they can be used in conjunction with the candi­ date gene approach to identify new gene targets (Comuzzie, 2004; Kowalski, 2004; Hirschhorn and Daly, 2005; Martinez-Hernandez et al., 2007).

For example, a variant in the apolipoprotein A-II (APOA2) gene, an HDL-related protein, was reported to be associated with total energy, total fat and protein intake (Corella et al., 2007). Correspondingly, an earlier genome-wide link­ age study identified a strong linkage for dietary energy and fat intake on chromosome 1p21.2, which places APOA2 as a potential candidate gene, although it was not mentioned in that study (Collaku et al., 2004). The fat mass and obesity-associated (FTO) gene was also first dis­ covered by GWAS (Frayling et al., 2007), and was subsequently associated with increased energy intake among children in two populations (Cecil et al., 2008; Timpson et al., 2008). Future research should consider utilizing the GWAS approach, because, unlike genome-wide linkage studies, families are not required and GWAS technology offers finer mapping in order to identify poten­ tial causal genes within the locus identified (Hirschhorn and Daly, 2005). Genetic variants not only shift the preference for tastes of food that are calorie-rich, but also lead to increased consumption. Therefore, in order to capture a comprehensive perspective of ingestive behavior, future studies could examine interactions between genes involved in sensory aspects of food intake, energy homeostatic path­ ways and reward circuits driving food intake, since each of these pathways might not function in isolation. Indeed, the melanocortin 4 recep­ tor, which is involved in energy homeostasis, is also expressed in regions of the brain involved in food reward (Huang et al., 2003). Discovering genetic variants involved in ingestive behaviors will ultimately help clinicians identify individu­ als predisposed to certain food intake pheno­ types that may increase the risk of obesity or other food intake related behaviors (Figure 12.1). Several studies have examined genetic variants that modify an individual’s response to a behavioral intervention of diet and/or exer­ cise (Fogelholm et al., 1998; Shiwaku et al., 2003; Masuo et al., 2005). Thus, once a genetic vari­ ant has been established to be associated with

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References

Sensory perception

Energy homeostasis

Food reward circutis

Ingestive behaviors

Energy expenditu re Food intake

Obesity risk

Figure 12.1  Genetic variation in sensory perception, energy homeostasis and food reward circuit pathways may i­ nfluence ingestive behaviors, favoring increased food intake. An imbalance between energy expenditure and energy intake results in weight gain and risk of obesity.

a particular ingestive behavior, research efforts should aim to determine what foods or dietary pattern help the individual control their predis­ posed ingestive behaviors. Ultimately, identify­ ing genetic variants involved in food intake has the potential to assist clinicians in understanding an individual’s behavior from a biological per­ spective, and help plan an appropriate strategy to prevent or treat obesity in the future as we move towards a more personalized medicine.

Acknowledgments This work was supported by the Advanced Foods and Materials Network (AFMNet). Karen Eny is a recipient of a Natural Sciences and Engineering Research Council of Canada Julie Payette Research Scholarship and a Canadian Institutes of Health Research Training grant. Ahmed El-Sohemy holds a Canada Research Chair in Nutrigenomics.

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1.  From Brain to Behavior

References

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1.  From Brain to Behavior

C H A P T E R

13 Development of Human Learned Flavor Likes and Dislikes Martin R. Yeomans School of Psychology, University of Sussex, Brighton, UK

o u tl i n e 13.1 Introduction

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13.2 Understanding Flavor Perception

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13.3 Why Innate Flavor-liking is Rare

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13.4 Flavor-preference Learning 13.4.1 Mere Exposure and the Importance of Familiarity 13.4.2 Flavor–Consequence Learning 13.4.3 Flavor–Flavor Models of Evaluative Conditioning 13.4.4 Social Acquisition of Flavor-liking

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13.6 Liking and Intake: The Role of Palatability in Overeating

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13.8 Individual Differences in Learning

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13.1  Introduction The human diet is extremely varied, and humans have the ability to recognize valuable sources of nutrition while avoiding items which are poisonous. Critical to this ability is an appetite control system that facilitates the development of liking for the flavor of foods which provide nutritional or other benefits, ambivalence to items with

Obesity Prevention: The Role of Brain and Society on Individual Behavior

13.5 Different Learning Mechanisms Interact to Enhance Flavor-liking

little or no benefit, and dislike of items which are harmful. These acquired likes and dislikes guide food choice, and in part determine the amount we consume. As will be detailed in this chapter, since it is clear that we rapidly acquire a liking for energy-dense foods, the system which underlies flavor preference development may also contribute to overconsumption and consequent risk of obesity. This chapter therefore explores current

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theories of how we acquire flavor likes and dislikes, examines the impact of these changes on eating behavior, and considers how individual differences in the ability to acquire such preferences may be a risk factor for development of obesity.

13.2  Understanding flavor perception In order to understand how we acquire liking for flavors, it is first necessary to outline current theories of how our experience of flavor arises. Unlike primary senses such as taste, smell, hearing and vision, flavor is a higher-level construct arising from the integration of multiple sensory inputs relating to the experience of food or drink in the mouth. There is now increasing understanding of how this integration occurs both at the phenomenological and the neural level (for recent reviews, see Small and Prescott, 2005; Rolls, 2006; Auvray and Spence, 2008). In brief, primary sensory characteristics are sensed by peripheral sensors, for example by taste receptors on the tongue and buccal cavity which are tuned to detect five taste qualities (Chandrashekar et al., 2006), and food-related odors stimuli by the olfactory bulb. These primary sensory qualities activate distinct areas of the cortex (primary cortex for taste, smell, etc.). These separate sensory qualities are then integ­ rated in different areas of the cortex, which can be thought of as secondary taste/smell cortex, including the orbitofrontal cortex. Critical to the current discussion is the observation that the palatability of this multi-sensory flavor percept appears also to be encoded in areas of the orbitofrontal cortex (O’Doherty et al., 2001). However, we are far from a full understanding of the neural processes underlying flavor perception: for example, how the brain knows that these separate sensory inputs all relate to the same external food or drink stimulus remains unclear

(Small, 2008), and we do not know how the types of flavor learning described in the main body of this discussion modify these neural responses. However, the general acceptance that flavor involves complex multi-sensory integration in the brain is crucial to understanding how flavor-liking may be acquired. An important issue is how the process of integration of sensory qualities into our perception of flavor and the processes underlying the hedonic experience of that flavor are related. In theory, three different possible relationships may exist. The first is that the processing which determines flavor perception also determines flavor-liking. However, although there have been many studies published which appear to assume that this is the case, there is a wealth of evidence that these processes are separate. For example, it is possible to pharmacologically modify liking for food flavors by blockade of opioid receptors in the brain, yet this blockade has no impact on either the ability to sense the flavor or indeed to alter flavor quality (see Yeomans and Gray, 2002). More importantly, in relation to this discussion, there is a wealth of evidence, reviewed later, that experience can modify flavor-liking without altering flavor perception. How, then, do the separate pro­cesses underlying liking and flavor relate to each other? The second possible relationship would be one where these represent neuronally distinct processes, and in non-human animals this is supported by evidence of distinct sensory and hedonic pathways in the brain (for review, see Sewards, 2004). However, in humans this seems a less plausible model, as the areas in the brain that appear critical to flavor-liking are downstream of those areas involved in flavor perception (see Rolls, 2006). This suggests that the most plausible model is one where the brain integrates signals relating to the sensory quality of an ingested food or drink and determines whether this is a liked or disliked experience – a decision that has major effects on behavior through rapid rejection of disliked items but

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13.3  Why innate flavor-liking is rare

acceptance and enjoyment in ingesting liked items. The question that then follows is: why are some flavors liked and others disliked?

13.3  Why innate flavor-liking is rare If the human diet were restricted to a limited set of foods, then there would be scope for the evolution of clearly defined genetically pre-determined flavor likes and dislikes. However, a feature of the adaptive success of humans is our ability to exploit an extraordinarily wide range of possible sources of nutrition, with minimal evidence for genetic flavor preferences. Indeed, genetically pre-determined flavor preferences are extremely rare across all species. The example of a genetic flavor preference in some populations of garter snakes for their most common prey, the banana slug (Arnold, 1977), illustrates why such preferences are rare. Coastal garter snakes eat banana slugs, but mountain garter snakes avoid them. Newly hatched snakes from coastal parents showed clear appetitive responses when presented with banana-slug odor, whereas naive offspring of mountain snakes showed little response. The rarity of this genetic preference can be attributed to two features of garter-snake biology: a limited diet, and a distinct flavor component of their primary prey. In contrast, the human diet is highly varied, and although many foods have distinctive flavors, the complexity and variety of both the sensory stimuli and source of nutrition offers little opportunity for innate flavor-liking to have evolved in the way that it has in garter snakes. A real difficulty in assessing innate components to human flavor likes and dislikes is the problem of providing unequivocal evidence that flavor-liking is unlearned, particularly now that there is clear evidence that flavor exposure in utero can impact on later food-liking (Mennella

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et al., 2004). The most widely cited approach has been to examine responses of newborn babies to elements of widely liked and disliked flavors, particularly primary tastants. For example, the application of sweet tastants to the tongues of newborn babies (Desor et al., 1973; Steiner, 1979; Berridge, 2000) found clear acceptance of sweet tastes, with positive facial responses consistent with flavor-liking, whereas the responses to bitter tastants were characteristic of a strong aversive response. A counter-argument might be that such sweet liking was acquired in utero, although this is countered by findings of acceptance of sweet tastes by premature babies (Maone et al., 1990). Liking for sweet tastes has been explained in terms of the reliable relationship in nature between a sweet taste and safe, nutritious foods rich in sugars (Hladik et al., 2002). Mammals have specific sweet-taste receptors (Matsunami et al., 2000), and the molecular structure of the vertebrate sweet-taste receptor has been conserved across species from fishes to humans, with only rare examples of sweet-insensitive species such as chickens (Shi and Zhang, 2006). Further evidence that sweet-taste preferences are genetically predetermined comes from breeding studies that have successfully reared separate lines of sweet-preferring and sweet-disliking rats (Bachmanov et al., 2002). The other reliable evidence for an innate component of hedonic evaluation of flavors is a dislike for bitter tastes, which has been interpreted as an innate avoidance of items that have the potential to be poisonous (Fischer et al., 2005; Behrens and Meyerhof, 2006), since most poisons have bitter tastes. There have been striking advancements in our understanding of bittertaste perception, with the identification of 25 different bitter-taste receptor genes to date in humans (Behrens and Meyerhof, 2006), which is consistent with the idea that bitter-taste aversion relates to avoidance of poisonous molecules. However, these initial aversive responses can be reversed if ingestion of bitter-tasting items fails to lead to illness, or leads to a ­ positive

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e­ xperience such as the effects of alcohol or caffeine, as discussed later. It is striking how little genetics predisposes humans to like or dislike food flavors, but it appears that evolution has favored the development of complex learning systems which allow us to assess potential foods for the nutritional content, and rapidly to acquire liking for potential foods which deliver useful nutrients and a profound dislike for flavors which lead to illness. The next set of questions thus relates to the nature of the learning processes that are involved in these processes.

13.4  Flavor-preference learning Among the myriad potential explanations for how flavor likes and dislikes may be acquired, four theoretical approaches to our understanding of flavor learning have been widely supported and are the focus of this review.

13.4.1  Mere exposure and the importance of familiarity One of the earliest learning concepts to be discussed in relation to acquired flavor-liking in humans was mere exposure (Zajonc, 1968), which essentially argues that repeated exposure to any stimulus in any modality results in increased liking through familiarity. Although specific studies examining how mere exposure alone may lead to flavor-liking have been limited in number and scope (Pliner, 1982; Crandall, 1984; Stevenson and Yeomans, 1995), the mere exposure concept remains a useful description of familiarity effects. Attempts to explain how mere exposure works make reference to reduced neophobia, or other explanations such as opponent-process affective responses (Solomon and Corbit, 1974). However, although overcoming

neophobia may be an important element in trying to direct food preferences – for example, in children faced with unfamiliar flavors (Birch and Marlin, 1982; Pliner, 1982) – this concept does not offer any explanation for why the flavors of certain classes of foods, most notably those high in fat and sugar, are usually the most liked items in the human diet.

13.4.2  Flavor–consequence learning A seminal discovery in our understanding of how humans and other animals may develop flavor likes and dislikes was the observation that pairing of a novel flavor with subsequent gastric illness leads to a profound and enduring aversive reaction to the flavor. This phenomenon was initially labeled conditioned taste aversion (CTA), but with increasing evidence that this learning could be supported by non-­gustatory flavor components (Capaldi et al., 2004) it is now better characterized as conditioned flavor aversion (CFA). Once CFA had been discovered, researchers speculated that just as a flavor which predicted illness would become an aversive (disliked) stimulus, so a flavor that reliably predicted a safe source of nutrition should lead to conditioned flavor-liking (Rozin and Kalat, 1971; Booth, 1985), with the general learning pro­cess underlying these types of changes classified as flavor-consequence learning (FCL). The ideas behind FCL are heavily influenced by broader concepts in associative learning, with the primary association being between the perceived flavor of the ingested food or drink (acting as the Pavlovian conditioned stimulus, CS) and the post-ingestive effects of the food or drink (the Pavlovian unconditioned stimulus, US). How flavor and consequence come to be associated in FCL is summarized in Figure 13.1a. Critical to this chapter is consideration of whether such learning could explain our avidity for energy-dense food, since it is wellknown that average ratings of liking for flavors

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13.4  Flavor-preference learning

(a) Flavor-consequence learning Conditioned stimulus (CS) Flavor

Repeat Pairing

Unconditioned stimulus (US) Post-ingestive effect of food/drink

(b) Flavor-flavor learning Conditioned stimulus (CS) Novel flavor component

Repeat pairing

Unconditioned stimulus (US) Liked/disliked flavor component

Acquired response

Normal response

Acquired response

Normal Response

Conditioned Response (CR) Altered flavor liking

Unconditioned Response (UR) Hedonically-significant consequence (e.g. illness, satiation, drug effects)

Conditioned Response (CR) Altered flavor liking

Unconditioned Response (UR) Positive or negative affective reaction

Figure 13.1  The associative substructure underlying (a) flavor–consequence learning and (b) flavor–flavor evaluative conditioning. Source: Adapted from Yeomans (2006), with permission.

correlates positively with energy density (Holt et al., 1995; Drewnowski, 1998; Yeomans et al., 2004a), consis­tent with predictions from FCL. Once research had established the robustness of CFA, studies explored whether consumption of energy-containing foods could come to enhance flavor-liking in humans and preference in animals (Sclafani, 1999; for reviews, see Capaldi, 1992; Gibson and Brunstrom, 2007). There is now a large body of evidence that exposure of animals and humans to novel flavors paired with consumption, or gastric delivery, of energy in the form of fat, carbohydrate or protein leads to enhanced preference for the nutrientassociated flavors. In animals, the evidence for these types of associations is particularly strong, with clear evidence of acquired flavor preferences for flavor CS paired with sucrose (Fedorchak and Bolles, 1987; Capaldi et al., 1994; Harris et al., 2000; Sclafani, 2002), glucose (Myers and Sclafani, 2001a), starch (Elizalde and Sclafani, 1988; Sclafani and Nissenbaum, 1988; Ramirez, 1994), fats (Lucas and Sclafani, 1989), protein (Delamater et al., 2006) and alcohol (Ackroff and Sclafani, 2001, 2002, 2003). The most convincing studies, based on the extensive work of Sclafani’s group, pairs consumption of one of two non-nutritive flavored drinks with intra-gastric nutrient infusion, and the second

i­ nfusion with water, resulting in a profound and enduring preference for the nutrient-paired flavor (see, for example, Elizalde and Sclafani, 1990; Azzara and Sclafani, 1998; Myers and Sclafani, 2001a, 2001b; Sclafani, 2002; Yiin et al., 2005; Ackroff and Sclafani, 2006). In humans, an increased interest in the importance of flavor-liking as a cause of overeating has resulted in many studies that have shown clear increases in liking for novel flavors which have been associated with ingestion of nutrients in humans (Appleton et al., 2006; Brunstrom and Mitchell, 2007; Mobini et al., 2007; Yeomans et al., 2005a, 2008a, 2009a), adding to a small but important older literature (Booth et al., 1982; Birch et al., 1990; Kern et al., 1993). The increased preference by children for the flavor of yoghurt that has been consumed in a high-fat version relative to a second flavor always experienced as a low-fat, low-energy version illustrates the effects of FCL with nutrient reinforcers (Figure 13.2). Note that not only do children come to prefer the high-fat flavor in spite of their ignorance of the difference in nutritional content, but also their expression of this liking is stronger when hungry than when satiated, an observation consistent with expression of acquired liking induced through FCL with sucrose as reinforcer in adults (Mobini et al., 2007). As with the animal studies,

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Preference ranking more preferred >

5 4 3 2 1

6 weeks conditioning

Hungry Pre-treatment

Hungry Full Post-treatment

Hungry Delayed

Figure 13.2  Changes in preference for the flavors of yoghurts consumed in low-fat (striped column) or high-fat (solid column) form by children. Source: Adapted from Kern et al. (1993), with permission.

acquired liking is not restricted to one source of nutrients, with studies to date showing changes both with carbohydrate and fat as energy sources. These data fit well with the broad observation that energy-dense foods (that is, foods rich in the major macronutrients) are generally the most liked (Stubbs and Whybrow, 2004). Further evidence of the broad significance of FCL as an explanation for acquired flavor-liking can be seen in the strength of acquired liking for the flavor of drinks that contain substances with psychoactive consequences, such as alcohol and caffeine – preferences that counteract the normal aversive reaction to the bitter taste of caffeine and alcohol. In humans, a wealth of research has shown clear and enduring increases in liking for the flavor of drinks that have been paired with ingestion of caffeine (see, for example, Rogers et al., 1995; Yeomans et al., 1998, 2005b; Tinley et al., 2003; Dack and Reed, 2008), showing that the effects of FCL extend beyond an ability to acquire liking for the flavors of nutrient-dense foods.

13.4.3  Flavor–flavor models of evaluative conditioning Evaluative conditioning (EC) involves transfer of affective value from a known liked or

­ isliked stimulus to a second, novel ­ stimulus d (Field and Davey, 1999; De Houwer et al., 2001). In the case of flavor-based learning, such changes in liking are usually interpreted within an associative learning framework based on the principles of Pavlovian conditioning, where repeated pairing of a previously hedonically neutral flavor or flavor component (interpreted as a Pavlovian CS) with a second flavor or flavor element that is already liked or disliked (interpreted as the UCS) results in transfer of liking to the previously neutral flavor CS (see Figure 13.1b). There are now many published examples of both acquired flavor-disliking (Baeyens et al., 1990; Dickinson and Brown, 2007; Wardle et al., 2007) and -liking (Zellner et al., 1983; Yeomans et al., 2006; Brunstrom and Fletcher, 2008) based on laboratory-based studies of flavor–flavor associations in humans. In terms of understanding the nature of ­flavor– flavor learning, one variation of flavor–flavor learning, where the CS is a food-related odor and the US is a taste (Stevenson et al., 1995, 1998, 2000a; Stevenson, 2003; Stevenson and Boakes, 2004), has proved particularly valuable since it helps define the different flavor elements more clearly than do studies that use more mixed flavors as CS. The typical design of these olfactory conditioning studies is relatively simple: odors are first presented orthonasally (i.e., sniffed) on their own, and evaluations of various sensory characteristics, including those using gustatory descriptors (e.g., sweetness, sourness, ­ saltiness etc), along with hedonic ratings, are made. The odor is then experienced repeatedly paired with a taste stimulus (e.g., 10% sucrose to give a sweet US) in a number of disguised training ­trials. Finally, the odor is re-evaluated orthonasally. The consistent finding was that ratings of the degree to which the odor possessed the sensory dimension related to the trained US increased. For example, when an odor was paired with sucrose, the rated sweetness of the odor post-training was consistently higher than it was before training started (Stevenson et al.,

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13.4.4  Social acquisition of flavor-liking Social learning may contribute to acquisition of flavor-liking in two different ways. Social facilitation refers to modified behavior due to

Change in rated pleasantness

Pleasantness 50.00 40.00 30.00 20.00 10.00 0.00 –10.00 –20.00

Sucrose

Water

US during training

(a)

Sweetness Change in odor sweetness

1995, 1998), even though the sucrose was not present when odors were rated orthonasally. EC would predict that odors paired with sweet tastes would become liked, but whereas some studies report increased liking for sweet-paired odors (Yeomans and Mobini, 2006; Yeomans et al., 2006, 2007), many of the earlier studies failed to find these effects (Stevenson et al., 1995, 1998, 2000a, 2000b). However, increased liking would only be predicted if the individual under test actually rated the sweet US as pleasant, and since there are individual differences in rated evaluation of sweet tastes (Looy et al., 1992; Looy and Weingarten, 1992), a simple explanation for the variability in these findings is that those studies that failed to find increased liking did not have sufficient sweet-likers to support this change – a suggestion supported by clear findings of increased liking when participants are preselected to be sweet-likers (Yeomans and Mobini, 2006; Yeomans et al., 2006, 2008a, 2009b, 2009c). This is illustrated in Figure 13.3, where changes in rated pleasantness and sweetness are shown in relation to classification of sweet-liking. Note that while in Figure 13.3b acquired sweetness was seen regardless of liker status, changes in odor pleasantness (Figure 13.3a) depended critically on hedonic evaluation of the 10% sucrose solution used during odor–taste pairing. Overall, development of a dislike for flavor components consistently paired with an aversive flavor US appears more robust than does acquired liking for a flavor paired with a second liked flavor element. Flavor–flavor learning, therefore, appears an important element of human flavor-preference development, although, as with FCL, more research is needed to determine the full scope and importance of flavor–flavor associations.

50.00 40.00 30.00 20.00 10.00 0.00 –10.00 –20.00

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Figure 13.3  Changes in the rated (a) pleasantness and (b) sweetness of odors rated orthonasally following repeated retronasal exposure to the same odor paired with 10% sucrose either by sweet-likers (solid bars) or by sweetdislikers (open bars). Source: Adapted from Yeomans et al. (2006), with permission.

the mere presence of others (Guerin, 1993). In the context of food, social facilitation has been shown to influence eating in ways that may influence flavor preference development. People reliably consume more when in groups than alone (De Castro, 1990; De Castro et al., 1990; Redd and De Castro, 1992). This social facilitation of meal-size may lead to acquired preference if the increased intake includes novel items, where exposure alone may enhance liking, perhaps reinforced further by the post-ingestive effects of the meal. Direct evidence for social facilitation of food preferences has been reported in species other than humans, such as capuchin monkeys (Visalberghi and Addessi, 2000). The

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acquisition of liking for the burning ­ sensation of spicy food by Mexican children could be interpreted as evidence of social facilitation effects: Mexican children are exposed to these foods in the context of meals where they are consumed by adults (Rozin and Schiller, 1980; Rozin, 1982), and the mere presence of others may reduce neophobia and so promote acceptance and later liking. Many other studies report increased acceptance of, and reduced neophobia towards, unfamiliar foods by children when exposed to these foods in a social context (Birch, 1980). A more recent study confirms the role of social facilitation: children showed neophobic responses to unfamiliar foods which were reduced by the presence of an adult consuming that food (Addessi et al., 2005). A more powerful social influence on flavorliking acquisition may be through social modeling (also referred to as observational learning or social imitation). Here, the observation by one individual of a second individual who is consuming and enjoying a food may lead to increased liking for the same food by the observer. Thus, children showed increased acceptance of an unfamiliar food when an adult was eating that food than when an adult merely offered the food to them (Harper and Sanders, 1975). Similarly, the presence of an enthusiastic teacher who modeled food acceptance was highly effective in encouraging repeated consumption and increased acceptance of novel foods by children (Hendy and Raudenbush, 2000). Combining observation of a peer consuming a food with positive social reinforcers has also proved an effective method of enhancing children’s preferences for less preferred foods, such as vegetables (Horne et al., 1995, 2004). Enhanced intake of foods through social modeling by peers may be particularly influential on development of food likes in children (Horne et al., 2004; Romero et al., 2009). Overall, social learning is clearly an important element in flavor preference development, which seems to operate primarily by reducing

neophobia and so allowing more direct flavorlearning (FFL and FCL) to occur.

13.5  Different learning mechanisms interact to enhance flavor-liking Although experimental studies have been able to establish multiple mechanisms through which flavor-liking may be acquired, the typical experimental study examines one putative mechanism while ensuring that as many alternative influences as possible are controlled for. Thus, for example, studies examining effects of multiple exposures of a flavor paired with ingestion of some form of nutrient typically run control groups exposed to the flavor alone (Kern et al., 1993). However, in real-life it is clear that flavor-liking for foods is likely to develop through multiple mechanisms at the same time. Consider, as an illustrative example, how liking for the flavor of chocolate might be acquired. Most chocolates consumed in Western society are sweetened, and our innate tendency to like sweet tastes should predispose us to find chocolate to be acceptable on first exposure. Our first exposure will confirm that the food is not poisonous, leading to reduced neophobia for the food through learned safety. It is also likely that our first exposure to chocolate will occur in the presence of others, and observation that other people are consuming it will further help reduce neophobic reactions through social facilitation. Also, if we observe pleasurable responses to consuming chocolate by people who we trust, this in turn may enhance liking through social modeling. The pairing of unique chocolate flavor elements with sweetness would be predicted to enhance liking through flavor– flavor associations, and, once ingested, the highfat and -sugar content of chocolate, along with small amounts of caffeine, should all promote flavor-liking through FCL. Thus, in the case of

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13.6  Liking and intake: the role of palatability in overeating

liking for foods like chocolate, which has been reported as the food most often named as a craved item (Hetherington and MacDiarmid, 1993; Gibson and Desmond, 1999; Parker et al., 2006), we can see plausible influences of all the major learning elements described in this chapter working together to generate a strong acquired like. Several experimental studies have confirmed how learning mechanisms interact to modify flavor-liking. For example, when a novel flavor was paired with sweetness (FFL), energy (FCL), or sweetness and energy (FFL and FCL), the largest increase in liking was seen where the opportunity for both associations was present, with smaller increases with either FFL or FCL alone (Yeomans et al., 2008a). Likewise, the increased liking for a drink flavor by association with caffeine consumption was enhanced when training was in a sweet context (where a flavor– sweet association could add to the flavor– caffeine association: Figure 13.4), but was retarded when caffeine was consumed in a bitter context (Yeomans et al., 2007). Thus, FFL and

Change in flavor pleasantness

40 30 20 10 0 –10 –20 –30 –40

Water

Aspartame

Quinine

Trained drink flavor

Figure 13.4  Rated pleasantness of drink flavors before and after repeated pairing with caffeine (hashed bar) or placebo (open bar), and with added sweetness (aspartame), bitterness (quinine) or no added flavoring (water). Source: Adapted from Yeomans et al. (2007), with permission.

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FCL appear to have additive effects on acquisition of flavor-liking. However, social effects seem to interact with FCL to generate liking (Jansen and Tenney, 2001), since the effectiveness of a social model in enhancing food preferences in children was greater when the ingested food was high energy than low energy, implying a synergistic effect between social reinforcement and post-ingestive effects. Overall, these studies provide clear predictions about the situations where flavor-liking will develop, and these models are consistent with actual observations of flavor preferences. The critical question now is how these acquired likes may modify food intake and so be a risk factor for overeating and consequent weight gain.

13.6  Liking and intake: the role of palatability in overeating Why does understanding the basis of flavorliking matter to obesity? The answer lies in the role of flavor hedonics as a driver of short-term food intake. Many studies in humans and other animals have established a clear relationship between hedonic evaluation of a food and consequent intake (Nasser, 2001; Sorensen et al., 2003; Yeomans et al., 2004a; Westerterp, 2006). Since it is harder to evaluate hedonic evaluation in nonhuman animals, this discussion concentrates on the human literature. The simplest studies take the same food and modify its flavor, either by adding a disliked component (or note in sensory terms) or by adding liked flavor elements. The outcome is very clear: a change in liking produces a predictable change in intake, with a linear relationship between hedonic evaluation and overall consumption (Yeomans et al., 2004a). In relation to short-term overconsumption, this implies that liking drives overeating and so may be a significant risk factor for development of obesity. Indeed, many people have suggested

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that the availability of energy-dense palatable food has been a major environmental component which has fostered the rapid increase in obesity (Wansink, 2004; Ulijaszek, 2007). It is also notable that intake does not decrease reliably as energy density decreases: it appears that, in the short-term, it is the volume of food that is regulated, leaving a risk of passive overeating as the energy density of our diet increases (Westerterp, 2006). As energy density is also enhanced with greater liking and so may actively drive overconsumption, it is easy to see how the combined active and passive overconsumption of energydense food greatly increases the risk of obesity. Until recently, what was not clearly known was whether this active overconsumption was also seen for acquired flavor likes. In terms of the mechanism through which palatability drives short-term intake, our understanding has increased at both the phenomenological and biological levels of explanation. Thus, there is clear evidence that increased flavor-liking leads to short-term increases in desire to eat (the experience of hunger). This appetizer effect (Yeomans, 1996; Figure 13.5) Very

100

Bland Palatable Strong

Rated hunger

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60

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Figure 13.5  Effects of manipulated palatability on the experience of appetite during a meal. Source: Adapted from Yeomans (1996), with permission.

offers a behavioral description of how evaluation of sensory quality modulates internal appetitive state and so alters short-term intake. Pharmacological studies have offered some insights into the biological basis of how liking enhances appetite. For example, blockade of opioid receptors both reduces food pleasantness (Drewnowski et al., 1989; Yeomans et al., 1990) and abolishes the appetizer effect (Yeomans and Gray, 1997). However, all of these types of study rely on contrasts between foods varying in immediate palatability, without consideration of whether this is a consequence of learned liking.

13.7  Acquired liking as a driver of overeating The previous discussion clearly shows that liking drives short-term intake, but was based on analyses of either manipulation or variation in liking on intake. Since most liking for flavors is acquired, one interpretation of these findings is that acquired liking then is a driver of intake. Two recent studies in our laboratory suggest this is the case. First (Yeomans et al., 2008a), liking and voluntary intake of a highly novel food (a fruit sorbet) was tested before and after the same flavor was associated with energy (provided by the non-sweet carbohydrate maltodextrin), sweetness (aspartame), or energy and sweetness (sucrose). Exposure to the same flavor paired with sucrose (i.e., where both flavor– sweetness and flavor–energy associations could be made) resulted in a large increase in liking for the flavor in the sorbet context and an increase in voluntary intake (Figure 13.6). In a different learning model, people evaluated and consumed a low-energy soup on separate days before and after repeated experience of the same soup either unaltered or with its flavor enhanced by monosodium glutamate (MSG: Yeomans et al., 2008b). As predicted by evaluative condition (EC), the greater liking for the

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13.8 Individual differences in learning

40 30 20 10

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Change in rated sweetness

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SUC MALT ASP

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Figure 13.6  Changes in (a) intake, (b) liking and (c) sweetness of a novel-flavored sorbet after experiencing the same flavor paired with ingestion of sweetness and energy (sucrose: SUC), energy alone (maltodextrin: MALT), sweetness alone (aspartame: ASP) or unaltered (exposure control: EXP), along with an unexposed control condition. Source: Adapted from Yeomans et al. (2008a), with permission.

MSG-enhanced version during the training sessions transferred to the soup alone, resulting in increased liking, an enhanced appetizer effect, and greater intake at post-training. Both these examples provide unequivocal confirmation that acquired liking can act as a driver of shortterm intake.

13.8  Individual differences in learning An important observation in relation to the recent increases in the incidence of obesity is that there are large phenotypic variations in whether individuals who are exposed to the modern, obesogenic environment become obese, with a significant proportion remaining lean. Thus, some people are susceptible to gaining significant weight in a weight-promoting environment, but others are resistant to weight gain (Blundell and Cooling, 2000; Blundell et al., 2005; Carnell and Wardle, 2008). There are myriad factors that may contribute to this variability, many of which are reviewed elsewhere

in this book. In the present context, it is notable that the idea that over-responsiveness to hedonic cues has been reported in obese participants (Nisbett, 1968; Price and Grinker, 1973; Rissanen et al., 2002), and has been cited as a factor underlying overeating (Drewnowski et al., 1985; Nasser, 2001; Sorensen et al., 2003; de Graaf, 2005). Moreover, it has recently been argued that in terms of external food cues driving short-term intake, a distinction can be made between normative cues such as portion size, and sensory cues such as palatability, with most people sensitive to normative cues but the obese over-responsive to sensory cues (Herman and Polivy, 2008). Thus, acquired flavor likes may be significant contributors to overeating and consequent obesity. Since the major argument of this chapter is that acquired flavor-liking may be a ­ significant driver of short-term overconsumption, one pos­ sible source of individual differences in response to food cues may relate to the extent to which individuals learn flavor-based associations, either through FCL or FFL. To date, no studies have specifically contrasted these learning mechanisms between obese and ­normal populations;

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however, a number of studies have started to identify significant differences in ability to learn through both FFL and FCL in subsets of normalweight individuals, some of which may contri­ bute to weight gain and later lead to increased body weight. The first studies to report individual differences in flavor-based learning did so in relation to dietary restraint (Brunstrom et al., 2001, 2005) – the tendency to self-restrict food intake in order to control body-weight. People who score high on measures of restraint do so because they are either trying to lose weight, or are aware that their unrestrained behavior places them at risk of gaining weight. Thus, in the absence of studies with obese patients, studies with restrained eaters might identify deficits in flavor-based learning that may be relevant to our understanding of obesity. Crucially, these studies reported that women who scored higher on a questionnaire measure of dietary restraint (restrained eaters) were insensitive to FFL (Brunstrom et al., 2001, 2005) and FCL (Brunstrom and Mitchell, 2007). In the original FFL study (Brunstrom et al., 2001), women were presented with a novel drink flavor (CS) followed by consumption of small sweets, with different contingencies between different flavors and the frequency with which sweet reinforcers were presented. Subsequent liking for the drink flavors increased as a function of the contingent relationship with sweet presentation in unrestrained eaters, but did not differ between restrained eaters. A second series of studies (Brunstrom et al., 2005) extended these findings. It was found that restrained women tended to experience increased liking for flavors that were least frequently paired with delivery of sweets, in contrast to unrestrained women who showed strongest liking for the flavors most frequently paired with sweet delivery. These effects were replicated in a further study where the CS consisted of pictures rather than flavors (Brunstrom et al., 2005). Finally, the most recent study examined acquired liking for flavors through

a­ ssociation with energy in a test of FNL, and again found impaired learning in restrained but not unrestrained participants (Brunstrom and Mitchell, 2007). Alongside restraint, a second measure also seems to identify significant individual differences in response to foods. The Three Factor Eating Questionnaire disinhibition scale (TFEQ-D) has been shown to reliably measure a number of aspects of eating that may increase the risk of becoming obese (Bryant et al., 2008). In relation to appetite, high scores on the TFEQ-D have been shown to be a better predictor than restraint of eating in response to stress (Oliver and Huon, 2001; Haynes et al., 2003), and to be associated with a heightened appetite response to palatability (Yeomans et al., 2004b) and greater selection of high-fat and sweetened foods (Lahteenmaki and Tuorila, 1995; Contento et al., 2005; Bryant et al., 2006). Many studies also report a positive association between TFEQ-D scores and BMI (Williamson et al., 1995; Provencher et al., 2003, 2004; Bellisle et al., 2004; Hays and Roberts, 2008). In relation to flavor-based learning, we recently reported that scores on the TFEQ-D were found to predict the extent to which women acquired liking for a flavor paired with sweetness in an FFL paradigm (Yeomans et al., 2009a: Figure 13.7). Notably, in that study, it was not an ability to acquire flavor-liking per se that was impaired, since all women acquired a dislike for a flavor paired with an unpleasant taste (the bitter taste of quinine). What the study indicated was that women scoring high on the TFEQ-D showed a greater increase in liking for the sucrose-paired flavor despite no differences in actual liking for sucrose between groups. Thus, the TFEQ-D appears to measure some aspect of overexpression of hedonic response, which in turn appears to be a risk factor for overeating. Although research into individual differences in the tendency to acquire flavor-liking is still at an early stage, the outcome of the few studies reported to date does suggest that differences in the way individuals acquire flavor-liking may

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references

Change in rated pleasantness

20 15

Sucrose Quinine

10 5 0 –5 –10 –15 –20

Low

High Disinhibition

Figure 13.7  Changes in pleasantness of a novel odor paired either with a pleasant sweet taste (sucrose: open bars) or with an unpleasant bitter taste (quinine: solid bars) by sweet-liking women who scored either high or low on the disinhibition scale of the Three Factor Eating Questionnaire. Source: Adapted from Yeomans et al. (2009a), with permission.

make people more or less at risk of overeating, and so becoming obese. Future studies are needed to confirm these findings in obese groups.

13.9  Summary Multiple learning mechanisms operate together to allow humans to identify safe and nutritious foods from the huge variety of potential food items in our environment. Social factors are likely to be key to our initial exposure to foods, and such exposure helps us rapidly to learn what is safe. Innate and previously learned flavor preferences direct our liking for associated novel flavors, and once ingested, the experience of nutrient and other effects of food constituents becomes associated with the flavors, leading to powerful acquired liking for energy-dense foods. Liking itself, including acquired flavor likes, is a short-term driver of food intake. In environments where food was scarce, this would have meant that the ­consumer took maximum advantage of rare but

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highly nutritious food sources. However, in the modern world, where such foods are abundant, the ability of acquired likes to drive intake is a risk factor for overeating and obesity. What remains less clear is the extent to which individual variation in response to the sensory quality of foods may help explain phenotypic variation in the tendency to become obese. Emerging evidence that women who are prone to overeat also show heightened responses in acquiring flavor likes, in addition to a large body of literature suggesting that obese individuals over-respond to hedonic food cues, suggests that liking may be a key factor in explaining individual differences in obesity risk.

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C H A P T E R

14 Biopsychological Factors and Body Weight Stability Jean-Philippe Chaput and Angelo Tremblay Department of Social and Preventive Medicine, Laval University, Quebec City, Canada

o u t l i n e 14.1  Introduction

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14.1  Introduction The maintenance of an adequate body weight is a major determinant of the survival of higher organisms, including mammals. Body-weight and body-composition stability over long periods of time require that energy intake matches energy expenditure. In human adults, there are mechanisms partly influenced by heredity that balance

Obesity Prevention: The Role of Brain and Society on Individual Behavior

14.4  W  eight Loss: Not Always Beneficial   for the Psychological Health 184 14.5  P  hysical Activity and Diet:   What is the Impact on Body-  weight Stability?

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energy intake and expenditure. Body-weight regulation requires the maintenance of not only energy balance but also nutrient balance – i.e., the mixture of fuel oxidized must be adjusted to match the composition of fuel mix ingested (Flatt, 1987). Because protein and carbohydrate reserves stored in adults vary relatively little, body-weight regulation mainly concerns adipose tissue mass. The chronic imbalance between energy intake

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and expenditure results in changes in the adipose tissue mass. Therefore, body-weight regulation implies that adipose tissue mass is “sensed” and leads to appropriate responses in individuals who maintain body weight and body composition constant during prolonged periods of time. A variety of factors determine body-weight balance and regulation, and the size of the adipose tissue mass is not subjected to a strict set point. Many individuals, whether lean or obese, maintain their body weight within small limits during long periods of time. If energy intake exceeds expenditure by 1 percent daily for 1 year, the result would be approximately 9000 kcal stored, or 1.15 kg of body weight (Rosenbaum et al., 1997). The mean weight gain of the average American between 25 and 55 years of age is about 9 kg, which represents a mean excess of circa 0.3 percent ingested calories over energy expenditure (Rosenbaum et al., 1997). The high precision of energy balance maintenance is achieved by several regulatory loops. Many pathways participate in homeostatic responses that tend to maintain adequate fuel storage. The combined responses that control energy intake and expenditure to maintain energy homeostasis have conferred a survival advantage to humans. Food is increasingly available, and advances in technology and transportation have reduced the need for physical activity. These two environmental changes challenge body-weight regulation, and contribute to the increasing prevalence of obesity worldwide. Beyond the “Big Two” factors (physical inactivity and poor diet), recent research has emphasized the potential roles of additional environmental factors in contributing to the obesity epidemic (Keith et al., 2006), including: sleep debt endocrine disruptors l reduction in variability in ambient temperature l decreased smoking l l

l l l l l l

pharmaceutical iatrogenesis changes in distribution of ethnicity and age increasing gravida age intrauterine and intergenerational effects assortative mating and floor effects body mass index-associated reproductive fitness.

The list is not exhaustive. Public health practitioners and clinicians need to take these into account when looking at anti-obesity policies and actions. In spite of a growing number of works in this field, the obesity crisis rages on. This suggests that the obesity problem is multifaceted, and requires a combination of therapies in order to be managed. This chapter focuses on two phenomena characterizing our modern society and that challenge body-weight stability: (1) the increase of knowledge-based work (KBW) in daily labor as well as in leisure time; and (2) the reduction of sleeping time. In addition, we discuss the psychological impact of dieting and weight loss, which may impede the success of diet/physical activity clinical interventions. Finally, integrative comments and novel insights are provided.

14.2  Is knowledge-based work a potential determinant of the current obesity epidemic? Technological changes have brought about a progressive shift away from physically demanding tasks to knowledge-based work (KBW), which solicits an enhanced cognitive demand (Mitter, 1999). This modern transition has also redefined the notion of “fatigue at work”, which is now more of a psychosomatic nature (such as a burnout) than physical exhaustion (Iacovides et al., 2003). From a physiological standpoint, KBW represents a type of activity that relies on the brain,

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14.2 Is knowledge-based work a potential determinant of the current obesity epidemic?

Energy expenditure (kJ/45 min)

Chaput and Tremblay (2007) undertook an interventional study with female students to evaluate the impact of KBW on feeding behavior and spontaneous energy intake, using a crossover design. They used a two-session protocol including an ad libitum buffet which was preceded by either a 45-minute cognitive task (reading a document and writing a 350-word summary on a computer) or a 45-minute resting period (in the sitting position). As shown in Figure 14.1, the mean energy expenditure of the two conditions was comparable (13 kJ difference), whereas the mean ad libitum energy intake in the KBW group task exceeded that in the resting group by 959 kJ (P  0.01). Furthermore, the

300 270 240 210 180 150 120 90 60 30 0

∆ = 13 kJ

Control

KBW

∆ = 959 kJ *

6000 Energy intake (kJ)

which essentially utilizes glucose for its energy metabolism. Physical activity solicits skeletal muscle metabolism, which, to a significant extent, relies on fat metabolism. In addition, tasks requiring a significant cognitive demand are more likely to be confounded with neurogenic stress, which is known to promote a positive energy balance (Akana et al., 1994; Pijlman et al., 2003). In humans, many observations support the idea that an increase in KBW and/or stress promotes excess energy intake. Indeed, it was found, for instance, that the increased workload associated to the preparation of an NIH grant application was associated with a high energy intake and percent energy from fat compared to a lower workload period (McCann et al., 1990). Also, Wardle and colleagues (2000) found that high workload periods in a department store – 47 hours of work over 7 days, with a high level of perceived stress – were related to higher energy, saturated fat and sugar intakes compared to low workload periods (32 hours of work per week). Other studies have shown that overtime hours are positively correlated with 3-year changes in body mass index (BMI) and waist circumference (Nakamura et al., 1998). Moreover, the excess weight gain in spouse caregivers of individuals with Alzheimer’s disease was also associated with increased energy intake compared to spouses in the control group (Vitaliano et al., 1996). The impact of stress on spontaneous feeding has been studied under well-standardized laboratory conditions. Macht (1996) demonstrated that subjective hunger motivation was potentiated by emotional stress when energy intake was low in the preceding hours. Epel and colleagues (2001) observed that stress-induced cortisol reactivity was associated with increased energy intake after a first stress session. This is consistent with Wallis and Hetherington (2004), who reported that chocolate consumption increased by 15 percent after a cognitive task (Stroop Test) as compared to a control session.

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5000 4000 3000 2000

Control

KBW

Figure 14.1  Energy expenditure of rest (control) and knowledge-based work (KBW) and spontaneous energy intake in a buffet-type meal offered after the completion of each task. Data are expressed as mean  standard error of the mean (SEM); *significantly different from control value (P  0.01). Source: Adapted from Chaput and Tremblay (2007).

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subjects did not compensate for the ad libitum buffet by eating less during the rest of the day. This suggests a net caloric surplus. Dallman and colleagues (2003) have suggested that the overconsumption of food may be perceived as a reaction, whereby eating serves as a consolation and/or compensation for emotional stress. According to these authors, people eat “comfort food” in an attempt to reduce the activity of the stress-response network. Beyond this interpretation, other data suggest that KBW can be viewed as its own specific entity, producing certain physiological effects that promote a positive energy balance, independently of the emotional stress with which it is occasionally paired. In this regard, KBW produced plasma glucose and insulin instability (defined as the sum of absolute changes between each time of blood collection at every 15 minutes) 2.2 and 8.3 times greater, respectively compared to the resting activity (Tremblay and Therrien, 2006). Furthermore, Chaput and colleagues (2008a) recently reported in another experimental study that cognitive work acutely induced an increase in spontaneous energy intake and promoted increased fluctuations in plasma glucose and insulin levels. According to the glucostatic theory of appetite control1, energy intake may be triggered with the goal of restoring glucose homeostasis (Mayer, 1953; Chaput and Tremblay, 2009a). Interestingly, Chaput and Tremblay (2009b) also observed that mental work solicited by computer-related activities produced an increase in cortisol levels, which was related to a compensatory increase in caloric intake. This observation is in line with the results from Epel and colleagues (2001), who found that high cortisol reactors (defined as the increase from baseline to stress levels of salivary

cortisol) consumed significantly more calories and more high-fat, sweet foods on the stress day compared with low reactors, but consumed similar amounts on the control day. Thus, computer-related activities represent a particular type of sedentary activities that are stressful and biologically demanding. According to Tremblay and colleagues (2009), this type of activity cannot be in any way considered a restful activity, and deserves to be counterbalanced by an adequate physical activity regimen. As opposed to KBW, physical exercise enhances the accuracy and cell sensitivity to numerous hormones and substrates (Tremblay and Therrien, 2006). Consequently, the progressive shift from physically demanding tasks to KBW, which necessitates cognitive demand, has changed the biological requirements of the human organism. It is therefore noteworthy to focus on the impact of cognitive tasks and their potential effect on the control of food intake. Taken together, these observations suggest that activities requiring significant cognitive demand favor overconsumption of foods and body-weight gain. Moreover, acute effects of KBW suggest that this work modality might promote a greater positive energy balance in comparison to what would be expected from a sedentary activity. This adds a new component to sedentary lifestyles, made more harmful when one is subjected to mental stress. It also raises an additional obstacle in the fight against obesity, in that KBW is now the modern way of working. The orexigenic effect of mental work implies, too, that modern societies might be in a conflictual state, as KBW could significantly handicap the ability to spontaneously match energy intake and expenditure, and thus promote weight gain.

1

More than 50 years ago, Jean Mayer proposed that changes in blood glucose concentrations or arteriovenous glucose differences are detected by glucoreceptors that affect energy intake. According to this theory, an increase in blood glucose concentrations results in increased feelings of satiety, whereas a drop in blood glucose concentrations has the opposite effect.

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14.3 Is short sleep duration a potential determinant of the current obesity epidemic?

14.3  Is short sleep duration a potential determinant of the current obesity epidemic? Reduced sleeping time has become a widespread phenomenon driven by the demands and opportunities of the modern “24-hour” society. Not surprisingly, reports of fatigue and tiredness are more frequent today than a few decades ago (Bliwise, 1996). Over the course of the second half of the twentieth century, the dramatic increase in the incidence of obesity appears to have paralleled the progressive decrease in the duration of self-reported sleep (Flegal et al., 1998; Van Cauter et al., 2005). Consequently, many researchers have suggested that our “cavalier attitude” toward sleep could be partly responsible for our expanding waistlines. Indeed, a good night’s sleep, an activity that should ideally occupy about onethird of our lives, is an integral part of a “good health package”. It is therefore relevant to ask whether the current emphasis on poor diet and

lack of exercise omits the importance of sleep in the battle against obesity, thereby hindering individuals’ ability to maintain a healthy body weight. Chaput and colleagues (2006a) reported a dose–response relationship between short sleeping hours and childhood overweight/obesity. The risk for overweight/obesity in children reporting sleeping 8–10 hours per night was 3.45 times greater than for those who reported 12–13 hours per night. As seen in Figure 14.2, short sleep duration was the most important determinant of the potential risk to overweight/ obesity in children. Other studies examined the sleep–body weight association in children, and the conclusions were concordant with the Chaput and colleagues (2006a) findings (Gupta et al., 2002; Sekine et al., 2002; Von Kries et al., 2002; Reilly et al., 2005). In adults, short sleep duration also predicted an increased risk of being overweight or obese (Hasler et al., 2004; Spiegel et al., 2004; Taheri et al., 2004; Gangwisch et al., 2005; Vorona et al., 2005; Chaput et al., 2007, 2008b). Importantly, it was shown that the neuroendocrine control of appetite

Low total family income

Physical inactivity

Low parental educational level

Long hours of TV watching

Parental obesity

Short sleep duration

4 3.5

Odds ratio

3 2.5 2 1.5 1 0.5 0

Figure 14.2 

183

Relationship between potential risk factors and childhood overweight/obesity.

Source: Adapted from Chaput et al. (2006a).

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14.  Biopsychological Factors

was affected as plasma levels of the anorexigenic hormone, leptin, were decreased. Levels of the orexigenic hormone, ghrelin, increased (Spiegel et al., 2004; Taheri et al., 2004). Hence, these neuro­ endocrine changes were associated with increased hunger and appetite, which may lead to overeating and weight gain. Other large-scale studies also showed that both short and long sleeping durations are independently linked to an increased risk of coronary events, symptomatic diabetes and mortality (Ayas et al., 2003a, 2003b; Patel et al., 2004; Tamakoshi and Ohno, 2004). In these studies, the cut-off point of minimal mortality and related events was at 7 hours of sleep daily. Thus, there may be an “optimal sleeping time” for the prevention of common diseases and premature death. However, the mechanisms behind these associations are not fully understood, and the effects of long sleep duration on body weight and/or other health outcomes appear to be different from those associated with shorter sleep duration. Besides decreased leptin and increased ghrelin levels, physiological data in adults suggest that short-term partial sleep restriction leads to striking alterations in metabolic and endocrine functions, including decreased glucose tolerance, insulin resistance, increased sympathetic tone, elevated cortisol concentrations, and elevated levels of pro-inflammatory cytokines (Spiegel et al., 1999; Vgontzas et al., 2000; Taheri et al., 2004). Thus, one could speculate that a chronic lack of sleep represents a stress factor stimulating appetite, promoting weight gain and impairing glycemic regulation, with a subsequently increased risk of impaired glucose tolerance and, eventually, type II diabetes. However, a good night’s sleep is different for each individual, and is subject to a broad range of potential confounding variables. Consequently, many experts doubt that more sleep, natural or drug-induced, can be the answer to successful weight loss. Once a person is overweight, poor sleep and uncontrolled

appetite could become part of a vicious cycle; obesity might make it hard to sleep, and poor sleep might make it harder to lose weight. Instead, researchers have focused on identifying individuals with “high-risk” sleeping patterns, in order to prevent weight-related problems before they arise. An early warning sign, such as altered leptin concentration, might alert physicians that the body is suffering more than is immediately obvious. In addition, it may be useful to identify children who do not sleep enough and to encourage parents to change these sleeping habits. Future research needs to examine the effect of short sleeping duration on appetite, food intake and obesity. These studies should use an interventional study design to establish the cause-and-effect relationship behind sleep duration and obesity. They should also examine the effects of restricted sleep on both sides of the energy balance equation, with the use of objective measures for sleep duration and quality. It may thus be demonstrated that the rise of obesity in many societies around the world is partly linked to sleep deprivation. Future studies can also examine whether increasing sleep to 7 or 8 hours per night can help individuals lose weight or prevent weight gain. This may prove to be a pleasurable way to control obesity.

14.4  Weight loss: not always beneficial for the psychological health From a psychosocial perspective, overweight and obesity adversely affect the quality of life. They carry a social stigma that may contribute to higher rates of anxiety, depression and low selfesteem (Puhl and Brownell, 2001; Kottke et al., 2003; McElroy et al., 2004). Depression may contribute to weight gain and obesity and, vice versa, obesity may contribute to depression (Wyatt et al., 2006). From a weight-loss standpoint, it

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14.4  Weight loss: not always beneficial for the psychological health

is realistic to say that the association between obesity and metabolic complications generally constitutes the main argument in justifying weight-reducing programs: the aim is to improve the metabolic risk profile of obese individuals. However, benefits to physical health should not be associated with detrimental effects on mental health or psychological wellbeing. In this regard, the psychological effects that accompany weight loss in obese individuals are of high importance in order to understand the psychological barriers to weight loss, and the optimal management of obesity. The majority of the evidence in this field of research shows the beneficial impact of weight reduction on mental wellbeing and health-related quality of life (Rippe et al., 1998; Fine et al., 1999; Fontaine et al., 1999; Kaukua et al., 2002; Karlsson et al., 2003). However, they fail to mention the possible psychological costs associated with weight loss, reflected by a destabilization of body homeostasis. Such negative psychological costs require a cautionary approach to weight reduction. As shown in Figure 14.3, Chaput and Tremblay found that depression symptoms increased significantly after a weight

Figure 14.3  Clinical threshold of depression. Evolu­ tion of depression symptoms, measured with the Beck Depression Inventory (BDI), over the course of a progressive body-weight loss program that consisted of a supervised diet and exercise clinical intervention. *significantly different from baseline mean score (P  0.05); **P  0.01. Source: Adapted from Chaput et al. (2005; 2006b).

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loss of 10 kg (Chaput et al., 2005; dynamic weight-loss phase). These symptoms were more pronounced at the static weight-loss phase, the plateau (Chaput et al., 2006b). Furthermore, the increase in the symptoms of depression was associated with an increased restraint of eating (Chaput et al., 2005, 2006b). This psychobiological phenomenon was observed concomitantly with a significant decrease in resting energy expenditure, and a significant increase in hunger and a desire to eat (Chaput et al., 2006b). In another recent study, Chaput and colleagues (2008c) linked the increase in depression symptoms with glucose homeostasis and thyroid function. Specifically, the significant increase in depression symptoms observed after an average loss of 11.2 percent of initial body weight induced via energy restriction (700 kcal/day) and an aerobic exercise program was shown to be highly associated with hypoglycemia at the end of an oral glucose challenge, and with a decrease in total triiodothyronine (T3) and free thyroxine (fT4) levels. Such results are not surprising, as glucose is the main substrate of the brain and thyroid function is related to metabolism, which represent effects that may influence mood and wellbeing as well as the perception of “body energy”. This suggests that weight loss up to a certain level has the potential to destabilize body homeostasis and induce a psychobiological vulnerability favoring weight regain. For health professionals, these observations indicate that body-weight management should maintain a reasonable balance between the health benefits associated with weight loss and the potential negative consequences for the control of energy intake and expenditure. Furthermore, various psychological and physio­ logical adaptations make body-weight maintenance following weight loss difficult, and render the individual vulnerable to weight regain. In this context, patients may need to accept a more modest weight-loss outcome (Foster et al., 1997).

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14.5  Physical activity and diet: what is the impact on body-weight stability? An individual looking to lose weight through a healthy diet and regular physical activity usually asks how much weight can be feasibly lost. There is no straightforward answer to this question, as it depends on numerous factors. From a physiological standpoint, the most realistic answer compares the loss of the regulatory impact on fat balance that occurs with weight loss with the gain in the regulatory impact on fat balance that can be promoted by a healthy lifestyle. This assumption means that the fat compartment contains beneficial molecules that aim to fight against a further weight gain. Indeed, fat gain facilitates the maintenance of body homeostasis because of an increased hormonal gradient which favors the regulation of energy balance. An increase in plasma free-fatty acids, fat oxidation, sympathetic nervous system activity, insulinemia at euglycemia, and leptin­ emia are all adaptations that contribute to promote body-weight stability over time (Tremblay and Doucet, 2000; Chaput and Tremblay, 2009a). Simply put, the increase in body fatness is accompanied by neuroendocrine adaptations that favor an increase in energy expenditure and a decrease in energy intake. Accordingly, if one wants to maintain a reduced-obese state, the stimulating effects of a healthy lifestyle on the regulatory processes should, theoretically, be equivalent to what is lost with body mass reduction. Up to now, we have not been able to promote weight losses exceeding 10–12 percent of the initial body weight without inducing metabolic and behavioral changes compromising the ability to maintain subsequent long-term weight stability. It is thus likely that individuals cannot continue to lose weight without more demanding activity and diet changes than those displayed at the end of the program, when the plateauing occurred.

The failure to adhere to healthy lifestyle habits following weight loss leaves the patient with two possible strategic choices in regards to maintaining subsequent body-weight stability. The first is a self-imposed energy restriction, irrespective of the hunger sensations that may be perceived. This option may be counterproductive in the long term; Drapeau and colleagues (2003) observed greater weight gain over time in women that displayed restraint behaviors. The second scenario is to simply not adhere to a healthy lifestyle, the consequence of which will be weight regain as the body will attempt to re-equilibrate the energy and fat balance. Consequently, the reduced-obese individual wishing to maintain the new morphological status is left with very few alternatives. In fact, the only real and valid option is to improve body functionality by healthy activity and diet habits, and thus to compensate for the loss of physiological impact of the decrease in body fat. However, even if a person displays an exemplary discipline in the implementation of a healthy lifestyle, the resulting beneficial impact is not limitless. Body-weight management imposes systematically a balance between the expectations of an individual and what his or her biology can tolerate in terms of lifestyle changes. In some cases, the management of this balance may be complicated by the increased practice of KBW and/or short sleep duration.

14.6  Conclusion and perspectives The modern world demands less energy, and is characterized by an improved quality of life. Modernity has thereby provided numerous products and services contributing to the comfort and wellbeing of people. Beside the obvious positive changes related to the health status and life expectancy of individuals, it has

1.  From Brain to Behavior

References

contributed to considerable gains in labor efficiency and productivity. However, this environment challenges body-weight stability, as decreased sleeping time and increased KBW provide stimuli that can induce a positive caloric balance over time. As described in this chapter, this new reality can partly explain the current obesity epidemic, and also mitigates the potential outcomes of a diet–physical activity weight-reducing program. In this context, an increased level of body fat might be necessary to maintain body-weight stability.

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C H A P T E R

15 Nutrition, Epigenomics and the Development of Obesity: How the Genome Learns from Experience John C. Mathers Human Nutrition Research Centre, Institute for Ageing and Health, Newcastle University, Newcastle on Tyne, UK

o u tl i ne 15.1 The Basics of Epigenetics and Epigenomics

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15.5 An Epigenetic Basis for Developmental Programming of Obesity? 197

15.2 Epigenetic Marks During Development and Aging 193

15.6 Physical Activity, Epigenetic Markings and Obesity 197

15.3 Nutritional Epigenomics

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15.7 Concluding Comments

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15.4 Epigenetics and Brain Function

196

Acknowledgments

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15.1  The basics of epigenetics and epigenomics The human DNA sequence defines biologi­ cal capacity in that it determines the genes, and the functionality of those genes, which can be expressed by the individual. However, although all nucleated cells in a person contain exactly the same genomic sequences, the diversity of structure

Obesity Prevention: The Role of Brain and Society on Individual Behavior

and function in different cells and tissues is mani­ fested by the expression of characteristically different consortia of genes. This cellular differen­ tiation is programmed, at least in part, by epigen­ etic mechanisms that regulate the expression of genes over long periods of time. Epigenetics is the science of chromatin modifications responsible for such altered regulation of gene expression occurring in the absence of changes in the pri­ mary DNA sequence. In other words, epigenetics

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© 2010, 2010 Elsevier Inc.

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15.  Nutrition, Epigenomics and Obesity

is a mechanism which facilitates phenotypic plasti­ city in the context of a fixed genotype. Chromatin can be considered as “smart packaging” which, in addition to helping package the  2 m of DNA into the nucleus (diameter  20 m), carries a sophisticated pattern of marks that regulate chro­ matin structure, DNA accessibility and transcrip­ tion of specific sequences. The best understood epigenetic mark is the covalent addition of a methyl group at the 5 position in a cytosine residue when this precedes a guanine residue – a so-called CpG dinucleo­ tide. The prevalence of the CpG dinucleotide in the human genome is much less than would be expected, and these dinucleotides tend to cluster in DNA domains known as CpG islands which are characterized by high G  C and CpG con­ tents (Bird, 2002). About 50 percent of human genes have CpG islands (CGI) in their promoter regions, sometimes extending into the first exon. The cytosines in these regulatory regions of genes are usually unmethylated, in contrast with cytosine residues elsewhere in the genome which are heavily methylated. Indeed, mam­ malian genomes are dominated by methylated DNA, with unmethylated domains (largely CGI) accounting for only 1–2 percent of the total (Suzuki and Bird, 2008). This divergent methyl­ ation landscape reflects the functionality of the individual DNA sequences with unmethylated promoters allowing transcription of the associated gene, whereas methylated regions are transcrip­ tionally silent. At its simplest, DNA methylation acts as a transcriptional switch which is in the “on” position when the CpG island is unmethyl­ ated and signals “off” when methylated. Within the nucleus, DNA is packaged by sophisticated wrapping around an octet of glob­ ular proteins known as histones i.e. two copies of each histone H2A, H2B, H3 and H4. These histones host further epigenetic marks in the form of post-translational chemical modifica­ tion of amino acid residues, including acetyla­ tion and ubiquitination of lysine residues,

­ hosphorylation of serines, and methylation of p lysine and arginines (Berger, 2007). In all, there are more than 100 distinct post-translational modifications of histones (Kouzarides, 2007). Individual histone modifications and patterns of modifications, described as histone decoration, constitute a histone code (Jenuwein and Allis, 2001) which, in conjunction with DNA meth­ ylation status, regulates the expression of asso­ ciated genes (Bernstein et al., 2007). Although there is some dispute about how inclusive the term “epigen­etics” should be, many in the field consider that the density of nucleosome packing along DNA, the presence of proteins that recog­ nize methylated DNA or modified histones, and higher-level topological organization of these elements into complex structures (Berger, 2007) contribute to the complexity of epigenetic infor­ mation (Feinberg, 2008). The term “epigenome” describes the totality of epigenetic marks in a given cell under specified conditions, and “epi­ genomics” is the science (and technology for the study) of genome-wide epigenetic marks. If epigenetic marks are important in defining over long time periods the complement of genes that characterize specific cell types, then it is evident that there must be mechanisms for sus­ taining patterns of epigenetic information across cell generations. For example, when a hepato­ cyte divides, its daughters “need to know” that they are liver cells rather than kidney or bone cells; the tissue of origin is remembered (LaddAcosta et al., 2007). Indeed, in mitotic tissues, this hypothesis would predict that epigenetic features characteristic of individual stem cells would be recapitulated in the progeny of those stem cells. This prediction holds, and the phe­ nomenon is best exemplified in the intestinal mucosa, where the patterns of DNA methylation differ between individual crypts. This reflects the diversity of methylation patterns in the stem cells populating those crypts (Kim and Shibata, 2004). The molecular mechanism responsible for “memorization” of DNA methylation marks

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15.2  Epigenetic marks during development and aging

through mitosis is well understood. During semi-conservative replication, DNA methylation transferase 1 (DNMT1) uses the parental strand as a template to methylate the daughter strand, with S-adenosyl methionine (SAM) acting as the methyl donor. In contrast, the molecular mechanisms for memorization of histone modi­ fications remain obscure. No enzyme has been identified that recognizes chromatin modifica­ tions in the parental cell and reproduces them in the daughter cells (Feinberg, 2008). However, it appears that histones segregate randomly dur­ ing mitosis so that each daughter cell acquires some of the marked proteins, which then spread to the newly deposited histones (Hatchwell and Greally, 2007).

15.2  Epigenetic marks during development and aging Each individual’s DNA sequence is fixed at conception, but their epigenetic state, as indi­ cated by DNA methylation patterns, changes throughout the life-course. The most dra­ matic of these changes occur very early after a highly methylated sperm fuses with a relatively unmethylated egg. In the first few cell divisions, the new individual undergoes genome-wide demethylation, which erases parental methyla­ tion marks for all genomic sequences with the exception of imprinted genes (Reik, 2007) – that is, genes which are expressed in a parent-oforigin-specific manner. Between the morula and the blastocyst stages there is genome-wide de novo methylation, with tissue-specific methyl­ ation patterns emerging later in embryonic development (Reik, 2007; Feinberg, 2008). In embryonic stem (ES) cells, there appears to be a novel chromatin-based mechanism for maintain­ ing pluripotency through which expression of developmentally-important transcription factors

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is regulated epigenetically by “bivalent domains”, which silence these genes in ES cells but keep them poised for activation (Bernstein et al., 2006). When compared with the tsunami-like remodeling of the epigenetic landscape seen in early embryonic life, DNA methylation patterns (and, by inference, other epigenetic marks) are relatively stable following birth. However, there is substantial evidence that these epigenetic pat­ terns continue to evolve over the life-course. A good illustration of this evolution is provided by Fraga’s study of monozygotic twins, which found that members of twin pairs were epigen­ etically indistinguishable when young but epi­ genetic portraits (DNA methylation patterns and histone acetylation) diverged with age (Fraga et al., 2005). These epigenetic differences in older twin pairs were reflected in greater dif­ ferences in gene expression (Fraga et al., 2005), suggesting that the greater epigenetic hetero­ geneity may have functional consequences. Epigenetics is emerging as an important field for those studying the biology of aging and agerelated diseases because of the potential functional consequences of the changes in epigenetic marks that accumulate with age (Fraga and Esteller, 2007). Studies of aging cells in culture, of animal models, and of older humans indicate that, in general, genomic DNA becomes progressively demethylated with age. In contrast, some genes (for example, some tumor suppressor genes and other DNA defense genes) become silenced by promoter methylation (Fraga and Esteller, 2007). Until recently, the understanding of aging’s effects in humans was handicapped by the restriction of cross-sectional studies which cannot provide infor­ mation on intra-individual changes in epigenetic marks over time. A study of an Icelandic cohort in whom DNA was collected 11 years apart, and that of a Utah (USA) cohort sampled 16 years apart (Bjornsson et al., 2008), has changed this land­ scape. This study showed that genome-wide DNA methyl­ation changed in a substantial proportion of each cohort, with individuals showing both gains

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and losses of cytosine methylation (Bjornsson et al., 2008). In contrast, previous studies found little evi­ dence of age-related changes in methylation of the human IGF2/H19 locus (Heijmans et al., 2007) and of human chromosomes 6, 20 and 22 (Eckhardt et al., 2006). However, both of these studies were cross-sectional and, because some individuals gain DNA methylation whilst others become relatively hypomethylated with age, the process of averag­ ing degrees of methylation for each age group is likely to obscure individual age effects (Bjornsson et al., 2008). Further, studies of epigenetic markings at an individual cell level may provide novel insights into the development of age-related frailty. Changes in DNA methylation over the lifecourse may not occur equally in all cells within a tissue – that is, aging may increase the extent of epigenetic mosaicism within a tissue. Since copying of DNA methylation patterns across cell generations is much less well-policed than is the primary sequence, methylation patterns may drift over time, leading to greater inter­ cellular divergence in methylation patterns within a tissue with age. By expanding HMEC cells from 1 to 106 followed by bisulfite sequen­ cing, Ushijima and colleagues (2003) quantified epigenetic error rates for a panel of genes and reported a mean of 0.1 percent “errors” per site per cell generation. This increased heterogen­eity in epigenetic markings with time may contri­ bute to the greater cell-to-cell variation in gene expression that is observed in cardiomyocytes of older mice (Bahar et al., 2006). The obser­ vation that this greater cell-to-cell variation appeared to be random (i.e., differed between genes within a cell) (Bahar et al., 2006) is con­ sistent with the mechanism of epigenetic drift over time. Increased cell-to-cell diversity in epi­ genetic marking with age may have important functional consequences and, at a tissue level, may explain some of the reduction in speed and magnitude of response to stimuli (loss of homeostasis) that characterizes aging and the ­development of frailty (Figure 15.1).

Young

Large, unified response

Old

Reduced, variable response

Figure 15.1  Conceptual functional consequences of increased inter-cellular heterogeneity in promoter methyla­ tion and subsequent silencing of a gene age in a given tissue. , unmethylated gene, expression; , methylated gene, no expression. Source: Mathers and Ford (2009).

15.3  Nutritional epigenomics There is indisputable evidence that nutritional exposures contribute to phenotypic plasticity and, indeed, that exposures early in life can have profound effects on health decades later. As mechanisms that play a significant role in orches­ trating the complex interplay between nutrition (and other lifestyle exposures) and the genome that determines individual phenotype, epigen­ etic processes are strong candidates. In other words, it is proposed that epigenetic markings (1) allow phenotypic plasticity in a fixed geno­ type, and (2) connect environmental exposures with gene expression and function (Feinberg, 2007). To help focus research attention on the key processes likely to be involved in linking envi­ ronmental (nutritional) exposure with altered phenotypes, we developed the simple concep­ tual model of the “4Rs of epigenomics” (Figure 15.2; Mathers and McKay, 2009). This model pro­ poses that nutritional exposures are “Received” and “Recorded” by epigenetic mechanisms, and that the environmentally determined epigenetic marks are “Remembered” across succeeding cell generations. Sometime later, the consequences of earlier environmental exposures are “Revealed”

1.  From Brain to Behavior

15.3  Nutritional epigenomics Environment (diet)

Receive and Record

Remember

Reveal

Time

Figure 15.2  The four Rs of epigenomics. Conceptual model of the key processes through which altered epigenomics markings as a result of nutritional expo­ sures are Received, Recorded, Remembered and Revealed. Source: Mathers (2008), reproduced with permission from Cambridge University Press.

as altered gene expression, which translates into changes in cellular and tissue function (Mathers and McKay, 2009). There is only fragmentary understanding of the mechanisms through which nutritional exposures are received and recorded as novel epigenetic marks (the first two “Rs”), yet the list of food components which modulate DNA methylation and histone decoration is expand­ ing (for reviews, see Arasaradnam et al., 2008; Mathers and Ford, 2009). In many cases, the functional consequences of the altered epigen­ etic marks are not known and the field is ripe for the systematic study of the relationships between specific epigenetic marks and transcrip­ tional responses (the fourth “R”). The impact of maternal nutrition on epigenetic markings, gene expression and phenotype is probably best exemplified by studies in the viable yel­ low agouti (Avy) mouse (Waterland and Jirtle, 2003). The offspring of mouse dams fed a diet enriched with methyl donors (folate, vitamin B12, choline and betaine) during pregnancy are more likely to have mottled or pseudo-agouti coats (rather than yellow coats) and a reduced risk of being obese (Waterland and Jirtle, 2003). The molecular mechanism for these effects

195

appears to involve greater methylation of the cryptic promoter in the proximal end of the Avy intracisternal A-particle (Waterland and Jirtle, 2003). Intriguingly, similar effects are seen when the diet of the mouse dam is supplemented with genistein, which is not a methyl donor. This epi­ genetic remodeling does not seem to be driven by availability of methyl groups (Dolinoy et al., 2006). The rapid increase in obesity prevalence in the past few decades is consistent with a hypothesis of transgenerational amplification of adiposity, which might be mediated by the effects of maternal adiposity on birth weight and subsequent adult adiposity (Lawlor et al., 2007). Recent data suggest that maternal ­obesity in Avy mice induces transgenerational amplification of obesity. This adverse effect, however, can be ameliorated by supplement­ ing the dams with dietary methyl donors (Waterland et al., 2008). Importantly, the effects in these mice were independent of epigenetic changes at the Avy locus (Waterland et al., 2008). The search for epigenetic mechanisms will need to be widened to include, for example, genes in pathways regulating food intake and/ or energy expenditure. These results also pro­ vide proof of concept that the putative cycle of transgenerational amplification of ­ obesity might be broken by readily implemented nutri­ tional interventions. The mandatory fortifica­ tion of staple foods with folic acid (one of the methyl donors used in the mouse studies) in the US, Canada and elsewhere has resulted in significant increases in folate status of the whole population, including women of childbearing age (Pfeiffer et al., 2005). This “­natural” experiment provides an opportunity to test the hypothesis that maternal methyl donor ­supplementation per se is effective in reducing the risk of obesity in the offspring by examin­ ing the relationships between maternal and offspring adiposity before and since the wide­ spread fortification of baked goods with folic acid in 1996.

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15.4  Epigenetics and brain function Investigation of epigenetically-mediated mechanisms in the brain is in its early stages, but it is already apparent that epigenetic marks are important for brain structure and func­ tion. For example, Rett syndrome (RTT), the single gene disorder caused by mutations in the gene encoding methyl-CpG-binding pro­ tein 2 (MeCP2 – located at chromosome Xq28), presents a progressive loss of developmen­ tal milestones associated with aberrant gene expression (Feinberg, 2008). In the healthy state, MeCP2 selectively binds CpG dinucleotides and mediates transcriptional repression through interaction with histone deacetylase and the corepressor SIN3A. The loss of this repression is the mechanism underlying the pathogenesis of RTT (Amir et al., 1999). Recent analysis of DNA methylation signatures in the human brain has shown that different brain regions (cere­ bellum, cerebral cortex and pons) are distin­ guished by characteristically different patterns of DNA methylation (Ladd-Acosta et al., 2007). Differences between brain regions within indi­ viduals were much greater than those between individuals due to potential confounders includ­ ing age, sex, post-mortem interval or cause of death. These authors suggested that epigenetic signatures may, in part, determine brain func­ tional programs (Ladd-Acosta et al., 2007). To date, there has been little research on the effects of altered supply of specific nutrients on brain epigenetic marks. However, a recent publication reported that long-term feeding of a diet low in methyl donors caused genomic DNA hyper­ methylation in the rat cortex which was associ­ ated with reduced expression of DNMT1 and increased expression of the de novo DNA methyl transferase DNMT3A (Pogribny et al., 2008). There is substantial proof of principle that environmental factors program gene expression in the brain, that this occurs through epigenetic

mechanisms, and that the sequelae are both long-lasting and important for health. In a rat model, high-quality maternal care characterized by licking, grooming and arched back nursing in the first week of life (“good” mothers) produces offspring with reduced fearfulness and more modest hypothalamo-pituitary-adrenal (HPA) responses to stress (Weaver et al., 2004). In this model, whole genome transcriptomic analy­ sis of hippocampal tissue revealed more than 900 genes that were differentially expressed between the adult offspring of “good” and “poor” mothers (Weaver et al., 2006). Maternal care was associated with alterations in the pat­ tern of methylation of the glucocorticoid recep­ tor (GR – also designated NR3C1, for nuclear receptor sub-family 3, group C, member 1) gene and altered histone acetylation within the hip­ pocampus which became apparent within the first week of life and persisted into adulthood (Weaver et al., 2004). Importantly, these aberrant epigenetic marks could be reversed by crossfostering. In addition, central infusion of tricho­ statin A (a histone deacetylase inhibitor) ablated the effects of maternal care on histone acetylation, DNA methylation, GR expression, and HPA responses to stress (Weaver et al., 2004). These findings support the hypothesis that epigenetic processes in the brain provide a mechanism through which maternal care influences longterm responses to stress in the offspring (Weaver et al., 2004). Interestingly, “good” maternal care resulted in demethylation of very specific CpG sites corresponding with the nerve growth factor-inducible protein A (NGFI-A) transcrip­ tion factor response element in exon 17 of the GR promoter (Weaver et al., 2004). A recent study in mother–infant pairs pro­ vides support for the hypothesis of environ­ mental “programming” of the HPA axis by maternal factors in humans (Oberlander et al., 2008). Methylation of specific CpG residues in the potential NGIF-A consensus binding site within exon 17 of the glucocorticoid receptor gene (NR3C1) in neonatal cord (venous) blood

1.  From Brain to Behavior

15.6  Physical activity, epigenetic markings and obesity

mononuclear cells correlated with exposure to maternal depression in the third trimester of pregnancy (Oberlander et al., 2008). Importantly, this increased methylation correlated posi­ tively with HPA stress reactivity assessed as the change in salivary cortisol concentration in response to a non-noxious stressor (Oberlander et al., 2008). Given the lower risk of childhood (Arenz et al., 2004) and perhaps adult (Owen et al., 2005) obesity among those who have been breastfed, it is tempting to speculate that the nature of maternal care in the early post-natal period may have profound effects on adult health through altered programming of behaviors mediated by epigenetic mechanisms in the brain.

15.5  An epigenetic basis for developmental programming of obesity? There is now strong evidence from both obser­ vational studies in humans and experiments in animal models that nutritional insults during intrauterine and early post-natal development enhance the risk of increased adiposity later in life. Intriguingly, both maternal under-nutrition (leading to low birth weight) and maternal obes­ ity (associated with greater birth weight and adiposity) increase risk of childhood and adult obesity (Taylor and Poston, 2007). Whether simi­ lar molecular and cellular mechanisms underlie the phenotypic convergence resulting from these two contrasting adverse nutritional exposures remains to be discovered, but it seems likely that both cause hypothalamic “malprogramming” (Plagemann, 2005). The adipokine leptin appears to be the dominant factor, providing the brain with long-term information about the status of energy reserves in adipose tissue by binding to the leptin receptor in the hypothalamus and acti­ vating the JAK–STAT and other signal transduc­ tion pathways (Badman and Flier, 2005). There

197

is growing evidence that leptin concentrations in the early post-natal period may play a central role in hypothalamic programming (reviewed by Taylor and Poston, 2007). For example, oral dos­ ing with physiological amounts of leptin during the suckling period in rats resulted in reduced body fat content in adulthood, and altered hypothalamic expression of a number of genes involved in leptin signaling (Pico et al., 2007). Of particular interest was the lower expression of suppressor of cytokine signaling 3 (SOCS3), an important mediator of leptin resistance, which may produce enhanced sensitivity to leptin in the regulation of food intake (Pico et al., 2007). Since leptin is present in human breast milk but not in infant formula, it is possible that leptin supply during breast-feeding may contribute to the “protection” against obesity (Pico et al., 2007) seen among those who have been breast­ fed (Arenz et al., 2004). The mechanism through which leptin (or other exposures) alters SOCS3 expression remains to be discovered. However, this may involve an epigenetic mechanism, since the SOCS3 gene contains a large CpG island extending from the promoter region into exon 2, and aberrant methylation is associated with altered expression of the gene and disruption of JAK–STAT signaling (Niwa et al., 2005). Given the centrality of the hypothalamus in the control of food intake, there is an a priori case that epi­ genetic dysregulation of expression of appetite regulatory genes and/or of associated receptors and signaling cascades may play an important role in the programming of obesity.

15.6  Physical activity, epigenetic markings and obesity In contrast with the expanding body of evi­ dence that dietary factors have wide-ranging effects on epigenetic marks and, in so doing, may modulate risk of obesity, very little is known

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about the impact of physical activity on the epi­ genome or, conversely, about how epigenetically altered regulation of gene expression might influ­ ence willingness to undertake (or capacity for) physical activity. However, in the cancer field, there is epidemiological evidence for associa­ tions between physical activity and gene methyl­ ation. In a study of promoter methylation of a panel of six genes in colonic tumors, the number of methylated CpG islands increased with age but, perhaps surprisingly, fewer were methylated in those with higher BMI (Slattery et al., 2007). This study found no relationship between level of physical activity and number of methylated genes, but there was evidence that those report­ ing high physical activity had a lower risk of both CIMP-low and CIMP-high tumors (CIMP  CpG island methylator phenotype) (Slattery et al., 2007). The widespread genomic derangements in tumors make it difficult to ascribe causality to such observations, and studies of non-tumor tis­ sue can be more informative. The likelihood of promoter hypermethylation of the tumor sup­ pressor gene APC in non-malignant breast tissue is inversely related to recent and lifetime meas­ ures of physical activity (Coyle et al., 2007). Given that physical activity appears to lower the risk of breast cancer, and that the loss of function of APC (by promoter methylation or mutation) is mecha­ nistically important in tumor development, these data support the hypothesis that physical activity might be protective through reducing the likeli­ hood that aberrant epigenetic marking will dis­ able key defense genes. The mechanism(s) through which physical activity appears to impact on epigenetic mark­ ings are not understood, but inflammation is potentially a critical mediator. There is good evi­ dence that ulcerative colitis (a common type of inflammatory bowel disease) is associated with higher methylation of several genes in the human colonic mucosa (Issa et al., 2001), and chronic gas­ tric inflammation is accompanied by increased methylation of several genes (Kang et al., 2003). More recently, global DNA hypermethylation in

peripheral blood leukocytes was correlated with chronic systemic inflammation (based on circu­ lating concentration of C-reactive protein) and shown to be associated significantly with both allcause and cardiovascular disease mortality even after adjustment for age, inflammation, and other risk factors (Stenvinkel et al., 2007). Since lack of physical activity and obesity are each associated with a chronic inflammatory state (Handschin and Spiegelman, 2008) it is reasonable to suppose that both may have effects on epigenetic marks, and disentangling cause from consequence will be a considerable challenge. Currently, there is major interest in the role of the power­ ful transcriptional co-activator PGC1 (peroxi­ some-proliferator-activated receptor  (PPAR) co-activator 1) as the master down-regulator of inflammation in response to exercise (Handschin and Spiegelman, 2008), and it will be important to discover whether expression of the gene encod­ ing PGC1 is epigenetically regulated.

15.7  Concluding comments The topology of the epigenomic landscape provides a sophisticated and long-lasting set of signals for regulating gene expression in a given cell under particular circumstances, and across cell generations. However, these epigenetic marks are plastic and respond to environmental exposures, including diet. It is therefore prob­ able that epigenetic processes are a major mecha­ nism through which nutrition modulates health throughout the life-course. Technologies for char­ acterizing the epigenomics landscape are readily available (Esteller, 2007) and developments in this area are expected to accelerate. Epigenetics has been identified by the National Institutes of Health as an emerging frontier of science (http://nihroadmap.nih.gov/epigenomics/). In contrast with the rapid advances in under­ standing of the role of epigenetics in the etiology of cancers (Esteller, 2008), there has been little

1.  From Brain to Behavior

References

research on epigenetic mechanisms in the devel­ opment of obesity, and the field is open for novel investigations of, for example, how expression of the genes responsible for regulating food intake and energy expenditure are controlled epigeneti­ cally. Small differences in expression sustained over long periods of time would be expected to have profound effects on energy balance and, therefore, risk of obesity. The tools, including bioinformatics approaches (McKay et al., 2008), necessary to support research on nutritional epi­ genomics and obesity are there to be used.

Acknowledgments Nutritional epigenomics research in my laboratory is funded by the BBSRC and EPSRC through the Centre for Integrated Systems Biology of Ageing and Nutrition (CISBAN) (BB/C008200/1), by the BBSRC (grant no. BH081097) and by NuGO “The European Nutrigenomics Organisation; linking genomics, nutrition and health research” (NuGO; CT2004-505944), which is a Network of Excellence funded by the European Commission’s Research Directorate General under Priority Thematic Area 5, Food Quality and Safety Priority, of the Sixth Framework Programme for Research and Technological Development. Further informa­ tion about NuGO and its activities can be found at http://www.nugo.org.

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C H A P T E R

16 The Role of Early Life Experiences in Flavor Perception and Delight Julie A. Mennella and Gary K. Beauchamp Monell Chemical Senses Center, Philadelphia, PA, USA

o u tl i n e 16.1 Introduction

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16.1  Introduction Food is much more than a source of calories, since its flavor can signal nutrient sources, provide pleasure (or pain) and, through experience, be identified with one’s family, community and culture. The pleasure experienced upon ingestion of a food is a complex process mediated by the chemical senses (taste and smell and irritant

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16.3 Taste and Development 16.3.1 Sweet Taste 16.3.2 Salt Taste 16.3.3 Bitter Taste

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Acknowledgments

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properties of foods) in the periphery and then multiple brain substrates, which are remarkably well conserved phylogenetically (Berridge and Kringelbach, 2008). The degree to which the chemicals that stimulate these flavor senses are liked or disliked is determined by innate or inborn factors, learning and experience, and the interactions among these. In essence, these senses, which are already well-developed at birth (for review, see Ganchrow and Mennella, 2003),

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function as gatekeepers throughout one’s life. They control one of the most important decisions an animal makes – whether to reject a foreign substance or take it into the body. Furthermore, these senses function to inform the gastrointestinal system about the quality and quantity of the impending rush of nutrients. Although the modernization and industrialization of the food supply has produced many benefits, unanticipated consequences from eating diets rich in sugars, salt and fats have become increasingly commonplace (Gidding et al., 2009). Excessive intake of foods containing high amounts of salt or sugars (and consequently foods that taste salty and sweet) causes or exacerbates a number of illnesses. For example, high intake of salt has been linked to hypertension in some individuals, and there is a broad, but not universal, agreement that decreasing salt intake on a population-wide basis could save many lives (Hooper et al., 2004). Similarly, excessive intake of refined sugars has been linked to the metabolic syndrome and, perhaps less persuasively, to obesity (Reed and McDaniel, 2006). Thus, it is recommended that both adults and children limit the amount of salt and simple sugars; minimize excessive intakes of energy, saturated fat, trans fat and cholesterol; and favor diets rich in vegetables and fruits, whole grains, low- and non-fat dairy products, legumes, fish and lean meat (Gidding et al., 2005; Lichtenstein et al., 2006). Despite such recommendations, neither adults nor their children are complying. The 2004 Feeding Infants and Toddlers Study in the US alarmingly revealed that while toddlers were more likely to be eating fruits than vegetables, one in four did not even consume one vegetable on a given day (Briefel et al., 2006; Mennella et al., 2006). Instead, they, like older children (Siega-Riz et al., 1998; Mannino et al., 2004; Nicklas et al., 2004; Schmidt et al., 2005), were more likely to eat fatty foods such as French fries, sweet- and salty-tasting snacks and sweet beverages, and less likely to eat

bitter-tasting vegetables (Briefel et al., 2006; Mennella et al., 2006). None of the top five vegetables consumed by toddlers was a dark green vegetable (Mennella et al., 2006). Not only is the consumption of fruits and vegetables generally low in pediatric populations (Briefel et al., 2006; Mennella et al., 2006), but acceptance of these foods is difficult to enhance beyond toddlerhood (Wardle et al., 2003a, 2003b). Moreover, despite participation in high-quality dietary intervention programs, snacks, desserts and pizza continue to contribute heavily in the diets of elementary school students (Van Horn et al., 2005). One reason for why it is difficult to alter children’s dietary intake is the remarkably potent rewarding properties of the flavors of foods. This chapter will focus on the biological imperatives that shape food and flavor likes and dislikes, and will take a developmental approach since, although some changes in preference occur during adolescence, many food preferences are firmly in place by the time a child reaches the age of 3 years (Resnicow et al., 1998; Skinner et al., 2002a; 2002b; Cooke et al., 2004; Nicklaus et al., 2004, 2005). Because the senses of taste and smell are the major determinants of whether young children will accept a food (that is, they eat only what they like (Birch, 1998)), these senses take on even greater significance in understanding the bases for food choices in children than they do for adults. In what follows, it will be argued that the type of foods preferred or rejected by children reflects their basic biology. We focus on the ontogeny of sweet, salty and bitter tastes because these tastes have been most extensively studied, are directly involved in choices of specific foods of concern (for example, sweet and salty snacks, green vegetables), and exhibit age-related changes in function. Although flavors associated with fats and fatty acids may also be detected, in part, by the sense of taste, there is insufficient evidence to review the ontogeny of fat taste. However, given children’s preference

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16.2  Flavor and the ontogeny of the senses

(Fisher and Birch, 1995) and the rewarding properties of fats (see, for example, Johnson et al., 1991), this is certainly a research topic worthy of further investigation. The inherent plasticity of the chemical senses and how, as a consequence of post-natal maturation and early life experiences, developmental processes act to ensure that a child is not restricted to a narrow range of foodstuffs by virtue of few preferences and strong aversions for foods will be discussed. First, though, the chapter begins by providing a basic understanding of taste, smell and chemical irritation, the differences between them, and how they interact to produce the overall impression of a food which we define as its flavor.

16.2  Flavor and the ontogeny of the senses The perceptions arising from the senses of taste, smell and chemical irritation combine in the oral cavity to determine flavor. These perceptions are often confused and misappropriated (Rozin, 1982), with such olfactory sensations as vanilla, fishy, chocolate and coffee being erroneously attributed to the taste system per se when, in fact, much of the sensory input is due to retronasal olfaction (see below).

16.2.1  Taste The taste system is attuned to a small number of perceptual classes of experience, the so-called basic tastes, each of which specifies crucial information about nutrients or dangerous substances. This small number of primary taste qualities (e.g., sweet, salty, bitter, sour and savory or umami) is detected by specialized receptors on the tongue, other parts of the oral cavity and even in the gastrointestinal system (Bachmanov and Beauchamp, 2007; Egan and Margolskee, 2008). These basic tastes either stimulate intake (sweet, salty and savory) or inhibit it (bitter and

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perhaps sour) when ingested within a generally restricted range of concentrations. Major progress has been made in identifying the initial events in taste recognition (for more extensive reviews, see Chandrashekar et al., 2006; Kim et al., 2006; Bachmanov and Beauchamp, 2007; Lumpkin and Caterina, 2007; Katz et al., 2008). It appears that two different strategies have evolved to detect taste molecules. For salty and sour tastes, it is widely believed that ion channels serve as receptors. Here, H (sour) and Na (salty) ions interact with channels in the taste cell membrane. The cell is then activated, and sends an electrical message to the brain. However, for both of these taste qualities the molecular identity of the receptors and their exact mechanisms are still unknown. For sweet, umami and bitter tastes, G-proteincoupled receptors (GPCRs) appear to play the most prominent roles. These GPCRs bind taste molecules in a sort of lock-and-key mechanism, thereby activating the taste cell to send an electrical message to the brain. For sweet and umami, a family of three GPCRs, named T1R1, T1R2 and T1R3, act in pairs (T1R1  T1R3 for umami and T1R2  T1R3 for sweet) to detect molecules imparting these taste qualities. Other GPCRs may also be involved. A substantially larger family of GPCRs, the T2Rs (n  25), constitutes the bitter receptors. From an evolutionary perspective, these taste qualities likely evolved to detect and reject that which is harmful and to seek out and ingest that which is beneficial. It has been hypothesized that the small number of taste qualities evolved because of the functional importance of the primary stimuli (e.g., sugars, sodium chloride, amino acids and protein, organic acids, bitter toxins) in nutrient selection, especially in children. Preference for salty and sweet tastes is thought to have evolved to attract us to minerals and to energy-producing sugars and vitamins, respectively. Rejection of bitter-tasting and irritating substances evolved to protect the animal from being poisoned and the plant producing

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these chemicals from being eaten (Jacobs et al., 1978; Glendinning, 1994). However, while bitter tastes are innately disliked, with experience people may come to like certain foods that are bitter, particularly some vegetables, and foods and beverages with pharmacologically active bitter compounds, such as caffeine or ethanol.

16.2.2  Olfaction The organization of the olfactory system reflects the need to recognize a wide range of odors and to discriminate one odor from another. In fact, the olfactory receptors are encoded by the largest mammalian superfamily of genes (Buck and Axel, 1991). In contrast to the taste system, there are thousands of diverse odor qualities. Volatile molecules (odorants) bind to olfactory receptors located on a relatively small patch of tissue high in the nasal cavity. Odor molecules can reach these receptors by entering the nostrils during inhalation (orthonasal route) or traveling from the back of the oral cavity toward the roof of the nasal pharynx (retronasal route). It is this retronasal stimulation arising from the molecules of foodstuffs that leads to many of the flavor sensations we experience during eating. Although there is some evidence that certain odors may be innately biased in a positive or negative direction (Khan et al., 2007), individual experiences largely determine how much a person likes or dislikes the odor component of a food or beverage flavor. Through experiences, odors acquire personal significance (Epple and Herz, 1999; Mennella and Forestell, 2008; Mennella and Garcia, 2000). Memories evoked by odors are more emotionally charged and resistant to change than those evoked by other sensory stimuli (Herz and Cupchik, 1995; Epple and Herz, 1999). The unique processing of olfactory information (Cahill et al., 1995) and the olfactory system’s immediate access to the neurological substrates underlying non-verbal

aspects of emotion and memory (Royet and Plailly, 2004) help explain the large emotional component of food aromas. This, coupled with the recent finding that the most salient memories formed during the first decade of life will likely be olfactory in nature (Willander and Larsson, 2006), explains how food aromas can trigger memories of childhood, and why flavors and food aromas experienced during childhood remain preferred and can, to some extent, provide comfort.

16.2.3  Chemical irritation Sensations resulting from chemicals stimulating receptors and free nerve endings of the trigeminal and vagal nerves lead to oral, nasal and pharyngeal sensations such as pain, heat, coolness, tingling, tickle and itch. Recent research has shown that a family of transient receptor potential (TRP) channels is involved in detecting many of these chemicals (Bautista et al., 2006; Liman, 2007). These channels also respond to actual heat and cooling. While “irritating” sensations are critical in food and flavor acceptability, and most likely have a huge impact on acceptance by children, there is virtually no research on their ontogeny. Thus, the remainder of this chapter focuses only on taste and smell.

16.2.4  Ontogeny of the flavor senses Both taste and olfactory systems are welldeveloped and functioning before birth (for review, see Ganchrow and Mennella, 2003). The anatomical substrates mediating the detection of taste stimuli make their first appearances at around the seventh or eighth week of gestation, and by the thirteenth to fifteenth weeks, the taste bud in which taste receptor cells arise begins to morphologically resemble the adult bud, except for the cornification overlying the papilla (Bradley and Stern, 1967; Bradley and

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16.3  Taste and development

Mistretta, 1975). Taste receptor cells are capable of conveying gustatory information to the central nervous system by the last trimester of pregnancy. This information is available to systems organizing sucking, facial expressions, and other affective behaviors. With regard to olfaction, the olfactory bulbs and receptor cells needed to detect olfactory stimuli have attained adult-like morphology by the eleventh week of gestation (Humphrey, 1940; Pyatkina, 1982). Olfactory marker protein, a biochemical correlate of olfactory receptor functioning, has been identified in the olfactory epithelium of human fetuses at 28 weeks of gestation (Chuah and Zheng, 1987). Because the external nares (nostrils) are opening between the sixteenth and twenty-fourth gestational weeks, there is a subsequent continual movement of amniotic fluid through the nasal passages such that, by the last trimester of pregnancy, the fetus inhales more than twice the volume of amniotic fluid it swallows. The chemical composition of this fluid, and hence its flavor, changes constantly, in part because of the passage of food flavors from the maternal diet (Hepper, 1988; Mennella et al., 1995; Schaal et al., 2000). Even in air-breathing organisms, volatile molecules must penetrate the aqueous mucus layer covering the olfactory epithelium to reach receptor sites on the cilia. Thus, there is no fundamental distinction between olfactory detection of airborne versus waterborne stimuli during fetal life.

16.3  Taste and development From the perspective of taste development, what children like to eat (e.g., sweet cereals, desserts, salty snacks) and do not like to eat (e.g., green vegetables) is not surprising. Children are programmed, through the sense of taste, to like foods and beverages that taste sweet or salty, and to dislike bitter ones (Cowart et al., 2004).

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16.3.1  Sweet taste Intense liking for sweet taste is evident early in ontogeny. Within the first few hours of life, consistent, quality-specific facial expressions such as smiling and relaxation of facial muscles are elicited when infants taste sweet­tasting solutions (Steiner, 1977; Rosenstein and Oster, 1988). This suggests that the liking for sweet reflects basic human biology, and is not solely a product of modern-day technology and advertising. For infants and children around the world, the general rule seems to be the sweeter the better (for review, see Liem and Mennella, 2002; Mennella, 2008). Preferences for sweets remain heightened throughout childhood (Beauchamp and Moran, 1984; Mennella et al., 2005; Pepino and Mennella, 2005a) and early adolescence (Desor et al., 1975), but then decline to adult levels during late adolescence (Desor and Beauchamp, 1987). In a cross-sectional study that measured sweet preference in more than 750 participants, 50 percent of the children and adolescents, but only 25 percent of the adults, selected a 0.60-M sucrose concentration as their favorite solution. To put this in perspective, a 0.60-M sucrose concentration is equivalent to approximately 12 spoonfuls of sugar in 230 ml of water (an 8-ounce glass), whereas a typical cola is about half of this sucrose (or sucrose equivalent) concentration. Making foods, beverages and even medications taste sweet can increase both liking and acceptance by children (Filer, 1978; Beauchamp and Moran, 1984; Sullivan and Birch, 1990). This strong preference for sweet tastes may have an ecological basis. At birth, a sweet-liking may help to ensure the acceptance of sweet-tasting mother’s milk. As children begin to eat solid foods, their sweet preference attracts them to foods, such as fruits, that are associated with energy-producing sugars, minerals and vitamins. Although strong evidence is lacking, it has been suggested that such preferences evolved to solve a basic nutritional problem

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of attracting children to sources of high energy during periods of maximal growth (Simmen and Hladik, 1998; Drewnowski, 2000; Coldwell et al., 2009). Although the liking for sweet-tasting substances is inborn, the degree to which early experiences alter or modulate sweet preferences later in life is largely unknown. Longitudinal studies revealed that babies who were routinely fed sweetened water during the first months of life exhibited a greater preference for sweetened water when tested at 6 months (Beauchamp and Moran, 1982) and then again at 2 years of age (Beauchamp and Moran, 1984) when compared to those who had little or no experience with sweetened water. Similarly, a more recent crosssectional study on 6- to 10-year-old children revealed that such feeding practices may have longer-term effects on the preference for sweetened water than previously realized (Pepino and Mennella, 2005a). However, there are no compelling data suggesting that repeated exposure to sugar water results in a generalized heightened hedonic res­ ponse to sweetness (Beauchamp and Moran, 1984). Rather, the context in which the taste exp­erience occurs is an important factor. Through familiarization, children develop a sense of what should, or should not, taste sweet (Beauchamp and Cowart, 1985). The cultures in which children live and their early-life experiences enable them to develop a sense of how foods should taste. If the goal is to limit consumption of sweet foods and beverages, children’s preferences for sweetness may not be the only barrier. Sweet-liking may also have its roots in how sweets make children feel. A small amount of a sweet solution placed on the tongue of a crying newborn can blunt expressions of pain and calm both preterm and full-term infants who have been subjected to painful events such as heel stick or circumcision, presumably via the involvement of the endogenous opioid system (Blass and Hoffmeyer, 1991; Barr et al., 1999).

Afferent signals from the mouth, rather than gastric or metabolic changes, appear to be responsible for the analgesic properties of sugars (Barr et al., 1999; Ramenghi et al., 1999; Bucher et al., 2000). The ability of sweets to reduce pain continues during childhood (Miller et al., 1994; Pepino and Mennella, 2005b), and the more children like sucrose, the better it works in increasing pain tolerance during the cold pressor test (Pepino and Mennella, 2005b). Thus, it is important to realize that trying to limit consumption of sweet-tasting foods and beverages may be more difficult for some children or certain ethnic groups (Desor et al., 1975; Bacon et al., 1994; Pepino and Mennella, 2005a) because of individual differences in the inherent hedonic value of sweet tastes and how sweets make a person feel.

16.3.2  Salt taste Children’s avidity for salt is more complex and less well understood than that for sweets. A liking for salt water relative to plain water is not evident at birth (Steiner, 1977; Rosenstein and Oster, 1988). Young infants (2–4 months of age) did not detect and differentiate salt solutions from plain water. Rather, the ability to detect salty tastes appears to develop later; it is in most children around 4–5 months of age that a preference begins to be observed (Beauchamp et al., 1986). Moreover, to a greater extent than that observed for sweet taste, the degree of avidity for salt seems to be affected by individual experiences, beginning in utero (Crystal and Bernstein, 1995, 1998; Stein et al., 2006). For example, severe maternal emesis can have an enduring influence on an offspring’s response to salty tastes (Crystal and Bernstein, 1995; Leshem, 1999). Similarly, several behavioral measures related to salty taste preference have been found to be invers­ely related to birth weight over the first 4 years of life (Stein et al., 2006). Because it

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16.3  Taste and development

is generally accepted that excess salt intake can lead to or exacerbate hypertension, we speculate that one mechanism predisposing to high salt intake is the heightened preferences caused by in utero events common to lower birth-weight babies, although the mechanisms underlying this effect of body weight are not known. Like sweet tastes, children prefer substantially higher levels of salt than do adults, and adding salt to many foods can drive consumption (Beauchamp and Moran, 1984; Beauchamp et al., 1994). Factors responsible for this age-related difference are not known. Nevertheless, we do know that salt-liking and preference in infants and young children are regulated to some extent by prior dietary exposure. For example, bottlefed infants exhibit higher salt preferences than do breastfed infants (Beauchamp and Stein, 2008), perhaps due to the greater amounts of sodium in formula relative to breast milk. Other evidence indicates that infants who are fed starchy foods (that likely also contain substantial amounts of salt) early in life have elevated salt preferences compared to infants whose early supplemental feedings do not contain these high-salt foods (Beauchamp and Stein, 2008). The findings relating preference for salty taste with amount of exposure were correlational, and hence do not prove cause and effect. However, studies on adults revealed that the experimental manipulation of salt intake can alter salt-taste perception and preference (Bertino et al., 1982; Beauchamp et al., 1990). When total salt intake is reduced over a substantial period of time, adults prefer lower levels of salt and perceive a given level of salt as being more intense. This taste change, which takes 2 to 3 months, can be rapidly reversed when individuals are returned to their typical dietary salt level (Beauchamp et al., 1990). In conclusion, salty taste preferences begin to be observed at about 4 months of age, and are apparently more plastic than are sweet preferences. Nevertheless, our knowledge of how early exposures impact later preferences and

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intake remains incomplete. It will be important to determine whether early exposure to lowersalt foods can help protect the developing child from excess intake later in life.

16.3.3  Bitter taste A rejection of bitter compounds is common across many phyla, and is thought to reflect the need to avoid consuming toxic compounds. There are, however, many species diff­ erences in sensitivities to bitter compounds and the number of different bitter receptors that are expressed (Go, 2006); these differences are thought to reflect differences in ecological niches and food choices. It is generally assumed that the existence of multiple bitter receptors (there are approximately 25 in humans; Chandrashekar et al., 2000; Mueller et al., 2005) reflects the wide structural variability of bitter compounds, which in turn reflects the evolution of protective compounds by plant species. Plants do not “want” to be eaten, and animals do not “want” to be poisoned. Thus, a strong rejection of bitterness by children is evolutionarily prudent: children may be at particular risk from the ingestion of toxic, bitter compounds. Rejection of bitter tastes is evident early in life, although there seem to be differences based on the bitter compound tested. For example, while human infants respond with highly negative facial expressions to concentrated quinine, significant rejection of urea does not occur until a few weeks after birth (Kajuira et al., 1992). A different developmental timetable for rejecting different bitter compounds may reflect the multiple controls of bitterness sensation that develop at different rates (Margolskee, 2002). Moreover, the 25 different bitter receptors, each likely responsive to one or several structurallyrelated bitter compounds, could be expressed at different times during development. One of the predominant flavor characteristics of the prototypical healthy foods – vegetables – is

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their bitterness. Indeed, many of the apparent health-related benefits of consuming vegetables come precisely from bitter ingredients such as glucosinolates, which at low levels are healthful but at higher levels can be harmful. However, there is a great deal of individual differences in how sensitive people are to specific bitter compounds. The classic example of genetic differences in taste sensitivity is for phenylthiocarbamide (PTC) and the related chemical 6-npropylthiouracil (PROP). Some people can detect these compounds at low concentrations, whereas others need much higher concentrations, or cannot detect them at all (Kim et al., 2003; Bufe et al., 2005; Hayes et al., 2008). The gene TAS2R38, variants which accounts for the majority of this taste polymorphism, codes for one member of the family of taste receptors that respond to bitter stimuli. Recently, it was discovered that variation in this bitter receptor specifically regulates adults’ bitterness perception of cruciferous vegetables known to contain PTC-like glucosinolates (e.g., turnips, broccoli, mustard greens) (Sandell and Breslin, 2006). Children are not only more likely to experience a strong bitter taste from PTC and PROP, but are also more sensitive to it, detecting it at lower concentrations than adults (Blakeslee, 1932; Karam and Freire-Maia, 1967; Anliker et al., 1991; Mennella et al., 2005). This agerelated change in sensitivity for PROP was recently shown to be affected by sequence diversity in the bitter taste receptor TAS2R38 gene. Children who were heterozygous for the common form of this receptor were more sensitive to the bitterness of PROP than were adults with this same form (Mennella et al., 2005). Like sweet and salt preference, the timing of the shift from child-like to adult-like PROP perception occurs during adolescence (Mennella et al., 2010). The age-related change in bitter perception is likely to have a broad impact because of the high allele frequencies of the taster and non-taster haplotypes in the human population.

One effective strategy in reducing the bitterness of certain foods, and thereby increasing their acceptability, is to add salt. This may partly explain the ubiquitous use of salt in cooking evident in many cultures. Sodium salts, particularly sodium chloride (i.e., table salt), impart a desirably salty taste to foods (Kemp and Beauchamp, 1994). One mechanism underlying this increase in palatability may be the suppressing activity of sodium on bitter taste by a mechanism that is still obscure. There are substantial compoundspecific differences in the effectiveness of salt in inhibiting bitterness, presumably reflecting the wide array of bitter compounds and the multiple receptor-transductive pathways for bitterness. Salt also enhances the intensity of sweetness, presumably by blocking bitterness and thereby releasing sweetness from suppression (Breslin and Beauchamp, 1997). Furthermore, like adults (Kroeze and Bartoshuk, 1985; Breslin and Beauchamp, 1995; Keast and Breslin, 2002), the perceived bitterness of some bitter compounds is reduced when such compounds are mixed with sodium salts in children (Mennella et al., 2003). Perhaps a little salt may go a long way in getting children to accept the taste of bitter vegetables. Childhood may represent a time of heightened bitter-sensitivity. As will be discussed in the next section, children’s acceptance of bittertasting foods such as leafy green vegetables can be facilitated with early and repeated exposure (Gerrish and Mennella, 2001; Forestell and Mennella, 2007; Mennella et al., 2008). However, it may be harder to ensure that children who are particularly sensitive to compounds in bitter vegetables are exposed to these, in comparison with bitter-insensitive children. An absence of early exposure to bitterness may, in turn, affect the development of their taste system. In rodents, early taste deprivation remodels the central nervous system (Mangold and Hill, 2007), and experience with bitterness during early life changes bitter taste preferences in adulthood (Harder et al., 1989).

1.  From Brain to Behavior

16.4  Learning about food flavors

16.4  Learning about food flavors The flavor of food is comprised of much more than the basic tastes of sweetness, sourness, bitterness, saltiness and umami or savoriness. The contribution to the overall flavor of the volatile odors of foods, perceived retronasally, is crucial for identifying foods. During the past two decades, a growing body of data has suggested that early experiences with these food volatiles serve as the foundation for lifelong habits. That is, in contrast to taste preferences, preferences for volatile flavor compounds detected by the sense of smell retronasally are generally more highly influenced by experiences, with those occurring early in life being particularly salient (Bartoshuk and Beauchamp, 1994). The sensory environment in which fetuses live, the amniotic sac, changes as a function of the mother’s food choices, since dietary flavors are transmitted and flavor amniotic fluid (Hepper, 1988; Mennella et al., 1995; Schaal et al., 2000). Prenatal experiences with food flavors, which are transmitted from the mother’s diet to the amniotic fluid, lead to greater acceptance and enjoyment of these foods during weaning. This flavor-learning continues when infants are breast-fed, since human milk is composed of volatile flavors which directly reflect the foods, spices and beverages ingested or inhaled (e.g., tobacco) by the mother (Mennella and Beauchamp, 1991, 1993, 1996). In common with other mammals (for review, see Mennella, 2007), early exposure leads to greater liking and acceptance. For example, infants whose mothers ate more fruits and vegetables during pregnancy and lactation were more accepting of these foods during weaning (Mennella et al., 2001; Forestell and Mennella, 2007). That amniotic fluid and breast milk share a commonality in flavor profiles with the foods eaten by the mother suggests that breast milk may “bridge” the experiences with volatile flavors

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in utero to those with solid foods. More­over, the sweetness and textural properties of human milk, such as viscosity and mouth-coating, vary from mother to mother, thus suggesting that breast-feeding, unlike formula feeding, provides the infant with the potential for a rich source of other variations in chemosensory experiences. The types and intensity of flavors experienced in breast milk may be unique for each infant, and serve to identify the culture to which the child is born and raised. In other words, the flavor principles of the child’s culture are experienced prior to their first taste of solid foods. When infants are exposed to a flavor in the amniotic fluid or breast milk and are tested sometime later, the exposed infants accept the flavor more than infants without such experience (Mennella et al., 2001). This pattern makes evolutionary sense, since the foods that a woman eats when she is pregnant and nursing are precisely the ones that her infant should prefer. All else being equal, these are the flavors that are associated with nutritious foods, or at least foods she has access to, and hence the foods to which the infant will have the earliest exposure. In a recent study, it was shown that breast-feeding conferred an advantage when infants first tasted a food, but only if their mothers regularly eat similar tasting foods (Forestell and Mennella, 2007). If their mothers eat fruits and vegetables, breast-fed infants will learn about these dietary choices by experiencing the flavors in their mother’s milk, thus highlighting the importance of a varied diet for both pregnant and lactating women (Forestell and Mennella, 2007). These varied sensory experiences with food flavors may help explain why children who were breastfed were found to be less “picky” (Galloway et al., 2003) and more willing to try new foods (Sullivan and Birch, 1994; Mennella and Beauchamp, 1996), which in turn contributes to greater fruit and vegetable consumption in childhood (Skinner et al., 2002a; Cooke et al., 2004; Nicklaus et al., 2005). Formula

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feeding, quite a new innovation in human infant eating practices, differs from breast milk in that it lacks sensory variety and does not reflect the foods the mother consumes. There is not enough known about how this lack of flavor experience impacts later food choices, but it is reasonable to hypothesize that formula-fed children are at a nutritional disadvantage. Nevertheless, recent research revealed that once infants, regardless if they are breast- or formula-fed, are weaned to solid foods, acceptance can be facilitated by different types of early dietary experience. One type of experience entailed repeated dietary exposure to a particular vegetable or fruit for at least 8–9 days (Sullivan and Birch, 1994; Gerrish and Mennella, 2001; Forestell and Mennella, 2007; Mennella et al., 2008). Like children (Birch and Marlin, 1982), infants ate significantly more of the fruit or vegetable to which they were repeatedly exposed. Merely looking at the food does not appear to be sufficient, since children have to experience the flavor of the food to learn to like it (Birch et al., 1987). Another type of dietary experience does not require actual exposure to the target fruit or vegetable, but rather experience with a variety of flavors. Infants who were repeatedly exposed to a different starchy vegetable each day ate as many carrots after the exposure as did infants who were repeatedly exposed to carrots (Gerrish and Mennella, 2001). Similarly, repeated dietary experience with a variety of fruits enhanced acceptance of a novel fruit, but had no effect on the infants’ acceptance of green vegetables (Mennella et al., 2008). Because rejection of bitter taste is largely innate (Kajuira et al., 1992), infants may need actual experience with bitter taste, more exposures, or a different type of variety experience to enhance acceptance of green vegetables. Additional experimental studies, as well as randomized nutrition interventions that focus on maternal dietary habits and infantile dietary experiences, are needed to better understand how liking for the taste of foods develops (Lucas, 1998).

16.5  Concluding remarks The child’s basic biology, a consequence of a long evolutionary history, does not predispose the child to favor low-sugar, low-sodium and vegetable-rich diets. The sensory and biological considerations reviewed herein shed light on why it is difficult to make lifestyle changes in young children, and why it is difficult for children to eat nutritious foods when these foods do not taste good to them. We cannot easily change the basic ingrained biology of liking sweets and avoiding bitterness. If this is the bad news, the good news arises from our growing knowledge of how, beginning very early in life, sensory experience can shape and modify flavor and food preferences. In other words, what we can do is modulate children’s flavor preferences by providing early exposure, starting in utero, to a wide variety of healthy flavors, and moderating exposure to salt. To this end, the pregnant and nursing mother should widen her food choices to include as many flavorful and healthy foods as possible. Infants of women who do not breastfeed should be exposed repeatedly to a variety of foods, particularly fruits and vegetables, from an early age. Further, mothers should be encouraged to focus on their infants’ willingness to eat the food, and not just the facial expressions made during feeding. They should also be made aware that, with repeated dietary exposure, it may take longer to observe changes in facial expressions than intake (Forestell and Mennella, 2007). Also, infant formula manufacturers should be encouraged to provide lower-salt infant formula that contains flavors of the foods that children will be weaned to (e.g., fruits, vegetables). These experiences, combined with provision of infants and children with nutritious foods and flavor variety as well, should maximize the chance that they will select and enjoy a more healthy diet. Moreover, many of these preferences may last throughout

1.  From Brain to Behavior

REFERENCES

the entire lifespan, and can help overcome the reluctance to consume vegetables. The best predictor of what children eat is whether they like the taste of the food (Resnicow et al., 1998). The reward systems that encourage us to seek out pleasurable sensations and the emotional potency of food- and flavor-related memories initiated early in life together play a role in the strong emotional component of food habits. An appreciation of the complexity of early feeding, and a greater understanding of the cultural and biological mechanisms underlying the development of food preferences, will aid in our development of evidence-based strategies and programs to improve the diets of our children.

Acknowledgments The preparation of this manuscript and much of the research described herein was supported by NIH Grant HD37119 from the National Institutes of Health, USA. We thank Dr Allison Ventura Rubenstein for helpful comments on the manuscript.

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C H A P T E R

17 Implications of the Glycemic Index in Obesity Julia M.W. Wong1,2, Andrea R. Josse1,2, Livia Augustin3, Nishta Saxena1,2, Laura Chiavaroli1,2, Cyril W.C. Kendall1,2 and David J.A. Jenkins1,2,4 1

Clinical Nutrition & Risk Factor Modification Center, St. Michael’s Hospital, Toronto, Canada 2 Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Canada 3 Unilever Health Institute, Unilever Research and Development, Vlaardingen, The Netherlands 4 Department of Medicine, Division of Endocrinology and Metabolism, St Michael’s Hospital, Toronto, Canada

o u t l i n e 17.1 Introduction

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17.5 GI and Obesity

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17.2 The Concept of the Glycemic Index

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17.6 GI and Diabetes

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17.3 Mechanisms of Action

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17.7 GI and Cardiovascular Disease

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17.8 Conclusion

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17.4 Effects of Low GI Foods on Appetite, Food Intake and Satiety 222

17.1  Introduction The growing prevalence of obesity in adults and children is an important public health concern, as these individuals are at greater risk of developing chronic diseases such as coronary

Obesity Prevention: The Role of Brain and Society on Individual Behavior

heart disease (CHD) and diabetes. Nutritional strategies to combat this growing concern have never been more important. The current recommendation of high-carbohydrate diets to help manage weight (Klein et al., 2004) has recently been challenged as the number of people who are classified as overweight (body mass index

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© 2010, 2010 Elsevier Inc.

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17. glycemic index and obesity

(BMI) 25 kg/m2) or obese (BMI 30 kg/m2) (WHO, 2000) continues to rise. Alternative dietary approaches have emerged which vary in their total calories, macronutrient (carbohydrate, fat, and protein) content, energy density and glycemic index, as well as portion control (Klein et al., 2004). However, at the center of this debate are the metabolic effects of carbohydrates: these diets focus on decreasing the total carbohydrate content and increasing the intake of protein. However, the nature of the carbohydrate may be important – that is, slow-release carbohydrates or low glycemic-index (GI) foods versus fast-release carbohydrates or high GI foods. Evidence suggests that there are metabolic advantages to increasing low GI food consumption, and that their consumption should be advised over that of high GI foods.

foods based on the rate of carbohydrate absorption as determined by its postprandial glycemic response compared to a reference standard (Jenkins et al., 1981, 1984). Thus, the GI differentiates carbohydrate-rich foods that result in a lower postprandial blood glucose rise (i.e., low GI foods) from those that produce a larger postprandial blood glucose rise (i.e., high GI foods). As a result, the GI is considered a specific pro­ perty of the food itself and differs from the term “glycemic response”, which is an individual’s change in blood glucose after ingestion of the food (Wolever, 2006). Many starchy staples of traditional cultures have a lower GI, including pasta, some wholegrain breads, cracked wheat or barley, some rices, dried peas, beans and lentils (Jenkins et al., 1980, 1986; Thorne et al., 1983) (Tables 17.1 and 17.2). In cultures such as the Pima Indians and the

17.2 THE CONCEPT OF THE GLYCEMIC INDEX It was traditionally believed that postprandial blood glucose responses were determined by the carbohydrate chain length, often referred to as simple or complex carbohydrates. Over time, increasing experimental evidence has questioned this classification and given rise to the concept of the GI. It suggests, as an extension to the dietary fiber hypothesis first proposed by Burkitt and Trowell (Burkitt and Trowell, 1977), that certain carbohydrates, by virtue of their rate of digestibility and absorption, may provide a strategy to prevent and manage chronic diseases such as diabetes and CHD (Jenkins and Jenkins, 1995). The GI is defined as the incremental area under the blood glucose response curve (IAUC) elicited by a 50-g available carbohydrate portion of a food, expressed as a percentage of the response after the consumption of 50 g of anhydrous glucose or white bread (Wolever, 2006). In other words, it is a numerical classification of carbohydrate

Table 17.1  Glycemic indices of some traditional and contemporary foods Food

GI*

Traditional foods Pasta (spaghetti)

60

Pumpernickel bread

58

Cracked wheat

68

Pearled barley

36

Parboiled rice

68

Beans

39–55

Lentils

36–42

Chickpeas

39

Contemporary foods White bread

101

White bagels

103

Instant mashed potatoes

122

Glutinous white rice

132

Corn flakes

116

*

GI values are based on white bread as the reference food, which has a glycemic index of 100. Source: Adapted from Foster-Powell et al. (2002).

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17.3 Mechanisms of action

Table 17.2  Classification of foods based on GI values*† Low GI (78 or less)

Medium GI (79–99)

High GI (100 or more)

Grains:

Grains:

Grains:

Barley

Wheat roti

White bread

Pasta/noodles

Brown rice

White rice

Fruits & vegetables:

Fruits & vegetables:

Fruits & vegetables:

Strawberries, raw

Pineapple, raw

Banana, ripe

Orange, raw

Grapes, raw

Watermelon

Peaches, raw and canned in natural juice

Pumpkin, boiled

Potato, baking (Russet)

Carrots, boiled

Potato, new/ white

Rice noodles

Extensive research has led to the compilation of data into comprehensive international GI food tables, which have greatly facilitated research and clinical applications of this concept (Foster-Powell et al., 2002; Atkinson et al., 2008). Furthermore, the concept of glycemic load (GL) has been developed to assess the total glycemic impact of the diet. The GL is the product of the dietary GI and the amount of available dietary carbohydrate in a food or diet (Salmeron et al., 1997a).

17.3  Mechanisms of action It has been suggested that the metabolic effects of low GI foods relate to their rate of absorption in the gut (Figure 17.1). Low GI foods are absorbed at slower rates, which in turn results in a lower rise in postprandial blood

Glucose

Australian Aborigines, the relatively recent shift from traditional low GI foods to high GI foods may partially explain the increasing rates of diabetes among these populations (Thorburn et al., 1987; O’Dea, 1991; Boyce and Swinburn, 1993). The GI of traditional common starch foods may also have been affected by recent changes in food processing and manufacturing that reflect a changing consumer demand (Bjorck et al., 2000).

Sweet potato, boiled Sweet corn Other:

Other:

Other:

Legumes

Popcorn

Rice cakes

Chickpeas

Potato crisps

Soda crackers

(a)

Time

Glucose

Kidney beans Lentils Soya beans Milk, skim & full fat Yogurt *

GI values are based on white bread as the reference food, which has a glycemic index of 100. † Canadian values where available. Conversion: 70/100 to glucose scale Source: Adapted from Foster-Powell et al. (2002) and Atkinson et al. (2008).

(b)

Time

Figure 17.1  Hypothetical effect of feeding diets with a (a) low or (b) high glycemic index on gastrointestinal glucose absorption and postprandial blood glucose. Source: Reproduced from Jenkins et al. (2002), with permission.

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glucose and insulin levels. Many factors may influence the rate of carbohydrate absorption of a food and, therefore, its GI value. These include the rate of digestion (Jenkins et al., 1981; Englyst et al., 1999) and transit time (Englyst et al., 1992), food form (physical form, particle size), type of preparation (cooking method and processing) (Haber et al., 1977; O’Dea et al., 1980; Jenkins et al., 1982a; Sheard et al., 2004), ripeness (Englyst and Cummings, 1986), nature of the starch (predominance of amylose or amylopectin) (Wursch et al., 1986; Sheard et al., 2004), monosaccharide components, presence of antinutrients such as -amylase inhibitors (Isaksson et al., 1982; Yoon et al., 1983), and the amount and type of fiber, fat and protein content (Thorne et al., 1983; Krezowski et al., 1986). The metabolic effect of a reduced rate of absorption has been demonstrated in studies of healthy individuals as well as of people with type 2 diabetes, and when carbohydrates are ingested slowly over a prolonged period of time. For example, when a glucose solution was sipped at an even rate over 180 minutes in comparison to the same amount of glucose taken as a bolus, a marked decrease in insulin secretion and lower serum free fatty acid (FFA) levels were observed (Jenkins et al., 1990). This improvement was also observed after consuming low GI foods. This may be due in part to a sustained tissue insulinization, a suppressed FFA release and the absence of counter-regulatory endocrine responses (Wolever et al., 1988; Jenkins et al., 1990; Ludwig et al., 1999), hence resulting in minimal hormonal fluctuations. Over time, glucose is removed from circulation at a faster rate and blood glucose concentrations return toward baseline despite continued glucose absorption from the gut. This results in an improved postprandial peak and incremental area under the glucose curve. Other studies have demonstrated an improved “second meal” effect, such that an intravenous glucose tolerance test shows a more rapid uptake of glucose (increased KG) after sipping than after the bolus

drink (Jenkins et al., 1990). The improved postprandial glycemia of the second meal may be related to the prolonged suppression of FFA levels (Jenkins et al., 1982b).

17.4  Effects of low GI foods on appetite, food intake and satiety It has been proposed that low GI foods have properties that may make them potentially bene­ ficial for weight control. These include the ability to promote satiety and delay hunger, reduce fluctuations in glycemia and insulinemia, promote higher rates of fatty acid oxidation, and minimize the decline in metabolic rate during energy restriction (McMillan-Price and BrandMiller, 2006). However, the reverse has also been observed in acute studies. High, not low, GI foods have been associated with satiety and reduced food intake (Anderson and Woodend, 2003a). This is observed in studies where subjects are given various preloads and short-term (e.g., 1–2 hours) food intake is measured after consumption of the preload (Holt et al., 1995; Woodend and Anderson, 2001; Anderson et al., 2002). Satiety may be increased in the short term with the rapid increase in blood glucose after the intake of high GI foods, whereas the intake of low GI foods may be more effective in sustaining satiety in the long-term (Anderson and Woodend, 2003b; van Amelsvoort and Weststrate, 1992). Over 50 years ago, the glucostatic theory first suggested a link between blood glucose concentrations and appetite sensations. More specifically, high blood glucose utilization was considered to signal satiety and the termination of feeding, whereas low blood glucose utilization was believed to trigger the onset of feeding (Mayer, 1955). This theory continues to generate interest, as seen by a growing number of studies still exploring this concept. Proponents of the theory agree that meal initiation is dependent on

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17.4  Effects of low GI foods on appetite, food intake and satiety

the transient declines in blood glucose (i.e., patterns in blood glucose) (Campfield and Smith, 2003), whereas opponents support the theory that low GI foods are more satiating because of their lower rate of digestion and absorption from the gut, by modulating the appetite-controlling gut hormones, and not just to postprandial gly­cemia alone (Holt et al., 1992; Jenkins et al., 1982c). Reviews of short-term studies of GI and appetite generally demonstrate that increased satiety, delayed return of hunger or decreased ad libi­ tum food intake after the consumption of low compared with high GI foods, as measured by visual analog scales or subsequent meal intakes (Ludwig, 2000; Roberts, 2000; Ebbeling and Ludwig, 2001). However, others have found no consistent association between GI, appetite and food intake (Raben, 2002), and it has also been reported that, acutely, high GI foods are more satiating (Anderson and Woodend, 2003a). It is worth noting that a number of these studies did not completely control for differences in the test diets; differences in variables such as energy density, macronutrient content or palatability may or may not have affected the results (Roberts, 2000). In another study, the effect of high, medium or low GI breakfast meals on subsequent ad libi­ tum meal intake was investigated in obese teenage boys (Ludwig et al., 1999). It was observed that voluntary energy intake was significantly reduced by 53 percent and 81 percent after the medium and low GI meals respectively, compared to the high GI meal. These results suggested that low GI meals had a greater effect on satiety and subsequent food intake compared to an isocaloric high GI meal. High GI foods tend to increase the rate of carbohydrate absorption, cause large blood glucose and hormonal (insulin/glucagon) fluctuations and, together with reduced satiety, promote excess food intake over time (Haber et al., 1977; Ludwig et al., 1999). Many studies looking at appetite, satiety and food intake were conducted in the short term, and may not be indicative of what might occur in the long term. Further studies will need to be

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conducted to determine whether these effects are observed in the long term. The gastrointestinal tract releases a number of regulatory peptide hormones that influence certain physiological processes, including gut motility and short-term feelings of hunger and satiety. These gut hormones include ghrelin, peptide YY, cholecystokinin (CCK), pancreatic polypeptide, amylin, glucose-dependent insulinotropic polypeptide (GIP), glucagon-like peptide-1 (GLP-1), oxyntomodulin, gastrin and secretin (Murphy and Bloom, 2006). Two specific hormones, GIP and GLP-1, have important effects on insulin action (Drucker, 2007), which may be relevant to the inclusion of low GI foods for the treatment of hyperinsulinemic conditions (e.g. metabolic syndrome, first stages of type 2 diabetes) and in diabetes prevention. There is some evidence indicating that slowly absorbed carbohydrates induce a lower acute response in GIP and GLP-1 (Jenkins et al., 1982b, 1990; Juntunen et al., 2002), but this does not appear to affect pancreatic polypeptide responses (Jenkins et al., 1990). The GI of a meal has also been found to be inversely associated with the perception of satiety and CCK levels, a hormone involved in appetite suppression (Holt et al., 1992). Furthermore, the addition of viscous fiber in the form of beans (Bourdon et al., 2001) or barley (Bourdon et al., 1999) has been shown to increase postprandial CCK responses. This suggests a possible role for the gastric volume and the bulkiness of food in the maintenance of appetite suppression. In clinical trials, dietary adherence is often an important consideration when relating findings to everyday practices. There are many factors that affect dietary adherence, including taste – also known as palatability (Lloyd et al., 1995; Glanz et al., 1998; Brekke et al., 2004). Many studies assessing the palatability of low GI diets in comparison to high GI diets have shown that they are equally acceptable (Jimenez-Cruz et al., 2003, 2004; Ebbeling et al., 2007; Jenkins et al., 2008; Wolever et al., 2008). Furthermore, not only is there a lack of studies demonstrating poor acceptability

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of low GI diets, but some studies also show they are the preferred diet choice (Gilbertson et al., 2001; Barnard et al., 2009). For example, the study by Gilbertson and colleagues (2001) looked at glycemic control in children with type 1 diabetes given dietary advice on following either a low GI diet or a carbohydrate-exchange diet. A clear preference for the low GI dietary regimen by both the children and their parents who experienced the two types of dietary advice was demonstrated by quality of life questionnaires, and was the choice of diet continued after completion of the study (Gilbertson et al., 2001).

17.5  GI and obesity Low GI diets may play a potential role in body-weight regulation in that the type of carbo­ hydrate may be more important than the total amount. This is supported by a number of popu­ lation studies. In a seasonal variation of blood cholesterol study of 572 individuals, the GI was associated with a higher body mass index, thereby suggesting that the type of carbohydrate is important in determining its effect on body weight (Ma et al., 2005). Similarly, the EURODIAB Complications Study of over 3000 individuals with type 1 diabetes found that a lower GI diet was associated with lower levels of waist-to-hip ratio and waist circumference (Toeller et al., 2001). Several studies have looked at the effects of low GI weight-loss diets on body weight or composition, compared to a high GI diet. Slabber and colleagues (1994) compared two energy-restricted diets of either high or low GI in healthy obese females for 12 weeks in a parallel study (n  30), followed by some subjects crossing over to the alternate treatment for another 12 weeks (n  16) after a washout period. Both diets resulted in a significant reduction in weight after the parallel study (9.3 kg low GI vs 7.4 kg high GI), but after the crossover study, the low GI diet resulted in a greater reduction in body weight than did the high GI diet (7.4 kg v. 4.5 kg respectively, P  0.04)

(Slabber et al., 1994). Bouché and colleagues (2002) looked at 11 healthy men who were randomized into a 5-week low or high GI diet in a crossover design. Body weight remained comparable between the two diets after the intervention periods. However, the low GI diet resulted in a greater reduction in body fat mass (700 g reduction) and an increased lean body mass as measured by dual-energy X-ray absorptiometry (DEXA). The reduction in body fat mass was mainly attributable to a decline in trunk fat (Bouché et al., 2002). Similarly, a study of 14 obese adolescents who received an energy-restricted low GI and low GL diet for 6 months followed by a 6-month follow-up demonstrated a significant reduction in both body weight (at 12 months) and fat mass (at 6 and 12 months), as measured by DEXA, as compared to the energy-restricted lowfat diet group (Ebbeling et al., 2003). Despite these positive findings of the effects of low GI diets on body weight and composition, some studies have shown no benefit (Frost et al., 2004; Sloth et al., 2004; Ebbeling et al., 2005, 2007). At this time, there is no consensus as to the effect of low GI diets on body weight or composition. However, low GI diets may still reduce risk factors for CHD and diabetes, which are often present in those who are overweight or obese (Grundy et al., 2004). This issue needs to be addressed in long-term studies with large sample sizes and well-controlled dietary interventions where only the GI differs. Care should be taken to ensure that the intervention diets are matched in palatability, energy density, fiber content and macronutrient composition (Sloth and Astrup, 2006).

17.6  GI and diabetes Several studies have looked at dietary GI in relation to the risk and management of type 2 diabetes. Large prospective cohort studies investigating the association between GI and the risk of type 2 diabetes have found a positive relation,

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17.7  GI and cardiovascular disease

where higher dietary GI resulted in increased diabetes risk (Salmeron et al., 1997a, 1997b; Hodge et al., 2004; Schulze et al., 2004). However, this was not observed in the Iowa Women’s Health Study and the Atherosclerosis Risk in Communities (ARIC) Study (Meyer et al., 2000; Stevens et al., 2002). The first found no association between GI and GL with type 2 diabetes; this is possibly because of the inclusion of an elderly cohort, which could have introduced a selection bias (Meyer et al., 2000). The ARIC Study also observed no association; this may relate to the dietary assessment tool used, which was not specifically designed to assess GI (Stevens et al., 2002). Two recent meta-analyses summarizing the effects of low GI diets on risk factors for diabetes and CHD demonstrated a significant reduction in fructosamine and hemoglobin A1c (HbA1c) in those receiving low GI diets (Kelly et al., 2004; Opperman et al., 2004), but no significant changes in blood glucose or insulin (Kelly et al., 2004). One meta-analysis of 14 randomized controlled trials comparing low GI diets to conventional or high GI diets and glycemic control in individuals with diabetes found that the low GI diets were able to reduce glycated proteins by 7.4 percent and HbA1c by 0.43 percent compared to high GI diets (Brand-Miller et al., 2003). The studies included in this meta-analysis were either of a randomized crossover or a parallel design, of 12 days to 12 months in duration (mean: 10 weeks), and comprised a total of 356 subjects. Subsequent studies have been consistent with the results of this meta-analysis (Jimenez-Cruz et al., 2003; Rizkalla et al., 2004), though there is an indication that larger and longer low GI studies have not found the benefit in glycosylated protein (Wolever et al., 2008). In addition to the positive effects of low GI foods on the treatment of diabetes, drug therapies that reduce the rate of glucose absorption have also been shown to be effective in the control of diabetes and its complications. Use of acarbose, an -glucosidase enzyme inhibitor which converts the diet into a low GI diet, at a dosage

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of 100 mg three times daily in subjects with type 2 diabetes in the UK Prospective Diabetes Study (UKPDS), resulted in significantly lower HbA1c compared to placebo at 3 years (Holman et al., 1999). This improvement in glycemic control was comparable to that achieved by monotherapy with a sulfonylurea, metformin or insulin. In the STOP-NIDDM trial, subjects with impaired glucose tolerance were randomized to receive either 100 mg of acarbose three times daily or a placebo (Chiasson et al., 2002). For those on acarbose, there was a 25 percent reduction in the risk of progression of diabetes and a significant increase in reversion of impaired glucose tolerance to normal glucose tolerance.

17.7  GI and cardiovascular disease Epidemiological and clinical studies assessing the role of GI on the development of cardiovascular disease (CVD) have shown that low GI diets are associated with reduced CVD risk, possibly suggesting a protective role. The Nurses’ Health Study of over 75,000 women demonstrated a direct positive relation between fatal and non-fatal myocardial infarction, and GI and GL. The association of dietary GI and GL with CHD risk was more prominent in those with a BMI  23 kg/m2, suggesting that dietary GI may be more important in those with a greater BMI who may also have a greater degree of insulin resistance (Liu et al., 2000). Similarly, a high carbohydrate intake or, more specifically, a high GI diet tended to be positively associated with atherosclerotic progression in postmenopausal women (Mozaffarian et al., 2004). However, the Zutphen Study of older men (van Dam et al., 2000) observed no significant association of GI or GL with CHD, possibly due to the smaller sample size and the age of the cohort at baseline. Drug therapies that reduce the rate of glucose absorption have also been shown to be effective in reducing the risk of CVD.

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The STOP-NIDDM trial demonstrated that de­creasing postprandial hyperglycemia with acarbose was associated with a 49 percent relative risk reduction in the development of cardiovascular events and a 2.5 percent absolute risk reduction in subjects with impaired glucose tolerance. Furthermore, acarbose was associated with a 34 percent relative risk reduction in new cases of hypertension and a 5.3 percent absolute risk reduction (Chiasson et al., 2003). Numerous studies have explored the effect of low GI diets on CHD risk factors. Epidemiologi­ cal studies have shown that a low GI dietary pattern is associated with lower serum triglycerides and/or higher serum HDL cholesterol levels, suggesting that low GI diets may help preserve HDL-C (Frost et al., 1999; Ford and Liu, 2001; Liu et al., 2001; Slyper et al., 2005). Furthermore, in the Women’s Health Study, GI was positively associated with C-reactive protein (CRP) (Liu et al., 2002). The effects of low GI diets in clinical trials on major risk factors for CVD have been summarized in recent meta-analyses (Kelly et al., 2004; Opperman et al., 2004); 15 or 16 clinical trials were included in each analysis, and these trials varied in terms of subjects’ disease classification (healthy, with CHD, or with type 1 or 2 diabetes). It was found that low GI diets resulted in no change in HDL-C, triglycerides and LDL-C compared to high GI diets. However, improvements in total cholesterol were observed (Kelly et al., 2004; Opperman et al., 2004), with greater reductions in those with a higher baseline level (Opperman et al., 2004). Interestingly, the observed improvement in HDL-C in epidemiological studies is not consistent with the clinical trials. Nonetheless, despite the appearance of only a weak effect of low GI diets on CHD risk factors, it was concluded that the studies conducted to date were short term, of poor quality, and small in sample size. Therefore, there is a need for more well-designed randomized controlled trials of adequate power and duration to assess the effect of low GI diets on CHD (Kelly et al., 2004). Other clinical trials have

started to investigate new and emerging risk factors for CHD. Plasminogen activator inhibitor-1 (PAI-1) levels were reduced on a low GI diet in subjects with type 2 diabetes (Jarvi et al., 1999; Rizkalla et al., 2004). A low GL diet compared to a low-fat diet during weight loss found marked improvements in heart disease risk factors such as insulin resistance, TG levels, CRP and blood pressure while on the low-GL diet (Pereira et al., 2004).

17.8  Conclusion The habitual consumption of low GI foods in the context of a high-carbohydrate diet may help to reduce the risk of obesity, type 2 diabetes and heart disease. Drastic dietary changes may result in short-term health benefits, but long-term compliance is often an issue. Despite continuing controversy, the concept of the GI may still have great clinical implications if it can be easily incorporated into dietary and lifestyle modification strategies to help in the selection of better quality starchy foods. Moreover, if lower GI foods were to contribute to greater satiation, reduced postprandial glycemia and/or insulinemia, bodyweight reduction or change in composition, these attributes may help to reduce the risk of CHD and diabetes. More long-term efficacy and effectiveness studies are required to better determine the potential health benefits of low GI diets.

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Sheard, N. F., Clark, N. G., Brand-Miller, J. C., Franz, M. J., Pi-Sunyer, F. X., Mayer-Davis, E., Kulkarni, K., & Geil, P. (2004). Dietary carbohydrate (amount and type) in the prevention and management of diabetes: A statement by the American Diabetes Association. Diabetes Care, 27(9), 2266–2271. Slabber, M., Barnard, H. C., Kuyl, J. M., Dannhauser, A., & Schall, R. (1994). Effects of a low-insulin-response, energy-restricted diet on weight loss and plasma insulin concentrations in hyperinsulinemic obese females. The American Journal of Clinical Nutrition, 60(1), 48–53. Sloth, B., & Astrup, A. (2006). Low glycemic index diets and body weight. International Journal of Obesity (London), 30(Suppl. 3), S47–S51. Sloth, B., Krog-Mikkelsen, I., Flint, A., Tetens, I., Bjorck, I., Vinoy, S., Elmstahl, H., et al. (2004). No difference in body weight decrease between a low-glycemic-index and a high-glycemic-index diet but reduced LDL cholesterol after 10-wk ad libitum intake of the low-glycemicindex diet. The American Journal of Clinical Nutrition, 80(2), 337–347. Slyper, A., Jurva, J., Pleuss, J., Hoffmann, R., & Gutterman, D. (2005). Influence of glycemic load on HDL cholesterol in youth. The American Journal of Clinical Nutrition, 81(2), 376–379. Stevens, J., Ahn, K., Juhaeri, R., Houston, D., Steffan, L., & Couper, D. (2002). Dietary fiber intake and glycemic index and incidence of diabetes in African-American and White adults: The ARIC Study. Diabetes Care, 25(10), 1715–1721. Thorburn, A. W., Brand, J. C., & Truswell, A. S. (1987). Slowly digested and absorbed carbohydrate in traditional bushfoods: A protective factor against diabetes? The American Journal of Clinical Nutrition, 45(1), 98–106. Thorne, M. J., Thompson, L. U., & Jenkins, D. J. (1983). Factors affecting starch digestibility and the glycemic response with special reference to legumes. The American Journal of Clinical Nutrition, 38(3), 481–488. Toeller, M., Buyken, A. E., Heitkamp, G., Cathelineau, G., Ferriss, B., & Michel, G. (2001). Nutrient intakes as

predictors of body weight in European people with type 1 diabetes. International Journal of Obesity and Related Metabolic Disorders, 25(12), 1815–1822. van Amelsvoort, J. M., & Weststrate, J. A. (1992). Amyloseamylopectin ratio in a meal affects postprandial variables in male volunteers. The American Journal of Clinical Nutrition, 55(3), 712–718. van Dam, R. M., Visscher, A. W., Feskens, E. J., Verhoef, P., & Kromhout, D. (2000). Dietary glycemic index in relation to metabolic risk factors and incidence of coronary heart disease: The zutphen elderly study. European Journal of Clinical Nutrition, 54(9), 726–731. WHO. (2000). Obesity: Preventing and managing the global epi­ demic. Report of a WHO Consultation. Geneva: World Health Organization. Wolever, T. M. (2006). The glycaemic index: A physiologi­ cal classification of dietary carbohydrate. Oxford: CABI Publishing. Wolever, T. M., Jenkins, D. J., Ocana, A. M., Rao, V. A., & Collier, G. R. (1988). Second-meal effect: Lowglycemic-index foods eaten at dinner improve subsequent breakfast glycemic response. The American Journal of Clinical Nutrition, 48(4), 1041–1047. Wolever, T. M., Gibbs, A. L., Mehling, C., Chiasson, J. L., Connelly, P. W., Josse, R. G., et al. (2008). The Canadian trial of carbohydrates in diabetes (CCD), a 1-y controlled trial of low-glycemic-index dietary carbohydrate in type 2 diabetes: No effect on glycated hemoglobin but reduction in C-reactive protein. The American Journal of Clinical Nutrition, 87(1), 114–125. Woodend, D. M., & Anderson, G. H. (2001). Effect of sucrose and safflower oil preloads on short term appetite and food intake of young men. Appetite, 37(3), 185–195. Wursch, P., Del Vedovo, S., & Koellreutter, B. (1986). Cell structure and starch nature as key determinants of the digestion rate of starch in legume. The American Journal of Clinical Nutrition, 43(1), 25–29. Yoon, J. H., Thompson, L. U., & Jenkins, D. J. (1983). The effect of phytic acid on in vitro rate of starch digestibility and blood glucose response. The American Journal of Clinical Nutrition, 38(6), 835–842.

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C H A P T E R

18 Characterizing the Homeostatic and Hedonic Markers of the Susceptible Phenotype

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John Blundell1, Eleanor Bryant2, Clare Lawton1, Jason Halford3, Erik Naslund4, Graham Finlayson1 and Neil King5 Institute of Psychological Sciences, Faculty of Medicine and Health, University of Leeds, Leeds, UK 2 Centre for Psychology Studies, University of Bradford, Bradford, UK 3 Psychology Department, University of Liverpool, Liverpool, UK 4 Clinical Sciences, Danderyd Hospital, Karolinska Istitutet, Stockholm, Sweden 5 Institute of Health and Innovation, Queensland University of Technology, Brisbane, Australia

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18.2 Susceptible and Resistant Phenotypes 232 18.3 What Would a Susceptible Phenotype Look Like? 18.4 What Level of Analysis is Appropriate? 18.5 Appetite is Not Rocket Science – It is More Complicated

Obesity Prevention: The Role of Brain and Society on Individual Behavior

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18.6 Diversity, Susceptibility and Homeostasis

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18.7 Hedonics: The Importance of Liking and Wanting

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18.8 Comparing Susceptible and Resistant Phenotypes

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18.9 Resistance to Weight Loss – The Other Side of Susceptibility

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18.10 Conclusions

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18.1  The approach Recently, after 50 years of concentrated research on the mechanisms underlying energy homeostasis, there has developed increased interest in the potency of non-homeostatic influences on appetite control and body-weight regulation. This is often expressed as the relationship between hedonic and homeostatic processes (see, for example, Saper et al., 2002; Berthoud, 2004, 2006; Blundell and Finlayson, 2004). To express the force of non-regulatory eating, the term “hedonic hunger” has been proposed (Lowe and Butryn, 2007). These conceptualizations have shifted the understanding of poor weight control towards the idea that hedonic processes overcome homeostatic regulation. Indeed, for some time it has been recognized that the homeostatic system operates asymmetrically, easily permitting overconsumption but more strongly defending against under-eating and weight decrease. The system therefore appears to be “permissive” of weight gain, and the term “passive obesity” has been used in a recent influential report to convey this idea (Foresight Report, 2007). This, in turn, reflects the concept of “passive overeating” (Blundell et al., 1996). The first stage in understanding susceptibility and resistance is to decide what questions to ask. Inevitably, susceptibility and ­ resistance will not be uni-dimensional constructs that apply universally and can be categorically defined. Different clusters of susceptibility ­ (individuals sharing patterns of physiology and behavior) can be envisaged (and demonstrated). Therefore, susceptibility will exist in several forms, or subtypes. These subtypes – or phenotypes – can  be an appropriate target for research. They define a construct between the truly universal or nomothetic approach, and the truly individual or idiographic approach (Allport, 1937). Different susceptible phenotypes can exist in

parallel, and “obesogenic” environments exploit this ­ susceptibility. This chapter will describe an approach to studying susceptibility to weight gain (and its partner construct, resistance to weight loss).

18.2  Susceptible and resistant phenotypes An obesogenic environment clearly encourages weight gain and obesity. However, not all people living in an obesigenic culture become obese: some remain of normal weight, or lean. Considerable individual differences exist in the capacity of people to succumb to weight gain or to resist it. This implies the existence of a spectrum of proneness or vulnerability within a population (for model, see Ravussin and Kozak, 2004). Along this spectrum it is possible to identify clusters of individuals who are susceptible and clusters who are resistant; we have called these contrasting groups “phenotypes” because they can be defined according to particular markers. Characterizing the ways in which these phenotypes differ can shed light on the particular biological and behavioral features, and their responsiveness to the environment, that encourage weight gain. Obesity is a heterogeneous entity. There is a need to differentiate between groups of obese individuals by assessing what risk factors predispose them to becoming obese and, in some cases, what characteristics prevent them from losing weight. Could the identification of a susceptible phenotype help in the prevention of obesity? The first stage concerns how to detect a suscep­ tible phenotype. By definition, a susceptible ­person is gaining weight or has already become obese. It is more difficult to detect a person in the process of weight gain than it is to identify, for instance, someone who has already attained a BMI of 35. However, the stratification of BMI

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18.4  What level of analysis is appropriate

that is ­ commonly recognized (normal, 20–24.9; overweight, 25–29.9; obese, 30; super obese, 40) should not be regarded as definitive. This handy classification, now a rule of thumb, is based solely on the accumulation of risk factors and morbidity; it has no grounding in etiology, and nothing to say about causation. Therefore, a susceptible phenotype can exist at any BMI, and reflects the capacity of a person to persistently gain weight even after accumulating a significant amount of fat. However, the easiest first step in characterizing susceptibility would be to study someone with a high BMI. Such identifying features could, though, be a consequence of obesity as well as a cause. Therefore, longitudinal studies of weight-gaining individuals are necessary. Studies already carried out have identified predictors (moderators) of weight gain (see, for example, Hays et al., 2002; Dykes et al., 2004). In order to be of use in the prevention of obesity, it is necessary to identify markers of susceptibility in a lean or normal-weight person; at this stage, a strategy to oppose or offset the susceptibility features could be initiated. Such markers can be identified.

18.3  What would a susceptible phenotype look like? The idea of susceptibility implies a set of biobehavioral processes that favor the achievement of a positive energy balance. In simple terms, this means the promotion of overconsumption together with a sedentary lifestyle, which conjointly would lead to an increased accretion of energy. Although these features often coexist, the weight of evidence suggests that increased energy intake is more pernicious and of greater potency. Therefore, the major features of a susceptible phenotype relate to an excessive food intake

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in an obesogenic environment. The phenotypic expression of this feeding could take the form of extended eating periods (defective satiation leading to large meals), frequent initiation or rapid re-initiation of eating episodes (weak postprandial satiety), binge-eating episodes (features of weak satiation and satiety), heightened sensitivity to the pleasurable aspects of eating, tendency to seek high-energy dense foods, etc. A susceptible phenotype may not show all of these features. Just as there are multiple routes to weight gain and obesity, there will be subgroups of susceptible phenotypes (Blundell and Cooling, 2000). The common element among susceptible individuals is the expression of a poorly restrained willingness to eat.

18.4  What level of analysis is appropriate? It should be clear that the ideology of psychobiology contains the belief that susceptibility incorporates a genetic component. Therefore, the susceptible phenotype will be associated with specific polymorphic markers of particular genes related to weight gain or obesity itself (see Bouchard, 2008). At one level, therefore, there will be a genetic analysis of susceptibility. This genetic “explanation”, however, may be distant from the observed expression of susceptibility that can be studied in research units or be managed in clinical or public health settings. Psychobiology implies a two-way bridge between physiology and the environment, with the bridge reflecting behavior itself. Therefore, one way of defining susceptibility (and, by implication, resistance) is through the architecture of behavior and the proximal processes that influence this behavior. This approach gives susceptibility a form that is an accessible target for physiological (pharmacological), behavioral and public health approaches to dealing with the obesity epidemic.

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18.5  Appetite is not rocket science – it is more complicated

sweet foods. There are two reasons why this form of overconsumption is difficult to manage. The first is the likelihood that the selection of foods with these properties (fattiness and sweetness) connotes a biologically useful, It is often remarked that appetite control energy-yielding capacity which humans would is not rocket science; indeed, it is much more be genetically predisposed to find attractive. complicated than that. The reason for believ-  The second is the unwillingness of humans ing this apparent absurdity concerns predict- to relinquish a potent source of pleasure. The ability. Whereas physical science embodies  importance of such food choices in susceptible sufficient predictability to enable a rocket to be people is therefore both subconscious (medisent to a distant planet, most of us cannot pre- ated via implicit predispositions) and condict what we are going to eat for the next meal scious (perceived loss of rewarding stimulus) (or how much). In an obesogenic environment, (Finlayson et al., 2008). An important demonthe act of eating is unpredictable because the stration (by means of visual evoked potenenvironment contains a huge range of possibili- tials) has been that the brain has the capacity ties creating a tapestry of eating opportunities, to detect and recognize the fat content of foods requiring choice. This is part of the legacy of within 150–200 ms of exposure to visual images humans being omnivores. For omnivores, food of foods (Toepel et al., 2008). choice is not an option, it is an obligation – and it extends the range of edible foods beyond the limits of optimal nutrition. 18.6  Diversity, susceptibility Coupled with this lack of predictability and homeostasis is the tremendous diversity in the expressed forms of eating behavior. This is apparent when Homeostasis is an inherent property of a comparing dietary profiles and patterns of eating among different geographical and cultural biologically regulated system. One of the rearegions, yet there is also equal diversity within sons why humans (as omnivores) are successethnic or social groups. Consequently, the eat- ful is because whatever the profile of foods ing behavior of humans is characterized by consumed (from the huge range available), the huge individual variability; there is no univer- biological system can adapt. Therefore, greatly sal, normal pattern, nor is there any unique divergent patterns of eating are biologically pathological pattern. An attribute of eating viable – the physiological and biochemical that can be predicted with some certainty is processes operate to maintain the system. This that it will be enjoyable. Although there are means that behavioral adaptation (to dietary exceptions to this rule, food is a common and possibilities) is not always necessary. Thus, potent source of pleasure. This is made possible behavioral regulation of food choice, although because of the links between sensory receptors feasible, is not an imperative. However, behav(mainly sight, taste and smell) and the neu- ioral regulation of internal states is clearly an ral pathways mediating liking and wanting. adaptive strategy that serves a homeostatic purIndependent of the dietary profile and the top- pose, since behavior can be initiated and termiographical pattern, eating normally generates a nated to optimize biological requirements. In hedonic response that can be extremely potent. the control of ­appetite, this motivated behavior One feature of the susceptible phenotype is the takes the form of an increase in drive (hunger) high hedonic response, especially to fatty and in response to signals of need (low glycogen ­ 

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18.7  Hedonics: the importance of liking and wanting

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levels or an empty stomach), and a positive 18.7  Hedonics: the inhibition of eating (satiation) and the mainimportance of liking tenance of inhibition (satiety) in response to and wanting signals of repletion. In a closed or tightly controlled situation the operation of this biobehavJust as homeostatic processes can be analyzed ioral system can be demonstrated. However, in some situations (see, for example, Levitsky,   to reveal components such as orexigenic drive 2005), eating appears to be unregulated by (hunger), satiation and satiety, hedonic pro-  internal signals and dominated by environmen- cesses also have a structure that can be dissected.  tal stimuli (such as portion size). Data of this The fundamental processes of “liking versus type are often used to argue that the biological wanting” inform current theory and research regulation of eating is irrelevant to the obesity (Berridge, 1996). Liking and wanting have the epidemic. There are many examples of eating logical status of hypothetical constructs that being initiated in the absence of a drive (hun- mediate between a neuropsychological process ger), and of eating persisting in the presence of and a directive behavior. These processes can be inhibitory satiety signals. Therefore, although investigated in humans (Finlayson et al., 2007a), the homeostatic system displays regulatory and have a dominant role to play in food preferproperties, these mediating processes can be ences. Consequently, they also play a key role in readily over-ridden. Susceptibility can involve a susceptibility to overeating. Many people would assume that liking and pattern of eating that operates with weak influwanting are identical phenomena, both of which ence of homeostatic regulation. It has been authoritatively stated that “a well- signify a positive attraction to food. In behavioknown response in nutrition research and prac- ral terms, we assume that a change in liking will tice is the dramatic variability in inter-individual lead to proportional adjustments in wanting response to any type of dietary intervention” and, likewise, differences in wanting will pre(Ordovas, 2008: S40). The difference between dict changes in liking. This would be the natural susceptible and resistant individuals reflects the view of a layperson. However, there are strong spectrum of this inter-individual responsive- grounds for recognizing that liking and wanting ness. In research, it is clearly possible to work can be clearly dissociated, and constitute diswith the variance itself. Sectioning the variance tinct identities. This means that they have much and working with subunits (phenotypes) is a greater resolving potential for understanding manageable and transparent approach (Blundell the role of hedonics on eating and, therefore, on overconsumption. Thus, the importance of likand Cooling, 1999). Therefore, on theoretical grounds, suscep- ing versus wanting reflects the functional sigtibility to weight gain is likely to involve weak nificance of these two distinguishable processes, homeostatic regulation (that would permit a operating within the hedonic domain, for overready initiation of eating and a weak inhibition) consumption and weight regulation in humans. A reasonable proposal is that wanting rather  and a potent hedonic influence (strong attraction to energy-dense foods and a dispropor- than liking may be the crucial process in maintionately strong liking and wanting for specific taining an obese state. For this to be confirmed,  foods). These attributes would be expressed it is necessary that wanting and liking can through enduring traits (reflecting biologically be dissociated. This is clearly shown in the based predispositions) and through episodically parallel field of research on chronic drug oscillating states (such as hunger sensations) abusers which shows that repeated drug-  taking behavior and strong motivation to obtain (see, for example, Blundell et al., 2008).

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a “fix” (wanting) can occur in the absence of any pleasant sensations (liking) during ingestion (Lamb et al., 1991). Moreover, food-liking is often a rather stable characteristic within an individual, and appears relatively uninfluenced by increasing weight status (Cox et al., 1999). The implication is that liking may be important in establishing the motivational properties of food, but once these are formed it is the up-regulation of wanting in an obesogenic environment – insensitiv- Figure 18.1  Frequency distribution of BMI for highity to homoeostatic signals but over-reactivity to fat consumers, defined by the absolute amount of fat conexternal cues – that promotes overconsump- sumed after the database had been cleaned by the exclusion tion by influencing what, and how much, is of implausible reports. The distribution for low-fat consumers also shows wide variation in BMI, but only one person eaten from moment to moment (Finlayson et al.,   reached a BMI of 30. 2007b). Source: Adapted from Macdiarmid et al. (1996). This situation in humans is a parallel of the phenomenon seen in rats habitually exposed to a potentially weight-inducing, high-fat diet (Levin et al., 1989). Susceptible individuals show a clusVariability can begin with preferences for, ter of characteristics when appetite regulation is and selection of, particular foods in the habit- challenged. There is a relatively weak suppresual diet. In an environment that contains a sur- sion of hunger in response to consumed high-fat feit of all types of foods, people “choose” to eat foods, suggesting weak, fat-induced satiety sigquite diverse ranges of foods within a single naling (Blundell et al., 2005). This may involve culture (of course, there are major intercultural CCK, PYY or some other gut peptide. A weak differences). For example, high-fat and low- satiation response leads to larger meals. These fat phenotypes have been identified (Cooling factors suggest variable strength (impairment) and Blundell, 2000). Habitual low-fat diets of homeostatic signaling systems. Other studappear to confer a resistance to weight gain ies indicate a weak compensation to high-fat (Macdiarmid et al., 1996), a characteristic also loads related to insulin resistance (Speechly and shown by successful weight-losers (Klem et al., Buffenstein, 2000) and poor com­pensation to 1997), although one must note that a low-fat, enforced overconsumption (Cornier et al., 2004). Evidence also points to a differential responhigh-carbohydrate diet may not be beneficial for all (particularly for some obese, highly sed- sivity in the hedonic processes influencing eatentary people). However, clear variability can ing (Blundell and Finlayson, 2004). There is be demonstrated in the response to a high-fat a preference for high energy-dense over low diet. Although a high fat intake is a potent risk ­  energy-dense foods (Westerterp-Plantenga et al., factor for weight gain, the relationship between 1998), and an increased wanting for high-fat the preference for high-fat foods and weight foods under postprandial satiation conditions gain is not a biological inevitability (Blundell (Le Noury et al., 2004). Long-standing evidence and Macdiarmid, 1997). Some people habitually points to a link between adiposity and sensory consuming a high-fat diet are obese (susceptible) preference for fat (Mela and Sacchetti, 1991). Susceptible high-fat phenotypes also report whilst others are lean (resistant) (Figure 18.1).

18.8  Comparing susceptible and resistant phenotypes

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18.9  Resistance to weight loss – the other side of susceptibility

more dramatic hedonic responsivity to foods than do lean people (Blundell et al. 2005). A high binge-eating score on the Binge Eating Scale (BES) (Gormally et al., 1982) is also a feature of susceptibility and, even in normal-weight women, is associated with an increased liking for all foods and an increased wanting for sweet, high-fat foods (Blundell and Finlayson, 2008). Susceptible and resistant individuals also differ in the strength of certain traits measured by psychometric tests. Men defined as susceptible on a habitual high-fat diet score much higher on traits of Disinhibition and Hunger (but not Restraint) on the Three Factor Eating Questionnaire (TFEQ) than do resistant (same age) men. Such individuals can be regarded as opportunistic eaters who are often in a state of high readiness to eat, and also are likely to be easily provoked into eating by environmental triggers (for a review, see Bryant et al., 2007). The trait of Disinhibition is also associated with weight gain or obesity in large-scale surveys (Hays et al., 2002; Dykes et al., 2004) and smaller intervention studies (Lawson et al., 1995). There is, moreover, presumptive evidence that this trait has a genetic basis (Steinle et al., 2002; Bouchard et al., 2004) and may be linked to the GAD-2 gene. Other evidence suggests that the Disinhibition trait is associated with fasting levels of leptin and adiponectin which may influence the tonic control of appetite (Blundell et al., 2008). Consequently, individuals susceptible to weight gain appear to display a portfolio of risk factors which, acting together, make such people extremely vulnerable in the obesogenic environment.

18.9  Resistance to weight loss – the other side of susceptibility It may be claimed that the rate of increase in the prevalence of obesity is driven by three intrinsic features: the susceptibility of people to

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gain weight, the failure of people to maintain weight loss, and the resistance to lose weight. Therefore, the natural increase in weight gain combined with the failure (of those already obese) to lose or maintain weight will contribute to the overall increase in obesity. The resistance to lose weight is different from the failure to maintain lost weight, which reflects a susceptibility to weight (re)gain. When groups of people are subjected to weight-loss regimes, the physiological system generates automatic compensatory metabolic processes to adjust for the energy deficit (Rosenbaum et al., 2005). The system can also make behavioral adjustments by up-regulating the orexigenic drive and increasing energy intake (Heini et al., 1998). This can be demonstrated very clearly by the response to imposed and supervised exercise regimes (King et al., 2008). In a scientifically controlled study, obese people participating in a fully supervised 12-week program of exercise showed an average weight loss of 3.3 kg. However, the most remarkable effect was the diversity of individual responses, which ranged from a loss of 14 kg to a weight gain of 2 kg, despite individuals achieving similar levels of exercise-induced energy expenditure (Figure 18.2). This type of diversity in response to imposed exercise was noted many years ago (see, for example, Bouchard, 1994; Bouchard and Rankinen, 2001) but apparently overlooked by  most researchers. It follows that any interpretation based on the average weight-loss response would obliterate the true response of the individuals doing the exercise. “This kind of variation is an example of normal biological  diversity … and is beyond measurement error and day-to-day variation” (Rankinen and Bouchard, 2008: S47). It reflects the degree of individual variation in physiologically and behavioral adaptive processes. In the investigation by King and colleagues (2008), the design of the study permitted the source of variability in the compensatory response to be identified and measured. In those individuals who were

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Figure 18.2  Individual changes in body weight and body fat at the end of the mandatory exercise program. BW, body weight; FM, fat mass. Source: Adapted from King et al. (2008).

“resistant” to the theoretical weight loss, the exercise increased the orexigenic drive, reflected in a persistently high hunger, accompanied by an increased food intake, a preference for high energy-dense fatty foods and a relative aversion for low energy-dense foods (fruit and vegetables) (Caudwell et al., 2009). In turn, these food preferences represent an altered hedonic response to exercise (Finlayson et al., 2009). The characteristics of this “resistant” phenotype are still under investigation, but clearly illustrate the diversity of the human psychobiological response. Inter-individual variability is a dominant feature of both nutritional and exercisebased interventions.

is readily apparent. Investigation of the spectrum reveals clusters of individuals who can be termed susceptible phenotypes, and clusters that are resistant. Scientific comparison  between these contrasting phenotypes is a legitimate and powerful approach that can throw light on the way in which bio-social processes influence individual behavior. The susceptible phenotype is a suitable target for scientific study and for management of clinical and public health programs, and early identification of a susceptible phenotype in children (see, for example, Carnell and Wardle, 2008) would be very valuable.

References 18.10  Conclusions The heterogeneity of the human response to interventions that impact on energy balance and weight regulation is a demonstrable fact. The existence of a spectrum of susceptibility

Allport, G. W. (1937). Personality: A psychological interpretation. New York, NY: Holt Rinehart & Winston. Berridge, K. C. (1996). Food reward: Brain substrates of wanting and liking. Neuroscience and Biobehavioral Reviews, 20, 1–25. Berthoud, H. R. (2004). Neural control of appetite: Crosstalk between homeostatic and non-homeostatic systems. Appetite, 43, 315–317.

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successful at long term maintenance of substantial weight loss. American Journal of Clinical Nutrition, 66, 239–246. Lamb, R. J., Preston, K. L., Schindler, C. W., Meisch, R. A., Davis, F., Katz, J. L., Henningfield, J. E., & Goldberg, S. R.   (1991). The reinforcing and subjective effects of morphine in post-addicts: A dose response study. Journal of Pharmacology and Experimental Therapeutics, 259, 1165–1173. Lawson, O. J., Williamson, D. A., Champagne, C. M.,  DeLany, J. P., Brooks, E. R., Howat, P. M., Wozniak, P. J.,  Bray, G. A., & Ryan, D. H. (1995). The association of body weight, dietary intake, and energy expenditure with dietary restraint and disinhibition. Obesity Research, 3, 153–161. Le Noury, J., Lawton, C., Stubbs, J., Whybrow, S., & Blundell, J. E. (2004). Is hedonic response a risk factor for overeating in individuals susceptible to weight gain? International Journal of Obesity, 28(1), S218. Levin, B. E., Hogan, S., & Sullivan, A. C. (1989). Initiation and perpetuation of obesity and obesity resistance in rats. American Journal of Physiology, 256 (Regulatory Integrative Comp 25), R766–R771. Levitsky, D. (2005). The non-regulation of food intake in humans: Hope for reversing the epidemic of obesity in humans. Physiology & Behavior, 86, 623–632. Lowe, M. R., & Butryn, M. L. (2007). Hedonic hunger: A new dimension of appetite? Physiology & Behavior, 91, 432–439. Macdiarmid, J. I., Cade, J. E., & Blundell, J. E. (1996). High and low fat consumers, their macronutrient intake and body mass index: Further analysis of the National Diet and Nutrition Survey of British Adults. European Journal of Clinical Nutrition, 50, 505–512. Mela, D. J., & Sacchetti, D. A. (1991). Sensory preferences for fats: Relationship with diet and body composition. American Journal of Clinical Nutrition, 53, 908–915.

Ordovas, J. M. (2008). Genotype–phenotype associations: Modulation by diet and obesity. Obesity, 16(Suppl. 3), S40–S45. Rankinen, T., & Bouchard, C. (2008). Gene–physical activity  interactions: Review of human studies. Obesity, 16(Suppl. 3), S47–S50. Ravussin, E., & Kozak, L. P. (2004). Energy homeostasis. In K. G. Hofbauer, U. Keller, & O. Boss (Eds.), Pharmacotherapy of obesity – options and alternatives   (p. 488). Boca Raton: CRC Press. Rosenbaum, M., Goldsmith, R., Bloomfield, D., Magnano, A.,  Weimer, L., Heymsfield, S., Gallagher, D., Mayer, L., Murphy, E., & Leibel, R. L. (2005). Low-dose leptin reverses skeletal muscle, autonomic and neuroendocrine adaptations to maintenance of reduced weight. Journal of Clinical Investigation, 115, 3579–3586. Saper, C. B., Chou, T. C., & Elmquist, J. K. (2002). The need to feed: Homeostatic and hedonic control of eating. Neuron, 36, 199–211. Speechly, D. P., & Buffenstein, R. (2000). Appetite dysfunction in obese males: Evidence for role of hyperinsulinaemia in passive overconsumption with a high fat diet. European Journal of Clinical Nutrition, 54(3), 225–233. Steinle, N. I., Hsueh, W. C., Snitker, S., Pollin, T. I., Sakul, H., St Jean, P. L., Bell, C. J., Mitchell, B. D., & Shuldiner, A. R. (2002). Eating behavior in the Old Order Amish: Linking eating behaviors and susceptibility to obesity. American Journal of Clinical Nutrition, 80, 1478–1486. Toepel, U., Knebel, J. F., Hudry, J., Le Coutre, J., & Murry,  M. M. (2008). The brain tracks the energetic value in food images. NeuroImage, 44, 967–974. Westerterp-Plantenga, M. S., Kempen, K. P., & Saris, W. H. (1998). Determinants of weight maintenance in women after diet-induced weight reduction. International Journal of Obesity and Related Metabolic Disorders, 22(1), 1–6.

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C H A P T E R

19 The Carnivore Connection: Crosspopulation Differences in the Prevalence of Genes Producing Insulin Resistance Stephen Colagiuri1, Scott Dickinson1 and Jennie Brand-Miller2 1

Sydney Medical School, Boden, Institute of Obesity, Nutrition & Exercise, and School of Molecular and Microbial Biosciences, University of Sydney, NSW, Australia

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o u t l i n e 19.1 Background

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19.2 The Evolution of Insulin Resistance

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19.3 Determinants of Insulin Resistance 19.3.1 Physiological Determinants 19.3.2 Pathological Determinants

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19.1  Background Although insulin has a number of metabolic effects, insulin resistance is usually defined as a state in which physiological levels of insulin have a decreased biological action on plasma glucose. Glucose uptake by skeletal muscle and adipose tissue, and suppression of hepatic glucose production, are affected. To maintain normoglycemia in the insulin-resistant state,

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19.4 Candidate Genes and Cross-population Genetic Differences 246 19.5 Conclusion

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excessive compensatory increases in insulin are required which may eventually lead to a decline in and exhaustion of insulin-producing pancreatic beta cells, the development of glucose intolerance, and, finally, type 2 diabetes (Polonsky, 1999). Insulin resistance is associated with a constellation of traits other than glucose intolerance, including visceral obesity, dyslipid­ emia, hypertension, and a prothrombotic state (Reaven, 1988). Epidemiological studies consistently show an independent association between

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© 2010, 2010 Elsevier Inc.

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insulin resistance and risk of cardiovascular ­disease (McFarlane et al., 2001). The term ­insulin resistance is often used interchangeably with decreased insulin sensitivity or reduced insulin action. There are wide differences in the ability of insulin to mediate glucose disposal among individuals. Insulin sensitivity is a continuous variable, and distinguishing normal and abnormal insulin sensitivity in individuals is difficult because there is no uniform quantitative definition of what constitutes insulin resistance. Individuals are considered insulin resistant if they have normal glucose tolerance but lie in the most insulin-resistant quartile of a given population (Reaven et al., 1993). In individuals with normal glucose tolerance, insulin resistance can vary by four- to ten-fold, with some measures of resistance not differing substantially from those of people with impaired glucose tolerance (IGT) or type 2 diabetes (Reaven et al., 1989; Clausen et al., 1996). However, those subjects in the most insulin-resistant quartiles are generally significantly more obese and less glucose tolerant compared with more insulinsensitive individuals (Clausen et al., 1996). Also, those with insulin resistance but normal glucose tolerance are hyperinsulinemic compared with insulin sensitive controls, allowing insulinresistant individuals to overcome the defect in the short term. Quantitative comparisons of insulin resistance across racial/ethnic groups are difficult because of the need to take into account the effects of age, gender, weight, physical fitness, and glucose tolerance. However, some data support real differences. Comparative data are available for Mexican Americans (Haffner et al., 1992) and Australian Aborigines (Proietto et al., 1992) consistent with the view that insulin resistance is more common in individuals without diabetes in these populations. AfricanAmericans maintain glycemia following a carbohydrate load by producing a much larger insulin response, two- to three-fold greater than

that seen in matched European Caucasians (Osei and Shuster, 1994). African-Americans also have lower adiponectin levels than Caucasians, which is associated with insulin resistance (Osei et al., 2005). Asian Indians are more insulin resistant than matched European Caucasians, as demonstrated by reduced rates of glucose disposal adjusting for confounding variables (Chandalia et al., 1999). Dickinson and colleagues (2002) studied 60 lean, healthy young adults from five racial/ethnic groups (European Caucasians, Chinese, Southeast Asians, Asian Indians and Arabic Caucasians) and assessed glucose and insulin responses following a 75-g carbohydrate meal, and insulin sensitivity by HOMA or the hyperinsulinemic euglycemic clamp technique. While mean fasting glucose concentrations were similar among the groups, Southeast Asian and Chinese subjects showed markedly higher postprandial glycemia than did European Caucasians, with 1.5- to 2.0-fold higher mean incremental areas under the glucose curve. The groups also differed significantly in insulin sensitivity, with European Caucasians being the most sensitive, whereas Southeast Asians were the most resistant. The results were not explained by differences in sex, age, BMI or birth weight. The variation in insulin resistance across ethnic groups could be due to genetic and/or biochemical differences, and in the absence of definitive data it is not currently possible to separate these influences.

19.2  The evolution of insulin resistance Several theories have been proposed to explain the current high prevalence and population differences in insulin resistance and the associated type 2 diabetes, both of which increase in populations transitioning from a traditional lifestyle. The “thrifty genotype”

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19.2  The evolution of insulin resistance

hypothesis was first proposed by Neel (1962), and postulates the presence of genetic traits that had survival value for hunter-gatherers, allowing the ability to go without food for extended periods of time. A “thrifty” metabolism made for efficient storage of fat during times of plenty, providing an energy buffer during scarcity. Such a gene(s) would now be detrimental, with the abundant food supply and lack of physical activity predisposing to obesity. Subsequently, it was proposed that insulin resistance was the phenotypic expression of the thrifty genotype (O’Dea, 1991). In the presence of modern-day constant food supply, insulin resistance would result in hyperinsulinemia and eventual diabetes. Diamond (1992) described it as the “collision of our old hunter-gatherer genes with our new twentieth-century life style”. The “not-so-thrifty genotype” was suggested by Reaven (1998), who hypothesized that the purpose of insulin resistance was not to increase fat storage, as Neel suggested, but to spare the proteolysis of muscle tissue during periods of famine. Hales and colleagues (1991) proposed a “thrifty phenotype” to explain metabolic adaptations to allow survival of a malnourished fetus. The hypothesis, based on anthropometric records of infants, associates poor early fetal and infant growth with insulin resistance and the later development of type 2 diabetes and other metabolic abnormalities. Subsequently, it has been suggested that low birth weight may be genetically determined (Poulsen et al., 1997). Hattersley and Tooke (1999) proposed the “fetal insulin” hypothesis, which argues against the thrifty phenotype and in favor of genetically determined insulin resistance resulting in low insulin-mediated fetal growth in utero as well as insulin resistance later in adult life. They propose that low birth weight, hypertension, IGT and eventual diabetes are the phenotypic expression of the insulin-resistant genotype. Brand-Miller and Colagiuri developed the “carnivore connection” hypothesis to explain

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the evolution of genes predisposing to insulin resistance (Miller and Colagiuri, 1994; Colagiuri and Brand-Miller, 2002), and proposed a critical role for the quantity and quality of diet­ ary carbohydrate in the evolution of insulin resistance and hyperinsulinemia. Insulin resistance offered survival and reproductive advantages during the Ice Age, which dominated the last two million years of human evolution and which was characterized by low-carbohydrate, high-protein diets (Richards et al., 2000). While carbohydrate was scarce, compensatory hyperinsulinemia would not have been needed to maintain normal glucose tolerance. Dietary carbohydrate increased beginning about 10,000 years ago, following the end of the last Ice Age and the development of agriculture. Traditional carbohydrate foods have a low glycemic index (GI) and produce only modest postprandial increases in plasma insulin. However, beginning with the Industrial Revolution, there is now a constant supply of highly refined high GI carbohydrate in modern diets, resulting in excessive postprandial hyperinsulin­emia, exposing the disadvantages of the insulin resistance genotype and predisposing to type 2 diabetes and other metabolic abnormalities. The situation has been further aggravated over the past 60 years by the explosion in the range of available convenience and “fast foods”, which expose most populations to caloric intakes far in excess of energy requirements. This overconsumption has been responsible for the increased prevalence of obesity in Western and developing societies, and an important factor in determining the prevalence of insulin resistance in any population. The carnivore connection also offers an explanation for the relative insulin sensitivity of European Caucasians. These theories are based on the assumption of the advantage of insulin resistance for reproduction and survival during periods of famine thought to have been common through human evolution. Extensive evidence now shows that

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starvation was in fact not common in prehistoric hominids or among modern hunter-­gathers (Cordain et al., 1999). However, evidence supports natural selection to a mixture of famines and seasonal food shortage in the postagricultural era mediated through fertility, rather than viability selection (Prentice et al., 2008). Speakman (2008) challenges this view, proposing instead the “drifty gene” hypothesis which puts forward possible scenarios based on random unselected genetic drift. The relative contribution of environmental and genetic influences to insulin sensitivity remains unclear. While the molecular basis of these theories remains unknown, the relative roles played by genetic and environmental factors will continue to be the subject of intense debate.

19.3  Determinants of insulin resistance Insulin action is influenced by physiological, pathological and genetic factors (Figure 19.1).

19.3.1  Physiological determinants Insulin resistance increases with age. This trend, however, diminishes when adjustments are made for the effect of BMI, body composition, and physical activity (Ferrannini et al., 1996; Basu et al., 2003). Physical activity increases insulin sensitivity, an effect that can be demonstrated after 4–6 weeks of intensive training (Koivisto et al., 1986). Diet also influences insulin action. Epidemiological studies suggest an association between high saturated fat intake and reduced insulin sensitivity in humans (Marshall et al., 1997; Mayer-Davis et al., 1997) while animal studies demonstrate that diets high in fat, particularly saturated fat, lead to insulin resistance (Lee et al., 2006). A study in women with advanced CVD awaiting bypass surgery (Frost et al., 1998) showed an improvement in glucose tolerance and insulin sensitivity after 4 weeks on a low GI diet (compared with a high GI diet). In overweight, middleaged men, Brynes and colleagues (2003) demonstrated that HOMA-insulin resistance increased significantly on a high GI diet compared with a macronutrient-matched low GI diet.

Evolutionary environment - food availability and type - reproduction and fertility

Genes

Genetic selection

Physiologic determinants - age - diet - physical activity - pregnancy

Genetic drift

Pathologic determinants - overweight/obesity Insulin action

Figure 19.1  Determinants of insulin action.

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19.3  Determinants of insulin resistance

Insulin sensitivity decreases during ­pregnancy (Reece et al., 1994) and may result in gestational diabetes, a common and increasing problem, especially in women of non-European background (Kaaja and Greer, 2005).

19.3.2  Pathological Determinants Regardless of glucose tolerance, body weight has a major influence on insulin sensitivity. An increase in body weight of 35–40 percent above the normal range results in an insulin sensitivity decline of 30–40 percent (DeFronzo and Ferrannini, 1991). The worldwide increasing rates of overweight and obesity are a major determinant of individual and population insulin resistance. Insulin resistance, in the context of obesity, is the most common risk factor for type 2 diabetes and metabolic abnormalities (Eckel et al., 2005). There is a strong association between abdominal adiposity and insulin resistance (Abate et al., 1995; Cnop et al., 2002; Wagenknecht et al., 2002) for any level of total body fat; the subgroup of individuals with excess intra-abdominal fat has a substantially greater risk of having insulin resistance (Despres and Lemieux, 2006). Several mechanisms may result in obesityrelated insulin resistance and have provided a focus for the search for the genetic determinants of insulin resistance. Impaired non-esterified fatty acid (NEFA) metabolism is an important contributor to insulin resistance in the viscerally obese (Despres et al., 1990; Pouliot et al., 1992; Chan et al., 1994; Folsom et al., 2000; Hayashi et al., 2008). Adipose tissue not only stores and mobilizes lipids, but also releases a number of cytokines and pro-inflammatory molecules. The macrophage infiltration in adipose tissue in the obese is likely to play a role in the inflammatory profile characteristic of people with abdominal obesity (Weisberg et al., 2003), and may be responsible for obesity-related insulin resistance (Wellen and Hotamisligil, 2005). Other possible mechanisms

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include endoplasmic reticulum stress (Ozcan et al., 2004) leading to up-regulation of JNK, which in turn suppresses insulin action through inhibition of the insulin receptor substrate-1 and its associated downstream signaling pathways. Insulin-resistant subjects have elevated levels of lipid in their skeletal muscle cells compared with matched insulin-sensitive subjects. Falholt and colleagues (1988) observed a relationship between insulin resistance and intramyocellular lipid (IMCL) independent of overweight or obesity. Storlien and colleagues (1991) fed rats a high-fat diet and found that mean muscle tri­ glyceride accumulation was inversely correlated with insulin sensitivity, suggesting involvement of the intracellular glucose–fatty acid cycle. In Pima Indians without diabetes, skeletal muscle triglycerides were inversely correlated with insulin sensitivity even after controlling for all other measures of fat (Pan et al., 1997). With the availability of magnetic resonance spectroscopy to measure IMCL, more data have emerged showing significant associations between skeletal muscle triglycerides and insulin resistance (Jacob et al., 1999; Krssak et al., 1999; Perseghin et al., 1999; Virkamaki et al., 2001). However, this relationship was not observed in a group of South Asian subjects, but was present in a European Caucasian control group (Forouhi et al., 1999). Shulman proposed a unifying hypothesis for a number of forms of human insulin resistance. He suggested that an accumulation of intracellular fatty acid metabolites in muscle or liver, whether by increased caloric intake or by a failure of mitochondrial fatty acid oxidation, could produce an insulin-resistant state (Shulman, 2000) through activation of a serine/threonine kinase cascade, downstream activation of IB kinase- (IKK-) and c-JUN NH2-terminal protein kinase (JNK-1), phosphorylation at serine sites on insulin receptor substrate-1 (IRS-1) and decreased activation of glucose transport due to the inability of serine-phosphorylated forms of IRS-1 to associate with phosphatidylinositol 3-kinase (PI3K) (Shulman, 2004).

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19.3.3  Genetic Determinants Since insulin sensitivity varies between individuals and populations even when standardized for confounders, it is likely that there is a contributing genetic component. This is supported by familial clustering (Lillioja et al., 1987), twin studies (Newman et al., 1987; Mayer et al., 1996; Narkiewicz et al., 1997) and other lines of evidence (Rewers and Hamman, 1995; Abate et al., 1996; Rique et al., 2000; Malecki and Klupa, 2005). However, the precise genetic determinants of the more common form of insulin resistance remain unclear, and it is likely that multiple genes in various combinations are responsible.

19.4  Candidate genes and cross-population genetic differences There are several possibilities where genetic variation and candidate genes could play a role, including the insulin receptor, cellular signaling and glucose metabolism. Studies have highlighted some population-specific genes associated with insulin action. Pima Indians without diabetes show a familial aggregation of insulin sensitivity suggestive of a single gene with a co-dominant mode of inheritance (Bogardus et al., 1989) linked to chromosomal markers on 4q (Prochazka et al., 1993). This region is also linked to insulin resistance and 2-h plasma insulin concentrations in Mexican Americans (Prochazka et al., 1993; Mitchell et al., 1995). The FABP2 (a protein that binds saturated and unsaturated long-chain fatty acids) is linked to this chromosomal region and is expressed in the epithelial cells of the small intestine, and likely plays a role in the absorption of fatty acids. A missense mutation of FABP2 (Ala54Thr) has been identified, and has an allele frequency of 0.29 in Pima Indians,

0.34 in Japanese, 0.31 in Caucasians, 0.28 in Finns, and 0.14 in indigenous Canadians. In Pima Indians, the Ala54Thr variant was associated with both reduced insulin sensitivity and elevated fasting insulin levels (Baier et al., 1995). The 3-adrenergic gene is mainly expressed in visceral adipose tissue, where it plays an important role in lipid metabolism (Walston et al., 1995). A missense mutation in the gene (Trp64Arg) is associated with early onset of type 2 diabetes, overweight (visceral fat accumulation), and insulin resistance (Sakane et al., 1997). Pima Indians homozygous for Trp64Arg have a higher predisposition to early onset of type 2 diabetes, a higher BMI and lower resting metabolic rate (Walston et al., 1995). Finns, heterozygous for this mutation, show earlier onset of type 2 diabetes and decreased glucose disposal rates (Widen et al., 1995). PPARy has two isoforms determined by differential splicing of the gene on chromosome 3p25. PPARy1 is present in most tissues, whereas PPARy2 is predominantly expressed in adipose tissue. While both positive (Deeb et al., 1998) and negative associations (Meirhaeghe et al., 2000) between the gene and insulin resistance have been reported, recent studies show that substitution of proline to alanine at position 12 in the y2-specific exon (Pro12Ala) is associated with significantly less insulin resistance (Ek et al., 2001; Gonzalez-Sanchez et al., 2002; Helwig et al., 2007). Insulin resistance has been linked with the ectoenzyme plasma cell membrane glycoprotein-1 differentiation antigen (PC-1) in humans where levels of PC-1 are elevated two- to threefold in key tissues (Frittitta et al., 1996, 1997). PC-1 binds to the insulin receptor but does not block the ability of insulin to bind the receptor; instead, it interferes with insulin-induced autophosphorylation of the receptor and tyrosine kinase activation (Maddux and Goldfine, 2000). Abate and colleagues (2003) reported that the PC-1 K121Q polymorphism was associated with insulin resistance in Asian Indians compared with Caucasians.

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19.4  Candidate genes and cross-population genetic differences

The insulin receptor substrate-1 gene (encoded on chromosome 2q36 as a single exon) plays a role in determining insulin resistance. Several IRS-1 gene mutations have been identified in humans, but only G971R and Gly972R appear to have an association with obese subjects with type 2 diabetes (Clausen et al., 1995; Baroni et al., 2004). Horikawa and colleagues (2000) identified a susceptibility gene for type 2 diabetes in Mexican Americans on chromosome 2q in association with the gene encoding cysteine protease calpain-10. Although the exact role of calpain-10 in insulin resistance remains controversial, it appears to affect glucose uptake pathways in skeletal muscle (Otani et al., 2004) and adipose tissue (Paul et al., 2003). Adiponectin is encoded by the gene ADIPOQ and is involved in glucose and lipid metabolism; it is decreased in insulin-resistant states (Yamauchi et al., 2001; Bajaj et al., 2004). Adiponectin acts through its receptors ADIPOR1 and ADIPOR2, with ADIPOR2 being the main isoform for the insulin-sensitizing effects in human skeletal muscle (Civitarese et al., 2004). Polymorphisms in the ADIPOQ gene have been studied in various populations, including Caucasians and Japanese, and suggest that gene variation predisposes to insulin resistance (Gu et al., 2004; Nakatani et al., 2005). The conversion from pre-diabetes to type 2 diabetes in the STOP-NIDDM trial was predicted by SNP 45 and SNP 276 polymorphisms of the ADIPOQ gene (Zacharova et al., 2005). Damcott and colleagues (2005) found an association between ADIPOR1 and ADIPOR2 variants and type 2 diabetes in an Old Order Amish population, while Stefan and colleagues (2005) showed that a variation in ADIPOR1 may affect insulin sensitivity in Europeans. Studies from Canada suggest that insulin resistance may be a significant inherited trait contributing to the onset of type 2 diabetes (Hegele et al., 2003). Linkage of type 2 diabetes to chromosome 20q12-q13.1 has been

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reported (Klupa et al., 2000; Permutt et al., 2001). Localized in this region is the transcription factor hepatic nuclear factor (HNF)-4, which plays a critical role in the regulation and expression of genes essential to the normal functioning of the liver, pancreas and gut (Stoffel and Duncan, 1997). Hegele and colleagues (1999) identified a population-specific HNF-1 G319S mutation among a group of Oji-Cree Indians in Northern Canada which conferred susceptibility to type 2 diabetes. Individuals carrying this mutant gene had significantly higher post-challenge plasma glucose levels and fasting hyperinsulinemia suggestive of insulin resistance. Other studies suggest a major locus on chro­ mosome 6 (near marker D6S403) strongly influ­encing plasma insulin concentrations and insulin resistance in Mexican Americans (Duggirala et al., 2001); two diabetes susceptibility loci on chromosome 6q associated with insulin resistance and insulin secretion in Finns (Watanabe et al., 2000; Shtir et al., 2007) and, in the same region, diabetes in both Pima Indians (Hanson et al., 1998) and Japanese (Iwasaki et al., 1999). Insulin levels and body fat in the Quebec Family Study were linked to a region on chromosome 1p32-22 (Chagnon et al., 2000). In a study involving 2684 Asian Indians from the UK (Chambers et al., 2008), a genome-wide association study found a significant association between four SNPs in the MC4R gene and insulin resistance (HOMA-IR) with the association with the SNP rs12970134 persisting after adjusting for waist circumference, BMI and body mass. Several syndromes of insulin resistance based on single mutations of genes have been described. Over 50 mutations in the insulin receptor gene (located on chromosome 19p13.213.3) have been reported and are associated with severe insulin resistance and hyperinsulinemia (Taylor et al., 1992). These syndromes result in severe outcomes, including intrauterine growth retardation, fasting hypoglycemia and death, within the first year of life (Mercado et al., 2002).

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19.5  Conclusion Insulin resistance is common, and is implicated in a number of metabolic abnormalities, particularly the development of type 2 diabetes. Differences in insulin resistance are apparent across populations, and likely contribute to the differences in prevalence of diabetes and other metabolic abnormalities. While insulin action is influenced by many factors, including age, diet, physical activity and especially body weight, ethnic/racial differences also exist, implying underlying genetic variations. Various theories have been proposed to explain the evolution of variations in insulin resistance and the interaction with the modern-day environment. The carnivore connection hypothesis is based on the evolutionary changes in the quantity and quality (GI) of dietary carbohydrate and the advantages of insulin resistance for reproduction and during times when dietary carbohydrate, rather than energy, was scarce. These theories will remain speculative, however, until progress is made in identifying the specific molecular and genetic basis for population and individual differences in insulin action. Although our genetic make-up may exacer­ bate the impact of our current lifestyle, finding individual and societal solutions to combat these evolutionary changes is proving challenging. Without a major global catastrophe we are unlikely to be able to turn back the clock, and neither would many want to, considering the benefits of modernization. Increasing physical activity is arguably the most amenable way of increasing an individual’s insulin sensitivity, especially when coupled with appropriate dietary changes. Increased attention to urban design and providing individuals with the opportunity to exercise is fundamental. However, effective and sustainable strategies to address the excessive and inappropriate energy intake are more problematic.

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C H A P T E R

20 Neuroanatomical Correlates of Hunger and Satiaty in Lean and Obese Individuals Angelo Del Parigi Senior Medical Director, Medical Affairs, Pfizer Inc., New York, NY, USA

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Eating behavior in humans is not a stereotypical behavior driven only by the need to compensate for acute changes in energy status. It is clear that emotional, cognitive and cultural factors play a major role in the initiation and termination of an eating episode. To put it simply, a negative energy balance is sufficient but not necessary to initiate eating. However, homeostatic, hedonic and cognitive controls of eating behavior are intimately intertwined. Their separation as discrete neurophysiological processes is, in fact, supported by theoretical principles rather than by empirical evidence (Berthoud and Morrison, 2008).

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20.1  Physiology of hunger and satiety in human eating behavior Hunger and satiety are at the crossroads of this complex interplay between metabolic and non-metabolic factors regulating human eating behavior. In fact, energy balance is continuously monitored by the brain through multiple endocrine and neural mechanisms, which include long- and short-term signals of changes in energy stores, and changes in energy currency, respectively. On this dynamic background which steers

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the individual toward the decision to start eating or not, or to stop eating or not, the information from the external environment, either sensorial (such as the sight, smell or taste of food) or social (such as the availability of a scheduled “break” for lunch) may in fact act as triggering factors. If we add additional layers of complexity that pertain to cultural, psychological and environmental constraints related to the logistics of food consumption, and/or to the projected body image, and/or to the choice of specific foods (for example, inducted by a commercial or by religious beliefs), we encompass the multitude of factors which define the rhythm and the gears of human eating behavior. Although a simple model of meal control might suggest that feeding terminates when a sufficient quantity of nutrients has been ingested to meet individual nutritional needs, it is clear that the normal rate of eating indicates that meal termination occurs too early to reflect absorption of the ingested nutrients. The satiety response can actually be resolved in different chronological phases (the so-called “satiety cascade”) characterized by different underlying phenomena, mainly sensorial and cognitive, leading to the actual termination of a meal (i.e., satiation), or post-ingestive and post-absorptive phases, supporting the duration of fasting intervals between meals – that is, the properly defined satiety (Blundell and Tremblay, 1995). As such, satiation defines the discrete transition from eating to fast, while satiety characterizes the period of fast that follows. During this interval, as satiety declines, the subjective feeling of the drive to eat reaches the threshold of conscious appreciation, which is what is generally defined as hunger. In fact, operationally, satiety can be defined as the state of hunger suppression. Although fasting is a common denominator of both satiety and hunger, satiety is associated with a feeling of comfort and low desire for food, whereas hunger is associated with discomfort and high desire for food. Furthermore, in normal conditions, while satiety

is mainly a digestive-metabolically driven pro­ cess, where the gastrointestinal processing of the alimentary bolus and consequent absorption of nutrients and elicited hormonal responses are the leading factors, hunger can also be triggered by externally or internally generated cues, such as the sight or smell of food or the desire for prompt gratification – for example, in stressful life conditions. Consistent with the view of protracted overeating, or eating in excess of metabolic needs, as the leading contributing factor to weight gain and obesity, dysregulation of hunger and/or satiety appears to be a plausible working hypothesis for the understanding of the pathophysiology of obesity. In fact, the search for the biological underpinnings of a positive energy imbalance and weight gain has been intensely focusing on the molecular signatures of hyperphagia or overeating in animals and in humans. It is beyond the scope of this chapter to review the evidence accrued on the molecular pathways associated with weight gain. Suffice it to say that many catabolic and anabolic signals have been identified and tested in rodent models of obesity, and that overwhelming evidence supports the notion that weight gain and obesity are associated with neurofunctional aberrations (Bray, 2004). However, the translation of this experimental evidence to common forms of human obesity has been disappointing. Part of this loss in translation is likely due to the limitations of access to the brain for scientific experiments in humans.

20.2  Functional neuroimaging evidence One of the few available options for a noninvasive exploration of the in vivo biology of the human brain is offered by functional neuro­ imaging, which, depending on the technique of choice, measures different proxies for changes in local neural activity or receptor binding,

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20.2  Functional neuroimaging evidence

ultimately allowing for the identification of regional brain responses to specific stimulations. As such, functional neuroimaging has also been used for the investigation of brain responses to food-related stimuli in the attempt to identify neurobiological patterns associated with different states of eating behavior and different metabolic conditions, including obesity and the risk for obesity. In this context, hunger and satiety have been investigated to identify possible neurofunctional markers of these conditions in obese and normal-weight individuals, and assess their importance to the pathophysiology of obesity. From a theoretical/behavioral standpoint, such an approach could contribute to answering the atavistic and always stimulating question: is overeating (and the consequent weight gain) induced by enhanced feelings of hunger, or by a weakened satiety response, or by both? From a methodological/experimental point of view, such an approach would rather test the question: are there neurofunctional markers of overeating in obese individuals expressed at a scale that can be investigated with functional neuroimaging? Within this theoretical and methodological framework, a pioneering neuroimaging program was designed and implemented at the National Institute for Diabetes, Digestive and Kidney Diseases (NIDDK) branch in Phoenix, Arizona, USA, using positron emission tomography (PET) and 15O-water to measure changes in regional cerebral blood flow (rCBF), a marker of local neural activity. This technique works by measuring the effect of the intravenous administration of a dose of 15O-water, which is a tracer conveyed and distributed to tissues throughout the body by the arterial blood flow. This tracer rapidly diffuses through the blood–brain barrier, making it suitable for the measurement of rCBF. The spatial resolution of this technique is limited by the precision of the localization of the positron emitting nucleus (1- to 6-mm radius). On the other hand, the short half-life of 15O (122.24 seconds)

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makes it possible to acquire multiple images during a single scanning session, allowing for each person to serve as his or her own control, which eliminates a series of confounders. For example, the assessment of a change in local neural activity in response to a stimulus is implemented by subtracting the map of rCBF associated with the application of the stimulus from the map of rCBF associated with the baseline condition. Automated algorithms transpose each individual PET subtraction map onto a standardized stereotactical space, perform group data analysis, and generate statistical parametric maps of statedependent changes in rCBF (Acton and Friston, 1998). When superimposed onto an MRI scan of the same subject, these maps allow precise identification of the neuroanatomical location of the change in neuronal activity subject by subject. We have used PET and 15O-water to study the brain responses to hunger (after a 36-hour fast) and to early satiety (in response to a liquid meal providing 50 percent of the resting energy requirements) in normal weight, obese and postobese men and women (Del Parigi et al., 2002a, 2004, 2005; Gautier et al., 2000, 2001; Tataranni et al., 1999; Tataranni and Del Parigi, 2003). Subjects were admitted to the clinical research unit of the NIDDK in Phoenix for approximately 1 week. On admission, all subjects were placed on a weight-maintaining diet (50 percent carbohydrate, 30 percent fat, 20 percent protein). Body composition was assessed by dual energy X-ray absorptiometry (DPX-l, Lunar, Madison, WI), and resting energy expenditure, after a 12-hour overnight fast, was measured for 45 minutes by using a ventilated hood system (DeltaTrac, SensorMedics, Yorba Linda, CA). Extreme abnormalities in eating behavior were excluded by using the Three-Factor Eating Questionnaire (Stunkard and Messick, 1985) which estimates three major dimensions of eating behavior – dietary restraint, a measure of cognitive control over eating behavior; disinhibition, a measure of susceptibility to sensory and emotional cues; and hunger, a measure of

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sensitivity to physiological cues. The imaging session took place after a 36-hour fast, during which only water and non-caloric, noncaffeinated beverages were provided. First, we obtained a structural MRI of the head, to rule out gross abnormalities and provide anatomical information for the precise localization of the functional findings. Soon afterwards, the functional session began. A PET transmission scan using a 68Germanium/ 68 Gallium ring source was performed to correct subsequent emission images for radiation attenuation. Next, the preparation of the subject for the functional imaging session continued with the insertion of a plastic extension tube into the mouth to the middle of the tongue, while the subject was lying supine on the PET table. This apparatus was then connected to a peristaltic pump (IMED 980, Imed, San Diego, CA), set to deliver, over a period of 25 minutes, a liquid formula meal (Ensure-Plus 1.5 kcal/ml, RossAbbott Laboratories, Columbus, OH) providing 50 percent of the previously measured daily resting energy expenditure. Two 1-minute PET scans were performed right before starting the administration of the liquid meal (i.e., with the subject hungry) and two PET scans were collected right after the administration of the liquid meal (i.e., with the subject sated), with intervals of 10 minutes between scans. For each scan, a 50-mCi intravenous bolus of 15O-labeled water was injected. To eliminate possible confounding factors such as tactile stimulation of the tongue and motor neuron activity, swallowing was consistently induced by administering 2 ml of water before each of the four PET scans. During each scan, subjects rested still in the supine position, the head immobilized in a custom-made solidified foam helmet, and were asked to keep their eyes closed and pointing forward. Subjective ratings of hunger and satiety were recorded after each PET scan, using a 100-mm visual analog scale (Lawton et al., 1993). Blood samples were also collected immediately after each scan for the measurement of plasma glucose, free

fatty acids, insulin and leptin concentrations. To familiarize each subject with the experimental setting and minimize the risk of learning-related artifacts, the feeding procedure was practiced on the research ward before PET scanning. PET images were reconstructed with an inplane resolution of 10 mm full width at halfmaximum (FWHM), and a slice thickness of 5 mm FWHM. We used this approach to seek the answer to three main experimental questions: 1. Can the functional neuroanatomical correlates of hunger and satiety be imaged in humans? 2. Are there selective differences in the brain responses to meal consumption between obese and normal-weight individuals? 3. What is the pathophysiological relevance, if any, of these differences? In regard to the first question, our results demonstrated that the administration of a satiating meal to hungry individuals was associated with increased neural activity in the prefrontal cortex (generally involved in the top-down control of behavior, especially inhibiting inappropriate response tendencies) and decreased neural activity in several limbic and paralimbic areas (regions involved in a wide array of functions spanning metabolic, affective and motivational processes), and cerebellum. Among the limbic/paralimbic areas, we observed decreased activity in response to the meal in the insular cortex (a visceral sensory area also involved in processing food craving (Pelchat et al., 2004a)), the anterior cingulate (selectively involved in response to noxious stimuli (Craig et al., 1996)) and the orbitofrontal cortex (involved in cross-sensorial processing). Some of these findings were also replicated in a study of the changes in brain activity related to eating solid food (Small et al., 2001). Taken together, these findings not only demonstrated the feasibility of a neuroimaging study applied to obesity-related questions, but also showed that hunger and early satiety

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20.2  Functional neuroimaging evidence

are associated with specific regional brain responses. These regional responses clustered in a hunger-related domain, encompassing areas involved in responses to emotional cues and sensory information as well as to metabolic changes. Several of these areas were also implicated by other neuroimaging studies in responses to other forms of urges, such as thirst (Denton et al., 1999) and hunger for air (Brannan et al., 2001). Conversely, early satiety appeared to be associated with the activation of only one brain region, the prefrontal cortex, an area that reached the greatest phylogenetic expansion in humans and functionally presides over cognitive processing of information, including topdown control over behavioral responses. In light of these results and the evidence that all these major regional brain domains are reciprocally interconnected, we postulated that the prefrontal cortex, through efferent inhibitory projections to the limbic and paralimbic areas, exerts inhibiting effects on eating by suppressing the hunger-related activation of these brain areas. As an aside, the presence of a distributed, and possibly redundant, network of brain areas activated by hunger seems to support the common notion that the control of energy balance is inherently biased, favoring anabolic processes such as food intake (Schwartz et al., 2003). In regard to the second question, we observed that obese individuals respond to hunger and early satiety with greater changes in some of the limbic/paralimbic areas and in the prefrontal cortex, respectively, compared to normal-weight individuals. Specifically, in obese compared to normal-weight individuals, we observed that limbic and paralimbic areas, including the orbitofrontal cortex, insula and hippocampus, showed a greater activity in response to hunger, whereas dorsal and ventral prefrontal areas showed a greater activity in response to satiety. These differences were generally consistent in men and women (Del Parigi et al., 2002b). However, in men only, the hunger response in the hypothalamus was attenuated in obese

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c­ ompared to normal-weight individuals (Gautier et al., 2000). A similar observation was reported in lean and obese individuals in response to the ingestion of a glucose solution (Matsuda et al., 1999). We also found significant associations between postprandial changes in plasma insulin, glucose and FFA, and postprandial changes in neural activity in several brain regions, which suggests that hormones and metabolites might contribute to the generation of postprandial neural responses. In some instances, the correlations between postprandial changes in hormones/ metabolites and neural activity were in opposite directions in obese and normal-weight individuals (Del Parigi et al., 2002a). To answer the third and most challenging question, we recruited post-obese individuals who had successfully achieved and maintained a normal body weight by lifestyle changes despite a past of morbid obesity (BMI  35) (Del Parigi et al., 2004). Anthropometrically, these individuals showed a normal-weight phenotype, not different from the normalweight group previously studied, while their past as formerly obese individuals indicated a high susceptibility to weight gain, constantly counteracted by an intense physical activity regimen and actively controlled dietary intake. Although in a cross-sectional fashion, we planned the study of these formerly obese individuals in order to explore functional similarities in the brain responses to hunger and satiety between a group of obese-prone and a group of currently obese individuals to be interpreted as putative markers for neurofunctional signatures of predisposition to weight gain and obesity. Similarities between postobese and obese were actually observed only in the posterior hippocampus, which exhibited a similar decrease of neural activity in the obese and post-obese groups, whereas in the normalweight group the regional activity increased (Del Parigi et al., 2004). The hippocampus is a brain region implicated in many cognitive phenomena, chiefly related to mnemonic and

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learning processes, but it is also rich in receptors of metabolic signals and has been associated with food craving by another neuroimaging study which reported a response in an overlapping hippocampal area (Pelchat et al., 2004b). While suggestive of putative neurofunctional markers of increased risk for weight gain and obesity, this finding still awaits testing in properly designed longitudinal studies in individuals at high risk for obesity before and after gaining weight. Until then, the assessment of the pathophysiological relevance of the neurofunctional differences observed between obese and normal-weight individuals remains exploratory. In conclusion, we believe that the use of functional neuroimaging in the search for the neurofunctional underpinnings of eating behavioral differences between obese and normal weight individuals has proven to be both feasible and useful. The study of the neurofunctional correlates of hunger and satiety is one of the viable experimental settings in pursuing a better understanding of the neurobiology of eating behavior and its aberrations. We have reported a series of exploratory findings that have been partially confirmed in independent studies. These observations have generated hypotheses that are amenable to proper testing in longitudinal studies, where the pathophysiological importance of obesity-related neural abnormalities can be determined and offer the rationale for new investigational targets for the pharmaco­therapy of obesity.

References Acton, P. D., & Friston, K. J. (1998). Statistical parametric mapping in functional neuroimaging: Beyond PET and fMRI activation studies. European Journal of Nuclear Medicine, 25, 663–667. Berthoud, H. R., & Morrison, C. (2008). The brain, appetite, and obesity. Annual Review of Psychology, 59, 55–92. Blundell, J. E., & Tremblay, A. (1995). Appetite control and energy (fuel) balance. Nutrition Research Reviews, 8, 225–242. Brannan, S., Liotti, M., Egan, G., Shade, R., Madden, L., Robillard, R., Abplanalp, B., Stofer, K., Denton, D., & Fox, P. T. (2001). Neuroimaging of cerebral activations

and deactivations associated with hypercapnia and hunger for air. Proceedings of the National Academy of Sciences USA, 98, 2029–2034. Bray, G. A. (2004). Obesity is a chronic, relapsing neurochemical disease. International Journal of Obesity and Related Metabolic Disorders, 28, 34–38. Craig, A. D., Reiman, E. M., Evans, A., & Bushnell, M. C. (1996). Functional imaging of an illusion of pain. Nature, 384, 258–260. Del Parigi, A., Gautier, J. F., Chen, K., Salbe, A. D., Ravussin, E., Reiman, E., & Tataranni, P. A. (2002a). Neuroimaging and obesity: Mapping the brain responses to hunger and satiation in humans using positron emission tomography. Annals of the New York Academy of Sciences, 967, 389–397. Del Parigi, A., Chen, K., Gautier, J. F., Salbe, A. D., Pratley, R. E., Ravussin, E., Reiman, E. M., & Tataranni, P. A. (2002b). Sex differences in the human brain’s response to hunger and satiation. The American Journal of Clinical Nutrition, 75, 1017–1022. Del Parigi, A., Chen, K., Salbe, A. D., Hill, J. O., Wing, R. R., Reiman, E. M., & Tataranni, P. A. (2004). Persistence of abnormal neural responses to a meal in postobese individuals. International Journal of Obesity and Related Metabolic Disorders, 28, 370–377. Del Parigi, A., Pannacciulli, N., Le, D. N., & Tataranni, P. A. (2005). In pursuit of neural risk factors for weight gain in humans. Neurobiology of Aging, 26(Suppl. 1), 50–55. Denton, D., Shade, R., Zamarippa, F., Egan, G., Blair-West, J., McKinley, M., & Fox, P. (1999). Correlation of regional cerebral blood flow and change of plasma sodium concentration during genesis and satiation of thirst. Proceedings of the National Academy of Sciences USA, 96, 2532–2537. Gautier, J. F., Chen, K., Salbe, A. D., Bandy, D., Pratley, R. E., Heiman, M., Ravussin, E., Reiman, E. M., & Tataranni, P. A. (2000). Differential brain responses to satiation in obese and lean men. Diabetes, 49, 838–846. Gautier, J. F., Del Parigi, A., Chen, K., Salbe, A. D., Bandy, D., Pratley, R. E., Ravussin, E., Reiman, E. M., & Tataranni, P. A. (2001). Effect of satiation on brain activity in obese and lean women. Obesity Research, 9, 676–684. Lawton, C. L., Burley, V. J., Wales, J. K., & Blundell, J. E. (1993). Dietary fat and appetite control in obese subjects: Weak effects on satiation and satiety. International Journal of Obesity and Related Metabolic Disorders, 17, 409–416. Matsuda, M., Liu, Y., Mahankali, S., Pu, Y., Mahankali, A., Wang, J., DeFronzo, R. A., Fox, P. T., & Gao, J. H. (1999). Altered hypothalamic function in response to glucose ingestion in obese humans. Diabetes, 48, 1801–1806. Pelchat, M. L., Johnson, A., Chan, R., Valdez, J., & Ragland, J. D. (2004a). Images of desire: Food-craving activation during fMRI. Neuroimage, 23, 1486–1493. Pelchat, M. L., Johnson, A., Chan, R., Valdez, J., & Ragland, J. D. (2004b). Images of desire: Food-craving activation during fMRI. Neuroimage, 23, 1486–1493.

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References

Schwartz, M. W., Woods, S. C., Seeley, R. J., Barsh, G. S., Baskin, D. G., & Leibel, R. L. (2003). Is the energy homeo­ stasis system inherently biased toward weight gain? Diabetes, 52, 232–238. Small, D. M., Zatorre, R. J., Dagher, A., Evans, A. C., & JonesGotman, M. (2001). Changes in brain activity related to eating chocolate: From pleasure to aversion. Brain, 124, 1720–1733. Stunkard, A. J., & Messick, S. (1985). The three-factor eating questionnaire to measure dietary restraint, disinhibition and hunger. Journal of Psychosomatic Research, 29, 71–83.

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Tataranni, P. A., & Del Parigi, A. (2003). Functional neuro­ imaging: A new generation of human brain studies in obesity research. Obesity Reviews, 4, 229–238. Tataranni, P. A., Gautier, J. F., Chen, K., Uecker, A., Bandy, D., Salbe, A. D., Pratley, R. E., Lawson, M., Reiman, E. M., & Ravussin, E. (1999). Neuroanatomical correlates of hunger and satiation in humans using positron emission tomography. Proceedings of the National Academy of Sciences USA, 96, 4569–4574.

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C H A P T E R

21 Neuroendocrine Stress Response and Its Impact on Eating Behavior and Body Weight Beth M. Tannenbaum1, Hymie Anisman2 and Alfonso Abizaid2 1

McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada 2 Institute of Neuroscience, Carleton University, Ottawa, Canada

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21.5 Peripheral Signals Regulating Energy Balance 21.5.1  Leptin 21.5.2  Insulin 21.5.3  Ghrelin

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21.1  Introduction There is considerable overlap between the physiological systems that regulate food intake and those that mediate stress responses, and stressful events may influence food ingestion. Considering that the ability of an organism to mount an effective defensive response is highly dependent on available energy stores (i.e., the

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mobilization and utilization of blood glucose), it is not surprising that overlapping mechanisms exist among stress and consummatory systems. That said, under certain conditions it may be adaptive for processes that stimulate defensive behaviors to inhibit those relating to ingestive processes – for example, it would clearly be counterproductive for an organism facing a threat from predators to engage in a search for food.

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Under non-stressful conditions, individuals have the opportunity to adopt healthy eating practices and maintain a healthy body weight. However, under stressor conditions, particularly if these are chronic and unremitting, the wear and tear on biological and behavioral coping methods may be excessive (McEwen, 2007), and shifts may occur to food consumption patterns in regard to both quality and quantity. In order to maintain “allostasis”, the processes by which homeostasis is maintained in the face of stressors, numerous short-term adaptive coping strategies can be employed, although some of these may have negative long-term repercussions. In rodents, stressors typically result in reduced food intake. However, in humans there are marked individual differences: some individuals reduce their consumption, whereas others increase ingestion (particularly carbohydrate snacks). Some of these individuals may employ eating as a coping method, even if it is an ineffective or counterproductive one. Indeed, this coping strategy may involve a shift from the consumption of healthy foods to the overconsumption of “comfort foods” that are typically high in calories, fat and sugar, and low in nutritional value. As individuals turn to “comfort foods” to alleviate stress, the continued failure to cope with stressors may promote the development of obesity (Laitinen et al., 2002). This drive for comfort is associated with a shift from the homeostatic/allostatic system (dependent on energy stores and nutritional status) to the nonhomeostatic or reward-seeking system (invol­ved with the motivational aspects of eating). The “homeostatic” and “non-homeostatic” controls on food intake and energy expenditure are achieved through coordination between the hypothalamus, the brainstem and various limbic areas. However, if pleasure is experienced after the consumption of high-sugar/high-fat foods, the hedonic response might be capable of over-riding homeostasis/allostasis, and result in an elevated appetite and a drive to overeat “pleasurable” calories (McEwen, 2007).

The present chapter addresses some of the current research findings in both animal and human populations that have elucidated how and why food consumption patterns can be altered under stressor conditions. It is suggested that cortisol (or corticosterone in rodents) and several metabolic hormones, released under stress and anxiety conditions, are linked to changes in metabolic function. Moreover, through repeated experiences, individuals may learn that eating high-caloric foods can reduce some of the unpleasant effects of stress and thus, with further stressor encounters, individuals may “self-medicate” through eating “comfort foods”.

21.2  Hypothalamo-pituitaryadrenal axis The activation of the hypothalamo-pituitaryadrenal (HPA) axis is comprised of a network of regions that span both the central and peripheral nervous systems. In response to stressors, corticotrophin releasing hormone (CRH) is released from cells in the paraventricular nucleus (PVN) of the hypothalamus. CRH acts on the anterior pituitary corticotrophs to stimulate the synthesis and release of adrenocorticotropic hormone (ACTH), which then acts on the adrenal cortex to stimulate the release of cortisol (humans) or corticosterone (animals). Cortisol regulates its own levels via a series of negative-feedback loops at both brain and pituitary sites. Excessive and/or chronic stressors, possibly through actions on HPA functioning, can adversely impact a variety of physiologic functions and behavioral outputs, such as growth, reproduction, glucose metabolism (insulin resis­ tance and type 2 diabetes), immunocompetence, deposition of body fat, atherosclerosis, hippo­ campal atrophy, and depression (Kyrou et al., 2006). This makes negative-feedback efficacy essential, but under conditions of chronic stress

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21.3  Stress and food intake: it is not all homeostatic or automatic

the ability of the axis to “shut-off” or dampen circulating cortisol levels is often compromised, and the diurnal rhythms associated with stressors may be disturbed (Michaud et al., 2008). In addition to the hypothalamus, the amyg­ dala plays a fundamental role in mounting effective stress responses. It contains many of the peptides implicated in stress and anxiety regulation, such as CRH (Behan et al., 1996). However, in contrast to the hypothalamus, corticosterone stimulates CRH release in the amyg­ dala, which then affects anxiety/fear responses (Makino et al., 1994) as well as the regulation of food intake and appraisal. Like the amygdala, medial prefrontal cortical regions (mPFC) (i.e., the anterior cingulate gyrus, the subcallosal gyrus and the orbitofrontal cortex), appear to be involved in the memory of the emotional valence of stimuli, and thus play an active role in the inhibition of fear responses mediated by the amygdala (Pignatti et al., 2006; Petrovich et al., 2007). Therefore, the response to stress is highly complex and recruits varied brain regions that serve and support both the endocrine and cognitive aspects of the axis.

21.3  Stress and food intake: it is not all homeostatic or automatic Animals exposed to repeated stressors typically eat less, and therefore weight gain is limited. Yet, under some conditions, they also show increased consumption of palatable foods and liquids, accompanied by reduced HPA axis activity (Pecoraro et al., 2004; la Fleur et al., 2005). As indicated earlier, intake of “comfort foods” reduces HPA axis activity and promotes the activation of brain circuits implicated in reward-seeking behaviors (Dallman et al., 2003). In line with the view that “comfort foods” have positive effects, when provided to chronically stressed rats they may negate stress-induced

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abnormalities of cortisol and dopamine (DA) functioning (Dallman et al., 2006). In effect, it may be that the inhibition of neuroendocrine responses to stressors in rats eating “comfort foods” is explained by the interplay between the negative effects of chronic stressors and the positive effects of “comfort foods” on inputs to the ventral tegmental area (VTA) nucleus accumbens reward network – brain sites critically involved in dopamine DA regulation, the primary neuro­ chemical implicated in responses to reward and addiction. As such, DA modulation may be responsible for regulating the reward or reinforce­ ment necessary to enhance feeding as seen in obesity (Berridge, 1996). These reward-related areas are also activated in response to drugs of abuse (McQuade et al., 2004), and it is conceivable that the underlying brain mechanisms associated with stressor-provoked eating are similar to those that ultimately result in the compulsive drug consumption seen in addiction (Volkow and O’Brien, 2007). Data from human studies support the idea that stressors can enhance caloric intake as a means to cope with stressful events (Anisman et al., 2008). Daily hassles were associated with increased consumption of high-fat/high-sugar snacks, and with a reduction in the frequency of main meals and the consumption of veget­ ables. Interestingly, psychosocial stressors elicited hyperphagic responses in subjects, whereas physical stressors caused hypophagic responses (O’Connor et al., 2008). There appear to be premorbid features that predict the impact of stressors on eating and weight gain. Specifically, it was reported that among students followed over a 12-week stressful period, dietary restraint decreased and their body mass index (BMI) increased (Roberts et al., 2007). Further, those with the highest dietary restraint scores were those with the highest initial BMI and lowest daily salivary cortisol secretion. This is consistent with evidence that restrained eaters struggle to control food intake and weight, as well as with the predictions of the dietary restraint

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model; namely, that reliance on cognitive control over eating, rather than physiological cues, renders dieters vulnerable to uncontrolled eating. Paralleling such findings, healthy medical students who had higher urinary cortisol and insulin during academic exams had identified themselves as stress eaters and showed greater weight gain than non-stress eaters during a stressful academic period (Epel et al., 2004). Others showed that laboratory stressors (unsolvable anagrams, Trier Social Stress Test) were associated with increased caloric intake (and, in particular, fat) (Zellner et al., 2006), especially in those who showed substantial cortisol responses to the stressor (Epel et al., 2001).

21.4  Imaging studies in humans Brain imaging studies have been instrumental in elucidating the functional network that controls appetite, identifying the specific brain regions differentially involved in hunger and satiety in humans (Tataranni et al., 1999). For example, hunger was associated with increased regional cerebral blood flow (rCBF) in the hypothalamus, the anterior cingulate cortex, and the insular and orbitofrontal cortices (IC and OFC), whereas satiety was associated with changes in the prefrontal cortex (PFC) (Tataranni et al., 1999). It has been postulated that the PFC has inhibitory effects on the hypothalamic regions that regulate hunger in humans, thus promoting meal termination. In effect, there is decreased activity of the ‘‘hunger’’ areas when the individual is satiated. Since the PFC is known to have inhibitory projections to these areas, termination might be provoked by the “anorexigenic” PFC down-regulating neuronal activity in the orexigenic CNS regions. PET analyses indicate that gastric distension (as a mechanic visceral stimulus to simulate satiety) provokes activation of the inferior frontal

gyrus (a component of the PFC) (Le et al., 2006). This suggests that this region plays a pivotal role as a convergence zone for processing foodrelated/visceral stimuli, and for the coordination of states of appetite and satiety. In addition, it appeared that the amygdala was involved in the coordination of appetitive behaviors (Baxter and Murray, 2002; Cardinal et al., 2002; Holland and Gallagher, 2004). Specifically, it is thought that the amygdala, through interactions with the OFC, signals the hedonic value of a stimulus or object (Holland and Gallagher, 2004). By interacting with posterior visual areas, this region may be important in defining the salience of biologically relevant stimuli (LaBar et al., 2001). The processing of hunger and satiety cues appears to be contingent on the inherent reward value of the food, the individual’s motivational state, and other factors that could influence motivational processes (such as stressor exper­ iences). In this regard, it was found that when students were highly motivated to eat chocolate (and rated the chocolate as being highly pleasant), rCBF increased in the medial OFC and IC. Conversely, rCBF in the PFC and lateral OFC increased with satiety as the chocolate became less pleasant (Small et al., 2001). Killgore and colleagues (2003) likewise tested whether images of high-caloric foods would have greater motivational salience in the amygdala and PFC relative to images of low-caloric foods, as measured by fMRI (Killgore et al., 2003). They found activation of the amygdala irrespective of the caloric content of the food image, but significant activation in the PFC following the presentation of high-caloric foods. Given these data and those supporting the inhibitory role of the PFC on food intake (Del Parigi et al., 2007), it is likely that the inhibition of food reward is probably a goal of this prefrontal-orbitofrontal loop. Thus, in addition to the differential recruitment of brain areas in conditions of satiety and hunger, the neural activity in these areas can be modulated by the incentive value of food stimuli

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21.5  Peripheral signals regulating energy balance

(i.e., the inherent reward value) as well as differences in motivational state. Several studies have shown that there is overlap between brain regions associated with food intake and in anticipation of or in response to stressful stimuli. For example, anticipation of public speaking was associated with activation in the hippocampus (HC)/amygdala (Tillfors et al., 2001). Subjects asked to solve difficult mathematical problems showed increased activation of the ventral right prefrontal cortex (rPFC) and insula/putamen, as measured by perfusion MRI (Wang et al., 2007). rPFC activation persisted well beyond the termination of the stressor task, suggesting a heightened state of vigilance or emotional arousal (Wang et al., 2005). Based on several PET studies, it also appears that subcortical DA was increased in response to physiological (Adler et al., 2000) and psychological (Pruessner et al., 2004) stressors. For instance, Pruessner and colleagues reported that a mental arithmetic stressor produced brain activations involving the occipital, parietal and motor cortex. The most profound effect of the stressor, however, seemed to involve a deactivation across a network of limbic structures, including the hippocampus, amygdala, insula, hypothalamus, ventral striatum, medioorbitofrontal cortex and posterior cingulate cortices (Pruessner et al., 2008). Interestingly, indi­viduals who reacted to the stressor with a significant increase in circulating cortisol showed the greatest deactivation in the aforementioned brain regions (Pruessner et al., 2008). These results suggest that this set of limbic system structures shows high activity during nonstressful states, serving as a threat-detecting system. The system constantly scans the environment for signs of incoming danger or threat. Once such a condition is met, the activity of this system is actively curtailed to initiate the alarm response consisting of hormonal and physiological responses of the HPA and other axes to allow adaptation of the organism in response to the threat. As such, one mechanism for acute

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stress-induced changes in food consumption may be a redirecting of resources normally available for feeding processes towards basic survival needs. However, under conditions of chronic stress, continuously elevated adrenal glucocorticoids, through a compromised alarm system, may be associated with a shift towards hedonic feeding patterns.

21.5  Peripheral signals regulating energy balance The brain regions discussed here appear to be fundamental in integrating both internal and external cues, promoting appropriate physiological and behavioral responses to maintain homeostasis. Some of these brain regions are also influenced by peripheral hormones which are fundamental in determining consumption and satiety.

21.5.1 Leptin The discovery of leptin, the protein encoded by the Ob gene, could be considered among the most important research findings in the field of energy balance. Produced primarily by adipocytes, leptin is secreted into the circulation and targets the brain and peripheral organs to ultimately decrease food intake, increase energy expenditure and reduce adiposity (Zhang et al., 1994; Campfield et al., 1995; Halaas et al., 1995). Mutation of this gene or the gene encoding the leptin receptor results in morbid obesity, insulin resistance and infertility (Zhang et al., 1994; Chen et al., 1996). In addition to being a metabolic hormone, leptin also acts as a modulator of the HPA axis and might influence the effects of stressors on systems other than those regulating energy homeostasis (Lu et al., 2006). Indeed, leptin targets critical brain regions responsible for the regulation of the HPA axis, such as the

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hippocampus, the brainstem and the hypothalamic PVN (Hakansson et al., 1998; Hosoi et al., 2002). Leptin receptors are also found in regions where corticosterone and CRH may affect food intake and energy balance. For example, leptin targets the raphe nuclei and the VTA in the midbrain, where it modulates the activity and release of 5-HT and DA, respectively (Finn et al., 2001; Fernandez-Galaz et al., 2002; Clark et al., 2006; Fulton et al., 2006; Hommel et al., 2006). The presence of receptors in these regions also suggests that leptin may directly influence reward-seeking behaviors, and affective tone, related to feeding. Support for this idea comes from studies where leptin promoted anhedonia reflected by an in­crease in the reward threshold (i.e., reduced value of the reward) among rats responding to rewarding electrical stimulation from the lateral hypo­ thalamus (Fulton et al., 2000). Like several other hormones, leptin is influenced by stressors (Konishi et al., 2006). It has been suggested that, through actions on reward mechanisms, it might contribute to stressrelated pathology such as depression (Anisman et al., 2008). In fact, in rodents the depressivelike behavioral disturbances introduced by a chronic stressor could be antagonized by systemic or intrahippocampal (but not hypothalamic) leptin administration (Lu et al., 2006). However, the data concerning leptin variations in relation to depression in humans have been inconsistent (Deuschle et al., 1996; Kraus et al., 2001; Atmaca et al., 2002; Westling et al., 2004; Kauffman et al., 2005; Eikelis et al., 2006; Jow et al., 2006; Otsuka et al., 2006; Pasco et al., 2008), and the source for these inconsistencies is uncertain. However, these may have been related to variability of the features of depression across individuals. While some display typical features (e.g., reduced eating and sleeping), in atypical depression, symptoms may be comprised of reverse neurovegetative features (e.g., increased eating, sleeping). More research is needed to obtain a clearer picture of the specific role of leptin on depressive disorders. Given the relation

between leptin, CRH and glucocorticoid pro­ cesses, it can be suggested that leptin contributes to the different stressor-provoked changes of ingestion evident in depressive illness.

21.5.2  Insulin The role of insulin in the regulation of energy balance is well established, and it is suggested that its interactions with glucocorticoids, leptin, ghrelin and cytokines play a critical role in the development of obesity and metabolic anomalies seen after continuous exposure to stressful events (Landsberg, 2001). Like leptin, insulin secretion from the pancreas is increased in animals exposed to stressors (Black, 2006; Innes et al., 2007). Acutely, insulin increases glucose utilization in the periphery and targets the hypothalamic ARC to reduce NPY synthesis and ultimately decrease food intake (Woods et al., 1996). Nevertheless, chronic stressor exposure leads to insulin insensitivity through a number of mechanisms that may be mediated by elevated glucocorticoid action (Black, 2006). One reason for this is that insulin resistance is ameliorated by adrenalectomy (Saito and Bray, 1984; Duclos et al., 2005). Interestingly, insulin depletion also prevents some of the obesogenic effects of glucocorticoids, particularly those where stressors may promote increased consumption of high-caloric foods (la Fleur et al., 2004). Thus, in the absence of insulin, animals with elevated corticosterone levels may not experience the soothing effects of “comfort foods” (la Fleur et al., 2004). Given that dopamine cells in the VTA express insulin receptors (Figlewicz et al., 2003), it is possible that insulin targets midbrain dopamine cells to sensitize them to the action of glucocorticoids, thereby enhancing food-seeking behaviors.

21.5.3  Ghrelin Ghrelin is a stomach-derived peptide that has generated considerable attention because, unlike

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other peripheral signals, ghrelin stimulates food intake, increases adiposity and decreases metabolic rate (Kojima et al., 1999; Tschop et al., 2000). The effects of ghrelin, like those of glucocorticoids, leptin and the other hormones discussed to this point, occur both centrally and peripherally (Kojima and Kangawa, 2005). Within the hypothalamus, ghrelin receptors are concentrated in the VMH and ARC, where they target primarily NPY/AGRP neurons to modulate its orexigenic properties (Nakazato et al., 2001). Ghre­lin also directly stimulates orexin cells in the LH as well as cells in the PVN, VMH, DMH and SCN, suggesting a large degree of complexity in its effects within the hypothalamus (Guan et al., 1997; Toshinai et al., 2003; Zigman et al., 2006). In addition to its hypothalamic actions, ghrelin also targets extrahypothalamic structures that overlap with targets of glucocorticoid action, including the hippocampus, the brainstem and the midbrain VTA, the substantia nigra and the raphe nuclei (Guan et al., 1997; Abizaid et al., 2006; Zigman et al., 2006). Microinfusion of ghrelin into the VTA leads to increased food intake, whereas microinfusion of antagonists decreased compensatory food intake after a fast (Guan et al., 1997; Carlini et al., 2004; Naleid et al., 2005; Abizaid et al., 2006; Zigman et al., 2006). Interestingly, ghrelin injections increase food-related imagery and stimulate the activity of reward pathways in human subjects, including the PFC and amygdala. This further implicates ghrelin in appetitive responses to incentive cues (Schmid et al., 2005; Malik et al., 2008). Peripherally, ghrelin targets the pituitary to enhance the release of growth hormones and stress hormones such as ACTH and pro­ lactin (Arvat et al., 2001; Stevanovic et al., 2007). Ghrelin also stimulates the release of corticosterone, an effect mediated by the increases in the release of ACTH (Stevanovic et al., 2007). It is notable that although adrenalectomy reduces food intake and body weight, the orexigenic effects of ghrelin are not affected by this manipulation, supporting the idea that ghrelin does

not promote food intake through the stimulation of corticosterone secretion (Proulx et al., 2005). In this regard, ghrelin also stimulates the proliferation of adipocytes, which might underlie the obesogenic effects of this hormone (Kim et al., 2004; Zwirska-Korczala et al., 2007). There is evidence that ghrelin levels fluctuate in response to acute and chronic stressors (Kristenssson et al., 2006; Ochi et al., 2008), but little is known about the potential role of ghrelin in the metabolic alterations that follow continuous exposure to stressors. In humans, an acute stressor (the Trier Social Stress Test; TSST) increases plasma ghrelin and cortisol levels, although the post-stress increase in the urge to eat found to occur in some individuals was unrelated to acute changes in plasma ghrelin levels (Rouach et al., 2007). Nevertheless, little is known about ghrelin responses to chronic stressors and their possible interactions. There are several potential mechanisms by which ghrelin and corticosterone might influence metabolic processes. These include the interaction of stressor-induced corticosterone and ghrelin action on the melanocortin system to modulate sympathetic outflow; VTA and substantia nigra functioning to regulate motivational aspects of feeding; and actions at the hippocampus to regulate feedback mechanisms that keep HPA activity in check.

21.6  Conclusion The obesity epidemic is often viewed as the outcome of an inherited genetic predisposition to store energy in the form of adipose tissue in combination with sedentary lifestyles. The current review offers stress as a possible factor in the generation of obesity and metabolic syndrome. Here, we have reviewed evidence that acute activation of the HPA axis affects brain and peripheral organs to affect appetite. Continued stimulation of this system results in

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severe energetic dysregulation leading to obesity, insulin resistance, cardiovascular disease and early death. There are numerous mechan­ isms that engender these pathological conditions, including effects on brain regions that regulate metabolism, autonomic function, and behavioral processes that include motivational, cognitive and affective behaviors. Particular emphasis has been placed upon regulatory and autonomic processes. However, a therapeutic agent might be developed on the basis of an understanding of processes linking stress and the soothing effects of high-caloric foods, as well as a better understanding regarding the contribution of corticosterone in relation to the feeding, metabolic and rewarding effects of leptin, insulin, dopamine and ghrelin. It could be speculated that behavioral and cognitive-based therapies aimed at reducing stress, as well as modifying behavior via programs that diminish the incentive value of high-calorie diets while enhancing the incentive value of physical activity as a means of reducing stress, may prove to be effective clinical tools to reduce obesity. It has been shown that physical activity is associated with the release of so-called “feel-good” endorphins in frontolimbic brain structures that may mediate some of the therapeutically beneficial consequences of exercise on depression, stress and anxiety in patients. As such, interventions introducing physical activity into one’s daily life could serve to promote the same “reward” as high-calorie foods, but without the detrimental health consequences in the long term. In time, the observed health benefits of increased physical activity may serve as motivation to adopt more balanced and nutritious eating patterns as an adjunct to an overall healthier lifestyle. Thus, in order to reduce the physical, mental and economic costs of the current obesity epidemic, it is imperative that preventative strategies that involve a remodeling of the notion of “eating for pleasure” are introduced early on and promoted throughout the lifespan.

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C H A P T E R

22 Eating Behavior and Its Determinants: From Gene to Environment John M. de Castro College of Humanities and Social Sciences, Sam Houston State University, Huntsville, TX, USA

o u t l i n e 22.1  Introduction

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22.3  The Environment

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22.6 Discussion

22.1  Introduction A large set of compelling evidence has already been presented in regard to the role of physiology in the control of food intake and body weight. That is, however, only part of the story. The environment also plays a major role. In fact, the environment may be the single most important influence on intake in the short term, but also in the long-term control of body weight. However, distinctions between physiology and environment are no longer as clear as originally thought, since they appear to interact in subtle and important ways to control intake and body size (de Castro, 2004a).

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There are significant clues that physiology plays a less important role than currently acknowledged. First, if physiology were in complete control, then body weight would remain relatively stable. This is not in fact the case. On an individual level, significant body-weight changes can occur at any age and be maintained (Pearcey, 2000). At the population level, there has been a marked increase in body weight over the past several decades (Flegal, 1999; Mokdad et al., 1999; Flegal, et al., 2001; Ogden et al., 2002). Second, if the physiology were completely responsible for regulation, then daily intake should be fairly constant. This is also not the case: the total food energy intake fluctuates widely from day to day

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(Morgan et al., 1987; Tarasuk and Beaton, 1991; de Castro, 1998; Hartman et al., 1999). It might be possible that regulation occurs across days such that intake on one day affects intake on subsequent days – except that, again, this is not the case. There is no significant relationship between one day’s food intake and that on the subsequent day, and only very weak relationships to the intake two and three days later (de Castro, 1998, 2000). Hence, neither body size nor daily intake appears to be tightly regulated, suggesting that environmental influences have a wide range in which to affect intake.

22.2  Genes Genes have marked influences on body size and composition (Bouchard et al., 1985, 1986; Stunkard et al., 1986, 1990; Bouchard, 1991; Hewitt et al., 1991; de Castro, 1993a; Allison et al., 1996). It stands to reason that if body size is influenced by genes, then the ingestion of nutrients that underlies the development and maintenance of body mass should also show genetic influences. We investigated this notion by studying the food and fluid intakes of adult twins living independently in their normal environments. Detailed eating behaviors along with contextual and psychological variables were measured with a 7-day diet-diary technique (de Castro, 1994a, 1999a, 2006a). Twins were required to make detailed records of their intake in a pocket-sized diary for 7 consecutive days; in addition, they were asked to record their feelings and the nature of the environmental context. This procedure has been shown to have reasonable levels of reliability and validity (de Castro, 1994a, 1999a, 2006a). A heritability analysis of the twins eating behavior revealed that 42 percent of the variance in daily intake, independent of body size, was accounted for by inheritance (de Castro, 1993b). In addition, carbohydrate, fat, protein, alcohol

and water intakes were all significantly affected by inheritance in a manner that was, to some extent, independent of the overall daily intake (de Castro, 1993b). Since daily intake occurs in the form of meals, it follows that inheritance should also affect meal intake. Indeed, independent of the level of overall intake, heredity accounted for 28 percent of the variance in the meal sizes and for 34 percent of the variance in the meal frequencies (de Castro, 1993a). These findings demonstrate that genes have pervasive influences on body size and intake, including separate and independent effects on height, weight, overall and macronutrient intake, meal size, and meal frequency. One way that the physiology can affect intake is by influencing gastrointestinal physiology. Indeed, the fuller the stomach at the beginning of the meal, the smaller size of the meal eaten (de Castro et al., 1986). Genes appear to affect not only the amount of food in the stomach, but also the degree of restraint on intake exerted by stomach filling. Genetic influences have been found to affect the amount estimated to be in the stomach before and after meals (de Castro, 1999b) (Figure 22.1). When the relationship between stomach content and the size of the meals is established for each individual, the slope of the relationship provides a measure of how responsive that individual is to his or her stomach content. Genetic influences have also been demonstrated for the slope of the relationship between stomach content and the amount eaten (de Castro, 1999b) (Figure 22.1). Hence, how full the stomach is at the start of the meal and how big a suppressive effect the stomach content has on subsequent intake are both significantly influenced by genes.

22.3  The environment Although genes clearly influence intake, the majority of the variance in intake is accounted

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22.3  The environment

Heritability analysis of factors affecting intake Mean variable values %of the variance accounted for

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Figure 22.1  Heritability analysis of factors affecting intake. The proportion of the variance in the factor means (upper panel) and the slopes of the relationships between the factors and the meal size (lower panel) that could be accounted for by the individual environment (white), family environment (striped) and heredity (black) in the linear structural modeling heritability analysis of the twin data.

for by environment. While inheritance accounts for 42 percent of the variation in overall intake, 58 percent is due to environment. While inheritance accounts for 28 percent and 34 percent, respectively, of the variation in meal size and meal frequency, 72 percent and 64 percent, respectively, is due to environment. In addition, as we will see, the influence of genes is, at least in part, due to gene–environment interactions and genetic influences on environmental selections. Some of the most important environmental effects on behavior derive from social influences

and food intake is no exception. When people eat with other people, they eat, on average, 44 percent more than when they eat by themselves (de Castro and de Castro, 1989). The degree of social facilitation of food intake is related to the number of people present. When one other person is present, 33 percent more is eaten, while 47 percent, 58 percent, 69 percent, 70 percent, 72 percent and 96 percent increases were associated with two, three, four, five, six, and seven or more people, respectively (de Castro and Brewer, 1992). In addition, these social influences are greater when family and

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friends are present than with other types of eating companions (de Castro, 1994b). An important but rarely recognized environmental influence on intake is the time of day. Over the course of the day, the amount eaten in meals increases (de Castro, 1987) and the amount of time before the next meal decreases. Hence, the period of satiety produced per unit of food energy ingested declines over the day, becoming quite low by late evening. This decrease in the satiating effect of food over the course of the day can lead to differing intakes, depending on when food is ingested. When a large proportion of intake is eaten in the morning, lower overall intake occurs over the day. In contrast, when a high proportion of intake is eaten late in the day, there is a higher overall intake for the day (de Castro, 2004b). Not only does the time of day make a difference in intake; so too does the time of the week, with substantially larger intakes on weekend days (de Castro, 1991a). This extends even to the season of the year, with significantly greater intakes in the fall (de Castro, 1991b). Psychological phenomena are also major influences on intake. The greater the level of subjective hunger, the more will be eaten; the more that is eaten, the lower the level of hunger (de Castro and Elmore, 1988). To some extent, the influence of hunger appears to be independent of the stomach content. In addition, many humans attempt voluntarily to establish control over their food intake, in a phenomenon labeled dietary restraint. Higher restraint is associa­ ted with lower overall intake, smaller meals and lower fat intake (de Castro, 1995). In addition to the social environment, the physical environment also has important effects upon the amounts ingested (Stroebele and de Castro, 2004a). When eating while watching television, people tend to eat more over the day (Stroebele and de Castro, 2004b). In addition, people eat substantially more in restaurants than they do at home or at work (de Castro et al., 1990; Stroebele and de Castro, 2004a, 2006).

The characteristics of the foods consumed are also important determinants of the amounts ingested. Palatability is a hypothetical construct that stands for the stimulus qualities of a substance that affects its acceptability (Rogers, 1990). The more palatable a food, the greater the amount consumed, with highly palatable meals being 44 percent larger than neutral or unpalatable meals (de Castro et al., 2000). The energy density of the diet ingested is significantly associated with intake (Yao and Roberts, 2001): the more energy per gram of food, the more total energy is ingested in the meal (de Castro, 2004c, 2005). Thus, the social, temporal, psychological and dietary environments have substantial impacts on the amounts ingested in meals and over a day.

22.4  Genes–environment interactions Obviously, the control of intake is a complex phenomenon involving a myriad of physiological and environmental variables that independently and interactively influence intake. Classically, genes have been viewed as primarily determining anatomical structure. It appears that inheritance has much more subtle and complex effects, and can influence the environments that an individual chooses to occupy and the impact of these environments on intake. The influence of genes on the social facilitation of intake was explored by analyzing the relationship between the number of people present and the meal intake of twins, reported in the 7-day diet diaries. Interestingly, there were significant inheritance effects not only on the number of eating companions at meals, but also on the choice of the companions, accounting for over 25 percent of the variance in the likelihood of eating with family, friends and a spouse (de Castro, 1997) (see Figure 22.1). This observation is quite remarkable, and clearly

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22.4  Genes–environment interactions

muddies the distinction between inheritance and environment. Genes appear not only to affect physiology, but also the kind of environments that people choose to occupy. The genetic influence, however, extends beyond simply impacting the choice of environments. It also appears to impact the magnitude of the effect of these environments on intake. The twin data showed that heredity affected the extent to which intake was increased by the presence of other people. The correlation between the number of people present and the amount eaten was significantly heritable (de Castro, 1997). More significant, however, was that the slope of the regression between the number of people present and meal size was also considerably affected by inheritance (see Figure 22.1). The slope of the regression can be viewed as a measure of the responsiveness of the individual to social effects. These data indicate that genetic factors affect not only the number and types of people at meals, but also the impact of these companions on intake. This is a remarkable intrusion of genes into environmental influences on intake. Looking in a similar way at the heritability of time-of-day associations with intake, the twin data revealed the significant influences of genes on the time of day at which people choose to eat. Some people eat a larger portion of their daily intake in the morning, others do so in the afternoon and yet others in the evening, and these proportions were found to be heritable (de Castro, 2001a) (see Figure 22.1). The differences in the proportions of intake ingested during different periods of the day can be used as a metric of the individual’s responsiveness to time of day. The twin data showed that the differences in the proportions of intake eaten during the morning and afternoon, the morning and evening, and the afternoon and evening were significantly heritable (see Figure 22.1). This indicates that inheritance affects not only the time of day at which people choose to eat, but also the impact of that time selection on intake. As with social

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facilitation, the results with the diurnal rhythms of intake indicate that genes affect environmental choices and their impact on intake. Genes also appear to influence the psychological state of the individual and the effect of that state on intake. The twins were asked to rate their level of hunger before and after meals. Heritability analysis of these data revealed that the subjective levels of hunger at which individuals initiate a meal and at which they finish the meal are significantly heritable (de Castro, 1999c) (see Figure 22.1). The strength of the relationship between hunger and intake provides a metric of the individual’s responsiveness to the subjective state of hunger. Using this measure, the twin data suggest that this responsiveness is also heritable. Significant heritabilities are present for both the correlation and the slope of the regression between hunger and meal size (see Figure 22.1). The relationship between the amount eaten during the meal and the change in the level of subjective hunger produced by that intake provides a measure of individuals’ psychological responsiveness to their intake. The twin data for this measure indicate that this responsiveness is heritable. Significant heritabilities were calculated for both the correlation and the slope of the regression between meal size and change in subjective hunger. Thus, heredity has a variety of significant influences on the hunger–intake relationship, including how hungry the individual is at the start of the meal and how big of an effect that hunger has on subsequent intake, as well as how hungry individuals are when they have finished eating and how big an impact intake has on changing perceived hunger. Individuals’ tendency to restrain their energy intake, the psychological characteristic of cognitive restraint, also appears to be affected by genes. Cognitive restraint was measured by having the twins complete the three-factor eating questionnaire (Stunkard and Messick, 1985). An analysis of these data revealed significant genetic effects, accounting for 44 percent of the

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variance in cognitive restraint (de Castro and Lilenfeld, 2005) (see Figure 22.1). Hence, it ap­pears that individuals inherit a tendency to restrain their intake. Characteristics of the food, such as its attractiveness and palatability, appear also to be affected by inheritance, as well as their impact on the amounts eaten. Twins were asked to rate the attractiveness of the food before and after the meals. Heritability analysis of these data indicated significant heritability, accounting for 23 percent of the variance in the palatability ratings before the meal (de Castro, 2001b) (see Figure 22.1). Similarly, significant heritability was found for the amounts ingested in both low-palatability and high-palatability meals. A metric of the responsiveness of the individual to palatability is the difference in the amount ingested between meals of low and high palatability. This measure was also found to be significantly heritable (see Figure 22.1). The dietary energy density is another characteristic of the food that appears to be affected by inheritance. Analysis of the twin data revealed that dietary energy density is a highly heritable factor, with inheritance accounting for over 40 percent of the variance (de Castro, 2006b) (see Figure 22.1). Thus, the preferred diet density, which in turn has a major influence on intake, appears to be affected by genes. Hence, the data suggest that genes influence not only how much is eaten in a meal, but also the preferred palatability of the food, the reactivity of the individual to that palatability, and the selected level of dietary energy density of the food.

these factors and the magnitude of their impacts on intake appear to be affected by genes. Given this level of complexity, it is difficult to comprehend how all of these simultaneously present variables are combined to result in some form of control of intake. In order to summarize all of these variables’ influences on intake, we developed the general model of intake regulation (de Castro and Plunkett, 2002) (Figure 22.2). The conceptual system of the model includes the assumption that intake is affected by a wide range of physiological and environmental factors. Each of the factors is assumed to account for only a small portion of the variance in intake. In addition, the level and impact of these factors can vary from individual to individual, and these individual differences are affected by heredity. In the model, factors are sorted into two sets, labeled as uncompensated (primarily environmental) and compensated (primarily physiological) factors. A key difference between these types of factors is that compensated factors have negative-feedback loops with intake, simultaneously affecting and being affected by intake,

General intake regulation model

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22.5  A general model of intake regulation It is clear from the data reviewed above that food intake is affected by a large array of physiological, environmental, social, psychological and dietary factors. Both the preferred level of

Figure 22.2  General intake regulation model. The general intake regulation model, wherein intake (I) is controlled by two sets of factors; compensated factors (Ci) that both affect and are affected by intake via negativefeedback loops, and uncompensated factors (Ui) that affect but are not affected by intake. Inheritance affects the system by determining the preferred level for intake, and compensated and uncompensated factors also by determining the level of impact of the factors on intake (W).

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22.5  A general model of intake regulation

while uncompensated factors affect intake, but are not affected by intake. Each of the factors is assumed to have preferred levels that are in turn influenced by heredity. The model further specifies that each factor has a particular magnitude of influence on intake. These impact factors, with weights varying between 1 and 1, are assumed to differ among individuals and be affected by heredity. The general model of intake regulation is a descriptive system. In order to ascertain if the model can produce predicted outcomes that parallel intake and body-weight changes seen in the natural environment, a computer simulation was implemented. The simulation was designed to test the model’s response to changes similar to those that occur in the natural environment, and individual differences in responsiveness to environmental changes. For this simulation, an instantiation of the model was implemented, with overall daily food energy as the intake variable. It was found that the model’s behavior could be well represented by a rather simple instantiation that included only four hypothetical uncompensated factors and four hypothetical compensated factors, in addition to body weight. The parameterization of the model was arbitrary, except that it was specified that the sum of all

of the positive and negative weights would be equal to zero (de Castro, 2006b). The model’s response to a simulated change in the environment was investigated by doub­ ling the level of one uncompensated factor. In response to the change, initially the body weight became unstable and oscillated at a markedly higher level before stabilizing and sett­ ling at a 7 percent higher body weight (Figure 22.3). The model then maintained this new body weight as long as no further changes occurred. Subsequently, the model’s response to differences in individual responsiveness was investigated. The weighting factor was manipulated in conjunction with the doubling of the uncompensated factor, as above. When the weighting factor was low, the doubling of the uncompensated factor produced only a small increase in body weight. However, when the weighting factor was large, the model’s output reflected a large increase in body weight (Figure 22.3). The output body weight was found to depend upon both the amount of increase in the level of the uncompensated factor and the magnitude of the weighting factor. Hence, the model predicted that a sustained change in the environment would trigger a sustained change in body weight; its magnitude would depend on

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Figure 22.3  Model output after doub­ling uncompensated factor with varying weights. Results of a computer simulation of the general intake regulation model in response to a doubling of one uncompensated factor with seven different levels of impact weights. Four hypothetical compensated factors and four hypothetical uncompensated factors with varying weights were set to produce a stable output from the model of 60-kg body weight. One uncompensated factor’s level was doubled. Seven simulations were performed with differing weights for the doubled uncompensated factor.

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the individual’s inherited responsiveness to the factor.

22.6  Discussion It should be clear from the above review that the control of food intake regulation is a very complex phenomenon that defies simple description. It involves large numbers of factors and processes that not only act individually, but also interact. This should, however, come as no surprise. Food intake is so essential to the survival of the individual that there must have been extraordinary evolutionary pressures to produce control mechanisms with great flexibility and adaptability, able to operate in a wide variety of environments and conditions. This suggests that the genetic underpinnings of the system would be multifaceted and deeply involved in multiple mechanisms, from physiological to social and environmental. This is exactly what we observe. It is surprising, however, that genes and the environment are so intertwined. Historically, genes were perceived as influencing anatomical structure, while the environment had independent effects. However, the reviewed findings suggest that inheritance influences not only physiology, but also the environment and the responsiveness of the individual to the environment. These genetic influences on the envi­ ronment probably are the result of the operation of inherited psychological characteristics. For example, the preferred number of people present at meals may well result from an inherited extraversion or sociability factor (Saudino et al., 1997). These factors would then tend to prompt the individual to seek out preferred levels of companionship. Inborn differences in circadian oscillators (Kolker and Turek, 1999) or in the gustatory system (Matsunami et al., 2000) might explain how genes affect the time of day that people choose to eat, and their preferred

palatability levels. Nevertheless, whether direct or indirect, genes have the capacity to affect the selection of environments that an individual chooses to occupy, and also the impact those environments might have on that individual’s behavior. The level of food intake is a complex integ­ ral of the effects of a large number of influences. The general model of intake regulation is a useful heuristic to represent the complexity of intake regulation. It includes the ideas that some of the significant influences on intake originate in the environment, some from heredity, and many from the interaction of heredity and environment; some have negative-feedback loops with intake, while others do not. The simulations of the model suggest an explanation for how individual and societal changes may underlie large changes in the incidence of obesity. The model’s simulation results suggest that when an individual’s environment changes, there would be commensurate changes in intake and body weight. Indeed, body-weight changes occur most frequently during the late teens to late twenties (Pearcey, 2000). During this time, large chronic changes in environment occur: individuals leave home, enter college, marry, have children and begin careers. The model well predicts that such changes in the environment would be paralleled by changes in body weight, as observed in reality. The recent societal increase in body weight (Flegal, 1999; Mokdad et al., 1999, 2003; Flegal et al., 2000; Ogden et al., 2002) has been paralleled by unprecedented changes in the environment for both energy expenditure and intake. The modern world has produced a marked reduction in activity and thereby energy expenditure. The model would predict that this would be an “obesogenic” environment (Ravussin and Bouchard, 2000), resulting in a new, higher level for body weight. In addition, the eating environments have been markedly altered, with increases over the past few decades in dietary energy densities, portion sizes, palatability, variety and availability of

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REFERENCES

copious quantities of attractive foods, restaurant eating, breakfast-skipping and shifting of intake to the evening, television watching including incessant advertisements for food, home delivery, and attractive pre-prepared foods (Stroebele and de Castro, 2004). The model would predict that these changes would be more than sufficient to prompt an obesity epidemic (Hill and Peters, 1998). A further strength of the model is that it includes mechanisms than can account for individual differences in responsiveness. When individuals are immersed in new environments, some react and gain weight, while others appear unaffected. The model includes inborn responsiveness to environmental factors, and the simulations indicate that differences in responsiveness can markedly alter the effect of a change in the environment. This could also serve as a hypothesis to explain certain eating disorders. Inheritance appears to be an influence on the development of anorexia nervosa (Lilenfeld and Kaye, 1998; Bulik et al., 2000; Klump et al., 2001a, 2001b). From the ­perspective of the model, anorexia nervosa may be conceptualized as an inherited tendency to high levels of dietary restraint and a high inherited responsiveness to that restraint. The model also suggests what the nature of effective weight-control strategies might be in an individualized program that first detects what factor(s) the individual is most responsive to and then alters the level of these factors as required. The model clearly shows, however, that the changes must be maintained. Changes in intake and weight will only remain as long as the changes in the environment remain. As soon as the environment reverts to its prior condition, so too will body weight. Hence, the model predicts what is often observed: weight quickly reverts to its prior level when the dietary strategy is terminated. This strategy is applicable not only to weight loss, but also to weight gain – frequently desired in the elderly and in recovery from illness.

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In conclusion, the control of intake in freel­ iving humans is affected by a myriad of genetic, physiological, dietary, psychological, social and cultural variables. Each of these influences has large individual differences in both level and responsiveness. The general model of intake regulation provides an integrated and comprehensive account of how all these pieces might fit together to produce the level of intake and body weight in an individual and in populations.

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and fluid intakes of free-living humans. Appetite, 23, 179–192. de Castro, J. M. (1995). The relationship of cognitive restraint to the spontaneous food and fluid intake of free-living humans. Physiology & Behavior, 57(2), 287–295. de Castro, J. M. (1997). Inheritance of social influences on eating and drinking in humans. Nutrition Research, 17, 631–648. de Castro, J. M. (1998). Prior days intake has macronutrients specific delayed negative feedback effects on the spontaneous food intake of free-living humans. Journal of Nutrition, 28, 61–67. de Castro, J. M. (1999a). Measuring real world eating behavior. Progress in Obesity Research, 8, 215–221. de Castro, J. M. (1999b). Inheritance of pre-meal stomach content influences on eating and drinking in free-living humans. Physiology & Behavior, 66, 223–232. de Castro, J. M. (1999c). Inheritance of hunger relationships with food intake in free living humans. Physiology & Behavior, 67(2), 249–258. de Castro, J. M. (2000). Macronutrient selection in free feeding humans: Evidence for long term regulation. In H. R. Berthoud & R. J. Seeley (Eds.), Neural control of macro­ nutrient selection (pp. 43–59). New York, NY: CRC Press. de Castro, J. M. (2001a). Heritability of diurnal changes in food intake in free-living humans. Nutrition, 17(9), 713–720. de Castro, J. M. (2001b). Palatability and intake relationships in free-living humans: Influence of heredity. Nutrition Research, 21(7), 935–945. de Castro, J. M. (2004a). The control of eating behavior in free-living humans. In E. M. Stricker & S. C. Woods (Eds.), Handbook of the behavioral neurobiology, Vol. 14. Neurobiology of food and fluid intake (pp. 469–504). New York, NY: Plenum. de Castro, J. M. (2004b). The time of day of food intake influences overall intake in humans. Journal of Nutrition, 134, 104–111. de Castro, J. M. (2004c). Density and intake relationships in the eating behavior of free-living humans. Journal of Nutrition, 134, 335–341. de Castro, J. M. (2005). Stomach filling may mediate the influence of dietary energy density on the food intake of free-living humans. Physiology & Behavior, 86(1–2), 32–45. de Castro, J. M. (2006a). Varying levels of food energy selfreporting are associated with between group but not within subjects differences in food intake. Journal of Nutrition, 36, 1382–1388. de Castro, J. M. (2006b). Heredity influences the dietary energy density of free-living humans. Physiology & Behavior, 87, 192–198. de Castro, J. M., & Brewer, E. M. (1992). The amount eaten in meals by humans is a power function of the number of people present. Physiology & Behavior, 51, 121–125.

de Castro, J. M., & de Castro, E. S. (1989). Spontaneous meal patterns in humans: Influence of the presence of other people. American Journal of Clinical Nutrition, 50, 237–247. de Castro, J. M., & Elmore, D. K. (1988). Subjective hunger relationships with meal patterns in the spontaneous feeding behavior of humans: Evidence for a causal connection. Physiology & Behavior, 43, 159–165. de Castro, J. M., & Lilenfeld, L. (2005). The influence of heredity on dietary restraint, disinhibition, and perceived hunger in humans. Nutrition, 21(4), 446–455. de Castro, J. M., & Plunkett, S. (2002). A general model of intake regulation. Neuroscience and Biobehavioral Reviews, 26(5), 581–595. de Castro, J. M., McCormick, J., Pedersen, M., & Kreitzman, S. N. (1986). Spontaneous human meal patterns are related to preprandial factors regardless of natural environmental constraints. Physiology & Behavior, 38, 25–29. de Castro, J. M., Brewer, M., Elmore, D. K., & Orozco, S. (1990). Social facilitation of the spontaneous meal patterns of humans is independent of time, place, alcohol, or snacks. Appetite, 15, 89–101. de Castro, J. M., Bellisle, F., Dalix, A. M., & Pearcey, S. (2000). Palatability and intake relationships in free-living humans: Characterization and independence of influence in North Americans. Physiology and Behavior, 70, 343–350. Flegal, K. M. (1999). The obesity epidemic in children and adults: Current evidence and research issues. Medicine and Science in Sports and Exercise, 31(Suppl. 11), S509–S514. Flegal, K. M., Carroll, M. D., Ogden, C. L., & Johnson, C. L. (2000). Prevalence and trends in obesity among US adults 1999–2000. Journal of the American Medical Association, 288(14), 1723–1727. Hartman, A. M., Brown, C. C., Plamgren, J., Pietinen, P., Verkasalo, M., Myer, D., et al. (1999). Variability in nutrient and food intakes among older middle-aged men. American Journal of Epidemiology, 132, 999–1012. Hewitt, J. K., Stunkard, A. J., Carroll, D., Sims, J., & Turner, J. R. (1991). A twin study approach towards understanding genetic contributions to body size and metabolic rate. Acta Geneticae Medicae et Gemellologiae, 40, 133–146. Hill, J. O., & Peters, J. C. (1998). Environmental contributions to the obesity epidemic. Science, 280, 1371–1374. Klump, K. L., Kaye, W. H., & Strober, M. (2001a). The evolving genetic foundations of eating disorders. Psychiatric Clinics of North America, 24(2), 215–225. Klump, K. L., Miller, K. B., Keel, P. K., McGue, M., & Iacono, W. G. (2001b). Genetic and environmental influences on anorexia nervosa syndromes in a population-based twin sample. Psychological Medicine, 1(4), 737–740. Kolker, D. E., & Turek, F. W. (1999). The search for circadian clock and sleep genes. Journal of Psychopharmacology, 13(4, Suppl. 1), S5.

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C H A P T E R

23 The Molecular Regulation of Body Weight: The Role of Leptin, Ghrelin and Hypocretin John J. Medina Department of Bioengineering, University of Washington, Seattle, WA, USA

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23.1  Introduction In most mammals, energy homeostasis is modulated by two groups of signaling mechanisms. The first group is comprised of short-term signals arising from the gastrointestinal system that provide information about individual meal intake. The second group is comprised of collections of long-term signals arising from adiposity hormones. These provide information about overall energy stores. The interactions between

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these signaling mechanisms inform the organism about energy supply and expenditure, and profoundly influence consumptive behavior and the perception of satiation. The human brain assists in maintaining energy homeostasis by integrating information about the body’s current energy needs with an unrelenting analysis of its energy stores (Schwartz, 2001). From appetite creation to satiation, complex interlocking neuronal circuits have evolved to modulate both the consuming and the expending aspects of feeding behavior.

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These circuits are subject to a wide variety of environmental influences, including emotional, social and cognitive factors. Such influences can be modulated by the rewarding (and punishing) aspects of food consumption (Berthoud, 2004). The signals derived from these systems’ interactions are exerted at the cellular level. The up-to-the-minute energy requirements of mammalian cells, for example, are satisfied by circulating glucose levels in the vasculature. The body goes to great lengths to ensure those concentrations of glucose are kept within specific and firmly regulated limits. To maintain such tight ranges, physiological processes must exert strict control over energy homeostasis. In aggregate, the ideal adult state exists where energy expenditure equals energy uptake, resulting in the maintenance of a relatively stable body weight. A disruption in this energy balance sheet can result in sustained bi-directional weight problems, pathological examples of which can include morbid obesity and anorexia nervosa. A great deal of progress has occurred in our understanding of the molecular interactions involved in regulating appetite. These advances promise to increase our genetic understanding of both overconsumptive and underconsumptive food-related behaviors. This chapter describes three well-characterized proteins involved in both the maintenance and disruption of the energetic balancing act.

23.2  Leptin, ghrelin and hypocretin Many appetite-specific neuronal circuits are found within the brainstem and hypothalamus. These regions respond to information about energy homeostasis through an interrelated network of hormonal signals arising from tissues throughout the body (Morton et al., 2006). Three of the best characterized signals are leptin, ghrelin and hypocretin. Circulating leptin,

a hormone generated by adipose tissue, provi­ des information about overall energy stores. Ghrelin, a protein secreted by the gut, communicates with the arcuate nucleus of the hypo­ thalamus and functions as a short-term meal initiation signal. Hypocretin (also called orexin) has a wide variety of functions. While it is a powerful stimulator of feeding behavior, mutations in its sequence are also responsible for narcolepsy, a disorder of arousal (Faraco et al., 1999). Hypocretin’s involvement in disparate physiological roles serves as an important example of the multi-functionality of most hormones involved in the maintenance of energy homeo­ stasis. In the following sections, we will briefly summarize the effects of these three proteins on the creation of feeding behaviors and the maintenance of energy balance in adult humans.

23.3  Leptin protein Isolating the leptin protein, first characterized more than a decade ago (Zhang et al., 1994), was pivotal in the quest to characterize appetite regulation at the level of the gene. Leptin’s overall function is to provide the brain with information about the body’s energy supplies, and it is specifically involved in mediating sensations of satiety (Meister, 2000). The protein is also involved in a variety of physiological processes. Leptin is encoded by the human obese gene OB, a sequence found on chromosome 7 (7q31.3) (Isse et al., 1995). The gene spans over 18 kb, and is composed of two introns and three exons. The protein, which possesses a putative signal sequence, is comprised of 166 amino acids (Gong et al., 1996). Though leptin is produced in abundance in adipose tissue, it is also found in smaller quantities in the heart, stomach, placenta, and mammary epithelium (Klok et al., 2006). Its expression profile is fairly simple: the protein synthesized in adipocytes consists of a single mRNA species (Masuzaki et al., 1995).

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23.3  Leptin protein

The metabolic effects of leptin are mediated through the leptin receptor. The receptor is encoded by the OBR gene (also called LEPR gene), localized to chromosome 1 (1p31) (Chung et al., 1996). The sequence of the receptor is much larger than its ligand’s, comprising18 exons and 17 introns. The fully processed protein is of 1162 amino acids. In contrast to the OB gene, the OBR sequence gives rise to a number of splice variants. One species, the OBRb variant, has a large intracellular domain, retains full signaling capability, and is widely expressed throughout the human brain (Campfield et al., 1996). There are particularly high concentrations of this receptor in the hypothalamus and cerebellum (Burguera et al., 2000), though it has also been found in other tissues such as the stomach, the vasculature and the placenta.

23.3.1  Leptin function Leptin is probably best known for its function in maintaining energy homeostasis, a role most thoroughly characterized in laboratory animals (Pelleymounter et al., 1995). Leptin exerts its effects by controlling regulatory feedback mechanisms that cause the brain to inhibit food intake. It thus plays a powerful role in the normal regulation of body weight in laboratory animals (Halaas et al., 1995). Montague and colleagues (1997) first demonstrated an energy-balancing role for leptin in humans by examining the metabolic history of a pair of morbidly obese children. They subsequently characterized explicit examples of congenital leptin deficiency: the patients presented a normal birth weight, but rapidly developed severe obesity. Consumptive behavior was associated with impaired satiety and accompanying hyperphagia. The associated chromosomal deficiency turned out to be a homozygous frameshift mutation in the OB gene. Other researchers showed a higher prevalence of obesity in patients heterozygous for the same (Farooqi et al., 2001).

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While much of the research literature indicates that leptin plays a role in the long-term maintenance of energy homeostasis, other research shows that leptin functions in the regulation of short-term food intake as well (Sobhani et al., 2000). The protein may even play a role in the adaptation to energy deprivation, such as that experienced during dieting (Weigle et al., 1997). Because of this expanding role, its potential involvement in disease states such as anorexia nervosa is under active investigation (Chan and Mantzoros, 2005).

23.3.2  Leptin mechanism of action Leptin is released into the vasculature by adipose tissues as a direct function of their energy stores (Golden et al., 1997). Once released, leptin penetrates the blood–brain barrier, gaining access to the neural tissues that control appetite. Leptin provides information about the body’s energy supplies by associating with leptin receptors found throughout the hypothalamus, particularly the arcuate nucleus (Saahu, 2004). Once bound, the protein exerts a wide variety of effects on hypothalamic neurons, including the expression of a variety of orexigenic and anorexigenic neuro­ peptides (Schwartz et al., 1996). Once expressed, the interaction of these peptides with specific hypothalamic cell populations supplies the brain with information about both feeding status and energy supplies. The result is appetite regulation. Clinical experiments designed to assess reac­ tions of patients with naturally occurring leptin deficiencies to exogenously supplied leptin have found correlated behavioral changes (Kolaczynski et al., 1996). These treated patients show changes in ingestive and non-ingestive behaviors resulting in a decreased appetite. A concomitant weight loss occurs. There is even an increase in physical activity, which may be related to the weight loss. Other researchers have demonstrated just the opposite effect: circulating leptin levels are positively associated

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with determining resting metabolic rates in humans (healthy, non-obese males) (Jørgensen et al., 1998). Exactly how those interactions affect human appetite regulation is a contentious issue. For instance, a number of researchers do not find that vascular leptin levels are associated with overall metabolic efficiency in either obese or non-obese patients – something one might predict, given its role in maintaining energy homeostasis (Licinio et al., 2004).

23.4  Ghrelin protein The biological effects of leptin are counterbalanced by those of ghrelin. Whereas leptin induces weight loss by stimulating sensations of satiety, ghrelin affects metabolic balance by providing appetite-stimulation signals (Cummings et al., 2001). Human prepro-ghrelin is encoded by the GHRL gene, a sequence located on chromosome 3 (3p25-26) (Kojima et al., 1999). The gene spans a small distance – about 5 kb – and is composed of three introns and four exons (Ueno et al., 2005). The unprocessed protein is of 117 amino acids, though its fully processed form is whittled to 28 amino acids (Kojima et al., 1999). Ghrelin is produced in abundance in the stomach – the tissue from which it was originally isolated – though the peptide has also been found in the adrenal cortex, the ovaries and the pancreas (Tortorella et al., 2003; Gaytan et al., 2005). Ghrelin is additionally produced by neurons in a wide variety of regions in the brain, including the pituitary and various regions within the hypothalamus (Cowley et al., 2003). The metabolic effects of ghrelin are mediated through the ghrelin receptor, the growth hormone secretagogue GHS-R. The gene for this receptor is also located on chromosome 3 (3q26.2), spans 4 kb, and is comprised of two exons and one intron (McKee et al., 1997). There

are two mRNA moieties processed from the primary GHS-R transcript, GHS-R1a (Howard et al., 1996) and GHS-R1b (Petersenn et al., 2001). The GHS-R1a variant is 366 amino acids in length and the GHS-R1b is estimated to be 289 amino acids (Petersenn et al., 2001). GHSR1a is found in a wide variety of tissues, including the infundibular hypothalamus and the human pituitary (Howard et al., 1996). It has been further identified in the testis (Gaytan et al., 2004) and ovaries (Gaytan et al., 2005). It should be noted that GHS-R1b has never been isolated from in vivo tissues (Petersenn et al., 2001).

23.4.1  Ghrelin function Several lines of evidence support the hypothesis that ghrelin functions as a short-term mealinitiation signal in humans. Preprandial elevation of ghrelin, for example, corresponds with a rise in self-reported hunger scores in non-obese human volunteers (Cummings et al., 2004). Exogenously supplied infusions of ghrelin in both non-obese and obese subjects induce perceptions of hunger, and result in elevated food intake (Wren et al., 2001). Stimulatory effects on gastric emptying have been positively correlated with increasingly elevated levels of ghrelin as well (St-Pierre et al., 2003). In laboratory rats, this elevation negatively affects short-term energy expenditure (Tschöp et al., 2000). Though a similar finding awaits confirmation in human subjects, the role of ghrelin in the short-term perception of hunger is unambiguous. Mounting evidence suggests that ghrelin additionally functions in the maintenance of longterm energy balance. Laboratory rodents exposed to daily doses of ghrelin become obese, increasing the amounts of adipose tissue by inhibiting the animal’s ability to utilize fat (Tschöp et al., 2000). A similar mechanism may operate in humans. Human BMI is inversely correlated with levels of ghrelin; circulating levels of this

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23.5  Hypocretin protein

hormone increase when obese patients begin to lose weight (Hansen et al., 2002). Patients with Prader-Willi syndrome (a genetic condition characterized by an insatiable appetite and accompanying weight gain) possess circulating ghrelin levels that are dramatically elevated when compared to healthy controls (Paik et al., 2004). Plasma ghrelin levels are lower in patients who have undergone gastrectomy; this decrease is thought to be the primary reason why such procedures result in weight loss (Ariyasu et al., 2001). Ghrelin levels decrease when patients diagnosed with anorexia nervosa begin to gain weight (Otto et al., 2001). Given such data, understanding the role of ghrelin in such pathologies is an area of intense investigation. Though ghrelin may exert long-term regulatory effects, these data must be examined in light of findings in animals that were deliberately constructed without a functional ghrelin gene (knockout animals, homozygous for ghrelin mutation). These mice enter adulthood with normal body composition, are of typical size, possess normal food intake, demonstrate unremarkable growth rates, and show no obvious changes in feeding behavior (De Smet et al., 2006). These data suggest that ghrelin, unlike leptin, is not crucial for the overall maintenance of energy homeostasis.

23.4.2  Ghrelin mechanism of action The concentration of ghrelin is highly regulated. Indeed, whether or not ghrelin is secreted by the stomach depends primarily on the organism’s nutritional state (Ariyasu et al., 2001). There are both preprandial (Cummings et al., 2004) increases and postprandial (Tschöp et al., 2001a) decreases, and they appear to exhibit diurnal variation (De Smet et al., 2006). The target appears to be, at least in part, specific hypothalamic nuclei (Cowley et al., 2003). A number of pathways have been proposed to explain the appetite-stimulating effects of ghrelin. One popular explanation hypothesizes that upon secretion from the stomach,

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ghrelin migrates through the blood–brain barrier and finds its way to various hypothalamic targets, where it exerts its biological functions (Banks et al., 2002). Another explanation posits more localized control: this theory considers the finding that regions in the hypothalamus are capable of synthesizing endogenous ghrelin. It is argued that the hypothalamus simply responds to metabolic intermediaries and the protein is produced locally (Cowley et al., 2003). A final pathway proposes that ghrelin bypasses the vasculature, at least in part, and exerts its brain-related effects through the vagal nerve and nucleus tractus solitarus (Ueno et al., 2005). Whatever pathway is the correct explanation, it is clear that ghrelin exerts meal-initiating signals by controlling the expression of various hypothalamic peptides. These include NPY (Nakazato et al., 2001), AgRP (Kamegai et al., 2001) and hypocretin, also called orexin (Toshinai et al., 2003). Because of their opposing roles, it is tempting to consider that leptin and ghrelin interact directly and complementarily in maintaining energy supply. It has been hypothesized, for example, that leptin or leptin-induced signals lead to the inhibition of ghrelin secretion by the stomach (Yildiz et al., 2004). This notion, at least in humans, is not unambiguously confirmed. One study has demonstrated a negative correlation between ghrelin and leptin concentration in fasting obese patients (Tschöp et al., 2001b). Another study examining obese pediatric populations showed no such correlation (Ikezaki et al., 2002).

23.5  Hypocretin protein Hypocretin has a functionally opposing role to the meal-initiation role of ghrelin, appearing to be involved in the regulation of feeding behavior, specifically with an increase in consumptive behaviors. Its exact role in energy homeostasis is not universally accepted. It serves, however, as a canonical example of the extent to which proteins

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involved in energy homeostasis can be multifunctional (Siegel, 2004). Among other things, mutations in the hypocretin gene cause human narcolepsy (Peyron et al., 2000). The isolation of hypocretin was simultaneously announced in two papers in 1998, 4 years after leptin had been characterized. De Lecea and colleagues termed their protein hypocretin (de Lecea et al., 1998). The name was a fusion of the hypothalamus, its neuroanatomic region of expression, and secretin, the gastric peptide with which hypocretin appeared to possess structural homology. Two novel mRNAs synthesized in the hypothalamus were isolated, putatively encoding two peptides termed Hcrt-1 and Hcrt-2. Independently, Sakurai also described the isolation of two novel peptides (Sakurai et al., 1998). These moieties were termed orexin-A and orexin-B, after the term orexis, which refers to desire or appetite. Though the terms are synonymous, the peptides will be referred to as hypocretin throughout this chapter. The human hypocretin gene is located on chromosome 17q21 (Preti, 2002). It is a relatively short sequence, composed of two exons and an intervening exon. Hcrt-1 peptide consists of 33 amino acid residues, and is folded back on itself in a classic “hairpin” which is held in place by disulfide bonds. Hcrt-2 is a peptide consisting of a linear chain of 28 amino acids. Both peptides are generated from a single, large peptide, preprohypocretin (de Lecea et al., 1998; Sakurai et al., 1999). The molecules are extraordinarily conserved, with sequences essentially identical in humans, pigs, dogs, sheeps, cows, rats and mice (Sakurai, 2004). The metabolic effects of hypocretin are mediated through the hypocretin receptors. Two have been isolated, which for purposes of this chapter will be labeled type 1 Hcrt receptors and type 2 Hcrt receptors. The mammalian receptors share 65 percent amino acid identity with each other, though they have different binding profiles. Type 1 binds with greatest affinity to Hcrt-1 peptide, while type 2 binds Hcrt-1 and Hcrt-2 with equal affinity (Zhu et al., 2003).

Not surprisingly, neurons expressing hypocretin and attendant receptors are concentrated in the hypothalamus. A dense collection of Hcrt neurons is found in the dorsomedial hypothalamic nucleus, medial to the fornix (Thannickal et al., 2000). These cells project widely throughout the rest of the brain. Other densely innervated Hcrt-positive regions include the raphe nuclei of the brainstem and locus coeruleus. Less densely innervated regions include the neocortex and limbic regions of the brainstem (Peyron et al., 1998; van den Pol, 1999). Hcrt-1 and Hcrt-2 receptors are also found throughout these innervated regions, but their expression patterns receptors can be remarkably tissue-specific. Receptor 1 is found mostly in the anterior olfactory nucleus, the cingulate cortex, the anterior hypothalamus and the locus coeruleus. Receptor 2 is found in much smaller quantities in those regions. Hcrt-2 distribution patterns are predominant in the medial septal nucleus, the hippocampal CA3 field and the arcuate nucleus of the hypothalamus (Trivedi et al., 1998; Marcus et al., 2001). Hcrt-expressing neurons are found outside the central nervous system. The protein, and its attendant receptors, have been isolated from the intestines and the pancreas (Siegel, 2004), the testis and the adrenal tissue (Jöhren et al., 2001). Determining its biological role in these diverse tissues remains an intense area of investigation.

23.5.1  Hypocretin function As mentioned, hypocretin turns out to have an astonishing array of functions besides the regulation of feeding behavior. Indeed, some researchers have hypothesized that it plays no direct role in energy homeostasis, serving rather as a hormone involved in mediating motor activity during periods of waking and sleeping (Siegel, 2004). Support for this notion came a year after its isolation, when a group of researchers demonstrated that mutations in the Hcrt peptide system

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23.6  Concluding remarks

were responsible for narcolepsy in dog populations. These mutations could occur in the Hcrt receptor gene (Lin et al., 1999) or in the Hcrt gene itself (Chemelli et al., 1999). The non-primate symptoms were remarkably similar to human narcolepsy, and the role of Hcrt in human sleep dysfunction was eventually established (Peyron et al., 2000). These data have uncovered a complex relationship between sleep/wake states and feeding. From an evolutionary perspective, the purpose of this linkage may be to arouse the subject to resupply lowering energy stocks. Hypocretin has now been shown to mediate a wide range of human behaviors, including sympathetic activation (Chen et al., 2000), HPAmediated stress responses (Date et al., 2000), reward-seeking and chemical addiction (Harris et al., 2005), as well as eating behaviors. The subsequent discussion will focus on the role of hypocretin in appetite regulation.

23.5.2  Hypocretin mechanism of action The biological effects of hypocretin are mediated in part by a short 36 amino acid neurotransmitter called Neuropeptide Y (NPY) (Ganjavi and Shapiro, 2007). NPY peptide has been shown to be a powerful stimulator of feeding behavior. The interaction between hypocretin and NPY may represent a potentially critical regulatory point of energy-balancing control, clarifying the indirect role hypocretin may play in energy homeostasis. Several lines of evidence support this conclusion. Neuroanatomical studies revealed that hypocretin’s positive axons were connected to NPY-laden neurons in the arcuate nucleus of the hypothalamus (many NPY-laden neurons actually express hypocretin) (Rauch et al., 2000). When exogenous hypocretin was applied directly to the rodent arcuate nucleus, NPY expression was transiently increased. Other work with NPY antagonists in the presence of hypocretin showed alterations in consumptive behavior. If the animal was pre-treated with

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NPY antagonist, the appetite-stimulating effect of exogenously supplied hypocretin was attenuated (López et al., 2002). Researchers investigating the molecular biology of the previously described leptin have shed light on the relationship between hypocretin and NPY. Increases in leptin expression were found to be associated with a decrease in the synthesis of NPY. This resulted in an inhibition of feeding behavior. Neurons carrying hypocretin were shown to express leptin receptors, and may be regulated by leptin. Indeed, leptin appears to inhibit the activity of hypocretinproducing neurons (Kok et al., 2002). Behavioral work has shown that decreasing hypocretin levels dramatically decreases consumptive behavior by down-regulating NPY, which in turn may be a result of low circulating leptin levels (Hara et al., 2001).

23.6  Concluding remarks The isolation of leptin, ghrelin and hypocretin has profoundly deepened our understanding of how mammalian energy homeostasis is regulated at the molecular level. Irregularities in the interactions between the physiological systems in which these proteins interact may profoundly influence the development of human obesity. Specific abnormalities in the expression of any one of these protein systems may also shed light on consumptive disorders such as anorexia nervosa. These ideas are complemented by data demonstrating that the brain processes stimuli related to eating in a manner similar to how it responds to other addictive stimuli (Volkow and Wise, 2005). These addictive tendencies may have strong genetic roots (Ball, 2008). There is also great potential for components of these systems to serve as therapeutic targets in the design of future medications, though so far the results are uneven. Treating patients with leptin, for example, shows great

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promise for patients with leptin deficiencies, but little for obese patients without such deficiencies (Westerterp-Plantenga et al., 2001). Taken together, the view leptin, ghrelin and hypocretin affords us demonstrates the incredibly complex nature of maintaining energy homeo­ stasis. Fortunately, for the vast majority of us, the action steps we need to ensure a healthy balance are much simpler than the substrates that afford us the luxury. As with so many disorders involving food intake, lifestyle changes involving diet regulation and exercise promotion are the simplest and most effective of the many treatments proposed to address pathologies related to energy balance (Orzano and Scott, 2004). As we increase our understanding of these mechanisms, we will be in a much more powerful position to show how medical intervention can work hand in hand with these lifestyle changes. For the few of us for whom low-fat diets and sufficiently robust exercise programs are not enough, this is fortunate news indeed.

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C H A P T E R

24 Energy Balance Regulation: Complex Interplay between the Autonomic and Cognitive/Limbic Brains to Control Food Intake and Thermogenesis Denis Richard and Elena Timofeeva Centre de Recherche de l’Institut universitaire de Cardiologie et de Pneumologie de Québec, Canada

o u tli n e 24.1  Introduction

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24.3.2 The LH in Energy Homeostasis 305 24.3.3 The Brainstem as a Key Relay in Energy Homeostasis 307 24.3.4 The Ventral Striatum and the Brain Reward System in the Regulation of Energy Balance 308

24.2 The Regulation of Energy Balance 300 24.2.1 Energy Expenditure and Brown Adipose Tissue Thermogenesis 300 24.3 Brain Pathways Involved in the Control of Food Intake and Thermogenesis 301 24.3.1 The ARC–PVH Axis in Food Intake and Thermogenesis Control 303

24.1  Introduction The escalating prevalence of obesity together with the rising awareness of the detrimental impact of this condition on health and health costs have considerably stimulated research related to the etiology and complications of excess

Obesity Prevention: The Role of Brain and Society on Individual Behavior

24.4  Conclusion

309

fat deposition. Investigation of the main factors causing obesity has led to appreciable progress in our understanding of the respective and integrated roles of the environment and genetics in the development of this condition. Obesity largely results from complex gene–environment interactions (O’Rahilly and Farooqi, 2006; Speakman,

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© 2010, 2010 Elsevier Inc.

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2006; Wardle et al., 2008). The “obesogenic” milieu in which we live, characterized by a sedentary lifestyle, excess ingestion of energy-dense palatable food, stress, and pollution, proves to be particularly obesity-inducing in individuals who are genetically predisposed to positive energy balance. Genes that promote obesity might not only be energy-conserving genes acquired through evolution to combat periods of food shortage (Speakman, 2006), but all those genes (“thrifty” or not) that are involved in the overall adaptation/ inadaptation to the changes imposed by the “modern lifestyle” and whose under- or over­ expression potentially leads to overeating and other obesity-promoting behaviors. Excess fat deposition results from an imbalance between energy intake and energy expenditure, the two inescapable determinants of energy (or fat) balance. The understanding of the complex controls exerted on these determinants is therefore essential to decipher the etiology of obesity, and to envision effective behavioral or pharmacological strategies to prevent or reverse excess fat deposition. In recent years, major progress has been made in understanding the key metabolic systems and brain circuitries involved in the regulation of energy balance. This chapter aims at reviewing the role of certain systems involved in energy-balance regulation. It focuses on key axes and pathways implicated in the integrated controls of both food ingestion and regulatory/adaptative thermogenesis.

24.2  The regulation of energy balance Given the stability of body energy stores in response to attempts to change energy balance (such as food deprivation, overfeeding and excess physical activity), it has been argued that energy balance is regulated (Keesey and Corbett, 1984; Cabanac, 2001; Levin, 2006). The process of regulation is particularly efficient at

maintaining constant energy stores in the pro­ cess of reducing fat reserves. Excess fat stores are fiercely “defended”, which certainly sets hurdles in any attempts to tackle obesity. Fat losses are associated with an increase in hunger or appetite and a reduction in adaptive thermogenesis, a potential regulatory component of energy expenditure (Tremblay et al., 2007).

24.2.1  Energy expenditure and brown adipose tissue thermogenesis The impact of the expenditure component on energy balance is particularly meaningful in laboratory rodents, in which a strong regulatory control is exerted through the sympathetic nervous system (SNS) on brown adipose tissue (BAT), a potent effector of thermogenesis (Cannon and Nedergaard, 2004; Sell et al., 2004; Landsberg, 2006), whose role in energy expenditure in humans might be more important than previously anticipated (Nedergaard et al., 2007; Ravussin and Kozak, 2009). In contrast to white adipocytes, brown fat cells are highly adapted to dissipate chemical energy in the form of heat (Cannon and Nedergaard, 2004; Sell et al., 2004). The thermo­ genic power of BAT is conferred by the presence of uncoupling protein-1 (UCP1) (Nicholls, 2008). UCP1 is part of a subfamily of mitochondrial transporters also including UCP2 and UCP3, with which it shares homology of sequence (Bouillaud et al., 2001; Ricquier, 2005). UCP1 is unique to brown adipocytes, and is found in the inner mitochondrial membrane. It is the archetypical UCP, generating heat by “uncoupling” ATP synthesis from cellular respiration. Active UCP1 allows the dissipation of the electrochemical gradient, which is generated across the inner membrane by the electron transport along the respiratory chain, and which is normally used to generate ATP. Mitochondrial uncoupling prevents ATP synthesis, and energy is instead given off as heat, substrates being efficiently

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24.3  Brain pathways involved in the control of food intake and thermogenesis

catabolized without the respiratory constraint of ATP (Nicholls and Locke, 1984). BAT is very well vascularized so that the heat produced can travel throughout the body. UCP1 activity is driven by the activation of the SNS, whose post-ganglionic neurons densely innervate brown adipocytes. SNS-mediated UCP1 activity is governed by neurons (Bartness et al., 2005) found in brain structures implicated in energybalance regulation. In rodents, BAT thermogenesis not only allows for cold adaptation, but also contributes to energy balance regulation; food deprivation reduces SNS-mediated UCP1 activity, whereas excess food ingestion increases it. Up until recently, there was the general consensus that BAT was not present in significant amounts in adult humans. However, researchers/ clinicians using positron emission tomography and computed tomography (PET/CT) primarily to detect tumors demonstrated that some adipose tissue sites can capture significant amounts of the PET tracer F-18 fluoro­deoxyglucose (FDG, a glucose analog taken up by high-glucoseusing cells such as cancer cells) and these were not tumors but brown fat depots (Nedergaard et al., 2007). As typical white fat tissue does not capture FDG, one has to deduce that the deposits represent BAT sites. We recently calculated that between 6 and 7 percent of patients scanned for tumors show sites of intense FDG uptake in cervical, clavicular and spinal areas in deposits characterized by CT as being fat. The prevalence of brown fat was higher in female than in male subjects, and decreased as a function of age, body mass and ambient temperature (D. Richard, E. Turcotte and A. Carpentier, personal communication, 2009). The demonstration that BAT can exist in substantial amounts in certain individuals has rejuvenated interest in the role played by adaptive thermogenesis in humans (Ravussin and Kozak, 2009). Adaptive thermogenesis describes the changes in energy expenditure in response to alterations in energy balance resulting from

301

excess energy intake/energy gain or energy restriction/energy loss that are not attributable to changes in body mass or body composition. There is evidence that adaptive, or regulatory, thermogenesis could play a role in energy balance regulation and obesity in humans (Tremblay et al., 2007; Wijers et al., 2009), particularly in conditions leading to energy deficits (Dulloo, 2006). In laboratory rodents, it is clear that excessive energy consumption of palatable and energy-dense food items selected from the human obesogenic diet (the so-called “cafeteria diet”) induced BAT thermogenesis to limit excess energy deposition (Rothwell and Stock, 1979; Richard et al., 1988).

24.3  Brain pathways involved in the control of food intake and thermogenesis Regulation of energy balance (and hence regulation of fat mass and body weight) is determined by controls exerted on both food intake and thermogenesis (Saper et al., 2002; Blundell, 2006; Morton et al., 2006; Berthoud, 2007; Abizaid and Horvath, 2008; Adan et al., 2008; Berthoud and Morrison, 2008; Crowley, 2008) (Figure 24.1). The brain is critically involved in those complex controls, which are achieved through harmonized crosstalk between autonomic (hypothalamus and brainstem) and cognitive/limbic (hippocampus, amygdala, striatum and cortex) brain circuitries. The hypothalamus and the brainstem are key structures involved in the involuntary control of food intake and thermogenesis in response to changes in energy stores. The limbic structures are known to support functions such as emotion, learning, memory, pleasure, olfaction, vision and taste. The “autonomic” and “cognitive/limbic” brains work inseparably in regulating energy balance (Berthoud and Morrison, 2008). For instance, the strength of hedonic stimuli related to food is influenced by

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the deficit and surfeit of energy; palatable food items are undeniably more appetizing in conditions of energy restriction. It also has to be pointed out that the pleasurable signaling associated with the ingestion of palatable energy-dense foods inherent to an obesogenic environment has the power of distorting the autonomic controls exerted on food intake and energy expenditure (Berthoud and Morrison, 2008). The control of food intake and energy expenditure is insured by interconnected neurons

expressing varied receptor types and produ­cing diverse peptides or classic neurotransmitters that have been grouped into “anabolic” and “catabolic” mediators (Schwartz et al., 2000). Those mediators are found in nuclei such as the arcuate nucleus of the hypothalamus (ARC), paraventricular hypothalamic nucleus (PVH), lateral hypothalamus (LH), nucleus of the tractus solitarius (NTS), nucleus accumbens (NAcc), ventral tegmental area (VTA), amygdala and cortex. All these nuclei are tied to each other to

Limbic/cognitive

Autonomic

(Cortex, striatum, amygdala)

(Hypothalamus, brainstem)

Catabolic

Catabolic

Anabolic

Anabolic

NPY/AgRP -MSH/CART 5 HT MCH Orexins

Endocannabinoids Opioids Dopamine

Circulating signals Catabolic Anabolic Leptin (tonic) Insulin (tonic)

Ghrelin (episodic) Corticosteroids (tonic) GLP1/PYY (episodic) Nutrients (episodic)

Heat

Food energy Fat stores

Organ and cell metabolism Physical activity Regulatory thermogenesis

Figure 24.1  Overview of the regulation of energy balance presenting the main brain regions and chemical mediators involved in the control of food intake and energy expenditure. The stability of energy stores depends on controls exerted on both energy intake and energy expenditure. These controls are done by interconnected neurons comprised in the cognitive/limbic (cortex, striatum, amygdala) and autonomic (hypothalamus, brainstem) brains. These neurons express various receptor types and produce different peptides and classic neurotransmitters that have been grouped into “anabolic” and “catabolic” mediators. The neurons involved in control of energy intake and energy expenditure are influenced by peripheral hormones or other chemicals capable of informing brain cells on the status of the energy stores as well as on the nutritional status. These hormones or chemicals can be anabolic and catabolic, and have further been categorized as producing tonic (long-term) or episodic (short-term) effects. Gastrointestinal hormones produce episodic signals that mainly relate to the nutritional status. The controls exerted on regulatory (adaptive) thermogenesis are of major importance, in particular in small mammals.

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24.3  Brain pathways involved in the control of food intake and thermogenesis

form pathways that control the intake as well as the expenditure of energy. They comprise ­ neurons that produce energy-balance-­influencing mediators such as neuropeptide Y (NPY), agouti-related peptide (AgRP), alpha-melanocyte-stimulating hormone (-MSH), cocaineand amphetamine-regulated transcript (CART), melanin-concentrating hormone (MCH), orexins, endocannabinoids, opioids, dopamine and serotonin. The production of these diverse molecules is modulated by short- and long-term signals that inform the brain about the status of the energy stores and energy fluxes. Whereas leptin and insulin are recognized as the main long-term tonic signals, the gastrointestinal hormones ghrelin, peptide tyrosine-tyrosine (PYY), cholecystokinin (CCK) and glucagon-like peptide 1 (GLP-1) are known as short-term or episodic regulatory signals (Woods, 2005; Blundell, 2006). Circulating nutrients, including glucose, lipids and amino acids, are also sensed by brain “catabolic” and “anabolic” neurons (Obici and Rossetti, 2003; Lam et al., 2005; Potier et al., 2009). The peripheral signals have also been described as being “catabolic” and “anabolic”.

24.3.1  The ARC–PVH axis in food intake and thermogenesis control The ARC forms, with the PVH, perhaps the most important duet in the autonomic regulation of energy balance (Schwartz et al., 2000; Williams et al., 2001; Jobst et al., 2004; Elmquist et al., 2005; Morton et al., 2006; Adan et al., 2008). It consists of a tiny nucleus found in the basomedial hypothalamus just above the median eminence and adjacent to the third ventricle, in a position slightly caudal to the PVH (Williams et al., 2001). The ARC comprises two populations of neurons strongly involved in the control of energy intake and energy expenditure, and it integrates peripheral signals that influence energy homeostasis. One population of neurons synthesizes proopiomelanocortin (POMC)

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and CART, whereas the other synthesizes NPY and AgRP. POMC and CART are catabolic peptides, whereas NPY and AgRP are anabolic. The POMC/CART and NPY/AgRP neurons innervate both the descending and the parvocellular neurosecretory divisions of the PVH (Elmquist et al., 2005), through which nucleus they may influence energy homeostasis. The peptide CART is also likely an important player in the regulation of energy balance (Larsen and Hunter, 2006; Vrang, 2006). In the ARC, CART is co-localized with POMC in catabolic neurons. Similar to POMC, CART exerts anorexigenic effects. CART is also found in the retrochiasmatic area (RCh), which is seen as a hypothalamic structure distinct from the ARC (Vrang, 2006). CART neurons in the RCh have projections to the intermediolateral column (IML) (Elias et al., 1998) and could therefore represent a brain site for the autonomic action of the adipocyte-derived hormone leptin. Indeed, the RCh CART neurons, which also express POMC, have been reported to be involved in the leptin-mediated activation of the sympathetic outflow to BAT (Vrang, 2006). The melanocortin system in energy homeostasis POMC/CART neurons exert their catabolic effects in large part via -MSH, a peptidergic fragment ensuing from POMC cleavage. Within the brain, -MSH binds to melanocortin 3 (MC3R) and 4 (MC4R) receptors with which it essentially constitutes, together with AgRP, the “metabolic” melanocortin system (Adan et al., 2006; Butler, 2006; Ellacott and Cone, 2006). The functional significance of both MC3R and MC4R in energy homeostasis has been validated in Mc3r (Chen et al., 2002) and Mc4r (Huszar et al., 1997) knockout mice. Mc3r ablation seemingly enhances visceral fat accretion (Chen et al., 2002), whereas Mc4r disruption causes a significant and widespread body fat deposition (Huszar et al., 1997; Butler, 2006). In humans,

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Mc4r deficiency represents one of the most commonly known monogenic forms of obesity; up to 5 percent of severely obese patients apparently have pathogenic Mc4r mutations (Farooqi and O’Rahilly, 2006). The excessive fat deposition occurring in Mc4r knockout mice has been found to result from an increase in energy intake and a decrease in energy expenditure (Ste Marie et al., 2000; Butler, 2006). MC4R is endogenously antagonized by AgRP, which is solely expressed in the ARC neurons, where it is co-synthesized with NPY (Hahn et al., 1998). AgRP is over­ expressed in obese mice (Shutter et al., 1997) and, when injected centrally, it increases food intake (Rossi et al., 1998) and reduces energy expenditure (Asakawa et al., 2002), similar to synthetic melanocortin receptor antagonists. The MC4R is expressed in the three major parts of the PVH (parvocellular, magnocellular and descending divisions) (Kishi et al., 2003), and the role of the PVH in the hypophagia mediated by the MC4R has been ascertained (Balthasar et al., 2005). Reactivation of MC4R in the PVH by cre-recombinase appears sufficient to correct the hyperphagia seen in Mc4r null allele mice (loxTB Mc4r mice) (Balthasar et al., 2005), and microinjections of MC4R ligands into the PVH reduce food intake without apparently causing aversive effects (Giraudo et al., 1998). In ­addition, the MC4R seems to govern thermogenic effects. Transneuronal tract-tracing experiments have established a clear link between the PVH neurons expressing the MC4R and BAT (Voss-Andreae et al., 2007; Song et al., 2008), and such a link appears functional, as PVH microinjections of MC4R agonists elevate oxygen consumption (Cowley et al., 1999) and BAT temperature (Song et al., 2008). Peripheral metabolic influences on the ARC–PVH axis The role played by the ARC–PVH axis in energy balance regulation is strongly modulated by adipostatic signals such as leptin (Friedman, 2009) and insulin (Woods and D’Alessio, 2008),

and by acute satiety/hunger signals such as PYY (Karra et al., 2009), GLP1 (Chaudhri et al., 2008), oxyntomodulin (Wynne and Bloom, 2006) and ghrelin (Cummings and Overduin, 2007; Wiedmer et al., 2007). Nutrients also appear to be important modulators of the ARC activity (Woods and D’Alessio, 2008; Potier et al., 2009). The adipocyte-derived hormone leptin is certainly one of the most important controllers of the ARC–PVN axis. Leptin acts in the brain, into which it is actively transported, and where it binds to its long-form receptor (Ob-Rb) (Myers et al., 2008). It unquestionably exerts part of its catabolic action at the levels of the ARC, where, through the STAT3 signaling cascade, it reduces the production of NPY and AgRP while stimulating synthesis of POMC (Farooqi and O’Rahilly, 2008). Importantly, leptin also acts early in life as a trophic signal that stimulates ARC axon outgrowth to the PVH (Bouret et al., 2004). Novel pathways converting metabolic signals in the ARC and PVH Recent studies have highlighted the importance of AMP-activated protein kinase (AMPK) (Lage et al., 2008), mammalian target of rapamycin complex 1 (mTORC1) (Kahn and Myers, 2006; Woods et al., 2008), and forkhead box O1 (FoxO1) (Kim et al., 2006) in converting metabolic signals into anorectic (appetite-­suppressing) responses in the hypothalamus. AMPK is seen as a gauge of cellular energy status (Hardie, 2007). High and low levels of AMPK activity in the ARC stimulate and repress food intake, respectively (Lage et al., 2008). In keeping with this, expression and activity of AMPK in the ARC are increased by orexigenic stimuli such as fasting, ghrelin and cannabinoids, and are decreased by re-feeding and leptin (Kahn et al., 2005). AMPK may exert various actions to alter energy balance, including an inhibition exerted on mTORC1 (Kahn and Myers, 2006). mTORC1 has been clearly located in the ARC POMC and NPY/AgRP neurons, and its activation causes

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24.3  Brain pathways involved in the control of food intake and thermogenesis

anorexia (Cota et al., 2006a). mTORC1 can be activated by nutrients such as the branchedchain amino acid leucine (Cota et al., 2006a), as well as by insulin and leptin, two activators of phosphoinositide 3-kinase (PI3K) (Kahn and Myers, 2006). Activation of PI3K in turn leads to the phosphorylation of FoxO1 (Kido et al., 2001), which triggers its nuclear exclusion and its proteosomal degradation. Degradation of FoxO1 is catabolic, as it represses the orexigenic AgRP gene (Kim et al., 2006; Kitamura et al., 2006). It is still unclear whether FoxO1 also modulates NPY and POMC (Kim et al., 2006; Kitamura et al., 2006). AMPK, mTORC1 and FoxO1 are novel players in the brain regulation of energy balance. They appear to be of particular importance in the brain sensing of nutrients such as long chain fatty acids and branched-chain amino acids.

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that in turn results from a decreased activity of the NPY/AgRP neurons, which are particularly stimulated by the shortage of food (Sanacora et al., 1990). Also supporting a function for brain UCP2 in energy metabolism is the work from Parton and colleagues (Parton et al., 2007) suggesting that the genetic deletion of Ucp2 prevents obesity-induced loss of glucose sensing. Very recently, UCP2 was shown to be critical in the ARC action of the hormone ghrelin (Andrews et al., 2008), the production of which is stimulated by energy restriction (Cummings et al., 2002). The UCP2-dependent action of ghrelin on ARC NPY/AgRP neurons is apparently driven by a fatty acid oxidation pathway involving AMPK and free radicals that are scavenged by UCP2. The role of UCP2 in controlling reactive oxygen species (ROS) has been demonstrated (Arsenijevic et al., 2000).

ARC UCP2 in energy balance regulation UCP2 constitutes, together with UCPI, one of the main members of a subfamily of mitochondrial transporters/exchangers. In the brain, Ucp2 mRNA is distributed in a pattern that foretells neuroendocrine, behavioral, autonomic and neuroprotective functions (Richard et al., 1998, 1999, 2001). It is, for instance, expressed (1) within the neuroendocrine PVH; (2) within the ARC, in neurons co-expressing NPY and AgRP (Coppola et al., 2007) and expressing POMC (Parton et al., 2007); (3) in the brainstem within neurons controlling the sympathetic and parasympathetic nervous system (Richard et al., 1998, 1999, 2001); and (4) in the hippocampus (Clavel et al., 2003) and other regions sensitive to excitotoxicity. Recent animal studies have provided sound evidence for a role for brain UCP2 in energy homeostasis (Coppola et al., 2007; Parton et al., 2007; Andrews et al., 2008). UCP2 has been demonstrated to be involved in the rebound feeding induced by fasting (Coppola et al., 2007). Indeed, compared to wild-type mice, Ucp2/ mice exhibit reduced eating following fasting

24.3.2  The LH in energy homeostasis The LH has a long-established role in the regulation of energy balance (Anand and Brobeck, 1951; Berthoud, 2007). It is involved in food intake as well as in energy expenditure. More recently, the LH has been suggested to be a bridge between the cognitive/limbic and autonomic brain areas involved in energy balance regulation (Berthoud, 2006, 2007). The neurons comprised in this region indeed link the hypothalamus with the NAcc and VTA, two key parts of the brain reward system. The LH could also likely participate in the regulation of energy balance through descending projections to the brainstem and spinal cord areas involved in autonomic functions (Oldfield et al., 2002; Morrison, 2004). The LH control on energy expenditure has recently been further supported by data demonstrating the link between the LH and BAT. Transneuronal labeling experiments have indeed established a clear poly­ synaptic link between melanin-concentrating hormone (MCH) and orexin neurons and BAT (Oldfield et al., 2002, 2007; Zheng et al., 2005).

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The MCH system in the regulation of energy balance Evidence is accumulating to suggest that the MCH system is implicated in the regulation of energy balance (Nahon, 2006; Pissios et al., 2006). Indeed, chronic treatment with MCH (Gomori et al., 2003) and MCH overexpression (Ludwig et al., 2001) lead to obesity and an increased susceptibility to high-fat feeding, whereas antagonism of MCHR1 (Shearman et al., 2003) or ablation of MCH (Shimada et al., 1998), MCH neurons (Alon and Friedman, 2006) and MCHR1 (Chen et al., 2002; Marsh et al., 2002) promotes leanness. MCH mRNA-expressing cells are concentrated within the LH and the adjacent zona incerta (ZI). These neurons project from the LH to the rest of the brain (Bittencourt et al., 1992) in regions expressing the MCHR1 (Hervieu et al., 2000), the functional MCH receptor in rats and mice. Two subpopulations of MCH neurons have been characterized, based on the presence or absence of a CART co-localization (Cvetkovic et al., 2004). The MCH/CART cells, described as MCH type A neurons, project to the caudal brainstem and the spinal cord, whereas the MCH/CART cells, referred to as MCH type B neurons, terminate in the forebrain. MCH type A neurons likely fulfill autonomic functions and are also likely involved in the control of energy expenditure, as they are polysynaptically linked to BAT (Oldfield et al., 2002, 2007). In fact, more than 50 percent of the LH MCH neurons surrounding the fornix (the bundle of axons crossing the LH) are infected following the injection in BAT of the transneuronal retrograde tracer pseudorabies virus (PRV), a marker used to map the SNS outflow to BAT (Oldfield et al., 2002). MCH Type B neurons could also participate in the thermogenic function of MCH, as they connect with the ARC– PVH axis and the dorsal brainstem (Cvetkovic et al., 2004). Meanwhile, the type B neurons are likely to play a role in the control of food intake,

as they project to the NAcc, whose role in the rewarding effects of food and other substances is acknowledged (Pecina, 2008; Carlezon and Thomas, 2009). Injection of MCH in the NAcc increases food intake (Georgescu et al., 2005; Guesdon et al., 2009). There is strong evidence for the involvement of MCH not only in the control of food intake but also in the control of energy expenditure. In the leptin-deficient ob/ob mouse, deletion of MCH induces a dramatic fat loss without any food intake reduction (Segal-Lieberman et al., 2003). Mchr1 disruption (Chen et al., 2002; Marsh et al., 2002) even leads to leanness despite hyperphagia. The increased energy expenditure caused by deletion of MCH in ob/ob mice is accompanied by increases in both metabolic rate and locomotor activity (Segal-Lieberman et al., 2003). However, the observation that the enhanced metabolic rate (seen throughout the day) of the Mch/ob/ob mice is not totally paralleled by an increase in locomotor activity (seen only at night) suggests that the augmented energy expenditure cause by deletion of MCH (Segal-Lieberman et al., 2003) is not solely due to an increase in physical activity, which is consonant with recent results showing the inability of an i.c.v. injection of MCH to reduce locomotion in rats (Guesdon et al., 2009). Another determinant of MCH-induced energy expenditure could certainly be BAT thermogenesis, as Mch/ ob/ob mutants exhibited an increase in BAT expression of UCP1. In rats and mice, the effects of MCH, including those on energy metabolism, are mediated through the MCHR1, which appears to be the sole MCH receptor expressed in those species. However, humans (Hill et al., 2001; Rodriguez et al., 2001), monkeys (Fried et al., 2002), dogs and ferrets (Tan et al., 2002) also express a second MCH receptor, referred to as MCHR2. Not much is currently known about this receptor except that its highest mRNA levels are found in the frontal cortex, amygdala and NAcc (Wang et al., 2001), which suggests that MCHR2 could

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24.3  Brain pathways involved in the control of food intake and thermogenesis

be involved in the cognitive, motivational and hedonic (non-homeostatic) aspects of feeding. The orexin system in the regulation of energy balance Another LH neuronal entity liable to affect the regulation of energy balance is the orexin system. Orexin-producing neurons are concentrated in the posterior LH, from where they extend dorsally, medially and laterally from the fornix (Swanson et al., 2005). Their localization is rather similar to that of the MCH neurons, even though they form an entirely distinct population of cells. The orexin neurons project to the entire neuraxis excluding the cerebellum (Peyron et al., 1998; Date et al., 1999). There are two orexins: orexin A and orexin B. They are issued from the same precursor, and act on two distinct receptors (OX1R and OX2R). The brain distribution of the orexin receptors is in good correspondence with the orexin neuronal projections. Among other areas, orexinimmunoreactive nerve endings have been located in the ARC (OX2R), VTA, NAcc shell (OX2R), caudal raphe and locus coeruleus (LC) (OX1R) (Peyron et al., 1998; Date et al., 1999). Similar to MCH neurons, orexin cells have been separated into two populations, based on their locations, projections and functions (Harris and Aston-Jones, 2006). One population, which would be mainly involved in reward, includes LH neurons projecting to the VTA. The other, which would be implicated in arousal, comprises perifornical/dorsomedial-nucleus neurons sending projections to the brainstem. The stimulation of the orexin system increased appetite (Sakurai et al., 1998), reward (Harris et al., 2005), body temperature (Yoshimichi et al., 2001), BAT SNS drive (Yasuda et al., 2005) and locomotor activity (Kotz, 2006). All those effects are consonant with the action of orexins in waking and arousal (Sakurai, 2005; Matsuki and Sakurai, 2008; Ohno and Sakurai, 2008). Food deprivation and ghrelin stimulate orexin neurons, whereas

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leptin and glucose exert opposite effects (Ohno and Sakurai, 2008). Ablation of orexin neurons is associated with the development of a lateonset obesity that occurs even in the presence of hypophagia (Hara et al., 2001), strongly suggesting the stimulating effect of orexin neurons on energy expenditure.

24.3.3  The brainstem as a key relay in energy homeostasis The brainstem, which includes the hindbrain (pons and medulla oblongata) and the midbrain, forms, with the hypothalamus, a key autonomic arm in the regulation of energy balance. The brainstem comprises the NTS, which, together with the area postrema (AP) and the motor nucleus of the vagus nerve, composes the dorsal vagal complex (DVC). The DVC integrates peripheral signals conveyed by cholecystokinin, PYY and GLP1, which are gastrointestinal hormones significantly influencing energy balance regulation (Moran, 2006; Murphy and Bloom, 2006; Cummings and Overduin, 2007). The action of these hormones is exerted on vagal afferents, which terminate in the DVC or at the level of the AP, one of the circumventricular organs (devoid of a blood–brain barrier). The AP expresses key gastrointestinal hormone receptors (Fry and Ferguson, 2007). The key role of the DVC (and hindbrain in general) in the regulation of energy balance has been advocated by Grill and colleagues (Grill and Kaplan, 2001; Grill, 2006), who carried out elegantlydesigned experiments with decerebrated rats (rats subjected to a complete transection of the neuroaxis at the mesodiencephalic juncture) to decisively demonstrate the brainstem implication in the effects of leptin (Harris et al., 2007), melanocortins (Williams et al., 2000) and ghrelin (Faulconbridge et al., 2005). In addition to integrating peripheral inputs, the brainstem comprises nuclei, such as the caudal raphe, periaqueductal gray (PAG),

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pontine reticular nuclei, lateral paragigantocellular nucleus, and locus ceruleus (LC), that are likely involved in SNS-mediated thermogenesis (Bamshad et al., 1999; Cano et al., 2003; Morrison et al., 2008). These nuclei are capable of relaying information from the hypothalamus to the IML, from which originate SNS pre­ganglionic neurons involved in the control of BAT thermogenesis.

24.3.4  The ventral striatum and the brain reward system in the regulation of energy balance The ventral striatum is constituted of the olfactory tubercle and the NAcc, which has emerged as a crucial structure in the control of food intake (Salamone, 1994). Being part of the limbic system, the NAcc comprises two distinct sections: the core and shell (Heimer et al., 1997). The NAcc forms, with the VTA, the mesolimbic pathway, also referred to as the mesolimbic reward pathway. This pathway comprises dopaminergic neurons originating from the VTA, which terminates in the NAcc. These dopaminergic neurons likely play a role in the motivational component of reward (referred to as “incentive salience” or “wanting”) (Berridge and Robinson, 2003; Finlayson et al., 2007), and they do not appear to be essential in sensing the hedonic value or the “liking” aspects of food (Pecina et al., 2003). The opioid system and the brain reward system The main endogenous opioids are -endorphin, enkephalins, and dynorphins. They are widely found in the brain, and act through three receptor types:  (-endorphin, met- and leuenkephalins),  (met- and leu-enkephalins) and  (dynorphin). The opioid system plays a key role in the hedonic response to food (Levine and Billington, 2004; Cota et al., 2006b; Pecina et al.,

2006). Brain administration of opioid receptor agonists and antagonists has been shown to exert site-specific effects on feeding. Injection of the -opioid receptor agonist DAMGO in the NAcc shell markedly increases the hedonic value of sweetness (Pecina and Berridge, 2005). The NAcc shell expresses the -opioid receptor as well as the CB1 receptor (Fusco et al., 2004; Matyas et al., 2006), which are part of systems capable of strong interactions (Fattore et al., 2004; Robledo et al., 2008). The cannabinoid system in the brain reward system The endocannabinoid system is essentially composed of two receptors, namely the cannabinoid 1 and 2 (CB1 and CB2) receptors, and of “on-demand” produced endocannabinoids (the most notable being anandamide and 2-arachidonoylglycerol (2-AG)) (Di Marzo, 2008). The endocannabinoid system appears to be genuinely involved in energy balance regulation; it exerts actions on both energy intake and energy expenditure, and its activity is affected by the status of the fat stores in a way to prevent any major oscillations in the fat reserves. Administration of cannabinoid receptor agonists such as the plant-derived cannabinoids delta-9-tetrahydrocannabinol (9-THC), the active substance of the cannabis plant, caused hyperphagia and increased the preference for palatable food (Brown et al., 1977; Williams et al., 1998). These effects are mediated through the CB1 receptor, whose genetic ablation (Cota et al., 2003; Ravinet Trillou et al., 2004) or antagonism (Arnone et al., 1997; Jbilo et al., 2005) reduces energy deposition; CB2 receptor antagonism is without any apparent effect on energy balance (Wiley et al., 2005). CB1 receptor antagonism, which expectedly has no influence on ingestive behavior in CB1/ mice (Di Marzo et al., 2001), not only creates hypophagia but also seemingly stimulates energy expenditure (Doyon et al., 2006; Herling et al., 2008; Kunz et al., 2008). Food

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deprivation (Kirkham et al., 2002) and obesity (Di Marzo et al., 2001) elevate endocannabinoid levels in the hypothalamus. There is sound evidence to suggest that the endocannabinoid system can influence food intake by acting on the ventral striatum. The endocannabinoids apparently control the activity of the dopaminergic neurons of the mesolimbic reward pathway that project from the VTA to the ventral striatum (Matyas et al., 2008). The NAcc expresses a considerable amount of the CB1 receptor protein (Fusco et al., 2004; Matyas et al., 2006), and it is known to produce anandamide and 2-AG. Functionally, intra-NAcc injections of anandamide, 2-AG, fatty acid amide hydrolase (FAAH) inhibitors (anandamide inactivator), and inhibitors of anandamide uptake, all increase food intake (Kirkham et al., 2002; SoriaGomez et al., 2007). In addition, ventral striatum synthesis of the two main endocannabinoids is induced by food deprivation and normalized by re-feeding (Kirkham et al., 2002). Moreover, whereas food deprivation increases 2-AG levels only in the hypothalamus, it raises the levels of both anandamide and 2-AG in the limbic forebrain (Kirkham et al., 2002), revealing the relative importance of the brain reward system and the NAcc in the regulation of energy balance. The role of the ventral striatum and the brain reward system in the anabolic action of the endocannabinoids is further supported by series of experiments that have demonstrated that CB1 receptor agonism can enhance the preference for palatable foods such as sucrose (Brown et al., 1977) and fat (Koch, 2001), and increase food intake in sated rats by increasing the duration and number of meals (Williams and Kirkham, 2002). Also, it has been demonstrated that CB1 receptor antagonism can blunt the conditionedplace preference for food (Chaperon et al., 1998), the selective preference for sucrose (Arnone et al., 1997), the reinforcing and motivational properties of a chocolate-flavored beverage (Maccioni et al., 2008), and the desire for sweets and highfat food (Scheen et al., 2006). Altogether, these

experiments point to a role of endocannabinoids on the “liking”/”wanting” aspects of ingestive behavior (Williams and Kirkham, 2002). In fact, endocannabinoids appear to influence both the “liking” and “wanting” for food. Similar to -opioid receptor agonists, endogenous ligands of the CB1 receptor, such as anandamide, appear to be capable of enhancing sweet reward when injected in the NAcc shell (Mahler et al., 2007). The influence of the endocannabinoids on the “wanting” aspect of food (Thornton-Jones et al., 2005) is supported by the observation that the cannabinoid antagonism reduces the intra NAcc release of dopamine that is induced by a novel, highly palatable food (Melis et al., 2007). Finally, it appears worth mentioning that the effects of endocannabinoids on the brain reward system implicate the hypothalamus (Soria-Gomez et al., 2007) and the brainstem (DiPatrizio and Simansky, 2008); this is not unexpected, considering the link existing between the limbic/cognitive and autonomic circuitries controlling food intake.

24.4  Conclusion The regulation of energy balance depends on complex crosstalk between the autonomic and cognitive/limbic brain circuitries that control energy intake and energy expenditure. The “metabolic autonomic brain” includes (1) various hypothalamic structures, among which the ARC, PVH and LH are prominent; and (2) the brainstem, which per se constitutes a major afferent/efferent relay for metabolic signals. The ARC comprises the NPY/AgRP and POMC/ CART neurons, whose respective anabolic and catabolic roles in energy balance have been acknowledged. Both NPY/AgRP and POMC/ CART neurons project to the PVH, probably the most important brain neuroendocrine structure, which forms, with the ARC, one of the most notable circuitries in energy homeostasis.

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The PVH comprises a division referred to as the descending division, whose neurons project to the brainstem and the spinal cord to control food intake and energy expenditure. Both the hypothalamus and the brainstem harbor receptors for the main catabolic and anabolic hormones informing about the energy stores and the nutritional status. The LH hosts neurons, such as those expressing MCH, which are in a good position to interface the autonomic and cognitive/limbic brains. MCH neurons not only autonomically control food intake and BAT thermogenesis, but also influence the activity of the ventral striatum, a main component of the brain reward system. The latter, whose activity is also modulated by peripheral metabolic hormones such as leptin and ghrelin, plays a major role in energy balance, as it integrates various hedonic/anhedonic signals capable of reinfor­ cing or blunting behaviors modulating energy homeostasis.

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C H A P T E R

25 Stealth Interventions for Obesity Prevention and Control: Motivating Behavior Change Thomas N. Robinson Division of General Pediatrics, Department of Pediatrics and Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine; Center for Healthy Weight, Stanford University School of Medicine and Lucile Packard Children’s Hospital at Stanford, Stanford, CA, USA

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25.1  Motivation for behavior change Most past behavioral and lifestyle interventions to prevent and treat obesity have produced relatively modest and non-sustained effects on weight (Summerbell et al., 2003, 2005). Even state-of-the-art behavioral programs, that successfully reduce weight or weight gain during treatment, are generally followed by regain of some, if not all, of the lost weight. One possible

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reason is insufficient attention to motivational factors related to eating, physical activity and sedentary behaviors (Robinson, 2001). Cognitive social learning models of behavior indicate that motivational processes are key to influencing behavior (Deci and Ryan, 1985; Bandura, 1986, 1997). Many medical and public health interventions tend to emphasize outcome-based incentives for behavior change, such as reducing or maintaining weight, physical appearance, becoming more fit, and reducing risks of future chronic diseases – the eventual outcomes

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of adopting a healthy diet, increased physical activity and decreased sedentary behaviors. Because the beneficial outcomes of changing eating and activity behaviors often lag in time behind the behaviors, they may not be sufficient motivators to adopt, generalize and maintain new behaviors (Bandura, 1986, 1997). Our substantial clinical and research experiences in designing, implementing and evaluating obesity prevention and treatment interventions suggest that it may be more important to emphasize motivation for participating in the interventions themselves – the process of behavior change. To produce behavior changes over time, it is necessary that participating in those behaviors (the activities making up the behavior change process) is rewarding. Lack of sufficient attention to this process motivation may be one explanation for the difficulty that many children and adults have initiating and sustaining behavior changes to maintain or reduce their weight over time.

25.2  Self-efficacy Social cognitive theory specifies perceived self-efficacy, a belief in one’s capabilities to perform the specific actions required to produce expected attainments, as a primary determinant of behavior change (Bandura, 1986, 1997). While interventions focusing on outcomes may contribute to increased motivation for behavior change, conversely they also may undermine perceived self-efficacy for behavior and weight change when payoffs are not immediately forthcoming. Even when weight changes are immediate, biological homeostatic mechanisms tend to protect against a reduced weight, making additional incremental loss or maintenance more difficult (Friedman and Halaas, 1998; Schwartz et al., 2003). Thus, the potential incentive value of continued weight loss and its associated benefits become less salient. Unless other rewards are recognized, this reduced salience diminishes the

motivational impact and self-efficacy for behavior change, and individuals will likely start to regain or reaccelerate weight gain as is observed in traditional weight-loss programs (Wadden et al., 2005). Another potential outcome of intervention is no weight loss or continued gain in weight. If this occurs, individuals using weight as a primary motivator would likely have low perceived self-efficacy to perform the behaviors required to control their weight. Therefore, emphasizing an outcome goal could actually be detrimental to long-term weight control and obesity prevention, as unsuccessful attempts would further reinforce individuals’ perceived inability to lose or control their weight. In contrast, emphasizing process motivators in the design of prevention and treatment interventions is more likely to enhance selfefficacy for behavior change, thereby resulting in behavior change and weight control. To do so, it is necessary to specifically design prevention and treatment interventions that are motivating in themselves (the process of behavior change). Process motivators are the factors that elicit and sustain attention to and persistence in an activity. Table 25.1 contrasts examples of outcome motivations typically used in medical and public health interventions versus process motivations that increase intrinsic motivation for participating in the process of behavior change, based on research on intrinsic motivation and our own observations (Robinson and Borzekowski, 2006; Lepper et al., 2008).

25.3  Stealth interventions Accepting the strategy of emphasizing process motivators over outcome motivators then begs the question: if motivations are not tied to health outcomes, does a health behavior change intervention need to look, taste, sound, feel or smell like a health behavior change intervention? In fact, if it is the process that is important,

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25.3  Stealth interventions

Table 25.1  Examples of outcome and process motivators for physical activity and eating behaviors Outcome motivators

Process motivators

Obesity/weight loss

Fun

Diabetes

Taste

Hyperlipidemia

Choice

Hypertension

Control

Risk for cardiovascular diseases and/or cancer

Goals

Other chronic diseases

Curiosity

Personal appearance

Challenge Teamwork Competition Pride, sense of accomplishment Anticipated parent, peer, or social approval/ disapproval Social interaction

interventions can be designed to focus on intrinsically motivating characteristics of behaviors without appearing to be directly related to obesity, physical activity, nutrition, or any aspect of health at all, to be successful. Such interventions are referred to here as stealth interventions, because the primary emphasis is on maximizing the incentive value of the intervention activities themselves rather than their resulting healthrelated outcomes (Robinson and Sirard, 2005). From the perspective of participants, they may not adopt improved health-related behaviors for the purpose of reducing their weight or weight gain, but reduced weight or weight gain instead become beneficial “side effects” of their participation. This approach was central to designing effective screen-time reduction interventions for children and families (Robinson, 1999; Robinson et al., 2003; Robinson and Borzekowski, 2006). Intervention activities were designed to enhance the intrinsic motivation for reducing screen time by emphasizing

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challenge, curiosity, perceived choice and control, fantasy and contextualization, perceived individualization, cooperation and competition, social interaction, positive emotions, and anticipated peer and adult approval as integral to the process of reducing screen time (Robinson and Borzekowski, 2006; Lepper et al., 2008). This model stands in sharp contrast to the more traditional approach of trying to persuade persons to change their behaviors to achieve health and social outcomes, such as weight loss, or cholesterol or blood pressure reduction (Robinson and Borzekowski, 2006). The stealth intervention approach can be taken one step further by structuring environments and intervention activities to enhance the motivational value of behaviors that are directly health promoting. An example is the use of dance to promote physical activity among young girls. The original “Dance for Health” program was developed as a medical student public service and research project (Flores, 1995). The objective was to provide a motivating and active alternative to traditional physical education (PE) classes. To evaluate its efficacy, 81 seventh-grade children (mean age 12.6 years, 43 percent Latino and 44 percent AfricanAmerican, 49 girls) were randomized to either aerobic dance (treatment) or their usual PE classes (control). Both were delivered during the regular PE periods of 40–50 minutes, 3 times per week, for a 12-week period. The dance classes were led by Stanford undergraduate and graduate students. Each dance period was designed to include about 30 minutes of moderate- to high-intensity aerobic dance. Popular music was selected for dance routines developed by the instructors. Usual PE included standard PE instruction and playground activities, led by the school PE teacher. Assessments were completed by trained staff, blinded to treatment assignment, at baseline and after 12 weeks. Girls randomized to dance classes significantly reduced their body mass index (BMI) and resting heart rate, a measure of aerobic fitness, compared to

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girls in standard PE. There were no significant differences between groups for boys, possibly because co-ed. dance was not as motivating for boys at this age. However, this is still one of the few PE interventions to ever show benefits in body composition, and suggests that designing PE classes around a highly motivating activity can result in sufficient levels of physical activity to reduce weight gain. The success of the Dance for Health project, with its emphasis on process motivators rather than outcome motivators, led to the design of after-school ethnic dance classes to promote physical activity among pre-adolescent girls (Robinson et al., 2003). Physical activity falls dramatically as girls enter adolescence (Kimm et al., 2002). Experience has demonstrated that it is extremely difficult to persuade girls to exercise, even to dance, for the purpose of improving their health and preventing future chronic diseases. In contrast, it was found that many girls vigorously exerted themselves as part of a dance class if it was designed to maximize the immediate incentive value of participation. For the Stanford GEMS trial, both theory and extensive formative research (Kumanyika et al., 2003) were used to design intrinsically motivating intervention activities to encourage low-income African-American girls to participate in a dance class (Robinson et al., 2003). This approach resulted in an intervention that linked dance to the girls’ cultural heritage and included popular social dances, periodic performances for family, friends and community events, choreography, set and costume design, popular teachers, safe and supervised after-school care, and homework assistance. Notably, it did not overtly emphasize physical activity, obesity or health. Girls participated in dance classes primarily because it was an enjoyable and rewarding experience, not because it would improve their fitness, weight or health. From the perspectives of the girls and their parents, improved fitness, weight and health were side effects of participating in a dance class (Robinson et al., 2003).

In the Stanford GEMS pilot randomized controlled trial, 65 African-American girls aged 8–10 years, from 61 families/households, were enrolled from low-income areas of East Palo Alto and Oakland, CA. The treatment intervention included five family-based lessons to reduce television, videotape and video-game use, and the GEMS Jewels after-school dance program (emphasizing traditional African dance, step and hip-hop, and time for homework and mentoring). Dance groups were offered 5 days per week at three different neighborhood community centers. The comparison group received an active-placebo state-of-the-art information-based health education program. Compared to girls randomized to the health education condition, girls in the after-school dance and televisionreduction treatment condition showed trends toward reduced BMI, reduced waist circumference and reduced television viewing, in just 12 weeks (Robinson et al., 2003). This same approach can also be applied in the context of weight control among overweight and obese children. An example is the Stanford SPORT (Sports to Prevent Obesity Randomized Trial) team sports program for overweight children (Weintraub et al., 2008). Team sports can be highly motivating for some children, but are often avoided by overweight children (who may, for example, not want to be “picked last” or the slowest person on the field). However, when we designed a soccer team to specifically cater to the needs of overweight children, they enthusiastically participated in moderate and vigorous activity. We emphasized the process motivators from Table 25.1 with many of the rewarding features of team sports that are unrelated to preexisting skills and high levels of competition, such as simply belonging to a team, wearing a uniform, receiving attention from young adult coaches, opportunities to see improvement in their skills, and even some friendly competition with other similarly skilled players/teams or family members. In a test of Stanford SPORT, 21 overweight or obese (BMI  85th percentile

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25.4  Social and ideological movements as stealth interventions to change health behaviors

for age and sex on the 2000 CDC BMI reference) fourth- and fifth-grade children were enrolled and randomized to 6 months of either afterschool soccer, or health education. Of the 21 enrolled children, 14 (66 percent) had never previously participated in a sports team. Soccer was initially offered 3 days per week, but this was increased to 4 days per week at the request of participating children and parents. Children randomized to the active placebo health education condition received a 25-session, state-of-the-art information-based nutrition and health education intervention consisting of weekly after-school meetings conducted by health educators. All 21 children (100 percent) completed the study. We found medium to large beneficial effects in BMI, age- and sex-adjusted BMI (BMI-Z), total daily physical activity, and time spent in moderate physical activity and vigorous physical activity (measured by accelerometers) at both 3-month and 6-month follow-up. All 9 children (100 percent) randomized to the soccer group compared to 5 of 12 children (42 percent) randomized to the health education group had lower BMI Z-scores at both 3 and 6 months. At 6-month follow-up, 8 of 9 children (89 percent) in the soccer program stated that they wanted to continue to play on a soccer team (Weintraub et al., 2008).

25.4  Social and ideological movements as stealth interventions to change health behaviors As illustrated by the examples above, the stealth intervention approach encourages looking outside of traditional avenues for promoting physical activity and reducing energy intake to prevent and treat obesity. In doing so, we observe that some of the most compelling examples of widespread, dramatic and sustained behavior changes occur in the context

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of social and ideological movements. Some of the most obvious examples are the behaviors associated with religious traditions. For example, Seventh Day Adventist, Mormon, Hindu, Jewish and many other religious traditions include significant dietary prescriptions that many followers are able to follow throughout their life-course, despite a powerful food environment promoting contrary behaviors. At an extreme, some individuals sacrifice their lives as martyrs for their religious beliefs (Atran, 2003). Compare that to the difficulty many people have in following even comparatively small changes in their diet or activity behaviors for purposes of reducing their weight, cholesterol or blood pressure levels. Religious traditions, however, are not the only examples where participants endorse, adopt and sustain dramatic changes in their behaviors. Analogous to religious movements, similar dramatic and durable behavior changes can be seen as part of social justice, civil rights, environmental, anti-tobacco, political and other social movements. Think, for example, of the motivations encountered among vegetarians. In pediatric practice, it is not unusual to meet adolescents who have adopted vegetarianism because of their concern for animal welfare or to support environmental sustainability. In contrast, it is rare to meet a teenager who has adopted a vegetarian diet for its health benefits. Participation in these traditions and movements may be motivating because of their spiritual, social, economic, self-esteem enhancing, self-actualizing, or other qualities associated with the processes of belonging to such a social movement. Based on these observations, social and ideological movements may represent a potentially powerful stealth intervention model for changing obesity-related behaviors. Observations about social movements in general, and the anti-tobacco movement in particular, have triggered suggestions about harnessing the power of social movements to combat obesity (Dietz and Robinson, 2008). Some have focused on building a new anti-obesity movement de

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novo using the lessons learned from retrospective explanations of successful prior social movements (Economos et al., 2001). However, retrospective explanations of success may or may not be relevant to prospectively building a successful movement, as they are limited by the conceptual models and perspectives of those of us who are looking back. To date, obesity and appeals to improve health have not proven sufficiently compelling or motivating to stimulate a social movement to combat obesity. An alternative strategy, however, is to identify existing and emerging social/ideological movements that share behavioral goals with those for weight control. It may be possible to integrate efforts to control obesity into social movements that are already proving to be highly motivating to significant segments of the population. We have identified a number of existing social and ideological movements that include goal behaviors which overlap with some of the goal behaviors of obesity prevention and control. These movements, therefore, have the potential to produce obesity prevention and reduction as side effects, as discussed with other forms of stealth interventions. A number of these are illustrated in Table 25.2. These examples show that many existing movements from across the political, cultural and socio-economic spectrum share behavioral goals consistent with obesity prevention and control. Considering obesity prevention and treatment via social movements that are not overtly driven by concerns regarding obesity or health may be a particularly powerful stealth intervention. An additional benefit of promoting change through ideological and social movements is their potential to influence behavior and public policy to promote further change through the simultaneous actions of multiple societal sectors – family, government, markets and civil society. Attempting to reduce obesity by allying with and working through these larger social and ideological movements becomes the ultimate expression of the stealth intervention approach. If successful, reductions in individual

and population levels of obesity may occur without having to persuade patients or the public to change their eating and activity behaviors for purposes of attaining and maintaining a healthy weight – an approach that has proven ineffective to date. Linking obesity to existing ideological and social movements does not preclude turning obesity prevention and control into a social movement of its own, of course. Studying the characteristics of successful social movements may help us promote a similar movement around obesity, and this should also be pursued (Economos et al., 2001; Dietz and Robinson, 2008). The same principles, emphasizing process motivators that are inherent in the stealth intervention approach, will still apply.

25.5  Conclusion Stealth interventions can take multiple forms. As demonstrated in interventions to reduce children’s screen time (Robinson, 1999; Robinson et al., 2003; Robinson and Borzekowski, 2006), emphasis is placed on incentives for the process of behavior change rather than outcomes, by using frames to enhance intrinsic motivation such as perceived choice and control, individualization, fantasy and contextualization, challenge, curiosity, and cooperation and competition (Robinson and Borzekowski, 2006; Lepper et al., 2008). These design features make the process of behavior change rewarding, easy and desirable rather than a sacrifice or burden, as “diets” and “exercise” are often perceived to be. These same approaches can be applied to the design of other nutrition and physical activity interventions to raise the incentive value of participation, rather than trying to persuade participants to change through reason and logical arguments. Taking stealth interventions to the next level is to identify or structure healthpromoting environments and activities that are

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Table 25.2  Social and ideological movements with behavioral goals that overlap with the goals of obesity prevention and control Social movements

Overlapping behaviors

Environmental sustainability/climate change: reducing global warming and climate change, sustainable agriculture, organic farming, slow food, eating locally (locavores), water conservation, recycling/waste reduction, improving air quality

Greater consumption of fresh fruits and vegetables and reduced intake of meat, poultry, and processed and packaged foods that are transported over long distances; less automobile use, more walking, bicycling and mass-transit use

Food safety: infections (e.g., E. coli O157:H7, bovine spongiform encephalopathy/mad cow disease) and potentially harmful additives (e.g., contaminants in imported food)

Less meat consumption and greater consumption of locally grown fruits and vegetables

Human rights/social justice: workers’ rights, poor working conditions among fast-food workers and suppliers (e.g., slaughterhouses, meatpacking, farm workers), food justice, access to fresh fruits and vegetables in low-income areas

Lower fast-food, meat and poultry consumption, less processed foods, more fruits and vegetables from farmers’ markets, Community Supported Agriculture (CSA), boycotts of fast-food chains

Anti-globalization: movements by farmers, labor unions, human rights groups, nationalists, etc. supporting local economies and against corporate and cultural globalization

Greater consumption of locally grown foods, lower consumption of fast food and imported foods, lower patronage of multinational food chains and producers

Animal protection

Lower meat and poultry consumption, vegetarianism

Anti-consumerism: movements to reduce the impact of a consumer culture, advertising and marketing

Lower consumption of heavily advertised and marketed fastfood and snack foods/convenience foods; less screen media use

Cause-related fundraising: to raise awareness and money for charitable causes such as cancer or AIDS research and services (e.g., Team-in-Training).

Walk-a-Thons, door-to-door fundraising, training and participation in distance races, triathlons, long distance walks and bike rides, etc.

Energy independence/reduced dependence on foreign oil

Less automobile use, more walking, bicycling and masstransit use; greater consumption of locally grown fruits and vegetables; lower consumption of packaged and processed foods transported over long distance

Youth violence and crime prevention

Participation in sports programs to reduce youth involvement in crime/gangs (e.g., midnight basketball leagues, police sports leagues), participation in neighborhood policing/neighborhood watch

National security: movements to promote a physically fit military

Participation in military training or military reserve programs involving physical activity

Political action: as part of the movements listed above or others

Increased physical activity and displacement of sedentary behavior from door-to-door campaigning, public demonstrations, marches, etc.

motivating in themselves. As shown, after-school ethnic dance classes for girls can redefine sustained moderate to vigorous exercise (with its perspiration, fatigue and soreness) as a highly rewarding and fun, cultural, social, artistic and political

activity. These interventions are designed such that, from the perspective of the participants, physical activity, diet and weight changes are positive side effects of their participation, rather than the primary motivators. Finally, the next

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25.  Stealth Interventions for Behavior Change

logical step for the stealth intervention approach is to harness the motivational appeal of existing and emerging social and ideological movements, to achieve greater and more sustained effects on behavior. The stealth intervention approach has a number of important advantages as a model for obesity prevention and control. First, it is theorybased. The guiding conceptual framework has its origins in theories of motivation and behavior that are supported by substantial empirical research, including social cognitive theory (Bandura, 1986, 1997), self-determination theory (Deci and Ryan, 1985), and basic and applied experimental research in intrinsic motivation (Lepper et al., 2008). Second, the stealth intervention approach can be applied at all levels of intervention – individual, family, school, workplace, community, social and/or environmental – overcoming the limitations of models that focus on the level of intervention rather than the process of behavior change. Third, it is solution-oriented (Robinson and Sirard, 2005), emphasizing the discovery of solutions rather than causes of obesogenic behaviors, allowing intervention designers to entertain potential solutions that would not be identified from traditional etiological thinking. Finally, the stealth intervention approach prompts the possibility of building new alliances and synergies with other, previously unrelated, movements and causes that share similar behavioral goals. In this way, available resources, expertise and experience can be shared, potentially strengthening all efforts to produce greater and more expedient individual and society-wide changes.

References Atran, S. (2003). Genesis of suicide terrorism. Science, 299, 1534–1539. Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, NJ: Prentice-Hall. Bandura, A. (1997). Self-efficacy: The exercise of control. New York, NY: W.H. Freeman and Company.

Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and selfdetermination in human behavior. New York, NY: Plenum Publishing Co. Dietz, W. H., & Robinson, T. N. (2008). What can we do to control childhood obesity? Annals of the American Academy of Political and Social Science, 615, 222–224. Economos, C. D., Brownson, R. C., DeAngelis, M. A., Foerster, S. B., Foreman, C. T., Gregson, J., et al. (2001). What lessons have been learned from other attempts to guide social change? Nutrition Reviews, 59, S40–S56. Flores, R. (1995). Dance for health: Improving fitness in African American and Hispanic adolescents. Public Health Reports, 110, 189–193. Friedman, J. M., & Halaas, J. L. (1998). Leptin and the ­regulation of body weight in mammals. Nature, 395, 763–770. Kimm, S. Y., Glynn, N. W., Kriska, A. M., et al. (2002). Decline in physical activity in black girls and white girls during adolescence. New England Journal of Medicine, 347, 709–715. Kumanyika, S., Story, M., Beech, B. M., et al. (2003). Collaborative planning process for formative assessment and cultural appropriateness in the girls health enrichment multi-site studies (GEMS): A retrospection. Ethnicity and Disease, 13, S15–S29. Lepper, M. R., Master, A., & Yow, W. Q. (2008). Intrinsic motivation in education. In M. L. Maehr, S. A. Karabenick, & T. C. Urdan (Eds.), Advances in motivation in education: Vol. 15 (pp. 521–556). Bingley: Emerald Group Publishing Limited. Robinson, T. N. (1999). Reducing children’s television viewing to prevent obesity. Journal of the American Medical Association, 282, 1561–1567. Robinson, T. N. (2001). Population-based obesity prevention for children and adolescents. In F. E. Johnston, & G. D. Foster (Eds.), Obesity, growth and development: Vol. 3 (pp. 129–141). London: Smith-Gordon and Company Limited. Robinson, T. N., & Borzekowski, D. L. G. (2006). Effects of the SMART classroom curriculum to reduce child and family screen time. Journal of Communication, 56, 1–26. Robinson, T. N., & Sirard, J. R. (2005). Preventing childhood obesity: A solution-oriented research paradigm. American Journal of Preventive Medicine., 28, 194–201. Robinson, T. N., Killen, J. D., Kraemer, H. C., et al. (2003). Dance and reducing television viewing to prevent weight gain in African-American girls: The Stanford GEMS pilot study. Ethnicity and Disease, 13, s65–s77. Schwartz, M. W., Woods, S. C., Seeley, R. J., Barsh, G. S., Baskin, D. G., & Leibel, R. L. (2003). Is the energy homeostasis system inherently biased toward weight gain? Diabetes, 52, 232–238. Summerbell, C. D., Ashton, V., Campbell, K. J., Edmunds, L., Kelly, S., & Waters, E. (2003). Interventions for treating

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obesity in children Art. No. CD001872. DOI: 10.1002/ 14651858.CD001872. Cochrane Database of Systematic Reviews, 3. Summerbell, C. D., Waters, E., Edmunds, L. D., et al. (2005). Interventions for preventing obesity in children Art. No.: CD001871. DOI: 10.1002/14651858.pub2. The Cochrane Database of Systematic Reviews, 4.

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Wadden, T. A., Sternberg, A., Letizia, K. A., et al. (2005). Behavioral treatment of obesity. Psychiatric Clinics of North America., 28, 151–170. Weintraub, D. L., Tirumalai, E. C., Haydel, K. F., et al. (2008). Team sports for overweight children: The Stanford Sports to Prevent Obesity Randomized Trial (SPORT). Archives of Pediatrics and Adolescent Medicine, 162, 232–237.

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C H A P T E R

26 From Diets to Healthy and Pleasurable Everyday Eating Lyne Mongeau Department of Social and Preventive Medicine, University of Montreal, Quebec, Canada

o u t l i n e 26.1 The Diet Zeitgest

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26.2 A New Weight Paradigm

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26.1  The diet Zeitgest Eating is obviously a complex and central behavior to human life, but it is important not to neglect one’s relationship with the body as an influence on eating. To fully understand and effectively intervene in weight- and food-related problems, the issues linked to obesity must of course be examined, but also those associated with the quest for thinness, prevalent in modern societies. This section will outline the origins as well as the various aspects of life underlying the quest for thinness and weight loss. The quest for thinness and the world of diets are not flukes of history. Their origins are numerous and complex. While women’s bodies have often been targeted by transformations and diktats, never in the course of history have

Obesity Prevention: The Role of Brain and Society on Individual Behavior

26.3 The New Paradigm’s Contribution to Solving the Obesity Epidemic 337

women’s bodies been so thin (Hubert, 2004). While not explaining everything (Stearns, 1997; Sobal and Maurer, 1999), there are some fundamental factors that coherently represent the rise of the obsession with thinness and the oppression of overweight (Figure 26.1). In Figure 26.1, the religious precedent represents the base upon which rests three pillars of the rise of the thin ideal: (1) the industrial era and consumer society; (2) fashion and design; and (3) medicine and dietetics. These three factors contributed to the transformation of lifestyles, which progressively became more sedentary and urbanized and were the object of a major process of liberalization in various domains. The obsession with thinness being primarily a female concern, the gender aspect is represented by the horizontal arrow in the

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© 2010, 2010 Elsevier Inc.

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26.  From Diets to Healthy and Pleasurable Eating

Moral question restraint

prejudice

Progressive transformation of lifestyles

The industrial era and consumer society -publicity -media

Fashion and design -nature -nudity -ready-made clothing

Medicine and dietetics

The status of women -motherhood -sexuality

Religious morality

Figure 26.1  Illustration of the elements responsible for the rise of the obsession with thinness and the oppression of overweight. Reproduced from: Côté, D. and Mongeau L., (2005), Le programme Choisir de maigrir? Guide pour les intervenantes et les intervenants. Montreal: ÉquiLibre, Groupe d’action sur le poids. Reprinted and translated with permission.

figure, because it transcends eras and has not yet been resolved. Finally, at the top there is the crux of the issue, represented by the moral question, which is, in turn, manifested on an individual basis by self-control and restraint, and on a collective basis by prejudice. Religious imperatives have regulated life since the beginnings of time, and help to explain both physical representations of the body as well as the relationship with food. Greek wisdom advocated moderation, and Christian revulsion of appetite is stark. Fasting was a highly valued ritual during the Middle Ages, sustained by puritan Protestantism. Gluttony is one of the Seven Deadly Sins, a vice according to others, because people who are gluttonous eat more than is naturally required. Overweight is not so much condemned as is the nature of the relationship with food. In fact, the Bible ignores fat, and saints are usually represented as thin. In spite of this, historians have noted that the theme of self-sacrifice has long been associated with a belief that resisting food is a

sign of sainthood. Even recently, different religious practices pertaining to eating restrictions are still engaged in, such as forbidding children from eating, withholding dessert from them as punishment, doing penance and fasting on certain days of the Catholic calendar (Stearns, 1997; Fischler, 2001). Therefore, for many centuries humans deprived themselves willingly of food, but never with the aim of losing weight. The first pillar (Figure 26.1) is comprised of the far-reaching social transformations brought about by the Industrial Revolution and the market economy. At the beginning of the twentieth century, due to the unprecedented rise in productivity, tensions arose between capitalism’s profit imperative, and the Judaeo-Christian values of frugality and the importance of being parsimonious (Austin, 1999). In this regard, ecclesiasts denounced the new infatuation for consuming, describing it as frivolous and greedy. Gradually, their efforts to keep the faithful on the “straight and narrow” lessened. They encouraged listening to good music and

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26.1  The diet Zeitgest

buying novels. The array of products available for purchase grew, and marketing, until then strictly informational, began appealing to emotions (Stearns, 1997). To dissipate the tension, it was necessary to de-standardize the self-sufficient attitude of pre-Industrial families who would make themselves whatever they needed (Austin, 1999). The increased availability of products coupled with strategies to promote consumption profoundly modified domestic necessities and routines. This was the birth of “good housekeeping” (Ehrenreich and English, 1982). Individuals became attached to the act of purchasing per se (Stearns, 1997), and “shopping” was born. With the assault of electronic media and the influence of advertisements, the consumer world became even more important. Among the suddenly available array of products related to weight and the body we find healthy, “light” and ready-made foods. These products illustrate particularly the resolution of the Judaeo-Christian dilemma, reconciling consumption and abstinence. “Light” food is the perfect example: large quantities, but with fewer calories (Austin, 1999). In addition, manu­ factured products were found to be helpful to women who were now in employment, and thus had less time to prepare meals. Finally, the relationship with the body was also affected by the advent of the consumer era, referring here to the fashion and design pillar. The physical capabilities of the body are less and less needed and valued, as they are replaced by machines. Conversely, the body has become a selling tool in itself, an object of purchasable transformations (Hubert, 2004): slimming products and services, beauty and body objects, plastic surgery, cosmetic treatments and massage services, clothing and other accessories, etc. It represents the decline of the body as subject and the rise of the body as object: it can even be defined the “body project” (Brumberg, 1998). These products represent a significant share of the consumer market, referred to here as the “body industries”. Therefore, industrialization and the

331

new social conditions associated with it significantly influenced the modern relationship individuals have with food, their bodies and their weight. The second pillar represents fashion and design. Feminists often blame fashion as being one of the factors that has oppressed and subordinated women. While fashion certainly justified various harmful practices, such as corsets, feetbinding, the lengthening of the neck, etc., nevertheless, it has never been as destructive as today (Seid, 1994). Three principal elements related to fashion seem to have significantly influenced the rise of the cult of thinness: the corset, nudity, and the standardization of sizes linked to the development of ready-made clothing. While the use of corsets goes back to the dawn of time, the end of the eighteenth and the beginning of the nineteenth century marked the start of their demise. Two major influences encouraged this demise: the return to nature, and the freedom of movement needed to practice certain sports (Histoire de la lingerie, n.d.). While the corset was not very comfortable, it was not so much its use as its disappearance that affected the emergence of restraint eating. The corset compensated for body shapes that were too rounded or not rounded enough. Without the corset, it is the body itself that must respond to beauty ideals. This change imposed constraints of a difference nature on women: to alter the body from the inside. The progressive undressing of the body added other constraints on women’s bodies (Hubert, 2004): the naked body now has to be perfect in itself, because it is exposed to all. In the past, women approached beauty ideals by manipulating clothing and jewelry and modifying what they wore. Today, because of the nudity imposed by fashion, the body itself has become the subject of manipulations (Seid, 1994). The sales of beauty products, hair dyes, antiwrinkle creams, etc., have reached new heights. Physical modifications are no longer limited to transforming the body externally, changing

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body weight or using benign and temporary solutions such as massages, wraps or other such frivolities. It is literally transformed, altered permanently, by removing or adding a little something. Plastic and cosmetic surgery is no longer a medical discipline dedicated to the correction of accidental flaws or birth deformities; it constitutes a medico-cosmetic alternative, the provision of one service among many others in the world of physical transformations, a consumer good. The third element, ready-made clothing, made its appearance in the US in 1870. From then on, clothing was no longer tailored individually; the body had to fit into the clothing (Stearns, 1997). Yet the history of ready-made clothes has more to do with democratizing designer wear, too expensive for the poorer segments of society, than with constraining the body. Neither was the standardization of clothing aimed at encouraging thinness; it was rather a temporal coinci­ dence. Since fat was under attack, the advent of standard sizes and the public context in which people tried clothes (the store versus the home) naturally promoted an awareness of the body. Characteristics of the clothing industry, such as mass production, marketing principles, fashion magazines, etc., molded what we know today of fashion. Women’s clothing is often designed by male designers, to a greater or lesser degree mindful of women’s figures. Interestingly, clothes that hug curves are more difficult to adjust. The medical and dietetic discourse constitutes the final pillar upon which rests the focus on thinness. The causes of obesity are numerous, and our knowledge of them has evolved greatly. Explanations underpin the recommended treatments, and for obesity these alternate between biology and behavior. They rarely escape a certain moral judgment, which shifts back and forth between individual and collective responsibility (Bray, 1990). The quantity, variety and nature of the recommendations found in the literature for losing weight are astounding: encouraging

sweating, using laxatives, controlling passionate impulses and the frequency of sexual intercourse, bathing every 8 hours and rubbing oneself down with flannel, taking cold baths, taking hot baths, ingesting soap, eating only one meal per day – to give but a few examples. In regard to food, recommendations are just as varied and contradictory: drink the least possible/ as much as possible, eat as little/as much meat as possible, fruit-based diets, milk-based diets, chew each mouthful of food 32 times, etc. (Apfeldorfer, 2000). Spread over the course of a few decades, each of these recommendations or treatments benefited from a credible context. During the nineteenth century, public receptivity to such dietetic recommendations was also attributable to a more constricting form of dress, as well as to the emergence of very strict table etiquette for the upper class – etiquette that called for a certain degree of restraint. Therefore, even at that time weight control was popular. To medical and dietetic recommendations were added other weight-controlling products and gadgets. Subsequent to the nineteenth century’s miracle solutions, the twentieth century, through women’s magazines, saw the marketing and promotion of various slimming strategies. For example, “Rengo”, advertised in the Pittsburg Press in 1908, promised the loss of 1 pound per day, playing upon the embarrassment caused by obesity. The slogan urged clients not to wait: “Now – do not wait until you are a disgusting, frightful sight” (Stearns, 1997). Dietetics fads were decried by the medical and scientific communities, which were increasingly worried about related health risks. In addition, lifestyle changes were already manifesting themselves through the increasing inactivity of the population, which soon raised alarm in medical circles. On the other hand, the medicalization of women’s health offered doctors the opportunity to target fat surplus in middle- to upper-class women (Ehrenreich and English, 1982; Kohler Riessman, 1998). The rhetoric argued for the need to develop diets based on

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26.1  The diet Zeitgest

scientific facts, rather than on various tricks and gadgets (Stearns, 1997). In a culture where weight and appearance were growing in importance, weight control soon became the focal point of the new profession of dietitian/nutritionist. Recommendations for better weight control had two goals: health and esthetics (Parham, 1999). Parham (1999) examined the training curriculum of dietitians/ nutritionists during the first century of the profession’s existence. When the profession first began to develop, nutritional knowledge was limited to the role of nutrients responsible for energy provision, but concerns regarding obesity became more and more important. With the aim of influencing health, training focused on the knowledge of food and its related physiological processes; public demand, on the other hand, focused on weight control and maintenance. Restraint eating, self-sacrifice, and tracking calorie intake soon became the center of the dietetic approach, hence the rather pejorative label of “diet police”. In the 1960s and 1970s, with the support of scientific evidence, restraint eating slowly fell out of favor. Major articles reported the ineffectiveness of recommended approaches (Stunkard and McLaren-Hume, 1959; Stunkard and Penick, 1979), and others called into question the very issue of obesity treatment (Wooley et al., 1979; Wooley and Wooley, 1984; Brownell, 1993; Brownell and Rodin, 1994). Paradoxically, this launched a whole new series of dietary recommendations, also based on restraint eating: fasting, very low-calorie diets, surgery, medication and aerobic exercise (Parham, 1999). After a few relative successes, the long-term effectiveness of these various strategies was also deemed to be mixed (NIH, 1993). It was only in 1995 that the Institute of Medicine declared that the success criterion for a weight-loss program was a weight loss of 5 percent of the original weight, maintained for a year (IOM, 1995). Then, in 1998, the National Institutes of Health (NIH) recommended losing 10 percent of the original weight over the course of 6 months (NHLBI,

333

2000). These recommendations constituted a first step in the medical community towards moderation in regard to weight control. While the role of women has been mentioned several times in the description of the three pillars, Figure 26.1 illustrates that issues of gender transcend this history. Are weight-related problems so much the problems of women? While there have been recent reports documenting body image problems in males, weight remains primarily a female issue (Striegel-Moore et al., 2008). While the angle with which feminist ques­ tions are treated varies considerably from one author to another, there is a consensus regarding the issue of gender and the rise of thinness (Seid, 1994; Stearns, 1997). Historically seen as “the weaker sex”, in the nineteenth century, women began to fight for the right to vote, and sought to erase ancient perceptions of their bodies, their supposed weakness and vulnerability, which made them unfit for political power, education or employment (Weitz, 1998). However, the emancipation of women corresponded with the rise of the ideal of thinness (Orbach, 1978; Stearns, 1997). Women entering the workforce, the control of reproduction and sexual liberalization are all factors that contributed to modifying the relationship women had with their bodies. Along with fashion, the consumer era, and the decline in popularity of and investment in motherhood and domesticity, thinness brought back the idea of the fragile body, the body object and the esthetic body (Weitz, 1998). Many feminists do not support this correlational hypothesis, arguing that it was a reaction against the feminist movement, or a conspiracy to bring back women to these more traditional ideas. One of the reactionary elements is precisely the heightened pressure to control one’s appearance, thereby keeping women slaves to their bodies and moving them away from power (Wolf, 1991; Weitz, 1998). The final element of Figure 26.1 pertains to public opinion about weight. It is effectively the cement which binds all the elements together,

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and confers to the explanation its global nature. As mentioned previously, public opinion has traditionally oscillated between acceptance and judgment. While Hippocrates, the father of medicine, was reluctant to discuss the causes of obesity, this was certainly not the case for Galien, who stated: “The hygienic art promises to maintain in good health those who obey it; but to those who are disobedient, it is just as if it did not exist at all” (Bray, 1990: 911). It is a clear indication that Galien perceived obesity as the irrefutable proof of someone’s “inadequacy”. The nineteenth century saw the rise of negative perceptions of obesity and fatness (Stearns, 1997). As an example, the magazine Living Age wrote: “Fat is now regarded as an indiscretion and almost as a crime” (Stearns, 1997: 22). These prejudices soon morphed into a true phobia of fat, a conviction that animal fat, in all its forms – on the body, in the blood, on the plate – is dangerous. Concurrently, Americans began perceiving themselves as too fat, as continuously becoming fatter, eating too much, eating the wrong foods, too sedentary, and therefore flabby. They saw themselves as an ailing group, even if their life expectancy kept improving. In fact, the greatest fear they had was of becoming physically and psychologically “soft”. The most powerful and pernicious aspect of the phobia of fat is that being fat is as shameful as being dirty – as though you could become thin as easily as you could become clean (Seid, 1994). Fat prejudices have been found in every segment of the population, as well as in many groups of professionals (Crandall and Biernat, 1990). Discrimination against fat people exists in the workplace as well as in healthcare ser­vices (Puhl and Heuer, 2009). In their day-to-day lives, obese people are subjected to mockery. They are seen as lazy, stupid and unpleasant (Wadden and Phelan, 2002). If thinness symbolizes success and power, obesity means failure and powerlessness. Obesity therefore constitutes an issue of social power (Breseman et al., 1999). In fact, considering how far our societies

have come in eliminating prejudice, some believe that obesity is its last stronghold (Sobal and Stunkard, 1989; Andreyeva et al., 2008). The main discourse about weight can be summarized in three points: (1) overweight is always unhealthy; (2) overweight is mainly caused by a lack of individual self-control in regard to food and physical activity; and (3) any person who wants to be thin, can be. Self-control is an impor­ tant point because it represents the basis of prejudice and discrimination, as well as the primary argument of the weight-loss industry – i.e., that obesity can be eliminated if the individual takes the appropriate steps (Brownell, 1991; Ritenbaugh, 1991; Brown and Bentley-Condit, 1998). This idea fits well in the puritan values of individualism and individual willpower. In fact, according to Seid (1994), this belief system has fostered prejudice, and has turned the quest for thinness into almost a religion. Therefore, the fear of overweight feeds the desire for thinness. But how can we hold such negative opinions of our fellow human beings, who simply possess a different physical appearance? How can esthetic norms lead us to such extremes? A passing dietetic trend might have been accompanied by particular fashion requirements, but the effects there would have been short term. According to Stearns (1997), the new anxiety with regard to weight is not only an issue concerning health or even appearance; it has also deeply affected American conscience, to the point that the obsession continues to grow. Deepseated causes must have justified this truly moral condemnation. It is in self-control research, in the equation between laziness and obesity and in the disgust with people who cannot respect the values of self-control, individual will and effort that one finds the last link, the crux of the issue. Stearns (1997) hypothesizes that as liberalization spread through various spheres of life – sexuality, consumption, hobbies – feelings of guilt increased, and had to be contained by the establishment of new restraints. Physical and dietary discipline gave the impression that one was compensating

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26.2  A new weight paradigm

for the indulgence linked to other pleasures, was paying lip service to religious morality. Fischler (2001: 391) summarizes this idea well: “It is tempting to see in today’s diets a laicized version of yesterday’s fasts. Thinness has appeared to be a modern form of sainthood that can only be attained through restriction”. Therefore, the moral stance is manifested by a series of personal selfconstraints which allow for inner peace, followed by powerful moral judgment of others, to express one’s disapproval of hedonism.

26.2  A new weight paradigm Obesity is not a simple pathology that occurs in the presence of an easily definable condition. On the contrary, we gain weight for a multitude of reasons, and, less well-known, as individuals attempt to lose weight through various strategies, the problem becomes more complicated because physiological, behavioral and psychological homeostasis is disrupted. The problem’s recurrence is spectacular, and, after over half-acentury of serious efforts to find a way to attain successful and sustained weight loss, results remain mixed (Ayyad and Anderson, 2000; Jeffery et al., 2000; Anderson et al., 2001; Curioni and Lourenco, 2005; Shaw et al., 2005; Tsai and Wadden, 2005; Franz et al., 2007). Among researchers and practitioners, this failure has led to a deep questioning of obesity treatments and has even raised doubts regarding the need to lose weight (Wooley et al., 1979; Wooley and Wooley, 1984; Garner and Wooley, 1991; Brownell, 1993; Brownell and Rodin, 1994). This is one of the motivations behind developing a new vision of weight loss, and constitutes the first assumption of the new paradigm:

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(1) Weight-loss methods are ineffective. The two others are: (2) Weight loss methods are unhealthy; and (3) Commonly-held beliefs, past and present, about the causes and consequences of obesity are incorrect (Mongeau, 2005). The same line of questioning also emerged within the feminist movement (Stearns, 2004) and fed these assumptions. In the 1970s, various workshops led women to reflect upon the obsession with physical appearance, weight and food (Orbach, 1978; Hirschmann and Munter, 1988). The feminist movement evolved in two ways with regard to weight. First, they fed into a movement for the protection of the rights of overweight/obese people and the more general acceptance trend. Rights organizations, such as the National Association to Advance Fat Acceptance (NAAFA) (formerly the National Association to Aid Fat Americans), founded in 1969, fights primarily against inequalities, stigmatization and discrimination against overweight/ obese people, and aims at creating a society in which obese people can live with dignity and equality (NAAFA, 2008). The second evolutionary trend that adopted the feminist movement was to develop an intervention approach, focused on individual wellbeing, which aimed at rehabilitating the relationship between food and body image. Participants were asked to reconnect with hunger and satiety signals, as well as to relearn to respect their tastes, while eliminating restrictive eating. It is effectively giving oneself back the right to eat (Hirschmann and Munter, 1988). Between 1975 and today, other programs have been developed around the world.1 This trend was strongly driven by the development of restraint-eating theory, pioneered by Herman and Polivy (Herman and Mack, 1975; Herman and Polivy, 1984). This theory stipulated

1 

Bacon et al., 2002; Rapoport et al., 2000; Diet Free Forever (Steinhardt et al., 1999); Sbrocco et al., 1999; Undieting (Hetherington and Davies, 1998); Goodrick et al., 1998; Tanco et al., 1998; Beyond Dieting (Ciliska, 1990); The Solution Method (Mellin et al., 1997); If Only I Were Thin (Robinson and Bacon, 1996); Overcoming Overeating (Steinhardt and Nagel, 1995); HUGS, a Non-Diet Lifestyle Program (Omichinski and Harrison, 1995); Eat for Life (Carrier et al., 1994); Undieting (Polivy and Herman, 1992); Roughan et al., 1990; McNamara, 1989; What About Losing Weight? (Mongeau, 2005).

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that individuals in a state of cognitive restraint impose upon themselves rigid limits to regulate their dietary intakes, which are determined by rules and beliefs regarding “good” foods and the permitted quantities. Removed from the restraint framework, they are completely destabilized and become incapable of managing their dietary intake. They then eat without control until they feel ill (Herman and Polivy, 1984). Hence, cognitive restraint is about replacing a dietary behavior regulated by outside criteria with dietary behaviors planned and determined according to cognitive criteria, or with dietary behaviors modeled to specific diets (Herman and Polivy, 1980). Regarding the use of weight-loss products and methods, their consequences on health depend upon the length, nature, method and scope of the self-imposed restriction (Gregg and Williamson, 2002). They can be short term and minor but, with severe dietary restrictions and/ or frequent use, consequences may be major: cardiac arrhythmia, electrolytic imbalances (Gregg and Williamson, 2002), biliary calculations (Liddle et al., 1989; Weinsier et al., 1995), and loss of bone density (Langlois et al., 2001; Ensrud et al., 2003). In addition, many of the natural weight-loss products contain ingredients that may be toxic and/or may lead to serious health problems, some fatal (INSPQ, 2008). In a recent scientific report, the Institut national de santé publique du Québec questioned the safety and utility of many weight-loss products and methods and, given the vulnerability of individuals excessively concerned by their weight, called into doubt the capacity of this industry to adequately protect the health of users, especially in the current social context which idealizes thinness (INSPQ, 2008).

While some benefits of successful weight loss have been recorded (INSPQ, 2008), they only last as long as the weight loss is maintained. This weight maintenance has been shown to be very difficult (Wing and Phelan, 2005). Therefore, faced with the risks related to weight loss and the odds against maintaining the loss, what is the net benefit? This is where the paradigm’s second assumption, that weight loss is unhealthy, comes in. Finally, the last assumption refers to the discourse on the causes and consequences of obesity. The explosion of media articles and reports on the obesity epidemic that summarize research results without qualifying them contributes to the third assumption. As for the consequences of obesity, there is a clear link between real obesity and various illnesses, but the link between obesity and mortality as well as the consequences of overweight are not so clear (McGee, 2005; Romero-Corral et al., 2006; Flegal et al., 2007). Numerous factors enter the causal equation between weight and health or longevity, which are not always taken into account or well understood. Several research results suggest that, independent of weight, individuals with a good cardio-respiratory condition present a reduced morbidity and mortality compared to unfit individuals (Ross and Katzmarzyk, 2003; LaMonte and Blair, 2006). Considering the diverse points presented above, the new paradigm promotes “Health-atEvery-Size” (HAES), and represents a new way of conceiving weight problems. According to the definition of this paradigm,2 the vision that emerges completely breaks with the former approach. Based on the premise that it is best to improve one’s health and to honor one’s body, the new paradigm supports individuals in the adoption of new lifestyle habits to improve their

2 

According to Kuhn (1972), a paradigm is the system of beliefs, recognized values and techniques common to a particular scientific community. It consists of a framework of assumptions which defines what a problem is, a solution, and a method which molds and guides the researchers’ work. Guided by a new paradigm, researchers adopt new instruments and look to new directions. More importantly still, during scientific revolutions researchers discover new and different things, even though they are studying previously examined questions with familiar tools.

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26.3  The new paradigm’s contribution to solving the obesity epidemic

health and wellbeing rather than encouraging weight loss at any cost. The paradigm encourages the acceptance and respect of the natural diversity of physical forms, suggests eating in a flexible manner, responding to hunger and satiety signals, finding pleasure in an active body, and becoming more physically active (Bacon, 2008). Globally, the new paradigm allows indi­viduals to potentialize their health independently of their weight, which is difficult to change. The process is based on the exploration, knowledge and understanding of all facets of the weight problem. By focusing on self-esteem and acceptance, intervention empowers individuals to change what can be changed, and to accept what cannot be changed (Berg, 1999). The success of this process is measured more by the accomplishment of personal goals and the evaluation of wellbeing rather than exclusively in terms of the weight lost. Since the approach is non-directive and focuses on empowerment, it allows participants to learn how to take charge of their health; health practitioners are then partners or facilitators in the process. Different programs based on this paradigm have been evaluated. They tend to improve the relationship with food, body image, selfesteem and self-efficacy, and to reduce rates of depression and emotional eating (Ciliska, 1990; McFarlane, 1999; Miller and Jacob, 2001; Foster and McGuckin, 2002; Bacon et al., 2005; Mongeau, 2005; Provencher et al., 2007, 2009). These results occur in the absence of significant weight loss, and therefore cannot be attributed to this. Therefore, these results suggest that the changes are the fruit of the active principles of this approach.

26.3  The new paradigm’s contribution to solving the obesity epidemic To halt the obesity epidemic, there exists an important consensus on the necessity of adopting

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a mixed approach, composed of macrosocial and individual measures (James, 1995; Swinburn et al., 1999; Nestle and Jacobson, 2000; French et al., 2001; Kumaniyka et al., 2002). In the case of public health, the World Health Organization (2000) recommends the development of an obesity management strategy that covers a complete continuum of actions, prioritizing the following points in particular: 1. Actions that influence the weight of the entire population and aim to modify social, cultural, political and physical elements of the environment 2. Actions targeted to those individuals with weight-related problems and that aim at: • weight maintenance • the treatment of complications • weight loss. Can we conceive of possibilities whereby human individuals can pull themselves out of the great dependence on external solutions to find a path which fits them and will allow them to re-establish control over their life and health? In what way is this new paradigm a guide on the path towards a better balance between a healthy weight and a pleasurable way of life? What can this new paradigm bring to the fight against obesity? Psychology theory has recently undergone a paradigm shift (Bandura, 2001). From a rather mechanized vision, the conception of human behavior has evolved towards a vision of individuals as control agents of their own life (Baranowski et al., 2002). A high level of functional consciousness implies a deliberate manipulation of information with the aim of selecting, building, regulating and evaluating the course of actions. This is actualized by an intentional mobilization and a productive use of a semantic and pragmatic process of activities, goals and other future events. Therefore, building upon this vision of behavioral self-determination, the intervention model proposed by the new paradigm integrates the fundamental elements

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of awareness, self-control, and enlightened analysis of information. In addition, elements from the feminist approach and the theory of empowerment favor the convergence towards key concepts of self-confidence (self-esteem and self-efficacy). It is by considering the principles and values upon which public health actions are based (Donnan, 2001; MSSS, 2003) that we may hope to reconcile public health strategies and the new paradigm. In this regard, it is interesting to recall these principles. The Programme national de santé publique du Québec 2003–2012 (MSSS, 2003) presents the ethical values and principles guiding the practice of public health. First, promoting the common good is a value that occupies a central place in public health. Health, well­being, safety, and a healthy environment represent especially targeted common goods. The promotion of the common good involves defending the long-term interest of a population which is sometimes in the grip of individual and temporary preferences. Charity and no-harm are two ethical principles that cannot be ignored, and which enlighten the concept of common good. Charity requires that the benefits of an intervention be evaluated in relation to the harm it may cause. It highlights the importance of protecting the population against the misdeeds, iatrogenic or other, that may flow from certain interventions or inaction. Finally, they reflect, particularly, principles of another nature: doubt and precaution. Doubt invites practitioners periodically to question evidence, even solid evidence, in the planning of interventions. The principle of precaution guards against inaction when health risks are not well known or well characterized or, again, when the knowledge underpinning interventions is incomplete, whereas the risks are serious and irreversible. If charity and precaution invite action, no-harm and doubt invite prudence. Even though the new paradigm was not developed in response to a change of vision in public health intervention, but with regard to

individual-level intervention, the convergence between these assumptions and the values they underpin, and the principles and values of public health, is striking. This fact opens the door to concerted action between public health actors and supporters of the new weight paradigm. The cooperation between these two groups of actors could favor, for instance, respect of the psychosocial dimensions of the issue, core components of the new paradigm. Individuals’ autonomy being a common principle of both groups of actors, a favored educational strategy could be based on the empowerment of individuals and on social support. The Governmental Action Plan on the promotion of healthy lifestyle habits and the prevention of weight-related problems 2006–2012, Investir pour l’avenir (Investing for the Future; MSSS, 2006), developed in Quebec, Canada, is an illustration of the convergence between a public health strategy and the new paradigm. The latter made its appearance among nutrition professionals in Quebec in the 1980s, and has gradually inscribed itself into the practice of a significant number of them. Twenty years later, some of these professionals, now public health practitioners, have proposed a vision of public health that incorporates many elements of this paradigm (ASPQ, 2003, 2005). Within the Action Plan, we find specific elements from this mix of visions. For example, the use of the term “weight-related problems” rather than “obesity” takes into account both sides of the problem: overweight, and an excessive preoccupation with weight. Respect for different body shapes is one of the objectives of the Action Plan. Many actions aim at better controlling the weight-loss industry – a protection measure along the same lines as the control measures imposed on infectious agents in the environment. Finally, the plan also warns against a perverse consequence: we must not foster a population-wide weight psychosis and worsen the obsession regarding food. Similarly, the plan warns against increasing stigmatization.

1.  From Brain to Behavior

References

What, then, is the best approach to fighting obesity efficiently, while maintaining the pleasures of eating? Certainly not restrictive diets that, in addition to removing pleasure, pose risks to health and wellbeing, without offering a high probability of weight mainten­ance. Interventions based on the new paradigm, which permits the reconciliation between the body and food, activity and fun, coupled with societal interventions which can make healthy choices easy and unhealthy choices more difficult (Milio, 1986), offer the best hope.

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Wadden, T. A., & Phelan, S. (2002). Behavioral assessment of the obese patient. In T. A. Wadden & A. J. Stunkard (Eds.), Handbook of obesity treatment (pp. 186–226). New York, NY: Guilford Press. Weinsier, R. L., Wilson, L. J., & Lee, J. (1995). Medically safe rate of weight loss for the treatment of obesity: A guideline based on risk of gallstone formation. American Journal of Medecine, 98(2), 115–117. Weitz, R. (1998). A history of women’s bodies. In R. Weitz (Ed.), The politics of women’s bodies (pp. 3–11). New York, NY: Oxford University Press. Wing, R. R., & Phelan, S. (2005). Long-term weight loss maintenance. American Journal of Clinical Nutrition, 82(Suppl. 1), 222S–225S. Wolf, N. (1991). The beauty myth. New York, NY: Anchor Books. Wooley, S. C., & Wooley, O. W. (1984). Should obesity be treated at all? In A. J. Stunkard & E. Stellar (Eds.), Eating and its disorders (pp. 185–192). New York, NY: Raven Press. Wooley, S. C., Wooley, O. W., & Dyrenforth, S. R. (1979). Theoretical, practical and social issues in behavioural treatments of obesity. Journal of Applied Behavior Analysis, 12, 3–25. World Health Organization. (2000). Obesity, preventing and managing the global epidemic. Report of a WHO consultation WHO Technical Report Series No. 894. Geneva: World Health Organization.

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C H A P T E R

27 Resisting Temptations: How Food-Related Control Abilities can be Strengthened through Implementation Intentions Christine Stich1, Philip J. Johnson2 and Bärbel Knäuper2

1

Population Health, Prevention and Screening Unit, Cancer Care Ontario, Toronto, Canada 2 Department of Psychology, McGill University, Montreal, Canada

o u t l i n e 27.1 Introduction 27.2 The Motivational Nature of Food 27.2.1  The Rewarding Effects of Food 27.2.2 The Rewarding Effects of Food and Attention 27.2.3 The Rewarding Effects of Food and Eating Habits

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27.1  Introduction The past decade has witnessed a rise in the investigation of self-regulation, in particular in regard to the study of the processes underlying self-regulation failure and success in eating and weight control. Successful behavioral

Obesity Prevention: The Role of Brain and Society on Individual Behavior

27.3 Food-Related Control Abilities 27.3.1 Attention and Inhibitory Behavioral Control 27.3.2  Implementation Intentions 27.3.3 Implementation Intentions and Eating Behavior 27.3.4 Replacing Unhealthy Habits Through Implementation Intentions

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control of eating involves the inhibition of the impulse to eat, particularly if it conflicts with long-term goals, such as eating more healthily or losing weight (Baumeister et al., 2007). If an individual lacks the inhibitory control necessary to resist temptation, he or she will succumb to it (Westling et al., 2006). Lapses in self-regulation

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are thus an indication of a breakdown in inhibitory control. Some researchers suggest that, in the domain of eating behavior, being overweight and obese are linked to a lack of inhibitory control, and, moreover, this lack might have crucial consequences for the development, maintenance and treatment of obesity in children and adults (see, for example, Nederkoorn et al., 2006a, 2006b, 2007). For example, using a stop-signal task to assess inhibitory control in children, Nederkoorn and colleagues (2006a) found that obese children undergoing a weight-loss treatment showed less inhibitory control than normal-weight children. They also demonstrated that less successful children in response inhibition lost less weight in the program (Nederkoorn et al., 2006a). The researchers also compared obese women with normal-weight women and found that the for­ mer were less likely to demonstrate inhibitory control (Nederkoorn et al., 2006b). Like inhibition control, attention control processes are necessary for successful self-regulation (Mischel et al., 1989; Mischel and Ayduk, 2004; Rueda et al., 2005). For example, in their classic work on delayed gratification in children, Mischel and colleagues (Mischel and Ebbesen, 1970) showed that when 4-year-olds were provided with rewarding stimuli (cookies), attention paid to the rewards substantially decreased their ability to delay gratification – to wait for the reward. If not exposed to rewards, children waited on average over 11 minutes; however, if exposed to rewards, they waited less than 6 minutes. The ability to delay gratification has important implications, not only for successful self-regulation with food, but also in other domains, such as cognitive, academic, and social competences. Mischel’s delay of gratification paradigm in children has been shown to be predictive of various indices of self-regulation later in life. For instance, 4-year-old children willing to wait longer before receiving their reward were over 10 years later described by their parents as adolescents who were more academically and socially competent than their peers, more able to tolerate frustration and cope maturely with stress,

and better able to resist temptation. Moreover, the time that 4-­year-old children were willing to wait before receiving the reward was shown to be significantly related to their verbal and quantitative scores on the Scholastic Aptitude Test (Mischel et al., 1989). It should be noted that, depending on the rewarding effects of stimuli (such as cookies in Mischel’s paradigm), some stimuli are more attractive than others, thus producing an attentional bias (Waters et al., 2003). Consequently, when addressing people’s attention and inhibitory control abilities, it is also important to take into account the rewarding effects of the stimuli, as these will determine the degree to which such stimuli tax individuals’ attention and inhibitory control abilities. We first review the motivational nature of food, and in particular high-fat/high-sugar food, and its consequences for attention to food and the development of (maladaptive) eating habits. We then discuss how certain self-control strategies might help people control their food intake. We focus here particularly on implementation intentions, and speculate how better planning with implementation intentions could help people replace unhealthy eating habits with healthier ones.

27.2  The motivational nature of food 27.2.1  The rewarding effects of food In a relatively new theoretical approach to understanding food intake, Berridge (1996) suggests that “wanting” food and “liking” food are possibly two separate components affecting food intake, as they have distinct underlying brain substrates. In other words, although liking food is usually associated with wanting food, the two concepts can also be separated (Berridge, 1996; Berridge and Robinson, 2003). For example, the sight of a cake in a bakery can draw in an individual, causing an intense desire

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(“want”) for the cake, even if that individual does not necessarily expect to “like” the cake very much (Winkielman and Berridge, 2003). According to Berridge (1996), “liking” refers to the hedonic preference for food, to its perceived pleasantness or palatability, and thus represents an affective component of food. In contrast, “wanting” food refers to the incentive or reward associated with food, and corresponds more closely to appetite or craving. As such, wanting represents a motivational component of eating (Berridge, 1996). At the same time, even though food intake is influenced by both liking and wanting, sensory information such as the appearance, smell and taste of food is transformed into attractive and desired incentives that motivate food intake through the attribution of rewarding effects, which are guided by associative learning (Berridge and Robinson, 2003). Hence, once a specific food item is associated with rewarding effects, the mere perception of its attributes, such as appearance or smell, can motivate eating (e.g., Weingarten and Elston, 1990; Fedoroff et al., 2003; Tiggemann and Kemps, 2005).

27.2.2  The rewarding effects of food and attention As noted earlier, research has shown that stimuli with rewarding effects can seize an individual’s attention (Mischel and Ayduk, 2004), and therefore people may develop an attentional bias or hyper-vigilance toward such stimuli (Waters et al., 2003). It has been found for a wide range of stimuli, such as smoke-related stimuli in smokers (e.g., Sharma et al., 2001; Waters et al., 2003), alcohol stimuli in alcoholics (e.g., Sharma et al., 2001; Noël et al., 2007) and mood-congruent stimuli in manic and depressed individuals (Murphy et al., 1999). With respect to clinical populations, attentional bias for food stimuli has been found in both individuals with anorexia and those with bulimia nervosa (for review, see Faunce, 2002).

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Such a bias has also been demonstrated in individuals without an eating pathology. For instance, using a cued-target, covert attention paradigm, Leland and Pineda (2006; Study 1) found an attentional bias for food-related words in non-clinical normal-weight participants. In this study, participants were presented with food-related and neutral cue words. By appearing in the same or opposite hemifield, these cues served as either valid (75 percent) or invalid (25 percent) predictors of target (rectangles) location. Supporting the hypothesis that visual selective attention is biased by food-related stimuli, results showed that the effect of valid cues was larger for trials in which food words were cues than for trials in which neutral words were cues. Looking further to the motivational salience of food stimuli, we recently found an attentional bias for visual food stimuli. Using a version of a go/no-go task to investigate inhibitory control for food stimuli, we found that participants paid more attention to and showed a higher decision bias for food rather than for control stimuli. Specifically, participants responded faster to food pictures than to control pictures (landscapes), and tended to respond more to food than control pictures (Stich et al., 2008). The question then arises as to whether specific types of food possess particular rewarding effects. As mentioned earlier, the rewarding effects of food (i.e., wanting) have been shown to have different underlying brain substrates than its palatability (i.e., liking). The two processes, however, are most likely interrelated when it comes to food intake (Berridge, 1996). Hence, individuals want to eat food they find palatable (e.g., Epstein et al., 2003). Moreover, foods that taste good (i.e., are more palatable) are often those high in fat and/or sugar. In contrast, foods that are low in fat and/or sugar are often perceived as unpalatable (Drewnowski, 1998). Research has confirmed that, when given the choice between energy-dense snack foods and fruits and vegetables, most individuals choose

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snack foods (e.g., Goldfield and Epstein, 2002). Such findings suggest that people have a preference for palatable, energy-dense foods, and therefore they might show a higher attentional bias toward energy-dense foods. However, to our knowledge, research has yet to explore the relationship between types of food in terms of their palatability and energy density, and attentional bias.

27.2.3  The rewarding effects of food and eating habits The aforementioned findings of attentional bias to food stimuli are especially important, as food stimuli can serve as cues to activate well-practiced routines or habits (e.g., Sayette, 1999; Verplanken and Wood, 2006). In general, people acquire habits slowly, based on the covariation between features of contextual cues and responses that they repeatedly experience (Wood and Neal, 2007). Habits are mediated by memory representations of the cue–response link, which operate automatically (Tiffany, 1990), without intention, effort or conscious awareness (Shiffrin and Schneider, 1977). This way, once a habit is formed, the behavioral response is merely triggered by the perception of the cues. Because responses to contextual cues can occur intentionally or unintentionally, habits can be formed without being mediated by goals (Wood and Neal, 2007). One way in which habits can be formed is in contexts in which people repeatedly experience rewards for a specific response (e.g., Neal et al., 2006; Wood and Neal, 2007). For example, when regularly eating chips in front of the TV, this behavior can develop into a habit because eating in itself is rewarding. Over time and with sufficient repetition, a person will automatically associate TV-watching with eating chips, even if these are not available. Illustrating the automaticity associated

with habits, laboratory research and real-world studies have repeatedly demonstrated the facilitating effect of contextual cues on speed and acc­uracy of responses (for an overview, see Wood and Neal, 2007). The self-regulatory process of permanently over-riding or inhibiting habitual and automatic rewarding responses requires conscious effort (Baumeister et al., 1994). Thus, when a goal (e.g., losing weight) conflicts with a habitual response (e.g., eating chips in front of the TV), a person’s self-regulatory capacities will determine whether the goal or the habitual response will prevail. The undesirable habit will be inhibited only if sufficient self-regulatory capacities are available (Wood and Neal, 2007). Overall, research suggests that if foods associated with high rewarding effects are readily available, they will seize individuals’ attention. Food can activate eating habits via its appearance, smell and taste, as well as through the context in which its consumption frequently occurs. The rewarding effects of food in combination with eating habits might lead to pre-potent approach tendencies that make inhibitory control challenging. Exerting control to inhibit attention and habitual eating responses depends on the availability of self-regulatory capacities. Uninhibited, and in combination with a sedentary lifestyle, such pre-potent approach tendencies may contribute to the overconsumption of foods and, ultimately, to overweight and obesity, especially when the food consumed is energy dense.

27.3  Food-related control abilities 27.3.1  Attention and inhibitory behavioral control The task of controlling habitual food-related responses involves (1) attention control (Mischel

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and Ayduk, 2004) and (2) inhibitory control (Nederkoorn et al., 2006a). Attention control refers to the ability to keep oneself from getting distracted by attention-grabbing, goal-irrelevant stimuli, and to focus cognitive resources on goal-relevant behavior (e.g., avoiding fatty foods or sweet desserts and eating vegetables instead; Gollwitzer and Sheeran, 2006). As illustrated by ample research findings, failing to control attention, and therefore to inhibit the distraction through tempting stimuli, can greatly undermine goal pursuit (e.g., Kuhl, 1981; Gollwitzer and Schaal, 1998; Gollwitzer and Sheeran, 2006; also see Mischel and Ayduk, 2004). Inhibitory control is needed to inhibit or stop the execution of behavioral responses once a pre-potent or habitual behavior is initiated (e.g., Logan et al., 1997; Gollwitzer and Sheeran, 2006). In contexts where an undesired behavior can be avoided completely, inhibitory control refers to the inhibition of behavior in response to stimuli. For example, in the context of smoking cessation, it can mean avoiding stores where cigarettes are sold, not buying a package of cigarettes when seeing it in the store, or even inhibiting the act of lighting a cigarette, in order to avoid the occurrence of the undesired behavior (i.e., smoking). Inhibitory control with food, however, is more challenging, because individuals need to eat on a regular basis. Thus, instead of simply avoiding any behavior related to the tempting stimuli, people rather need to inhibit the consumption and overconsumption of particular types of food (e.g., energy-dense foods). In sum, to successfully abstain from giving in to eating temptations, both attention control and inhibitory behavioral control are necessary. Particularly in a world where food stimuli with significant rewarding effects are readily available, successful behavioral control entails the inhibition of consumptive responses to appetitive food stimuli (attention control), as well as the inhibition of behavioral impulses (inhibitory control) that would lead to an overconsumption of calories.

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27.3.2  Implementation intentions As mentioned, engaging in self-regulatory control processes such as attention and inhibitory behavioral control requires a significant amount of self-regulatory effort (e.g., Baumeister et al., 1994; Wood and Neal, 2007). For these control efforts to be successful over time, their activation needs to be transformed from a non-automatic, conscious and effortful process into an automatic response (Mischel and Ayduk, 2004). The conversion of effortful self-regulatory processes into automatic ones lies at the center of Gollwitzer’s research on implementation intentions (Gollwitzer, 1993, 1996, 1999). Research has shown that by forming implementation intentions, people can avoid giving in to temptations elicited by food. Implementation intentions are volitional strategies that can be used to translate behavioral intentions into actual behavior. While behavioral intentions (e.g., “I intend to eat a low-fat diet”) express people’s motivation to engage in a certain behavior, implementation intentions specify the steps to be followed in order to translate intentions into behavior. They do this by specifying the conditions, such as the critical environment or context, under which a target behavior will be performed. Implementation intentions are concrete if the plan of action specifies when, where and how one will perform a behavior in order to achieve a specific goal: “If situation Y arises, then I will perform goal-directed response Z!” (Gollwitzer, 1993, 1999; for review, see Gollwitzer and Sheeran, 2006). Forming implementation intentions is effective because it is a conscious commitment to perform a goal-directed behavior (ignore) when encountering certain critical cues (dessert; Gollwitzer, 1999). This commitment creates a strong mental link between the critical situation/ cue and one’s intended behavior. Thus, instead of relying solely on motivation and willpower, implementation intentions allow individuals to

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partly delegate control of their behavior to the situation and, as a result, the intended behavior is carried out quasi-automatically (Gollwitzer, 1999; Gollwitzer and Sheeran, 2006). Research has shown that specifically forming “if–then” links helps to partly delegate control of behavior to critical cues in the situation, thereby making action initiation immediate, efficient, and absent of conscious intent (e.g., Gollwitzer and Brandstätter, 1997; Aarts and Dijksterhuis, 2000; Brandstätter et al., 2001; Webb and Sheeran, 2004). Successfully establishing this “if–then” plan assists in activating effective self-­regulatory behavior, even under stressful or cognitively demanding situations (Gollwitzer, 1999; Gollwitzer and Sheeran, 2006). The formation of implementation intentions has been shown to be impressively effective. In a recent meta-analysis of effects of implementation intentions, Gollwitzer and Sheeran (2006) performed 94 independent tests, and found that implementation intentions had a positive effect of medium-to-large magnitude (d  0.65) on goal attainment. For health-related behaviors, a medium effect size was found. Particularly in the area of eating behaviors, research has shown that implementation intentions are effective in increasing the consumption of fruits and vegetables (Kellar and Abraham, 2005; Armitage, 2007), reducing fat intake (Armitage, 2004; Luszczynska et al., 2007a) and increasing healthy eating (Verplanken and Faes, 1999). More recently, implementation intentions have been found to enhance weight reduction among overweight and obese people (Luszczynska et al., 2007b). Gollwitzer and Sheeran (2006) observed implementation intentions to be effective in every step of goal pursuit. Namely, they are effective in promoting the initiation of goalstriving, shielding ongoing goal pursuits from unwanted influences, disengaging from failing courses of action, and, finally, conserving capability for future goal-striving. Such effectiveness is even more remarkable considering the sheer simplicity of the implementation intention

instructions employed in these studies, and that they are usually administered just once in standard implementation intention experiments. Finally, the effects of implementation intentions have been shown to be relatively enduring. For instance, they were still present after 48 hours between implementation intention formation and cue encounter (see Gollwitzer, 1999). In intervention studies, they have been found to be effective in promoting health behavior over a period of up to 6 months (Luszczynska, 2006). Furthermore, Sheeran and Orbell (1999) suggested that once the behavior initiated through implementation intentions has been performed regularly over a longer period of time (e.g., 3 weeks), it becomes habitual in nature. Thus, the effects of implementation intentions should not diminish greatly over longer periods of time. This, however, is an empirical question, and further studies are needed to test the long-term effects of implementation intentions on the maintenance of health behaviors (Luszczynska, 2006).

27.3.3  Implementation intentions and eating behavior With respect to eating behavior, while many people initiate dieting behaviors, the majority of diets are brief and unsuccessful (Stotland et al., 1991; Jeffery et al., 2000). The cardinal problem in this area is not goal initiation, but goal completion. As noted previously, people are derailed, in part, because of lack of attention and/or inhibitory behavioral control in the face of incentive food or food cues. Thus, to attain weight loss or healthy eating goals, people’s self-regulatory task is to shield their goal-striving from unwanted influences. Research by Gollwitzer and colleagues suggests that implementation intentions can prevent “derailment” and thus protect goalstriving from unwanted influences by suppressing (1) unwanted attention responses (e.g., “If I see something delicious, but unhealthy on the menu, then I will ignore it”) and (2) unwanted

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behavioral responses (e.g., “If I am tempted to eat a delicious, but energy-dense dessert, I will not eat it”; Gollwitzer and Sheeran, 2006). In Gollwitzer and Sheeran’s (2006) meta-analysis, all studies on the inhibition of unwanted attention responses and unwanted behavioral responses showed strong beneficial effects of implementation intentions. Forming implementation intentions to suppress unwanted attention toward food should prevent the individual from paying attention (the original behavioral response) to appetitive but unhealthy stimuli (i.e., the cue; Gollwitzer and Sheeran, 2006), and thus should increase attention control. More precisely, depending on the situation and on the individual’s personal eating goals, suppressing unwanted attention by forming implementation intentions should allow the individual to (1) ignore (new behavioral response) food (cue) or, when eating is appropriate, to (2) focus on healthy food alternatives or limit portion sizes (new behavioral responses). Looking at the effectiveness of implementation intentions in shielding goalstriving from distraction in eating behavior, Achtziger and colleagues (2008) posited that implementation intentions help people ignore thoughts about high-fat snacks that they are craving, resulting in better diet adherence. As illustrated earlier, it may not always be sufficient to inhibit unwanted attention responses to food in order to avoid eating, or eating too much. It is often also necessary to inhibit unwanted behavioral responses (see also Gollwitzer and Sheeran, 2006). Forming implementation intentions to inhibit unwanted behavioral responses should increase inhibitory behavioral control, and should also prevent unhealthy eating behaviors. Again, depending on the individual’s personal goals, it should allow that individual to avoid certain food or to eat less in general. Hence, implementation intentions could be used to replace the original behavioral responses to specific food cues (e.g., eating) with new, healthy and intended responses (e.g.,

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eating fruits instead of high-caloric snacks). Thus, the same attention-grabbing food cues that, in the absence of implementation intentions, would lead to self-regulatory failure could also be used as critical cues when forming implementation intentions. Future research needs to investigate in which format implementation intention-based interventions aimed at inhibiting unwanted attention and behavioral responses to food should be administered in the general population, in order to effectively promote healthy eating and weight loss, and to prevent obesity.

27.3.4  Replacing unhealthy habits through implementation intentions The automatic activation of habitual respon­ ses is key to habit persistence. After all, behavior modification approaches have long recognized that habits initiated by cues can be used for intervention. These approaches change undesired behavior, such as addictions, by altering the very contexts in which such maladaptive behaviors occur. Furthermore, people are typically encouraged to limit their exposure to critical cues with the potential to activate undesired habits. However, such attempts to avoid critical-cue exposure might require a significant amount of self-regulatory effort (Wood and Neal, 2007). Implementation intentions could be used to replace habitual behavioral responses to specific cues (e.g., eating chips when watching TV) with new, healthy and intended responses (e.g., ignoring the chips, eating healthy snacks instead) (cf. Verplanken, 2005). While the cues remain the same, when paired with an appropriate goal-directed response they serve to activate effective self-regulatory behavior. Like habits, implementation intentions allow individuals to partly delegate control over their behavior to the situation. No conscious effort is needed, and the behavior is carried out

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quasi-automatically when the relevant cue is encountered (Gollwitzer, 1999). Implementation intentions and habits bear important similarities (Sheeran et al., 2005). For instance, in both situations the cue is linked to the activation of behavior; once the cue is encountered, the behavior is activated without conscious effort (Verplanken and Faes, 1999). However, unlike habits, which are acquired slowly through repeated covariation of cue and response, implementation intentions are formed consciously and instantaneously (Gollwitzer, 1999; Sheeran et al., 2005; Verplanken and Wood, 2006). Because implementation intentions are associated with the automatic initiation of action, they can create what Gollwitzer (1999) calls “instant habits”, which can result in habitual behavior over time (Gollwitzer and Schaal, 1998). Future research needs to show whether, similar to habit formation, the rehearsal of implementation intentions increases the strength of cue–response link – whether they can slowly be turned into habits. If so, rehearsing implementation intentions would enhance their impact on behavioral responses (Sheeran et al., 2005). Since implementation intentions and habits share similar features, it seems plausible that, with sufficient repetition, the cue–response association formed initially through implementation intentions could help people form new, healthier habits (Verplanken, 2005; Verplanken and Wood, 2006). In sum, forming implementation intentions to inhibit unwanted attention and behavioral responses to food cues could be an important step in changing people’s unhealthy eating habits.

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C H A P T E R

28 The Dieter’s Dilemma: Identifying When and How to Control Consumption Ayelet Fishbach1 and Kristian Ove R. Myrseth2 1

Booth School of Business, University of Chicago, Chicago, IL, USA ESMT European School of Management and Technology, Berlin, Germany

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o u t l i n e 28.1  Introduction 28.2 A Two-Stage Model of Self-Control: Identification Versus Resolution

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28.1  Introduction Drawn to plentitudes of tempting foods, the dieters’ challenge to restrict consumption is two-fold. Not only must dieters employ the force of will to steer clear from temptation, but they must also know when such efforts are appropriate in the first place. Clearly, people need to eat, and, unlike other temptations (e.g., cigarettes, drugs and alcohol), abstinence is not a solution. The question is then when and under what circumstances should people exercise restraint? Having one extra sandwich alone will not incur serious costs even for the strict dieter, but having the extra sandwich every day

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28.2.1 The First Stage: Conflict Identification 28.2.2 The Second Stage: Choice Resolution 28.3  Conclusions

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may. Indulging in one chocolate alone will not cause significant problems for most dieters, but regular consumption may. Knowing when to exercise restraint is as important as knowing how to exercise restraint, and these two challenges together constitute the dieters’ dilemma. In this chapter, we review research on the two stages of the dieter’s dilemma. We first distinguish between the different challenges associated with each stage for success and failure at self-control. Subsequently, we review the research on conflict identification, focusing on factors that increase the dieter’s tendency to identify a self-control problem when facing tempting foods. Thereafter, we discuss the second stage, focusing on the role of

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counteractive self-control processes in promoting the pursuit of dieting and health goals.

28.2  A two-stage model of self-control: identification versus resolution While the psychological literature to date has mainly focused on the manifestations and mechanisms of self-control (Mischel et al., 1989; Baumeister et al., 1994, Loewenstein, 1996; Fishbach and Trope, 2007), a precondition for selfcontrol is that individuals perceive a self-control conflict and hence the necessity to harness temptation. Of course, there are circumstances in which the person will have no issue with recognizing a potential problem of indulging. We could imagine a gourmet diner faced with a delicious dessert, knowing that having that dessert could trigger a dangerous allergic reaction; she should not have it. In this case, the capacity to exercise self-control efforts determines the diner’s likelihood of resolving the conflict in favor of the goal to stay healthy (and alive). In other circumstances, however, recognizing conflict may not prove obvious. For example, another dieter could be facing the same dessert, though without allergy concerns. Having this one dessert alone will but trivially affect his

health, but having desserts in general may prove detrimental (e.g., Rachlin, 2000). The likelihood that the dieter indulges in dessert, therefore, depends jointly on his (1) identifying choice conflict and (2) invoking effective self-control strategies given conflict identification. Of course, the problem of identification for the dieter is commonplace. It characterizes most consumption decisions about food because the cost of eating too much on a single occasion usually is trivial. On the basis of this analysis, we propose a two-stage model of self-control to describe the dieter’s dilemma (Myrseth and Fishbach, 2009a). According to this model (see Figure 28.1), individuals facing a tempting stimulus either will identify self-control conflict or not (Stage 1). If selfcontrol conflict is identified, the individual will employ self-control processes to promote goalpursuit over indulgence in temptation (Stage 2). However, if self-control conflict is not initially identified, the individual will choose temptation without invoking self-control processes. The individual who has identified a conflict may then succeed to stay clear of temptation, in which case we have successful goal pursuit. Alternatively, self-control processes may fall short, and we have a classic case of acrasia: lacking command over oneself. Although the individual’s failure in applying effective self-control strategies after identifying a conflict will yield outcomes

Stage 1: Conflict identification

Stage 2: Conflict resolution Successful selfcontrol strategies (restraint)

Identify selfcontrol conflict Facing temptation

Do not identify conflict (indulging)

Figure 28.1  The two-stage model of self-control.

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Unsuccessful strategies (indulging)

28.2  A two-stage model of self-control: identification versus resolution

similar to those of the person who failed to identify conflict (Stage 1), the etiologies of the two are distinct. These distinct etiologies for success and failure at self-control are consequential for understanding and improving goal pursuit.

28.2.1  The first stage: conflict identification The problem of identification arises only in circumstances under which the cost of a single indulgence is trivial (or epsilon). We conceptually distinguish between “malignant” and “epsilon cost” temptation. The former is characterized by potentially serious costs associated with unit consumption (e.g., sugar for the dieter with diabetes); the latter is characterized by trivial costs (e.g., sugar for the dieter with no diabetes). Specifically, the unit consumption cost of epsilon temptation is trivial, but the extended consumption cost may prove quite serious. Epsilon cost temptation is distinct from malignant temptation by virtue of its ambiguous threat to goal pursuit (during Stage 1, Figure 28.1). The individual facing malignant temptation likely will identify self-control conflict, but conflict identification in the face of epsilon cost temptation is less clear. For most people, a serving of tempting food represents an epsilon cost temptation: there are trivial costs associated with consuming a serving of food, but potentially serious costs following extended consumption. Therefore, the question of conflict identification is central to understanding healthy eating. We propose two conditions necessary for the dieter to identify self-control conflict in the face of epsilon cost temptation. In general, the dieter must view the choice opportunity in relation to multiple similar choice opportunities. This interrelated choice frame has two key properties that distinguish it from its counterpart, the isolated frame: 1. Width: The interrelated frame must be wide, such that the individual sees multiple choice opportunities

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2. Consistency: The individual expects to make similar choices for each of the multiple opportunities. Successful resolution of the dieter’s dilemma depends on the width of the frame, and people make healthier food choices when they consider a wide (vs narrow) time frame (Read et al., 1999a; Rachlin, 2000). For example, when making a snack choice for the entire week, people may choose healthier options than when making a separate decision each day of the week (Kudadjie-Gyambi and Rachlin, 1996; Read et al., 1999b). We have recently demonstrated the effect of a wide frame in a study that manipulated the mere perception of the time frame as wide versus narrow (Myrseth and Fishbach, 2009b). Participants in our study approached a food stand offering free carrots and chocolates, unaware that they were participating in a study. The poster adjacent to the food stand announced either “Spring Food Stand” (the wide frame), or “April 12 Food Stand” (the narrow frame). We found that those who approached the “Spring” food stand took more carrots (the healthy option) and fewer chocolates (the tempting option) than did those who approached the “April 12” food stand. This is because “Spring” activated a wider time frame than a specific spring day, increasing the likelihood that participants approaching this stand considered the present choice in light of similar future opportunities. Thus, they more likely identified self-control conflict between staying healthy and indulging than did those approaching the “April 12” stand. However, adopting a wide time frame is not sufficient for identifying self-control conflict. In addition, individuals should see themselves making similar choices across multiple opportunities, leading them to highlight the same important goal across these opportunities. Research on the dynamics of self-regulation addresses this second criterion for conflict identification (Fishbach and Dhar, 2005; Fishbach et al., 2006;

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Fishbach and Zhang, 2008; Koo and Fishbach, 2008). This research examines how frames of goal pursuit influence patterns of self-regulation. These frames can lead either to a highlighting dynamic of choice, by which pursuit of the overriding goal is chosen across choice opportunities, or to a balancing dynamic, by which goal pursuit and indulgence in temptation are balanced across opportunities. Specifically, this work suggests that choices consistent with goal pursuit may signal either greater commitment to a goal or progress toward this goal. For example, after choosing to forego unhealthy food, individuals may conclude either that they are more committed to their health goals or that they have made progress on the health goals. The two possible inferences from the same choice, to eat healthy food, will have opposite consequences for subsequent course of action. As shown in Figure 28.2, a “commitment frame” leads to a dynamic of “highlighting” the important goal, whereas a “progress frame” leads to a dynamic of “balancing” this goal and short-term temptation. In a commitment frame, for example, choosing to eat healthy food increases the likelihood that a person will make another healthy choice at the next opportunity, because the strength of the health goal increases (high commitment). Conversely, choosing to eat unhealthy food decreases the likelihood of making healthy choices because the strength of the health goal decreases (low commitment). In contrast, in the progress frame, choosing to eat healthy food decreases the likelihood that a person will make another healthy choice,

because the strength of the partially fulfilled goal decreases (high progress). Conversely, choosing to eat unhealthy food increases the likelihood of making a healthy choice because the strength of an unfulfilled goal is high (low progress). In a study that tested this model, Fishbach and colleagues (2006) manipulated the frame of healthy behaviors for gym users by priming (or not) super-ordinate health goals. They hypothesized that when gym users consider the overall meaning of their workout for their super-ordinate health goals, they will infer their personal commitment from their level of exercise, and so subsequent food consumption would be consistent with initial performance through a dynamic of highlighting (i.e., greater exercise – healthier eating). In contrast, when gym users focus on the action itself (no health goal reminder), they infer their level of progress from their level of exercise, and subsequent behavior would compensate for initial performance through a dynamic of balancing (i.e., greater exercise – unhealthier eating). Consistent with these predictions, when gym users were reminded of the super-ordinate health goal, those who learned that they exercised more than others intended to eat healthier food than did those who learned that they exercised less (a highlighting dynamic). In contrast, when gym users were not reminded of the super-ordinate goal, those who learned that they exercised less than others intended to eat healthier food than did those who learned that they exercised more (a balancing dynamic). It appears that when the super-ordinate health goal is salient and conflict

Goal Commitment

Highlighting a focal goal and inhibiting alternative goals

Goal Progress

Balancing between a focal goal and alternative goals

Figure 28.2  Dynamics of self-regulation.

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28.2  A two-stage model of self-control: identification versus resolution

is identified, thinking about exercise achievement may further reinforce commitments to the goal of maintaining good health, thus promoting consistent healthy behavior. However, when the super-ordinate goal is not salient and conflict not identified, thinking about exercise achievement, paradoxically, may reduce healthy food consumption because people engage in a balancing dynamic of choice, whereby consistency of choice is not expressed across opportunities. In another study that tested how inferences of goal progress may allow individuals to indulge, Fishbach and Dhar (2005) manipulated perceived goal progress by asking participants to indicate the discrepancy between their current and ideal weight on scales with endpoints 5 lb or 20 lb. The same discrepancies in absolute terms (e.g., 3 lb) would appear larger in the former than in the latter case, and so participants were expected to infer more goal progress for the 20-lb scale than for the 5-lb scale. Accordingly, those indicating discrepancies on the 20-lb scale subsequently were more likely to choose an unhealthy chocolate over a healthy apple. That is, learning that one is closer to one’s ideal weight reduces efforts at healthy eating when one does not identify a self-control conflict between eating healthily or not. Further studies find that merely thinking about future goal-pursuit influence present choice (Zhang et al., 2007). For example, when considering future workouts, gym users may conclude either that they are more committed to their health goal, or that they will make progress toward the goal. These inferences will have opposite implications for what they presently decide to eat. People will indulge less in unhealthy foods when interpreting future workouts as commitment to health goals, but more when interpreting future workouts as progress toward health goals. Moreover, to the extent that people are optimistic and believe more goal attainment is achieved in the future than in the past (Weinstein, 1989; Newby-Clark et al., 2000; Buehler et al., 2002), future ­ expectations

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may have greater impact than past behaviors on present choice. For example, considering the intention to exercise in the future, more than considering past exercise, increased healthy food consumption in the commitment frame but reduced it in the progress frame. Research on the dynamics of self-regulation further shows that presenting alternatives as competing versus complementary influences whether individuals adopt, a highlighting or balancing dynamic of choice (Fishbach and Zhang, 2009). According to this research, presenting goal- and temptation-related options (e.g., healthy and unhealthy food) apart in two separate choice sets, versus together in one choice set (e.g., in two different bowls or in the same bowl), determines whether individuals perceive them as conflicting versus complementary. When the options are apart, they seem conflicting and thus promote a highlighting dynamic of choice; when the options are together, they seem complementary and hence promote a balancing dynamic of choice. In a highlighting dynamic, individuals employ selfcontrol processes to secure goal pursuit. In a balancing dynamic, however, they proactively postpone goal pursuit in favor of instant gratification. That is, when individuals plan to balance between complementary alternatives, they do not see themselves making the same choice in the future, and so there is no self-control conflict. Therefore, they choose to indulge presently, with the intention to choose goal-pursuit later. To demonstrate these effects, Fishbach and Zhang (2008) presented healthy and unhealthy food items in one of three formats: (1) together in one image, to induce a sense of complementarity and a dynamic of balancing; (2) in two separate images, to induce a sense of competition and a dynamic of highlighting; or (3) in two separate experimental sessions, as a control condition (see Figure 28.3). They selected healthy and unhealthy food items that were similarly positive when evaluated independently (e.g., fresh tomatoes vs a cheeseburger). As expected,

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Together

Apart

Figure 28.3  Presenting foods together or apart.

presenting these items together, in one image, increased liking for unhealthy foods because the items appeared complementary. However, presenting them apart, in separate images, increased liking for healthy foods because the items appeared conflicting. In another study, these researchers measured liking for healthy and unhealthy courses on a restaurant menu. The courses were presented either together on one menu (e.g., “garden salad” and “chili cheese fries” were on the same appetizer list), or apart, in two separate parts of a menu (e.g., one side included the “garden salad” and the other included the “chili cheese fries” on the appetizer lists). The researchers found that mixing these courses together on a single menu rendered them complementary and increased the value of unhealthy courses. However, presenting them separately, on two parts of a menu, rendered them conflicting and increased the value of healthy courses. Moreover, when healthy and unhealthy foods were mixed together, fewer participants chose a healthy entrée than chose a healthy dessert. This is consistent with a balancing dynamic of choice, where immediate gratification takes precedent over subsequent goal-pursuit. However, when healthy and unhealthy foods were separated on the menus into distinct sections, most participants chose both a healthy entrée and a healthy dessert, consistent with a highlighting dynamic of choice. Similar to reminding people of their super­ordinate goals, presenting alternatives apart, as conflicting with each other, facilitates successful self-control by helping people identify a conflict

between maintaining good health and indulging. When items are presented together and seem complementary, people fail to perceive a self-control problem in the present choice, leaving goal-pursuit for the future (“I start my diet tomorrow”). It follows that people’s concern with weight-­watching should positively predict choice of healthy items when these items are presented apart from unhealthy items, signaling conflict between important goals and temptations, but not when these items are presented together with unhealthy items, signaling no conflict. To examine this idea, Fishbach and Zhang (2008) offered participants a choice between a chocolate bar and a bag of baby carrots. They found that when the options were presented apart, in two different piles, more participants chose the healthy carrots than when the options were presented together. More importantly, when the chocolates and carrots were presented apart, participants’ concern with weight-watching positively predicted their choice of carrots over chocolate. That is, dieters preferred carrots more than did non-dieters. However, when the foods were mixed into the same pile, participants’ concern with weight-watching did not predict choice, because they failed to identify the choice set as a self-control conflict. Therefore, they did not adhere to their goals. In summary, research reviewed here demonstrates that identification of self-control conflict, first of all, depends, on a wide frame. That is, the individual must consider making multiple choices, such that the cost of yielding to temptation appears potentially high. A wide frame, however, is not sufficient for identifying selfcontrol conflict. In addition, individuals must perceive a choice pattern that highlights one type of choice and promotes consistency.

28.2.2  The second stage: choice resolution To the extent that self-control conflict is identified upon presentation of temptation, the

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28.2  A two-stage model of self-control: identification versus resolution

individual will exert self-control. Then, ­successful goal-pursuit will depend on the effectiveness of self-control strategies. In this section, we address the nature of the self-­control strategies from the standpoint of counteractive control theory (Trope and Fishbach, 2000; Fishbach and Trope, 2005; Myrseth et al., 2009). This theory describes the processes by which individuals offset (i.e., counteract) the influence of temptations on goal-pursuit. According to counteractive control theory, self-control strategies involve asymmetric shifts in motivational strength: an increase in motivation to pursue a goal and a reduction in motivation to pursue temptation. Such asymmetric shifts may result from behavioral strategies. For example, facing the tempting presence of cigarettes, alcohol or fattening food, people may choose to skip purchase opportunities for these items or maintain only a small supply, thereby constraining the availability of temptation (Thaler and Shefrin, 1981; Schelling, 1984; Ainslie, 1992; Wertenbroch, 1998). Because self-control is a process of asymmetric response, people also increase the availability of goal-related items. For example, individuals ­ maintain a large supply of healthy products and take advantage of purchase opportunities to pre-commit themselves to goalrelated choices: some purchase gym membership for the entire year, or buy extra supplies of healthy foods. Behavioral self-control strategies act directly on the physical availability of choice alternatives. Other self-control strategies act on the psychological representation of the choice alternatives, and involve selective attention, encoding, and interpretation of these alternatives. For example, research finds that people promote goal-pursuit by forming “cool” or abstract representations of temptations, thereby reducing their appeal (Metcalfe and Mischel, 1999; Mischel and Ayduk, 2004; Kross et al., 2005; Fujita et al., 2006). Correspondingly, it is possible people promote goal-pursuit by forming a “hot” or concrete representation of goal-consistent behavior.

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Furthermore, self-control involves changes in the valuation of goals and temptations. That is, individuals experiencing self-control conflict counteractively bolster the value of goals and dampen the value of temptation. For example, Trope and Fishbach (2000) document that students bolster the value of studying for an important exam when they consider tempting leisure. More recently, Myrseth and colleagues (2009) explored the parallel devaluation of temptation in the domain of food consumption. They show that individuals devaluate tempting foods when these interfere with dieting goals. Specifically, Myrseth and colleagues examined how availability of tempting food influences their evaluation. They argue that individuals with weight-watching goals, before choosing between healthy and unhealthy food, will dampen their valuation of unhealthy food relative to that of healthy food. However, this pattern should attenuate after choosing, when tempting foods no longer threaten dieting goals. For example, the dieter contemplating the dessert menu in a restaurant will perceive the napoleon as more appealing when the dessert cart is in the kitchen than when it is in front of her; the unavailable napoleon is less threatening to her dieting goals. In support of this analysis, Myrseth and colleagues found that individuals choosing chocolates bars over health bars valued chocolates less than health bars before but not after they had made their choice. Once they had chosen the health bars, chocolates no longer represented a threat to their weight-watching goals, and participants did not employ counteractive valuation. Asymmetric shifts need not be of conscious, deliberative nature. That is, in contrast to common belief that self-control is an explicit response requiring conscious deliberation and executive processing resources (Mischel, 1996; Muraven and Baumeister, 2000), some self-­control responses involve non-conscious modes of operation (Moskowitz et al., 1999; Fishbach et al., 2003; Amodio et al., 2004; Gollwitzer et al., 2005;

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Fishbach and Shah, 2006). These non-­conscious responses are efficient, and characterize successful self-regulators more than they do unsuccessful ones (Ferguson, 2008). For example, successful dieters are more likely to exhibit implicit self-control responses than do those who fail to follow their dietary goals (Fishbach et al., 2003). Non-conscious counteractive control takes several forms. One is that individuals alter the implicit value of goal- and temptation-related alternatives when goal-pursuit conflicts with indulging in temptation. Outside their conscious awareness, individuals boost the value of the goal while dampening the value of temptation (Fishbach et al., 2010). For example, in the presence of cues for unhealthy foods, Fishbach and colleagues find that individuals concerned with weight-watching increase the implicit positivity of concepts related to healthy alternatives (e.g., fruit, vegetable) by associating them with positive concepts. In another study, these authors find that weight-watching individuals further decrease the implicit positivity of concepts related to unhealthy foods (e.g., candy, cake) by associating them with negative concepts. Nonconscious processes, which boost the value of healthy foods while devaluing unhealthy foods, may increase the likelihood that individuals choose to eat healthy foods. Another form of implicit counteractive control entails changes in the accessibility of goals and temptations (Fishbach et al., 2003). Individuals shore up their goals by activating related constructs in response to interfering temptations, and by inhibiting tempting constructs in response to cues for the over-riding goal (see also Shah et al., 2002). For example, success in weight-watching entails activating concepts related to dieting when encountering a tempting chocolate cake, and inhibiting thoughts about fatty food when exercising. Fishbach and colleagues (2003) illustrated the former point with a subliminal sequential priming procedure (Fazio et al., 1995; Bargh et al., 1996). The more important weight-watching was to participants,

the faster they recognized words relating to weight-watching (e.g., diet) upon subliminal priming of concepts related to conflicting temptation (e.g., chocolate). This pattern held only for weight-watchers, who were generally successful at maintaining their weight, suggesting that the implicit operations facilitate pursuit of health and weight-watching goals. Another technique for promoting goal pursuit over temptation is to keep distance from tempting objects, but maintain physical proximity to objects that facilitate goal-pursuit (Thaler and Shefrin, 1981; Schelling, 1984; Ainslie, 1992). For example, anticipating problems posed by a previous romantic partner, people may move to a different location or maintain close proximity to others who help them cope. This asymmetrical response, to approach goals and avoid temptations, also occurs at the non-conscious level. To demonstrate this effect, Fishbach and Shah (2006) investigated dieters’ response time with a joystick for pulling words toward themselves (i.e., approaching) and pushing words away from themselves (i.e., avoiding). They found that committed dieters were quicker to push the joystick (avoiding) in response to temptationrelated words (such as chocolate or sweets) than in response to goal-related words (such as slim or shape). That is, they automatically avoided the temptation. Not only do self-control processes act at the basic level of approach and avoidance, but approaching goals and avoiding temptation also improves goal-pursuit. Accordingly, Fishbach and Shah (2006) demonstrated that participants who completed a joystick task, in which they responded to unhealthy food stimuli by pushing (avoiding) and to fitness stimuli by pulling (approaching), later expressed stronger preferences for healthy foods than did those who completed the opposite task (i.e., responded to food stimuli by pulling and to fitness-stimuli by pushing). These results suggest that simply expressing subtle behavior consistent with one’s weight-watching or health goals may prove

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28.3  Conclusions

sufficient to strengthen the goals and thus to increase goal-consistent behavior. That is, dieters who practice pushing away the dessert plate may increase their frequency of choosing healthy over unhealthy foods. In summary, this section has reviewed the processes of self-control. In line with counter­ active control theory, we propose that self-control involves asymmetric changes in motivational strength. We further propose that individuals more likely to succeed at goal-pursuit are better at employing these self-control strategies. Notably, individuals apply self-control strategies only to the extent that they have identified a self-control conflict. Specifically, research on counteractive control finds that self-control is elicited only when important goals are perceived to conflict with temptations, and when external mechanisms are not in place to ensure goal pursuit (Fishbach and Trope, 2005). Thus, the perception that temptation threatens goal pursuit (Stage 1) is necessary to activate subsequent self-control processes.

28.3  Conclusions The dieter’s dilemma has two components. First, individuals facing tempting foods either identify or not conflict between indulging and pursuing weight-watching or other health goals. Second, if they have identified conflict in the first stage, they will subsequently draw on self-control strategies to restrict consumption. If their strategies are successful, then they exercise restraint. However, if their strategies fall short, they will indulge, as will they if they do not identify conflict in the first place. We thus identify two distinct etiologies of indulgence in tempting foods: namely, the absence of selfcontrol conflict, and the failure of self-control strategies. While a significant proportion of psychological research to date has focused on implementation of self-control strategies (see, for example,

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Thaler and Shefrin, 1981; Mischel et al., 1989; Baumeister et al, 1998), the nature of self-control dilemmas for dieters entails that conflict identification often is not trivial. This is because one act of indulgence will have little impact on one’s overall success at maintaining good health; only if this act is repeated across many opportunities may it seriously undermine the health goal. In this type of dilemma, conflict identification in the face of epsilon cost temptation will depend on the frame of the choice opportunity. Specifically, it will depend on whether the frame is wide, capturing multiple choice opportunities, or narrow, capturing a single opportunity, and whether the individual perceives that the same choice will be made for each opportunity. For example, the question of having one bitesized chocolate alone may not be sufficient to activate self-control strategies for most dieters, but the prospect of regularly having this opportunity probably is, though only to the extent that the present choice is thought to be the same as future ones. The second component of the dieter’s dilemma involves the implementation of selfcontrol pro­cesses. In line with research on counteractive control (e.g., Fishbach and Trope, 2007), we propose that the essence of these self-control operations involves an asymmetric motivational response: increasing the motivational strength of the goals (e.g., weight-watching) while decreasing the motivational strength of indulging in temptation (e.g., eating dessert). For example, to resist the chocolate, the dieter can elaborate on what makes healthy eating valuable, while undermining the perceived appeal of the chocolate. We conclude that the problems of overeating may not be mere problems of acting against one’s better judgment, but also problems of determining better judgment in the first place. Better understanding of the etiology of successful weight-watching and health maintenance would lay the groundwork for better remedies against excessive indulging.

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Fishbach, A., Zhang, Y., & Trope, Y. (2010). Counteractive evaluation: Asymmetric shifts in the implicit value of conflicting motivations. Journal of Experimental Social Psychology, 46, 29–38. Fujita, K., Trope, Y., Liberman, N., & Levin-Sagi, M. (2006). Construal levels and self-control. Journal of Personality and Social Psychology, 90, 351–367. Gollwitzer, P. M., Bayer, U. C., & McCulloch, K. C. (2005). The control of the unwanted. In R. R. Hassin, J. Uleman, & J. A. Bargh (Eds.), The new unconscious (pp. 485–515). New York, NY: Oxford University Press. Koo, M., & Fishbach, A. (2008). Dynamics of self-regulation: How (un)accomplished goal actions affect motivation. Journal of Personality and Social Psychology, 94, 183–195. Kross, E., Ayduk, O., & Mischel, W. (2005). When asking “why” does not hurt: Distinguishing rumination from reflective processing of negative emotions. Psychological Science, 16, 709–715. Kudadjie-Gyambi, E., & Rachlin, H. (1996). Temporal patterning choice among delayed outcomes. Organizational Behavior and Human Decision Processes, 65, 61–67. Loewenstein, G. (1996). Out of control: Visceral influences on behavior. Organizational Behavior Human Decision Processes, 65, 272–292. Metcalfe, J., & Mischel, W. (1999). A hot/cool-system analysis of delay of gratification: Dynamics of willpower. Psychological Review, 106, 3–19. Mischel, W. (1996). From good intentions to willpower. In P. M. Gollwitzer & J. A. Bargh (Eds.), The ­ psychology of action: Linking cognition and motivation to behavior (pp. 197–218). New York, NY: Guilford Press. Mischel, W., & Ayduk, O. (2004). Willpower in a cognitiveaffective processing system: The dynamics of delay of gratification. In R. F. Baumeister & K. D. Vohs (Eds.), Handbook of self-regulation: Research, theory, and applications (pp. 99–129). New York, NY: Guilford Press. Mischel, W., Shoda, Y., & Rodriguez, M. L. (1989). Delay of gratification in children. Science, 244, 933–938. Moskowitz, G. B., Gollwitzer, P. M., Wasel, W., & Schaal, B. (1999). Preconscious control of stereotype activation through chronic egalitarian goals. Journal of Personality and Social Psychology, 77, 167–184. Muraven, M., & Baumeister, R. F. (2000). Self-regulation and depletion of limited resources: Does self-control resemble a muscle? Psychological Bulletin, 126, 247–259. Myrseth, K. O. R., & Fishbach, A. (2009a). Self-control: A function of knowing when and how to exercise restraint. Current Directions in Psychological Science, 18, 247–252. Myrseth, K. O. R., & Fishbach, A. (2009b). Seeing self-control  conflict: The problem of isolated versus interrelated temptation. Working Paper, The University of Chicago. Myrseth, K. O. R., Fishbach, A., & Trope, Y. (2009). Counteractive self-control: When making temptation available makes temptation less tempting. Psychological Science, 20, 159–163.

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C H A P T E R

29 Lifestyle Change and Maintenance in Obesity Treatment and Prevention: A Self-determination Theory Perspective Heather Patrick1, Amy A. Gorin2 and Geoffrey C. Williams1 1

Departments of Medicine and of Clinical and Social Psychology, University of Rochester, Rochester, NY, USA 2 Department of Psychology, University of Connecticut, Storrs, CT, USA

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29.1  Introduction As discussed elsewhere in this volume, obesity has become a serious public health problem, with both short- and long-term physical and psychological consequences. In recent years, rates of overweight and obesity have soared in developed and developing countries. In the United States

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alone, some 66 percent of adults are overweight or obese, as are 17 percent of adolescents and 19 percent of children (National Center for Health Statistics, http://www.cdc.gov/nchs/fastats/overwt. htm). The key to obesity prevention is lifestyle change: improving dietary intake and increasing physical activity. To date, several interventions have been developed to target these behaviors and

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increase weight-loss success. Behavioral wei­ght control, consisting of education about nutrition and physical activity and instruction in key behavioral techniques (e.g., self-monitoring and problem-solving), is the treatment of choice for overweight to moderately obese individuals (BMI 25–40 kg/m2) (Wing, 2002). Weight losses average out at 0.5 kg per week, for a total weight loss of 9.7 kg over 6 months of behavioral weight loss treatment (roughly 8–10 percent of initial body weight) (Wing and Phelan, 2009). However, despite these strong initial results, the long-term impact is disappointing. Unhealthy eating and exercise habits resurface within weeks to months of completing the treatment and, as a result, only 60–70 percent of weight lost during treatment is maintained at 1-year post-treatment, and nearly all weight is regained within 3–5 years (Perri et al., 2001). One reason for this is that existing weight-loss programs largely ignore the potentially crucial element of motivation for sustained behavioral change. Self-determination theory (SDT) (Deci and Ryan, 2000; Ryan and Deci, 2000a) is a general theory of human motivation that addresses the importance of motivation in behavior change and its maintenance. The purpose of this chapter is to describe the general tenets of SDT, to discuss applications of SDT to the lifestyle changes relevant to weight loss, and to provide suggestions for future research and interventions to prevent obesity.

29.2  Self-determination theory One of the key assumptions of self-determination theory is that human beings are naturally oriented toward growth, health and development. However, social-contextual circumstances may facilitate or impede this natural process of motivation and self-governance. Thus, self-determination theory offers an organismic dialectic perspective

on human motivation, which acknowledges the interplay between the person and the situation in various behavioral contexts. SDT posits that all humans possess three basic psychological needs: competence, relatedness and autonomy. The need for competence involves the need to feel optimally challenged in one’s endeavors and to feel capable of achieving desired outcomes. Relatedness pertains to the need to feel close to and understood by important others. Finally, autonomy refers to the need to feel volitional, as the originator of one’s actions. When these needs are met, people evidence optimal motivation and improved physical and psychological outcomes (Ryan and Deci, 2000a). SDT distinguishes between autonomous and controlled motivations. Autonomous motivation is characterized by feelings of choice, volition and self-integration. Controlled motivation is characterized by feelings of pressure, guilt, obligation and self-fragmentation. Importantly, SDT allows for this distinction between autonomous and controlled motivation at the individual differences level or personality level while also acknowledging the role of the broader social milieu in supporting or thwarting optimal motivation in particular domains (e.g., regular physical activity). Thus, according to SDT, individuals may be oriented toward relatively more or less autonomous (or controlled) functioning. The social surround may support or thwart optimal (i.e., autonomous) motivation within individuals in these same circumstances. SDT also addresses how, through the process of internalization, health behavior may be both changed and maintained.

29.3  Self-regulation In addition to its focus on psychological needs, SDT also speaks to self-regulation of behaviors. According to the theory, behaviors may be regulated in relatively more autonomous ways, in that behaviors may be engaged

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29.4 Need-supportive contexts

in because they are fun, interesting, and inherently enjoyable. In contrast, other behaviors may be regulated in relatively more controlled ways, in that they are engaged in because of inter- or intrapersonal pressure, such as demands, evaluation, pressure and guilt. A key distinction between autonomous and controlled behaviors is that the former are engaged in for the sake of the behavior itself, whereas the latter are en­gaged in for some separable outcome. People engaging in behaviors for more autonomous reasons experience greater interest, excitement and confidence for the target behavior. They subsequently evidence enhanced performance, persistence and creativity, as well as heightened vitality, self-esteem and general wellbeing (Deci and Ryan, 1995; Ryan et al., 1995a; Sheldon et al., 1997; Nix et al., 1999; Ryan and Deci, 2000b). Some behaviors may be inherently unenjoyable, or may be enacted largely for some separ­ able outcome. For many, lifestyle change may be experienced as such. This may be because the behavior itself is unpleasant (e.g., muscle soreness and stiffness associated with beginning a new exercise routine) or because the behavior is simply a means to some other end (e.g., changing one’s eating habits for the purpose of losing weight). Ryan and Connell (1989) thus proposed a continuum of motivation to address the fact that behaviors may be engaged for some separ­ able outcome, but they may be relatively more or less integrated with the broader sense of self and thus relatively more or less autonomous. The least autonomous form of self-regulation is external regulation. Externally regulated behaviors are engaged in to gain some reward or avoid a punishment. For example, someone may change her diet to get her spouse to stop nagging her about her eating habits. Introjected regulation refers to behaviors that are engaged in to avoid guilt or shame, or to gain the approval of others. For example, someone may begin an exercise regimen because he is embarrassed about his weight and feels guilty about not taking better care of his health. Identified behaviors

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are enacted because the goal is important or valued by the person. For example, someone may try to improve his eating habits because he values eating in a healthy manner. Finally, integrated regulation refers to behaviors that are integrated to one’s sense of self and are concordant with one’s values. For example, an individual may begin exercising regularly because it is consistent with his goals for healthy eating and of losing 10 kg, and these goals are consistent with his overall value for health. It is the integration with other autonomously- and innately-held values that leads to greater levels of motivation for making and sustaining these behaviors. A growing body of research has demonstrated the importance of autonomous self-regulation for a range of health behaviors. The more autonomously motivated patients are for a health behavior, the more adherent they are to treatment, and the better their health outcomes. This finding has emerged in mandatory treatment for alcohol use (Ryan et al., 1995b), participation in a weight-loss program (Williams et al., 1996), diabetes self-management (Williams et al., 1998a), adherence to medication prescriptions (Williams et al., 1998b), and tobacco abstinence (Williams et al., 1999, 2002; Williams and Deci, 2001). Thus, one key way in which researchers, clinicians and policy-makers may positively influence obesity outcomes is by facilitating the process of internalization in the people with whom they attempt to intervene.

29.4  Need-supportive contexts SDT maintains that motivation is dynamic. Thus, relatively less autonomous forms of selfregulation can become more autonomous through the process of internalization. Although a primary assumption of SDT is that individuals are inherently oriented toward health and optimal

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motivation and may thus naturally tra­verse thr­ough the internalization process, the theory also acknowledges the role of the social context in supporting or thwarting internalization. Optimal motivation and internalization arise out of needsupportive contexts (Ryan and Deci, 2000a). Traditionally, need-support has been studied within “vertical” relationships, where one person is in a position of authority over another (e.g., doctors and patients, parents and children, teachers and students, etc.), though more recent research has begun examining need-support in horizontal relationships between equals (e.g., romantic partners, peers, etc.). In the context of lifestyle change related to weight loss, both types of relationships may offer support in ways that facilitate or impede the process of internaliz­ ation. The concept of need-support represents an interpersonal climate whereby one takes the perspective of another into consideration, asks the individual what he or she wants to achieve, provides relevant information and opportunities for choice, encourages the other to accept personal responsibility for his or her behavior, and refrains from judgment or evaluation when asking about past behavior. In healthcare contexts (i.e., practitioner–patient interactions), this also involves providing clear recommendations, encouraging questions, providing meaningful rationale for treatment recommendations and satisfactory answers to questions, and acknowledging that the patient does not have to change or accept an unwanted treatment. Thus, needsupport involves actively engaging individuals in a discussion about their health and health behaviors with minimal pressure, judgment and control (Ryan, 1993; Reeve, 1998; Williams, 2002). A growing body of research has provided evidence for the critical role of need-support in facilitating autonomous self-regulation for health behaviors. For example, adolescents who were provided a need-supportive message about choosing not to start smoking were more autonomously motivated not to smoke compared to those who received a more controlling

message. Those who were more autonomously motivated not to smoke, in turn, smoked less frequently and less intensely 4 months after the intervention (Williams et al., 1999). In other research, students enrolled in a gymnastics class reported a more intrinsic interest in and greater intentions to continue participating in the class when they perceived their instructor as need-supportive (Goudas et al., 1995). When coaches were seen as need-supportive, competitive swimmers experienced greater autonomous motivation for the sport and were more likely to persist in it (Pelletier et al., 2001). A similar set of findings emerged for competitive gymnasts with regard to perceived need-support from parents and coaches (Gagne et al., 2003). In addition to having need-supportive healthcare providers, who have little direct contact with individuals outside the clinical setting, having need-supportive family members appears to confer health benefits. Williams et al. (2006) demonstrated that increases in perceived need support from important others (e.g., family members) were associated with increases in autonomous self-regulation and perceived competence, as well as better outcomes in a smoking cessation and dietary intervention trial. Interestingly, need-support from important others provided variance distinct from need-­support from healthcare providers, suggesting that both make independent contributions to health outcomes. When allowed to compete for variance, need-support from both healthcare providers and important others uniquely contributed to abstinence from tobacco. In dietary outcomes (e.g., percent calories from fat), the importantothers measure accounted for a greater percent of the variance than the healthcare-providers measure. In ongoing work, Gorin and colleagues (2009) have found that increases in perceived need-support from another adult in the home are associated with increases in autonomous self-regulation for weight control. These increases in autonomous self-regulation predict weight losses over that same time period (controlling

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29.5  SDT and weight loss

for relevant baseline values), providing more evidence of the crucial role family members can play in the weight-loss process.

29.5  SDT and weight loss To date, very little research has focused on SDT applications to weight loss and healthy eating. However, an impressive body of evidence is emerging regarding SDT applications to leisure time physical activity – an important lifestyle change relevant to weight loss. In the first study to apply SDT to weight loss, Williams and colleagues studied severely obese patients enrolled in a 26-week, medically-supervised, very lowcalorie weight loss program (Williams et al., 1996). Additionally, patients attended weekly meetings during which they met with a medical practitioner and participated in a group counseling session. Results indicated that those who were more autonomously motivated for weightloss treatment were more likely to attend treatment sessions and evidenced greater weight loss throughout the treatment program (as indicated by body mass index; BMI). Importantly, autonomous motivation for treatment was associated with greater weight-loss maintenance and better maintained physical activity 2 years post-intervention. Thus, autonomous motivation for weight-loss treatment was associated with both initiated and maintained weight loss and behavioral outcomes. Patients in the Williams et al. (1996) study also completed measures of perceived need-support from treatment practitioners. Perceiving one’s treatment practitioners as more need-supportive was associated with more autonomous reasons for treatment, better treatment attendance, and greater long-term reduction in BMI. Further, path analyses indicated that the link between perceived needsupport and treatment attendance and weightloss outcomes was mediated by autonomous self-regulation. Those who experienced greater

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need-support were more treatment-adherent and had more sustained weight-loss outcomes because of the influence of need-support on autonomous self-regulation. Thus, both need-support and auto­ nomous self-regulation are important to weightloss initiation and maintenance. In a randomized controlled trial examining the impact of weight loss on urinary incontinence in over 300 overweight women, Gorin and colleagues (2008) explored whether baseline levels of autonomous and controlled selfregulation, and changes in self-regulation over 6 months, were associated with 6-month weightloss outcomes. Controlling for baseline weight, the results suggest that higher levels of controlled self-regulation at study entry were associated with worse weight-loss outcomes, whereas better weight-loss outcomes were associated with increases in autonomous self-regulation and decreases in controlled self-regulation over the 6-month period. Silva and colleagues (2008) recently reported on the development of an SDT-based random­ized controlled trial for weight loss among over­weight individuals. Participants were randomly assigned to either the SDT weight-loss intervention or a general health improvement intervention. The intervention period is 1 year, and is followed by a 2-year, no intervention followup. The SDT weight-loss intervention consists of 30 2-hour group sessions conducted with groups of up to 30 participants. Participants received information from physical activity, nutrition and behavior-change specialists about the lifestyle changes needed to achieve weight loss. Those in the general health intervention attended a similar number of sessions, but their session content was based on a series of 3- to 6-week long educational topics, including preventive nutrition, self-care, stress management and effective communication skills, among others. Preliminary analyses suggest that the SDT-based intervention resulted in greater increases in physical activity, improvement in dietary intake, and better weight-loss outcomes. Importantly,

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participants in the SDT-based intervention evidenced more autonomous self-regulation for physical activity and weight loss, which independently predicted the relevant behavior change outcomes (Patrick et al., 2009). While relatively little attention has been given to SDT applications to weight loss and dietary changes, much research has focused on SDT approaches to physical activity, with a recent focus on recreational physical activity in adults. Those who have more autonomous self-regulation for exercise are more ready to initiate exercise and report enjoying exercise more (Mullan and Markland, 1997; Mullan et al., 1997). Thogersen-Ntoumani and Ntoumani (2006) found that those who had more autonomous self-regulation for recreational exercise had fewer periods of relapse into a sedentary lifestyle, and greater intentions to continue exercising. Autonomous self-regulation has also been associated with consistently exercising over a period of 6 months (Matsumoto and Takenaka, 2004) and participation in moderatelevel physical activity consistent with public health recommendations (Standage et al., 2008). Together, this set of findings supports the importance of autonomous self-regulation in recreational physical activity initiation (Mullan and Markland, 1997) and recreational physical activity consistency and maintenance (Matsumoto and Takenaka, 2004; Thogersen-Ntoumani and Ntoumani, 2006). These findings can inform obesity interventions as they speak to the underlying mechanisms of lifestyle changes necessary to achieve and maintain weight loss.

29.6  Potentional limitations of current interventions: an SDT perspective As noted previously, one reason that weightloss maintenance has remained elusive may be the failure of these interventions to consider

the critical role of motivation in health behavior change and maintenance. One key issue may be the focus on weight loss as an outcome. While weight loss is an important outcome because of its potential impact on health, the focus on weight loss inherently creates a circumstance whereby lifestyle change becomes a means to an end. That is, changes to one’s levels of physical activity and quality of dietary intake are enacted primarily for the purpose of losing weight, and not necessarily for the inherent value or pleasure of the activities themselves. Thus, lifestyle change is enacted based on relatively less autonomous self-regulation. When lifestyle change is engaged for some separable outcome – particularly when that outcome is clearly measureable (e.g., lose 10 kg) – the outcome attains especially significant meaning. If the outcome is not achieved, then it is easy to infer that the lifestyle change “didn’t work” and is therefore not useful to continue. Yet the focus on separable, measurable outcomes may also explain why even those who achieve initial weight loss may discontinue lifestyle change and thus regain weight. Once the outcome has been achieved, there is no need to continue the utilitarian lifestyle changes. In many ways, failing to sustain lifestyle change may be even more problematic than failure to maintain weight loss. Research indicates that the health benefits of lifestyle change – particularly physical activity – are independent of weight or weight status (US Department of Health and Human Services, 2008). Another potential problem with a focus on weight loss is that, for many, weight loss as a goal is in large part about one’s physical appearance or impressing others. These are goals that are relatively extrinsic or external to the self. This is in contrast to goals about health and personal growth which are more intrinsic or internal to the self (Kasser and Ryan, 1993, 1996). Weight loss is a prime example of the conflict between people’s inherent value for health and societal and cultural values for image and appearance. Extrinsic goals about image and appearance can

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29.7 Directions for future research based on SDT

further thwart autonomous self-regulation, as they create additional pressure for the individual to attain outcomes that may not be feasible and that are beyond that which is required for improved health. The result of repeated efforts to lose weight and then regain it may be an amotivated state whereby individuals are unable to muster the energy needed to initiate, much less sustain, health behavior change.

29.7  Directions for future research based on SDT The past three decades have seen substantial improvement in the efficacy of interventions for weight loss. Yet much remains to be done to better elucidate the characteristics of these interventions that are likely to foster not only weight-loss initiation but also sustained weight loss and lifestyle change over time. As a broad theory of human motivation, SDT is uniquely positioned to address many of these issues, particularly through the development of interventions that target the social-contextual characteristics likely to facilitate autonomous self-regulation. These interventions may target healthcare practitioners as well as the broader social context within which individuals interact on a more consistent basis, such as family and friends. SDT may also address how interventions can be framed with a focus on health – rather than an exclusive focus on weight loss – to support optimal motivation. While it is unlikely that weight-loss interventions can fully get away from weight loss as a goal, practitioners can work with patients to facilitate a focus on health as part of that goal. Importantly, interventions that focus on the health benefits of lifestyle change in the form of improved dietary intake and increased physical activity independent of weight loss may be particularly beneficial. Indeed, increasing evidence suggests that health benefits from these lifestyle

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changes occur much sooner than weight loss. For example, engaging in moderate levels of physical activity for 30 minutes a day on 5 or more days a week has been shown to improve various health outcomes, including decreasing heart disease and diabetes risks. However, engaging in more vigorous levels of physical activity and for longer periods of time is required to achieve weight loss (Jakicic et al., 2001). There is also increased evidence regarding the weight loss needed to achieve health benefits. Specifically, weight loss of 5–10 percent results in improved health with respect to cardiovascular disease and diabetes (Diabetes Prevention Program Re­s­earch Group, 2002; Knowler et al., 2002; Look AHEAD Research Group, 2007). For overweight or obese individuals, this amount of weight loss is unlikely to change their weight status and move them from being classified as overweight to normal weight based on BMI. Modest weight loss may also fail to result in substantial changes to one’s physical appearance, which may be frustrating and disheartening for many patients, particularly when the energy behind wanting to lose weight centers on a desire to look better. According to SDT, practitioners can support patients’ needs by acknowledging their interest in and concern about weight. Thus, we do not recommend that practitioners ignore weight concerns or minimize their patients’ interest in weight loss as an outcome. Beyond considering the patients’ perspective on the importance of weight loss, practitioners may serve to further facilitate autonomous self-regulation by working with patients to shift their focus to their innate tendency toward health. Although motivation for health may not necessarily be at the forefront of all patients’ minds as they embark on weight-loss efforts, SDT maintains that this energy source exists and can be activated. While concern for achieving a particular weight or an interest in one’s image will likely not dissipate completely, practitioners may be better positioned to facilitate autonomous self-regulation and work with patients to achieve long-term

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lifestyle change and maintenance by bringing to the forefront the patient’s inherent value for health. Research is needed on how best to sustain this focus on health in the face of conflicting societal and cultural messages. Much of the research to date examining SDT in healthcare settings has focused on the importance of need-support as provided by healthcare practitioners. This is indeed an important part of any intervention focused on motivating health behavior change and its maintenance. However, given that eating and physical activity are behaviors that occur almost entirely in the home, work and community contexts, and not in the clinical environment, need-support from family members and other members of patients’ social network is needed. Without proper training, and despite the best of intentions, family and friends may provide help and support in controlling ways that undermine autonomous self-regulation and sabotage the very success these efforts are intended to promote. For example, Goldsmith and colleagues (2006) found that cardiac patients attempting weight loss and other lifestyle changes described talking with their spouse about their health behavior changes as “pressure”, “control”, “demands”, “poli­cing” and “gatekeeping”. If perceived as control­ling, important others’ attempts at providing support may backfire. In a study with cardiac rehabilitation patients, Franks and colleagues (2002) found that social control from important others (e.g., trying to the stop the other from doing unhealthful things) was associated with worse adherence to lifestyle goals at 6 months. An additional study indicated that young adults attempting to lose weight reported significantly more weight loss when they perceived their family and friends as need-supportive, but not when they perceived that support to be controlling (Powers et al., 2009). The need to develop and evaluate interventions specifically designed to improve the social context of weight loss is supported by both SDT and these empirical findings.

In addition to being a serious public health problem, obesity and its related health behaviors are extraordinarily complex, with roots in individual, familial/relational, cultural–societal and healthcare contexts. While the decision to make lifestyle changes and to sustain them ultimately rests with individuals and their willingness to put forth effort to these ends, there is much that can be done within the social-contextual environment to facilitate these efforts. Healthcare practitioners – whether they are physicians, nurses, nutritionists/dietitians, personal trainers or other professionals – are uniquely positioned to provide need-support to patients in ways that facilitate optimal motivation. They can do this by acknowledging their patients’ perspectives, including their ambivalence about health behavior change, providing clear recommendations about patients’ health and their behavior, and providing a rationale for treatment recommendations. Although healthcare practitioners play a critical role in facilitating patients’ motivation, it is also important to develop interventions that teach laypeople – particularly family members and friends – how to be need-supportive with important others who are trying to make lifestyle changes and lose weight. Intervening with family and friends who are part of a patient’s daily life and routine is crucial to enhancing the effects of need-supportive treatment environments and creating sustainable interventions. Finally, while it is unlikely that social and cultural contexts will dramatically shift away from a focus on image, appearance and weight, interventions that also draw attention to our innate value for health will serve to facilitate long-term lifestyle change maintenance by harnessing this inherent energy source that will catalyze optimal motivation and autonomous self-regulation.

References Deci, E. L., & Ryan, R. M. (1995). Human autonomy: The basis for true self-esteem. In M. Kernis (Ed.), Efficacy, agency, and self-esteem (pp. 31–49). New York, NY: Plenum Press.

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Williams, G. C. (2002). Improving patients’ health through supporting the autonomy of patients and providers. In E. L. Deci & R. M. Ryan (Eds.), Handbook of selfdetermination research (pp. 233–254). Rochester, NY: University of Rochester Press. Williams, G. C., & Deci, E. L. (2001). Activating patients for smoking cessation through physician autonomy support. Medical Care, 39(8), 813–823. Williams, G. C., Grow, V. M., Freedman, Z. R., Ryan, R. M., & Deci, E. L. (1996). Motivational predictors of weight loss and weight-loss maintenance. Journal of Per­ sonality and Social Psychology, 70(1), 115–126. Williams, G. C., Freedman, Z. R., & Deci, E. L. (1998a). Supporting autonomy to motivate glucose control in patients with diabetes. Diabetes Care, 21(10), 1644–1651. Williams, G. C., Rodin, G. C., Ryan, R. M., Grolnick, W. S., & Deci, E. L. (1998b). Autonomous regulation and adherence to long-term medical regimens in adult out­patients. Health Psychology, 17(3), 269–276. Williams, G. C., Cox, E. M., Kouides, R., & Deci, E. L. (1999). Presenting the facts about smoking to adolescents: The effects of an autonomy supportive style. Archives of Ped­ iatrics and Adolescent Medicine, 153(9), 959–964. Williams, G. C., Gagné, M., Ryan, R. M., & Deci, E. L. (2002). Facilitating autonomous motivation for smoking cessation. Health Psychology, 21, 40–50. Williams, G. C., McGregor, H. A., Sharp, D., Kouides, R. W., Levesque, C., Ryan, R. M., et al. (2006). A selfdetermination multiple risk intervention trial to improve smokers’ health. Journal of General Internal Medicine, 21(12), 1288–1294. Wing, R. R. (2002). Behavioral weight control. In T. A. Wadden & A. J. Stunkard (Eds.), Handbook of obesity treatment (pp. 301–316). New York, NY: Guilford Press. Wing, R. R., & Phelan, S. (2009). Behavioral treatment of obesity. In R. H. Eckel (Ed.), Obesity: An academic basis for clinical evaluation and treatment (pp. 415–435). New York, NY: Lippincott Williams & Wilkins.

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C H A P T E R

30 Nutritional Genomics in Obesity Prevention and Treatment Branden R. Deschambault, Marica Bakovic and David M. Mutch Department of Human Health and Nutritional Sciences, University of Guelph, Guelph, Ontario, Canada

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30.2 The Genetics of Obesity

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30.3 Nutritional Genomics

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30.1  Background

illustrated by NHANES, an important increase in the prevalence of childhood obesity has also The prevalence of both obesity (BMI  30 kg/m2) occurred during the past 50 years (Ogden et al., and morbid obesity (BMI   40 kg/m2) have 2007). Taken together, obesity is now considered an now established a firm foothold in our society epidemic that has placed an unsustainable burden (Sturm, 2007). This trend is visible in data from on social and public health programs around the the National Health and Nutrition Examination world. Indeed, the metabolic abnormalities and Surveys (NHANES) in the United States, where chronic disease risks associated with obesity (Field a shift in BMI measurements in the upper per- et al., 2001; Calle and Thun, 2004), which include centiles was observed in adult men and women type 2 diabetes, hypertension, heart disease between NHANES II (1976–1980) and NHANES and certain cancers, reinforces the urgency with III (1999–2004) (Ogden et al., 2007). As further ­ which public health agencies must innovatively

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respond to curb this epidemic. Current public health initiatives designed to address the increasing prevalence of obesity, albeit in their infancy, tend to provide general exercise, nutritional and lifestyle recommendations (McKinnon et al., 2009). Such recommendations can yield positive and encouraging results; however, for any one individual in a population there is considerable variability in response to these lifestyle recommendations. As such, pioneering methods to address the obesity epidemic must come in the form of a paradigm shift away from medical and nutritional recommendations based on research from genetically diverse populations exposed to highly heterogeneous lifestyles, towards more personalized approaches in which the genetic make-up and lifestyle are considered for each individual. The field of nutritional genomics exemplifies this paradigm shift (Khoury et al., 2007). It is now becoming more common that people are familiar with such terms as “personalized nutrition” and “nutrigenomics”, partly because of the technological advances made in the post-genomic era and partly because commercial laboratories have begun to market genotyping services to the public (Morin, 2009). However, while the framework for this conceptual progression has been insightfully drafted (Agurs-Collins et al., 2008), its realization requires further delineation of how gene–gene and gene– ­environment interactions are able to impact health. There is a considerable ongoing effort in the scientific community to identify and catalog those genes that affect parameters associated with body weight, such as the regulation of food intake, energy expenditure, lipid and glucose metabolism, and adipose tissue development. Genetic studies have revealed familial aggregation (Allison et al., 1996a) and heritability for quantitative traits associated with obesity, such as BMI (Allison et al., 1996b), total and regional adiposity (Malis et al., 2005), and waist– hip ratio (Rose et al., 1998). Heritability has also been documented for numerous facets of eating behavior (Tholin et al., 2005). Such efforts have revealed that the obese phenotype is highly

heterogeneous, and varies in genetic complexity from person to person. Monogenic obesity, which stems from a single dysfunctional gene, is characterized by an extremely severe phenotype that presents itself in childhood and is often associated with additional behavioral, developmental and endocrine disorders; however, monogenic obesity accounts for less than 5 percent of the severe obesity cases (Mutch and Clement, 2006). Rather, the more common polygenic form of obesity is the result of numerous genes (i.e., up to several hundred) each having a minor contribution to the overall phenotype. Moreover, these genes not only interact with each other (gene–gene), but are also sensitive to environmental factors (gene–environment). Thus, defining the genetic component of common obesity continues to prove challenging.

30.2  The genetics of obesity In the ongoing search for genetic variants that independently predispose certain individuals to common obesity, a variety of modern populationlevel approaches have been employed. These approaches vary in scope and resolution, and can be broadly divided into genome-wide studies (hypothesis-generating) and candidate gene studies (hypothesis-driven) (Hirschhorn and Daly, 2005; Li and Loos, 2008) (Figure 30.1). Genomewide studies use linkage mapping or association approaches to survey the entire genome for unknown variants that correlate with a particular phenotypic endpoint or incidence of disease (Hirschhorn and Daly, 2005; Balding, 2006). In this regard, the researcher is not looking at a particular gene a priori, but rather hypothesizes that unrecognized genes and/or gene variants are associated with a phenotype of interest and remain to be discovered. In contrast, hypothesisdriven studies are based on association or resequencing approaches, and can vary in scale from individual single nucleotide polymorphisms

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30.2  The genetics of obesity

Hyphenate generating approach

Genome-wide linkage analyses (populations of related individuals)

Gene expression profiling (microarrays) Genome-wide association studies (unrelated individuals–cases & controls)

Highly penetrant genes/loci (monogenic obesity)

Associated gene variants (polygenic obesity)

Differentially expressed genes (polygenic obesity)

Hyphenate driven approach

Transgenic animal models

Candidate genes

Transfected in vitro systems

Functional studies large population validation studies

Figure 30.1  Relationship between hypothesis-generating and hypothesis-driven research. Candidate genes can be identified via a multitude of interconnected approaches. To definitively demonstrate that a candidate gene has a real role in obesity requires epidemiological studies as well as functional cellular and animal transgenic models.

(SNPs) to fine mapping of candidate regions (typically 1–10 Mb) (Hirschhorn and Daly, 2005; Balding, 2006). In other words, the researcher aims to demonstrate that a particular gene or chromosomal region is significantly associated with a phenotype of interest. Hypothesis-driven candidate gene association studies in humans are typically designed to validate functional data obtained using transgenic animal and cellular models or positional data (Risch and Merikangas, 1996; Hirschhorn and Daly, 2005; Balding, 2006). By genotyping allelic variants (i.e., SNPs), the association between a gene and an obesity-related trait can be evaluated in cases versus controls at the population level (Balding, 2006; Li and Loos,

2008). The 2005 Human Obesity Gene Map update reported associations between 127 candidate genes and obesity-related phenotypes (Rankinen et al., 2006); however, conflicting results have been reported for several of these genetic variants. The poor replication of associations has been attributed to insufficient sample sizes, low frequencies (a function of ethnicity) and modest effects associated with causal alleles (Balding, 2006; Li and Loos, 2008), leading some to speculate on the excess of false-positive associations for complex diseases (Lohmueller et al., 2003). Fortunately, recent studies have managed to sample tens of thousands of subjects to provide further insight regarding some previous conflicting results. For example, the association

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between obesity and the K121Q polymorphism (rs1044498) in the ectoenzyme nucleotide pyrophosphate phosphodiesterase (ENPP1) gene, which codes for a transmembrane glycoprotein capable of inhibiting insulin receptor-mediated signal transduction, has not been validated using larger cohorts (Meyre et al., 2007; Seo et al., 2008). Thus, the candidate gene approach remains a useful confirmatory tool that is capable of either substantiating or refuting the role of a specific gene in polygenic obesity. Linkage studies were the first genome-wide hypothesis-generating approach used in the initial searches for human obesity susceptibility loci or genes. Genome-wide linkage analysis requires the recruitment of related individuals for investigation of co-segregation of genetic markers with disease phenotype. The first genome-wide linkage scan for an obesity-related quantitative trait loci (QTL) was published in 1997, and identified an association between a region at chromosome 11q21-q22 and percentage body fat in Pima Indians (Norman et al., 1997). Since this initial study, the number of published linkage scans has increased considerably and highlighted promising candidate genes for further in-depth analyses. In 2005, the final update to the Human Obesity Gene Map reported 253 QTLs derived from 61 genome-wide linkage scans, 15 of which were replicated in 3 or more studies (Rankinen et al., 2006); however, the genes or gene variants related to these replicated QTLs have not yet been discovered (Li and Loos, 2008). While replicated results tend to alleviate doubts regarding the validity of previous findings, a rank-based meta-analysis of 37 genome-wide linkage scans was unable to achieve convincing evidence linking BMI or obesity to any of the QTLs evaluated (Saunders et al., 2007), despite generating substantial statistical power (31,000 individuals, 10,000 families). It has been suggested that genome-wide linkage scans are better suited to the discovery of highly penetrant gene variants, such as those underlying the monogenic and clinically severe forms of obesity that exist in a common chromosomal background amongst related

individuals (Hirschhorn and Daly, 2005; Li and Loos, 2008). As such, Li and Loos recently postulated that genome-wide association will replace genome-wide linkage as the hypothesis-generating tool of choice for the identification of genes underlying common obesity (Li and Loos, 2008). Genome-wide association scans (GWAS) have been made possible due to technological advancements, as well as initiatives such as the International HapMap Consortium. Indeed, the HapMap initiative has resulted in a public database describing over 10 million human SNPs (International HapMap Consortium et al., 2007) and has also identified the subset of markers that best capture genetic variability in humans (Daly et al., 2001), referred to as tag SNPs. An important caveat with regard to this impressive database is that it has been generated using genetic information from “only” four populations: Nigerian, Japanese, Chinese and American. Thus, while this genetic sampling provides an excellent starting point, the tag SNPs identified in these populations may not provide the same degree of information in other populations. In 2006, the first GWAS was published, and described an association in obese children and adults with a common SNP (rs7566605) in the insulin-induced gene 2 (INSIG2) (Herbert et al., 2006). Although the authors replicated this association in four separate samples, subsequent independent investigation in large, populationbased studies (i.e., 20,000 individuals) has failed to confirm these initial findings (Dina et al., 2007; Loos et al., 2007a; Rosskopf et al., 2007). In 2007, the Welcome Trust Case-Control Consortium used a GWAS approach to search for genetic variants that predispose individuals to type 2 diabetes (T2D) (Frayling et al., 2007). A cluster of SNPs in the fat mass and obesity associated (FTO) gene region were found to be strongly associated with T2D; however, when data were adjusted for BMI the association was lost. In other words, this positioned FTO as a gene predisposing people to obesity rather than T2D

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30.3  Nutritional genomics

(Frayling et al., 2007). The association between FTO variants and BMI was found in each of the 13 cohorts used (Frayling et al., 2007), and later in 3 additional and independent cohorts (Scuteri et al., 2007). A more recent GWAS identified common variants near the melanocortin 4 receptor (MC4R) gene (rs17782313, rs17700633) which were associated with fat mass, weight and risk of obesity (Loos et al., 2008). Two subsequent studies have confirmed the associations between FTO and MC4R variants, and BMI, as well as identifying previously unrecognized associations between other sequence variants and obesity traits (Thorleifsson et al., 2009; Willer et al., 2009). Despite the exciting progress in our ability to identify gene variants associated with obesity-related traits, it remains important to recognize the phenotypic impact of such variants. For example, the Genetic Investigation of Anthropometric Traits (GIANT) consortium noted that individuals (n  178) with 13 or more “standardized” (weighted by relative effect size) BMI-increasing alleles were only 0.59 kg/m2 heavier than an average individual in the cohort studied (n  14,409) (Willer et al., 2009). This finding highlights the modest effects typically associated with common obesity-predisposing variants, and reinforces the complexity of such a task. Other possible contributors to the established heritability of BMI that are now being explored are epistasis (Dong et al., 2005), epigenetics (Campion et al., 2009) and copy number variations/polymorphisms (Willer et al., 2009). Ultimately, complex traits such as common obesity are seldom solely dictated by genetic factors. Rather, it is more likely that genetic factors will determine the susceptibility of individuals to their surrounding “obesogenic” environment – i.e., an environment favoring energy abundance and storage versus energy expenditure (Bouchard and Rankinen, 2007; Chung and Leibel, 2007). It is this concept that has reinforced the research community’s interest in unraveling gene–gene and gene–environment interactions, with the

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goal of identifying those factors that are causative for the increased prevalence of obesity. In the context of common obesity, the environmental challenges that have typically garnered the most attention for their potential interaction with genetic factors include dietary composition, smoking, and physical activity. While the term “interaction” may mean different things to different people, we use the term here to describe a deviation from the additive or multiplicative effects of combining genotype and environment factors (Loos et al., 2007b). The remainder of this chapter will be focused on diet–gene interactions as they pertain to the study of human obesity.

30.3  Nutritional genomics When searching for the culprit underlying the dramatic increase in obesity, it is perhaps easiest to point the “finger of blame” at diet. Although our dietary habits and the composition of our foods have changed considerably over the past century, it is evident that not everyone in society who has experienced an energy surplus has gained weight similarly. So how can we explain the existence of these inter-individual differences? It is the field of nutritional genomics that has taken center stage in an attempt to answer this question. The term “nutritional genomics” is best considered as an umbrella term that describes two distinct but highly related disciplines: nutrigenetics and nutrigenomics; however, a quick search in published literature reveals that the abridged term “nutrigenomics” is also used as an umbrella term. Irrespective of the term used, both subdisciplines describe the study of diet–gene interactions; however, nutrigenomics and nutrigenetics tend to approach the question from different perspectives. The former describes a global functional response to a nutrient or diet at the level of gene expression, protein expression or, more recently, metabolite abundance. The latter is concerned with understanding the role of genetic

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variation in modifying an individual’s response to a dietary stimulus (Mutch et al., 2005). Both approaches are starting to yield insight into how diet–gene interactions may alter an individual’s susceptibility to gaining weight. Furthermore, it is expected that nutritional genomics research will generate knowledge that will assist clinicians eventually to assess and predict those patients susceptible to weight gain and, more importantly, help tailor lifestyle modifications that will improve an individual’s propensity for weight loss.

30.4  The role of gene polymorphisms Diet–genotype interactions in the context of obesity have been explored using either observational or intervention approaches, where the focus has been on candidate genes related to energy balance and/or adipogenesis. Observational approaches have been previously used to reveal interactions between lifestyle factors and polymorphisms in genes coding for leptin (LEP), the -adrenoceptor (ADRB) family, uncoupling proteins (UCPs), peroxisome proliferator-activated receptor-alpha and -gamma (PPAR, PPAR), the interleukin 6 receptor (IL6R), and numerous others that are associated with obesity-related traits (recently reviewed by Qi and Cho, 2008). While the aforementioned review highlights promising diet–genotype interactions and their influence on weight parameters, a major limitation with some of the studies discussed is the small number of subjects used to identify associations. As such, it is imperative that these exciting preliminary findings are replicated in much larger populations. Nevertheless, these studies have generated novel hypotheses that suggest diet can influence obesity differently based on genetic variants. Several studies have begun to explore the impact of high energy intake on obesity risk in subjects with particular gene variants. For example, Miyaki and colleagues studied the interaction

between the adrenergic beta-3 receptor (ADRB3) and high energy intake in 295 Japanese males. ADRB3 is a gene predominantly expressed in adipose tissue, and is involved in the regulation of lipolysis and thermogenesis. The authors found that the ADRB3 Trp64Arg polymorphism (rs4994) was not systematically associated with obesity; however, the authors did find that subjects with the Trp64Arg polymorphism and the highest level of energy intake displayed an increased risk for obesity (OR  3.37; 95% CI  1.12–10.2) (Miyaki et al., 2005). Interestingly, a recent study examined FTO gene variants in order to determine whether dietary energy density predisposes children to an increased fat mass several years later, and whether this predisposition is related to FTO variant status. The authors were unable to identify an association between dietary energy density and FTO polymorphisms that influence fat mass later in life (Johnson et al., 2009). Taken together, these studies are encouraging, as they reflect the importance of incorporating nutritional genomics into ongoing research programs; however, it will be important to independently verify these results (both positive and negative results) in larger populations. While the interaction between energy intake and genetic variation has revealed intriguing results, other researchers have begun to assess interactions between dietary macronutrient content and genetic variation, and their potential impact on obesity risk. Similar to ADRB3, the ADRB2 gene is involved in the regulation of adipose tissue lipolysis, and its activity is downregulated in subcutaneous fat of obese subjects (Rasmussen et al., 2003). The ADRB2 Gln27Glu polymorphism (rs1042714) was shown to cause aberrant agonist-mediated down-regulation of receptor expression (Green et al., 1994). Addition­ally, the ADRB2 Gln27Glu variant was shown to modify the probability of obesity in relation to carbohydrate intake (Martinez et al., 2003). Although the incidence of obesity was not affected by the polymorphism, the authors found that women with the Gln27Glu variant

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30.5  The role of gene expression

consuming a higher carbohydrate intake ( 49 percent total energy) displayed significantly elevated fasting insulin levels (P   0.01) and a greater risk for obesity (OR  2.56) when compared to female Gln27Glu carriers with a lower carbohydrate intake. These trends were not observed in female Gln27Gln homozygotes, or in men. The increased propensity for obesity in female Gln27Glu carriers was hypothesized to reflect established alterations in fat oxidation in obese Glu27Glu homozygotes (Macho-Azcarate et al., 2003) that potentially contribute to a hyperlipidemic state and thereby set the stage for high carbohydrate consumption to induce insulin resistance and subsequent hyperinsulinemia (Marti et al., 2008). The transcription factor PPAR2 plays an important role in the regulation of adipocyte differentiation and is the predominant isoform in adipose tissue. The PPAR2 Pro12Ala polymorphism was investigated for interactions with total fat intake and dietary fatty acid intake, as well as the ratio of dietary polyunsaturated to saturated (PUFA:SFA) fats. This line of thinking stemmed from research exploring PPAR-regulated highfat diet induced adipocyte hypertrophy and insulin resistance in mice, where PPAR heterozygous null mice were found to be resistant to high-fat diet-induced obesity (Kubota et al., 1999). Additionally, fatty acids and their derivatives are thought to be the endogenous ligands for PPARs (Jump, 2004). It was therefore intriguing to speculate that the PPAR2 Pro12Ala variant may be a candidate genetic factor influencing an individual’s metabolic response to dietary fat. The Nurses’ Health Study cohort revealed an inverse association between monounsaturated fat (MUFA) intake and BMI, but only among carriers of the Pro12Ala variant-allele (Memisoglu et al., 2003). In the Quebec Family Study, the Pro12Ala variant was shown to modulate the interaction between diet SFA and various anthropometric measures, such as BMI and waist circumference (Robitaille et al., 2003). Additionally, Pro12Ala carriers consuming a diet with a low PUFA: SFA

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ratio had an increased BMI, while the opposite was found true when the PUFA: SFA ratio was high (Luan et al., 2001). As the examples above indicate, evidence ex­ists demonstrating that genetic polymorphisms can modulate the effect of diet on an individual’s risk for developing an obesity-related trait (Figure 30.2a); however, identifying single diet–gene interactions (already a challenge!) is merely scratching the surface when it comes to unraveling the impact of diet on the risk of obesity. Foods are not single nutrients, but complex mixtures of macronutrients, micronutrients and non-nutritive phytochemicals. Furthermore, numerous cultural, social, familial and environmental factors mean that a particular food in one country may be very different in another. As such, a diet–gene interaction observed in one cohort may not be observed in another cohort because of the aforementioned factors. Genetic differences between populations will also be a major factor that affects the independent replication of diet–gene interactions. Thus, while current nutritional genomic studies tend to make conclusions based on data stemming from the study of a single gene variant, the future challenge will be to integrate and consider all of these data simultaneously.

30.5  The role of gene expression Investigating the role of genetic variants on an individual’s susceptibility to weight gain, both independently and in conjunction with diet, will remain an important component in deciphering the etiology of common obesity. It is also crucial to consider the role of gene expression in the development of obesity, as well as the different influences that dietary components will have on modulating gene expression (Figure 30.2b). The advent and integration of microarray technologies, which enables the

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t/ or sp ion n a Tr iffus d

Nutrient

Obesity-related trait (e.g., BMI)

36

Protein

35 Intracellular pool

34 33

mRNA

Ligand

32 TF

31

TF

30 29

5’regulatory sequence

Homozygote WT carrier

28

pre-mRNA DNA Pol

Phenotypic response (e.g., adipogenesis, lipolysis, etc.)

Expression (+/-)

Gene

Minor allele carrier Low level

(a)

Alternate metabolic fates

High level

Nucleus

Exposure to dietary factor

(b)

Figure 30.2  Diet–gene interactions from nutrigenetic and nutrigenomic perspectives. (a) Hypothetical data illustrating a gene–lifestyle interaction in the context of obesity. This example represents one possible relationship between the level of exposure to a dietary factor and the phenotypic outcome (e.g., BMI). (b) Crude mechanism by which a nutrient could contribute to the regulation of gene expression, by acting as a ligand for nuclear transcription factors which interact with regulatory sequences upstream of target genes, thereby potentially altering cellular activities. TF, transcription factor; DNA Pol, DNA polymerase.

analysis of global gene expression, has greatly facilitated this effort (Mutch, 2006). In recent years, microarray studies have revealed that the adipose tissue transcriptome is responsive to weight loss induced by lifestyle and surgical interventions, and can thus be used to identify both differentially expressed genes and predictors (Figure 30.3). For instance, participants in the European project NUGENOB (NUtrient-GENe interactions in human OBesity) demonstrated that the consumption of a 10-week, low-energy diet in obese women, irrespective of the carbohydrate and fat content, led to modest, yet significant (P  0.001 for both moderate- and low-fat diets), weight loss. Furthermore, over 100 genes were identified as differentially regulated when comparing adipose tissue gene expression before and after weight loss, where genes involved in polyunsaturated fat biosynthesis were down-regulated (e.g., fatty acid synthase, stearoyl coenzyme A desaturase 1 (SCD1), and fatty acid desaturase 1 and 2) (Dahlman et al.,

2005). The authors suggested this may be important for depletion of lipids from human fat cells during the consumption of a low-energy diet. In an alternate study, inflammation-related genes were down-regulated in obese subjects who lost weight in response to the 28-day consumption of a very low calorie diet (VLCD; 800 kcal/day) (Clement et al., 2004). The transcriptional changes induced by the VLCD led to a “normalization” of the expression profiles in obese subjects (i.e., the expression profiles tended to resemble those of lean subjects). The transcriptome is sensitive not only to changes in total energy intake, but also to the macronutrient composition in diet; however, the NUGENOB consortium demonstrated that energy restriction, rather than variations in the dietary content of carbohydrate and fat, was the dominant factor influencing gene expression following the 10-week consumption of lowenergy diets (Capel et al., 2008). Nevertheless, such knowledge has prompted the suggestion that understanding why individuals respond

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30.5  The role of gene expression

(a)

Which genes discriminate cases from controls?

vs.

(Differentially expressed genes)

Controls

Cases

Candidate genes

(b)

Which genes predict a response? 1. Identify ‘predictors’ with Training Set (9/10 available data) 2. Verify accuracy of ‘predictors’ in test set (1/10 available data)

Responders and non-responders known (Training set)

Unknown sample (Test set)

Figure 30.3  Identifying candidate genes with microarrays. Using supervised bioinformatic algorithms, microarrays can be used to identify both discriminators and predictors. Discriminators, or differentially expressed genes, are identified by comparing one group (e.g., obese subjects) to another group (e.g., lean subjects). Predictors are identified using a two-step approach in which a classifier (i.e., genes considered to be predictive) is built using 90 percent of the subjects (responders and non-responders) and then tested in a blinded manner using the remaining 10 percent of the subjects. If the classifier is valid, responders and non-responders in the remaining 10 percent of the subjects will be correctly classified and predictive genes are identified.

differently to weight-loss diets varying in macronutrient composition will pave the way for personalized nutrition. As previously mentioned, both the obesity phenotype and response to lifestyle interventions demonstrate considerable inter-individual variability. While gene variants influence this variability, gene expression has also been found to play an important role in shaping an individual’s response to an “obesogenic” environment. Indeed, it can be hypothesized that the existence of a coordinated transcriptional response that favors energy expenditure over energy storage in times of overfeeding may provide a degree of “resistance” to weight gain. Several studies have been published in which the authors have

attempted to address the utility of gene expression to predict changes in weight. For example, the transcriptomic response to short-term energy surplus in lean and obese men was recently investigated by Shea and colleagues. Sampling abdominal subcutaneous adipose tissue before and after a 7-day hypercaloric diet, the authors found 45 genes were differentially expressed in response to the diet, of which 6 of them were also different between lean and obese individuals (Shea et al., 2009). These six genes, which revealed an interaction between adiposity and the hypercaloric diet, included transferrin, transaldolase 1, cathepsin C, insulin receptor substrate 2, pyruvate dehydrogenase kinase isozyme 4 and SCD1. The authors suggested

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this group of genes may constitute a protective molecular profile in lean individuals that prevents excessive weight gain despite a significant surplus of energy. A study by Koza and colleagues found that mice could be characterized as “high gainers” and “low gainers” as early as 6 weeks, and that these phenotypic differences persisted during times of diet-induced weight gain or dietinduced weight loss (Koza et al., 2006). Gene expression profiling in mouse adipose tissue and hypothalamus revealed important differences between high and low gainers, most notably in the Wnt signaling pathway. The authors concluded that this differential expression profile may serve to predict whether mice will be high or low gainers. A human study performed by Mutch and colleagues examined whether subcutaneous adipose tissue gene expression could be used to predict subjects who will respond to a low-calorie diet (LCD) by losing weight (8–12 kg) versus those subjects who fail to lose weight (4 kg) (Mutch et al., 2007). The authors demonstrated that the global gene expression profiles of responders and non-responders could be distinguished, but the use of predictive bioinformatic methods failed accurately to predict responders from non-responders. When such studies are considered, it appears that gene expression profiles do differ between individuals who lose weight during a dietary intervention versus those subjects who do not; however, further work is required before it will be possible to predict weight loss with a high degree of accuracy.

30.6  From bench to bedside: predicting outcome An ultimate goal of nutritional genomic re­search is to predict a particular outcome. In other words, a clinician, using genetic knowledge, will be able to make informed lifestyle recommendations for a patient that will help reduce

disease risk and/or improve treatment efficacy. The search for reliable predictors for successful weight loss is ongoing, and focus over the past decade has begun to consider genetic make-up (Moreno-Aliaga et al., 2005). Current methods to identify successful responders to a weight-loss intervention tend to be based on weight loss during an initial evaluation phase (Finer et al., 2006); however, there is an increasing interest in exploring the utility of candidate gene variants to predict a patient’s response to hypo­caloric or macronutrient-restricted diets (Martinez et al., 2008). Indeed, support for gene variants related to appetite control (leptin and melanocortin pathway genes), energy expenditure (UCPs, ADBRs), lipid metabolism (PLIN) and adipogenesis (PPAR2) is substantiated by the various epidemiological studies previously discussed. The UCP gene family (UCP1-3) can modulate energy expenditure by uncoupling respiration and phosphorylation at the mitochondrial inner membrane. The UCP1 proton transporter is predominantly expressed in brown adipose tissue, and the -3826A/G polymorphism (alternatively referred to as Bcl-1) was previously found to modify the recovery to long-term positive energy balance (Ukkola et al., 2001). Furthermore, subjects homozygous for the Bcl-1 allele lost significantly less weight following a 10-week LCD intervention than did heterozygotes, who in turn lost less weight than wild-type homozygotes (P   0.05) (Fumeron et al., 1996). Another study further demonstrated a potential role for UCP1 haplotype in determining the magnitude of weight lost after a 1-month VLCD (Shin et al., 2005). Interestingly, research suggests a possible synergistic effect for the UCP1 Bcl-1 and ADRB3 Trp64Arg polymorphisms, which confers an additional resistance to weight loss during periods of negative-energy balance that is not observed in those with only one of the variants (Kogure et al., 1998). Evidence also exists for the related UCP2 and UCP3 genes, where polymorphisms and certain haplotypes can modify an individual’s response to a VLCD,

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30.7  Outlook

in terms of body and fat mass reduction (Cha et al., 2006; Yoon et al., 2007). These findings suggest that polymorphisms in UCPs may be of considerable interest for predicting diet-induced weight loss. The perilipin (PLIN) protein is an important regulator of lipase activity in adipocytes, and inhibits triglyceride catabolism until phosphory­ lated by catecholamine-driven signal transduc­tion pathways (Brasaemle et al., 2009). The 13041A/G (rs2304795) and 14995A/T (rs1052700) SNPs in the PLIN gene were previously shown to be associated with waist circumference and percentage body fat in a gender-specific manner (Qi et al., 2004). In another study, Corella and colleagues found that subjects with the 11482G/A polymorphism (rs894160) minor allele were resistant to weight loss, even with a 1-year LCD intervention (Corella et al., 2005). Furthermore, genetic variation at this locus in general has been shown to influence weight loss after both caloric restriction (12-week) (Jang et al., 2006) and the consumption of a VLCD (6-week) (Soenen et al., 2009). Thus, studies are emerging which demonstrate that using specific gene polymorphisms to predict diet-induced weight loss in individuals may not be wishful thinking, yet translating this knowledge from the laboratory bench to the actual clinic requires further validation and replication studies. Indeed, it must be determined whether predictive SNPs are broadly applicable in the general population or whether they are best suited for particular subsets of the population (e.g., ethnic-specific, male versus female, adult versus children, diseased versus healthy, etc.).

30.7  Outlook As the body of research presented above demonstrates, genetic factors acting both independently and in conjunction with lifestyle factors play a notable role in the etiology of obesity. More importantly, this research has begun to

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provide an explanation for the heterogeneous response of individuals to energy imbalances stemming from diet and/or alternate lifestyle factors. Recent progress has revealed that other factors influencing genetic variation, such as epigenetics (Campion et al., 2009) and copy number variants (Sha et al., 2009; Willer et al., 2009), also have an important role and need to be considered in future nutritional genomic research. Furthermore, the recent concept of “genetical genomics”, an approach combining DNA sequence variation with gene expression data, may identify susceptibility genes that are causative, rather than responsive, for disease traits (Mehrabian et al., 2005; Schadt et al., 2005). Incorporating this knowledge and approach into future nutritional genomics research will also be of paramount importance. Despite this encouraging progress, a deal of skepticism remains regarding the utility of nutritional genomics research in the fight against the obesity epidemic; however, as discussed in the preceding sections, this relatively new axis of nutritional sciences has provided glimpses of its potential to contribute to substantial advances in the clinical prediction of individuals susceptible to weight gain and/or loss, as well as the synchron­ ization of weight-loss diets with the genetic make-up of individual obese persons. While nutritional genomics is inherently translational research (i.e., moving research from bench to bedside), there is considerable controversy regarding the appropriateness and timing for the mass dissemination and commercialization of this research. Although all parts of the spectrum (i.e., from the researcher to the medical/ nutritional practitioner to the consumer) are aware of nutritional genomics and recognize its potential, there remains a paucity of scientific studies in which genetic information has been used to drive nutritional counseling. To the best of the authors’ knowledge, only a single research paper has been published along this line. In 2007, a personalized energy-restricted diet (i.e., nutrigenetic testing complemented

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by specific dietary advice and/or supplement consumption to account for deficiencies) was implemented in a clinical trial, and compared to a generic energy-restricted diet (Arkadianos et al., 2007). In other words, some subjects simply ate a common energy-restricted diet, whereas other subjects consumed an energyrestricted diet in accordance with genetic information. The authors clearly stated the intention was not to design personalized weight loss diets per se; rather, they sought to optimize a subject’s nutrient intake in light of the current understanding of genomic variation and diet–gene interactions to better motivate these individuals for compliance and weight loss. A panel of SNPs related to folic acid metabol­ism, phase II enzyme detoxification, oxidant balance, bone health, inflammation and lipid metabolism were genotyped, as opposed to the polymorphisms modulating obesity risk discussed in the present chapter. Nevertheless, results indicated that those in the “nutri­genetictested” group were significantly more likely to maintain weight loss in the longer term (300 days) as compared to the “non-tested” group (OR  5.74; 95% CI  1.74–22.52). There was also a significant decrease after 90 days (P  3  106) in fasting glucose levels amongst “nutrigenetic-tested” subjects with baseline levels 100 mg/dl. Although encouraging, such a result will need to be replicated in larger cohorts prior to the widespread implementation of nutritional genomics research as a means to combat obesity. While the aforementioned study was a joint endeavor between medical practitioners and a commercial company, most nutritional genomic companies provide direct-to-consumer tests. In 2006, the Government Accountability Office in the United States released a report stating that directto-consumer nutrigenetic tests for chronic disease risks were misleading, and provided ambiguous and medically unproven dietary recommendations (Kutz, 2006). There is also a consensus in the scientific community that the potential for

nutritional genomics in chronic disease risk reduction and personalized weight-loss regimens requires considerably more research into diet–gene interactions, and must therefore be approached with caution at the present time (Arab, 2004; Adamo and Tesson, 2007; Rimbach and Minihane, 2009). Although the causality between diet–gene interactions and chronic disease requires further scientific validation, evidence suggests there may be an alternate benefit of discussing genetics during consultations with obese individuals. For instance, a study found that providing a one-session consultation on weight management to obese subjects, in which genetic information was included (i.e., heredity, twin studies), led to novel insights on weight problems, and improved a subjective rating of negative mood regarding obesity (Rief et al., 2007). Similarly, in individuals with a family history of obesity, this type of consultation resolved some feelings of self-blame, and led to more achievable weight-loss goals in subjects with and without familial predisposition (Conradt et al., 2009). Taken together, nutritional genomics fills an important niche in the scientific foundation of a “Brain-to-Society” systems approach for addressing the obesity epidemic. It is clear that common obesity is a multi-factorial disease that has direct links with aspects of biochemistry, nutrition, sociology and psychology, amongst others. While nutritional genomics generates critical information regarding diet–gene interactions, it is only by considering all aspects simultaneously that we can hope to alleviate the current obesity epidemic.

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C H A P T E R

31 Physical Activity for Obese Children and Adults Ross Andersen and Catherine Sabiston Department of Kinesiology and Physical Education, McGill University, Montreal, Canada

o u t l i n e 31.1 Introduction

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31.2 Adults and Physical Activity

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31.3 Physical Activity and Young People 392 31.4 Linking Physical Activity and Obesity 393 31.4.1 Sedentary Activities and Obesity 393 31.4.2 Lifestyle Physical Activity 393 31.5 The Model 31.5.1 Environmental Factors 31.5.2 Self-perceptions, Attitudes and Beliefs

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31.1  Introduction The obesity epidemic has become one the leading health problems in developed countries around the world. This is a public health challenge, and countries such as Canada, Brazil and Mexico are experiencing dramatic increases in the prevalence of obesity. Recent data from the National Center

Obesity Prevention: The Role of Brain and Society on Individual Behavior

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31.6 Supporting Overweight Individuals in Overcoming real and/or Perceived Barriers to Physical Activity 397 31.7 Outcomes 31.7.1 Physical Outcomes 31.7.2 Psycho-emotional Outcomes 31.7.3 Social Outcomes

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for Health Statistics (NCHS) show that more than 33 percent of Americans are overweight, and 34 percent are obese (Ogden et al., 2006). More than 6 percent are “extremely” obese. In the US, over one-third of adults (or 72 million people) were classified as obese in 2005–2006, as reported by the NCHS (Ogden et al., 2006). The causes of the obesity epidemic are complex and multifaceted. Clearly, at a population

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level, an energy imbalance is necessary for such widespread increases in body mass index (BMI) to occur. While much has been written about the increased energy intake as the root cause of the epidemic, it is critical to examine the energy expenditure component of the energy balance equation at the same time.

31.2  Adults and physical activity The Surgeon General has reported that a sedentary lifestyle is hazardous to our health, and a growing body of scientific evidence supports recommendations of increasing physical activity to lose weight and maintain good health (Hagan et al., 1986; Klem et al., 1997; Schoeller, 1999; Department of Health and Human Services and US Department of Agriculture, 2005). Despite this knowledge, few adults perform enough physical activity to derive health benefits from it. Most exercise scientists agree that performing at least three bouts of vigorous exercise per week can result in significant health benefits (Haskell et al., 2009). Recently, it has become apparent that the health benefits of physical activity may be achieved at intensities that are lower than those traditionally recommended. Many countries are now encouraging people to accumulate moderate intensity activity throughout the day if they cannot exercise vigorously. The American College of Sports Medicine has recommended that obese individuals accumulate between 200 and 300 minutes of moderate intensity physical activity per week to enhance long-term weight management (Jakicic et al., 2001). This is similar to the recommendation from the Institute of Medicine (IOM) that suggests doing 60 minutes of moderate intensity physical activity per day for weight management (Institute of Medicine, 2002). The International Association for the Study of Obesity (IASO) also advocates 45–90 minutes of

physical activity per day to control body weight (Saris et al., 2003). Nonetheless, obese adults are more likely to be sedentary and not participate in leisure time activity than are their leaner counterparts (Shields and Tremblay, 2008). A recent report found that 19 percent of obese men and only 16 percent of obese women met minimum public health recommendations for physical activity (Centers for Disease Control and Prevention (CDC), 2000). Moreover, in community settings, obese individuals are more likely to choose passive versus active options to ambulate and commute (Andersen et al., 2006). It is paradoxical that 62 percent of obese men and 57 percent of obese women report attempting to use physical activity to lose weight (CDC, 2000).

31.3  Physical activity and young people The World Health Organization (WHO) recom­ mends that school-age children engage in moderate to vigorous physical activity for at least 60 minutes per day (World Health Organization, 2009). Appropriate physical activity may help children better manage their weight, and has been associated with the promotion of healthy development (Floriani and Kennedy, 2008). Despite the numerous physiological and psychological benefits of active living for young people, levels of physical activity are decreasing around the globe. The following outlines key reasons why physical activity has been engineered out of many young people’s lives: more opportunities for sedentary leisure time (television, Internet, video games); lack of time; greater pressure for academic performance and increased amounts of homework; less recess, intramural and after-school sports in the schools; lack of safe places to exercise; reduced active commuting to and from school; changes in neighborhood design that result in reduced physical activity; increased parental concerns

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about child safety; unskilled children not being encouraged to remain in sports activity; unskilled children feeling embarrassed to exercise in front of peers; and more dual-career families (Floriani and Kennedy, 2007, 2008).

3.4 3.1 29.2

Watching TV Watching time-shifted TV

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Time on the Internet

31.4  Linking physical activity and obesity Inactivity and overweight/obesity have been identified as significant health risks (Physical Activity Guidelines Advisory Committee, 2008). While inactivity and overweight/obesity do not necessarily co-occur, they have been linked. Increasing physical activity has been identified as one way of battling the obesity epidemic (Wing, 1999). We propose that increasing physical activity, in particular lifestyle activity, and reducing time spent in sedentary activities are strategies to help offset the public health risks associated with obesity across the lifespan.

31.4.1  Sedentary activities and obesity A direct association between the hours of tele­vision watched and BMI or body fatness in American children has been reported (Andersen et al., 1998a). Others have found that time spent playing video games, in front of a computer and, to lesser extent, reading are also related to an increased prevalence of obesity. This link has been made primarily as a result of the low energy expenditure that occurs during sedentary behaviors. It has also been reported that children who watch 5 or more hours of television per day consume on average 200 kcal per day more than their counterparts who watch 1 hour or less of television per day (Crespo et al., 1998). We suspect that television watching may be a cue to eat for many overweight individuals. Recent data from the Neilson Organization (Figure 31.1) have reported that the average American adult watches over 127 hours of television and browses

Watching video on the Internet

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Watching video on a mobile phone

Figure 31.1  Average monthly hours among American adults doing sedentary activities. Source: Nielson Three Screen Report (2008).

the Internet for 26 hours per month. This represents 22 percent of all hours each month – or 39 percent of typical waking hours are spent in front of a screen. Epstein and colleagues (1997) have developed a treatment for sedentary overweight children that encourages them to reduce the time spent engaging in sedentary activities. Children in these studies are given television allowances and are taught to limit the time they spend on the Internet and playing video games. They are also encouraged to look for opportunities to walk or cycle to and from school. While this model has not been tested in adult populations, it may offer promising results.

31.4.2  Lifestyle physical activity Many investigators have also demonstrated that traditional vigorous exercise may not be the optimal way to help sedentary overweight individuals adopt more active lifestyles (Andersen et al., 1998b, 1999; Jakicic et al., 2001). This is particularly true if they do not enjoy or are not able to perform traditional vigorous, continuous exercise. Lifestyle physical activity encourages patients to look for opportunities to accumulate moderate-intensity physical activity throughout the

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day (Andersen, 1999; Andersen et al., 1999). It has been found that sedentary overweight adults often feel that this may be a do-able way to begin increasing their physical activity. Moreover, patients who begin increasing their activity with lifestyle activity seem to gain confidence in their ability to exercise, and over time begin to transition into a more traditional, moderate-to-vigorous exercise program. Jakacic and colleagues have also found that accumulating short 10-minute bouts of aerobic exercise may offer obese adults a suitable alternative to traditional uninterrupted exercise (Jakicic et al., 1999). This is important, given that a perceived lack of time remains the top reason that sedentary overweight individuals report for not participating in regular activity.

31.5  The model The model depicted in Figure 31.2 has been developed by the authors to summarize physical activity motivation and health behavior change models – e.g., the expectancy–value model (Eccles, 1983); self-determination theory (Deci and Ryan, 1985); social cognitive theory (Bandura, 1997); the social-ecological model (Bronfenbrenner, 1977); and theory of planned behavior (Ajzen and Madden, 1986) – as well as empirical evidence demonstrating direct and mediating relationships as outlined. This non-linear model describes the individual, social and environmental factors that influence physical activity in overweight individuals. Positive changes, as a result of ­physical activity

Weight status Antecedents Self-perceptions, attitudes, beliefs

Physical environment

Social context

Physical activity Moderators -diet -sedentary behavior

Psychoemotional

Physical/ biological Outcomes

Figure 31.2  Model relating weight status and physical activity.

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31.5  The model

intervention, have also impacted these factors; hence the feedback loop depicted in the model (Kahn et al., 2002; Gallagher et al., 2006). The outcomes of physical activity are classified as having psycho-emotional, social and physical underpinnings.

31.5.1  Environmental factors The relationship between weight status and physical activity levels may be explained in part by environmental factors. For example, neighborhood features, such as lower perceptions of safety and characteristics that preclude walking, have been linked to higher body weight. Neighborhoods with higher walkability indices (grid-like structures with less cul-de-sacs and more street connectivity and intersections, the presence of sidewalks, and perceptions of safety) tend to promote physical activity and support a greater number of active transportation than low-walkability neighborhoods (Gordon-Larsen et al., 2006; Smith et al., 2008; Spence et al., 2008). There is a lower prevalence of overweight in these safe and walkable neighborhoods, which tend to be located in more urban living areas (Joens-Matre et al., 2008). Notwithstanding the walkability index, there is also lower prevalence of overweight in urban compared to rural areas. Furthermore, access to facilities and opportunities can be a barrier to increased physical activity levels for overweight individuals (Gallagher et al., 2006; Holt et al., 2008). Strategies to increase safe play spaces and to provide specific sports and physical activity programs to overweight youth appear to be effective in reducing weight and enhancing health and well-being (Farley et al., 2007; Weintraub et al., 2008). Greater (or increased awareness of) opportunities for physical activity among overweight individuals are necessary. One approach may be to promote lifestyle physical activity. Integrating 30–60 minutes, 5–7 days per week, of lifestyle physical activity has adaptive physical and

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psychological outcomes, including reduced anxiety and depression, increased self-esteem, and reduced fatigue (Andersen et al., 1999). Environmental manipulations, such as simple signs and visual prompts encouraging physical activity (for example, taking the stairs instead of the elevator), appear to be beneficial in increasing lifestyle physical activity (Andersen et al., 1998b; Rees, 2007). Other environmental attributes include the development and implementation of institutional policies, and community-level program development. Physical activity opportunities that enable overweight individuals to exercise together may also be particularly important.

31.5.2  Self-perceptions, attitudes and beliefs Understanding the individual-level factors that are linked to physical activity participation among overweight/obese individuals is challenging and overwhelming. Nonetheless, most emphasis has been placed on self-perceptions such as self-concept, self-efficacy, perceptions of competence, enjoyment, intrinsic motivation and interest in physical activity, and perceived barriers and drawbacks of exercising. Specifically, individuals who have weaker perceptions of the physical self and higher weight concerns are more likely to use physical activity as weight management (Page and Fox, 1997). Self-esteem tends to be directly associated with physical activity levels (Dunn et al., 2001), and overweight individuals may be more likely to report lower self-esteem than their healthyweight counterparts (Pesa et al., 2000). Perceptions of competence/physical activity self-efficacy and/or beliefs of enjoyment and interest have shown some of the strongest associations to behavior both in theory (Bandura, 1997; Eccles and Wigfield, 2002) and in empirical evidence (Deci and Ryan, 1985; van der Horst et al., 2007; Sabiston and Crocker, 2008). Unfortunately, overweight persons report lower

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perceptions of competence and generally feel less pleasure while exercising, and thus exhibit lower enjoyment beliefs (Garcia-Bengoechea et al., 2010). Once physically active, overweight individuals have reported empowering increases in perceptions of competence and self-concept that foster increased physical activity participation and greater confidence to try new activities (Gallagher et al., 2006; Sabiston et al., 2009). This relationship is illustrated in Figure 31.2 by the circular nature of the model. Research from the Australian Longitudinal Study on Women’s Health reports that depressive symptoms are higher among women who are overweight or obese as well as among more sedentary individuals (Ball et al., 2008). Physical activity can promote and maintain mental health by protecting individuals from depression and anxiety (Paffenberger et al., 1994; Motl et al., 2004). Physical activity may also be used as a treatment for non-clinical depressive symptoms and diagnosed depression (North et al., 1990; Craft and Landers, 1998). Overweight individuals face unique socialcontext barriers, such as stereotypes, embarrassment, body-image concerns, time barriers and lack of motivation, and obstacles (e.g., weather, access, support) are commonly reported (Godin et al., 1986; Gallagher et al., 2006). Efforts aimed at reducing the perception and/or existence of these barriers for overweight individuals are necessary. Simply engaging in structured physical activity programs seems to reduce the extent of reported barriers (Gallagher et al., 2006), thus providing some justification for the circular nature of the model.

31.5.3  Social context The social context includes others’ beliefs and behaviors regarding physical activity and weight status. Most individuals need to perceive strong social-normative beliefs about exercising in order to engage in the behavior. Drawing

from the sport and exercise literature, social support primarily consists of the network of providers (who) as well as the types of strategies provided (what) (Rees, 2007). For example, having physically active friends, co-workers and family members who can act as role models has a positive impact on physical activity levels for youth and adults, independent of weight status (Sallis et al., 2000). In fact, overweight youth reported that a barrier to their activity levels was not having someone with whom to engage in physical activities (Zabinski et al., 2003). The functions of social support are important factors in promoting physical activity throughout the lifespan for overweight individuals (Trost et al., 2003, Sabiston et al., 2009). Providers of physical activity-related support may provide information (i.e., advice, suggestions and guidance), emotional strategies such as encouragement and praise, esteem functions (i.e., comfort, concern, and care), and tangible support, which includes providing instrumental and practical assistance (such as gym memberships, equipment, etc.). Socially-created barriers, such as stereotypes that overweight individuals are lazy and unmotivated, and weight discrimination/fat biases may act as barriers to physical activity among overweight individuals (Ball et al., 2000; Zabinski et al., 2003; Davison et al., 2008). Furthermore, overweight individuals who internalize these stereotypes themselves are at risk of poor psychosocial functioning and wellbeing (Davison et al., 2008). Overweight girls and women report embarrassment and body image as additional factors hindering their engagement in physical activity (Hooper and Veneziano, 1995; Treasure et al., 1998; Zabinski et al., 2003). Taken together, social support and a strong social-normative belief can promote physical activity, whereas stereotypes and fat stigmatiz­ ation may act as deterrents to activity among overweight individuals. In addition to the direct effect that social factors may have on physical

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31.7  Outcomes

activity, they also indirectly influence behavior by affecting an individual’s self-perceptions, attitudes and beliefs about exercise (Brustad, 1996). Taken together, the factors outlined in the model are strong correlates of physical activity behavior. The following outlines ways to support overweight individuals in overcoming barriers to being physically active, and in turn increase the likelihood of engaging in physical activity.1 1. Since changing the physical environment is difficult and costly, it is important to help overweight individuals become aware of available opportunities and reframe their conception of environmental factors. An intention implementation plan has been demonstrated to be very efficient in overcoming barriers. Individuals write down barriers that they encounter or anticipate encountering in their neighborhood, and prepare a plan for what they will do when this barrier is encountered. It usually reads as follows: “If... [barrier], then... [plan]”. 2. Using visual cues and prompts to remind individuals to exercise is also an effective

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strategy. The key is to use personal photos and affirmations, rather than unrealistic magazine photos or impersonal slogans. 3. Seeking social support and using available resources, whether a trainer in a gym, an Internet website, or a colleague or friend, has been found to be helpful. Another strategy is identifying exercise partners. 4. Highlighting the positive emotions that result from exercise, rather than those of guilt, shame and embarrassment that keep individuals from being physically active, can also help overcome barriers. 5. Finally, it is crucial to help individuals set SMART (Specific, Measureable, Attainable, Realistic, and Time-based) goals.

31.5.4  Outcomes Physical activity confers physical, mental (psychological and emotional) and social health benefits (World Health Organization, 2009). The physical benefits of exercise include (but are not limited to) the reduced risk of cardiovascular disease, ischemic stroke, diabetes, various

1

In order for healthcare providers to help overweight patients adopt more active lifestyles, it is always helpful to know how much physical activity they are currently engaging in. Direct measures of physical activity tend to be the most accurate, since they do not rely on patient recall. While unpractical for most clinical settings, the best technique to measure physical activity remains the water technique (i.e., the individual consumes a stable isotope to examine hydrogen and carbon utilization over a period of time to calculate total energy expenditure). A more practical approach may be to use motion sensors (i.e., accelerometers and pedometers). The accelerometers can measure activity in two or three different planes, using sensors that can reflect speed and intensity of effort. They are more costly (approximately US $350) than pedometers, and are often used more commonly in research settings. In contrast, pedometers are available in most sporting goods stores and essentially measure the number of steps taken per day. Approximating the individual’s stride length and multiplying this value by the number of steps taken will calculate daily walking distance. Good quality devices can be purchased for about US $30, and they have been found to provide motivation to patients. Patients should be encouraged to wear a pedometer for a week to get a baseline activity assessment. They should then strive to increase their daily step count by 1000 steps per day each week until they reach 10,000 steps per day. Finally, questionnaires have also been used to assess physical activity. Several valid and reliable questionnaires are available for use in a written format or administered by a trained clinician. As with all self-report questionnaires, an inability to recall past physical activity episodes can lead to problems of inaccuracy and lack of precision.

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types of cancers, and osteoporosis (Ford and Mokdad, 2008). Men and women who are physically active also have a 20–35 percent reduced relative risk of death (Macera and Powell, 2001; Hu et al., 2004; Myers et al., 2004). Moreover, physical activity leads to improved body composition (i.e., reduction in adiposity and weight control), which in turn further improves the already mentioned physical health outcomes (Tremblay et al., 1990; Warburton et al., 2006).

31.5.5  Psycho-emotional outcomes As stated above, overweight individuals often have poorer psychological profiles than their healthy-weight counterparts and chronically ill individuals (Ball et al., 2008). Physical activity benefits are often most effective for those individuals who have the worst mentalhealth profiles – providing justification for the enhanced need to help overweight individuals become more physically active. Additionally, increases in physical activity may be protective against depressive symptoms (Fox, 2000). In addition to alleviating mental health problems such as depression and anxiety, physical activity also appears to influence stress levels. Research has shown that physically active individuals are more likely to have dampened stress reactions in general. If they experience stressful events, their stress levels tend to return to baseline faster than those of individuals who are not physically active (Buckworth and Dishman, 2002; Boutcher et al., 2009). Physical activity can also be a coping mechanism used to deal with stress. Since overweight individuals tend to have lower levels of self-esteem, greater bodyimage disturbance and overall heightened selfpresentation issues, physical activity can also confer positive effects on these body-related affects and cognitions (Treasure et al., 1998). Specifically, when weight loss or perceptions of increased aerobic capacity and muscular strength are reported as a result of physical activity participation,

individuals tend to have increases in perceptions of physical self-concept. This association is depicted by the feedback loop in the model. Furthermore, with the identity shift that may be experienced as a result of regular physical activity participation, there may be the opportunity for overweight individuals to experience psychological growth. Whereas the concept of positive psychological growth has been studied in populations who have suffered trauma (i.e., cancer, death, abuse) (Tedeschi and Calhoun, 2004), it can be speculated that overweight individuals who lose weight and/or become physically active also experience this growth. The concept suggests that as overweight individuals struggle with their weight and attempt to become physically active, changes to their physical self-concept and perceptions (i.e., feelings of being more muscular, having more energy and endurance, and reduced weight) will lead to psychological growth (Tedeschi and Calhoun, 2004). This means realizing new possibilities, developing personal strength and empower­ ment, a new appreciation for life, and new or stronger relationships with others (Tedeschi and Calhoun, 2004). More research is needed to better understand the experiences of psychological growth for overweight individuals.

31.5.6  Social outcomes The idea of developing new or stronger relationships with others through exercise is the final outcome of the model. Generally, individuals appear to benefit most from group-based interventions as compared to individual, home-based programs or information/education efforts (van der Horst et al., 2007). Regardless of the nature of physical activity, be it groups or individual participation, the behavior appears to enhance social normative beliefs, and provide individuals with greater support networks and the ability to seek social support when needed. Moreover, when accomplished in groups, it enhances connectedness among participants (Kayman et al., 1990).

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References

31.6  Fit or fat It is well known that excess weight and physical inactivity are independently related to mortality. However, many public health scientists have argued that it is important to consider fitness and levels of physical activity when evaluating the health risks of obesity. A recent report examined the association between physical activity and overweight status to evaluate whether activity can reduce the adverse impact of a higher BMI on coronary heart disease (CHD) (Weinstein et al., 2008). Women were classified as active if they met public health guidelines and engaged in 30 minutes or more of moderate activity most days of the week, including brisk walking or jogging. Women who engaged in less were classified as inactive. After adjusting for confounding variables, inactive normal-weight women had a slightly (8 percent) higher risk of CHD than those who were fit and active. Conversely, the risk of developing CHD was 54 percent more likely among overweight women and 88 percent more likely for obese women compared to their normal-weight active counterparts. The data show that overweight and obese women can considerably alter the risk of heart disease by remaining physically active. This is particularly important for those obese individuals who may not be ready to lose weight. However, an active lifestyle did not entirely eliminate the risks of obesity, which reinforces the importance of active living combined with maintaining a healthy body weight.

31.7  Conclusion In conclusion, the obesity epidemic is now one of the greatest public health challenges facing healthcare professionals in developed countries around the globe. In addition to sensible meal planning, it is critical to examine strategies

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to encourage both youth and adult patients to adopt more active lifestyles and reduce sedentary activities. Accumulating appropriate amounts of activity may help to promote healthier living, even if weight status is not changed.

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1.  From Brain to Behavior

C H A P T E R

32 Economic Growth as a Path Toward Poverty Reduction, Better Nutrition and Sustainable Population Growth* T.N. Srinivasan Yale University, New Haven, CT, USA; Stanford Center for International Development, Stanford University, Stanford, CA, USA

o u t l i ne 32.1 Introduction and a Definition of Terms

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32.2 What is Needed to Accelerate and Sustain Growth?

409

32.4 The Case of Undernutrition and Obesity

413

Acknowledgments

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32.3 Country Case Study: China and India 410

32.1  Introduction and a definition of terms The late nineteenth century was the glorious period for the first wave of globalization. There were no restrictions on capital and labor movements, and neither passports nor visas were

required then. The world was one of free movement of goods, people and capital. The gold standard insured that there were no exchange risks. It was a period during which global growth exploded. It all came to an abrupt end with World War I. John Maynard Keynes’ Economic Consequences of Peace, written after the Treaty of Versailles, highlights the glories of the first wave.

*

The following is an edited transcript of a talk given by Dr. T. N. Srinivasan during the 2006 McGill Health Challenge Think Tank, hosted in Montreal, Canada, October 25–27, 2006.

Obesity Prevention: The Role of Brain and Society on Individual Behavior

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© 2010, 2010 Elsevier Inc.

408

32.  Globalization, growth and poverty reduction

Between the two World Wars, the global economic system broke down with the abandonment of the gold standard and competitive devaluation of currencies, the erection of tariff barriers exemplified by the infamous Smoot-Hawley tariffs in the US, and the collapse of world trade and capital flows, all of which contributed to the Great Depression and to World War II. With the intention of preventing the disaster which befell the world between the two World Wars, institutions were set up, among which are the International Monetary Fund and the World Bank. The attempt to set up an International Trade Organization in 1948 failed, and it finally succeeded only in 1995, when the World Trade Organization (WTO) was established. Between 1948 and 1995, the General Agreement on Tariffs and Trade (GATT) governed trade. This chapter will focus primarily on globalization, because it has been a major force in eliminating poverty in many regions of the world. Indeed, trade and globalization have been major elements in the saga that have led us to where we are today. We will examine the relationship between globalization and poverty reduction as it pertains to developing countries across time. The instrumentality of sustained and rapid growth for poverty reduction cannot be denied. The only known effective method to reduce poverty across the world over time has been the acceleration of economic growth. The late Nobel laureate Jan Tinbergen – a saint among economists and economist among saints – said this beautifully: While the main aims of the new strategy are of a multiple character, with important social elements in it, the most important single figure representing the set of aims is the rate of growth in real product of the developing world. This is because production is the source of financing social measures, because production implies elements such as food, housing, education and other social services, because employment is directly dependent on the volume of production envisaged and because more equal income distribution can be more easily attained from a high than from a low average income. (Tinbergen, 1971: 12)

There are several links in the globalization– growth–poverty reduction chain, which this chapter will address in later sections. Not all links are present all the time or in all countries, and therefore threshold effects are possible. Their good effects cannot be realized until some threshold, by way of infrastructure, for instance, is reached. Not all links are unidirectional. Some can improve poverty reduction while others, coming the other way, can be offsetting influences. The assertion, without taking the context into account, that globalization is either all bad or all good, is misleading and simplistic. From an economic perspective, there are national poverty lines based on the situation in different countries, but the concept of US $1 per day poverty line at Purchasing Power Parity (PPP) exchange rates has become the most popular. This is, however, far too crude an index to describe a poverty situation. It purportedly compares poverty across countries, adjusting for the differences in the purchasing power of national currencies. However, this adjustment is crude, since the poor usually pay different prices than the rest of the population within each country, and it does not take into account the variation in prices and poverty across regions within large countries such as China and India. Measuring poverty within countries raises other issues as well. If you compare data from household surveys with national income data, there are always some discrepancies between the two for legitimate reasons, such as differences in coverage and data collection and estimation methods, and necessarily biases in one or both. In general, there are no convincing reasons to conclude that one source is better than the other. Growth according to one may not be the same as growth according to the other. Moreover, in the case of poverty alleviation, growth of particular sectors (such as agriculture) is far more important than growth of other sectors or of the overall economy.

2.  From Society to Behavior: Policy and Action

32.2 What is needed to accelerate and sustain growth?

Poverty is a socio-political as well as an economic concept. Adam Smith knew it very well when he talked about necessities and luxuries. He argued that a linen shirt was a necessity for the Englishmen of his day, even though it would have been a luxury for less privileged individuals. Poverty cannot be disassociated from its historical, social and political contexts. However, most of the literature on the subject focuses on an economic notion of poverty.

32.2  What is needed to accelerate and sustain growth? There are essentially three sources of growth. The first source is through the use of more inputs into production, such as land, labor, capital – and, more specifically, human capital, in terms of education, skills, etc. The second is improving the productive efficiency of the inputs. The third is the innovation that creates new products, new uses for existing products, and again improves the efficiency of input use. Innovation has been a very important source of economic growth. There are limits to increasing use of inputs, such as capital, labor and exhaustive resources. For example, China saved 55 percent of its GDP in 2007 according to the World Bank, but there will come a time when the Chinese will want to consume rather than save more than 50 percent of their output. However, even if high savings and investment can be sustained, the marginal returns to investment diminish rapidly. The same is true with respect to labor. The only source of growth that is sustainable is productivity-raising innovation. It is therefore important to focus on policies and institutions that encourage productivity growth. Globalization contributes to all three sources of growth. Opening up to trade increases the efficiency of resource allocation by exploiting national comparative advantage. In this case,

409

one country specializes in something that it is more successful at producing than another is; the latter, in turn, specializes in the production of other things at which it is better than the former. The two countries exchange, and trade becomes mutually beneficial. Openness to flows of capital, labor, natural and other resources augment their domestic availability. If we have too little capital, allowing capital to come in from elsewhere increases the capital for our productive uses at home and reduces its cost. The same is true for skilled and unskilled labor flowing in from poorer to richer countries: their rewards are raised above what they would have been without such flows. Openness to technology flows ensures that fruits of productivity-raising and growth-sustaining innovation anywhere are available everywhere, provided one does not create institutional bottlenecks that impede the flow of technology. Finally, institutions, both domestic and international, matter. Policies as well distortions in markets and the functioning of institutions, both domestic and international, can limit the operation of the globalization–growth link. Globalization and growth reinforce each other. Trade is a handmaiden of growth – that is to say, faster growth means greater trade. Trade as an engine of growth means that greater trade accelerates growth; thus, there is a two-way relationship between trade and growth. When we look at poverty at the individual or household level, its main determinants are resource endowments of poor households and their returns. In countries such as India, where 60 percent of the population still depends on agriculture for its employment and where almost 70 percent of the population lives in rural areas, access to land is a major issue. Access to employment and productive employment of labor are also important. Capital certainly is relevant. Access to resources through market and non-market transactions between households and institutions is important. The institutions in the financial system that

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32.  Globalization, growth and poverty reduction

relate to credit markets play significant roles. Infrastructure – transportation and communications – are also crucial in facilitating the distribution and marketing of a product. Resource allocation within households also has important repercussions for growth and poverty. The discrimination against female children in developing countries’ households is quite well-known. Whether a female child is fed as well as her male sibling, whether she is allowed to go to school and, if so, whether she will be given the same degree of education as the male child, matters. These decisions take place at the individual household level, rather than at the governmental level, and the government’s ability to influence them is limited.

32.3  Country case study: China and India This section will focus on China and India as case studies to illustrate the arguments made above. According to Maddison (2009), in 2006 China and India together represented 2.4 billion people, a little less than 40 percent of the global population, and 23 percent of global GDP at 1990 PPP dollars. Their share of global population in 2030 will fall to 33 percent, and

in global GDP will rise to 37 percent. Almost half the world’s poor lived in China and India in 2005, according to the World Bank. Growth in both accelerated only after they opened their economies and began globalizing. China’s globalization started in 1978, while India’s began hesitantly in the mid-1980s, and more resolutely and systemically after 1991. Before turning to their experience, let us review the trends in growth of world trade and output after 1950. The GATT was established after World War II, following eight rounds of multilateral trade negotiations under its auspices that resulted in trade barriers being brought down from 15 percent or more in the early 1950s to about 5 percent in 2007. As can be seen in Table 32.1, trade expanded rapidly. From 1950 to 1963 and 1963 to 1973, trade grew by 8 percent and 9 percent respectively per year, compared to only 6 percent growth of output. The 1973 and the 1980 oil shocks, followed by the 1980s debt crisis, caused export and output growth to slacken during 1973 and 1990. The former fell to 4 percent; the latter to 3 percent. Since the 1990s, the modern era of globalization that started in the late 1980s has revived annual growth rate of exports to around 6 percent, although output growth has not yet revived, remaining below 3 percent per year.

Table 32.1  Growth in world merchandise exports and output (average annual percentage change in volume terms) Agriculture Exports

Output

Fuels Exports

Manufacture Output

Exports

Output

Total Exports

Output

1950–1963

4.2

3.0

7.0

5.0

8.5

6.8

7.9

5.8

1963–1973

4.0

2.8

8.0

5.0

11.2

7.8

9.0

6.0

1973–1990

2.2

2.2

0.5

1.0

5.8

3.2

4.0

30

1990–2000

4.0

2.0

3.9

1.9

6.1

2.2

6.0

2.1

2000–2007

4.0

2.5

3.5

1.5

6.5

4.0

5.5

3.0

Sources: WTO (2006: Chart I.1); WTO (2009: Table 1.1).

2.  From Society to Behavior: Policy and Action

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32.3  Country case study: China and India

Table 32.2  Share in world merchandise trade by region and economy (percent) 1948

1953

1973

1983

2007

Exports

Output

Exports

Output

Exports

Output

Exports

Output

Exports

Output

North America

28.1

18.5

24.8

20.5

17.3

17.2

16.8

18.5

13.6

19.4

US

21.7

13.0

18.8

13.9

12.3

12.3

11.2

14.3

8.5

14.5

Europe

35.1

45.3

39.4

43.7

50.9

53.3

43.5

44.2

42.4

43.4

South and Central America

11.3

10.4

9.7

8.3

4.3

4.4

4.4

3.8

3.7

3.3

Africa Asia China

7.3

8.1

6.5

7.0

4.8

3.9

4.5

4.6

3.1

2.6

14.0

13.9

13.4

15.1

14.9

14.9

19.1

18.5

27.9

25.3

0.9

0.6

1.2

1.6

1.0

0.9

1.2

1.1

8.9

6.8

Japan

0.4

1.1

1.5

2.8

6.4

6.5

8.0

6.7

5.2

4.4

India

2.3

2.3

1.3

1.4

0.5

0.5

0.5

0.7

1.1

1.6

Source: WTO (2009: Tables 1.6 and 1.7).

Table 32.2 highlights the level of globalization of different continents and regions in the past 50 years. We can see that China’s share of world merchandise exports dropped in 1948, just before the Communists took over, to less than 1 percent, and remained around 1 percent until the 1980s. In 2007, China was the second largest exporter in the world, with a share of 8.9 percent. India, on the other hand, had a 2.3 percent share of world exports in 1948. Assiduously following an import substitution strategy for 30 years, it brought it down to 0.5 percent in 1983. Since the 1980s India has slowly reintegrated with the world economy, and its 2007 share of world export is only 1.1 percent. The lesson here is that policies matter. If a country is going to forego integration for 30 years, it will pay a price: China began opening up in 1978, but in India, this only started in 1991. Tables 32.3a and 32.3b feature, respectively, growth in GDP and poverty (share of population living on less than $1.25 a day at 2005 purchasing power parity) over the three decades since 1980 in low- and middle-income countries (LMICs). China has achieved a spectacular 10 percent growth per year on average since the 1980s, as compared to India’s 6 percent. During

Table 32.3a  Growth of GDP (percent per year, average), for low- and middle-income countries 1980–1990*

1990–2000

2000–2007**

7.9

8.5

8.1

10.3 5.5 5.7 1.7

10.6 5.5 5.9 2.5

10.3 7.3 7.8 5.1

East Asia and Pacific China South Asia India Sub-Saharan Africa

Sources: *World Bank (2006: Table 4.1); **World Bank (2009a: Table 4.1).

Table 32.3b  Poverty (share of people living on less than $1.25 a day; percent)

East Asia and Pacific China South Asia India Sub-Saharan Africa World

1981

1990

2005

77.7 84.0 59.4 59.8 53.4 52.2

54.7 60.2 51.7 51.3 57.6 42.0

16.8 15.9 40.3 41.6 50.9 25.3

Source: *World Bank (2009a: Table 2.8).

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32.  Globalization, growth and poverty reduction

2000–2007, India inched towards a 7.8 percent rate of growth, but still lags behind China. Turning to poverty, 77.7 percent (84 percent) of East Asia’s (China’s) population lived on less than $1.25 per day in 1981. That figure came down significantly to only 16.8 percent (15.9 percent) in 2005. South Asia’s (India’s) poverty rate has moved from 59.4 percent (59.8 percent) to 40.3 percent (41.6 percent) during the same period. The sad story is still Sub-Saharan Africa. Its rate of growth has accelerated from 1.7 percent during 1980–1990 to 5.1 percent during 2000–2007, in part due to the effects of globalization. Yet there has been very little poverty reduction from its 1981 level of 53.4 percent, when the region had a lower poverty rate compared to the regions of East and South Asia, to 50.9 percent in 2005 – the highest among the three. Looking at the development of China and India in greater depth, let us start with the first wave of globalization in the late nineteenth century, and then move to selected features for GDP, GDP growth and poverty in the postWorld War II era. The data are presented in Tables 32.4a–d. During the first wave of globaliz­ ation (1870–1918), China was in turmoil due to numerous conflicts, such as the Boxer Rebellion and the Opium Wars. India, on the other hand, had come under British direct rule about a decade earlier, which had brought some peace and stability. India benefited from the first wave of globalization whereas China did not, so that by 1913, China’s GDP per capita at $552 was only

82 percent of India’s $673. When China and India began their current growth era after World War II, China’s per capita income at $448 was still significantly lower, at 72 percent of India’s $619. China was merely catching up with India during the entire Mao era. The Mao era was associated with the disasters of a famine that killed 30 million, and of the Great Leap Forward and the Cultural Revolution. India has not exper­ ienced disasters of corresponding magnitude since Independence in 1947. When Deng Xiao Ping opened up the Chinese economy in 1978, the Chinese began globalizing and China took off. India continued to lag behind, because it did not begin to globalize seriously until the 1990s. In India, according to the official data, from the 1950s to the early 1980s, annual growth stagnated at around 3.75 percent and the proportion Table 32.4a  GDP per capita at Purchasing Power Parity exchange rates China

India

1870

530

533

1913

552

673

1950

448

619

1973

838

853

1990

1871

1309

2006

6048

2598

2030

17,394

7472

Sources: Maddison (2008: Table 12) for 1870–1990; Maddison (2009a: Table 5a) for 2006 and 2030.

Table 32.4b  Growth of real GDP (average, percent per year) 1950–1980

1980–1990

1990–2000

2000–2007

2008–2009

China

4.39*

10.3

10.6

9.4

6.5†

India

3.75**

5.7*

6.2

5.9†

6.0





Sources: *Maddison (2009b: Table 6) for 1952–1978; **Author’s estimates; World Bank (2006: Table 4.1);  World Bank (2009a: Table 4.1); †World Bank (2009b). GDP in constant 2000 dollars. The data for China are for 2009, and GDP is in 2000 PPP dollars. 2.  From Society to Behavior: Policy and Action

413

32.4  The case of undernutrition and obesity

of poor in the population fluctuated at around 50 percent. There was no downward trend. Only as annual growth began accelerating to 5.7 percent in the 1980s, 5.9 percent in the 1990s and 7.8 percent since 2000, did poverty begin declining in India. The latest figure for 2004–2005 shows that 27.5 percent of the Indian population is below the very modest national poverty line. Tables 32.5a–c present globalization measures for China and India at different points in time, respectively featuring the share of trade in goods in GDP, foreign capital inflows, and tariff barriers. As can be seen, the share of traded goods – imports, and exports in GDP – in 2007 was 67.8 percent for China and 30.8 percent for India. India’s tariff barriers continue to be higher than China’s. In terms of foreign capital inflows, China is, once again, far ahead of India. Table 32.4c  Poverty (proportion of population below poverty line) for China 1981

1990

1996

2002

2005

China (New National Poverty Line)*

52.8

22.2

9.8

7.3

5.2

China (World Bank)** $1.25/day Poverty Line

84.0

60.2

36.4

28.4

15.9

India (World Bank) ** $1.25/ day Poverty Line

59.8

51.3

46.6

43.9

41.6

Sources: *Chen and Ravallion (2007: Table 4); **World Bank (2009a: Table 2.8).

That is again another measure of the integration with respect to capital.

32.4  The case of undernutrition and obesity Poverty in India, China and other poor countries remains the primary determinant of undernourishment, stunting and wasting. Figure 32.1 brings together data from the National Family Health Survey from developing countries, in regard to under- and over-nourishment in India. The curve to the left is the distribution curve for Indian children, and the curve to the right is the “international reference” curve. As can be seen, comparing the weight-for-age distribution for children under the age of 3 years in India to the global reference population, the problem for India is on the left-hand side of the distribution, with the emergence of mild overweight at the other extreme of the distribution. The children are found predominantly under the “severe underweight” and “moderate underweight” headings. Globalization has led to moderate progress in reducing undernourishment. Figures 32.2 and 32.3 reveal that the reduction in prevalence of undernutrition during the 1990s in India was modest to moderate, whether measured in terms of prevalence variation for moderate or for severe underweight. Looking at the distribution quintiles, the wealthiest show a 36 percent prevalence in 1992–1993. The figure is almost double (61 percent) for the poorest.

Table 32.4d  Poverty (proportion of population below poverty line) for India India (Official)

1951–1952 1961–1962 1973–1974 1977–1978 1983

1987–1988 1993–1994 1999–2000 2004–2005

Rural India

47.4

47.2

55.7

53.1

45.7

39.1

37.3

27.1

28.3

Urban India

35.5

43.6

48.0

45.2

46.8

38.2

32.4

23.6

25.7

Combined

45.3

46.5

54.1

51.3

44.5

38.9

36.0

26.1

27.5

Sources: Datt (1998, 1999); MOF (2009).

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32.  Globalization, growth and poverty reduction

Table 32.5a  Measure of global integration: share of trade in goods in GDP China

India

1950–1951

11.5

1960–1961

10.3

1970–1971

6.9

1980–1981

13.4

1990

32.5*

China

13.1*

1990–1991

14.6

2000–2001

22.5

2007

Table 32.5b  Measure of global integration: foreign capital inflows

67.8*

30.8*

Sources: India MOF (2006); *World Bank (2006, 2009a: Table 6.1).

India

1990

2007

1990

2007

Gross Private Capital 2.5 (% of GDP) Gross Foreign Direct 1.2 Investment (% of GDP) Foreign Direct 3.48 Investment ($ billion) Portfolio Investment ($ billion):   Bonds 0.48  Equity 0

10.0

0.8

5.9

4.8

0.1

3.1

0.24

23.0

0.15 0

8.2 35.0

138.4

1.72 18.5

Sources: World Bank (2006: Tables 6.1, 6.8); World Bank (2009a: Tables 6.1 and 6.11).

Table 32.5c  Measure of global integration: tariff barriers (all products) China 1992

India 2007

1990

2005

Simple Mean Tariff

40.4

8.9

79.0

17.0

Weighted Mean Tariff

32.1

5.1

56.1

13.4

Share of Lines with International Peaks

77.6

14.9

92.4

15.4

Sources: World Bank (2006: Table 6.7); World Bank (2009a: Table 6.8).

Normal distribution curve (international reference) Distribution curve for indian children

Moderate underweight

Mild overweight Moderate overweight

Server underweight

–6.0

–5.0

–4.0

–3.0

–2.0

–1.0

.0

1.0

2.0

3.0

4.0

5.0

6.0

Source: Calculated from NFHS data

Figure 32.1  Weight-for-age distribution: children aged under 3 years in India compared to the global reference population. Source: Gragnolati et al. (2005).

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415

32.4  The case of undernutrition and obesity

80

73

69

Percentage of children

70 60

68

53 47

50

47

49

45

46

40 30

25

22

23

18

20

18

10

15 3

3

0 1992

1998

1992

Underweight

1998

1992

Stunting Mild

Moderate

1998 Wasting

Severe

Source: Underweight figures calculated directly from NFHS I and NFHS II data; other figures obtained from StatCompiler DHS (ORC Macro 2004). Note: Figures are children under the age of three

Figure 32.2  A modest reduction in the prevalence of undernutrition during the 1990s. Source: Gragnolati et al. (2005).

% Rural children undernourished

80 70 60 50 40 30 20 10

Underweight Severe underweight

1996− 97

1995− 96

1991− 92

1988− 90

1974− 79

1996− 97

1995− 96

1991− 92

1998− 90

1974− 79

0

Stunting Moderate underweight

Source: WHO Global Database on Child Growth and Malnutrition (WHO 2004a); original data from NNMB (1974–79, 1988–90, 1991–92), DWCD (1995–96) and Vijayaraghavan and Rao (1996–97). Note: Prevalence is not strictly comparable across time periods since each round of surveys used different sampling methodologies and calculated prevalence across different age groups.

Figure 32.3  Trends in the prevalence of underweight and stunting among children aged under 5 years in rural India Source: Gragnolati et al. (2005).

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32.  Globalization, growth and poverty reduction

Table 32.6  Underweight, stunting and wasting, by global region (2000) Percentage of under-5s (2000) suffering from: Region

Underweight

Stunting

Wasting

6

14

2

Africa

24

35

8

Asia

28

30

9

India

47

45

16

Bangladesh

48

45

10

Bhutan

19

40

3

Maldives

45

36

20

Nepal

48

51

10

Latin America and Caribbean

Pakistan

40

36

14

Sri Lanka

33

20

13

22–27

28–32

7–9

All developing countries

Source: Gragnolati et al. (2005: Table 3).

Table 32.6 shows similar prevalence variation across countries. In conclusion, while the global upward obesity trends draw attention to the double burden of over- and undernutrition in LMICs, it is also extremely important that attention continues to be paid to the many forms of undernourishment that continue to exist in developing countries.

Acknowledgments I acknowledge with thanks the permissions of the following: M. Gragnolati, his co-authors and the World Bank for data from their paper for the World Bank; Angus Maddison for data from his unpublished (2009a) and published (2009b) papers, and his paper in the Asian Economic Policy Review; Gaurav Datt for data from his unpublished papers of 1998 and 1999; Shaohua Chen and Martin Ravallion and the Journal of Development Economics for their paper of 2007; Ministry of

Finance, Government of India for data from the Economic Survey for 2005–2006 and 2008–2009; Wiley Science for data from Angus Maddison’s papers published in the Asian Economic Policy Review, and The Review of Income and Wealth; United Nations Institute for Training and Research (UNITAR) to quote Jan Tinbergen for his lecture at UNITAR in 1971; World Bank for data from World Development Indicators for 2006 and 2009; and the World Trade Organization for data for International Trade Statistics, 2006 and 2008.

References Chen, S., & Ravallion, M. (2007). China’s (uneven) progress against poverty. Journal of Development Economics, 32(1), 1–42. Datt, G. (1998). Poverty in India and Indian states: An update Working Paper 47. Washington, DC: International Food Policy Research Institute. Datt, G. (1999). Has poverty in India declined since the economic reforms? Washington DC: World Bank. Gragnolati, M., Shekar, M., Das Gupta, M., Bredenkamp, C., & Lee, Y.-K. (2005). India’s undernourished children: A call for reform and action. Washington DC: World Bank. Maddison, A. (2008). Shares of the rich and the rest in the world economy: Income divergence between nations, 1820-2030. Asian Economic Policy Review, 3, 67–82. Maddison, A. (2009a August). The world economy in 2030: A quantitative assessment. Utrecht: paper presented at International Economic History Association. Maddison,   A. (2009b July). Measuring the economic perform­ ance of transition economies: Some lessons from Chinese exper­ ience, the review of income and wealth, Series 55, (1). MOF. (2006). Economic Survey, 2005–06. New Delhi: Ministry of Finance. MOF. (2009). Economic Survey, 2008–09. New Delhi: Ministry of Finance. Tinbergen, J. (1971). Towards a better international order. New York, NY: United Nations Institute for Training and Research Lecture Series 2. World Bank. (2006). World development indicators. Washington, DC: World Bank. World Bank. (2009a). World development indicators. Washington, DC: World Bank. World Bank. (2009b). Prospects for the global economy. Washington, DC: World Bank. WTO. (2006). International trade statistics 2005. Geneva: World Trade Organization. WTO. (2009). International trade statistics 2008. Geneva: World Trade Organization.

2.  From Society to Behavior: Policy and Action

C H A P T E R

33 The Human Agent, Behavioral Changes and Policy Implications Transcribed Remarks by Daniel Kahneman Center for Health and Well-Being, Princeton University, Princeton, NJ, USA

o u t l i n e 33.1 The Economic and Psychological view of Human Nature

417

33.2 Culture as an Economic Externality 418 33.3 A Psychologist’s Explanation of Behavior

419

33.5 An Argument for some Paternalism

420

418

33.1  The economic and psychological view of human nature Proponents of the Chicago school of thought perceive the human agent as completely rational. In their seminal article “A theory of rational addiction”, Becker and Murphy (1988) present a human agent who anticipates everything and chooses between two options as a function of their derived utility. The agent, in principle, knows he is going to become addicted because

Obesity Prevention: The Role of Brain and Society on Individual Behavior

33.4 Happiness, or the Power of Human Adaptability

he is discounting the future, and discounting the future at the interest rate. For example, if we look at the case of a 15-year-old who is considering losing a year at the age of 70, discounted at 5 percent per year, that year of life is not worth much. When one discounts the future at 5 percent per year, one can rationalize a great deal of rather unhealthy behaviors. It is a powerful idea, but also perhaps a questionable one too. There is a fundamental difference that shows itself here between the economic analysis – or the Chicago economic analysis – and what most psychologists feel to be the case. Psychologists

417

2010 Elsevier Inc. © 2010,

418

33.  The Human Agent, Behavioral Changes and Policy Implications

and economists adopt very different perspectives of the mainsprings of behavior and decisions. Psychologists tend to take a retrospective view of the consequences of action. For example, looking at someone who began smoking at a young age, and suffering and regretting it later, psychologists would say that it was not worth it. This retrospective view of the whole arc stretches from the decision to the consequences. An economist’s perspective is solely based on the point of decision. Theirs is a forward-looking perspective. It is generally agreed that people discount very sharply. On the one hand, if one assumes that the human agent is rational, one accepts his discount rate. On the other, if one takes the retro­ spective view, one does not. An issue with the economic point of view is that it does not take regret into consideration. We return to the example of the person who began smoking young, but now hates it, cannot quit, and regrets ever starting. According to the Chicago school of thought, that person is perceived in the same way as a person who goes to a restaurant, eats a meal, and then refuses to pay. There is very little sympathy for regret. When one takes the retrospective view – the view that, in the author’s opinion, most people are inclined to take – regrets matter. There is a lot of regret for different kinds of behavior in which people engage early in life and that have delayed consequences.

33.2  Culture as an economic externality Considering the human agent in all its imperfect complexity also calls for examining choice and behavior in their cultural context, with culture having the potential for both protective and aggravating effects. Using “culture” as an explanation for a behavior is often seen as an admission that we are dealing with something we do not know how to change. In a study conducted with Fischler and other colleagues, we surveyed

the wellbeing of women in two cities: 800 women in Columbus, Ohio, and 800 women in Rennes, France (Kahneman et al., 2009). Results showed a striking difference in the body mass index (BMI) of these women: the average BMI in the Columbus group was of 28; in Rennes, it was 23. The authors suggested a few reasons which, altogether, may explain this significant discrepancy. For instance, they found that French women walk more, approximately 36 minutes per day, whereas American women only walk approximately 20 minutes per day. The authors also looked at women’s use of time and activities to which they paid attention. They found that French women spent more time at the table than American women. The difference was not great, however. What dramatically differed, though, was the incidences of focal eating. Sixty percent of the French women surveyed reported that, when eating, it was the main activity in which they engaged. Only 30 percent of American women reported eating as a focal activity. Researchers also looked at the time French mothers spent with their children. There were two distinct peaks at meal times. Clearly, these mothers were with their children when the children were eating. Nowhere else in Europe did the researchers come across this behavior. It was a cultural difference unique to France, which in this case appears to have had a protective effect against nutritionally poor eating habits and high BMI.

33.3  A psychologist’s explanation of behavior How do psychologists explain behavior? First, psychologists tend to avoid intentions as explanations of behavior. Behavior is, as much as possible, a result of the environment, human biology (what is colloquially known as “instinctive behaviors”) or, sometimes, culture, related to historical and cultural learning interpretations. How behavior is explained by psychologists has

2.  From Society to Behavior: Policy and Action

33.4  Happiness, or the power of human adaptability

important implications on how they think about controlling and changing behavior. One crucial psychological observation in explaining behavior – which sharply distingui­ shes the psychological point of view from the economic one – is the huge impact of factors that should not matter: namely, inconsequential details. Psychologists call this the “framing effect”. Bertrand and colleagues conducted a study in South Africa, where they convinced banks to mail 55,000 loan offers to customers who had previously taken out loans (Bertrand et al., 2005). Loan offers had different interest rates, but also had other variations – for example, some of the promotional literature had pictures of a man, some of a woman. The power of including a picture was worth 4 percent of interest rates. It demonstrates the relative powers of factors that rationally do matter (e.g., financial information) and factors that do not matter (e.g., a picture). It certainly puts into perspective the role of communication in conveying important nutritional information. Another example of this is demonstrated in Brian Wansink’s research. In one of these studies examining eating behavior, a jar of candy was placed within reach of participants. For another group, it was placed 6 feet away. Participants in the former group ate significantly more than those in the latter. Automatic behaviors requiring little thought are engaged in differently than those requiring decisions about physical effort and displacement.

33.4  Happiness, or the power of human adaptability A final factor to consider in examining the full psychological complexity of the human agent is happiness. However, applying notions of happiness to public health problems turns out to be extremely difficult because things do not work the way we would like them to work,

419

with variation in psychological wellbeing and happiness hardly mapping objective reality. A significant proportion of our population lives in appalling conditions, dreadful health or other circumstances. We would expect them to be unhappy, yet they are not. They have adapted to the worst of conditions. The differences between healthy and unhealthy individuals are not as stark as what one would expect. Studying happiness does not provide much support for interventions. With colleagues from around the world, we have begun studying happiness and wellbeing in the hopes of measuring, among other things, the burden of disease, and thereby providing justification for intervention. We found that people are so good at adapting to circumstances that the differences are much smaller than they would have to be in order to justify interventions. Those who become blind or paraplegic are, inevitably, devastated, yet they eventually recover. It has been found that they get as much pleasure as anyone else in listening to a joke, eating a meal or interacting with their grandchildren. It is therefore difficult to bring happiness to bear on health problems and thereby justify intervention. Returning to the comparison of women in Columbus, Ohio and Rennes, France, reported earlier, Fischler and colleagues found a correl­ ation between BMI and life satisfaction. While there was little correlation between BMI and average mood, women with high BMI tended to be less satisfied with their lives. One reason for this is that BMI is largely associated with other problems, such as low income, low education and low status. Keeping income and education constant, the association between BMI and life satisfaction essentially vanishes. In the same study, women were also posed a 32-item questionnaire about what gives them “joy” in life: How much joy do you get from different aspects of life? From spiritual experiences? Gardening? Sex? Eating with friends? Results showed that a high BMI was clearly

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33.  The Human Agent, Behavioral Changes and Policy Implications

correlated with a constriction in the sources of joy. This remains the case when keeping status as a constant. Women with higher BMI find less joy in their weight (the correlation here is of 0.45), in the physical condition, in their physical activities, in their looks, in taking walks, in nature and the outdoors, in community activities, in gardening, etc. As for the sources that gave high-BMI women more joy than to their leaner counterparts, two significant differences were identified: television and pets. As one can see, there is extreme diversity in human happiness, and it is evident that people can cope, making scientific research on happiness and wellbeing particularly challenging.

33.5  An argument for some paternalism Kurt Lewin, one of the great twentieth-century psychologists, looking at the complexity of behavioral change, suggested that the question “How do we get people to do what we want them to do?” was wrong. He rephrased it: “Why are they not already doing what we want them to do?” This small but powerful difference alters the way one thinks about behavioral change. The first question, “How do we get people to do what we want them to do?”, implies argument, explanations, incentives and threats. The second, “Why are they not already doing what we want them to do?”, directs the attention to something entirely different; namely, how do you change the situation of those making the choices so that they become more likely to do what it is they are supposed to be doing? As a result, one ends up with very different prescriptions. Lewin talks about driving forces that push people where one wants them to go, and restraining forces that prevent them from going where one does not want them to go. He suggests that one should focus on limiting

the restraining forces rather than on limiting the driving forces. In the context of obesity, this concept may be applied to the issue of enlisting industry cooperation in such a way as it is easy for corporations to act in the public interest. At the individual level, while not explaining why people prefer French fries to broccoli, it certainly does suggest an interesting avenue in attempting to change eating behaviors so that people come to prefer broccoli to French fries. By focusing on creating conditions that help people do what they are supposed to do, we come to an argument in favor of some paternalism. In this regard, the contrast between the US and the rest of the world is striking: most nations take a fair amount of paternalism for granted. The US, on the other hand, apologizes for it. There is a deep sense that individuals have the right to operate without any interference from government. It is very apparent that Americans are conditioned to worry about paternalism and intervention. Paternalism is often seen as a phenomenon whereby adults are treated as if they were children. The main justification for paternalism is that humans have self-control problems. The gurus of behavioral economics spoke of the human agent, as viewed in behavioral economics, as having bounded rationality, bounded selfishness (unlike the economic man) and bounded self-control. It is those with bounded self-control who tend to justify paternalism and interventions. The main concept driving paternalism is the idea that people want to behave differently from the way they are inclined. In addition, they are willing to pay for it. One only has to look at the popularity of small packages of sweets to see that people are paying to help with a selfcontrol problem. Paternalism is on the rise in the US, and behavioral economics is having a significant impact on this. Behavioral economics does not so much provide an argument for paternalism as undermine arguments against paternalism.

2.  From Society to Behavior: Policy and Action

References

References Becker, G. S., & Murphy, K. M. (1988). A theory of rational addiction. Journal of Political Economy, 96(4), 675–700. Bertrand, M., Karlan, D. S., Mullainathan, S., Shafir, E., Zinman, J. (2005) What’s psychology worth? A field exp­e­ riment in the consumer credit market. Yale University Economic Growth Center Discussion Paper No. 918.

421

Kahneman, D., Schkade, D. A., Fischler, C., Krueger, A. B., & Krilla, A. (2009). The structure of well-being in two cities: Life satisfaction and experienced happiness in Columbus, Ohio; and Rennes, France. In E. Diener, J. F. Helliwell, & D. Kahneman (eds.), International differences in well-being. New York, NY: Oxford University Press.

2.  From Society to Behavior: Policy and Action

C H A P T E R

34 The Four Pillars of the Industrial Machine: Can the Wheels be Steered in a Healthier Direction? William Bernstein efficientfrontier.com, North Bend, OR, USA

o u t l i n e 34.1 Introduction

423

34.2 Malthus’ World

424

34.3 How Nations Become Wealthy

425

34.4 The Progress of Economic Development

426

34.5 Measuring Economic Development

429

The bourgeoisie, during its rule of scarce one hundred years, has created more massive and more colossal productive forces than have all preceding generations together. Karl Marx, Communist Manifesto

34.1  Introduction Around 1820, the world tilted on its economic axis. Before that date, the life of the average per­ son on the planet improved little, if at all, from

Obesity Prevention: The Role of Brain and Society on Individual Behavior

34.6 The 2 Percent Productivity Cruise Control

429

34.7 The Obesity Connection

430

34.8 The Way Forward

431

Acknowledgments

432

year to year, decade to decade, or even century to century. Then, in the early nineteenth century, the world slowly became a much richer place. Material wellbeing, as measured by world per capita GDP, began to increase at 2 percent per year, meaning that, for the past two centuries, the life of the child has been approximately twice as prosperous as that of the parent, doub­ ling the standard of living once every 36 years (Maddison, 2001). This improvement in the material condition of mankind resulted largely from developments

423

© 2010, 2010 Elsevier Inc.

34.  The Four Pillars of the Industrial Machine

in manufacturing productivity owing to advanced technologies, which were in turn effected by four institutional pillars: secure property rights, scientific rationalism, efficient capital markets, and advanced communications and transport mechanisms. The same factors that caused improved manufacturing productiv­ ity also resulted in a revolution in agricultural technology. This has been a two-edged sword: on the one hand, the cheap, high-energy food­ stuffs available from these technologies have mitigated world hunger; unfortunately, these same foodstuffs have also triggered a world­ wide obesity epidemic.

6

Population (millions)

424

4

2 40

50

60

70

80

90

Real per capita GDP (1865 = 100)

Figure 34.1  The Malthusian trap in England 1265–1595. Data from Anderson (1996) and Clark (2001).

34.2  Malthus’ world Before 1820, the regime described by Thomas Malthus operated with its inexorable logic. In his harsh world, a nation’s food supply, along with its population, grew slowly, if at all, so that, in the short term, the standard of living was inversely proportional to the number of mouths to feed. Were population to increase, there would not be food enough to go around. Prices would rise, while wages and the standard of living in general would fall. If, on the other hand, the population were suddenly to fall, as happened in the mid-fourteenth century in the wake of the Black Death, the survivors’ food supply, wages and standard of living would rise dramatically. History, as well as Malthus’ life experi­ence, had burned this sequence of events into his consciousness. Figure 34.1 plots the per cap­ ita GDP of England from 1255 to 1545 versus population. The thin, crescent-shaped distribu­ tion of the data points depicts the “Malthusian Trap”, as summarized by historian Phyllis Deane (1979): When population rose in pre-industrial England, product per head fell: and, if for some reason (a new

technique of production or the discovery of a new resource, for example, or the opening up of a new market), output rose, population was not slow in fol­ lowing and eventually leveling out the original gain in incomes per head.

In this eternal cycle, food output might rise, but population followed in lock step, dooming mankind to a near-subsistence existence. Paradoxically, soon after Malthus immortal­ ized this grim state of affairs, it abruptly came to an end in Western Europe. Figure 34.2 shows that, sometime around 1600, a bulge developed in the crescent, and Figure 34.3 shows that, after 1800, population cleanly broke out of the crescent, never again to return to starvation’s edge. The escape from the trap was made possible not by an increased birth rate, but rather by a 40 percent drop in the death rate, the result of rapidly improving living stand­ ards born out of vigorous economic growth (Ashton, 1967). The nature of that growth changed dramati­ cally in the centuries following 1600. Initially the growth was “extensive”, consisting of a significant increase in the size of the national economy caused purely by population growth,

2.  From Society to Behavior: Policy and Action

34.3  How nations become wealthy

34.3  How nations become wealthy

6 Population (millions)

1655

1705

1615

4

Before 1600 After 1600

2 40

50

60

70

80

90

Real per capita GDP (1865 = 100)

Figure 34.2  The trap breaks down 1600–1705. Data from Anderson (1996) and Clark (2001).

Population (millions)

20

1865

Before 1710 After 1710

15

1845 1825

10 `

1715

1785

5 0 40

60

425

80

100

Real per capita GDP (1865 = 100)

Figure 34.3  Breaking out of the trap 1715–1865. Data from Anderson (1996) and Clark (2001).

unaccompanied by any real improvement in the wealth or material comfort of the aver­ age citizen. In other words, for the first time, the economy mustered just enough growth to keep pace with population. By the nineteenth century growth had become “intensive”, out­ pacing even the human urge to reproduce, with advances in per capita income and an increase in wellbeing at the individual level (Jones, 1988).

Beginning around 1820, the pace of economic advance picked up noticeably, making the world a better place in which to live. What happened? An explosion in technology, the likes of which had never been seen before. New technology is the powerhouse of per capita economic growth; without it, significant increases in productivity and consumption do not occur. Four things are needed to develop new technologies: 1. Property rights. Innovators and tradesmen must rest secure that the fruits of their labors will not be arbitrarily confiscated, by the State, by criminals or by monopolists. The assurance that a person can keep most of his or her just reward is the right that guarantees all other rights, though the right to property is never absolute. Even the most economically libertarian governments, such as in Singapore and Hong Kong, levy some taxes, enforce some form of eminent domain and maintain some restrictions on commercial freedom of action. A government that fails to control inflation or maintain proper banking and property controls, such as Brazil in the 1980s or present-day Zimbabwe, steals as surely as, and on a much greater scale than, a common thief. 2. Scientific rationalism. Economic progress depends on the development and commercialization of ideas. Their creation requires an intellectual framework that supports the inventive process – an infrastructure of rational thought which relies on empirical observation and on the mathematical tools that support technologic advance. The scientific method that we take for granted in the modern West is a relatively new phenomenon. Only in the past 400 years have Western peoples

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426

34.  The Four Pillars of the Industrial Machine

freed themselves from the dead hand of the totalitarian, Aristotelian mindset, and even today, in areas of Africa, Asia and the Middle East, honest intellectual inquiry is at risk from the forces of state and religious tyranny. 3. Capital markets. The large-scale production of new goods and services requires vast amounts of money from others – “capital”.1 Even if property and the ability to innovate are secure, capital is still required to develop schemes and ideas. Since almost no entrepreneur has enough money to massproduce his or her inventions, economic growth is impossible without substantial capital from outside sources. Before the nineteenth century, society’s best, brightest and most ambitious had scant access to the massive amounts of money necessary to transform their dreams into reality. 4. Fast and efficient communications and transportation. The final step in the creation of new technologies is their advertisement and distribution to buyers hundreds or thousands of miles away. Even if entrepreneurs possess secure property rights, the proper intellectual tools and adequate capital, their innovations will languish unless they quickly and cheaply put their products into the hands of consumers. For example, only two centuries ago did sea transport become safe, efficient and cheap. Land transport followed suit 50 years later, with the invention of the steam engine. Of the four factors, property rights fell into place first in early medieval England and Holland. Scientific rationalism followed hard on

the heels of Francis Bacon’s Novum Organum in 1620, and the modern capital markets saw their origins in Amsterdam and London around the same time. The final piece of the puzzle, the trans­ port and communications revolutions, resulted from the advent of steam and telegraph in the late eighteenth and early nineteenth centuries, respectively, unleashing the floodgate of global prosperity and, along with it, an epidemic of global obesity. Not until all four of these factors – property rights, scientific rationalism, effective capital markets and efficient transport/communication – are in place can a nation prosper. The absence of even one factor endangers economic progress and human welfare; kicking out just one of these four legs will topple the table of a nation’s bounty. This occurred in eighteenth-century Holland with the British naval blockade, in the world’s Communist states with the loss of property rights, and in much of the Middle East with the absence of capital markets and Western rationalism. Finally, and most tragically of all, in most of present-day Africa, all four factors remain essentially absent.

34.4  The progress of economic development The portrait economic historian Angus Maddison and others painted of economic development is as stunning as it was unex­ pected. The lot of the average individual, meas­ ured as real per capita GDP, did not change at all during the first millennium after the birth of Christ. Over the next 500 years, between 1000 and 1500, things did not get much better.

1 

The term “capital” is fraught with economic meaning. Economists frequently employ a broad definition of the term, encompassing human capital, knowledge, or “intellectual” capital, as well as physical capital such as plant and equipment. Here, “capital” is defined in the narrowest possible sense: money available for investment.

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427

34.4  The progress of economic development 3%

$1,000

1%

8 99

3 19

73

–1

97

0 19

50

–1

95

3 19

13

–1

91

0 18

70

–1

87

0 18

20

–1

82

0 00

(a)

–1

50

2000

15

1500

–1

1000

00

500

10

0

00

0%

$100

10

(a)

2%

0–

Per-capita GDP (1990$)

$10,000

Per-capita GDP (1990$)

$10,000

(b)

2% $1,000

1%

$100 1700

1750

1800

1850

1900

1950

2000

Figure 34.4  (a) World per capita GDP (inflation adjusted). (b) World per capita GDP (inflation adjusted). Data from Maddison (2001).

0% 0–1000

1000–1500

(b)

1500–1820

1820–1998

Years A.D.

Figure 34.5  (a) Annualized growth of world per cap­ ita GDP. (b) Annualized per capita world GDP growth. Data from Maddison (2001).

Figure 34.4(a & b), which plots world per capita GDP2 since the dawn of the Common Era, brings the welfare of the average person into sharp focus. Before 1820, there was only minus­ cule material progress from decade to decade and century to century. After 1820, the world steadily became a more prosperous place. Figure 34.5(a & b), which summarizes the aver­ age annual growth in world real per capita GDP,

displays the breakout occurring around 1820 from a different viewpoint. Once again, prior to 1820, there was little improvement in the mater­ ial welfare of the average human. At the height of the Greek and Roman peri­ ods, the “urbanization ratio” – the proportion of the population living in cities of more than 10,000, an excellent barometer of an econo­ my’s margin over subsistence – was estimated

2 

Economists have found it easy to criticize Maddison’s estimates of income and production from centuries ago. After all, how can he be sure that the annual per capita GDP of Japan at the birth of Christ was $400 in current dollars, rather than $200 or $800? Maddison himself concedes the point: “To go back earlier involves use of weaker evidence, greater reliance on clues and conjecture. If you want to measure economic progress over the centuries, you first must ask, ‘How much money is necessary to sustain a subsistence level of existence?’“ Maddison’s answer was that, in an underdeveloped nation in 1990, that amount was about $400 per year. Next, economic historians use whatever data they can find to determine what percentage of the population existed at this level. A society in which nearly 100 percent of the population is engaged in farming and that does not export any substantial amount of its agricultural products lives, by definition, very close to Maddison’s $400 per year subsistence level. It is highly artificial to assign, as he did, a $400 per capita GDP to Europe in AD 1, China in 1950, or modern day Burkina Faso, but doing so at least provides economic historians with a working standard against which to measure economic growth (Maddison, 2001).

2.  From Society to Behavior: Policy and Action

34.  The Four Pillars of the Industrial Machine

at only several percent. In 1500, the largest city in Europe was Naples, with 150,000 inhabit­ ants; only 865,000 Europeans lived in cities of more than 50,000, or about 1 percent of the total European population; only 6 percent lived in towns of more than 10,000. In the great civili­ zations of Asia, which during the medieval era were far more advanced than those in Europe, the percentage of the population engaged in agriculture was even closer to 100 percent, and the opulence of the tiny ruling elites did not much raise the overall level of prosperity. So it seems likely that before 1500 the world’s overall per capita GDP was close to a subsist­ ence level. Even in the US until as late as 1920, fully 70 percent of the working population was employed on farms. Today, that figure stands at 1 percent. Europe did produce some economic growth after the fall of the Roman Empire. The early medieval period saw the switch from a two-crop to a three-crop rotational system; the invention of the horseshoe and horse collar, water mill and windmill; and the replacement of the twowheeled cart with the four-wheeled variety (Jones, 1987). Economic historians, however, disagree about just when these changes began to result in growth, with estimates ranging from the eighth to the fifteenth century. These advances, although they produced growth, merely resulted in increases in popula­ tion, leaving the wellbeing of the average citi­ zen unchanged. The wide range of opinion on the dating of the renaissance of growth in the post-Roman world is proof enough that per capita growth (the best measure of the well­being of the individual) cannot have been sub­ stantial or sustained. Yet, before 1820, there were hints of the com­ ing prosperity. Maddison estimates that around AD 1500, European per capita GDP averaged $774, with Renaissance Italy clocking in at $1100 (Maddison, 2001). Italy’s relative pros­ perity would not last long. After 1500 it would

$100,000 Per-Capita GDP (1990$)

428

$10,000 England Japan

$1,000

China

$100 1500

1600

1700

1800

1900

Figure 34.6  Growth versus starting wealth.

2000

Data from Maddison (2001).

s­ tagnate, while Holland began to experience persistent, if sluggish, economic growth. Around the same time, however, Britain’s growth rate began to increase as well, although more slowly than Holland’s. The Glorious Revolution of 1688 brought a stable constitutional monarchy to England, along with a Dutch king, the cream of Holland’s financiers, and Dutch advances in the capi­ tal markets from across the North Sea. It took more than a century for English growth to accelerate rapidly following these events. Not until the middle of the nineteenth century did the average Englishman live better than the average Dutchman, and even then only after the crippling of Holland’s economy by dec­ ades of British naval blockade, followed by the destruction of the Dutch Republic by Napoleon Bonaparte. The British seeded their overseas colonies not only with their people but, more critically, with their legal, intellectual and financial institutions. The great economic transformation did not begin to spread to the rest of Europe and Asia until much later. Its effects were highly uneven there, as depicted in Figure 34.6, with the “takeoffs” of England, Japan and China occurring in 1820, 1870 and 1950, respectively.

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429

34.6  The 2 percent productivity cruise control

34.5  Measuring economic development The beauty of examining a very long his­ torical sweep is that it “washes out” even large uncertainties about growth. For example, over a period of 1000 years, if we overestimated the beginning or ending per capita GDP by a fac­ tor of two, this would entail an error of just 0.07 percent per year in the annual growth rate. Put another way, world per capita GDP growth since the birth of Christ could not possibly have been as high as 0.5 percent, since if it were, per capita GDP would have grown from $400 in current dollars to over $8.6 million by the year 2000! So we can be certain that, for most of this period, growth was indeed very close to zero. Putting it yet a third way, even the most wildly optimistic estimates suggest no more than a doubling or tripling in per capita global GDP between AD 1 and AD 1000, versus the eightfold increase in the 172 years following 1820. During this same 172-year period, per capita GDP in the UK grew 10-fold, and in the US, 20-fold.

34.6  The 2 percent productivity cruise control The vigor of modern economic growth astounds. Throughout the 1800s, real per capita GDP growth in what is now called the devel­ oped world gradually accelerated to about 2 percent per year, then maintained that pace throughout the entire turbulent twentieth cen­ tury. Table 34.1 lists the growth of real per capita GDP in 16 nations during the twentieth century, dividing them into nations that were physically ravaged by world war or civil war, and those that were not.

Table 34.1  Annualized per capita GDP growth, 1900–2000 Per capita GDP growth War-damaged Belgium

1.75%

Denmark

1.98%

France

1.84%

Germany

1.61%

Italy

2.18%

Japan

3.13%

Netherlands

1.69%

Spain Average for war-damaged countries

1.91% 2.01%

Not war-damaged Australia

1.59%

Canada

2.17%

Ireland

2.08%

Sweden

1.96%

Switzerland

1.72%

United Kingdom

1.41%

United States

2.00%

Average for countries not war-damaged

1.85%

Source: Bernstein and Arnott (2003).

Notice how tightly around 2 percent the growth rates cluster – 13 of the 15 nations increased their per capita GDP between 1.6 percent and 2.4 per­ cent per year. It is as though an irresistible force – a sort of economic cruise ­control – propelled their productivity upwards at almost exactly 2 percent per year – not faster and not slower. Notice also the similarity between the average growth rates of the war-torn and non-war-torn nations. The devastation of war does no long-term damage to the economies of developed nations. Figure 34.7 displays another fascinating char­ acteristic of Western economies – the wealthiest advanced nations of 1900 tended to grow the

2.  From Society to Behavior: Policy and Action

430

34.  The Four Pillars of the Industrial Machine

Annualized per capita GDP growth, 1900-2000

3.0%

2.5%

2.0%

1.5%

1.0% $1,000

$2,000

$3,000

$4,000

$5,000

Per capita GDP in 1900 (constant 1990$)

Figure 34.7  Per capita GDP versus US (US  100%). Data from Maddison (2001).

100% 80% 60%

Germany

40%

economic machinery during World War II is clearly visible at the left edge of Figure 34.8. Japan began World War II with a per capita GDP 40 percent of the US value; by the war’s end, it had fallen to just 15 percent. Germany’s per capita GDP fell from 80 percent to 40 per­ cent of that of the US during the same period. By 1990, however, both nations had completely recovered. The Western growth machine reduces the catastrophe of conquest to a mere historical hiccup. Within just two decades, Japanese and German economies completely recovered their former prosperity relative to the US. In the fol­ lowing 20 years, Japan’s relative per capita GDP doubled yet again. The beginning of the nineteenth century did not transform every corner of the world. At first, only Europe and its New World offshoots pros­ pered. Nonetheless, over the ensuing 200 years, the Western variety of growth spread over the rest of the globe.

Japan

20% 0% 1940

1950

1960

1970

1980

1990

Figure 34.8  Per capita GDP (inflation adjusted). Data from Maddison (2001).

slowest, while the least wealthy tended to grow the fastest. In other words, the per capita wealth of the most advanced nations tends to converge. Japan, which started out the twentieth century as the poorest of the nations listed, saw its pro­ ductivity grow at 3 percent per year, while the leader in 1900, Great Britain, grew at only 1.4 percent per year. The most spectacular example of the resil­ iency of Western economies – the tendency to “catch up” – is provided by examining the per capita GDP of Germany and Japan from 1940 to 1990. The devastation of the Axis Powers’

34.7  The obesity connection As advanced technologies have made work­ ers more productive, societies wealthier and manufactured products cheaper, so too have these four pillars of economic growth applied to agriculture. At its birth, the US needed more than two-thirds of its population to feed itself; now, 1 percent feeds not only the nation, but also much of the rest of the world. Conversely, whereas a century ago the average American family spent more than half its income on food, today that figure stands at less than 10 percent. While almost all agricultural products have become much cheaper in real terms, it stands to reason that the prices of the most highly processed foods – the high-energy sug­ ars and fats largely responsible for the mod­ ern obesity ­ epidemic – have fallen the most.

2.  From Society to Behavior: Policy and Action

34.8  The way forward

One commodity, sucrose,3 has fallen in price more, and over a longer period, than almost any other. Also, in virtually every part of the world, its per capita consumption has increased stead­ ily over the course of recorded history. It nicely illustrates the progress of economic growth in agriculture (Mintz, 1986). During the medieval period, sugar was con­ sidered a “fine” spice, as rare and expensive as its four trade cousins: cloves, nutmeg, mace and cinnamon. Economic historians estimate that during the fifteenth century, European per cap­ ita consumption was just one teaspoon per year (Hobhouse, 1986). Sugar’s mass production and consumption only began around 1500, with the invention of the three-cylinder mill, which could be driven by water or animal power. With it, the important problem of crushing sugar cane was remediated. A second problem, the lack of fuel, which had resulted from the deforestation of the Middle East, Europe, and the Atlantic Islands, was resolved by the discovery of the New World’s endless forests. By the time of Columbus’s transatlantic voy­ ages, cane had just been transplanted to the Spanish Canaries, from which his expeditions were staged. It quickly spread throughout the New World tropics, and touched off an explo­ sion of cane production that powered much of the world economy for the next three centu­ ries. The “sugar belt” of the New World, which

431

spread from Northern Brazil to Surinam and up the Caribbean chain all the way to Cuba and even the Louisiana Delta, attracted large ­numbers of European settlers lured by the rela­ tively short transatlantic passage, the lack of organized Native opposition, and agricultural profits unimaginable in their homelands. Over the next five centuries, improvements in growing, refining and transportation technol­ ogies for sugar have made it a bulk commod­ ity that sells so cheaply that it is given scarcely a second thought. Today, the average American consumes 66 pounds per year and the average European consumes 87 pounds – a perfect pub­ lic health storm born of the marriage of a highly addictive foodstuff and the genius of modern industrial productivity (FAO, 2006; CIA, 2008; US Census Bureau, 2008).

34.8  The way forward In the unlovely jargon of economics, the global obesity crisis is a classic “externality”, in which the same market forces that have yielded an everincreasing global prosperity impose unintended costs on society at large, not unlike industrial pollution. It is not difficult to propose solutions, none of which are mutually exclusive: taxa­ tion of corporations that produce energy-dense foods, taxation of the end products themselves,

3

 The cane plant, Saccharum officinarum, requires a frost-free growing season of about 12–18 months, steady and copi­ ous rainfall or irrigation, and year-round temperatures averaging more than 70°F. Cane harvesting and the subse­ quent extraction of pure, granulated sugar from the cut stalks is hot, backbreaking work that consumes vast amounts of both fuel and human effort. The production of sugar is as much an industrial process as an agricultural one. It occurs in three stages. First, the cane is crushed to release the sweet cane juice. For millennia, this was accomplished with crude and inefficient mortar-and-pestle devices, and made cane juice a luxury product, even where abundant slave labor was available. Next, the sweet juice has to be “reduced” by boiling it down to a concentrated sucrose solution, a process that consumes massive amounts of fuel. Finally, the solution is repeatedly heated and cooled in a refining process that separates out the sugar into granules of purity ranging from clear crystalline rocks to a brown residue – treacle or molasses – that cannot be further crystallized. This final process – sugar refining – not only con­ sumes yet more fuel, but also requires great skill – so much so that during the colonial age, it was accomplished mainly in the advanced industrial centers of Europe (Galloway, 1977).

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34.  The Four Pillars of the Industrial Machine

intensive public education, and subsidies of lowenergy density foods, to name but a few. An analysis of the relative merits of these pos­ sibilities is well beyond the scope of this chapter, but a good starting point is the classic model of externalities proposed by University of Chicago economist Ronald Coase,4 who explored the arcana of government regulation of conflicts among private parties. Consider, for example, a corn farm adjacent to a cattle ranch. The cattle wander, as is their wont, onto the corn farm and eat the farmer’s crop. This is a classic “negative externality”, similar to industrial pollution or a noisy neighbor. Coase realized that there were two possible ways to settle this sort of conflict. The first and most obvious way required the cattleman to pay for the damage. The second and less intuitive way allowed the cattle rancher to request payment from the farmer in exchange for fencing his cattle. In the first case, the lia­ bility is the cattleman’s; in the second, it is the farmer’s. Coase’s genius lay in the realization that it did not matter who initially “owned” the liabil­ ity. In each case, the end result would be the same – an identical amount of money would change hands, just in different directions. Economically, the two possible outcomes were equivalent (Coase, 1960). In the Coase para­ digm, only three things matter: 1. That ownership and liability be clearly defined 2. That property and liability can be bought and sold at will 3. That the expenses of negotiating, selling and enforcement are low. Coase’s third condition is critical. When many parties are involved, negotiation costs can be high. Such is the case with the obesity crisis,

in which the damage is suffered by hundreds of millions of people and is produced by a rela­ tively small number of large corporations. (The same is true of atmospheric pollution.) The problem, then, is that having millions of citizens negotiate the payment of damages with a myriad of food companies (or payment from millions of citizens to the companies to produce healthier foods) is impossibly cumbersome. Thus, the best economic model we have to deal with the crisis strongly suggests that governments must intervene (McMillan, 2002). Just how they do so is one of the most important inter­ national public policy questions of our era.

Acknowledgments The author would like to thank Atlantic Monthly Press and McGraw-Hill Inc. for grant­ ing permission for the adaptation of material from A Splendid Exchange and The Birth of Plenty for use in this chapter.

References Anderson, M. (1996). British Population History from the Black Death to the Present Day. Cambridge: Cambridge University Press. Ashton, T. S. (1967). The industrial revolution. Oxford: Oxford University Press. Bernstein, W. J., & Arnott, A. D. (2003). Earnings growth: The two-percent dilution. Financial Analysts Journal, 59(5), 51. Central Intelligence Agency. (2008). The 2008 World Fact Book. Online. Available: https://www.cia.gov/library/­ publications/the-world-factbook/index.html/. Clark, G. (2001). The secret history of the Industrial Revolution. Working Paper. Coase, R. H. (1960). The problem of social cost. Journal of Law and Economics, 3, 1–44.

4

 Coase’s name is known mainly among economists and lawyers. The paper, “The Problem of Social Cost”, Journal of Law and Economics, is one of the most cited articles in economic literature. In 1991, he was awarded the Nobel Prize in Economics for this and related work.

2.  From Society to Behavior: Policy and Action

References

Deane, P. (1979). The first industrial revolution. Cambridge: Cambridge University Press. Food and Agriculture Organization. (2006). Sugar. The Food Outlook. Online. Available: http://www.fao.org/­ documents/show_cdr.asp?url_file/docrep/009/ J7927e/j7927e07.htm/. Galloway, J. H. (1977). The Mediterranean sugar industry. Geographical Review, 67(2), 182–188. Hobhouse, H. (1986). Seeds of change. New York, NY: Harper and Row. Jones, E. L. (1987). The European miracle. Cambridge: Cambridge University Press.

433

Jones, E. L. (1988). Growth recurring. Ann Arbor, MI: University of Michigan Press. Maddison, A. (2001). The world economy: A millennial perspective. Paris: OECD Press. McMillan, J. (2002). Reinventing the bazaar. New York, NY: W.W. Norton. Mintz, S. W. (1986). Sweetness and power. New York, NY: Penguin. US Census Bureau. (2008). US POPClock Projection. Online. Available: http://www.census.gov/population/www/ popclockus.html/.

2.  From Society to Behavior: Policy and Action

C H A P T E R

35 Libertarian Paternalism: Nudging Individuals toward Obesity Prevention Laurette Dubé James McGill Chair, Consumer Psychology and Marketing, Desautels Faculty of Management, McGill University, Montreal, Canada

o u tline 35.1  Introduction 35.2 Biases and Shortcomings in Human Decision-Making 35.2.1 Status quo and Default Bias 35.2.2 Limited Cognitive Capacity 35.2.3 Neglect of Consequences of Distributed Choices and Concreteness Bias 35.2.4 Present-biased Preference and Hyperbolic Discounting 35.2.5 Prediction Failure

435 436 436 437 437 437 437

35.1  Introduction At the root of obesity and chronic diseases lie, as argued by Julian Le Grand, the “giants of excess”. These include the excess consumption of alcohol, tobacco, drugs, high-fat, high-salt and

Obesity Prevention: The Role of Brain and Society on Individual Behavior

35.2.6 Reference Dependence and Loss Aversion 438 35.2.7 Vulnerability to Framing Effects 438 35.3 On Libertarian Paternalism

438

35.4  Libertarian Paternalism Applied 35.4.1 Resetting the Default Option 35.4.2 Immediate Rewards and Pre-commitment

440 440

35.5  Limitations and Conclusion

440

440

high-sugar foods, as well as the increase of sedentary activities. They emerged from industrialization and economic development, a phenomenon which is also responsible for tremendous social and economic benefits. Industrialization has primarily flourished in market economies, rooted in the power of individual creativity, self-interest and

435

2010 Elsevier Inc. © 2010,

436

35.  Libertarian Paternalism AND Obesity Prevention

freedom of choice. Modern society evolved within the clear boundaries of a two-pronged institutional framework: on the one hand, market mechanisms align supply and demand; on the other, social and political governance prevents/addresses market failures and takes care of the social domains of health and human development. Standard approaches to policy-making and regulation assume the full rationality of individual agents, be these consumers, firms or other organizations. In this context, policy-making and regulation lie outside the purview of individual decision-making and market mechanisms (Camerer et al., 2003). It is assumed that (1) decision-makers have well-defined preferences or goals and make decisions to maximize these preferences; (2) preferences reflect the true costs and benefits of available options; and (3) in situations that involve uncertainty, decision-makers have well-formed beliefs about how the uncertainty will resolve itself and such probabilistic assessments will be updated in light of new information according to Bayes’ law (Camerer et al., 2003). Government regulation can take a variety of forms; some aim at redistribution, others to counteract externalities. The form that concerns us here is paternalistic regulation, which is designed to help on an individual basis: it treads on individual sovereignty by forcing, or preventing, choices for the individual’s own good. Behavioral economists have suggested that libertarian paternalism (also called asymmetric paternalism) can better guide individual choices (Camerer et al., 2003; Thaler and Sunstein, 2003). Relying on a sophisticated understanding of human biases and shortcomings, such interventions and policies aim at “nudging” individuals and organizations to act in their own and in society’s best interest while preserving freedom of choice. In societies where large proportions of the population are overweight or obese, traditional and rationality-based interventions and policies have obviously failed. An argument can be made in favor of a libertarian paternalistic approach to obesity prevention.

The next section reviews biases and shortcomings most relevant to lifestyle behavior choices, presents key premises of libertarian paternalism vis-à-vis traditional policy approaches, and examines applications of libertarian paternalism to behavior change strategies and policy interventions addressing obesity.

35.2  Biases and shortcomingS in human decision-making The emerging field of behavioral economics has highlighted that human decision-making is the end product of multiple competing motives, reasons, considerations and perspectives. This body of evidence has unraveled systematic ways in which individuals make less-than-optimal choices, revealing that individuals (1) rarely exhibit rational expectations, (2) fail to update their forecast according to Bayesian rules, (3) use heuristics that ignore utility maximiz­ ation, and/or (4) shift their preferences and choices as a function of contextual changes in the ways options or outcomes are presented. These decision biases and pitfalls may help explain why and how individuals engage (or do not) in obesity-promoting lifestyle behaviors. Here, we briefly review the most relevant.

35.2.1  Status quo and default bias Changing any state imposes physical, cognitive and, in some cases, emotional costs that human beings intuitively tend to avoid by mere inertia. As a result, status quo or default options have a powerful impact on decision-making. According to status quo and default biases, even if an individual knows the best course of action, what is automatic and/or what has always been done in the past represents the path of least resistance (Lowenstein et al., 2008). In the modern environment of plenty, where high-calorie, high-fat and

2.  From society to behavior: policy and action

35.2 Biases and shortcomings in human decision-making

high-sugar food options and sedentary activities are ubiquitous, the current default options (i.e., those that are made most natural by the choice architecture) appear to be setting the stage for obesogenic behavior.

35.2.2  Limited cognitive capacity Individuals have processing difficulties which can mean cognitive overloads when too many options are presented to them (Ratner et al., 2008). It has also been found that when humans deliberate excessively about a decision, they are more likely to focus too much on less important criteria, to develop an attachment to certain options, and later to feel a sense of loss toward the unchosen options and a lower sense of post-choice satisfaction.

35.2.3  Neglect of consequences of distributed choices and concreteness bias Lifestyle decisions are made several times a day, every day. The marginal impact of each decision is negligible. Indeed, in regard to eating, no single indulgence has a discernable effect on weight; it is only in aggregate that their cumulative consequences are manifested (Hernstein and Prelec, 1991). Most individuals are motivated by action that produces measurable, tangible benefits. Actions that do not produce tangible progress toward a goal are less motivating (Weber and Chapman, 2005). For many behaviors that undermine health, factors working against adherence (such as time costs) are tangible. Adverse outcomes, such as long-term risks, are intangible and often delayed.

437

ones (Ratner et al., 2008). Hyperbolic discounting implies that agents are relatively far-sighted when making trade-offs between rewards at different times in the future, but pursue immediate gratification when available (O’Donaghue and Rabin, 1999). Loewenstein (1996) attributes present biases to the overwhelming power of visceral factors, such as hunger, cravings or emotions felt at the time of decision. Such factors distort the perceived utility of choice alternatives, increasing the utility of options that alleviate visceral factors while decreasing that of options unrelated to visceral factors, such as long-term body weight or health consequences. In the context of inter-temporal choice, people exhibit dynamic inconsistency, valuing present consumption much more than future consumption. In other words, people have self-control problems. This explains why many behavioral patterns that undermine health involve immediate bene­ fits (such as the pleasure of eating a high-caloric food or the convenience of eating processed food) or immediate costs (such as the inconvenience of taking a drug or undergoing a medical procedure), coupled with delayed, and often uncertain, benefits. Caring less about the future than the present can be rational, but most individuals place much greater weight on the present than would follow from a consistent tendency to discount the future. A major consequence is that behavior is not consistent over time: decisionmakers do not make the decision they expected they would (when they evaluated the decisions in prior periods) when the actual time arrives. This account for both self-control and procrastin­ ation (Camerer et al., 2003).

35.2.5  Prediction failure 35.2.4  Present-biased preference and hyperbolic discounting Individuals place disproportionate weight on present costs and benefits relative to future

A stream of literature on affective forecasting documents several common mistakes people make when predicting how they will feel in the future, underestimating the degree of emotional adaptation to changes in their lives (Gilbert and

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35.  Libertarian Paternalism AND Obesity Prevention

Ebert, 2002). People are also poor at predicting what they will do, overestimating their ability to commit to a plan of action (for example, I will renounce dessert and start exercising tomorrow) (Zauberman, 2003).

35.2.6  Reference dependence and loss aversion Violating expected utility theory, the valuation of choice options is examined in relation to the reference point considered as the status quo against which the choice options are assessed. A given distance from the reference point that represents a loss looms larger than a gain of the same magnitude.

1981), in which professionals and policy-makers are asked to choose between two alternative programs to combat the outbreak of an Asian disease (Box 35.1), respectively presented in a “gain” (i.e., outcomes expressed in terms of number of lives saved) and a “loss” (i.e., outcomes expressed in terms of number of live lost). Although there is no substantial difference between the two versions, in the “gain” version of the problem a substantial majority of respondents favor Program A, indicating risk aversion. In the “loss” version of the problem, a clear majority favor Program B, the risk-seeking option. Numerous framing effects have also been found in how individuals respond to commercial marketing and healthpromoting communications.

35.3  On libertarian paternalism

35.2.7  Vulnerability to framing effects Preferences are affected by variations of irrele­ vant features of options and outcomes. One of the best demonstration of framing effects is the Asian Disease Problem (Tversky and Kahneman,

Libertarian paternalism brings together two otherwise contradictory concepts. Libertarianism refers to the concept by which individual freedom

Box 35.1

Th e A sia n D is e as e P r o bl e m Imagine that the United States is preparing for an outbreak of an unusual Asian disease, which is expected to kill 600 people. Two alternative programs to combat the disease have been proposed. Assume that the exact scientific estimates of the consequences of the programs are as follows:

two-thirds probability that no people will be saved. Which one of the two programs would you favor?

Version Gain Frame

If Program A is adopted, 400 people will die. If Program B is adopted, there is a one-third probability that nobody will die and a two-thirds probability that 600 people will die. Which one of the two programs would you favor?

If Program A is adopted, 200 people will be saved. If Program B is adopted, there is a one-third probability that 600 people will be saved and a

Version Loss Frame

Source: Tversky and Kahneman (1981).

2.  From society to behavior: policy and action

35.3  On libertarian paternalism

is maximized and the presence and intervention of the state is minimized or completely eliminated. The individual is viewed as a completely rational agent who is forward-looking, and whose choices are made to maximize utility and self-interest (Murphy, 2006). Paternalism, however, refers to the concept by which the State makes decisions on behalf of individuals for their own good. It assumes that the individual is incapable of making his or her own decisions, and must therefore be taken care of by a “fatherly” State. Libertarianism is based on a false assumption and two misconceptions. The false assumption is that individuals always make choices that are in their best interest – an assumption that has been found time and time again to be false. The first misconception is that the choices made by government or other actors shaping the environment have no effect on the choices other individuals and/or organizations make. The second misconception is that paternalism always involves coercion (Camerer et al., 2003). As illustrated in the following example, the choice of the order in which to present food items does not coerce anyone to do anything, but may influence an individual’s choice. Consider the problem facing the director of a company cafeteria who discovers that the order in which food is arranged influences the choice that people make. To simplify, consider three alternatives: the director could (1) make choices that she thinks would make the customers better off; (2) make choices at random; or (3) maliciously choose the items he or she thinks would make the customers as obese as possible. Option 1 appears to be paternalistic, which it is, but would anyone advocate option 2 or 3 (Thaler and Sunstein, 2003: 175)? From the perspective of libertarian paternalism, a policy counts as “paternalistic” if it is selected with the goal of influencing the choices of affected parties in a way that will make those parties better off (Thaler and Sunstein, 2003). “Better off” is to be measured as objectively as possible. Libertarian paternalism accounts for the possibility that

439

individuals could improve inferior choices if they had complete information, unlimited cognitive abilities, and no lack of will power. Since this is usually not the case, it is assumed that a form of paternalism cannot be avoided, and that the altern­ atives to it (such as choosing options that make people worse off) are unattractive. Libertarian paternalism helps individuals who are prone to making irrational decisions, while imposing minimal or no restrictions on and not harming those making informed, deliberate decisions. In the purest cases, people behaving sub-optimally are benefited without imposing any costs on those behaving optimally (Camerer et al., 2003). Crucial tenets of libertarian paternalism are to (1) shift behavior in a self-interested direction without abridging individuals’ freedom to choose and (2) help those behaving in a selfdestructive fashion without distorting the decisions of those behaving in a self-interested fashion (Down et al., 2009). The overall aim is to find policies that maximize the health and wellbeing of individuals and society while minimizing the effects on autonomy and freedom of choice (Le Grand, 2008). While some criticize libertarian paternalism as the first step towards government control (Goldstein, 2008), it offers a vision whereby the government “nudges” individuals and organizations in the healthiest direction. Reasoning, judgment, discrimination and self-control are seen as burdens which the State can help lighten. Indeed, some (Loewenstein and O’Donoghue, 2006) go so far as to argue that government and the law have a responsibility to take into account how the choice architecture impacts human behaviors and emotions, and the choices that follow from these. In the context of obesity and the “giants of excess”, by changing the incentive structure that is faced by individuals and organizations we can bring the costs from unhealthy activities (or the benefits from healthy ones) back from the future, and/or reduce the benefits from unhealthy activities (or reduce the costs of healthy ones) in the present (Le Grand, 2008).

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440

35.  Libertarian Paternalism AND Obesity Prevention

35.4  Libertarian paternalism applied Modifying the choice architecture in which individuals make health-related decisions can yield behavioral changes. Various studies an­c­hored in the field of behavioral economics have attempted to demonstrate the effectiveness of such an approach to driving behaviors.

35.4.1  Resetting the default option Novel research has yielded insights into the importance of the default option. Benartzi and Thaler (2007), for instance, showed that introducing some small design features in the way 401(k) plans are presented can go a long way to overcoming faulty biases and heuristics in retirement savings behavior. They highlighted behavioral research that illustrated common individual behaviors related to choosing a retirement savings plan: individuals were usually slow to join advantageous plans, were unlikely to make frequent changes, and adopted naïve diversification strategies. Yet small changes to the retirement savings plan, such as automatic enrollment, precommitment to synchronize pay raises and savings increases, and simplifying the investment selection process, can go a long way in optimizing an employee’s 401(k) (Benartzi and Thaler, 2007). Automatic enrollment to a retirement savings program meant an enrollment increase from 49 percent to 86 percent (Madrian and Shea, 2001; Choi et al., 2003). It was also found that employees saved more if the employer automatically deposited a significant share of the salary in a retirement savings plan than if the default option placed the onus with the employee.

35.4.2  Immediate rewards and pre-commitment Volpp and colleagues (2008) conducted a study to determine whether common decision

errors, such as prospect theory, loss aversion and regret, could be used to design effective weight-loss interventions. Participants engaged in a 16-week weight-loss program that combined monthly weigh-ins, a lottery incentive program and a deposit contract. The study was designed upon the following assumptions: (1) that there is a significant incentive value to small rewards and punishments; (2) that people are motivated by past rewards and the prospect of future rewards; (3) that people are emotionally attracted to the small probabilities of large rewards; and (4) that the desire to avoid regret is a potent force in decision-making under risk–loss aversion. The use of economic incentives produced significant weight loss during the intervention; the mean weight loss was 5.5 kg. It ought to be noted, however, that the weight loss was not sustained following the intervention (and the end of the incentives). Down and colleagues (2009) conducted a study in which they assessed the effects of information versus a paternalistic intervention that made healthier sandwiches more convenient to order. While there was no effect when customers were presented with calorie information or daily calorie recommendations, the convenience manipulation had a strong impact on sandwich choice, such that participants were more likely to choose a low-calorie sandwich when it was more convenient to do so. This was found in both dieters and non-dieters. Also significant, it was found that while the provision of calorie information may influence the choice of some people, it could also have the perverse effect of promoting greater calorie consumption.

35.5  Limitations and conclusion While libertarian paternalism offers significant promise in addressing major societal problems, care must be taken when tweaking the choice architecture. Ariely and colleagues (2009) demonstrated the complexity of the choice architecture

2.  From society to behavior: policy and action

References

and the risks involved in attempting to “nudge” individual and organizational behaviors. They studied the incentives behind charitable donations, and found that monetary incentives interacted negatively with image motivation, and therefore diluting charitable behavior. Indeed, monetary incentives had no effect on pro-social and charitable behavior because of the negative perception of the incentive (Ariely et al., 2009). Therefore, challenges in order to address the choice architecture supporting and promoting unhealthy eating and lifestyle behaviours lie in judiciously matching role, context and measure.

References Ariely, D., Bracha, A., & Meier, S. (2009). Doing good or doing well? Image motivation and monetary incentives in behaving prosocially. American Economic Review, 99(1), 544–555. Benartzi, S., & Thaler, R. H. (2007). Heuristics and biases in retirement savings behavior. Journal of Economic Perspectives, 21(3), 81–104. Camerer, C., Issacharoff, S., Lowenstein, G., O’Donaghue, T., & Rabin, M. (2003). Regulation for conservatives: Behavioral economics and the case for “asymmetric paternalism”. University of Pennsylvania Law Review, 151(3), 1211–1254. Choi, J., Laibson, D., Madrian, B. C., & Metrick, A. (2003). Optimal defaults. American Economic Review, 93(2), 180–185. Down, J. S., Loewenstein, G., & Wisdom, J. (2009). Strategies for promoting healthier food choices. American Economic Review: Papers & Proceedings, 99(2), 159–164. Gilbert, D. T., & Ebert, J. E. (2002). Decisions and revisions: The affective forecasting of changeable outcomes. Journal of Personality and Social Psychology, 82(4), 503–514. Goldstein, E. R. (2008). When nudge comes to shove. Chronicle of Higher Education, 54(35), B10–B11. Hernstein, R. J., & Prelec, D. (1991). Melioration: A theory of distributed choice. Journal of Economic Perspectives, 5(3), 137–156.

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Le Grand, J. (2008). The giants of excess: A challenge to the nation’s health. Journal of the Royal Statistical Society: Series A (Statistics in Society), 171(4), 843–856. Loewenstein, G. (1996). Out of control: Visceral influences on behavior. Organizational Behavior and Human Decision Processes, 65(3), 272–292. Loewenstein, G., & O’Donoghue, T. (2006). We can do this the easy way or the hard way: Negative emotions, selfregulation, and the law. The University of Chicago Law Review, 73(83), 183–206. Loewenstein, G., Brennan, T., & Volpp, K. G. (2008). Asymmetric paternalism to improve health behaviors. Journal of the American Medical Association, 298(20), 2415–2417. Madrian, B. C., & Shea, D. F. (2001). The power of suggestion: Inertia in 401k participation and saving behavior. Quarterly Journal of Economics, 116(4), 1149–1187. Murphy, K. (2006). Obesity and the Economic Man. Presen­ tation given during the McGill Health Challenge Think Tank, hosted in Montreal, Canada, 2006, October 25–27. O’Donaghue, T., & Rabin, M. (1999). Doing it now or later. American Economic Review, 89(1), 103–124. Ratner, R. K., Soman, D., Zauberman, G., Ariely, D., Carmon, Z., Keller, P. A., et al. (2008). How behavioral decision research can enhance consumer welfare: From freedom of choice to paternalistic intervention. Marketing Letters, 19, 383–397. Thaler, R. H., & Sunstein, C. R. (2003). Libertarian paternalism. American Economic Review, 93(2), 175–179. Tversky, A., & Kahneman, D. (1981). The framing of decision and psychology of choice. Science, 453–458. Volpp, K. G., John, L. K., Troxel, A. B., Norton, L., Fassbender, J., & Loewenstein, G. (2008). Financial incentive-based approaches for weight loss: A ran­ domized trial. Journal of the American Medical Association, 300(22), 2631–2637. Weber, B. J., & Chapman, G. B. (2005). Playing for peanuts: Why is risk seeking more common for low-stakes gambles? Organizational Behavior and Human Decision Process, 97, 31–46. Zauberman, G. (2003). The intertemporal dynamics of consumer lock-in. Journal of Consumer Research, 30(3), 405–419.

2.  From society to behavior: policy and action

C H A P T E R

36 The Current State of the Obesity Pandemic: How We Got Here and Where We Are Going* Philip James International Association for the Study of Obesity, and   International Obesity Task Force, London, UK

o u t l i n e 36.1 The Current State of the Obesity   Pandemic 36.1.1 World Data on Overweight, Obesity and their Related Chronic Diseases

36.1.2 Regional Data on Overweight and Obesity

445 445

36.1  The current state of the obesity pandemic 36.1.1  World data on overweight, obesity and their related chronic diseases According to the World Health Organization, approximately 1.6 billion adults were overweight

450

36.2 How Did We Get Here?

456

36.3  The Complexity of the Problem

460

in 2005, and 400 million adults were obese. Furthermore, the WHO estimates that by 2015, these numbers will rise to 2.3 billion and 700 million respectively.1 Even more worrisome is the growing trend of overweight/obesity among children. It is estimated that 287 million children are overweight and 74 million are obese around the world.2 Furthermore, whereas once overweight and obesity were considered

* The following is based on two presentations given by Dr. Philip James during the 2006 and 2007 editions of the McGill Health Challenge Think Tank, which were hosted respectively in Montreal, October 25–27, 2006 and November 7–9, 2007. 1  http://www.who.int/mediacentre/factsheets/fs311/en/index.html 2  International Obesity Task Force estimates made in 2006.

Obesity Prevention: The Role of Brain and Society on Individual Behavior

445

2010 Elsevier Inc. © 2010,

446

36.  Current State of the Obesity Pandemic

Global Totals 2002 Obese: 356 million O/wt ≥ 25: 1.4 billion

35 USA

% Obese (BMI =>30 kg/m2)

30 25 Finland

20

Sweden (Goteborg)

15 10 5 0 1970

2015 Obese: 704 million O/wt ≥ 25: 2.3 billion

England

Cuba

Australia Brazil

Norway (Tromsø)

1975

1980

1985

1990

Japan 1995

2000

2005

Year

Figure 36.1  Escalating obesity rates in adults. Source: IOTF (2006).

a problem only in high-income countries, they are now dramatically on the rise in low- and middle-income countries, particularly in urban settings.3 Figure 36.1 illustrates current overweight/ obesity trends for adults in various countries, based on data collected from various sources by the International Obesity Task Force (IOTF). Except for Cuba, where the collapse of Soviet aid together with the US economic blockade created major but temporary food shortages, every other country shows a growing rate of overweight/ obesity, with the sharpest inclines being in the US, England and Australia. The only country, other than Cuba, where the trend has seemingly leveled off is in Finland. (Indeed, Finland has engaged in strong anti-obesity action in recent years and has, for instance, successfully lowered their fat intake from 43 percent to nearly 30 percent.4

This intake, however, is still too high given the degree of Finnish physical inactivity.) Figure 36.2 shows the prevalence of overweight in children within WHO regions. While still a relatively minor problem in most of Africa, information from South Africa shows that as these countries develop, they too develop the problem of overweight and obesity. This trend is further emphasized in other developing countries, such as the Caribbean Islands, other Middle East countries, for example in Bahrain, and in the Pacific Islands. Analyses highlight the link between economic development and urbanization, and the rise of overweight and obesity (Ezzati et al., 2005). Furthermore, as economic development occurs, there is a shift away from overweight being a disease of the rich and successful: in most middleincome countries, the overweight and obese

3 

http://www.iotf.org/database/documents/GlobalPrevalenceofAdultObesityOctober2009v2.pdf http://www.ktl.fi/attachments/suomi/esittely/organisaatiokaaviot/pj_esityksia/warsova.ppt

4 

2.  From Society to Behavior: Policy and Action

447

36.1  The current state of the obesity pandemic

45 40

% Over weight

35 30 25

Boys Girls

20 15 10

WHO africa region

WHO south east asia region

WHO Western Pacific

WHO Eastern Mediterranean Region

WHO European Region

USA

Chile

Brazil

Portugal

Spain

England

Czech Republic

Bahrain

Iran

Saudi Arabia

Australia

New Zealand

China

Japan

Thailand

India

Sri Lanka

Algeria

South Africa

00

Mali

05

WHO Americas Region

IOTF 200 6

Figure 36.2  Childhood overweight (including obesity) by WHO region. Source: IOTF (2006).

members of society are found among the poorer segments (Monteiro et al., 2007). Overweight/obesity has profound repercussions on health, and is directly linked to cardiovascular diseases, cancer, and type 2 diabetes. Childhood obesity is also now recognized as a major amplifier of disease, with a much higher chance of premature death and early disability in adulthood. Across the world, in both developed and developing countries, obesity’s related chronic diseases place a huge burden on societies so that now the WHO finds that cardiovascular disease is the world’s main cause of death, killing approximately 17 million people every year with the majority of these deaths occurring in low- and middle-income countries (LMICs) – not the affluent West (Lopez et al., 2006). Diabetes itself affects 246 million people throughout the world, and this figure, it is expected, will reach 380 million by 2050. By then,

80 percent of diabetes cases will be in LMICs – countries that do not necessarily have the means to deal with such a widespread problem (Diabetes Atlas, 2003). Figure 36.3 shows that cardiovascular disease is the leading cause of death in these LMICs. Also of note is the number of deaths attributed to cardiovascular disease in LMICs – just over 10 million deaths per year, compared with just over 2 million deaths in high-income countries. This again emphasizes the scale of the problem in developing countries, which have a hopelessly inadequate health system and socio-economic infrastructure to tackle the diseases properly. The underlying major risk factors for such diseases in the affluent world are smoking, high blood pressure and overweight/ obesity; in LMICs, the major killers are childhood underweight, unsafe sex and high blood pressure (Lopez et al., 2006). Nevertheless, in these LMICs, smoking, high blood cholesterol levels,

2.  From Society to Behavior: Policy and Action

448

36.  Current State of the Obesity Pandemic

Low- and Middle-income countries Cause

High-income countries

Deaths (millions)

% total deaths

Cause

Deaths (millions)

% total deaths

1.

Ischemic heart disease

5.70

11.8

Ischemic heart disease

1.36

17.3

2.

Cerebrovascular disease

4.61

9.5

Cerebrovascular disease

0.76

9.9

3.

Lower respiratory infections

3.41

7.0

Trachea, bronchus & lung cancers

0.46

5.8

4.

HIV/AIDS

2.55

5.3

Lower respiratory infections

0.34

4.4

5.

Perinatal conditions

2.49

5.1

Chronic obstructive pulmonary disease

0.30

3.8

6.

Chronic obstructive pulmonary disease

2.38

4.9

Colon and rectal cancers

0.26

3.3

7.

Diarrhoeal diseases

1.78

3.7

Alzheimer's & other dementias

0.21

2.6

8.

Tuberculosis

1.59

3.3

Diabetes mellitus

0.20

2.6

9.

Malaria

1.21

2.5

Breast cancer

0.16

2.0

10

Road traffic accidents

1.07

2.2

Stomach cancer

0.15

1.9

Amplified by excess weight gain

Figure 36.3  The ten leading causes of death in low- and middle/high-income countries. Source: Lopez et al., (2006); reproduced with permission of the World Health Organization.

high alcohol and low fruit and vegetable intakes with overweight/obesity still come into the top 10 primary risk factors. So it is notable that in both developed and developing countries, these risk factors and therefore the principal causes of death and disability are preventable. Yet only a fraction (usually 1 percent) of national budgets for health are devoted to prevention. If the direct medical costs are not enough of an incentive for action, the financial costs to society should raise serious alarms. Figure 36.4 illustrates the financial impact of chronic diseases amplified by overweight/obesity (WHO, 2005). The sheer population sizes of India and China, compounded by their rapid economic development, mean that costs will be enormous in

coming years. In the next 10 years, China is going to be handicapped by at least $100 billion, which amounts to 2 percent of its GDP. Figure 36.5 looks at the costs of different degrees of excess weight in a study from the US (Arterburn et al., 2005). The curvilinear line indicates the increasing cost as one gets fatter. Of note, an interesting feature is that although we know that when one is very fat costs are very high, the graph shows that the biggest absolute cost to the United States arises not in the group of exceptionally obese people with a BMI of over 40, but from those who have only modest levels of overweight (BMI from 25 to 30). Their individual extra cost is small, but for the society as a whole it is huge. The implication

2.  From Society to Behavior: Policy and Action

449

36.1  The current state of the obesity pandemic

600

Projected foregone national income due to heart disease, stroke and diabetes in selected countries, 2005–2015

International dollars (billions)

500 400 300 200 100 0

Brazil

Canada

China

India

Nigeria Pakistan Russian United United federation kingdom republio of tanzania

Figure 36.4  The financial impact of chronic diseases amplified by obesity. Source: WHO (2005), reproduced with permission of the World Health Organization.

Per capita costs $

18

5000

16

4500

14

4000 3500

12

3000

10

2500 08

2000

06

1500

04

1000

02

500

00

<18.5

18.5–24.9

25.0–29.9

30.0–34.9

35.0–39.9

>40

BMI

Figure 36.5  The costs of different degrees of excess weight in the USA ($56 billion). Source: Arterburn D et al., (2005).

2.  From Society to Behavior: Policy and Action

0

Per capita costs $

Total excess expenditure $ billion

Total excess expenditure $ billion

450

36.  Current State of the Obesity Pandemic

1985– 1989

1995–1999

1990– 1994

2000–2008

% Obesity <5% 5–9.9 % 10–14.9 % 15–19.9 % 20–24.9 % ≥ 25 %

Figure 36.6  Increasing rates of obesity in Europe (1985–2008) – males. Source: IASO (2009).

here is clear: we cannot simply take a high-risk approach. The whole population must be considered, since the majority of people will fall into that 25–35 BMI category.

36.1.2  Regional data on overweight   and obesity Europe5 Figures 36.6 and 36.7 show the growing rates of obesity for males and females, respectively, in Europe since 1985. Obesity is responsible for 2–8 percent of health costs and 10–13 percent of deaths, with very notable national differences. Box 36.1 provides examples of various antiobesity initiatives that have been developed by

European countries. A WHO European Obesity Summit was held in Istanbul in November 2007, and gathered health ministers from 48 different countries. The ministers agreed on a joint strategy to combat the European obesity epidemic, and signed the European Charter on Counteracting Obesity.6 The charter stipulates various tools and approaches to dealing with the growing epidemic in Europe. Policy tools range from legislation to private–public partnerships with a particular importance being attached to regulatory measures. International approaches emphasized the need to include the development of a Code of Marketing for foods high in fat, sugar and salt (HFSS), particularly to children, which was then planned to be included in the Second Food and Nutrition Action Plan for Europe.7

5 

http://www.iotf.org/database/index.asp http://www.euro.who.int/Document/E89567.pdf 7  WHO European action plan for food and nutrition policy 2007–2012 (see http://www.euro.who.int/nutrition/ actionplan/20070620_3). 6 

2.  From Society to Behavior: Policy and Action

451

36.1  The current state of the obesity pandemic

1985–1989

1990– 1994

1995– 1999

2000– 2008

% Obesity <5% 5–9.9 % 10–14.9 % 15–19.9 % 20–24.9 % ≥ 25 %

Figure 36.7  Increasing rates of obesity in Europe (1985–2008) – females. Source: IASO (2009).

Box 36.1

E u r o p e a n i n i t i at i v e s European Commission: Obesity Platform. Nordic Ministers: Agreed intersectoral policies (August 2006). ● WHO Europe: Forty-eight Ministries of Health agreed to the Obesity Charter in Istanbul (November 17, 2006). ● Denmark: Focus on trans fats, but has formal prevention plan. Includes altering sales taxes in such that government revenue is preserved, but healthy food consumption is encouraged. ● Sweden: Developed a brilliant proposal, but it was blocked by the Prime Minister. ● ●

Finland: Has a diabetes prevention program implemented in three states. ● Norway: Initiatives have been developed at the Prime Ministerial level and include key intersectoral initiatives. ● France: Has imposed legal measures to restrict advertising; further legislation is possible. Velib ( public bicycle use) in Paris. ● Spain: New initiatives in cooperation with the food industry. ● Slovenia: Far ranging nutrition action plan. ●

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36.  Current State of the Obesity Pandemic

Asia and the Pacific

in that it had managed to reverse obesity trends in children. It involved monitoring children, keeping the overweight after school to help them exercise, ensuring parental involvement in changing their diet, eating in a separate lunch area, and levying fines on those schools who allowed children to become overweight. The program was abandoned in 2007 in spite of promising initial results because the targeted families objected to the clear discrimination in their treatment (Associated Press, 2007).

Figure 36.8 focuses on Asian susceptibility to impaired glucose tolerance (IGT) and diabetes, and indicates the predicted increase in the prevalence of diabetes and IGT in Asian populations (Diabetes Atlas, 2003). Note that China’s levels are still low compared with Singapore, where one in three adults already has diabetes or glucose intolerance. Not shown here is India, where diabetes is sweeping through the population. It has also been shown that individuals of Indian, Bangladeshi, Pakistani, etc. origin living in the UK – so traditionally thinner than the British – will, as their weight increases, dramatically outstrip their British counterparts and succumb to diabetes in far greater numbers. Box 36.2 provides examples of obesity initiatives currently underway in Asia. China and India are just beginning to grapple with the problem. In Thailand, the Prime Minister is acting as a power engine for change, having launched new socio-economic development plans. Singapore had developed an apparently excellent strategy

The Americas In the US, obesity has exploded. Figures 36.9 and 36.10 show the prevalence of overweight/ obesity in the US in 1990 and 2007, respectively. In 1990, among states participating in the Behavioral Risk Factor Surveillance System, 10 states had a prevalence of obesity of less than 10 percent, and no state had prevalence equal to or greater than 15 percent. In 2007, only a single state (Colorado) had a prevalence of obesity of less than 20 percent;

Diabetes

IGT

40

Singapore

35

Malaysia

% prevalence

30

Hong kong Korea

25

Australia

20

Thailand Philippines

15 China

10 05

Taiwan

Vietnam

Figure 36.8  The predicted escalation of the burden from diabetes and IGT. Source: Diabetes Atlas (2003).

2.  From Society to Behavior: Policy and Action

2003 2025

2003 2025

2003 2025

2003 2025

2003 2025

2003 2025

2003 2025

2003 2025

2003 2025

2003 2025

00

36.1  The current state of the obesity pandemic

453

Box 36.2

A s i a - O c e a n i a i n i t i at i v e s China: Ten minutes of play for children in school. ● India: Creation of new Public Health Institutes. ● Australia: State vs Canberra debate on marketing restrictions, but now led by a $10 billion diabetes prevention plan. ● New Zealand: Ministerial frustration with academics’ and NGOs’ lack of initiatives, but school and Maori initiatives. ● Pacific Islands: Action plan has been developed, but has not been implemented. The proposal regarding junk food dumping was negated by Australia and New Zealand but new legal restrictions being introduced. ●

Singapore: Childhood program of Slim and Fit recently changed. ● Malaysia: New Global Alliance. The Minister’s proposal on the marketing of junk food was sabotaged by the food industry and the Nutrition Society’s representatives. New Minister wanting a major educational approach. ● Thailand: Major initiative at both the Prime Ministerial and the Royal Family level. New economic and social development plan incorporating dietary change. ● Pakistan: Focus on heart disease and diabetes. ●

(*BMI ≥30, or ~ 30 lb. overweight for 5’4”person)

No Data

<10

10–14

Figure 36.9  Obesity trends among US adults, 1990. Source: Centers for Disease Control and Prevention.

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454

36.  Current State of the Obesity Pandemic

(*BMI ≥30, or ~ 30 lb. overweight for 5’4”person)

15–19%

20–24%

25–29%

30%

Figure 36.10  Obesity trends among US adults, 2007. Source: Centers for Disease Control and Prevention (1985–2008).

30 states had a prevalence equal to or greater than 25 percent, and three of these (Alabama, Mississippi and Tennessee) had a prevalence of obesity equal to or greater than 30 percent. Furthermore, telephone questionnaires, such as used here, are recognized to markedly underestimate the true prevalence of obesity – so the problem in the US is immense. The situation is no better in Canada, where obesity trends have been on the rise since the early 1970s. Canada’s adult obesity rate is still significantly lower than that in the United States; 23.1 percent compared with 29.7 percent. Yet in 2004, nearly one-quarter (23.1 percent) of adult Canadians, 5.5 million people aged 18 or older, were obese, and an additional 36.1 percent (8.6 million) were overweight. The 2004 obesity figure was up substantially from 1978–1979, when Canada’s obesity rate had been 13.8 percent (Starky, 2005). Indeed, Canadian daily calorie availability of food jumped from 2358 kcal

per person per day in 1976 to 2788 kcal per person per day in 2002 – an 18 percent increase. The situation is not isolated to North America. Studies in Chile (Uauy et al., 2001) and Mexico (Sanchez-Castillo et al., 2005) have shown that 7–12 percent of children under 5 years old and one-fifth of adolescents are obese, while in adults surveys carried out between 2002 and 2007 in Central America and Belize, for example, have shown rates of overweight and obesity close to 60 percent. The Pan-American Health Organiz­ ation (PAHO) reveals that the number of persons suffering from diabetes in Latin America could reach 32.9 million by 2030 (up from 13.3 million in 2000). Right now, according to the available data, the highest prevalence rates for diabetes are in Belize (12.4 percent) and Mexico (10.7 percent). Furthermore, some of the large Latin American capitals, such as Managua, Guatemala City and Bogotá, are reporting prevalence rates of between 8 and 10 percent. In 2000, the cost of diabetes in

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36.1  The current state of the obesity pandemic

80 ♦ Kuwait

Percentage BMI ≥25.0

70 60

Russia ♦ Guyana

40 30 20

Jamaica

S. Africa♦

50

+20% sugar

Trinidad and Tobago

♦ Australia

♦ Brazil

Cuba

♦ Kyrgyzstan ♦

Morocco ♦

New Caledonia ♦ ♦ USA

Barbados

♦ Tunisia

♦ Italy

♦ Malaysia

Philippines ♦

10 ♦ India Dietary fat (%)

♦China ♦ Mali

♦ Congo

20

25

r = 0.88

30

35

40

Figure 36.11  Dietary fat and overweight: Caribbean comparisons and sugar effect. Source: Adapted from Bray and Popkin (1998), and data from the Food and Agriculture Organization of the United Nations (FAO), CFNI and national surveys.

Box 36.3

T r i n i d a d S u m m i t o f P r i m e M i n i s t e r s ( S e p t e m b e r 2 0 0 7 ) Collaboration between CARICOM, PAHO, WHO and partners. ● Established National Commissions. ● Legislation: Immediate implementation of the Tobacco Framework. Ban marketing of HFSS to children. Taxes being proposed. ● Funds obtained from taxes on tobacco, alcohol and other products to be redirected to noncommunicable disease prevention. ● Ministers of Health were due to develop an action plan with other ministries by the end of 2008. ●

the region was estimated at US $65.2 billion, of which $10.7 billion were direct costs of medical management and $54.5 billion were the indirect societal costs (Barcelo et al., 2003). In 2006, the cost of diabetes in some countries was reported as being between 0.4 and 2.3 percent of GDP (data from http://www.eatlas.idf.org/downloadables/ Graphics/index.html).

Reintroduce immediately physical education in schools. ● Progressively eliminate trans fats. ● Nutritional labeling to be organized on a regional level. ● Work site and other areas: Plans for physical activity for the entire community. ● Extensive public education. ● Surveillance. ● CARICOM (the economic and trade policy arm of Caribbean governments) to continue to develop an action plan. ●

In the Caribbean, the situation is alarming. Figure 36.11 shows that all Caribbean countries are above the expected level for their rising dietary fat intakes. The sugar cane history has also affected their intake, and this has played a significant role in amplifying the problem of obesity in this region (Bray and Popkin, 1998; Caribbean Food and Nutrition Institute).

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36.  Current State of the Obesity Pandemic

In 2006, Ministers of Health throughout the Americas adopted the Regional Strategy and Plan of Action on an Integrated Approach to the Prevention and Control of Chronic Diseases, including Diet, Physical Activity, and Health. Box 36.3 highlights the outcome of the Trinidad Summit of Prime Ministers, which was held in September 2007, addressing the issue of overweight and obesity in CARICOM countries (CARICOM, 2007).

36.2  How did we get here? How did we get here? What are the factors that have led to this sudden explosion of obesity around the world? The following section looks at the factors that have contributed to the spread and growth of the obesity pandemic over the last 40–60 years. The case of the United Kingdom illustrates much of the shift in thinking that occurred in previous decades. By the early twentieth century, it became obvious that the UK was suffering from a slew of socio-economic problems. Following its defeats during the Boer Wars, it was evident that British troops were no longer the tall, resilient fighters that had conquered the world during the nineteenth century. Indeed, 40 percent of candidates presenting themselves for military recruitment were turned down: they were short, anemic, and incapable of carrying substantial, heavy army equipment. Stunting was, until then, considered to reflect a genetic problem. However, a survey of the British pre-World War II working class revealed that short people were the poorest, living on a terrible diet, in deprived environments and with a variety of medical problems (Boyd Orr, 1936). A group of scientists decided to apply a concept based on the recently discovered vitamins and other compounds which helped animals to grow faster, leading to the hypothesis that stunting might not be genetic. When applied to young

children, it was found that those who grew the fastest had been given milk (Corry Mann, 1926). Similar trends in weight were found among those who were given sugar and butter, and a comparable study in the US found similar results if you gave children meat. The studies made it clear that stunting was not an intrinsic, genetic problem, but an acquired feature of the working classes. These studies linked with major developments early in World War II because it soon became obvious that the British would have to survive on limited rations of food as a result of so many ships being sunk. These ships carried in, from the colonies such as Canada, the Caribbean, Australia and India, approximately 70 percent of the food consumed by Britons. Since milk, meat, butter and sugar had come to be recognized as the major contributors to the successful nutrition of the poor, this required a massive, fundamental shift in socio-economic thinking. Agriculture and the proper feeding of children and the vulnerable, and pregnant and nursing mothers, became of fundamental importance for national security and survival. Within 4 years, the major national agriculture effort pushed food production in the UK up to 70 percent self-sufficiency. Along with it, a national feeding and rationing program was implemented. Governments the world over then came to realize that agriculture must become a national security issue, and that, globally, no country could afford not to be self-sufficient in food. Agriculture was transformed with the premise that meat, milk, butter and sugar were “wonder foods” which the poor needed but could not afford. A “cheap food” policy was therefore necessary to bring these commodities within easy reach of the poor. The cheap food policy was a brilliant success and was copied globally, with efforts being launched to support nutrition work in the developing world. In the 1960s and 1970s, the Green Revolution began in Asia, and the other pro-meat, -milk, -butter, -oil and -sugar policies ensured that world food commodity prices dropped significantly. This coincided with the nutrition and medical world

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36.2  How did we get here?

advocating a “balanced diet”. So the epidemic of heart disease took off in the 1950s, and continued to escalate throughout the Western world as, globally, meat, butter, oil and sugar intakes were promoted. Thus it slowly became clear that disease patterns would rise or fall depending on the agricultural and food-processing policies being enacted for national security and socioeconomic reasons; health was then only of s­econdary importance. Indeed, these shifts in dietary habits reflected the impact of government, international policies and scientific endeavor – all collaborating together to impact individual and population-level nutritional changes.

A major contributor to the spread of the obesity pandemic was the significant investments being made in agricultural research, and the introduction of the Common Agricultural Policy (CAP). The CAP (Common Market – 1957) aimed at providing farmers with a reasonable standard of living whilst preserving the rural heritage and providing consumers with quality food at fair prices. The CAP support radically transformed the wellbeing of French and German small farmers, and also affected other European countries. Thus, in 1982, for instance, any UK dairy farmer could have 50 percent of any capital investment in his government farm subsidies to increase

Billion € 50

€43.5 billion

40 30 20 10 0

1993

1994

1995

1996

Export subsidies

1997

Market support

1998

1999

Direct aids

$ Billions 25

2000

2001

2002

Rural development

US

20 15 10 5 0

1995

1997

1999

2001

2003

Figure 36.12  Annual EU and US farm subsidies (billions). Source: Elinder (2005).

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EU

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36.  Current State of the Obesity Pandemic

his yield. In addition, the state guaranteed milk prices and many other safeguards to reduce the farmers’ risks. This led to the production of huge surpluses. The retail prices of butter, milk powder and beef became the subject of numerous discussions in Europe involving the manipulation of prices to get rid of the “mountains of butter and beef”. Furthermore, huge agricultural subsidies for the US and European farmers continue (Figure 36.12) (Elinder, 2005). In the US, many would argue that farming subsidies are out of sensible control. Thus, the major change in the relative price of foods in the US has involved an increase in the relative cost of fresh fruit and vegetables, whereas other foods, such as poultry, sugar, sweets, fats, oils and soft drinks, have become remarkably cheap (Figure 36.13) (USDA ERS, 2003; Shoonover and Muller, 2006). The US continues to be locked into this system of agricultural support through a wide range of mechanisms, with

the provision of agricultural support for many different food commodities. Thus, government support for producing grain and oilseed crops comes in many forms – from money invested in public universities and government agencies to research such crops, to subsidy payments that make up for low prices, to continued promises of increased export markets for these crops. Changes in food systems have also affected how food is produced, processed, distributed and consumed. Supermarkets are now playing a new and increasingly important role in determining what reaches consumers’ plates. Indeed, an analysis of supermarkets in Great Britain found that approximately 10 supermarket chains control 70 percent of the British food supply. Furthermore, supermarket buyers choose the commodities and nutrient content of their branded food, and this control by supermarkets is increasingly present in developing countries. Throughout the Caribbean, for instance, and the

Change in US food prices 1985–2000 40

Fresh fruits and vegetables

35 30 25 20

Total fruits and vegetables

% change

15 10

Cereals and bakery

5 0 –5 –10 –15

Dairy Red meats

Poultry Sugar and sweets Fats and oils

–20 –25

Soft drinks

Figure 36.13  Change in US food prices 1985–2000. Source: USDA ERS (2003). Converted to real dollars.

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36.2  How did we get here?

UK National survey of adults 60

Men Women

50 20

20.2 16.6

15

12.5

Percentage

25 40 30

10

20

5

10

0

1997

1990

2001

US National Survey of children 5–15 yrs.

0

16–24 25–34 35–44 45–54 56–64 65–74 75+ Age range (yrs)

Adults achieving suggested 30 mins walking x 5 / w

Figure 36.14  Declining activity: recent trends in children and age effects. Source: Allied Dunbar (1992), US EPA (2003).

Figure 36.15  The Foresight causal map of obesity. Source: Foresight (2007).

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36.  Current State of the Obesity Pandemic

Pacific Islands, one finds that the key decisionmakers are importers or supermarket buyers, who thereby influence in major ways consumer food choices. Individual food needs and circumstances have also greatly changed over the past decades. Economic development has involved a shift away from physical-based labor to knowledgebased work. This has decreased the levels of energy expended by the average human being. Indeed, our energy needs have dropped by 500–800 kcal per day from perhaps 3000 kcal per day, simply because we have every mechanical,

technological and computer-based help at home and work, with cars simplifying travel. So, to maintain normal body weights, we either decrease our consumption of food or increase our level of physical activity. As shown in Figure 36.14, the percentage of children making their own way to school in the US has declined markedly (US EPA, 2003), and in the UK a study conducted about 20 years ago showed that only approximately 40 percent of men and 30 percent of women are meeting physical activity norms, with a marked decline as adults age (Allied Dunbar, 1992).

Individual responsibility e.g. Focus on Health Education – but need understandable food labeling; campaigns selectively help upper socio-economic groups

Change in the environment • Establish nutritional standards for food provision in all government facilities/schools; involve business and all catering in Finnish provision of fruit + veg. within meal costs • Selectively increase costs of high fat/sugary products; soft drinks • Social/medical policies for breast feeding as the norm • Abolish all marketing to children • Progressively adapt all to wns/cities to favor pedestrian/cycling as norm with car restrictions

Figure 36.16  Complementary approaches to obesity prevention. Source: Adapted from Puska (2007).

2.  From Society to Behavior: Policy and Action

References

36.3  The complexity of the problem The UK’s Foresight Report came out in 2007 and, anchored in systems sciences approaches, mapped out the multi-level antecedents of obesity (Foresight, 2007). Figure 36.15 presents this causal map of obesity and illustrates the sheer number of levels and sectors upon which we must operate if we are to deal with this issue effectively. Included are factors such as individual and social psychology, physiology, food production and consumption, individual physical activity, and the physical activity environment. The Foresight Report specified that the epidemic of obesity reflected the normal biological response of humans to a completely inappropriate environment – i.e., it was “passive obesity”, with the normal innate biological response to an obesogenic environment becoming apparent in everybody unless they were blessed with being genetically resistant to weight gain. Obesity must therefore be seen within the entire complex system, and its fundamental environmental drivers must be transformed if we are to allow people to have a more effective life with a normal body weight. Figure 36.16 illustrates the social policies and processes that impact the prevalence of obesity at the population level, and further adds to the argument in favor of broad societal plans as the major requirement which now needs to be added to any individually-tailored interventions that tend to help preferentially the wealthier, more educated members of society.

References Allied Dunbar. (1992). Allied Dunbar National Fitness Survey: Summary Report. London: Sports Council and Health Education Authority. Arterburn, D. E., Maciejewski, M. L., & Tsevat, J. (2005). Impact of morbid obesity on medical expenditure in adults. International Journal of Obesity, 29, 334–339.

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Associated Press. (2007). “Singapore to Scrap Anti-Obesity Program”, March 20. Barcelo, A., Aedo, C., Rajpathak, S., & Robles, S. (2003). The cost of diabetes in Latin America and the Caribbean. Bull WHO, 81, 19–27. Boyd Orr, J. (1936). Food, health and income. London: MacMillan. Bray, G. A., & Popkin, B. M. (1998). Dietary fat intake does affect obesity! The American Journal of Clinical Nutrition, 68, 1157–1173. Caribbean Food and Nutrition Institute. Basic Country Health Profiles for the Americas: Summaries. Online. Available: http://www.paho.org/English/DD/AIS/cp_index.htm. Accessed January 2010. CARICOM. (2007). Communique issued at the Conclusion of the Regional Summit of Heads of Government of the Caribbean Community (CARICOM) on Chronic NonCommunicable Diseases (CNDs), September 15, Port of Spain, Trinidad and Tobago. Online. Available: http:// www.caricom.org/jsp/communications/­communiques/ chronic_non_communicable_diseases.jsp. Centers for Disease Control and Prevention. US Obesity Trends. Trends by State 1985–2008. Online. Available: http://www.cdc. gov/obesity/index.html. Accessed January 2010. Corry Mann, H. C. (1926). Diets for boys during the school years. MRC Spec. Rep. Series 105. HMSO. Diabetes Atlas (2003) 2nd edn. Brussels: International Diabetes Federation. Online. Available: http://www.idf. org/e-atlas. Elinder, L. S. (2005). Obesity, hunger, and agriculture: The damaging role of subsidies. British Medical Journal, 331, 1333–1336. Ezzati, M., Hoorn, S. V., Lawes, C. M. M., Leach, R., James, W. P. T., Lopez, A. D., et al. (2005). Rethinking the “diseases of affluence” paradigm: economic development and global patterns of nutritional risks obesity and other cardiovascular risk factors 2005 in relation to economic development 404–412. PLoS Medicine, 2(5), E133. Foresight. (2007). Tackling obesities: Future choices. London: UK Government Office of Science Online. Available: www.foresight.gov.uk. Lopez, A. D., Mathers, C. D., Ezzati, M., Jamison, D. T., & Murray, C. J. L. (Eds.), (2006). Global burden of disease and risk factors. New York, NY: Oxford University Press. Monteiro, C. A., Conde, W. L., & Popkin, B. M. (2007). Income-specific trends in obesity in Brazil: 1975–2003. American Journal of Public Health, 97, 1808–1812. Puska, P. (2007). European Society of Cardiology. Vienna, September 3, Geoffrey Rose Lecture on Population Science: How to Affect the Risk Profile of a Nation. Online. Available: http://www.ktl.fi/portal/english/ktl/organization/director_general/publications_­presentations_ and_article.

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Sanchez-Castillo, C. P., Velasquez-Monroy, O., Lara-Esqueda, A., Berber, A., Sepulveda, J., Tapia-Conyer, R., & James, W. P. (2005). Diabetes and hypertension increases in a society with abdominal obesity: Results of the Mexican National Health Survey 2000. Public Health Nutrition, 8, 53–60. Shoonover, H., & Muller, M. (2006). Food without thought: How US agriculture policy contributes to obesity. Minneapolis, MN: The Institute for Agriculture and Trade Policy. Starky, S. (2005). The Obesity Epidemic in Canada. Online. Available: http://www2.parl.gc.ca/Content/LOP/ResearchPublications/prb0511-e.pdf.

Uauy, R., Albala, C., & Kain, J. (2001). Obesity trends in Latin America: Transiting from under- to overweight. Journal of Nutrition, 131, 893S–899S. USDA ERS. (2003). Food Review, 25(3). US Environmental Protection Agency. (2003). Travel and environmental implications of school siting. Washington, DC: EPA 231-R-03-004. WHO. (2005). Preventing chronic diseases: A vital investment. Geneva: World Health Organization Online. Available: http://www.who.int/chp/chronic_disease_report/contents/en/index.html.

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C H A P T E R

37 The Underweight/Overweight Paradox in Developing Societies: Causes and Policy Implications 1

Carlos A. Monteiro1, Corinna Hawkes1 and Benjamin Caballero2 Department of Nutrition, School of Public Health, University of São Paulo, São Paulo, Brazil 2 Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA out l i n e

37.1 Introduction

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37.2 The Reasons Underlying the Underweight /Overweight “Paradox” 464 37.2.1 Causes of Underweight and Overweight 464 37.2.2 The Coexistence of Underweight and Overweight among Low SES Groups 465 37.2.3 The “Transition” to the Dual Burden of Overweight and Underweight 465 37.3 Public Policies Needed to Tackle the Coexistence of Underweight / Overweight

466 467 467 467

37.4 Applying the WHO Global Strategy on Diet, Physical Activity and Health 468 37.5 Conclusion

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37.1  Introduction In nearly half of all developing countries (mostly classified as lower-middle and uppermiddle income economies), underweight and overweight are equally relevant public health

Obesity Prevention: The Role of Brain and Society on Individual Behavior

37.3.1 Policies that Promote Universal Access to Public Services 37.3.2 Policies on Appropriate Marketing Practices 37.3.3 Policies that Promote Access to Food 37.3.4 The Role of Economic Growth

problems: they rank fourth and fifth, respectively, on the list of risk factors contributing to the total burden of disease (WHO, 2002). For many years, underweight and overweight (which here refers to overweight and obesity) have coexisted in developing countries, reflecting

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© 2010, 2010 Elsevier Inc.

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37.  The Underweight/ Overweight Paradox

the social and economic disparity in the population: a large segment lacking access to basic needs – and therefore exposed to risk of underweight – coexisting with an affluent social elite exposed to overweight and nutrition-related chronic diseases. Today, the underweight/overweight paradox goes beyond this: it concerns the growing number of developing countries where underweight and overweight have become common in low socio-economic status (SES) groups. In many countries where underweight and overweight coexist, the prevalence of overweight is in fact higher in low SES groups than in high SES groups, at least among women (Monteiro et al., 2004a, 2004b). In the remaining developing countries (mainly low-income economies), underweight is still the risk factor that contributes most to the total burden of disease; overweight ranks only seventeenth (WHO, 2002). Yet even in many of these countries, overweight is still more prevalent in higher SES groups than in lower ones (Monteiro et al., 2004a, 2004b). Since economic growth in developing countries is positively associated with both the overall prevalence of overweight and the shifting of overweight onto the poor, the situation in these countries may change in the near future (Monteiro et al., 2004a, 2004b). In this context, the present chapter addresses two key issues: (1) the reasons underlying the underweight/overweight “paradox” among low SES groups, and (2) the public policies needed to confront this new phenomenon.

37.2  The reasons underlying the underweight/ overweight “paradox” 37.2.1  Causes of underweight and overweight The apparent paradox of the coexistence of underweight and overweight in low SES

populations is based on the belief that both conditions are associated with food availability and intake. Obviously, it is not possible for a population to have both too little and too much food at the same time. However, limited access to food is not the main underlying determinant of underweight. Only in very poor developing countries or under extreme conditions of natural or man-made disasters is underweight primarily determined by limited access to food. In these situations, underweight is usually endemic across all age groups. On the other hand, in countries with some economic development and in the absence of catastrophes, other factors are as important, or more so, than access to food. In fact, the majority of cases of underweight in most developing countries occur among children below 2 years of age; they are primarily the result of inappropriate weaning practices and high vulnerability to infectious diseases, especially diarrhea and acute respiratory infections. In this situation, the main underlying determinants of underweight are poor maternal education, lack of adequate healthcare, and unsanitary housing and water supply. Like underweight, overweight has multiple and complex causes, but is invariably the result of an energy imbalance: excess intake and/or insufficient expenditure. Overweight is usually associated with excess dietary energy intake – frequently promoted by an “obesogenic” environment – and with a sedentary lifestyle, also greatly conditioned by environmental and social constraints to physical activity (Swinburn et al., 1999). Several dietary factors have been proposed as key drivers of obesity in developing nations, including rising consumption of meat, vegetable oils, sugar, and processed foods, and an overall increase in dietary energy intake. The decline of physical activity is the result of a shift towards service-sector employment, changing use of transportation, and increased mechanization of activities in the home (Prentice, 2006).

2.  From Society to Behavior: Policy and Action

37.2  The reasons underlying the underweight/ overweight “paradox”

37.2.2  The coexistence of underweight and overweight among low SES groups It is clear that limited access to food is incompatible with overweight in low SES groups. This explains why the coexistence of underweight and overweight is still not a relevant public health problem in countries where economic development is incipient and food supplies are insufficient. Where there is some economic development and food availability matches the population energy requirements, underweight and overweight can readily coexist; low SES populations experience underweight owing to limited access to education and healthcare, poor housing conditions and lack of basic environmental services, and are also at risk of overweight because they have easy access to dietary energy and, at least in urban areas, expend less energy in everyday life. The coexistence of underweight and overweight is thus only an apparent paradox, since the two conditions are perfectly compatible. In the majority of cases, underweight among children coexists with overweight in adults.1 This situation is found not only within communities, but also within households (Doak et al., 2000, 2005; Caballero, 2005). These “dual burden” households are usually comprised of an underweight child and an overweight, non-elderly adult, and are more likely to be found in poorer areas. For example, data from Mexico’s 1999 National Household Survey show that women with a waist-to-hip ratio (WHR) greater than 1 have twice the probability of having a stunted child as do those with a WHR of 0.65 (Barquera et al., 2007); the data also shows that “dual burden” households are more prevalent in poor areas of the country. A rural community-based survey conducted in Mexico in 2003 also showed that low SES was directly associated with the coexistence of stunting and overweight (Fernald and Neufeld, 2007). Similar results have

465

been found in other places in Latin America: in Guatemala, 16 percent of stunted children live in households with an overweight mother, 55 percent of which reside in rural areas (Garrett and Ruel, 2005); in Nicaragua, the figure is 6.9 percent, 45 percent of whom live in rural areas.

37.2.3  The “transition” to the dual burden of overweight and underweight The growth of overweight in developing countries is frequently attributed to “nutrition transition” (Popkin, 2002), which conceptualizes dietary change as a series of stages: 1. Collecting food (when diets are high in carbohydrates and fiber, and low in fat) 2. Famine (which still characterizes diets in some low-income countries) 3. Receding famine (the consumption of fruits, vegetables and animal protein increases, and starchy staples become less important in the diet) 4. Dietary patterns associated with overweight and nutrition-related chronic diseases (increased consumption of fats and sweeteners, and a decline in fiber – the stage popularly referred to as “the nutrition transition”) 5. Behavioral change (when populations desire a healthier diet and change accordingly). The nutrition transition model suggests that dietary change follows a path from famine to adequate food supply/intake. This implies a shift away from underweight to overweight. It is expected, then, that the problem of underweight (particularly moderate and severe cases) in developing countries is brought under control before overweight becomes a public health problem. Indeed, historically, when developed

1 

It is notable that WHO estimates of the contribution of underweight to the burden of disease of each country take into consideration only young children and women of childbearing age, while the contribution of overweight takes into account the entire adult population.

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countries were at the level of economic development (as measured by the GNP per capita) that currently characterizes middle-income developing countries, underweight had already been controlled and overweight was much less prevalent than it is in many of today’s middleincome countries. But even though underweight has not been brought under control in many developing countries, overweight has been rising rapidly. This presents a significant departure for the situation experienced by developed countries, and seems to indicate that the underweight/overweight coexistence is far from “natural” or expected. Why are develop­ ing countries experiencing earlier rises of overweight while their underweight problems remain still unresolved? A key factor explaining this is the strong history of social inequalities and exclusion of the poor from basic services such as education, healthcare and housing. This explanation is supported by the fact that developing countries with lower levels of social inequalities have a much lower prevalence of underweight than expected based on their GNP per capita. A good hypothesis for the early rise of the overweight epidemic in developing countries is the relatively recent integration of these countries into the global economy (globalization). This integration has brought about rapid and strong changes in the production and trade of agricultural goods in these developing countries, growing foreign direct investments in food processing and retailing, and the increasing sophistication of food marketing with clear implications for dietary patterns and the risk of obesity. For instance, changes in the production and trade of agricultural goods has facilitated the recent increase in the consumption of vegetable oils seen in most developing countries, while changes in both foreign direct investments and global food marketing have certainly facilitated the consumption of highly processed, energy-dense, nutrient-poor foods (Hawkes, 2006).

37.3  Public policies needed to tackle the coexistence of underweight / overweight Underweight and overweight are different problems with different causes, but alleviating both is crucial in order to improve public health in developing countries. Given that they both deal with nutritional concerns, they need to be addressed in a complementary fashion. Below, we analyze briefly several policy options available for simultaneously tackling underweight and overweight.

37.3.1  Policies that promote universal access to public services Given the importance of universal access to basic public services, clean water and housing, policies to prevent and control underweight should focus on insuring the provision of these services. Two key public services are health and education. Policies that support health services are critical to the alleviation of underweight. Special attention should be given to prenatal care, the promotion of exclusively breast-feeding for 6 months followed by appropriate complementary feeding, child growth monitoring, immunizations, and micronutrient supplementation. Comprehensive health service interventions focused on prevention rather than just on treatment are also needed to address overweight and other nutrition-related chronic diseases. For example, employers could develop preventive health services in the workplace as a means of preventing overweight among the workforce. Community heath service workers could also be used more effectively in communities to spread information about overweight prevention and control. It is well known that women’s education is a critical factor in the fight against malnutrition: women’s education has been estimated to have a greater influence on child nutrition than food

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37.3  Public policies needed to tackle the coexistence of underweight/ overweight

availability, women’s status, and access to safe water (Smith and Haddad, 2000). Policies and programs are therefore needed to increase education among girls and mothers. Improving overall education levels can also be used to promote healthy food choices, since low levels of education, more so than income, are associated with overweight in women in developing countries (Monteiro et al., 2004a, 2004b). Education specific to nutrition is another more targeted tool available to address overweight, but it is likely to be limited without complementary interventions which address environmental factors; there is little evidence supporting nutrition education interventions among adults (Brown et al., 2007). Among children, however, multi-faceted, school-based interventions do show real potential to address overweight (Doak et al., 2006; Haby et al., 2006).

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access to food is not a key concern. Public policies focusing on food distribution, although politically appealing, are thus not appropriate in addressing the double burden. Yet many programs designed to ensure people are sufficiently fed, such as school meal programs, food-for-work programs and income transfer programs, still tend to focus on the provision of calories, which introduces risks for obesity. For example, the food voucher program for workers in Brazil has been found to be associated with unhealthy weight gain (Wanjek, 2005). Evidence from the United States suggests that such programs need to be designed to promote healthy eating if they are to avoid encouraging excessive caloric intake (Guthrie et al., 2007; Fox et al., 2009).

37.3.4  The role of economic growth 37.3.2  Policies on appropriate marketing practices Many nutritional professionals and advocates have raised concerns both about the marketing of breast-milk substitutes, and of high-calorie, nutrient-poor foods to children. The WHO Code on the Marketing of Breastmilk Substitutes aims at decreasing inappropriate marketing as a means of promoting breast-feeding. It should be implemented and monitored by policy-makers. Evidence also suggests that food marketing to children has a deleterious effect on diets (Hastings et al., 2003; McGinnis et al., 2006). This has led to calls around the world for greater restrictions on the marketing of high-caloric, low-nutrient foods (Hawkes, 2007). These policies are necessary to ensure that populations receive consistent messages about balanced diets and healthy weights.

37.3.3  Policies that promote access to food As stated earlier, where both overweight and underweight are present in low SES populations,

The effective implementation of these policies requires not only good governance, but also significant resources that can only become available with sustained economic growth. Yet the current form of economic development adopted by many developing countries (largely based on the globalization of internal markets) has in itself increased the risk of overweight in the population. Data show that higher GNP per capita in developing countries is associated with a higher prevalence of overweight, particularly in lower SES groups (Monteiro et al., 2004a, 2004b). Economic development may be a doubleedged sword: on the one hand, it is essential to combat poverty and underweight; on the other, it may result in increased overweight. There are concerns that policies tackling overweight pose a threat to economic development. For example, the sugar industry opposed the development of the WHO Global Strategy on Diet, Physical Activity and Health, arguing that it would harm the economies of sugar-producing countries. However, there is ample evidence that investing in prevention as economies grow is far less costly than dealing with the adverse

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37.  The Underweight/ Overweight Paradox

consequences afterwards, even after considering the impact of prevention programs on economic growth (O’Connor, 1996). Thus, what is needed is healthy economic development which incorporates the policies and programs necessary to prevent overweight and other nutrition-related chronic diseases.

37.4  Applying the who global strategy on diet, physical activity and health To address broader environmental concerns, policies and programs to prevent and control overweight in the developing countries should follow the blueprint formulated by the WHO Global Strategy on Diet, Physical Activity and Health. Specifically, they need to contemplate intersectoral initiatives to (1) enable individuals to make informed choices and take effective action toward healthier diet and physical activity behaviors, and (2) create and protect macro- and micro-environments that make healthy choices feasible, attractive and rewarding (WHO, 2004). Two principles of the Global Strategy deserve special attention in the context of the “double burden” in the developing world: 1. A life-course perspective is essential. The actions for the prevention of obesity in developing countries should follow a life-course approach that starts with maternal and child health programs and stretches to children and adolescents in schools and adults in worksites, encouraging healthy diets and regular physical activity from youth to old age. The reinforcement and improvement of maternal and child health programs aiming at reducing intra-uterine growth retardation, premature births, lack of breast-feeding, and infant and child growth retardation – all factors that may increase the risk of obesity (and other chronic diseases) later in life – is

an integrative element that unifies the agenda for the prevention of obesity and underweight in developing countries. 2. Since dietary habits and patterns of physical activity are often rooted in local and regional traditions, national strategies should be culturally appropriate, able to challenge cultural influences and to respond to changes over time. Actions to promote, support and protect healthy local and ethnic dietary patterns (such as the rice and bean diet in Brazil, the vegetable-rich cuisine in China or the kimchi cuisine in South Korea) and traditional active leisure habits are particularly relevant for the developing world, where these patterns and habits still prevail. Examples of specific programs and actions implemented in Brazil and China following the principles and orientations formulated by the WHO Global Strategy on Diet, Physical Activity and Health, as well as relevant initiatives to promote healthy diets and active lifestyles in other developing countries, are described elsewhere (Coitinho et al., 2002; Doak, 2002; Matsudo et al., 2002; Zhai et al., 2002).

37.5  Conclusion Health-promoting nutrition is an important strategy for improving public health worldwide, for individuals who are at risk of underweight and overweight. The two conditions, although often termed a “paradox”, are in fact perfectly compatible. Addressing both of them needs to be done in a way that reflects both their differences and similarities.

References Barquera, S., Peterson, K. E., Must, A., Rogers, B. L., Flores, M., & Houser, R. (2007). Coexistence of maternal central adiposity and child stunting in Mexico. International Journal of Obesity, 31, 601–607.

2.  From Society to Behavior: Policy and Action

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Brown, T., Kelly, S., & Summerbell, C. (2007). Prevention of obesity: A review of interventions. Obesity Reviews, 8(Suppl. 1), 127–130. Caballero, B. (2005). A nutrition paradox – underweight and obesity in developing countries. New England Journal of Medicine, 352, 1514–1516. Coitinho, D., Monteiro, C. A., & Popkin, B. M. (2002). What is Brazil doing to promote healthy diets and active lifestyles? Public Health Nutrition, 5, 263–267. Doak, C. (2002). Large-scale interventions and programs addressing nutrition-related chronic diseases and obesity: Examples from 14 countries. Public Health Nutrition, 5, 275–277. Doak, C. M., Adair, L. S., Monteiro, C. A., & Popkin, B. M. (2000). Overweight and underweight coexist within households in Brazil, China and Russia. Journal of Nutrition, 130, 2965–2971. Doak, C. M., Adair, L. S., Bentley, M., Monteiro, C. A., & Popkin, B. M. (2005). The dual burden household and the nutrition transition paradox. International Journal of Obesity, 29, 129–136. Doak, C. M., Visscher, T. L. S., Renders, C. M., & Seidell, J. C. (2006). The prevention of overweight and obesity in children and adolescents: A review of interventions and programmes. Obesity Reviews, 7, 111–136. Fernald, L. C., & Neufeld, L. M. (2007). Overweight with concurrent stunting in very young children from rural Mexico: Prevalence and associated factors. European Journal of Clinical Nutrition, 61, 623–632. Fox, M. K., Dodd, A. H., Wilson, A., & Gleason, P. M. (2009). Association between school food environment and practices and body mass index of US public school children. Journal of the American Dietetic Association, 109(Suppl. 2), S108–S117. Garrett, J. L., & Ruel, M. T. (2005). Stunted child–overweight mother pairs: Prevalence and association with economic development and urbanization. Food and Nutrition Bulletin, 26(2), 209–221. Guthrie, J. F., Andrews, M., Frazao, E., Leibtag, E., Lin, B. H., Mancino, L., et al. (2007). Can food stamps do more to improve food choices? An economic perspective.’ Economic Information Bulletin No. (EIB-29). Washington DC, USDA. Retrieved from . Haby, M. M., Vos, T., Carter, R., Moodie, M., Markwick, A., Magnus, A., et al. (2006). A new approach to assessing the health benefit from obesity interventions in children and adolescents: The assessing cost-effectiveness in obesity project. International Journal of Obesity, 30, 1463–1475. Hastings, G., Stead, M., McDermott, L., Forsyth, A., MacKintosh, A. M., Rayner, M., et al. (2003). Does food promotion influence children? A systematic review of the evidence. London: Food Standards Agency.

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Hawkes, C. (2006). Uneven dietary development: Linking the policies and processes of globalization with the nutrition transition, obesity and diet-related chronic diseases. Globalization and Health, 2, 4. Hawkes, C. (2007). Regulating food marketing to children: Trend and policy drivers. American Journal of Public Health, 97, 1962–1973. Matsudo, V., Matsudo, S., Andrade, D., Araujo, T., Andrade, E., Oliveira, L. C., et al. (2002). Promotion of physical activity in a developing country: The Agita São Paulo experience. Public Health Nutrition, 5, 253–261. McGinnis, J. M., Gootman, J. A., & Kraak, V. I. (2006). Food marketing to children and youth: Threat or opportunity. Washington, DC: National Academies Press. Monteiro, C. A., Conde, W. L., Lu, B., & Popkin, B. M. (2004a). Obesity and inequities in health in the developing world. International Journal of Obesity, 28, 1181–1186. Monteiro, C. A., Moura, E. C., Conde, W. L., & Popkin, B. M. (2004b). Socioeconomic status and obesity in adult populations of developing countries: A review. Bulletin of the World Health Organization, 82(12), 940–946. O’Connor, D. (1996). Grow now/clean later, or the pursuit of sustainable development? Technical Paper No. 111. Paris: OECD Development Centre. Popkin, B. M. (2002). An overview on the nutrition transition and its health implications: The Bellagio meeting. Public Health Nutrition, 5(1A), 93–103. Prentice, A. M. (2006). The emerging epidemic of obesity in developing countries. International Journal of Epidemiology, 35, 93–99. Smith, L. C., & Haddad, L. (2000). Overcoming child malnutrition in developing countries: Past achievements and future choices Food, Agriculture, and the Environment Discussion Paper No. 30. Washington, DC: International Food Policy Research Institute. Swinburn, B., Egger, G., & Raza, F. (1999). Dissecting obesogenic environments: The development and application of a framework for identifying and prioritizing environmental interventions for obesity. Preventive Medicine, 29, 563–570. Wanjek, C. (2005). Food at work: Workplace solutions for mal­ nutrition, obesity and chronic diseases. Geneva: ILO. World Health Organization. (2002). The world health report 2002: Reducing risks, promoting healthy life. Geneva: WHO. World Health Organization. (2004). Resolution WHA57.17, Global strategy on diet, physical activity and health. Geneva: Fifty-seventh World Health Assembly. Zhai, F., Fu, D., Du, S., Ge, K., Chen, C., & Popkin, B. M. (2002). What is China doing in policy-making to push back the negative aspects of the nutrition transition? Public Health Nutrition, 5, 269–273.

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C H A P T E R

38 The Drivers of Body Weight, Shape and Health: An Indian Perspective of Domestic and International Influences Manoja Kumar Das and Narendra K. Arora International Clinical Epidemiology Network, New Delhi, India

o u t l i n e 38.1 Introduction 38.2 Overweight and Obesity in Indian Children and Youth 38.2.1 Current State of Overweight and Obesity in India 38.2.2 Measurement of Overweight and Obesity in India 38.2.3 Birth Weight and State of Overweight  / Obesity in Indian Children and Youth 38.2.4 Advertisements and Children 38.2.5 Schools and Physical Activity 38.2.6 Urban Planning and Children’s Play 38.2.7 Existing Child-Targeted Programs 38.3 Trends Influencing Intake 38.3.1 Current State of Intake

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38.3.2 Increasing Income 38.3.3 Food Globalization 38.3.4 Agriculture

476 477 478

38.4 Trends in Energy Expenditure 38.4.1 Urbanization 38.4.2 Transportation 38.4.3 Motorization and Changes in the Mode of Transportation 38.4.4 Poor Sports Culture

480 480 480

472 472 472 473 473 474 474 474 475 475

481 482

38.5 Cross-Cutting issues 482 38.5.1 Undernutrition to Overweight/  Obesity: Unfinished Agendas Versus New Challenges 482 38.5.2 Policy Dilemma 482 38.5.3 Implications for Research and Scientific Advancement 483 38.6 Conclusions

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2010 Elsevier Inc. © 2010,

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38.  Drivers of Body Weight, Shape and Health in india

38.1  Introduction Any deviation from ideal body weight, in the form of overweight/obesity – and particularly abdominal obesity – is associated with increased risk of morbidity and mortality. Unfortunately, this problem of overweight/obesity has reached epidemic proportions in developed nations, and has been increasing in developing countries. Among Indian adults and children, the prevalence of overweight/obesity is on the rise. While this is first and foremost an urban issue, a similar trend is also gradually becoming visible in rural areas. The causes of body fatness have been well documented in developed countries, yet factors within the Indian context have not been so explored. This chapter reviews the available evidence regarding body fatness among Indians, especially Indian children. Indeed, there is a great concern about the increasing prevalence of obesity in Indian children and adolescents. This document also seeks to draw attention to the important issues regarding the emergence of the lifestyle-related epidemic in India.

11 percent in NFHS-2 (1998–1999) to 15 percent in NFHS-3 (2005–2006). The prevalence can be seen to be increasing significantly after the age of 30 in both men and women. Children Most of the reports from India in regard to children pertain to urban areas and are based on school surveys. These surveys indicate the prevalence of overweight/obesity among children to range from 15 to 25 percent (Kapil et al., 2002;  Marwaha et al., 2006; Sharma et al., 2007). Limited  reports from rural areas indicate the prevalence of overweight/obesity among children to be about 5 percent (Pandher et al., 2004). Lack of comparable study methodology and classification norms make comparison difficult, yet it is apparent that the burden of overweight/obesity  among Indians, both adults and children, is rising.

38.2.2  Measurement of overweight and obesity in India

Given the small body frame of Indians and other Asians, both the current BMI and waist 38.2  Overweight and obesity circumference criteria may be inappropriate. The World Health Organization Expert in Indian children and Consultation (2004) has reduced the “obesyouth ity related action point” in Asians to 23 kg/m2 (WHO Expert Consultation, 2004). It is now clear that the metabolic and vascular risks for 38.2.1  Current state of overweight and “obesity” are manifested at a lower BMI in popobesity in India ulations in developing countries compared with Adults those in developed countries. For a given BMI, According to a recent National Family Health Indians have a higher percentage of body fat Survey (NFHS-3, 2005–2006), overweight/obesity than White Caucasians and African Americans among women aged 15–49 years was 24 percent  (Yajnik, 2004). In the past few decades, there has in urban areas and 7 percent in rural areas. been an increasing realization that the distribuAmong adult males (15–54 years), 16 percent tion of fat is also an important determinant of in urban areas and 6 percent in rural areas were morbidity and mortality, and that “central” overweight/obese. Overall, the proportion of obesity may be more pathological than generaloverweight or obese women has increased from ized obesity (measured as BMI) (WHO, 2000).

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38.2.3  Birth weight and state of overweight / obesity in Indian children and youth

determinants operating during the lifetime need further exploration.

Nearly one-third of Indian infants weigh less than 2.5 kg. at birth. Low birth-weight incidence has remained unaltered over the past three decades (Nutrition Foundation of India, 2004). The incidence of low birth-weight (LBW) is highest among low-income groups. There is clear correlation between birth weight, maternal body weight, and maternal hemoglobin levels  below 8 g/dl (Ramachandran, 1989a). With improved survival of LBW newborns, concern regarding the relationship between LBW and poor early-life growth, and the increased risk of chronic degenerative diseases in later life, has been highlighted. Concepts of intrauterine origin for adulthood diseases such as type 2 diabetes and CHD are generally accepted around the world. This is more relevant for developing nations, like India, where a substantial proportion of babies are born with a low birth weight, and mothers suffer from undernutrition. It is now clear that the metabolic and vascular risks for “obesity” are manifested at a lower BMI in populations in developing countries compared with those in developed countries. These studies suggest the existence of two types of relationship between birth weight, later weight and BMI. First, thin–fat babies (i.e., babies who are light, short and thin, but who have relatively preserved skinfold) are at higher risk of being obese and diabetic in late life. Second, high birth-weight babies are expected to be more obese. As per the available evidence, both the relationships are valid; each one partly explains obesity in a population. So, birth weight is a poor indicator of the adiposity of the fetus, and alone is not sufficient to indicate the later outcomes. These relationships are also modified and modeled as per other individual, environmental factors related to food and physical activity. The contribution of genetic factors, intrauterine environment and other 

38.2.4  Advertisements and children There is little literature pertaining to the influence of media on health and behavior in India. Advertising is an immensely potent tool to influence the judgment of even the most aware consumer. Children are especially vulnerable to advertising, because they are less able than adults to understand fully and judge critically. A study on the influence of televised food ads on children was conducted in six developing countries, including India (The Junk Food Generation, 2000–2002). It revealed that most children and parents watch between 2 and 4 hours of television on weekdays. During vacations, this increases beyond 8 hours per day for 10 percent of children and 3 percent of parents in India. In India, the advertising-to-program ratio was 1 : 3 – i.e., 15 minutes in each hour is spent on advertisements. The 10 percent of Indian children watching over 8 hours of television per day during vacations would therefore be exposed to 2 hours of advertising. Of advertisements during children’s programming, 40–50 percent pertain to food, and the diet advertised to children contrasts strongly with the nationally recommended or cultural diet. When Indian children were surveyed, 62 percent stated they loved watching advertisements and considered them necessary. Children have important spending power and considerable influence on family purchases. Advertisers recognize children as the teenage and adult shoppers of the future, and hence  attempt to instill brand loyalty from an early age.  Children are vulnerable, and have low cognitive defenses against television advertising. Parents also recognize advertising as a critical behavior modification medium. More than two-thirds of parents suggested reducing the frequency

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38.  Drivers of Body Weight, Shape and Health in india

of advertisements on TV, and making health protection messages in food advertisements mandatory. In India, there is still too little regulation controlling program-to-advertising ratios, especially during children’s programming. The Advertising Standards Council of India (ASCI), a voluntary, self-regulatory council, was formed in 1985 with the overarching goal of maintaining and enhancing the public’s confidence in advertising. This council stresses self-regulation, and has issued advertisement guidelines.

38.2.5  Schools and physical activity School is considered a place to build attitudes and behavior for future life. While physical activity is an integral part of the school curriculum, many schools do not have playgrounds or sports facilities. Increasing academic pressures have also meant shorter structured physical education sessions. For instance, while Chennai has increasing rates of childhood obesity and adolescent diaebetes, only 10 percent of the 457 schools in Chennai have playgrounds, and only 2 percent are equipped for games such as football, cricket, basketball and volleyball.

38.2.6  Urban planning and children’s play While there are technically specifications pertaining to residential housing areas, local shopping, park areas, playing areas, community halls, schools, roads and transport provisions, these are, in reality, often modified. For example, the Municipal Corporation of Delhi (MCD) is in the process of converting 24 parks across the city into multi-level parking lots, and is setting up ATMs and eateries outside these parks to generate revenue. It has already designated 16 areas of the city for building. Conversely, there are about 14,000 parks in Delhi, of which about 5000 are ornamental parks where play is

prohibited. The MCD plans to convert an additional 2000 parks to ornamental ones in coming years. Many of the other parks are either poorly maintained or encroached upon. Additionally, parks maintained by locals end up as esthetic greens, out of bounds for playing, which means that children tend to play in the streets. Yet, given the increased traffics as well as the inevitable fallout of children playing (noise, broken windows, etc.), even this is an increasing rarity. Children under 15 years of age – about one-third of the population – are not included in urban planning, and therefore playing is now becoming a difficult and expensive commodity. The relevance of play, recreation and physical activity as a tool for early childhood development is understood differently in different cultural contexts. In India, it is generally believed that going to school and gaining academic qualifications is more important than play. It is commonly thought that play is accompanied by mischief and, at times, interaction with unwanted elements. Therefore, it is not socially desirable. Virtue and goodness of a child is judged by the hours a child spends with a book as opposed to playing outside the house. Lack of space and lack of time both have made the lives of students very stressful. The Indian examination system causes considerable stress, as these exams predict a child’s scholastic destiny. With cut-throat competition ensuring that only the top one-eighth achieve admission into good colleges, securing high grades has become the one-point agenda in the lives of many students and their parents.

38.2.7  Existing child-targeted programs The Reproductive and Child Health Pro­ grammes 1 and 2 (Ministry of Family and Health Welfare, India) have stressed providing effective prenatal care and reducing the rates of low birth weight. While anemia, pregnancyinduced hypertension and low maternal weight

2.  From society to behavior: policy and action

38.3  Trends influencing intake

gain during pregnancy can be managed, other factors – such as maternal nutritional status (especially height and body composition), which has a significant influence on birth weight –   cannot be improved with short-term corrective interventions. The Integrated Childhood Development Scheme (ICDS) is India’s largest multi-package program consisting of health, growth monitoring, nutritional and educational services for children below the age of 6 years. It is mostly located in rural and disadvantaged urban areas. The program operates through the Anganwadi center in each village (approximately 1000 inhabitants), which is manned by a trained Anganwadi worker. The benefits of stimulation/non-formal education and other health and nutritional interventions on the growth and development of children have been recognized. These facilities, located within the low-income communities, also act as daycare centers, and often house approximately 40–50 children in a cramped space. The original objective of providing for the overall development of the child has thus been compromised due to inadequate space and the character of the setting (Vazir and Kashinath, 1999).

475

distinct food habits even after migrating to different parts of the country. Most Indians prefer to eat home-cooked foods, and take immense pride in the variety of food cooked at home. Looking at Indian dietary intake over the past three decades, there is no increase in individual per capita daily caloric intake. According to the National Sample Survey Organization (NSSO), which collects household level data at regular intervals in India, between 1972–1973 and 2004–2005 per capita daily caloric intake in rural areas decreased from 2250 calories to 2050 calories (Figure 38.1). During the same period, per capita daily caloric intake in urban areas decreased from 2100 calories to 2025 calories. Protein intakes in both rural and urban areas have remained stable. Yet, over the past two decades (between 1983 and 2004–2005), per capita daily fat consumption has increased by 10 g (from 25 to 35 g) in rural areas and by 15 g (from 35 g to 50 g) in urban areas (Figure 38.2). These changes have occurred as per capita income increased more than eight-fold, from less than US $50 to about US $400. Yet, looking at the intake of specific food products against the Human Development Index (HDI), Indian states with higher HDI ( 0.65) had significantly greater intakes (1.5–2 times greater) of fat, milk and milk products, and sugar than did states 38.3  Trends influencing with low HDI ( 0.65) (Antony and Laxmaiah, intake 2008). Following Indian Independence in 1947, political leaders vigorously propounded the 38.3.1  Current state of intake preference for national products, swadeshi manWhile an overwhelming majority of Indians tra, and pursued economic nationalism aimed at (83 percent) do not eat beef or pork, only  self-sufficiency until the late 1980s. Government 20 percent of the population is completely veg- control over the economy and public sector-led etarian (Anthropological Survey of India, economic growth were given critical impor1997). Food preferences vary widely across tance. Such strategies produced a low economic states, and some communities, such as Jains, growth rate of 3.5 percent between 1965 and Buddhists and Brahmins, are pure vegetarians 1980. India’s wide range of economic reforms in (Anthropological Survey of India, New Delhi, the 1990s, coupled with a potentially large con1997; Balasubramanian, 2004). Each region and sumer market, lured global food chains to enter sub-region in India has distinct food traditions India (Ahluwalia, 1994). While India’s sensitivity  and preferences, and inhabitants maintain their to cultural imperialism and the importance

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38.  Drivers of Body Weight, Shape and Health in india

2300 2250 2200

kcal

2150 2100 2050 2000 1950 1900 1850

27th round

38th round

50th round

55th round

Rural

61st round

Urban

Figure 38.1  Per capita per diem calorie intake. Source: National Sample Survey Organization (NSSO).

70 60

Grams

50 40 30 20 10 0

27th round

38th round Protein Rural

50th round Urban

55th round Fat Rural

61st round

Urban

Figure 38.2  Per capita per diem intake of protein and fat (in grams). Source: National Sample Survey Organization (NSSO).

attributed to its own values, traditions, religious 38.3.2  Increasing income beliefs, customs and food habits have made the penetration of global food chains difficult, treThe income distribution is highly skewed in mendous changes have still taken place in the  India. Just 20 percent of the richest Indians share dietary intake of Indians in both rural and urban  more than 40 percent of the national income. areas. According to the National Council of Applied

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477

38.3  Trends influencing intake

Table 38.1  Per consumer unit intake of calorie, protein and fat per day by monthly per capita calorie consumption (MPCE) class Rural

Urban

Lowest MCPE

Highest MCPE

All

Lowest MCPE

Highest MCPE

All

% Expenditure on food

68.5

  33.7

55.0

64.9

  23.7

42.5

% Expenditure on cereal

34.7

    6.8

18.0

26.3

  3.2

10.1

% Expenditure on non-food items

31.5

  66.3

45.0

35.1

  76.3

57.5

2475

Calorie intake

1746

3722

2540

1764

3496

Protein

46.8

104.7

70.8

50.4

  96.7

69.9

Fat

17.8

  91.5

44.0

24.4

114.9

58.2

Source: National Sample Survey Organization, 61st round, 2004–2005.

Economic Research, in 2006–2007, 5 million households had an annual income of over US $25,000 and 76 million households had incomes between US $5200 and US $25,000 (NCAER, 2006–2007). Market research groups project that these numbers will increase to 20 million and 127 million respectively by 2014–2015. In addition, reduced family size is a growing trend (Economist Intelligence Unit, 2005; NCAER, 2004–2005, 2006–2007; Dash, 2005; Rabobank Estimates, 2008). This impacts the availability of disposable income of a significant proportion of households to spend on food and luxury. This early trend can be seen in the recent NSSO 61st round (2004–2005), as reflected in Table 38.1.

of cereal consumption will reduce from 29 percent to 18 percent, and that of processed foods and drinks will increase from 11 percent to  23 percent (NSSO data; Shah, 2007). The declining trend in cereal consumption in rural areas can largely be attributed to a shift in tastes and preferences resulting from the increasing availability of a greater variety of food items other than food grains. India is seen as a mass market for processed, convenience and snack foods. Almost all global fast-food chains face three challenges in entering the Indian market: (1) the religious sensibilities of Indian consumers – Hindus, Muslims and others; (2) conflict with the Indian government and political activists; and (3) food prices (Dash, 2005). Most global fast-food chains have had to change 38.3.3  Food globalization their menus, composition and preparation to Milk and milk-product sales increased two-  make their offerings suitable for Indian customfold between during 1995 and 2001. Over ers. A vocal group of environmental and anithe same period, the sales of soft drinks  mal activists has opposed the entry of fast-food  increased ten-fold and of potato chips three-fold  chains, and companies have responded by adopt(Dasgupta, 2004). Market research projects that ing strategies to counter such negative camby 2015, the food habits of Indian consum- paigns, participating in the Green movement’s ers will have changed drastically compared to campaigns, engaging in corporate social respon2001 (NSSO data). It is projected that the share sibility activities, promoting active and healthy 

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lifestyles, and adding healthy food options to their product lists. Food prices are always a sen-  sitive issue, even for the Indian middle class, despite their much improved income levels, and companies have kept prices within a lower and more affordable range. Still, penetration and expan­-  sion remain slower than anticipated.

(16 percent) followed by the Association of South East Asian Nations (ASEAN) (14 percent), the US and the Middle East (10 percent). Trade with neighbors Bangladesh and China (currently 7.5 percent) is growing fast (MAP, 2007). Agricultural commodities account for one-third of all agricultural exports; intermediate products account for one-fourth; and final products account for the remaining 40 percent. 38.3.4  Agriculture The biggest growth has been in the export of The share of agriculture in India’s GDP fell commodities, which increased by 134 percent from 29 percent in 1991 to below 17.5 percent between 1993–1995 and 2003–2005. The top five in 2006. Compared to agriculture, industry con- exports from India during 2003–2005 included tributed about 28 percent and the service sector four foodstuffs: milled rice, cashew nuts, buffalo about 55 percent to the 2006 GDP. Yet approxi- meats and soybean meal. The other was cotton. Yet most agricultural production is aimed for mately 60 percent of the labor force is still local consumption. Indeed, exports are comproemployed in the agriculture sector, compared to mised by the inefficient supply-chain mechan­ 70 percent in the early 1990s. Of India’s 116 milisms and storage facilities. The Food and lion farmers in 2006, approximately 60 percent Agricultural Policy Research Institute expects owned less than 1 hectare of farm. Together, they India to play a bigger role in world markets in farmed only 17 percent of the land. The World the future, but believes that it is likely to remain Bank predicts that the shift towards the service sector will continue at the expense of agriculture, an overall small net exporter (MAP, 2007). India whose share could decline by 30 percent by 2030 is forecast to consolidate its position among the world’s leading exporters of rice, increasing its (Monitoring Agri-trade Policy (MAP), 2007). India is among the world’s leading produ­cers market share from 16 percent to 20 percent by of paddy rice, wheat, buffalo milk, cow milk and 2015 (MAP, 2007). The OECD meanwhile takes a sugar cane. It is also a leading consumer. The more conservative view of production prospects agricultural and food products trade accounts  and of export potential. India is expected to shift for a relatively small share of overall Indian trade, from a net importer of sugar to a net exporter, at 9 percent of the total exports and 5 percent  with its share of the world market increasing of total imports. Thus, India is a net exporter of from 4 percent to 6 percent. Indian buffalo beef agricultural food products with a small surplus. exports are projected to increase to 11 percent of This can be explained by the fact that although the world market share. India is a leading world producer of agricultural products, it is also a major consumer. India’s imports Agricultural trade in the past has been relatively India’s top five imports include palm oil, limited due to government policies seeking to soybean oil, cashew nuts, dried peas and cotachieve self-sufficiency. Both imports and exports ton. ASEAN is by far the biggest supplier of have grown steadily since 2000 (MAP, 2007). agricultural products to India, accounting for a massive 40 percent of its imports in 2003–2005, India’s exports followed by Argentina and Brazil. India’s agriBetween 2003 and 2005, the European cultural imports were dominated by inter­ Union (EU) was India’s top market for exports  mediate products (56 percent), followed by final 

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38.3  Trends influencing intake

products (31 percent) and commodities (13 percent) in 2003–2005. The biggest growth has been in intermediate products, like vegetable oils, which increased nearly fourfold over the period. Palm oil is by far the biggest import, at 29 percent of the total and, together with soybean oil, represents over 40 percent of imports. Growth in imports of vegetable oils has been dramatic, with an increase of over 800 percent between 1993–1995 and 2003–2005. For vege­ table oil imports, India depends on a few key suppliers. Over 72 percent of India’s palm oil imports come from Indonesia, and 27 percent from Malaysia. This pattern is similar in the soybean oil market, with Argentina supplying 72 percent and Brazil around 24 percent (MAP, 2007). India is projected to remain a leading vegetable oils importer, absorbing one-fourth of world soybean oil imports and 14 percent of palm oil imports over the next decade. Although the share is not expected to increase much, the  quantity of oil imports will increase given the expansion in world trade in vegetable oils.  Indian consumption of vegetable oils has grown faster than production since the mid-1990s, and the trend is expected to continue. The increase in consumption and imports is driven by population growth and the increase in purchasing power.

479

Private investment in agriculture has increased modestly in recent years. The government also supports agriculture through other measures, which include minimum support prices (MSP) for the major agricultural crops, farm input subsidies, and preferential credit schemes. Concerns over the food security of essential food items have led the government both to reduce some import restrictions and to impose export restrictions on items such as wheat, rice and vegetable oils. In 2008–2009, this translated into stockbuilding of government supplies of wheat and rice. Rapid growth in wheat and rice production has contributed to food security mainly by inducing a decline in the real prices of rice and wheat, although the decline in cost per unit of output has been variable across regions and states. The fall in staple food grain prices has benefited the urban and rural poor more than the upper income groups. Agriculture occupies a prominent position in Indian policy-making not only because of its contribution to the GDP, but also because a large proportion of the population is dependent upon the sector for its livelihood. Yet, as the service economy grows, the share of agriculture will diminish, which may have implications for India’s trade and agriculture policy in the future. Food regulatory systems

Agricultural policy Improving food self-sufficiency and allev­ iating hunger through food distribution has been the overriding goal of agricultural policy in India. The Green Revolution triggered food grain production from 72 million metric tonnes in the mid-1960s to about 200 million tonnes in the late 1990s. However, in recent times declining public investments in agriculture and its decreased contribution to the GDP are of concern. The share of input subsidies (in fertilizers, electricity, irrigation, and credit, for example) from public expenditure increased from 44 percent in the early 1980s to 83 percent by 1990.

While there are numerous regulations controlling food safety (Food Safety and Stand­ards Authority), agricultural production and marketing (Agricultural Produce Marketing Com­ mittee), and commodities procurement, storage, and transportation (Essential Commo­dities Act), and supporting the establishment of private markets, direct purchasing centers, consumer/ farmer markets for direct sale through public–  private partnerships, and contract-farming arrangements (Model Act), there are no rules in place pertaining to food content and nutrient composition. More importantly, there is no stringent food safety checking and certification 

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system available for the street vending and unorganized local food markets, and what little there is, is not implemented properly.

these official statistics, due to the substantial transient population not included in the census.

38.4.2  Transportation 38.4  Trends in energy expenditure

Between 1980 and 2005, real per capita income (adjusted for inflation) more than doub­ led in India. Accordingly, since 1990 the total number of motor vehicles has roughly tripled 38.4.1  Urbanization in India. The rapid growth in Indian cities has The proportion of urban population in India dramatically increased demand for land and increased from 20 percent in 1971 to 28 per- travel in urban areas, thus putting enormous cent in 2001 (Registrar General of India, 2001), pressure on transport and other kinds of public and is likely to reach 36 percent by 2025 (Dyson   infrastructure. The skyrocketing motorization et al., 2004). The trend of urban population has generated serious transport problems. The growth is shown in Figure 38.3. Over 70 percent developed area of Indian cities increased threeof affluent urban Indian consumers live in the fold between 1985 and 2005, while the total 10 most populated and cosmopolitan cities in urban population only doubled. This is partly India. Three mega-cities in India have popula- due to deliberate government policies to decontions of over 10 million (Mumbai, Kolkata and gest crowded city centers by adopting land-use Delhi); another three (Chennai, Hyderabad and regulations (limiting the ratio of floor area to Bangalore) have populations of over 5 million; land area), and thus restricting the heights of and 35 cities have populations of more than  buildings and density of development in the 1 million (Registrar General of India, 2001). Actual  center (Bertaud, 2002). In addition, local authoriurban population growth probably exceeds ties in suburban jurisdictions have less stringent 30 26.13 25

23.31 19.91

20 15

27.78

11.91

17.26

17.97

1951

1961

13.86

10 5 0 1931

1941

1971

1981

1991

Years % of urban population to total population

Figure 38.3  Urbanization in India. Source: Registrar General of India.

2.  From society to behavior: policy and action

2001

481

38.4  Trends in energy expenditure

land-use regulations than their inner-city counterparts, and even have more permissive policies to lure away economic development from urban centers. This virtually forces new development on the suburban fringe. Indian suburbs are generally unplanned, and rarely have adequate public transport services (Ramachandran, 1989b). The expansion of cities has increased travel time for urban residents, increasing overall demand for and traffic on roadways and the public transport system. Additionally, increased traveling distances make walking and cycling less feasible than before, thus encouraging a shift from non-motorized to motorized modes of transportation.

vehicles (bicycles and cycle rickshaws) for transport. Additionally, para-transit, such as auto rickshaws, jeep taxis and tempos (large auto rickshaws), contributes up to 30 percent of the transport share in the large cities (Singh, 2005). The most dramatic transport development in India has been the striking growth in private motorized travel, especially cars and motor­ cycles. As represented in Figure 38.4, from 1981 to 2002 the total number of motorized twowheelers rose from about 3 million to 42 million (Ministry of Road Transport and Highways, 1999, 2000, 2003a). In parallel, between 1991 and 2003, the number of cars in India more than doubled (Ministry of Road Transport and Highways, 2004). Car ownership has spread from the political and economic elite to the middle classes and become a symbol of prestige. 38.4.3  Motorization and changes in the The traffic fatality rate per million inhabitmode of transportation ants has roughly tripled in India in the past The walking share of all travel has fallen three decades (Ministry of Road Transport and from 37 percent to 28 percent in cities of over  Highways, 2003b), and many more moderate 5 million inhabitants. Similarly, the cycling share and minor accidents are not even recorded in has declined from 26 percent to only 9 percent, reports. Although walking and cycling account and use of public transport has increased from for about half of all travel in some urban areas, 16 percent to 63 percent. Smaller cities still rely they do not receive funding, infrastructure prosignificantly on walking and non-motorized vision, legal rights or traffic priority. Most of the 80

14

70

12

60

10

50

8

40 6

30

4

20

2

10 0

1981

1986

1991

Two wheelers

1996 Cars/Jeeps/Taxis

2002 Cycle

Figure 38.4  Growth of India’s individual transport. Source: Ministry of Road & Transport and Highways (Auto Junction, 2007; Bicycle India, 2007).

2.  From society to behavior: policy and action

2007

0

482

38.  Drivers of Body Weight, Shape and Health in india

roads in Indian cities do not have separate cycle lanes, and sidewalks are either non-existent, or so cluttered that pedestrians are usually forced to walk in the roadway. Pedestrians and cyclists are exposed to extraordinary traffic dangers, and forced to share crowded roads with a wide range of both motorized and non-motorized transport. The recent sharp increase in motorized travel has greatly raised the danger for pedestrians and cyclists, who now account for almost three-fourths of India’s traffic fatalities.

about equally undernourished. Undernutrition in children is substantially higher in rural areas than in urban areas. Almost half (46 percent) of 15- to 19-year-old girls are undernourished, and more than one-third (36 percent) of women have a BMI below 18.5 kg/m2. Similarly, more than half (58 percent) of boys aged 15–19 years and 34 percent of men age 15–49 are thin (BMI  18.5 kg/m2). It is also evident that as the prevalence of undernutrition is declining over time, that of overweight/obesity is rising. Similarly to adults, under-5 s undernutrition and overweight/obesity among older children is 38.4.4  Poor sports culture being increasingly documented. The presence of There is a serious lack of sports culture in both extremes of poor nutrition – undernutrition  India, as demonstrated by India’s consistently and overnutrition – simultaneously creates a poor showing at the Olympics. Physical educa- nutritional environment characterized by a doution – which is supposed to be an integral part ble burden. This does not fit neatly into any of the of the school and college curriculum – is solely classic stages of nutrition transition. It has been missing. This is largely because of the mindset noted that low birth weight and early childhood of Indian parents and teachers, who accord lit- undernutrition contribute to non-communicable tle importance to such education. The majority disease epidemics, which may be amplified with of government-funded schools, which enroll the improved child survival rates and rapid ecoabout 80 percent of Indian students, do not offer nomic transformation taking place in India. The double burden is important due to (1) physical education or development. According lack of knowledge about its magnitude; (2) lack to a Public Report on Basic Education (PROBE of knowledge regarding the determinants leadTeam, 1999), 48 percent of government schools ing to this problem within the same population; in India do not possess a playground. India’s (3) lack of knowledge about the consequences of sports disaster can be attributed to lack of politineonatal and early childhood undernutrition on cal will, long-term strategy and persistence. This the risks of adult obesity and non-communicable is reflected in the smaller budgetary allocation of India of about US $300 million, compared to diseases; and (4) lack of clarity in formulating policies to tackle both over- and undernutriUS $2 billion in China. tion simultaneously in inadequately equipped nations.

38.5  Cross-cutting issues 38.5.1  Undernutrition to overweight/ obesity: unfinished agendas versus new challenges According to NFHS-3 (2005–2006), about 45 percent of children under the age of 5 years are underweight. Overall, girls and boys are

38.5.2  Policy dilemma Looking at all the above issues, nutritional problems, undernutrition and overweight/ obesity must be seen as parts of the same spectrum and in continuum, although they manifest themselves differently across age groups, sexes, geographical areas, social classes, etc. These

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38.6  Conclusions

nutritional problems must not be seen simply as a technical, food and/or healthcare problem, but rather as a challenge for the whole of society, challenging the fundamental ways it is constructed. This involves closer interactions and a sharing of vision among the sectors involved –   agriculture, manufacturing, retail markets and the supply chain, trade, economics, education and culture – and society as a whole. Policy decisions at the state and/or national level are also significantly influenced by private market players, the scope of globalization, trade arrangements and ongoing WTO negotiations. Complexity lies in judging the appropriate level of policy action and intervention, assessing the most appropriate intervention, deciding who is responsible, and determining how radical the policy should be, as well as defining the level of participation of markets and civil society. Lack of appropriate evidence to feed into and support such policies in India and other LMICs constitutes a major handicap.

38.5.3  Implications for research and scientific advancement While it is impossible to prevent advances in technology, economy and environment, corrective measures at every level are needed in order to ensure that future generations of human beings are functional, healthy and productive. This is only possible through state-of-the-art research as the foundation of policies that influence individual and societal changes. From available evidence, the initiatives that are underway to address these food and nutrition problems are clearly not effective, sustainable or rapid enough to effect the changes necessary. These are needed throughout society –   at the individual, family and community levels; within education, health, media and business; and in trade, investments and governments –   to address the present health and economic

divide that shapes the behavior of individuals, families, communities and organizations, in order to evolve towards a healthier and productive society. From the limited success of various interventions tried in the past, it is clear that a new science is needed to inspire the design and implementation of revolutionary innovations and policies to tackle these issues effectively. This new science must address the broad, complex and dynamic biological–psychological–  environmental interplay that shape individual, family and society behaviors and preferences for food, physical activity and healthcare. To achieve this, we must cross the boundaries between disciplines by bridging theories and data on genetics and epigenetics, brain, behavior and environment shaping individual choices, in a novel and integrative manner. There is also a need to generate and correlate information from various sources through bridging hypotheses and multi-level models involving multiple non-health domains of determinants. Research must refine these complex dynamics within and between developed and developing countries, as they are part of the same world system in need  of realignment. Additional issues of low birth weight, genetic and nutritional programming in fetal life, maternal nutritional and other environmental factors, and patterns of early-life growth and their influence on evolution of diabetes and other noncommunicable diseases (NCD), is also important. This context is particularly significant for India, as for many other developing countries.

38.6  Conclusions The spectrum of health and nutrition problems of Indian children is a big challenge for policymakers attempting to develop and implement health and nutrition policies. According to the National Nutrition Monitoring Bureau (NNMB)  and the National Family Health Survey (NFHS),

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half of Indian children suffer from under­ nutrition. While India has not yet overcome the problems of poverty, undernutrition and communicable diseases, it is increasingly facing additional challenges related to the affluence that results from industrialization, urbanization and economic betterment. Health is a composite outcome of interaction between biology and behavior, and behavior is dependent on the environment within which individuals live and function. It is clear that risk behavior adopted early in life will persist through adulthood and increase the risk of noncommunicable diseases. It is essential that longterm preventive strategies are identified and implemented to tackle the epidemic early on.  Basic steps regarding food, physical activity and the immediate environment are urgently warranted. Attempts at every level – individual, family, school and peer group, society and policy – are needed, even in the face of a number of difficulties. An increase in the political will to tackle lifestyle diseases is becoming evident. The importance of enhancing physical activity and improving dietary habits from childhood and creating opportunity to sustain them cannot be overemphasized. Education regarding a healthy lifestyle must begin at home, then be promoted and sustained at school and throughout society for long-term impact. A lifecycle approach and multi-disciplinary platform, with health as the central outcome, needs to be developed. Health should be a key agenda in every policy/strategy that is likely to influence individual and society behavior. Moreover, strategies must consider the socio-cultural realities. If the community is considered as an equal partner at all stages of planning, implementation and evaluation, the program is likely to be effective and sustainable. The biology of the emerging epidemic of obesity in India is only beginning to be understood. In addition to the much discussed role of diet, lifestyle factors and genes, new exciting issues such as the intrauterine environment and social norms and practices also need consideration.

References Ahluwalia, M. S. (1994). India’s quiet economic revolution. Columbia Journal of World Business, 29(1), 7. Anthropological Survey of India. (1997). People of India. New Delhi: Anthropological Survey of India. Antony, G. M., & Laxmaiah, A. (2008). Human development index: Human development, poverty, health & nutrition situation in India. The Indian Journal of Medical Research, 128, 198–205. Auto Junction. (2007). Online. Available: www.auction. autojunction.in. Balasubramanian, D. (2004, October 21). Changes in the Indian menu over the ages. The Hindu. Bicycle India. (2007). Online. Available: www.bicycleindia. com.  Dasgupta, P. (2004). Confederation of Indian Food Trade and Industry (CIFTI), New Delhi. In A weighty crisis. Health Environment Newsletter, 3(2), 3. Online. Available: http://www.cseindia.org/html/healthnews/march_ april05/march-april05.pdf.  Dash, K. (2005). McDonald’s in India. Glendale, AZ: Thun­-  derbird: The Garvin School of International Management   pp. 1–25. Dyson, T., Cassen, R., & Visaria, L. (2004). Twenty-first century India: Population, economy, human development and the environment. New Delhi: Oxford University Press. Economist Intelligence Unit. (2005). The states-1: Part of the whole. Business India Intelligence, 12(2), 5. Kapil, U., Singh, P., Pathak, P., Dwibedi, S. N., & Bhasin, S.   (2002). Prevalence of obesity amongst affluent adolescent schoolchildren in Delhi. Indian Pediatrics, 39, 449–452. Marwaha, R. K., Tandon, N., Singh, Y., Aggarwal, R., Grewal, K., & Mani, K. (2006). A study of growth parameters and prevalence of overweight and obesity in school children from Delhi. Indian Pediatrics, 43, 943–952. Ministry of Road Transport and Highways. (1999, 2000 2003a  2004). Annual report. Delhi: Government of India. Ministry of Road Transport and Highways. (2003b). Handbook on transport statistics in India. Delhi: Government of India. Monitoring Agri-trade Policy (MAP). (2007). India’s role in world agriculture. Delhi: Directorate-General for Agri­ culture and Rural Development No. 03/07. National Council of Applied Economic Research, NCAER. (2004–2005, 2006–2007). Survey report. New Delhi: NCAER. National Family Health Survey – India (NFHS-3, 2005–2006).  Online. Available: www.nfhsindia.org.  National Sample Survey Organization (NSSO). Ministry of statistics and program implementation, Government of India. Nutrition Foundation of India. (2004). Twenty-five years report 1980 to 2005. New Delhi: Nutrition Foundation of India.

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Pandher, A. K., Sangha, J., & Chawla, P. (2004). Childhood obesity among Punjabi children in relation to physical activity and their blood profile. Journal of Human Ecology (Delhi, India), 15, 179–182. PROBE Team. (1999). Public report on basic education in India. New Delhi: Oxford University Press. Rabobank Estimates. (2008) Online. Available: http://www.isb.edu/simc2006/htmls/PDFfiles/ RajeshSrivastava_Hyderabad.pdf.   Ramachandran, P. (1989a). Nutrition in pregnancy. In C. Gopalan & S. Kaur (Eds.), Women and nutrition in India (pp. 153–193). New Delhi: Nutrition Foundation of India. Ramachandran, R. (1989b). Urbanization and urban systems in India. Oxford: Oxford University Press. Registrar General of India. (2001). The census of India. New Delhi: Government of India. Shah, S. (2007). Yes Bank Market Research. Online. Available: http://www.ficci.com/media-room/speeches-  presentations/2007/sep/agri/SessionIV/SonalShah. pdf.  Sharma, A., Sharma, K., & Mathur, K. P. (2007). Growth pattern and prevalence of obesity in affluent schoolchildren of Delhi. Public Health Nutrition, 10, 485–491.

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Singh, S. K. (2005). Review of urban transportation in India. Journal of Public Transportation, 8(1), 75–97. The Junk Food Generation. (2000–2002). A multi–country survey of the influence of television advertisements on children. Consumers International: Asia Pacific Office. Vazir, S., & Kashinath, K. (1999). Influence of ICDS on psychosocial development of rural children in southern India. Journal of the Indian Academy Applied Psychology, 25, 11. World Health Organization. (2000). The problem of overweight and obesity. In Obesity: Preventing and managing the global epidemic (pp. 5–15). WHO Technical Report Series no. 894, WHO: Geneva. World Health Organization Expert Consultation. (2004). Appropriate body mass index (BMI) for Asian populations and its implication for policy and intervention strategies. Lancet, 363, 157–163. Yajnik, C. S. (2004). Obesity epidemic in India: Intrauterine origins? Proceedings of the Nutrition Society, 63, 387–396. Yajnik, C. S., Fall, C. H. D., Vaidhya, U., Pandit, A. N., Bavdekar, A., Bhat, D. S., et al. (1995). Fetal growth and glucose and insulin metabolism in four-year-old Indian children. Diabetic Medicine, 12, 330–336.

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C H A P T E R

39 Diets and Activity Levels of Paleolithic versus Modern Humans: Societal Implications for the Modern Overweight Pandemic Peter J.H. Jones and Dylan MacKay Richardson Center for Functional Foods and Nutraceuticals, University of Manitoba, Manitoba, Canada

o u tli n e 39.1  Introduction

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39.2 The Four Eras of Change of Human Diets

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39.3 Contrasting Food Intake During the Paleolithic Era Versus Today 489 39.3.1 Energy Density 489 39.3.2 Diet Composition 489 39.3.3 Portion Size 489

39.1  Introduction Human diets and physical activity patterns have changed substantially over the past several millennia. Prior to the advent of the agricultural technologies that emerged 10,000 years ago, human ancestors were primarily hunter­gatherers (Cordain et al., 1998). While their

Obesity Prevention: The Role of Brain and Society on Individual Behavior

39.3.4 Intake and Obesity

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39.4 Energy Expenditure and Physical Inactivity

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39.5 The Tipping Point of Energy Imbalance

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39.6 Insights from Paleolithic Diets to Fight the Obesity Pandemic

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patterns of intake differed depending on geographic location, climate, and season, it is clear that ancestral diets overall were substantially different from modern-day North-American diets, particularly in terms of energy density and overall food availability (Gerrior et al., 2004) (Figure 39.1). Concurrently, our ­ ancestors had profoundly different activity patterns.

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2010 Elsevier Inc. © 2010,

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39  Paleolithic environment and obesity Paleolithic diet

Carbohydrate 31%

Fat 42%

Protein 27%

Lean game, wild fowl and seafood 35%

2000 AD

Carbohydrate 50%

Fat 39%

Protein 11%

Other 21% Fruits, vegetables, nuts, honey 65%

Sugars and sweeteners 19%

Grains 24%

Meat, fish, poultry 12% Separated fats and oils 22%

Figure 39.1  Energy source comparison. Adapted from Eaton and Cordain (1997), Cordain et al. (2002) and Gerrior et al. (2004).

The energy required to forage and hunt for food imposed substantial demands on energy expenditure, and thus represented a vastly different energy balance model than that typical of modern North Americans (Cordain et al., 1998). The objective of the present chapter is to compare modern-day energy intake and expenditure patterns to those of the Paleolithic era, gleaning insight into tackling the current obe­s­ ity pandemic.

39.2  The four eras of change of human diets Four fundamental eras of change in human diets have resulted in significantly altered patterns of food intake and expenditure (Cordain, 2007); (1) the pre-Paleolithic era, (2) the Paleo­ lithic era, (3) the Neolithic era, and (4) the period of the Industrial Revolution. Across the

pre-Paleolithic (approximately 4 million – 2.6 million years BCE) humanoid diets appeared to be rich in plants, including vegetables, seeds and nuts (Eaton and Konner, 1985). These diets were low-­calorie, low-fat, and abundant in fiber and vegetable proteins. A similar consumption profile existed during the Paleolithic period (approximately 2.6 million – 11,000 years BCE), when diets continued to be based on plantderived foods but also incorporated animal meats obtained largely from wild animals and seafood (O’Keefe and Cordain, 2004). With the advent of agricultural technologies during the Neolithic period, human beings began their transition from a nomadic to a sedentary lifestyle, characterized by more stable and larger societies. Societal attributes relating to culture and knowledge, such as music, literature and sciences, developed rapidly as less time was devoted to energy-demanding food-seeking behavior. Individuals began consuming large amounts of grains, milk and domesticated meat (Cordain et al., 2002). Starchy foods in the form of grains and legumes became major dietary components. Vegetable oils, notably olive oil, emerged as an important dietary ingredient, as did alcohol, largely in the form of wine. However, of all these transitions in the human diet, no period was associated with as profound a series of changes as those linked to the Industrial Revolution. The Industrial Revolution brought on a gradual “whitening” of the human diet. Increased industrial processing, together with the introduction of packaged and convenience foods, almost entirely overturned prior approaches to feeding and dietary patterns. In particular, refined and whitened bread, pasta and rice resulted in the emergence of high glycemic-index carbohydrates, and lowfiber and micronutrient-poor dietary patterns (Brand-Miller et al., 2002). The proportion of dietary fat as energy has also increased substantially over the past 100 years. Presently, grains, fats, sugars and oils comprise 65 percent of the daily caloric intake of Americans (Cordain, 2007).

2.  From SOCIETY TO BEHAVIOR: POLICY AND ACTION

39.3  Contrasting food intake during the Paleolithic era versus today

Nutritionist Jean Bogert noted in 1939 that “the machine age has had the effect of forcing upon the peoples of the industrial nations (especially the United States) the most gigantic human feeding experiment ever attempted” (Bogert, 1939).

39.3  Contrasting food intake during the Paleolithic era versus today A myriad of societal shifts have meant that modern-day human beings consume conside­r­ ably more calories than their ancestors.

39.3.1  Energy density The energy densities of foods now commonly consumed are greater than those of the foods our ancestors ate. Eaton and Cordain compared commonly consumed modern foods to uncultivated fruits and vegetables and game species, and found that the latter were of considerably lower energy density (Eaton and Cordain, 1997). Similarly, Prentice and Jebb (2003) examined the energy density of foods from typical fast-food restaurants, and found that the average energy density was 145 percent higher than that of traditional African diets, which, they posited, were representative of human diets prior to the agricultural era.

39.3.2  Diet composition The relative contributions of protein, carbohydrates and fat to total energy in Paleolithic diets were estimated to range from 19–35 percent, 22–40 percent and 28–58 percent, respectively (Cordain et al., 2002), with relatively high MUFA and PUFA content and low 6/3 fatty acid ratios. In today’s micronutrient ­distribution, fat has climbed as high as 42 percent of energy intake, with much higher levels of saturated fat and 6/3 fatty acid ratios than those seen in the

489

diets of hunter-gatherers (Bang et al., 1980; Uauy and Diaz, 2005). Today, fewer than 12 percent of Americans eat the five daily servings of fruits and vegetables recommended by dietary guidelines (Casagrande et al., 2007). Beyond that, even people who regularly do eat fruits and vegetables generally limit themselves to a rather undiversified selection of foods (Patterson et al., 1990). It was estimated that over the course of a single year, ancestral hunter-gatherers may have consumed a considerable number of fruit and vegetable species, providing up to 100 grams of fiber daily. Fiber sources in pre-agricultural diets originated almost exclusively from fruits, roots, legumes, nuts and other naturally occurring noncereal plant sources. Coupled with low-fiber and high glycemic-index carbohydrate sources, the modern American diet is currently more micronutrient poor and energy dense than ever before.

39.3.3  Portion size Modern dietary trends are characterized by enlarging portion sizes. Perceptions of what constitutes a “normal” portion size have shifted dramatically in North America: both supermarket and restaurant serving sizes are considerably greater than what was considered “normal” a mere few decades ago, and is still considered to be so in many other regions of the world (Fisher and Kral, 2008). Market demands and mass production systems have resulted in the food industry currently competing lucratively on the basis of quantity, rather than quality.

39.3.4  Intake and obesity Together, the high-energy density of modern diets and the distorted perception of a “normalsized” portion present an unprecedented situation of runaway energy intakes, exceeding the level of daily calories needed to balance energy expenditures (Figure 39.2). This shift has occurred so swiftly that human beings have been unable to adapt effectively. In fact,

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39  Paleolithic environment and obesity

Easy a cces to food s

Sede nta lifesty ry le

Calor

Weight maintenance

ic bala

Incre asin por tio g n sizes

Weight loss

nce

Ener gy dens e food

Weight gain

Figure 39.2  The modern human lifestyle.

the numerous dietary changes occurring over the past 10,000 years have greatly outpaced the human ability to adapt to them: More than 4000 generations of human beings existed as huntergatherers, followed by 500 generations which depended on agriculture. There have been only 10 generations since the start of the Industrial Age, and just two of these have grown up with highly processed convenience foods (Eaton and Konner, 1985).

39.4  Energy expenditure and physical inactivity It is interesting to compare human energy expenditure to that of other mammals in their natural habitat. In a group of large, free-­ranging mammals, it was estimated, using doubly labeled water and other approaches to assess total energy expenditure, that the ratio of total caloric expenditure to resting energy expenditure (PAL  TEE/BMR, where PAL  physical activity level, TEE  total energy expenditure in 24  h and BMR  basal metabolic rate in 24  h) averaged 3.1 (Bishop, 1993). In 33 species of smaller mammals, the PAL has been estimated to average 3.6 (Hayes et al., 2005). In contrast, modern humans in the developing world possess PAL values of 1.67 (Hayes et al., 2005), representing a greatly more inactive physiological

state than that of other mammals and primates. It has been estimated that to achieve the activity level of larger mammals, modern human beings would have to augment their typical daily acti­ vities with an additional 6 hours of walking over fields and small hills (Hayes et al., 2005). Paleolithic hominids represent a more appropriate benchmark against which to compare activity levels of modern-day humans. Foodgathering to meet energy requirements entailed 6–8 miles of mobility per day. It has been estimated that Neanderthal hominids possessed a PAL value of 2.3, based on skeletal structure (Sorensen and Leonard, 2001; Leonard, 2002). Gathering and hunting behaviors of our ancestors required considerable physical effort, meaning that they exercised regularly, burned fat, and likely lowered circulating lipid levels. Skeletal remains indicate that hunter-gatherers were more muscular than modern human beings (Eaton and Eaton, 2003). It is likely that life during the agricultural period also required considerable physical exercise. Industrialization and mechanization, however, have progressively reduced obligatory physical exertion. The average American’s total energy expended per unit of body weight represents about 65 percent of that of Paleolithic Stone Age humanoids. As such, modern human beings would need to add an additional 12 miles of walking per day to attain a comparable level of physical activity (Panter-Brick, 2002).

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39.6  Insights from Paleolithic diets to fight the obesity pandemic

This comparison with Paleolithic humans suggests that modern levels of activity are as low as half of what we have been genetically engineered to engage in. Eaton and Eaton have estimated that human beings in the Paleolithic era expended about 5.4 MJ (1230 kcal/day), compared to the modern-day expenditure, estimated at 2.3 MJ (555 kcal/day) (Eaton and Eaton, 2003). The implications of the low level of activity of modern humans are being recognized. It has long been understood that physical inactivity leads to the erosion of lean body mass, a decrease of both muscular and bone mass, and numerous physiological changes: a 25 percent decrease in maximal stroke volume, maximum cardiac output and aerobic exercise peak oxygen uptake (Saltin et al., 1968). Similarly, whole body insulin sensitivity declines immediately after the onset of physical inactivity (Lipman et al., 1972; Burstein et al., 1985), as does the capacity to oxidize fatty acids (Ferretti et al., 1997). Additionally, physical inactivity is associated with metabolic changes such as decreased resting metabolism and reduced metabolic compartment mass (Ritz et al., 1998; Blanc et al., 2000; Bell et al., 2004). Together these factors result in a significant decline in energy expenditure, which, unless met by commensurate reductions in energy intake, will result in weight gain.

39.5  The tipping point of energy imbalance Modern-day human beings possess a metabolic regulatory machinery that is designed to accommodate a very different environmental scenario than that we currently experience. As summarized by Eaton, “that the vast majority of our genes are ancient in origin means that nearly all of our biochemistry and physiology are fine-tuned to conditions of life that existed before 10,000 years ago”(Eaton et al., 1988).

491

More specifically, current energy management patterns would suggest that modern human digestive systems are geared towards optimi­z­ ing energy absorption from low-energy foods, presented in smaller portions and more intermittently than humans are presently exposed to. Human digestive systems were likely not designed for high-energy foods, in large portions, readily available at any time. The disassociation of energy intake from energy expenditure needs only to be very modest to amount in a sizeable surfeit of calories over time. As pointed out by Hill and colleagues (2003), it requires only an imbalance of 100 kcal per day between calories consumed and those expended to cause a 1-lb increase of body fat per month. The impact of such a gain over a period of even a single decade is notable. Just over 2000 years ago the average life expectancy was about 23 years, with a high infant mortality rate and hard living conditions (Alesan et al., 1999). Longer life expectancy, brought about by improved hygiene and sanitation, means that even modest energy imbalances can become significant. As individuals live longer, they are increasingly susceptible to the effects of energy imbalance. As such, the tipping point for weight gain is easily disturbed by even minor disengagement of calories consumed relative to those expended. When the potential for substantial intakes and reduced energy expenditure of modern human beings is considered, it can be surprising that an even greater proportion of North Americans are not overweight or obese.

39.6  Insights from Paleolithic diets to fight the obesity pandemic Useful lessons can be gleaned by examining the pattern of food consumed before the development of technology. Paleolithic diets indicate

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39  Paleolithic environment and obesity

that low-energy, low-fat and high-fiber dietary patterns are desirable. Dairy and cereals, while important in moderation, should not be consumed in excess. It is also clear that modern energy-expenditure patterns need to be maximized and regular activity encouraged. Caloric balance represents a basic process of ensuring that the sum of energy absorbed is equivalent to the net total energy expended. In this manner, weight gain can be avoided. The possibility does exist to increase fiber and vegetable protein consumption as well as change the fatty acid profile of modern diets. Ideally, eating small, energy-dilute, lower-fat, high-fiber meals every couple of hours throughout the day, choosing a wider variety of fruits, legumes, nuts and non-cereal plants, reducing the amount and frequency of red meat and poultry, and eating more foods containing marine oils would be better suited to our evolution. This, however, would often be very difficult, given the typical “three large meals” routine that is pervasive in most developed countries. Eating the wide variety of foods available and required by our ancestors would be equally as challenging in the modern, rather homogeneous, food system. In our modern food culture and system, returning to the eating habits and patterns of our nomadic ancestors who struggled just to survive may be an unreasonable goal. Functional foods, which provide health benefit beyond their nutritive value, are being designed with the insights from Paleolithic diets in mind. Functional foods can replace similar foods currently included in modern diets to provide added health benefits, with minimal impact on day-to-day eating habits. These functional foods should not be confused with junk foods, which may be fortified with nutrients in order to market them as healthy. The addition of extra fiber or vitamins and/or minerals to a candy, cookies or a bag of potato chips does not suddenly make it a healthy food choice. However, modifying staple healthy foods to ­further increase their health benefit and reduce

disease risk has enormous potential. Products with increased fiber, such as baked goods made with yellow pea flour, are showing promise in fighting the development of insulin resistance (Weickert et al., 2006). Certain vegetable proteins, such as plant hydrolysates, are being investigated for their anti-hypertensive effects (Moller et al., 2008). Plant sterols and stanols, plant constituents that chemically resemble cholesterol, have been repeatedly shown to favorably lower plasma cholesterol levels (Berger et al., 2004; Abumweis et al., 2008). These vegetable proteins and plant sterols/ stanols can either be incorporated into functional foods, or extracted and taken as nutraceuticals (isolated bioactives) in pill form. Numerous food products and nutraceuticals containing plant sterols and stanols are already sold around the world. The successful incorporation of functional foods into a healthy balanced diet can make the modern diet more appropriate from an evolutionary standpoint, and may be beneficial in reducing the risk of certain diseases. In conclusion, both energy-intake and -ex­­ pen­diture profiles of modern human beings have undergone a substantial transformation from those characterizing ancestral societies. Lessons can be learned by examining the societal drivers that modulated such societies and suggest directions to promote healthy lifestyle changes and reduce the prevalence of obesity in the twenty-first century and beyond.

References Abumweis, S. S., Barake, R., & Jones, P. J. (2008). Plant sterols/stanols as cholesterol lowering agents: A meta­analysis of randomized controlled trials. Food & Nutrition Research, 52. Alesan, A., Malgosa, A., & Simó, C. (1999). Looking into the demography of an Iron Age population in the Western Mediterranean. I. Mortality. American Journal of Physical Anthropology, 110, 285–301. Bang, H. O., Dyerberg, J., & Sinclair, H. M. (1980). The composition of the Eskimo food in north western Greenland. The American Journal of Clinical Nutrition, 33, 2657–2661.

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Bell, C., Day, D. S., Jones, P. P., Christou, D. D., Petitt, D. S., Osterberg, K., et al. (2004). High energy flux mediates the tonically augmented -adrenergic support of resting metabolic rate in habitually exercising older adults. The Journal of Clinical Endocrinology and Metabolism, 89, 3573–3578. Berger, A., Jones, P. J., & Abumweis, S. S. (2004). Plant sterols: Factors affecting their efficacy and safety as functional food ingredients. Lipids in Health and Disease, 3, 5. Bishop, C. (1993). Wildlife feeding and nutrition. San Diego, CA: Academic Press. Blanc, S., Normand, S., Pachiaudi, C., Fortrat, J.-O., Laville, M., & Gharib, C. (2000). Fuel homeostasis during physical inactivity induced by bed rest. The Journal of Clinical Endocrinology and Metabolism, 85, 2223–2233. Bogert, L. (1939). Nutrition and physical fitness. New York, NY: Saunders. Brand-Miller, J., Holt, S. H., Pawlak, D. B., & McMillan, J. (2002). Glycemic index and obesity. The American Journal of Clinical Nutrition, 76, 281S–285S. Burstein, R., Polychronakos, C., Toews, C. J., MacDougall, J. D., Guyda, H. J., & Posner, B. I. (1985). Acute reversal of the enhanced insulin action in trained athletes. Association with insulin receptor changes. Diabetes, 34, 756–760. Casagrande, S. S., Wang, Y., Anderson, C., & Gary, T. L. (2007). Have Americans increased their fruit and vegetable intake? The trends between 1988 and 2002. American Journal of Preventive Medicine, 32, 257–263. Cordain, L. (2007). Implications of pilo-pleistocene hominid diets for modern humans. In P. Ungar (Ed.), Evolution of the human diet: The known, the unknown, and the unknowable (pp. 363–383). New York, NY: Oxford University Press. Cordain, L., Gotshall, R. W., Eaton, S. B., & Eaton, S. B. (1998). Physical activity, energy expenditure and fitness: An evolutionary perspective. International Journal of Sports Medicine, 19, 328–335. Cordain, L., Eaton, S. B., Miller, J. B., Mann, N., & Hill, K. (2002). The paradoxical nature of hunter-gatherer diets: Meat-based, yet non-atherogenic. European Journal of Clinical Nutrition, 56(Suppl 1), S42–S52. Eaton, S., & Cordain, L. (1997). Evolutionary aspects of diet: Old genes, new fuels. World Review of Nutrition and Dietetics, 81, 26–37. Eaton, S. B., & Eaton, S. B. (2003). An evolutionary perspective on human physical activity: Implications for health. Comparative Biochemistry and Physiology, 136, 153–159. Eaton, S. B., & Konner, M. (1985). Paleolithic nutrition. A consideration of its nature and current implications. The New England Journal of Medicine, 312, 283–9289. Eaton, S., Shostak, M., & Konner, M. (1988). The paleolithic prescription: A program of diet & exercise and a design for living, (p. 39). New York, NY: Harper & Row.

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Ferretti, G., Antonutto, G., Denis, C., Hoppeler, H., Minetti, A., Narici, M., & Desplanches, D. (1997). The interplay of central and peripheral factors in limiting maximal O2 consumption in man after prolonged bed rest. The Journal of Physiology, 501, 677–686. Fisher, J. O., & Kral, T. V. E. (2008). Super-size me: Portion size effects on young children’s eating. Physiology & Behavior, 94, 39–47. Gerrior, S., Bente, L., & Hiza, H. (2004). Nutrient content of the US food supply, 1909–2000. Home Economics Research Report No. 56. Washington, DC: US Department of Agriculture, Center for Nutrition Policy and Promotion. Hayes, M., Chustek, M., Heshka, S., Wang, Z., Pietrobelli, A., & Heymsfield, S. B. (2005). Low physical activity levels of modern Homo sapiens among free-ranging mammals. International Journal of Obesity, 29, 151–156. Hill, J. O., Wyatt, H. R., Reed, G. W., & Peters, J. C. (2003). Obesity and the environment: Where do we go from here? Science, 299, 853–855. Leonard, W. R. (2002). Food for thought. Scientific American, 287, 106. Lipman, R. L., Raskin, P., Love, T., Triebwasser, J., Lecocq, F. R., & Schnure, J. J. (1972). Glucose intolerance during decreased physical activity in man. Diabetes, 21, 101–107. Moller, N. P., Scholz-Ahrens, K. E., Roos, N., & Schrezenmeir, J. (2008). Bioactive peptides and proteins from foods: Indication for health effects. European Journal of Nutrition, 47, 171–182. O’Keefe, J. H., Jr., & Cordain, L. (2004). Cardiovascular disease resulting from a diet and lifestyle at odds with our Paleolithic genome: How to become a 21stcentury hunter-gatherer. Mayo Clinic Proceedings, 79, 101–108. Panter-Brick, C. (2002). Sexual division of labor: Energetic and evolutionary scenarios. American Journal of Human Biology, 14, 627–640. Patterson, B. H., Block, G., Rosenberger, W. F., Pee, D., & Kahle, L. L. (1990). Fruit and vegetables in the American diet: Data from the NHANES II survey. American Journal of Public Health, 80, 1443–1449. Prentice, A. M., & Jebb, S. A. (2003). Fast foods, energy density and obesity: A possible mechanistic link. Obesity Reviews, 4, 187. Ritz, P., Acheson, K., Gachon, P., Vico, L., Bernard, J., Alexandre, C., & Beaufrere, B. (1998). Energy and substrate metabolism during a 42-day bed-rest in a headdown tilt position in humans. European Journal of Applied Physiology, 78, 308–314. Saltin, B., Blomqvist, G., Mitchell, J., Johnson, R. J., Wildenthal, K., & Chapman, C. (1968). Response to exercise after bed rest and after training. Circulation, 38, VII1–VII78.

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Sorensen, M. V., & Leonard, W. R. (2001). Neanderthal energetics and foraging efficiency. Journal of Human Evolution, 40, 483–495. Uauy, R., & Diaz, E. (2005). Consequences of food energy excess and positive energy balance. Public Health Nutrition, 8, 1077–1099.

Weickert, M. O., Mohlig, M., Schofl, C., Arafat, A. M., Otto, B., Viehoff, H., Koebnick, C., et al. (2006). Cereal fiber improves whole-body insulin sensitivity in overweight and obese women. Diabetes Care, 29, 775–780.

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C H A P T E R

40 Agriculture, Food and Health 1

Kraisid Tontisirin1 and Lalita Bhattacharjee2

Institute of Nutrition Mahidol University, Thailand and Former Director, Food and Nutrition Division, Food and Agriculture Organization of the United Nations, Italy 2 National Food Policy Capacity Strengthening Programme, Food and Agriculture Organization of the United Nations, Bangladesh

o u t l i n e 40.1  Introduction and Context 40.2 Food Consumption and Nutrition Situation 40.2.1  Dietary Energy Supplies 40.2.2  Undernourishment 40.2.3  Micronutrient Deficiencies 40.2.4  Obesity and Chronic Diseases 40.3  Agriculture–Nutrition Linkages 40.3.1  Health and Food Crop Diversity 40.3.2 Changing Food Consumption Patterns and Health 40.3.3 Agriculture, Nutrition and the Environment

498 498 498 498 499 499 500 500 501 501

40.5 Dietary Transition in Asian Countries 40.5.1  Carbohydrates 40.5.2  Protein 40.5.3  Milk and Dairy Products 40.5.4  Vegetables and Fruits

503 503 503 504 504

40.6  The Impact of Urbanization

504

40.7  Overweight and Obesity in Asia 40.7.1  Diabetes in India 40.7.2  The Case of Thailand

505 505 506

40.8  Policy Interventions

506

40.9  Conclusion and Recommendations

508

40.4 Analysis of South Asian Dietary Calorie Energy and Nutrition Status 502

Obesity Prevention: The Role of Brain and Society on Individual Behavior

497

2010 Elsevier Inc. © 2010,

498

40.  Agriculture, Food and Health

40.1  Introduction and context There is a growing recognition that agriculture influences health and health influences agriculture; both in turn have profound implications for poverty reduction. This recognition suggests that opportunities exist for agriculture to contribute to better health, and for health to contribute to agricultural productivity. To take advantage of these opportunities, it is crucial to understand the linkages between the two sectors, how these linkages operate, where opportunities for joint action lie, and what the main obstacles to such action are. Indeed, how can the agricultural and health sectors work more closely together to address food security, nutrition and health? This chapter will focus on Asian developing countries, and examine food consumption (dietary energy supplies) and the nutrition situation. It will point out the agriculture–nutrition linkages, review dietary transition in selected Asian countries, discuss links of food and agriculture with healthy diets, suggest communityand societal-level strategies, and highlight some policy recommendations for consideration.

worldwide basis. The availability of energy per capita from the mid-1960s to the late 1990s increased globally by approximately 450 kcal/ capita/day, and by over 600 kcal/capita/day in developing countries (Table 40.1). This change, however, has not been equal across regions. The world has made significant progress in raising food consumption per person. Current energy intakes range from 2681 kcal/capita/day in developing countries to 2906 kcal/capita/day in transition countries and 3380 kcal/capita/day in industrialized countries. The growth in food consumption has been accompanied by significant change and shifts in diets away from staples such as roots and tubers towards more meat products and vege­ table oils (Food and Agriculture Organization of the United Nations (FAO), 2003). Per capita energy supply from both animal and vegetable sources has declined in the transition countries, but has increased in developing and industria­ lized countries (FAO, 2003).

40.2.2  Undernourishment

According to recent estimates (FAO, 2009), there were 872.9 million people in the world who were undernourished in 2004–2006: 857.7 million in the developing countries, 566.2 mil40.2  Food consumption and lion in Asia Pacific, and 336.6 million in South nutrition situation Asia (Table 40.2). Over the past two decades, progress has been made to reduce undernourishment in the The food security and nutrition situation developing countries. The incidence of underis presented here in four categories: dietary  nourishment has declined from 28 percent two energy supplies, undernourishment, micro­ decades ago to 16 percent at present. However, nutrient deficiencies. and overweight and obesas a result of population growth and the afterity and related chronic diseases. math of the recent food and economic crises, the decline in absolute numbers has been markedly slow. In addition, while the decline was 40.2.1  Dietary energy supplies pronounced during the 1980s, it slowed down The analysis of FAOSTAT data shows that significantly in the 1990s. It must be pointed dietary energy measured in calories per capita  out that most of the improvement has been per day has been increasing steadily on a  concentrated in Asia and the Pacific, where the

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499

40.2  Food consumption and nutrition situation

Table 40.1  Global and regional per capita food consumption (kcal/capita/day) Region

1964–1968

1974–1976

1984–1986

1997–1999

2015

2030

World

2358

2435

2655

2803

2940

3050

Developing countries

2054

2152

2450

2681

2850

2980

Near East and North Africa

2290

2591

2953

3006

3090

3170

Sub-Saharan Africa*

2058

2079

2057

2195

2360

2540

Latin America and the Caribbean

2393

2546

2689

2824

2980

3140

East Asia

1957

2105

2559

2921

3060

3190

South Asia

2017

1986

2205

2403

2700

2900

Industrialized countries

2947

3065

3206

3380

3440

3500

Transition countries

3222

3385

3379

2906

3060

3180

*Excludes South Africa Source: FAO (2002).

Table 40.2  Global prevalence of undernourishment Region

No. undernourished (millions)

% undernourished

World

872.9

13

Developing countries

857.7.2

16

Asia and the Pacific

566.2

16

East Asia

136.3

10

China

127.4

10

Southeast   Asia

84.7

15

South Asia

336.6

23

India

251.5

22

Source: SOFI, FAO (2009).

i­ ncidence of undernourishment was halved in the past two decades. Estimates of the prevalence of undernourishment also show considerable variation in the pattern and distribution within Asia. The prevalence of child undernourishment is two- to threefold higher in South Central Asia and parts of Southeast Asia compared to East and West Asia (Table 40.3).

40.2.3  Micronutrient deficiencies Micronutrient deficiencies, especially deficiencies in iron, iodine and vitamin A, are even more widespread worldwide than protein deficiencies (UNICEF/MI, 2004). Besides being important causes of disability in themselves, micronutrient deficiencies often underlie other types of morbidity. Using underweight as a marker of malnutrition, almost 150 million children were reportedly undernourished at the turn of the century, most of them residing in Asia (Standing Committee on Nutrition (SCN), 2004). The lack of progress in the reduction of intrauterine growth retardation (IUGR), an important determinant of subsequent growth and development, is of particular concern in South Asia, where one child in every three is born with a low birth weight (less than 2.5 kg). This contrasts with prevalence rates of less than 10 percent in industrialized countries or even in other developing countries, such as Mexico and China (UNICEF, 2004).

40.2.4  Obesity and chronic diseases Along with the problem of undernutrition among children and chronic energy deficiency

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40. AGRICULTURE, FOOD AND HEALTH

TABLE 40.3 Parameter of malnutrition Underweight (5 y) Stunting (5 y)

Prevalence of malnutrition in Asia East

South Central

Southeast

West

9.3

40.8

27.4

11.3

14.8

39.7

32.1

18.7

IDD (all)

16.3

41.9

60.5

55.8

Anemia (women 15–49( y))

35

60

60

45

Source: SCN (2004).

(CED) in adults in many parts of Asia, the burden of overweight and obesity is becoming increasingly widespread (WHO, 2007). In some countries, this situation exists amidst continued food shortages and nutrient inadequacies. Over the last decade, there has also been a progressive increase in overnutrition. Reduced physical activity is identified as a major factor behind this. In affluent urban segments, increased energy intake from fats, refined cereals and sugar, combined with simultaneous reductions in physical activity, have contributed to steep increases in overnutrition in all age groups.

40.3 AGRICULTURE–NUTRITION LINKAGES The development of agriculture has a direct impact on food consumption and health. Agriculture underpins both household incomes and community wealth. Over 70 percent of all undernourished people who live in rural areas depend directly on agriculture – crops, livestock, fish, forests – for their food and livelihoods. In all countries of South Asia, agriculture remains a major sector providing employment and livelihood. While most countries are selfsufficient with regard to their food production, the percentage of people dependent upon agriculture for their livelihood varies greatly: the data stand at 89 percent in Bangladesh, 70 percent in India, 35 percent in Sri Lanka and

42 percent in Pakistan. Given these numbers, it is increasingly recognized that a major paradigm shift must occur to change the focus of food production from self-sufficiency to nutritional adequacy. Many Asian countries have attempted to introduce nutrition into the goals and objectives of their food security policies. The agriculture sector has a responsibility to provide an adequate diet that contributes to human health and encourages the sustainability of food production. There is need to conserve agricultural resources and promote actions and strategies that are critical to the food system’s sustainability. In strengthening the linkages between agriculture and nutrition for health and the promotion of dietary diversity, the use of locally available, nutrient-rich indigenous and traditional foods has been recommended as an important strategy.

40.3.1 Health and food crop diversity There is a crucial link between the maintenance of food crop diversity and effective strategies that ensure optimum nutritional status. Unfortunately, food production strategies to date have resulted in increasing dependence on cereals and other starchy staples, especially in poor communities. This has been linked to poorer nutrition. In this regard, the narrowing of the food base, a global phenomenon, is seen as an important factor affecting dietary diversity. Of the perhaps 100,000 edible plant species, just

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501

40.3  Agriculture–nutrition linkages

three (maize, wheat and rice) supply the bulk Figure 40.1 shows the share of various food of humans’ protein and energy needs, with  groups as percentage of dietary energy supplies 95 percent of the world’s food energy needs (kcal/capita/day) in selected countries of South being supplied by just 30 plant species (Welch Asia. and Graham, 1999). Conversely, crop diversification is critical not only for meeting the nutritional needs of the population, but also to ensure 40.3.3  Agriculture, nutrition and the the sustainability of soil health and productivity. environment

40.3.2  Changing food consumption patterns and health While the Green Revolution and agricultural research have facilitated a rapid increase in food production in developing countries, nutritionrelated issues – in particular the micronutrient needs of the population – have been addressed to a lesser degree. In developing countries, the emphasis has largely been on increasing the production of wheat and rice, which has resulted in a substantial increase of the per capita availability of these cereals. Pulses and legumes, which contribute to the nutrient quality of cereal-based diets, have lagged behind.

Agriculture is tightly linked to environmental, cultural and technological factors. Indeed, agricultural production can adversely affect the quality of the soil, air and water. It can contribute to the loss of biodiversity and climate change. These same methods can result in hazards for workers, local communities and consumers, raising issues of food safety. Changes in climate and other important environmental factors pose a major threat to food security, notably in the Asian region. Such changes not only directly threaten food production from land and sea for local consumption, but also affect income generation and livelihoods at household and farm levels. The adverse impacts of climate change are a major

Maldives Pakistan Cereal Sri Lanka

Sugar Pulses

India

Oil Vegetables and fruits

Nepal

Meat, milk, egg and fish Other

Bangladesh 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Figure 40.1  Share of food groups as percentage of total dietary calorie supply in South Asian countries, 2005. Source: FAO STAT (2007).

2.  From society to behavior: policy and action

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40.  Agriculture, Food and Health

barrier to food security and achievement of sustainable development goals in South Asia. They are anticipated to exacerbate the impact of existing development challenges, such as loss of market and declining value of traditional exports, declining domestic food production and increasing imports, and environmental degradation. Arable land, water resources and biodiversity are under pressure, and, with climate change, negative impacts on agriculture are predicted. Coral reefs and mangroves will be threatened by increased sea surface temperatures and sealevel rises. Predicted impacts of climate change in the region include extended inundation of arable land, salinity intrusion and reduced fresh water availability. For example, in India, freshwater availability is predicted to decrease by 47 percent in 2025 due to climate change and population growth (FAO, 2008). Moreover, the productive sectors which include agriculture accounted for over half of associated damages and losses. Climate change will superimpose itself on the existing trends, significantly increasing production risks and rural vulnerability, particularly in regions that already suffer from poverty and hunger.

South Asia is the poorest region in the world after sub-Saharan Africa. Looking at Figure 40.1, from, 1995 to 2005, all countries of the region, except Pakistan, improved their food energy supply. Yet the average per capita food energy supply of the region is still the lowest when compared  to other regions of Asia and the Pacific. Within the region, Bangladesh, the second poorest country after Nepal, still has the lowest per capita energy supply, along with the highest dietary energy supply (DES) from cereals (Bangladesh’s DES Cer %, an indicator of poverty, is 80 percent). It is followed by Nepal, which has a DES Cer % of 72 percent. In comparison, the Maldives has the highest per capita gross national income (GNI) (US $2680), the highest per capita energy supply (2600 kcal) and the lowest DES Cer % (39 percent). The Maldives and Pakistan consume the most animal products: the Maldives consume mainly fish; Pakistan consumes most beef and milk. The diet in the Maldives is also rich in fruits and vegetables (7.3 percent of  total energy), compared to only 2.6 percent in  Pakistan and less than 2 percent in Bangladesh. The data reflect the poor diet diversification of Bangladesh, Nepal and Pakistan. Diets of other countries, particularly the Maldives, are relatively more diversified. Prevalence of undernourishment is highest 40.4  Analysis of South Asian in Bangladesh (30 percent) and lowest in the dietary energy supply and Maldives (10 percent). Bangladesh, India, Nepal nutrition status and Pakistan experience high stunting and underweight rates in the under-5 s (43–51 perThe following is an analysis of the dietary cent and 47–48 percent, respectively). Sri Lanka  energy supply1 and nutrition status of South has the lowest child stunting rate (about oneAsian countries, drawing upon data from FAO third those of Bangladesh, India or Nepal, and  Food Balance Sheets2 (Yusuf et al., 2008) and half of that in the Maldives). A one-way ANOVA using the FAO method of estimating the distri- test of the data shows that total per capita calobution ­ function of dietary energy consumption rie supply is negatively associated with the on a per-person basis. percentage of undernourishment in the total 1

Calculated as dietary energy supplies by FAO. For the trend and pattern of dietary intake (energy supply from different food groups) in the six countries of the region, data from the FAO Food Balance Sheets were taken for three year-points during the decade of 1995–2005 (1995, 2000 and 2005, respectively) (FAOSTAT, 2007). 2

2.  From society to behavior: policy and action

503

40.5 Dietary transition in Asian countries

population (r2  0.937, P  0.001). There is also a positive, but insignificant (r2  0.511, P  0.110) association between national income and total per capita energy supply. Thus, national income largely, but not wholly, determines the food consumption and nutritional status of a country.

to a rise in overweight and obesity throughout the developing world (Popkin, 2002).

40.5.1  Carbohydrates

Among food grains, there has been a shift from starchy roots and tubers to polished rice and refined wheat. The most undesirable fea40.5  Dietary transition in ture of this nutritional transition is the substitution of millets with socially more prestigious Asian countries and more refined grains. Indeed, the per capita availability of “coarse grains” (millets) has sufFood availability data from the FAO show that fered through relative neglect. The resulting disadverse shifts in dietary composition are taking tortion in the pattern of food-grain production place at a much more rapid pace than are benhas in turn been reflected in the relative market eficial changes. Many of them have been driven prices of these food grains. Since this trend has by dietary globalization that has on the one hand coincided with the total decline in intake of cereincreased dietary diversification, and on the other als, the net effect is a 50 percent decrease in the hand increased consumption of fats and refined diet’s fiber content. An emerging issue is that, carbohydrates (Uusitalo and Pushka, 2003). while grain yields are rising, the rate of increase Traditional Asian diets are cereal-based but, with is slower than population growth. The adoption a growing middle class, changes are taking place of modern rice varieties has slowed in many in the structure and patterns of diets. Relative to countries, reaching a plateau of 75–90 percent. traditional carbohydrate-dominated Asian diets, Table 40.4 shows the relative rice consumption the evolving diets are distinctly higher in fat and in the Asian region in terms of kcal/per capita/ protein content. More specifically, these changes day during the 1995–2005 period ((FAO RAP), have included shifts towards higher energy-  2008). density diets, characterized by increased consumption of fat and added sugars, saturated fat intake (mostly from animal sources), and reduced 40.5.2  Protein intakes of complex carbohydrates, dietary fiber, Meat consumption in Asia (mainly Southeast, and fruits and vegetables (Drewnowski and Popkin, 1997). Technical advances in agriculture Central and East Asia) has more than doubled have also led to changes in the source of nutri- in the past 20 years. During the 1990s, the proents, which can have negative implications for duction of pigs and poultry almost doubled health. Altogether, these shifts have contributed in China, Thailand and Vietnam (FAO, 2006).  Table 40. 4  Consumption of rice: kcal/capita/day (1995 and 2005) Central Asia

South Asia

Southeast Asia

East Asia

Oceania

1995

2005

1995

2005

1995

2005

1995

2005

1995

2005

100

  140

  780

  800

1420

1370

  980

  790

  300

  300

Source: FAO RAP (2008).

2.  From society to behavior: policy and action

504

40.  Agriculture, Food and Health

By 2001, these countries accounted for more than half the pigs and one-third of the chickens consumed in the world.

population living in urban areas. About 3 billion people, or nearly half the world’s population, now lives in urban settlements (UNESCAP, 2004). In 2007, for the first time in history, the world had more urban than rural dwellers. 40.5.3  Milk and dairy products Although Asia is one of the least urbanized areas Improved milk and dairy production can in the world, the size of its urban population mean a rise in undesirable fats. Milk production (1.5 billion in 2003), is greater than the combined has grown more rapidly in Asia than anywhere number of urban dwellers in Europe, Latin in the world. For instance, milk consumption America and The Caribbean, North America has increased by 50 percent in India. There and and Oceania (1.2 billion). The urban population in Pakistan, which has a strong dairy tradition, of Asia is expected to rise to 2.7 billion – 58 permilk prices have remained insulated from inter- cent of the world’s population – by 2030. Urbanization changes the environment in national price variations, and thus these have which people live, with marked effects on diet­ had no affect on milk consumption. ary patterns. A dramatic shift has been seen in dietary patterns in the Asia Pacific region in 40.5.4  Vegetables and fruits recent years (Pingali, 2007). As women enter Vegetable and fruit consumption accounts for the workforce, there is increased consumpabout 2 percent of total energy intake in lower- tion of processed foods. Smaller urban famiincome Asian countries. The low consumption lies also tend to eat out more often. Changes in of fruit and vegetables in most Asian countries socio-cultural environment, infrastructure and can be attributed to the lack of a clear and effi- resources also provide greater access to Western cient strategy to strengthen the horticulture sec- foods, which influences tastes and preferences tor. Consumers on low incomes do not have (Mendez et al., 2005). There is also a stronger access to affordable, nutritious foods from which preference for meat, fish and dairy products, they can select a healthy diet. The deficit of fresh fruits, such as apples, and highly processed conproducts has to be covered by imports. Efforts venience foods and drinks, which are available should be made to increase the domestic pro- in the emerging supermarkets and fast-food duction through implementation of new farm- outlets. Urbanization also has significant impact ing technologies that will permit a more rational on physical activity levels (Ferro-Luzzi and exploitation of each country’s potential and land Martino, 1996). Urban work requires less physiresources. Promoting home-gardening and growcal exertion and is more sedentary compared to ing local vegetables and fruits are considered rural activities. Previous dietary intake surveys sustainable food-based strategies to diversify (Taylor et al., 1992) of rural and urban comhousehold diets and address problems of food munities in several Pacific island communities security (Bhattacharjee et al., 2007). have compared total energy and macronutrient intakes, and obesity, hypertension, diabetes mellitus, serum cholesterol and physical activ40.6  The impact of ity levels. Urban subjects were more obese than urbanization rural ones, had higher prevalence rates of diabetes and hypertension, and generally had higher In the past 50 years, there has been a two-  cholesterol levels. Rural subjects were leaner, fold increase in the percentage of the world’s suffered less from diabetes and hypertension,

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505

40.7 Overweight and obesity in Asia

and had greater total energy intakes than urban dwellers. Rural people ate a greater proportion of carbohydrates, while urban subjects ate proportionally more protein and in general more fat. Rural subjects had higher levels of physical activity. These studies provide persuasive evidence that exercise as well as diet has a significant effect on rural/urban differentials in obesity and non-communicable diseases, and that energy intake reflects energy expenditure.

40.7  Overweight and obesity in Asia Although undernourishment remains a serious problem throughout Asia, more than 1 in 10 children in many countries of the region are already overweight. Surveys in Taiwan, for example, indicated that around one in four children has a weight problem. Generally, rates of childhood obesity are higher in urban areas than in rural areas, and highest in countries with the greatest economic development, such as Japan, Malaysia and the Republic of Korea. Indeed, even countries like Indonesia, that are just beginning the process of economic transition, have not escaped this problem. A study of pre-school children from high-income families in Jakarta found that around 16 percent of children were obese (Gill, 2006). Table 40.5 shows selected countries in East Asia (China) and Southeast Asia (Timor Leste, Lao PDR, Thailand) that have overweight prevalence in adults exceeding 30–40 percent. In contrast, South Asian countries, which are among the poorest in the region, in general have lower proportions of overweight adults. Besides the increasing prevalence of overweight and obesity, diabetes and other non­communicable diseases are also on the rise in the Asian region. Six countries in Asia were listed among the top 10 countries globally with the highest number of diabetes cases (Figure 40.2).

Table 40.5  Prevalence of overweight/obesity for ages 15 years and above in 2002 BMI 25–30 kg/m2

BMI 30 kg/m2

Country

Male

Female

Male

Female

Timor Leste

35.9

46.4

6.0

14.2

Bhutan

34.0

44.7

5.3

13.1

Lao PDR

30.4

43.5

2.3

  9.2

Thailand

27.7

32.5

2.5

  7.0

China

27.5

22.7

1.0

  1.5

Malaysia

22.5

34.2

1.6

  6.8

The Philippines

21.7

25.4

1.1

  2.8

Pakistan

16.7

23.2

0.8

  2.9

India

15.0

13.7

0.9

  1.1

Indonesia

  9.6

20.3

0.2

  2.0

Cambodia

  9.6

  7.1

0.1

  0.1

Nepal

  7.7

  8.0

0.1

  0.2

Bangladesh

  5.9

  4.3

0.1

  0.1

Vietnam

  2.7

  7.0

0

  0.2

Source: WHO (2007).

40.7.1  Diabetes in India Indian populations have demonstrated a predisposition for adiposity – especially abdominal – hyper-triglyceridemia, CVD, and insulin resistance and diabetes. Gupta (2007) examined risk-factor determinants of diabetes. Risk associations demonstrate that lifestyle factors such as urbanization, socio-economic status, stress, sedentary lifestyle, dietary calorie excess, certain specific dietary factors, and generalized and central obesity are important risk factors for diabetes. Such risk factors tend to develop early in the lifecycle in Indian subjects, and consequently type 2 diabetes occurs at least 10–15 years earlier compared to people of non-Indian origin (Bhargava et al., 2004). While environmental factors seem partly to explain the prevalence  of diabetes in the Indian population, it ought to  be noted that the ­population is ­genetically prone

2.  From society to behavior: policy and action

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40.  Agriculture, Food and Health

90 2000 2030

79.4

80 70

Millions of cases

60 50 42.3

40 31.7

30.3

30 20.8

20

21.3

17.7

13.9 8.4

10

6.8

11.3 5.2

11.1 4.6

8.9 4.6

4.3

7.8 3.2

6.7

sh

ly Ba

ng

la

de

Ita

il az Br

n

Fe uss de ia ra n tio n

R

an

ta is Pa k

ne do In

Ja p

si

a

SA U

na hi C

In

di

a

0

Country

Figure 40.2  Diabetes situation in selected countries (2000 and 2030). Source: Wild et al. (2004).

to the disease. From a ­ preventive perspective, there is a need to bring in change in the economic and environmental structures in urban areas of India so that physical activity and healthy diet­ ary choices are available (Reddy et al., 2005).

40.7.2  The case of Thailand Thailand has undergone social and economic transitions in the past three decades. These are demonstrated by an increase in life expectancy at birth, and the decline in total fertility and infant mortality rates. Several nationwide surveys have also shown that the food consumption pattern of the population has changed considerably. Thai staples and traditional dishes are being replaced by foods rich in fat and

a­ nimal meat, and less vegetables and fruits are consumed (Kosulvat, 2002). Studies have also shown that in Thailand, the prevalence of childhood obesity (over the 97th percentile for weight and for height) was 22.7 percent in urban areas and 7.4 percent in rural populations. Marked relationships between childhood obesity and parents’ educational level and household income were seen (Sakamoto et al., 2001).

40.8  Policy interventions It is obvious from this analysis that it is urgent to enrich food and agriculture ­ policies with a nutrition perspective that can effect major changes on Asian diets. The new ­nutrition 

2.  From society to behavior: policy and action

40.8  Policy interventions

challenges now facing Asia necessitate the development of effective nutrition programs and policies aimed at preventing and controlling both under- and overnutrition. With regard to overnutrition, food and nutrition policy initiatives must address problems of dietary excesses and lifestyle changes that are key factors leading to overweight and obesity. The types of initiatives needed to shift policies and processes towards the promotion of healthy population diets, activity levels and weight status will differ culturally, and between and within countries. Physical activity policy initiatives are also essential, and may be motivated by fields other than health – e.g., sustainable transport, family recreation. As part of a comprehensive approach to interventions, all aspects of production, access and utilization of food should be incorporated in the national food policies. Commencing with the food supply chain, possible solutions should begin with providing a nutrition orientation in agriculture and food supply, in addition to adequate and safe food supply and services. Production and consumption diversification, with a focus on horticulture crops, small livestock and fisheries and promoting biodiversity, should be a key approach. Intercropping and intensive and integrated home gardens and communityand school-based nutrition education (rural and  community and school gardens) should be emphasized, to deal with diabetes mellitus using holistic and participatory approaches. Targeting of vulnerable groups, provision of massive training and capacity-building, and implementation of nutrition promotion actions should be strengthened. The reduction of supply of fatty/sugary/salty foods, replacing them with fruits and vegetables and encouraging modified/appropriate cooking methods, along with increased physical activities, should be promoted. In setting process outcomes, many countries have established food-based dietary ­ guidelines

507

that can be mainstreamed into national agricultural and health policies. Key messages are reiterated to promote healthy food intake,  such as: eat lots of fruit, vegetables, fish and complex carbohydrate foods l limit fatty, sugary and salty foods and, to promote appropriate nutrient intake, aim for about 10 to 15 percent energy from protein, 15–30 percent energy from fat, more than 50 percent energy from complex carbohydrates l limit salt and alcohol intakes. l

Citing an example from Thailand, where community-based nutrition programs have been sustainable in addressing undernutrition, nutrition and health literacy programs are now playing an important role to address problems of obesity and non-communicable diseases. Nutrition labeling has been mandatory for the past 10 years, and national education strategies are largely being centered on nutrition promotion and advocacy at various levels (Tontisirin and Bhattacharjee, 2001). Detailed knowledge regarding patterns of food and nutrient intake is needed to compare existing nutrient intakes and give meaningful advice about appropriate food choices (Tontisirin and Kosulvat, 1998). Expanded nutrition education and consumer awareness programs will be required, as will information on the composition of the processed and packaged food. Improvement in nutritional status has often been the result of programs designed simultaneously to improve food security and nutrition (Bloom and Canning, 2000). Major efforts need to be made through convergent efforts of the agriculture and health sectors to improve national diets through a demand-led food policy whereby nutrition education policies (influenced by food-based dietary guidelines) stimulate consumer demand for healthier food choices (Tontisirin and Bhattacharjee, 2007).

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40.  Agriculture, Food and Health

40.9  Conclusion and recommendations The agriculture, food and health sectors play prominent roles in the promotion of healthy diets for individuals and societies. Food strategies must be directed at ensuring food security in the holistic sense, achieving all aspects of availability, access and utilization along with the consumption of adequate quantities of safe and good-quality foods that together make up a healthy diet. Recommendations for consideration include the following: 1. A nutrition orientation to food production should be adopted to enable greater possibilities for long-term sustainability of food resources for communities. 2. National food policies should strengthen achievement of balanced production and availability of a range of diverse foods   so that larger sections of the population   can widen reliance on a variety of foods   and dietary diversification can be   promoted. 3. The impact that agricultural policies, particularly subsidies, have on the structure of production, processing and marketing systems – and, ultimately, on the availability of foods that support healthy food consumption patterns – needs to be examined. 4. The implementation of nutrition education should be strengthened through food-based dietary guidelines (FBDGs), school nutrition programs, and information technology at national, district, household and community levels as the key to promoting diversity in the diets of the population. 5. Urban agriculture activities should be promoted through innovative means to ensure food availability within urban households, slum communities, urban labor sectors and urban locales.

References Bhargava, S. K., Sachdev, H. P. S., Fall, C. H. D., Osmond, C., Lakshmy, R., Barker, D. J. P., et al. (2004). Relation of serial changes in childhood body-mass index to impaired glucose tolerance in young adulthood. The New England Journal of Medicine, 350, 865–875. Bhattacharjee, L., Saha, S., & Nandi, B. K. (2007). Food based nutrition strategies in Bangladesh: Experience of integrated horticulture and nutrition development. Bangkok: FAO, RAP Publication, 2007/05. Bloom, D. E., & Canning, D. (2000). The health and wealth of nations. Science, 287, 1207–1209. Drewnowski, A., & Popkin, B. M. (1997). The nutrition transition: New trends in the global diet. Nutrition Reviews, 55, 31–43. FAO. (2002). World agriculture: Towards 2015/2030. Summary report. Rome: FAO. FAO. (2003). Global and regional food consumption patterns and trends. In Diet, nutrition and the prevention of chronic disease. Rome: Agriculture and Consumer Protection Division, FAO. FAO. (2006). Livestock report. Rome: FAO. FAO. (2008). Implications of Climate Change on Agriculture and Food Security in South Asia. Paper presented by the FAO Representative in Bangladesh at the International Symposium on Implications of Climate Change on Agri­ culture and Food Security in South Asia, Dhaka, 25–30 August. FAO. (2009). The state of food insecurity in the world 2009. Economic crisis: Impact and lessons learned. Rome: FAO. FAO Regional Office for Asia and the Pacific (RAP). (2008). The state of food and agriculture in Asia and the Pacific Region. Bangkok: RAP Publication Series 2008/03. FAOSTAT. (2007). World food and agriculture statistics. Rome: FAO Statistics Division. Ferro-Luzzi, A., & Martino, L. (1996). Obesity and physical activity. Ciba Foundation Symposium, 201, 207–221. Gill, T. (2006). Epidemiology and health impact of obesity: An Asia Pacific perspective. Asia Pacific Journal of Clinical Nutrition, 15, 3–14. Gupta, R. (2007). Type 2 diabetes in India: Regional disparities. British Journal of Diabetes Vascular Diseases, 7(1), 12–16. Kosulvat, V. (2002). The nutrition and health transition in Thailand. Public Health Nutrition, 5(1A), 183–189. Mendez, M. A., Monteiro, C. A., & Popkin, B. M. (2005). Overweight exceeds underweight among women in most developing countries. The American Journal of Clinical Nutrition, 81(3), 714–721. Pingali, P. (2007). Westernization of Asian diets and the transformation of food systems: Implications for research and food policy. Food Policy, 32(3), 198–281.

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References

Popkin, B. M. (2002). The shifts in stages of the nutrition transition in the developing world differs from past experiences. Malaysian Journal of Nutrition, 8(1), 109–124. Reddy, K. S., Shah, B., Varghese, C., & Ramadoss, A. (2005). Responding to the threat of chronic diseases in India. Lancet, 336, 1744–1749. Sakamoto, N., Wansorn, S., Tontisirin, K., & Marui, E. (2001). A social epidemic study of obesity among preschool children in Thailand. International Journal of Obesity, 25(3), 389–394. Standing Committee on Nutrition (SCN). (2004). Fifth report on world nutrition survey – nutrition for improved development outcomes. Geneva: UN/SCN. Taylor, R., Badcock, J., King, H., Pargeter, K., Zimmet, P., Fred, T., et al. (1992). Dietary intake, exercise, obesity and non-communicable disease in rural and urban populations of three Pacific Island countries. Journal of the American College of Nutrition, 11(3), 283–293. Tontisirin, K., & Bhattacharjee, L. (2001). Nutrition actions in Thailand: A country report. Nutrition Research, 21(1–2), 425–433. Tontisirin, K., & Bhattacharjee, L. (2007). Food based dietary guidelines from around the world: An overview. In  E. Kennedy & R. Deckelbaum (Eds.), Nation’s nutrition. Washington, DC: ILSI.

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Tontisirin, K., & Kosulvat, V. (1998). Food based dietary guidelines in Asian Countries. In P. Shetty & C. Gopalan (Eds.), Diet, nutrition and chronic disease: An Asian perspective. London: Smith-Gordon. UNESCAP. (2004). Population data sheets. UNICEF. (2004). The state of the world’s children. New York, NY: UNICEF. UNICEF/MI. (2004). Vitamin and mineral deficiencies. A global progress report. Ottawa: Micronutrient Initiative (MI). Uusitalo, U. P., & Pushka, P. (2003). Dietary transition in developing countries: Challenges for chronic disease prevention. Ithaca, NY: Cornell University. Welch, R. M., & Graham, R. D. (1999). A new paradigm for world agriculture: meeting human needs – productive, sustainable, nutritious. Food Crops Research, 60, 1–10. Wild, S., Roglic, G., Green, A., Sicree, R., & King, H. (2004). Global prevalence of diabetes. Estimates for the year 2000 and projections for 2030. Diabetes Care, 27, 1047–1053. World Health Organization. (2007). WHO global infoBase. Online. Available: http://www.who.int/ncd_surveillance/infobase. Yusuf, H. K. M., Bhattacharjee, L., & Nandi, B. K. (2008). Trends and patterns of dietary energy supplies and nutrition status in South Asian countries, 1995–2005. South Asian Journal of Population Health, 1, 1–12.

2.  From society to behavior: policy and action

C H A P T E R

41 Changing Food Systems in the Developing World Prabhu Pingali Deputy Director, Agricultural Development, The Bill and Melinda Gates Foundation, USA

o u t l i n e 41.1  Introduction

511

41.2 Factors Driving Changes in Food Demand 41.2.1  Increased Incomes 41.2.2  Urbanization 41.2.3  Changing Lifestyles

512 512 512 513

41.3 Factors Driving Changes in Food Supply 41.3.1  Decrease in the Price of Food Commodities

514

41.4 Impact of Changes in Food Supply and Demand 41.4.1  Diet Diversification 41.4.2  Supermarkets 41.4.3  Smallholder Farmers

515 515 516 517

514

41.5 The Key Role of Institutions and Research

518

41.1  Introduction Eating and diets in the developing world have been affected by major changes in food systems in the past few decades. Food supply and demand have both driven and been driven by these changes. While changing diets is a well-known consequence of economic growth and development, what is unique in current

Obesity Prevention: The Role of Brain and Society on Individual Behavior

41.3.2 Globalization and Liberalization of FDI 514 41.3.3 The Trade Balance: Importers and Exporters 515

trends is the convergence with Western diets, as well as the speed at which this transformation is occurring. Also of note is the increasing presence of the double burden of over- and undernutrition. This chapter first reviews the factors currently driving changes in the food supply and demand, and then discusses the impact of these on diet. Their impact on the dynamics between smallholder farmers and leading actors

511

2010 Elsevier Inc. © 2010,

512

41.  Food Systems in the Developing World

in modern food value chains is also examined. The implications for policy and government action are then discussed.

41.2  Factors driving changes in food demand 41.2.1  Increased incomes Per capita incomes have risen substantially in many parts of the developing world over the past few decades. In developing countries, per capita income growth averaged around 1 percent per year in the 1980s and 1990s, but jumped to 3.7 percent between 2001 and 2005 (World Bank, 2006a). East Asia has led the world, with sustained per capita growth of 6 percent per year in real terms since the 1980s. In South Asia, growth rates have been consistently positive since the 1980s although not as spectacular (Figure 41.1). Latin America and parts of sub-Saharan Africa have also observed positive

per capita growth trends over the past decade. A consequent swelling of middle-class populations is observed in the developing world – the group that is driving the diet diversification trends.

41.2.2  Urbanization Economic growth has been accompanied by the rapid spread of urbanization worldwide. These shifts in population distribution are considerable. At the beginning of the 1960s, only about 20 percent of developing countries’ population lived in urban areas. By 2000 the share had risen to nearly 40 percent, and by 2007 it had exceeded the 50 percent mark (UN, 2006). The growth in urban populations is most evident when we look at the growth of the megacities in the developing world. In 1950, New York City was the only city with a population greater than 10 million people. By 2000, there were 19 cities with populations greater than 10 million people, and it is projected that by 2015

Real GDP per capita, annual average percent change

7 1980

6

1990 2001–2005

5

2006–2015

4 3 2 1 0 –1

Developing Countries

–2 –3

East Asia and Pacific

Europe and Central Asia

Latin America and Caribbean

South Asia

Middle East and N.Africa

Figure 41.1  GDP per capita (annual average percent change). Source: World Bank (2006b).

2.  From society to behavior: policy and action

SubSaharan Africa

513

41.2  Factors driving changes in food demand

there will be 23 mega-cities around the world (UN, 2000) (Figure 41.2). Feeding the billions of urban dwellers represents a vitally important food policy challenge which is only just being recognized. More importantly, though, it is at the intersection between increased incomes and rising urbanization that lifestyle changes begin to become evident.

41.2.3  Changing lifestyles A clear feature of urbanization is the greater percentage of women working outside the

home. Women’s participation in the labor force is rising. Female employment has at least kept pace with population growth in developing countries since 1980 (World Bank, 2006a). Higher rates of female participation in the workforce have been linked to a greater demand for processed foods (Popkin, 1999; Regmi and Dyck, 2001; Pingali, 2007). The share of processed and high-value food in trade has increased, now accounting for more than 60 percent of all food trade internationally (Figure 41.3). Increasing global interconnectedness, resulting from travel, communications, Internet and

6

Billion people

5

actual

expected

4 3 2

Urban Rural

1 0 1950

1960

1970

1980

1990

2000

2010

2020

2030

Figure 41.2  Urban population to outnumber rural. Source: UN, World Population Assessment 2002.

100 Processed products

Percent

80

60

Fresh horticultural products

40

Semi-processed products

20 Bulk commodities 0

1980

1985

1990

1995

1998

Figure 41.3  Processed and high-value products are increasing in share of food trade. Source: Regmi and Dyck (2001), USDA.

2.  From society to behavior: policy and action

514

41.  Food Systems in the Developing World

Index (1991–92 = 100) 350

Cereals Oilcrops Meat Dairy

300 250

Sugar Horticulture Tropical beverages Raw materials

200 150 100 50 0 1961

1966

1971

1976

1981

1986

1991

1996

2002

Figure 41.4  The long-run real commodity prices have been declining. Source: FAO Statistics.

television, has led to a more homogenized concept of a superior diet, usually mimicking a Western diet (Pingali, 2007).

41.3  Factors driving changes in food supply 41.3.1  Decrease in the price of food commodities Except for a peak in food prices in 2008, the past four decades have generally been characterized by a steady decline in the real prices of food (Figure 41.4). Though prices for major commodities spiked sharply between the falls of 2007 and 2008, they returned to their pre-spike levels by early 2009 (FAO, 2008). Some evidence suggests the world may be experiencing a reversal of the sustained decline in commodity prices due to structural shifts in demand, such as rising demand for food and animal feed in emerging economies, and for biofuels stock. However, the speed at which prices rose and fell indicates that the acute price crisis cannot be attributed to changing demand alone. Critical factors in the food price spike were supply shocks, especially drought in important export-oriented bread baskets, and commodity speculation, which may have been partially fuelled by expectations of rising demand for biofuels

stock. Reactionary policy measures, such as banning exports and grain-hoarding, exacerbated the problem (Pingali and McCullough, 2010). It is anticipated that the renewed interest in enhan­ cing agriculture productivity growth and rising productivity trends in the developing world will keep global food prices on their long-term declining trend in the future as they have done in the past. Falling food prices (in real terms) have been particularly beneficial to poorer consumers, since they can purchase their food requirements with a smaller share of their income.

41.3.2  Globalization and liberalization of FDI “Globalization” is marked by the liberalization of trade as well as of foreign direct investment in retail and in agri-business. The 1990s and 2000s were characterized by the partial or full liberalization of foreign direct investment, often included in structural adjustment programs and bilateral or multilateral trade agreements. Foreign direct investment (FDI) in agriculture and the food industry grew substantially in Latin America and in Asia between the mid-1980s and mid-1990s, although investment remained very low in sub-Saharan Africa (FAO, 2004). In Asia, FDI in the food industry nearly tripled, from $750 million to $2.1 billion, between 1988 and 1997. During that same period,

2.  From society to behavior: policy and action

515

41.4 Impact of changes in food supply and demand

14

Billion US$

12 10 8 6 4 2 0 61

65

69

73

77

81

Total agricultural exports

85

89

93

97

01

03

Total agricultural imports

Figure 41.5  The agricultural trade deficit of LDCs is widening. Source: Food and Agriculture Statistics Database (various years).

food industry investment exploded in Latin America, from around $200 million to $3.3 billion1 (Pingali and McCullough, 2010). Companies such as Carrefour (France), Tesco (UK), Ahold (Netherlands) and Metro (Germany) began transforming the foodscape in developing countries. This was a crucial driver of the “supermarket revolution”, which will be discussed further in this chapter (Reardon et al., 2003; Traill, 2006).

41.3.3  The trade balance: importers and exporters A consequence of decreasing food prices and the liberalization of FDI has been a switch in the trade balance between developed and developing countries, which has been compounded by decreases in freight and transportation costs. The least developed countries (LDCs) of the world, which used to be net exporters of agricultural commodities, have now become net importers. Trends in the inter­ national trade of foodstuffs are expected to continue in the future. In 1961–1963, developing countries had an overall agricultural trade surplus of US $6.7 billion. By the end of the 1990s, the trade was broadly in balance. The outlook to 2030, however, suggests that the agricultural trade deficit of

developing countries will widen markedly, reaching an overall net import level of US $31 billion. The trends in total agricultural exports and imports in LDCs (Figure 41.5) indicate that this will continue to draw a net deficit. Many developing countries’ policy-makers find it more convenient to buy the food from international markets at the margins than to invest in domestic productivity improvements and bring the food from the hinterlands of their own countries to urban areas. This is particularly true of urban areas on the coast. It is much cheaper to ship rice from Bangkok (Thailand) to Dhaka (Bangladesh), for instance, than to increase rice production within Bangladesh. Moreover, trade is shifting towards higher-value and more processed products, and away from bulk commodities (Regmi and Dyck, 2001).

41.4  Impact of changes in food supply and demand 41.4.1  Diet diversification The changes in food supply and demand reviewed above have had a significant impact on individual diets in developing countries.

1

 Since 1997, it has been difficult to track foreign direct investment in the food industry due to changes in data reporting by the UN Conference on Trade and Development. 2.  From society to behavior: policy and action

516

41.  Food Systems in the Developing World

3,000

2,500 Other Pulses

kcal/capita/day

2,000

Roots and tubers Meat

1,500

Sugar Vegetable oils

1,000

Other cereals Wheat Rice

500

0

1964–66

1997–99

2030

Figure 41.6  Diets in developing countries are diversifying rapidly… Source: Food and Agriculture Organization (2002).

As stated in the introduction, diet diversification is considered to be a natural economic phenomenon: as economic development occurs and incomes rise, one begins to see a shift from highly staple-based diets (rice diets, wheat diets, etc.) to more complex and diversified diets, characterized by a higher consumption of meat products, fruits and vegetables (Figure 41.6). Per capita meat consumption in developing countries tripled between 1970 and 2002, while milk consumption increased by 50 percent (Steinfeld and Chilonda, 2005). Also, current food consumption patterns are showing signs of convergence towards a Western diet (Pingali, 2007). In Asia, for instance, these changes have been characterized by (1) decreased consumption of rice; (2) increased consumption of wheat and wheat-based products; (3) increased diversity of food groups consumed; (4) increased consumption of high-protein, energy-dense diets; (5) increased consumption of temperate zone

products; and (6) increased popularity of convenience foods and beverages. This raises the issue of the health consequences of such a diversification. These changes could lead to an increase in the incidence of obesity and of diet-related chronic diseases (see Shetty, 2002). Indeed, the prevalence of overweight and obesity in many low- and middle-income countries (LMICs) has been steadily rising as these countries’ economies grow. Popkin and colleagues (2001), for example, analyzed diet trends and nutritional status in India and China and calculated the economic costs of these changes. While undernutrition is in decline, the incidence of obesity, diabetes and hypertension is rising.

41.4.2  Supermarkets Growth in the number and size of large urban centers creates opportunities for the establishment

2.  From society to behavior: policy and action

517

Number of countries where operating

41.4 Impact of changes in food supply and demand

35 1980

30

2001

25 20 15 10 5 0 Carrefour (France)

Ahold (Netherlands)

Metro (Germany)

Walmart (USA)

Tesco (UK)

Figure 41.7  Global expansion of transnational supermarkets, 1980–2001. Source: UK Food Group (2003), Food, Inc., International Institute for Environment and Development, London, UK.

of large supermarket chains, which attract foreign investments and advertising from global corporations (Pingali, 2007). Supermarkets are in the ideal position to deal with the quantitative, qualitative and location elements of changes in the urban food market. Moreover, supermarkets also play an active role in accelerating and broadening the scope for diet diversification. With economic development, and driven by potential economies of scale, supermarkets tend to replace central food markets, neighborhood stores and street sellers of food in urban areas. Structural transformation of the retail sector took off in Central Europe, South America, and East Asia outside China in the early 1990s. The share of food retail sales by supermarkets grew from around 10 percent to 50–60 percent in these regions. By the mid- to late 1990s, in Central America and Southeast Asia, the shares of food retail sales accounted for by supermarkets reached 30–50 percent. Starting in the late 1990s and early 2000s, substantial structural changes were taking place in East Europe, South Asia and parts of Africa. Here, supermarkets’ share approached 5–10 percent in less than a decade, and is growing rapidly (Reardon and Stamoulis, 2006). Figure 41.7 shows the tremendous increase in the number of countries in which supermarkets

are operating. In Central America, almost 40 percent of food now comes from supermarkets (up from 10 percent); proportions have risen to 50 percent in South America and 50 percent in China (Figure 41.8). It is interesting to note that while international supermarkets are leading the trend, there is also a growing sector of domestic supermarkets.

41.4.3  Smallholder farmers An important consequence of the transformation of the food supply and the supermarket revolution is the impact on the lives and livelihoods of smallholders, which includes the transformation of the intermediaries. Increasingly, trade is being controlled by a small group of enterprises, agri-food companies and retailers who act as a conduit between millions of farmers and millions of consumers. This small but powerful group determines what is produced at the farm level, the types of food produced, its quality and timing, how it is processed, and how it is delivered to consumers. For example, the case of coffee, just 4 international traders, 3 roasters and 30 grocers link approximately 25 million farmers and their families to 500 million consumers (UK Food Group, 2003).

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1992

2002

Central America South America Southeast Asia East Asia* Central Europe South Africa Kenya Zimbabwe China (urban)

0

20

40

60

*excl.China

Figure 41.8  Supermarket share of retail food sales. Source: Reardon et al. (2006).

Smallholder farmers are increasingly commercializing and diversifying their production, and are facing higher transaction costs in the process. The new food system is extremely differentiated in terms of its products and outputs, more intensive in terms of post-harvest processing issues, and highly based on science, which is then applied at the farm level, as well as founded on highly skilled-based farming techniques. Indeed, as economies grow and the opportunity cost of labor rises, the returns from intensive production systems which require high levels of family labor are generally lower than those from exclusive reliance on purchased inputs. With the expected rise in operational holding size, the ability of the household to supply adequate quantities of non-traded inputs declines. Power, soil fertility mainten­ ance and crop care are the primary activities for which non-traded inputs are used in subsistence societies. With the increased opportunity costs, family labor will be used less as a source of power and more as a source of knowledge (technical expertise), management and supervision. Farm decisions become increasingly responsive to market signals – domestic as well as international – and less driven by traditional practice.

41.5  The key role of institutions and research Figure 41.9 summarizes the key components of the changing food system, emphasizing the role of institutions with their rules, regulations and contracts to govern the arena in which individuals and enterprises interact along the food chain. Appropriate investments in research and technology generation, as well as policy reform, could assist in alleviating many of the constraints discussed in the last section, and help small farmers benefit from the transformation process. The primary objective of the research system during the process of commercialization and diversification remains to generate new technologies that improve productivity and farmer income. In responding to diversification trends, the research should not abruptly shift from an exclusive focus on one set of commodities to another set of commodities. In addition to the productivity objective, the focus of research should be to provide farmers the flexibility to make crop choice decisions, and to move relatively freely between crops. Both substantial crop-specific research and system-level research efforts will be required to provide farmers the

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References

Individuals

Inputs

Primary production

Enterprises

Processing and packaging

Transport

Distribution and retail

Consumption

Services

Governed by Institutions: Rules and regulations Markets (Contracts)

Figure 41.9  The changing food system.

flexibility of crop choice. Crop-specific research should investigate increases in yield potential, shorter duration cultivars, improved quality characteristics and greater tolerance to pest stresses. System-level research would include land management and tillage systems that allow for shifts of cropping patterns in response to changing incentives, and farm-level water management systems that can accommodate a variety of crops within a season. Also important at the system level is research on the carryover effect of inputs and management practices across crops – for instance, high insecticide and herbicide applications, or the effects of intensification in terms of prolonged water saturation, the build-up and carry-over across crops of pest populations, rapid depletion in soil micronutrients and changes in soil organic matter could lead to reduced productivity of rice monoculture systems over the long term. The transformation of food systems presents a set of problems that vary drastically between countries, based on the characteristics of the food system, the place, or the households involved. Various countries will prioritize problems differently, depending on the context.

There is no one policy response, but a common objective between all situations is to see smallholders through the transition, in a manner that enhances their incomes and improves their livelihoods. Facilitating the transformation ultimately requires a three-pronged policy approach: 1. Facilitating the inclusion of smallholders in modern chains by reducing costs of participation 2. Upgrading traditional marketing systems 3. Supporting those who cannot supply traditional markets, with social safety nets and exit strategies from agriculture.

References FAO. (2002). World agriculture: Towards 2015/2030 Summary Report. Rome: FAO. FAO. (2004). The state of food insecurity in the world. Rome: FAO. FAO. (2008). Food outlook: Global market analysis November 2008. Global information and early warning system on food and agriculture. Rome: FAO. Pingali, P. (2007). Westernization of diets and the transformation of food systems: Implications for research and policy. Food Policy, 32(3), 281–298.

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Pingali, P., & McCullough, E. (2010). Drivers of change in global agriculture and livestock systems. In H. Steinfeld, H. A. Mooney, F. Schneider, & L. E. Neville, (Eds.), Livestock in a changing landscape. Volume 1, Drivers, consequences, and responses (pp. 5–10). Washington, DC: Island Press. Popkin, B. (1999). Urbanization, lifestyle changes and the nutrition transition. World Development, 27(11), 1905–1916. Reardon, T., and Stamoulis, K. (2006). Impacts of agrifood market transformation during globalization on the poor’s rural nonfarm employment: lessons for rural business development programs. Plenary paper presented at the 2006 meeting of the International Association of Agricultural Economists, in Queensland, Australia, August 12–18. Reardon, T., Timmer, C. P., Barrett, C. B., & Berdegue, J. (2003). The rise of supermarkets in Africa, Asia and Latin America. American Journal of Agricultural Economics, 85(5), 1140–1146.

Regmi, A., & Dyck, J. (2001). Effects of urbanization on global food demand. In A. Regmi Changing (Ed.), structure of global food consumption and trade. Washington, DC: Economic Research Service, United States Department of Agriculture. Shetty, P. S. (2002). Nutrition transition in India. Public Health and Nutrition, 5(1A), 175–182. Steinfeld, H., & Chilonda, P. (2005). Old Players. New players: Livestock report 2006. Rome: FAO. Traill, W. (2006). The rapid rise of supermarkets? Development Policy Review, 24(2), 163–174. United Nations. (2006). World population prospects: the 2006 revision. New York, NY: Population Division, Economic and Social Affairs Department, United Nations. World Bank. (2006a). Global economic prospects: Economic implications of remittances and migration. Washington, DC: International Bank for Reconstruction and Development. World Bank. (2006b). World development indicators. Washington, DC: World Bank.

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42 Green Revolution 2.5: From Crisis to a New Convergence Between Agriculture, Agri-Food and Health for Healthy Eating Worldwide 1

Laurette Dubé1,  Janet Beauvais1, Louise Fresco2 and Patrick Webb3 James McGill Chair, Consumer Psychology and Marketing, Desautels Faculty of Management,   McGill University, Canada 2 Universiteit van Amsterdam, Amsterdam, The Netherlands  3Friedman School of Nutrition Science and Policy, Tufts University, Medfore, MA, USA

o u t l i ne 42.1  Introduction

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42.4.2  Revisiting the Role of Business and Communities 526 42.4.3  Empowering Smallholders through Early-stage Development Pathways 527 42.4.4  Integrating the National and Global Food Chains through Later-stage Development Pathways 527 42.4.5  Policy, Innovation and Financial Levers for Convergence along the Local and Global Food Chains 527

42.2  N  ovel and Convergent Solutions for Agriculture, Agri-Food and Health 522 42.3  A  n Integrated Approach to the Food   and Nutrition Value Chain 523 42.4  C  hallenges and Opportunities in Developing Green Revolution 2.5

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42.4.1  Taking into Consideration the Structural Underpinnings of Food Crises

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42.1  Introduction

need to be addressed simultaneously as related symptoms of a more insidious systemic illness afflicting agriculture, agri-food and health worldThree major food crises are assailing the wide. The second premise is that both the causes world today. First, an epidemic of obesity and and solutions to these crises may lie at the interdiet-related non-communicable diseases looms face of agriculture, agri-food and health. In this in developing and developed countries alike, context, the solutions lie in the creation of novel unleashed by the terrible irony that development and convergent pathways for sustainable agriand prosperity bring their worst habits with cultural, agri-food and health innovation in both them, including overconsumption, poor diet and developing and developed countries. Building unhealthy lifestyles. Secondly, the world is ­facing upon the original Green Revolution (which hara global food security crisis, with food-price  nessed science and technology to preempt wideinflation sending markets into turmoil, and the spread famine between 1945 and 1970) and Green increasing prevalence of food insecurity espe- Revolution 2.0 (unveiled in 2006 by the Alliance cially for the roughly 1 billion people who sub- for a Green Revolution in Africa (AGRA), a sist on less than US $1 per day. Finally, food group co-funded by the Bill and Melinda Gates safety is becoming an increasingly major con- Foundation and the Rockefeller Foundation to cern, with practices in one part of the world often orchestrate radical change in African agriculresulting in food-borne illnesses in others. With a ture), Green Revolution 2.5 is designed to scale steady stream of headlines about bovine spongi- up the latter. It will foster and support a global form encephalopathy (“mad cow disease”), food social change movement to build greater converrecalls prompted by outbreaks of salmonella and gence in mind-sets, policies, strategies, practices, E. coli and fears of avian and swine influenza methods and metrics prevailing in agriculture, contagion from animal supply, the pressures  agri-food and health. The aim is to have them facing all stakeholders in both local and global work, singly and jointly, toward the creation of food systems have never been greater. societal and economic value while promoting This chapter argues that the pathways of agri- healthy eating, food and nutrition security, and cultural and industrial development that have food safety around the world. led to the current situation need to be revisited in an integrated manner in order to build, from the outset, greater convergence between 42.2  Novel and convergent agriculture, agri-food and health systems and solutions for agriculture, their respective policies and programs. In line agri-food and health with the recent orientation taken by the World Health Organization (WHO) and the Food and Agriculture Organization (FAO), it proposes Novel and convergent solutions for sustainthe Green Revolution 2.5 model, coined by the able development are needed worldwide in both McGill World Platform for Health and Economic developing and developed countries to develop Convergence, to take an integrative approach a shared, integrative approach to healthy eating to current food-related issues. It views all three that goes beyond its recent definition of food food-related crises as convergent facets. and nutrition security. It must encompass stabilThe Green Revolution 2.5 model is anchored ity in availability, access, and utilization of safe in a double premise. The first is that the “silent and nutritious food to prevent both over- and tsunami” of catastrophic food price increases under-nutrition in an economically, environmen(The Economist, 2008) is in fact three crises that tally, socially and culturally sustainable way. 

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42.3  An integrated approach to the food and nutrition value chain

It is also increasingly recognized that to assemble the knowledge, technology, competency and resource basis needed, partnerships at local, national and global levels, between governments, governmental agencies, civil society and private sector organizations within and across agriculture, agri-food and health sectors must be formed. If great strides have been made in the past toward such multi-level and multi-­sector responses to such issues, the scale, scope and speed remain insufficient to meet the needs of today. Significant work is needed to tap into the innovation and entrepreneurship of the private sector and the community and grassroot levels to (1) better understand the needs of those acting in the field and how they can contribute to the overall effort to ensure food and nutrition security, and (2) provide capacity for and trigger the social changes needed to move toward better convergence. The developing and developed worlds must be seen as part of the same local and global system. Furthermore, agriculture, agri-food and health must also be viewed as an integral part of a same overall system as that of education, transportation, civil engineering and all other sectors that shape food supply and demand. Local systems supporting these domains of social and economic activities in developed and developing countries, and the global organizations, markets and societies they form, singly and jointly possess the solutions to the three food crises. Such solutions must ensure social and economic effectiveness with environmental and ­ financial sustainability. They must also be scalable, to reach the scale, scope and speed of change needed. Finally, they must be sustainable over time, anticipating, for instance, long-term  consequences of policy and interventions. Conceiving new models of convergence between agriculture, agri-food and health systems worldwide is particularly urgent, given the precipitous rise in food demand due to newly prosperous middle classes in high-population developing economies. This is further exacerbated

523

by constraints on exports from some markets, and inflated imports due to high fuel prices. The need to trace novel pathways for agricultural, industrial and social development is further reinforced by statistics on childhood obesity in countries such as Mexico and China, where obesity rates parallel the rise of the Gross Domestic Product (GDP). While governments and international institutions have played and will continue to play leading roles in managing these issues, particularly in crisis situations, current food crises around the world highlight the pressing need for a deeper engagement from all actors of society. In this regard, we present an integrated perspective of food supply and demand along a value chain that combines agriculture, agri-food and health in a novel manner, all sectors being critical to guiding the dynamics along the chain toward sustainable healthy eating.

42.3  An integrated approach to the food and nutrition value chain Understanding the complex processes of agri-food, industrial and commercial development at the local and national levels, as well as in the context of global markets, is critical to developing appropriate strategies and policies at the interface of health, agriculture and  agri-food. Indeed, industrialization, urbaniz­ ation and globalization have significantly changed the agriculture and agri-food systems, notably through the interrelated expansion of trade, foreign direct investment and trans­ national corporations (Hawkes et al., 2010). Globalization has resulted in increasingly complex cross-national and cross-continental supply chains of agriculture and agri-food products. It connects daily grocery shoppers in Canada and the United States, for example, to small farmers in developing countries, with transnational agrifood corporations as the bridge between them. 

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Developed Countries

Global Agro-Business

Transnational Food Manufacturers

Global Fast-food Franchises (McDonald’s, KFC)

Global Retailers (supermarkets, discount foods, Wal-Mart)

Global value chain Local food production system

Local Farmers

Local Food Producers

Transnational Fast Food Franchises

Food Consumption Patterns (un/healthy eating)

Local Franchises (fast-food & traditional) Developing Countries

Figure 42.1  The value-chain approach.

The value-chain approach provides a theoretical framework that situates why, how and what global economic processes are felt at the national and local levels, and vice versa. It maps systematically all actors involved, from the farm to the plate (Figure 42.1). The value-chain approach can identify various points along the chain where change and intervention can be targeted to create a healthier food supply while maintaining taste and pleasure. It can also help identify the points where demand can be modified in order to support the shifts in supply. The aim is to integrate health considerations at every point along the chain, and move toward an integrated health and agriculture and agri-food strategy (Figure 42.2). This means that all actors will have to be engaged along the chain to work in shifting the chain in a healthier direction. On the supply side, the private and public sectors in the health, agriculture and agri-food

realms have undertaken a significant effort to entice producers, processors, marketers, retailers and restaurants to shift the drivers of food supply in a healthier direction. At the same time, the health community has made efforts to educate consumers about healthy eating. Despite these initiatives, and even though policy-makers in both sectors are aware that supply and demand are highly intertwined, no complete and systematic approach has been developed to move supply and demand toward health and nutrition in a convergent and sustainable manner. On the demand side, these changes must place the consumer at the center of focus. Indeed, shifting demand underlies shifting supply. Consumers, far from being generic entities, vary along numerous dimensions and hold a variety of behavioral motivations which impact their food choices in different ways and to different extents. Culture and norms must also be

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Media and culture Community and environment

Home preparation and consumption

Restaurant and food service

Food retailing

Added-value food import and export

Food and social marketing

Food branding and nutrition labeling

Food processing

Agriculture import and export

Agriculture production

Breeding

Nutrition, health and education professions, organizations and systems

Figure 42.2  Moving towards an integrated health and agriculture and agri-food strategy.

taken into account, since the value attached to food and its relationship to health varies according to one’s culture. Cultural values and social norms also shape the activities, interactions and transactions within and between health, agriculture and agri-food systems.

must be undertaken in a manner that is culturally, socially, environmentally and economically sustainable, both in the early stages of local development, when subsistence agriculture and early-stage commercialization prevail, and in the subsequent phase of economic growth, during the industrialization and integration of the local agricultural and food production sectors and markets into global value chains. In other 42.4  Challenges and opportunities in developing words, Green Revolution 2.5 charts agricultural, social and economic development pathways Green Revolution 2.5 that better balance the transition from tradition to modernity. It identifies what works best in Building upon principles of pro-poor and both tradition and modernity, and revisits some sustainable economic growth, Green Revolution of the basic ways in which each operates to 2.5 encompasses the whole of society to develop improve worldwide food and nutrition security pathways where all actors at all levels of for the health of all, and the competitiveness of ­decision-making, singly and jointly, work toward developing and developed nations. the creation of sustainable societal and ecoGreen Revolution 2.5 will strive to chart develnomic value. These actors include the national opment trajectories in the poorest countries of and transnational businesses, community lead- the world to promote healthy and sustainable ers and civil society organizations, governments development; to reduce hunger, nutritional defiand philanthropic organizations in both the ciencies and poverty in its early stage; to control developed and the developing world. Changes  social and health inequities; to improve rural

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livelihoods while creating health-friendly cities; to ensure safety in local and global food systems; and to facilitate environmentally, socially and economically sustainable development. Such trajectories will enable the poorest countries to progressively reap the many benefits of development without having to pay the high toll of obesity and chronic diseases. Aligned with this vision, pathways at the early stage of development achieve maximal societal and economic value, and contribute to solve food security issues, while laying the foundations of local societies and economies that learn to interact with global societies and economies in a balanced and safe manner. Even the most innovative approaches to address health challenges within the local and global food systems have had limited success, with the agenda of “food security” focusing exclusively on the reduction of hunger and nutritional deficiencies. Healthy eating, food security and food safety have typically been the object of different interventions, different systems and different institutions. Yet the ever-increasing interconnections between local and global agriculture, food, and financial systems potentially both underlie the causes and point to solutions to the three food crises. Thus, it is important to examine the most pressing challenges and key lever points for change as we lay the foundations of a Green Revolution 2.5 model.

42.4.1  Taking into consideration the structural underpinnings of food crises While current high food prices may or may not remain high, there is a pressing need to understand the structural underpinnings that relate to these. Different causes of that phenomenon include, but are not limited to, biofuels. High prices also have a significant impact on farmers’ choice of crops. Another important factor relates to the paradox of the poverty concept. Initiatives that are successful in reducing food insecurity through poverty alleviation and economic growth

lead to increased food demand worldwide. This phenomenon has knock-on effects through market structures on those countries that are struggling in their fight against poverty. There are important health consequences linked to this, such as malnutrition. Fundamentally, this ties into globalization: changing the conditions of the food supply and demand in one area of the world affects those of others, and defines a new equilibrium that may not necessarily translate into better food and nutrition security worldwide.

42.4.2  Revisiting the role of business   and communities The private sector is an important player in society, as an engine of economic development, a key driver of evolving working and living conditions shaping risk and preventive factors for population health, and a stakeholder  (like others in society) that brings its unique competencies and resources to contribute to the sustainable social and economic development of nations worldwide. Resources and power have increasingly shifted to the private sector over the past decades: of the world’s largest 150 economic entities, 95 are corporations. The revenues of corporations such as Wal-Mart, BP and Exon-Mobil all rank above the GDP of countries like Indonesia, Norway, Saudi Arabia or South Africa (World Bank, 2005). Wal-Mart, for example, has a single-day revenue larger than the annual GDP of 36 independent countries. If it were a country, Wal-Mart would be China’s eighth largest trading partner. Together with actors from the agricultural value chains, the many value-adding industrial, commercial and social actors, operating at all levels from farm to fork, shape local and global food supply and demand, and contribute to the balance of opportunities and constraints afforded by the environment in which individuals can eat healthy food or not. Private sector and community actors have core competencies and resources

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42.4  Challenges and opportunities in developing Green Revolution 2.5

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that, through a process of health and nutrition, agriculture and agri-food convergence, could be used to improve current practices. Novel agricultural development pathways could facilitate the production of healthy foods and diets, and provide solutions to some of the world’s most pressing health and nutrition problems.

breeds and feeds as well as in business processes and market institutions along the agriculture and agri-food chains, need to be examined.

42.4.3  Empowering smallholders through early-stage development pathways

Reviewing social and business innovations in local developing economies and key features of industrial local and global chains, we examine best practices, innovation and collective actions that can help smallholders and firms to better integrate the national and global food chains. What could be the quantitative, qualitative and organizational changes in the food supply and demand to ensure the sustainability of healthy food and diets? How do you balance traditional markets and retail shops vis-à-vis supermarkets? What capacity-building is needed to improve the food safety of food products? What processing facilities are needed? What integration strategies are possible to integrate small farmers into local and global industrial chains in a way that improves the safety, quality and affordability of healthy food and diets in an economically sustainable manner? In both developing and developed countries, can practices and policies in processing, retail and foodservice business development, foreign investment and marketing be such that the transition is sustainable? What other capacity-building activities are needed to facilitate the co-existence of major distribution chains, and smaller-scale retail outlets or networks of small producers?

Challenges in conceiving early-stage development pathways to ensure the food and nutrition security of smallholders and their local, national and transnational consumers are manifold. How do you ensure access to agricultural input, technology, market knowledge and capital to smallholders and micro-businesses in order to reduce constraints to diversification, including market availability and size, land suitability and rights, irrigation infrastructure and labor supply? How do you ensure that innovative cooper­ative arrangements helping small fishing or farming households selling on the international markets are not accompanied by other changes in trade, agriculture and business policies and practices that compromise their own diets? How do you develop smallholder-friendly food safety systems to reduce barriers to market access for smallholders? How do you guide the main cultural, social and economic determinants of the changes in consumer preferences, in the demand for food and in lifestyle in the first stage of development toward a better balance between traditional and western-type food, diets and lifestyle? How do you anticipate and prevent adverse long-term caloric excess consequences? What education and other capacity-building activities can be given to small farmers to help them better know the players, rules and relationships within new commercialized food systems? Strategies for convergence that relate to “bestpractices”, innovation and collaborative action in methods and technologies embodied in seeds,

42.4.4  Integrating the national and global food chains through later-stage development pathways

42.4.5  Policy, innovation and financial levers for convergence along the local and global food chains In making improved policy choices and better managing the food system, attention to the political drivers of policy change is necessary.

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What are the incentives for a government official in Kenya or Vietnam to bring about the policy changes that the international commun­ ity collectively considers to be the appropriate ones? For example, many of the countries of eastern and southern Africa are in the pro­ cess of updating or establishing national plans of action and/or national policies for nutrition and food security (some of the “push” comes from the NEPAD activity, AGRA and other large-scale initiatives aimed at finally resolving Africa’s underlying vulnerability to food constraints). Most represent sound syntheses of existing areas of consensus, taking stock of advances in nutrition over the past 20 years. However, few are genuinely forward looking. While future public health is an important basis on which to judge policy priorities, domestic support for changes to agricultural systems, tariff structures on food trade, food safety systems, and welfare/social safety net support all matter in determining the “policy space” available to making changes towards a more “healthy” food system. That is, there are many entrenched interest groups even within the public policy sphere (let alone in the private sector) which determine the parameters of the possible when it comes to making what are significant shifts in national priorities. What, then, are the lessons of political economy for shepherding decisions for the public good, when existing incentive structures seem to be stacked in favor of private goods and benefits? National and global public policies and investments can help create an enabling environment that facilitates a transformation of smallholder agriculture and an opening on the world’s markets and societies that support convergence between health, development and economic considerations. This may be possible through infrastructure investment, institutional reform, and regulations that reflect appropriate trade-offs between nutrition, agriculture and agri-food priorities. This may also be possible

through programs that equip small farmers and other actors in the food systems with appropriate technology and knowledge that make them able to face the requirements of the changing market conditions in a manner that contributes to economic development and healthy food and diets for all. Also needing examination are public sector interventions that would build on existing regulatory (e.g., Codex Alimentarius) or strategic (WHO Global Strategy on Diet, Physical Activity and Health) initiatives to promote the joint development, with local and global private sector and civil society organizations, of an actionable system of codes of conduct or guiding principles, norms, and/or standards that will place consideration of food safety, quality and security on the agenda of all actors in the local and global food chains in order to make healthy foods and diets the easy choice for consumers.

42.5  Conclusion In sum, the Green Revolution 2.5 model has the potential to scale up the Green Revolution 2.0 and adapt it to twenty-first century realities. It can harness the power of business and communities, in line with but going beyond government policies and investments, to mobilize the resources needed to support smallholders worldwide and empower them to become key actors in agricultural, industrial and social development progresses.

References The Economist. (2008). The silent tsunami: The food crisis and how to solve it. The Economist, April. Hawkes, C., Blouin, C., Henson, S., Drager, N., & Dubé, L. (2010). Trade, food, diet and health: Perspectives and policy options. London: Wiley-Blackwell. World Bank. (2005). World Development Indicators database. Online. Available: http://devdata.worldbank.org/ wdi2005/Section2.htm.

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43 How High-level Consumer Research can Create Low-caloric, Pleasurable Food Concepts, Products and Packages* Howard R. Moskowitz and Michele Reisner Moskowitz Jacobs Inc., White Plains, NY, USA

o u tl i n e 43.1  Introduction

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43.2 Where Did This Systematic, RDE Approach Come From?

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43.3 Designing the Product and Communicating it 530 43.3.1 What Works – How Do Companies Create Product Concepts? 530

43.1  Introduction C   onsumer research is divided into two assess­ ments: in the early stage, the developer and mar­ keter create the basic idea of a product (or service), and in the second stage the product’s physical

43.3.2 Creating a Framework for Knowledge-Driven Development Using Rule Developing Experimentation 531 43.3.3 Running the Basic Ice Cream Study 531 Acknowledgments

541

characteristics, packaging and communication are  developed. This chapter presents ways to dev­ elop product ideas for low-caloric, pleasur­ able products, packages and communications using structured research or RDE (rule develop­ ing experimentation) (Moskowitz and Gofman,

*Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Ideamap® and Ideamap®.net are registered trademarks of Luxton Enterprises. US patent 6,662,215. Other patents pending. Moskowitz Jacobs Inc. ©2006. All rights reserved. 2002 Crave it!® database courtesy of It! Ventures.

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43.  Pleasurable Food Concepts, products and packages

2007). The key to the approach is the experimental design, an “architecture” of the stimulus, popu­ lated with options, creation of test combinations, consumer evaluation and identification of “what works” and “with whom”. Systematic approaches, such as conjoint analysis, are now used by con­ cept and product developers worldwide (Box  et al., 1978; Cornell, 1981; Wittink and Cattin, 1989; Wittink et al., 1994; Moskowitz et al., 2005a). This chapter shows how systematic approaches identify “what works” for foods, using ice cream as an example. These approaches, which can be used to create concepts for low-caloric, pleasur­ able foods, cut across the different corporate functions involved in a new product – R&D func­ tion (product development), marketing (commu­ nication, advertising) and packaging.

43.2  Where did this systematic, RDE approach come from? In the early to middle twentieth century, statisticians showed developers and consumer researchers ways to increase the likelihood of success in creating successful foods and bever­ ages. Conventional scientific methods, which seek to understand the consumer’s mind, are dif­ ficult to apply in a real setting, where time and budgets are short, and actionable results must be presented quickly. Using systematic response, pairs of ingredients, messages and package fea­ tures can interact with each other, so that the response, jointly determined by several variables acting in concert, often identifies “what works”. While this method does not reveal the underly­ ing mechanisms, it does provide a database that can be interrogated to identify winning ideas or to suggest research problems for the basic scien­ tist. Nonetheless, knowing that some ingredients or messages drive acceptance, the food scientist or researcher can use the data to produce better products or concepts.

43.3  Designing the product and communicating it Product ideas come in two types: (1) product concepts – details about the product’s appear­ ance, aroma, taste/flavor, texture, ingredients, packaging, etc.; and (2) positioning concepts –   motivational statements that tell consumers why they should buy/use the product. In designing foods, we begin with product concepts. Figure 43.1 illustrates a product con­ cept (left panel) and a positioning concept (right panel) for ice cream. Most concepts comprise some part product concept and some part posi­ tioning concept, or procepts.

43.3.1  What works – how do companies create product concepts? Product concepts can be created by look­ ing at competitors’ or one’s own products and re-engineering them in a concept format with a twist in flavor, ingredient, etc. Companies do this because it is easy and risk-free, but it does not always generate winning ideas. An effective way to create product concepts is to screen ideas through promise or benefits testing. Consumer researchers use this approach to identify win­ ning ideas, which can be combined into new concepts. The Internet is a test venue that allows Examples of ice cream concepts Product concept

Compact containers Made with pure ingredients Real, natural flavorings Premium, full-flavored 210 calories per serving

Positioning concept Compact containers Pure ingredients All natural, not artificial Indulge yourself Brought to you by one of America’s oldestic cream producers

Figure 43.1  Product versus positioning concepts for ice cream. Source: Moskowitz et al. (2009).

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43.3 Designing the product and communicating it

the researcher to execute such tests quickly and cheaply. These tests provide randomization to eliminate bias and generate a ranking of bene­ fits (Dahan and Srinivasan, 2000). Many researchers skip benefits screening and move directly to the evaluation of complete  concepts/ideas. Traditionally, these were created by advertising agencies, marketing or product developers and evaluated by respondents in socalled “screening tests” or “ConScreen”. A typi­ cal ConScreen exercise involves 5, 10 or even  20 concepts, rough “stabs” at product ideas. Good concept performers become the raw mate­ rial for the next stage of development. They are gathered and maintained in normative data­ bases which give a sense of how well a new concept performs versus an already-tested one. However, the databases do not tell corporate clients how to create winning product concepts.

43.3.2  Creating a framework for knowledge-driven development using rule developing experimentation Beginning in the 1950s and 1960s, rule devel­ oping experimentation (RDE) allowed mathe­ matical psychologists/researchers to prepare the ground through a process of systematic inves­ tigation of how people can act as measuring instruments. A key issue was how they could measure consumers’ reactions to systematic combinations of concepts/ideas. This was the origin of the experimental design of concepts. When applied to food concepts, RDE adapts many procedures from various approaches. Consumers face many choices in each food product category, and a product’s unique quali­ ties may be lost among the clutter. This is a major challenge for a manufacturer trying to differentiate an item. Properly designing the product, its package, contents and messaging (health benefits, low caloric content, etc.) can help the product to sell in an increasingly com­ petitive environment.

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Packaging rules and guidelines vary, with many levels of government having jurisdiction over what claims and information are allowed/ required to be placed on labels. It are not within the scope of this chapter to describe the specific guidelines that package designers have to fol­ low (for a thorough review, see Moskowitz et al., 2009). Rather, we wondered if we could deter­ mine what would work within these frameworks. To demonstrate the systematic approach, this chapter will focus on ice cream. The global ice cream market is estimated to be worth US $40 billion, with US citizens being the largest per capita consumers (Faron, 2008). An indulgent food, it is an ideal product for testing health- and nutrition-based messages. Researchers (BechLarsen and Grunert, 2003) found that consum­ ers react differently to health-based messages depending upon whether the base product is perceived as healthy or unhealthy. Health mes­ sages might perform better on “unhealthy” foods than on “healthy” ones (Kähkönen et al., 1997). The objective was to see how differing types of health- and nutrition-type ice cream package messages drive consumer responses. Do responses vary when the nutritional information panel “facts” change?

43.3.3  Running the basic ice cream study The different steps, as applied to ice cream, can be summarized as follows. Creating a basic structure of the concept The concept comprises three different silos (features) that typify standard ice cream packages: 1. Silo 1 Brand Name. We chose five brands (Breyers®, Dreyer’s, Häagen-Dazs®, Ben & Jerry’s, and Stonyfield Farm®) to represent a range of existing product qualities/prices available in the US market (Figure 43.2).

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43.  Pleasurable Food Concepts, products and packages

Figure 43.2  Brand logos for the five ice cream brands. Source: Moskowitz et al. (2009).

Nutrition Facts

Nutrition Facts

Nutrition Facts

Nutrition Facts

Serving Size ½ cup Servings Per Container 14

Serving Size ½ cup Servings Per Container 14

Serving Size ½ cup Servings Per Container 14

Serving Size ½ cup Servings Per Container 14

Amount Per Serving Calories 140 Calories from Fat 65

Amount Per Serving Calories 110 Calories from Fat 30

Amount Per Serving Calories 65 Calories from Fat 30

Amount Per Serving Calories 90 Calories from Fat 15

Total Fat 7g Saturated Fat 4.5g Trans Fat 0g Cholesterol 20mg Sodium 40mg Total Carbohydrate 16g Dietary Fiber 0g Sugars 16g Protein 3g Vitamin A 10% Vitamin C 0%

% Daily Value* 11% 23% 7% 2% 5% 0% 6% Calcium 10% Iron 0%

*Percent Daily Values are based on a 2,000 calorie diet.

Total Fat 3.5g Saturated Fat 2g Trans Fat 0g Cholesterol 10mg Sodium 50mg Total Carbohydrate 16g Dietary Fiber 0g Sugars 16g Protein 3g Vitamin A 10% Vitamin C 0%

% Daily Value* 5% 10% 3% 2% 5% 0% 6% Calcium 10% Iron 0%

*Percent Daily Values are based on a 2,000 calorie diet.

Total Fat 3g Saturated Fat 2g Trans Fat 0g Cholesterol 10mg Sodium 50mg Total Carbohydrate 6.5g Dietary Fiber 0g Sugars 6.5g Protein 3g Vitamin A 10% Vitamin C 0%

% Daily Value* 5% 10% 3% 2% 2% 0% 6% Calcium 10% Iron 0%

*Percent Daily Values are based on a 2,000 calorie diet.

Total Fat 1.5g Saturated Fat 1g Trans Fat 0g Cholesterol 10mg Sodium 50mg Total Carbohydrate 16g Dietary Fiber 0g Sugars 16g Protein 3g Vitamin A 10% Vitamin C 0%

% Daily Value* 2% 5% 3% 2% 5% 0% 6% Calcium 10% Iron 0%

*Percent Daily Values are based on a 2,000 calorie diet.

Figure 43.3  Different nutritional label for 140-Calorie Original. Source: Moskowitz et al. (2009).

2. Silo 2 Type of Ice Cream. We selected   original, low-fat, etc., to represent the   range of standard products and included the nutritional health facts panel (Figures 43.3, 43.4). Original Ice Cream is the basic ice cream product. Organic Ice Cream is a healthful production method. Light Ice Cream is an example of a nutritional content claim   panel based on the US FDA definition of nutrient content claim “Light” (21 CFR 101.60[b]). Reduced Calorie Ice Cream is an example of a nutritional content claim panel based on the US FDA definition of nutrient content claim “Reduced Calorie” (21 CFR 101 60[b]). Heart Smart Ice Cream is an example of a nutritional content claim   panel based on the US FDA definition of

health claim “plant sterol/stanol esters and risk of coronary heart disease” (21 CFR 101.83). 3. Silo 3 Identification of Flavor with Visual. We chose flavors representative of the three most popular single flavors (vanilla, strawberry, and chocolate) and two of the many available flavor combinations (strawberry–banana and vanilla–chocolate chip) (Figure 43.5). A fourth feature was Total Calorie Content. We divided the ice cream evaluation into two stud­ ies which separated the two calorie levels (140 and 210). Study 1 was reduced-fat/calorie and Study 2 was more traditional, using nutritional facts panels for the ice cream types, including

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43.3 Designing the product and communicating it

“Original” & “Organic”

“Light”

“Reduced Calorie”

“Heart Smart”

Nutrition Facts

Nutrition Facts

Nutrition Facts

Nutrition Facts

Serving Size ½ cup Servings Per Container 14

Serving Size ½ cup Servings Per Container 14

Serving Size ½ cup Servings Per Container 14

Serving Size ½ cup Servings Per Container 14

Amount Per Serving Calories 210 Calories from Fat 125

Amount Per Serving Calories 145 Calories from Fat 65

Amount Per Serving Calories 95 Calories from Fat 55

Amount Per Serving Calories 100 Calories from Fat 15

Total Fat 14g Saturated Fat 10g Trans Fat 0g Cholesterol 50mg Sodium 50mg Total Carbohydrate 18g Dietary Fiber 0g Sugars 18g Protein 3g Vitamin A 10% Vitamin C 0%

% Daily Value* 22% 50% 17% 2% 6% 0% 6% Calcium 10% Iron 0%

*Percent Daily Values are based on a 2,000 calorie diet.

Total Fat 7g Saturated Fat 5g Trans Fat 0g Cholesterol 25mg Sodium 50mg Total Carbohydrate 18g Dietary Fiber 0g Sugars 18g Protein 3g Vitamin A 10% Vitamin C 0%

% Daily Value* 11% 25% 8% 2% 6% 0% 6% Calcium 10% Iron 0%

*Percent Daily Values are based on a 2,000 calorie diet.

Total Fat 6g Saturated Fat 4g Trans Fat 0g Cholesterol 20mg Sodium 50mg Total Carbohydrate 7g Dietary Fiber 0g Sugars 7g Protein 3g Vitamin A 10% Vitamin C 0%

% Daily Value* 9% 20% 7% 2% 2% 0% 6% Calcium 10% Iron 0%

*Percent Daily Values are based on a 2,000 calorie diet.

Total Fat 1.5g Saturated Fat 1g Trans Fat 0g Cholesterol 10mg Sodium 50mg Total Carbohydrate 18g Dietary Fiber 0g Sugars 18g Protein 3g Vitamin A 10% Vitamin C 0%

% Daily Value* 2% 5% 3% 2% 6% 0% 6% Calcium 10% Iron 0%

*Percent Daily Values are based on a 2,000 calorie diet.

Figure 43.4  Different nutritional label for 210-Calorie Original. Source: Moskowitz et al. (2009).

Vanilla

Strawberry

Chocolate

Strawberry Banana

Vanilla Chocolate Chip

Figure 43.5  The five different flavors and pictures. Source: Moskowitz et al. (2009).

organic, light, reduced calorie and heart-smart, changing appropriately for 140-calorie original and 210-original messages.

invitation text in Figure 43.6, with changes in information, can be used for future studies as well.

Recruiting study participants

Respondents clicked on invitation links to begin the study, and were led to the welcome page (Figure 43.7), which did not tell them what to expect in terms of the appropriate answer for any test package design. All the respond­ ent knew was that there would be a 15-minute interview to evaluate a new set of ice cream package labels, on two scales.

Study welcome page

We recruited participants by e-mail invitation to join a randomly allocated study. Invitations for the studies were identical, and did not men­ tion the specific study content. Well-written invi­ tations increased the likelihood of acceptance: they must have an engaging, respectful opening, and a legitimizing reason why the study is being done. The survey promised a possible reward, which attracts and helps retain respondents (see The actual evaluations Moskowitz and Martin (2008) for guidelines  Respondents evaluated 35 different combi­ on improving e-mail survey invitations). The nations. They first chose the appropriate scale

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43.  Pleasurable Food Concepts, products and packages

i-Novation Inc. I-Novation, an independent research company, has been asked to find out what consumers like YOU think about different types of ice cream. Your opinions are very important and will help us design the next generation of ice cream products. Here’s your chance to tell us what you think! Simply click on the link below (if your email does not support hotlinks, cut and paste the link into your browser) and complete the short, easy-to-answer survey. http://12.109.160.59/NJL734/NJL7344091 Front.asp Depending on your connection speed, the survey should take about 15 minutes to complete. As our way of saying “Thank You” for your input, everyone who completes the survey before 9 PM Eastern Time on Thursday, April 27th, will be entered in a prize drawing featuring a first prize of $100 and a second prize of $50. Because this is a Web-based survey, you will be able to take it when and wherever is most convenient for you, as long as you have access to a Windows-based computer with an Internet connection. Unfortunately, the survey software will not support Mac or Web TV. Please be assured that any information you provide will be held in the strictest confidence. You will not be contacted by any sales or other research organization as a result of your participation in this survey. Thanks in advance for your input, and good luck! The I-Novation team. We respect your privacy. If you feel that you received this message in error, or no longer wish to receive invitations to participate in market research surveys from our company, please click on the “Unsubscribe” link. I-Navation, 1025 Westchester Avenue, Suite 444,White Plains, NY 10604 Copyright  2004-ACME Publishing Inc.

Figure 43.6  Text of email invitation for the ice cream studies. Source: Moskowitz et al. (2009).

point for evaluation of purchase intent, getting overall interest but not why. Then, they chose the appropriate point on another scale for description of indulgence–healthfulness, which got at the nature of the “type” of product being communicated. Researchers get more data with many pack­ age designs, varied systematically, as well as with a number of rating questions. However, they are cautious about asking one respond­ ent questions about many systematically var­ ied stimuli. After two or three ratings of the same package on different scales, a normal respondent stops paying attention, UNLESS the respondent is paid and highly motivated!

The respondent experience Each respondent saw different combinations  of package designs. Repeated evaluations en­sured a good, reliable measure of the mean and topthree box values. If there was a hidden bias  in the combinations, we propagated it across all the respondents. By giving each person different combinations of the same elements and unique sets of pictures, we minimized this bias. The screens were followed by a self-profiling classification. It told us about each respondent, from standard demographics (age, gender, etc.), to his or her attitudes toward ice cream and health. The study was modestly time-consuming,  and took about 20 minutes to complete.

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43.3 Designing the product and communicating it

535

Welcome to ice cream survey. in this survey, we are going to evaluate Ice Cream for different groups of people. The following rating question will be asked for Calories and Flavors of each Ice Cream Concept seen. you will be using a 1–9 rating scale: Rating 1 How interested are you in this container of Ice Cream? 1 = Not at interested...9 = Very interested Rating 2 How would you DESCRIBE this ice cream? 1 = Very indulgent...9 = Very Healthful Below is example of what you might see:

140 Calories only Rich chocolate flavor Creamy

Figure 43.7  Welcome page for the ice cream study. Source: Moskowitz et al. (2009).

Looking at the data and what we learn about ice cream We dealt with the data in a number of differ­ ent ways, changing the topics and questions. We looked at aspects other than the ice cream and ice cream label data alone. The experimental designs were rich with information about people. Who logs in and how many complete? If a topic interested respondents, we expected to see a high number of log-ins and completion rate. Study 1 had 278 total log-ins and 199 completes,

a completion rate of 72 percent. Study 2 had 301 total log-ins and 205 completes, a completion rate of 68 percent. Therefore, 70 percent of the respondents who started the study completed it. Ice cream was clearly a popular topic. Once respondents agreed to participate in the ice cream study, the content of the elements held interest for the majority of them. Let us put this into perspective with some other data. Maier and colleagues reported completion rates of 50 percent (fruit smoothie), 46 percent (flavored water), 55 percent (yogurt beverage) and 47 percent (flavored tequila) for similar types of conjoint analy­ sis studies (Rabino et al., 2007; Maier et al., 2008).

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43.  Pleasurable Food Concepts, products and packages

Food Chocolate Candy Cheesecake French Fries Tortilla Chips Cinnamon Rolls Taco Potato Chips Olives Gravy Pretzels Ice Cream Cola Cheese Peanut Butter Coffee Nuts Pizza BBQ Ribs Chicken Hamburger Steak

% Female 86% 84% 81% 80% 80% 79% 76% 76% 76% 75% 74% 74% 73% 69% 69% 67% 67% 62% 62% 60% 56%

At the individual level, we began with 15 independent variables corresponding to the graphical 15 elements (3 silos, 5 elements per silo) and 35 cases (35 test concepts or graphics combinations). For each respondent, ordinary least-squares regression analysis created an indi­ vidual model showing the contribution of each ice cream element to the binary rating of not interested or interested (Fox, 1997; SYSTAT, 2004). The model equation is the sum of the additive constant (k0) and the part-worth combinations of the 15 elements, Element 1 to Element 15.



We can see the parameters of this model in Figure 43.9. Baseline results for ice cream

What do we learn about interest in ice cream?

The additive constant (k0) told us the esti­ mated probability that our concept about ice cream (our package design) was interesting (i.e., rated 7, 8 or 9 on the 9-point scale), if no ele­ ments were present. All concepts in the study comprised elements; no concept comprised zero elements. Our additive constant was a con­ struct or estimated parameter that told us the respondent’s predisposition to being interested in buying the ice cream. Previous packages and communications de­signs, using this specific type of experimental  design (main effects, with categories absent) have generated very powerful, databasable and comparable values of the constant (Moskowitz et al., 2005a). Some rules that provided data interpretation structure included:

Our analysis focused on measures of inter­ est or membership in the group of respondents interested in the ice cream, based on what they saw. We used the binary transformation, where a rating of 7–9 for purchase was transformed to 100, and ratings of 1–6 for purchase were trans­ formed to 0.

1. Constants above 50 represented a high degree of basic interest; the respondent really liked the packages, in general, and was predisposed to buy. 2. Constants lower than 30 represented a low degree of basic interest, so that, on average, the respondent did not like what he or she saw.

Figure 43.8  Percent female respondents for concept studies, using Ideamap.net®. Source: Data courtesy of IT! Ventures, and taken from the 2002 Crave IT!® database.

In these studies, typically more women than men participate. Females comprised 74 percent of respondents in the reduced calorie study and 81 percent in the full-calorie study. The ratio of women to men in the study changed, depend­ ing upon the food. Figure 43.8 gives a sense of the proportion of women to men in the It! Studies (Beckley and Moskowitz, 2002).

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43.3 Designing the product and communicating it

Indulgent vs

140

Health

Indulgent vs

210

Health

Purchase

Purchase 140

210

Calories Calories Calories Calories Base Size

199

205

Additive Constant (K0)

25

22

44

45

Häagen-Dazs

4

2

0

0

Ben & Jerry’ s

3

3

0

0

Beryers

3

2

0

0

Dreyers

2

1

0

0

Stonyfield Farm

1

0

2

0

6

12

18

17

4

16

20

23

Ice cream with nutritional facts panel

–1

–4

–2

–8

Light Ice cream with nutritional facts panel

–3

0

12

13

Organic ice cream with nutrition facts panel

11

–12

3

–3

Strawberry flavor

6

5

2

–1

Vanilla chocolate chip

6

8

0

–1

Strawberry banana flavor

5

6

0

–1

Silo A: Brand

Silo B: Line (type of product) Reduced calorie ice cream and nutritional facts panel Heart Smart ice cream with nutritional facts panel

Silo C: Flavor

4 top 3 box 1 purchase–2 Chocolate flavor Figure 43.9  Base size, additive constant and utility values4for percent interest (1–6  0; 7–9  100), and for indulgent (numbers) vs healthful (numbers) for ice cream positioned as having 140 calories or 210 calories. 3 0 2 –1 Vanilla flavor Source: Moskowitz et al. (2009).

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43.  Pleasurable Food Concepts, products and packages

3. Constants around 20 or lower represented a very low degree of basic interest, with the product or package representing almost a commodity.

something to the imagination. This theory has been tested in studies ranging from fragrance to teas (Moskowitz et al., 2005b).

Ice cream scored a 25 on the purchase scale for ice creams positioned at 140 calories and 22 for those having 210 calories. Knowing that people like ice cream, we asked why the addi­ tive constant for package designs scored so low. Did we have the wrong group of respondents, or did we select poor graphics to show? The wrong group of respondents would drop the constant because they might not like ice cream. As Figure 43.10 shows, other concept studies with words and pictures conveying ideas about products generate higher values for the addi­ tive constant (Beckley and Moskowitz, 2002). Indeed, for ice cream the additive constant is a much-higher value: 39. A more likely explanation is that graphics design may generate a lower constant because we are dealing with pictures, not with ideas. Pictures have to be more concrete and are more subject to “immediacy”, whereas ideas can be supported with less information and leave

What design features drive purchase interest?

Item BBQRibs

Con

Item

Con

44 Ice Cream

Item

Con

39 Pizza

29

38 Pretzel

29

Pearut Steak

41 Butter Cinnamon

Tortilla

Potato Chips

41 Rolls

38 Chips

Chicken

40 Cola

37

Coffee Chocolate

40 Cheese

36

Candy

40 Nuts

36

Cheesecake

40 Olives Tacos

36

29

35

French Fries

35

Gravy

33

Hamburger

31

Figure 43.10  Additive constant (con) for different food studies when the test stimuli are word concepts. Source: Data courtesy of IT! Ventures, and taken from the 2002 Crave IT!® database.

The coefficients for our 15 elements in 3 silos told it all. In Figure 43.9, the first data column pertains to impact/contributions of the ele­ ments when the ice cream was positioned at the low (140)-calorie level, and the second data col­ umn pertains to impact when the ice cream was positioned at the high (210)-calorie level. Statistical design enabled our researchers to compare data across elements, the three cat­ egories, and the two product positions. They drew conclusions about how well brands per­ formed in two different positioning conditions, as well as how much negative brand names can counteract. The coefficients showed the contribution of the element (positive or negative) to purchase interest (the response). Positive coefficients indi­ cated that when the element was present in the concept, the probability that a person was inter­ ested in the concept increased. For example, when a coefficient was 10, it indicated that an additional 10 percent of the respondents said that they would buy the product. Some rules of thumb for the data include: 1. Utility above 15 corresponds to extremely impactful and important elements. 2. Utility between 10 and 15 corresponds to very impactful elements. 3. Utility between 5 and 10 corresponds to impactful elements. 4. Utility between 0 and 5 means that the element adds little to interest. 5. Utility below 0 means that the element detracts from interest and should be avoided. Let us see how the three categories of five ele­ ments performed, and whether the number of calories in the ice cream affected what won and

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43.3 Designing the product and communicating it

539

what lost. With these types of data, one might  simple versus mixed flavors seemed to do try to draw a single coherent “picture”, which equally well. There was a slight hint that might, but often does not, lead to success. flavors would do slightly better in higherAnother, more productive way includes obser­ calorie ice cream. vations of what is happening and synthesizes 5. The simple experimental design of what might be occurring. Let us try this second packages with health information and enumeration strategy, listing some of our obser­ positioning generated significant amounts vations to see what emerged. of information, even for a simple, overall evaluative attribute “purchase intent”. 1. The brands did not do much. They all had utilities around 0–4, which means that What design features communicate when brand name (and logo) came into healthful and indulgence? the package concept, no more than about 4 percent of the respondents shifted their Recall that the respondents evaluated each vote from would not buy to buy. However, no of the 35 packages on another rating scale: How brands were negative. Ice cream brands did would you DESCRIBE this ice cream? 1  Very not do much good, but did not do much bad indulgent … 9  Very healthful. “Purchase/noneither! We often think of brands as carrying purchase” was not used for the second rat­ the product, which they do not, even when ing scale, because we needed the whole scale. the logo is clearly on the package. Our interest was in what the package design  2. We saw a range in the specific type of communicated – how much indulgence or how product (i.e., the line). The 140-calorie much healthfulness, not good or bad. ice cream rated a 6 when the package We were interested in where the package fell. said “Reduced calorie ice cream and features We were interested in nuances; if we wanted to a nutritional facts panel”. The 210-calorie “move the package” to communicate a bit more “reduced calorie ice cream and features health or more indulgence, we needed to use a nutritional facts panel” convinced an the scale itself, not the binary equivalent. additional 12 percent of respondents to However, we also wanted to stay with the say that they would buy the ice cream. Are 0–100 thinking, which was easy to use. We did consumers more responsive to claims of an affine transformation (Zwillinger, 1995) general calorie reduction when dealing with of the data before analysis: 1 transforms to 0;  a higher-calorie product? 2 transforms to 12.5; and 3 transforms to 25, etc. 3. The line can make a difference when we Affine transformation preserves the original introduce specific health benefits. Talking information acquired using the 9-point scale, about “Heart-smart ice cream” did a lot but stretches the scale, changing the origin. The more with 210 calories than with 140. When original 1-point difference on the 9-point healththe calories were high, health messages indulgence scale transformed to a 12.5-point did far better than when the product was difference. The origin of the scale at point 1 was perceived to be healthier in general. We now at point 0. can only speculate about the effect of such Let us look at the results of the ice cream study, statements in the super-premium category. concentrating on the communications. Low addi­ 4. Flavors were important. Although vanilla tive constants (below 50) meant indulgence; high is perhaps the world’s most popular flavor, constants meant health. Negative utilities pushed it was the weakest performing, reasonably the package towards communicating indulgence; acceptable to everyone. The strength of positive utilities communicated health. There may

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43.  Pleasurable Food Concepts, products and packages

be some overt health messages or indulgence mes­ sages. Those “blunt statements” should come out as very strong communication drivers. The com­ munication might be subtle, not direct, but was always in the mind of the respondent. Instead of thinking of large-scale, over-riding patterns, we found specific, noteworthy points. Refer back to Figure 43.9 and look at the data columns on the right, which showed the results for the indulgent versus healthful scale. 1. The additive constants were 44 for 140 calories and 45 for 210, both below 50. The fact that the constants were low was not surprising; after all, we were dealing with ice cream, and additive constants below 50 mean indulgence. 2. The five brands communicated neither indulgence nor health; they were all around 0. 3. The line clearly communicated. It was evident that respondents picked up what the label was saying. For example, when the label said “heart smart”, the communication jumped far over to healthful. 4. Calories modified the impact of communication. “Heart smart” was more powerful with 210 calories than with 140 calories. Ice cream was more indulgent at 210 calories compared with 140 calories. 5. Flavors did not drive communication. For 140 calories, utilities were between 0 and 2, while flavors in the context of 210 calories were negative and a little more towards the indulgent side.

individual model or pattern for purchase intent for the 15 utilities that we created. The segmenta­ tion method has been shown to drive new product opportunities (Moskowitz et al., 1985; Moskowitz, 1994). (For more information about segmentation and mind-set, refer to Moskowitz et al., 2006.) How we get there Originally, we were interested in whether a person would buy or not buy the ice cream, so we transformed the rating to a binary value  (1–6 transforms to 0; 7–9 transforms to 100). We ran the regression model on original data and got a set of utility values (persuasion values), which we used to cluster the respondents: Analysis 1 (INTEREST) – What drove purchase interest? We transformed the ratings to 0 or 100, placing people into the category of would buy (100) or would not buy (0). We found the key drivers we have been concentrating on. This was the Interest analysis. l Analysis 2 (COMMUNICATION) – What drove indulgent versus healthful? We transformed the 1–9 to 0–100, but kept   9 rating points. This was the communication analysis. l Analysis 3 (PERSUASION, used only for clustering or segmentation) – placing people into different groups or clusters or segments, based on the patterns of their utilities. We transformed the data, keeping 9 rating points. We ran the purchase intent model and kept the fine-grained results because we had metric information about purchase intent. l

Overall, experimental designs of packages can generate differences in how they are per­ ceived, not just how they are liked. Bipolar  rating scales, allowing the respondent to show how the product communicates, do well in What did we find when we divided our respondents? picking up these differences. We ran our segmentation twice, once for Study 1 data on 140 calories, and again for Study Using the sensory-liking curve to identify 2 on 210. If the two segmentations came up with sensory preference segments the same set of segments, then we could be sure Because people differ from each other,  that we were onto a strong way to divide the we can divide them, or segment them, by the people.

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REFERENCES

Our analysis suggested two key mind-sets in the population: 1. Segment 1 – Health Seekers. 140-calorie products: Segment 1, 51 percent of our respondents, were health seekers (our term) and interested in ice cream lines (elements in Silo 2): reduced calorie ice cream, heart-smart ice cream, and light ice cream (all with nutritional facts panels). Health seekers were least interested in flavor (Silo 3), including strawberry, strawberry banana, and chocolate. 210-calorie products: Almost the same proportion (54%), were health seekers interested in ice cream lines and least interested in flavor (Silo 3) including strawberry banana, strawberry, and vanilla. 2. Segment 2 – Flavor Seekers. 140-calorie products: Flavor seekers were most interested in Silo 3, specifically strawberry and vanilla chocolate chip. They were uninterested in ice cream lines (Silo 2) including the health messages for organic, light, heart-smart, and reduced calorie ice cream. 210 calorie products: Flavor seekers were most interested in Silo 3 (flavors), specifically strawberry banana, strawberry, and vanilla chocolate chip. Respondents in this group were uninterested in ice cream lines (Silo 2). The same segmentation occurred, with the same winning and losing elements reappearing. This gave us confidence that the segmentation was real, and independent of the basic calorie level of the ice cream. An overview Experimental design of packages worked for ice cream. It differentiated messages, and showed that textual messages with “content” made a dif­ ference. It has not always been clear whether the information on a package, or the logo and brand,

influenced respondents. We witnessed firsthand that information was key, at least in these two studies. The segmentation results were also worth noting, and should not be surprising. What was surprising was the division of the popula­ tion into two equal groups, independent of the calorie level of the ice cream, and the fact that almost the same exact elements did well or poorly in each.

Acknowledgments The authors wish to thank Linda Lieberman, Editorial Coordinator, Moskowitz Jacobs Inc., for editing and preparing this chapter for publication.

References Bech-Larsen, T., & Grunert, K. G. (2003). The perceived healthiness of functional foods: A conjoint study of Danish, Finnish and American consumers’ perception of functional foods. Appetite, 40, 9–15. Beckley, J., & Moskowitz, H. (2002). Databasing the consumer mind: The Crave It!™, Drink It!™, Buy It! ™, and Healthy You! ™ databases. Anaheim, CA: Institute of Food Technologists. Box, G. E. P., Hunter, J., & Hunter, S. (1978). Statistics for experimenters. New York, NY: John Wiley & Sons. Cornell, J. A. (1981). Experiments with mixtures. New York, NY: John Wiley & Sons. Dahan, E., & Srinivasan, V. (2000). The predictive power of internet-based concept testing using visual depiction and animation. Journal of Product Innovation Management, 17(2), 99–109. Fox, J. (1997). Applied regression analysis, linear models, and related methods. Thousand Oaks, CA: Sage Publications, Inc. Kähkönen, P., Tuorila, H., & Lawless, H. (1997). Lack of effect of taste and nutrition claims on sensory and hedonic responses to a fat-free yogurt. Food Quality & Preference, 8, 125–130. Maier, A., Moskowitz, H., & Ashman, H. (2008). Comparing across categories: Using mind-set information about health, pleasure, and function to understand consumer choices in bottled water. Bentham Open Food Science Journal, 2, 131–150.

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Moskowitz, H. R. (1994). Food concepts and products. Just in time development. Trumbull, CT: Food and Nutrition Press. Moskowitz, H., & Gofman, A. (2007). Selling blue elephants: How to make great products that people want before they even know they want them. Upper Saddle River, NJ: Wharton School Publishing. Moskowitz, H. R., & Martin, B. (2008). Optimising the language of e-mail survey invitations. International Journal of Market Research, 50(4), 491–510. Moskowitz, H. R., Jacobs, B. E., & Lazar, N. (1985). Product response segmentation and the analysis of individual differences in liking. Journal of Food Quality, 8, 168. Moskowitz, H. R., Porretta, S., & Silcher, M. (2005a). Concept research in food product design and development. Oxford: Blackwell Publishers. Moskowitz, H. R., Gupton, A., & Beckley, J. H. (2005b). Capturing the algebra of the customer’s mind for func­ tional fragrance through pictures, text and decomposi­ tional research analyses. In Proceedings of the European Society for Opinion and Marketing Research (ESOMAR) conference – Fragrance research: Unlocking the sensory experience. New York, NY: ESOMAR.

Moskowitz, H. R., Gofman, A., Beckley, J., & Ashman, H.   (2006). Founding a new science: Mind genomics. Journal of Sensory Studies, 21, 266–307. Moskowitz, H., Reisner, M., Lawlor, J. B., & Deliza, R. (2009). Understanding nutritional labeling: Case study: Ice cream. In H. Moskowitz, M. Reisner, J. B. Lawlor, & R. Deliza (Eds.), Packaging research in food product design and development. Ames, IA: Wiley-Blackwell. Rabino, S., Moskowitz, H., Katz, R., Maier, A., Paulus, K., Aarts, P., et al. (2007). Creating databases from crossnational comparisons of food mind-sets. Journal of Sensory Studies, 22(5), 550–586. SYSTAT. (2004). SYSTAT for Windows, version 11. Chicago, IL: SYSTAT Software, Inc. Wittink, D. R., & Cattin, P. (1989). Commercial use of con­ joint analysis: An update. Journal of Marketing, 53, 91–96. Wittink, D. R., Vriens, M., & Burhenne, W. (1994). Commercial use of conjoint analysis in Europe: Results and critical reflections. International Journal of Research in Marketing, 11, S41–S52. Zwillinger, D. (Ed.). (1995). Affine transformations. §4.3.2 in CRC standard mathematical tables and formulae. Boca Raton, FL: CRC Press (pp. 265–266).

2.  From society to Behavior: policy and action

C H A P T E R

44 Reductions in Dietary Energy Density to Moderate Children’s Energy Intake Barbara J. Rolls and Kathleen E. Leahy Department of Nutritional Sciences, Pennsylvania State University, PA, USA

o u t l i n e 44.1 Introduction

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44.2 What is Energy Density?

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44.3 Why is Energy Density Important?

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44.4 Does Energy Density Influence Energy Intake?

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44.5 Practical Strategies to Reduce Energy Density

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44.1  Introduction Several major health organizations have recently made recommendations concerning the prevention of childhood overweight (WHO, 2003; Committee on Prevention of Obesity in Children and Youth, 2005; Barlow, 2007). One strategy advised is to change children’s eating patterns in order to modify dietary energy density

Obesity Prevention: The Role of Brain and Society on Individual Behavior

44.6 Will Reducing the Energy Density of the Diet Benefit Every Child? 550 44.7 Future Directions in Energy Density Research

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44.8 Conclusions

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(the concentration of calories in food). For example, the World Health Organization recommends that children and adolescents restrict their intake of energy-dense, micronutrientpoor foods in order to prevent obesity (WHO, 2003). The Institute of Medicine Committee on Prevention of Obesity in Children and Youth recommends that “parents should promote healthful food choices among toddlers and young

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children by making a variety of nutritious, low-energy-dense foods, such as fruits and vegetables, available to them” (Committee on Prevention of Obesity in Children and Youth, 2005). These recommendations rely on research in adults showing that consuming a diet low in energy density is an effective way to moderate energy intake (Ledikwe et al., 2006a, 2007b; Rolls et al., 2006) and improve diet quality (Ledikwe et al., 2006a), both of which are important goals in light of the childhood obesity epidemic and mounting concern for children’s nutritional status. The objective of the present chapter is to consider whether reducing the energy density of the diet may be an effective strategy to moderate energy intake and improve nutrient intakes in young children.

44.2  What is energy density? Energy density refers to the concentration of energy (kcal) in a given portion of food. The energy density of a food can range from 0 kcal/g to 9 kcal/g, and is influenced by both its macronutrient composition and its water content (Figure 44.1). The largest influence on energy density is water, which contributes weight and volume to a food without supplying any energy (0 kcal/g) (Ledikwe et al., 2007b). The macronutrient with the greatest influence on energy density is fat, which is high in energy density at 9 kcal/g. Carbohydrate and protein are moderate in energy density, and each provides 4 kcal/g.

Water 0 kcal/g

Fiber 1.5–2.5 kcal/g

Fiber has a relatively low energy density of 1.5–2.5 kcal/g. Modest changes in energy density can have a significant impact on energy intake. For example, on a typical day an adult might consume 1200 g of food with an overall energy density of 1.8 kcal/g, amounting to an energy intake of 2160 kcal. If the average energy density of the diet was decreased by 0.1 kcal/g while the same weight of food was consumed, then the individual would ingest 2040 kcal. Thus, a relatively small change in the overall energy density of the diet would reduce energy intake by 120 kcal per day. A significant proportion of young children’s energy intake comes from beverages (Fox et al., 2006; Storey et al., 2006). Data from the National Health and Nutrition Examination Survey suggest that some 2- to 5-year-old children may consume as much as 35 percent of their daily energy from beverages (LaRowe et al., 2007). Beverages also vary in energy density. For instance, non-fat milk has an energy density of 0.35 kcal/g, while whole milk has an energy density of 0.6 kcal/g. Because beverages contribute a significant amount of energy to the diets of young children, the energy density of beverages may be important to consider in this age group. However, assessments of the impact of energy density on energy intake have been shown to be more informative if beverages are considered separately from foods; this is because beverages have a high water content and may disproportionately influence dietary energy density values (Ledikwe et al., 2005).

Carbohydrate 4 kcal/g

Protein 4 kcal/g

Fat 9 kcal/g

Figure 44.1  Energy density values for water, fiber, carbohydrate, protein, and fat. Each dot represents 1 kilocalorie per gram (kcal/g). Energy density values range from 0 kcal/g to 9 kcal/g. Source: Reprinted with permission from HarperCollins Publishers, Inc., New York (Rolls, 2005).

2.  From society to behavior: policy and action

44.4  Does energy density influence energy intake?

Foods can be categorized according to their energy density (Table 44.1). For instance, non-starchy fruits and vegetables are classified as very-low-energy-density foods, while starchy fruits and vegetables are low-energy-density foods (Rolls and Barnett, 2000; Rolls, 2005). Verylow-energy-density and low-energy-density foods tend to have high micronutrient density. These foods should be consumed frequently as part of a healthful balanced diet (Ledikwe et al., 2007a). At the other end of the energy density spectrum, low-moisture and high-fat foods are categorized as high-energy-density foods. These foods should be consumed in limited amounts (Ledikwe et al., 2007a), not only because they facilitate excess energy intake (Bell et al., 1998; Kral et al., 2004; Kral and Rolls, 2004) but also because they are often low in micronutrient density. Table 44.1  Typical energy density values for different types of foods

Description

Typical energy density (kcal/g)

Food examples

Very-lowenergy-density

  0–0.6

Non-starchy fruits and vegetables, broth-based soups

Low-energydensity

0.6–1.5

Starchy fruits and vegetables, cooked grains, beans and legumes, extra lean meats, low-fat dairy foods

Mediumenergy-density

1.5–4.0

Bread and bagels, French fries and other fried vegetables, chicken nuggets, ground beef, lunch meats, pizza, hard cheeses, eggs, dried fruits

High-energydensity

4.0–9.0

Low-moisture foods such as crackers, cookies, chips, and high-fat foods such as peanut butter, nuts, margarine, and bacon

Adapted from Rolls and Barnett (2000); Rolls (2005); Ledikwe et al. (2007a).

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44.3  Why is energy density important? Consuming a diet rich in energy-dense foods could lead to excess energy intake and consequent weight gain (Stubbs et al., 1998a, 1998b). This is concerning, because by the age of 2 years a significant proportion of toddlers’ daily energy intake comes from energy-dense foods that are low in nutrient density (Webb et al., 2006). Epidemiological data from the Feeding Infants and Toddlers Study indicate that more than 17 percent of toddlers’ energy intakes can be accounted for by sweetened cereals, butter/ margarine, cookies, processed meats, cakes/pies, pancakes/waffles/French toast and chips/salty snacks (Fox et al., 2006). These foods tend to be high in energy density due to their high fat and sugar content. In addition to consuming energy-dense foods, many toddlers are consuming amounts of energy in excess of their energy needs. It has been estimated that the average energy intake of toddlers exceeds estimated energy requirements by 31 percent (Devaney et al., 2004). Data examining relationships bet­ ween dietary energy density, energy intake and body weight in young children are limited, and in some cases come from small convenience samples (Kral et al., 2007a, 2007b; Johnson et al., 2008a, 2008b). However, a recent longitudinal study of 521 children reported that consuming an energy-dense, low-fiber, high-fat diet at ages 5 and 7 years was associated with excess body fat at age 9 years (Johnson et al., 2008a). The results of this study suggest that interventions to reduce dietary energy density may help to prevent childhood obesity.

44.4  Does energy density influence energy intake? Numerous well-controlled studies have shown that the energy density of foods influences

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energy intake in adults because they tend to consume a consistent weight of a food even when it is reduced in energy density (Bell et al., 1998; Rolls et al., 2006). Lowering the energy density of foods led to a significant reduction in energy intake at a single meal (Kral et al., 2004), throughout a day (Bell and Rolls, 2001; Kral et al., 2002), and over multiple days (Bell et al., 1998; Stubbs et al., 1998b; Rolls et al., 2006). For example, in one recent study, adults were served all meals and snacks during 2-day sessions (Rolls et al., 2006). During one 2-day session, the energy density of all foods was reduced by 25 percent compared to the other session. Participants consumed a similar weight of food over 2 days in both sessions, and therefore consumed significantly (24 percent) less energy when served the lower-energy-density version of the foods. Young children have been thought to be better at regulating energy intake than adults (Birch and Deysher, 1986). This suggestion is based primarily on studies showing that young children adjust their subsequent energy intake in response to the energy density of a compulsory first course (a preload). In some cases, children compensate for energy intake from the first course by reducing their intake of the main course so that they consume a consistent amount of total energy across meals (Birch and Deysher, 1986; Birch et al., 1989; Hetherington et al., 2000). However, in other cases compensation has been incomplete, with some children overcompensating for energy intake from the first course and thus under-eating at the meal, and others under-compensating and thus increasing meal energy intake (Birch et al., 1993a; Johnson and Birch, 1994; Cecil et al., 2005). While these studies have shown that children can make some adjustments to their energy intake in response to the energy density of a compulsory first course, they do not indicate how children would respond to foods varying in energy density that are freely consumed. Several recent studies have addressed this by presenting children with different versions of

foods (higher- and lower-energy-density) and allowing them to eat as much or as little as they wanted. These studies have demonstrated that young children behave similarly to adults when served foods varying in energy density that are consumed ad libitum. One study assessed the effects of the energy density of a popular and familiar entrée on energy intake in 5- to 6-yearold children (Fisher et al., 2007a). The energy density of macaroni and cheese was lowered by reducing the fat content. Despite the difference in the energy density, children, like adults, consumed a consistent weight of the entrée and of the other unvaried foods served at the meal. Therefore, when served the lower-energydensity version of the macaroni and cheese, children consumed significantly less energy from the entrée and, therefore, from the entire meal. Similar findings have been reported in a younger sample of children (2–5 years old) using a comparable protocol and entrée (Figure 44.2) (Leahy et al., 2008a). In these studies, children consumed a similar weight of both versions of the entrée, demonstrating that the energy density of a familiar food can be lowered without affecting its acceptability to young children. The previously mentioned studies have shown that decreasing the energy density of an entrée moderates children’s energy intake at a meal, but does this effect persist beyond a single meal? It is possible that children compensate for reduced energy intake at a meal by consuming more energy at subsequent eating occasions. This possibility was examined in a study among preschool children in which the energy density of foods and beverages was reduced over 2 days (Leahy et al., 2008b). Children in a childcare facility were provided with all of their meals (breakfast, lunch, afternoon snack, dinner, and evening snack) for 2 days in each of two experimental sessions. During one session, the foods and beverages served at breakfast, lunch and afternoon snack on both days were reduced in energy density by 19 percent to 33 percent. Decreases in energy density were

2.  From society to behavior: policy and action

44.5  Practical strategies to reduce energy density

450 Energy intake (kcal)

Food intake (g)

450

300

150

0

a b

300

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Milk Broccoli Apple sauce Macaroni and cheese

150

0 Higher

Lower

Higher

Lower

Energy density Energy density Figure 44.2  Food and energy intakes (mean  standard error) of 77 preschool children served meals in which the energy density of a macaroni and cheese entrée was varied (higher  2.0 kcal/g; lower  1.4 kcal/g). Means with different letters are significantly different (P  0.0001). Compared to when served the higher-energy-density entrée, children consumed significantly less energy from both the macaroni and cheese and the meal when served the lower-energy-density entrée. Source: Reprinted from Journal of the American Dietetic Association, with permission from Elsevier (Leahy et al., 2008a).

achieved using various strategies, such as redu­ cing fat and sugar content and increasing fruit and vegetable content. Both commercially available products and foods prepared from recipes were served. Similar to findings from singlemeal studies, the children ate a consistent weight of foods and beverages over 2 days in both sessions, and therefore consumed significantly less energy when served the lower-energy-dense versions of foods and beverages. The results of this study show that the energy density of food has a persistent effect on children’s energy intake, suggesting that reductions in energy density could be used strategically to prevent excess energy intake in young children.

44.5  Practical strategies to reduce energy density As mentioned in the previous section, the energy density of food can be altered in various ways. One strategy is to decrease the fat content. For example, a popular children’s food such as macaroni and cheese is often prepared with more fat than is necessary to make

this dish appealing. The energy density of this dish can be reduced from 2.0 kcal/g (medium energy density) to 1.4 kcal/g (low energy density) without affecting its acceptability, simply by decreasing the fat content of the cheese sauce. Another way to reduce the energy density of a food is to increase the proportion of water-rich ingredients in a recipe, such as fruits and vegetables. For instance, doubling the amount of blueberries in a blueberry muffin recipe would lower the energy density of the muffins. This strategy can not only decrease the energy density of a food, but also simultaneously increase the nutrient density of a food. Combinations of approaches, such as decreasing fat content and increasing vegetable content, are especially effective for changing the energy density of foods. Many frequently consumed commercial foods and beverages are available in reduced energy density versions that are acceptable to children. For example, substituting 1%-fat milk or non-fat milk for whole milk or 2%-fat milk is an easy way to decrease the energy density of the diet. Small changes like serving 1%-fat milk to children instead of whole milk can have a substantial impact on energy intake without

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44.  Energy Density aND young Children’s Energy Intake

450 Energy intake (kcal)

Food intake (g)

450

300

150

a b

300

Milk Carrots Apple sauce Pasta

150

0

0 Higher

Lower

Higher

Lower

Energy density Energy density Figure 44.3  Food and energy intakes (mean  standard error) of 61 preschool children served meals in which the energy density of a pasta entrée was varied (higher  1.6 kcal/g; lower  1.2 kcal/g). Means with different letters are significantly different (P  0.0001). Compared to when served the higher-energy-density entrée, children consumed significantly less energy from both the pasta dish and the meal when served the lower-energy-density entrée. Source: Reprinted with permission from Macmillan Publishers Ltd: Obesity ©2008 (Leahy et al., 2008c).

detrimentally affecting nutrient intakes. Con­ suming two cups of whole milk per day would provide 292 kcal, but two cups of 1%-fat milk would provide 204 kcal, an energy saving of 88 kcal per day. Since the palatability of foods is an important determinant of food intake (Sorensen et al., 2003), maintaining the palatability of a food is part of the challenge when reducing energy density. One strategy to decrease energy density is to incorporate water-rich vegetables into the food. Vegetables may be less palatable than other foods for some individuals (Drewnowski, 1998), making their addition to a dish challenging because palatability of the dish can be affected. A recent study demonstrated that it is possible to incorporate vegetables into a dish while maintaining palatability (Leahy et al., 2008c). Preschool children were served two versions of a pasta entrée with a vegetable-based tomato sauce that was varied in energy density by decreasing fat and increasing vegetable content. While both versions of the pasta had some vegetables puréed into the sauce, the lowerenergy-density version of the entrée had three times more than the higher-energy-­density

version. As was found in the previously mentioned studies with macaroni and cheese (Fisher et al., 2007a; Leahy et al., 2008a), children consumed a consistent weight of both versions of the pasta and the other items offered at the meal. Therefore, they consumed significantly less energy from the pasta and the entire meal when served the lower-energy-density version of the pasta compared to when served the higher-energy-density version (Figure 44.3). Additionally, because of the greater amount of vegetables in the sauce, children consumed significantly more vegetables when served the lower-energy-density version. Children’s vege­ table intake increased by more than half of a serving (serving  3 tablespoons). Despite the substantial increase in vegetable content of lower-energy-density pasta, there was no significant difference in children’s taste ratings of the two versions of the dish. This study demonstrated that decreasing fat content and increasing vegetable content of a food could increase children’s vegetable intake while moderating their energy intake. In addition to lowering the energy density of the diet by making substitutions and recipe

2.  From society to behavior: policy and action

44.5  Practical strategies to reduce energy density

modifications, it is important to make a variety of low-energy-density foods, such as fruits and vegetables, available to children, because this can promote consumption of these foods. A recent study demonstrated that the availability of a variety of foods increased children’s food intake compared to offering only a single food (Temple et al., 2008). In this study, 9- to 12-year-old children were assigned to one of four groups: higher-energy-density single food, higher-energy-density variety of foods (hamburger, pizza, chicken nuggets and French fries), lower-energy-density single food, or lower-energy-density variety of foods (yogurt, pudding, carrots, grapes, mandarin oranges, pineapple and peaches). Children in each group responded to a computer task to earn points for access to foods that they could consume. The results of the study showed that the effect of variety was independent of energy density, meaning that food intake increased with variety for both higher-energy-density foods as well as lower-energy-density foods. In a subsequent study, Epstein and colleagues found that overweight children increase their energy intake more in response to variety, particularly of foods high in energy density, than do leaner children (Epstein et al., 2009). These findings suggest that increasing the variety of lower-energy-density foods such as fruits and vegetables, while limiting the variety of high-energy-density foods, could be used strategically to lower the energy density of children’s diets, and this is likely to moderate their energy intake. Epstein et al. (2001) have also tested whether encouraging parents to provide their children with a variety of fruits and vegetables leads to an increase in children’s consumption of these foods as well as a decrease in their consumption of energy-dense foods. They designed a parentfocused behavioral intervention to change eating behaviors of 6- to 11-year-old normal-weight children (body mass index 85th percentile). One group of parents received positive messages to increase fruit and vegetable intake, and

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the other group was told to decrease fat and sugar intake. Children in the Increase Fruit and Vegetable group showed trends toward greater consumption of fruits and vegetables than children in the Decrease Fat and Sugar group. Children in both groups decreased their intake of energy-dense foods. Epstein et al. (2008) also conducted a family-based intervention with 8- to 12-year-old children who were either overweight or at risk for overweight (body mass index 85th percentile) and their parents. In this study, the positive message was to increase intake of fruits, vegetables and low-fat dairy foods. The negative message was to reduce intake of high-energy-dense foods. Children in the positive message group had a greater reduction in body mass index for age z-score compared to the children in the negative message group at both 12- and 24-month follow-up appointments. These studies suggest that positive messages to increase intake of lowenergy-dense foods, such as fruits, vegetables and low-fat dairy foods, may be more effective at improving children’s eating behavior and weight status than restrictive messages. Similar findings have previously been reported in a clinical trial among adults (Ello-Martin et al., 2007). Providing children with a variety of nutrient­dense foods that are low in energy density, as well as modifying recipes to lower the fat content or increase water-rich ingredients, are simple strategies that are likely to be successful because they do not restrict or drastically change the diets of young children. Lowering the energy density of foods that are already familiar to children may be particularly appealing to parents who are struggling to feed picky children. Incorporating fruits and vegetables into familiar foods may increase nutrient variety in the diets of children prone to “food jags” (when a child will only eat one food item meal after meal). Decreasing the energy density of familiar foods may also be feasible in schools, where food service directors are reluctant to

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change menus due to concerns that children will reject new food items.

44.7  Future directions in energy density research

44.6  Will reducing the energy density of the diet benefit every child?

It is unclear why young children can sometimes compensate for variations in the energy content of a compulsory first course, yet show little compensation for variations in the energy density of foods consumed ad libitum. Per­ haps dissimilarities between study protocols or between-child differences in responsiveness to energy density (Cecil et al., 2008) may account for the apparent disparity in children’s responses to energy density. Regardless of the differences in study outcomes, reducing the energy density of the diet has the potential to significantly improve children’s nutrient intakes and moderate their energy intakes. Decreasing the energy density of foods consumed ad libi­ tum has been consistently shown to moderate children’s energy intake across several studies (Fisher et al., 2007a; Leahy et al., 2008a, 2008b, 2008c). The previously described 2-day study raises the question of how long the effects of energy density will persist in young children (Leahy et al., 2008b). Several studies have shown that children’s adjustments in energy intake are not always immediate, but may become apparent over multiple meals or days (Wilson, 1991, 1999; Birch et al., 1993b; Fisher et al., 2007b). Additionally, it has been suggested that it may take up to 3 to 4 days for the biological systems responsible for energy balance to detect and respond to changes in energy intake (Bray et al., 2008). Future research should focus on whether the effects of energy density on children’s energy intake persist for more than 2 days. In order to adjust their food intake to variations in energy density, children would have to recognize and respond to the post-ingestive consequences of food. Several studies have demonstrated that infants (Fomon et al., 1969, 1975) and children (Birch and Deysher, 1985; Birch et al., 1987, 1990; Louis-Sylvestre et al., 1989)

Consuming diets rich in energy-dense, micronutrient-poor foods may place children at risk for inadequate intakes of vital micronutrients (Stang, 2006). Additionally, many young children are not consuming recommended amounts of fruits and vegetables (Guenther et al., 2006) or fiber (Kranz et al., 2005). Lower-energy-density diets are associated with high diet quality because these diets tend to be rich in fruits and vegetables, whole grains and low-fat dairy foods (Ledikwe et al., 2006b). These types of diets also tend to be high in micronutrients and fiber, and are generally consistent with the Dietary Guidelines for Americans (US Department of Health and Human Services, US Department of Agriculture, 2005; Ledikwe et al., 2006b; Newby, 2006, 2007). Strategies that lower the energy density of foods, such as incorporating fiber-rich fruits and vege­ tables into favorite recipes, are likely to improve children’s micronutrient and fiber intakes. There are a number of ways to reduce the energy density of the diet. The various app­roaches can have beneficial effects on nutrient and energy intakes, and can be tailored to meet the individual nutritional needs of a child. For instance, a child who consumes too much saturated fat may benefit from consuming 1%-fat milk instead of whole milk. A child who does not like yellow vegetables might consume more of them if they are incorporated into a mixed dish. A child who consumes too much sugar might benefit from consuming natural apple sauce instead of regular sweetened apple sauce. These different approaches to reduce energy density may be used individually or together to moderate energy intake and improve nutrient intakes.

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REFERENCES

can learn to adjust their food intake in response to the energy density of foods. For instance, Fomon et al. (1969) conducted an experiment in which infants were given formulas that varied in energy density. For the first 6 weeks of the study, infants who were fed a higher energydense formula consumed more energy and gained more weight than those fed a formula with a standard energy concentration. After 6 weeks, however, the infants fed the higherenergy-dense formula adjusted their intake so that they were consuming the same amount of energy per kilogram of body weight as the other group of infants. Although infants in this study took 6 weeks to learn to adjust their formula intake, teenagers in another study demonstrated that they could learn to adjust their food intake in less time (Louis-Sylvestre et al., 1989). Teenagers 15–17 years of age consumed a snack every afternoon before dinner for a week. Initially the teenagers did not compensate for the snack by reducing their energy intake at dinner, but by the end of the week they were compensating for the snack energy by eating less at dinner. The results of these studies suggest that children may be able to make associations between foods and their post-ingestive consequences. These studies, however, were conducted in a relatively simple eating environment in which children were learning about a single food. Little is known about how children learn about foods consumed in typical food environments, which are more complex and varied because multiple foods are eaten together. Perhaps reductions in the energy density of foods will have persistent effects on energy intake because it may be difficult for children to learn about the post-ingestive consequences of foods in complex environments. This suggestion is supported by several longitudinal studies of freely chosen diets showing that dietary energy density can have persistent effects that influence children’s body fatness (Johnson et al., 2008a; McCaffrey et al., 2008). While such observational data cannot show causality, they

do suggest that variations in dietary energy density have the potential to be used strategically to influence children’s body fat. Further long-term interventions are needed to determine the effectiveness of dietary energy density reductions for the prevention or reversal of the onset of childhood overweight.

44.8  Conclusions Current research indicates that reducing the energy density of the diet is a promising approach to moderating energy intake in young children as a means to prevent childhood overweight. The energy density of foods appears to have a robust effect on energy intake in young children, similar to the effect demonstrated repeatedly in adults. Lowering the energy density of foods has been shown to lead to a reduction in children’s energy intake at a single meal and over the course of 2 days. Decreasing energy density by incorporating fruits and vegetables into foods also has the potential to improve the nutrient quality of children’s diets. Developing strategic child-feeding policies for reducing energy density is a sensible step in countries where childhood obesity has reached epidemic proportions. Even before such policies are enacted, parents and childcare providers concerned about children’s weight and nutritional status may find energy density reduction strategies to be useful for moderating children’s energy intake and improving their nutrient intakes.

References Barlow, S. E. (2007). Expert committee recommendations regarding the prevention, assessment, and treatment of child and adolescent overweight and obesity: Summary report. Pediatrics, 120(Suppl. 4), S164–S192. Bell, E. A., & Rolls, B. J. (2001). Energy density of foods affects energy intake across multiple levels of fat content in lean and obese women. American Journal of Clinical Nutrition, 73, 1010–1018.

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Bell, E. A., Castellanos, V. H., Pelkman, C. L., Thorwart, M. L., & Rolls, B. J. (1998). Energy density of foods affects energy intake in normal-weight women. American Journal of Clinical Nutrition, 67, 412–420. Birch, L. L., & Deysher, M. (1985). Conditioned and uncondit­ ioned caloric compensation: Evidence for self-regulation of food intake in young children. Learning and Motiv­ ation, 16, 341–355. Birch, L. L., & Deysher, M. (1986). Caloric compensation and sensory specific satiety: Evidence for self-regulation of food intake by young children. Appetite, 7, 323–331. Birch, L. L., McPhee, L., Shoba, B. C., Steinberg, L., & Krehbiel, R. (1987). Clean up your plate: Effects of child feeding practices on the conditioning of meal size. Learning and Motivation, 18, 301–317. Birch, L. L., McPhee, L., & Sullivan, S. (1989). Children’s food intake following drinks sweetened with sucrose or aspartame: Time course effects. Physiology and Behavior, 45, 387–395. Birch, L. L., McPhee, L., Steinberg, L., & Sullivan, S. (1990). Conditioned flavor preferences in young children. Physiology and Behavior, 47, 501–505. Birch, L. L., McPhee, L. S., Bryant, J. L., & Johnson, S. L. (1993a). Children’s lunch intake: Effects of midmorning snacks varying in energy density and fat content. Appetite, 20, 83–94. Birch, L. L., Johnson, S. L., Jones, M. B., & Peters, J. C. (1993b). Effects of a nonenergy fat substitute on children’s energy and macronutrient intake. American Journal of Clinical Nutrition, 58, 326–333. Bray, G. A., Flatt, J. P., Volaufova, J., Delany, J. P., & Champagne, C. M. (2008). Corrective responses in human food intake identified from an analysis of 7-d food-intake records. American Journal of Clinical Nutrition, 88, 1504–1510. Cecil, J. E., Palmer, C. N. A., Wrieden, W., Murrie, I., Bolton-Smith, C., Watt, P., et al. (2005). Energy intakes of children after preloads: Adjustment, not compensation. American Journal of Clinical Nutrition, 82, 302–308. Cecil, J. E., Tavendale, R., Watt, P., Hetherington, M. M., & Palmer, C. N. (2008). An obesity-associated FTO gene variant and increased energy intake in children. New England Journal of Medicine, 359, 2558–2566. Committee on Prevention of Obesity in Children and Youth (2005). In J. P. Koplan, C. T. Liverman, & V. I. Kraak (Eds.), Preventing childhood obesity: Health in balance. Washington, DC: The National Academies Press. Devaney, B., Ziegler, P., Pac, S., Karwe, V., & Barr, S. I. (2004). Nutrient intakes of infants and toddlers. Journal of the American Dietetic Association, 104, S14–S21. Drewnowski, A. (1998). Energy density, palatability, and satiety: Implications for weight control. Nutrition Reviews, 56, 347–353.

Ello-Martin, J. A., Roe, L. S., Ledikwe, J. H., Beach, A. M., & Rolls, B. J. (2007). Dietary energy density in the treatment of obesity: A year-long trial comparing 2 weightloss diets. American Journal of Clinical Nutrition, 85, 1465–1477. Epstein, L. H., Gordy, C. C., Raynor, H. A., Beddome, M., Kilanowski, C. K., & Paluch, R. (2001). Increasing fruit and vegetable intake and decreasing fat and sugar intake in families at risk for childhood obesity. Obesity Research, 9, 171–178. Epstein, L. H., Paluch, R. A., Beecher, M. D., & Roemmich, J. N. (2008). Increasing healthy eating vs. reducing high energy-dense foods to treat pediatric obesity. Obesity, 16, 318–326. Epstein, L. H., Robinson, J. L., Temple, J. L., Roemmich, J. N., Marusewski, A. L, & Nadbrzuch, R. L. (2009). Variety influences habituation of motivated behavior for food and energy intake in children. American Journal of Clinical Nutrition, 89, 746–754. Fisher, J. O., Liu, Y., Birch, L. L., & Rolls, B. J. (2007a). Effects of portion size and energy density on young children’s intake at a meal. American Journal of Clinical Nutrition, 86, 174–179. Fisher, J. O., Arreola, A., Birch, L. L., & Rolls, B. J. (2007b). Portion size effects on daily energy intake in low-income Hispanic and African American children and their mothers. American Journal of Clinical Nutrition, 86, 1709–1716. Fomon, S. J., Filer, L. J., Jr., Thomas, L. N., Rogers, R. R., & Proksch, A. M. (1969). Relationship between formula concentration and rate of growth of normal infants. Journal of Nutrition, 98, 241–254. Fomon, S. J., Filer, L. J., Jr., Thomas, L. N., Anderson, T. A., & Nelson, S. E. (1975). Influence of formula concentration on caloric intake and growth of normal infants. Acta Paediatrica Scandinavica, 64, 172–181. Fox, M. K., Reidy, K., Novak, T., & Ziegler, P. (2006). Sources of energy and nutrients in the diets of infants and todd­ lers. Journal of the American Dietetic Association, 106, S28–S42. Guenther, P. M., Dodd, K. W., Reedy, J., & Krebs-Smith, S. M. (2006). Most Americans eat much less than recommended amounts of fruits and vegetables. Journal of the American Dietetic Association, 106, 1371–1379. Hetherington, M. M., Wood, C., & Lyburn, S. C. (2000). Response to energy dilution in the short term: Evidence of nutritional wisdom in young children? Nutritional Neuroscience, 3, 321–329. Johnson, S. L., & Birch, L. L. (1994). Parents’ and children’s adiposity and eating style. Pediatrics, 94, 653–661. Johnson, L., Mander, A. P., Jones, L. R., Emmett, P. M., & Jebb, S. A. (2008a). Energy-dense, low-fiber, high-fat diet­ ary pattern is associated with increased fatness in childhood. American Journal of Clinical Nutrition, 87, 846–854.

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Johnson, L., Mander, A. P., Jones, L. R., Emmett, P. M., & Jebb, S. A. (2008b). A prospective analysis of dietary energy density at age 5 and 7 years and fatness at 9 years among UK children. International Journal of Obesity, 32, 586–593. Kral, T. V. E., & Rolls, B. J. (2004). Energy density and portion size: Their independent and combined effects on energy intake. Physiology and Behavior, 82, 131–138. Kral, T. V. E., Roe, L. S., & Rolls, B. J. (2002). Does nutrition information about the energy density of meals affect food intake in normal-weight women? Appetite, 39, 137–145. Kral, T. V. E., Roe, L. S., & Rolls, B. J. (2004). Combined effects of energy density and portion size on energy intake in women. American Journal of Clinical Nutrition, 79, 962–968. Kral, T. V., Berkowitz, R. I., Stunkard, A. J., Stallings, V. A., Brown, D. D., & Faith, M. S. (2007a). Dietary energy density increases during early childhood irrespective of familial predisposition to obesity: Results from a prospective cohort study. International Journal of Obesity, 31, 1061–1067. Kral, T. V., Stunkard, A. J., Berkowitz, R. I., Stallings, V. A., Brown, D. D., & Faith, M. S. (2007b). Daily food intake in relation to dietary energy density in the free-living environment: A prospective analysis of children born at different risk of obesity. American Journal of Clinical Nutrition, 86, 41–47. Kranz, S., Mitchell, D. C., Siega-Riz, A. M., & SmiciklasWright, H. (2005). Dietary fiber intake by American preschoolers is associated with more nutrient-dense diets. Journal of the American Dietetic Association, 105, 221–225. LaRowe, T. L., Moeller, S. M., & Adams, A. K. (2007). Beverage patterns, diet quality, and body mass index of US preschool and school-aged children. Journal of the American Dietetic Association, 107, 1124–1133. Leahy, K. E., Birch, L. L., & Rolls, B. J. (2008a). Reducing the energy density of an entrée decreases children’s energy intake at lunch. Journal of the American Dietetic Association, 108, 41–48. Leahy, K. E., Birch, L. L., & Rolls, B. J. (2008b). Reducing the energy density of multiple meals decreases preschool children’s energy intake over two days. American Journal of Clinical Nutrition, 88, 1459–1468. Leahy, K. E., Birch, L. L., Fisher, J. O., & Rolls, B. J. (2008c). Reductions in entrée energy density increase children’s vegetable intake and reduce energy intake. Obesity, 16, 1559–1565. Ledikwe, J. H., Blanck, H. M., Khan, L. K., Serdula, M. K., Seymour, J. D., Tohill, B. C., & Rolls, B. J. (2005). Dietary energy density determined by eight calculation methods in a nationally representative United States population. Journal of Nutrition, 135, 273–278. Ledikwe, J. H., Blanck, H. M., Khan, L. K., Serdula, M. K., Seymour, J. D., Tohill, B. C., & Rolls, B. J. (2006a). Dietary

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energy density is associated with energy intake and weight status in US adults. American Journal of Clinical Nutrition, 83, 1362–1368. Ledikwe, J. H., Blanck, H. M., Khan, L. K., Serdula, M. K., Seymour, J. D., Tohill, B. C., & Rolls, B. J. (2006b). Lowenergy-density diets are associated with high diet quality in adults in the United States. Journal of the American Dietetic Association, 1006, 1172–1180. Ledikwe, J. H., Blanck, H. M., Khan, L. K., Serdula, M. K., Seymour, J. D., Tohill, B. C., & Rolls, B. J. (2007a). Reductions in dietary energy density as a weight management strategy. In R. F. Kushner & D. H. Bessesen (Eds.), Contemporary endocrinology: Treatment of the obese patient (pp. 265–280). Totowa, NJ: Humana Press Inc. Ledikwe, J. H., Rolls, B. J., Smiciklas-Wright, H., Mitchell, D. C., Ard, J. D., Champagne, C., et al. (2007b). Reductions in dietary energy density are associated with weight loss in overweight and obese participants in the PREMIER trial. American Journal of Clinical Nutrition, 85, 1212–1221. Louis-Sylvestre, J., Tournier, A., Verger, P., Chabert, M., Delorme, B., & Hossenlopp, J. (1989). Learned caloric adjustment of human intake. Appetite, 12, 95–103. McCaffrey, T. A., Rennie, K. L., Kerr, M. A., Wallace, J. M., Hannon-Fletcher, M. P., Coward, W. A., et al. (2008). Energy density of the diet and change in body fatness from childhood to adolescence; is there a relation? American Journal of Clinical Nutrition, 87, 1230–1237. Newby, P. K. (2006). Examining energy density: Comments on diet quality, dietary advice, and the cost of healthful eating. Journal of the American Dietetic Association, 106, 1166–1169. Newby, P. K. (2007). Are dietary intakes and eating behaviors related to childhood obesity? A comprehensive review of the evidence. Journal of Law and Medical Ethics, 35, 35–60. Rolls, B. (2005). The volumetrics eating plan. New York, NY: HarperCollins Publishers, Inc. Rolls, B., & Barnett, R. A. (2000). The volumetrics weight­control plan. New York, NY: Quill. Rolls, B. J., Roe, L. S., & Meengs, J. S. (2006). Reductions in portion size and energy density of foods are additive and lead to sustained decreases in energy intake. American Journal of Clinical Nutrition, 83, 11–17. Sorensen, L. B., Moller, P., Flint, A., Martens, M., & Raben, A. (2003). Effect of sensory perception of foods on appetite and food intake: A review of studies on humans. International Journal of Obesity, 27, 1152–1166. Stang, J. (2006). Improving the eating patterns of infants and toddlers. Journal of the American Dietetic Association, 106, S7–S9. Storey, M. L., Forshee, R. A., & Anderson, P. A. (2006). Beverage consumption in the US population. Journal of the American Dietetic Association, 106, 1992–2000.

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Stubbs, R. J., Johnstone, A. M., Harbron, C. G., & Reid, C. (1998a). Covert manipulation of energy density of high carbohydrate diets in ‘pseudo free-living’ humans. International Journal of Obesity, 22, 885–892. Stubbs, R. J., Johnstone, A. M., O’Reilly, L. M., Barton, K., & Reid, C. (1998b). The effect of covertly manipulating the energy density of mixed diets on ad libitum food intake in “pseudo free-living” humans. International Journal of Obesity, 22, 980–987. Temple, J. L., Giacomelli, A. M., Roemmich, J. N., & Epstein, L. H. (2008). Dietary variety impairs habituation in children. Health Psychology, 27, S10–S19. US Department of Health and Human Services, US Department of Agriculture. (2005). Dietary guidelines for Americans 2005. Washington, DC: The National Academies Press.

Webb, K. L., Lahti-Koski, M., Rutishauser, I., Hector, D. J., Knezevic, N., Gill, T., et al. (2006). Consumption of “extra” foods (energy-dense, nutrient-poor) among children aged 16-24 months from western Sydney, Australia. Public Health Nutrition, 9, 1035–1044. Wilson, J. F. (1991). Preschool children maintain intake of other foods at a meal including sugared chocolate milk. Appetite, 16, 61–67. Wilson, J. F. (1999). Preschoolers’ mid-afternoon snack intake is not affected by lunchtime food consumption. Appetite, 33, 319–327. World Health Organization. (2003). Diet, nutrition and the prevention of chronic diseases. Geneva: World Health Organization (WHO Technical Report Series, No. 916).

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C H A P T E R

45 Nurturing and Preserving the Sensory Qualities of Nature Tanya L. Ditschun Food Science and Technology Group, Senomyx, Inc., San Diego, CA, USA

o u t l i ne 45.1  Introduction

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45.2  Determinants  of Individual Food   Choices and Current “Healthful   Eating” Trends 557 45.2.1 Factors Influencing Food Choices 557 45.2.2 Current Trends in Consumer Purchasing and their Ability to Influence Healthful Eating and the Perception of Healthful Eating 559

45.1  Introduction Understanding how consumers choose food products is a challenging task for the food industry. As more is learned about how dietary choices affect obesity, food manufacturers are looking to change products in order to support consumers in making healthful food choices. Two challenges exist in this area: (1) consumers have grown accustomed to consuming food in excessive quantities, and (2) consumer tastes favor processed foods which, if consumed in

Obesity Prevention: The Role of Brain and Society on Individual Behavior

45.3  P  reserving the Natural Sensory   Qualities of Food 561 45.3.1 How Food Science Can Help Improve Food Palatability and/or Reduce the Caloric Value of Foods 561 45.3.2 In Summary: the Consumer’s Willingness and Ability to Change their Food Choices 564

large quantities, may not be considered a healthful choice. Indeed, Figure 45.1 (top) indicates a 30-year trend of increasing overall caloric intake per capita. Foods rich in added sugars, fat and refined flours are being consumed in greater quantity. Conversely, as seen in Figure 45.1 (bottom), vegetable, fruit and dairy consumption has remained stable over the same 30-year period (USDA/Economic Research Service, 2009). The typical North American consumes over 500 additional calories per day than 30 years ago, yet does not achieve the recommended daily number of

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2010 Elsevier Inc. © 2010,

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3000 Total calories consumed

2900

Calories per capita

2800 2700 2600 2500 2400 2300 2200 2100

80 19 82 19 84 19 86 19 88 19 90 19 92 19 94 19 96 19 98 20 00 20 02 20 04 20 06 20 08

78

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Year

Meat, eggs, and nuts Dairy

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Added fats

Calories per capita

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86 19 88 19 90 19 92 19 94 19 96 19 98 20 00 20 02 20 04 20 06 20 08

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Figure 45.1  Average daily per capita calories from the US food supply, adjusted for spoilage and other waste. Top, Yearper food group. total calories per capita consumed; bottom, calories per capita consumed Source: USDA/Economic Research Service (2009).

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45.2 Determinants of individual food choices and current “healthful eating” trends

servings of produce and whole-grain products (USDA/Economic Research Service, 2009). Food-processing technologies have created new tastes: for example, US consumers perceive refined grains to be more palatable and to have a superior taste to whole grains (Putnam et al., 2002). While consumer education is necessary to promote healthful diets (low in refined sugars and flours and high in fruits, vegetables and whole grains), taste seems to be the over-riding factor for making food choices (Buzby et al., 2005). Without making changes to food tastes and textures, it seems unlikely that consumers will willingly choose more beneficial products over unhealthful ones. Sensory food science can respond to these changes in consumer preferences and create foods that are more healthful as well as pleasurable. This chapter will examine (1) how consumer behavior can be changed in favor of a more healthful diet, thus better controlling obesity, and (2) the role that food manufacturers can play in supporting healthful food choices.

45.2  Determinants of individual food choices and current “healthful eating” trends While healthful food options are readily available, consumers may be overlooking these in favor of processed foods. Simply providing healthful food products is not enough to address the growing obesity pandemic and its related health issues. A number of factors determine how individuals make food choices.

45.2.1  Factors influencing food choices Some important factors determining how food choices are made include socio-economic factors, such as income and education, the consumer’s

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knowledge and awareness of health and nutrition issues (or lack thereof), and the expected value of food purchases. These factors are briefly examined below. Income and education A strong predictor of consumer behavior in food choices may be income. While obesity rates are climbing in all American states, it may be more of a problem in the poorest states (Levi et al., 2006). Turrell and Kavanagh (2006) confirmed that food purchasing behavior is related to education and, possibly more strongly, to household income. Consumers with both low levels of education and low incomes were least likely to purchase healthful foods, high in fiber and low in fat, salt and sugar. Previously, Turrell and colleagues (2002) showed that residents of low-income households, and blue-collar workers, purchased fewer types of fruits and vegetables, and on a less frequent basis, than those with higher incomes. A recent Canadian study has shown that consumers holding university degrees purchase significantly more fruits and vegetables than do households with lower levels of education (Ricciuto et al., 2006). The higher proportion of Americans expected to obtain high-school and college diplomas is expected to yield an increased consumption of healthful foods (Blisard et al., 2002). What drives this potential relationship between low income and increased obesity? Healthful, low-cost foods could be difficult to access. Kirkpatrick and Tarasuk (2003) demonstrated that low-income Canadians might have reduced access to milk products and fruit and vegetables, both from a financial and a geographic point of view. Drewnowski and Specter (2004) suggested that diets high in fats and refined sugars are a lower-cost option than healthful diets. Healthful foods represent both a financial cost and a time investment, as it takes time to prepare healthful food. The fast-food restaurant industry is still the fastest, cheapest,

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easiest way to obtain convenient foods, although the choices are often not the most healthful. It is important that inexpensive, healthful food options be made available to all people, regardless of income or education level, in order to fight the obesity epidemic. Health and nutrition awareness Several healthful food trends (outlined below) seem to indicate that consumers are becoming increasingly aware of the health issues related to food choices. These include increasing consumption of whole grains and fiber, as well as choosing different types of fat. Another trend is food functionality, which involves adding healthful additives, supplements or vitamins to an existing food product. An example of this is the practice of adding omega-3 fatty acids in foods such as eggs and yogurt (Mellgren, 2006). Similarly, Buzby and colleagues (2005) found that the consumption of whole milk declined by 70 percent from 1970 to 2003, while the consumption of lower-fat milk increased by 140 percent. They attribute this to behavioral change in response to health warnings regarding the consumption of saturated fats. While there does appear to be a move towards more healthful eating, it may not be enough to impact health. In 2003, only one of an American’s average 10 servings of grains per day was a whole grain (Buzby et al., 2005). While consumers may be aware that they need to eat more healthful foods, they may not know how much is required to effect changes in health. Based on consumer intake questionnaires, 70 percent of Americans think they are consuming enough whole grains, yet nearly 40 percent of Americans consume no whole grains at all (Buzby et al., 2005). Another trend has been the growing development of low-fat and low-carbohydrate diets over the past decade. In response to consumer complaints that low-fat and fat-free foods did not taste good, the food industry responded

by reformulating products with improved palatability (Putnam et al., 2002). However, not all consumers accepted the changes, and many returned to consume full-fat products. While consumers may be aware of healthful food choices, choosing for taste may prevail over other factors. Perceived value The perceived value of a food product is dependent on many factors, some of which have been touched on previously. A low-income individual may not be able to justify the purchase of a fruit or vegetable when the same amount could purchase an entire fast-food meal. Conversely, high-income consumers will justify spending a relatively significant amount on a specialty item which will only make up a part of a meal. Affluent consumers will make food choices based on attributes other than taste (Blisard et al., 2002); fair trade, environmental concerns and animal welfare are factors that less wealthy consumers may not (or cannot afford to) consider. A good example of this is the growing organic market (Dimitri and Greene, 2002). Understanding how food choices are made is extremely complex. How many consumers consider the long-term environmental effects of food production? How many consider their immediate health? Most consumers do not understand the complexities of manufacturing choices; are consumers choosing organic foods because of a perceived positive effect on the environment, or do they believe that pesticide residues cause cancer? Do consumers reject genetically modified crops because of the potential evolution of highly resistant insects, or do they believe that “Frankenfoods” will cause personal harm? Would consumers reject products with added omega-3 fatty acids if the product was termed “genetically-modified”? Consumers may not understand the full implications of some technologies, and may thus thwart a movement toward a safer and more healthful food supply.

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45.2 Determinants of individual food choices and current “healthful eating” trends

45.2.2  Current trends in consumer purchasing and their ability to influence healthful eating and the perception of healthful eating Consumers drive the trends of food purchasing around the world. Below, several trends in consumer purchasing and eating patterns are examined. The purpose of this section is to examine what trends are currently driving food purchases and highlight how the food industry can build upon these trends to foster better food choices among consumers. Organic and natural foods Organic food sales increased by a dramatic 80 percent between 1997 and 2006, reaching US $17.7 billion, according to the Organic Trade Association’s 2007 Manufacturer Survey (2007). Natural and organic food stores offer foods with fewer preservatives, hormones and artificial ingredients, and are quickly becoming mainstream, as evidenced by the prevalence of “organic and natural foods” aisles in major grocery chains in North America (Dimitri and Greene, 2002). Regardless of whether organic and natural foods are more healthful than traditionally manufactured and processed foods, consumers are purchasing these products because of their perceived benefits. The dramatic increase in sales of organic food products seems to indicate that the industry has successfully convinced consumers that these products are healthful. Survey results from The Natural Foods Merchandiser (as reported in Dwyer, 2006) indicated the following trends in consumer perceptions of organic and natural foods: 1. Why do consumers choose natural and organic foods? • 42 percent choose natural and organic foods to avoid harmful ingredients • 22 percent consume natural and organic foods because of perceived health benefits

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• 8 percent of consumers suggested taste as the reason for purchasing these foods. 2. What events caused consumers to try natural, organic, or health products? • 21.7 percent of consumers began using these foods after learning something through a local or national news story • 14.2 percent of consumers tried natural, organic and health products because of a health issue or because their healthcare provider urged them to do so. Regardless of the reasons for rising organic food sales, food manufacturers should use this trend to encourage consumers to purchase more bene­ ficial foods. Gourmet and ethnic foods Much of this movement stems from an increasingly diverse population with a wealth of ethnicities and nationalities which demand diverse food-product and restaurant choices. This is fed by increased exposure to difference cultures and cuisines through travel, the media, and friends and family who travel (Mellgren, 2006). Supermarket chains stocked three times as many products in 2001 as in 1980 (Martinez and Stewart, 2003), and now have whole or partial aisles devoted to ethnic food products. Gourmet products typically refer to foods meticulously prepared by chefs. They can also be products of very high quality, such as a good wine or cheese. These specialty products, once only found in gourmet stores, are now available in supermarkets, natural food stores and “big box” stores. For example, many supermarkets across North America now feature pre-­packaged sushi at the deli counter (Mellgren, 2006). Consumers interested in purchasing varied, gourmet and ethnic foods are willing to spend more money on these high-quality products. When these products are healthful, this trend is a good opportunity for food manufacturers to help consumers make better food choices while catering to their desire for a varied and diversified diet.

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However, gourmet or ethnic food purchases can also be motivated by consumers wanting to “treat themselves”. Trends in gourmet foods include indulgence (Mellgren, 2006). Indulgent food products are not always the most healthful products; a half-cup serving of gourmet Haagen-Dazs vanilla ice cream contains 270 calories and 18 grams of fat. The consumer needs to be aware that not all gourmet or ethnic foods are healthful choices. Convenience foods While most foods may still be eaten in the home, it does not necessarily mean that they are homemade. The food industry produces convenient alternatives to “home-cooked meals” by offering pre-made sauces, spice mixes, and other pre-prepared ingredients (Mellgren, 2006). While items such as boxed cake mixes and side dishes have been around since the 1950s, the fastest growing area currently is in freshly-­prepared foods at grocery store deli counters. These offer a “homemade” taste but save the consumer the trouble of preparing it. Nearly 80 percent of supermarkets have delis featuring a variety of prepared foods, including side dishes and entrées, and in 2000 their sales increased by 6.1 percent (Martinez and Stewart, 2003). Today, prepared foods make up over 57 percent of the sales in supermarket delis (Chanil and Major, 2006a). Pre-cut fruits and vegetables are other popular convenience items, responsible for the 50 percent increase in stocked produce items between 1994 and 2004 (Clemens, 2004). In addition to saving time, these offer the added benefits linked to increased fruit and vegetable consumption. Away-from-home consumption of vegetables (excluding potatoes) is also expected to increase (Clemens, 2004). “On-the-go” foods, such as breakfast sandwiches and bars, are also increasingly popular. Pre-prepared produce and nutritious restaurant fare may become a good way to foster more healthful eating habits, in spite of the fact that the added cost may be difficult to overcome for some.

Trust marks and other symbols of goodness In order to sell products, food packaging often displays statements or symbols, called “trust marks”, which attest to the healthful nature of the product. A What’s In Store survey of consumer shopping habits commissioned by Con Agra Foods indicated that 95 percent of Americans consider these statements and symbols when food shopping (Chanil and Major, 2006b). These include symbols representing whole grains, heart-healthy, zero grams trans fat, low sodium, natural, dietary guidelines, organic, and kosher foods. Chanil and Major (2006b) suggested that consumers are increasingly interested in organic and kosher foods because producers must adhere to strict manu­ facturing standards, including quality and purity of ingredients, in order to label products as such. Similarly, ecolabels indicate that a product has met certain standards (Martinez and Stewart, 2003); consumers are willing to pay a premium for items that are environmentally friendly, such as Fair Trade-certified coffee, dolphin-friendly tuna and environmentally friendly pork. Products with trust marks and symbols of goodness are easily recognized, and using similar indicators on food packaging for healthful products could help consumers make more healthful food choices. Diets The diversity and popularity of diets indicate that consumers are indeed willing to change their consumption habits. The “low-carb” South Beach and Atkins diets gained significant popularity at the beginning of the twenty-first century. Food manufacturers responded by creating products bearing these names, so that consumers could easily identify the products that adhered to their diet plans. Many manufacturers are also providing consumers with options for portion control, such as 100-calorie packages of Nabisco cookies and crackers, Pringles potato chips, and small cans of Coca-Cola. Similar to

2.  From Society to Behavior: Policy and Action

45.3  Preserving the natural sensory qualities of food

trust marks, consumers may view the brands associated with diets as healthful food choices.

45.3  Preserving the natural sensory qualities of food While not necessarily the primary factor determining food purchases, taste is an important determinant of the quantity of food ingested.

45.3.1  How food science can help improve food palatability and/or reduce the caloric value of foods Several examples of how food science can help improve and maintain the sensory properties of foods are presented below. Improving freshness, availability, and variety of produce The typical North American does not consume enough fruits and vegetables (Putnam et al., 2002). Replacing calorie-rich foods with fruits and vegetables is one of the main components of a balanced weight-loss program. Three strategies exist for increasing the consumption of fruits and vegetables: improve availability; improve taste and consumer appeal; and, most importantly, reduce cost. In order to do so and respond to the increasing demand for fruits and vegetables, the industry has invested considerable resources and made various technology improvements (Pollack, 2001). Several examples are listed in Pollack (2001): 1. Improvement of storage facilities. The use of controlled atmosphere storage allows produce to be available even when it is out of season. 2. Improved shipping and packaging. New trade agreements have allowed increased imports, giving the consumer the opportunity to obtain fresh produce year-round. More

561

efficient routes and means of shipping could be added to further improve availability and reduce cost. Modified atmosphere packaging also helps maintain the freshness, taste qualities and nutrition of fruit and vegetable products that require shipping. Through these initiatives, manufacturers increased the production of fruits and vegetables (including potatoes) by 19 percent and 29 percent per capita, respectively, between 1980 and 2001 (Clemens, 2004). 3. Improved plant breeding. Better plant breeding is another way to increase both availability and consumer appeal (Pollack, 2001). Creating new varieties of fruits and vegetables could appeal to consumers’ needs and interests. For example, new varieties of traditional fruits, such as the seedless grape and the tangerine, have successfully become staples in North-American grocery stores. Breeding hardier varieties of plants that could survive more extreme weather or longer periods of storage could also improve the availability of produce. Improving the appeal of whole grain foods Consumers are becoming more aware of the health benefits of consuming whole-grain foods. It is well known that whole-grain consumption is associated with reduced body weight and reduced risk of cardiovascular and other diseases (Katcher et al., 2008; Nettleton et al., 2008; van der Vijver et al., 2009). Just as with fruits and vegetables, convenience and cost are important determinants of whether consumers include whole grains in their diet. Whole grains generally require longer preparation time, and are typically more expensive (Buzby et al., 2005). Further, wholegrain foods have a different color, flavor and texture than the more familiar refined-grain foods that consumers tend to prefer (Camire, 2004). Consumers may confuse whole-grain and multi-grain products as having the same charac­ teristics. Consumers may also be confused by

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45.  Nurturing and Preserving Sensory Qualities

descriptors used on food packaging, often without additional defining information, such as wheat bread, stone-ground bread and sevengrain bread (Buzby et al., 2005). For example, products described as “multi-grain”, perhaps thought to be a healthful whole-grain food choice, could merely contain refined flours from numerous grain sources. Consumers may also perceive bread products that are darker in color to contain whole grains, but breads can be colored with molasses to appear darker. In response to these hurdles to consumer acceptance of high-fiber foods, food manufacturers are reformulating products to meet consumer taste demands. In 2004, General Mills announced that it would manufacture all its cereal products with whole grains only, and other companies have quickly followed suit. Also in 2004, ConAgra announced a new processing technique designed to improve the

texture and flavor of whole-grain products. ConAgra’s “Ultragrain” flour provides a fine texture like that of wheat flour, is smoother than other whole-wheat flours, and has less visible bran specks which consumers seem to dislike. According to Mintel, a leading market research company, there has been a marked increase in whole-grain product launches over the past 8 years, from under 200 products in 2000 to over 2500 in 2008, as seen in Figure 45.2 (Mintel, 2008). Reducing caloric intake The food industry has reacted to current health trends and created low-calorie food products to appeal to new consumer needs. Stubbs and colleagues (2000) have provided an excellent review of how dietary energy density of foods can be altered.

3000

Number of products

2500

2000

1500

1000

500

0 2000

2001

2002

2003

2004

2005

2006

2007

2008*

Year

Figure 45.2  Number of new whole-grain product launches per year. Data from 2008 includes launches up to September 2008. Source: Mintel (2008).

2.  From Society to Behavior: Policy and Action

45.3  Preserving the natural sensory qualities of food

Adding air and water When air and water are added to a product, the manufacturer cuts ingredients costs and the consumer can cut calories. While aerating a product does change its texture, its taste is unaltered. Aerating a product can address the issue of producing pleasurable “diet” products. Yet some argue that the consumer responds by simply eating more of the product. Adding an acaloric ingredient (such as water) to food will most definitely reduce the food’s energy density (Stubbs et al., 2000). Water, unlike air, may affect taste by diluting the other constituents. Furthermore, the texture can be altered, and in extreme cases the food may lose its integrity if too much water is added. Stubbs and colleagues (2000) point out that the effect this type of calorie reduction has on appetite and eating patterns is not obvious. Increasing water intake and its efficacy in curbing energy (caloric) intake is unclear. Reducing sugar Reducing the carbohydrate content in foods is a common method of cutting calories. Diet soft drinks are a notable example of the popularity of such foods; a sugar-sweetened soft drink can contain 140 calories or more, while diet alternatives contain 1 calorie or less, depending on the sweetener used. Sweeteners, however, do not always replicate the characteristics of sucrose in a product, and the average consumer can discriminate fairly easily between a regular soft drink and its diet version. High-potency sweeteners also tend to have some undesirable attributes, such as bitterness, aftertastes and lingering characteristics (Wiet and Beyts, 1992; Hanger et al., 1996). Another challenge is that artificial sweeteners may not be able to achieve the intensity of sweetness found in natural sweeteners (Dubois et al., 1991). The temporal profile of artificial sweeteners can be quite different from that of natural sweeteners (Ayya and Lawless, 1992). For example, the high-potency sweetener neotame has a delayed onset of

563

sweetness and a lingering sweetness character quite different from that of sucrose, which has a quick onset and is comparatively less lingering (Nofre and Tinti, 2000). The type of sweetener used can also have an impact on other taste characteristics of foods, aside from sweetness. The impact of sweeteners on taste profiles is highly dependent on the food system in which it is used. Further, artificial sweeteners cannot duplicate other functions of sugar in foods, such as sensory mouthfeel (Muir et al., 1998), and a wide variety of functional properties such as bulking (Dies, 1994). Using artificial sweeteners in some foods may also necessitate the use of gums or starches for textural purposes, which counteracts any reduction of calories from replacing sugars with low-calorie sweeteners. The safety of artificial sweeteners continues to plague product developers and confuse consumers. Safety data are not always easy to interpret, and concerns about safety often stem from the difficulty in translating results of studies where quantities of a particular ingredient are fed to rodents at levels up to thousands of times higher than a human would be exposed to in a typical diet. However, in some cases safety concerns are warranted. For example, aspartame-­containing products must indicate that phenylketonurics should avoid the sweetener due to their increased sensitivity to phenylalanine, one of the two amino acids in aspartame. High-potency sweeteners from natural sources are available, though their uses are limited by acidity, heat stability and sensory qualities (Gibbs et al., 1996). Some consumers are always willing to accept artificial ingredients in food products, just as there will be those who will never consume such substances. Safety remains a top priority for food producers as they work to create better sweeteners for food use. Reducing fat As fats are more energy dense than carbohydrates, replacing fat in foods could have a more

2.  From Society to Behavior: Policy and Action

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45.  Nurturing and Preserving Sensory Qualities

significant impact on the caloric value of food products. As with high-potency sweeteners, fat replacers have not been successful in imitating fat in terms of taste, texture, physical and chemical properties, and winning consumer acceptance. The food industry has developed a wide range of ingredients that can be used instead of fat in products. Carbohydrate-based fat repla­ cers are popular, as they aid in maintaining the organoleptic qualities of full-fat foods. Textural characteristics, including cohesiveness, viscosity and juiciness, are dictated by fat content, and food product developers are being challenged to create similar products using ingredients like gums, maltodextrins and emulsifiers (Shulka, 1995; Sandrou and Arvanitoyannis, 2000). Unlike the sugar replacers aspartame and sucralose, which do not contribute significantly to energy density, carbohydrate-based fat replacers add calories to foods. Consumers also seem to prefer natural alternatives for reducing fat in foods. One success story is in the dairy industry, where low-fat milk, yogurt, cheese and ice cream continue to make strong retail sales. Improving the texture of dairy products while reducing fat is achieved through thickeners like gelatin and pectin, as well as other carbohydrate-based ingredients previously described (Sandrou and Arvanitoyannis, 2000). However, the true success of these products may lie in the fact that consumers either inherently like the taste or have adapted to the lower-fat versions, learning to like their characteristics. Perhaps consumers are initially willing to give up acceptability of some desirable sensory attributes, like creaminess and thickness, in favor of reducing their fat intake, and after repeated exposure grow to like these products just as much or more than their full-fat counterparts. Despite the vast number of reduced-calorie choices that consumers have been given over the last few decades, the obesity problem is increasing in severity. One must ask whether

consumers are compensating by eating more of low-calorie foods, whether they are less satiated when consuming low-calorie foods, and whether low-calorie foods can truly help consumers in their weight-loss efforts. Changing the nutritional profile of foods: taste enhancers Advancements in biotechnology show promising results in modulating the taste of foods and beverages using novel compounds such as flavor enhancers and taste modulators. Consumer products companies are hoping that revolutionary technologies will lead the way for both the industry and the consumer in the development of food products with better nutritional profiles. Technologies used to find these flavor enhancers focus on modulating the taste-signaling pathways so as to block or amplify sensations perceived by the taster (for a review on taste receptors, see Chandrashekar et al., 2006). This may help the food industry to provide consumers with healthful food alternatives. For example, the development of bitter-taste blockers may reduce the unpleasant taste of some foods, such as products containing some vegetables, soy, and other bitter-tasting compounds. As stated above, reducing carbohydrate sweeteners and their associated calories in foods and beverages is a critical challenge in the food industry. Sweet-enhancers aim to modulate the activity of the sweet receptor, thereby amplifying the sweet taste sensation without adding calories to the product (Senomyx Inc., 2008).

45.3.2  In summary: the consumer’s willingness and ability to change their food choices It seems that today’s consumers either embrace or avoid “food science foods”. As stated by the media’s “Supermarket Guru” Phil

2.  From Society to Behavior: Policy and Action

References

Lempert in Mellgren (2006), the best tool for fighting obesity may be “recreating our daily foods”. While this may be the answer, how foods are recreated will have much to do with what the consumer is willing to accept. How can manufacturers preserve the natural qualities of foods, while creating what the consumer wants? If consumers are willing to eat healthful foods, making fresh produce and whole grains more accessible will help. If the consumer associates natural/reduced processing/organic foods with better-tasting and more healthful products, then marketing strategies and nutritional information on packaging can boost sales and support healthful food choices. Reducing the caloric value of foods while imitating the natural taste and texture of foods will clearly aid consumers in making more healthful food choices. Alternatively, portion control, as modulated through packaging, can also help the consumers moderate their intake. In order to control obesity, people will need to eat fewer calories and/or make more healthful food choices. Yet identifying the consumer’s needs and desires for taste, texture, convenience and satiation is an important part of the equation. Consumer habits are hard to break and, generally speaking, major diet changes will be sustained with difficulty. If one looks to the dairy industry’s successful replacement of high-caloric milk products by lower-calorie versions, one can draw some interesting lessons for other industry segments. The key to success here was that the consumer was not required to make a very important shift; buying patterns and habits did not change, nor did availability or cost. Consumers may be willing to change their behavior, but only as little as possible. Food science can and will provide the innovation needed to provide food manufacturers, marketers, retailers and, ultimately, consumers with successful new products that can effectively address the health issues attributed to obesity in the North-American population.

565

References Ayya, N., & Lawless, H. T. (1992). Quantitative and qualitative evaluation of high-intensity sweeteners and sweetener mixtures. Chemical Senses, 17(3), 245–259. Blisard, N., Lin, B.-H., Cromartie, J., & Ballenger, N. (2002). America’s changing appetite: Food consumption and spending to 2020. Food Review, Economic Research Service, USDA, 25(1), 2–9. Buzby, J., Farah, H., & Vocke, G. (2005). Will 2005 be the year of the whole grain? Amber Waves, Economic Research Service, USDA, 3(3), 12–17. Camire, M. E. (2004). Technological challenges of whole grains. American Association of Cereal Chemists, 49(1), 20–22. Chandrashekar, J., Hoon, M. A., Ryba, N. J., & Zuker, C. S. (2006). The receptors and cells for mammalian taste. Nature, 444(7117), 288–294. Chanil, D., & Major, M. (2006 a). On the case. Progressive Grocer June 1. Chanil, D., & Major, M. (2006 b). Proofing positive. Progressive Grocer June 1. Clemens, R. (2004). The expanding US market for fresh produce. Iowa Agricultural Review, 10(1), 8–9. Dies, R. C. (1994). Adding bulk without adding sucrose. Cereal Foods World, 39, 93–97. Dimitri, C., & Greene, C. (2002). Recent growth patterns in the US organic foods market. US Department of Agriculture, Economic Research Service, Market and Trade Economics Division and Resource Economics Division. Agriculture Information Bulletin 777. Drewnowski, A., & Specter, S. E. (2004). Poverty and obesity: The role of energy density and energy costs. American Journal of Clinical Nutrition, 79(1), 6–16. Dubois, G. E., Walters, D. E., Schiffman, S. S., Warwick, Z. S., Booth, B. J., Pecore, S. D., et al. (1991). Concentrationresponse relationships of sweeteners: A systematic study. In D. E. Walters, F. T. Orthoefer, & G. E. DuBois (Eds.), Sweeteners: Discovery, molecular design, and chemo­ reception (pp. 261–276). Washington, DC: American Chemical Society. Dwyer, K. (2006). Natural flock expands with converts. Natural Foods Merchandiser, 27(8), 16–17. Gibbs, B. F., Alli, I., & Mulligan, C. (1996). Sweet and tastemodifying proteins: A review. Nutrition Research, 16(9), 1619–1630. Hanger, L. Y., Lotz., A., & Lepeniotis, S. (1996). Descriptive profiles of selected high intensity sweeteners (HIS), HIS blends, and sucrose. Journal of Food Science, 61(2), 456–464. Katcher, H. I., Legro., R. S., Kunselman, A. R., Gillies, P. J., Demers, L. M., Bagshaw, D. M., & Kris-Etherton, P. M. (2008). The effects of a whole grain-enriched hypo­caloric diet on cardiovascular disease risk factors in men and women with metabolic syndrome. American Journal of Clinical Nutrition, 87(1), 79–90.

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Kirkpatrick, S., & Tarasuk, V. (2003). The relationship between low income and household food expenditure patterns in Canada. Public Health Nutrition, 6(6), 589–597. Levi, J., Juliano, C., Segal, L. M. (2006). F as in fat: How obesity policies are failing in America. Trust for America’s Health Issue Report, August 2006. Online. Available: http://healthyamericans.org/reports/obesity2006/ Obesity2006Report.pdf/. Martinez, S., & Stewart, H. (2003). From supply push to demand pull: Agribusiness strategies for today’s consumers. Amber Waves, Economic Research Service, USDA, 1(5), 22–29. Mellgren, J. (2006). Evolving food trends. The Gourmet Retailer, VNU eMedia Business Publication. Online. Available: http://www.gourmetretailer.com/gourmetretailer/ search/article_display.jsp?vnu_content_id1001804386/. Mintel (2008). Mintel global new products database. Online. Available: www.mintel.com/. Muir, D. D., Hunter, E. A., Williams, S. A. R., & Brennan, R. M. (1998). Sensory profiles of commercial fruit juice drinks: Influence of sweetener type. Journal of the Science of Food and Agriculture, 77, 559–565. Nettleton, J. A., Steffen, L. M., Loehr, L. R., Rosamond, W. D., & Folsom, A. R. (2008). Incident heart failure is associated with lower whole-grain intake and greater high-fat dairy and egg intake in the atherosclerosis risk in communities (ARIC) study. Journal of the American Dietetic Association, 108(11), 1881–1887. Nofre, C., & Tinti, J. M. (2000). Neotame: Discovery, properties, utility. Food Chemistry, 69, 245–257. Organic Trade Association. (2007). 2007 Manufacturer survey. Organic Trade Association. Online. Available: www. ota.com/. Pollack, S. L. (2001). Consumer demand for fruit and vegetables: The US example. In A. Regmi (Ed.), Changing structure of global food consumption and trade, WRS-01-1. Washington, DC: Economic Research Service/USDA.

Putnam, J., Allshouse, J., & Kantor, L. S. (2002). US per capita food supply trends: More calories, refined carbohydrates, and fats. FoodReview, 25(3), 2–15. Ricciuto, L., Tarasuk, V., & Yatchew, A. (2006). Socio-demographic influences on food purchasing among Canadian households. European Journal of Clinical Nutrition, 60(6), 778–790. Sandrou, D. K., & Arvanitoyannis, I. S. (2000). Low-fat/calorie foods: Current state and perspectives. Critical Reviews in Food Science and Nutrition, 40(5), 427–447. Senomyx Inc. (2008). Senomyx annual report, 2008. Online. Available: www.senomyx.com/. Shulka, T. P. (1995). Problems in fat-free and sugarless baking. Cereal Foods World, 40(3), 159–160. Stubbs, J., Ferris, S., & Horgan, G. (2000). Energy density of foods: Effect on energy intake. Critical Reviews in Food Science and Nutrition, 40(6), 481–515. Turrell, G., & Kavanagh, A. M. (2006). Socio-economic pathways to diet: Modeling the association between socioeconomic position and food purchasing behaviour. Public Health Nutrition, 9(3), 375–383. Turrell, G., Hewitt, B., Patterson, C., Oldenburg, B., & Gould, T. (2002). Socioeconomic differences in food purchasing behaviour and suggested implications for dietrelated health promotion. Journal of Human Nutrition and Diet, 15(5), 355–364. USDA/Economic Research Service. (2009). Loss adjusted food availability. Economic Research Service (ERS) Food Availability (Per Capita) Data System. Online. Available: http://www.ers.usda.gov/Data/FoodConsumption/. van de Vijver, L. P. L., van den Bosch, L. M. C., van den Brandt, P. A., & Goldbohm, R. A. (2009). Whole-grain consumption, dietary fibre intake and body mass index in the Netherlands cohort study. European Journal of Clinical Nutrition, 63, 31–38. Wiet, S. G., & Beyts, P. K. (1992). Sensory characteristics of sucralose and other high intensity sweeteners. Journal of Food Science, 57(44), 1014–1019.

2.  From Society to Behavior: Policy and Action

C H A P T E R

46 Aligning Pleasures and Profits: Restaurants as Healthier Lifestyle Enablers Jordan LeBel Marketing Department, John Molson School of Business, Concordia University, Montreal, Canada o u tl i n e 46.1  Introduction

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46.4  How Restaurants Compete

572

46.2  Industry Overview 46.2.1 All Restaurants are not the Same 46.2.2 Challenges Facing Foodservice Operators

568

46.5  Ways Forward 46.5.1  Changing Mind-Sets 46.5.2  Changing Practices

573 573 575

46.6  Conclusion

577

46.3 Food-Away-From-Home Demand Drivers

568 570 570

46.1  Introduction Consumers now spend close to half of their food dollar on food-away-from-home, and slightly more than 15 percent of their meals come from restaurants. Deservingly or not, the foodservice industry has recently come under heavy criticism for its role in and slow response to the obesity epidemic. This chapter aims to inform restaurateurs’ efforts to more actively support the consumers’ quest for healthier and balanced lifestyles.

Obesity Prevention: The Role of Brain and Society on Individual Behavior

Health and pleasure have long been perceived as irreconcilable motivations for eating: if it tastes good it cannot be good for you, and vice versa. Approach-avoidance conflicts over food are evidenced in many ways: while Food Network and other food media enjoy unprecedented popularity, sales of all things “diet” and exercise equipment increase steadily. Consumers demand healthier fare, yet sales receipts tell a different story. “Health”, as a selling point, reaches only the already health-conscious consumers, whereas

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2010 Elsevier Inc. © 2010,

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46.  Restaurants and healthier lifestyles

for others the mere word itself triggers avoidance behaviors. A similar divide exists for many industry professionals who believe that healthy items will not sell. Making matters worse is the fact that healthier foods are often perishable and cost more than less healthier items. Finding the right mix of menu items, and packaging, branding and aligning the myriad of other processes and systems necessary to take healthier menu items to market, at a profit, is a daunting challenge. This chapter begins with an overview of the industry and then outlines some of the key demand drivers. Next, common dimensions or bases used by restaurants to develop a competitive edge are explained. Finally, various ways forward are outlined, focusing on three critical mind-set changes that must take place for restaurants to play a more active role in solving the obesity epidemic.

46.2  Industry overview This section serves as an introduction to the industry, and outlines some of its key operational challenges. This will allow the reader to understand the structure of the industry and to “follow the money”, for along that path ideas and opportunities surface for addressing the obesity epidemic. It will also provide the rudimentary knowledge necessary to collaboratively envision successful ways forward.

46.2.1  All restaurants are not the same The foodservice industry is large and multisegmented. In the US alone, 2008 industry sales were expected to reach a staggering $558 billion, generated by more than 945,000 restaurants.1 To this economic impact, add the fact that for each dollar spent by consumers in the restaurant industry, $2.34 are spent in supplier industries (NRA, 2007). An overview and sales breakdown

by major industry segment for both the American and Canadian foodservice industries appears in Table 46.1. As seemingly alike as restaurants appear, the foodservice industry is composed of nearly 40 different segments or groups of foodservice operators with distinctively different operating realities and/or business models. To make sense of this industry, it is insightful to consider different, non-mutually exclusive characteristics of foodservice operators. First, foodservice operations differ in terms of their commercial orientation. Commercial foodservice operators are in business to make money: profit maximization is their primary objective. Non-commercial operators, by contrast, focus on Table 46.1  American and Canadian foodservice industry overview and revenue projections, 2008 US

Canada

Units

954,000

Units

62,000

Labor force

13.1 million

Labor force

1.04 million

Commercial restaurants

$510.4

Commercial restaurants

$42.6

Eating places Bars and taverns l Managed services l Lodging places l Retail, vending, mobile

$376.7 $16.4 $38.3 $27.6 $51.4

l

Full service Limited service l Caterers l Pubs, taverns, nightclubs

$19.9 $16.7 $3.6 $2.4

Non-commercial restaurants

$45.9

Non-commercial restaurants

$12.5

Military

$2.0

Total

$558.3

Total

$55.1

l l

l

Note: “Eating places”, according to the NRA, includes caterers, snack bars, and what most consumers would imagine when thinking of a “restaurant”. Managed services are companies, such as Aramark or Sodexo, that contract with a host (e.g., school) to handle its foodservice needs. Lodging places include restaurants and banquet facilities located in hotels. Sources: NRA (2008), www.crfa.ca.

1

Unless otherwise noted, statistics and figures for this section are derived from the 2008 forecasts by the National Restaurant Association (US) and the Canadian Restaurant and Foodservices Association.

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46.2 Industry overview

covering their costs, and leftover profits are usually earmarked for reinvestment in the business. School districts, hospitals, universities, prisons, and employers who run their own foodservice operations are considered non-commercial vendors. For them, keeping costs within acceptable limits is a constant struggle. Another distinction involves the difference between full-service and quick-service restaurants (also known as “fast food”; hereafter “QSRs”). The QSR (e.g., Burger King, McDonald’s, etc.) segment, often erroneously believed to account for the lion’s share of the industry’s sales, is expected to reach US $156.8 billion in 2008 (NRA, 2007). By contrast, the full-service segment is projected to reach $187.4 billion, and includes sub-segments like “family dining” (e.g., IHOP, Big Boy) and “casual dining” (e.g., TGIFridays; Olive Garden). Three other characteristics help in understanding the structure of the industry: (1) the price or quality tier, (2) the ownership type, and (3) the concept. The industry’s price tiers are undergoing a transformation. QSR typically commands an average check of $6 or less per customer; the mid-scale (or casual) dining sector comes in at $6–20 per person; anything higher than $20 is labeled as upscale or fine dining. The arrival of a new segment known as “fast casual” a decade ago altered this classification and forced competitors to modify their offering. “Fast casual” restaurants (e.g., Au Bon Pain, Panera Bread, and Chipotle) command a $6–9 average check, delivering higher-quality food (particularly in terms of freshness) than their QSR competitors. The success of fast casual restaurants has prompted QSR operators to improve their menu selections and mid-scale operators to “casualize” their offering and/or lower their prices. A restaurant’s ownership also influences its ability to compete and invest resources to develop healthful fare. “Independent” operators are usually “mom and pop” businesses with no brand

569

affiliation. By contrast, a chain consists of mul­ tiple units operating under a single brand name, such as Wendy’s or Outback Steakhouse. Although chains account for the bulk of the industry’s total sales, “independents” represent the majority of restaurants. In the US, seven out of ten restaurants are independently owned and have less than 50 employees (NRA, 2007). In Canada, 62 percent of restaurants are independents.2 Independents must find success in the shadow of chains with larger budgets for marketing and research and development. Chains, on the other hand, have a different set of challenges, one of which is to manage their relationship with franchisees who can often resist proposed changes to the menu or general concept. The type of concept, often defined on the basis of central menu items or cuisine (e.g., Chinese, pizza, chicken, etc.), is another important factor, and each concept has a unique competitive dynamic. For example, the pizza segment is a mature segment (meaning sales are not increasing significantly), and its challenges and dynamics are different from those of, say, a chicken-based concept. For instance, pizza is the number one take-out food – 78 percent of all orders are taken home or delivered (Mintel, 2007) – and, for this reason, pizza restaurants have been particularly sensitive to the rising cost of gasoline. Some concepts are inherently more successful than others: the five top companies in the “sandwich” segment (e.g., McDonalds, Burger King, Taco Bell, etc.) generate over $57 billion in sales in the US, more than 10 percent of the industry’s total sales (Walkup, 2007). The popularity of sandwiches is due to the fact that they are easy to prepare and to standardize, and, from the consumer’s point of view, they offer portability and convenience. The king of sandwiches is still the burger, accounting for 14 percent of all restaurant orders in 2007, representing 8.5 billion burgers in the US alone (Newman, 2008).

2

As per the website of the Canadian Restaurant and Foodservice Association, March 2008.

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Finally, a brief mention about “managed services” that operate foodservice units in a variety of locations, such as employee cafeterias, sports centers, and schools and universities. These companies often serve somewhat “captive” audiences, but must still be competitive; they have upscaled their offerings recently. For example, in various colleges and universities, contractors like Sodexo and Aramark have been offering better service and healthier fare, publishing nutritional information, and reaching out to parents in an effort to keep students enrolled in dining plans longer (Sheridan, 2005). The total US educational foodservice market is valued at $28.7 billion, with $9.28 billion going to commercial managed services, of which primary and secondary schools are the largest market at $4.18 billion per year.

46.2.2  Challenges facing foodservice operators Controlling food and labor costs is a key concern of any restaurant manager. In 2008, the industry was expected to spend a total of more than $202 billion on securing the foods and beverages it needed for its operations (NRA, 2007). Cost of goods (food plus beverages) and labor costs together typically represent over 60 percent of a restaurant’s sales, leaving many restaurants struggling to cover the numerous remaining costs, such as rent, uniforms and laundry, permits, repairs and maintenance, marketing, not to mention interest payment. Hiring and retaining qualified employees remains a challenge for all foodservice operators. Among other issues facing the sector, the obesity epidemic has certainly loomed large, as well as pressures from various consumer groups to “go green” and to develop more sustainable practices. The pressure to remove trans fats from menus, whether locally mandated or voluntary, has added to the costs of doing business. Other issues include the responsible service of alcohol (with significant training

costs), reducing marketing to children (particularly for QSRs), and food safety and preventing food-borne illnesses. These challenges place ever-increasing pressures on already slim profit margins. For operators who may not have the resources to remain abreast of changing laws or the latest research (e.g., in nutrition), finding and implementing solutions to some of these issues can be daunting.

46.3  Food-away-from-home demand drivers Food-away-from-home (FAFH) now accounts for almost half of the consumer’s food dollar. In 2006, the typical American household spent $2694 on FAFH, which represents 46.4 percent of its total expenditure on food (NRA, 2008). In Canada, FAHF now accounts for 41.4 percent of a household’s food budget, according to the CRFA. On a per capita basis, American consumers spent $1264 on foodservice, followed by Japanese consumers with $1195, and Canadian consumers with $1166 (Anon., 2007a). Moreover, many restaurant meals are no longer consumed in restaurants. In 2006, the average American ate 81 meals in restaurants and another 127 restaurant meals at home (Horovitz, 2007). In the case of QSRs, fully 54 percent of all take-out orders are eaten at home (Mintel, 2007). Increased spending on FAFH and the pene­ tration of prepared meals into the home may be explained in part by socio-demographic factors. Higher income correlates with greater spending on prepared meals: a 1 percent annual growth in real income translates into more than a 20 percent increase in spending on FAFH (Stewart et al., 2004). Not surprisingly, higher socio-economic status households (i.e., income above $70,000) spend more on FAFH – approximately $4502 annually (NRA, 2008). An aging population and the growing number of single-person households (expected to increase from 22.7 percent

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46.3 Food-away-from-home demand drivers

in 1980 to 28.6 percent in 2020) are also increasing the demand for foodservice (Stewart et al., 2004). Finally, changes in ethnic composition affect demand for FAFH – for example, more than half of the foreign-born US population now comes from Latin America, influencing new flavors brought to market and fueling the popularity of Mexican-inspired concepts. Our modern lifestyle also creates demand for FAFH. Today’s consumers frequent restaurants out of necessity and for pleasure, producing two key demand drivers fueling much of the industry’s innovations. The first driver is actually the result of two joint forces: convenience and customization. Time-stressed consumers place a premium on convenience, and FAFH offers a less time-consuming alternative to cooking at home. According to a survey conducted by the NPD Research Group, “not having to cook” was the number one reason given for eating out by 41 percent of respondents, distantly followed by “celebrating a special occasion” (10 percent of respondents) (Anon., 2006). Convenience and ease of cooking even drive the decision regarding what to cook and eat at home, ahead of health and taste (NPD, 2006). Moreover, consumers want to be able to customize their menu choices, which drives menu development efforts (Smith Hamaker and Panitz, 2002). In short, consumers want it now, and they want it their way. Another important demand driver is the fact that consumers no longer simply want a meal, but rather a dining experience (NRA, 2008). This requires restaurateurs to think about the nature and level of involvement that consumers will experience throughout their meal (Pine and Gilmore, 1999), as well as the emotional responses and specific type(s) of pleasure (Dubé and LeBel, 2003) to be produced. Whether that emotional wellbeing and experience comes in the form of a Happy Meal box with a clown, in a décor that affords a tropical escape in the dead of winter, or involves a chef providing entertainment by

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playfully sautéing ingredients in front of the diner, eating out has become part of our lifestyle. Young consumers are also fueling demand for FAFH. The more than 70 million Generation Y or “millennium” consumers, born between 1978 and 2000, are avid restaurant users. Males aged 18–24 are particularly influential, as they represent the most frequent buyers of fast food. “Tweens” aged 6–12, with their spending power and eventual lifetime loyalty, are another attractive segment for restaurants. These young consumers have more than $20 billion to spend, with up to 30 percent going on food and beverages (Lempert, 2004). Their influence over family eating-out decisions makes them even more attractive to the industry: over 55 percent of FAFH expenditure originates from households with children (UniPro, 2007a). Research conducted by McCain Foods,3 one of the industry’s largest suppliers, puts the value to the industry of America’s 41 million children as high as $43 billion. For these consumers, more so than for their parents, eating out is a way of life and must be fun (UniPro, 2007b). In a survey conducted by the US National Restaurant Association, 6 out of 10 teenagers said they would rather eat at a restaurant than at home (NRA, 2008). Their eating out decisions are often based on convenience and motivated by their lack of cooking skills (Mintel, 2007). Their menu choices gravitate towards popular fast-food items, with chicken fingers, grilled cheese sandwiches and burgers being top choices (Mintel, 2006). Even when making healthy choices, convenience is still a driving factor: in school cafeterias, for example, sliced apples and wedged oranges sell better than whole fruits (Martin, 2005). Younger consumers are also highly technology savvy, and drive new product and service innovations. In the same NRA survey, about two-thirds of teenagers said they would use Wireless if available in a restaurant; a similar proportion said they would use an MP3 docking station while eating out.

3

http://www.mccainkids.com

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46.4  How restaurants compete To envision new ways that the foodservice industry could contribute to healthier lifestyles, it is useful to understand how restaurants compete and attempt to win customer loyalty. The total offering of a brand can be conceptualized as a combination of a core product or benefit and an augmented product (Levitt, 1983). At the core of a restaurant’s offering is a unique marketing mix, characterized by a combination of product, price, promotion and place, otherwise known as the “4 Ps”. The “product” includes a number of elements, food and service being the most critical. Far from being merely an obvious observation, this fact has important implications. Processes surrounding the production and delivery of food are often secretly guarded, as they constitute the core competencies of the restaurant and lie at the heart of the brand’s identity and the experience it offers. Portion size, taste and variety are typically the food-related attributes that restaurants focus on to earn customers’ approval and loyalty. Considerable time, money and efforts go into developing the processes necessary to ensure that each new item can be produced quickly and reliably across multiple locations (with the shortage of qualified labor adding to this challenge). For example, McDonald’s spent years developing the recipe and production processes, and aligning some 34 suppliers, before it introduced its new upscale Angus Beef Burger. The centrality of food in a restaurant’s brand identity, and the fact that chefs like to cook with fat and are convinced that customers do not want to count calories (Sagon, 2006), partly explain why many are reluctant to make dramatic changes to their core offering in favor of smaller portions and/or lower calorie-density foods. Changing the food core offering can be risky for a brand and confusing for consumers. For example, KFC’s recent introduction of

grilled chicken items, while a welcome move towards more balanced options on the menu, is a risky departure from the brand’s core feature, which has long been fried chicken with “secret” spices. The investments and planning may not yield a high return if consumers do not embrace the new message and take to these innovative items. Service processes are also a core competitive variable for restaurants, and technology is being used to involve customers in various parts of the service. For example, Domino’s online ordering promotion, called “Big Fantastic Deal”, lets customers make their own pizza through an Internet website and then save and name their creation. Such tactics are changing the ordering process and forging a relationship between the brand and its customers. The payment process is also changing: cashless transactions are now the norm, and not only increase efficiency but also impact sales. Sodexo, for instance, reported a 30 percent increase in sales when it introduced a cashless payment option (Leahy, 2006). Competition can also focus on specific meal periods (“dayparts” in industry lingo). At the moment, competition over breakfast is fierce. In a recent survey of its members, the NRA found that 61 percent of QSR operators said breakfast now accounts for a larger share of their revenues than a year ago (NRA, 2008). To win market share, portability and speed of service are critical. In 2006, for instance, each American purchased on average 7.8 breakfasts from restaurants to eat in a car (Anon., 2007b). The dinner meal period, however, has seen decreasing sales during the current economic downturn: in 2007, there were 377 million fewer dinner visits than in 2006. To fill the valleys between meal periods and to offset declining dinner sales, many chains are exploring between-daypart opportunities with snack items: Quiznos has introduced its $2 Sammies, McDonald’s has its Snack Wraps, and KFC has its Twister. In the crowded competitive landscape, marketing has emerged as an important competitive

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tool and a means to craft a strong brand. The most successful brands use marketing more strategically, as a means to carve an intimate relationship with their customers and to solidify the attachment they feel towards the brand. As a strategic differentiator, marketing involves more than merely selling or advertising one’s product, and instead becomes concerned with every point of contact between the brand and customers. This requires a more comprehensive understanding of customers’ needs to seize on every opportunity to deliver a better brand experience. Will customers choose a brand because of its marketing efforts? Neuro-imaging (see, for example, McClure et al., 2004) and behavioral studies (Fitzsimons, Chartrand, & Fitzsimons, 2008) indeed support the claim that brands imbued with stronger and more positive associations generate different thought patterns and approach behaviors. Forward-looking chains are indeed moving towards a greater emphasis on marketing. McDonald’s, for example, has embraced new technology (e.g., wireless) and new tactics (e.g., customer-generated content) to reach its varied audiences, and has developed new messages, such as the need for physical activity (via Olympic sponsorship, for instance). Other competitive tactics currently used include upstreaming, upselling and upscaling. Upscaling refers to the use of higher-end ingredients, accompanied by a higher selling price. Upselling involves suggestive selling tactics designed to get customers to spend more than they intended. Upstreaming involves remodel­ ing to improve the appearance of the premises, with or without changes to other aspects. Finally, it should be noted that restaurants often compete by augmenting their basic or core product with features that are designed to keep customers interested in the brand. Many such tactics are prompted by the need to reach younger consumers. McDonald’s, for example, has installed a RedBox terminal in many restaurants, where customers can rent DVDs. Others, such as Starbuck’s, offer Wireless Internet access,

often in an effort to become the “third place” (favourite hang out) of choice for its customers. The challenge is to select features that will matter to customers and will not dilute the brand’s strengths and take its focus away from its core competencies.

46.5  Ways forward 46.5.1  Changing mind-sets For any obesity-reducing intervention to be more than a quick-fix idea, restaurants must adopt a broader perspective and reassess some fundamentals about their business. In this section, three mind-set changes are proposed. Restaurants as lifestyle enablers How a firm views its core product defines how it sees itself and positions itself in the ­market place. To play a more active part in solving the obesity epidemic, restaurants must redefine themselves as “healthy lifestyles enablers”. Embracing social issues, such as healthy lifestyles, as part of their core strategy can help companies protect shareholder value for the long term (Davis, 2005). Perhaps the most forwardlooking company in this regard has been Sub­way, which has gone the furthest in positioning itself as a healthy lifestyle enabler, essentially by inviting consumers to “lose weight together”. Making health a core preoccupation, as opposed to merely the focus of a short-lived ad campaign or flavor-of-the-month sponsorship, requires companies to rethink their offering, not by providing exclusively healthy fare but by striking a better balance and making healthier fare more appealing and easy to obtain. By means of an example from an industry known for selling snacks and junk foods, consider the case of Couche-Tard, operator of more than 5700 convenience stores across the US (under the brand Circle-K) and Canada. In a move to put health at the forefront

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of its strategy, the company recently introduced a new concept to sell fresh food items and healthy prepared meals. Appealing dishes were developed with the assistance of nutritionists, and the strategy includes a complete store-redesign to entice consumers to select the healthier options. The environment in which selections are made and the presentation of different options are as important as the options themselves. In the restaurant industry, finding the right combination of fresh healthy food with proper delivery and service processes can be profitable (Holloway, 2006). Many small upstarts and entrepreneurs have developed unique healthoriented concepts with considerable success. For instance, Massachusetts-based Fresh City has expanded quickly by offering what it calls “fresh fusion” cuisine with evocatively named menu items like its Kyoto Wrap and made-toorder salads and sandwiches. Delivering more “pleasures per calorie” For many, a restaurant meal is an efficient refueling opportunity, with at best some immediate sensory gratification. The industry’s emphasis on convenience has certainly contributed to strengthen that attitude. However, as Schmitt (1999) points out, many companies focus on functionality and convenience and thus overlook opportunities to deliver more pleasurable brand experiences. In fact, pleasure is one of the key purchase and consumption motivators, and individuals readily recognize and distinguish among four different types of pleasures: sensory, social, emotional/esthetic, and intellectual/discovery (Dubé and LeBel, 1999, 2003). Consumers also associate product categories (Dubé et al., 2002), specific brands (Sears et al., 2003) and even websites with specific types of pleasure (Dubé et al., 2002). Naturally, a single pleasurable experience may involve more than one pleasure. Restaurants should explore new ways to deliver low-calorie pleasures and re-examine the cues and physical elements that can produce more

pleasures. In general, the industry emphasizes a very limited range of sensory stimulations, mostly short-lived gustatory pleasure driven by fat–sugar or fat–salt combinations followed by the well-known pangs of regret and heartburn, and long-term consequences for the waistline. Maximizing “pleasures per calorie”, restaurants could enhance each or some of the four pleasures listed earlier while reducing calorie content. Lindstrom (2005) recommends seeking ways to leverage all sensory touch points in the brand experience in a coherent and synergistic manner. Appealing visual presentation, smell and even tactile sensations could be enhanced. Sushi, for example, is popular amongst college students, in part because of its appealing presentation (UniPro, 2007c). As for social pleasure, consider “social caterers”, which now number almost 700 across the US. In large communal kitchens, these caterers provide customers with the opportunity to gather and cook together and package the cooked food to take home. The resulting conviviality and social pleasure is a key driver of these caterers’ success. As for intellectual pleasure, Disney’s success in turning “edu-tainment” into a profitable business suggests untapped potential for restaurants to do the same. These four pleasures offer a useful and simple heuristic through which to evaluate tactics or changes to a brand experience: any proposed change should be evaluated for its ability to deliver more or one or a combination of these pleasures. By delivering more “pleasures per calorie”, restaurants can enrich their brand experience and its competitiveness. They can also begin to move away from the relentless titillation of the tastebuds and emphasis on hunger satisfaction and instead, as lifestyle enablers, begin to address other psychological needs that can be fulfilled by the act of eating. Revisiting the role of marketing Uncovering the particular pleasures associated with balanced and healthy lifestyles and

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46.5 Ways forward

devising novel means to link health and pleasure should be a priority. In that regard, exper­ iential communications should be used for their ability to communicate and support the delivery of the specific pleasures promised by the brand (Schmitt, 2003). Making use of technology, restaurants could devise interactive games and promotions to build children’s and older consumers’ knowledge of nutrition in order to make educated choices. Tools like animated trade characters, for instance, have a unique ability to draw audiences into deeply engrossing narratives and emotional relationships (LeBel and Cooke, 2008), and could be used to engage consumers and to communicate more effectively the key concepts of a balanced lifestyle. Research and development, and menu item development in particular, should be top marketing priorities. For instance, while comfort foods have long been thought to be exclusively high-calorie items, recent research revealed that young consumers do associate low-calorie foods with comfort (Dubé et al., 2005; LeBel et al., 2008). More research into the role of taste but also, importantly, texture, packaging and appearance in driving preferences and consumption of low-calorie foods should be a priority for developing successful healthy menu items.

46.5.2  Changing practices Following from the three mind-set changes described in the preceding section, this section outlines interventions that would move restaurants closer to becoming healthy lifestyle enablers.

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point. However, as portion size is related to the perception of value, this must be done creatively and perhaps gradually. As an example, TGIFriday’s “Right Portion, Right Price” menu contains portions that are about two-thirds smaller than usual, at prices ranging from $5.99 to $9.99. Beyond portion control, cooking techniques and ingredients must be re-examined. Many companies have been proactively taking steps to deliver fresher, tastier and healthier fare. For example, Seasons 52, a new concept by Darden Restaurants, where entrées are no more than 475 calories, took years of research and development, as every recipe was scrutinized to eliminate butter and to explore different cooking techniques such as steaming. Although Seasons 52’s average check is over $50 per person, the concept’s success illustrates the feasibility of producing good-tasting food with fewer calories. Focusing exclusively on “healthy” often fails to sell; instead, healthier food should be fun and hip. UK-based Wagamama cleverly exploited the influence of décor, branding and packaging as powerful determinants of healthy food selection, but also as means of conveying pleasures other than sensory. Foodservice providers can also help reach consumers at home. As restaurants and grocery stores (with their prepared food sections) battle to keep their share of the consumer’s food dollar, take-out foods and home meal replacements are appropriate vehicles to reach consumers and promote healthier balanced choices. Take-out containers and other objects, once in the home, have profound and lasting influences on eating practices (LeBel and Richman Kenneally, 2009). Education

Product Since portion size is one of the chief determinants of intake (Wansink and Kim, 2005) and thus contributes to rising obesity rates, reducing portion sizes is a logical starting

Moving towards more balanced lifestyle choices will require re-educating an entire population regarding the pleasures and benefits of healthier eating, targeting not just the end consumers but all stakeholders and gatekeepers

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as well. Learning to appreciate healthier foods and eating habits starts at home. Public policies and programs that build children’s knowledge, cooking skills and interest in food, along with their parents’, are necessary first steps. Schools must also play a role in working with their foodservice provider to promote healthier foods and teach students about the different pleasures of eating. For example, the College of the Holy Cross in Worcester (MA) offers Slow Food dinners as a means to show college students why they need to take time to dine and enjoy their meals (Boss, 2006). Educational efforts should also target industry professionals, many of whom are grossly unaware of the magnitude and consequences of the obesity epidemic. More than awareness-building, professional education should also provide industry workers with the skills necessary to devise effective obesityreducing solutions of their own. Proposals to place calorie count on menus have led to a heated debate. Building on the lessons from integrated marketing communications (where multiple media and vehicles are used to deliver a message), delivery of nutritional information should target different points along the decision process and consider consumers’ existing knowledge, motivation, and barriers to behavior. Solely printing calories on a menu, without proper support material, fails to consider the entire decision process. Far ahead of the point of selection, consumers may feel strongly motivated to eat healthy, and information can focus on building awareness for healthy-eating alternatives. However, motivation to eat healthy may wane the closer one gets to the point of selection, and as hunger pangs and other visceral inputs prevail. Information delivered closer to the point of selection should be user-friendly (i.e., visible, easy to grasp) and accompanied by support material. For example, how is a consumer to evaluate whether 500 calories for a sandwich is appropriate if he or she has no notion of what an appropriate daily calorie intake should be? The point of selection

(i.e., when a customer orders) and the point of consumption (e.g., food eaten at home or in the car) represent distinct opportunities to deliver balanced lifestyle messages. Still, more evidencebased research is needed on the impact of such strategies and on the more appropriate means of communicating nutritional information. As public officials press for more legislation, they should also be more proactive and provide industry professionals with the resources to develop and enact effective solutions. Alliances The magnitude of changes needed to achieve healthier lifestyles beg for alliances to be forged with actors within and outside the restaurant industry. Product- or distribution-based deals between manufacturers and restaurants are probably the most common. Consider, for example, the recent introduction of yogurt by Danone to more than 20,000 Subway shops. With 26 million visitors to Subway restaurants each week, that alliance has significant potential. Another good example is Subway’s partnership with Discovery Kids to develop television messages, in-store material and online promotions encouraging children to adopt healthier behaviors and “play hard and eat fresh” (Cebrzynski, 2006). To develop the needed capacities and skills, and increase the reach and scale of such efforts, alliances must enable knowledge transfer and more intense marketing of low-density foods. A recent initiative by the Greater Dallas Restaurant Association and the Medical City Heart illustrates the kind of capacity-sharing needed. Participating restaurateurs highlight healthier dining options for consumers at risk, and receive City Heart’s endorsement and the services of dietitians to evaluate and improve recipes (Ruggless, 2007). Actors with considerable reach, such as media outfits, can also be useful partners to promote healthier foods. One such notable example is Disney’s decision to license its characters to products meeting specific

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references

nutritional criteria, and its partnership with Imagination Farms to launch a line of characterthemed fruit and vegetable snacks and side dishes.

46.6  Conclusion Reversing the obesity epidemic requires a departure from business-as-usual by all actors whose decisions and interventions, directly or not, affect the root causes of obesity. Moreover, achieving healthier lifestyles will require nothing less than a cultural and industrial re­volution. Processes and techniques, from production methods to delivery mechanisms, must be realigned to deliver and promote healthier eating. The success of entrepreneurs like Fresh City, Saladworks and others, as well as the success of individual interventions such as salads from giants like McDonald’s, show that producing and delivering tasty, healthy food in a commercial setting is achievable and commercially viable. In addition, a cultural shift is needed to make such healthier options more desirable. Changing attitudes towards low energy-density foods will require the involvement of cultural gatekeepers, for, in effect, we must change the web of metaphors and associations whereby health and pleasure are perceived as mutually exclusive. Also needed is a fundamental examination of the very purpose of restaurant meals. While more consumers eat at restaurants out of necessity, their choices are often motivated by a decision mind-set that still views a restaurant meal as a treat. Confronted with a menu board, consumers care more about immediate sensory pleasure maximization than health, where the impact or consequences are less immediate. That is why the relationship between health and pleasure must be better understood and more creatively packaged or presented in a foodservice context.

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Interventions should also consider the broad spectrum of food-at-home and food-away-from-home alternatives. Although the foodservice industry’s share of the food dollar has steadily increased over time, foodaway-from-home only accounts for less than 20 percent of all meals consumed. Partnerships, such as Danone’s alliance with Subway, that can promote healthier food choices in and out of the home are urgently needed.

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Pine, B. J., & Gilmore, J. H. (1999). The experience economy. Boston, MA: Harvard Business Press. Ruggless, R. (2007, November 12). Dallas hospital teams up with restaurants to promote heart-healthy dining options. Nation’s Restaurant News. Sagon, C. (2006, May 3). At the chefs’ table, the subject was fat. Washington Post, F01. Schmitt, B. (2003). Customer experience management. Hoboken, NJ: Wiley & Sons. Sears, D., LeBel, J., Dubé, L. (2003). Differentiating hedonic consumption on the basis of experiential qualities and emotional make-up. In, B.E. Kahn, and, M.F. Luce (Eds.), Advances in consumer research. ACR Proceedings, 30(1). Sheridan, M. (2005, June 15). Perfect pitch. Restaurants & Institutions, 47-48, 50. Smith Hamaker, S., and Panitz, B. (2002, May). In vogue: what’s hot in the restaurant industry. Online. Available: http://www.restaurant.org/rusa. Stewart, H., Blisard, H., Bhuyan, S., & Nayga, R. N. (2004). The demand for food away from home Agricultural Economic Report Number 829. Washington, DC: United States Department of Agriculture. UniPro. (2007a, August). Innovative menus and – yes – healthier foods capture kids’ attention. Operator’s Edge Online Newsletter. UniPro. (2007b, August). Kids’ eating habits are changing. Are your menus reflecting their demands? Operator’s Edge Online Newsletter. UniPro. (2007c, August). Attracting the 20-something crowd with share menus. Operator’s Edge Online Newsletter. Walkup, C. (2007). Sandwich leaders lure customers back from upscale rivals by expanding offerings and operating hours. Nation’s Restaurant News, 41(26), 84. Wansink, B., & Kim, J. (2005). Bad popcorn in big buckets: Portion size can influence intake as much as taste. Journal of Nutrition Education and Behavior, 37(5), 242–245.

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C H A P T E R

47 A Study of Corporate Social Responsibility Activities of 12 Giant Food Companies (1980–2008) in Promoting Healthy Food Shanling Li, Dan Zhang and Wenqing Zhang Desautels Faculty of Management, McGill University, Montreal, Canada

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47.1  Introduction The prevalence of obesity is rapidly increasing globally, with at least 155 million overweight or obese children worldwide, according to the International Obesity Task Force (Hossain et al., 2007). In 2004, 26 percent of Canadian children and adolescents aged 2–17 years were overweight and 8 percent were obese (Shields, 2005).

Obesity Prevention: The Role of Brain and Society on Individual Behavior

Similar phenomena have been observed in many other countries. Any factor that raises energy intake or decreases energy expenditure, even by a small amount, may cause obesity in the long-term (Ebbeling et al., 2002). Nielsen and Popkin (2004) point out that consumption of sugar-sweetened beverages and fruit juice has increased considerably among children and adolescents over the past few decades. Ludwig and colleagues (2001)

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stated that for each additional can or glass of a sugar-sweetened drink consumed per day, 11year-old children were 1.6 times more likely to become overweight. Obesity in childhood causes a wide range of serious complications and increases the risk of premature illness and death later in life, raising public health concerns. In recent years, many research studies have focused on the effects of marketing on children’s dietary habits. The North-American food and beverage industries view children and adolescents as a major market force (Story and French, 2004), turning them into targets of intense and specialized food marketing and advertising efforts. Multiple techniques and channels are used to reach youth, as young as toddlers. Many studies (see, for example, Robinson, 1999; Swinburn and Egger, 2002), however, argue that children under the age of 12 cannot understand the content of advertising, marketing and commercialism, and therefore the unregulated food advertisement by food manufacturers can be hazardous to children’s health. Food and beverage companies have been under increasing pressure to take actions against the obesity epidemic, and this issue is often viewed as a crucial component of corporate social responsibility (CSR). In November 2006, the Council of Better Business Bureaus (CBBB) launched the Children’s Food and Beverage Advertising Initiative (CFBAI)1 to use advertising to promote healthy dietary habits and lifestyles in children under the age of 12. As a voluntary, selfregulatory program for food and beverage companies, this initiative is designed to shift the mix of advertising messages to children to encourage healthier dietary choices and lifestyles. Under the terms of the CFBAI, participating companies agree to devote at least 50 percent of their advertising expenditures to children under 12 in order to promote healthy food. Twelve giant food and beverage companies joined the initiative, and

each company prepared a pledge describing its commitments to comply with the CFBAI. The objective is to provide a transparent and accountable self-regulation mechanism. Key to the effectiveness of such initiatives is how the 12 firms implement and comply with their pledges. While ideally such a question should be answered using the compliance data after the initiatives have been implemented by the companies, CFBAI data will not be available in the near term. It is possible, however, to measure past firm CSR activities, and then compare them to CFBAI pledges to obtain an understanding of the relationship between overall past CSR activities and the companies’ compliance and commitments to pledge stipulations. Such a comparison can provide considerable insights into CSR activities. We argue that a high CSR activity level in the past suggests strong CSR effort, which heightens the probability of pledge compliance. On the other hand, the firms with low CSR activity levels in the past and weak commitments in the pledges will likely require close monitoring by the CBBB. Inconsistencies (high past CSR activity and low commitment level, or low past CSR activity and high commitment level) indicate mixed messages, and we suggest that further investigations need to be conducted. The research proceeds in several steps. First, we evaluate the pledges proposed by the 12 giant companies. Next, we examine CSR activities related to the promotion of healthy foods initiated by these companies from 1980 to 2008. Finally, we compare the commitments in the current pledges with the past CSR performance, and predict future compliance with their respective pledges. Specifically, the goal of our research is to answer three major questions: 1. Can we measure the commitments stated in the company pledges? 2. What have past CSR activities been with respect to the promotion of healthy foods?

1

For more information, please refer to http://us.bbb.org/WWWRoot/SitePage.aspx?site113&iddba51fbb-9317-4f889bcb-3942d7336e87.

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47.2 Literature review

3. Given their past CSR performances, how will these companies comply with their pledges? Our research shows that the CFBAI pledges of some companies match their previous CSR efforts in promoting healthy food. Interestingly, some companies with strong CSR records in the promotion of healthy food proposed relatively weak pledges. Also, some companies’ strong commitments were not corroborated by their past CSR efforts. Although it is not entirely clear whether this implies that these companies are making an effort to improve their corporate reputation or that CSR is beginning to be built into their business core foundations, the discrepancies may lead us to investigate the motivation of the participating firms and whether there are changes in their business missions and strategies. We also conduct a sensitivity analysis to investigate the impact of data sources on the overall research conclusion and show that the main results are robust against the use of alternative data sources. We first provide a brief literature review, followed by an outline of the data, sample and evaluation methodology. We then discuss the result and sensitivity analysis, before making some concluding remarks.

47.2  Literature review In this section, we briefly review the literature on CSR and the methodologies that can be used to evaluate CSR. The concept of CSR centers on the idea that a corporation may be held socially and ethically accountable by an expansive array of stakeholders such as customers, employees, governments, communities, NGOs, investors, supply-chain members, unions, regulators and media (Maloni and Brown, 2006). CSR can be viewed as an extension of the earlier concepts of the social responsibilities of businesses and

581

businessmen (Bowen, 1953; Heald, 1970; Jones, 1980). Recent years have seen the development of new CSR concepts such as the stakeholder concept, social contracts and the legitimacy concept (Freeman, 1984; Suchman, 1995; Donaldson and Dunfee, 1999). Conversely, as CSR concepts are redefined, CSR performance evaluation is also studied. Wood (1991) recognized the need to measure corporate social performance. Carroll (1991) identified four criteria when evaluating CSRs: economic, legal, ethical and philanthropic activities. Abbott and Monsen (1979) and CSR Europe (2000) discussed the data collection and an input–output approach to measure CSR. In this chapter, we are primarily interested in firm CSR performance in the promotion of healthy food, for which the related literature is rather limited. Rigby and colleagues (2004) proposed an effective implementation of the WHO Global Strategy on Diet, Physical Activity and Health, and Maloni and Brown (2006) developed a comprehensive framework of CSR to apply to the food supply chain. While this work related to our research, they do not focus on the CSR evaluation. CSR initiatives and activities are often difficult to quantify, as many researchers have pointed out (Abbott and Monsen, 1979; Clarkson, 1995; Murray and Vogel, 1997). Aupperle and colleagues (1985) proposed to specify criteria and standards. We are not aware of any study that quantitatively evaluates the CSR performance in the food industry. Our research contributes to the existing literature in three ways. First, it empirically studies the healthy food CSR activities of 12 food companies. Second, it provides a novel perspective by comparing the pledges made by these companies to their past CSR, making it possible to predict their future compliance to their commitments. Third, analytical hierarchy process (AHP) is used to measure company pledges, which opens new spheres of applicability for the classic technique.

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47.  Corporate social responsibility

47.3  Data, sample and methodology In this section, we first provide a brief description of the CFBAI and the purpose of the pledges. We then introduce an approach to quantitatively evaluate the pledges and rank them. To assess future compliance, we collected and evaluated past health-related CSR activities from 1980 to 2008. By comparing current pledge commitments with past CSR initiatives, we provide an initial prediction of future compliance with the pledges by 12 food companies. As mentioned above, the purpose of the CFBAI is to change the nutritional profile of food and beverage products and to seek a balance in the types of food and beverage products that are advertised primarily to children under 12. This new voluntary self-regulation program reflects the participants’ commitment to responsible advertising. In July 2007, CBBB announced the commitments or “pledges” of 12 participants. Together, these 12 companies accounted for more than two-thirds of children’s food and beverage television advertising expenditures in 2004. Each participating company prepared a pledge following the guidelines of the Initiative. The approved pledges of the 12 participating companies are available on the CBBB website. For example, General Mills promised that it would only advertise healthy dietary choices to children under 12. In particular, only those products that have 12 grams or less of sugar per serving will be advertised to children under 12. Companies who implemented their pledges immediately include the Campbell Soup Company, the Coca-Cola Company, the Hershey Company, Kraft Foods Global, Inc., Mars, and Unilever. To ensure compliance with the pledges by the participating companies, the CFBAI adopted yearly reviews to monitor the progress of the participating companies. While specific commitments vary, each ­participating company agrees to commit to a core set of principles, which pertain to third-party

licensed character use, product placement, interactive games and elementary school advertising. By joining the CFBAI, the companies agree to comply with their stated pledges and to be held accountable in the event of non-compliance. While the implementation of participant pledges is very important, we notice the commitments made by the 12 companies vary ­dramatically. An interesting challenge is how to evaluate the commitments and compare them in order to assess their future compliance. The process of measuring the commitments involved identifying, weighting and evaluating the content and context of the pledges made by the 12 firms. Analytical hierarchy process (AHP), a formal decision-making technique that emphasizes decision-making with intangible criteria (see Saaty, 1980, 1986, 1990) is adopted in this research to measure the commitments in the pledges. AHP is a multi-criteria decision-making tool that can reflect judgment, ideas and emotion (Brock, 1980). Rather than prescribing a “correct” decision, the AHP helps decision-­makers determine a course of action that suits their needs, wants and understanding of the ­ problem. The AHP provides a comprehensive and rational framework for structuring a problem, for representing and quantifying its elements, for relating those elements to overall goals, and for evaluating alternative solutions. It is used throughout the world in a wide variety of decision situations, in fields such as government, business, industry, healthcare and education. To evaluate the degrees of the commitment made by the 12 companies in their pledges, we completed the following AHP steps. We first identified our goal to evaluate the commitments made by the 12 food companies in their pledges. We then proposed four criteria for evaluation: (1) coverage, to examine the scope of each pledge to cover in accordance with the CFBAI program guidelines; (2) implementation, to measure whether a pledge provides detailed plans and schedules for fulfilling the commitments; (3) a benchmark, to compare a pledge against the guidelines of the CFBAI; and (4) specification,

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583

47.3  Data, sample and methodology

to refer to the clarity and healthy standards described in their products. Third, we developed the hierarchy of criteria (Figure 47.1). Fourth, we developed pairwise comparison matrixes to rate the relative importance between each pair of food companies for each criterion. We then prepared pairwise comparison matrixes to evaluate firms for each criterion, and a criteria pairwise matrix to prioritize the criteria. The composite scores computed from the pairwise matrixes ranged from 0.016 to 0.265, as shown in Table 47.1. From Table 47.1, we observe that General Mills and ConAgra are ranked first and second, while Coca-Cola and Cadbury Adams received the lowest scores. Would General Mills and ConAgra live up to their ranks? Will the firms in the bottom comply with their pledges? To assess the firms’ future compliance with their pledges,

we looked at past CSR efforts pertaining to healthy food promotion and introduction. We focused on news releases (called events) in popular newspapers and journals, and identified a total of 46 sources. Appendix A provides a list of the top 10 popular newspapers from which the data were derived. We then conducted a content analysis of news releases and article reports in the 46 sources. Content analysis technique consists of codifying qualitative information in anecdotal and literary form into categories in order to derive quantitative scales of varying levels of complexity (Abbott and Monsen, 1979). The earliest food-related CSR initiative among the 12 food companies dated back to 1980. Thus, the CSR activities cover the time period 1980–2008, and include all the announcements that reported healthy food promotion. Search

Evaluate Commitment

Coverage

Implementation

Benchmark

Specification

Figure 47.1  Framework of AHP. Table 47.1  AHP results for the twelve pledges Factor

Coverage

Implementation

Benchmark

SP

Total

Rank

General Mills

0.006838

0.017758

0.149016

0.091158

0.264771

1

ConAgra

0.017078

0.001414

0.081022

0.03437

0.133884

2

Unilever

0.006838

0.010747

0.054237

0.03393

0.105751

3

Burger King

0.016526

0.005462

0.032551

0.03437

0.088909

4

McDonald’s USA

0.011168

0.002195

0.032551

0.03437

0.080284

5

PepsiCo, Inc

0.004061

0.001414

0.053535

0.021205

0.080215

6

Kellogg Co

0.002635

0.003494

0.033252

0.023838

0.06322

7

Hershey

0.004061

0.003494

0.032551

0.006951

0.047057

8

Kraft Foods

0.006838

0.00552

0.020013

0.013722

0.046093

9

Campbell Soup

0.006217

0.003333

0.020714

0.011798

0.042062

10

Coca-Cola

0.005548

0.00099

0.013493

0.011921

0.031952

11

Cadbury Adams

0.001575

0.002258

0.007414

0.004555

0.015803

12

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47.  Corporate social responsibility

keywords included: healthy, healthful, nutritious, nutritional, wholesome, wellness, balanced, active, new, and other relevant phrases. Events were then divided into four categories: 1. New health-related product introduction. This refers to the introduction of new healthy products. For example, The Wall Street Journal (September 10, 1985) reported that Coca-Cola unveiled a plan to launch Enviga, a sparkling green-tea based soft drink infused with a tantalizing claim: consuming three 12-ounce cans of Enviga over a 24-hour period can make a healthy, normalweight individual burn 60–100 additional calories. 2. Improvement of existing products. Companies improved old products by making them healthier. For example, The Wall Street Journal (February 9, 2005) announced that “General Mills added whole grains to breakfast cereals”. The Washington Post (September 4, 2002) reported that McDonald’s would soon start frying its foods in a new oil that would significantly reduce the most harmful type of fat in its menu. 3. Healthy notion or idea announcement. Firms communicated with their customers about healthy lifestyles, or announced some caveats. For example, The New York Times (August 1, 2003) reported that General Mills said marketing in school was wrong. The Wall Street Journal (June 20, 2005) announced that General Mills planned to launch a national advertisement campaign, targeting children, that would tout the health benefits of eating breakfast cereal. 4. Public health affair participation. Firms participated in social activities to improve health. For example, The New York Times (July 7, 1995) reported PepsiCo would start a health clinic in Russia. The New York Times (July 15, 1990) stated that a growing number of employers, like Campbell Soup, were making special efforts to help retirees stay healthy.

Table 47.2  Yearly distribution of 299 CSR activities Year

Number

Year

Number

1980

1

1995

11

1981

0

1996

12

1982

0

1997

5

1983

0

1998

10

1984

2

1999

8

1985

5

2000

4

1986

5

2001

7

1987

2

2002

14

1988

1

2003

19

1989

4

2004

23

1990

11

2005

33

1991

14

2006

21

1992

11

2007

34

1993

8

2008

25

1994

9

Note: The events are collected before March, 2008.

We collected 299 events which reported CSR activities on healthy foods. Table 47.2 provides the distribution of the events between 1980 and 2008, and Appendix B provides the chronicle details of CSR activities, according to company and above category. Table 47.2 indicates a significant increase in CSR activities after 2002. Between 1980 and 2001 there were 130 activities, but there were 169 from 2002 to March 2008. In the next section, we compare the pledges with past CSR activities.

47.4  Results and sensitivity analysis The matrix in Figure 47.2 describes the commitments and CSR performance of the 12 companies. The center of the matrix is the intersection of

2.  From Society to Behavior: Policy and Action

47.4  Results and sensitivity analysis 45 Kellogg co

40 Il

35

CSR

30 25 20

Conagra Kraft foods McDonald’s USA Pepsico, Inc Median Cambell soup Average Coca-cola

15

General mills

Unilever

Hersney

Burger king

IIl

10

I

IV

Companies

5

Average

Cadbury adams

Median

0

0

0.05

0.1

0.15

0.2

0.25

0.3

Commitments

Figure 47.2  CSR activities vs. pledge commitment for the 12 companies.

the average commitment score (0.083) and the average event count of CSR activities (24.91). The matrix also indicates the median count of CSR activities as 28. The average lines divide the matrix into four quadrants: (I) high commitment and high CSR activities; (II) low commitment and high CSR activities; (III) low commitment and low CSR activities; and (IV) high commitment and low CSR activities. This matrix helps us answer the question about the relationship between the commitments in the pledges and past CSR activities described in the first section of this chapter. We notice that only General Mills and ConAgra are located in quadrant I: both companies demonstrated strong commitments to their pledges and had above average CSR activities. Kellogg, Kraft, McDonald’s USA, Campbell Soup and PepsiCo, Inc. belong to quadrant II: they have higher than average CSR levels but relatively weak commitment scores in their pledges. In particular, Kellogg has the highest CSR activities among the 12 companies. In quadrant III, Coco-Cola, Hershey and Cadbury

585

Adams have both below average commitment scores and below average CSR activities. Unilever and Burger King appear in quadrant IV; they have higher than average commitment scores but lower than average CSR activities. Firms in quadrants I and III show consistent CSR performances and pledge commitments. This consistency could be due to well-established business strategies. The firms in quadrant I can be viewed as leaders in the CSR for healthy food. They are the firms that have done the most in the past and are likely to continue their endeavors in the future. Thus, we may predict that the firms in quadrant I will comply well with their pledges. Firms in quadrant III demonstrate low CSR performances and pledge commitments. Thus, it would be very interesting to investigate the reason why firms in quadrant III chose to participate in CFBAI. Were these firms under pressure to be involved in such a program? We would suggest that the CBBB examines the motivation of and monitors closely the firms in quadrant III. The firms in quadrants II and IV show inconsistency between CSR performance and pledge commitment. Quadrant II presents the interesting case where a firm’s commitment is weak compared with its CSR performance. Explanations for this are that the pledge commitments are measured in relation to all other firms participating in CFBAI, a group with strong commitment across the board, or it may be that, due to administrative complexities, the pledges were not well prepared and documented. Nevertheless, this inconsistency certainly works against the firms, and improvement is needed to ensure that public documentation reflects CSR activities. The firms in quadrant IV show weak CSR performance and relatively strong pledge commitments. There are at least two equally reasonable explan­ ations. First, given the prominence of the CSR issue, these firms may have recently realized the importance of CSR, which was reflected in their pledge commitments. Second, it is possible that these firms are using CFBAI as part of their ­

2.  From Society to Behavior: Policy and Action

47.  Corporate social responsibility

marketing strategy to improve their public image. It is interesting to note that although McDonald’s USA and Burger King are both in the fast-food industry, McDonald’s has excelled in CSR activities but proposed a relatively weak pledge. This is in contrast to Burger King, which made a relatively strong pledge but demonstrated poor performance in its CSR. CSR leads to many debates and arguments. Some researchers argue that corporations bene­ fit in multiple ways by operating with a perspective broader and longer than their own immediate, short-term profits. Others argue that CSR distracts from the fundamental economic role of businesses, and it might be nothing more than superficial window-dressing. Thus, further research can be done to investigate the motivation, the business strategy and ethical practice of each company in quadrants III and IV. More sensible conclusions might be reached about their future compliance. A sensitivity analysis, using alternative data sources, was also conducted to verify our results. We focused on the three most influential and popular newspapers: The Wall Street Journal, The New York Times and USA Today. Based on these three sources, the total number of CSR activities reduced from 299 to 85. Appendix C provides a comparison of the reported CSR activities in the 46 sources, and in the 3 sources. Figure 47.3 presents the new matrix constructed based on the three selected sources. While the number of CSR activities was significantly reduced, all companies, with the exception of Kellogg, remained in the same quadrants as in Figure 47.2. This shows that the initial classification of CSR performance is robust and not sensitive to the data sources. Future investigation is required to understand why Kellogg has the highest CSR activities in Figure 47.2 but lower than average CSR in Figure 47.3. A possible explanation is that Kellogg did not draw attention to its CSR activities in the top media, which could be the result of ineffective communication

14 McDonald’s USA

12

Pepsico, Inc Conagra Il

10

I

Campbell soup Kraft foods

General mills

8 CSR

586

Average Median

6

Kellogg co

IIl

4

Coca-cola

Cadbury adams

0

0.05

IV Companies

Hersney

2 0

Burger king

0.1

Unilever

Average Median

0.15 0.2 Commitments

0.25

0.3

Figure 47.3  CSR activities vs. Pledge commitment for the 12 companies using only top three sources.

strategy or the relatively low impact of their CSR activities. A more detailed analysis of its events is needed to reach a sensible conclusion. In summary, we have used AHP to measure the commitment levels stated in the CFBAI pledges by the 12 food and beverage companies. We found that General Mills and ConAgra received highest scores in the commitment, and Coca-Cola and Cadbury Adams ranked the lowest. We then collected data about the CSR activities related to healthy food promotion in the period 1980–2008 from 46 media sources, and identified the leaders in the CSR. In order to assess the future compliance with the pledges made by the 12 firms, we have compared the commitment levels with the past CSR activities and derived mixed information: some firms are consistent in that they have high levels of past CSR activities and express strong commitment. We conclude that those firms will be likely to comply with their pledges. Some firms are weak in both aspects, which raises the question of whether these firms will comply with their

2.  From Society to Behavior: Policy and Action

587

47.5  Conclusion

pledges. On the other hand, a few firms with weak CSR activities made strong commitments in their pledges and others had high performances in CSR in the past but show weak commitments in the pledges. The mixed information regarding the CSR and commitment suggests that further research on those firms’ business strategies, policies and motivation to participate in the CFBAI is needed in order to reach a final conclusion about their compliance with their pledges in the future.

47.5  Conclusion In this chapter, we have examined the recent pledges made by 12 giant food and beverage companies in response to the CFBAI program

initiated by the CBBB. The CFBAI program requires participants to commit to some core advertising principles to combat the growing childhood obesity epidemic. Key to the ­success of the CFBAI program is compliance to the pledges. We first used AHP to evaluate the commitments in the pledges, and then collected healthy food CSR activities over the period of 1980–2008. By matching the past CSR performances with the current commitments, we made an initial prediction of the future compliance of the 12 companies. Our results show that some companies have performed consistently well in CSR and current pledges, while other companies show poor or mixed performances. Monitoring efforts should be directed to the latter group of companies. On the other hand, to reach further conclusions, more investigation and studies may need to be conducted.

Appendix A Data sources in top 10 newspapers Rank

Newspaper

Circulation

1

USA Today (Arlington, VA)

2,528,437

2

Wall Street Journal (New York, NY)

2,058,342

3

Times (New York, NY)

1,683,855

4

Times (Los Angeles, CA)

1,231,318

5

Post (Washington, DC)

6

Tribune (Chicago, IL)

957,212

7

Daily News (New York, NY)

795,153

8

Inquirer (Philadelphia, PA)

705,965

9

Post/Rocky Mountain News (Denver, CO)

704,806

10

Chronicle (Houston, TX)

692,557

960,684

Notes: By largest reported circulation, as of March 31, 2006. Source: Audit Bureau Circulation. By the largest reported circulation, as reported to the Audit Bureau of Circulation (http://www.infoplease.com/ipea/A0004420.html).

2.  From Society to Behavior: Policy and Action

588

Kellogg Co

Unilever

CocaCola

Kraft Foods

General Mills

ConAgra

PepsiCo, Inc

Campbell Soup

Burger King

McDonald’s USA

Hershey

Cadbury Adams

1984

1993

1986

1980

1985

1989

1985

1984

1985

1989

1985

2004

New healthy product introduction

10

5

11

6

4

11

10

7

7

10

7

2

Existing product improvement

11

4

1

10

6

10

5

4

1

5

1

0

Health idea and notion

12

8

6

9

13

8

11

10

4

11

5

2

8

2

2

8

7

6

4

5

3

5

1

1

41

19

20

33

30

35

30

26

15

31

14

5

The first activity reported Events

Public affair participation Total number of events

47.  Corporate social responsibility

2.  From Society to Behavior: Policy and Action

Appendix B A distribution of the announcements by firms and by categories

589

references

Appendix C Comparison of activities based on different sources Companies

Pledge score

Rank

CSR 1

CSR 2

General Mills

0.26477058

1

30

9

ConAgra

0.133884076

2

35

12

Unilever

0.105751243

3

19

2

Burger King

0.088908604

4

15

4

McDonald’s USA

0.080284109

5

31

13

PepsiCo, Inc

0.080215119

6

30

12

Kellogg Co

0.063219603

7

41

5

Hershey

0.047057132

8

14

2

Kraft Foods

0.046093018

9

33

10

Campbell Soup

0.042061776

10

26

10

Coca-Cola

0.031952197

11

20

4

Cadbury Adams

0.015802543

12

5

2

Mean

0.083333333

24.91667

7.083333

Median

0.071717361

28

7

Note: CSR 1 reports the activities based on 46 sources and CSR 2 reports the activities based on top three newspapers: USA Today, The Wall Street Journal and The New York Times.

References Abbott, W. F., & Monsen, R. J. (1979). On the measurement of corporate social responsibility: Self-reported disclosures as a method of measuring corporate social involvement. Academy of Management Journal, 22, 501–515. Aupperle, K. E., Carroll, A. B., & Hatfield, J. D. (1985). An empirical examination of the relationship between corporate social responsibility and profitability. Academy of Management Journal, 28(2), 446–463. Bowen, H. R. (1953). Social responsibilities of the businessman. New York, NY: Harper & Row. Brock, H. W. (1980). The problem of “utility weights” in group preference aggregation (in preference models. Operations Research, 28, 176–187. Carroll, A. B. (1991). The pyramid of corporate social responsibility: Toward the moral management of organizational stakeholders. Business Horizons, 34, 39–48. Clarkson, M. B. E. (1995). A stakeholder framework for analyzing and evaluating corporate social performance. Academy of Management Review, 20, 92–117.

CSR Europe. (2000). Communicating corporate social responsibility. Brussels: CSR Europe. Donaldson, T., & Dunfee, T. W. (1999). Ties that bind. Boston, MA: Harvard Business School Press. Ebbeling, C. B., Pawlak, D. B., & Ludwig, D. S. (2002). Childhood obesity: Public health crisis, common sense cure. Lancet, 360, 473–482. Freeman, R. (1984). Strategic management: A stakeholder perspective. Englewood Cliffs, NJ: Prentice-Hall. Heald, M. (1970). The social responsibilities of business: Company and community, 1900–1960. Cleveland, OH: Case Western Reserve University Press. Hossain, P., Kawar, B., & Nahas, M. E. (2007). Obesity and diabetes in the developing world – A growing challenge. New England Journal of Medicine, 356, 213–215. Jones, T. M. (1980). Corporate social responsibility revisited, redefined. California Management Review, 59–67. Ludwig, D. S., Peterson, K. E., & Gortmaker, S. L. (2001). Relation between consumption of sugar-sweetened drinks and childhood obesity: A prospective, observational analysis. Lancet, 357, 505–508.

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Maloni, M. J., & Brown, M. E. (2006). Corporate social responsibility in the supply chain: An application in the food industry. Journal of Business Ethics, 68(1), 35–52. Murray, K. B., & Vogel, M. C. (1997). Using a hierarchy-ofeffects approach to gauge the effectiveness of corporate social responsibility to generate goodwill toward the firm: Financial versus non-financial impacts. Journal of Business Research, 38, 141–159. Nielsen, S. J., & Popkin, B. M. (2004). Changes in beverage intake between 1977 and 2001. American Journal of Preventive Medicine, 27, 205–210. Rigby, N. J., Kumanyika, S., & James, W. P. T. (2004). Confronting the epidemic: The need for global solutions. Journal of Public Health, 3–4, 418–434. Robinson, T. N (1999). Reducing children’s television viewing to prevent obesity: A randomized controlled trial. Journal of the American Medical Association, 282, 1561–1567. Saaty, T. L. (1980). The analytic hierarchy process. New York, NY: McGraw-Hill Book Co.

Saaty, T. L. (1986). Axiomatic foundation of the analytic hierarchy process. Management Science, 32, 841–855. Saaty, T. L. (1990). An exposition of the AHP in reply to the paper “Remarks on the Analytic Hierarchy Process”. Management Science, 36(3), 259–268. Shields, M. (2005). Measured obesity: Overweight Canadian children and adolescents. Statistics Canada Catalogue No 82-620-MWE. Story, M., & French, S. (2004). Food advertising and marketing directed at children and adolescents in the US. International Journal of Behavioural Nutrition and Physical Activity, 1, 3. Suchman, M. C. (1995). Managing legitimacy: Strategic and institutional approaches. Academy of Management Review, 20, 571–610. Swinburn, B., & Egger, G. (2002). Preventive strategies against weight gain and obesity. Obesity Review, 3, 289–301. Wood, D. J. (1991). Corporate social performance revisited. Academy of Management Review, 16, 691–718.

2.  From Society to Behavior: Policy and Action

C H A P T E R

48 The Injunctive and Descriptive Norms Governing Eating Robert J. Fisher Department of Marketing, Business Economics and Law, University of Alberta, Edmonton, Canada

o u t l ine 48.1  Introduction

593

48.2  I njunctive Versus Descriptive   Eating Norms

593

48.3  Norms are Situational

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48.4  S  ocialization and the Creation of   Eating Norms 48.4.1  Culture 48.4.2  Family 48.4.3  Peers 48.4.4  Government 48.4.5  Business

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48.5  Norm Violations 48.5.1  Social Censure 48.5.2  Threats to Self-Esteem 48.5.3  Negative Emotions

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48.6  T  he Effect of Eating Norms on   Health Outcomes

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48.7  A  ffecting Norms Through   Marketing

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48.8  Conclusion

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48.2  Injunctive versus descriptive eating norms

48.1  Introduction Norms are dynamic, constantly ­shifting, and are a reflection of our individual and collective wisdom about food consumption and dietary health. This chapter is an attempt to synthesize extant literature on this topic, and to organize it in a way that will stimulate further thinking, debate and research.

Obesity Prevention: The Role of Brain and Society on Individual Behavior

It is important to distinguish between injunctive and descriptive norms because they are conceptually distinct and have very different implications for eating behaviors. Injunctive norms involve “shoulds” or “oughts” that reflect a collective obligation to behave in a specific

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way (Cialdini et al., 1990). When internalized, norms reflect people’s personal beliefs about what is an appropriate behavior (Schwartz, 1973). Injunctive norms related to eating proscribe or prescribe behaviors, such as what foods are allowed, how much food is consumed, fat content, daily caloric intake, the number of eating occasions in a day, etc. Although these norms are more likely to be explicit for dieters, athletes or the health conscious, most people have implicit norms that guide their daily food intake. Consider the following statements that proscribe or prescribe specific eating behaviors: I shouldn’t eat between meals I ought to eat whole-grain foods l Always finish everything on your plate l Only eat when you are hungry l Never eat after 8 pm. l l

When explicit, these norms are expressed as rules within a family or peer group, such as when a mother admonishes her children to avoid snacking before dinner. Implicit norms are discussed or communicated only rarely. We are socialized within our culture, for example, to prefer certain foods and to avoid others, to eat at specific times of the day, and to expect defined portions. When injunctive norms are internalized, people adhere to them regardless whether the focal behavior is public or not (Schwartz, 1973). The norm is followed because it is a part of the individual’s belief system, not out of fear of how others will react. When an individual’s behavior is contrary to what he or she believes is correct or appropriate, that individual is likely to engage in self-deprecation and experience guilt, shame or anger. These negative appraisals and their associated emotions might occur after failing to stick to a diet, indulging in an unhealthy food, or binge ­eating. Recent research has illustrated the negative ­cognitive and emotional consequences of violating eating behavior norms related to women’s prenatal nutrition (Copelton, 2007), binge drinking (Yanovitzky and Stryker, 2001; Cho,

2006) and snack food consumption (Herman et al., 2003). In contrast, descriptive norms relate to what is normal or typical behavior within a social context: in simple terms, “if everyone is doing it, it must be the right thing to do” (Cialdini et al., 1990). Descriptive norms affect behavior by providing information about what others are doing, and they typically lead people within a group to behave similarly. If a family meal typically consists of meat, potatoes and vegetables followed by dessert, this becomes the descriptive norm. Descriptive norms also develop with respect to the frequency of snacks and fast-food consumption, and the types of foods that are eaten. As such, descriptive norms create the foundation for eating rules, many of which are likely to be implicit assumptions about what constitutes a meal, healthy eating, or a diet. Evidence suggests that descriptive norms have a significant impact on human behavior and, in many instances, are underappreciated (Cialdini et al., 1990; Cialdini, 2007). Descriptive norms help explain recent findings in social contagion effects related to food and diet. For example, high incidences of bulimia and binge eating have been found in members of sports teams (Crago et al., 1985) and dance groups (Garner and Gartinkel, 1980). Christakis and Fowler (2007) found evidence of social contagion effects in the form of a person-to-person spread of obesity through social networks: weight gain in one person was linked to weight gains in his or her friends, neighbors and family members. Consistent with a descriptive norm perspective, they reason that people’s norms about the acceptability of being overweight are affected by the behaviors of others, which in turn affect food consumption. Social contagion occurs through subtle or implicit changes in what is perceived to be normal or typical, rather than through explicit influence. The distinction between injunctive and descriptive norms is important because they both are likely to have significant effects on

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48.4 Socialization and the creation of eating norms

eating behaviors, yet they operate in distinctly different ways – sometimes even in opposition. Injunctive norms such as “Don’t snack between meals” may or may not be descriptive of actual eating behavior within a family or peer group. Similarly, injunctive norms contained in government food guides are at odds with individual behavior. A further issue is that some injunctive norms are known to be contrary to healthy eating practices, such as “Always eat everything on your plate”, “Don’t refuse food offered by your host”, and, “Always get your money’s worth at a buffet”.

48.3  Norms are situational Norms are situational, meaning that they are specific to a particular situation or context and the people involved within it (Handel, 1979). As a consequence, it is relatively easy to make an exception for every normative rule. For example, the norm “I should avoid refined sugar” can easily be dismissed because of extenuating circumstances, such as being on holiday, running late for an appointment, missing lunch, having no healthy alternatives available, or having exercised more than normally that day. Salvy and colleagues (2007) found that whereas the eating behaviors of overweight girls were affected by the weight of their eating partner, the same was not true of girls who were of normal weight. Specifically, overweight girls who ate with an overweight peer consumed more calories than overweight participants eating with a normal-weight peer. In contrast, normal-weight participants eating with overweight peers ate similar amounts as those eating with lean eating companions. Roth and colleagues (2001) found that sometimes norms are in ­ conflict within the same situation. Participants ate alone or while being observed by the experimenter, and were either given information about how much others had eaten (norm condition) or not (no norm

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condition). They found that participants were influenced by the information about what others had eaten only when they were alone. Participants always ate minimally when they were observed. Situational factors can make it difficult to predict the salience and impact of norms on behavior.

48.4  Socialization and the creation of eating norms Eating norms can exist within a culture or ethnicity, religion, family, peer group, organiz­ ation, or any other social group. People are informed of a group’s norms through a socializ­ ation process in which learning is a function of exposure both to the attitudes, opinions, values and behaviors of other group members, and also through organizations in the government, education and business sectors. Each group has its own values and objectives, which often results in confusing and contradictory messages (e.g., government norms are represented and promoted via a healthy eating guide, whereas friends encourage high-fat and high-sugar content foods). We briefly review the impact of culture, family, peers, government and business on eating norms.

48.4.1  Culture Culture can be defined as the processes of meaning-making that operate in social life (Spillman, 2002). Culture connects us to other people through the way we view the world, our assumptions and rules about how things are done, and our preferences and behaviors. Our norms and expectations surrounding how and what we eat is an essential part of culture (Montanari, 2006). Indeed, culture and food are so intertwined that many countries are defined by specific foods. Each culture creates or adopts

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its own foods, transforms them through various culture-specific cooking methods and technologies, and selects and eats what is pleasing.

48.4.2  Family The family environment is clearly an important social factor shaping eating behaviors, particularly during formative years (Birch and Fisher, 2000; Birch et al., 2003; Campbell et al., 2006; van Assema et al., 2007). Parents affect both descriptive (“this is what we eat”) and prescriptive (“this is what should be eaten”) norms to the extent that they are responsible for food purchases, meal planning and food preparation (Wilson et al., 2004). Research in the area has also found that parental influence is important in terms of when and how much is consumed (Iannotti et al., 1994), body image (Abramovitz and Birch, 2000; Birch and Fisher, 2000), the likelihood of eating disorders (Fisher and Birch, 2001), and dieting (Francis and Birch, 2005). Norms within families are often reinforced through rewards, such as “If you eat your vege­ tables you can eat dessert” (Birch et al., 1984). Parents may also use coercion to impose their preferences on their children. Research in this area has found that children consume more food when they are not pressured to eat, and that coercion has a negative effect on children’s affective responses to an intake of healthy foods (Galloway et al., 2006). A study of college students found that nearly 70 percent had experienced at least one forced consumption of food, and most of them (72 percent) would not eat that food as an adult (Batsell et al., 2002).

48.4.3  Peers Peer influence is linked to various effects, which include how much we eat (Salvy et al., 2007), the type of food that is consumed (Ariely and Levav, 2000) and how hungry we feel

(Herman et al., 2003). Importantly, it has also been found that peer influence is related to the drive for thinness (Stice, 2002; Gravener et al., 2008), and food-related disorders including bulimia (MacBrayer et al., 2001). These effects have been found to depend on characteristics of the eating environment’s social context. To illustrate, a variety of studies have found that females eat less when in the presence of a male than alone (Mori et al., 1987; Pliner and Chaiken, 1990). Eating restraint is associated with femininity and female attractiveness (Pliner and Chaiken, 1987). Females should or ought to limit their food intake because overeating reduces the likelihood of maintaining or achieving a desirable body size and shape. Peer influence can lead to a higher incidence of negative eating behaviors such as obesity (Christakis and Fowler, 2007) and binge eating (Crandall, 1988). As noted previously, these effects can be partly explained by descriptive norms alone – the greater the exposure to others who are undertaking a behavior, the more normal the behavior becomes. Hammond and Epstein (2007) illustrate how simple conformity can explain the dramatic increase in weight among the US population over the past 40 years. The authors developed a model that relies on basic assumptions about physiology and weight distributions within the US population. A “Follow the Average” weight adjustment rule generated an increased mean weight that is consistent with the observed pattern of weight gain within the population.

48.4.4  Government By developing and promoting dietary guidelines, governments are an important source of injunctive food norms. Rules for eating have been published in the United States in the form of Dietary Guidelines for Americans (DGAs) every 5 years since 1980. In Canada, the Official Food Rules were introduced in 1942 and have since been routinely updated. At a global level,

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48.4 Socialization and the creation of eating norms

the World Health Organization has a strategy on diet, physical activity and health (World Health Organization, 2009). Each of these programs provides science-based recommendations to improve both diet and exercise, and contribute to the establishment of beliefs about what is normal or expected. Governments also introduce informational programs on eating and health through educational institutions and national programs. In 2005–2006, the Canadian government launched an integrative social marketing campaign involving a national advertising campaign, an informational website and a 1-800 number to promote healthy behaviors. In the US in 2006, the USDA spent nearly $13 billion on five major domestic food programs focused on the needs of children. The USDA programs provide free or subsidized breakfasts, lunches and snacks to lower-income households. From a normative perspective, these programs create descriptive norms as to what is an appropriate food and what constitutes a meal or snack. Although these programs cannot force children to make healthy food choices in the long term, they do communicate what children should or ought to eat. It is an open question as to the effects of these programs on eating behaviors. Many studies have found that adherence to food guide standards is poor. For example, Knol and colleagues (2006) found that 2- to 8-year-old children in the US do not follow USDA Food Guide recommendations for whole grains, total vegetables, deepgreen vege­tables, deep-yellow vegetables, other vegetables and fruit. Moreover, the authors found that adherence to recommended daily allowances decreased with age.

48.4.5  Business Within a market economy, businesses tend to be motivated by short-term profits, which can lead to the promotion of food products and eating behaviors that are not necessarily consistent

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with the long-term interests of society. The business sector is an important source of descriptive norms, given the extent to which the media are dominated by images of food and eating. What is normal? Consider the following statistics: The food industry is the largest advertiser in the US, spending over $30 billion in 1994 (Gallo, 1995). The vast majority (89.4 percent) of food product advertisements seen by adolescents were for foods high in fat, sugar or sodium (Robert Wood Johnson Foundation, 2005). l The United States fast food market grew by 4.1 percent in 2007 to reach a value of $57.5 billion, and it is forecast to have a value of $68.4 billion by 2012 (Datamonitor, 2008). l In 2005, the number of fast-food restaurants in the US exceeded 280,000. McDonald’s alone has 30,000 restaurants worldwide (Datamonitor, 2008). l The percentage of meals eaten at fast-food establishments increased 200 percent between 1977 and 1996. In excess of 45 percent of today’s food dollars are spent on food consumed in a restaurant, and this value is expected to exceed 53 percent by 2010 (Demory-Luce, 2005). l Restaurant portion sizes have increased dramatically since the late 1970s. On average, the energy intake has increased by 93 kcal for salty snacks, by 49 kcal for soft drinks, by 68 kcal for French fries, and by 97 kcal for hamburgers (Nielsen and Popkin, 2003). Preferences for large portion sizes have followed suit (Colapinto et al., 2007). l A study of secondary schools in the US found that the vast majority had vending machines with soft drinks and snack foods such as ice cream, chips and candy (French et al., 2003). l

Beyond their impact on the availability of calorie-dense foods, these statistics describe aspects of the environment that are likely to influence perceptions of what are normal or accepted eating behaviors – norms such as “If

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there are lots of fast-food restaurants, it must be what most people eat”, and “If this is the portion of food I receive from the restaurant, it must be the right amount of food”. These factors do not necessarily emphasize that people should eat more fast food than they have in the past, but they do provide clear information about what is normal and typical. The business sector has also been blamed for societal norms and expectations about body image, particularly for females, that have emphasized dieting and levels of thinness that are for the most part unattainable (Dwyer et al., 1970). Examining the social aspects of food consumption created by business interests has the potential to provide significant insights into how to help people develop healthy eating behaviors.

48.5  Norm violations When a group member does not act in accordance with a norm, that norm has been violated (DeRidder, Schruijer, & Tripath, 1992). Violations can occur for norms related to the number of eating occasions per day, the number of fruits, vegetables or meats consumed, and so forth. The types of sanctions depend on factors such as who has violated the norm, the magnitude and direction of the violation, and the occasion. Norms vary significantly according to the violator’s gender (Mori et al., 1987), the gender of those who are present (Pliner and Chaiken, 1990), whether the person is on a diet (Herman and Polivy, 2005), and the eating occasion (Arnould and Wallendorf, 1994). This ­section examines how norm violations can ­create social censure, threats to self-esteem, and negative emotions.

48.5.1  Social censure Social censure is one possible consequence of a norm violation. Social censure can take

various forms, ranging from non-verbal expressions of disapproval (e.g., a frustrated sigh, a shake of the head, a scowl or defensive body posture) to mild reproach (e.g., “You’ll spoil your supper”) to outright displeasure (e.g., “You shouldn’t be snacking right before dinner!”). Even stronger forms of social censure in the form of abusive language or physical violence are also possible. In each instance, the social censure occurs after someone has publically violated a norm that is shared among group members. Social censure is also possible when personal norms – norms that are held by the individual but which are not necessarily shared within the group – are made visible to others. A publicly stated intention to go on a diet creates the potential for socially-mediated rewards and punishments. For example, visible eating behaviors that are inconsistent with a stated commitment to a low-glycemic index or lowcarbohydrate diet might result in criticism by others.

48.5.2  Threats to self-esteem We define a threat to self-esteem as any event or communication that implies something unfavorable about the self (cf. Baumeister et al., 1993). The first and most obvious threat comes from the social rejection that accompanies a norm violation (Rudman et al., 2007). The second and more fundamental threat comes from a norm violation contradicting an important self-perception, such as “I live up to my commitments” or “I have self-discipline”. As noted by Herman and Polivy (2005), people judge themselves harshly, whether overeating in public or in private. Acting in a way that contradicts one’s personal values or beliefs can affect self-image and therefore self-esteem. Some evidence suggests that violations of normative standards have very significant self-esteem consequences and can lead to

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48.5  Norm violations

self-destructive eating behaviors. Low selfesteem (Steiger et al., 1990) and self-criticism (Dunkley and Grilo, 2007) are strongly related to eating disorders such as anorexia and bulimia (see Safner and Crowther, 1998). Low self-esteem increases the desire to control overeating behaviors in order to overcome a negative self-image (see Fairburn et al., 2003), whereas chronic negative self-evaluation maintains a gap between the desired and actual self that creates a negative self-image. Yet the reverse causality is also likely to be true. A commitment to a normative standard, such as avoiding refined sugar, places an obligation on the self and therefore implies self-regulation. When self-regulation fails, there are negative self-esteem consequences and a further loss of self-control (Baumeister et al., 1993).

48.5.3  Negative emotions Norm violations, particularly if they relate to an internalized, norm create a perceived inconsistency between the individual’s actual and desired self, which can lead to shame, guilt and embarrassment. Three differences between the emotions suggest that they result from different types of norm violations. The first difference is that embarrassment occurs in response to the real or imagined reaction of others, whereas shame and guilt can take place in private situations (Edelmann & Hampson, 1981). An individual who is embarrassed experiences a discrepancy between a desired and actual presentation of the self, which results in a loss of situational self-esteem (Modigliani, 1968; Silver et al., 1987; Miller, 1992). In contrast, guilt and shame are derived from a failure of the core self that may exist in both public and private ­circumstances (Lazarus, 1991). For instance, a person might feel guilty or ashamed of binge eating even though no one witnessed it, but would be embarrassed only if others became aware of his or her behavior.

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Thus, embarrassment is likely to occur when there is a socially visible norm violation, whereas guilt and shame occur whenever a behavior violates an internalized normative standard that reflects the self. A second difference among these emotions pertains to their intensity. Embarrassment is a self-referencing emotion that follows from unintentional and undesired circumstances or mistakes in the presence of one or more others (Edelmann & Hampson, 1981). Embarrassment is a relatively mild, transitory state that often results because of factors beyond the control of the individual, and it is based on the real or anticipated reaction of others. The emotion might occur because a person has been observed sneaking a “guilty snack”, or because he or she overindulges at the buffet or dessert tray. Thus, embarrassment is associated with “temporary” errors or accidents that are typically minor in nature, and the emotion quickly abates once the individual is removed from the situation (Miller and Tangney, 1994). In contrast, shame is a much more intense emotion – to experience shame is to feel inherently deficient as a person because it reflects a personal flaw or failure. As a consequence, shame can be experienced for very long periods of time because there is a significant threat to the person’s self-image. Finally, these three emotions are also differentiated on the basis of who or what is responsible for the transgression or mistake. Embarrassment often occurs because of factors that can be attributed to causes that are beyond the control of the individual (Harre, 1990). For example, a person might be embarrassed because his or her spouse inadvertently “super sized” their orders at a fast-food restaurant. In contrast, guilt and shame result from behaviors that are ostensibly under the person’s control. Guilt occurs when there is a willful violation of norms and obligations, whereas shame is a feeling of not having lived up to one’s aspirations (Lazarus, 1991).

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48.6  The effect of eating norms on health outcomes Evidence on the relationship between norms and health outcomes is limited and equivocal. In the area of parental control over food and health outcomes, Brink and colleagues (1999) report that adults’ recollection of childhood food rules is higher for both successful dieters and the obese, suggesting that rigid prescriptions related to controlling food intake are a double-edged sword. Similarly, Puhl and Schwartz (2003) indicate that adult binge eating and dietary restraint are significantly related to recollections of food being used as a means of control during childhood. It is possible, however, that recall of food norms and rules is higher in the two groups simply because dieters or the obese are more preoccupied with food and therefore are more likely to have food-related memories. For adults, there is some evidence that adherence to eating rules leads to weight loss. Knaüper and colleagues (2005) found that dieters who had clear rules about reducing their caloric intake and increasing exercise had greater success in achieving their weight-related goals. The study also found that weight-loss success increased when dieters adhered to these rules, but also that adherence rates were low.

48.7  Affecting norms through marketing Although marketing might be used to create new normative standards that promote healthy eating practices, this is a very difficult, long-term task. The ability of governments, schools and other organizations to change adults’ beliefs about what they should and should not eat through marketing activities is limited because norms are created through powerful socialization processes that begin at birth. These

norms originate from our culture as interpreted by our parents, siblings and extended family, our peers, and social institutions. Nevertheless, marketing can be used to make existing norms more salient and to reduce the ambiguity of the situation so that the appropriate norms that govern the situation are clear. Unless a norm is salient, it will not affect behavior. Norms can be made salient by marketing communications that remind people of what is appropriate in a specific context, but can also be enhanced by self-directed attention, which leads people to focus on their internal behavioral standards (Kallgren et al., 2000). Eating environments could be designed to enhance compliance with normative standards through feedback via mirrors or closed circuit television. These design elements enhance self-directed attention by providing diners with real-time information on their eating behaviors. A further strategy is to increase the social visibility of one’s actions (Fisher, 1993), which could be achieved through marketing communications but also through an open restaurant design that enables diners to observe the behaviors of others in the environment. One outcome of increasing the perceived visibility of food consumption is a reduction in the amount of food that is eaten during a sitting (Salvy et al., 2007). Of course, reducing the amount of food consumed and increasing its healthiness is contrary to the profit motive, and therefore these recommendations are difficult to implement in the business sector. Nevertheless, these mechanisms might be used in the design of eating environments that are not governed by the profit motive (e.g., kitchens in the home, school cafeterias). Marketing can also affect eating norms by reducing the ambiguity of the eating situation because they are less relevant in uncertain social contexts. Leone and colleagues (2007) found that people ate significantly more than would be predicted by either “eat minimally” or “avoid eating excessively” norms when the

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references

eating behaviors of others were ambiguous. Participants tended to eat as much as possible when they were not constrained by clear descriptive norms. Because norms are situational, a restaurant might influence the types of foods that are preferred, whether alcohol or dessert is appropriate, and the size of the meal, simply by labeling a mid-morning meal as breakfast, brunch or lunch.

48.8  Conclusion Although the evidence suggests that norms can exert a powerful effect on eating behaviors, particularly when we are in the presence of others, little is known about the specific types of norms that exist, their structure, and the degree to which they are shared across the variety of groups we belong to. Also, little is known about their association with specific health outcomes. Eating norms seem to hold tremendous promise in linking macro- and micro-forces in society because they are affected by social institutions such as business, government and educational actors, but also by families, peer groups, and the communities and the cultures within which we live.

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C H A P T E R

49 Family Meal Patterns and Eating in Children and Adolescents Nicole Larson and Mary Story Division of Epidemiology and Community Health, School of Public Health,   University of Minnesota, Minneapolis, MN, USA

o u t l i n e 49.1  Introduction

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49.5  Do Family Meals Have Other Benefits? 611

49.2  D  o Family Meals Promote Good   Nutrition?

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49.6  W  hat Are Strategies to Promote   Family Meals?

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49.3  D  o Family Meals Promote Healthy   Weights? 609

49.7  W  hat Actions Can Communities   Take to Promote Family Meals?

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49.4  D  o Family Meals Promote Health in Overweight Children? 611

49.8  W  hat Remains to be Learned About   Family Meals? 614

49.1  Introduction Families shape the food preferences and eating behaviors of young people in many ways (Savage et al., 2007). Mealtime socialization may be a particularly powerful influence, as youth learn about eating through active observations of their relatives and direct participation in mealtime practices. Family mealtime practices and social norms surrounding the nature of mealtimes have shifted over time (Cinotto, 2006).

Obesity Prevention: The Role of Brain and Society on Individual Behavior

They vary across cultures and economic circumstances (Ochs et al., 2006). However, the practice of sharing food and eating together is universally central in defining the family as a social group (Ochs et al., 2006). It is therefore important to consider how family meals may promote good nutrition and healthy weights in children and adolescents. This chapter will explore: (1) the nutritional and weight-related benefits of family meals; (2) other benefits of family meals for children and adolescents; and

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49.  Family Meal Patterns

(3) strategies for increasing the frequency of family meals. The chapter will close with suggestions for future research to improve our understanding of the potential benefits of family meals and ultimately enhance the design of nutrition programs for youth and families.

49.2  Do family meals promote good nutrition? When children (Kusano-Tsunoh et al., 2001; Haapalahti et al., 2003; Cooke et al., 2004; Veugelers et al., 2005; Ayala et al., 2007; Yuasa et al., 2008; Crombie et al., 2009) and adolescents (Gillman et al., 2000; Neumark-Sztainer et al., 2003; Videon et al., 2003; Mamun et al., 2005; Utter et al., 2008) regularly share mealtimes with their families, they are more likely to have diets of higher nutritional quality (see Table 49.1). With few exceptions (De Bourdeaudhuij et al., 2000), research studies conducted in a number of different countries around the world (Japan, New Zealand, Australia, Canada, Scotland, England, Finland, Belgium and the US) have found evidence that having frequent family meals is associated with better nutritional outcomes. Having frequent family meals is related to higher intakes of fruit, vegetables, grains, calcium-rich foods, protein, fiber and several key micronutrients. In addition, frequent family meals are related to lower intakes of soft drinks, saturated fat and trans fat. The development of healthful eating patterns at family meals during childhood and adolescence may also improve the likelihood that individuals will consume a nutritious diet in adulthood (Larson et al., 2007). In the US, several studies on family meals have been reported by Project Eating Among Teens (EAT). Project EAT collected survey data from 4746 American adolescents attending middle school or high school in the Minneapolis/St. Paul metropolitan area in 1998–1999. In school classrooms, adolescents completed a dietary

questionnaire and a survey that assessed how many times all or most of the family members living in their home had eaten a meal together in the previous week. Adolescent report of family meal frequency was related to higher intakes of healthy foods (e.g., fruit, vegetables) and key nutrients (e.g., calcium, iron) (Neumark-Sztainer et al., 2003). When compared to adolescents who never had family meals, those who reported having daily family meals consumed an average of one additional serving of fruits and vegetables per day. Five years later, the same adolescents were re-surveyed by mail and again reported on their dietary intake. Family meal frequency during adolescence predicted higher intakes of fruit, vegetables and key nutrients, and lower intakes of soft drinks, among young people aged 18–24 years making the transition into adulthood (Larson et al., 2007). Research from the Project EAT study and other studies has also found evidence that the mealtime environment at home and the foods served at meals contribute to better nutritional outcomes for youth. Studies relating to the mealtime environment have focused on the practice of watching television during dinner. These studies suggest that turning off the television during dinner is related to higher diet quality among parents (Boutelle et al., 2003) and children (Coon et al., 2001; Feldman et al., 2007). Many children and adolescents watch television during meals; in the US, more than 60 percent of children aged 8–18 years and 30 percent of those aged 6 and under usually have the television playing during meals (Roberts et al., 2005; Rideout et al., 2006). Among children and adolescents, watching more television is related to greater caloric intake, as well as higher consumption of high-fat, highsugar foods and lower consumption of fruits and vegetables (Boynton-Jarrett et al., 2003; Wiecha et al., 2006). It is interesting to note that watching television during family meals is still related to a better dietary intake in comparison with not eating regular family meals (Feldman et al., 2007). Nonetheless, when compared to not watching

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49.2  Do family meals promote good nutrition?

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Table 49.1  Research on family meals and nutrition Author, year

Study population

Type of study

Meal variable

Summary of nutritional benefits

Yuasa et al., 2008

Male and female students in elementary and junior high school, Japan

Crosssectional, observational

Family eats meals together every day (parental and child report)

Youth who ate family meals every day were more likely to consume breakfast daily and eat vegetables at least once per day

Utter et al., 2008

Male and female adolescents, mean age  14.8 years, New Zealand

Crosssectional, observational

Frequency of eating the evening meal with family members during the school week

Meal frequency was related to meeting the recommended intake of fruit and vegetables, having fruit for an afternoon snack, and eating breakfast at home before school

Crombie et al., 2009

Two-year old males and females living in low-income areas, Scotland

Crosssectional, observational

Family ate a meal together in the past week (maternal report)

Children whose families had not eaten a meal together in the previous week had an increased risk of a poor diet (poor diet was defined by failing to achieve a balance of grains, fruit, vegetables, dairy, and meat or meat alternatives daily, or having more than two fatty and sugary snack foods per day)

Ayala et al., 2007

Males and females aged 8–18 years, US

Crosssectional, observational

Daily number of meals eaten together as family; weekly number of times the family consumed fast food at home

Meal frequency was related to higher fiber intake; fast food for meals was related to higher intake of fat and soft drinks

Feldman et al., 2007

Males and females aged 11–18 years, US

Crosssectional, observational

Usually watching television while eating dinner; past week frequency of eating a meal with all or most of family members living in the home

Television watching during family meals was related to higher intake of soft drinks and fried foods, and lower intake of vegetables and grains as compared to having family meals without watching television

Boutelle et al., 2007

Males and females aged 11–18 years, US

Crosssectional, observational

Past week frequency of purchasing food from a fastfood restaurant for a family meal (parental report)

Fast-food restaurant purchase frequency was related to higher adolescent intake of fast foods and salty snack foods

Larson et al., 2007

Males and females, aged 18–23 years at 5-year follow-up, US

Longitudinal, observational

Past week frequency of eating a meal with all or most of family members living in the home

Meal frequency during middle adolescence was related to higher intake of fruit, vegetables, and micronutrients, and lower intake of soft drinks 5 years later in young adulthood

Mamun et al., 2005

Males and females aged 14 years, Australia

Crosssectional, observational

Usual weekly frequency of family eating together and perceived importance of eating together (maternal report)

Having infrequent family meals was associated with higher adolescent intake of fast food and red meat; lower perceived importance of eating together was related to higher adolescent intake of fast food and soft drinks (Continued)

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49.  Family Meal Patterns

Table 49.1  (Continued) Author, year

Study population

Type of study

Meal variable

Summary of nutritional benefits

Veugelers et al., 2005

Male and female students in grade 5, Canada

Crosssectional, observational

Weekly frequency of eating supper with family members

Family supper frequency was associated with a lower risk for poor diet quality, defined using the Diet Quality Index International

Cooke et al., 2004

Male and female nursery school students, aged 2–6 years, UK

Crosssectional, observational

Family mealtime score determined by children eating their evening meal at the same time and in the same place as grownups, and by eating the same foods as grown-ups (parental report)

Family mealtime score was related to higher child consumption of vegetables but was not related to fruit consumption

Haapalahti et al., 2003

Males and females aged 10– 11 years, Finland

Crosssectional, observational

Family eats together daily or almost daily on weekdays (parental and child report)

Children with no regular family dinner ate sweets and fast foods more often

NeumarkSztainer et al., 2003

Males and females aged 11–18 years, US

Crosssectional, observational

Past week frequency of eating a meal with all or most of family members living in the home

Meal frequency was related to higher intake of fruit, vegetables, grains, calcium-rich foods, and micronutrients, and lower intake of soft drinks

Videon et al., 2003

Males and females in grades 7–12, US

Crosssectional, observational

Past week frequency of eating the evening meal with at least one parent present

Meal frequency was related to lower risk for poor consumption of fruit, vegetables, and dairy foods, and lower risk for skipping breakfast

Boutelle et al., 2003

Male and female parents of middle school students, US

Crosssectional, observational

Frequency of watching television during mealtimes

Frequency of television watching was related to higher parent fat intake and lower fruit and vegetable intake

Coon et al., 2001

Male and female students in grades 4–6, US

Crosssectional, observational

Television usually on in the presence of children during two or more daily meals (parental report)

Television watching during meals was related to higher intake of salty snacks and soft drinks, and lower intake of fruits, vegetables, and juices

KusanoTsunoh et al., 2001

Male and female students in primary school and junior high school, Japan

Crosssectional, observational

Weekly frequency of eating dinner and breakfast meals with all family members

Breakfast and dinner frequency were associated with higher intake of fruit, vegetables, and milk; associations differed according to meal type, gender, and school level

Gillman et al., 2000

Males and females aged 9–14 years, US

Crosssectional, observational

Frequency of dinner or supper with family members

Meal frequency was related to higher intake of fruit, vegetables, fiber, and micronutrients, and lower intake of fried foods, soft drinks, saturated fat, and trans fat

De Bourdeaudhuij and Van Oost, 2000

Males and females aged 12–18 years, Belgium

Crosssectional, observational

Usually share breakfast meal or hot meals; shared meals given a special meaning (e.g., taken at a fixed hour)

None of the meal variables were related to adolescent healthy food scores

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49.3  Do family meals promote healthy weights?

television during family meals, watching television is related to lower intakes of vegetables and calcium-rich foods, and to higher intake of soft drinks. The relationship between family meals and dietary intake probably represents the opportunity for children and adolescents to be exposed to healthy food choices that are prepared, served and consumed by their parents. Some research suggests that the frequent consumption of fast food during family meals is related to poorer diet quality among children and adolescents (Ayala et al., 2007; Boutelle et al., 2007). In contrast, young people who report helping with dinner preparation at home tend to have higher intakes of fruit, vegetables and key vitamins (i.e., vitamin A and folate), and lower intakes of fat (Larson et al., 2006). Among adolescents participating in the Project EAT study, those who usually or always had vegetables at dinner were found to have higher intakes of vegetables 5 years later compared to their peers who were never or only sometimes served vege­ tables (Arcan et al., 2007). Likewise, parental intakes of fruit, vegetables and dairy foods predicted higher adolescent intakes of these foods 5 years later during the period of transition from adolescence to young adulthood (18–24 years).

49.3  Do family meals   promote healthy   weights? The observation that having frequent family meals is related to diets of higher quality raises the possibility that family meals may also help to promote healthy weights among children and adolescents (see Table 49.2). However, few studies have investigated whether family meals are related to weight status, and these findings have provided only mixed support. Some research has found that children and adolescents who regularly share mealtimes with their families are less likely

609

to be overweight. There was, however, notable differences according to gender, age, and racial and ethnic background (Taveras et al., 2005; Sen, 2006; Gable et al., 2007; Fulkerson et al., 2008; Gundersen et al., 2008; Yuasa et al., 2008). Yet, an approximately equal number of studies have found no evidence of a relationship between family meals and weight status (Mikkila et al., 2003; Mamun et al., 2005; Utter et al., 2008; Wurbach et al., 2009). Findings from the Early Childhood Longitudinal Study support the importance of having frequent family meals during the elementary school years (Gable et al., 2007). This study measured over 8000 American children and collected parent report of family meal frequency (breakfast and dinner meals) four times between kindergarten entry and the end of third grade. Report of greater meal frequency was related to a lower risk of becoming overweight (body mass index 95th percentile) between kindergarten and the end of third grade. Similarly, children who shared more meals with their families were less likely to be persistently overweight over the 4 years. These relationships were found above and beyond any relationships between overweight and child gender, race and family socio-economic status. While some research suggests that regularly sharing meals with family also promotes healthy weights among older children and adolescents, support for this relationship has been limited to females and non-Hispanic white youth in the majority of these studies (Sen, 2006; Fulkerson et al., 2008; Gundersen et al., 2008; Yuasa et al., 2008). For example, the National Longitudinal Survey of Youth surveyed more than 5000 American adolescents (12–15 years) to collect their height, weight, and the number of days in a typical week that their family had eaten dinner together in the past year (Sen, 2006). Family dinner frequency was related to a lower risk of being overweight at the time of the initial assessment, a lower risk of becoming overweight over the next 3 years, and a greater chance of ceasing

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Table 49.2  Research on family meals and weight status Study Author, year population

Type of study

Meal variable

Summary of findings relevant to weight status

Wurbach et al., 2009

Males and females aged 7–14 years, Germany

Crosssectional, observational

Usual weekday frequency of main meals eaten together with family members (parental report)

Daily frequency of family meals was not related to child weight status

Yuasa et al., 2008

Male and female students in elementary and junior high school, Japan

Crosssectional, observational

Family eats meals together every day (parental and child report)

Young females who ate family meals every day were less likely to be overweight, but no relationship between weight status and family meals was found among males or older females

Utter et al., 2008

Male and female adolescents, mean age  14.8 years, New Zealand

Crosssectional, observational

Frequency of eating the evening meal with family members during the school week

Meal frequency was not related to adolescent weight status

Gundersen et al., 2008

Males and females aged 10–15 years, US

Crosssectional, observational

Family eats breakfast or dinner meals together (parental report)

Females that ate dinner together with their families were less likely to be overweight; no association was observed between child weight status and eating breakfast with one’s family

Fulkerson et al., 2008

Males and females aged 11–18 years at baseline, US

Crosssectional and longitudinal, observational

Past week frequency of eating a meal with all or most family members living in the home

Family meal frequency was related to lower odds of being overweight among young adolescent females at baseline, but the relationship did not persist over 5 years of follow-up; no relationship was found between family meal frequency and weight status among males or older females; associations differed by race/ethnicity

Gable et al., 2007

Males and females, in kindergarten at baseline and in grade 3 at followup, US

Longitudinal, observational

Typical frequency of eating breakfast and frequency of eating dinner meals together as a family (parental report)

Children who ate fewer family meals were more likely to be overweight for the first time at follow-up in grade 3; children who ate fewer family meals were also more likely to be persistently overweight during the study period

Mamun et al., 2005

Males and females aged 14 years, Australia

Crosssectional, observational

Usual weekly frequency of family eating together and perceived importance of eating together (maternal report)

Frequency of eating together was not related to child weight status; children were at increased risk of overweight if their mother did not feel that the family eating together was important

Sen, 2006

Males and females aged 12–15 years at baseline, US

Crosssectional and longitudinal, observational

Frequency of eating dinner with family during a typical week in the past year

For white youth only, dinner frequency was related to reduced odds of being overweight at baseline; reduced odds of becoming overweight at 3-year follow-up; and increased odds of ceasing to be overweight by 3-year follow-up (Continued)

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49.5  Do family meals have other benefits?

611

Table 49.2  (Continued) Taveras et al., 2005

Males and females aged 9–14 years at baseline, US

Crosssectional and longitudinal, observational

Frequency of sitting down to eat dinner or supper with other members of one’s family

Frequency of family dinner was related to lower odds of being overweight at baseline; no relationship was found between past year frequency of family dinner and 1-year incidence of becoming overweight

Mikkila et al., 2003

Males and females aged 14–16 years, Finland

Crosssectional, observational

Usually having the evening meal at home (daily with the family, daily without the family, or evening meal not consumed at home)

Not having evening meals at home was associated with overweight status in females, but weight status was not related to whether adolescents ate the evening meal with family versus alone

to be overweight over the next 3 years. However, these relationships were found only among non-Hispanic white adolescents and not among African-American or Hispanic adolescents. Family meals may be an important means of promoting healthy eating and reducing overweight among young people, but more needs to be learned to ensure that the benefits of mealtimes can be shared equally by all children and adolescents. The relationship between family meals and weight status may depend on multiple aspects of the family meal, such as the types and amounts of foods served, the eating behaviors modeled by parents, the style of meal service, and mealtime rules.

49.4  Do family meals promote health in overweight children? Overweight children and adolescents are socially marginalized and report poor social or emotional health more often than young people at a healthy weight (Institute of Medicine, 2005). For overweight youth, the family mealtime environment is likely to also be important for promoting psychosocial wellbeing. The Project

EAT survey included measures of the mealtime atmosphere and priority for shared meals. A positive mealtime atmosphere was defined by enjoyment of mealtimes and agreement that mealtimes are an occasion for talking with family members. Greater priority for meals was defined by the expectation for having shared meals and the importance placed on sharing meals. Among overweight adolescents in the Project EAT study, positive mealtime atmosphere was related to better self-esteem and body satisfaction (Fulkerson et al., 2007). A greater priority for shared meals was related to better self-esteem, lower risk of depression, and decreased manifestation of unhealthy behaviors to control weight (e.g., taking diet pills).

49.5  Do family meals have other benefits? It appears that frequently sharing mealtimes with family, making shared mealtimes a priority and engaging in affirmative mealtime conversation benefits the health of young people. Having family meals can also help families maintain close relationships, and benefit the

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49.  Family Meal Patterns

learning and development of children and adolescents. When young people feel connected to their parents, they are more likely to avoid problem or risky behaviors. More frequent family meals and extended mealtime conversations are related to stronger vocabulary skills in young children, and higher academic achievement in adolescents (Eisenberg et al., 2004; Snow et al., 2006). Beyond general family communication and support, family meal frequency is related to a number of developmental assets (e.g., strong commitment to learning, a sense of purpose, high social competency) in adolescents (Fulkerson et al., 2006a). Adolescents who have more frequent family meals are less likely to use alcohol, tobacco or illicit drugs, engage in acts of violence, get involved in antisocial behavior, or attempt suicide (Eisenberg et al., 2004; Fulkerson et al., 2006a).

49.6  What are strategies to promote family meals? Despite the many potential benefits of sharing mealtimes with family, there is considerable variability in the frequency with which children and adolescents have family meals around the world and within different countries according to individual demographic factors. For example, surveys of adolescents (13–15 years) in six European countries found the percentage of adolescents that have family meals every day or most days to vary by country from 72 percent to 92 percent in males and 65 percent to 90 percent in females (Zaborskis et al., 2007). In Japan, 92 percent of males and 93 percent of females have daily family meals in the first grade of elementary school, while only 62 percent of males and 66 percent of females report having family meals when they reach junior high school (Yuasa et al., 2008). While the majority of children (6–11 years old) in the US have

family meals 6–7 days a week, 25 percent have family meals only 4–5 days a week and 20 percent have family meals on 3 or fewer days per week (Child Trends DataBank, 2003). Among American adolescents (12–17 years), fewer than half have family meals 6–7 days a week, 27 percent have family meals on 4–5 days a week and 31 percent have family meals on 3 or fewer days per week (Child Trends DataBank, 2003). The frequency of family meals among youth in the US tends to decline with increasing age, and differences have also been found according to gender, ethnic background and family income (Child Trends DataBank, 2003). National survey data suggest that Hispanic youth are more likely than non-Hispanic children (66 percent of Hispanic, 53 percent of non-Hispanic white, and 50 percent of non-Hispanic black children) and adolescents (54 percent of Hispanic, 39 percent of non-Hispanic white, and 40 percent of non-Hispanic black adolescents) to have family meals 6–7 days a week. In regard to family income, the percentage of adolescents who have family meals 6–7 days a week tends to decline with increasing income. Among adolescents living at 100 percent of the poverty level, 55 percent have family meals 6–7 days a week, compared to only 37 percent of those living at 200 percent or more of the poverty level. While most parents and children feel it is important to eat together as a family, they also report several barriers to sharing mealtimes. In the Project EAT study, it was found that 98 percent of parent participants and 63 percent of their teenage children agreed it is important for their family to eat at least one daily meal together (Fulkerson et al., 2006b). Furthermore, 97 percent of parents and 63 percent of their teenage children agreed that family meals bring them together in an enjoyable way. Research in adolescents has found that major barriers to sharing meals with family are scheduling difficulties, a desire to spend time with friends, a dislike of the foods served at family meals,

2.  From society to behavior: policy and action

49.7 What actions can communities take to promote family meals?

and dissatisfaction with family relations (Neumark-Sztainer et al., 2000). Families clearly face a number of challenges to sharing meals, even when they have adequate food and resources to prepare meals. Health professionals and others who work with youth and families can help by sharing strategies to overcome these barriers. Families can address scheduling difficulties by taking the time to plan when and where they will gather for meals, and by being flexible about the time and location of meals. Planning the menu in advance with input from the entire family will help ensure that everyone enjoys the meal. For example, parents can offer their children a choice about the type of vegetables to be served, or prepare a few varieties. The amount of time required for meals can be reduced by working together as a family to prepare the food, and by purchasing simply-prepared, healthy foods (e.g., frozen vegetables, instant whole-grain rice, pre-seasoned lean meat). Avoiding contentious topics of conversation can help to make mealtimes more pleasant. Finally, turning off the television and ignoring the phone will create a relaxed environment.

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pledged to share 4 or more dinners per week over 6 months, and then shared with the community the positive impact of this pledge on their lives. The Mayor of Minneapolis was among those to take the pledge; he rearranged his schedule so that all City Hall staff could be home between 6 and 7 pm for dinner with their families. A similar initiative, started in Ridgewood, New Jersey, has engaged community organizations (e.g., sport, faith-based and art organizations) in a family night during which all activities are canceled, so that families can spend time together (Anderson et al., 2005). These examples show how simple community-level changes, such as making work schedules flexible and scheduling events outside of typical dinner hours, can allow families to share more meals together. At least two programs targeting changes in the home environment to promote family meals for school-age girls and low-income families have also been evaluated (Johnson et al., 2006; Rosenkranz et al., 2009). These programs are described below, as they might be modified or further refined by community organizations with the goal of increasing family meals in the populations they serve.

Kansas State University activity sessions 49.7  What actions can communities take to promote Kansas State University developed 4 activity sessions for summer day-care programs serving family meals? Barriers to family mealtimes need to be addressed not only by families, but also by businesses, schools and other community organizations. A community in Minneapolis, Minnesota became concerned about the loss of family time to outside activities. It developed an initiative to make family life, including time for family meals, a priority (Anderson et al., 2005). Time IN for Family has encouraged families to make and protect time for connecting during the dinner hour. As part of the initiative, families publicly

girls aged 6–12 years (Rosenkranz et al., 2009). Discussions and activities designed to promote family meals stressed the importance of sharing mealtimes and how to make healthful improvements to the family mealtime environment. The specific behaviors enforced were: 1. Asking permission to turn off the television during meals 2. Asking to replace soda with water at mealtime 3. Helping parents prepare food and clean up after meals

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4. Asking to include a fruit and vegetable in the meal 5. Using good table manners 6. Asking to be physically active with a parent before or after the meal. Program participants were engaged in physical activities, preparing fruit and vegetable snacks, and role-playing the target family mealtime behaviors. Parent surveys showed the frequency of family meals increased following their daughters’ completion of the program. The Promoting Family Meals module The Promoting Family Meals module was designed by the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) in Washington State (Johnson et al., 2006). A variety of educational materials and 7 key messages were developed for the program: 1. Eating together strengthens the family 2. Eating together is part of parenting 3. Eating together helps children eat better 4. Children can help with family meals 5. There are many benefits to eating together as a family 6. It is possible to work through barriers to eating together 7. Try eating together at unconventional times and places. WIC staff were trained to select and match these educational materials and messages to each client’s level of interest and to help them improve on current mealtime behaviors as part of individual counseling sessions. In addition, local WIC agencies were encouraged to share program materials and messages with other agencies, programs and clinics in their communities. Small but significant gains in the frequency of family meals were observed over 6 months among clients at agencies where Promoting Family Meals was delivered compared to control agencies.

49.8  What remains to be learned about family meals? Research suggests that family meals may offer many benefits for youth, including improved nutrition and weight-related health. There is clearly sufficient evidence to support the promotion of family meals; however, much remains to be learned, and care should be taken when interpreting existing research. Causality cannot be inferred from the findings of non-experimental studies. The authors are unaware of any experimental research relating family meals to the health outcomes reviewed in this chapter. The majority of research relating family meals to nutrition and weight-related outcomes has been observational and cross-sectional in nature. It may be that youth at greater risk for poor nutrition and excess weight gain choose not to participate in family meals for a variety of reasons. Research must address the types of foods served at meals, and how foods are portioned. There is also a need to evaluate relationships between family meals and the outcomes of interest using consistent definitions in different populations. This research is required to confirm that the benefits of sharing meals occur across age, race, gender and income groups. Research examining family meals should be prioritized as part of efforts to enhance the design of future nutrition programs for diverse youth and families.

References Anderson, J., & Doherty, W. (2005). Democratic community initiatives: The case of overscheduled children. Family Relations, 54, 654–665. Arcan, C., Neumark-Sztainer, D., Hannan, P., van den Berg, P., Story, M., & Larson, N. (2007). Parental eating behaviors, home food environment and adolescent intakes of fruits, vegetables, and dairy foods: Longitudinal findings from Project EAT. Public Health Nutrition, 10, 1257–1265. Ayala, G., Baquero, B., Arredondo, E., Campbell, N., Larios, S., & Elder, J. (2007). Association between ­family ­variables

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Fulkerson, J. A., Neumark-Sztainer, D., & Story, M. (2006b). Adolescent and parent views of family meals. Journal of the American Dietetic Association, 106, 526–532. Fulkerson, J., Strauss, J., Neumark-Sztainer, D., Story, M., & Boutelle, K. (2007). Correlates of psychosocial wellbeing among overweight adolescents: The role of the family. Journal of Consulting and Clinical Psychology, 75, 181–186. Fulkerson, J., Neumark-Sztainer, D., Hannan, P., & Story, M. (2008). Family meal frequency and weight status among adolescents: Cross-sectional and 5-year longitudinal associations. Obesity, 16, 2529–2534. Gable, S., Chang, Y., & Krull, J. (2007). Television watching and frequency of family meals are predictive of overweight onset and persistence in a national sample of school-age children. Journal of the American Dietetic Association, 107, 53–61. Gillman, M., Rifas-Shiman, S., Frazier, A., Rockett, H., Camargo, C., Field, A., et al. (2000). Family dinner and diet quality among older children and adolescents. Archives of Family Medicine, 9, 235–240. Gundersen, C., Lohman, B., Eisenmann, J., Garasky, S., & Stewart, S. (2008). Child-specific food insecurity and overweight are not associated in a sample of 10- to 15-year old low-income youth. Journal of Nutrition, 138, 371–378. Haapalahti, M., Mykkanen, H., Tikkanen, S., & Kokkonen, J. (2003). Meal patterns and food use in 10- to 11-year-old Finnish children. Public Health Nutrition, 6, 365–370. Institute of Medicine, Committee on Prevention of Obesity in Children and Youth, Food and Nutrition Board, Board on Health Promotion and Disease Prevention. (2005). Preventing childhood obesity: Health in the balance. Washington, DC: National Academies Press. Johnson, D., Birkett, D., Evens, C., & Pickering, S. (2006). Promoting family meals in WIC: Lessons learned from a statewide initiative. Journal of Nutrition Education and Behavior, 38, 177–182. Kusano-Tsunoh, A., Nakatsuka, H., Satoh, H., Shimizu, H., Sato, S., Ito, I., et al. (2001). Effects of family-togetherness on the food selection by primary and junior high school students: Family togetherness means better food. Tohoku Journal of Experimental Medicine, 194, 121–127. Larson, N. I., Story, M., Eisenberg, M. E., & NeumarkSztainer, D. (2006). Food preparation and purchasing roles among adolescents: Associations with sociodemographic characteristics and diet quality. Journal of the American Dietetic Association, 106, 211–218. Larson, N., Neumark-Sztainer, D., Hannan, P., & Story, M. (2007). Family meals during adolescence are associated with higher diet quality and healthful meal patterns during young adulthood. Journal of the American Dietetic Association, 107, 1502–1510.

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Mamun, A., Lawlor, D., O’Callaghan, M., Williams, G., & Najman, J. (2005). Positive maternal attitude to the family eating together decreases the risk of adolescent overweight. Obesity Research, 13, 1422–1430. Mikkila, V., Lahti-Kosk, M., Pietinen, P., Virtanen, S., & Rimpela, M. (2003). Associates of obesity and weight dissatisfaction among Finnish adolescents. Public Health Nutrition, 6, 49–56. Neumark-Sztainer, D., Story, M., Ackard, D., Moe, J., & Perry, C. (2000). The “family meal”: Views of adolescents. Journal of Nutrition Education and Behavior, 32, 329–334. Neumark-Sztainer, D., Hannan, P. J., Story, M., Croll, J., & Perry, C. (2003). Family meal patterns: Associations with sociodemographic characteristics and improved dietary intake among adolescents. Journal of the American Dietetic Association, 103, 317–322. Ochs, E., & Shohet, M. (2006). The cultural structuring of mealtime socialization. New Directions for Child and Adolescent Development, 111, 35–49. Rideout, V., & Hamel, E. (2006). The media family: Electronic media in the lives of infants, toddlers, preschoolers and their parents. Menlo Park, CA: Kaiser Family Foundation. Roberts, D., Foehr, U., & Rideout, V. (2005). Generation M: Media in the lives of 8–18 year-olds. Menlo Park, CA: Kaiser Family Foundation. Rosenkranz, R., & Dzewaltowski, D. (2009). Promoting better family meals for girls attending summer programs. Journal of Nutrition Education and Behavior, 41, 65–67. Savage, J., Orlet Fisher, J., & Birch, L. (2007). Parental influence on eating behavior: Conception to adolescence. Journal of Law, Medicine & Ethics, 35, 22–34. Sen, B. (2006). Frequency of family dinner and adolescent body weight status: Evidence from the national longitudinal survey of youth, 1997. Obesity, 14, 2266–2276.

Snow, C., & Beals, D. (2006). Mealtime talk that supports literacy development. New Directions for Child and Adolescent Development, 111, 51–66. Taveras, E., Rifas-Shiman, S., Berkey, C., Rockett, H., Field, A., Frazier, A., et al. (2005). Family dinner and adolescent overweight. Obesity Research, 13, 900–906. Utter, J., Scragg, R., Schaaf, D., & Mhurchu, C. (2008). Relationships between frequency of family meals, BMI and nutritional aspects of the home food environments among New Zealand adolescents. International Journal of Behavioral Nutrition and Physical Activity, 5, 50. Veugelers, P., & Fitzgerald, A. (2005). Prevalence of and risk factors for childhood overweight and obesity. Canadian Medical Association Journal, 173, 607–613. Videon, T., & Manning, C. (2003). Influences on adolescent eating patterns: The importance of family meals. Journal of Adolescent Health, 32, 365–373. Wiecha, J., Peterson, K., Ludwig, D., Kim, J., Sobol, A., & Gortmaker, S. (2006). When children eat what they watch. Impact of television viewing on dietary intake in youth. Archives of Pediatrics & Adolescent Medicine, 160, 436–442. Wurbach, A., Zellner, K., & Kromeyer-Hauschild, K. (2009). Meal patterns among children and adolescents and their associations with weight status and parental characteristics. Public Health Nutrition, 12, 1115–1121. Yuasa, K., Sei, M., Takeda, E., Ewis, A., Munakata, H., Onishi, C., & Nakahori, Y. (2008). Effects of lifestyle habits and eating meals together with the family on the prevalence of obesity among school children in Tokushima, Japan: A cross-sectional questionnaire-based survey. Journal of Medical Investigation, 55, 71–77. Zaborskis, A., Zemaitiene, N., Borup, I., Kuntsche, E., & Moreno, C. (2007). Family joint activities in a crossnational perspective. BMC Public Health, 7, 94.

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C H A P T E R

50 Social Influences on Eating in Children and Adults 1

Sarah-Jeanne Salvy1 and Patricia P. Pliner2 Division of Behavioral Medicine, Department of Pediatrics, University at Buffalo, State University of New York, USA 2 Department of Psychology, University of Toronto at Mississauga, Canada

o u t l i n e 50.1 Introduction

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50.2 Social Influences on the Control of Intake in Adults 50.2.1 Social Facilitation 50.2.2 Impression Management 50.2.3 Modeling and Conformity 50.2.4 Social-normative Framework

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50.3 Social Influence on Food Selection in Adults 50.3.1 Impression Management 50.3.2 Modeling and Conformity 50.3.3 Family and Peer Resemblance

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50.1  Introduction Christakis and Fowler (2007) recently described the spread of obesity among adults with shared social networks: individuals are more likely to

Obesity Prevention: The Role of Brain and Society on Individual Behavior

50.4 Social Influences on the Control of Intake in Children 621 50.4.1 Influences of Parents on Control of Intake in Children 622 50.4.2 Influence of Peers on Control of Intake in Children 622 50.5 Social Influences on Food Selection 623 in Children 50.5.1 Influence of Adults on Children’s Food Preferences and Food Choices 623 50.5.2 Influence of Peers on Children’s Food Preferences and Food Choice 624 50.6 Concluding Remarks

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gain weight over three decades if their same-sex friends are obese, thus implicating some form of social influence as a causal factor (Christakis and Fowler, 2007). This chapter, consisting primarily of descriptions of laboratory studies of children

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and adults, will review the literature on the effects of social influence on two aspects of eating – the control of intake (with an emphasis on how much people eat), and food selection (with an emphasis on the foods that people prefer and choose to eat).

50.2  Social influences on the control of intake in adults Social influence has important effects on the control of intake in adults, as revealed by research in three separate areas: social facilitation, impression management and modeling. These demonstrate that the effect of social influence on human eating behavior is complex, and the extent to which people influence others to eat more or less depends on a variety of situational factors.

50.2.1  Social facilitation Briefly, the research on social facilitation shows that individuals eat more in the presence of others than when they are alone, and that intake increases as the number of co-eaters increases (see, for example, de Castro et al., 1990; de Castro and Brewer, 1992; de Castro, 1994a). The effect of the presence of others on eating, assessed by food diaries generated by members of a community sample, occurs across a range of meal occasions and contexts. It occurs when individuals are eating breakfasts, lunches, dinners and snacks; when they are eating on weekdays and weekends; whether or not they are consuming alcohol at the meal; and whether they are eating at home or in a restaurant (de Castro et al., 1990; de Castro, 1991a, 1991b, 1991c). Social facilitation has been demonstrated in many different populations beyond de Castro’s original one, including American soldiers, and French and Dutch students (Hirsch and Kramer, 1993; Feunekes et al., 1995; Bellisle et al., 1999), and across a variety of research methodologies, including the food diary approach,

direct observation and the “gold standard” expe­rimental method (Klesges et al., 1984; Berry et al., 1985; Edelman et al., 1986; Clendenen et al., 1994). Given the ubiquity of the effect, it is also worthwhile to note that social facilitation is attenuated or even absent when people eat with strangers in contrast to eating with friends and family (Clendenen et al., 1994; de Castro, 1994b; Salvy et al., 2007a).

50.2.2  Impression management In contrast to social facilitation, the research on impression management shows that the presence of others usually results in decreased amounts eaten, as individuals attempt to project positive images of themselves. This research takes some of its impetus from the classic and modern literatures on stigma, which show that obesity is a highly stigmatized state (Vann, 1970; Laslett and Warren, 1975; DeJong, 1980; Brownell et al., 2005; Puhl and Brownell, 2007). Subsequently, a literature on “consumption stereotypes” developed, indicating that a set of negative traits is attributed to those who consume large (as opposed to small) amounts of food, particularly if they are women (Chaiken and Pliner, 1987; Martins et al., 2004; Vartanian et al., 2007). Large eaters are perceived as less feminine, less likeable and less desirable as a friend, less attractive and even less moral than those who eat little. Conversely, small eaters are assumed to possess positive traits. One might expect that, in order to avoid the imputation of such negative characteristics and/or to attract the attribution of positive traits, people who are motivated to make a good impression on another person will eat less than those who are not so motivated. The results of several studies confirm that expectation, showing that both males and females eat less in the presence of a partner of the opposite sex, particularly if that partner is deemed socially desirable (Mori et al., 1987; Pliner and Chaiken, 1990).

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50.2  Social influences on the control of intake in adults

Other research shows that overweight individuals, presumably at risk from negative attributions related to eating, suppress their eating in the presence of others, especially others who are not obese (Maykovich, 1978; de Luca and Spigelman, 1979). For example, de Luca and Spigelman found that obese college students ate close to nothing in the presence of a lean group, but consumed a large amount in the company of an obese group. In contrast, the intake of normal-weight participants was unaffected by the group’s weight (de Luca and Spigelman, 1979). It seems likely that the absence of a social facilitation effect when individuals are eating with strangers is also related to impression management. Presumably, the importance of conveying a positive image is greater during interactions with strangers than with friends or relatives (Leary et al., 1994), as individuals are generally more assured of their mutual affection in the latter cases and have less need to use behavioral strategies to obtain each other’s approval (Jellison and Gentry, 1978; Leary et al., 1994).

50.2.3  Modeling and conformity Early research in social psychology found that when individuals made perceptual judgments in ambiguous situations, those judgments converged (Sherif, 1936). The same kind of convergence in behavior can be seen when two individuals are studied in a free-eating situation (Herman et al., 2005; Salvy et al., 2007a): correlations between amounts eaten by members of the pairs are high. These results suggest that individuals are conforming to or modeling each other’s behavior; however, the methodology does not directly demonstrate conformity or modeling, since there is no systematic manipulation of the amount of the food intake of one member of the pair. Thus, it is not clear whether individuals are matching the behavior of their eating companions or whether the mere presence of others impacts food consumption. However, modeling or conformity studies, in which a participant is paired with one or more individuals whose eating

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beh­avior has been pre-determined (i.e., an experimental accomplice), have consistently found that the participant’s eating closely follows that of the accomplice. They eat very little or a lot depending on the behavior of their eating partners. When left to their own devices in a no-model control condition, they eat intermediate amounts (Nisbett and Storms, 1974; Rosenthal and McSweeney, 1979; Conger et al., 1980). This conformity effect is extremely powerful, capable of over-riding even strong physiological influences. Goldman and colleagues (1991) found that the effects were as strong among participants who had been deprived of food for over 24 hours as among those who had not been deprived: participants exposed to a model who ate very little ate minimally themselves, even when they had been severely deprived. In a complementary study, modeling effects were as strong among participants who were very full as among those who were not: participants exposed to a model who ate a great deal followed their lead, eating a great deal themselves, even if they had just consumed a large amount of food (Herman et al., 2002). A modeling effect also appears when a model is not physically present in the situation but is represented symbolically by information about how much others ate (Roth et al., 2001; Pliner and Mann, 2004). This holds even if the hypothetical “others” is supposedly only one person (Larkin and Pliner, 2008). Feeney and Colleagues (2008) showed that participants continue to consume, on a different day and in the absence of the model, the amount originally consumed by the model. Thus, model­ing or conformity effects are extremely powerful, trans-situational, and durable to a certain extent.

50.2.4  Social-normative framework The disparate findings from these three areas of research have been neatly incorporated into a social-normative framework (Herman et al., 2003) which posits that, in the presence of palatable

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food and in the absence of other constraints, people are motivated to eat as much as they can. One major constraint, however, is a social norm that proscribes eating excessively. Thus, the social norm serves an inhibitory function, indicating at what point individuals must stop eating if they are to avoid excess. What is appropriate (as opposed to excessive) in a given situation can be defined partially in terms of the individual’s prior experience or cultural knowledge. Appropriateness is also importantly determined by the intake of other eaters in that situation, and socially-derived appropriateness norms will often prevail. The research clearly indicates that norms related to eating can operate to either increase or decrease individuals’ eating. This fact is reflected in the main tenet of the normative model, which posits that norms often inhibit intake, in oppos­ ition to individuals’ motivation to maximize intake in the presence of palatable food. When the relevant norms restricting intake are not clear or when norms are high (i.e., others eat a lot), people may eat large amounts (Leone et al., 2007). By the same token, norms can suppress individuals’ eating if they are clear and if they are low (i.e., if others clearly eat a small amount of food). Thus, individuals’ behavior in eating situations is strongly determined by the prevailing social norms – their judgment about what is the appropriate amount to eat is dependent on the judgments and behavior of others in the situation. Indeed, Christakis and Fowler (2007), whose social network and obesity study opened this chapter, suggest similarly that norms about the acceptability or appropriateness of being overweight may underlie their finding of an effect of shared social networks on obesity.

50.3  Social influence on food selection in adults There is less literature on the effects of social influences on food selection in adults than on the control of food intake. For purposes of comparison,

the paradigms and specific research questions asked have unfortunately tended to differ. Al­tho­ugh there are some studies that might be considered to be counterparts of the impression management and modeling literatures, there is no counterpart to social facilitation literature.

50.3.1  Impression management There is some work indicating that eating unhealthy foods attracts negative attributions, similar to those resulting from eating large amounts (Mooney et al., 1994; Stein and Nemeroff, 1995; Barker et al., 1999; Oakes and Slotterback, 2004–2005). Although there are no studies directly testing the idea that people choose healthy foods in order to make a good impression on others, one recent study examined choice of food after an ego-threatening experience (Pliner et al., 2008). Participants who had been bested by a companion on a series of intellectual tasks subsequently chose a healthier (and less palatable) lunch than those who had succeeded, possibly in an attempt to repair (in the eyes of the experimenter, the competing companion and/or themselves) the negative impression produced by their poor performance.

50.3.2  Modeling and conformity In the case of modeling and conformity, the few data that exist show no effect of social influence. Roth found that although the number of cookies that participants ate was strongly influenced by the number supposedly eaten by earlier participants in the study, the type of cookies they ate (of the three available) was uninfluenced by the “remote” models’ reports of their “favorite”. Furthermore, whereas Pliner and Mann (2004; Study 1) obtained a strong conformity effect for amounts consumed, in a second study they found no evidence for models’ influence on participants’ choice between a healthy but relatively unpalatable food versus an unhealthy but

2.  From society to behavior: policy and action

50.4  Social influences on the control of intake in children

palatable one. Participants chose the unhealthy palatable food independently of the imaginary models’ behavior. Strengthening the conformity manipulation by making the hypothetical models’ choice unanimous, by allowing participants to make their choices in private, and by making the palatability difference between the two choices smaller, still failed to produce any evidence for social influence on food choice (Pliner and Premji, 2005).

50.3.3  Family and peer resemblance Research on the resemblance between parents and their adult children is relevant to the question of social influences on food preferences, if one assumes social transmission. Although the possibility of genetic effects is obvious (Falciglia and Norton, 1994), Rozin and Millman (1987) found no evidence for heritability in an adult twin cohort. Thus, it seems likely that children’s preferences are developed at least partly through modeling of parental preferences (Pliner and Pelchat, 1986). Surprisingly, the correlations representing family resemblance tend to be fairly low, accounting for only about 2–6 percent of the variance (Pliner, 1983; Rozin et al., 1984; Rozin and Millman, 1987). Hypothesizing that individuals might be more likely to model the food preferences of their peers than those of their parents, Rozin and colleagues (2004) examined peer similarity in a group of college student roommates and found no similarity in food preferences, despite 7 months of shared accommodation (Rozin et al., 2004). Thus, the family/peer resemblance literature, like the modeling literature, shows little effect of social influence on food choice in adults. Thus, the social facilitation, impression management and modeling/conformity studies demonstrate the power of social influence in the control of food intake in adults, yet the evidence for effects of social influence appear to be much weaker in the case of food selection. While the amount that adults eat appears to be strongly

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affected by the social norms conveyed by others’ behavior, food choices and preferences seem to be weakly affected at best. Why might this be the case? The following paragraph describes some early research on social influence in which the type of judgments that participants were required to make was systematically manipulated (Crutchfield, 1955); these included judgments about matters of fact and about preferences. An example of a judgment about a matter of fact was to choose which of two shapes, a star and a circle, was larger in area; an example of a judgment of preference was for participants to choose which of two drawings they preferred. There were strong conformity effects for all types of judgments with the notable exception of those concerning preferences. Here, conformity effects were weak or absent. Interpreting Crutchfield’s results, Sabini (1995) suggested that while people look to others for information about how to behave in ambiguous circumstances in which those others are perceived to know the “correct answer”, they do not need the guidance of others in matters related to preferences – what they like or what they want. Thus, in situations where there might be assumed to be a “correct” answer – how much one should eat – there are robust conformity effects. In matters of preference – what one likes or chooses to eat – the effects seem to be weaker.

50.4  Social influences on the control of intake in children An ever-increasing number of children and adolescents in the United States and abroad are considered overweight and obese. Thirty-one percent of children and adolescents are either at risk for overweight or overweight, and 16 percent are obese (Hedley et al., 2004). As is the case for adults, the social environment clearly influences youths’ eating behaviors; in this case, parents and peers are the primary influences. However,

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in contrast to the adult literature, there is little research describing the underlying mechanisms of social influences on children. Furthermore, little is known about the developmental trajectory of the effects of social influences on children’s and youth’s eating behavior. For this reason, the research presented below describes studies of children of all ages, from preschoolers to teens, even though there are almost certainly huge differences in the source(s) and nature of the social influence. Arguably, parents may be most influential in early childhood, whereas peers and friends may become more influential as children get older.

50.4.1  Influences of parents on control of intake in children As primary sources of socialization, parents are likely role models for eating habits and food consumption. Despite this widespread assumption, questions remain about how parental infl­ uences work to promote the development of (over)eating in children (Birch and Davison, 2001). Perhaps the most widely-cited literature on parental influences on children’s eating behavior is the work of Dr Leann Birch. In a series of studies, Birch and colleagues have shown that parenting styles and feeding practices can impact children’s food consumption (Klesges et al., 1983, 1986; Cutting et al., 1999; Fisher and Birch, 1999, 2002; Birch and Fisher, 2000; Carper et al., 2000; Wardle et al., 2002; Faith et al., 2004). These findings indicate that more rigid and controlling approaches to child feeding can potentiate preferences for high-fat, energy-dense foods, limit acceptance of a variety of foods, and disrupt regulation of energy intake by altering their responsiveness to internal cues of hunger and satiety. This can occur when well-intended but concerned parents assume that children need help in determining what, when and how much to eat, and impose child-feeding practices that provide children with few opportunities for self-control (Birch and Davison, 2001).

These findings are consistent with the work of others. For instance, Klesges and colleagues, observing family dinners, found significant correlations between a child’s weight and (1) parental prompts to eat, (2) parental food offers and (3) parental encouragement to eat, in 1- to 3-year olds (Klesges et al., 1983). Similarly, Koivisto and colleagues (1994) found that children’s energy intake was positively correlated with the child taking food upon parental prompting, reinfor­ cing the idea that some parental behaviors are related to the weight and energy intake of their children and to the development of overeating in overweight children (Koivisto et al., 1994). Laessle and colleagues (2001) examined the impact of parental presence on the eating behavior of obese and non-obese children (8–12 years old), using a repeated-measure design in which mothers were either present or absent while children ate. When the child’s mother was present in the laboratory, overweight, but not normal-weight children, showed an increased caloric intake. The authors hypothesized that this would promote a positive energy balance in the long term.

50.4.2  Influence of peers on control of intake in children There is a surprising lack of research on the effects of peers on youth’s food intake, especially in light of the fact that the influence exerted on youths by parents is of a different nature than that exerted by peers (Huon and Walton, 2000; Huon et al., 2000). It is recognized that peers and friends are important agents of change in childhood and adolescence. Extensive literature also suggests that eating and body-image concerns (Oliver and Thelen, 1996), as well as dieting during childhood and adolescence, are the result of social influences (Paxton et al., 1999; Huon and Walton, 2000; Huon et al., 2000). Romero and colleagues (2009) assessed the effect of modeling on the food intake of 8- to 12-year-old overweight and non-overweight

2.  From society to behavior: policy and action

50.5  Social influences on food selection in children

girls, using a video model who ate a large or a small amount. The weight status of the video model was voluntarily ambiguous. The model was at the 75th BMI percentile, and she wore a light jacket over her clothes to conceal her body weight. Both overweight and non-overweight girls exposed to the large serving-size condition consumed substantially more cookies than those exposed to the small serving-size condition. The effects of social influence on eating in children are moderated by some of the same variables as in adults. Familiarity moderates the social facilitation effect: children ate more with siblings than alone, while they ate no more with strangers than alone (Salvy et al., 2008a). Overweight is a stigmatized state for children, as it is for adults (Sigelman, 1991; Bell and Morgan, 2000). Studies have shown that weight status moderates the effects of modeling on children’s eating, as it does in adults. Overweight children decrease their food intake in front of others, especially in front of those who are not overweight. For example, Salvy and colleagues (2007a, 2007b), examining dyads made up of lean and overweight pre-adolescent girls (8–12 years old), assessed the effects of peer influence on snack intake as a function of the co-eaters’ weight status: weight discordant (one lean and one overweight participant) or weight concordant (both members of the dyad were either lean or overweight). Overweight girls eating with a normal-weight peer consumed 200 fewer kilocalories than overweight participants eating with an overweight peer (Salvy et al., 2007b). Because research on the effects of peers and friends on youth eating behavior is still in its infancy, there is a little systematic research investigating the explanatory mechanisms underlying the effects of social influences on eating. Future research ought to explore the social motives in children, and examine how these factors impact children’s selection and regulation of food intake. Based on the findings reviewed here, it appears that peer influence may be a suitable medium to teach children healthy eating habits.

623

An exciting and important future research direction is to determine whether peers and friends may be used to modify youths’ eating consumption and preferences, as part of a more comprehensive weight-loss program.

50.5  Social influences on food selection in children 50.5.1  Influence of adults on children’s food preferences and food choices Influence of models In an early study, Marinho (1942) assessed the effects of an adult’s expressed food preferences on those of 4- to 6-year-old children. The modeling manipulation produced a strong immediate effect on preferences, although 1 year later the children’s preferences had largely reverted to their original state. However, in children who had neither strong preference nor distaste for the foods, the manipulation produced lasting after-effects (Marinho, 1942). Harper and Sanders (1975) found that toddlers and preschoolers were more likely to ingest novel foods when adults modeled the behavior than when the adults simply offered the foods. The children were also more likely to eat unfamiliar food when it was offered by their mother, as opposed as by a friendly adult male “stranger” (Harper and Sanders, 1975). More recently, Addessi and colleagues (2005) showed that, in young children, the influence of others on food preference is specific to the food consumed; children are more likely to eat a novel food if others are eating it than when others are merely present or eating a different food (Addessi et al., 2005). In contrast, Hendy (1999) found that teacher model­ ing was no more effective than simple exposure in increasing the intake of a novel food (Hendy, 1999). In a later paper, Hendy and Raudenbush (2000) found that while “silent” teacher modeling was ineffective, “enthusiastic” modeling (i.e., the

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50.  Social influences on eating

teacher expressed liking for the food in an animated way) increased acceptance of the novel food. However, with the addition of a competing peer model, the enthusiastic teacher modeling was no longer effective in encouraging the acceptance of new food (Hendy and Raudenbush, 2000). Family resemblance Several studies have examined similarities between the food likes and dislikes of young children and those of their parents (Birch, 1980; Pliner and Pelchat, 1986). Overall, the association between child and parent food preferences appears to be statistically significant, but small in magnitude. Borah-Giddens and Falciglia conducted a meta-analysis of five studies testing the association between child ( 25-year-old) and parent food preferences. Their work indicated a mean parent–child correlation of r  0.19 for mother–child dyads and of r  0.14 for father– child dyads (Borah-Giddens and Falciglia, 1993). More recent studies have reported comparable findings (Skinner et al., 2002).

50.5.2  Influence of peers on children’s food preferences and food choice In a study of preschoolers, Birch (1980) arranged lunchtime seating so that a target child who preferred vegetable A to vegetable B was seated with three or four peers with the opposite preference pattern. The children were offered both vegetables at lunch for 4 consecutive days and allowed to choose in the presence of the other children which vegetable they wanted to eat. The likelihood that the target child would choose the non-preferred vegetable increased significantly. Birch further found that changes in preferences were maintained after the experiment when children were tested in the absence of the original peers (Birch, 1980). In a conceptually similar experiment conducted

many years earlier, Duncker (1938) obtained similar results. In both studies, younger children were more influenced than older children (Duncker, 1938). More recently, Hendy (2002) examined the effectiveness of trained peer models to encourage food acceptance in children during preschool meals, retesting them 1 month later. Findings indicated that peer models influenced food intake, but the effectiveness of models did not last beyond the modeled meals (Hendy, 2002). Salvy and colleagues (2008a) recently assessed the effects of the presence versus absence of peers on the consumption of healthy (grapes and baby carrots) and unhealthy (cookies and chips) snacks in 9- to 11-year-old overweight and normalweight children. The best predictor of participants’ consumption of healthy snack foods was whether the other youth also consumed healthy snack foods. These findings support previous studies in indicating that the presence of peers can influence food selection in both overweight and normal-weight children (Salvy et al., 2008b). However, this research did not assess whether children would model peers’ choices in their absence. The Pliner and Pelchat (1986) study of family resemblance also examined the target child’s closest age sibling. Here, in contrast to the typic­ ally low parent–child correlations, the average correlation between sibling profiles computed across foods was substantial, r  0.50. Finally, Rozin and Colleagues (2004) examined correlations bet­ween the preference profiles for 13 foods for mutu­ally-selected “best friends” of 8 and 9 years of age, finding absolutely no evidence for peer influence.

50.6  Concluding remarks We conclude that there may be more evidence for social influence on children’s food preferences than for adults – although the magnitude

2.  From society to behavior: policy and action

References

of the effects is typically not large and there are studies showing no influence. Referring to the earlier discussion pertaining to the distinction between matters of fact and matters of preference, and the relative immunity of adults to social influence with respect to the latter, we propose very tentatively that children may still be in the process of forming their preferences and, accordingly, may be more susceptible to social influence in matters of preference. If this is the case, we would expect to find evidence for influence declining from very early childhood through adolescence and early adulthood. Unfortunately, such data do not exist at present.

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Herman, C. P., Koenig-Nobert, S., Peterson, J. B., & Polivy, J. (2005). Matching effects on eating: Do individual differences make a difference? Appetite, 45, 108–109. Hirsch, E. S., & Kramer, F. M. (1993). Situational influences on food intake. In B. M. Marriott (Ed.), Nutritional needs in hot environments (pp. 215–243). Washington, DC: National Academy Press. Huon, G. F., & Walton, C. J. (2000). Initiation of dieting among adolescent females. The International Journal of Eating Disorders, 28, 226–230. Huon, G. F., Lim, J., & Gunewardene, A. (2000). Social influences and female adolescent dieting. Journal of Adolescence, 23, 229–232. Jellison, J. M., & Gentry, K. W. (1978). A self-presentation interpretation of the seeking of social approval. Personality and Social Psychology Bulletin, 4, 227–230. Klesges, R. C., Coates, T. J., Brown, G., Sturgeon-Tillisch, J., Moldenhauer-Klesges, L. M., Holzer, B., et al. (1983). Parental influences on children’s eating behavior and relative weight. Journal of Applied Behavior Analysis, 16, 371–378. Klesges, R. C., Bartsch, D., Norwood, J. D., Kautzman, D., & Haugrud, S. (1984). The effects of selected social variables on the eating behavior of adults in the natural environments. The International Journal of Eating Disorders, 3, 35–41. Klesges, R. C., Malott, J. M., Boschee, P. F., & Weber, J. M. (1986). The effects of parental influences on children’s food intake, physical activity, and relative weight. The International Journal of Eating Disorders, 5, 335–346. Koivisto, U. K., Fellenius, J., & Sjoden, P. O. (1994). Relations between parental mealtime practices and children’s food intake. Appetite, 22, 245–257. Laessle, R. G., Uhl, H., Lindel, B., & Muller, A. (2001). Paren­ tal influences on laboratory eating behavior in obese and non-obese children. International Journal of Obesity and Related Metabolic Disorders, 25(Suppl. 1), S60–S62. Larkin, J., & Pliner, P. (2008). Consumption stereotypes re-examined: Does the anticipation of eating moderate the effect? Unpublished manuscript, Toronto, Ontario. Laslett, B., & Warren, C. (1975). Losing weight: The organiz­ ational promotion of behavior change. Social Problems, 23, 69–80. Leary, M., Nezlek, J., Downs, D., Radford-Davenport, J., Martin, J., & McMullen, A. (1994). Self-presentation in everyday interactions: effects of target familiarity and gender composition. Journal of Personality and Social Psychology, 67, 664–673. Leone, T., Pliner, P., & Herman, C. P. (2007). Influence of clear versus ambiguous normative information on food intake. Appetite, 49, 58–65. Marinho, H. (1942). Social influence in the formation of enduring preferences. Journal of Abnormal and Social Psychology, 37, 448–468.

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C H A P T E R

51 Church and Other Community Interventions to Promote Healthy Lifestyles: Tailoring to Ethnicity and Culture Shiriki Kumanyika Department of Biostatistics and Epidemiology and Department of Pediatrics (Gastroenterology; Section on Nutrition), University of Pennsylvania School of Medicine, Philadelphia, PA, USA

o u tli n e 51.1  Introduction

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51.2  Background

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51.3 Cultural Targeting and Tailoring in Community Settings

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51.4 Religious Organizations as Communities within Communities

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51.1  Introduction Population approaches to obesity prevention require interventions that can influence proximal to distal environments that shape food, physical activity, and weight-control behaviors (Kumanyika et al., 2002, 2008). The pivotal role of

Obesity Prevention: The Role of Brain and Society on Individual Behavior

51.5 Challenges 51.5.1 Cultural and Other Contextual Issues 51.5.2 Research and Program Delivery Issues

642

51.6 Conclusion

646

643 644

communities – positioned as mediators and filters between these proximal and distal influences –is evident in all ecological models of individual behaviors. Figure 51.1 illustrates this for the ecological contexts of childhood obesity (Koplan et al., 2005). Community environments include physical and social settings, infrastructures

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© 2010, 2010 Elsevier Inc.

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51.  Ethnic and cultural tailoring C u lt ur e a nd s o c i e t y str y, gover nment Indu C o m mu n i t y ool and peers h c S and ho m m il y e Fa

Child or adolescent

conventions that distinguish cultural adaptations for a population subgroup (targeting) from those designed for individuals (tailoring) (Kreuter et al., 2003). This distinction helps to avoid the fallacy of assuming that all individuals within a target group are homogeneous and will respond to an initiative in identical ways. Both targeting and tailoring are relevant to community initiatives for lifestyle change.

51.2  Background Figure 51.1  Simplified ecological systems theory model. Source: Koplan et al., (2005). Reprinted with permission from Preventing Childhood Obesity. Health in the Balance, © 2005 by the National Academy of Sciences, Courtesy of the National Academies Press, Washington, DC.

and systems. Geographically or politically defined communities, including some ethnic communities, comprise formal institutions and infrastructure such as local governments, schools, religious organizations, social and civic organizations, services and other resources. Other communal entities, such as professional or trade communities, operate without a geographical boundary. Community processes include laws, rules, regulations or other policies, as well as shared customs, traditions, rituals, perceptions and expectations, with a continuing blending of historical and current experiences and exposures (Koplan et al., 2005; Robinson, 2005). The concept of adapting lifestyle interventions to fit ethnic and cultural characteristics of communities applies to all populations. This chapter focuses on particular considerations for targeting and tailoring to ethnically and culturally distinctive, minority populations. After a brief background, the remainder of this chapter describes and illustrates concepts and approaches to cultural targeting and tailoring, and discusses the associated challenges. The terms “targeting” and “tailoring” are used here according to current

The scenarios and examples discussed in this chapter are from communities of color in the United States and Canada, Australia and New Zealand – indigenous populations and others. To the extent that these communities may be numerically small or not powerful politically, their issues may seem unimportant in the larger global scheme. However, because of the spectrum of issues that must be considered, insights derived from cultural targeting and tailoring for these populations are potentially applicable to other populations. The public health rationale for targeting communities of color relates to the high levels of obesity and obesity-related health risks such as diabetes or metabolic syndrome in these populations. These risks are high relative to European-descent populations in the same societal contexts, high in the absolute sense, affect both adults and children, and may predate the obesity epidemic in the respective populations at large (Kumanyika, 1993a; Story et al., 1999; Simmons et al., 2001; Welty et al., 2002; Tremblay et al., 2005; Adams and Schoenborn, 2006; Ogden et al., 2006; Kondalsamy-Chennakesavan et al., 2008; Kumanyika et al., 2008). Illustrative data for adults and for children in two US ethnic groups are in Table 51.1. Populations of Asian descent, although not further addressed here, are also of interest because of cardiovascular diseases and diabetes rates that are higher

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51.2  Background

Table 51.1  Estimates of obesity prevalence for ethnic minority populations in the United States, Australia and New Zealand Population

Age group

Males

Females

USA (Ogden et al., 2006)

Adults aged 20 and over

%

%

National Health and Nutrition Examination Survey, 2003–2004 (measured weight and height) adults: % with BMI  30 kg/m2 children: % with BMI  95th reference percentile

non-Hispanic white

31.1

30.2

non-Hispanic black

34.0

53.9

Mexican American

31.6

42.3

Children ages 2–19

%

%

non-Hispanic white

17.8

14.8

non-Hispanic black

16.4

23.8

Mexican American

22.0

16.2

USA (Welty et al., 2002)

%

%

Strong Heart Prospective Study of adults in 13 American Indian tribes in Arizona, Oklahoma, and South Dakota/North Dakota, 1993–1995 (measured weight and height); % with BMI  30 kg/m2

Adults aged 49–78 (mean age 56)

43.9

56.4

USA (Adams and Schoenborn, 2006)

Adults aged 18 and over

%

%

National Health Interview Survey, 2002–2004 (self-reported weight and height) % with BMI  30 kg/m2

non-Hispanic white

23.4

20.7

non-Hispanic black

28.1

39.1

Mexican American

26.5

30.2

Hispanic

24.9

26.9

American Indian / Alaska Native

33.2

33.0

Asian Native Hawaiian or other Pacific Islander

6.9 33.3

5.7 Reliable estimate not available

Canada (Tremblay et al., 2005)

Adults aged 20 to 64

%

%

Canadian Community Health Survey, 2000/2001 and 2003 (self-reported weight and height); % with BMI  30 kg/m2 (estimated from graph)

White

18

16

Black

10

19

Latin American

15

14

Off-reserve Aboriginal

27

28

South Asian

7

9

Adults aged 25–74

%

%

Australians in AusDiab’s

19.7

22.7

Wadeye community

2.6

10.8

Nauiyu community

12.7

31.5

Borroloola community

33.6

42.1

Australia (Kondalsamy-Chennakesavan et al., 2008) Screening of Australian Aboriginals in three communities in the Northern Territory of Australia, 2000–2003 and comparison data from the Australians in the national, AusDiab’s study, 1999–2000 (measured weight and height); % with BMI  30 kg/m2

(Continued)

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51.  Ethnic and cultural tailoring

Table 51.1  (Continued) Population

Age group

New Zealand (Simmons et al., 2001) Household survey of Europeans, Maori, or Pacific Islands populations in urban South Auckland, 1991–1994 (measured weight and height) Mean BMI in kg/m2 NOTE: Overall obesity prevalence (BMI  30.0) was 25% in Europeans, 63% in Maori, and 69% in Pacific Islanders

Males kg/m

2

Females kg/m2

Adults aged 40–59 Europeans

27.8

28.8

Maori

34.0

34.0

Pacific

33.4

32.1

Europeans

27.1

28.1

Maori

31.5

36.8

Pacific

31.6

33.0

Adults aged 60–79

than would be expected based on their average BMI levels (WHO, 2004; Smith et al., 2005). These ethnic differences may imply genetic differences. However, trends showing the emergence of obesity in association with exposure to obesity-promoting environments, over time or after migration, provide very convincing evidence that environmental factors have a greater impact than genetic factors on population differences in obesity (Lauderdale and Rathouz, 2000; Luke et al., 2001; Gordon-Larsen et al., 2003; Ogden et al., 2006; Schulz et al., 2006). Obesitypromoting environments are characterized by high availability of calories and relatively low demand for physical activity. Some communities of color have experienced enslavement, colonization or other social trauma. Others experience marginalization, cultural isolation or discrimination because of their ethnicity or occupational status (e.g., labor migrants). The higher than average risks of obesity and related lifestyle diseases constitute social inequities that warrant special attention. Some inequities are also socio-economic but may not be alleviated by interventions on income (e.g., changing food prices, or income transfers), education or occupational status. Such interventions may not alter social class factors that

define life chances, lifestyle opportunities and preferences, or may not eliminate vulnerability to negative stereotyping, stigmatization, discrimination, or lack of political power related to ethnicity. Biosocial and cultural adaptations to adverse circumstances (including stress-related physiological and behavioral responses) may concentrate and potentiate obesity-related health risks; they may also influence the effectiveness of both targeted and non-targeted interventions.

51.3  Cultural targeting and tailoring in community settings Cultural targeting and tailoring first require an understanding of what culture means and how it relates to the potential for lifestyle change. Culture is a very complex concept that, although central to considerations of ethnically targeted programming, is often oversimplified in the public health literature (Institute of Medicine, 2002; Page, 2005). Cultural influences apply to all human behavior, and are identified by gender, group dynamics, region, and many other dimensions. Ethnocultural variables are

2.  From Society to Behavior: Policy and Action

51.3  Cultural targeting and tailoring in community settings

imprinted on the individual’s psychobiologic core very early in life and transmitted intergenerationally, both gestationally and postnatally through infant feeding and subsequent parenting practices. These imprints are reinforced through “embodiment” – defined by Krieger (2005) as the biological incorporation of the effects of exposure to material and social influences – of social and environmental stimuli throughout life (Booth et al., 2001; Institute of Medicine, 2001; Wetter et al., 2001; Krieger, 2005). Food and eating, both in general and as culturally mediated, are two of the strongest pathways for imprinting this ethnocultural habitus (Mintz, 1996; Counihan and Van Esterik, 1997; Bourdieu, 2007). Biomedical uses of the term “culture” often focus on shared norms, attitudes, values, and customs of ethnic groups that are evident crossculturally – for example, between providers and patients or researchers and communities – and which seem to relate to program acceptance or adherence to treatment advice. Such views of culture ignore social and environmental context variables that are integral to cultural perspectives and processes. Also, acquisition of European or Western norms, attitudes and practices is termed “acculturation”, and may suggest that the culture of reference has been exchanged for attitudes and practices that are more desirable from a lifestyle and health perspective. This may refer to, for example, shifts away from: culturally influenced preferences for high-calorie or high-fat foods or food preparation methods; cultural uses of food for coping with stress; celebratory traditions involving large meals; preferences for sedentary forms of entertainment; parenting practices that are indulgent with respect to child feeding; or beliefs that heavier bodies are healthier and more attractive than thin bodies (Kumanyika, 1993b, 2006, 2008; Kumanyika and Morssink, 1997). However, in some circumstances acculturation increases rather than decreases risk, such as when breast-feeding or traditional diets that include relatively more

633

plant foods and less meat are replaced with bottle-feeding or eating patterns characterized by energy-dense foods and low intake of fruits and vegetables (Kumanyika, 2006; Duffey et al., 2008; Renzaho et al., 2008). In commenting on the study of cultural influences as they relate to health inequities, Page (2005) points out the problem of using “culture” as a noun that implies a fixed, tangible entity; he argues for recognizing the dynamic nature of cultural processes as a “context for human behavior that involves constant adaptation and reframing” (Page, 2005: iii). Distinctive cultural traditions and groups endure in part because some aspects of cultural contexts are very conservative, protected by robust social institutions, and remain stable over centuries, whereas other aspects may change readily in response to environmental and societal changes. Such changes have not only shaped social institutions and social interactions directly, but also created high levels of intercultural contact that result in cultural exchanges and mixing of cultural influences (WHO, 2000; Page, 2005). The understanding that cultural processes are in a dynamic relationship with social and environmental contexts is particularly important with respect to socially disadvantaged or marginalized ethnic and cultural groups (Institute of Medicine, 2002; Hopson, 2003; Dressler, 2005; Page, 2005; Robinson, 2005; Kumanyika et al., 2007). Applicable contexts include historical influences that define current social and power relationships and the ability to establish trust between groups. To focus on norms, values and behaviors as if they are independent from relevant life circumstances leads to an incomplete and sometimes distorted picture of underlying causes, a picture that places the blame primarily upon individual “bad behaviors” or poor health motivations. Even very wellintended cultural adaptations, if based on such faulty premises, will have limited value. Narrowly framed views of cultural influences may be tempting in a research tradition that limits the number of manipulated variables to facilitate inferences of

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51.  Ethnic and cultural tailoring

causality. These same narrow views also reinforce an ethnocentric (i.e., Euro or Anglo, Western, middle-class) and individually-oriented perspective in which it seems logical to attribute bad behaviors to members of a negatively stereotyped group – to view them as people who “don’t know better” or “do not care” about their health. Conveniently, this also avoids any need to attribute problems to structural variables (policies, infrastructure, economic inequities) that might place responsibility on society as a whole. Such attributions have political overtones that are controversial and may be uncomfortable for health professionals and researchers.

An expanded obesity research paradigm to encourage deeper and more contextualized understandings of cultural variables has been proposed by the African American Collaborative Obesity Research Network (AACORN) (Kumanyika et al., 2007) (Figure 51.2). Taking a research focus on eating, physical activity, and weight interventions, the elements of this paradigm are as follows: 1. “Research lenses” indicate the different perspectives and agendas of various actors involved in community initiatives and community based research – those of (i) community members, (ii) researchers who

AACORN’s Expanded Obesity Research Paradigm Research Lenses

Research focus

African Americans in researched communities

African American researchers

• Community and family life (content) Interventions on eating, physical activity, and weight in African Americans

Cultural and psychosocial processes Energy balance

Historical and social contexts

Physical and economic environments

• Historical legacy and core values (content) • Ethnographic and literary content analysis (methods) • Engaging communities (methods)

Researchers in general and research sponsors

Focus of traditional obesity research

Research content and methods

Expanded knowledge domains

• Leveraging insider status (methods) Progression toward more effective research to improve weight and quality of life in African American communities © African American Collaborative Obesity Research Network

Figure 51.2  African-American Collaborative Obesity Research Network expanded obesity research paradigm. See text for explanation. Source: Kumanyika et al., (2007). Reprinted with permission from Preventing Chronic Disease © 2007 by AACORN.

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51.3  Cultural targeting and tailoring in community settings

are of the same ethnicity and who identify as members of the community, and (iii) other researchers, or research sponsors who impose external agendas. 2. The Venn diagram in the center places energy balance – the traditional biomedical obesity research focus – as subordinate to life contexts for which understanding can only be obtained with reference to expanded knowledge domains from the social sciences (e.g., family sociology; transcultural psychology; communications; economics), the humanities (historical accounts and literary works, for example), and other disciplines. 3. The process of generating knowledge in these contextual domains is facilitated by emphasizing the content areas and methodologies at the right of the diagram. Community and family life, historical context and core values must be studied directly. Qualitative research that facilitates observations, reflections and insights among and about community members and life contexts is essential. The increasing recognition in the research literature of the value of qualitative inquiry is indicated by qualitative needs assessments in both program development and evaluation (Gittelsohn et al., 1999; Resnicow et al., 1999; Ammerman et al., 2003; Kumanyika et al., 2003; Stefanich et al., 2005; Bachar et al., 2006; Frable et al., 2006; Punzalan et al., 2006; Bopp et al., 2007; Campbell et al., 2007a, 2007b; Ramirez et al., 2007; Adams et al., 2008). Content analyses of literary works – for example, about the lives of African-American women – provide insights about food-related roles and ideas about body size; analyses of historical accounts provide insights on how slavery shaped the development of trust and loyalty as core values that continue to be prominent in African-American culture (Kumanyika et al., 2007). Qualitative or experimental research conducted by marketing scholars is another potentially

635

useful approach (Grier & Brumbaugh, 1999; Lamont & Molnar, 2001; Tharp, 2001). 4. “Leveraging insider status” refers to the potential for researchers who, trained in research methods, part of the academic culture, but who also identify themselves as members of the community of interest, can create bridges between the community and academic knowledge and experience domains. Figure 51.3 depicts a continuum of differences in perspectives on cultural targeting and tailoring – not actually linear, but shown as such for simplicity. The more narrow view is at the left – a provider- or outsider-oriented focus on specific behaviors. The broader, more community- or population-focused perspective is at the right. The view of cultural characteristics as barriers that “get in the way” or facilitators that may “help to get the message across” is contrasted with identification of community assets and liabilities. Objectives framed in terms of compliance – implying bending to the will of a dominant other – are contrasted with objectives of generating community-owned and -driven sustainable solutions, for example by raising awareness, facilitating critical consciousness and transformative thinking (Esperat et al., 2005, 2008). Addressing cultural influences through superficial accommodations and motivating change with artificial incentives is contrasted with community-based social marketing (Bryant et al., 2007) and empowerment approaches (Laverack and Labonte, 2000). The typical lifestyle counseling approach of teaching people to negotiate their individual behavioral change options with those in their social context is contrasted with approaches that incorporate and build upon the social context to improve environments for behavioral change and render new behaviors more normative. Cultural targeting approaches relevant to obesity prevention in diverse communities of color have been summarized in several reviews (Yancey et al., 2004, 2006; Lombard et al., 2006; Teufel-Shone, 2006; Campbell et al., 2007a),

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636

51.  Ethnic and cultural tailoring

Adherencefocused

Populationfocused

Dominant cultural and contextual perspective

•Institutional •Health professional

•Population •Community

Perspective on cultural influences

•Barriers •Facilitators

•Assets •Liabilities

Intervention objectives

•Adherence •Compliance

•Ownership •Sustainability

Approaches to cultural adaptations

•Accommodations •Incentives

•Social marketing •Empowerment

Perspective on social and environmental context

•De-emphasize •Negotiate

•Incorporate •Facilitate

Power dynamic

•Professional expertise •Provider control •Provider comfort

•Community expertise •Client control •Client comfort

Figure 51.3  Contrast between conventional and community oriented perspectives on cultural targeted and tailoring.

characterized as to whether adaptations address “surface” or “deep structure” (Resnicow et al., 1999), and classified by the type of adaptation approach (Kreuter et al., 2003). Selected examples are in Tables 51.2 and 51.3 (Macaulay et al., 1997; Davis et al., 1999; Gittelsohn et al., 1999; Bell et al., 2001; Yanek et al., 2001; Ammerman, 2002; Fitzgibbon et al., 2002, 2005, 2006; Ammerman et al., 2003; Caballero et al., 2003; Corbie-Smith et al., 2003; McAuley et al., 2003; Murphy et al., 2003; Potvin et al., 2003; Resnicow et al., 2004; Simmons et al., 2004; Beckham et al., 2005; Paradis et al., 2005; Stefanich et al., 2005; Bachar et al., 2006; Baker et al., 2006; Gracey et al., 2006; Campbell et al., 2007b; Nacapoy et al., 2008; Plescia et al., 2008). Although some of the cited references include outcome data, these tables are not intended to describe impact. Considerations in determining impact are discussed under the Challenges section. The approaches described in Tables 51.2 and 51.3 involve community assessment and engagement and go beyond superficial adaptations, but

they vary in the nature and extent of community empowerment or ownership. The cultural adaptation is often achieved by and inherent to community-driven processes. Several programs listed illustrate highly participatory, empowermentoriented approaches (see Figure 51.3). These more comprehensive approaches acknowledge that expertise rests with both community members and academic collaborators, and that the academic collaborators must not only listen to community members but also learn to work outside of their accustomed comfort zones in other respects.

51.4  Religious organizations as communities within communities Church programs are given special consideration here because of their unique roles as cultural institutions relevant to individuals and

2.  From Society to Behavior: Policy and Action

Table 51.2  Examples of culturally targeted and tailored initiatives relevant to obesity prevention in diverse community settings Key characteristics of the initiative relevant to cultural adaptation and community change

Location, setting, and goals

(McAuley et al., 2003; Gracey et al., 2006)

Australia; Aboriginal communities in remote areas in the far north of Western Australia Goal was to develop a diabetes management and control program, including the promotion of healthy lifestyles, in Aboriginal communities

• Community partnerships were developed with the Unity of First People of Australia, a non-profit, Aboriginal-run organization, after an extensive process of community consultation that involved community elders, governance committees, school authorities and teachers, health care providers, shops, and elected health committees • Main strategies involved engaging children as well as older community members in education and health promotion activities related to healthy living • Community-based health assessments promote awareness, early detection and appropriate use of health services • Program delivery is through “Carers” from within and outside of the communities

(McAuley et al., 2003; Murphy et al., 2003)

New Zealand; Maori community in Dunedin “Te Whai matauranga o te ahua noho/ Lifestyle Intervention Programme” Goal was to develop and evaluate a specially designed lifestyle change program for prevention of type 2 diabetes among Maori

(Fitzgibbon et al., 2002, 2005, 2006)

USA; African-American and Latino pre-school children in 24 Head Start Programs in Chicago, Illinois “Hip Hop to Health, Jr” Goal was to evaluate, after 2 years, the effects of a 14-week (three times per week) nutrition and physical activity program designed to prevent progression toward obesity among African-American and Latino preschool children

• Program development was undertaken under the auspices of Dunedin Hospital, with the approval of several community groups, and under the leadership of a Maori Diabetes Educator; non-Maori personnel were introduced to the community and attended immersion weekends to learn about and experience Maori traditions and culture • Community leaders were identified to participate in and also recruit family members and coworkers to the program • Adaptations included: emphasizing traditional foods and healthy cooking classes, at which family members were welcome, exercise prescriptions based on low-cost home or community-based activities and cultural activities; direct participation by the Maori Diabetes Educator; moving the program to a setting outside of the hospital environment that provided space for education, healthy food demonstrations, group exercise, showers, and an indoor swimming pool; and influencing food and activity habits at traditional gatherings and meeting houses • Culturally specific adaptations included: family-oriented program; easy and safe access to the program; addressing both cognitive and environmental influences on exercise and diet; encouraging participants to identify with the interventionists; use of demonstrations of recommended lifestyle changes; adapting content and delivery for diverse literacy levels; bilingual (English and Spanish) curriculum, with presentations in both language at the Latino sites • Results suggested that the cultural adaptations may have worked better for AfricanAmericans than for Latinos

51.4  Religious organizations as communities within communities

2.  From Society to Behavior: Policy and Action

Reference(s)

(Continued)

637

Location, setting, and goals

Key characteristics of the initiative relevant to cultural adaptation and community change

(Plescia et al., 2008)

USA; 14 African-American communities in the northwest corridor in Charlotte, North Carolina; “Charlotte REACH (Racial and Ethnic Approaches to Community Health) Project” Goal was to improve nutrition and physical activity and decrease smoking in the targeted communities through community mobilization and environmental and policy changes

• An advisory committee to a community health center expanded into broader coalition that involved other health and human services providers and members of a grassroots community group • Health-promotion activities were conducted by a group of well-trained lay health advisors, supervised by a full-time coordinator and supported by a nurse and by nutrition, fitness and smoking-cessation specialists. • Environmentally focused activities included: partnering with a neighborhood association to create a farmers’ market; expanded availability of community physical activity programs through the YMCA; a mass media campaign conducted by a local African-American public relations firm; tobacco advocacy efforts; and a diabetes care registry and quality improvement project at the local health center

(Beckham et al., 2005)

USA; Native Hawaiians in Waianae, Hawaii Waianae Comprehensive Health Center obesity prevention programs Goal was to implement an innovative, multipronged approach to address the obesity epidemic

• Three programs were developed by a large comprehensive health center providing primary care to low-income Native Hawaiians • “Lifestyle Enhancement Program” – a holistic approach to individual counseling that blends traditional Hawaiian medicine and complementary healing • “KidFit” – teen weight-management program based on preferences identified and ranked through teen focus groups; priority is given to weight training and other fitness or sports activities; trainers who are also dietitians integrate healthy eating advice • “Hawaii Community Resource Obesity Project (HCROP)” – brings together food producers and vendors with consumers to create a community-based agricultural network that will increase availability and access to locally produced healthy food in an economically sustainable way

(Nacapoy et al., 2008)

USA; Native Hawaiians and other Pacific Island Populations in Honolulu, Hawaii “PILI (Partnership for Improving Lifestyle Interventions) Ohana Project” Goal was to form a co-equal community-academic partnership that would engage in obesity prevention research – the priority chosen by a network of 10 community organizations initially convened by the Department of Native Hawaiian Health within a school of medicine

• Five organizations became active partners in the research collaboration: two Native Hawaiian grassroots organizations, two community health centers (one private and one public); and a Native Hawaiian, non-profit health care system, together with a local medical school as the academic partner; the other five organizations chose to serve in an advisory role • Partnership development explicitly addressed the balance of power among partners; a Community Action Board and an Intervention Steering Committee were formed; written governing principles and guidelines were developed to provide a framework for interactions and operations • A research design was developed that met the community requirement for all participants to receive a service by beginning with a phase in which all participants were enrolled in the weight-loss program; in a second phase participants were randomly assigned to one of three maintenance approaches. • Intervention delivery and control of primary data would be by community members

51.  Ethnic and cultural tailoring

2.  From Society to Behavior: Policy and Action

Reference(s)

638

Table 51.2  (Continued)

Table 51.2  (Continued) Key characteristics of the initiative relevant to cultural adaptation and community change

(Bachar et al., 2006)

USA; Eastern Band Cherokee Indian Nation in western North Carolina “Cherokee Choices” Goal was to implement community action plan developed to address a community-chosen priority for diabetes prevention

• Four community mentors were hired to develop and implement in-school and after-school programs with students and teachers in the community elementary school; health promotion activities focused on healthy eating and physical activity; in-class lessons focused on self-esteem, community pride, conflict resolution, emotional wellbeing, stress management, and health knowledge • Worksite wellness activities included challenges and competitions among teams of tribal workers, with supportive policy changes, related to nutrition and physical activity • Churches served as sites for various health promotion activities and classes; congregations organized walking groups that accumulated and mapped their miles on a “Walk to Jerusalem” • Media components included three TV ads based on cultural themes (family, spirituality, tradition) and a seven-part TV series based on interviews with people who had personal or family experiences with diabetes

(Davis et al., 1999; Gittelsohn et al., 1999; Caballero et al., 2003)

USA; Children in American Indian communities in Arizona, New Mexico, and South Dakota The “Pathways” Study Goal was to develop (first 3 years) and evaluate (second 3 years) a culturally appropriate, school-based, multicomponent intervention to promote healthful eating and physical activity behaviors in 7-to 10-year-old children (grades 3 through 5)

• Intervention involving classroom, food service, physical education, and family components was developed and piloted in close consultation with tribal representatives and American Indian members of the study team and informed by extensive formative assessment activities • Program framework was based in American Indian cultural heritage relating to physical activity, active games, nutritious low fat foods and the incorporation of customs, traditions, beliefs, values of participating American Indian nations • Several indigenous learning modes were incorporated, including storytelling, learning through play, learning cooperatively, and learning through reflection

(Stefanich et al., 2005)

USA; Alaska Native Women in Anchorage, Alaska and the surrounding community “Traditions of the Heart” Goal was to develop a culturally specific cardiovascular health promotion program for Alaska Native women

• An extensive, community-partnered process of formative data collection and pilot testing was undertaken under the auspices of an Alaska Native-owned regional health corporation • Two existing programs were identified for adaptation: a nutrition program developed for American Indians, and a nutrition and physical activity program for cardiovascular risk reduction in African-American women • Cultural adaptations for Alaska Native women involved selecting and merging the most useful and culturally relevant elements from each program, developing a culturally specific program name and insignia, incorporating traditional wellness concepts, and addition of related health topics such as smokeless tobacco and stress management

(Macaulay et al., 1997; Potvin et al., 2003; Paradis et al., 2005)

Canada; Mohawk Indian Community near Montreal “Kahnawake Schools Diabetes Prevention Project” Goal was to promote healthy eating and increased physical activity in elementary-school aged children in the two schools in this community

• Addressed all pillars of the Ottawa Charter for Health Promotion • Included school- and community-based activities for teachers, students and families and school policy changes • School teachers (who were mostly from the local Mohawk community) were trained to deliver the curriculum, which incorporated traditional learning styles • Community mobilization involved creating a 40-member Community Action Board as the mechanism for community involvement, empowerment, sustainability, and programmatic advice • Community activities included building a recreation path, promotion of the production and sale of traditional food; food preparation and tasting; role modeling; fitness activities and events; conferences, and media activities

639

Location, setting, and goals

51.4  Religious organizations as communities within communities

2.  From Society to Behavior: Policy and Action

Reference(s)

640

Table 51.3  Examples of culturally targeted and tailored initiatives relevant to obesity prevention based in church settings Key characteristics of the initiative relevant to cultural adaptation and community change

Location, setting and goals

(Bell et al., 2001)

New Zealand; three Samoan church communities in Auckland “Samoan Ola Fa’autauta (Life-Wise) Study” Goal was to promote weight loss in Samoan church communities through nutrition education and exercise

• A health committee that included the minister’s wife was developed to advise and facilitate the project at each church • Informal nutrition education sessions were offered to families, caterers, and the whole church • Weekly aerobics sessions were built into regular church activities, with a $2 per class donation requested to allow instructors to be paid • Church members were trained to assume roles of group leaders for nutrition education and aerobics sessions

(Simmons et al., 2004)

New Zealand; Samoan and Tongan church congregations in South Auckland Goal was diabetes control through church-based diabetes awareness and lifestyle change programs; Samoan program was conducted during an earlier time period

• Program development and adaptation were supervised by church committees, and included diabetes and nutrition education and physical activity programs; healthy food policies were underscored or developed as appropriate • Adaptations included language, format, foods, cooking methods and types of exercise and music, and incorporation of culture specific traditions, local humor and content on relevant local issues; relevance was maintained by ongoing discussions with church leaders • Program effectiveness differed and may have been influenced by several differences in the church contexts, e.g., size, denomination (Seventh Day Adventist or Latter Day Saints) and other structural or participation variables

(Yanek et al., 2001)

USA; 16 African-American churches in Baltimore, MD “Project Joy” Goal was to facilitate lifestyle change for cardiovascular health promotion in African-American women ages 40 and older over 12 months

• Participants named project based on a Bible verse • Created a spiritually-enhanced group behavior change program focused on nutrition and physical activity for comparison with non-spiritually enhanced, standard program, and a non-spiritual self-help program • Created Community Expert Panel to advise on program and study • Spiritual component included group prayers during weekly sessions; enrichment of health messages with scripture; gospel music or praise dancing for aerobics component; telephone calls from lay leaders; pastor-authored, church newsletter feature; annual events • Lay leaders trained to implement program

51.  Ethnic and cultural tailoring

2.  From Society to Behavior: Policy and Action

Reference(s)

USA; 60 African-American churches in North Carolina “The PRAISE! (Partnership to Reach African-Americans to Increase Smart Eating) Project” Goal was to develop and evaluate an approach to increasing consumption of fruits and vegetables and fiber and decreasing consumption of fat over 12 months in partnership with churches

• All African-American staff, including two pastors’ wives; pastor consultant • Created a Health Action Team of 15–35 people, recruited by pastors, to guide program within each church • Prominent role for pastors • Changes in church menus • Option to try out some aspect of programs to determine fit • Designed interventions for sustainability, e.g., low-cost programs; curriculum designed to be updated; church members trained as program leaders; activities designed to be blended with regular activities rather than added on

(Resnicow et al., 2004; Campbell et al., 2007b)

USA; 15 African-American churches in California, Georgia, North Carolina, South Carolina, Delaware and Virginia “Body and Soul” Goal was to test the effectiveness of interventions to increase fruit and vegetable consumption among church members over a six-month period, as a cancer prevention strategy

• Church-wide and individually-focused activities were adapted from prior studies in African-American churches, retaining essential components but simplified and modified for delivery by lay volunteers; a culturally relevant video and cookbook from a prior study were also used • Required core activities in each church were: a kick-off event; at least three food-related events (e.g., food demonstrations or field trips to food markets); one pastor event (such as a health-related sermon), and at least one policy change (e.g., creating nutrition guidelines for church meal functions or youth programs) • Individual tailoring included telephone support provided to a subset of members; this was implemented by church members trained and certified for a modified form of motivational interviewing

(Baker et al., 2006)

USA; African-American community in St Louis, Missouri “The Garden of Eden” Goal was to increase availability of healthy foods as essential for community-based efforts to prevent obesity

• An established partnership between faith-based health ministries and academics led to the development of the Garden of Eden, a community-run produce market within a local church, also with involvement from local business leaders; pricing and staff wages were geared to community needs • Need for greater access to healthy foods was identified by health advocates and confirmed by data from a prior project in which food store locations were mapped and product availability within stores was assessed • Associated activities include providing transportation from other churches and from senior centers to a variety of church or community health promotion activities by community member “health advocates”

51.4  Religious organizations as communities within communities

2.  From Society to Behavior: Policy and Action

(Ammerman et al., 2002, 2003; CorbieSmith et al., 2003)

641

642

51.  Ethnic and cultural tailoring

families and to people of all ages. DeHaven’s review of studies published between 1990 and 2000 identified 53 church-based health promotion programs or studies conducted in the United States – a mixture of program descriptions and evaluations, pilot studies, and larger and more rigorous, formal research projects (DeHaven et al., 2004). These efforts addressed general health maintenance as well as cardiovascular health, cancer screening, smoking cessation, mental health, nutrition and weight control. No church-based studies with a specific focus on obesity prevention (i.e., that combined changes in food intake and physical activity to achieve energy balance or avoid weight gain) were identified. Many were relevant in that they targeted changes in fruit and vegetable intake, or physical activity, or weight loss. Where a target population was identified, most programs targeted African-Americans (n  22), and four targeted Hispanics, consistent with the impression that church-based health promotion has focused primarily on underserved ethnic communities. A subsequent review by Campbell highlighted seven additional articles on churchbased health promotion relevant to eating and activity lifestyle change; all were with AfricanAmerican churches (Campbell et al., 2007a). In part, this appears to reflect funding by US government agencies of specific community initiatives to address lifestyle-related health disparities, combined with observations that African-American churches are especially good settings for reaching African-American communities (Eng et al., 1985). Table 51.3 includes examples of church-based initiatives in African-Americans and Pacific people in New Zealand. The Pacific Obesity Prevention in Communities (OPIC) Project (not shown), designed to prevent obesity among youth in Fiji and Tonga, was also identified as an example of involving churches in communitywide initiatives (Utter et al., 2008). As exemplified in the table, churches lend themselves to health promotion programming in multiple respects

(Peterson et al., 2002). Faith organizations provide repeated access to community members who are long-term or lifetime members of the congregation. Working with religious organizations may provide opportunities to address shared core values, deeply held beliefs and traditional practices, and leverage these for motivation or directly link them to efforts to change behaviors and social norms. From a practical perspective, churches and other religious settings are gathering places where people come together for social support, to share meals and to interact in other ways. Ethnic identity incorporates religious views and practices. All or the majority of a congregation may come from one ethnic group, such that the setting is essentially an ethnic community. Participation – including attendance at services, financial contributions and volunteer service – may be a defining aspect of ethnic group membership. Some religious organizations have been historically, and continue to be, core community self-help, social support and safety-net organiz­ ations providing a variety of social and healthrelated services in addition to spiritual guidance, support and opportunities to exercise the principles of the faith through service. Services and resources provided may extend to the larger community and include infrastructure such as housing, day-care centers, recreation facilities or gathering places such as coffee houses. Characteristics and the impact of religious organizations vary according to size and demographics (e.g., ethnicity, income level and geographic location), denomination and mission.

51.5  Challenges The literature on culturally-targeted and -tailored lifestyle interventions in churches and other community settings is remarkably consistent in pointing to certain challenges. Some relate to cultural and other contextual influences on the targeted behaviors, while others relate to the complexities of research or program delivery in

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community settings. These challenges, of which many apply to health promotion and community interventions generally, are highlighted below.

51.5.1  Cultural and other contextual issues Many challenges are inherent in working in the complex and sensitive (socially and politically) domains of cultural influences, culture– structure dynamics, and cultural change. The multiple realities and contexts – the necessity of living in multiple worlds – that apply particularly to ethnic minority communities within larger societies must be taken into account. For example, living in worlds that are incongruent with respect to norms about eating, body size, or other aspects of lifestyle may lead to internal conflicts or ambivalence about lifestyle-change goals (Diaz et al., 2007). The assumption that adapting to a new cultural perspective necessarily negates the original cultural perspective is simplistic, and may be misleading. For example, Renzaho and colleagues (2008) identify variations in the prevalence of obesity and related diet and physical activity behaviors among African migrant children in Australia in relation to four acculturation categories: “integration”, “assimilation”, “traditional” and “marginalization”. These categories reflect both the extent and nature of retention of original cultural identity, and the extent and nature of orientation to the Australian society. Furthermore, individual heterogeneity in filtering and responding to cultural influences, which it is impossible to predict on the basis of group-level variables, mandates tailoring even for interventions that are well targeted at the group level (Kreuter et al., 2003). The Body and Soul church-based program is one example. The program targeted members of African-American churches as a group, but also included telephone support to provide individualized counseling (Resnicow et al., 2004; Campbell et al., 2007b).

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Basic cross-cultural communication and relationships skills are essential for researchers and providers who engage in culturally-targeted and tailored programs – particularly cultural selfawareness and the ability to be non-hierarchical and non-judgmental with other cultures (Kavanagh & Kennedy, 1992). The need for effective crosscultural communication is implicit in the descriptions of all of the programs cited in Tables 51.2 and 51.3. Working across cultures carries the implicit risks of imposing one’s own cultural perspectives on another cultural group, or inappropriately interfering in others’ social processes (Leininger, 1991). On the other hand, “cultural relativism” refers to the concept that the merits of different norms, values and beliefs differ across cultures and should be evaluated based on validity for members of the specific ethnic group. This does not mean that all cultural attitudes and beliefs are, therefore, “untouchable” and not to be challenged. However, it is sometimes difficult to strike a balance between respecting cultural practices and encouraging community members to change those that may once have been adaptive but now have become maladaptive with respect to effects on health. Supporting cultural practices while also challenging some aspects is illustrated in church programs such as PRAISE! (Ammerman et al., 2002). As noted by these researchers, foods served at special events may define these events from a symbolic perspective but also exemplify just the types of high-fat or high-calorie items that are targeted for change from a health perspective. The traditions must be respected, but creative ways can be identified to invoke other cultural influences that motivate gradual change in the types of foods served (e.g., linking a spiritual health message to physical health). Airhihebuwa’s PEN-3 model for educational diagnosis and differentiation of different types of cultural influences that may influence interventions positively or negatively may be a helpful guide (Airhihenbuwa, 1995). Also, community priorities may differ from those of researchers or programmers. Research is often offered where services are wanted and

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needed, and where the funded research topic is not at the top of the community’s list of issues. Negotiating priorities to develop a mutually beneficial and desirable agenda may be difficult and time-consuming.

51.5.2  Research and program delivery issues Insiders and outsiders Various actors in community intervention scenarios will inevitably be perceived or perceive themselves as either insiders or outsiders, or a combination of these, as shown in the AACORN paradigm (see Figure 51.2). Community partnered approaches that directly involve community members benefit from the insider knowledge and status of these community members. However, insiders might not be available to fill all research roles, even when desired. In a New Zealand study, when Maori professionals were not available to fill key intervention roles, non-Maori staff were hired (Murphy et al., 2003). To facilitate their effectiveness, non-Maori staff were required to spend immersion weekends on the marae in order to become familiar with Maori traditions and customs. Outsiders must earn the trust of community members and develop effective partnerships with formal and informal community leaders, governing bodies and members at large. The long and unpredictable time-course for developing trust may present practical problems. The process of partnership building, which cannot be skipped, requires time and patience, and demonstration of sincerity and respect. Personal discomfort of outsiders with being in different cultural surroundings (for example, having to attend church services in a faith other than their own (Campbell et al., 2007a)) or in unfamiliar and sometimes physically unsafe communities (e.g., in some lowincome urban areas) may also hinder trust and partnership development. Working with community members on an equal basis and sharing

power and ownership of data may be challenging for professionals, who are accustomed to deference on the basis of their expertise. Research partnerships may also be challenging for community members who fear exploitation or infringement on tribal sovereignty. Nacapoy and colleagues (2008) point out that skepticism and distrust were voiced by both academic and community critics, even after their academic-community partnership and program had demonstrated success. Working with insiders may facilitate the trust-building process, although this cannot be taken for granted. People who are community members but who are acting as representatives of outside interests may have to prove themselves as retaining perspectives that give priority to the best interests of the community. As described in several of the articles cited in Tables 51.2 and 51.3, trust-building and partnership development are aided by explicit provisions for sharing power and resources (e.g. providing grant funds directly to community members, or setting up formal written agreements about decision-making structures) and by initiatives that build community capacity, emphasize assets and consider sustainability from the outset. Descriptions of projects with Aboriginal commun­ ities in Australia (Gracey et al., 2006) and Canada (Macaulay et al., 1997), with Native Hawaiian community organizations (Nacapoy et al., 2008), and with pastors of African-American churches (Yanek et al., 2001) make explicit mention of formal written agreements, covenants, or codes of research ethics. Capacity-building examples are evident in projects in which school teachers or church members were trained to provide counseling or involved in other ways that would allow the community to continue the program after the end of the research funding (see Tables 51.2 and 51.3). Study design issues Whereas randomized controlled trials, or at least trials with control groups, are the gold

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51.5  Challenges

standard for research interventions, neither control groups nor random assignment to a control group can be achieved in many community studies. Designs in which changes (e.g., in body weight) are assessed on a post- versus preprogram basis are common in reports of commun­ ity programs. This does not allow comparison of program effects with changes that might have occurred over the same time period in those who did not receive the program. However, conducting studies in underserved communities in which only some people receive a tangible benefit or service while others serve as “untreated controls” raises ethical concerns. For example, McAuley and Nacapoy (McAuley et al., 2003; Nacapoy et al., 2008) have noted that control groups were either not feasible in their studies, because community members assigned to different treatments shared information, or were simply not acceptable to community members. Another issue of particular relevance to church programs is that of attempting to experimentally manipulate the spiritual component. Specifically, in Project Joy an attempt was made to compare two versions of the program that did or did not include a spiritual component (Yanek et al., 2001). However, the leaders of the program that was intended to be “non-spiritual” immediately and spontaneously introduced a spiritual component, considering this appropriate and essential in the church setting. Project Joy also provides an interesting example of problems with randomization in a community setting (Yanek et al., 2001). The investigators had intended to assign churches randomly to intervention or control conditions only after pastors had agreed to participate in the study, which is the typical sequence in research settings. However, many pastors refused to sign up without having the opportunity to agree to their randomization assignment, and the investigators ultimately revised their protocol to accommodate this preference. To work around the feasibility issues with random­ ization, quasi-experimental designs, in which

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comparison groups are not randomized (and therefore possibly not equivalent), are used (Bell et al., 2001; Simmons et al., 2004; Plescia et al., 2008). A delayed intervention or “waiting list” control in which the comparison population receives the intervention after study completion may also be employed. The comparison group in PRAISE! received the intervention 12 months after the study started (Ammerman et al., 2002). Comparing two active interventions that impact on different outcomes is another approach. This approach requires the additional cost and expertise to mount two interventions rather than only the one of interest. In the Memphis GEMS study in African-American girls, the main intervention was an after-school obesity prevention program; the comparison group received a culturally appropriate intervention designed to build selfesteem (Klesges et al., 2008). Impact The ultimate challenge relates to the need to demonstrate that culturally targeted community interventions have the desired impact. Relevant questions include whether these efforts are as effective as standard interventions, and whether the cultural adaptations as such are responsible for any improved effects. Studies of welldesigned, culturally-targeted lifestyle-change programs often report very modest or null effects on individual behavior or body weight (Bell et al., 2001; Yanek et al., 2001; Caballero et al., 2003; Paradis et al., 2005; Resnicow et al., 2005). This raises questions as to whether the interventions have been delivered as intended (fidelity, as conventionally defined in the behavioral intervention literature) and in sufficient dose. Fidelity issues are approached by identifying program elements that should be retained as originally developed and those for which fit with cultural and other contextual issues is deemed essential (Castro et al., 2004). Those issues may relate in part to program exposure – i.e., attendance or participation.

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However, it is also possible that designing a program to fit the community delivers a less than adequate dose because of inherent factors that compete with or undercut the intervention effect. For example, to render programs cost-feasible as well as sustainable, organization staff or members (peer or lay leaders) are often trained to deliver the intervention. Several of the studies cited in Tables 51.2 and 51.3 note the variability in implementation and dilution of the dose of intervention associated with this approach to program delivery (Bell et al., 2001; Yanek et al., 2001; Resnicow et al., 2004; Simmons et al., 2004; Paradis et al., 2005). Lay leaders drawn from organization members have pre-existing relationships with program participants that may influence their effectiveness in program delivery. Yanek notes that, even after special training, lay leaders drawn from program participants may not be viewed as leaders or “experts” by their peers (Yanek et al., 2001). Bell comments that some intervention sessions might not have been delivered because lay leaders were not confident in their ability to deliver those sessions (Bell et al., 2001). These authors also note that the time needed for training may shorten the time available to actually offer the intervention program. In church or other closely-knit communities where the majority of adults are obese, the strong interpersonal networks in which obesitypromoting behaviors are embedded and normative may cause latent or explicit resistance to change, thereby reducing program impact. Also, for both church leaders (e.g., pastors) and members, attending to other and perhaps more mission-relevant church obligations may limit the consistency or level of participation even when initiatives have their strong support. Another example of factors that may limit impact is the potential for mixed messages about health. For example, scriptural references to preserving health are often incorporated into program content, but the healing mission of churches may convey a message that disease is inevitable.

Evaluation The shift toward more community-based interventions triggers shifts in multiple dimensions of the paradigm, including outcomes measurement and evaluation (Laverack and Labonte, 2000; Hopson, 2003). The appropriateness of using conventional biomedical or behavioral outcomes to assess the success of these programs can be questioned, for example the use of changes in body mass index or percent body fat or changes in eating and physical activity behaviors as measures of success after a relatively short course of intervention, or the use of individual behavior change measures to evaluate environmental and policy change interventions given that changing environments and shifting norms to create cultural changes are long-term prospects with only indirect effects on individuals. An Institute of Medicine Committee has created an evaluation framework for childhood obesity prevention that emphasizes the need to match evaluative questions with appropriate outcome measures (Koplan et al., 2007). As an example, the PRAISE! Program listed in Table 51.3 evaluated perceived changes of church members and pastors in trust, their perceived benefit, and their satisfaction with the program (Corbie-Smith et al., 2003). A tool developed by Vinh-Thomas and colleagues (2003) to facilitate the design of culturally competent HIV-AIDS prevention programs has general applicability, and may be useful in identifying important operational elements related to obesity prevention. There are also guidelines for evaluating community processes such as participation and coalition development (Butterfoss and Francisco, 2004; Butterfoss, 2006).

51.6  Conclusion Community settings are pivotal to lifestyle change. They mediate the macro- and microenvironments that shape individual and aggregate

2.  From Society to Behavior: Policy and Action

References

eating and physical activity behaviors. There is increasing recognition that cultural influences on lifestyle change include social, physical and other environmental contexts. This is most clear in populations that are culturally distinct and whose environmental contexts include the larger societal context as well as distinctive sub-contexts that are ethnic and culture-specific. Culture-specific contexts influence obesity prevention by enhancing or impeding effects of extant obesity-promoting forces and of initiatives designed to counteract these forces in the population at large. They also define the settings and processes for cultural targeting and tailoring. The same types of multilevel cultural and contextual issues also apply to the population at large, but are easier to identify, more difficult to ignore, and more challenging to address across cultural boundaries. Targeting communities of color provides contrasts that can be useful for understanding risk and intervention pathways. In this respect, learning to reach the “minority” may inform the understanding of how to reach the majority. The process of developing targeted interventions for high-risk populations can trace backwards to identify the responsible factors that may be more prevalent or more salient in these populations (Kumanyika and Grier, 2006; Kumanyika and Teff, 2007), but this may lead to partial understanding and piecemeal solutions. Proactively identifying group-specific ethnocultural and contextual influences, including strengths and assets, for forward mapping of pathways to sustainable interventions may be of greater value for pursuing integrative, brain-to-society and lifecourse perspectives.

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Resnicow, K., Baranowski, T., Ahluwalia, J. S., & Braithwaite, R. L. (1999). Cultural sensitivity in public health: Defined and demystified. Ethnicity & Disease, 9, 10–21. Resnicow, K., Campbell, M. K., Carr, C., McCarty, F., Wang, T., Periasamy, S., et al. (2004). Body and soul. A dietary intervention conducted through African-American churches. American Journal of Preventive Medicine, 27, 97–105. Resnicow, K., Taylor, R., Baskin, M., & McCarty, F. (2005). Results of go girls: A weight control program for overweight African-American adolescent females. Obesity Research, 13, 1739–1748. Robinson, R. G. (2005). Community development model for public health applications: Overview of a model to eliminate population disparities. Health Promotion Practice, 6, 338–346. Schulz, L. O., Bennett, P. H., Ravussin, E., Kidd, J. R., Kidd, K. K., Esparza, J., & Valencia, M. E. (2006). Effects of traditional and western environments on prevalence of type 2 diabetes in Pima Indians in Mexico and the US. Diabetes Care, 29, 1866–1871. Simmons, D., Thompson, C. F., & Volklander, D. (2001). Polynesians: Prone to obesity and type 2 diabetes mellitus but not hyperinsulinaemia. Diabetic Medicine, 18, 193–198. Simmons, D., Voyle, J. A., Fou, F., Feo, S., & Leakehe, L. (2004). Tale of two churches: Differential impact of a churchbased diabetes control programme among Pacific Islands people in New Zealand. Diabetic Medicine, 21, 122–128. Smith, S. C., Jr., Clark, L. T., Cooper, R. S., Daniels, S. R., Kumanyika, S. K., Ofili, E., et al. (2005). Discovering the full spectrum of cardiovascular disease: Minority Health Summit 2003: Report of the Obesity, Metabolic Syndrome, and Hypertension Writing Group. Circulation, 111, e134–e139. Stefanich, C. A., Witmer, J. M., Young, B. D., Benson, L. E., Penn, C. A., Ammerman, A. S., et al. (2005). Development, adaptation, and implementation of a cardiovascular health program for Alaska native women. Health Promotion Practice, 6, 472–481. Story, M., Evans, M., Fabsitz, R. R., Clay, T. E., Holy Rock, B., & Broussard, B. (1999). The epidemic of obesity in American Indian communities and the need for childhood obesity-prevention programs. The American Journal of Clinical Nutrition, 69, 747S–754S. Teufel-Shone, N. I. (2006). Promising strategies for obesity prevention and treatment within American Indian communities. Journal of Transcultural Nursing, 17, 224–229. Tharp, M. (Ed.) (2001). Marketing and consumer identity in multicultural America. Thousand Oaks, CA: Sage. Tremblay, M. S., Perez, C. E., Ardern, C. I., Bryan, S. N., & Katzmarzyk, P. T. (2005). Obesity, overweight and ethnicity. Health Reports, 16, 23–34.

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C H A P T E R

52 On Gluttony: Religious and Philosophical Responses to the Obesity Epidemic William B. Irvine Department of Philosophy, Wright State University, Dayton, OH, USA

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52.1  Introduction North America is currently in the grip of an obesity epidemic. Obesity is substantially more common here than it was a decade ago, and vastly more common than it was in 1970. The epidemic also appears to be spreading to other regions of the globe, affecting not only industrial­ ized countries but developing countries as well. The victims of this epidemic have paid a con­ siderable price. At the minimum, they have had to change their lifestyle to accommodate their

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increased corpulence: activities that once gave them pleasure, such as dancing or playing tennis, have had to be abandoned. More important, their obesity has undermined their health: the obese are more likely to experience hypertension, osteo­ arthritis, diabetes, heart disease, strokes, and some forms of cancer. Those wishing to understand the obesity epidemic would do well to think about glut­ tony, inasmuch as there is clearly a connection between obesity and gluttony. It is, of course, possible for a glutton not to become obese: even though he gorges himself at meals, a glutton

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might lead such a physically active lifestyle that he maintains his ideal weight. It is also possi­ ble for a person who is not a glutton to become obese: a moderate eater may grow fat because of an unusually sedate lifestyle. As a general rule, though, gluttons become obese, and the obese have been guilty of gluttony in the past. Even though obesity and gluttony are con­ nected, physicians and public health officials have chosen to ignore gluttony. They do not, in particular, refer to the current situation as a glut­ tony epidemic, even though gluttony is argu­ a­bly the root cause of obesity. Although they are happy to warn people about the dangers of becoming obese, they rarely attempt to deter peo­ ple from the gluttony that so often leads to obes­ ity. This reluctance to think in terms of gluttony is presumably a consequence of the non-judgmental age in which we live. To label someone obese is not to judge him; rather, it is to categorize him in a scientific manner: by definition, anyone with a body mass index (BMI) over 30 is obese. To label someone a glutton, however, is to pass judgment on him in a very personal way. It is to imply that his character is seriously flawed. This is some­ thing most people – physicians and public health officials included – are reluctant to do. The con­ cept of gluttony is passé. If we look back in history, though, we will discover that those who lived in the ancient and medieval worlds were perfectly willing to label someone a glutton. More generally, our ances­ tors not only felt comfortable criticizing peo­ ple’s character flaws, but also thought they were doing people a favor by pointing them out. It is important to realize that these ancestors were critical of gluttony not because it led to obesity; as we shall see, even if a person could be a glut­ ton without becoming obese, they still would have criticized his gluttony. Gluttony should be of interest to anyone concerned with the obesity epidemic. It seems clear, after all, that if we could motivate people to overcome their gluttonous tendencies, we would do much to help them regain or maintain

a healthy weight. Let us therefore explore the concept of gluttony. Among the questions to be addressed are the following: What is gluttony? Why did our ancestors chide the gluttons among them? And what can we today learn from these ancestors that might help us fight the obesity epidemic?

52.2  What is gluttony? There is some disagreement regarding the exact nature of gluttony. The Merriam-Webster Dictionary defines a glutton as “one given habit­ ually to greedy and voracious eating and drink­ ing”, and the Oxford English Dictionary (OED) defines a glutton as “one who eats to excess, or who takes pleasure in immoderate eat­ ing”. Notice that although the Merriam-Webster Dictionary counts excessive drinking as a form of gluttony, the OED mentions only excessive eating. Notice, too, that the OED definition of glutton differs from the Merriam-Webster defi­ nition in that it classifies as a glutton not only someone who eats too much, but also someone who takes too much pleasure in his eating. Faced with this semantic dispute, the author would side with the Merriam-Webster Dictionary in including both eating and drinking as potentially gluttonous activities. Someone who subsisted only on milkshakes – drinking, say, 20 of them each day – would clearly count as a glutton. Even more significantly, drinking alcoholic beverages should count as a potentially gluttonous activity for a pair of reasons. First, alcohol is itself a signif­ icant source of calories. Furthermore, those who overconsume alcohol are likely, in their inebriated state, to consume more food than they otherwise would. Thus, the consumption of alcohol can lead to gluttony with respect to food. The author would, however, agree with the OED’s suggestion that an inordinate desire for food can in itself make one a glutton. Consider, for example, a jailed person whose supply of

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food and drink is restricted, meaning that he cannot inordinately consume food and drink. If this person wolfs down the (moderately-sized) meals he is served and, in between meals, does nothing but fantasize about his next meal, he would still count as a glutton – not because his eating was immoderate, but because his desires with respect to food were.

52.3  What is wrong with gluttony? Physicians routinely counsel their obese patients to eat less, but their primary motive for doing so is not to overcome these patients’ glut­ tonous tendencies; rather, it is to make them lose weight. In support of this claim, let us perform the following thought experiment. Suppose researchers come up with a drug that lets peo­ ple eat and drink as much as they want without gaining weight. Suppose the drug has no appre­ ciable side-effects. Suppose, finally, that tests of the drug have shown that people who use it capitulate to their gluttonous tendencies: they start gorging themselves at meals, treat them­ selves to triple helpings of dessert, and continu­ ously snack between meals. Physicians would probably be willing to prescribe this drug to their patients. It is a drug, after all, that would prevent their patients from becoming obese, and would thereby prevent them from falling victim to the various health problems that accompany obesity. In other words, physicians would probably prescribe this anti-obesity drug in much the same way as they currently prescribe contracep­ tives. Rarely, when their patients express a desire to avoid pregnancy, do doctors counsel them to have less sex. Instead, they recommend the use of contraceptives. Contraception allows women and men to have as much sex as they want – to surrender, that is, to their lustful tendencies – with little chance that pregnancy will result.

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Now let us make this somewhat fanciful thought experiment even more fanciful by add­ ing an element of time travel. Suppose we could transport forward in time the theologian St Thomas Aquinas and the Stoic philosopher Musonius Rufus. Suppose we ask for their reac­ tion to the drug described above. They would both chastise physicians for making such a drug available to patients. The drug, after all, has been shown to encourage patients’ gluttonous tenden­ cies. Gluttony, they would assert, is something to be overcome, not something to be encour­ aged. Furthermore, they would maintain that gluttony is objectionable regardless of whether it leads to obesity or has negative medical con­ sequences. (Aquinas and Musonius, by the way, would make a similar claim regarding contra­ ception: a doctor who cares about the wellbeing of his patients will not recommend that they use contraception, since doing so encourages them to surrender to their lustful feelings rather than try­ ing to overcome these feelings.) To many modern individuals, it will not be clear why a thoughtful person would be criti­ cal of gluttony (or, for that matter, lust). Let us, therefore, explore the religious and philosophi­ cal objections against gluttony.

52.3.1  The religious perspective Many religions offer their adherents advice on what kinds of food and drink to consume, and on how much of them to consume. In Islam, for example, the consumption of pork and of alcohol is proscribed. Likewise, Muslims are instructed to fast during Ramadan. The goal in this fast­ ing, though, is not to curb gluttony; indeed, once the sun has set, Muslims are free to eat their fill. More generally, the Qur’an allows Muslims to eat as much food as they want, as long as they do not waste it (Qur’an, 7:31) (The Qur’an, 1997). Buddhism (which might or might not count as a religion – some describe it as a way of life) coun­ sels against gluttony. More generally, Buddhism

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counsels its adherents to follow the “middle path” between hedonism and asceticism. They should, to begin with, avoid hedonism: they should not have as their primary goal the pursuit of pleas­ ure, and for this reason they need to overcome their gluttonous tendencies. They should likewise avoid asceticism, which encourages them to hold all sources of pleasure at arm’s length, including the pleasure to be derived from eating and drink­ ing. Buddha himself had experienced both these extremes. While he lived in the palace, he enjoyed a life of sensual indulgence. After leaving the pal­ ace, he tried a life of asceticism; for 5 years, he fasted until he was consuming only one grain of hemp per day (Carus, 34). In the end, he realized that such fasting was contrary to physical health, which in turn was connected to mental and spir­ itual health. Buddha’s great insight is that in order to over­ come pain and suffering, we must master our desires; otherwise, our desires will master us – they will dictate how we spend our days and, as a result, they will determine how we spend our lives. Among the desires we must overcome are our craving for sex, fame, fortune and food. If we give in to our gluttonous tendencies, we will be miserable not only in this life, but in future lives as well; our gluttony will prevent us from escap­ ing the cycle of rebirth (The Dhammapada, 246)1 (The Dhammapada, 1987). In Catholicism, gluttony is not just a sin; it is one of the Seven Deadly Sins. The sins in ques­ tion are not listed in the Bible; indeed, it was not until the sixth century that Gregory the Great sin­ gled out certain sins – pride, envy, wrath, sloth, greed, gluttony and lust – as being particularly dangerous because, if committed, they increase the likelihood of committing other sins. Why is

gluttony included among the deadly sins? In part because gluttony – a fixation on eating and drinking – will get in the way of the proper wor­ ship of God. Furthermore, overcoming our desire to eat and drink is the first step in overcoming a wide range of unwholesome desires. In The Institutes, monastic theologian John Cassian (360–435) describes the institutes and rules of Egyptian monasteries, the vices those undertaking monastic life encounter, and the remedies for those vices. The first vice he con­ siders is gluttony. There are, he says, three forms that gluttony can take: wanting to eat before mealtime, eating more than your nutri­ tional needs require, and favoring “refined and delicate foods” (Cassian, XXIII.1). And what is wrong with gluttony? For one thing, “the stom­ ach that has been fed with all kinds of food begets the seeds of lasciviousness, and the mind that is suffocated and weighted down by food cannot be guided by the governance of discre­ tion”. Too much food, he adds, makes the mind “stagger and sway and robs it of every possibil­ ity of integrity and purity” (Cassian, VI). More importantly, “whoever is unable to check the desire to gormandize will be incapable of curb­ ing the urges of burning lust” (Cassian, XI). After all, Cassian asks, if a person “has been unable to curb legitimate and insignificant pas­ sions that are in the open, how will he, under the governance of discretion, be able to fight against hidden ones that itch him when no one is looking?” (Cassian, XX). For this reason, says Cassian, the desire to glut ourselves with food and drink “is the first thing that we must tram­ ple upon” (Cassian, XIV.1). Along similar lines, St Thomas Aquinas (1225–1274) argues that gluttony is a deadly sin

1

At this point, some readers might be perplexed: if Buddha was opposed to gluttony, then what are we to make of the “fat Buddha” statues one sees in Chinese shops and restaurants? One theory is that these statues depict a “Buddha” – that is, a person who has gained enlightenment – other than Siddhartha Gautama, the man we think of as the Buddha. Another theory is that Chinese Buddhists depicted the Buddha as fat in order to indicate his pro­ found level of satisfaction. The Buddha himself did a lot of walking and begged for his meals; it is therefore unlikely that he was corpulent.

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because the glutton “adheres to the pleasure of gluttony as his end, for the sake of which he contemns God, being ready to disobey God’s commandments, in order to obtain those pleas­ ures” (Aquinas, article 2). Gluttony is also dangerous, inasmuch as it gives rise to the “daughters of gluttony”: unseemly joy, scurril­ ity, uncleanness, loquaciousness and dullness of mind in regards to the understanding (Aquinas, article 6). How are we to avoid the temptations of glut­ tony? Cassian recommends that “we should approach with restraint the food that we are obliged to eat in order to sustain our lives” (Cassian, VII). He adds that “the monk who wishes to advance to the struggles of the inner contest should first beware of ever allowing himself to take anything to drink or to eat, as one who is overcome by pleasures of any sort” (Cassian, XX). If Catholics find it difficult to act on this advice, their religion provides them with a powerful incentive not to fall into glut­ tony: God will punish them if they do. Indeed, in the Middle Ages and Renaissance, artists and writers took apparent delight in describing the horrors that awaited gluttons in the next world. Gluttons, for example, occupy the third circle of Dante’s hell, where they shiver in eternal foul weather, and in Hieronymus Bosch’s painting “The Last Judgment”, gluttons have themselves become food.

52.3.2  The philosophical perspective Modern philosophers have shown little inter­ est in gluttony: a search of the Philosopher’s Index turns up only three articles that deal with it directly. Ancient Greek and Roman philoso­ phers, however, thought gluttony was worth their attention. They held human beings to be a curious hybrid – half animal and half god. The animal element can be found in our body, which is “programmed” to seek pleasure and avoid pain, and the godly element can be found

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in our mind, with its rational powers. In human beings, the animal and godly elements can and often do come into conflict; the animal element might want something that the godly element knows would be bad for us. If we allow the godly element to be subverted by the animal element, though, we will be no better than ani­ mals. This is precisely what a glutton does: he allows his palate and gut to overrule his brain. And why is it bad for us to behave like an ani­ mal? Because, ancient philosophers argued, doing so will prevent us from living a good life. In a good life, in the sense that ancient phi­ losophers had in mind, we will achieve a degree of tranquility. What prevents most people from achieving this tranquility is their insatiability: they are never satisfied with what they have. They work hard to obtain something – a con­ sumer gadget, a job, or even a spouse – and, upon obtaining it, become disenchanted and set off in pursuit of something new. We are unlikely to have a good life, these philosophers argued, unless we master our desires. The suggestion is not that we should eliminate desire from our lives – this would be a foolish thing to do even if it were possible to do so. Instead, the sugges­ tion is that we should work to prevent ourselves from acting on many of the desires generated by our animal element. If we cannot do so, ancient philosophers warned, we will spend our lives in a state of dissatisfaction. It is useful to contrast ancient philosophers’ views on gluttony with those of the Catholic theologians described above. Those theologians thought that if we are unable to curb our glut­ tonous tendencies, we will have a bad afterlife; ancient philosophers, by way of contrast, thought that if we are unable to curb our glut­ tonous tendencies, we will have a bad life. It is also useful to contrast ancient philosophers’ views on gluttony with those of modern physi­ cians and public health officials. These modern individuals would agree with ancient philoso­ phers that we are likely to have a bad life if we are unable to curb our gluttonous tendencies.

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In particular, they would point to the health problems we will encounter when, because of our gluttony, we become obese. But this sense of a “bad life” is different from what ancient philos­ ophers had in mind. Indeed, those philosophers would argue that even if our gluttony does not lead to obesity, it can and probably will prevent us from having a good life. Gluttony, after all, is a symptom of not having mastered our desires, making it unlikely that we will ever attain the contentment and tranquility that ancient philos­ ophers thought should be our goal. How can we master our desires? The first and most important step is to develop a degree of self-control. These days, of course, self-control is a character trait that many deride. They argue that self-controlled individuals lack spontane­ ity in their lives, and that this lack of sponta­ neity, in turn, prevents them from having fun. Individuals with self-control, they might add, are therefore likely to live grim lives. Ancient philosophers, though, thought that an indi­ vidual with self-control not only can enjoy life, but is also more likely to do so than a “sponta­ neous” individual. They would point out, most significantly, that self-control is the ability to control ourselves – the ability to decide how we are going to spend our days and, ultimately, how we are going to spend our lives. If we lack self-control, it means that we do not control our­ selves; someone or something else does. There is little reason to think that this someone or some­ thing else will control our lives with the goal of bringing us the greatest possible happiness. Of course, a person with self-control is unlikely to succumb to gluttony. Instead, he will take the advice of philosopher Socrates (469–399 BC) (Laertius, 1979) and, instead of living to eat, he will eat to live (Diogenes Laertius, II.34). He will also heed the warning of Cynic philosopher Diogenes of Sinope (c. 412–323 BC), that pleas­ ure “hatches no single plot but all kinds of plots, and aims to undo men through sight, sound, smell, taste, and touch, with food too, and drink and carnal lust, tempting the waking and the

sleeping alike” (Dio Chrysostom, 389), and that pleasure, “with a stroke of her wand … cooly drives her victim into a sort of sty and pens him up, and now from that time forth the man goes on living as a pig or a wolf” (Dio Chrysostom, 391) (Chrysostom, 1961). Among ancient philosophers, the Stoics took a particular interest in our relation to food. The Stoics, contrary to popular belief, were not stoical: they did not advocate living grim, emotionless lives. Rather, they advocated living lives that were as free as possible of negative emotions such as anxiety, anger and grief; they had nothing against experiencing positive emotions such as joy. According to first-century Stoic philoso­ pher Musonius Rufus, gluttony is one of man’s most shameful vices. He characterizes gluttony as a “lack of self-control with respect to food” (Musonius, 18B.1). He offers the following important insight into eating: although there are many pleasures which per­ suade human beings to do wrong and compel them to act against their own interests, the pleasure con­ nected with food is undoubtedly the most difficult of all pleasures to combat. We encounter the other sources of pleasure less often, and we can therefore refrain from indulging in some of them for months or even years. But we will necessarily be tempted by gastronomic pleasures daily or even twice daily, inas­ much as it is impossible for a human being to live without eating. (Musonius, 18B.3)

This in turn means that if we wish to improve our self-control – as any rational person will – one of the best places to do so is at meals. If we can teach ourselves to control our desire for food – that is, if we can learn to restrain our gluttonous tendencies – we will have made important progress toward achieving what should be our more general goal of mastering our desires. To many people, showing restraint at meals sounds like no fun at all. The Stoics, however, were perceptive enough to realize that although we will forgo certain gastronomic pleasures if

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we restrain our eating, this loss will be coun­ terbalanced by our experience of certain other pleasures. Stoic philosopher Epictetus (55–135), for example, points out that when you pass up some tidbit that you know it would be unwise to eat, you will “be pleased and will praise yourself” for passing it up (Epictetus, 1983, 34). Or, as Stoic philosopher Seneca (4 BC–AD 65) put it, “there is a pleasure in having succeeded in enduring something the actual enduring of which was very far from pleasant” (Seneca, 1969, 135). This is a phenomenon that many dieters will recognize: when they successfully resist temptation, they are rewarded by a rather pleasant feeling of success – a feeling that they have won a battle against the temptation to eat. Seneca, by the way, also realized that a glutton could derive pleasure not only from overcoming his tendency to glut himself with food, but also by overcoming his craving for gourmet fare: “barley porridge, or a crust of barley bread, and water,” he wrote, “do not make a very cheerful diet, but nothing gives one keener pleasure than the ability to derive pleasure even from that” (Seneca, 68). The above discussion makes it sound as though ancient philosophers were unanimous in their disapproval of gluttony. But what about the Epicureans (who happen to have been the princi­ pal philosophical rivals of the Stoics)? Did they not advocate that people stuff themselves with gour­ met foods? In fact, they did not. Just as the ancient Stoics were not stoical in the modern sense of the word, the ancient Epicureans were not epicureans in the modern sense of the word. In support of this claim, consider the following quotation from Epicurus (341–270 BC) (Epicurus, 1940): It is not continuous drinkings and revellings, nor the satisfaction of lusts, nor the enjoyment of fish and other luxuries of the wealthy table, which produce a pleasant life, but sober reasoning, searching out the motives for all choice and avoidance, and banishing mere opinions, to which are due the greatest distur­ bance of spirit. (Epicurus, 32)

There were doubtless ancient philosophers who saw nothing wrong with gluttony, includ­ ing, perhaps, the Cyrenaics, but most of the schools of philosophy, despite their differences in doctrine, agreed that gluttony was something we should overcome. A modern philosopher, having read the ancients and having pondered the obesity epi­ demic, might offer the following observations. First, the obesity epidemic is merely a ramifica­ tion of a gluttony epidemic. Second, this glut­ tony epidemic is only one aspect of a larger philosophical concern – namely, people’s ten­ dency to spend their lives in the grip of desire; because they are unable to distinguish between wholesome and unwholesome desires, they work to fulfill almost any desire that pops into their head. Third, until people learn how to master their desires – until they learn how to prevent and extinguish unwholesome desires – they will be unlikely to have a life that a philos­ opher would think is worth living. It is the opinion of the author that these observations, although valid, are unlikely to help us deal with the current obesity epidemic. Across cultures and down through the millen­ nia, only a handful of people have been willing to listen to what philosophers have to say – much less heed their advice about the impor­ tance of mastering our desires. It is unlikely that this attitude will change any time soon. Here is another way to make this point: if Catholic priests could not curb their parishioners’ glut­ tony with threats of damnation, what chance do philosophers have of curbing people’s gluttony with mere appeals to reason?

52.4  Conclusions This chapter describes gluttony and the con­ nection between gluttony and obesity. While modern individuals are far more concerned with obesity than gluttony, in the ancient and medie­ val world this concern was reversed. This paper

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also explains the rationale that lay behind our ancestors’ intolerance of gluttony: Catholic theo­ logians worried that gluttony would get in the way of worshipping God (and would therefore result in gluttons being consigned to hell), and ancient philosophers worried that unless peo­ ple could overcome their gluttonous tendencies, they would be unlikely to have a good life. This suggests that one way to deal with the obesity epidemic is to restore the concept of gluttony to its former prominence: convince people to overcome their gluttony, and you will go far toward preventing and reversing obesity. This suggestion, however, is not very practical, inasmuch as people are unlikely to take to heart the recommendations of priests and philoso­ phers to eat less. Suppose that instead of priests and philoso­ phers leading a campaign against gluttony, the medical community were to do so. Doctors, for example, could start badgering patients to overcome their gluttony. Public health officials could design an anti-gluttony advertising cam­ paign, reminiscent of anti-smoking campaigns. Such measures are unlikely to succeed. A doctor who started carping about his patients’ gluttony would likely lose patients to doctors who were less judgmental. And given how sensitive we are to hurting people’s feelings, it is unlikely that we will any time soon encounter, say, a television advertisement that cuts between scenes of pigs gorging themselves and humans doing the same, with the tag line, “Don’t pig out!” The public

health official responsible for such an advertise­ ment would probably soon be out of the job. What this means is that although gluttony is arguably the root cause of the obesity epidemic, it seems unlikely that we will be able to curb the epidemic in the obvious way – by trying to con­ vince people to overcome their gluttony and, more generally, to master their desires.

References Aquinas, S. T. Summa theologica (Second Part of the Second Part: “Of Gluttony”; Question 148). Carus, P. The gospel of Buddha. La Salle, IL: Open Court. Cassian, J. (2000). The institutes (B. Ramsey, Trans.). New York, NY: The Newman Press. Chrysostom, D. (1961). Dio chrysostom (Vol. I), The eighth discourse: diogenes or on virtue (J. W. Cohoon, Trans.). Cambridge, MA: Harvard University Press. Epictetus (1983). The handbook (The Encheiridion) (N. White, Trans.). Indianapolis, IN: Hackett. Epicurus (1940). Epicurus. In: W. J. Oates (Ed.), The Stoic and Epicurean philosophers. Letter to Menoeceus. New York, NY: The Modern Library. Laertius, D. (1979 version). Lives of eminent philosophers (Vol. II), Socrates (R. D. Hicks, Trans.). Cambridge, MA: Harvard University Press. Rufus, M. In: W.B. Irvine (Ed.), The lectures and sayings of Musonius Rufus (C. King, Trans.). Typescript. Seneca (1969). Letters from a Stoic. (R. Campbell, Trans.). London: Penguin. The Dhammapada (1987). The dhammapada (J. R. Carter and M. Palihawadana, Trans.). New York, NY: Oxford University Press. The Qur’an (1997). The qur’an: The eternal revelation vouchsafed to Muhammad, the seal of the prophets (M. Z. Khan, Trans.). New York: Olive Branch Press.

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C H A P T E R

53 Social Alliances: Moving Beyond Corporate Social Responsibility to Private–Public Partnerships Xiaoye Chen1, Karl J. Moore2 and Lise Renaud3 1

Marketing Department Strategy and Organization Department, Desautels Faculty of Management, and Dept. of Neurology & Neurosurgery, McGill University, Montreal, Canada 3 Social and Health Communication, Université du Québec à Montréal (UQAM), Montreal, Canada 2

o u t l i n e 53.1  Introduction

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53.2  Partnership in Social Alliances

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53.3 Social Alliances as a Strategy for Corporate Branding

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53.4 Societal Interventions as Strategic Alliances 663 53.4.1  Motivations and External Drivers 664 53.4.2  Social Alliance Characteristics 665

53.1  Introduction Two out of three adults in the US, Canada, and other industrialized countries are either overweight or obese. The outlook is no better for children: the International Obesity Task Force’s (IOTF)

Obesity Prevention: The Role of Brain and Society on Individual Behavior

53.4.3  Social Alliance Governance 53.4.4  Partnership Outcomes

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53.5 The Case Study Intervention 53.5.1  Research Method 53.5.2  The Case Study Results

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53.6  Discussion of the Case Study

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53.7  Conclusion

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Acknowledgments

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estimates suggest that nearly 287 million children could become overweight or obese by 2010 – 85 percent more than a decade ago (Dubé et al., 2006). It is increasingly recognized that the drivers of the obesity pandemic – the overconsumption of food, and physical inactivity – are rooted in the way

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2010 Elsevier Inc. © 2010,

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modern society operates. However, paradoxically, only 2–3 percent of health expenditures in most industrialized countries is devoted to obesity prevention efforts (Le Galès-Camus, 2006). Given that 10–15 percent of most countries’ GDP is dedicated to health, it seems unlikely that the additional resources needed to prevent childhood obesity could be sourced from within the formal boundaries of the health system. It is urgent that childhood obesity is curbed in a timely and economically sustainable manner. As such, obesity prevention needs to move from the traditional realm of public health interventions to new forms of collectively created engagements or public–private partnerships, referred to here as “social alliances”. The choice of this term refers to strategic alliances, the win–win partnerships that have become commonplace in the business world. Social alliances would be created among business, non-profit, academic and professional organizations at all levels, and driven and maintained by the creative and sustained resources, commitments and actions of all the above-mentioned stakeholders. In order to underscore the action-oriented role of stakeholders within social alliances, they are referred to as “agents”. Along with strategic alliance partners, all relevant agents, each with their own respective missions, core competencies and investments of sufficient scale and scope, would achieve their own respective objectives while simultaneously partaking in critical and much needed interventions to address critical social issues. In the context of childhood obesity prevention, ambitious novel social alliances of agents in food, physical activity, health, agriculture and business – defined as multi-agent interventions (Dubé et al., 2006) – are needed. Within these, agents from all sectors must actively participate to create a society where healthy lifestyles are the natural option for both children and adults. The objective of this chapter is to examine whether companies can transcend their currently peripheral and limited funding-related

role in corporate social responsibility (CSR) and corporate societal marketing (CSM) projects and become fully active agents in childhood obesity prevention social alliances (Loewenson, 2003).

53.2  Partnership in social alliances Many health interventions have features of social alliances involving the public sector, business and civil society, together collaborating to tackle complex and expensive public problems (Davis, 2005a). For instance, the World Health Organization (WHO) has engaged in an initiative to promote partnerships between government and non-government organizations (NGOs) in the social arena in order to better manage national health systems’ resources (Davis, 2005b). Within the specific context of obesity prevention, the novel and innovative partnership between the Clinton Foundation, the American Heart Association and Nickelodeon’ Networks, “Creating a Healthier Generation”, is a good example of such collaborations. In parallel, leading thinkers in strategic management (Varadarajan and Menon, 1988; Porter and Kramer, 2002; Aaker, 2004; Warner and Sullivan, 2004) suggest that firms could improve their “corporate brand” by placing social responsibility at the core of their strategic agenda. Firms could create “shared value”, simultaneously creating value for society as well as for their shareholders. However, this means companies must transcend their currently peripheral, limited and purely financial investment in CSR and CSM projects to actively engage in broader social alliances. A paradigm shift must also occur among non-business partners to see companies as capable not only of providing financial resources but also of providing unique competencies that can enhance the effectiveness of the social alliance’s efforts and interventions.

2.  From Society to Behavior: Policy and Action

53.4  Societal interventions as strategic alliances

In this chapter, social alliances are presented as a more complex and innovative corporate social responsibility approach than the conventional CSR practices. A case study of an existing “social alliance” intervention targeting childhood obesity is then examined. A well-established framework of strategic alliances is used as a guide for conducting in-depth interviews with all relevant agents: managers and professionals in business, NGOs, community and health organizations.

53.3  Social alliances as a strategy for corporate branding Currently, typical CSR initiatives do not capture the potential importance of social issues for a company’s business strategy (Aaker, 2004). Conventional corporate philanthropic practices consist of financial donations to a general cause, with emphasis on gaining goodwill or meeting legal and contractual requirements (Davidson, 1997). In conjunction with marketing goals, other current popular practices, such as causerelated marketing (Dean, 2002) and cause–brand alliances (Rifon et al., 2004), focus on selected social issues to contribute a share of product sales, thereby simultaneously boosting brandcentered marketing efforts. With the ultimate goal of encouraging the purchase of corporate products, these practices are often perceived by both the consumers and the health community as exploitative (Harnack et al., 1999, 2000), and have been found to produce fairly limited social benefits. A challenge lies in the fact that potential agents for change may also be at the root of a social problem; in the case of childhood obesity, for instance, the overconsumption of food and media brands is deemed to be one of the main culprits (Holder, 2000, 2004; Nielsen et al., 2002). There have been precedents, however: alcohol is

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a domain where joint efforts, deployed by social alliances among producers, retailers, media, school and health organizations as well as with civil society, have successfully educated and promoted moderate consumption (J. P. Morgan, Chase & Co., 2005; Lang et al., 2006). In regard to food production, advertising and consumption, firms that have proactively placed health issues on their strategic agenda have seen improved industry and consumer ratings (Rao et al., 2004; Berger et al., 2006). As demonstrated in the environmental sphere (Berger et al., 2006), such ratings are factored into the price that both consumers and investors are willing to pay. Price premiums, linked to a socially responsible “corporate brand” (Gulati, 1998), could also motivate food-producing and advertising companies to place obesity prevention at the forefront of their strategic agenda. It is advocated that by progressively adapting R&D and marketing efforts and/or actively engaging in social alliances to promote healthy food consumption, these companies could contribute resources and competencies to schools and health systems, beyond what is currently offered. By shifting the focus away from brand marketing to a more integrative level of corporate brand leverage, firms that engage in social alliances can present more powerful, credible and distinct social and marketing messages than those presented by CSM efforts, and this without compromising their profit returns.

53.4  Societal interventions as strategic alliances Over past decades, strategic alliances have emerged in various sectors of economic activity around the world. These relationships are characterized by the “pooling” of resources, competencies, capacities and expertise. These create added value to what each party could not have achieved by acting alone. Firms involved in a

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business strategic alliance have specific goals within the partnership, such as market positionrelated, product-related, or risk-reduction motives (Nooteboom, 1992). Viewing social alliances in this perspective not only provides valu­ able insights into identifying and defining the strategic nature and components of the partnership, but also sheds light on the fundamental issues underlying the partnering process. The framework of strategic alliance employed here is articulated in terms of its formation, implementation and outcomes, and draws from the management (Ring and Vandeven, 1992; Brown and Dacin, 1997; Waddell and Brown, 1997), public policy (Varadarajan and Menon, 1988; Davis, 2005a; London et al., 2005) and marketing (Davidson, 1997; Yin, 2003) literature. By utilizing the three components of the above-mentioned framework, three main principles emerge: (1) the decision to become a partner rests upon specific drivers or motivations that are influenced by the context; (2) to ensure that the alliance functions efficiently, it is necessary to find the partners and fit among resources, investment and governance; and (3) a social alliance must also ensure the highest return on objectives while maintaining a reliable evaluation system. Conversely, a social alliance is conceptualized in this chapter around four major building blocks: (1) motivations and drivers of alliance entry; (2) social alliance characteristics; (3) governance; and (4) outcomes. Figure 53.1 depicts these four blocks.

Motivations and external drivers Social and environmental drivers Internal motives

53.4.1  Motivations and external drivers One important distinction between traditional philanthropic partnerships and social alliances is the juxtaposition of the social and economic drivers of the partners when entering the partnership. A successful social alliance must be driven by social problems of significance which call for a set of actions that affect the potential partners. It is important to frame the issue based on commonality in decisionmaking (Waddell and Brown, 1997). From a social alliance perspective, the challenge arises in building convergence between the social and economic drivers. Beyond the common social goal, companies and non-profit organizations may have different motivations to collaborate. A social alliance works best when it helps each partner achieve its respective individual goals (Brown and Dacin, 1997). Generally speaking, a firm’s specific goals in entering into a social alliance can include fulfilling social obligations and consumer expectations, building brand and corporate equity, and maintaining community relations and seeking convergence between public and business interests. For non-profit organizations the motivations include fundraising objectives, increased influence on public policy and enhanced legitimacy of non-profit work, participation and building of social capital, and improved access to public service and greater involvement in public programs. The

Characteristics

Governance

Outcomes

Alliance features

Structural governance

Social/non-profit outcomes

Motivational governance

Business outcomes

Core competencies

Figure 53.1  Conceptual components of social alliances.

2.  From Society to Behavior: Policy and Action

53.4  Societal interventions as strategic alliances

internal drivers of government in entering into such alliances include integration of new perspectives, technical expertise and capacities on certain public issues, as well as increased service provision and implementation of public programs (Varadarajan and Menon, 1988; Brown and Dacin, 1997; London et al., 2005). At its core, a social alliance is the process of recognizing and reconciling the interests of individual partners.

53.4.2  Social alliance characteristics Social alliances differ from past forms of relationships in which a business would simply donate funds to a non-profit or government organization for obligatory or defensive purposes. In social alliances, business and non-profit organizations both bring competencies and specializations that are an intrinsic part of their respective internal functions. The partnerships are built around each party’s core competencies, and distinct elements are brought together (such as resources, roles, responsibilities or types of behaviors) that are either the true strengths of each party or add value to other parties (Varadarajan and Menon, 1988). The literature identifies the key assets of business, for non-profit organizations, as the financial, administrative and technical resources as well as the ability efficiently to produce short-term outcomes. Identified key non-profit organization and civil society competencies are their ability to facilitate commun­ ity interactions, their knowledge of issues and local communities, their influence on public attitudes, and their credibility and experience with effective common good processes. Finally, government authorities offer comprehensive and strategic coordination through development plans and access to budgets to launch social campaigns (Davidson, 1997; London et al., 2005).

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53.4.3  Social alliance governance Governance consists of the system of rules, norms and processes through which power and decision-making are exercised (Loewenson, 2003). Social alliances pose complex obstacles and challenges to governance, both in theory and in practice (McCracken, 1988). To be more specific, the private and public sectors usually have divergent backgrounds, values and expertise, as well as fundamentally different organizational structures. Because of their incongruent values and historic opposition to one another, the public and private sectors almost always have initial low trust and confidence levels in each other, which may considerably impede partnership entry and sustainability (Yin, 2003). In building a successful partnership, each partner must learn to adopt a negotiation approach that accommodates partners’ distinct differences and fosters consensus.

53.4.4  Partnership outcomes Possible outcomes of the integration of these sectors’ various interests include governance efficiencies, product development innovations, and expression of local values. Broadly speaking, partnership outcomes can be monitored and measured according to four distinct cate­ gories: (1) the extent to which the partnership objective has been achieved; (2) unintended or unexpected outcomes in business and community development; (3) the “added value” of partnership in terms of its organizational and business impacts; and (4) the cost–benefit indications (Davidson, 1997). In terms of performance and alliance outcome assessments, Gulati (1998) comments that, due to the multi-faceted and asymmetric nature of the alliance, it is extremely difficult to measure partnership performance itself. Other factors which make social alliance outcomes difficult to measure are the multitude of societal goals, as well as the wide scope of the impacts.

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Hence, carefully developed measurement tools are required to capture the different facets and nuances of partnership performance.

53.5  The case study intervention We examined a multi-agent intervention that aims at promoting healthy lifestyles and preventing obesity and other chronic diseases in children, the Canadian national campaign Long Live Kids (LLK). This campaign is organized by the Canadian Children’s Advertisers (CCA), a corporate entity which is comprised of 25 Canadian companies primarily from the food and media industries. They include worldleading food companies such as Coca-Cola Ltd, Frito Lay Canada, McDonald’s, Nestle Canada, etc. CCA partnered with a diverse set of non-profit organizations, government as well as agents and individuals from civil society. The non-profit organizations, specializing in a range of social, community and health issues, include the Boys’ and Girls’ Clubs of Canada, Dietitians of Canada, and YMCA Canada. Governmental support and guidance from Health Canada was

Business

also obtained to launch the LLK campaign. Other agents from civil society who were brought into the collaborative network include communities, issue experts, educators and parents. The campaign employs two main mechanisms to achieve its overarching objective of childhood obesity prevention: a series of scientific and child-directed public service messages (PSAs) in the form of television commercials aired across Canada, and an education program delivered to educators, parents and community leaders to equip them with specific tools to combat obesity. The LLK program can be viewed as an industry-led, issuebased, multi-agent intervention created through a corporate-based, non-profit intermediary (CCA), with members and partners taking distinct roles and responsibilities in supporting and sustaining program implementation. Figure 53.2 maps the partnering sectors, and lists examples of the organizations involved from each sector. The case study had two main objectives: (1) to examine the practical application of a multiagent intervention and gain an in-depth understanding of the partners’ strategic motives, inputs, as well as of the societal and organizational outcomes; and (2) to examine the extent to which business managers conceive of such

CCA Members

Industry Media

Health Canada Civil Society NGOs

Governme nt authorities

Community Parents Educators

CCA Partners

e.g., McDonald’s; Nestle Canada; Coca-Cola Ltd; Frito Lay Canada; Kellogg Canada; Kraft Canada; Weston Bakeries Ltd; Corus Entertainment Inc; Teletoon Canada; etc.

e.g., Dietitians of Canada; Coalition for Active Living; YMCA/YWCA Canada; Boys and Girls Clubs; Canadian Diabetes Associations; etc.

Figure 53.2  The Long Live Kids alliance Source: Adapted from Concerned Children’s Advertisers (http://longlivekids.ca/).

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53.5  The case study intervention

interventions as a strategic alliance, to delineate the embedded strategic objectives of achieving core business and societal goals, and to identify the aspects which could lead to improved strategic plans.

53.5.1  Research method Data were collected through in-depth interviews, conducted in the fall of 2006. Addi­ tionally, other secondary sources, such as media releases and organizations’ website material, were used as supplementary data. Twenty members, drawn equally from the private and public sectors, were interviewed. They were either directly involved in LLK, or were brought in as organizational strategic decision-makers. Interviewees from the non-profit sector were primarily the executive directors of their respective organizations, whereas all of the 10 corpor­ ate representatives were marketing or public affairs vice-presidents of the Canadian subsidiaries. Investigators posed “how” and “why” questions to describe an intervention and the real-life context which are less controllable by researchers (Yin, 2003). Elite interviews with decision-makers were designed to ascertain the decision-makers’ understanding of the phenomenon, its meaning or connotation, as well as to highlight factors which they considered important (McCracken, 1988).

53.5.2  The case study results External environments and internal motivations Most of the participants shared a sound assessment of the childhood obesity problem and described it as both a politically- (considering all kinds of public policy positions being adopted by NGOs and the government) and an emotionally-charged social issue resulting from a complex array of factors. Additionally,

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the results indicated that the current partnership had effectively accomplished a satisfactory issue-framing process by elucidating and maintaining the program’s focus as promoting healthy and balanced lifestyles while positioning this objective in relation to each of the partners’ individual goals. In terms of the organizations’ specific motiv­ ations, all partners sought out certain synergies and complementary resources that were based on different perspectives of “fit” between the campaign and their organizations. For the NGOs, the current program was perceived to dovetail with their organizational missions and mandates (such as nutrition, lifestyles and education), as well as with their target market or audiences. In addition, synergies in resources and networks, such as expertise and research properties in different areas of community development, were also highlighted. From the corporate perspective, and according to one of the managers interviewed, the partnership was seen as embodying the efforts to “find the intersection between doing the right thing for consumers and the community, and a business solution”. Similarly to their non-profit counterparts, convergence was found among the companies’ social efforts, especially in terms of the fit with the companies’ brands, the healthrelated CSR initiatives and strategic directions. In contrast to the most important motive for conventional CSR practices, this particular social alliance offers no direct corporate visibility. Consequently, building consumer brand awareness/image was a less important motive than others such as ongoing communication with corporate stakeholders. Also noteworthy, almost all partners mentioned the future possibility of setting up new partnerships and extending organizational influences. Partnership characteristics A social alliance ideally possesses distinct features that are suitable to solve complex social

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issues. In that respect, the participants discussed the nature of the relationships as well as the features of the LLK alliance that contributed to the successful implementation of the program. These included the large-scale and longterm orientation of the alliance, the multiple resource inputs and synergies, the large-scope network sharing, and the legitimacy of corpor­ ate participations. In contrast to other social initiatives, the LLK multi-agent alliance features a highly efficient allocation of distinctive and compatible resources and skill sets among the public and private sector as well as civil society. The nonprofit sector contributed knowledge about obesity and local communities, representation of credible health information to safeguard the reliability of program messages, the ability to mobilize and widely reach families and educators, as well as credibility and experience with effective common good processes. Business contributions can be classified into three cate­ gories: funding, ideas and expertise, and staff and services. Beyond the conventional funding, the current alliance requires a higher degree of participation from the business in terms of time, strategic counsel and advice to develop the PSA proposal. Additionally, the companies’ professional expertise and management experience, especially in terms of financial efficiencies, were also regarded as key resources for the program. Governance and success factors The managers stressed the importance of both structural vehicles and motivational means. Examples cited of the former included a grievance mechanism to resolve differences and procedures for transparency; and of the latter, trust- and confidence-building in terms of partnership negotiation and program implementation. In terms of structural governance, the LLK program is an intermediary-led and centrally coordinated partnership, with CCA shouldering the majority of daily administrative and

communication tasks. A governance structure of this kind has both advantages and drawbacks. On the one hand, the CCA functionality largely reduces partnership tensions and conflicts. On the other hand, the joint decision-making and working plan process are hard to reflect in the current alliance. Nonetheless, almost all representatives emphasized the importance of such a grievance intermediary in facilitating and organizing partnership constituents and ensuring the transparency of the process. The current social alliance, however, is not totally free from tensions and potential partnership risks encountered by other cross-sectoral collabor­ ations. Some non-profit partners indicated that there are risks in their organization affiliating with private sponsors accused of contributing to inactivity and poor nutrition, and thus possibly damaging the non-profits’ credibility. Some expressed the concern of an imbalance of power between corporations and NGOs as a result of the funding dynamic. To resolve this issue, the LLK partners accentuated the importance of shared objectives and a sound mutual understanding of the partnership positions of others, including interests, incentives, resources, working issues and values. Other managerial factors were suggested to ensure higher partnership returns. These included having a strong and sustained funding base, choosing appropriate partners who can add value and credibility to the program, and possessing a mandate based on science and research to ensure credibility and avoid potential challenges and conflicts among partners. As some informants indicated, it is essential to bring in relevant partners at the very beginning of the decision process so that each party has an equal opportunity to participate in the entire joint planning procedure. Additionally, a good and sound governance structure was perceived to be critical to ensure the partnership could properly adapt as issues evolved. Lastly, engaging senior levels and being pragmatic and action-oriented were cited as valuable in promoting highly efficient partnerships.

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53.7  Conclusion

Outcomes In the LLK social alliance, corporate partners appeared to have an explicit understanding of the costs and benefits associated with their involvement in the alliance. Several key points were mentioned: consumer education, enhanced CSR capability and social networking, and organizational learning opportunities, which directly and indirectly impact corporate competitiveness. From the non-profits’ perspective, partners considered the partnership as an opportunity to resonate with and enhance their own mandates. Some considered it helpful as it complemented their current messaging work, while others emphasized the assets and outcomes as added value. In addition, bene­ ficial outcomes to their organizations included (1) a wider target market reach, (2) expanded networking, and (3) a stronger organizational influence. A systematic methodology of evaluation has not yet been developed for LLK. Several interviewees mentioned that the chronic feature of obesity and the characteristics of the target population make it exceptionally tricky to identify proper indicators. Others, however, mentioned that measuring the outcomes or the “added value” of the social partnership is a subtle pro­cess which can be undertaken on an ongoing basis through diverse formal and informal approaches.

53.6  Discussion of the case study The main results of the case study are highlighted in Table 53.1. The intervention of Long Live Kids presents key features of a multi-agent social alliance effort. The majority of corporate managers perceived the LLK as a CSR project rather than a strategic alliance with added value. Moreover, the study indicated that the strategic planning processes (e.g., joint decisionmaking governance and outcome monitoring

mechanisms) are not manifested in the partnership management as well as the program intervention process. In other words, the potential strategic importance of the social alliance model has not been fully and profoundly leveraged to its legitimate extent. It is evident that there could be a broader use of the firms’ core competencies – i.e., their logistic, marketing and managerial techniques and skills. There is also the possibility of increasing their current investments. This, however, will require the development of new mindsets and novel models of governance in both private and public organizations so that firms can build corporate equity as they increase the scale and scope of their investment and involvement in multi-agent interventions while avoiding conflict of interests. Further research is needed in the disciplines of health and management to conceive, develop and evaluate such novel approaches.

53.7  Conclusion Social alliances have been increasingly advocated as an important initiative for both the public and the private sectors in order to achieve various strategic goals. The approach is innovative because it requires companies to engage in enduring and strategic relationships with other stakeholders. It represents a significant leap away from traditional corporate societal marketing initiatives in order for firms to fulfill their social legitimacy objectives in a more holistic and strategic manner. Few studies so far have attempted systematically and empirically to investigate the working mechanisms of social alliances and the roles of the firms involved. The presentation and empirical examination of the framework of social alliances will contribute to research and practical developments to support more effective societal plans in which the private sector can come to play a more critical and unprecedented role.

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Table 53.1  Results of the Case Study of Long Live Kids Non-profits

Corporations

Motivations

- Fit in terms of organizational mandate, target audience, expertise, and research properties - Network expansion - Policy implementation and health policy influence

- Genuine interest in addressing consumption-related issues - Demonstrating CSR - Preclusion to legislative risks - Consumer education and meeting wellness trends

Features of the partnership

Broad base of partners; long-term commitments; multiple and complementary resources; legitimacy of corporate participation

Resources

- Knowledge of local communities - Credible health information safeguard - Mobilize and reach families and educators - Network resources

Governance

CCA shouldering daily administrative work as general organizer; rare direct linkages between partners; both structural and motivational governance methods are in place; general lack of resources, e.g., funding and human resources are the biggest challenges

Risks and challenges

- Vision or credibility distortion by affiliating with private sponsors accused of contributing to obesity - Possible power imbalance between firms and NGOs from funding dynamics

- Suspicions or preconceptions from civil society and wider society of selfserving purposes

Outcomes

- Wider target market reach - Expanded networking - Stronger organizational influence

- Consumer education - Enhanced CSR capabilities and social networking - Organizational learning

- Funding - Staff and service commitments to CCA* - Ideas and expertise in designing and delivering program messages - Free media “air time”

*

CCA: Concerned Children’s Advertisers.

Acknowledgments Financial support for this research was provided by team grants from the Quebec research agencies Fonds de la recherche en santé du Québec and Fonds de recherche québécois sur la société et la culture to Lise Renaud. We would also like to thank the many informants from businesses, NGOs and government who gave generously of their time for our interviews.

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C H A P T E R

54 Social Networks, Social Capital and Obesity: A Literature Review Spencer Moore School of Kinesiology and Health Studies, Queen’s University; Centre de recherche   du Centre hospitalier de l’Université de Montréal, Montreal, Canada o u tl i ne 54.1  Definition of Terms

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54.2  Methodology

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54.3  Two Debates 54.3.1 Individual or Area-level Social Capital 54.3.2  Measuring Social Capital

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54.4  Social Capital and Obesity Literature 680 54.4.1  Social Networks and Obesity 680

54.1  Definition of terms Social networks consist of the patterns of relationships existing among a set of actors (Wasserman and Faust, 1994). Social capital is to be distinguished from social networks in that social capital refers to the resources to which indi­ viduals or groups have access through their social networks (Bourdieu, 1986; Portes, 1998). Unlike other forms of capital, such as economic, human or cultural, social capital is not the property of

Obesity Prevention: The Role of Brain and Society on Individual Behavior

54.4.2 Individual Social Capital and Obesity 54.4.3  Area-level Social Capital 54.5  Final Considerations 54.5.1 Well-connected in a Disadvantaged Network 54.5.2  Interpersonal Mechanisms 54.5.3 On the Importance of Cross-sectional Research

681 682 683 683 683 683

individuals or groups per se but instead emerges in the social networks that individuals or groups maintain. Individuals may access these resources through their own personal networks, or they might have access to a more generalized set of resources from living in an area having dense social connections and a propensity for recipro­ city. At the individual or area levels, social capital may provide benefits for the health of individuals through a variety of network mechanisms, such as the provisioning of affective support, access

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to information, or greater sense of belonging (Kawachi and Berkman, 2000). Studies on social capital and health cover a range of outcomes. For instance, its associations with self-reported health and mental health have found particular prominence in the litera­ ture (Almedom, 2005). This may be due in part to the fact that the health-related pathways link­ ing social capital to these are less opaque. Social capital is often seen as contributing to the psy­ chological wellbeing of individuals through mechanisms related to social integration or the availability of support. There is less research addressing the influence of social capital or social networks on obesity (Kim et al., 2008).

54.2  Methodology To describe findings in this field of research, a comprehensive literature review was under­ taken on social networks, social capital and obesity. PubMed, Medline and Sociological Abstracts databases were searched separately. Search terms for obesity included, among oth­ ers, obesity, body weight, adiposity, waist cir­ cumference (WC) and body mass index (BMI). For research on social capital, we searched for terms such as social capital, social participa­ tion, trust, social networks, and collective effi­ cacy. Terms on social influence or social support were identified only in the context of obesity. Although distinct from social capital, measures used to assess collective efficacy, which consists of neighborhood cohesion and the willingness to intervene for the common good, often rely on questionnaire items on social cohesion, trust and participation. Collective efficacy was there­ fore included for the purposes of this review. Abstracts of the retrieved articles were exam­ ined for indication of an empirical study of the association or effect of social networks or social capital on obesity. Articles were excluded if obesity was not the outcome but a covariate or

confounder of the relationship of social capital with another health outcome, such as cardiovas­ cular disease or diabetes. Sixteen articles were found using this search strategy; the author was aware of an additional article that was in-press that fulfilled the search criteria, for a total of 17 articles. These articles were grouped into the following three categories: (1) social networks, (2) individual social capital, and (3) area-level social capital (Table 54.1). Before describing the literature, however, two prominent debates on social capital and health should be set forth. The first debate concerns the most appropriate or relevant level for the analysis of social capital and health – the indi­ vidual or area level; the second debate concerns the measurement of social capital.

54.3  Two debates 54.3.1  Individual or area-level social capital Individual social capital refers to the resources that individuals might access through their own personal networks or relationships. The earliest studies on social capital in public health tended to equate individual-level social capital with social support (Moore et al., 2005), conflating affective support resources with other resources that may be accessible to individuals. This tendency began to decline after sociologi­ cal approaches to social capital began to dis­ seminate more thoroughly into public health research. Current research on individual social capital highlights two important ideas: (1) a per­ son’s access to social capital is not only limited to those near whom they reside, and (2) some people have better access than others to sociallyvalued resources. Research on area-level social capital focuses on the importance of social capital as a contex­ tual factor – i.e., the characteristics of places that influence individual health through mechanisms

2.  From Society to Behavior: Policy and Action

Table 54.1  Literature on social networks, social capital, and obesity Sample size, population

Social capital measure

Obesity measure

Analysis

Covariates

Results

Christakis and Fowler, 2007

12,067 adults (Framingham Heart Study)

Social connections

Obesity (objective BMI   30)

Longitudinal logistic regression

Smoking Education Geographical proximity

Obese friend: 57% (95%CI: 6–123%) Obese sibling: 40% (95%CI: 21–60%) Obese spouse: 37% (95%CI: 7–73%)

Cohen-Cole and Fletcher, 2008

1988 adults (National Longitudinal Study of Adolescent Health (Add Health))

Social connections

BMI (selfreported)

Longitudinal linear regression

Homophily School-level covariates Individual variables

Alter BMI: ns

Trogdon et al., 2008

2800 adolescents (Add Health)

Social connections

BMI (selfreported)

OLS, Probit, Quantile regression

Individual sociodemographic variables Household characteristics Smoking Self-rated health

Peer weight: 0.30 (SE: 0.03) (P    0.01)

Individual sociodemographic variables, Smoking, Exercise, Alcohol, Coping, Neighborhood

Associational involvement: OR: 0.89 (P    0.05)

Empirical studies Social networks

Veenstra et al., 2005

1504 adults in Hamilton, Ontario

Participation

Overweight (self-reported BMI   27)

Logistic Area fixed effects

Ali and Lindström, 2005

1967 females (18–34 years old) (2000 Scania, Sweden Public Health Survey)

Trust, Participation

Overweight (self-reported BMI   25 and   30) Obesity (BMI   30)

Logistic

(Continued)

675

Low trust: Overweight: ns; Obesity: ns Low participation: Overweight OR: 2.01, 95%CI: 1.51–2.66; Obesity OR: 1.52, 95%CI: 0.99–2.34 Low instrumental support: Overweight OR: 1.71, 95%CI: 1.26–2.33 Obesity OR: 2.07, 95%CI: 1.35–3.17

54.3  Two debates

2.  From Society to Behavior: Policy and Action

Individual social capital

Table 54.1  (Continued) Obesity measure

14,836 adults (2003 Health Survey of England)

Trust, Support, Participation

Burdette et al., 2005 2445 women in 20 US cities

Ellaway and Macintyre, 2007

Peterson et al., 2007

Poortinga, 2006

2.  From Society to Behavior: Policy and Action

Analysis

Covariates

Results

Obesity (objective BMI  30)

Multilevel logistic

Individual sociodemographic variables, Walking, Sports, Activity, Rural

Trust: OR: 0.87 (0.79–0.97) Support: ns Participation: ns

Perceived collective efficacy (social cohesion, trust, informal social control)

BMI, Obesity (BMI  30) (72% objective measure, 28% self-reported BMI)

General linear model

Income, Education, Race/Ethnicity, Marital status Neighborhood safety

Collective efficacy: ns

2334 adults (West of Scotland Twenty-07 Study)

Participation

BMI Waist–hip ratio (objective BMI)

General linear model

Age cohort Social class

BMI: Females: Sports club: f  6.37, P  0.05 Males: Social clubs: f  7.30, P  0.01 Any group: f  5.66, P  0.05 Waist–hip ratio: Females: Religious activities: f  3.85, P  0.05 Education/cultural activities: f  4.88, P  0.05 Sports clubs: f  11.60, P  0.001 Any group: f  5.41, P  0.05 Males: ns

3514 adults

Support

BMI (selfreported)

General linear model

Individual sociodemographic variables, Motivation, Physical activity, Place to exercise

Support: ns

54.  Social Networks, Social Capital and Obesity

Social capital measure

676

Sample size, population

Empirical studies

46,707 children (10–17 years old) (2003 National Survey of US Children’s Health)

Index of perceived parental neighborhood social trust, support, cohesion (quintiles)

Obesity (95th percentile BMI cut points)

Multivariable logistic

Individual socioLowest v. highest social demographic, capital: Perceived (OR: 1.47, 95%CI: neighborhood safety, 1.21–1.80) Television viewing, Computer use, Physical activity

Moore et al., 2009

291 adults (18–55 years old) (Montreal Neighborhood Survey of Lifestyle and Health)

Trust, Participation, Networkresources

BMI Waist circumference: (objective BMI: overweight, obesity)

Ordinal logistic (clustered robust standard errors)

Individual sociodemographic variables, Smoking, Alcohol consumption, Physical activity, Fruit and vegetable intake

BMI Overweight/obesity: Trust: ns Participation: ns Network accessed resources: OR: 0.81, 95%CI: 0.69–0.96 Waist circumference risk level Trust: ns Participation: ns Network accessed resources: OR: 0.81, 95%CI: 0.71–0.92

Area-level social capital 48 US states

Social capital index (trust, participation, sociability (Putnam Public Use data)

BMI (self-reported: BMI    30)

Bivariate correlation and ecological regression analyses

Poverty and income inequality

Social capital: r  0.344 P  0.017

Greiner et al., 2004

4601 adults (Kansas, US)

Participation

Obesity (selfreported: BMI    30)

Multilevel logistic regression

Individual sociodemographic variables,

Level 2: Community involvement: ns

Cohen et al., 2006

807 adolescents and 3000 adults in Los Angeles

Perceived collective efficacy (social cohesion, informal social control)

At-risk for overweight and overweight (self-reported BMI)

Multilevel logistic regression

Individual sociodemographic, Neighborhood disadvantage

At risk of overweight: Collective efficacy: OR, 0.20 Overweight: Collective efficacy: OR, 0.26

Kim et al., 2006

Over 167,000 adults in 48 US states

Social capital (Putnam), Social participation, trust

Obesity (self-reported ht/wt: BMI    30)

Multilevel logistic analysis

Individual sociodemographic, and State- or countylevel factors

County-level High social capital: ns State level High social capital: OR, 0.93, 95%CI: 0.85–1.00 (Continued)

677

Holtgrave and Crosby, 2006

54.3  Two debates

2.  From Society to Behavior: Policy and Action

Singh et al., 2008

Social capital measure

Obesity measure

Brabec et al., 2007

Approx. 387 Bolivian Amazonian households

Individual gift-giving, reciprocity, Village level average

McKay et al., 2007

37,390 adolescents (10–17 years old) (National Survey of Children’s Health)

Trust, Support

2.  From Society to Behavior: Policy and Action

Analysis

Covariates

Results

BMI (higher BMI in this context is seen as representative of better health)

Multilevel linear regression

Relative and mean village income

Higher village gift-giving: higher individual BMI Individual gift-giving: higher individual BMI

Overweight or at-risk for overweight) (BMI)

Multilevel logistic

Individual level factors State poverty

State level State social trust*adolescent stage : OR 3.11, 95%CI: 1.50–6.47 State mutual aid*adolescent stage : OR 1.68, 95%CI: 1.07–2.64 Individual level Social trust: OR 0.94, 95%CI: 0.90–0.98 Mutual aid: 0.90, 95%CI: 0.86–0.94

54.  Social Networks, Social Capital and Obesity

Sample size, population

Empirical studies

678

Table 54.1  (Continued)

54.3  Two debates

beyond the composition of those areas. Regardless of one’s own social capital, health benefits are said to accrue for individuals who reside in places with higher levels of social capital. Within the research, the meaning of “place” often shifts depending on the focus of the investigation. Studies range from those that use American states as the spatial unit of analysis to those that use neighborhoods (often operationalized as census tracts). While commentaries on social capital and health are not likely to dispute the idea that social capital can work at individual or area levels (Harpham, 2008), only a few multilevel studies have analyzed individual and area-level social capital variables together in the same model. In the earliest studies on social capital and health, this absence was due to the types of data available for research. Data on social capi­ tal often came from studies or surveys that were not specifically intended for health research – for example, the United States General Social Survey (GSS). Yet, with the growth of research on social capital and health, there has been a growing number of studies modeling simulta­ neously individual and area-level social capital.

54.3.2  Measuring social capital There is a disjuncture in health science research in regard to the conceptualization and measurement of social capital (Moore et al., 2005). To be theoretically precise, a measure of social capital should consist of (1) a measure of network connectivity and (2) an indication of the resources accessible in a network. Yet stud­ ies of social capital and health rely primarily on different sets of proxy indicators of social capi­ tal. These proxy indicators fall into four catego­ ries: (1) trust, (2) participation, (3) support, and (4) perceptions of social cohesion. Trust, which has been described as “cognitive social capi­ tal,” can be differentiated into two forms: gen­ eralized trust, and particularized or localized trust. Although different question formats exist,

679

generalized trust is often measured using the standard United States General Social Survey item: “Generally speaking, would you say that most people can be trusted, or that you can’t be too careful dealing with people?” Particularized trust often comes from items such as “People in your neighborhood can be trusted”, and is embedded within scales or indices of social cohesion or collective efficacy. Participation measures the degree to which individuals are active in associations and community organiz­ ations, including political associations, labor unions, school boards, social clubs or cul­ tural associations. Participation is occasionally referred to as structural social capital, since it refers to what people do and can be objectively verified (Harpham, 2008). Support can be either emotional or instrumental. Emotional support refers to the degree to which individuals feel that their emotional needs are being satisfied, and reflects individual opportunities for care, trust and confidence. Instrumental support refers to a person’s access to material resources, information, or guidance from others (Ali and Lindström, 2005). Measures of support often rely on questions about perceived support – i.e., if individuals feel that they receive emotional or instrumental support from the people they know. Indices constructed from individual per­ ceptions of social cohesion have also been used to measure social capital. These items assess in varying degrees if individuals trust others in their area, feel like they belong, or are able to get support when needed. Although not frequently found in the health science literature, social capital can be measured in terms of “networkaccessed resources” using instruments such as the position generator (Lin, 2001), resource gen­ erator (van der Gaag and Snijders, 2004) and name generators. These instruments contain questions that assess a person’s degree of social connectivity and the types of resources that they have access to through those connections. Area-level proxies of social capital are fre­ quently constructed through the aggregation of

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individual indicators of social capital to the area level. As a result, area-level measures of social capital are often based on individual proxy measures, such as the percentage of persons in an area with high or low levels of generalized trust, or high or low participation. However, researchers have proposed the development of more objective indicators of area-level social capital, such as the number of community asso­ ciations (Lochner et al., 1999). Given the ready availability of indicators of trust and participation, and the relatively low respondent burden required for such questions in household population surveys, there are obvious advantages to using proxy measures of social capital in public health research. Yet lit­ tle is known about the content validity of such indicators. Is generalized or particularized trust indicative of strong neighborhood cohesion or ties? Does participation contribute to one’s gen­ eral social connections, or more specifically to one’s neighborhood social connections?

54.4  Social capital and obesity literature 54.4.1  Social networks and obesity The literature search found three empiri­ cal studies that examined the effects of social networks on obesity (see Table 54.1). Although studies on social networks and obesity are few in number, the research that has been conducted has been prominent. In 2007, Christakis and Fowler (2007) published a longitudinal study in the New England Journal of Medicine examining the spread of obesity in social networks. Using data from the Framingham Heart Study, Christakis and Fowler found that a person’s (i.e., ego’s) chance of becoming obese increased if close mem­ bers (i.e., alters) of their social network became obese. The closer or the stronger the relationship

between the ego and the alter, the greater the odds of the ego becoming obese. For example, if the ego reported an alter as their friend, the ego’s chances of becoming obese increased by 57 per­ cent (95% confidence intervals (CI): 6–123) if the alter became obese; if the alter reciprocated in identifying the ego as a friend, the ego’s chances of becoming obese increased by 171 percent (95%CI: 59–326) if the alter became obese; yet, if the ego did not report a friendship tie to an alter but the alter reported one to ego, there was no significant increase in the risk of becoming obese. In network terms, the directionality of the friend­ ship ties thus influenced the ego’s obesity risk. Christakis and Fowler also reported evidence showing that among same-sex friendships, male egos had a 100 percent (95%CI: 26–197) increased chance of becoming obese if his male friend became obese. Among same-sex female friend­ ships, there was no significant increase. Neither was there a significant increase in the odds of the ego becoming obese if a friend from the oppo­ site sex became obese. The researchers found no effect of an immediate neighbor’s obesity status on the ego’s obesity risk. On examining family social connections, Christakis and Fowler found that among adult siblings, the ego’s chances of becoming obese increased by 40 percent (95%CI: 21–60) if their adult sibling became obese. There was similar evidence of stronger effects between adult siblings of the same sex. Among married couples, when an ego’s spouse became obese, the ego was 37 percent (95%CI: 7–73) more likely to become obese. Christakis and Fowler examined two ­ factors that would possibly mediate or moderate the social network effects on obesity: smok­ ing behavior and geographic distance. Neither smoking behavior nor geographical distance were found to modify the social network effects of the alter’s obesity status on the ego’s obesity risk. The researchers concluded their study with two main findings. First, based on the lack of evidence showing an effect of (1) a neighbor’s

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54.4  Social capital and obesity literature

obesity status on the ego’s probability of becoming obese and (2) geographical distance, Christakis and Fowler concluded that common environmental exposures may be secondary to social network effects in explaining the spread of obesity. Second, in terms of potential explan­ ations for person-to-person induction of obes­ ity, the researchers suggested that psychosocial mechanisms may be operating, possibly by changing the ego’s norms in the acceptability of being overweight or health behaviors. In a study published a year after the Christakis and Fowler article, Cohen-Cole and Fletcher used data from the National Longitudinal Study of Adolescent Health (Add Health) to exam­ ine the argument that social network effects play a more important role than environmental factors in explaining the spread of obesity (see Table 54.1). Unlike the Framingham Heart Study population, Add Health participants rep­ resent a 1994–1995 national sample of adoles­ cents. Like the Framingham Heart Study, Add Health provides longitudinal data on social networks and BMI. Despite the different popu­ lation, Cohen-Cole and Fowler found compa­ rable results to those of Christakis and Fowler when they used the same modeling strategy. However, when Cohen-Cole used a broader set of environmental contextual factors, particularly school-level confounders, and alternative speci­ fication procedures than Christakis and Fowler, Cohen-Cole and Fletcher found a decrease in the coefficient representing the effect of the alter’s BMI on the ego’s obesity status. Cohen-Cole and Fletcher concluded that community-level fac­ tors explained a large share of the social network effect. Fowler and Christakis (2008) responded to Cohen-Cole’s and Fletcher’s conclusions in an accompanying commentary. In a cross-sectional analysis of the Add Health data, Trogdon and colleagues (2008) found that the weight of the ego’s peers is asso­ ciated with the ego’s weight, particularly for females and adolescents with a high BMI.

681

54.4.2  Individual social capital and obesity Research on individual social capital includes studies that measure and model social capital as an individual-level attribute. Search of published articles on social capital and obesity found seven articles to date on individual social capital and obesity; there was an additional article known to the author which was in press but fulfilled the search criteria (see Table 54.1). All studies were cross-sectional and, with the exception of one article, all articles focused on adult obesity. Among the eight articles on individual social capital and obesity, three used trust to examine the likelihood or prevalence of obesity. Using self-reported measures of height and weight to calculate BMI, Ali and Lindstrom (2005) found that among women in Scania, Sweden, low trust was not associated with overweight or obese status. Poortinga (2006) found, using nurse-collected measures of height and weight, that individuals with high trust were 14 per­ cent less likely to be obese than people with low trust. After adjustment for health behaviors (i.e., walking, sports and overall physical activity), individuals with high trust remained 13 percent less likely to be obese than low-trust persons. Moore and colleagues (2009) found no associa­ tion between generalized trust and obesity risk and obesity levels as measured by waist circum­ ference and BMI. Five studies examined the association of participation with obesity. Veenstra and col­ leagues (2005) found individual involvement in community groups to be associated with a lower likelihood of having a BMI    27 (odds ratio (OR): 0.27; P  0.05; no 95% CIs reported). Ali and Lindström (2005) found those who had low levels of participation were more likely to be overweight (OR: 2.01; 95%CI: 1.51–2.66) but not obese (OR: 1.52; 95%CI: 0.99–2.34). Poortinga (2006) found no association of participation with obesity. Ellaway and colleagues (2006) found

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682

54.  Social Networks, Social Capital and Obesity

that males had a lower BMI if they participated in any group, organization (  f  5.66, P  0.05) or social clubs (  f  7.30, P   0.01); females had lower BMI if they participated in sports clubs (  f  6.37, P   0.05) and lower waist-hip ratio if they participated in any organization (  f  5.41, P   0.05), religious activities (  f  3.85, P   0.05), education/cultural activities (  f  4.88, P   0.05), or sports clubs (  f  11.60, P   0.001). Moore and colleagues (2009) found no association between participation and waist circumference risk lev­ els of adiposity or overweight/obesity levels. Three studies have examined the associa­ tion of support, emotional or instrumental, with obesity. Ali and Lindström found those with low instrumental support to have higher likeli­ hoods of being overweight (OR: 1.71, 95%CI: 1.26–2.33) or obese (OR: 2.07, 95%CI: 1.35–3.17). Poortinga (2006) found no association of sup­ port with obesity. Peterson and colleagues (2007) also found no association of support with obesity. Research on individual perceptions of neigh­ borhood social cohesion or informal social con­ trol and obesity were found in two studies. In a sample of young women with children, Burdette and colleagues (2005) found no asso­ ciation between a mother’s perceptions of col­ lective efficacy and BMI, or the likelihood of them being obese. Singh and colleagues (2008) found using their index of perceived neighbor­ hood trust and support that those children with parents who have low social capital, had higher odds of being obese (OR: 1.47, 95%CI: 1.21–1.80). In terms of studies of social capital as “network-accessed resources”, there was only one known article. Moore and colleagues (2009) showed, using a position generator instrument to measure social capital, that individuals with higher levels of social capital were less likely to be overweight or obese (OR: 0.81, 95%CI: 0.71–0.92) or have higher levels of waist cir­ cumference risk (OR: 0.81, 95%CI: 0.69–0.96). These findings adjusted for a range of socio­demographic and health behavioral variables.

54.4.3  Area-level social capital Six studies were found that examine possible contextual influences of area-level social capital on obesity (see Table 54.1). Among the six stud­ ies, five used multilevel methods to adjust for individual level factors; two studies adjusted for individual-level social capital when examining the influence of area-level social capital. In the one ecological regression analysis, Holtgrave and Crosby (2006) found that American states with higher social capital had lower preval­ ence rates of obesity. In the multilevel studies, Greiner and colleagues (2004) found no asso­ ciation between community-level involve­ ment and individual obesity. In a sample of Los Angeles adolescents and adults, Cohen and colleagues (2006) showed an association of neighborhood collective efficacy and individu­ als “at-risk for being” overweight (OR: 0.20) or being overweight (OR: 0.26). Adolescents in neighborhoods with high collective efficacy were predicted to have a BMI of almost one full unit below those adolescents in neighbor­ hoods with average levels of collective effi­ cacy. Kim and colleagues (2006) found, using a 14-item index of social capital, that statelevel but not county-level social capital was associated with obesity. Individuals resid­ ing in American states with high social capital had lower odds of being obese (O: 0.93, 95%CI: 0.85–1.00). In separate multilevel models, McKay and colleagues (2007) found the asso­ ciation of state-level social trust (OR: 3.11, 95%CI: 1.50–6.47) and mutual aid (OR: 1.68, 95%CI: 2.64) with an adolescent’s overweight risk to be conditional on the adolescent stage of development. McKay and colleagues’s (2007) models adjust for individual social trust and mutual aid in addition to other individual-level factors. In a study of social capital in the Bolivian Amazon, Brabec and colleagues (2007) found village social capital associated with higher indi­ vidual BMI. In this setting, Brabec and colleagues

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54.5  Final considerations

argued that higher BMI is representative of higher health status. Their findings also account for individual social capital.

54.5  Final considerations Early findings on social networks, social capital and obesity hold promise for the poten­ tial design of public health interventions lev­ eraging social connections (e.g., close peers) in the prevention of obesity. Yet certain issues call for further consideration before public health interventions can exploit more fully social net­ works and social capital to address the obesity epidemic.

54.5.1  Well-connected in a disadvantaged network Social networks consist of the patterns of relationships existing among a set of actors, but social capital concerns the types of resources that individuals or groups may be able to access through those patterns. Although Christakis and Fowler’s (2008) article highlights the clustering of obesity in a social network and the role of close ties in the clustering process, there is little information provided about the resource com­ position of these clusters. Do clusters of obesity consist of connected individuals with high or low quality of socially-valued resources? Being well-connected in a disadvantaged network may have different implications for obesity than being well- or even loosely-­connected in an advantaged network. For example, Caughy and colleagues (2003) found that children of mothers living in low-income neighborhoods reported fewer behavioral and mental health problems when their mothers had lower levels of attach­ ment to their communities. Bonding social capi­ tal may be detrimental to health (particularly when they are between disadvantaged persons

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or within disadvantaged communities), while bridging social capital may provide individu­ als or communities access to more diverse sets of resources and benefits. Disentangling these separate dimensions is important for identify­ ing the communities and groups that might benefit more fully from social capital related interventions.

54.5.2  Interpersonal mechanisms Research on social networks, social capital and obesity needs to address the question of how social relationships or social capital adds to one’s weight or adiposity. Poortinga (2006) and Moore (2009) found no evidence of health behavioral factors, such as physical activity or smoking, mediating the association of social capital with obesity. Yet both studies assess the individual study participant’s behavior and not the behavior of those people in their core networks. Christakis and Fowler’s (2007) and Trogdon and colleagues’s (2008) studies sug­ gest, however, the need to extend the analysis of health behavioral mechanisms beyond the indi­ vidual to those with whom they associate. In so doing, we may be better able to understand how social capital as an emergent property of social relationships comes to influence indi­ vidual propensities to be physically inactive or consume unhealthy foods.

54.5.3  On the importance of   cross-sectional research Longitudinal data are required to identify the causal pathways linking social connections and resource accessibility with obesity. Before investing financial resources in the collection of longitudinal data, however, there is still much to be gained through cross-sectional research. Not only do we need to better understand the content validity of the different proxy indicators

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and how they compare against network meas­ ures; we also need to refine the instruments that we use to measure social capital. If network measures of social capital are shown to have greater content validity and statistical robust­ ness, there remains work to be done to identify the types of relationships that matter for specific types of health outcomes. Are those with whom you discuss important matters more influential on your fruit and vegetable intake than those with whom you socialize? Do the same types of relationships matter for physical activity? Being able to leverage social networks and social capi­ tal for the prevention of obesity requires greater precision and specificity in conceptualizing and measuring social capital, as well as in disentan­ gling the influence of connectivity from capital accessibility.

References Ali, S. M., & Lindström, M. (2005). Socioeconomic, psycho­ social, behavioral, and psychologic determinants of BMI among young women: Differing patterns for under­ weight and overweight/obesity. European Journal of Public Health, 16, 324–330. Almedom, M. (2005). Social capital and mental health: An interdisciplinary review of primary evidence. Social Science & Medicine, 61, 943–964. Bourdieu, P. (1986). The forms of capital. In J. Richardson (Ed.), Handbook for the theory and research for the sociology of education (pp. 241–258). New York, NY: Greenwood Press. Brabec, M., Godoy, R., Reyes-García, V., & Leonard, W. R. (2007). BMI, income, and social capital in a native Amazonian society: Interaction between relative and community variables. American Journal of Human Biology, 19, 459–474. Burdette, H., Wadden, T., & Whitaker, R. (2006). Neighbour­ hood safety, collective efficacy, and obesity in women with young children. Obesity, 14, 518–525. Caughy, M. O., O’Campo, P. J., & Muntaner, C. (2003). When being alone might be better: Neighborhood poverty, social capital, and child mental health. Social Science & Medicine, 57, 227–237. Christakis, N. A., & Fowler, J. H. (2007). The spread of obes­ ity in a large social network over 32 years. New England Journal of Medicine, 357, 370–379. Cohen, D. A., Finch, B. K., Bower, A., & Sastry, N. (2006). Collective efficacy and obesity: The potential influence

of social factors on health. Social Science & Medicine, 62, 769–778. Cohen-Cole, E., & Fletcher, J. M. (2008). Is obesity conta­ gious? Social networks vs environmental factors in the obesity epidemic. Journal of Health Economics, 27, 1382–1387. Ellaway, A., & Macintyre, S. (2007). Is social participation associated with cardiovascular disease risk factors? Social Science & Medicine, 64, 1384–1391. Fowler, J. H., & Christakis, N. A. (2008). Estimating peer effects on health in social networks: A response to Cohen-Cole and Fletcher; and Trogdon, Nonnemaker, and Pais. Journal of Health Economics, 27, 1400–1405. Greiner, K., Li, C., Kawachi, I., Hunt, D. C., & Ahluwalia, J. S. (2004). The relationships of social participation and com­ munity ratings to health and health behaviors in areas with high and low population density. Social Science & Medicince, 59, 2303–2312. Harpham, T. (2008). The measurement of community social capital through surveys. In I. Kawachi, V. Subramian, & D. Kim (Eds.), Social capital and health (pp. 51–62). New York, NY: Springer. Holtgrave, D. R., & Crosby, R. (2006). Is social capital a pro­ tective factor against obesity and diabetes? Findings from an exploratory study. Annals of Epidemiology, 16, 406–408. Kawachi, I., & Berkman, L. F. (2000). Social cohesion, social capital, and health. In L. F. Berkman & I. Kawachi (Eds.), Social epidemiology (pp. 174–190). New York, NY: Oxford University Press. Kim, D., Subramanian, S. V., Gortmaker, S. L., & Kawachi, I. (2006). US state- and county-level social capital in rela­ tion to obesity and physical inactivity: A multilevel, multivariable analysis. Social Science & Medicine, 63, 1045–1059. Kim, D., Subramanian, S. V., Gortmaker, S. L., & Kawachi, I. (2008). Social capital and physical health: A systematic review of the literature. In I. Kawachi, V. Subramanian, & D. Kim (Eds.), Social capital and health (pp. 139–190). New York, NY: Springer. Lin, N. (2001). Social capital: A theory of social structure and action. Cambridge: Cambridge University Press. Lochner, K., Kawachi, I., & Kennedy, B. (1999). Social capi­ tal: A guide to its measurement. Health Place, 5, 259–270. McKay, C., Bell-Ellison, B., Wallace, K., & Ferron, J. (2007). A multilevel study of the associations between economic and social context, stage of adolescence, and physical activity and body mass index. Pediatrics, 119, S84–S91. Moore, S., Shiell, A., Haines, P., & Hawe, P. (2005). The privileging of communitarian ideas: Citation practices and the translation of social capital into public health research. American Journal of Public Health, 95, 1330–1337. Moore, S., Daniel, M., Paquet, C., Dubé, L., & Gauvin, L. (2009). Association of individual social capital with

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abdominal adiposity, overweight, and obesity. Journal of Public Health, 31, 175–183. Peterson, K., Dubowitz, T., Stoddard, A., Troped, P., Sorensen, G., & Emmons, K. (2007). Social context of physical activity and weight status in working class populations. Journal of Physical Activity and Health, 4, 381–396. Poortinga, W. (2006). Do health behaviors mediate the asso­ ciation between social capital and health? Preventive Medicine, 43, 488–493. Portes, A. (1998). Social capital: Its origins and applications in modern sociology. Annual Review of Sociology, 24, 1–24. Singh, G., Kogan, M., van Dyck, P. C., & Siahpush, M. (2008). Racial/ethnic, socioeconomic, and behavio­ ral determinants of childhood and adult obesity in the

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United States: Analyzing independent and joint associa­ tions. Annals of Epidemiology, 18, 682–695. Trogdon, J., Nonnemaker, J., & Pais, J. (2008). Peer effects in adolescent overweight. Journal of Health Economics, 27, 1388–1399. Van der Gaag, M., & Snijders, T. (2004). Proposals for the measurement of individual social capital. In H. Flap & B. Völker (Eds.), Creation and returns of social capital (pp. 27–50). London: Routledge. Veenstra, G., Luginaah, I., Wakefield, S., Birch, S., Eyles, J., & Elliott, S. (2005). Who you know, where you live: Social capital, neighbourhood and health. Social Science & Medicine, 60, 2799–2818. Wasserman, S., & Faust, K. (1994). Social network analysis. Cambridge: Cambridge University Press.

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55 From Society to Behavior: Neighborhood Environment Influences Josh van Loon School of Community and Regional Planning, University of British Columbia, Vancouver, Canada

o u t l ine 55.1  Introduction

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55.2  Identification of Neighborhoods

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55.3 Neighborhood Boundary Definition 690 55.4 Identification and Assessment of Neighborhood Environment Characteristics

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55.6  Conclusions and Implications

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55.1  Introduction Numerous reviews and government reports have recently highlighted the potential importance of environmental characteristics in influencing obesity and its determinants (WHO, 2003; Transportation Research Board (TRB), 2005; Sallis and Glanz, 2006). The body of research underlying such documents has developed out of a broad recognition that individual influences alone cannot account for health related behaviors, but rather that context also matters (Dunn and Cummins, 2007;

Obesity Prevention: The Role of Brain and Society on Individual Behavior

55.5  Findings and Limitations 55.5.1 Physical Activity and Obesity 55.5.2 Food Consumption and Obesity 55.5.3 Research Limitations

Fisher, 2008). According to ecological models of health behavior, contextual influences arise within multiple levels of environments external to individuals, ranging from home and school environments to national institutional environments (Sallis and Owen, 2002). The focus of this chapter lies somewhere between these extremes, at the scale of community or neighborhood environments. Characteristics of these environments are generally hypothesized to influence obesity through their influence on aggregate physical activity and eating opportunities (Wells et al., 2007). Having recreation facilities close to homes

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has, for instance, been hypothesized to enable residents to engage in physical activity simply because accessibility to such facilities translates into ease of participation in recreational activities (Dowda et al., 2007). This hypothesis has been borne out in numerous studies. For example, in a review summarizing research on environmental determinants of the use of sports facilities by youth, Limstrand (2008) found consistent negative associations between measures of distance to recreational facilities and measu­res of physical activity. Similarly, associations between patterns of food availability and food consumption have been the subject of many recent studies (Jeffery et al., 2006; Jago et al., 2007). Although studies of neighborhood environment influences on obesity and its determinants take a variety of forms, analytical steps common to most studies can be identified. These are as follows. 1. Acquisition of weight status or behavioral data. Data on weight status or its determinants (primarily food consumption or physical activity behavior) may be collected through a variety of means, including objective measurements or surveys. 2. Characterization of neighborhood environments. This step can be broken down into: • Identification of neighborhoods. This involves the identification of specific locations that can be used to situate research subjects within environments to which they are exposed and that are thought to exert influence on their behaviors. For example, characteristics of the area surrounding an individual’s workplace might be hypothesized to influence their daytime food consumption (Jeffery et al., 2006). In this case, a neighborhood could be defined as centered on the individual’s work address. • Neighborhood boundary definition. Once the locations of neighborhoods are identified, boundaries defining the extent

of the neighborhoods may be specified. A research subject’s neighborhood might thus be defined as the area contained within a 500-meter radius of their work address. • Identification and assessment of relevant neighborhood environment characteristics. After neighborhoods are clearly defined, characteristics of those neighborhoods thought to influence obesity or its determinants can be identified and assessed. For example, the presence or absence of sidewalks might be hypothesized to influence the likelihood that neighborhood residents will engage in physical activity. A measure of the proportion of streets with sidewalks within research subjects’ neighborhoods could then be calculated to assess this characteristic. 3. Modeling relationships between neighborhood environment characteristics and weight status or behavioral data. This step typically involves various forms of regression analyses to estimate associations between variables. For example, multiple regression might be used to assess the relationship between prevalence of obesity (as a dependent variable) and measures of access to grocery stores (as independent variables), while controlling for socio-demographic characteristics such as gender and household income. Because Step 2 in particular is unique to studies of neighborhood environment influences on obesity, conceptual and methodological consi­d­erations related to this step are examined in the following sections. These sections further focus on examples of measures of neighborhood environment characteristics created using Geographic Information Systems (GIS). Geographic Information Systems are ­computerbased tools designed to process, analyze and display multiple layers of spatially referenced data (Leslie et al., 2007). Such data primarily consist of pre-existing, archival data sets (Brownson

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55.1  Introduction

et al., 2009) collected for specific purposes, such as property taxation or as part of censuses. These data may in turn be manipulated to derive useful information to characterize neighborhoods for health research. For example, municipal property tax assessment data spatially referenced by individual parcels of land can be used to derive measures of access to land uses such as fast-food restaurants or parks (Setton et al., 2005).

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As a result of GIS spatial data processing functionalities, its usefulness for public health re­s­earchers studying neighborhood environment influences on health-related behaviors is increasingly recognized, and GIS is now widely used in research on obesity and its determinants (see, for example, Zhang et al., 2006; Brownson et al., 2009). The use of GIS in the characterization of neighborhood environments is illustrated in Figure 55.1.

(a) integration of neighborhood environment data Grocery stores

+ Parks

+ Streets

(b) dentification of neighborhood locations

+ neighborhood locations

(c) neighborhood boundary definition

Figure 55.1  Conceptual illustration of the use of GIS to integrate and analyze multiple layers of spatially referenced environmental data. In (a), data on the location of grocery stores, parks and streets are integrated in a single database. In (b), the location of research subjects’ neighborhoods are identified. These might, for example, represent workplace locations. In (c), neighborhood boundaries are defined. In this case, the boundaries are defined using circular buffers.

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As an alternative to the use of GIS, neighborhood environment data may also be assessed using surveys and observational methods, each of which has unique benefits. Survey methods can, for example, be used to gauge perceptions of environments (such as perceptions of neighborhood safety), which may not be measurable using other means. Conversely, GIS methods are valued specifically because they are not subject to uncertainty and bias arising from individual perceptions. In addition, GIS methods are more feasible for studies involving individuals or neighborhoods dispersed over large areas (Brownson et al., 2009).

55.2  Identification of neighborhoods Widely varying levels of complexity can be applied to identifying neighborhoods to which research subjects are exposed, and that are thought to exert influence on their physical activity and eating behaviors. Neighborhoods are perhaps most simply specified as an area surrounding the dwellings of research subjects, since these may be hypothesized to be the environments to which individuals are most regularly exposed. However, this is likely a gross simplification of reality, since people are regularly exposed to a much wider range of settings in the course of daily life that may influence nutrition and physical activity behavior (Ball et al., 2006). In the case of high school students, for example, the area surrounding schools, in addition to home neighborhoods, may constitute important environments from the perspective of eating opportunities. Thus, in some cases, two or three neighborhoods could be identified as relevant (e.g., the areas surrounding home and school for high school students, or the areas surrounding home and place of employment for adults). Taking this logic further, Cummins and colleagues (2007) have argued that a more

complex relational view is required to ­adequately model contextual influences. According to this view, individuals are exposed to widely varying environments because of their unique personal trajectories. Assessing such complex patterns of individual exposure might require the novel use of GPS (global positioning system) technologies to track individuals as they move from context to context (Cummins et al., 2007). In practice, the majority of studies rely on the former approaches, identifying one or two critical neighborhood environments, such as the home or workplace. Once relevant neighborhood environments are specified conceptually, GIS can be used with geographic data, such as addresses or postal codes, to specify the ­locations the neighborhoods are centered on (see Figure 55.1b). These locations effectively act as focal points around which neighborhood boundaries can subsequently be defined.

55.3  Neighborhood boundary definition For convenience, neighborhood boundaries are often defined using geographic units for which data are readily available. Examples include census tracts (e.g., Larsen and Gilliland, 2008) or administrative boundaries established by munici­ palities (e.g., Li et al., 2005). Thus, if the area surrounding a home is identified as a relevant neighborhood, neighborhood boundaries might be specified as the census tract containing the home addresses of research subjects. Use of such arbitrary boundaries, however, may lead to misestimation of neighborhood effects on health (Riva et al., 2008) because the selection and delimitation of specific spatial units used for the aggregation of data are likely to strongly influence the outcomes of the analysis (Green and Flowerdew, 1996). An alternate approach, referred to as buffering, is designed to more realistically model exposure of individuals to environmental influences

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55.4  Identification and assessment of neighborhood environment characteristics

based on accessibility or physical proximity. Rather than defining an individual’s neighborhood boundaries as arbitrary pre-existing spatial units, such as census tracts, this approach involves creating unique neighborhood boundaries centered on previously identified neighborhood focal points, such as home or workplace addresses. Two key parameters are in turn necessary to define this area: shape and size. A buffer’s shape can most simply be defined as a circle with a fixed radius extending out from a focal point, referred to as a circular or straightline buffer (see, for example, Nelson et al., 2006; Liu et al., 2007). This approach assumes that individuals can move freely in all directions, while in reality their movement may be constrained primarily to movement networks such as roads or sidewalks (Larsen and Gilliland, 2008). Hence, approaches have been developed to define buffers that reflect such constraints (see, for example, Frank et al., 2007; Oliver et al., 2007). The second parameter, size, remains a point of contention in research to date, with little consensus on appropriate buffer sizes (Kligerman et al., 2007). For example, for children, although radii of approximately 800 meters to 1 kilo­ meter around children’s homes are commonly used to define neighborhood boundaries (see, for example, Frank et al., 2007; Kerr et al., 2007; Roemmich et al., 2007), some studies have used radii of up to 3 kilometers (Nelson et al., 2006). Because mobility is a strong function of individual characteristics, such as gender and age (Hillman, 1993; O’Brien et al., 2000; Veitch et al., 2008), different buffer sizes might be appropriate for different populations. Colabianchi and colleagues (2007) provide an example of one such population-specific buffer, suggesting the use of a 0.75-mile buffer to represent an older female adolescent’s neighborhood as accessible through walking. Rather than relying on a single neighborhood boundary, neighborhood environments can also be modeled as multiple areas of different shapes and sizes, based on the premise that nested

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geographic areas exert independent influences on behaviors (Sallis and Owen, 2002; Ball et al., 2006). In this vein, Zhu and Lee (2008) constructed measures to assess the influence of both street level and larger neighborhood environments on walking behavior of youth. Similarly, Li and colleagues (2005) modeled the influence of nested environments by using measures of environmental characteristics assessed within both a 0.5-mile circular buffer surrounding the homes of individuals as well as neighborhood boundaries defined by the City of Portland (in which the study took place). Regardless of the approach to defining neighborhood boundaries, GIS plays a critical role. GIS software can be used to create both simple circular buffers (see Figure 55.1c) and more complex buffers, such as those that model movement constrained to road networks (Oliver et al., 2007).

55.4  Identification and assessment of neighborhood environment characteristics After neighborhood environments have been delimited, characteristics of these neighbor­ho­ods can be identified and assessed. These characteristics are generally identified based on hypothesized mechanisms of influence on obesity, such as through their influence on aggregate physical activity and eating opportunities. Some characteristics may exert highly behaviorspecific influences, while others might influence a range of behaviors. The presence of bicycle lanes in a neighborhood may, for instance, influence obesity primarily by influencing cycling behavior and hence physical activity. In contrast, average neighborhood income may be associated with obesity through multiple influences related to eating and physical activity opportunities. Low-income neighborhoods have been associated with both reduced access

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to healthy foods (Reisig and Hobbiss, 2000) and fewer recreational amenities (Gordon-Larsen et al., 2006). Similarly, while some characteristics may be associated with the behavior of mul­tiple populations, others might only be relevant to the behavior of one or two clearly identifiable populations. For example, proximity to schools might influence the physical activity behavior of school-age children and their parents, but is less likely to influence other populations. Neighborhood environment characteristics can also be classified as either social or physical (see Sallis and Owen, 2002, for a broader discussion of dimensions of environments). Frequently investigated social environment characteristics include measures of: socio-economic status (SES), such as average household income in a neighborhood (Gordon-Larsen et al., 2006; Inagami et al., 2006) and neighborhood crime and disorder (Molnar et al., 2004). Physical environment characteristics can be further subdivided into those relating to the natural environment and those relating to the built environment. Natural environment characteristics include weather and topography, both of which have been associated with physical activity behaviors (Timperio et al., 2006; Tucker and Gilliland, 2007). Frequently investigated characteristics of the built environment include measures of: access to different kinds of food establishments (e.g., supermarkets, corner stores), land-use mix, density of development, access to recreational facilities, and street connectivity (Jago et al., 2007; Brownson et al., 2009). Finally, there are often many options for operationalizing measures of environmental characteristics using GIS. For example, street connectivity can be calculated as the number of intersections per unit area within a neighborhood, or alternately as average block size. Density of development may similarly be operationalized in many different ways (Churchman, 1999). In addition, as an alternative to assessing characteristics within fixed neighborhood boundaries, measures characterizing neighborhood environments by the

shortest distance from neighborhood focal points to specific land uses are also frequently employed. For example, Jago and colleagues (2007) incorporated measures of distance to food stores and restaurants from participants’ home addresses in their analysis of neighborhood effects on food consumption. GIS may thus be used to create a vast number of quantitative metrics to characterize neighborhood environments, limited only by data availability.

55.5  Findings and limitations 55.5.1  Physical activity and obesity Research to date provides moderate evidence that obesity and its determinants are influenced by characteristics of neighborhood environments. Neighborhood environment influences on physical activity behavior are particularly well studied, highlighting the importance of higher densities, connected streets and a mix of land uses in supporting walking and cycling (Saelens et al., 2003). Such findings have drawn attention to the potentially negative health consequences of prevailing patterns of suburban development commonly referred to as “urban sprawl” (Frumkin et al., 2004; KellySchwartz et al., 2004). This type of development is characterized by the separation of land uses, disconnected street networks and low­density development, all of which effectively increase distances between origins and destinations (e.g., between houses and schools, or houses and workplaces) and hence reduce options for cycling and walking relative to more traditional forms of development (Saelens et al., 2003). Neighborhood influences on physical activity patterns of populations at risk of physical inactivity, such as children and adolescents, are also increasingly well documented (Poulsen and Ziviani, 2004). Youth participation in physical activity has been positively associated with level of access to recreational facilities and

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55.5  Findings and limitations

pedestrian friendly design of transportation infrastructure (e.g., presence of sidewalks), and negatively associated with traffic density/speed and local area deprivation and crime (Davison and Lawson, 2006). Neighborhood characteristics associated with physical activity have subsequently been studied for relationships with measures of weight status. In one study, Frank and colleagues (2004) found measures of the built environment to be important predictors of obesity in adults. In this study, land-use mix was found to be negatively associated with obesity, with each quartile increase in mix being associated with a 12.2 percent reduction in the likelihood of obesity across gender and ethnicity. Rundle and colleagues (2007) similarly found that body mass index (BMI) of adults was significantly inversely associated with mixed land use, density of bus and subway stops, and population density. Mobley and colleagues (2006), in a study focusing on low-income women, found that the BMI of women living in an environment of maximum mixed land use was 2.60  kg/m2 lower than that of women living in single-use uniform environments. Mobley and colleagues also found that an additional fitness facility per 1000 residents was associated with a BMI that was lower by 1.39 kg/m2. Finally, Sallis and colleagues (2009) found that a composite index of walkability, incorporating measures of density, land-use mix and street connectivity, was both positively associated with daily moderate to vigorous physical activity and negatively associated with prevalence of overweight/obesity. Because access to recreational facilities has been associated with increased physical activity and decreased rates of obesity in some populations, spatial patterns in access to such facilities have also been studied. A recent US study on the subject found that census block groups characterized by low SES and large minority populations had reduced access to recreational facilities, which was associated with decreased physical activity levels and increased relative

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odds of overweight (Gordon-Larsen et al., 2006). A recent Canadian study similarly found a social gradient in which fewer children living in lower SES neighborhoods participated in organized physical activities than their counterparts in higher SES neighborhoods, possibly as a result of a lack of local facilities (Oliver and Hayes, 2005). In this study, the prevalence of overweight and obese children was also found to be significantly higher in low-SES neighborhoods.

55.5.2  Food consumption and obesity Research on neighborhood influences on food consumption generally characterizes neighbor­hood food environments using measures of access to certain types of grocery or eating establishments, such as supermarkets, corner stores and fast-food restaurants (Glanz et al., 2005). The premise for such research is that foods available at these establishments vary in cost and nutritional quality (e.g., fat and sugar content), and hence exposure to specific types of establishments may be associated with particular food consumption behaviors which may in turn be associated with prevalence of overweight and obesity. Morland and colleagues (2006), for instance, found the presence of supermarkets to be associated with lower prevalence of obesity in adults, while the presence of convenience stores was associated with higher prevalence of obesity. These findings might reflect the relative abundance of healthy foods at supermarkets and, conversely, the availability of unhealthy foods at convenience stores, although limited evidence is available on the composition of food available in different types of food stores (Morland et al., 2006). Other studies provide more direct evidence that access to different types of food establishments is associated with varying food consumption behaviors. Timperio and colleagues (2008) found numerous associations between proximity of food outlets to home and children’s fruit

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and vegetable intake. Their findings include a declining likelihood of consuming fruit more than twice per day with the number of fast-food restaurants and convenience stores close to home, and a declining likelihood of consuming veget­ables more than three times per day with increasing density of convenience stores. However, some studies have produced more ambiguous results. While Jago and colleagues (2007) found associations between distance to fast-food restaurants and small food stores and the fruit and vegetable consumption of male adolescents, measures of access to other food establishments (e.g., supermarkets) did not explain food consumption. Jeffery and colleagues (2006) also failed to find a relationship between BMI of adults and access to fast-food restaurants. Because access to different types of food establishments may be associated with food consumption and obesity, some researchers have used GIS to map and analyze patterns in the spatial distribution of such establishments. Particular interest has arisen in the identification of food deserts, socio-economically deprived areas characterized by a lack of access to healthy foods (Reisig and Hobbiss, 2000). As a result of suburbanization of food retailers, food deserts are often located in inner cities (Larsen and Gilliland, 2008). Evidence of food deserts has been widely documented in North America and the UK (Wrigley, 2002; Mari Gallagher Research & Consulting Group, 2006, 2007; Larsen and Gilliland; 2008), although they have not been found to be a problem in certain cities, such as Montreal (Apparicio et al., 2007). Other studies have also found similar evidence of links between neighborhood deprivation and access to healthy foods (Reidpath et al., 2002; Burns and Inglis, 2007). For example, Burns and Inglis (2007) found that residents of more advantaged areas in a suburban municipality of Melbourne, Australia, were found to have closer access to supermarkets (a proxy for healthy diets), while residents of less advantaged areas had closer access to fast-food outlets (a proxy for unhealthy diets).

Finally, some studies have examined the influences of neighborhood environment characteristics related to both food consumption and physical activity, on prevalence of overweight and obesity. In one such study, Stafford and colleagues (2007) found lower levels of obesity in adults living in areas with more swimming pools and supermarkets, although the associations were not significant. In a study focusing solely on older adults, Li and colleagues (2008) found that land-use mix was negatively associa­ted with the prevalence of overweight/obesity, and density of fast-food outlets was positively associated with the prevalence of overweight/obesity. Li and colleagues also found that land-use mix was positively associated with walking activities and the likelihood of meeting physical activity recommendations. Thus, both neighborhood environment characteristics associated with physical activity (land-use mix) and food consumption (density of fast-food outlets) were found to influence prevalence of overweight/obesity.

55.5.3  Research limitations Several methodological limitations highlight the need for additional research on neighborhood influences on obesity and its determinants. These include the following. 1. Reliance on cross-sectional study designs. With few exceptions (for example, Krizek, 2003), studies of neighborhood environment influences on obesity and its determinants rely on cross-sectional research designs. Cross-sectional studies examine different research subjects in different contexts at a single point in time, and can only be used to identify associations between variables, rather than causal influences. In contrast, longitudinal and experimental studies allow causal inferences to be made (Morland et al., 2006). 2. Difficulties in neighborhood specification. Because individuals are exposed to widely varying environments in the course of

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55.6  Conclusions and implications

their day, the specification of one or two neighborhood environments (e.g., surrounding the home or workplace) may not adequately represent reality, and may therefore result in an underestimation of neighborhood effects (Cummins et al., 2007). Poor specification of neighborhoods could explain anomalous findings, such as Jeffery and colleagues’ (2006) finding that proximity of fast-food restaurants to home or work was not associated with eating at fast-food restaurants. In this instance, neighborhoods may have been incorrectly specified because people do not necessarily eat at fast-food restaurants close to either their home or work. They may instead choose to eat at such establishments in different neighborhoods which they access for other purposes, such as running errands. 3. Inconsistent operationalization of variables. The flexibility of GIS software in manipulating spatial data has enabled researchers to create widely varying measures of environmental characteristics (Brownson et al., 2009), with the consequence that comparability of studies is limited. Some of the variation can be explained in terms of different neighborhood definitions employed (e.g., one study might employ a 0.5-mile buffer around a home when calculating access to grocery stores while another might use a 1-mile buffer), but many other sources of variation also exist. For example, operationalization of measures of land-use mix are highly sensitive to GIS processing assumptions, including how to handle missing values and outliers (Bodea et al., 2008), and neighborhood environment data sources may contain considerable errors (Boone et al., 2008). 4. Model misspecification and inconsistent model specification. Specification of the relationships between neighborhood environment characteristics and obesity or its determinants also differs substantially from

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study to study. Notably, studies frequently include different covariates (e.g., measures of age and income) in their models, and also model relationships between variables inconsistently (Bodea et al., 2008). Important covariates might also be excluded from analyses, and many models are overly simplistic in not accounting for mediating and moderating factors underlying complex behaviors (McMillan, 2005).

55.6  Conclusions and implications Geographic Information Systems offer re­se­archers powerful tools for integrating and analyzing spatial data which can be used to objectively characterize neighborhood environments. Despite the limitations of studies employing these methods, sufficient evidence exists to support the hypothesis that characteristics of neighborhood environments influence physical activity patterns, food consumption and obesity. Even if these influences are weak, policy interventions targeting neighborhood environments may be beneficial simply because they influence large numbers of people relative to individuallyfocused interventions. Certain interventions, such as the development of neighborhood parks to promote physical activity, might also have longer lasting effects than other interventions because of the permanence of the built environment (Saelens et al., 2003). However, implementation of policies targeting neighborhood environments presents serious challenges because of the complexity of jurisdictional landscapes involved in governing those environments. For instance, modifying neighborhood food environments might require participation of both public-sector and private-sector stakeholders, ranging from municipal planners to provincial and federal bodies involved with the regulation and taxation of food to private-sector food retailers.

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55.  Neighborhood Environment Influences

Examples of policy interventions suggested by research to-date include: 1. Land use and transportation interventions to promote the development of communities that enable residents to engage in regular physical activity, such as walking or cycling, in the course of daily life (Frank and Engelke, 2001). 2. Interventions targeting populations at risk of physical inactivity or obesity. These include children, the elderly and lowincome populations, who may be especially dependent on their local environments (Day and Cardinal, 2007). 3. Interventions to address inequities in neighborhood environments, including inequities in access to recreational resources (Gordon-Larsen et al., 2006) and healthy food choices (Larsen and Gilliland, 2008). Finally, while this chapter has emphasized neighborhood environment influences on obesity, it is important to note that initiatives targeting these influences on their own may have limited effectiveness and should be seen as part of a suite of complementary strategies addressing the individual and the broader set of environmental influences to which they are exposed (Giles-Corti and Donovan, 2002; Giles-Corti, 2006).

References Apparicio, P., Cloutier, M., & Shearmur, R. (2007). The case of Montréal’s missing food deserts: Evaluation of accessibility to food supermarkets. International Journal of Health Geographics, 6(4). Ball, K., Timperio, A. F., & Crawford, D. A. (2006). Understanding environmental influences on nutrition and physical activity behaviors: Where should we look and what should we count? International Journal of Behavioral Nutrition and Physical Activity, 3(33). Bodea, T. D., Garrow, L. A., Meyer, M. D., & Ross, C. L. (2008). Explaining obesity with urban form: A cautionary tale. Transportation, 35, 179–199. Boone, J. E., Gordon-Larsen, P., Stewart, J. D., & Popkin, B. M. (2008). Validation of a GIS Facilities Database: Quantification and implications of error. Annals of Epidemiology, 18, 371–377.

Brownson, R. C., Hoehner, C. M., Day, K., Forsyth, A., & Sallis, J. F. (2009). Measuring the built environment for physical activity: state of the science. American Journal of Preventive Medicine, 36(4S), S99–S123. Burns, C. M., & Inglis, A. D. (2007). Measuring food access in Melbourne: Access to healthy and fast foods by car, bus and foot in an urban municipality in Melbourne. Health & Place, 13, 877–885. Churchman, A. (1999). Disentangling the concept of density. Journal of Planning Literature, 13, 389–411. Colabianchi, N., Dowda, M., Pfeiffer, K. A., Porter, D. E., Almeida, M. J. C. A., & Pate, R. R. (2007). Towards an understanding of salient neighborhood boundaries: Adolescent reports of an easy walking distance and convenient driving distance. International Journal of Behavioral Nutrition and Physical Activity, 4(66). Cummins, S., Curtis, S., Diez-Roux, A. V., & Macintyre, S. (2007). Understanding and representing “place” in health research: A relational approach. Social Science & Medicine, 65, 1825–1838. Davison, K. K., & Lawson, C. T. (2006). Do attributes in the physical environment influence children’s physical activity? A review of the literature. International Journal of Behavioral Nutrition and Physical Activity, 3(19). Day, K., & Cardinal, B. J. (2007). A second generation of active living research. American Journal of Health Promotion, 21(Suppl. 4), iv–vii. Dowda, M., McKenzie, T. L., Cohen, D. A., Scott, M. M., Evenson, K. R., Bedimo-Rung, A. L., et al. (2007). Commercial venues as supports for physical activity in adolescent girls. Preventive Medicine, 45, 163–168. Dunn, J. R., & Cummins, S. (2007). Placing health in context. Social Science & Medicine, 65, 1821–1824. Fisher, E. B. (2008). The importance of context in understanding behavior and promoting health. Annals of Behavioral Medicine, 35, 3–18. Frank, L., & Engelke, P. (2001). The built environment and human activity patterns: Exploring the impacts of urban form on public health. Journal of Planning Literature, 16(2), 202–218. Frank, L., Kerr, J., Chapman, J., & Sallis, J. (2007). Urban form relationships with walk trip frequency and distance among youth. American Journal of Health Promotion, 21(4), 305–311. Frank, L. D., Andersen, M. A., & Schmid, T. L. (2004). Obesity relationships with community design, physical activity and time spent in cars. American Journal of Preventive Medicine, 27(2), 87–96. Frumkin, H., Frank, L., & Jackson, R. (2004). Urban sprawl and public health. Washington, DC: Island Press. Giles-Corti, B. (2006). People or places: What should be the target? Journal of Science and Medicine in Sport, 9, 357–366.

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accessibility in London, Ontario, 1961-2005. International Journal of Health Geographics, 7(16). Leslie, E., Coffee, N., Frank, L., Owen, N., Bauman, A., & Hugo, G. (2007). Walkability of local communities: Using geographic information systems to objectively assess relevant environmental attributes. Health & Place, 13, 111–122. Li, F., Fisher, K. J., Brownson, R. C., & Bosworth, M. (2005). Multilevel modelling of built environment characteristics related to neighbourhood walking activity in older adults. Journal of Epidemiology and Community Health, 59, 558–564. Li, F., Harmer, P. A., Cardinal, B. J., Bosworth, M., Acock, A., Johnson-Shelton, D., et al. (2008). Built environment, adiposity, and physical activity in adults aged 50–75. American Journal of Preventive Medicine, 35(1), 38–46. Limstrand, T. (2008). Environmental characteristics relevant to young people’s use of sports facilities: A review. Scandinavian Journal of Medicine & Science in Sports, 18, 275–287. Liu, G. C., Colbert, J. T., Wilson, J. S., Yamada, I., & Hosch, S. C. (2007). Examining urban environment correlates of childhood physical activity and walkability perception with GIS and remote sensing. In R. R. Jensen, J. D. Gatrell, & D. McLean, (Eds.), Geo-Spatial technologies in urban environments, Vol. 2, policies, practice and pixels (pp. 121–140). Berlin: Springer. Mari Gallagher Research & Consulting Group. (2006). Examining the impacts of food deserts on public health in chicago. Online. Available: http://www.marigallagher. com/site_media/dynamic/project_files/Chicago_Food_ Desert_Report.pdf (accessed June 20, 2009). Mari Gallagher Research & Consulting Group. (2007). Examining the impacts of food deserts on public health in detroit. Online. Available: http://marigallagher.com/site_ media/dynamic/project_files/1_DetroitFoodDesertReport_ Full.pdf (accessed June 20, 2009). McMillan, T. E. (2005). Urban form and a child’s trip to school: The current literature and a framework for future research. Journal of Planning Literature, 19(4), 440–456. Mobley, L. R., Root, E. D., Finkelstein, E. A., Khavjou, O., Farris, R. P., & Will, J. C. (2006). Environment, obesity, and cardiovascular disease risk in low-income women. American Journal of Preventive Medicine, 30(4), 327–332. Molnar, B. E., Gortmaker, S. L., Bull, F. C., & Buka, S. L. (2004). Unsafe to play? Neighbourhood disorder and lack of safety predict reduced physical activity among urban children and adolescents. American Journal of Health Promotion, 18(5), 378–386. Morland, K., Diez Roux, A. V., & Wing, S. (2006). Supermarkets, other food stores, and obesity. The Atherosclerosis Risk in Communities Study. American Journal of Preventive Medicine, 30(4), 333–339.

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Nelson, M. C., Gordon-Larsen, P., Song, Y., & Popkin, B. M. (2006). Built and social environments associations with adolescent overweight and activity. American Journal of Preventive Medicine, 31(2), 109–117. O’Brien, M., Jones, D., Sloan, D., & Rustin, M. (2000). Children’s independent spatial mobility in the urban public realm. Childhood, 7(3), 257–277. Oliver, L. N., & Hayes, M. V. (2005). Neighbourhood socioeconomic status and the prevalence of overweight Canadian children and youth. Canadian Journal of Public Health, 96(6), 415–420. Oliver, L. N., Schuurman, N., & Hall, A. W. (2007). Comparing circular and network buffers to examine the influence of land use on walking for leisure and errands. International Journal of Health Geographics, 6(41). Poulsen, A. A., & Ziviani, J. M. (2004). Health enhancing physical activity: Factors influencing engagement patterns in children. Australian Occupational Therapy Journal, 51, 69–79. Reidpath, D. D., Burns, C., Garrard, J., Mahoney, M., & Townsend, M. (2002). An ecological study of the relationship between social and environmental determinants of obesity. Health & Place, 8, 141–145. Reisig, V. M. T., & Hobbiss, A. (2000). Food deserts and how to tackle them: A study of one city’s approach. Health Education Journal, 59, 137–149. Riva, M., Apparicio, P., Gauvin, L., & Brodeur, J. (2008). Establishing the soundness of administrative spatial units for operationalising the active living potential of residential environments: An exemplar for designing optimal zones. International Journal of Health Geographics, 7(43). Roemmich, J. N., Epstein, L. H., & Raja, S. (2007). The neighborhood and home environments: Disparate relationships with physical activity and sedentary behaviors in youth. Annals of Behavioral Medicine, 33(1), 29–38. Rundle, A., Diez Roux, A. V., Freeman, L. M., Miller, D., Neckerman, K. M., & Weiss, C. C. (2007). The urban built environment and obesity in New York City: A multi­level analysis. American Journal of Health Promotion, 21(4S), 326–334. Saelens, B. E., Sallis, J. F., & Frank, L. D. (2003). Environmental correlates of walking and cycling: Findings from the transportation, urban design, and planning literatures. Annals of Behavioral Medicine, 25(2), 80–91. Sallis, J. F., & Glanz, K. (2006). The role of built environments in physical activity, eating, and obesity in childhood. The Future of Children, 16(1), 89–108. Sallis, J. F., & Owen, N. (2002). Ecological models. In K. Glanz, B. K. Rimer, & F. M. Lewis (Eds.), Health behavior and health education: Theory, research and practice (pp. 403–424). San Francisco, CA: Jossey-Bass.

Sallis, J. F., Saelens, B. E., Frank, L. D., Conway, T. L., Slymen, D. L., Cain, K. L., et al. (2009). Neighborhood built environment and income: Examining multiple health outcomes. Social Science & Medicine, 68, 1285–1293. Setton, E. M., Hystad, P. W., & Keller, C. P. (2005). Opportunities for using spatial property assessment data in air pollution exposure assessments. International Journal of Health Geographics, 4, 26. Stafford, M., Cummins, S., Ellaway, A., Sacker, A., Wiggins, R. D., & Macintyre, S. (2007). Pathways to obesity: Identifying local, modifiable determinants of physical activity and diet. Social Science & Medicine, 65, 1882–1897. Timperio, A., Ball, K., Salmon, J., Roberts, R., Giles-Corti, B., Simmons, D., et al. (2006). Personal, family, social, and environmental correlates of active commuting to school. American Journal of Preventive Medicine, 30(1), 45–51. Timperio, A., Ball, K., Roberts, R., Campbell, K., Andrianopoulos, N., & Crawford, D. (2008). Children’s fruit and vegetable intake: Associations with the neighbourhood food environment. Preventive Medicine, 46, 331–335. Transportation Research Board (TRB). (2005). Does the built environment influence physical activity? examining the evidence. Washington, DC: TRB Special Report 282. Tucker, P., & Gilliland, J. (2007). The effect of season and weather on physical activity: A systematic review. Public Health, 121, 909–922. Veitch, J., Salmon, J., & Ball, K. (2008). Children’s active free play in local neighborhoods: A behavioral mapping study. Health Education Research, 23(5), 870–879. Wells, N. M., Ashdown, S. P., Davies, E. H. S., Cowett, F. D., & Yang, Y. (2007). Environment, design, and obesity opportunities for interdisciplinary collaborative research. Environment and Behavior, 39(1), 6–33. World Health Organization (WHO). (2003). Global strategy on diet, physical activity and health. Online. Available: http: www.who.int/dietphysicalactivity/media/en/gsfs_ obesity.pdf (accessed June 20, 2009). Wrigley, N. (2002). “Food deserts” in British cities: Policy context and research priorities. Urban Studies, 39(11), 2029–2040. Zhang, X., Christoffel, K. K., Mason, M., & Liu, L. (2006). Identification of contrastive and comparable school neighborhoods for childhood obesity and physical activity research. International Journal of Health Geographics, 5, 14. Zhu, X., & Lee, C. (2008). Walkability and safety around elementary schools: Economic and ethnic disparities. American Journal of Preventive Medicine, 34(4), 282–290.

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C H A P T E R

56 Social Determinants of Health and Obesity* Michael Marmot1 and Ruth Bell2 1

International Institute for Health and Society Department of Epidemiology and Public Health, University College London, London, UK

2

o u t l i n e 56.1  Introduction

701

56.2 The Social Gradient of Health

702

56.3 Obesity and the Social Gradient of Health

703

56.4 The Burden of Disease

704

56.5 The WHO Commission on the Social Determinants of Health and a Possible Explanatory Framework

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56.1  Introduction Even though the obesity pandemic runs across a whole spectrum of economic and social contexts, it is also strongly related to socio­economic disparities. Childhood obesity prevalence is higher in poorer population segments

56.6  Applying the Framework to Policy 56.6.1  Social Stratification 56.6.2 Differential Exposure to Health Damaging Conditions 56.6.3  Differential Vulnerability 56.6.4 Differential Consequences of Ill Health

707 707

56.7 Targeted and Universal Policies

709

56.8  Conclusion

710

708 708 709

not only in Canada and other developed countries, but also in the developing world, where it follows economic growth and development. Bluntly stated, poverty is bad for health and for BMI, and addressing obesity has everything to do with addressing low income, insufficient education, and the other afflictions of poverty.

*

The following is an edited transcript of a talk given by Dr. Michael Marmot during the 2006 McGill Health Challenge Think Tank, hosted in Montreal, Canada, October 25–27, 2006.

Obesity Prevention: The Role of Brain and Society on Individual Behavior

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2010 Elsevier Inc. © 2010,

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56.  Social Determinants of Health and Obesity

Research on the socio-economic gradients of health shows the importance and significance of the poverty–health–obesity relationship in the form of a gradient.

56.2  The social gradient of health While it is well known that poverty is bad for health, it is interesting to note that health runs along a social gradient. Figure 56.1 shows data from the Whitehall Study of British Civil Servants (Marmot and Shipley, 1996). In this study, civil servants are classified according to their position in their occupational hierarchy. The baseline, relative risk of 1, represents the average mortality of the population. The administrators are the top grades, and have about half the average mortality; the office support grades, the lowest grades, have about twice the average mortality. There is a significant relationship between mortality and grade of employment.

Relative rate

Admin 2 1.9 1.8 1.7 1.6 1.5 1.4 1.3 1.2 1.1 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

40–64 years

Beyond its scientific importance, the notion of social gradient is valuable for health policy from a motivational standpoint as well. As long as health policy focuses uniquely on the disadvantaged, the rest of society will not buy into it to a sufficient extent because it depends too heavily on peoples’ altruism, which is in short supply. However, if we are all affected, as the social gradient of health seems to indicate, it is in our own interest to do something. The social gradient changes the equation, because most of us are not at the top. This is further reinforced by the fact that the social gradient of health is present in all regions of the world. In Bangladesh, for instance, for men aged 45–90, the number of years of education is linked to mortality (Hurt et al., 2004) (Figure 56.2). Many countries have seen increasing health inequalities in recent years. This is true of the UK and the US, but also in countries around the world. Figure 56.3 presents data from a study conducted in Russia (Murphy et al., 2006), which looked at the probability that 20-year-olds will survive to age 65. A university-educated individual’s probability increased between 1989

Prof/Exec

Clerical

64–69 years

Other

70–89 years

Figure 56.1  Mortality by grade of employment, Whitehall men: 25-year follow-up. Source: Marmot and Shipley (1996).

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56.3  Obesity and the social gradient of health

Rate ratio

Own education

1.05 1 0.95 0.9 0.85 0.8 0.75 0.7 0.65 0.6

None

Wife's education

Koranic

1 to 4 years formal

5+ formal

Education

Figure 56.2  Mortality and education in men aged 45–90 in Matlab, Bangladesh, 1982–1998. Source: Data from Hurt et al. (2004).

elementary

university

0.7

45 P20

0.65 0.6 0.55 0.5 0.45

01

00

20

99

20

98

19

97

19

96

19

95

19

94

19

93

19

92

19

91

19

90

19

19

19

89

0.4

Calendar year 45 P20 = probability of living to 65 yrs when aged 20 yrs

Figure 56.3  Trends in probability of survival in Russian men by education (relatives study). Source: Data from Murphy et al. (2006).

and 2001, whereas for someone who only has an ­ elementary education the probably of surviving to age 65 decreased. As a result, the gap between these two groups increased dramatically in this period.

56.3  Obesity and the social gradient of health The following data show the changing social gradient of BMI. Figure 56.4(a&b) reports data from studies of men and women’s BMI ­according

to years of education in Russia, Poland and the Czech Republic (Pikhart et al., 2007). In Russia, men with more education have a higher BMI. In the Czech Republic, which is more economically developed than Russia, the reverse trend appears: higher education is linked to lower BMI. We are seeing education as a social determinant of health and obesity. As Russia’s economic development progresses, it will presumably follow the Czech Republic’s pattern (as that of other Western countries), where the least educated are the heaviest. Indeed, we see this already in the data pertaining to women: in all three countries, women

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704 (a) 32 31 30 29 28 27 26 25 24

56.  Social Determinants of Health and Obesity

MEN

Russia Primary

(b) 32 31 30 29 28 27 26 25 24

Poland Vocational

Czech

Secondary

University

WOMEN

Russia Primary

Poland Vocational

Czech

Secondary

University

Figure 56.4  (a) BMI (age adjusted) – Russia, Poland, Czech by education (men). (b) BMI (age adjusted) – Russia, Poland, Czech by education (women). Source: Data from Pikhart et al. (2007).

MEN

1 0.99 0.98 0.97 0.96 0.95 0.94 0.93 0.92 0.91 0.9 Russia

Poland

Primary Secondary

with higher levels of education have lower BMIs. The trend is also illustrated in Figure 56.5, which illustrates the waist–hip ratio (WHR) in the same sample. Again, in all three countries and for both sexes, education is linked to a lower WHR. In the Whitehall II Study of British Civil Servants, we see similar trends to those in the Russia–Poland–Czech Republic data: low status linked to higher WHR. The Whitehall II Study also finds links with work stress. Iso-strain is a combination of low control, high demand and social relations in a work context. The study found that the more iso-strain is reported, the greater the odds of having metabolic syndrome (Chandola et al., 2006) (Figure 56.6).

56.4  The burden of disease The following data (Figure 56.7), gathered by Beaglehole and colleagues (WHO, 2005a), presents the global burden of disease by regions of the world. In developing countries, commun­ icable diseases and chronic diseases are almost equally prevalent. For every other region of the world, including lower- and middle-income WOMEN

0.9 0.89 0.88 0.87 0.86 0.85 0.84 0.83 0.82 0.81 0.8 Czech

Russia

Vocational University

Poland

Primary Secondary

Czech

Vocational University

Figure 56.5  WHR (age adjusted) – Russia, Poland, Czech by education. Source: Data from Pikhart et al. (2007).

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56.4  The burden of disease

Odds ratios of metabolic syndrome

4.00

Age, sex, emp grade adj. Age, sex, emp. grade, behav. adj.

3.00 2.00 1.00 0.00 No exposures (491/5178)

1 exposure (134/1253)

2 exposures (54/383)

3 or more exposures (41/220)

Exposures to iso-strain

Figure 56.6  Metabolic syndrome by ISO-Strain at work – Whitehall II, phases 1 to 5. Source: Data from Chandola et al. (2006).

14,000

Total deaths ('000's)

12,000 10,000 8,000 6,000 4,000 2,000 0

Low income Lower middle Upper middle High income countries income countries income countries countries Communicable diseases, maternal and perinatal conditions, and nutritional deficiencies Chronic diseases Injuries

Figure 56.7  Adult mortality: the double burden of disease. Source: WHO (2005a).

countries (LMICs), chronic diseases far outpace communicable diseases. Since obesity is linked to cardiovascular diseases, diabetes, high cholesterol and blood pressure, it must be a key target for intervention in order to curb the trend that indicates that the chronic diseases of concern in developed countries are now those of developing countries. Figure 56.8 looks at the

prevalence of overweight and obesity in Mexico, Brazil, Egypt and South Africa. The high prevalence of overweight and obesity is striking. For instance, 60 percent of Mexican men are overweight/obese; in Egypt, the prevalence is 70 percent of women (Popkin, 2003). The increase in prevalence of adult overweight and obesity in developing countries

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56.  Social Determinants of Health and Obesity

% 80 70 60 50 40 30 20 10 0

32.1

24.4

35.2

Mexico males

31.8

12.4

6.4

12.4

32.1

26.6

Brazil males

Brazil females

6

37.9

36

19.4

Egypt males

25 ≤ BMI < 30

Egypt females

26.7

S. Africa S. Africa males females

BMI > 30

Figure 56.8  Obesity patterns across the developing world. Source: Popkin (2003).

50

450 40

400 350

30

300 250

20

200

Percent

Number of children ('000's)

500

150 100

10

50 0

No parent overweight/obese

One parent overweight/obese

Both parents overweight/obese

Number of boys obese ('000's)

Percent boys obese

Number of girls obese ('000's)

Percent girls obese

`0

Figure 56.9  Obese children by parental obesity and sex: England. Source: Zaninotto et al. (2006).

is worrisome for childhood obesity, since evidence from England shows that both are correl­ ated. Figure 56.9 assembles data from a health survey in England (Zaninotto et al., 2006) that looked at the percentage of obese girls and boys in relation to whether their parents were obese. If one has an obese parent, the prevalence of obesity is higher. If both parents are obese, it is higher still.

56.5  The WHO Commission on the social determinants of health and a possible explanatory framework Launched in 2005, the WHO Commission on the Social Determinants of Health aims at improving health and health equity through action on

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56.6  Applying the framework to policy

Social Context

G L O B A L I Z A T I O N

Social Stratification Differential access to “capital” by e.g., education, class, sex, ethnicity

Differential Exposures & Vulnerabilities

VULNERABILITIES: Material Psychosocial Behavioral Constitutional

G O V E R N A N C E for

H E A L Differential Health T H Consequences

Average & Differential Health Impact

Figure 56.10  The WHO Commission on the Social Determinants of Health’s causal framework. Source: CSDH (2008).

the socially determined causes of health inequities (CSDH, 2008). Figure 56.10 shows a version of the causal framework that has been developed. It begins with the social context, then moves to social stratification, and differential exposures and vulnerabilities. Vulnerability is material, psycho­ social, behavioral, constitutional – each with average and differential health impacts and health consequences, which can feed back onto social stratification. It happens in the context of globaliz­ ation and governance for health.

56.6  Applying the framework to policy 56.6.1  Social stratification What can be done about social stratification in relation to health? Within developed countries there is little relation between GDP, as expressed by Purchasing Power Parity (PPP), and life expectancy

(Table 56.1). Indeed, comparing Greece with the US, one sees that in Greece the life expectancy is 1.9 years longer than in the US, in spite those in Greece having about half the income. The same can be seen in comparing Costa Rica with the US. Even more striking, Cuba’s life expectancy is still only slightly below that of the US, in spite of having a seventh of its income. Spain and France have the same life expectancy, in spite of a US $5000 income difference. Table 56.2 shows life expectancy for men and GDP per capita in several developing countries. Sri Lanka’s GDP is a third of Russia’s, but its life expectancy is 10 years longer. Costa Rica, with almost the same GDP as Russia, has a greater life expectancy, by 17 years. Absolute income, therefore, is not a very good guide to life expectancy. If country-level relationships between income and health are weak or non-existent, these are much stronger when examined within countries. For instance, in the US, comparing the mortality rates of households with incomes of $37,500

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56.  Social Determinants of Health and Obesity

Table 56.1  Life expectancy and GDP in US$ (PPP) in 2003 LE at birth

GDP

Table 56.3  Share of total household income “enjoyed” by the top 20% and the bottom 20% of households Lowest 20%

Highest 20%

Japan

82

27,967

Sweden

80.2

26,750

Japan

10.6

35.7

  9.1

36.6

Switzerland

80.5

30,552

Sweden

Spain

79.5

22,391

Germany

  8.5

36.9

Canada

  7.0

40.4

France

79.5

27,677

UK

78.4

27,147

United Kingdom

  6.1

44.0

USA

  5.4

45.8

Greece

78.3

19,954

Costa Rica

78.2

  9,606

US

77.4

37,562

Cuba

77.3

  5,400

Source: Human Development Report (United Nations Development Programme, 2005) and World Health Report 2005 (WHO, 2005b).

Table 56.2  GDP per capita and male life expectancy: selected countries GDP per capita (ppp US$)

LE at birth (males)

Sri Lanka

  3,778

68

Costa Rica

  9,606

75

Russia

  9,230

58

Chile

10,274

74

with those with incomes of $19,900, lowerincome households have approximately twice the mortality rates of higher-income households (McDonough et al., 1997). The stronger withincountry relationships between income and health can be explained by the fact that it has little to do with income per se, and more to do with relative position, inequality, and the consequences of stratification (Marmot, 2004). Income inequality is therefore a better indicator than absolute income as a social determinant of health. In Japan, for instance, the highest 20 percent of earners hold 35 percent of total household income; the lowest 20 percent hold

Source: World Development Report 2005 (World Bank, 2004).

10.6 percent. The ratio is 3.5. In Sweden, the figure is slightly higher than that; in the UK it is about 8.5, in Chile 20, Brazil 31, and Zimbabwe 35 (Table 56.3). There is a clear relation between income inequality and aspects of health.

56.6.2  Differential exposure to health damaging conditions A second potential area for policy intervention is differential exposure to health-damaging conditions. The Whitehall II Study looked at job stress and, in particular, low job control (Bosma et al., 1998). The lower you are in the hierarchy, the lower the level of control you have over work. People with low job control have about twice the incidence of coronary heart disease, as compared to those with high control. This result adjusts for the standard coronary risk factors: plasma cholesterol, overweight, blood pressure and smoking.

56.6.3  Differential vulnerability A third policy intervention level targets individual responses and differential vulnerability. In a study that looked at behavioral problems in children (Kelly et al., 2001), parents were asked to complete a Strength and Difficulties Questionnaire (a measure of psychological health) on behalf of their children, and children

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56.7  Targeted and universal policies

were classified according to their birth weight and their parents’ social class. For children from lower social classes, birth weight significantly impacted behavioral problems. For those from high social classes, birth weight had little impact on behavioral problems. This suggests differential vulnerability to the various health consequences of low birth weight (Figure 56.11).

56.6.4  Differential consequences of ill health The following evidence comes from the English Longitudinal Study of Ageing (2006). SC I+II

SC IIInm

% high total SDQ

12

709

At baseline, people were classified according to their health state. After 2 years, the study looked at whether they were employed. Non-manual workers who reported their health as being poor were less likely to be working 2 years later (close to 20 percent) than those who reported their health to be very good or excellent (12 percent). This difference in work status as a function of reported health at baseline is, however, much larger for manual workers, a category lower on the social and economic continuum: the difference in the likelihood of moving out of work between a manual worker who reported poor health and one who reported good or excellent health is staggering (31 percent in comparison to 13 percent), with healthy manual workers showing approximately the same rate as healthy nonmanual workers (Figure 56.12).

10

56.7  Targeted and universal policies

8 6 4 2

High

Middle Birthweight tertile

Low

Figure 56.11  Behavioral problems by social class and birthweight. Source: Kelly et al. (2001).

Fair/poor

The fact that childhood obesity is more prevalent and complex in poorer population segments demands a comprehensive set of policies and interventions that span the health, social and economic domains. It underscores the need Good

V. good/excellent

% Men who move out of paid work

35 30 25 20 15 10 5 0 Manual workers

Non-manual workers

Figure 56.12  Differential consequences of poor health: employment and self-rated health. Source: English Longitudinal Study of Ageing (2006).

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56.  Social Determinants of Health and Obesity

for comprehensive and integrated governmental action. In such broad interventions, we might want to see (1) universal policies rather than only policies targeted to specific population segments; (2) inequalities placed at the forefront of a comprehensive portfolio of health policies; and (3) healthy public policies in domains other than health that are necessary to fight childhood obesity. Universalist interventions would take into consideration macro psychosocial and socio-economic variables, such as social stratification, income inequality and social inequities, seeking to improve the social determinants of health across the life-course. Figure 56.13 shows parents’ education and their children’s (aged 16–25 years) literacy levels (Willms, 1999). Parents’ levels of education have a significant effect on their children’s literacy scores. The trends here are from three different countries. In the US, the relation is far more pronounced than in Sweden, which has tended to adopt more universal policies. This graph also reinforces the point that if policies only target the poorer segments, they miss other segments of the population represented by the gradient. In Sweden, public health policies are universal. They include five of what are usually deemed public health policies: protection

against communicable diseases; sexual and reproductive health; increased physical activity; good eating habits and safe food; and policies on alcohol, drugs, tobacco and gambling. They also include participation in society, economic and social security, conditions in childhood and adolescence, healthier working life, environments and products, and a more healthpromoting health service – policies that concern the wider social drivers of health inequalities (Hogstedt et al., 2004).

56.8  Conclusion Two principles are needed to tackle obesity effectively: inequalities must be kept at the forefront of interventions, and action on the social determinants of health must involve the whole of government. On the one hand, there is a rational approach to policy, laying out the problem, examining the causes and planning interventions. On the other is the Realpolitik, which engages politicians and fits the evidence to pre-conceived policies. The key is to bring the rational and the Realpolitik to work together. Ministries of Health must learn to collaborate with their counterparts

Literacy scores 1.5 1

Sweden

0.5

Canada

0 –0.5

US

–1 –1.5 6

7

8

9

10

11

12

13

14

15

16

17

Parents’ education (years)

Figure 56.13  Youth literacy scores (16–25 years) and parents’ education. Source: Adapted from Willms (1999).

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19

References

operating in other sectors, because these have significant impact on health and health inequities. Conversely, other ministries must recognize that they too have an impact on health. Government considers that it has both sectoral and wholeof-government issues, and health must become part of the latter. Health is a crucial marker of the population’s wellbeing, which is influenced by social, economic and political factors across society.

References Bosma, H., Peter, R., Siegrist, J., & Marmot, M. G. (1998). Two alternative job stress models and the risk of coronary heart disease. American Journal of Public Health, 88, 68–74. Chandola, T., Brunner, E., & Marmot, M. (2006). Chronic stress at work and the metabolic syndrome: Prospective study. British Medical Journal, 332, 521–525. CSDH. (2008). Final Report: Closing the gap in a generation: Health equity through action on the social determinants of health. Geneva: World Health Organization. English Longitudinal Study of Ageing. (2006). Retirement, health and relationships of the older population in England: The 2004 English Longitudinal Study of Ageing, wave 3. J. Banks, E. Breeze, C. Lessof, & J. Nazroo (Eds.). London: IFS. Hogstedt, H., Lundgren, B., Moberg, H., Pettersson, B., & Agren, G. (2004). The Swedish public health policy and the national institute of public health. Scandinavian Journal of Public Health, 32(Suppl. 64), 1–64. Hurt, L. S., Ronsmans, C., & Saha, S. (2004). Effects of education and other socioeconomic factors on middle age mortality in rural Bangladesh. Journal of Epidemiology and Community Health, 58, 315–320. Kelly, Y. J., Nazroo, J. Y., McMunn, A., Boreham, R., & Marmot, M. (2001). Birthweight and behavioural

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problems in children: A modifiable effect? International Journal of Epidemiology, 30(1), 88–94. Marmot, M. (2004). Status syndrome. London: Bloomsbury. Marmot, M. G., & Shipley, M. J. (1996). Do socioeconomic differences in mortality persist after retirement? 25 year follow up of civil servants from the first Whitehall Study. British Medical Journal, 313, 1177–1180. McDonough, P., Duncan, G. J., Williams, D., & House, J. S. (1997). Income dynamics and adult mortality in the United States, 1972 through 1989. American Journal of Public Health, 87, 1476–1483. Murphy, M., Bobak, M., Nicholson, A., Rose, R., & Marmot, M. (2006). The widening gap in mortality by educational level in the Russian Federation, 1980–2001. American Journal of Public Health, 96(7), 1293–1299. Pikhart, H., Bobak, M., Malyutina, S., Pajak, A., Kubinova, R., & Marmot, M. (2007). Obesity and education in three countries of the central and eastern Europe: The HAPIEE Study. Central European Journal of Public Health, 15(4), 140–142. Popkin, B. M. (2003). The nutrition transition in the developing world. Development Policy Review, 21, 581–597. United Nations Development Programme. (2005). Human development report 2005: International cooperation at a crossroads. Aid, trade and security in an unequal world. New York, NY: UNDP. Willms, J. D. (1999). Inequalities in literacy skills among youth in Canada and the United States (International Adult Literacy Survey, cat no: 89-552-MIE, No 6). Ottawa: Human Resources Development Canada National Literacy Secretariat, and Statistics Canada. World Bank. (2004). World development report 2005: A better investment climate for everyone. New York, NY: World Bank and Oxford University Press. World Health Organization. (2005a). Preventing chronic diseases: A vital investment. Geneva: WHO. World Health Organization. (2005b). The World Health Report 2005: Making every mother and child count. Geneva: WHO. Zaninotto, P., Wardle, H., Stamatakis, E., Mindell, J., & Head, J. (2006). Forecasting obesity to 2010. London: National Centre for Social Research.

2.  From Society to Behavior: Policy and Action

C H A P T E R

57 The Role of the Environment in Socio-Economic Status and Obesity Gary W. Evans, Nancy M. Wells and Michelle A. Schamberg College of Human Ecology, Cornell University, Ithaca, NY, USA

o u t l i n e 57.1  Introduction

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57.2  Food Consumption 57.2.1  Availability of Healthy Food 57.2.2  Affordability of Healthy Food

714 714 718

57.3  Physical Activity 720 57.3.1 Accessibility to Physical Activity Resources and Physical Activity 720 57.4  Summary and Conclusions

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Acknowledgments

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57.1  Introduction Rather than encouraging individuals to eat more healthily or to take more exercise, perhaps it would be more useful to try and improve the availability, quality and prices of healthy foodstuffs in poor localities, or to improve the availability of sports grounds and green spaces there (Macintyre et al., 1993: 230). Obesity does not occur randomly in populations. It has been shown that lower socioeconomic status (SES) persons are more likely to be overweight (Day, 2006; Taylor et al., 2006) and their physical environment may play a role. The primary objective of the present chapter is

Obesity Prevention: The Role of Brain and Society on Individual Behavior

to identify obesogenic environmental factors that are plausible underlying mechanisms for the linkage between SES and obesity. The chapter is organized according to the energy balance equation. We focus first on caloric intake and then on energy expenditure. We examine the ecological contexts of food consumption and physical activity, and then tie these contexts to SES. In each section, we briefly review evidence of the importance of ecological context for caloric intake or energy expenditure and then examine how contextual factors are linked to SES. This is followed by a summary and conclusion.

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2010 Elsevier Inc. © 2010,

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57. the Environment in SES and Obesity

57.2  Food consumption Two major environmental factors embedded within SES have implications for caloric intake: availability of healthy food and affordability of food.

57.2.1  Availability of healthy food Availability and healthy food consumption The availability of healthy food has been associated with obesity. Chicago neighborhoods close to fast-food outlets and far from grocery stores have higher average BMI (Gallagher, 2006). Neighborhoods at the highest tertile of BMI had the worst ratio of proximity to fast food and distance to grocery stores (2.22), whereas the middle and lowest BMI neighborhood tertiles had fast-food proximity to grocery store proximity ratios of 1.26 and 0.95, respectively. The availability of chain supermarkets is also associated with adolescent BMI and overweight status. Each additional chain supermarket outlet per 10,000 capita reduced BMI by 0.11 units and reduced the prevalence of overweight by 0.6 percent among youth (Powell et al., 2007a). In addition, BMI and overweight were higher where there were more convenience stores. In contrast, Wang and colleagues (2007) studied neighborhoods in agricultural regions of California and found that only a few aspects of the food environment (e.g., proximity to chain supermarkets and higher density of small grocery stores) were associated with BMI, and only among women. In addition to the studies linking grocerystore access with BMI or obesity rates, researchers have examined connections between the local food environment and actual dietary intake. Edmonds and colleagues (2001) found that greater availability of fruit juice and vege­ tables in local restaurants was associated with higher juice and vegetable intake among

African-American boys. Morland and colleagues (2002a) documented that the presence of a supermarket within a census tract was associated with a 32 percent increase in consuming the recommended number of daily fruit and vegetable servings and a 25 percent increase in meeting recommendations for fat intake among African-Americans. Parallel but smaller in magnitude relations were found among white Americans. Wrigley and colleagues (2003) took advantage of a natural experiment to assess the impact of the construction of a supermarket within a low SES, food desert or “retail-poor” area in the UK. Among the respondents who completed the survey before and after the construction of the supermarket, mean daily intake of fruit and vegetables increased only marginally (2.88 to 2.92 portions per day). However, these aggregate level analyses do not tell the entire story. Among respondents with poor diets pre-intervention, 60 percent increased their fruit and vegetable consumption post-intervention. Even larger increases occurred among the subset of residents with the worst diets prior to the supermarket construction: 75 percent of people eating less than one portion of fruit and vegetables per day increased their consumption from an average of 0.59 to 1.41 portions per day. In addition, proximity was relevant. The effect of the new store was strongest among those living closest. Similarly, Laraia and colleagues (2004) found that for pregnant women living more than 4 miles from a supermarket, the probability of having a poor diet increased significantly after controlling for socio-demographic factors. Availability of healthy food and SES Several types of evidence indicate that lower SES households have less access to healthy food. At the most basic level, there are simply fewer stores selling healthy food per capita in lower-income neighborhoods, and low SES families typically must travel farther to access healthy food. Alwitt and Donley (1997) found

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57.2  Food consumption

that low SES neighborhoods had significantly fewer retail stores of all types, including large grocery stores and supermarkets. At the same time, these same low SES neighborhoods had more small grocery stores (i.e., with less than 10 employees). Morland and colleagues (2002b) found nearly four times more supermarkets in the wealthiest compared to the poorest neighborhoods across several metropolitan areas (27 supermarkets per highest-income census tract versus seven in lowest). Moore and Diez-Roux (2006), studying a large number of census tracts across a different set of metropolitan areas, showed that low-income neighborhoods had four times more small-scale grocery and convenience stores and half the number of supermarkets compared to wealthy neighborhoods (see Figure 57.1). Not only are there fewer places to buy healthy food in low SES neighborhoods; there are also fewer supermarkets per capita, and the distance to travel to the nearest supermarket is farther in comparison to the density and location of supermarkets in higher SES neighborhoods (Black and Macinko, 2007). National data on residential and food-store zip codes reveal that the lowest-income neighborhoods (bottom income quintile) had 25 percent fewer

chain supermarkets compared to remaining neighborhoods (Powell et al., 2007   b). Low SES households live far from food retailers that sell healthy food, and closer to small convenience stores that tend to stock fewer fresh fruits and vegetables or fish. At the same time, a higher proportion of food in convenience stores consists of ready-made, packaged products that tend to have more calories, higher fat content and more sugar (Sallis et al., 1986; Sloane et al., 2003). These same stores lack an adequate selection of healthy foods such as whole-grain products, low-fat dairy and low-fat meat (Jetter and Cassady, 2006). Wang and colleagues (2007) found similar results for proximity to convenience stores and small grocery stores in four medium-sized California cities. The closest distance to a supermarket, however, was not farther for the poorest neighborhoods. The latter finding matches in Edmonton, Canada, where having a supermarket within walking distance was unrelated to neighborhood SES (SmoyerTomic et al., 2008). Zenk and colleagues (2005) have also shown that in addition to income levels, neighborhoods that are also predominantly African-American are at highest risk for supermarket unavailability. Distance to nearest supermarket, number of supermarkets within

Supermarkets per 100,000 population

Supermarkets by Neighborhood Income 9 8 7 6 5 4 3 2 1 0

Poorest

Moderate income

Wealthiest

Neighborhoods

Figure 57.1  Supermarkets by neighborhood incomes. Source: Adapted from data provided in Moore and Diez-Roux (2006).

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57. the Environment in SES and Obesity

a 3-mile radius, and the inverse of distances between the neighborhood and all supermarkets, all revealed evidence of economic and racial inequality in access to food retail outlets that sell more healthy foods. Baker and colleagues (2006) took their analysis of spatial inequalities in access to healthy foods a step further by not only examining the number of supermarkets and fast-food restaurants (see discussion below), but also assessing the availability of fresh fruits and vegetables, lean meats and low-fat dairy products. As expected, census tracts with higher levels of poverty had fewer supermarkets. In fact, only one supermarket in the poorest census tract met the threshold for healthy food (i.e., highest tertile), while in the wealthiest census tracts of St Louis 19 supermarkets met the threshold. Similar to the findings of Zenk et al. (2005), Baker and colleagues (2006) noted that predominantly African-American households living in low-income neighborhoods were especially at risk. In a comparison of an upper-class white community and a lower middle-class AfricanAmerican community in Chicago, the latter had fewer supermarkets and more small grocery stores and liquor stores selling food (Block and Kouba, 2006). Moreover, the only place where poor-quality produce existed was in small grocery and liquor stores in the AfricanAmerican community. In another study focusing on the quality of available food supplies, Horowitz and colleagues (2004) compared the availability and cost of diabetes healthy foods in East Harlem to that in an adjacent, affluent white neighborhood. Whereas 58 percent of stores on the Upper East Side stocked diabetes healthy foods, only 18 pecent of East Harlem stores did so. Within the block in which a resident lived, 50 percent of East Harlem residents had no store selling diabetes healthy food; the comparable percentage for Upper East Side residents was 24 percent. Taking an in-depth look at one low-income minority community in Los Angeles with a very high rate of childhood

obesity, Kipke and colleagues (2007) noted that only 18 percent of the grocery stores in this section of the city sold fresh fruits and vegetables. Moreover, only four of them were within walking distance of a school. Although most of the attention to SES and access to healthy food has been focused on metropolitan areas, the challenge of finding healthy food may, somewhat ironically, be greatest where it is grown. Morton and Blanchard (2007) defined a food desert as a county in which all residents must drive more than 10 miles to the nearest supermarket. Ninety-eight percent of food deserts are rural counties. These counties, relative to others, have higher poverty rates, lower median family incomes and a larger percentage of individuals without a high school degree. In a detailed case study of four food-desert counties in rural Iowa, Morton and Blanchard (2007) further demonstrated that 45 percent of the residents did not consume daily US-recommended portions of fresh fruits and vegetables. Availability and fast-food consumption As noted earlier, Gallagher’s (2006) study of Chicago neighborhoods documented that regions in the highest tertile of BMI scores had the greatest proximity to fast-food outlets and greatest distance to groceries. While in the Gallagher (2006) study the unit of analysis was the neighborhood or region, relatively few studies have examined linkages between fast-food availability and diet or BMI at the individual level. One exception is provided by Jeffery and colleagues (2006), who found that eating at fastfood outlets was positively associated with children eating a high-fat diet and having higher BMI, and negatively associated with vegetable consumption and physical activity. Proximity to fast food at home or work, however, was not associated with eating at fast-food outlets, or with BMI. Other researchers have examined linkages between fast-food consumption and obesity risk.

2.  From Society to Behavior: Policy and Action

57.2  Food consumption

Duffey and colleagues (2007) examined linkages between both restaurant and fast-food consumption with BMI. Cross-sectionally, fast-food consumption, but not restaurant-food consumption, was associated with BMI. Higher consumption of fast food was also associated with a 0.16 unit increase in BMI 3 years later. Jeffery and French (1998) examined associations between TV viewing, fast-food eating and BMI. Both fast food and TV were positively associated with energy intake and BMI among women, but not among men. Relationships were stronger among lowincome women than among high-income women. Similarly, among children, Bowman and colleagues (2004) documented that those who ate fast food, in comparison with those who did not, consumed more total energy, more energy/gram of food, more total carbohydrates, more added sugar, more sugar-sweetened beverages, fewer fruits and fewer non-starchy vege­ tables. Analogous results were found based on within-group comparisons – comparing children to themselves on days they ate fast food versus days they did not eat fast food. Availability of fast food and SES Not only do poor people have less access to healthy foods sold in larger supermarkets while simultaneously living closer to small convenience stores; the poor, especially low-income ethnic minorities, also live in places saturated by fast-food restaurants and liquor stores. Block and colleagues (2004) studied the prevalence of fast-food restaurants by race and income in New Orleans. The number of fast-food restaurants per square mile within a census tract and its immediate surroundings was significantly related to both income and percent of African-Americans. For example, a 5 percent decrease in neighborhood median household income was associated with a 10 percent increase in fast-food restaurant density. Predominantly African-American neighborhoods had 2.4 fast-food restaurants per square mile compared to 1.5 for predominantly

717

white neighborhoods. Of all restaurants in a moderate- to low-income area of Los Angeles, 26 percent were fast-food outlets (Lewis et al., 2005); in an affluent comparison neighborhood, 11 percent of restaurants were fast food. Across all restaurants in each of these two areas, healthier food options were promoted and available significantly more often in the upper-class neighborhood. Contrasting those who lived within walking distance to a fast-food outlet to those who do not in Edmonton, Canada, multiple indicators of SES were significantly lower for neighborhoods within walking distance to fast food (Smoyer-Tomic et al., 2008). The proportion of indigenous people was also higher in neighborhoods closer to fast food. There is also evidence of a dose–response function between neighborhood SES and the proportion of fast-food stores per capita (Reidpath et al., 2002). Amongst the lowest fifth in income areas in Melbourne, Australia, there is 1 fastfood establishment per 5641 persons, versus 1 per 14,256 among the highest-income quartile. As shown in Figure 57.2, Cummins and colleagues (2005) found a similar dose–response function between the number of McDonald’s restaurants per 1000 persons by neighborhood IMD (a composite index of neighborhood deprivation) in the UK. Finally, in an analysis of food environments in an area of Los Angeles with a very high prevalence of childhood obesity, Kipke and colleagues (2007) noted that nearly half of all restaurants were fast food and over 60 percent of these establishments were within walking distance of a school. Despite the studies linking SES with fast-food availability, a few investigators have uncovered contradictory data (Morland et al., 2002b; Macintyre et al., 2005; Wang et al., 2007). Using national data, Powell and colleagues (2007b) found a more complex pattern of relations among income, race, and fast-food restaurant location. Similar to Wang and colleagues’ analysis of four California cities, neighborhoods with low-to-medium incomes had the highest number of fast-food restaurants compared to low-income

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57. the Environment in SES and Obesity

Mean number of McDonald’s restaurants per 1000 people by IMD quintile of England and Scotland 0.03 0.025 0.02 0.015

Mean number of McDonald’s restaurants per 1000 people by IMD quintile

0.01 0.0005 0 1

2

3 IMD Quintile

4

5

Figure 57.2  Fast-food restaurants and neighborhood deprivation in the UK (neighborhood IMD is a composite index of neighborhood deprivation). Source: Adapted from data provided in Cummins et al. (2005).

and more affluent neighborhoods. The latter clearly had the fewest fast-food restaurants. Ethnicity also mattered: African-American urban neighborhoods had a much higher proportion (nearly three times) of fast-food to all restaurants in Powell’s national data set. As noted above, Gallagher (2006) calculated for census blocks the ratio of the average distance to grocery stores divided by the average distance to fast-food restaurants. This food balance ratio was significantly higher for African-Americans than for white or Latino residents. The median income level of white neighborhoods was more than double that of black neighborhoods. Recall that Gallagher also showed that this food balance ratio metric predicted BMI.

57.2.2  Affordability of healthy food Food affordability and consumption Economics play a role in food decision-making (Cawley, 2004; Hill et al., 2004). When asked,

low-income individuals cite both lack of availability and high cost as constraints to eating more healthfully (Reicks et al., 1994). Drewnowski has suggested it is the economics of food choices which mediate between macro-level food pri­ cing trends (e.g., low cost of energy-dense foods) and rising obesity rates (Drewnowski, 2004; Drewnowski and Darmon, 2005). Others suggest that the cost of food is second only to taste as an influence on food consumption (Glanz et al., 1998). French and colleagues (2001) examined the effects of manipulating price on food purchased from vending machines. Price reductions of 10 percent, 25 percent and 50 percent on low-fat snacks resulted in increased sales of 9 percent, 39 percent and 93 percent, respectively, while average monthly profits did not differ across conditions (French et al., 2001; French, 2003). In a workplace cafeteria study, sales of fruit and salad increased threefold during a 3-week 50-percent price reduction period, and returned to baseline when original prices were reinstated (Jeffery et al., 1994).

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57.2  Food consumption

Similarly, halving the price of healthy food in a high school cafeteria led to a four-fold increase in fruit purchases, a two-fold increase in carrot purchases, but no change in salad sales (French et al., 1997; French, 2003). In a laboratory experiment, Epstein and colleagues (2006) examined the effects of cost among 10- to 12-year-old youth who were given $5.00 and allowed to purchase multiple portions of foods. The children were presented with two preferred foods, one healthy and one less healthy, one with a fixed price, and one with a changing price. Increasing the price reduced purchase of the same food type (healthy versus less healthy), but did not lead to substitution of the alternative food. In a second experiment, the researchers used the same protocol but added another variable: amount of funds provided. Substitution of the alternative food increased markedly when the amount of money provided was reduced from $5 to $1. The findings of the two experiments suggest that the more money children have available, the more they will spend on a preferred food that is increasing in price and the less they will substitute another food. Interestingly, these experimental data conform to analyses of food purchases among food-stamp recipients. When food stamps are newly made available, families do not purchase healthier alternatives to their previous commodity choices prior to when they had food stamps. Instead, families purchase more of the less healthy foods they had been eating beforehand (Wilde et al., 1999). On a more aggregate level, Sturm and Datar (2005) examined the association between area food prices with BMI changes in a national survey of elementary school children. They found that lower prices for fruits and vegetables predicted a smaller increase in BMI between kindergarten and third grade. Lower meat prices had the opposite association. SES and food affordability Low-income families spend a higher proportion of their budget on food relative to their

719

more affluent counterparts (Federman et al., 1996), and indicate that price and availability are major obstacles to purchasing healthy food (Reicks et al., 1994). Energy-dense foods with high sugar and fat content typically cost less, whereas fruits and vegetables are more expensive. A typical family market basket of groceries averages $36/basket lower than a healthy market basket in 2003–2004 (Jetter and Cassady, 2006). This difference amounted to more than one-third of the income of a lowincome family’s typical food budget. Cummins and Macintyre (2002) found that among the foods that tended to be cheaper in low- versus middle-income areas of Glasgow, most tended to be high in fats and sugar. Furthermore, the difference in price between a thrifty food basket and a healthy food basket was greater in the low- versus middle-income neighborhoods of Glasgow (Macintyre et al., 1993). As indicated above, low-income households have less discretionary income to choose more expensive, healthy food options than do their wealthier counterparts, and have to travel farther to reach stores that carry such foods. Low-income households also have a harder time overcoming the friction of travel distance, since they are much less likely to own a personal automobile, spend a larger portion of their household income on transit, and spend much more time in transit to work because of both distance and greater reliance on public transportation (Macintyre et al., 1993; Federman et al., 1996; Sanchez and Brenman, 2007). Focusgroup interviews with residents of Oakland California neighborhoods revealed that people in lower-income neighborhoods noted how the absence of supermarkets made it difficult to obtain healthy food, whereas affluent residents either had healthy food options nearby or could easily use their private cars to access healthy food (Altschuler et al., 2004). Although it may be harder for lower SES households to get to healthy food sources, one advantage of using public transit compared to the private automobile is physical activity. Train commuters are

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57. the Environment in SES and Obesity

much more likely than automobile commuters to meet the US Center for Disease Control recommended 10,000 steps per day of walking (Wener and Evans, 2007). SES, parenting and food In addition to access and affordability of healthy food, some aspects of low SES parenting may influence healthy eating. Low-income families are less likely to have regular meals together (Neumark-Sztainer et al., 2003; Evans et al., 2010), and eating together is positively associated with healthy food consumption and less obesity (Fiese and Schwartz, 2008). Moreover, low SES children do not have as much adult supervision as more advantaged children (Bradley and Corwyn, 2002). Reduced parental supervision may be associated with watching more television, snacking, and less participation in recreational activities. Low SES homes are also more stressful (Evans, 2004), and elevated stress affects metabolic efficiency and fat intake (McEwen, 2002).

57.3  Physical activity Physical and social contextual factors influence physical activity as well.

57.3.1  Accessibility to physical activity resources and physical activity Access to nature Proximity to parks and green space is associated with higher rates of physical activity and/or lower rates of obesity. Giles-Corti and colleagues (2005) found that people who use public open space are three times more likely to achieve recommended levels of physical activity than those who do not use the spaces. Similarly, in a study of 7000 European adults, those living

in areas with the highest levels of greenery were three times more likely to be physically active and 40 percent less likely to be overweight or obese than those living in the least green settings (Ellaway et al., 2005). A study of several rural counties in Missouri showed that respondents who had access to walking trails used trails more than those without access; furthermore, persons using trails were more likely to have increased their amount of walking since using them (Brownson et al., 2000). In Japan, senior citizens who had nearby space for a stroll and parks and tree-lined streets near their residence were more likely to be alive 5 years later (Takano et al., 2002). Although this study did not look explicitly at the mediating mechanisms, physical activity and maintaining healthy weight are good candidates. Access to recreational facilities One aspect of the physical environment that is correlated with physical activity is access to recreational facilities. Physical activity correlates indicate that actual access to facilities is positively associated with physical activity; however, perceived access to facilities has shown an inconsistent association with physical activity (see Humpel et al., 2002; Trost et al., 2002). One early study found a significant positive association between pay-for-use facilities and frequency of exercise, but no relation with free facilities (Sallis et al., 1990). MacDougall and colleagues (1997) found that dissatisfaction with local recreational facilities was significantly associated with greater inactivity. In a study of older adults in Australia, Booth and colleagues (2000) found that a greater proportion of older adults who were sufficiently active, compared with those who were inactive, reported having access to a recreation center, cycle or golf course, park or swimming pool. In contrast, another Australian study found that although access to the beach, river, tennis courts and public open space was associated with use, the association

2.  From Society to Behavior: Policy and Action

57.3  Physical activity

of access to recreational facilities with use was less clear (Giles-Corti and Donovan, 2002). In a national survey of US residents, Brownson and colleagues (2001) found that among the respondents who reported engaging in some physical activity, 21.3 percent indicated that their activity took place in an indoor gym. After adjusting for potential confounders, access to indoor gyms as well as access to parks and treadmills was associated with physical activity. An association between home exercise equipment and physical activity has also been documented (Humpel et al., 2002). In addition to studies of adults, several studies have examined the association between recreational facility access and physical activity among adolescents. Positive associations between recreational facilities and physical activity have been documented among adolescent girls (Norman et al., 2006). Gordon-Larsen and colleagues (2000) documented that among adolescents, use of a community recreation center was associated with a 75 percent greater likelihood of falling in the highest category of moderate to vigorous physical activity. In a more recent national study of American adolescents, Gordon-Larsen and colleagues (2006) found the odds of being overweight were inversely linked with the number of physical activity facilities within the neighborhood. Specifically, compared with having no facilities, having one physical activity facility per census block group was associated with a 5 percent decrease in the relative odds of being overweight; the odds of engaging in five or more bouts of moderate to vigorous physical activity per week was positively associated with the presence of facilities. Accessibility to physical activity resources and SES A few studies have examined the link between SES and access to nature and open spaces. New York City residents living in low-income

721

neighborhoods have more than 50 percent less square yards of park space per capita than their more affluent counterparts (Sherman, 1994). In a national study of adolescents, the odds ratio of having at least one park in the youth’s immediate neighborhood was 2.97 for each 100 percent increase in the proportion of neighborhood adults with a college degree, adjusted by population density and proportion minority (Gordon-Larsen et al., 2006). In Britain, manual laborers are four times less likely to have a yard large enough to sit outside in compared to white-collar employees (Townsend, 1979). In a study of a low-income neighborhood in Los Angeles, Kipke and colleagues (2007) reported that the community’s five parks amounted to approximately one half-acre of open space for every 1000 residents! Elementary school aged children in Sydney, Australia, who live in lower SES neighborhoods reported having fewer good parks, less open space, and fewer big backyards compared to children in higher SES neighborhoods (Homel and Burns, 1987). When asked for the one thing they would improve, nearly half of all children asked for more parks and playspaces. Studies consistently show that various recreational and sports facilities (e.g., outdoor playing fields) are less common in low- compared to middle-income areas (Macintyre et al., 1993; Brownson et al., 2001; Estabrooks et al., 2003; Huston et al., 2003; Boslaugh et al., 2004; Powell et al., 2004; Gordon-Larsen et al., 2006). In a national survey of adults, Brownson and colleagues (2001) found that 28 percent of males with incomes below $20,000 reported access in their neighborhood to indoor and outdoor places where they could exercise; this compared with 68 percent among affluent males. As shown in Table 57.1, among a national sample of adolescents, a 100 percent increase in the proportion of the neighborhood population with college or greater education significantly increased the odds of having various types of facilities in the neighborhood (Gordon-Larsen et al., 2006).

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57. the Environment in SES and Obesity

Table 57.1  Odds ratio of at least one physical activity resource in relation to every 100% increase in proportion of the population with at least a college education Physical activity facilities

Adjusted odds ratio*

All

2.18

Schools

1.73

Public

2.15

Youth organizations

2.84

Parks

2.97

YMCA

2.57

Fee

1.41

Instruction

2.65

Outdoor

4.20

Member

4.22

*

[n  42,187], Adjusted for population density and proportion minority. Source: Adapted from data in Gordon-Larsen et al. (2006).

On the other hand, Giles-Corti and Donovan (2002) found the opposite relationship between SES and access to physical activity facilities in a Western Australian city, whereas Wilson and colleagues (2004) found no SES differences in access to physical activity resources in a rural county in the US. SES and barriers to physical activity Disadvantaged neighborhoods have elevated traffic volumes and air pollutants (Evans, 2004), which may curtail physical activity. Playgrounds in low SES neighborhood contain substantially greater safety hazards (Suecoff et al., 1999; Cradock et al., 2005). Crime is inversely related to SES (Hsieh and Pugh, 1993), and some persons may exercise less because of crime (Ross, 1993; US Centers for Disease Control, 1999; Brownson et al., 2001; Boslaugh et al., 2004; Wilson et al., 2004; Weir et al., 2006). Two studies did not find this link (Sallis et al., 1997; King et al., 2000).

57.4  Summary and conclusions We have presented a case that some of the SES–obesity link can plausibly be explained by the ecological context of low SES households and neighborhoods. Families with fewer resources have less access to healthy food while simultaneously being exposed to greater unhealthy food alternatives sold in corner markets and in fast-food restaurants. Although obvious, it bears repeating that lower SES households spend proportionately more of their income on food, and healthier food options (fresh fruits and vegetables, low-fat dairy alternatives, lean meat and fish) cost more than energy-dense foods high in fat and sugar. Each of these SES correlates can influence caloric intake and obesity. At the same time, lower SES families have less access to nature and open space, and fewer recreational facilities in their neighborhoods. Thus, both on the caloric intake side as well as on the energy expenditure side of the weight equation, lower SES adults and children are placed a greater risk for weight gain by virtue of the social and physical characteristics of the immediate settings wherein they reside. Poverty and low SES are associated with social and environmental conditions that are obeso­genic. The ecological context of SES provides a partial explanation of why deprivation is positively related to obesity. As suggested by Macintyre in 1993, this perspective also provides great promise for more effective interventions to help support healthier food and physical activity choices, not only for poor people but for everyone.

Acknowledgments Preparation of this chapter was partially supported by the W. T. Grant Foundation and the John D. and Catherine T. MacArthur Foundation Network on Socioeconomic Status and Health.

2.  From Society to Behavior: Policy and Action

References

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2.  From Society to Behavior: Policy and Action

C H A P T E R

58 The Economics of Obesity: Why are Poor People Fat? 1

Adam Drewnowski1 and Petra Eichelsdoerfer2

Center for Public Health Nutrition, School of Public Health, University of Washington, Seattle, WA, USA 2 Bastyr University Research Institute, Bastyr University, Kenmore, WA, USA

o u t l i n e 58.1  Introduction

727

58.2  How do People Make Food Choices? 728 58.3  Energy-dense Foods Cost Less

729

58.4  Healthier Diets Cost More

730

58.5 The Growing Price Disparity in Food Costs

732

58.1  Introduction Rising rates of obesity, both in North America and worldwide, have been blamed on the food environment (French et al., 2001; WHO/FAO, 2003; Swinburn et al., 2004). Human physiology, conditioned through evolution, is said to respond poorly to the constantly evolving modern food supply. Processed energy-dense foods (Popitt and Prentice, 1996; Stubbs and Whybrow, 2004), fast foods (McCrory et al., 1999; French

Obesity Prevention: The Role of Brain and Society on Individual Behavior

58.6 Does Restricting Food Costs Lead to Energy-dense Diets?

734

58.7  Why are Poor People Fat?

735

58.8  Approaches to Obesity Prevention

736

et al., 2000; Prentice and Jebb, 2003; Bowman et al., 2004), snacks and sweets (Zizza et al., 2001), soft drinks (Berkey et al., 2004; Bray et al., 2004; Wiehe et al., 2004) and larger food portions (Rolls et al., 2002; Young and Nestle, 2002) have all been linked to rising obesity rates. Researchers have long sought to single out a food class underlying the global obesity epidemic. Diet composition was an early focus, with suspicion falling alternately upon sugars and fats. However, the data were rarely consistent.

727

2010 Elsevier Inc. © 2010,

728

58.  The Economics of Obesity: Why are Poor People Fat?

Obesity has now been linked – through a variety of metabolic mechanisms – with excess consumption of dietary sucrose (Yudkin, 1986), corn sweeteners (Bray et al., 2004), high glycemicindex carbohydrates (Brand-Miller et al., 2002), fructose (Bray et al., 2004), fat (Bray and Popkin, 1998) and even protein (Rolland-Cachera et al., 1995). Mechanisms regulating food intake were also blamed. Various researchers have suggested that the human body cannot perceive dietary energy provided by liquid beverages (DiMeglio and Mattes, 2000), or by energydense solid foods (Prentice and Jebb, 2003). Mechanistic interference by intense sweeteners was also mentioned as a contributing factor (Davidson and Swithers, 2004). This research has failed to explain why obesity rates follow a social gradient, with higher obesity rates observed in disadvantaged neighborhoods and among the working poor. Instead, researchers have published multiple – sometimes contradictory – physiological explanations for why obesity is caused by the consumption of protein, starch, sugar and fat; by caloric and noncaloric sweeteners; by meals and snacks (Zizza et al., 2001); by beverages and solid foods; by eating in restaurants (McCrory et al., 1999; French et al., 2000; Chou et al., 2004; Diliberti et al., 2004) and by eating at home (Nielsen and Popkin, 2003). The only food groups that have not been associated with obesity are fresh vegetables and fruit (Lin and Morrison, 2003; Rolls et al., 2004). Many obesity prevention strategies arise from the belief that drastically reducing consumption of a given food group will reduce rates at the population level. Encouraging people to replace sweets and fats with more healthful but more costly foods, along with limiting consumer access to problematic foods, have dominated public policy (Dietz et al., 2002; Caraher and Coveney, 2004). Defining “problematic” foods shifts with research results. Regulatory measures, including outright bans, taxation, or cost increases have all been proposed to discourage the consumption of sweetened beverages

and fast foods (Jacobson and Brownell, 2000; Associated Press, 2008). Nutrition standards for school foods (French et al., 2001; Fried and Nestle, 2002; Wiehe et al., 2004), fast-food menu labeling regulations (Odza, 2008) and new fastfood restaurant moratoria (Mair et al., 2005; Abdollah, 2007) have all been adopted by one or more jurisdictions. Often, these interventions target lower-cost foods, lower-income areas, blue-collar worksites and the working poor. The rationale lies in obesity’s growing cost to society at large in the form of lost productivity and medical care. Whereas the portion borne by insurance companies has received much attention, far less has been placed on the individual’s personal economic environment (Chou et al., 2004). The relation between diet costs and diet quality across diverse economic strata in the US remains unexplored (Darmon et al., 2002; Drewnowski, 2003a; Drewnowski and Specter, 2004). The cross-cutting hypothesis is that sugars and fats, beverages and fast foods share one critical feature: they provide dietary energy at relatively low cost. Low diet cost may predict rising obesity rates more powerfully than the macronutrient composition of the diet. If so, the search for a single food class underlying the obesity epidemic is misguided, as is the search for underlying physiologic mechanism(s). Obesity-promoting foods are – in a word – cheap, whereas foods that may stem the obesity epidemic are likely to be more expensive. Choosing healthful versus unhealthful foods is an economic decision, especially for people with limited resources.

58.2  How do people make food choices? People choose foods based on taste, cost, convenience and, to a lesser extent, health concerns and dietary variety (Glanz et al., 1998; Darmon et al., 2003). Conceptually, taste encompasses

2.  From Society to Behavior: Policy and Action

58.3  Energy-dense foods cost less

palatability, aroma and texture, and reflects the sensory appeal of foods (Drewnowski, 1995). Palatability is closely linked to energy density; high energy-dense foods are almost invariably the most palatable, and vice versa (Drewnowski, 1998). The energy density of foods is typically defined as available dietary energy per unit weight or volume (MJ/kg). Energy cost refers to the purchase cost per unit of either dietary energy ($/10 MJ) or a daily diet ($/day). Convenience relates to time spent buying or otherwise obtaining, preparing and cooking food. Variety refers to the innate drive to secure a varied diet, whereas health refers to concerns with nutrition, chronic disease and body weight. Figure 58.1 shows a model representing these factors. Identifying the factors responsible for food selection should precede efforts to promote dietary change among different social strata. Health promotion literature generally emphasizes the psychosocial aspects of food selection (Contento et al., 2002; Shepherd, 2002; Story et al., 2002). Underlying this is the unspoken middle-class premise that successfully adopting a healthful diet depends primarily on increased

Cost

Energy density

Taste

Food purchases

Metabolic consequences

Convenience

Variety

Obesity

Health

Figure 58.1  The influences on food purchases: consumer and marketing approach. Source: Reproduced from Drewnowski and Levine (2003), with permission from the American Society for Nutritional Sciences ©2003.

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awareness, heightened motivation, and making better choices. Consideration of food prices and diet costs has been notably absent from the research literature in health promotion (Blaylock et al., 1999). Setting obesity aside, it is worth remembering that an average person needs 2000–2500 kcal of dietary energy each day. Daily energy needs must be compared to the prevailing cost of dietary energy in the food supply. In 2006, very low-income families (50 percent of federal poverty level) spent as little as $35 per person on food and beverages per week (US Bureau of Labor Statistics, 2007). In 2007, the average American consumer spent double that – or just over $10 per day on food and beverages per person (US Department of Agriculture, Economic Research Service (USDA ERS): Table 15).

58.3  Energy-dense foods cost less One of Wilbur Atwater’s early tasks was teaching the poor how to satisfy protein and energy needs at the lowest cost (Atwater, 1887), now regarded as the beginning of significant nutrition research in the US (Carpenter, 2003). At the time, working families spent approximately 50 percent of their income on food (Carpenter, 2003). Atwater established that wheat flour and dried beans provided energy and protein at lower cost than did meat or fruit. This hierarchy of food prices has remained unchanged in the intervening 120 years. Dry foods with stable shelf lives remain less expensive (per kcal) than perishable meats, fish, dairy or fresh produce. However, contemporary data related to the relationship between energy density and energy cost of foods remain limited (Drewnowski, 2003b; Darmon et al., 2004; Monsivais and Drewnowski, 2007). One obstacle has been the difficulty of linking householdlevel data on food prices and expenditures with

2.  From Society to Behavior: Policy and Action

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58.  The Economics of Obesity: Why are Poor People Fat?

individual-level data on consumption patterns, diet quality and health. Many studies on diet quality and diet costs have been conducted outside the US. The INCA study (Enquête individuelle et nationale sur les consommations alimentaires) was a national study of food consumption conducted by the French government. Using national price information databases, mean national retail prices were assigned to each of the 895 foods in the nutrient database. Food composition tables were used to calculate energy density (kcal/100 g) as well as the mean cost of 100 g and 100 kcal of food (USDA Agricultural Research Service (ARS), 2007). Figure 58.2 shows a scatterplot of energy density of foods (MJ/kg) and their energy cost (€/100 kcal), separately for each food group. Fats and oils, sugar, refined grains, potatoes and beans provided dietary energy at the lowest cost. At retail prices, the energy cost of sugar or oil was below €0.10 per 1000 kcal. An immense gap separated grains, sweetened beverages and vege­ table oils from meats, fish and shellfish, dairy products, vegetables and fruit. The logarithmic

Meats Fruit & Vegetables Dairy Grains Fats & Sweets 40 Oil

Food ED (MJ/kg)

30

Margarine Butter Potatoes Chocolate

20

Paetries Beans Sugar

Crackere Chees Bread Red meat

10

Vogurt Milk

0 1

10

Carret

100

Lean meat Cltrue Fish Vegetables Lettuce (Log scale)

1000

Energy cost (Eurocents/MJ)

Figure 58.2  Relation between energy density (kcal/ 100 g) and energy cost (Euros/100 kcal). Source: Reproduced from Drewnowski et al., (2004), with permission from the American Public Health Association ©2004.

scale indicates a several thousand percent differential in energy costs between “unhealthful” and “healthful” foods.

58.4  Healthier diets cost more If healthier foods cost more, then so should healthier diets. French observational studies explored the relation between energy density and the cost of freely-chosen diets in a sample of 837 adult women and men (Darmon et al., 2004). The Val-de-Marne dietary survey (Preziosi et al., 1991; Drewnowski and Popkin, 1997) used a dietary history interview to estimate dietary intake, representative of a habitual diet, over 6 months (Cubeau and Pequignot, 1980). Food consumption was assessed in a manner similar to a food frequency questionnaire using a nutrient composition database (Preziosi et al., 1991). The analyses were based on 57 food items, excluding drinking water, alcohol, and infant and baby foods. Mean national food prices for the year 2000 were provided by the French National Institute of Statistics (INSEE) and from market research sources. Priced foods were the more frequently consumed and lower-cost options, including frozen and canned foods. For example, vegetables were represented by potatoes, tomatoes, carrots and endive (all fresh), mixed vegetables, peas and beans (all canned), and dried lentils. To calculate dietary energy density (MJ/kg), energy intake was divided by the estimated edible weight of all foods and caloric beverages (Cox and Mela, 2000; Gibson, 2000). To calculate energyadjusted diet costs, prices for the consumed foods were adjusted for portion size and summed over all foods consumed by that individual. The procedure assumes that all foods were purchased, prepared and consumed at home. It is analogous in every way to US Department of Agriculture procedures for estimating the cost of healthful diets, including the Thrifty Food Plan (TFP) (USDA

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731

58.4  Healthier diets cost more

4000

examined the relation between diet composition and cost separately by quintiles of energy intake (MJ/day), adjusting for gender and age. As shown in Figure 58.4, each 100-g increment in fruit and vegetables consumption was associated with an increase in diet costs of €0.18–0.29 per day, depending upon energy intake. 1200 1000 Diet cost (Eurocents)

CNPP, 1999), the cornerstone of federal food assistance. Excluding alcohol, mean energy intakes were estimated at 9.89 MJ for men and 7.38 MJ for women. After adjusting for energy, more energydense diets were associated with higher grains, fats and sweets consumption, compared to fruits and vegetables. Consistent with past reports, diet­ ary energy density (MJ/kg) was associated with higher energy intakes (r²  0.31, P  0.0001). Mean overall estimated diet cost was around €5 per day (€5.59 per day for men, €4.63 per day for women), similar to the mean national expenditures for at-home foods calculated by INSEE (€4.9 per person per day) (Clément et al., 1997). An inverse association between dietary energy density and energy cost was also observed (Darmon et al., 2004). Women consumed more vegetables and fruit, had lower energy-density diets and higher mean estimated energy cost per 10 MJ (€6.56 per day) as compared to men (€5.85 per day) (Darmon et al., 2004) (Figure 58.3). Subsequent studies determined that repla­ cing fats and sweets with more vegetables and fruit would be associated with higher diet costs Another study based on the Val-de-Marne data

800 600 400 200 0 0

200

400 600 800 1000 Fruit and vegetables (g/day)

1200

Figure 58.4  Relation between fruit and vegetable consumption (g/day) and diet costs (Euros/day). Regressions are for each quintile of energy intake. Source: Reproduced from Drewnowski et al., (2004), with permission from the American Public Health Association ©2004.

F+V Meat, fish, eggs Added fats and sweets

3000

F+V Meat, fish, eggs Added fats and sweets

3000 KJJd

KJJd

2000 2000

1000 1000

0

2.5 3 3.5 4 4.5 5 5.4 Diet cost (Euros/d) for men

0

1.3 2 2.5 3 3.5 4 4.3 Diet cost (Euros/d) for women

Figure 58.3  Relation between diet cost (Euros/day) and diet composition by food group for men (left panel) and women (right panel).

2.  From Society to Behavior: Policy and Action

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58.  The Economics of Obesity: Why are Poor People Fat?

In contrast, consuming more fats and sweets was associated with net diet cost savings. Figure 58.5 shows a €0.40 per day reduction in daily diet costs associated with each 100 g of fats and sweets for persons in the lowest energy quintile. Although the relationship flattened with increased energy intake, the diets of persons in the highest energy quintile were reduced by €0.13 per day for each additional 100 g of fats and sweets. In other 1200

Diet cost (Eurocents)

1000

words, sweets and fats reduced energy-adjusted diet cost, whereas each additional serving of vege­ tables and fruit measurably increased energyadjusted diet cost (Darmon et al., 2004). Replacing starches and fats with isocaloric amounts of lean meats and fresh produce was associated with higher energy-adjusted diet costs. Consistent with the French data, the 2004 per capita diet cost in the US was estimated at $8.98 (USDA ERS: Table 15). The same year, the estimated average cost of the low-carbohydrate Atkins diet was $14.27 per day while that of the South Beach Diet was $12.78 per day (Hellmich, 2004).

800

58.5  The growing price disparity in food costs

600 400 200 0 0

40

80 120 160 200 Fats and sweets (g/day)

240

280

Figure 58.5  Relation between fats and sweets consumption (g/day) and diet costs (Euros/day). Regressions are for each quintile of energy intake. Source: Reproduced from Drewnowski et al. (2004), with permission from the American Public Health Association ©2004.

Economic studies on food purchases typically use relative prices and relative price increases. Over the past several decades, global developments in agricultural and food technologies have reduced the cost and increased the availability of refined grains, caloric sweeteners and veget­able oils relative to other foods. Analyses of price increases between 1985 and 2000 (Figure 58.6) show that fresh fruit and vegetable prices rose at

Fresh F+V Fish Cereals Dairy Meat/poultry Sugar/sweets Fats and oils Soft drinks 0

20

40

60 80 % increase

100

Figure 58.6  Increase in retail prices 1985–2000 for foods in different categories. Source: Based on data from USDA ERS (2002).

2.  From Society to Behavior: Policy and Action

120

140

58.5  The growing price disparity in food costs

double or more the rate seen for sweets, fats and caloric beverages (Putnam et al., 2002). Since 2000, prices for more healthful foods have risen even more sharply. Between 2000 and 2008, worldwide food prices rose overall by 75 percent (World Bank). These figures disguise significant price disparities across food cate­ gories. For example, the price of wheat rose by 200 percent (World Bank). By contrast, the world market price for raw sugar was $0.968 per pound in 1998 (US General Accounting Office, 2000) and $0.966 per pound in 2007 (FAO, 2007). In global markets, the commodity cost of refined sugar (sucrose) fell from 2006 through the first 9 months of 2007, with the price averaging just over $0.10 per pound (FAO, 2007). During the same time frame, the cost of most vegetable oils rose sharply to around $0.45 per pound (FAO, 2007); the nearly 70 percent increase led some media outlets call this “the other oil shock” in an analogy to petroleum products (Bradsher, 2008). Low relative prices may be one reason why refined grains, fats and sweets have come to dominate the food supply. All are tasty, energydense, inexpensive, convenient to use and readily available (Putnam et al., 2002). Even with recent price increases, approximately 40,000 kcal from vegetable oils and caloric sweeteners can be obtained at world market rates for as little as $2.35. The cost of producing one ton of cane sugar in Brazil was recently estimated at $15.70, or less than $0.01 per pound (De Almeida et al., 2007). Although little relation exists between the commodity and finished food product retail costs, added sugars and added fats probably help hold down diet costs. The US per capita consumption of added sugars was estimated in 2006 at 117 g and the consumption of added fats at 68 g (USDA ERS, 1970–2006). The same report estimated daily energy intakes at 2679 kcal per day, so apparently added sugars and fats may account for 41 percent of total daily energy (USDA ERS, 1970–2006). However, this information is based on available foods, adjusted for losses, rather than direct reports of consumption. Comparison

733

of the American food supply with the 2005 Dietary Guidelines for Americans and MyPyramid (USDA MyPyramid; Frazao and Allshouse, 2003) reveals that the availability of sweets and fats far exceeds recommendations – a contrast especially noticeable in comparison to low consumption of green, leafy vegetables and fruit. Reported intake data from the National Health and Nutrition Examination Survey (NHANES) 2005–2006 indicate that the average person 2 years of age consumes 124 g of sugars, 82 g of fats, and a total of 2,157 kcal per day (US Centers for Disease Control (CDC), 2005–2006). Consumption data indicate that fully 1200 kcal, or nearly 56 percent of daily energy intakes, are consumed in the form of total sugars and total fats (US CDC, 2005–2006). Of note, the NHANES dataset does not differentiate between added sugars and fats and those occurring naturally in foods, reporting only total sugars and fats. The consumption of energy-dense diets high in sugars and fats apparently follows a social gradient. Diet quality, but not total daily calories, is a function of socio-economic position and social class. Older and wealthier consumers have higher-quality, healthier, and more varied diets, with a higher proportion of highquality meats, seafood, vegetables and fruit (James et al., 1997; Irala-Estevez et al., 2000; Darmon et al., 2002; Leibtag and Kaufman, 2003; Martikainen et al., 2003). In contrast, lowerincome households tend to select diets high in low-cost meats, inexpensive grains, added sugars and fats (Smith and Baghurst, 1992; Roos et al., 1996; Drewnowski, 2003a; Hulshof et al., 2003; Worsley et al., 2003). A 2003 study of lowincome families found low fruit and vegetable expenditures, with bananas far more likely to be purchased than other, more expensive, fruits or berries (Leibtag and Kaufman, 2003). In USDA focus groups, food assistance recipients expressed primary interest in obtaining sufficient calories at low cost to minimize hunger complaints (Wilde et al., 2000). Real or perceived diet costs may represent a barrier to the adoption of healthier diets by lower-income groups.

2.  From Society to Behavior: Policy and Action

734

58.  The Economics of Obesity: Why are Poor People Fat?

The price gap between the recommended and unhealthy foods continues to widen. The overall rise in US food prices was 4 percent in 2007, and the anticipated increase for 2008 was similar, at around 3.5–4.5 percent (Capehart and Richardson, 2008). In 2007, Monsivais and colleagues reported a cross-sectional study of retail supermarket food prices in the Seattle, Washington, metropolitan area. Analyses compared the 2004–2006 rise in prices for 372 foods with different energy densities. High energy-density foods (excluding beverages) were associated with a small decrease in price (1.8%) and lower overall cost. In contrast, the recommended low energy-density foods were more expensive per MJ, and increased in price by as much as 19.5 percent over the 2-year period. This sharp rise in healthier food costs may be another barrier to the adoption of healthful eating habits by low-income groups (Monsivais and Drewnowski, 2007). The disparities in eating habits may not be remedied by small shifts in income or food prices. 2005 US Bureau of Labor Statistics data show that low-income households spent about $1.68 less per person per week on fruit and vegetables compared to the overall average, and $4.19 less than higher-income households (US Bureau of Labor Statistics, 2005). A 2004 USDA study found that higher-income households increased fruit and vegetable consumption following an increase in income; lower-income households did not. One interpretation is that low-income households placed lower priority on fruit and vegetables, choosing to spend limited resources on more essential items such as meat, clothing or rent (Blisard et al., 2004).

58.6  Does restricting food costs lead to energy-dense diets? To date, observational studies on diet quality and diet cost have relied upon cross-sectional

data (Darmon et al., 2004). Absent longitudinal cohort data, computer models were used to test the hypothesis that reducing diet costs would necessarily lead to a lower-quality diet. Diet optimization models based on linear programming try to minimize the distance between the observed and recommended diets, subject to a variety of nutritional, social or economic constraints. The computer model optimization analyses sought to assess the impact of reduced diet costs, without considering nutritional factors. When reduced diet cost was the only factor in the model, the computer-generated diet was energy dense (Balintfy, 1979), with energy mostly provided by cereals and added fats. Fruits and vegetables disappeared first from the market basket, followed by dairy products and lean meats (Darmon et al., 2002). In essence, computer-generated diets were similar to those already consumed by the poor. In contrast, a deliberate search for a more energy-dense diet did not lead to significant diet cost savings (Balintfly, 1979). Figure 58.7 shows the relative contribution to energy intakes and diet costs by six major food groups (from the Val-de-Marne study). These data show that energy-dense diets are selected as a means of reducing food expenditures, not as a result of endogenous preferences for energy-dense foods. Deliberately selecting an energy-dense diet will not necessarily reduce diet costs. However, restricting food expenditures inevitably leads to diets that are energy dense yet nutrient poor. One can eat healthfully using inexpensive products (Mitchell et al., 2000; Raynor et al., 2002), and the USDA Thrifty Food Plan (USDA CNPP, 1999) was developed as a least-expensive nutritious diet in consideration of US consumption and consumer acceptance patterns (Smith, 1959; Balintfy, 1979; Foytik, 1981). Unfortunately, consumers with limited budgets will find consuming healthier diets difficult unless they are willing to adopt unfamiliar eating habits, depart from social norms and/or subsist on unpalatable foods. USDA researchers assume

2.  From Society to Behavior: Policy and Action

735

58.7  Why are poor people fat?

45 % cost

40

% energy

% contribution to

35 30 25 20 15 10 5 0 Meat

F+V

Dairy

Grains

Sweets

Added fats

Figure 58.7  Relative contribution to dietary energy intakes and diet cost of foods from 6 major food groups in the Val-de-Marne study. Source: Figure by Briend and Darmon, based on Val-de-Marne study data (Drewnowski et al., 2004).

this willingness, acknowledging that following the TFP “may require some sacrifices in taste” (Braylock et al., 1999; Carlson et al., 2003). While good nutrition in the form of liver, dry legumes, peanuts and canned fish can be obtained at a low cost, such a diet may also be low in taste, variety and convenience, and possibly alien or unfamiliar to most. Persons facing economic constraints will preferentially select lower-cost, energy-dense diets, rather than abandon long-time eating habits. Strategies for dietary change ought to consider the mechanisms of food choice. Studies in economics have begun linking poverty, relative food costs and higher obesity rates. Overall, Americans have the lowest-cost food supply in the world, and spend the lowest proportion of disposable income on food (approximately 12 percent) (Meade and Rosen, 1996). Lakdawalla and colleagues reported that technological advances led to a decline in the price of food, which led in turn to higher energy intakes (Lakdawalla and Philipson, 2002; Lakdawalla et al., 2005). Up to 40 percent of the increase in body mass index since 1980 was ascribed to the drop in food prices during those years (Lakdawalla and Philipson, 2002).

Another study, based on national Behavioral Risk Factor Surveillance System (BRFSS) data, linked higher obesity rates to a growing number of restaurants, lower food prices, and the higher cost of cigarettes (Chou et al., 2004). However, the downward trend in food prices was most marked for energy-dense foods containing added sugar and fat.

58.7  Why are poor people fat? Obesity rates in North America and other industrialized countries clearly follow a social gradient, with the highest rates observed among racial/ethnic minorities and the poor (Brunner et al., 1997; James et al., 1997; Evans et al., 2000; Molarius et al., 2000; Paeratakul et al., 2002; Tang et al., 2003). For individuals, obesity is linked to low income, low education, minority status, and a higher incidence of poverty (Brunner et al., 1997; James et al., 1997; Lantz et al., 1998; Molarius et al., 2000). Geographically, higher obesity rates have been observed in lowincome neighborhoods, low-income ZIP codes, and low-income counties (California Center for

2.  From Society to Behavior: Policy and Action

736

58.  The Economics of Obesity: Why are Poor People Fat?

Public Advocacy, 2005; Drewnowski et al., 2007). Although obesity rates climbed steadily in both sexes, at all ages, in all races, and at all educational levels (Molarius et al., 2000; Mokdad et al., 2003), the highest rates continue among the most disadvantaged groups, as noted in the Healthy People 2010 report (US Department of Health and Human Services (DHHS) 2004). Research efforts to explain the rising obesity rates continue to focus on metabolic and physiologic mechanisms such as hunger and satiety, rather than on the physical, social and economic environment. The concepts of satiety deficits and passive overeating were used to help explain excessive consumption of energy-dense foods, added sugars and added fats (Drewnowski, 1995, 1998; Popitt and Prentice, 1996; Blundell and MacDiarmid, 1997; Rolls et al., 1998). Neurotransmitter imbalances were invoked to help explain cravings for energy-dense fats and sweets (Drewnowski, 1998; Levine et al., 2003; Yanovski, 2003). Addictive personality types, stress, depression and comfort-seeking were used to further explain consumption of sweets and desserts. Finally, the failure to select healthy diets has been explained in terms of distance to supermarkets and grocery stores, marketing and distribution of healthy foods, urban sprawl, and the time spent commuting to work (Morland et al., 2002). The economic hypothesis cuts through the diverse metabolic and physiologic explanations. The varied “obesogenic” foods and beverages share a low energy cost, relative to other foods. Price may be the key factor, rather than the amount of sugar in the diet. Low-cost sugar in sweetened beverages has been associated with weight gain; the same amount of sugar in liquid meal replacements, sold at 10 times the price, is typically associated with weight loss. Diet quality also follows a socio-economic gradient. The present hypothesis argues that the social gradient in obesity can be partly explained by more limited economic access to healthful foods (Darmon et al., 2002). It is well established that the diets of lower-income households

obtain cheap, concentrated energy from fat, sugar, cereals, potatoes and meat products, yet offer relatively few whole grains, vegetables and fruit (Reicks et al., 1994; Dittus et al., 1995; Quan et al., 2000). Similarly, low-income consumers are more likely to frequent fast-food rather than full­service restaurants, and to live in areas with poor physical access to healthier foods. In contrast, higher diet quality, measured by the Healthy Eating Index (HEI), is associated with higher incomes, more education, and lower rates of obesity and overweight (Guo et al., 2004).

58.8  Approaches to obesity prevention The complexity of the economics of obesity poses a challenge to economists. The typical approach considers the costs of obesity to society, estimated on either an annual basis or over the life-course. Total costs are spread throughout society, with medical costs split between public and private sectors, in the form of private insurance, indigent care clinics and hospitals, and government programs such as Medicaid and Medicare. Costs of private medical insurance are further distributed among employers, employees, and purchasers of individual plans (Finkelstein et al., 2008). Indirect costs exist in the form of disability, absenteeism, presenteeism, workers’ compensation, and premature mortality (Trogdon et al., 2008). Finally, there are social costs in the form of job discrimination, lower wages, a reduced likelihood of marriage, lower self-esteem and general devaluation (Benson et al., 1980; Averett and Korenman, 1999; Baum and Ford, 2004). Annual US medical expenditures attributable to obesity were estimated at $75 billion in 2003 (Finkelstein et al., 2004), and borne by Medicare, Medicaid, private insurance and indigent care through hospital emergency departments and public health clinics. As the number of people

2.  From Society to Behavior: Policy and Action

58.8  Approaches to obesity prevention

without health insurance rises, some costs are passed on to individuals (Denavas-Walt et al., 2007). After consideration of differential survival rates, studies typically conclude that the medical costs associated with overweight/ obesity over the lifetime substantially deplete healthcare resources (Finkelstein et al., 2008). If obesity’s societal costs, including those for intervention, exceed those of the free market, obesity represents a valid public health concern. Finkelstein and colleagues suggest that policy-makers enact legislation emphasizing subsidized wellness programs and mandating obesity treatment coverage (Finkelstein et al., 2008). However, some economists, focusing on the monetary cost–benefit ratio, question the wisdom of considering obesity as a public health problem (Philipson and Posner, 2008), and consider overweight and/or obesity to be at least partly “the product of choice” (Philipson and Posner, 2008). Others suggest that the higher cost of health insurance to employers for the overweight or obese individual leads to lower wages and job discrimination (Bhattacharya and Bundorf, 2005). The societal cost of obesity needs to be balanced against the cost of healthier diets to the population. Total US expenditures on foods consumed at and away from home were estimated at $900 billion in 2002 (USDA ERS), equivalent to approximately $8.00 per person per day. By 2007, those daily food expenses had risen to just over $10.00 per person per day (De Almeida et al., 2007). An increase in daily food expenditures of as little as $0.75 per person per day would add $80 billion per year to US consumer expenditures. The cost of healthier diets to the consumer may potentially exceed the cost of obesity to society. The higher cost of healthier diets poses a major problem for current health promotion strategies. It is easier to pretend that all foods cost the same and that consumers freely “choose” from a wide assortment of healthful foods. The Healthy People 2010 report recommended a healthful

737

assortment of foods, including vegetables, fruit, whole grains, low-fat milk products, and fish, lean meat, poultry or beans (US DHHS, 2004). The USDA’s 2005 Dietary Guidelines for Americans include recommendations that consumers “choose” healthier foods (USDA, MyPyramid). The Surgeon General’s Call to Action called for more nutritious diets, including more vegetables and fruit (US DHHS, 2001). African-American men are now the focus of a public awareness campaign encouraging them to consume nine servings of fruit and vegetables per day (African-American Public Awareness Campaign, 2003). The NIH Obesity Education Initiative advised obese patients to look for guavas, persimmons, star fruit, kiwi and papaya rather than bologna and American cheese (NHLBI, 1998). Studies conducted in Australia, Canada and the European Union have generally found that healthier diets cost more. One UK study (Cade, 1999) found higher diet costs associated with vegetarian diets high in fruit and vegetables. In Denmark, low-fat diets for children were associated with higher costs (Stender et al., 1993). A French study showed diets with a higher content of vitamins and minerals to be associated with higher diet costs (Andrieu et al., 2006). The prevailing American view stands in direct contrast, suggesting that healthful diets represent no additional expenditure to the consumer, and may actually cost less (Mitchell et al., 2000; Raynor et al., 2002). Current strategies targeting obesogenic foods resemble past arguments about food assistance fattening the poor (Fried and Nestle, 2002; Gibson, 2003; WHO, 2004; Chen et al., 2005; Kaushal, 2007). Lower-income persons, some receiving food assistance, are more likely to be obese. However, obesity was probably not caused by receiving food assistance. Even if lower-income groups consume more low-cost foods and beverages, limiting access to such foods while failing to address the underlying issues of poverty, unemployment, lack of health

2.  From Society to Behavior: Policy and Action

738

58.  The Economics of Obesity: Why are Poor People Fat?

insurance (Rashad and Markowitz, 2007), food insecurity (Townsend et al., 2001) and rising food prices (Philipson and Posner, 2008) is not a productive strategy for public health (Darmon et al., 2002, 2003). One promising approach to obesity prevention is food policy interventions at the national and international levels (WHO/FAO, 2003). However, one cannot separate the twin tides of obesity and poverty; intervention strategies need to take this into account. Several public health approaches under consideration, including mandated labeling, taxes and tariffs, advertising campaigns and advertising limitations, rely heavily upon the precedent set by very successful anti-tobacco campaigns (Townsend et al., 2001). Obesity lawsuits targeting various sectors of the food, grocery and restaurant business for their alleged role in promoting weight gain represent another example of this approach (Cade et al., 1999; Darmon et al., 2002; Bradford, 2003; Fulwilder, 2003; Mello et al., 2003; Bartlett, 2004; Obesity and Legal Action, 2009). The demonstrated existence of a social gradient calls for a more sophisticated approach. Are the various sectors of the food, grocery, and restaurant business legally liable for providing low-income consumers with inexpensive foods? Or do rising obesity rates reflect the increasingly unequal distributions of incomes and wealth (Subramanian and Kawachi, 2004)? Does the problem lie with fast-food outlets, or with the falling value of the minimum wage, and lack of unemployment benefits or health insurance? Obesity in America is largely an economic issue.

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C H A P T E R

59 Challenges in Making Broad Healthy Lifestyle Plans: Revisiting the Nature of Health Interventions Catherine Le Galès CERMES3, National Institute for Health and Medical Research, U988, Paris, France

o u tline 59.1  T  he Context of Non-communicable   Diseases 747

59.5  I mproving the Global Policy   Framework

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59.2  T  he Current Health Policy   Framework

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59.6  I nsights from Tobacco Control   Efforts

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59.3  T  he Need for Joined-up   Policy-making

59.7  Engaging the Private Sector

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59.8  Conclusions

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59.4  The WHO Global Strategy for   Diet, Physical Activity and Health

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59.1  The context of noncommunicable diseases As many countries continue to battle against infectious diseases – diarrhoea, HIV, tuberculosis, neonatal infections and malaria – most witness an astonishingly rapid upsurge in premature deaths and disabilities from non-communicable diseases, mental disorders, injuries and

Obesity Prevention: The Role of Brain and Society on Individual Behavior

violence. Non-communicable diseases (or chronic diseases) such as heart disease, stroke, cancer, chronic respiratory diseases and diabetes are the leading cause of mortality in the world, representing 60 percent of all deaths. Out of the 35 million people who died from chronic diseases in 2005, half were under 70 and half were women (WHO, 2005). The shifting health trends indicate that leading infectious diseases will become less important causes of death globally

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59. the Nature of LIFESTYLE Interventions

over the next 20 years (WHO, 2008a), while in the developing world the burden from chronic diseases is projected to rise rapidly (WHO, 2005). These chronic diseases represent an underappreciated cause of poverty that hinders the economic development of many countries. Contrary to common perception, 80 percent of chronic disease deaths occur in low- and middle-income countries. The estimated total loss of national income from premature deaths due to chronic diseases in Brazil, China, India, Nigeria, Pakistan, the Russian Federation and Tanzania was US $43 billion in 2005. In the 23 countries that account for approximately 80 percent of the total chronic diseases mortality burden in developing countries, an estimated US $84 billion of national income will be lost between 2006 and 2015 (Abegunde et al., 2007).

59.2  The current health policy framework While some continue to hold that obesity and its related chronic diseases are a purely individualistic problem, and therefore its solution is the sole responsibility of the consumer, most recognize that individual responsibilities cannot be exercised in an environment that does not make the healthy choice the easy option. In an unsupportive policy environment, it is difficult for people, especially vulnerable groups, to benefit from existing knowledge on the causes and prevention of non-communicable diseases and their risk factors (Commission on the Social Determinants of Health (CSDH), 2008). Accumulated evidence shows that education and motivational campaigns as stand-alone strategies are effective neither at the individual level nor at the population level. An effective approach requires working on what influences individuals’ eating and physical activity choices (Ottawa Charter, 1986). The general policy environment is, then, a major determinant in

population health. Public sector policies on agriculture, finance, food, media and advertising, trade, transport, urban design and the built environment shape opportunities for people to access good health and make healthy choices. In addition, as demonstrated by the recently published work of the WHO Commission on the Social Determinants of Health (CSDH, 2008), the underlying determinants of the global increase of non-communicable diseases are a reflection of the major forces driving socio-economic change – globalization, urbanization, aging population and the general policy environment. Public health experts and advocates have now accumulated evidence on how globalization is partly driving non-communicable disease population risks, particularly diet-related ones. Affecting both the supply and the demand in complex ways, globalization has increased the affordability and availability of alcohol, tobacco products, and diets high in total energy fats, salt and sugar. This process has been facilitated by technological developments, the liberalization of foreign direct investments, the growth of transnational food companies, the liberalization of international food trade regimes, and deregulation in selected domestic markets. Cultural expectations have been reshaped via global and national food advertising and marketing (Rayner et al., 2007). Urbanization also creates conditions in which people are exposed to new products, technologies, and marketing of unhealthy goods, and in which they adopt less physically active types of employment. This has fostered tobacco use, physical inactivity, unhealthy diet and alcohol consumption – all risk factors for chronic diseases. Chronic diseases and their related challenges cut across national boundaries, weakening the ability of countries to deal with the problems individually. This strengthens the case for closer and more effective international action and cooperation (Bangkok Charter, 2005), especially since there is unequivocal evidence that low-cost, high-impact, evidence-based interventions exist

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59.4  The WHO global strategy for diet, physical activity and health

to prevent and treat non-communicable diseases and their risk factors, and that these interventions are excellent economic investments (WHO, 2002; Jamison et al., 2006a, 2006b). To control and decrease the diet-related health burden, multi-level, multi-sectoral and multi-stakeholder cooperating actions are needed.

59.3  The need for joined-up policy-making While a growing number of countries have begun to address the spread of noncommunicable diseases (WHO, 2005; Jamison et al., 2006b), policy coherence among health and non-health sectors is essential in order to improve the im­pact of these policies. Joinedup policy-making is considered to be a crucial feature of modern and improved institutional policy-making. This approach lies upon four basic princi­ples: (1) cross-cutting objectives are clearly defined at the outset; (2) joint working arrangements with other sectors are established; (3) barriers to effective joined-up policy-making are identified with a strategy to overcome them; and (4) implementation is considered as part of the policy process (Mulgan, 2008). Policy dialo­gues have highlighted other necessary compo­ nents, needed to ensure effective joined-up policy-making in matters related to non-communicable diseases. These included: horizontal public health committees, ad hoc comm­ittees on specific initiatives, formal consulta­tions, and public health reporting. These dialogues have also recognized potential obstacles to intersectoral cooperation: workload, inconsistencies bet­ween sectoral objectives, abse­nce of interest for health, and lack of evidence of what works (Sihto et al., 2006). In practice, competing mandates, interests and objectives among different governmental ministries and the existence of different levels of decision-making (from local to global)

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have supported approaches which focus on one particular group of diseases and their management. Adopting and implementing intersectoral actions represents the most difficult phase of joined-up policy-making. Aims and values are not always compatible, or may be in direct conflict with one another. Moreover, getting non-health stakeholders within sectors to contribute to or promote health-related factors adds a degree of complexity. Working towards policy coherence entails adoption and leadership at the highest political level, and a stewardship role for the ministry of health within government. However, important backing from beyond government and a strong political leadership must also be complemented by a whole-of-government mechanism, specific financing, and appropriate evaluation and monitoring (CSDH, 2008). To this end, policy options must meet the needs of each sector in order to create political buy-in.

59.4  The WHO global strategy for diet, physical activity and health In 2000, the 192 member states of the World Health Organization (WHO) adopted a global strategy to prevent and control non-communicable diseases (WHO, 2000). This strategy emphasized integrated prevention by targeting three main risk factors: tobacco, unhealthy diets, and physical inactivity. In 2002, the development of a Global Strategy for Diet, Physical Activity and Health (DPAS) was decided, and three guiding principles were agreed upon: stronger evidence for policy, advocacy for change, and stakeholder involvement. It was also argued that the strategy should propose appropriately tailored policies and interventions for countries which would then decide how these should be further developed and implemented. An Expert Consultation was jointly organized by the WHO and the Food and

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Agriculture Organization (FAO), and a technical report, published in 2003, assembled and reviewed the latest evidence and made recommendations (Joint WHO/FAO Expert Consultation on Diet, Nutrition and the Prevention of Chronic Diseases, 2003). Subsequently, as requested by the World Health Assembly (WHA), a consultation process involving country representatives, organizations of the United Nations system, civil society organizations and the private sector was launched. The consultation process provided an opportunity for all interested parties not otherwise involved in the consultation to provide input to the development of the strategy. In addition, a group of international experts was brought in to advise the WHO Secretariat. Four issues were particularly debated during the consultation process and development of the strategy: the strength of existing evidence, the possible impact on trade, the nature of an effective relationship with the private commercial sector, and the use of law. A draft strategy that took into account comments made by member states was forwarded to the WHA in January 2004. In May 2004, it was finally adopted as part of a resolution which captured the outcomes of the discussion process, most of which related to trade and agriculture (WHO, 2004a). DPAS specifies roles for WHO member states, United Nations agencies, civil society and the private sector in helping to reduce the occurrence of non-communicable diseases. It also calls for private sector engagement, and addresses the role of non-communicable disease prevention in health services, food and agriculture policies, fiscal policies, surveillance systems, regulatory policies, consumer education and communication (including marketing, health claims and nutrition labeling), and school policies as they affect food and physical activity choices. It suggests limiting the intake of sugars, fats and salt in foods, and increasing the consumption of fruits, vegetables, legumes, whole grains and nuts. The strategy emphasizes

the need for countries to develop national strategies with a long-term, sustainable perspective (WHO, 2004b). In 2006, 25 countries had implemented (some) policy options and 17 were planning to do so. As reported by the WHO secretariat: some progress has been made with implementation of the Strategy’s recommendations, but results are limited. Some Member States have responded positively but more countries need to do the same. Similarly, selected actions have been taken by other stakeholders, but much more needs to be done – and urgently. (WHO, 2006a)

In addition, based on public information provided by the 25 largest food companies, progress remained limited in many areas, such as reformulation of products, and the limitation of advertisement and promotion to children under the age of 12 (Lang et al., 2006). Since the publication of these reports (for more information, see http://www.who.int/ dietphysicalactivity/implementation/en/), progress has been marked by the adoption by the Ministers of Health of a European Charter on Counteracting Obesity. This provides guiding principles and a policy framework for the 54 countries of the WHO European region (WHO European Office, 2006). Other WHO regional offices have taken important steps to tackle noncommunicable diseases. For instance, the “Trans Fat Free Americas” expert task force initiative recommends eliminating industrially produced trans fatty acids from foods in the Americas (Trans Fat Free Americas, 2008). In 2007, the WHO Secretariat was asked to develop a set of recommendations on the marketing of foods and non-alcoholic beverages to children and, for the first time in a WHA resolution, prevention of obesity was explicitly targeted (WHO, 2007). In 2008, WHO member states adopted an action plan for non-communicable diseases which includes “supporting the healthier composition of food by .... eliminating industrialized produced trans fatty acids” (WHO, 2008b, 2008c).

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59.5  Improving the global policy framework

59.5  Improving the global policy framework In light of the limited outcomes of the existing global framework for action against noncommunicable and diet-related diseases, what other effective instruments could be used to improve the strategy’s impact worldwide? The analysis of international aid policy is beyond the scope of this chapter. Nevertheless, the fact that non-communicable diseases and their risk factors are excluded from the international development agenda is a critical obstacle to effective action. This is particularly the case in lowand low-middle income countries where a public health policy could aim at preventing, and not just controlling, the surge of non-communicable diseases. This exclusion is also prejudicial to emerging economies in which non-communicable diseases are not considered a health priority while their risk factors progress at a pace incomparable to what has occurred in the developed world (Popkin, 2008). Diet-related diseases are not yet recognized by the global community as a challenge for global development. Consequently, they are not inclu­ded in instruments such as the Millennium Dev­elopment Goals (MDGs) or any other global process aiming at enhancing and harmonizing development efforts. While some countries have highlighted the importance of non-communicable disease prevention in their national MDGs (for example, the Czech Republic, Mauritius, Poland, Slovakia and Thailand have included non-communicable diseases as part as their national health related MDGs, while Hungary, Indonesia, Jordan and Lithuania have highlighted the importance of chronic diseases in their MDG reports), generally-speaking, noncommunicable diseases are ignored by national development plans and other strategic plans and frameworks. They also remain absent from other strategic plans and frameworks, such as the Poverty Reduction Strategy Papers, which are used

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by donors and international development organ­izations. In such a context, mobilizing resources (proportionate to the situation) from private donors remains challenging. Some have begun to invest in this issue. Michael Bloomberg, for instance, donated US $125 million to tobaccocontrol initiatives in low- and middle-income countries; in July 2008, the Bill and Melinda Gates Foundation recognized that “tobacco-caused diseases have emerged as one of the greatest health challenges facing developing countries” (Bill and Melinda Gates Foundation (BMGF), 2008)

and joined Michael Bloomberg’s renewed initiative, increasing the total contribution of the two philanthropic organizations to US $500 million. While these commitments are encouraging, prevention and control of non-communicable diseases in general, and diet-related diseases in particular, remain severely under-funded. As highlighted by the WHO, “implementation of the Strategy has been limited by resource constraints, both human and financial, and reflects continuing low investment in prevention and control of chronic non-communicable diseases at local and global level” (WHO, 2006a)

In this constraint environment, and within the strategic framework provided by DPAS, action in several areas could strengthen global efforts to prevent diet-related diseases. This could include the following: 1. Accelerating country implementation, with a particular focus on technical cooperation and capacity-building in selected high healthburden low- and middle-income countries 2. Increasing the coordinated response to childhood obesity across different programs aiming at improving children’s health in cooperation with appropriate United Nations organizations and civil society, using the experience of the Global Strategy for infant and young child feeding as a foundational basis

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3. More effective interaction with the commercial sector on product reformulation, starting with the elimination of trans fatty acids 4. Improving consumer information, in particular through a more strategic engagement with Codex Alimentarius, on matters related to health claims and labeling 5. Strengthening global action on less controversial issues, such as workplace wellness or population-based approaches to increase levels of physical activity 6. Surveillance and monitoring of DPAS implementation based on existing frameworks (WHO, 2006b, 2008d, 2008e).

59.6  Insights from tobacco control efforts The WHO Framework Convention on Tobacco Control (WHO FCTC), a legally-binding document, was adopted in May 2003 (WHO, 2003). Country-level implementation is still in its early stages (WHO, 2008e). Global tobacco control efforts have been typically assessed as successful, mainly due to the legal nature of the WHO FCTC. Indeed, by ratifying the convention, countries are committed to implementing a set of regulatory measures. In addition, the WHO FCTC comprises accountability mechanisms and a monitoring system. There is no doubt that the WHO FCTC establishes stronger obligations than those adopted for diet and physical activity. The Diet, Physical Activity and Health Strategy (DPAS) is based on a quite different approach, relying on the “voluntary engagement” of a range of interested parties. This choice of approach has been debated since the beginning of the development of the DPAS (Daynard, 2003; Yach et al., 2003; Chopra and Darnton-Hill, 2004; Daynard et al., 2004; Hayne et al., 2004; Rigby et al., 2004; Weiss and Smith, 2004), and debate continues in multiple forums (Yach et al., 2005; Friel et al., 2007;

Gostin, 2007; Magnusson, 2007, 2008a, 2008b; Hodge et al., 2008; Swinburn, 2008). Should this approach be (partly) revised? Should legal and international standards be developed in order to accelerate progress?

59.7  Engaging the private sector As mentioned earlier, an important component of the DPAS has been engaging the private sector in the fight against non-communicable diseases. Yet its involvement has remained limited. Much of the current debate now lies in how to make the food and non-alcoholic beverage industry more responsive to obesity trends, and many have argued for legal and international standards to accelerate and improve privatesector efforts. The “voluntary” approach adopted by the DPAS relies on the evidence that, while tobacco is hazardous, food per se is not. There is no unhealthy food but only unhealthy diets, diet being the outcome of choices made by individuals. Consequently, while food safety should be regulated, establishing standards or norms is not justifiable. There is no threshold above which the consumption of a food product is healthy and beyond which it becomes dangerous for the consumer’s health. The solution lies in assisting the consumer to make wider and wiser food choices by encouraging food manufacturers, retailers and the foodservice industry to provide more diversified products and information. Accordingly, public health efforts, including at global level, should aim at encouraging the food-processing industry to reformulate products and inform consumers on their nutritional qualities. While several aspects of diet are inappropriate for international legal standards, there are areas where the development of global standards could leverage the DPAS. The first relates to labeling and health/nutrition claims. Some

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transnational food manufacturers and retailers have taken positions on on-product labeling, and provide information about nutrition claims. In developed countries, consumers are often (over-)exposed to information difficult to understand, making comparison of products a quasi-impossible task – in particular when supplied by different companies. In other parts of the world, nutritional information is missing. A standardized and easy-to-understand nutritional information system could make health a source of competitive advantage, while continuing to provide the consumer with choice. A standardized process for labeling could also warn consumers of the health risks associated with the overconsumption of certain product ingredients, such as salt, sugar and fat. However, to be effective these mechanisms need to be developed at the international level, in dialogue with all relevant stakeholders. Their use must be regulated and monitored. Another domain frequently considered for international standards and regulation is marketing; more specifically, the marketing of food and non-alcoholic beverages to children. Since the adoption of the DPAS, changes have taken place in the global environment regarding the marketing of food and non-alcoholic beverages to young people. Indeed, evidence has emerged from internationally recognized institutions, such as the American Institute of Medicine, that: among many factors, food and beverage marketing influences the preferences and purchase requests of children, influences consumption at least in the short-term, is a likely contributor to less healthful diets, and may contribute to negative diet-related health outcomes and risks among children and youth. (Institute of Medicine, 2006)

While industry has developed self-regulatory approaches, some have observed that transnational companies may not implement these commitments in the developing world. Some leading analysts have proposed that these commitments become a condition of direct foreign investments (Hawkes, 2007). In addition, civil

society has campaigned for statutory restrictions, and governments have dealt with a range of regulatory proposals. Building on those considerations and the experience of the WHO’s international code of marketing for breast-milk substitutes, in 2006, experts consulted by WHO concluded that “for the purpose of substantially reducing the volume and impact of commercial promotion of food and beverages to children, self-regulation is not sufficient”,

and the “WHO should take the lead in the development of an international code on the commercial promotion of food and beverages to children” (WHO, 2006c)

59.8  Conclusions To reverse the current trends of obesity and diet-related diseases, the implementation of the policy framework provided by the DPAS must be urgently accelerated. In order to be effective, this implementation must not only be rapid and comprehensive, but also be combined with the development of international standards and regulations in selected areas. In low- and middle-income countries facing a double burden of diseases, this implementation must be a component of an intersectoral, multi-level response to address premature mortality and ill-health due to non-communicable diseases and their risk factors. To improve national situations, it is imperative to support the operationalization of non-communicable disease strategies and action plans where they are already in place, develop strategies and action plans where they are absent, and coordinate existing and ongoing activities, while concentrating on strategic priorities in order to bridge existing gaps and provide an effective monitoring and evaluation mechanism. Combating obesity and, more generally, noncommunicable diseases and their risk factors calls for abandoning a narrow understanding of what public health policy is about. No healthy

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lifestyle plan will be successful without recognizing that improvements are no longer achievable without tackling the social determinants of health. It will require “policy coherence within and between sectors with contribution at all levels of governance from global through to local” (Commission on the Social Determinants of Health , 2008)

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Hodge, J. G., Jr., Garcia, A. M., & Shah, S. (2008). Legal themes concerning obesity regulation in the United States: theory and practice. Australia and New Zealand Health Policy, 5, 14. Institute of Medicine. (2006). Food marketing to children and youth: Threat or opportunity? In J. McGinnis, V. Appleton Gootman, & J. Kraak (Eds.), Committee on food marketing and the diets of children and youth. Washington, DC: The National Academies Press. Jamison, D. T., Breman, J. G., Measham, A. R., Alleyne, G., Claeson, M., Evans, D. B., Jha, P., Mills, A., & Musgrove, P. (Eds.), (2006a). Priorities in health. Washington, DC: World Bank. Jamison, D. T., Breman, J. G., Measham, A. R., Alleyne, G., Claeson, M., Evans, D. B., Jha, P., Mills, A., & Musgrove, P. (Eds.), (2006b). Disease control priorities in developing countries (2nd edn). New York, NY and Washington, DC: Oxford University Press and the World Bank. Joint WHO/FAO Expert Consultation on Diet, Nutrition and the Prevention of Chronic Diseases. (2003). Diet, nutrition and the prevention of chronic diseases: Report of a joint WHO/FAO expert consultation, Geneva, 28 January–1 February 2002. WHO Technical Report series 916. Geneva: World Health Organization. Lang, T., Rayner, G. and Kaelin, E. (2006). The food industry, diet, physical activity and health: A review of reported commitments and practice of 25 of the world’s largest food companies. London: Centre for Food Policy, City University. Online. Available: http://www.tescopoly. org/images//food%20co%20health%20monitoring%20 final%2004%2004%2006.pdf Accessed 10.13.8. Magnusson, R. S. (2007). Non communicable diseases and global health governance: Enhancing global processes to improve development. Globalization and Health, 3 ,2. Magnusson, R. S. (2008a). What’s law got to do with it? Part I: A framework for obesity prevention. Australia and New Zealand Health Policy, 5 ,14. Magnusson, R. S. (2008b). What’s law got to do with it? Part II: Legal strategies for healthier nutrition and obesity prevention. Australia and New Zealand Health Policy, 5, 14. Mulgan, G. (2008). Joined-up government now and in the future. Public Health Bulletin South Australia, 5, 1. Ottawa Charter. (1986). Ottawa Charter for Health Promotion. Online. Available: http://www.who.int/hpr/NPH/ docs/ottawa_charter_hp.pdf Accessed 10.6.08. Popkin, B. (2008). Will China’s nutrition transition overwhelm its health care system and slow economic growth? Health Affairs, 27(4), 1064–1076. Rayner, G., Hawkes, C., Lang, T., & Bello, W. (2007). Trade liberalization and the diet transition: A public health response. Health Promotion International, 21(51), 67–74. Rigby, N. J., Kumanyika, S., & James, W. P. T. (2004). Confronting the epidemic: The need for global solutions. Journal of Public Health Policy, 25, 418–434.

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on the Marketing of Food and Non-alcoholic Beverages to Children, Oslo, Norway, 2–5 May 2006. Geneva: World Health Organization. WHO. (2007). Prevention and control of non-­communicable ­diseases: Implementation of the global strategy. World Health Assembly Resolution 60.23. Geneva: World Health Organization. WHO. (2008a). World health statistics 2008. WHO report. Geneva: World Health Organization. Online. Available: http://www.who.int/whosis/whostat/EN_WHS08_ Full.pdf Accessed 10.20.08. WHO. (2008b). Prevention and control of non-­communicable ­diseases: Implementation of the global strategy. World Health Assembly Resolution 61.14. Geneva: World Health Organization. WHO. (2008c). Action plan for the global strategy for the preven­ tion and control of non-communicable diseases. Online. Available: http://www.who.int/gb/ebwha/pdf_files/ A61/A61_8-en.pdf Accessed 10.10.08. WHO. (2008d). Global tuberculosis control – surveillance, planning, financing. WHO Report. Geneva: World Health Organization. WHO. (2008e). WHO report on the global tobacco epidemic. WHO Report. Geneva: World Health Organization. WHO. European Office (2006). European charter on coun­ teracting obesity. WHO European document EUR/06/ 5062700/8. Online. Available: http://www.euro.who. int/Document/E89567.pdf Accessed 10.10.08. WHO/FAO Expert Consultation on Diet, Nutrition and the Prevention of Chronic Diseases. (2003). Diet, nutrition and the prevention of chronic diseases: Report of a joint WHO/ FAO expert consultation, Geneva, 28 January–1 February 2002. World Health Organization technical report series 916. Geneva: World Health Organization. Yach, D., Hawkes, C., Epping-Jordan, J. E., & Galbraith, S. (2003). The World Health Organization’s framework convention on tobacco control: Implications for global epidemics of food-related deaths and disease. Journal of Public Health Policy, 24, 274–290. Yach, D., McKee, M., Lopez, A., & Novotny, T. Oxford Vision 2020. (2005). Improving diet and physical activity: 12 lessons from controlling tobacco smoking. British Medical Journal, 330, 898–900.

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C H A P T E R

60 Social Interactions and Obesity: An Economist’s Perspective Katherine G. Carman and Peter Kooreman Department of Economics, Tilburg University, Tilburg, The Netherlands

o u t l i n e 60.1  Introduction 60.2 The Different Guises of Social Interactions 60.2.1  Separating the Sources of Correlation Across Peers in Empirical Research 60.3  The Literature So Far

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60.1  Introduction The importance of social ties as a force shaping human behavior has been recognized since ancient times, with references going back as early as Horace. It is only in recent decades, however, that researchers in the social sciences have begun to refine the concept of social interactions and distinguish between its various forms. Mathematical models are used to advance coherent thinking, and datasets on actual behavior are used to quantify the relationships of interest.

Obesity Prevention: The Role of Brain and Society on Individual Behavior

60.4 Policy Interventions Related to Social Interactions 762 60.4.1  “Obesity Reports” in Arkansas 763 60.4.2  Role Models 763 60.4.3  Workplace Programs 763 60.4.4  Weight-Loss Support Groups 763 60.5 Conclusions

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Measurement of the impact of social interactions is complex, and often confounded by other factors that may lead to a correlation across individuals. This literature has also been limited because there are few data sources that contain information about peer networks. Traditionally, policies for combating obesity are linked to factors like the nutritional composition of food in cafeterias at schools and at work, food prices, facilities for exercising, the proximity of schools to fast-food restaurants, etc. However, the dramatic increase in obesity in industrialized countries around the world – documented

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in various chapters elsewhere in this volume – requires researchers and policy-makers to think beyond standard policies. Some recent new policy initiatives attempt to use social forces to boost the effectiveness of existing policies. Examples include issuing weight report cards to schoolchildren (with information on their weight and how it compares to the weight of their peers), and the use of role models in promoting healthy behaviors. Weight-reduction programs, such as WeightWatchers® and TOPS (Take Off Pounds Sensibly), have long used group meetings as part of their programs, with the idea that social support may facilitate weight reduction. Social interactions in relation to obesity have been a focus of research in various disciplines (see, for example, Chapter 61). In this chapter, we look from an economist’s perspective at published contributions in the social interactions literature. As will become clear, economists’ contributions have largely focused on how to identify the various forms of social interactions from empirical data. We will also highlight the implications for studying and combating obesity by discussing a number of recent policy initiatives.

60.2  The different guises of social interactions Researchers investigating the relationship between an individual’s weight and the weight of his or her peers must be aware of the many possible explanations for a correlation in weight across individuals. The identification of various sources of this correlation has played a central role in the economics of peer effects. Under­ standing the precise nature of the correlation between individuals and their peers is neces­sary to properly design policy interventions. The various causes of the correlation across

peers have been carefully discussed in the literature on identification of peer effects (see, for example, Manski, 1993; Moffitt, 2001). Most researchers are interested in identifying direct effects: having obese or overweight peers causes an individual to be obese or overweight as well. These direct effects are typically referred to as endogenous social or peer effects in the economic literature, as defined by Manski (1993). These may also be called social network effects or induction. One explanation of why endogenous social effects regarding obesity may be observed could be that there are endo­ genous social effects related to exercise or diet­ ary behavior, especially if peers eat or exercise together. The data sources used in this literature do not provide sufficient information to identify these effects separately, so they may be captured in the effects of BMI. Another explanation could be that people prefer to be like others, so if they observe that their peers are all of normal weight they will also prefer to be of normal weight. This idea has been discussed in several theoretical economic models – see, for example, Burke and Heiland (2007) and Etile (2007).1 Endogenous social effects could have a powerful effect on obesity rates because they imply a social multiplier or process of mutual influence as described above. Manski (1993) highlights one of the key problems with measuring endogenous social effects, in that measurement is biased by the simultaneous relationship between peers: individuals affect their peers, and peers simultaneously affect the original individual. Manski has dubbed this “the reflection problem”. While researchers are interested in identifying endogenous effects of social interactions, it is possible that other factors lead individuals to be similar to their peers. The first possibility is that individuals are affected by the background characteristics of their peers; this is often referred to as a contextual effect. For example, the BMI of

1

 It is also possible that there is actually a contagious effect; Turnbaugh and colleagues (2006) show that certain infections can increase the likelihood of weight gain.

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60.2  The different guises of social interactions

one person could be affected directly by a peer’s knowledge of healthy behaviors, irrespective of any effect of the peer’s BMI. Second, individuals and their peers share a common environment, and this common environment may lead to a positive correlation between their behaviors. The effects of a common environment are referred to as correlated effects. For example, students at the same school may have access to the same food options in their cafeteria, or similar requirements to participate in physical education. Also, individuals and their peers may share characteristics that cause them both independently to gain weight. One example of this is shared genetic characteristics of siblings, which could cause them both to gain weight regardless of their interactions. Finally, there may be a problem of selection (or homophilly) where people with similar tastes and attitudes are more likely to be peers. If researchers do not adequately control for these factors, estimates of endogenous social effects will be biased. To illustrate these complexities, consider the following example. Two sisters, Cindy (32) and Daisy (33), live in different cities. They (physically) meet at least once a year to engage in some joint activity, like shopping, hiking, or just sitting together in a restaurant. The inter­ relationships between Cindy’s and Daisy’s BMIs might be mathematically represented as follows:

 

BMICindy  F  1 xCindy  2 BMI Daisy (60.1)  3 xDaisy  εCindy

 

BMI Daisy  F  α 1 xDaisy  2 BMICindy (60.2)  3 xCindy  εDaisy

Here, BMICindy represents Cindy’s Body Mass Index (all variables for Daisy are defined analogously). Cindy and Daisy have a joint genetic

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background, and were exposed to the same food habits and lifestyle while growing up; these joint influences are represented by the “family” parameter F. The variable xCindy is an exogenous characteristic that applies to Cindy. For example, xCindy could be a dummy variable which is 1 if Cindy’s employer promotes a program for exercising during lunch breaks at work. If 2  0, then there is a direct relationship between Cindy and Daisy’s BMI; if one gains weight, the other gains weight.2 This could represent a competition between the sisters, a desire to be similar, or changes in their perceptions of the ideal weight. If 3    0, the exercising program at Cindy’s work also has a direct effect on Daisy’s BMI (a contextual effect). This could represent the effect of information; Daisy learns from Cindy about the existence of such a program, which might trigger a behavioral response from Daisy, independent of the effect the program might have on Cindy’s BMI. Cindy and Daisy represent all other factors that affect BMI, but that are unobserved to researchers.3 How important is the distinction between direct and indirect effects? To answer this question, we consider a simple numerical example with xCindy  1, and xDaisy  0; Cindy’s employer has implemented a lunch-break exercising program, but Daisy’s employer has not. First, consider the case with 1   1, 2  0 and 3  0. These parameter values imply that the lunch-break exercising program results in a decrease in Cindy’s BMI by 1 full point, while leaving Daisy’s BMI unchanged. Now consider the case where 2  0.5 (with the values for the other parameters unchanged). This means that when Daisy observes that Cindy’s BMI has dropped (for whatever reason), this will cause a drop in Daisy’s BMI. This drop will be half as large as the (initial) drop in Cindy’s BMI. This

2

 It is of course also possible that 2  0, but this seems less likely given the empirical research that has already been done in this area. 3  In the simplest case, it is assumed that Cindy and Daisy are independent (orthogonal) to both xCindy and xDaisy.

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follows from Equation (60.2). The causation could work through social mechanisms like imitation, encouragement and competition, along with a change in the perceived ideal weight. Recall that 2 does not represent an informational effect, which would work through 3. Conversely, the resulting drop in Daisy’s BMI will be observed by Cindy, which will cause a further drop in Cindy’s weight. This follows from Equation (60.1). This process of mutual influence will continue and converge to an equilibrium where Cindy’s BMI has decreased by 1.333 points ( 1  1/4  1/16  …) and Daisy’s BMI has decreased by 0.667 points (1/2  1/8  1/32  …). Note that with social interactions, the total effect of Cindy’s lunch-break exercise program is twice as large as (1.333  0.667) as without social interactions (1  0). Thus, in the presence of social interactions, changes in the behavior (BMI) of one person in the social group are typically magnified as a result of a “social multiplier”. Clearly, the social multiplier not only has the potential to make health interventions more effective; it can also exacerbate health problems in a social group. Note that the same total effect on BMI would result if the parameter values were 1    1.333, 3    0.667 and 2  0 – that is, a case without endogenous peer effects. This illustrates the difficulty of telling from empirical data to what extent the effects are the result of direct or indirect social interactions. Yet the distinction is important for policy design. In the former case it could make sense to use social mechanisms to promote healthy behaviors; in the latter case it does not.

60.2.1  Separating the sources of correlation across peers in empirical research Suppose that we have a large sample available of sister pairs like Cindy and Daisy. Even if there were no social interactions at all (2  0 and 3  0), we would find a correlation between sisters’ BMI because of their similar genetic, parental and family backgrounds (through parameter F). As highlighted by Manski (1993), Moffitt (2001) and others, it is usually impos­sible to identify all parameters in Equations (60.1) and (60.2) without further assumptions. One such assumption is 3  0 (absence of “contextual effects”). In our example, this means that we would assume that the lunchtime exercising program of Cindy’s employer does not have a direct effect on Daisy’s BMI. If we were to find an effect of Cindy’s lunchtime exercising program on Daisy’s BMI, the implication – with the assumption 3  0 – would necessarily be that this was the result of an indirect effect. Cindy’s lunch exercise program has an effect on Cindy’s BMI, and Cindy’s BMI has a causal effect on Daisy’s BMI.4 Data that involve random variations in peer groups (Sacerdote, 2001) or random treatment of a part of a peer group (Kuhn et al., 2008) offer improved prospects for identification. Another important issue in the analysis of social interactions is the composition and formation of social groups (reference groups, peer groups). In general, the larger the disparity in people’s behavior, the less likely it is that they will belong to the same social group. The mere fact that the BMI of two individuals is very different could be a reason in itself for not counting

4

 Standard econometric theory implies that applying OLS to (60.1) and (60.2) is meaningless as it leads to a simultaneity bias. One solution is to estimate the so-called reduced form, which entails regressing BMI on a constant and xDaisy and xCindy. However, this yields only three estimated regression coefficients which are insufficient to separately identify the four structural parameters F, 1, 2 and 3. If, in our example, we find in the reduced form regression that the lunch exercising program of one sister affects the BMI of the other, we know that there is some form of social interaction. However, we do not know which type it is, without further assumptions.

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60.3  The literature so far

one another as friends. The selection of peers makes especially difficult the interpretation of research that analyzes social interactions between self-reported friends. An example is research based on the National Longitudinal Adolescent Health Survey (Add Health), in which respondents are asked to nominate five female and five male friends.

60.3  The literature so far A growing but still limited number of empirical studies have analyzed social interactions in obesity. No doubt the most influential paper so far is that by Christakis and Fowler (2007), which was featured on the front pages of newspapers and news websites around the world. Christakis and Fowler analyzed a social network of 12,000 people whose weight was measured every 4 years during a 32-year period. Their statistical model analyzed the association in weight gains between friends, siblings, spouses and neighbors (not between co-workers). They report that a person’s chance of becoming obese increased by 57 percent if he or she had a friend who became obese in a given interval; among pairs of adult siblings, if a sibling became obese, the probability that the other would become obese as well increased by 40 percent; if one spouse became obese, the chance that the other spouse would become obese increased by 37 percent; and no significant effects were found for interactions between neighbors. Interactions between people of the same sex were generally found to be stronger than between people of opposite sex – a result also reported by Soetevent and Kooreman (2007) for various behaviors in adolescents. Although Christakis and Fowler note that their results may not merely represent endo­ genous social interactions (2    0 in our notation), their original article strongly suggests that they can be interpreted as such. Cohen-Cole and Fletcher (2008) have criticized Christakis

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and Fowler’s research for this reason, and performed additional analyses using the National Longitudinal Adolescent Health Survey (Add Health). Their general finding is that social interactions are not significant once fixed effects for social groups or individuals are accounted for. This is essentially a statistical tool for controlling for the joint effects represented by F in Equations (60.1) and (60.2). Fowler and Christakis (2008) responded by adding fixed effects to the model reported in Christakis and Fowler (2007), and still found statistically significant effects. There are three reasons for the differences across the two papers. First, CohenCole and Fletcher showed that the results are sensitive with respect to the specification of the dependent variable (for example, using a dummy variable indicating overweight (BMI  25) versus using the continuous measure of BMI). In particular, the precise distribution of the population’s BMI relative to the cutoffs for overweight and obesity is important. Second, it is possible that the endogenous social effects regarding obesity are different among adults and adolescents. The third difference derives from how each paper deals with common environmental factors. Christakis and Fowler (2007) address this issue by comparing the results across different types of peers. For example, when both the individual and the peer report a relationship, there is a positive and significant correlation, but when only the peer reports a relationship there is no significant effect. Cohen-Cole and Fletcher (2008) use school fixed effects to deal with the common environment faced by students in the same school, and this causes the significant effects on peer BMI to disappear. In a related paper, Cohen-Cole and Fletcher (2010) show that implausible social network effects (for example, related to height) can be found if joint factors are not controlled for. Typically, the fixed-effect approach is preferred by economists. Trogdon and colleagues (2008) focus on the reflection problem in a study of peer effects in

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adolescents’ BMI. They also use the Add Health data. The central explanatory variable in their model is the average BMI of self-reported friends, similar to Equations (60.1) and (60.2). They attempt to control for the selection of friends’ BMI by using obesity of friends’ parents, and friends’ birth weight, as instrumental variables. This amounts to assuming that obesity of friends’ parents and friends’ birth weight do not have a direct effect on a given student’s BMI (3  0 in our notation). With this identifying assumption, the coefficient of friends’ average BMI is about 0.50. Using quantile regression, they find that peer effects are larger for individuals at the higher end of the BMI distribution, especially for girls. However, in an alternative specification in which the authors use all grade-level students in a school as peer group (rather than the self-reported friends), the peer effects are smaller, and – when performed separately by gender – peer effects are not significant for males. This points at a friend-selection effect driving the former results (and casts doubt on the appropriateness of the instrumental variables). While all of these papers have made attempts to address the complexities of social inter­ actions, their primary drawback is that they rely on the nomination of one’s closest peers. With self-nominated peers, researchers have the ad­v­antage of focusing on the people that a person considers to be the most important in their network of friends, but this is always subject to the problem of selection: who one chooses to be friends with. Ideally, research on endogenous social effects using self-nominated peers would carefully consider the endogenity of the peer connections themselves. As mentioned, Trogdon and colleagues (2008) try to address this by looking at peer effects within an entire grade in the school. However, research on peer effects

in education finds that within-classroom effects are much more important than within-grade effects.5 If peer groups are defined too broadly, researchers run the risk of failing to measure an effect when one actually exists. Another problem is that self-reported peer groups are usually assumed to remain unchanged. For example, Fowler and Christakis (2008) consider only those peers in the Add Health data who remain friends over time. This does not take into account the fact that people may stop being friends because of changes in weight. Cohen-Cole and Fletcher (2008) focus only on friendships that existed in the first wave of data. This will bias results, because some “peers” will in fact no longer be peers.

60.4  Policy interventions related to social interactions A number of policies have been proposed and implemented that attempt to take advantage of social interactions in obesity-related behavior. First, we discuss policies designed around schools and workplaces where entire social groups are affected by the same policy. By bringing attention to the issue in a group setting, they may help to motivate people to lose weight. Second, we consider programs that explicitly use a group setting to help individuals lose weight. As mentioned by Christakis and Fowler, weight-loss support groups have been in place for many years (Christakis and Fowler, 2007; Fowler and Christakis, 2008). For example, TOPS, a network of such groups, has been operating for 60 years.

5

 Examples of papers that measure peer effects at the classroom level include Hoxby and Weingarth (2005), Cooley (2007), and Carman and Zhang (2008). Examples of papers that measure peer effects at the grade level include Hanushek et al. (2003), and Hoxby (2000).

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60.4  Policy interventions related to social interactions

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60.4.1  “Obesity reports” in Arkansas

60.4.3  Workplace programs

In 2006, the State of Arkansas supplemented children’s school reports with an “obesity report”, indicating – by means of a percentile – the child’s position in the weight distribution of same-age children in school. For example, the report mentioned that the child’s BMI was in the 80th percentile. Similar initiatives have been developed in Delaware, South Carolina, Tennessee, Pennsylvania and New York. The weight report cards have been reported to stigmatize children, which can have negative effects on their emotional wellbeing. While regrettable, stigmatization is one possible channel of endogenous social effects. Also, these reports are sometimes inconsistent with other policies in school, such as unhealthy food in the school’s cafeteria or limited opportunities for physical education. The scientific basis for mandating this policy seems to be lacking, given the absence of randomized trials to evaluate its effectiveness.

Employers are increasingly interested in improving the health of their employees to improve the productivity of their workforce and reduce the costs of health care. Many of their programs exploit social networks. Encouraging exercise breaks, providing access to on-site gyms, or even a recent program in Japan to make employers responsible for their employees’ weight, all bring weight-reduction programs into a context where people know each other. The large interest in this area is illustrated by an entire issue of the journal Obesity devoted to the evaluation of workplace interventions.7 While rarely the central issue of study, these programs all exploit the fact that group members can motivate and support each other, especially with interventions like facilitation of exercise groups and pedometer challenges. Previous research has found evidence of endogenous social effects in the workforce, although in other contexts.8 However, again, workplace policies aimed at reducing obesity have not been evaluated in terms of endogenous social effects. Most studies of the effectiveness of workplace programs randomize treatment at the level of the worksite. Thus, either all employees in a particular location are treated or all are untreated. A randomized trial that treated only some employees within a worksite could be used to track endogenous social effects.

60.4.2  Role models In the realm of endogenous social effects, it is possible that some individuals have a larger impact than others. Certain individuals may act as role models for their peers, and thus their weight gain or loss may have a larger social impact. For example, ACTION! Wellness Program for Elementary School Personnel was designed with this in mind.6 The program targets employees in elementary schools in part because of the influence that they may have as role models for the children attending the school. Most research on endogenous social effects has treated all peers as equal, but models could be adapted to allow for the possibility that some peers matter more than others.

60.4.4  Weight-loss support groups Weight-loss support groups are an important part of many commercial weight-loss programs. These groups are explicitly designed to use social support to help individuals lose weight. It is believed that social influences from other individuals trying to lose weight will aid in

6

 The program is summarized in Pratt et al. (2007) and Webber et al. (2007).  Pratt and colleagues (2007). 8  See, for example, Duflo and Saez (2003) and Carman (2006). 7

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weight reduction. This strategy has been popular in groups dealing with addictions, such as Alcoholics Anonymous. While many different organizations make use of these programs, the effectiveness of weight-loss support groups independent of the other aspects of these programs has not been carefully studied. Tsai and Wadden (2005) reviewed the literature on commercial weightloss programs (many of which use support groups) and reported that most studies of commercial weight-loss programs find no significant benefits. Perri and colleagues (1987) found that peer support groups have no significant effect on weight loss. In contrast, Song and colleagues (2008) and Orth and colleagues (2008) found that support-group attendance is associated with increased weight loss for people who have undergone bariatric surgery. However, their results may be due to selection bias; those who choose to attend groups may be different from those who do not attend groups, and people who have chosen to undergo bariatric surgery are likely very different from those who have not followed this path. Despite the fact that there is little evidence of the effectiveness of support groups, they continue to be a prominent part of the weight-loss landscape. Recently, Internet weight-loss support groups have started to gain popularity. Long-term provision of and participation in traditional support groups is costly for both the provider and the participant. Internet support groups, on the other hand, have the potential to provide longterm support in a cost effective way. However, several studies by Harvey-Berino and colleagues (2002a, 2002b, 2004) have evaluated participation in Internet support groups and found no significant effect. While actual participation in support groups by individuals interested in losing weight indicates a revealed preference for this type of weight-loss program, there is no evidence that they actually aid in the weight loss through peer effects.

60.5  Conclusions The empirical literature makes clear that social influences can be an important channel to affect weight-related behavior and boost the effectiveness of more traditional policies. The literature also makes clear, however, that the magnitude of these effects is generally modest once confounding factors – in particular the similarity of the backgrounds of people in a peer group – are accounted for. Several policies have been proposed and implemented that attempt to take advantage of social mechanisms in shaping health-related behaviors. None of them, however, seem to have been accompanied by careful scientific investigation of their effectiveness. A prerequisite for developing successful future policies in this domain is pretesting their effectiveness, preferably through randomized trials. There are several open questions in the literature to be addressed in future research. One is whether a change in a peer’s BMI has a temporary or more permanent effect on an individual’s BMI. Another is whether peer effects are symmetric for weight loss and weight gain. Answering such questions requires high­frequency longitudinal data on the same individuals, with objective rather than self-reported weight measurements, and models that control for panel attrition and changes in peer groups.

References Burke, M. A., & Heiland, F. (2007). Social dynamics of obesity. Economic Inquiry, 45, 571–591. Carman, K. G. (2006). Social influences and the private provision of public goods: Evidence from charitable contributions in the workplace. Working Paper, Tilburg, The Netherlands: Tilburg University. Carman, K. G., & Zhang, L. (2008). Classroom peer effects and academic achievement; evidence from a chinese middle school. Working Paper, Tilburg, The Netherlands: Tilburg University. Christakis, N. A., & Fowler, J. (2007). The spread of obesity in a large social network over 32 years. The New England Journal of Medicine, 357, 370–379.

2.  From Society to Behavior: Policy and Action

References

Cohen-Cole, E., & Fletcher, J. M. (2008). Is obesity contagious? Social networks vs environmental factors in the obesity epidemic. Journal of Health Economics, 27, 1382–1387. Cohen-Cole, E., & Fletcher, J. M. (2010). Detecting implaus­ ible social network effects in acne, height, and headaches: Longitudinal analysis. British Medical Journal (in press). Cooley, J. (2007). Desegregation and the achievement gap: Do diverse peers help? Unpublished manuscript, University of Wisconsin-Madison, WI. Duflo, E., & Saez, E. (2003). The role of information and social interactions in retirement plan decisions: Evidence from a randomized experiment. Quarterly Journal of Economics, 118, 815–842. Etile, F. (2007). Social norms, ideal body weight and food attitudes. Health Economics, 16, 945–966. Fowler, J., & Christakis, N. A. (2008). Estimating peer effects on health in social networks; A response to Cohen-Cole and Fletcher; and Trogdon, Nonnemaker, and Pail. Journal of Health Economics, 27, 1400–1405. Hanushek, E. A., Kain, J. F., Markman, J. M., & Rivkin, S. G. (2003). Does peer ability affect student achievement? Journal of Applied Econometrics, 18, 527–544. Harvey-Berino, J., Pinauro, S., Buzzell, P., Di Giulio, M., Casey Gold, B., Moldovan, C., & Ramirez, E. (2002a). Does using the internet facilitate the maintenance of weight loss? International Journal of Obesity, 26, 1254–1260. Harvey-Berino, J., Pinauro, S., Buzzell, P., & Casey Gold, B. (2002b). Effect of internet support on the long-term maintenance of weight loss. Obesity Research, 12(2), 320–329. Harvey-Berino, J., Pinauro, S., & Casey Gold, B. (2002c). The feasibility of using internet support of the maintenance of weight loss. Behavior Modification, 26, 103–116. Hoxby, C. M. (2000). Peer effects in the classroom: Learning from gender and race variation. NBER Working Paper #7867. Hoxby, C. M., & Weingarth, G. (2005). Taking race out of the equation: School reassignment and the structure of peer effects. Unpublished manuscript. Kantor, J. (2007 January 8). As obesity fight hits cafeteria, many fear a note from school. New York Times. Kuhn, P., Kooreman, P., Soetevent, A., & Kapteyn, A. (2008). The own and social effects of an unexpected income shock:

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Evidence from the dutch postcode lottery. NBER Working Paper #14035. Manski, C. F. (1993). Identification of endogenous social effects: The reflection problem. The Review of Economic Studies, 60, 531–542. Moffitt, R. A. (2001). Policy interventions, low-level equilibria, and social interactions. In S. N. Durlauf & H. P. Young (Eds.), Social dynamics (pp. 45–82). Cambridge, MA: MIT Press. Orth, W., Atul, K., Madan, R. J., Taddeucci, M. C., & Tichansky, D. S. (2008). Support group meeting attendance is associated with better weight loss. Obesity Surgery, 18, 391–394. Perri, M. G., McAdoo, W. G., McAllister, D. A., Lauer, J. B., Jordan, R. C., Yancey, D. Z., & Nezu, A. M. (1987). Effects of peer support and therapist contact on long-term weight loss. Journal of Consulting & Clinical Psychology, 55(4), 615–617. Pratt, C. A., Fernandez, I. D., & Stevens, V. J. (2007). Introduction and overview of worksite studies. Obesity, 15(1S-3S), 2007. Sacerdote, B. (2001). Peer effects with random assignment: Results for dartmouth roommates. Quarterly Journal of Economics, 116, 681–704. Soetevent, A., & Kooreman, P. (2007). A discrete choice model with social interactions; with an application to high school teen behavior. Journal of Applied Econometrics, 22, 599–624. Song, Z., Reinhardt, K., Buzdon, M., & Liao, P. (2008). Association between support group attendance and weight loss after Roux-en-Y gastric bypass. Surgery for Obesity and Related Diseases, 4, 100–103. Trogdon, J., Nonnemaker, J., & Pail, J. (2008). Peer effects in adolescent overweight. Journal of Health Economics, 27, 1388–1399. Tsai, A. G., & Wadden, T. A. (2005). Systematic review: An evaluation of major commerical weight loss programs in the United States. Annals of Internal Medicine, 142(1), 56–66. Turnbaugh, P. J., Ley, R. E., Mahowald, M. A., Magrini, V., Mardis, E. R., & Gordon, J. I. (2006). An obesity­associated gut microbiome with increased capacity for energy harvest. Nature, 444, 1027–1031. Webber, L. S., Johnson, C. C., Rose, D., & Rice, J. C. (2007). Development of “ACTION!” Wellness program for elementary school personnel. Obesity, 15, 48S–56S.

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C H A P T E R

61 A Complex Systems Approach to Understanding and Combating the Obesity Epidemic Ross A. Hammond Center on Social and Economic Dynamics, Economic Studies Program, The Brookings Institution, Washington, DC, USA

out l i n e 61.1  Introduction

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61.2 Challenges for Study and Intervention Design

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61.3  Complex Adaptive Systems

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61.4 Applying a Complex Systems View to Obesity 61.4.1  Bottom-up Approaches

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61.1  Introduction The obesity epidemic has become a major public health challenge in the US and worldwide. Between 1970 and 2000, the percentage of Americans classified as obese doubled to almost 30 percent (Bray et al., 2003), with fully two-thirds of Americans now classified

Obesity Prevention: The Role of Brain and Society on Individual Behavior

61.4.2  Diversity Matters 61.4.3  Tipping Points 61.4.4  Designing Interventions

772 772 773

61.5 Agent-based Computational Modeling

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61.6  Conclusion

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Acknowledgments

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as overweight (Ogden et al., 2006). The problem of obesity is not limited to the United States; similar obesity epidemics are underway across the globe, from Europe to South America, to the Middle East and Asia (Albala et al., 2002; Kain et al., 2002; Cameron et al., 2003; MohammadpourAhranjania et al., 2004; Rennie and Jebb, 2005; Yumuk, 2005). Overall, nearly half a billion

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people worldwide were overweight or obese in 2002 (Rossner, 2002). Since obesity is linked with diabetes, high blood pressure and high cholesterol, this sharp increase in obesity rates has important implications for public health (National Heart, Lung, and Blood Institute (NHLBI), 1998). In addition to higher mortality and morbidity, obesity increases healthcare costs dramatically (Finkelstein et al., 2005). Some estimates suggest that obesityrelated medical expenses accounted for as much as 9 percent of total US medical expenditures in 1998 (roughly $78.5 billion) – a proportion that is expected to increase in the years to come (Finkelstein et al., 2003). Obesity in children, strongly linked to adult obesity, is also increasing at an alarming rate (Rossner, 2002; Dietz, 2004). Besides immediate obesity-related health risks for children (Dietz, 2004), childhood obesity rates suggest the risk of significant future increases in adult obesity, unless the epidemic is contained.

61.2  Challenges for study and intervention design In the words of one researcher, obesity is “the gravest and most poorly controlled public health threat of our time” (Katz, 2005). The scope and scale of the obesity epidemic highlight the urgent need for well-crafted policy interventions to prevent further spread and potentially reverse the tide. Yet several characteristics make obesity an especially challenging problem to study and to address. The first of these is the significant range in levels of scale involved (Figure 61.1) – from genes to social systems to environment – forming a “hierarchy” of levels (Glass and McAtee, 2006). Each of these levels is the traditional domain of a different field of science, and each entails very different methodologies for measurement. This breadth means that better understanding of obesity may require interdisciplinary approaches and new techniques.

Levels of scale implicated in obesity

Environmental

Policy

Environmental Economic, markets Social Social/networks Cognitive Intraindividual

Neurochemical/metabolic Genetic

Figure 61.1  Levels of scale that play a role in the obesity epidemic. Sources: For more information on each level, see: genetic (Stunkard et al., 1986; York and Bouchard, 2000; Comuzzie, 2002; Rossner, 2002; Keller et al. 2003; Perusse et al., 2005), neurochemical/metabolic (Tschop et al., 2000; Schwartz and Morton, 2002; Woods and Seeley, 2002), cognitive (Killgore et al., 2003; Broberger, 2005), social networks (Burke and Heiland, 2006; Christakis and Fowler, 2007), economic/markets (Cutler et al., 2003; Drewnowski and Darmon, 2005; Finkelstein et al., 2005) and environmental (Booth et al., 2005; Papas et al., 2007).

Another related challenge is the diversity of actors who have an impact on individual energy balance (and thus on obesity). A partial list includes consumers, the food industry, families, schools, retailers, government agencies, policy-makers, trade associations, NGOs, public health agencies, the media, and healthcare providers. Each of these actors has different goals, motivations, modes of decision-making, and forms of connection to other actors and levels above and below them in the hierarchy of levels. Policy shifts or other interventions will affect each differently, and each has a different sphere of potential influence as an agent of change. Without taking into account the diversity of these actors, policies cannot leverage potential synergies. In addition, they run the risk that successful interventions in one area might be counteracted by responses elsewhere in the system. Policies that do not take into account the

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61.3  Complex adaptive systems

full set of actors and their responses can backfire dramatically.1 A third challenge is the number of mechan­isms implicated in the obesity epidemic. For example, a wealth of evidence documents the roles of prices (Cutler et al., 2003; Drewnowski and Darmon, 2005; Finkelstein et al., 2005), social networks (Christakis and Fowler, 2007), genetics (Stunkard et al., 1986; Keller and Pietrobelli, 2003), neurobiology (Tschop et al., 2000; Schwartz and Morton, 2002; Killgore et al., 2003; Broberger, 2005), environment (Booth et al., 2005; Papas et al., 2007) and norms (Rand and Kuldau, 1990; Kumanyika et al., 1993; Kemper et al., 1994; Parker et al., 1995; Powell and Kahn, 1999; Fitzgibbon et al., 2000; Lovejoy, 2001; Patrick and Nicklas, 2005). However, the linkages and feedback between these mechanisms are neither well studied nor well understood. Furthermore, no single explan­ ation seems to account for all that we know about the obesity epidemic. For example, prices provide a compelling explanation of the overall upward trend in obesity incidence (Figure 61.2), but may not be able to account for the important disparities in incidence across sociodemographic groups (Figure 61.3) (see Burke and Heiland, 2006), or explain why obesity appears to move through social networks (Christakis and Fowler, 2007). Neurobiology and genetics help explain the resilience of obesity at both the social and individual levels, but have difficulty explaining the timing and speed of the epidemic, or its spatial clustering. Environmental

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explanations capture much of the spatial variability in obesity incidence, but cannot explain its spread across longer distances through networks, or variations within spatially coherent demographic groups. In sum, the obesity epidemic results from a system with diverse sets of actors, at many different levels of scale and with differing individual motivations and priorities. This system has many moving parts that interact in complicated ways to produce rich variation in outcomes and cannot be reduced to a single mechanism. Taken together, these features are classic characteristics of a complex adaptive system (CAS). Indeed, organisms, societies, economies and public health systems in general are complex adaptive systems; valuable insights about how to manage them can be gained from the relatively new, interdisciplinary field of complexity science.

61.3  Complex adaptive systems A complex adaptive system is one composed of many diverse pieces, interacting with each other in subtle and non-linear ways that strongly influence the overall behavior of the system. Complex adaptive systems, sharing many general properties, can be found in fields of study as diverse as economics, political science, sociology, anthropology, physics, public policy, biology, geology,

1

A particularly good illustration of this general point can be found in the case of the Lake Victoria environmental catastrophe (Murray, 1989; Fuggle, 2001). In 1960, the Nile perch, a non-native species of fish, was introduced into Lake Victoria. The policy goal was to provide a new, valuable source of protein and improve the health of the human communities surrounding the lake, in Kenya, Tanzania and Uganda. However, the policy did not take into account the other actors in the system – specifically, the other organisms and ecosystems of the lake. The introduction of the perch set off a chain reaction in these ecosystems. The perch wiped out the native cichlid species of fish, which were crucial in controlling a species of snail living in the lake, and so the snails flourished. Unfortunately, these snails are hosts to the larvae of Schistosomes, which cause the disease bilharzia in human beings – a disease that is always fatal if not treated promptly. The Schistosomes also flourished. Thus, the original policy goal (improving the health of the surrounding human communities) backfired, because the reaction of another set of actors in the system was not anticipated. Efforts to reduce obesity might face similar difficulties if systemic diversity is not factored into policy design.

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70.0

Incidence of overweight and obesity in United States 1960–2004

60.0 50.0

%

40.0 30.0 20.0 10.0

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Figure 61.2  Upward trend in incidence of overweight and obesity, United States. Also of interest is the apparent acceleration in the 1980s. Sources: Data are from the National Health and Nutrition Examination Survey and National Health Examination Survey, compiled by CDC at http://ftp.cdc.gov/pub/Health_Statistics/NCHS/Publications/Health_US/ hus06tables/Table073.xls.

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Incidence of overweight, United States 1976–2004

80.0 70.0 60.0 50.0 Total overweight White male White female Black male Black female

40.0 30.0 1976–1983

1988–1994

1999–2002

2001–2004

Figure 61.3  Disparities in incidence of overweight by race and gender, US. Sources: Data are from the National Health and Nutrition Examination Survey and National Health Examination Survey, compiled by CDC at http://ftp.cdc.gov/pub/Health_Statistics/NCHS/Publications/Health_US/hus06tables/Table073.xls.

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61.4  Applying a complex systems view to obesity

computer science and business. The general properties of such systems include the following. 1. Agent-based. Complex systems often contain many levels of hierarchy, but are usually driven from the “bottom up”, through the decentralized, local interaction of constituent parts. Each level is made up of autonomous individual actors who adapt their behavior (based on observations of the system as a whole or of others around them) through mechanisms such as learning, imitation or evolution. 2. Heterogeneity. Substantial diversity among actors at each level of the hierarchy may play an important role in the rich dynamics of the system. Actors have separate and possibly diverse goals, rules, adaptive repertoire, etc. 3. Interdependence. Complex systems usually consist of many interdependent interacting pieces, connected across different levels. There is often feedback between the different levels of the system, as well as substantial nonlinearity resulting from the interconnectedness of actors at each level. 4. Rarely in equilibrium. Complex systems are generally very dynamic and spend little time in any long-run equilibrium. Therefore, dynamics “far from equilibrium” are important. The particular order in which events occur may strongly affect the subsequent direction of the system (a characteristic often known as “path dependence”). 5. Emergence and tipping. Complex systems are often characterized by emergent, unexpected phenomena – patterns of collective behavior that form within the system often cannot be predicted from separate understanding of each individual element. In other words, the sum can be greater than the parts. Such emergent features may be “self-organized” (occurring from the bottom-up with no centralized direction). Complex systems are also often characterized by “tipping”

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behavior at system level. Non-linearity means that the impacts caused by small changes can seem hugely out of proportion. The system may spend long periods in a relatively stable state, yet be easily “tipped” to another stable state by a disturbance that pushes it across a threshold. Prediction is particularly difficult in complex systems because there are multiple forces shaping the future, and their effects do not aggregate simply. Non-linear interactions among pieces of the system mean that a coincidence of several small events can generate a large systemic effect. Yet, even in the face of substantial uncertainty about the future, insight regarding the right questions can prove valuable for decision-making (Axelrod and Cohen, 1999). The study of complex adaptive systems in a unified framework is emerging as a relatively new, interdisciplinary scientific field. The science of complex systems can help to guide research identifying the types of situations most amenable to policy intervention, and the points where leverage may best be applied for any particular policy goal.

61.4  Applying a complex systems view to obesity Complexity can be a source of concern for policy-makers because it creates uncertainty and makes the interconnected dynamics of societies and economies difficult to understand or uncover. It can be hard to know where to begin intervening, or how different approaches may be linked. Complexity can also be an opportunity, however, because it provides possibilities for creating large-scale changes with relatively small, focused policies. Although it is often difficult to predict their behavior, complex systems do have significant structure and organization, and can be managed or directed through careful intervention. Several general lessons from the study of com­plex systems are especially applicable to the

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study of energy balance and to the design of strategies to prevent or reverse the obesity epidemic. These are presented below.

61.4.1  Bottom-up approaches As described above, the dynamics of most complex adaptive systems are driven “from the bottom up”, by individual actors throughout the system. The individual is a natural initial focus in studying obesity – the core concern is, after all, individual energy balance. The range of systems implicated in the obesity epidemic is much broader, however – markets, societies, governments and food chains all play a role. Yet each of these is itself also a complex system, driven by the decision-making of relevant individual actors (firms, policy-makers, family members, etc.). This focus on individually-driven dynamics leads to a first major lesson from complexity science: decentralized solutions to policy can sometimes be most effective, even if traditional policy tools are macro ones. “Bottom-up” dynamics may respond most readily to “bottom-up” interventions. Of course, exploring which combinations of national, regional or local policies might provide the most effective decentralized intervention is a challenge. The link between the macro-world of policy space and the micro-level of individual incentives and decision-making is usually not a transparent or simple one. Fortunately, tools and techniques from complexity science can help elucidate these links. In particular, agent-based computational models (described in detail below) mirror the bottom-up structure of complex systems and can provide valuable “laboratories” for studying decentralized dynamics and discovering novel policy approaches.

61.4.2  Diversity matters Complexity research also highlights the importance of diversity (see Page, 2007). Complex systems often involve diverse types of actors

(as in the case of obesity), with different goals and decision-making processes for each type. There may also be substantial diversity among actors of any given type, in socio-demographics, age, gender, experience, network structure, genetics, etc. These within-type differences can affect individuals’ decision-making in important ways as well. Diversity is therefore a major challenge for policy, since no solution or intervention necessarily fits all circumstances or affects all decision-makers in the same way. Diversity can also be an opportunity, since it allows for rich adaptation and “tipping” (see “Tipping points” below). The design of policy interventions must take diversity into account, however, or they may be ineffective or even counterproductive. For example, a recent model of adolescent smoking and public health messages (Axtell et al., 2006) showed how the distribution of psychological “reactance” responses in networks could have a strong impact on the most effective kind of policy message – a message that worked well in some groups proved counterproductive in others.

61.4.3  Tipping points Non-linearity and feedback in complex systems mean that small changes at either the micro- or the macro-levels can have large effects on the system. Marginal shifts can be enough to “tip” an entire system from one relatively stable outcome to another. Tipping has important implications for policy-making in a complex world. It means that policies that do not take complexity into account may have unanticipated consequences, but it also means that, if properly targeted, very small changes in policy can have a very large impact: local changes can yield global effects. For example, work on the dynamics of bureaucratic corruption has shown how small changes in enforcement might lead to widespread reduction of corrupt behavior, “tipping” society from high to low levels of corruption (Hammond, 1999). Complexity science shows that the most effective

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61.5  Agent-based computational modeling

role for policy can sometimes be a suite of small, targeted interventions that “tip” systems, rather than large ones that attempt to restructure or reshape them.

61.4.4  Designing interventions A central theme in the general lessons described above is that, unless we recognize and understand the complexity of a system, interventions may have unanticipated (and sometimes unwanted) consequences. The best policies for any particular goal may be subtle and unconventional. Complex systems techniques can help uncover the intricate dynamics of economic, social and biological systems, and inform effective policy intervention. Agent-based computational modeling is one such technique.

61.5  Agent-based computational modeling In order to study the rich dynamics of complex systems, the methodology of agent-based computational modeling (ABM) is often used. This is a powerful and relatively new approach in which complex dynamics are modeled by constructing “artificial societies” on computers. In an ABM, every individual actor (or “agent”) in the system is explicitly represented in computer code. These agents are placed in a spatial context with specified starting conditions, and are given a set of adaptive rules for interaction with each other and with their environment. The agents’ decision processes and their interactions produce the output for agents themselves, and for the system as a whole. In this way, the computer simulation “grows” macro-level patterns and trends from the bottom up (see Epstein, 2006). While maintaining a high degree of analytical rigor, agent-based models offer several particular advantages for modeling complex systems, such as the obesity epidemic. First, they allow for

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substantial diversity among agents – no “representative agents”, homogeneous pools or other forms of aggregation are required, because every individual is explicitly modeled. This means ABMs can easily capture and incorporate diversity in the types of actors in a system, as well as any relevant heterogeneity within types (in socio-demographics, networks, location, goals, decision-making, psychology, physiology, genetics, culture, etc.). As discussed above, taking diversity into account is often critical in designing successful interventions in complex systems. The agent-based approach also allows for much more flexible cognitive assumptions about individual decision-making and informationprocessing than do many standard forms of modeling. Agents in simulation models are not required to be “hyper-rational” or “optimizing”, but merely goal-oriented in the context of limited and changing information. This kind of “bounded rationality” is often more plausible for modeling real-world decision-making (Simon, 1982), and is an important source of diversity as well. In addition, agent-based models can incorporate feedback dynamics and explicit spatial contexts that can be difficult to capture with mathematical formalism. At the individual level, for example, they can model multiple inter­ dependent sources of influence on a health outcome. At the aggregate level, they can model the interaction of actors and environments across multiple levels of analysis – agents can be implemented at multiple levels of scale simultaneously. ABMs can include explicit representations of geography – from GIS data, for example (Axtell et al., 2002) – as well as detailed social network structures. Explicit space is often hard to include in standard analytic models (see Page, 1999). A particular advantage of the ABM approach for studying complex adaptive systems comes from its focus on mechanisms and its ability to study non-equilibrium dynamics. Since com­plex systems are rarely in equilibrium and are often susceptible to dramatic “tipping”, this flexibility is especially important. Because of its

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focus on mechanisms, ABM allows adaptation (evolution, learning, imitation) to be modeled explicitly, and “emergent” social level phenomena to be uncovered. Finally, agent-based models are especially useful as a “computational laboratory” for policy. With an ABM, research can systematically explore the potentially complex impacts of each item on the existing menu of policy interventions, and can even uncover new ones; as discussed above, the best policies may be subtle and unconventional. Recent computing advances, in fact, allow researchers using agent-based models to search the entire space of possible combinations of interventions to find novel “policy cocktails” that best exploit potential synergies between policies.2 Agent-based models have been used to study a wide variety of topics in social science and public health, including: cooperation, coordination and conflict (Axelrod, 1984; Epstein, 2002; Cederman, 2003); prejudice (Hammond and Axelrod, 2006); social norms (Axelrod, 1986; Epstein, 2001; Macy and Willer, 2002; Centola et al., 2005; Hammond and Epstein, 2007); archaeology (Axtell et al., 2002); epidemiology (Longini et al., 2005; Ferguson et al., 2006; Epstein et al., 2007); cancer research (Axelrod et al., 2006); firm organization, economics and business strategy (Axelrod and Cohen, 2000; Tesfatsion and Judd, 2006); and markets (Kirman and Vriend, 2001; Macy and Sato, 2002). ABMs have been able to provide important policy guidance in several instances. Recently, for example, work using agent-based modeling helped the US government understand the potential impact of travel restriction policies on the pattern of global epidemics (Epstein et al., 2007) and various vaccination strategies for containing a smallpox epidemic (Longini et al., 2007). Other work using this technique (Axtell and Epstein, 1999) helped explain the unexpectedly slow response to changes in US retirement policy.

An ABM approach to obesity would permit the modeling of multiple mechanisms simultaneously, across several levels of scale, with the inclusion of important sources of diversity. For example, “agents” might be individual consumers, placed in an environment with opportunities for eating and for physical activity. Within each agent might be a representation of metabolic mechan­ isms or genetics, with the appropriate degree of population diversity reflected in variations among agents. These agents could be embedded in a social structure with multiple sources of influence on eating and activity (peers, parents, media, etc.). Individual behaviors would then adapt over time through interaction with the environment and society, in ways shaped by genetics and the metabolism. Such an approach could offer a deeper understanding of the full complexity of the obesity epidemic, and could permit experimentation with different forms of policy intervention, both to slow and to reverse the epidemic. (For a more detailed outline of how such a model might take shape, see Hammond and Epstein, 2007.)

61.6  Conclusion Obesity has become an important public health crisis worldwide. The obesity epidemic urgently requires well-crafted policy interventions, but also represents an especially challenging problem for study and for policy design due to its complexity. Many of its features – breadth of scale, diversity in actors, and multiplicity of mechanisms – are hallmarks of a complex adaptive system. The lessons and tools of complexity science can help to understand and to combat the obesity epidemic, but only if complexity is taken seriously and the problem of obesity is approached from a systems viewpoint. Agent-based computational modeling is an especially promising avenue for future research and for policy exploration.

2

Efficient searching of large parameter spaces in agent-based models is made possible with additional techniques from complexity science, such as Genetic Algorithms (Holland, 1992).

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References

Acknowledgments I would like to thank Joshua Epstein and Matthew Raifman for valuable comments.

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62 Conclusion: A Whole-of-Society Approach to Obesity Prevention: New Frontiers in Science, Policy and Action, and the Emerging Models of Capitalism and Society to Make it Possible Laurette Dubé, on behalf of the Editorial Team James McGill Chair, Consumer Psychology and Marketing, Desautels Faculty of Management, McGill University, Montreal, Canada

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62.1  Introduction The Brain-to-Society (BtS) model has been introduced as the conceptual anchor of this handbook. It entails a “brain” and a “society” system. It delineates the critical role of the brain and other biological systems in guiding individual food choice in response to the environment.

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62.4.1  Individual Level 62.4.2  The Whole of Society 62.5 Emerging Models of Capitalism and Society

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“Society” refers to the power of organizational and collective choices made by organizations and governments in matters of health, education, agriculture, agri-food, transportation, urban planning, media, and the other areas that shape the environment and define, at the aggregate level, the exposure conditions that shape individual-level relationships.

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62.  Conclusion

The BtS model views the health and economic motives, processes and outcomes of all individual, organizational and collective choices as being part of the same complex system in need of re-alignment to ensure a healthy eating lifecourse for all. The various accounts presented in this handbook have painted a complex picture of the rise of obesity trends. Human biological conditioning has been challenged by contem­porary social and economic conditions arising from industrialization, globalization and urbanization. Calories are plentiful, and opportunities for physical activity are limited. The consequence of such a combination has translated into an obesity pandemic with severe health and economic consequences. Revolutionary changes in practices and policies are needed not only in health, but also in domains as varied as education, agriculture, agri-business, media, and community planning. Only such action can ensure that we impact individual choice and behavior with suf­ ficient power to provide a nutritious diet and healthy lifestyle for all. A Whole-of-Society (WoS) approach to obesity prevention progressively emerges as the next frontier. From this perspective, health must be mainstreamed into the everyday lives of individuals, communities, organizations, markets and societies, in a manner that is hedonically, socially, culturally and economically sustainable. As mentioned in the introductory chapter, the WoS approach is: (1) transdisciplinary – scientists, researchers, decision-makers and strategists from all fields work together to develop shared conceptual and methodological frameworks and strategies for policy change and innovation, which not only integrate but also transcend their respective disciplinary perspectives; (2) multi-sector – actors from public agencies, communities and the private sector from all social and economic domains that contribute to lifestyles are mobilized to place health on their strategic agenda; and (3) multi-level – ­individuals, policy-makers and strategists whose decisions at the community, ­ municipal, provincial/state, national, transnational and global levels influence

the environment in which individual lives are involved. This chapter further elaborates upon the new frontiers of science, policy and action this approach entails, as well as the new models of capitalism and society that can make it possible.

62.2  New frontiers in science In a context where many of the needed changes to prevent obesity and chronic diseases lie outside the boundaries of the health sector, and where the environmental forces that shape the choice architecture for individual behavior operate at local, national and global levels, it is clear that obesity prevention must be informed by a highly sophisticated understanding of the complex mechanisms, motives and success criteria that guide individuals’ and social and economic actors’ decision-making and actions. A new science is needed to inspire the design and implementation of individual, social and business innovations and policies to effectively tackle these issues. This new science must address the broad, complex and dynamic biology–psychology–environment interplay that determines everyday individual and family food choices and eating behavior. It must also examine the interplay of choices made by schools, communities, business, civil society organizations and governments, which shape dynamics along local, national and global value chains and communities. This means promoting a lifespan approach to healthy eating for individuals and populations, and improving the ability to test causal health and non-health mechanisms and outcomes of interventions, and social and business innovation or policy. To do this, science needs to: (1) map the genetic, epigenetic, and other “brain systems” pathways shaping human decision-making and lifestyle choices, and their physical and mental health; (2) examine the collective choices made by actors in “society systems”, such as families, communities and

2.  From Society to Behavior: Policy and Action

62.2  New frontiers in science

businesses, in relation to the motives, processes, policies and outcomes driving decision in their respective mainstream domains; and (3) study the structural and dynamic features of the complex system linking biology to lifestyle choice behavior to environment, in order to inspire innovation, entrepreneurship and leadership in the design of feasible, impactful, sustainable and scalable interventions and policy changes in health, society, culture and the economy. Therefore, this new frontier of science transcends boundaries across disciplines, bridging theories and data on genes, brain, behavior and environment. It supports the multi-level and cross-sectoral changes needed to create a choice architecture that will prevent obesity. It must

781

also better understand the complex dynamics within and between developed and developing countries, as they are part of the same world system in need of re-alignment. This new science must also integrate systems science approaches in order to capture non-linear terms and multiple-level interactions, which typical epidemiological research does not seize. The UK Foresight Report, released in 2007, highlighted the significant potential which such an approach holds for obesity prevention, in the form of an impressive map of the multi-level antecedents of obesity (Figure 62.1; Foresight, 2007). To fully tap into the potential of systems science to address obesity and chronic diseases, it is critical to go beyond the mapping of the factors

Figure 62.1  Foresight: the obesity system map. Reproduced with permission from the Foresight Project on Mental Capital and Wellbeing (Foresight, 2007).

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62.  Conclusion

that influence health, and explore in a tract­able and actionable fashion how these factors act upon and interact with each other. Computational systems science methods, by encompassing bottomup approaches such as agent-based modeling (see Chapter 61) and top-down methods such as systems dynamics modeling, are particularly promising because of their ability to bridge the different levels of theories, data and evidence bearing on individual choice and their underlying biological and environmental determinants. Finally, the new frontiers for science to fully support a WoS approach must also be translational and networked. This is a prerequisite to fully examine the pathways by which biology influences individual choice and behavior, which are in constant interaction with the local, national and global features of the environment. Science must inform and be informed by the organizational and collective choices made by community, business, government, and other organizations and systems. Ultimately, systems science will be valuable only if its results can be translated and widely disseminated into science-informed strategies for behavior and policy change at both the individual and aggregate levels to make an impact on population.

62.3  New frontiers in policy New frontiers in public policy must not only occur in health, but also in the broad array of social and economic domains which impact the lifestyle choices individuals make. These include social and welfare support, education, transportation, urban planning, media, agriculture, trade, and industrial development. Lifestyle-related policy often takes the form of broad societal plans varying in the degree of the specificity of their goals and recommendations. More recently, many have adopted whole-ofgovernment approaches to policy development

and implementation. For instance, the European Anti-Obesity Charter, adopted by 53 health ministries in November 2006 in Turkey, sets goals and timelines, principles to guide action, as well as a framework linking main actors, policy tools and settings to translate these principles into actions (WHO, 2007). The Quebec Governmental Action Plan (2006) aims to reduce, in youth and adults, the obesity prevalence rate by 2 percent and the overweight prevalence rate by 5 percent, by 2012. A set of 75 actions along 5 main directions were proposed, namely: to foster healthy eating, to foster a physically active life, to promote supporting social norms, to provide better services to persons with weight problems, and to foster research and knowledge exchange. ActNow British Columbia specifies in great detail the lifestyle change and obesity targets for 2010: to increase the percentage of people eating at least five servings of fruits and vegetable every day by 20 percent, to increase the percentage of people physically active by 20 percent, to reduce the percentage of overweight or obese adults by 20 percent, and to increase the number of women counseled about alcohol use during pregnancy by 50 percent (ActNow BC, 2009). In setting new frontiers for policy development, we propose scaling up whole-of-government/joined-up approaches to a Whole-of-Society approach to policy development. This holds the potential to better harness the power of individuals, communities, businesses and the rest of society in the fight against obesity. The diversity of actors involved in WoS policy development goes beyond governmental actors to encompass all actors who have the power to drive change on the ground. The success of policy development and implementation in this context critically depends on action being taken at all levels of decision-making by a rich diversity of stakeholders, with each investing resources and competencies into the strategy (Figure 62.2). This presents both challenges and opportunities. A Whole-of-Society (WoS) approach to the development and implementation of an effective

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62.4  New frontiers in action

obesity prevention strategy will require that governments take up a diversity of roles like never before, being in turn “commander in chief”, imposing mandatory regulations that define boundaries and rules for consumers and all stakeholders; provider of public goods and services; steward of public resources; and partner in various collaborative undertakings with other jurisdictions, businesses, and civil society organiz­ ations (Figure 62.3). Challenges lie in judiciously matching role, context and measure – for example, determining when mandatory rules and regulations are needed for public good and consumer protection versus when participation- and trust-based approaches are relevant. The possibilities lie at the heart of the creative power and determination of all actors. Fresh analysis and dialogue can emerge from disciplinary breaks at

Consumers

Grassroots community

Small and medium businesses

the sectoral or within-level silos, and result in the creation of unforeseen horizons that are worth the investment.

62.4  New frontiers in action 62.4.1  Individual level A WoS approach to obesity prevention begins with the individual and what really drives behavior. Most societal plans assume that, because the plans are based on sound evidence, individuals should behave accordingly. Yet decisions made by individuals – in their personal or family lifestyle decisions, or in setting the strategic and policy agenda of organizations and governments – do not always correspond with

Cooperatives

Large NGOs (global and national)

Large businesses (national and transnational)

Public policy in its many roles Figure 62.2  Consumers and stakeholders involved in Whole-of-Society policy development and implementation. Source: Adapted, with permission, from a presentation by C. K. Prahalad at the Global Convergence Building Workshop, Montreal, 2008, commissioned by the Bill and Melinda Gates Foundation.

Regulator

Provider of public goods and services

Steward of public resources and investments

Partner in multi-sector collaboration

Enabler of social and business innovation

Enabler of Whole-ofSociety action

Figure 62.3  Public policy in its many roles. Source: Adapted, with permission, from a presentation by C. K. Prahalad at the Global Convergence Building Workshop, Montreal, 2008, commissioned by the Bill and Melinda Gates Foundation.

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62.  Conclusion

rational choice. Shortcuts, biases, gut feelings and other neurobiological signals, irrational cultural beliefs and habits often compete with deliberate consideration of knowledge and rules of utility maximization. While consumers may be aware of the factors that contribute to obesity, their simple, everyday lifestyle choices do not reflect this knowledge. Rather, these choices are often made in a less conscious and more impulsive manner. Individual behavior is not completely free and independent from the environment in which it occurs. For instance, children, owing to their neurobiological, cognitive, emotional and social development, are highly vulnerable to caloric excess – especially when surrounded by products, services and opportunities promoting food consumption and sedentarity. However, individual rationality remains a defining feature of the human species and a core value of society. It is vital to take fully into account all factors that drive individual behavior, both rational and less rational. Such a perspective could lead to the revision of broad societal plans and anti-obesity interventions. Rather than simply providing information, interventions can tap

into the non-rational drivers of behavior in order to motivate healthier behaviors. Conversely, for businesses that have long tapped into the less-rational side, factoring more of the rational side in their plans and actions could open novel avenues to creating value for the individual and the corporation, while contributing to preventing childhood and adult obesity. For policy- and decision-makers, it highlights the importance of garnering personal commitments at all levels to improve the health and wellbeing of individuals while simultaneously promoting economic growth.

62.4.2  The whole of society A lens is needed to foster convergence among conflicting perspectives in regard to behavioral change, grassroot, community, social and business innovation, and public policy. Figure 62.4 offers such a convergence lens. It combines a paradigm of principles and a mechanism for innovation and implementation which contribute to obesity prevention. It balances the focus

Economic sustainability

Health and social equity

Environmental sustainability

Convergence architects for innovation and action

Revised cost/price, social and economic performance

-Individuals -Communities -Governments Local/global balance and scalability

Risk management and local/global balance

-NGOs -Businesses

Market focus

Society focus

Figure 62.4  The convergence lens.

2.  From Society to Behavior: Policy and Action

62.5  Emerging models of capitalism and society

on society and market in a socially, environmentally and economically sustainable manner. It is also transferable and scalable on a local, national and global scale. Finally, and importantly, it reduces health and business risks for all. The convergence lens is based on a model introduced by Prahalad (2008) as a set of principles that can guide policy-making and social and business decision-making and action in health and economics. The convergence lens assembles a portfolio of perspectives that must be brought to bear upon convergence goal-setting and decision-making throughout the whole of society.

62.5  Emerging models of capitalism and society Modern society has evolved within the clear boundaries of a two-pronged institutional framework with, on the one hand, market mechanisms aligning supply and demand, and on the other hand, public governance for overseeing market operations and addressing externalities, and for providing goods, services and programs in social domains such as health and human development, traditionally funded by taxes and other public or philanthropic sources. It is assumed that what business does best is doing business, and therefore health and other social parameters should remain peripheral to their strategic agenda. As good members of society, corporations are expected to pay taxes and to comply with regulations when these are deemed necessary to solve or prevent “market failures”. Clearly, such a model of society and capitalism has been a barrier to previous efforts to curb the obesity pandemic because of the pervasive and intimate alignment of what is produced and consumed in society, and because many causal agents are not readily identified as market failures. This has been further reinforced by the collapse of the financial markets and subsequent

785

global economic downturn that has highlighted the inability of both governments and markets to tackle complex issues that transcend this twopronged framework. It is now increasingly recognized that health needs to be mainstreamed into the everyday and strategic agenda of economics, and, conversely, that business and economic practices must be integrated into health, public health, and other social organizations and systems involved in obesity prevention. Indeed, there has been a recent growing interest in the re-examination of traditional capitalism as we make sense of recent turmoil. The intersection of the post-crisis re-examination of our economic assumptions, America’s overhaul of its healthcare system, and the continuing rise in the prevalence of obesity and chronic diseases around the world represents a time bomb from both health and economic perspectives, which may facilitate the development of emerging, more humanitarian, socially and environmentally responsible models of capitalism. These models promote business, social and health innovation and technology to create sustainable health and wealth for all. Such new models of capitalism offer promising alternatives to the current status quo. Creative Capitalism, for example, was introduced by Microsoft CEO Bill Gates. It seeks to address global inequity by expanding capitalism into new areas and using it to solve problems previously assigned to charities or government (Kinsley, 2008). Inclusive Capitalism was pioneered by C. K. Prahalad to harness innovation to improve both economic performance and the social determinants of health for the 2 billion individuals living in extreme poverty (Prahalad and Krishnan, 2008). Value-Based Capitalism, promoted by the Harvard Business School’s Rosabeth Moss-Kanter, argues for corporate innovation based on clearly articulated values and principles (Kanter, 2009). Sustainable Capitalism, as articulated by Stuart Hart, Founding Director of the Center for Sustainable Global Enterprise at Cornell University, views

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social, environmental and economic sustainability as key pillars of the long-term success of a business strategy (Hart, 2007). Finally, Social Business, introduced by Nobel Peace Laureate and micro-credit pioneer Muhammad Yunus, fosters whole-of-society investments and partnerships focusing exclusively on the maximization of social goals, which empower the poorest segments of society (Yunus and Weber, 2007). It brings to bear the full logistic and technological power of business, with investors allowed to withdraw their capital at any time, and with no redistribution of profit into dividends. Instead, revenue surpluses are reinvested to ensure the sustainability and scalability of the social goal and/or improve the livelihood and welfare of communities. In doing so, it turns the basic foundations of traditional capitalism on their head, bringing the power of the self into the common good, while embedding externalities into market transactions. In sum, the time may be ripe for the WoS approach to obesity prevention.

References ActNow BC. (2009) ActNow BC. Online. Available: http:// www.actnowbc.ca/everyone/ Accessed 11.27.09.

Foresight (2007) Tackling obesities: Future choices. Foresight Project on Mental Capital and Wellbeing. London: UK Government Foresight Programme. Online. Available: http://www.foresight.gov.uk/Obesity/Obesity_ final/17.pdf   Hart, S. L. (2007). Capitalism at the crossroads: The unlimited business opportunities in solving the world’s most difficult problems. Upper Saddle River, NJ: Wharton School Publishing. Kanter, R. M. (2009). Supercorp: How vanguard companies create innovation, profits, growth, and social good. New York, NY: Crown Business. Kinsley, M. (2008). Creative capitalism: A conversation with Bill Gates, Warren Buffett and other economic leaders. New York, NY: Simon & Schuster. Prahalad, C. K. (2008). Presentation offered during the Gates Foundation meeting 2008: From crisis to a new convergence of agriculture, agri-food and health. Held in Montreal, Quebec, November, 7–9. Prahalad, C. K., & Krishnan, M. S. (2008). The new age of innovation: driving co-created value through global networks. New York, NY: McGraw-Hill. Quebec Governmental Action Plan. (2006) Plan d’action gouvernemental de promotion des saines habitudes de vie et de prévention des problèmes reliés au poids 2006–2012 – Investir pour l’avenir. Québec: Ministère de la santé et des services sociaux. WHO. (2007). WHO European ministerial conference on counteracting obesity – conference report. Geneva: World Health Organization. Yunus, M., & Weber, K. (2007). Creating a world without poverty. New York, NY: Public Affairs.

2.  From Society to Behavior: Policy and Action

Index A Acculturation 633, 643 Acquired liking individual differences 171–3 and overeating 170–1 Acrasia 354 ActNow British Columbia 782 Adaptability 419–20 Addiction 74 cocaine 74 model of hunger 16–17 promotion by food restriction 75–6 sugar 74 Adiponectin 237 genetic variation 247 Adipose cells 119 Adipose tissue 245 brown 300–1 white 301 Adolescents brain response to food images 65–7 family meals 612–13 see also Children ADRB2 gene 380 ADRB3 gene 380 Adrenocorticotrophic hormone (ACTH) 262, 267 Adults influence on children’s food preferences 623–4 obesity rates 446 India 472 physical activity 392, 459 Advertising, effect on children 473–4 Affective change 7–11 African-American Collaborative Obesity Research Network 634–5 Age-related frailty 194 Ageing, epigenetic marks 193–4 Agent-based computational models 772, 773–4 Agouti-related protein 5, 8, 29, 154, 303 genetic variation 154 Agri-food, sustainability 522–3

Agricultural trade deficit 515 Agriculture 498–508 advances in 428 and environment 501–2 food crop diversity 500–1 India 478–80 agricultural policy 479 exports 478 food regulatory systems 479–80 imports 478–9 links with nutrition 500–2 as national security issue 456 smallholder farmers 517–18 sustainable 522–3 Air, addition to products 562 Alliance for a Green Revolution in Africa (AGRA) 522 Alliances 576–7 Subway and Danone 576 Subway and Discovery Kids 576 Alliesthesia 75 Allostasis 262 2a-adrenoceptor, genetic variation 153 Ambiguity 95 Americas, obesity rates 452–6 Amniotic fluid, chemical composition 207, 211 AMP-activated protein kinase (AMPK) 304 Amygdala 17, 18, 60, 106, 107, 302 role in stress response 263 Amylin 8, 223 Anabolic mediators 302 Analytical hierarchy process 581, 582–3 Anandamide 308, 309 Animals caloric restriction 140–1 feeding behavior 24–31 Anorexia nervosa 33 ghrelin levels in 291 inherited tendency 283 Anterior cingulate cortex 264 Anti-nausea agents 12 Anticipatory satiety 127, 129–30

787

Apolipoprotein AII (APOA2) gene 156 Appetite control 234 effect of low GI foods 222–4 learned 126–7 mechanisms controlling 47–52 Appetizer effect 170 Appropriate behavior 620 Apreptitant 12 2-Arachidonoylglycerol 308–9 Arcuate nucleus 28, 302 metabolic pathways 304–5 UCP2 in energy balance regulation 305 Arcuate nucleus-paraventricular nucleus pathway peripheral metabolic influences 304 role in thermogenesis 303 Area postrema 307 Area-level social capital 674–9, 682–3 Area-level social networks 682–3 Artificial sweeteners 563 Asia agriculture-nutrition linkages 500–2 dietary calorie supply and nutrition status 502–3 dietary transition 503– 4 food consumption and nutrition 498–500 impact of urbanization 504–5 overweight and obesity 505–6 policy interventions 506–7 Asia and Pacific anti-obesity initiatives 453 obesity rates 452, 453 rising incidence of diabetes 452 Asian Disease Problem 438 Associative learning 125–31 Atherosclerosis Risk in Communities (ARIC) Study 225 Attention control 344, 346–7 Attentional bias 345–6 Attitudes and beliefs 395–6 Au Bon Pain 569 Autonomous behaviors 367

788

index

Autonomy 366 Availability 51, 136 and fast-food consumption 716–17 improving 561 and income 715–16 and reward value 93 and self-control 359 Aversion-inducing agents 6–7

B Balanced diet 457 Bayes’ law 436 Behavior individual 783–4 instinctive 418 psychological explanation for 418–19 Behavior change 319–26 motivation for 319–20 self-efficacy 320, 321 social and ideological movements 323–4 stealth interventions 320–3 Behavioral goals 323–4, 325 Behavioral self-determination 337–8 Bill and Melinda Gates Foundation 751 Binge Eating Scale 237 Binge-eating 52 and caloric restriction 141 in obesity 76 restrained eaters 137–8 selective advantage of 75–6 Biopsychological factors 179–87 Birch, Leann 622 Birthweight, and obesity 473 Bitter taste 152, 205 association with poisons 163–4, 209 dislike for 163–4 ontogeny of 209–10 reduction by salt 210 Bivalent domains 193 Bloomberg, Michael 751 Body composition 355–6 Body image 396 Body industries 331 Body mass, and response to food 61–3 Body mass index (BMI) 62, 110, 116, 181, 219–20, 263 and availability of healthy food 714 developed vs developing countries 472 and education 703–4 and fast food outlets 716–17 and life satisfaction 419

and mixed land use 693 and monounsaturated fat intake 381 Body project 331 Body weight and -endorphin 31 and insulin sensitivity 245 molecular regulation 287–94 and self-control 121–2 stability 179–87 physical activity and diet 186 and stress response 261–8 Bombesin 8 Bovine spongiform encephalopathy 522 Brain function, epigenetics 196–7 Brain responses food images, adolescents 65–7 food stimuli 58–61 high-calorie foods 59, 60 low-calorie foods 60, 61 modulating factors 61–5 Brain reward system 308–9 cannabinoid system 308–9 opioid system see Opioids Brain-derived natriuretic peptide 8 Brain-to-Society model 779 Brainstem, role in energy balance 307–8 Breast milk, chemical composition 211 Brown adipose tissue 300–1, 306 Buffering 690–1 Bulimia, dysregulation of -endorphin signaling 31 Burger King 569, 585 Burgers 569 Business sector role of 526–7 as source of eating norms 597–8 Business4Health compact convergence lens 784

C Cadbury Adams 583, 585 Cafeteria diet 301 Caloric intake 555–6 reduction of 562 Caloric restriction 75–6, 140–1 animals 140–1 and binge-eating 141 effectiveness in humans 141–2 in presence of food cues 142–4

Caloric value, reduction of 561–4 Calorie counts on menus 576 Calorie supply and nutrition status 502–3 Campbell Soup Company 582, 585 Canada, obesity rates 454 Canadian Children’s Advertisers (CCA) 666 Candidate genes identification of 383 insulin resistance 246–7 Cannabinoid system 308–9 Capital 426 Capital markets 426 Capitalism 785–6 Carbohydrates 503 Cardiovascular disease and glycemic index 225–6 incidence 447 mortality 448 Caribbean, obesity rates 455–6 Carnitine palmitoyltransferase I (CPT1) gene 154 Carnivore connection hypothesis 243 CART 8, 29, 303 Cassian, John, The Institutes 656 Catabolic mediators 302 Cathepsin C 383 Caudal raphe 307 Causal map of obesity 459 Cerebellum 60 Cerebral blood flow 255 Cheap foods 430–1 Chemical irritation 206 Child-targeted programs 474–5 Childhood obesity long-term health effects 447 rates of 447 Children and advertising 473–4 benefits of family meanl 605–14 as consumers 571 dietary intake 204 energy intake 544, 547, 548 food choices 571 food intake 547, 548 food jags 549 food marketing to 467 learning about flavor 211–12 obesity rates by parental obesity and sex 706 India 472 overweight 611 physical activity 392–3, 459

789

index

playing areas for 474 sensory development 205–7 social influences, food intake 621–3 taste development 207–10 time spent with parents at mealtimes 418 Children’s Food and Beverage Advertising Initiative (CFBAI) 580, 582–4 China economic growth 410–13 GDP 412 global integration 414 poverty 413 Chipotle 569 Choice resolution 358–61 Cholecystokinin 8, 118, 152–3, 223, 303 Chromatin 191–2 Chronic diseases contribution of obesity to 448–9 financial costs 448–9 prevalence of 447–8 Chronic energy deficiency 499–500 Circadian rhythms, and food intake 278, 279 Classical conditioning 127 Coase, Ronald 432 Coca-Cola Company CSR pledge committment 582, 583, 586 Enviga 584 Cocaine addiction 74 Cocaine- and amphetamine-regulated transcript see CART Cognitive effects on food response 46–7, 48, 52 Cognitive overload 437 Cognitive performance, effects of nutrients on 130 Cognitive restraint 279 Cognitive social capital 679 Colors 128 Comfort foods 182, 262, 263 Commitment frame 356 Common Agricultural Policy 457–8 Communications 426 Communities, role of 526–7 Community interventions 629–47 contextual issues 643–4 cultural influences 643–4 cultural targeting 632–41 evaluation 646 impact of 645–6

insiders vs outsiders 644 religious organizations 638, 642–3 research and program delivery 644–6 study design 644–5 Competence 366 Competition among restaurants 572–3 brand loyalty 573 core foods 572 meal periods 572 service processes 572 technology 573 upscaling 573 upselling 573 Complementary approaches 460 Complex adaptive systems 769–71 agent-based 771 application to obesity crisis 771–3 bottom-up approaches 772 designing interventions 773 diversity matters 772 tipping points 772–3 emergency and tipping 771 heterogeneity 771 interdependence 771 ConAgra 583, 585, 586 Concreteness bias 437 Conditioned cues, response to 17–19 Conditioned flavor aversion 164 Conditioned place aversion 7, 10 Conditioned place preference 7, 9–10 Conditioned preference 126 Conditioned stimulus 10, 164 Conditioned taste aversion 7, 9, 11, 12–13, 164 Conflict identification 355–8 Conformity food selection 620–1 intake control 619 ConScreen 531 Consequences, neglect of 437 Consumer purchasing trends 559–61 convenience foods 560 diets 560–1 gourmet and ethnic foods 559–60 organic and natural foods 559 trust marks 560 Consumer research 529–41 origins of 530 product concepts 530–1 rule developing experimentation 531–41

Consumerism condemnation of 330–1 rise of 330–1 Contentment 8 Contextual issues 643–4 Controlled behaviors 367 Convenience foods 560 Copy number variants (CNVs) 150, 151, 379 Corporate branding 663 Corporate social responsibility 579–89 activities 589 vs pledge commitment 583, 585–6 literature review 581 pledge commitments 583, 585–6, 588 yearly activity 584 Corsets 331 Corticosterone 8 Corticotrophin-releasing hormone 8, 9, 262 Couch-Tard 573–4 Council of Better Business Bureaus (CBBB) 580 Counteractive control theory 359–60 conscious control 359–60 non-conscious control 360 CpG dinucleotide 192 CpG islands 192, 198 Creative capitalism 785 CREB 77 Cross-sectional research 683–4 CSR see Corporate social responsibility Cue reactivity 19 fMRI 20 hypersensitivity in obesity 108–10 Cue-potentiated feeding 19 Cue-response links 350 Cultural influences 643–4 Cultural relativism 643 Cultural targeting 632–41 Culture and eating norms 595–6 as economic externality 418 Cytokine signaling 3 197

D DAMGO 26, 28 Dance for Health program 321–2 Darden Restaurants, Seasons 52 575 Deane, Phyllis 424 Decentralized solutions 772

790 Decision-making 96–9 biases and shortcomings 436–8 hyperbolic discounting 437 limited cognitive capacity 437 neglect of consequences 437 prediction failure 437–8 present-biased preference 437 reference dependence and loss aversion 438 status quo and default bias 436–7 vulnerability to framing effects 438 deficit in obesity 108–12 hypersensitivity to reward of food/food-related cues 108–10 hypoactivity in reflective system 110–12 neural systems 106–8 Default option bias towards 436–7 resetting 440 Delta opioid receptor 25–6 Delta-9-tetrahydrocannabinol 308 Depression 184, 396 clinical threshold 185 and glucose homeostasis 185 Descriptive eating norms 593–5 Developing world body mass index 472 changing food systems 511–19 factors driving food demand changes 512–14 changing lifestyles 513–14 increased incomes 512 urbanization 512–13 factors driving food supply changes 514–15 decreasing price of food 514 globalization and liberalization of trade 514–15 trade balance 515 impact of food supply/demand changes 515–18 diet diversification 515–16 smallholder farmers 517–18 supermarkets 516–17 obesity patterns 706 role of institutions and research 518–19 see also Asia; India Developmental programming 197 epigenetic marks 193–4 maternal care 196 taste 207–10

index

bitter taste 209–10 salt taste 208–9 sweet taste 207–8 Diabetes Asian populations India 505–6 rising incidence 452 and glycemic index 224–5 incidence 506 rates of 447 Diet and body-weight stability 186 cost vs composition 731, 734–5 diversification of 515–16 energy from 498, 499 four eras of change 488–9 historical perspectives 487–92 low-fat 236 Paleolithic vs modern 489 very low-calorie 333 Diet optimization 734–5 Diet, Physical Activity and Health Strategy (DPAS) 752 Diet police 333 Diet-gene interactions 386 Diet-genotype interactions 380 Dietary energy supply 502 Dietary transition in Asia 503–4 carbohydrates 503 milk and dairy products 504 protein 503–4 vegetables and fruit 504 Dieter’s dilemma 353–61 Dieting 329–39, 353–61, 560–1 current views 329–35 ease of 144 effectiveness of 333 fad diets 332–3 failure of 75 and food choices 358 Dining experience 571 Disinhibition 237 Diurnal rhythms 130 Diversity 234–5, 772 DNA methylation 194 DNA methylation transferase 1 (DNMT1) 193 Domino’s Pizza 572 Dopamine 18, 74, 303 effect of food restriction 79–80 release of 16 Dopamine agonists, food restriction and response to 76–7

Dopamine receptors, D1, activation of 77–9 Dorsal vagal complex 307 Dorsolateral prefrontal cortex 107 DRD2 genotype 155 Driving forces 420 Drugs of abuse, food restriction and response to 76–7 Dual burden households 465 Dual-energy X-ray absorptiometry (DEXA) 224 Dynorphins 8, 26, 308

E Early Childhood Longitudinal Study 609 Early life experiences 203–13 Eating as focal activity 418 restraint 135–44 sensory-specific anticipatory 127–30 Eating behavior 15, 16, 125–6, 253, 275–83, 594 consummatory phase 150 effect of government guidelines 597 environmental influences 276–8 and food reward 346 genetic factors 149–57, 276 hunger and satiety 253–4 and implementation intentions 348–9 initiation phase 150 learned 126 models of 16, 20 procurement phase 150 and stress response 261–8 see also Eating norms Eating habits 346 Eating norms 593–601 and healthy outcomes 600 injunctive vs descriptive 593–5 and marketing practices 600–1 situationality of 595 and socialization 595–8 violations 598–600 negative emotions 599–600 social censure 598 threats to self-esteem 598–9 Eating rate 51–2 Ecological systems theory model 630 Economic development 426–8 measurement of 429 per capita GDP 427, 429–30

791

index

Economic externalities culture 418 negative 432 obesity crisis as 431 Economic growth 407–16, 467–8 accelerating and sustaining 409–10 China and India 410–13 GDP 411, 412 global integration 414 and obesity 413–16 poverty 408, 411 trade 409, 411 vs starting wealth 428 Economics of obesity 727–38 food choices 728–9 high cost of healthy foods 730–2 low cost of energy-dense foods 729–30 Education access to 466–7 and body mass index 703–4 and food choices 557–8 healthy eating 575–6 and mortality rate 702, 703 Effort 94–5 Embarrassment 396, 599 Embodiment 633 Embryonic stem cells 193 Emotional eating 63 Employment and metabolic syndrome incidence 705 and mortality rate 702 Endocannabinoids 303, 308–9 Endocrine factors 50 -Endorphins 268, 308 and bulimia 31 and underweight 33 and weight control 31 Energy, chronic deficiency 499–500 Energy balance 5–6, 234–5 brainstem in 307–8 failure of 15 and food intake 152–5 lateral hypothalamus in 305–7 markers of 231–8 regulation of 265–7, 300–1 ghrelin 266–7 insulin 266 leptin 265–6, 289 MCH 306 melanocortin 303–4 orexin 307 UCP2 305 ventral striatum in 308–9

Energy density 51, 543–51 definition of 544–5 and energy intake 545–7 food values 545 importance of 545 and palatability 169–70, 729 Paleolithic vs modern 489 reduction of 547–50 benefits for children 550 research directions 550–1 Energy expenditure 119–20 and brown adipose tissue thermogenesis 300–1 decrease in 116 and physical inactivity 490–1 Energy imbalance 491 Energy input 52 Energy intake children 544, 547, 548 and energy density 545–7 increase in 116 Paleolithic vs modern 489–90 Energy regulation, evaluation of 7–11 Energy sources, Paleolithic vs modern 490 Energy-dense foods cost of 729–30, 734–5 palatability of 169–70 preference for 117–18, 165–6 English Longitudinal Study of Aging 709 Enkephalins 28, 308 ENPP1 gene 378 Environment and agriculture 501–2 and socio-economic status 713–22 Environmental influences 276–8 physical activity 395 Epictetus 659 Epigenetic marks 193–4 effects of physical activity 197–8 Epigenetics 191–3, 379 and brain function 196–7 developmental programming of obesity 197 Epigenomics 191–3 four Rs 194–5 nutritional 194–5 Epistasis 379 Epsilon cost temptation 355 ERK 1/2 MAP kinase signaling cascade 77 activation by drugs of abuse 78 upregulation by food restriction 78

Ethnic foods 559–60 Ethnic minority populations, obesity prevalence 631–2 Ethnicity-directed interventions 629–47 EURODIAB Complications Study 224 Europe anti-obesity initiatives 451 Common Agricultural Policy 457–8 obesity rates 450–1 European Anti-Obesity Charter 782 European Charter on Counteracting Obesity 450 European Prospective Investigation into Cancer and Nutrition (EPIC) study 152–3 Evaluative conditioning 166–7 Exercise see Physical activity Expectancy-value model 394 Exports 515 Exposure to flavors 164

F Fad diets 332–3 Familiarity 164 Family dining 569 Family eating norms 596 Family meals 605–14 adolescents 612–13 benefits of 611–12 in overweight children 611 good nutrition promotion 606–9 healthy weight promotion 609–11 promotion of 612–14 Family resemblance food choices 624 food intake 621 Fashion as constraint on women’s bodies 331–2 ready-made clothing 332 Fast food consumption availability of fast foods 716–17 and socio-economic status 717–18 Fast food restaurants 569 Fasting 330 Fat deposition 300 Fat prejudice 330, 334 Fat taste 152 Fats and learning 130–1 preference for 28 reduction of 563–4 storage 119 texture of 44, 46

792 Feeding behavior see Eating behavior Feminism 335 Fetal insulin hypothesis 243 Financial impact of obesity 449 Fit and fat 399 Flavor 44, 46 learning about 211–12 ontogeny of 206–7 Flavor perception 161–73 understanding 162–3 Flavor-based learning, individual differences 171–3 Flavor-consequence learning 164–6 Flavor-flavor learning 166–7 Flavor-liking 162 enhancement by learning 168–9 innate 163–4 and overeating 165 social acquisition of 167–8 Flavor-preference learning 164–8 evaluative conditioning 166–7 exposure and familiarity 164 flavor-consequence learning 164–6 social acquisition of flavor-liking 167–8 FMRI 18, 264 cue reactivity 20 Food access to 467 attentional bias towards 345–6 availability of 51, 136, 561 energy density 51 falling price of 430–1, 514 healthy vs unhealthy 358 “light” 331 motivational nature of 344–6 nutritional profile 564 palatability of 50–1 perceived value 558 pleasantness of 49, 50 reward value 344–5, 346 saliency of 51 variety of 561 Food affordability 718–20 and socio-economic status 719–20 Food Balance Sheets 502 Food chains, national and global 527–8 Food choices 358, 557–61 consumer purchasing trends 559–61 convenience foods 560 diets 560–1 gourmet and ethnic foods 559–60 organic and natural foods 559 trust marks 560

index

economics of 728–9 factors affecting 557–8 health and nutrition awareness 558 income and education 557–8 perceived value 558 Food companies, corporate social responsibility 579–89 Food consumption 714–20 changing patterns and health 501 healthy food affordability 718–20 availability 714–18 neighborhood influences 693–4 and nutrition 498–500 Food craving 52 Food crises 526 Food crop diversity 500–1 Food cues and caloric restriction 142–4 cephalic-phase responses 138 and dieting 144 effects on eating patterns 136 hypersensitivity to 108–10 indirect 138–9 portion size 139–40 removal of 140 response to 136–8 effect of food deprivation 143–4 induction of eating 143 self-control 118–19 Food demand, factors driving 512–14 changing lifestyles 513–14 increased incomes 512 urbanization 512–13 Food deserts 694 Food globalization 477–8 Food intake children 547, 548 parents’ influence 622 social influences on 621–3 control of 125–31, 343–50 adults 618–20 brain pathways 301–9 children 621–3 social influences 618–20, 621–3 current levels 476–7 effect of low GI foods 222–4 effects of ghrelin on 290–1 and energy homeostasis 152–5 need-driven 15 and palatability 278 and physical environment 278 pleasure-driven 15

regulation 280–2 reporting of 125–6 reward circuits 155–6 sensory determinants 151–2 social facilitation 277–8 and stress 263–4 and time of day 278, 279 trends in 475–80 and variety 43–4 Food jags 549 Food marketing 467 Food photographs 138–9 Food policy, and disease patterns 456–7 Food preference opioid effects 28–9 and self-control 117–18 Food prices 732–4 energy-dense foods 732 healthy foods 730–2 Food purchase 89–101 habits 100–1 negative valuations 93–5 effort 94–5 influences on 95–6 positive valuations 90 influences on 90–3 selection 96–100 Food restriction and promotion of addictive behavior 75–6 promotion of synaptic plasticity 77–9 and response to dopamine agonists 76–7 and response to drugs of abuse 76–7 striatal neuroadaptation 79–80 Food safety 522 Food security 526 Food selection 89–101 social influence adults 620–1 children 623–4 Food stimuli brain responses 58–61 high-calorie foods 59, 60 low-calorie foods 60, 61 modulating factors 61–5 Food supply, factors driving 514–15 decreasing price of food 514 globalization and liberalization of trade 514–15 trade balance 515 Food value chain 523–5

793

index

Food words 138–9 Food-away-from-home 570–1 demand for 571 Food-related control 346–50 attention and inhibitory behavioral control 346–7 eating behavior 348–9 implementation intentions 347–8 replacing unhealthy habits 349–50 Food-related cues, hypersensitivity to 108–10 Foodservice industry see Restaurants Forbidden foods, desire to eat 140 Foresight Report 461 causal map of obesity 459 obesity system map 781 Forkhead box O1 (FoxO1) 304–5 Formula feeding 211–12 Fos immunoreactivity 25 Framing effect 419 vulnerability to 438 Free fatty acids 222 Freshness of foods 561 Fruit consumption 504 FTO gene 378 Functional magnetic resonance imaging see fMRI Functional neuroimaging 254–8

G G-protein-coupled receptors 205 GAD-2 gene 237 Galanin 8 orexigenic effects 28 Galanin-like peptide 8 Gastrin 223 GDP see Gross domestic product Gender, and thinness 329–30, 333 Gene association studies hypothesis-driven 377 hypothesis-generating 377 Gene expression 381–4 Gene polymorphisms 380–1 Gene-environment interactions 278–80 General Agreement on Tariffs and Trade (GATT) 408, 410 General Mills 582, 583, 585, 586 Generation Y 571 Genetic drift 244 Genetic factors 49–50 food preferences 120–1 ingestive behavior 149–57

Genetic Investigation of Anthropometric Traits (GIANT) consortium 379 Genetical genomics 385 Genetics of obesity 376–9 Genome-wide association studies 156, 378–9 Geographic Information Systems 688–9 Ghrelin 6, 17, 18, 28, 223, 288, 290, 303, 304 in anorexia nervosa 391 functions of 290–1 regulation of energy balance 266–7 mechanism of action 291 and sleep deprivation 184 GHRL gene 290 GI see Glycemic index Giants of excess 435, 439 Global burden of disease 704–6 Global integration 414 Global policy framework 751–2 Globalization 407–16, 466 food 477–8 impact on food supply 514–15 GLP1 304 Glucagon-like peptide-1 223, 303 Glucose homeostasis, and depression 185 Glucose intolerance 241 Glucose tolerance, Asian susceptibility to impairment in 452 Glucose transporter, genetic variation 153 Glucose-dependent insulinotropic polypeptide 223 Glutamate receptors, inotropic 77–9 Gluttony 653–60 connection with obesity 654 definition of 654–5 as vice 330, 655–9 philosophical perspective 657–9 religious perspective 655–7 Glycemic index 219–26 and cardiovascular disease 225–6 concept of 220–1 and diabetes 224–5 examples 220 food classification 221 low GI foods 222–4 mechanisms of action 221–2 and obesity 224 Glycemic load 221

Goal pursuit 356–7 Goals extrinsic 370–1 intrinsic 370 SMART 397 Gourmand syndrome 111 Gourmet foods 559–60 Governance 668–9 Government guidelines 596–7 Gratification 357 Green Revolution 521–8 challenges and opportunities 525–8 food and nutrition value chain 523–5 solutions for agriculture, agri-food and health 522–3 Green spaces, access to 720 Gross domestic product (GDP) 411, 412, 424, 707, 708 correlation with obesity 523 and life expectancy 708 per capita growth 427, 429 subsistence level 427–8 Guilt 599

H Habits 100–1 instant 350 unhealthy 349–50 Happiness 419–20 Hcrt-1 peptide 292 Hcrt-2 peptide 292 Health changing food consumption patterns 501 family meals in promotion of 611 and food crop diversity 500–1 social determinants of 701–11 solutions for 522–3 Health at every size 336–7 Health benefits of physical activity 397–8 as positive goal 371 Health damaging conditions, differential exposure to 708 Health inequalities 702–3 Health policy framework 748–9 Health services, access to 466–7 Health-related products 584 Healthful eating, consumer trends 559–60 Healthy Eating Index 736 Healthy foods 558 affordability 718–20 availability 714–18 high cost of 730–2

794

index

Healthy weight, family meals in promotion of 609–11 Hebb, Donald 16 Hedonic hot spots 27 Hedonic hunger 232, 345 Hedonic markers 231–8 Hedonics 235–6 Hershey Company 582, 585 High-calorie foods brain response to 59, 60, 61 preference for 117–18 High-income countries, causes of death 448 Hippocampus 17, 18, 66, 265 Histone decoration 192 Histones 192 Homeostatic markers 231–8 Human nature, economic and psychological view 417–18 Human Obesity Gene Map 378 Hunger 59, 170 as addiction 16–17 anticipatory 129–30 cerebral response to 257 functional neuroimaging 254–8 neuroanatomical correlates 253–8 physiology of 253–4 5-Hydroxytryptamine (5HT3) see Serotonin Hyophagia 307 Hyperbolic discounting 437 Hyperinsulinemia 243 Hyperphagia 308 Hypertension 204 Hypocretin 5, 288, 291–2, 303, 305 functions of 292–3 mechanism of action 293 regulation of energy balance 307 Hypophagia 308 Hypothalamo-pituitary-adrenal axis 196, 262–3 Hypothalamus 60, 120, 264

I Identified behaviors 367 Ideological movements 323–4, 325 Ill health, consequences of 709 Imaging studies 44–6 fMRI 18, 20, 264 functional neuroimaging 254–8 odor 45–6 olfactory-taste convergence 46 oral viscosity and fat texture 46

sight of food 46 taste 44–5 Immediate reward 105–12, 440 Implementation intentions 347–8 and eating behavior 348–9 replacement of unhealthy habits 349–50 Imports 515 Impression management food selection 620 intake control 618–19 Impulse control 105–12 Impulsive system 107 Impulsivity 19, 115 Inclusive capitalism 785 Income and food choices 557–8 increase in 512 inequality as health indicator 708 and mortality rate 705 India 471–84 diabetes 505–6 economic growth 410–13 energy expenditure trends 480–2 motorization 481–2 poor sports culture 482 transportation 480–1 urbanization 480 food intake trends 475–80 agriculture 478–80 current state 475–6 food globalization 477–8 increasing income 476–7 GDP 412 global integration 414 Integrated Childhood Development Scheme (ICDS) 475 overweight and obesity 472–5 adults 472 birthweight 473 child-targeted programs 474–5 children 472 children and advertising 473–4 measures of 472 schools and physical activity 474 urban planning and children’s play 474 policy dilemmas 482–3 poverty 413 see also Asia Indirect food cues 138–9 Individual behavior 783–4 Individual differences in learning 171–3

Individual social capital 674–9, 681–2 Individual social networks 681–2 Industrialization 423–32 and changing lifestyle 435–6 Inflammation 198 Ingestive behavior see Feeding behavior Inheritance 276, 277, 279 food preferences 280 twin studies 276, 278–80 Inhibitory control 343–4, 346–7 Injunctive eating norms 593–5 Innate flavor preferences 163–4 INSIG2 378 Instant habits 350 Instinctive behavior 418 Instrumental conditioning 127 Instrumental support 679 Insula 17, 64, 264 activation 108–9 role of 18–19 Insulin 8, 17, 305 determinants of 244–6 genetic 246 pathological 245 physiological 244–5 regulation of energy balance 266 release of 118 Insulin receptor substrate 2 383 Insulin resistance 241–8 candidate genes 246–7 carnivore connection 243 ethnic differences 242, 246–7 evolution of 242–4 survival advantages 243 Intake regulation 280–2 Integrated regulation 367 Interleukin-1 8 Interleukin-6 8 Interleukin-6 receptor 380 International HapMap Consortium 378 International Monetary Fund 408 International Obesity Task Force (IOTF) 446 Interpersonal mechanisms 683 Interventions Asia 506–7 community 629–47 design of 773 focus of 371 limitations of 370–1 nutritional 195 societal 663–6 stealth 320–3

795

index

Intrauterine growth retardation 499 Introjected regulation 367 Iodine deficiency 499 Iowa Gambling Task 111 Iron deficiency 499

J JAK-STAT pathway 197

K K121Q polymorphism 378 Kansas State University activity sessions 613–14 Kappa opioid receptor 26 Kellogg 585, 586 Keynes, John Maynard, Economic Consequences of Peace 407 KFC 572 Knowledge-based work 180–2 energy expenditure 181 Kraft Foods Global Inc. 582, 585

L Lateral hypothalamus 16, 302 in energy homeostasis 305–7 Lateral paragigantocellular nucleus 308 Latin America, obesity rates 454–5 Le Grand, Julian 435 Learned behavior 136 flavor-liking 168–9 individual differences 171–3 Learned ingestive response 126–7 Learned response 130 LEP gene 380 Leptin 6, 8, 17, 18, 50, 120, 153, 237, 288–90, 303 in breast milk 197 effects on brain pathways 304 functions of 289 protective effect against obesity 197 regulation of energy balance 265–6, 289 genetic variations in 153 mechanism of action 289–90 signaling 197 and sleep deprivation 183–4 Leucine 305 Lewin, Kurt 420 Liability 432 Libertarian (asymmetric) paternalism 436, 438–9 application of 440

Libertarianism 439 Life expectancy 707, 708 Lifestyle changes to 365–72 and food demand 513–14 modern 488 physical activity in 393–4 role of restaurants 573–4 “Light” foods 331 Liking 235 Linkage studies 378 Lithium chloride 12 Living standards, improvements in 424 LMICs cardiovascular disease 447, 448 causes of death 448 diabetes 447 disease burden 704–5 Locus ceruleus 308 Long Live Kids (LLK) 666 Long-term consequences 105–12 Loss aversion 328 Low SES groups coexistence of underweight and overweight 465 dual burden households 465 underweight/overweight paradox 463–8 Low- and middle-income countries see LMICs Low-calorie foods, brain response to 60, 61 Low-calorie meals 574 Low-fat diets 236

M Mc4r gene 303–4 McDonalds 569, 572, 585 RedBox terminals 573 Maddison, Angus 426 Malignant temptation 355 Malnutrition, prevalence 500 Malthus, Thomas 424–5 Malthusian Trap 424 breakdown of 424–5 Mammalian target of rapamycin complex 1 (mTORC1) 304 Managed services 570 Market failures 785 Marketing practices 467 and eating norms 600–1 restaurants 574–5 Mars 582

Maternal care, and stress response 196 Maternal diet, importance of 211 MC4R gene 379 MCH see Melanin-concentrating hormone Meal-times, fixed 51 Medicaid 736 Medicare 736 Melanin-concentrating hormone (MCH) 5, 303, 305 role in energy balance 306 Melanocortin 303–4 Melanocortin receptors 303–4 Melanocyte stimulating hormone 50, 303 -Melanocyte-stimulating hormone 5, 8, 30 Melatonin 130 Metabolic autonomic brain 309 Metabolic rate 119–20 Metabolic syndrome 204 and employment 705 Methyl-CpG-binding protein 2 196 Micronutrient deficiencies 499 Milk and dairy products 504 Millennium Development Goals 751 Mitochondrial uncoupling 300–1 Modulating factors 61–5 body mass 61–3 mood 63–4 Monetary Incentive Delay Task 94 Monogenic obesity 376 Monotony 140 Mood 63–5 Morphine, and preference for fats 28 Mortality rate and education 702, 703 and employment 702 and income 705 Motivation 664–5, 667 Motivational nature of food 344–6 Motivational processes 319–20 Motorization 481–2 Mu opioid receptor 25 Multinational corporations 526

N Nalmefene, anorexigenic effects 32 Naloxone, decreased preference for fats 29 Naltrexone anorexigenic effects 24, 32 decreased preference for fats 29

796 National Association to Advance Fat Acceptance (NAAFA) 335 National Health and Nutrition Examination Surveys (NHANES) 375, 733 National Longitudinal Survey of Youth 609 Natural foods see Organic/natural foods Nausea, generation of 12 Need-support 367–9 positive effects of 372 in self-regulation 368 Negative affect 64, 65 Negative emotions 599–600 Negative valuations 93–5 effort 94–5 influences on 95–6 Neighborhood environments 687–96 boundary definition 688, 690–1 characteristics of 691–2 findings and limitations 692–5 food consumption and obesity 693–4 physical activity and obesity 692–3 research limitations 694–5 identification of 688, 690 Nerve growth factor-inducible protein A (NGFI-A) 196 Networked-accessed resources 679, 682 Neuroeconomics 89–101 Neuropeptide Y 5, 8, 28, 154, 293, 303 Neurotensin 8 Non-communicable diseases 747–8 Non-esterified fatty acids 245 Non-governmental organizations 662 Norepinephrine 8 Nucleus accumbens 302 opioid receptors in 25–6 NUGENOB project 382 Nurses’ Health Study 225 Nutrigenetics 379 Nutrigenomics 376, 379 Nutrition and calorie supply 502–3 and family means 606–9 personalized 376 Nutrition awareness 558 Nutrition transition 465–6 Nutrition value chain 523–5 Nutritional epigenomics 194–5 Nutritional genomics 379–80 Nutritional interventions 195

index

O OB gene 288 Obesity 15 Asia 505–6 and chronic diseases 499–500 definition of 47 developmental programming 197 drivers of 464 effect of knowledge-based work 180–2 -endorphin role in 31 as evidence of inadequacy 334 financial costs of 449 fMRI response to food as biomarker of 20 genetic factors 49–50, 376–9 glycemic index 224 and lack of sleep 183–4 neighborhood influences 692–3 passive 232 social determinants of 701–11 societal cost of 737 understanding of 47–52 Obesity epidemic 116 solutions for 337–8 Obesity pandemic 445–61 complexity of 461 current situation 445–56 regional data 450–6 world data 445–50 development of 456–60 Obesity prevention 736–8 Obesity rates adults 446 Americas 452–6 Asia and Pacific 452, 453 Canada 454 Caribbean 455–6 children 375 by parental obesity and sex 706 India 472 Europe 450–1 Latin America 454–5 and poverty 446–7, 735–6 rising trends 770 USA 452–4 Obesity reports 763 Obesogenic environment 300, 383, 464 Obesogenic foods 736 see also Energy-dense foods OBR gene 289 Observational learning 168 Odor, imaging studies 45–6 Odorants 128

Olfaction 206 Olfactory marker protein 207 Olfactory pathways 44 Olfactory-taste convergence 46 Olive Garden 569 Ondansetron 12 Ontogeny of senses 205–7 chemical irritation 206 flavor 206–7 olfaction 206 taste 205–6 Opioid receptors, effects of blockade 170 Opioids 23–34, 303 central feeding-related reward network 25–7 dysregulation of eating patterns in humans 31–3 effects of palatability on 27–8 endogenous 308 feeding behavior in rodent models 24–31 feeding for need vs pleasure 29–31 and hedonic reaction to food 26–7, 308 promotion of intake of palatable foods 24–5 Opportunistic eaters 237 OR13G1 150, 152 Oral viscosity 46 Orbitofrontal cortex 17, 18, 44, 47, 62, 106, 107, 162, 264 lesions of 98 Orexin see Hypocretin Organic/natural foods 559 preservation of sensory qualities 561–5 whole grain foods 561–2 Outback Steakhouse 569 Outcome prediction 384–5 Overeating and acquired liking 170–1 and flavor-liking 165 and palatability 169–70 passive 232 Overweight Asia 505–6 causes 464–5 measurement of 472 public perceptions of 334 social stigma of 184, 623 Ownership 432 Oxyntomodulin 223, 304 Oxytocin 8, 30 and sugar consumption 28

index

P Pacific Obesity Prevention in Communities (OPIC) Project 642 Packaging 561 Palatability 50–1, 548 and energy density 169–70, 729 improvements in 561–4 inheritance of 280 and overeating 169–70 Palatable foods hypersensitivity to 108 opioids promoting intake 24–5 Palatability, and food intake 278 Paleolithic diet, benefits of 492 Paleolithic era 489–90 Pancreatic polypeptide 223 Panera Bread 569 Parahippocampal gyrus 60, 66 Paraventricular nucleus 28, 262, 302 metabolic pathways 304–5 Parenting practices influence on children’s food intake 622 and socio-economic status 720 Parietal operculum 108 Participation 679 Partnerships 662–3 characteristics of 667–8 outcomes 665–6 “Passive obesity” 232, 461 Passive overeating 232 Paternalism 420, 435–41 libertarian (asymmetric) 436, 438–9 application of 440 PAV haplotype 151 Pavlov, Ivan Petrovich 127 Peer influence children’s food intake 622–3 children’s food preferences and choice 624 eating norms 596 Peer resemblance 621, 624 PepsiCo 584, 585 Peptide tyrosine-tyrosine 303 Peptide YY 223 Perceived value of foods 558 Periaqueductal gray 106, 307 Perilipin protein 385 Peroxisome-proliferator-activated receptor  380 Peroxisome-proliferator-activated receptor  198, 246, 380, 381

Phenotype 232–3 psychometric testing 237 resistant 232–3, 236–7 susceptible 232–3, 236–7 resistance to weight loss 237–8 Phenylthiocarbamide 152, 210 Philosophical perspective on gluttony 657–9 Phosphoinositide 3-kinase (PI3K) 305 Physical activity 182, 391–9 accessibility 720–2 adults 392, 459 barriers to 396 and body-weight stability 186 children 392–3, 459 effects on epigenetic marks 197–8 neighborhood influences 692–3 and obesity 393–4 lifestyle 393–4 sedentary activities 393 outcomes 397–9 health-related 397–8 psycho-emotional outcomes 398 social 398–9 promotion of 321 recommended levels 392 in schools 474 and socio-economic status 721–2 support for overweight individuals 397 and weight status 394–6 environmental factors 495 self-perceptions, attitudes and beliefs 395–6 social context 396 young people 392–3 Physical inactivity 490–1 health consequences 491 Pizza 569 Planned behavior theory 394 Plant breeding, improvements in 561 Plastic surgery 332 Pleasantness of food 49, 50 Pleasures per calorie 574 Policy dilemmas 482–3 Policy initiatives agriculture 479 Asia 506–7 and disease patterns 457 health 747–8 social interaction-related 762–4 targeted and universal 709–10 Policy-making 749 Pontine reticular nuclei 307–8

797 Population growth 424–5 Portion size 51, 235 and estimation of stomach content 276 as food cue 139–40 Paleolithic vs modern 489 reduction of 575 Positive affect 64, 65 Positive and Negative Affect Schedule (PANAS) 64 Positive valuations 90 influences on 90–3 recent availability 93 relative reward 93 satiety 92–3 temporal discounting 90–2 Positron emission tomography 255, 264 Poverty 408, 411 China 413 household 409 India 413 and obesity rates 446–7, 735–6 Poverty lines 408 Poverty Reduction Strategy Papers 751 Prader-Willi syndrome 291 Pre-commitment 440 Prediction failure 437–8 Prefrontal cortex 59, 106 Pregenual cingulate corte 47 Prejudice 330, 334 Prepro-ghrelin 290 Preprodynorphin 77 Preprotachykinin 77 Present-biased preference 437 Primary inducers 106, 109 Primitive brain 131 Private sector involvement 752–3 role of 526–7 PRL-RL 8 Pro12Ala variant 381 Procepts 530 Process motivation 319–20 Prochlorperazine 12 Product concepts 530–1 Productivity, growth in 429–30 Program delivery 644–6 Progress frame 356 Project Eating Among Teens (EAT) 606, 612 Proliferator-activated receptor- 153 Promoting Family Meals module 614 Proopiomelanocortin (POMC) 28, 303 genetic variation 154

798

index

Property rights 425 6-n-Propylthiouracil 151, 210 Protein 503–4 Psycho-emotional outcomes of physical activity 398 Psychobiology 233 Psychological influences 278 Psychometric testing 237 Public health participation 584 Public health strategies 338 Public opinion 333–4 Public policy 782–3 Public service messages 666 Public services, universal access to 466–7 Purchasing Power Parity (PPP) 408, 707 Pyruvate dehydrogenase kinase isoenzyme 4 383 PYY 8, 18

Q Quantitative trait loci 378 Quebec Governmental Action Plan 338, 782 Quiznos 572

R Ready-made clothing 332 Recreational facilities, access to 720–1 Reference dependence 328 Reflection problem 758 Reflective system 107 hypoactivity in 110–12 Regional cerebral blood flow 264 Regional data on obesity 450–6 Americas 452–6 Asia and Pacific 452, 453 Europe 450–1 Relatedness 366 Religion-based health programs 638, 642–3 Body and Soul 643 GEMS study 645 PRAISE! 643, 645 Project Joy 645 Religious imperatives for thinness 330 Religious view of gluttony 655–7 Buddhism 655–6 Catholicism 656–7 Islam 655 Research cross-sectional 683–4 investment in 518–19 Resistant phenotype 232–3, 236–7

Resource allocation 410 Restaurants 567–77 casual dining 569 challenges facing 470 changing practices 575–7 competition 572–3 concepts 569 dining experience 571 diversity of 568–9 family dining 569 fast casual 569 fast food 569 full-service 569 as lifestyle enablers 573–4 managed services 570 marketing 574–5 pleasurable experience of 574 Restrained eaters, response to food cues 136–8 Restraint 330 as religious duty 330 Restraint-eating theory 135–44, 335–6 Retrochiasmatic area 303 Rett syndrome 196 Reward hypersensitivity to 108–10 immediate 105–12, 440 Reward circuits 155–6 Reward value 344–5, 346 increase with nearness of reward 90 relative 93 Rice, consumption of 503 Risk 95 Risk factors differential exposure to 708 differential vulnerability to 708–9 Rolandic operculum 108 Role models 763 Rufus, Musonius 658 Rule developing experimentation 531 baseline results 536–8 concept structure 531–3 data analysis 435 design features communicating healthful and indulgence 539–40 driving purchase interest 538–9 evaluations 533–4 interest in product 536 number of respondents 535–6 recruitment of participants 533 respondent experience 534–5 sensory-liking curve 540 study welcome page 533

S S-adenosyl methionine 193 Saliency of food 51 Salmonella 522 Salt taste, ontogeny of 208–9 Salty food, preference for 117 Sandwiches 569 Satiation 235 Satiety 235 anticipatory 127, 129–30 cerebral response to 257 effect of low GI foods 222–4 effect of time of day 278 functional neuroimaging 254–8 learned 126–7 neuroanatomical correlates 253–8 physiology of 253–4 and reward value 92–3 sensory-specific 43–4, 51 Satiety agents 6–7, 8 Satiety cascade 254 Satiety peptide 11 Satiety sequence 9 Satisfaction 8 SCD1 gene 383 Schools obesity reports 763 physical activity in 474 Scientific rationalism 425–6 Screening tests 531 Secondary inducers 106, 109 Secretin 223 Sedentary activities 393 Selection of food 96–100 integration of positive and negative 99–100 positive vs positive decisions 96–9 Self-control 115, 116–17, 658 desirability of 330, 334 and healthy body weight 121–2 physiological influences on 117–21 adipose cells and set point 119 food cues 118–19 genetic contributions 120–1 hypothalamus 120 metabolic rate and energy expenditure 119–20 preference for calorically dense food 117–18 two-stage model of 354–61 choice resolution 358–61 conflict identification 355–8

index

Self-determination 366, 394 future research 371–2 and weight loss 369–70 Self-efficacy 320, 321 Self-esteem 337, 395 threats to 598–9 Self-perceptions 395–6 Self-regulation 356, 366–7 autonomous 367, 370 need-support in 368 undermining of 372 Seneca 659 Senses, ontogeny of 205–7 Sensory determinants of food intake 151–2 Sensory factors, brain processing of 49 Sensory preference 127 Sensory-specific anticipatory eating 127–30 Sensory-specific satiety 43–4, 51 Serotonin 303 Serotonin pathway 8, 12 role in ingestive behavior 155–6 SES see Socioeconomic status Set point 119 Shame 599 Shared value 662 Shipping 561 Shopping 331 Sight of food 44, 46 Single nucleotide polymorphisms (SNPs) 150, 151, 376–7 Situational eating norms 595 Sleep, lack of 183–4 Smallholder farmers 517–18 empowerment of 527 SMART goals 397 Smith, Adam 409 Smoot-Hawley tariffs 408 Social alliances 661–70 characteristics of 665 conceptual components 664 and corporate branding 663 governance 665 motivations and external drivers 664–5 partnerships 662–3 outcomes 665–6 Social business 786 Social capital 673–84 area-level 674–9, 682–3 cognitive 679 individual 674–9, 681–2 measurement of 679–80

obesity literature 680–3 structural 679 Social caterers 574 Social censure 598 Social cognitive theory 394 Social context 396 Social control 372 Social determinants 701–11 Social facilitation 618 of food intake 277–8 of food preference 167–8 Social imitation 168 Social influences 617–25 control of intake 618–20 children 621–3 food selection 620–1 children 623–4 Social interactions 757–64 policy interventions related to 762–5 obesity reports 763 role models 763 weight-loss support groups 763–4 workplace programs 763 varieties of 758–61 Social modeling 168 Social movements 323–4, 325 Social multipliers 760 Social networks 673–84 area level 682–3 individual 681–2 and obesity 680–1 Social outcomes 398–9 Social stratification 707–8 Social support 397 Social-ecological model 394 Social-normative framework 619–20 Socialization, and eating norms 595–8 Societal cost of obesity 737 Societal interventions 663–6 Society 785–6 Socio-economic status 464, 692 and availability of healthy food 714–16 and environment 713–22 and fast food consumption 717–18 and food affordability 719–20 and parenting practices 720 and physical activity access 721–2 barriers to 722 Socrates 658 Sodexo 571, 572 Somatic marker hypothesis 106–8

799 Stanford SPORT program 322–3 Starbucks, wireless internet access 573 STAT3 signaling cascade 304 Status quo 436–7 Stealth interventions 320–3 social and ideological movements 323–4 Stereotyping 396 Stigma of overweight 184, 623 Stomach content 276 STOP-NIDDM trial 225, 226 Storage of foods 561 Strategic alliances 663–6 Stress 52 and feeding behavior 17, 181–2 and food intake 263–4 maternal care and response to 196 Stress response 261–8 amygdala 263 hypothalamo-pituitary-adrenal axis 196, 262–3 imaging studies 264–5 Striatum 17, 18 Structural social capital 679 Study design 768–9 Stunted growth of low SES children 456, 465 Substance P 12 Suburbanization 694 Subway 573 Sugar addiction 74 falling price of 431 reducing content of 563 Sugar-seeking behavior 74 Supermarkets 516–17, 527 lack of healthy foods in 715–16 and neighborhood incomes 715–16 Support, instrumental 679 Susceptibility 234–5 Susceptible phenotype 232–3, 236–7 characteristics of 233 resistance to weight loss 237–8 Sustainability 522–3 Sustainable capitalism 785 Sweet cravings, and opiate addiction 31 Sweet foods, preference for 117–18, 163 Sweet taste 205 ontogeny of 207–8 pain reduction 208 Sympathetic nervous system 300 Synthesis 47

800

index

T Taco Bell 569 Take Off Pounds Sensibly (TOPS) 758 TAS2R38 gene 152, 210 TAS2R50 gene 152 Tastants 128 Taste 205–6 developmental programming 207–10 bitter taste 209–10 salt taste 208–9 sweet taste 207–8 imaging studies 44–5 Taste cortex primary 42 secondary 42 Taste enhancers 564 Taste pathways 44 Taste perception 151–2 Taste receptors 152, 162 Taste-processing in primates 42–4 pathways 42, 43 primary taste cortex 42 reward value 43–4 secondary taste cortex 42 Technological development 425–6 and obesity epidemic 430–1 Television watching, and calorie intake 606 Temporal discounting 90–1 Temptation 355 counteractive control theory 359–60 Texture of food 44, 46, 128 TGIFriday 569 Right Portion, Right Price menu 575 Thailand 506 Thalamus 60 Thermogenesis 300–1 brain pathways controlling 301–9 Thinness 332 gender aspects 329–30 quest for 329 religious imperatives 330 social pressures for 330 Three Factor Eating Questionnaire 237 disinhibition scale (TFEQ-D) 172 Thrifty genotype 242–3, 300 Thrifty phenotype 243, 279 Time of day, and food intake 278, 279 Tinbergen, Jan 408 Tipping points 772–3 Toxic environment 144 Tractus solitarius nucleus 302

Trade 409, 411 liberalization of 514–15 world share 411 Trade balance 515 Trans Fat Free Americas 750 Transaldolase 1 383 Transferrin 383 Transient receptor potential channels 206 Transportation 426 changes in mode of 481–2 India 480–1 Trier Social Stress Test 267 Trust generalized 679 localized 679 Trust marks 560 Tumor necrosis factor alpha 8 Twin studies 276, 278–80 Tyrosine hydroxylase 79, 80

U U50488 26 UCP gene family 384 UK Prospective Diabetes Study 225 Ultrasonic vocalizations 10 Umami 42, 45, 152, 205 Uncoupling proteins 300, 380 Underfeeding see Caloric restriction Undernourishment 498–9 global prevalence 499 Undernutrition, reductions in 413–16 Underweight 464 causes 464 Underweight/overweight paradox 463–8 public policies 466–8 reasons for 464–6 Unhealthy habits, replacement of 349–50 Unilever 582, 585 Unit bias 139 United Kingdom economic transformation 428 rise of obesity in 456 World War II 456 Unrestrained eaters, response to food cues 136–8 Upscaling 573 Upselling 573 Urban planning, and children’s play 474

Urban sprawl 692 Urbanization and dietary transitions 504–5 and food demand 512–13 India 480 Urbanization ratio 427–8 Urocortin 8 USA ethnic minority populations 631–2 farming subsidies 458 food prices 458 obesity rates 452–4 USDA Thrifty Food Plan 730–1, 735

V Value-based capitalism 785 Value-chain approach 523–5 Variety of foods 561 Vegetables bitter taste 209–10 consumption 504 Ventral striatum 308–9 Ventral tegmental area 26, 79, 263, 302 Ventromedial prefrontal cortex 106, 107 Verbal food cues 138–9 Visual inputs 44, 46 Vitamin A deficiency 499

W Wagamama 575 Waist circumference 385 Waist-hip ratio 704 Wansink, Brian 419 Wanting 235 Water, addition to products 562 Wealth of nations 425–6 Weight, public opinion 333–4 Weight control 332 recommendations 333 Weight gain high gainers 384 low gainers 384 resistance to 236 tipping point 491 Weight loss 184–5 as extrinsic goal 370 new vision of 335–6 recommendations for 332 resistance to 237–8 and self-determination 369–70

801

index

Weight loss products 336 Weight status and physical activity 394–6 environmental factors 495 self-perceptions, attitudes and beliefs 395–6 social context 396 Weight-loss support groups 763–4 Weight-reduction programs 758 WeightWatchers 758 Welcome Trust Case-Control Consortium 378 Wendy’s 569 Whitehall Study of British Civil Servants 702

WHO Global Strategy on Diet, Physical Activity and Health 467–8 Whole grain foods 561–2 Whole-of-Society approach 780, 782–3 Willpower 106–8 Wnt signaling pathway 384 Women, body image problems 329–30, 333 Workplace programs 763 World data on obesity 445–50 adults 446 children 447 World Health Organization Commission on Social Determinants of Health 706–7

Framework Convention on Tobacco Control 752 Global Strategy for Diet, Physical Activity and Health 749–50 World Trade Organization (WTO) 408

Y Young people, physical activity 392–3 Yunus, Muhammad 786

Z Zona incerta 306