Obesity: Dietary and Developmental Influences

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Author: Gail Woodward-Lopez | Lorrene Davis Ritchie | Dana E. Gerstein | Patricia B. Crawford

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OBESITY Dietary and Developmental Influences

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OBESITY Dietary and Developmental Influences Gail Woodward-Lopez Lorrene Davis Ritchie Dana E. Gerstein Patricia B. Crawford

Boca Raton London New York

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Published in 2006 by CRC Press Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742 © 2006 by Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group No claim to original U.S. Government works Printed in the United States of America on acid-free paper 10 9 8 7 6 5 4 3 2 1 International Standard Book Number-10: 0-8493-9245-4 (Hardcover) International Standard Book Number-13: 978-0-8493-9245-0 (Hardcover) Library of Congress Card Number 2005054943 This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission, and sources are indicated. A wide variety of references are listed. Reasonable efforts have been made to publish reliable data and information, but the author and the publisher cannot assume responsibility for the validity of all materials or for the consequences of their use. No part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, please access www.copyright.com (http://www.copyright.com/) or contact the Copyright Clearance Center, Inc. (CCC) 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. CCC is a not-for-profit organization that provides licenses and registration for a variety of users. For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged. Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Library of Congress Cataloging-in-Publication Data Obesity : dietary and developmental influences / Gail Woodward-Lopez … et. al.]. p. cm. Includes bibliographical references and index. ISBN 0-8493-9245-4 1. Obesity. 2. Obesity--Nutritional aspects. I. Woodward-Lopez, Gail. RC628.O255 2005 616.3’980655--dc22

2005054943

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Preface Although there is little doubt that diet plays a role in the development of obesity, and experts frequently recommend a “healthy” diet for the prevention and treatment of overweight, there is remarkably little consensus regarding the deﬁnition of a healthy diet in general, and in particular the characteristics of a diet that would protect against excess weight gain. This debate is often derailed by a shift in focus to ﬁndings from weight loss studies that indicate that all sorts of diets can be effective for weight loss. Findings from weight loss studies are seductive, because dramatic results may be achieved over a relatively short period of time. It is easy to lose sight of the fact that very few of these diets are effective in the long term, seriously limiting any applicability to the prevention of obesity. Rather than weight loss, our greatest challenge — and our greatest opportunity — is prevention of excess weight gain. Research has shown that in a 5-year period, up to 7% of the population may move into the overweight category. Stemming the obesity epidemic in the U.S. will require that many individuals slow their rate of weight gain or that substantial number of obese individuals lose weight. This former strategy has two advantages: improving the health of a greater segment of the population and providing sustainable change. Even a small decline in the number of new cases of obesity has the potential to dramatically shift the BMI distribution. If adults are able to maintain their current weights, and if children are able to maintain or slow their current rate of weight gain, society could reverse the obesity epidemic in just a few generations. Accordingly, this book shifts the focus of the obesity debate to prevention and therefore relies on studies that can evince a preventive approach. It is particularly critical, at a time when the public is bombarded by nutrition advice from multiple sources, that nutrition experts, educators, and decision-makers in government, academia, clinical practice, and public health provide clear, actionable, and unequivocal dietary recommendations for the prevention of overweight. Messages such as “all foods can ﬁt” and “no food is a bad food” are meaningless detractors that offer no compelling alternative to the latest nutrition fads and weight loss trends. In this manuscript, we therefore systematically review all of the literature regarding 26 different aspects of dietary intake including macronutrients, micronutrients, speciﬁc types of foods and beverages, meal and snack patterns, and feeding practices to arrive at 9 dietary recommendations most strongly supported by currently available evidence. These recommendations are intended to form the basis not merely for messages to the public but, more importantly, as the focus of interventions to improve the dietary environments where Americans work, live, and play so that “healthy eating” is convenient, enjoyable, and affordable. For these environmental interventions to be effective, they must have a clear and speciﬁc emphasis on the behaviors they are intended to support. Our aim is that this book provide the evidence base to deﬁne those foods and dietary behaviors that should be supported and encouraged as well as those that should be discouraged by the multiple sectors of society that are involved in the provision and promotion of foods and beverages. Although the evidence presented here is extensive, it is by deﬁnition not conclusive. It is, however, certainly strong enough to merit action. Clearly, additional research is warranted. But the stakes are too high to await the results of decades of further study. Ultimately, the risk of inaction is greater than the risk of action. The evidence presented in this book is the most comprehensive on the subject to date and provides what we anticipate will be a critical step forward in our quest to identify actionable strategies to prevent obesity.

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Acknowledgments This publication was made possible with funding from the Centers for Disease Control and Prevention, Cooperative Agreement U48/CCU909706-10; United States Department of Agriculture (USDA) Food and Nutrition Service award 43-3AEM-2-80087; USDA Cooperative State Research, Education, and Extension Service (CSREES) award 2001-35200-10658; the College of Natural Resources, University of California, Berkeley; and the American Dietetic Association. Its contents are solely the responsibility of the authors and do not necessarily represent the ofﬁcial views of the funders. The authors would like to thank Eve Donovan, Sarah Kim, and Lisa Diemoz for their assistance with background research, review, and editing of this manuscript.

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About the Authors Gail Woodward-Lopez, M.P.H., R.D. is Associate Director of the Center for Weight and Health at the University of California, Berkeley. She received her Bachelor of Arts in Neurobiology and Masters in Public Health with an emphasis in nutrition at UC Berkeley. She has over 15 years experience developing, implementing, and evaluating public health programs in a variety of settings here and abroad. The focus of her current work is the evaluation of school- and community-based programs to prevent overweight among children. She also directs activities to gather, synthesize, interpret, and disseminate information concerning the determinants of obesity and promising approaches for the prevention of excess weight gain. She has served on numerous obesity-related advisory panels and committees and served as Co-Chair of the national Obesity Prevention Network’s workgroup on the determinants of energy imbalance. Other areas of expertise include maternal and child health, breastfeeding promotion, curriculum development, education, and training. She is bilingual and has worked extensively with the Latino community in California and Latin America. Her previous positions include Senior Technical Advisor for Nutrition and Academic Program Development with Wellstart International, Population Fellow with The Center for Adolescent Education in Mexico City, Perinatal Director at Vacaville Community Clinic, Education Program Manager for Planned Parenthood of Western Washington, and Evaluation Specialist for the Mission Neighborhood Health Center in San Francisco. She has served as a consultant for several international agencies including UNICEF in Nicaragua, the Arizona-Mexico Border Health Foundation in Tucson and PATH (Program for Appropriate Technology in Health) in Mexico. Patricia Crawford, Dr.P.H., R.D., is on the faculty of the Department of Nutritional Sciences and Toxicology and the School of Public Health at the University of California, Berkeley. She is a Cooperative Extension Nutrition Specialist and was instrumental in founding the Center for Weight and Health, for which she now serves as Co-Director. Nationally recognized as an authority on childhood obesity, Dr. Crawford has served as Principal Investigator on several large research projects, including the ten-year longitudinal NHLBI Growth and Health study and the ﬁve-state FitWIC Child Obesity Intervention Initiative. A widely published author and consultant, she is the lead author on the American Dietetic Association’s position paper on childhood obesity. Currently, Dr. Crawford is assessing obesity prevention programs in California schools and communities. Dana E. Gerstein, M.P.H., R.D., received her M.P.H. from the University of California at Berkeley in Public Health Nutrition and Epidemiology and her R.D. credential from the San Diego State University. She is currently an Academic Specialist at the UC Berkeley Center for Weight and Health, where she conducts research to design and evaluate programs to prevent obesity, and has been involved in conducting several literature reviews concerning determinants of and practices to prevent overweight. Prior to this current position, she completed her Masters thesis on the determinants of weight gain during pregnancy. Ms. Gerstein has also held clinical positions as a renal dietitian, cardiac dietitian, and eating disorders specialist. She is on the Member Council of the California Dietetic Association and has worked to increase the organization’s involvement in statewide childhood obesity prevention efforts. She has published and given talks on the topics of determinants of overweight in children, the role of parental inﬂuences in the prevention of childhood overweight, and the design of an impact evaluation of participatory learning in WIC. Lorrene Davis Ritchie, Ph.D., R.D. is the Director of Research and an Associate Researcher at the U.C. Berkeley Center for Weight and Health. She obtained her doctorate in Nutrition at U.C. Berkeley. Dr. Ritchie has recently been involved in several evidence-based reviews of the scientiﬁc literature to identify promising target behaviors for the prevention of obesity and its comorbidities.

She writes grant proposals and research papers, disseminates results of research and literature reviews at professional meetings, and works to develop cooperative relationships with investigators and public health professionals on campus and outside the University setting. She is a member of the American Dietetic Associations Pediatric Weight Management Workgroup and is one of the authors of the American Dietetic Associations most recent evidence-based position paper on pediatric overweight interventions. Dr. Ritchie manages a research project to identify and track eating patterns and determine their relation to obesity in a large cohort of black and white females followed from preadolescence through young adulthood. She also works in the school arena to facilitate the implementation of nutrition programs and policies to promote health and prevent overweight among school children.

Contributors SENIOR AUTHORS Patricia Crawford, Dr.P.H., R.D. Principal Investigator Co-Director Center for Weight and Health University of California, Berkeley Berkeley, California

Lorrene Davis Ritchie, Ph.D., R.D. Lead Writer Associate Researcher Center for Weight and Health University of California, Berkeley Berkeley, California

Dana E. Gerstein, M.P.H., R.D. Project Coordinator Associate Specialist Center for Weight and Health University of California, Berkeley Berkeley, California

Gail Woodward-Lopez, M.P.H., R.D. Determinants Workgroup Chair Associate Director Center for Weight and Health University of California, Berkeley Berkeley, California

ADDITIONAL AUTHORS Alexandra E. Evans, Ph.D. Assistant Professor Arnold School of Public Health Department of Health Promotion, Education and Behavior University of South Carolina Columbia, South Carolina Donna Johnson, Ph.D., R.D. Assistant Professor, Nutritional Science Nutritional Science University of Washington Seattle, Washington Susan L. Ivey, M.D., M.H.S.A. Associate Adjunct Professor School of Public Health Center for Family and Community Health University of California, Berkeley Berkeley, California

Kristine Kelsey, Ph.D., R.D. Research Assistant Professor School of Public Health University of North Carolina Chapel Hill, North Carolina Allen Knehans, Ph.D. Professor College of Allied Health Department of Nutritional Sciences University of Oklahoma Health Sciences Center University of Oklahoma Oklahoma City, Oklahoma

OBESITY PREVENTION NETWORK DETERMINANTS OF ENERGY BALANCE WORKGROUP MEMBERS CALIFORNIA DEPARTMENT OF HEALTH SERVICES/PUBLIC HEALTH INSTITUTE Cancer Prevention and Nutrition Section Michelle Oppen, M.P.H., C.H.E.S. Health Educator Sharon B. Sugerman, M.S., R.D., F.A.D.A. Research and Evaluation Research Scientist Ofﬁce of Clinical Preventive Medicine Seleda Williams, M.D., M.P.H., P.H.M.O. III Public Health Medical Ofﬁcer

Division of Adult and Community Health Behavioral Surveillance Branch Earl Ford, M.D., M.P.H. Team Leader, Analysis and Epidemiology Team

CONNECTICUT DEPARTMENT OF PUBLIC HEALTH Charles Slaughter, M.P.H., R.D. Nutrition Consultant

FLORIDA DEPARTMENT OF HEALTH Obesity Prevention Program

California Project Lean Tammie Voss, M.A., R.D. Public Health Nutrition Consultant

Bureau of Chronic Disease Prevention Susan Ladd, M.S. Program Planner and Evaluator

CENTERS FOR DISEASE CONTROL AND PREVENTION

HARVARD UNIVERSITY

Division of Nutrition and Physical Activity

School of Public Health

The National Nutrition and Physical Activity Program to Prevent Obesity and Other Chronic Diseases

Harvard Prevention Research Center

Robin Hamre, M.P.H., R.D. Program Lead Sarah Kuester, M.S., R.D. Public Health Nutritionist Physical Activity and Health Branch David R. Brown, Ph.D. Research Behavior Scientist

Elizabeth Walker, M.S. Project Associate Jean Wiecha, Ph.D. Deputy Director

MASSACHUSETTS DEPARTMENT OF HEALTH Massachusetts Overweight Prevention and Control Initiative Division of Community Health Promotion

Sarah Levin Martin, Ph.D. Health Scientist

Maria Bettencourt, M.P.H., L.N. Principal Investigator

Chronic Disease Nutrition Branch Beth Carlton Tohill, Ph.D., M.S.P.H. Epidemiologist

Vivien M. Morris, M.P.H., M.S., R.D., L.D.N. Program Coordinator

NORTH CAROLINA DEPARTMENT OF HEALTH AND HUMAN SERVICES

College of Natural Resources

Division of Public Health, Children and Youth Branch

Dana Gerstein, M.P.H., R.D. Associate Specialist

Dorothy Caldwell, M.S., R.D., L.D. Coordinator, Initiative for Healthy Weight

Lorrene Ritchie, Ph.D., R.D. Afﬁliated Scholar

ST. LOUIS UNIVERSITY School of Public Health Obesity Prevention Center

Center for Weight and Health

Gail Woodward-Lopez, M.P.H., R.D. Associate Director

UNIVERSITY OF COLORADO

Debra L. Haire-Joshu, Ph.D. Professor of Community Health in Behavioral Science Director, Obesity Prevention Center

AMC Cancer Research Center

Kimberly Hessler, M.S., R.D. Research Coordinator

UNIVERSITY OF NEW MEXICO

SOUTH CAROLINA DEPARTMENT OF EDUCATION South Carolina Healthy Schools Project Regina M. Fields, M.S., C.H.E.S. Comprehensive Health Education Coordinator and Training Director

Center for Behavioral and Community Studies Kim Reynolds, Ph.D. Research Scientist, Associate Professor

Center for Health Promotion and Disease Prevention Pediatrics Health Promotion & Disease Prevention Leslie Cunningham-Sabo, Ph.D., R.D. Research Assistant Professor Sally M. Davis, Ph.D. Professor

TEXAS DEPARTMENT OF HEALTH Public Health Nutrition Program

Shirley Pareo, M.S. Research Scientist

Claire Heiser, M.S., R.D. Chronic Disease Nutrition Consultant

UNIVERSITY OF NORTH CAROLINA

UNIVERSITY OF CALIFORNIA, BERKELEY

Center for Health Promotion and Disease Prevention

Department of Nutritional Sciences Patricia Crawford, Dr.P.H., R.D. Co-Director, Center for Weight and Health

Lisa Macon Harrison, B.S.P.H., M.P.H. Candidate Project Coordinator School of Public Health

School of Public Health Center for Family and Community Health

Kristine Kelsey, Ph.D., R.D. Research Assistant Professor

Susan L. Ivey, M.D., M.H.S.A. Associate Adjunct Professor

Dianne Ward, Ed.D., M.S. Professor

UNIVERSITY OF OKLAHOMA

Roger Sargent, Ph.D. Professor

Health Sciences Center

Department of Exercise Science

College of Allied Health

Sara Wilcox, Ph.D. Associate Professor

Department of Nutritional Sciences Allen Knehans, Ph.D. Professor

UNIVERSITY OF WASHINGTON

College of Public Health

Lynne T. Smith, Ph.D., M.P.H., R.D. Information Specialist Nutritional Sciences

Center for Public Health Nutrition

Department of Health Promotion Sciences Roy F. Oman, Ph.D. Associate Professor

Donna Johnson, Ph.D., R.D. Assistant Professor

UNIVERSITY OF SOUTH CAROLINA

WASHINGTON STATE DEPARTMENT OF TRANSPORTATION

Arnold School of Public Health Department of Promotion and Education

Highways and Local Transportation Program

Alexandra E. Evans, Ph.D. Assistant Professor

Charlotte Claybrooke, M.S. Bicycle and Pedestrian Coordinator

REVIEWERS UNIVERSITY OF CALIFORNIA, BERKELEY

UNIVERSITY OF SOUTH CAROLINA

Department of Nutritional Science and Toxicology

Arnold School of Public Health

Sharon Fleming, Ph.D.

Susan Kayman, Dr.P.H., R.D. Research Associate Professor and Deputy Director

Professor University of California, Berkeley

Center for Research in Nutrition and Health Disparities

CORNELL UNIVERSITY Joanne Ikeda, M.S., R.D. Nutrition Education Specialist University of California, Berkeley

Division of Nutritional Sciences Wendy Wolfe, Ph.D. Research Associate

Abstract Obesity rates in the U.S. have risen dramatically in recent decades. Bold action is needed, yet there is a lack of consensus regarding speciﬁcally what type of diet and which dietary behaviors are most likely to favor weight maintenance and hence prevent obesity. This book describes and quantiﬁes the evidence supporting the potential role of 26 different dietary factors and 8 developmental periods in the prevention of obesity among both children and adults. The dietary factors examined include macronutrients, micronutrients, speciﬁc types of foods and beverages, snack and meal patterns, portion size, parenting practices, breastfeeding, and more. The factors from each developmental period encompassing the entire life cycle are examined in context of the likelihood of obesity development. For each dietary factor and developmental period, four lines of evidence were examined: secular trends, plausible mechanisms, observational studies, and prevention trials. Evidence was systematically gathered from studies published from January of 1992 through March of 2003. The literature review was conducted under the guidance of the Obesity Prevention Network, convened by the Centers for Disease Control and Prevention (CDC), with representatives from 7 universities and 12 state health departments across the nation. The book features 38 tables that summarize observational studies, 38 graphs depicting trends in dietary intake, and 9 tables that summarize prevention trials. Conclusions are drawn regarding which dietary factors show the most promise for prevention of obesity.

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Table of Contents Chapter 1 Introduction...................................................................................................................1 The Obesity Epidemic .......................................................................................................................1 The Obesity Prevention Network Determinants of Energy Imbalance Workgroup .........................1 A Focus on Prevention.......................................................................................................................1 Deﬁning Target Behaviors: A First Step ...........................................................................................2 Chapter 2 Methodology.................................................................................................................5 Search Process and Criteria ...............................................................................................................5 Article Abstraction and Manuscript Format......................................................................................7 Lines of Evidence ..............................................................................................................................9 Chapter 3 Critical Periods ...........................................................................................................13 Intrauterine Growth and Birth Weight.............................................................................................13 Infancy..............................................................................................................................................16 Adiposity Rebound ..........................................................................................................................16 Early Puberty....................................................................................................................................18 Pregnancy .........................................................................................................................................21 Postpartum........................................................................................................................................22 Menopause .......................................................................................................................................24 Elderly ..............................................................................................................................................26 Summary of Critical Periods ...........................................................................................................28 Chapter 4 Dietary Inﬂuences on Energy Balance.......................................................................31 Total Calories ...................................................................................................................................31 Energy Density.................................................................................................................................36 Macronutrient Intake........................................................................................................................40 Fat ....................................................................................................................................40 Carbohydrate (total, ﬁber, reﬁned/sugars) .............................................................................63 Protein ....................................................................................................................................87 Prevention Trials (all macronutrients)....................................................................................98 Summary and Conclusions (all macronutrients)..................................................................114 Minerals and Vitamins ...................................................................................................................115 Vegetables and Fruits .....................................................................................................................140 Sweetened Beverages and Fruit Juice ...........................................................................................153 Restaurant-Prepared Foods ............................................................................................................168 Dietary Patterns..............................................................................................................................178 Variety of Foods.............................................................................................................................185 Reduced-Fat Food Products...........................................................................................................195 Portion Size ....................................................................................................................................207 Meal and Snack Patterns (eating frequency, snacking, breakfast skipping).................................212 Parenting Inﬂuences .......................................................................................................................233 Child-Feeding Practices........................................................................................................237 Dietary Self-Restraint and Disinhibition .............................................................................242

Concern about Child’s Weight Status ..................................................................................245 Family Functioning ..............................................................................................................247 Breastfeeding..................................................................................................................................250 Food Insecurity ..............................................................................................................................255 Chapter 5 Conclusions...............................................................................................................267 When to Intervene: Critical Periods in the Development of Obesity...........................................267 What to Recommend: Dietary Inﬂuences on Energy Balance .....................................................269 Implications ....................................................................................................................................278 Priorities for Further Research ......................................................................................................279 References .....................................................................................................................................285 Index ..............................................................................................................................................331

1

Introduction THE OBESITY EPIDEMIC

The World Health Organization has referred to the trends in body weight currently observed worldwide as a “global epidemic of obesity” (Sorenson, 2000). National survey data show signiﬁcant increases in mean body mass index (BMI) and the prevalence of overweight and obesity for adults and children in the United States (Flegal and Troiano, 2002). Obesity represents a signiﬁcant risk factor for type 2 diabetes, cardiovascular disease, and other morbidities. The causes of overweight and obesity are complex, with genetic and environmental risks. However, given the likelihood that the underlying genetic makeup of the population hasn’t altered in the past 30 years, most of the recent dramatic change in the distribution of childhood and adult overweight and obesity surveyed in recent decades by the National Health and Nutrition Examination Survey (NHANES) I, II, III, and 1999–2002 is likely attributable to change in environmental factors. Our focus on dietary determinants of energy imbalance is a response to the necessity of synthesizing the current research on potential modiﬁable risk factors for obesity. While a focus on physical activity is similarly key to our understanding of the determinants of energy imbalance, it is outside the area covered by this review.

THE OBESITY PREVENTION NETWORK DETERMINANTS OF ENERGY IMBALANCE WORKGROUP The Obesity Prevention Network (OPN) was ﬁrst organized by the Centers for Disease Control and Prevention (CDC) Division of Physical Activity and Nutrition for the purpose of helping to identify effective strategies for the prevention of obesity. Members include (1) the CDC, (2) seven Prevention Research Centers at Universities across the nation [University of New Mexico (Coordinating Center), University of California-Berkeley, Harvard University, University of North Carolina, University of Oklahoma, University of South Carolina, and University of Washington], and (3) twelve State Departments of Health (California, Colorado, Connecticut, Florida, Massachusetts, Michigan, Montana, North Carolina, Pennsylvania, Rhode Island, Texas, and Washington). The CDC funded two OPN workgroups, one to evaluate methods of obesity surveillance in the United States and the other to identify factors and circumstances that contribute to obesity. The present literature review is the culmination of part of the work conducted by the latter. The emphasis of this review, coordinated by the Center for Weight and Health at the University of California, Berkeley, is on dietary determinants. The group chose to focus on the dietary determinants because of the lack of scientiﬁc consensus on the role of speciﬁc nutrients, foods, and dietary behaviors in the development of obesity. The group selected for review those dietary determinants that have been implicated in the literature as probable contributors to obesity and for which there was a sufﬁcient body of evidence to consider. The group also reviewed the role of television viewing in the development of obesity; however, this topic is covered in a separate document. A review of physical activity as a determinant of energy imbalance was coordinated by the University of Oklahoma and will also be presented elsewhere.

A FOCUS ON PREVENTION It is important to keep in mind that this document focuses exclusively on energy balance and the prevention of overweight and obesity. Therefore, weight loss studies are not reviewed or discussed 1

2

Obesity: Dietary and Developmental Inﬂuences

in any depth (see page 6 for the rationale for excluding weight loss studies). The workgroup chose to focus on prevention of overweight and obesity, as opposed to weight loss, for a variety of reasons: • Weight loss programs are rarely successful with maintenance of weight loss. Although many Americans have had some success at losing weight, very few are successful in the long run. It has been documented that 95–97% of persons losing weight by dietary restriction and exercise regain this weight within 5 years (Barner, 1991). Although a return to the preweight-loss lifestyle may be to blame in part, some evidence suggest that metabolic responses to weight loss may be largely responsible for weight regain (Liebel, 1995). Therefore, sustained weight loss may not be a realistic goal for many people. • Most “diets” are unsustainable. Most diets severely restrict the variety and quantity of foods the dieter can consume. Some diets are so restrictive they even fail to provide the recommended levels of required nutrients. It also appears unrealistic to expect individuals to conform to a diet that requires reduced intakes of the most convenient, affordable, and commonly consumed foods in their homes and communities. Unless the whole community commits to similar dietary adjustments and corresponding changes in the availability of foods and beverages, it is unlikely that overweight individuals will be able to depend upon self-control alone to lose weight and maintain the loss. • The majority of the U.S. population is “at risk.” Almost two thirds of the current adult population is overweight or obese. Treatment for such a large portion of the population is untenable and, therefore, prevention of excess weight gain appears to be the only viable option. • Prevention is probably less costly in the long run. The cost of providing weight management programs to a majority of the U.S. population is not ﬁnancially feasible; societal changes that make healthy eating and active living easier are likely to have a more enduring and sustainable impact. • Healthy lifestyles provide added beneﬁts. The promotion of healthy eating and active living is associated with lower morbidity and mortality even in the absence of weight loss. Although sustained weight loss may not be realistic for many individuals, all individuals can beneﬁt from healthier lifestyles.

DEFINING TARGET BEHAVIORS: A FIRST STEP Before answering the question as to “how” and in what venues to improve eating to facilitate energy balance, one must ﬁrst deﬁne “which” behaviors to target. Given this review’s focus on the dietary side of the energy balance equation, the goal is to deﬁne what dietary composition and behaviors are most likely to facilitate energy balance.

TABLE 1.1 Research Questions What • What types of physical activity and how much • Which changes in what and how we eat* • Other lifestyle changes *

How • Environment • Media/Marketing • Education

Where • School • Community • Workplace

The question of interest for this review

When planning an intervention program, the conclusions of this review provide a basis for choosing target behaviors. Other considerations include resource availability, other efforts currently

Introduction

3

underway, other health outcomes of interest, and the particular needs and priorities of the target population. This review, therefore, assists the reader in identifying which behaviors to target but does not provide information regarding how best to affect the targeted behavior changes. Speciﬁcally, these target behaviors are not synonymous with messages that should be directed to the public. Tailoring of messages that would be effective in producing the targeted behavior changes would require formative research with the target population.

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2

Methodology SEARCH PROCESS AND CRITERIA

A systematic search process was undertaken of dietary factors in relation to a measure of adiposity. Searches of the PubMed database were conducted using the search terms listed in Table 2.1. The following limits were applied to all searches: publication date from January 1992 through March 2003, English, and human. The additional limits, “randomized controlled” or “clinical trial,” were applied to the searches for prevention trials using the terms “overweight prevention OR obesity prevention.” However, not all trials identiﬁed were randomized and/or controlled; studies did not have to be randomized, controlled trials to be included. Abstract lists generated by the searches were reviewed by at least one, and not more than three, reviewers (OPN members) to determine which studies would be included. Additional studies on children were obtained from a previously conducted review (Ritchie et al., 2001). Articles identiﬁed through the formal search process were also supplemented with studies referenced in the other articles or previously known to the reviewers. Previously unpublished data for secular trends were obtained directly from the source. Data regarding food supply trends were obtained from the United States Department of Agriculture (USDA) web site. Data from the National Food Consumption Survey/Continuing Survey of Food Intake by Individuals (NFCS/CSFII) were obtained directly from USDA, and food intake data from NHANES were obtained directly from CDC. In all cases, these data were nationally representative. Obesity prevention trials were also identiﬁed by Yale’s SIP-08 project: Evidence-Based Guidelines for Obesity Prevention and Control. Regardless of the source of studies, the same inclusion and exclusion criteria were applied in selecting articles for review. Inclusion criteria included the following: • Studies reporting on secular trends with regard to the dietary factor of interest covering at least a six-year time period since 1975. • Experimental and other types of studies designed to elucidate the relationship between the dietary factor of interest and adiposity. • Observational studies that examined the relationship between the dietary factor of interest and some measure of adiposity. • Intervention trials that aimed to modify at least one of the dietary factors of interest with the aim of preventing weight gain, improving health, or preventing chronic disease, but not designed to promote weight loss. • Intervention trials whose primary outcome was a change in a health index other than adiposity (e.g., blood pressure, total cholesterol) were included as long as adiposity was also measured and examined in the analysis. • Reviews that covered the types of studies listed above. Exclusion criteria included the following: • Weight loss studies — studies that examined weight loss interventions, studies that speciﬁcally targeted caloric restrictions at levels intended to promote weight loss, or studies that included only overweight and/or obese subjects. • Training studies — studies conducted among trained athletes. • Studies of populations with chronic diseases (e.g., diabetic populations). • Studies conducted in developing countries. 5

6

Obesity: Dietary and Developmental Inﬂuences

TABLE 2.1 Search and Exclusion Terms by Dietary Factor1 Dietary Factor Energy intake/ Energy density Dietary fat

Search Terms Dietary energy, Calorie intake Energy density Dietary fat

Carbohydrate2

Carbohydrate, Dietary carbohydrate, Dietary sugar, Reﬁned carbohydrate, Sweets, Fiber Protein Calcium, dairy, vitamin, mineral Vegetables, Fruits Soda, Soft drinks, Sweetened beverage, Fruit drinks, Fruit juice

Protein Vitamins and minerals Vegetables and fruits Sweetened beverages and fruit juice Restaurant-prepared foods Dietary patterns Dietary variety Reduced fat food products Portion size Meal and snack patterns Parental inﬂuences2 Breastfeeding Food insecurity

Fast food, Restaurant Eating patterns, Dietary patterns Dietary variety, Diet AND Variety3 Reduced fat foods, Low fat foods, Fat modiﬁed foods, Low calorie foods Portion size Breakfast, Lunch, Dinner, Snacks, Snacking, Meal frequency, Meal pattern, Meal size, Eating style Parenting, Parent AND Inﬂuence Breastfeeding Food insecurity, Hunger, Food insufﬁciency

Exclusion Terms Diabetes, Cancer Orlistat, Sibutramine, Diabetes, Cancer Diabetes, Cancer

Diabetes, Cancer Diabetes, Cancer Diabetes, Cancer Diabetes, Cancer Diabetes, Cancer Diabetes, Cancer Diabetes, Cancer Diabetes, Cancer Diabetes, Cancer Prader Willi, Diabetes Prader Willi, Diabetes Diabetes, Cancer

Notes: 1

“Obesity” was used in all searches; exclusion terms were included using the “NOT” Boolean operator.

2

“Overweight” was also included as a search term.

3

“AND” was used as a Boolean operator.

• Studies published only in abstract form or in journals that are not peer reviewed (with the exception of some food intake or disappearance data as described above). • Studies published before January 1992 or after March 2003. • Prevention trials or observational studies that did not include a measure of adiposity (e.g., body weight, body mass index, skinfolds, and/or percent body fat). Weight loss trials were not included in this review chieﬂy because the primary aim of this review was to identify determinants of energy balance — in other words, factors that inﬂuence our ability to maintain energy balance and hence maintain weight. Weight loss, on the other hand, is a state of energy imbalance. Those factors that help us endure a state of negative energy balance over the short term might logically be quite different from those that help us maintain energy balance. Indeed, this appears to be the case. Although most diets have met with a fair degree of success in weight loss, they have been largely unsuccessful in terms of weight maintenance. Maintenance after loss may represent a greater challenge than weight loss. Experimental studies (Liebel, 1995) suggest, and empirical observations support, the notion that, after weight loss, the body involuntarily reduces energy expenditure and maintains a “state of hunger” until the previous weight is regained. Therefore, strategies for weight maintenance after loss may meet with less success than primary prevention of weight gain. Furthermore, since negative energy balance is a state that, in its natural course, impels individuals to increase intake and reduce energy expenditure, strategies that help to overcome this physiological reaction may not be practical or healthy for the

Methodology

7

long term. It would be an error to assume that strategies that successfully promote weight loss in the short term are necessarily effective for long-term weight maintenance. Many people are able to endure hunger and/or very limited dietary variety over the short run but are not willing or able to do so for along period of time. Therefore, the results of weight loss studies must be applied to prevention with caution. The reader is referred to recent reviews for a discussion of successful weight loss strategies (McTigue, 2003; Summerbell, 2003; Anderson, 2004).

ARTICLE ABSTRACTION AND MANUSCRIPT FORMAT The identiﬁed articles that met the above criteria are abstracted onto an article review table by at least one OPN member (Table 2.2). A writing group for each topic was formed. The abstracted articles were reviewed and discussed by the writing group members. Four lines of evidence were examined for each dietary factor: secular trends, mechanisms, observational studies, and prevention trials. Within each section, the number and merits of the studies were reviewed and a conclusion reached regarding the strength and consistency of evidence supporting the hypothesis that the dietary factor of interest is related to adiposity. The conclusions of the authors regarding each dietary factor are summarized in table format at the end of each section. For each line of evidence (secular trends, mechanisms, longitudinal, nationally representative and other cross-sectional observational studies, randomized controlled trials and other prevention trials) the authors answer the question as to whether the preponderance of the examined evidence supports (or does not support) the hypothesis regarding the relationship between the dietary factor and adiposity. The speciﬁc questions and criteria for answering the questions for each line of evidence can be found in Table 2.3. All questions were answered with either yes, no, inconclusive, or no studies, and a conclusion was included regarding the overall consistency of the evidence. The number of observational and intervention studies examined was provided, both for studies of adults and children (deﬁned as ≤18 years of age). This by no means is intended to imply that the authors’ conclusions were based merely on the number of studies with a given result. The merits of the studies were also considered and are discussed in the text. The process for arriving at the conclusion was systematic but also involved a degree of professional judgment. Since the nature of evidence is outlined and discussed in detail, the reader is encouraged to consider all the evidence so as to assess the validity of the conclusion. At the end of the document, the summary tables for each dietary factor are grouped together into one comprehensive table, allowing the reader to compare and consider the dietary factors relative to one another. Dietary factors that are rated more highly in terms of consistency of the evidence are presumed to have a higher likelihood of preventing obesity if successfully modiﬁed. However, many of the dietary factors lacked evidence and hence were rated inconclusive. Therefore, their promise as a target behavior for preventing obesity is unknown and cannot be ruled out. The section on Critical Periods covers the following stages of the life cycle: (1) intrauterine growth, as assessed by birth weight, (2) infancy, (3) adiposity rebound, (4) early puberty, (5) pregnancy, (6) postpartum, (7) menopause, and (8) the elderly. The intent of this section is to provide an overview that describes the contribution of each of these stages of the life cycle to the cumulative risk of obesity. The methodology employed was similar to that described above for the dietary factors with certain exceptions. The review focuses on studies published between 1992 and 2003, however an exhaustive review was not conducted. The same lines of evidence described above were considered with the exception of prevention trials given that these developmental factors can not be readily subjected to intervention. Likewise, conclusions are stated in more general terms and are not presented in evidence-based tables. The focus of this review on dietary factors is not meant to imply that physical activity and inactivity are not also important determinants of energy balance. Physical activity and inactivity in relation to adiposity were examined through a number of searches of recent reviews, book chapters,

8

Obesity: Dietary and Developmental Inﬂuences

TABLE 2.2 Article Review Table and Instructions PRC Obesity Prevention Network — Determinants of Energy Imbalance Article Review and Summary Table

Article Citation Name of Study

Sample Size and Demographics

Methodology Design: Duration: Sampling strategy: Statistical analyses: Design/methodological strengths and weaknesses:

Control Variables (How Measured)

Dependent and Independent Variable(s) (How Measured) Dependent: Independent:

Results

Other comments:

Instructions Please ﬁll out each section of the table completely but be as brief and concise as possible. If there is no information available or applicable for a given section write NA. Group studies which address the same determinant or group of determinants together in the same table. Save a copy of the blank table to cut and paste from in the event you need to lengthen the table or use a new table. 1. Article citation: Use the following format: Author. Title. Journal (using acceptable abbreviations) Year;vol:pp. Capitalize only the ﬁrst word in the title (and proper nouns). If the study or intervention has a name that is not included in the title, please include the study name in bold below the citation. If the name of the study is part of the title, put the name of the study in bold. 2. Sample size and demographics: Include the following when available: Sample size, race/ethnicity, gender, age, SES, location (city, state, country and/or region), other characteristics of interest. 3. Methodology: Include the following: Design (list all that apply): — Randomized trial (experiment) clinical or community — Nonrandomized trial (with at least one comparison) clinical or community — Prospective cohort — Retrospective cohort — Cross-sectional study — Case-control study — Qualitative (brieﬂy describe) — Intervention (brieﬂy describe) — Other (brieﬂy describe) Duration: How long did the study last? Sampling strategy: How was the sample selected? Statistical analyses: Which statistical analyses were used? 4. Control variables: List variables that were controlled for (in analysis or design) and, if relevant, include how the variable was measured in parentheses. 5. Dependent and independent variable(s): List dependent and independent variables under the appropriate header and include how the variable was measured in parentheses next to the variable. Include all variables that were measured and analyzed regardless of the results. 6. Results: Describe results including p values or other measures of signiﬁcance. 7. Design/methodological strengths and weaknesses: Include any strengths or weaknesses you feel are worth considering. 8. Other comments: Include here any other points you feel are important when interpreting this study. Factual information and/or opinion and interpretation can be included. For a lengthier summary or interpretation, use a separate page (cite the study at the top of page).

Methodology

9

TABLE 2.3 Summary Table Criteria Hypothesis • The dietary factor or critical period is related to adiposity. Secular Trends • Does the preponderance of nationally representative secular trend data support the hypothesis? Mechanisms • Does the preponderance of ﬁndings concerning plausible mechanisms support the hypothesis? Observational Studies • Does the preponderance of ﬁndings from longitudinal studies support the hypothesis? • Does the preponderance of ﬁndings from nationally representative cross-sectional studies support the hypothesis? • Does the preponderance of ﬁndings from other cross-sectional studies support the hypothesis? Overweight Prevention Trials • Does the preponderance of ﬁndings from randomized controlled prevention trials support the hypothesis? • Does the preponderance of ﬁndings from other prevention trials support the hypothesis? Criteria for Answers to the Questions Above • Yes/No — The results of at least half of the studies of similar merit signiﬁcantly support/refute the hypothesis AND — None, or very few, of the studies of similar merit refute/support the hypothesis AND — No more than about half of the studies of similar merit had nonsigniﬁcant ﬁndings.1 • Inconclusive — Several studies were conducted and found conﬂicting results (both positive and negative associations) with no clear trend in either direction OR — Of the studies examined, none or a very small percentage found signiﬁcant results in either direction OR — Too few studies of sufﬁcient merit were identiﬁed. • No studies — No studies were identiﬁed. 1

Merits and limitations of the studies were considered when arriving at conclusions, especially when results were not consistent among studies. Please refer to the text for a discussion of the relative merit and limitations of the studies and how study merit was considered when arriving at conclusions. It should be noted that nonsigniﬁcant ﬁndings neither support nor refute the hypothesis, given that the null hypothesis cannot be proven. Therefore nonsigniﬁcant ﬁndings do not establish a lack of association but merely leave in doubt the presence of one. Furthermore, insigniﬁcant ﬁndings may be a result of inadequate power or other methodological limitations, and therefore a lack of association is not considered deﬁnitive for our purposes.

and recent peer-reviewed articles. A separate document to summarize that line of research based on decisions by the researchers overseeing that section is to be produced by those authors.

LINES OF EVIDENCE During the process of identifying articles, it became apparent that studies that shed light on determinants of obesity or energy balance fell in four main categories: secular trends, deﬁned as studies that examined changes in behavior over the same time period that obesity has risen most steeply (i.e., since the mid-1970s); mechanisms, deﬁned as those studies that examined the characteristics of a food or nutrient, such as palatability and energy density, that are likely to impact intake, experimental studies relating dietary and other behaviors directly to calorie intake, and other

10

Obesity: Dietary and Developmental Inﬂuences

studies that explain why a certain dietary factor would be likely (or not) to impact adiposity; observational studies, deﬁned as those studies that examined the relationship between a certain dietary factor and some measure of adiposity among free-living populations in the absence of any intervention (this category includes both longitudinal and cross-sectional studies); and prevention trials, deﬁned as those studies that examined the impact on adiposity of interventions that aimed to prevent weight gain or overweight, prevent chronic disease, or maintain health, but were not speciﬁcally designed to promote weight loss. Each of these lines of evidence has both strengths and limitations that should be considered when evaluating the evidence presented (Table 2.4). Secular trend data are particularly useful for determining the identity and magnitude of factors that have changed since the 1970s, when obesity began to rise so steeply. Short-term experimental and mechanistic studies provide well-controlled and precisely measured information about inﬂuences on intake over the short term. Observational studies describe the difference between the food intake and behaviors of people who are at (or maintain) a healthy weight and those who are overweight. Longitudinal studies are considered more compelling because they measure change over time. Nationally representative studies are given greater weight than other cross-sectional studies because of the rigorous methodologies employed in these studies as well as their generalizability to the U.S. population as a whole. Prevention trials are typically considered the most convincing line of evidence, being closest to proving cause and effect. However, the studies examined for the purposes of this book were not designed to determine the impact of speciﬁc behavior changes on adiposity and therefore, in most cases, it was not possible to distinguish which factors may or may not have contributed to program outcomes. Furthermore, prevention intervention studies are few in number compared to weight loss intervention studies, because they are more costly and more difﬁcult to conduct. Given the caveats inherent to each line of evidence, when taken as a whole, the ﬁndings provide a more complete and accurate picture of the inﬂuence any particular variable has on energy balance and, ultimately, obesity.

Strengths

1. Secular Trends

• Of all the lines of evidence, this is the most remote from causation, given that no analysis is conducted relating the dietary factor of interest to any measure of energy intake, expenditure, or adiposity. • Provides no data on who in the population is contributing most, or at all, to the trend. (i.e., a small percentage may have changed their intake dramatically while the rest of the population’s intake remains unchanged). • Since the consumption of most nutrients, foods, and beverages in the American diet increased over the time period of interest, it is not possible to pinpoint one or a few as the causal factor(s) using this data alone.

• Identiﬁes those behaviors that have changed most during the period of rapid increases in obesity rates • Identiﬁes the relative contribution of speciﬁc foods and macronutrients to increases in calorie intake

Food supply data

• Tends to overestimate absolute intake, as it is not possible to adjust completely for waste. • Identiﬁes the relative contribution of each food or nutrient to the observed rise in calorie intake over the period in question; however, it is not possible to determine if those nutrients caused the increase in calorie intake.

• Provides a more accurate measure than intake, because it is not subject to recall error, error due to social desirability, or other apparent directional bias.

Dietary intake data

• Subject to recall bias and social desirability error (for example, underreporting of foods perceived as “bad” or overreporting of foods perceived as “good”), which may change over time with prevailing trends in dietary recommendations. • Particularly inaccurate with regard to calorie intake. • Heavier individuals tend to underestimate intake more than leaner individuals, creating a directional bias of particular importance to this paper. (See section on energy intake for a more detailed discussion.)

• Provides an average intake for age and gender groups.

2. Mechanistic/ Experimental Studies

• Usually of short duration. Since long-term body weight regulation mechanisms also exist, it is problematic to extrapolate short-term changes in intake or appetite to long-term weight change. • Settings are not natural; the same effects may not be observed in free-living populations. • Small number of subjects may not be representative of larger populations.

• Compared to observational studies, it is easier to test “causation,” because the dependent variables are being manipulated and confounders controlled for. • Food intake and other behaviors are more likely to be accurate, because they are usually observed and/or measured directly by the researchers rather than being recalled or documented by subjects.

3. Observational/ Epidemiological Studies

• Difﬁcult if not impossible to control for all possible covariates: — Commonly recognized components of “healthy” habits tend to co-vary among themselves and tend to co-vary with other factors known to impact adiposity; therefore, it is difﬁcult to sort out the causative factor. — Most studies don’t control for physical activity in particular. There may be different optimal relative levels of intake at different physical activity levels; the physiologic impact of nutrient intake may also vary with ﬁtness levels. Many studies do not control for ethnicity or SES, two factors that have a strong inﬂuence on adiposity. • Cross-sectional data — not possible to discern which came ﬁrst, the dependent or independent variable. Once a person becomes overweight, he may change his intake, especially if attempting to lose weight. • Longitudinal data — difﬁcult to characterize dietary intake over time, since it tends to be highly variable. • Adiposity is an imperfect proxy for energy balance. Although these studies establish the relationship between a measure of adiposity (such as BMI) and dietary intake, they cannot directly relate intake to energy balance. In other words, at the time of the study, a thin person might actually be gaining weight, and an overweight person might be maintaining or losing weight. Many studies do not control for dieting. • Subject to the same limitation as described above for dietary intake data in the secular trends section.

• Causation can be inferred (though not proven). • Conducted among free-living individuals in their usual environments and therefore more likely to describe realistic (and proven) approaches to the prevention of weight gain. • More likely to reﬂect the results of long-term behavioral patterns than the other lines of evidence. • Often include large, randomly selected, and/or representative samples, the results of which can be extrapolated to others in that population group.

11

Limitations

Type of Evidence

Methodology

TABLE 2.4 Limitations and Strengths of the Four Lines of Evidence Examined

12

TABLE 2.4 (CONTINUED) 4. Prevention trials

• • • • •

•

• • •

Subject to the same limitation as described above for dietary intake data in the secular trends section. Often take place over a relatively short period of time and therefore may not be realistic for the long term. May require a high level of intervention effort to maintain behavioral changes. Subjects may not be representative of a larger group, and therefore results may not be generalizable. Because many of the studies examined in this review included populations at high risk for chronic disease, they may have been more motivated than the general population and/or have other unique attributes that limit the generalizability of the ﬁndings. Many of the studies identiﬁed for the purpose of this review failed to measure change in the target behavior and included several target behaviors; therefore, the relative contribution of the behavior (or behavior change) to the resultant outcome cannot be determined. Studies may have had (or failed to have) an impact for multiple reasons other than the targeted behaviors, such as program quality, intensity, or duration. The studies examined herein are difﬁcult to compare, because they varied greatly with regard to such factors as setting, program quality, target population, intensity, and duration. Because many of the studies were not attempting to inﬂuence adiposity, they may not have been appropriately designed or adequately powered to inﬂuence or detect such changes.

• When a comparable control group is employed and group assignment is random, the problem of covariates is reduce. • Because the variables are intentionally manipulated, and change are observed over time, interventions provide information most relevant to causation compared to the other lines of evidence.

Obesity: Dietary and Developmental Inﬂuences

3

Critical Periods INTRODUCTION TO CRITICAL PERIODS

Each individual’s body composition represents the cumulative and interactive effects of unique genetic potential, multiple lifecourse environments and experiences, and conscious decisions about lifestyle behaviors. Cameron and Demerath (2002) propose that the classic concept of narrow “critical periods” requires modiﬁcation if it is to be applied to chronic diseases. They suggest a more broad “lifespan perspective” that encompasses accumulating and interacting risks that are manifest from prenatal life onward. In the case of obesity, it is necessary to take this approach one step further to include an intergenerational “lifecourse approach.” The risk of obesity is cumulative for each individual, and exposure begins before conception. For example, women who are obese, still growing, or diabetic may provide an intrauterine environment for the fetus that differs from women who do not have these characteristics. Children, in turn, may pass on their intrauterine and environmental risks to their own children, and so on. While “lifecourse” factors together may not explain all of the rapid increase in obesity prevalent in Western countries (Parker, 2003; Prentice, 2001), they do provide insight about potential approaches to obesity prevention and when during the life cycle obesity prevention is likely to be most warranted. What follows are brief summaries on current knowledge about the contributions to the cumulative risk of obesity of the major stages of life: (1) intrauterine growth period, as assessed by birth weight, (2) infancy, (3) adiposity rebound, (4) early puberty, (5) pregnancy, (6) postpartum, (7) menopause, and (8) elderly. Breastfeeding in relation to obesity is discussed in the dietary determinants section of the review. Although breastfeeding occurs only during a particular time in the life cycle, breastfeeding constitutes a dietary factor, and therefore the decision was made to include it with the other dietary factors. For each life stage, to the extent possible, data on secular trends, plausible mechanisms, and observational relationships with obesity were brieﬂy summarized.

INTRAUTERINE GROWTH AND BIRTH WEIGHT INTRODUCTION Fetal development and intrauterine growth involves complex interactions between numerous factors: (1) the characteristics the mother brings to pregnancy from her own genetic, nutritional, growth and early programming experiences, (2) fetal genes, and (3) the pattern of delivery of nutrients, oxygen, and other requirements for growth and development to the fetus throughout gestation (Brown, 2002; Ritchie, 2001). To the extent that the determinants of intrauterine growth are modiﬁable, they may offer opportunities for reducing the burden of obesity in the population. There is a large and rapidly growing body of research that provides evidence of the importance of the fetal environment on later risk for chronic disease in adulthood (Cameron, 2002; Barker, 1993; Jackson, 1996; Osmond, 2000). In population-based studies, data on speciﬁc rates of growth during periods of gestation are generally not available. Instead, fetal growth is assessed indirectly by measurement of birth weight. Risk for obesity has been examined at both ends of the birth weight spectrum, low birth weight (LBW) and high birth weight (HBW). Although various criteria have been adopted, commonly used cutoffs are below 2500 g (5.5 lb) for LBW and above 4000 g (9 lb) for HBW. 13

14

Obesity: Dietary and Developmental Inﬂuences

SECULAR TRENDS The percentage of infants born with LBW has increased in the U.S. over the past several decades (Martin, 2002). In 1980, 6.9% of infants weighed less than 2500 g at birth; in 2001, 7.7% of infants weighed less than 2500 g at birth (Martin, 2002). Singleton infants born to black mothers were more than twice as likely as non-Hispanic white or Hispanic infants to weigh less than 2500 g at birth (Martin, 2002). The risk of delivering an LBW infant was highest for the youngest (<15 years) and oldest (>45 years) mothers (Martin, 2002). The extent to which advances in reproductive technologies, which improve the chances of survival of smaller babies, is inﬂuencing this trend is not known, but these technologies are a likely contributing factor. Rates of HBW peaked in the 1980s, when 10.7% of infants weighed more than 4000 g. In 2001, 9.4% of infants had birth weights of more than 4000 g (Martin, 2002). The national mean birth weight reﬂects the increase in LBW and decrease in HBW and has fallen slightly from 3365 g in 1990 to 3339 g in 2001 (Martin, 2002). However, this trend toward reduced prevalence of HBW merits additional examination. Evidence suggests that, when birth weight is adjusted for gestational age, a slightly different picture emerges. In a cohort study that covered 20 years of births at a large Canadian hospital, Kramer et al. (2002) found that mean birth weight for gestational age increased more than actual weight at birth unadjusted for gestational age. The proportion of births to women who were overweight or obese also increased over this time period. The increased birth weight for gestational age was largely explained by increase in prepregnancy BMI, gestational weight gain, and gestational diabetes and a decrease in maternal smoking and postterm delivery (Kramer, 2002).

PLAUSIBLE MECHANISMS The intrauterine experience appears to “program” the fetus in ways that may lead to risk of obesity later in life. Programming is deﬁned as persisting changes in structure and function caused by environmental stimuli during critical periods of early development (Gale, 2001). The timing and degree of compromised maternal intake or delivery of nutrients to the fetus appear to determine the strength and severity of the programming, and postnatal experience and genetic potential combine to determine the degree to which the programming impacts the individual. Several possible mechanisms for the inﬂuence of the fetal period on attained obesity in childhood and adulthood have been suggested. These include changes in the central nervous system that affect appetite and satiety, permanent changes in the ratio of fat to lean body mass, alterations in insulin metabolism, pancreatic structure and function, long-term changes in the hypothalamic-pituitary-adrenal axis, and alterations in growth hormone and insulin-like growth factors (Oken, 2003; Young, 2002). Recent reports about programming of appetite and growth hormone secretion provide insight about possible mechanisms. The intrauterine environment may program appetite through effects on insulin or leptin metabolism (Breier, 2001; Vickers, 2000). Undernutrition in utero may increase the number of insulin receptors on adipocytes or enhance the insulin sensitivity of adipocytes (Catalano, 2003). Programmed leptin resistance at the hypothalamus and pancreatic beta cells have also been proposed as potential pathways for hyperphagia resulting in obesity later in life. In addition, growth hormone excretion at age 20 is lower in those individuals who were LBW, even after adjustment for gestational age (Flanagan, 1999). High BMI in adulthood is associated with reduced growth hormone excretion, and, in turn, reduced growth hormone is associated with truncal obesity.

OBSERVATIONAL STUDIES More than 30 published studies have reported an association between birth weight and attained BMI in childhood or adulthood (see reviews by Oken, 2003; Whitaker, 1998; Parsons, 2001; Gale, 2001; Moore, 2001). Results of these studies can be summarized as follows:

Critical Periods

15

• HBW is associated with higher BMI later in life but, in many cases, this relationship was not robust and was no longer signiﬁcant after adjusting for confounders such as gestational age, socioeconomic status, and parental BMI. • Intrauterine exposure to diabetes is associated with HBW and higher attained weights later in life, but this relationship may change with the type of diabetes and maternal metabolic milieu. • The combination of LBW with subsequent rapid weight gain during childhood is associated with increased BMI, increased central obesity, and increased risk of chronic disease in adulthood. While both HBW and LBW have been associated with higher attained BMI in childhood and adulthood, chronic disease risk appears to be higher in those with lower birth weights (Moore, 2001). The high BMI that occurs in individuals who are small at birth may differ from the high BMI found in those who are large at birth, in part because BMI is only a surrogate measure of body fatness. Recent cross-sectional (Singhall, 2003) and longitudinal (Gale, 2001; Loos, 2001, 2002; Eriksson, 2002) studies that have examined actual body composition may explain the paradoxical impact of birth weight on obesity and chronic disease risk. These studies failed to ﬁnd a positive association between HBW and attained fat mass later in childhood or adulthood. Indeed in some studies, higher birth weight was associated with lower fat mass later in life (Loos, 2001, 2002). They did, however, consistently ﬁnd an association between HBW and lean body mass and adult height. In contrast, LBW may be associated with higher body fat, particularly centrally located adiposity. It is clear that simple measures of weight and height are not sufﬁcient to fully describe the contribution of growth patterns and metabolic programming to risk of obesity and chronic disease later in life. Maternal cigarette smoking is one of several variables that may confound the association between birth weight and attained BMI. It has been proposed that nicotine exposure may program neurotransmitters or hormones that are involved in regulating food intake (von Kries, 2002; Widerøe, 2003). Four recent studies found that intrauterine exposure to maternal cigarette use was associated with increased risk of obesity in children and adults (Montgomery, 2002; Power, 2002; von Kries, 2002; Widerøe, 2003). The majority of the studies assessed maternal smoking at birth or during gestation; only one (von Kries, 2002) assessed smoking retrospectively. All studies found a doseresponse relationship with those exposed to the highest levels of maternal smoking having the greatest risk of attained obesity. Although infants born to mothers who smoke tend to be smaller at birth, risk of obesity associated with maternal smoking appears to be independent of birth weight and intrauterine growth retardation. In several studies, adjusting for birth weight only further increased the observed risk of obesity (Power, 2002; Widerøe, 2003). In general, adjustment for factors such as maternal obesity, socioeconomic status, and breastfeeding did not substantially change the risk of obesity. These data provide further support for the need to prevent tobacco initiation and promote tobacco cessation in women of childbearing age. The relationship between size at birth and subsequent obesity risk may be modiﬁed by growth patterns in early childhood. In a study of 3,641 Finnish boys followed into adulthood, risk of adult abdominal or visceral fat deposition and high BMI were greatest for those born LBW who experienced rapid “catch up” growth in weight during childhood (measured between ages 7 and 15 years) (Eriksson, 1999). In two studies, however, one involving 276,033 Swedish men (Lundgren, 2001) and the other 43,872 Swedish women (Lundgren, 2003), linear catch up growth reduced the risk of subsequent overweight in males born short for gestational age. Clearly, more work is needed to deﬁne the parameters of catch up growth that may impact subsequent risk of obesity.

SUMMARY The relationships between fetal growth, attained obesity, patterns of adult adiposity, metabolic abnormalities, and conditions such as diabetes and cardiovascular diseases appear to be complex.

16

Obesity: Dietary and Developmental Inﬂuences

In general, secular trends for LBW support a role for intrauterine growth as a critical period for risk of obesity; trends for HBW are less clear. Mechanistic data are also more evident with respect to LBW. Based on observational studies, both HBW and LBW appear to contribute to higher BMI later in life, but HBW may be related more to an increase in lean tissue, whereas LBW appears related to an increase in adiposity. Studies that discriminate between HBW concurrent with and without maternal diabetes may help clarify the relationship of HBW and obesity risk. In conclusion, evidence suggests that birth weight is related to obesity later in life, but more so for LBW than for HBW.

INFANCY INTRODUCTION Rapid growth during childhood may be associated with risk of chronic disease, suggesting that metabolic alterations occurring early in life may persist. However, few studies of growth pattern during early infancy in relation to subsequent risk of obesity have been reported.

OBSERVATIONAL STUDIES Results from several recent observational studies suggest that rapid weight gain in infancy increases the risk of overweight during later childhood. For example, Stettler and colleagues (2002) studied a large (n = 28,000) longitudinal national cohort of children from birth to age 7 years and found that infants who gained weight more rapidly between 0 and 4 months of age had a 38% increased risk of obesity at age 7 compared with infants who did not gain weight as quickly. This relationship was signiﬁcant after adjusting for numerous potential confounders, such as birth weight, gestational age, weight at 1 year of age, gender, race, birth order, maternal BMI, and maternal education. Stettler and colleagues (2003) also followed 300 full-term African-American infants from birth through 20 years of age. Rapid weight gain in the ﬁrst 4 months of life (deﬁned as an increase in weight for age greater than 1 SD) was associated with a ﬁve-fold increased risk of obesity in adulthood (odds ratio 1.55–17.6). In another study, rapid gain in weight between birth and 8 months of age was associated with overweight at age 3 (May, 2002). In yet another, children who gained weight for age faster than height for age in the ﬁrst two years of life had higher BMI, body fat, and waist circumference at age ﬁve (Ong, 2000).

SUMMARY Secular trend data during early infancy are lacking. Likewise, the mechanism whereby rapid growth during infancy could affect subsequent obesity risk is not known. Although observational evidence suggests that rapid weight gain during the ﬁrst few months or years of life may be a risk factor for the development of obesity later in early childhood, follow-up studies into later childhood, adolescence, and adulthood are needed. Weight status tracks throughout childhood; one of the best predictors of BMI at any given age is the BMI from the proceeding age. It has not yet been established that gaining large amounts of weight in earlier childhood is more of a risk factor for persistent obesity than weight gain later in life.

ADIPOSITY REBOUND INTRODUCTION Adiposity rebound is the point at which BMI reaches a nadir, after a decline in infancy, and begins to climb again. This point is reached typically between 4 and 8 years of age. The rebound point

Critical Periods

17

can only be determined retrospectively by frequent sequential measurements of height and weight during the expected rebound period. The term “adiposity” rebound is technically inaccurate, because research in this area has been based on measure of BMI rather than actual adiposity (Dietz, 2001). Additional studies verifying the relationship between an early adiposity rebound and subsequent obesity risk are needed that use measures of fat mass. Although “BMI rebound” is more accurate at present, the term adiposity rebound remains in common usage.

PLAUSIBLE MECHANISMS Some hypotheses for possible etiologies of earlier adiposity rebound include modiﬁable environmental factors. Whitaker and colleagues (1998) suggest that parental child feeding practices that do not allow children to respond to internal hunger and satiety cues, particularly by those parents who are obese, may promote early expression of both early obesity and early adiposity rebound. Results from a large cohort study indicate that breastfeeding may be protective against development of obesity during the rebound period (Bergmann, 2003). Infants who grow rapidly may be at risk of early rebound. Macronutrient intake may also inﬂuence the timing of rebound. Rolland-Cachera (1995) found protein intake at age two to be signiﬁcantly and negatively associated with age of adiposity rebound. However, a larger British study of more than 800 children found no evidence of any association between protein intake and adiposity rebound timing (Dorosty, 2000). Early adiposity rebound has also been associated with early maturation throughout childhood, including early puberty (discussed below).

OBSERVATIONAL STUDIES Rolland-Cachera et al. (1984) ﬁrst described an association between age of adiposity rebound and adult obesity in 1984. In a longitudinal study of 151 children, adiposity rebound at or before 5.5 years of age was signiﬁcantly associated with increased BMI in adolescence. Subsequently, early age of adiposity rebound has been associated with higher attained BMI in childhood and adulthood in most studies. In a study of nearly 900 children, Dorosty and colleagues (2000) found that adiposity rebound at or before 5 years of age was associated with a higher BMI 5 years later. Data from 105 children in the Bogalusa Heart Study showed that those with an earlier adiposity rebound tended to be heavier throughout childhood and into early adulthood (Freedman, 2001). Williams and colleagues (1999) found a similar relationship in 922 participants followed up at ages 18–21 years. In another study of nearly 3700 participants, the probability of BMI above 23 kg/m2 at 18 years of age increased with increased absolute BMI during the period of adiposity rebound (He, 2002). Likewise, Guo and colleagues (2000) found a signiﬁcantly greater likelihood of obesity at ages 35–45 years in females (but not males) with an earlier rebound. Wisemandle and colleagues (2000) found a signiﬁcantly greater BMI in adults with early onset of obesity (prior to age 25) as compared to those with late onset obesity (after age 25). This difference ﬁrst became apparent around the age of adiposity rebound. Whitaker and colleagues (1998) found those who experienced early adiposity rebound (<4.8 years) were 6 times more likely to be obese adults (adjusted odds ratio 6.0; 95% CI 1.3–26.6) compared to those who experienced adiposity rebound later in live (≥6.2 years of age). Early adiposity rebound has been associated with high parental BMI, high BMI before the rebound period, and increased height (Whitaker, 1998). However, even after adjusting for parental BMI, Williams (2002) found that children’s BMI z-score at rebound remained predictive of earlier rebound (Williams, 2002). The association between early age of adiposity rebound and subsequent development of obesity appears to be stronger for girls and children who are heavier at the age of adiposity rebound. Recent studies have also explored the association of rapid growth as evidenced by height for age during early childhood and the development of adulthood obesity. There appears to be an association between a child’s height at adiposity rebound and adult weight (Williams,

18

Obesity: Dietary and Developmental Inﬂuences

2002; Freedman, 2001). Information about childhood height improves the ability to predict adult obesity, but childhood weight is more strongly associated with adult obesity than childhood height (Freedman, 2002). The Fels Longitudinal Study did not ﬁnd signiﬁcant differences in stature until after the adiposity rebound period (Wisemandle, 2000).

SUMMARY Adiposity rebound appears to be occurring at younger ages than in the past, diet and weight gain during childhood may impact the timing of adiposity rebound, and numerous observation studies have demonstrated earlier adiposity rebound is related to an elevated BMI in later childhood and adulthood. Although clinical use of adiposity rebound as a critical period for possible interventions to prevent obesity in individuals is limited because adiposity rebound can be identiﬁed only retrospectively, the predominance of evidence supports the conclusion that adiposity rebound is a critical period for overweight prevention.

EARLY PUBERTY INTRODUCTION Puberty is often deﬁned as the appearance of secondary sexual characteristics; however, true puberty starts with the activation of the hypothalamic-pituitary-gonadal unit, which produces the hypothalamic gonadotropin-releasing hormone (GnRH). In girls, the ﬁrst visible sign of puberty is the appearance of breast buds, which is followed by the appearance of pubic hair 6–12 months later. Menses or menarche typically follows 2–2.5 years later but may occur as much as 6 years later (Behrman, 2000). In boys, the growth of testes and thinning of the scrotum are the ﬁrst signs of puberty, which are followed by pigmentation of the scrotum and the growth of the penis (Behrman, 2000). The assessment of puberty relies largely on a rating system devised by researchers by the name of Marshall and Tanner in the late 1960s, which has since been termed Tanner Staging (Kaplowitz, 1999). In practice, however, studies have frequently relied on a more convenient measure, age of menarche, to assess early puberty. Hence, the predominance of research in this area has focused on girls.

SECULAR TRENDS Girls from all ethnic backgrounds across the U.S. are developing pubertal characteristics at younger ages than suggested in standard pediatric textbooks and in previous studies (Herman-Giddins, 1997; Wattigney, 1999). Herman-Giddens et al. (1997) examined a cross-sectional sample of 17,077 girls in the U.S. (~90% white and 10% black, ages 3–12 years) to describe the prevalence of secondary sexual characteristics and the occurrence of menarche. The mean age of onset of Tanner stage 2 breast development for white girls was 9.96 ± 1.82 years and for African American girls 8.87 ± 2.0 years (Herman-Giddins, 1997), which were both earlier than the 10.4–12.9 years of age standard for normal onset of puberty in girls (Kaplowitz, 1999). In another study, two biracial (white/black) cohorts were examined for age of menarche and different measures of adiposity (Wattigney, 1999). The researchers found that the average onset of menarche for black girls during the time period of 1992–1994 was 11.4 years of age while, during the time period of 1978–1979, it was 12.3 years of age. Similarly, for white girls during the time period 1992–1994, the average age of onset was 11.5 compared to 12.3 years during the time period 1978–1979. Compared to the earlier cohort, twice as many girls in the later cohort had reached menarche before 12 years of age. While, on average, the age of menarche in girls decreased by 9.5 months from 1973 to 1994, the secular trend for lower age of menarche was especially marked and consistent in African-American girls (Freedman, 2002).

Critical Periods

19

PLAUSIBLE MECHANISMS When examining the plausible mechanism for a relationship between obesity and puberty, it is necessary to look at the relationship from two different angles: how can childhood obesity cause early puberty, and how can early puberty cause excess adiposity and obesity later in life? The biological explanation for the observation that an early onset of puberty may contribute to greater adiposity and the development of obesity later in life is probably multifactorial, partly determined by endocrine factors and partly explained by behavioral factors (Laitinen, 2001). Early-maturing girls tend to have increased blood concentrations of estradiol, which promotes the accumulation of body fat (van Lenthe, 1996). In addition, compared to early maturers, late-maturing girls have been described to be more physically active (Post, 1993). Early puberty in girls may be related to hormonal differences later in life as well. In a Swedish study, a positive association was observed between menarchial and menopausal ages. Those girls who started menstruating at an earlier age also tended to be younger at menopause (Rodstrom, 2003). Inasmuch as menopause is associated with increased risk of obesity (see discussion below), early puberty may be related to increased risk of obesity many years later. The biological explanation for the observation that childhood obesity may contribute to early puberty can be explained by the excess of sex steroids, especially estrogen, produced by greater body fat, which can trigger earlier onset of puberty. The high-caloric Western diet increases body fat (for discussion of speciﬁc dietary patterns and relationship to early onset of puberty see paragraph below), most notably intra-abdominal fat mass, which in turn can determine the time of the growth spurt and menarche (Frisch, 1971). Both amount and distribution of body fat are related to the plasma concentrations of sex steroids in girls experiencing early puberty, which play a major role in their sexual maturation (De Ridder, 1990). Obesity is also related to higher concentrations of leptin, which facilitates the development of the reproductive system and can thus have a direct effect on the onset of puberty in girls. Recent studies have also looked at dietary patterns of children and their relationship to the early onset of pubertal development. One study found that pubertal girls who consumed diets high in dietary grain ﬁber and vegetable protein had lower concentrations of plasma gonadotropin and estradiol compared to girls who consumed diets with lower concentration of vegetable products. Because both gonadotropin and estradiol are related to the breast development and menarche, this observation suggests that a diet rich in ﬁber can inhibit the maturation or secretion of the hypothalamus-pituitary-gonad system, which delays pubertal onset (De Ridder, 1991). In addition, results from another recent study suggest that lowering fat intake during puberty is associated with reduction in several sex hormone concentrations that are consistent with changing the function of the hypothalamus-pituitary-gonad system as mentioned above (Dorgan, 2003). In conclusion, there is a biological mechanism that can explain how both the amount and distribution of body fat, and certain dietary patterns can be linked to the early development of puberty among girls.

OBSERVATIONAL STUDIES Several recent review articles have concluded that earlier maturation in childhood is consistently related to increased risk of obesity in adolescence and adulthood (Parsons, 1999; Ritchie, 2001). Recent studies have substantiated this conclusion, in relation to obesity in both teens and adults. A longitudinal study, which followed 180 white girls from 5 to 9 years of age, found that percentage of body fat and height at age 5 and age 7 predicted earlier pubertal development (as assessed by hormone levels and breast changes) among girls at age 9. Nearly 60% of girls with early puberty were overweight compared to only 21% of later developers (Davison, 2003). Pirke et al. (1999) examined the weight development of both normal and overweight white girls over a 2-year period during puberty. They found that overweight girls (>85th percentile) had an earlier onset of pubertal development than the normal weight girls. In addition, the girls in the overweight

20

Obesity: Dietary and Developmental Inﬂuences

group gained signiﬁcantly more total body fat throughout puberty (Pirke, 1999). In the Bogalusa Heart Study, earlier menarche was signiﬁcantly associated with all measures of adiposity in adolescent black and white girls (Wattigney, 1999; Freedman, 2002). Another recent study used data from the second wave of the National Longitudinal Study of Adolescent Health, a nationally representative sample of female adolescents (6,507 Hispanic, African-American, White, and Asian-American girls living in the U.S.) to look at the relationship between early maturation and BMI. Results indicated that those girls who matured early (younger than 11) had a two-fold increased risk of being overweight (BMI > 85%). Although this relationship was noted for early maturing adolescents of all racial and ethnic groups, it was strongest among early-maturing African-American girls (Adair, 2001). A ﬁfth study (cross-sectional) examined the relationship between overweight/obesity and early pubertal maturation among a nationally representative sample of both boys (n = 1,501) and girls (n = 1,520). Interestingly, results indicated that adiposity (measured by BMI and skinfold thickness) was associated with sexual maturation stage and early maturation in both boys and girls, but that the associations were in opposite directions. Early maturing boys had lower BMI and skinfold measures compared to their later-maturing counterparts, whereas early-maturing girls had greater measures of BMI and skinfold thickness than their later-maturing counterparts (Wang, 2002). Although fewer studies have examined obesity outcome in adulthood, results have been similar to studies completed in childhood. A study conducted in Finland examined the association between BMI at 31 years of age; family social class during early childhood; mother’s prepregnancy BMI; BMI at birth, 1 year, and 14 years of age; and age at menarche among 3,404 white girls. Early onset of puberty (as measured by early menarche) was associated with a higher BMI at 14 and 31 years. In addition, BMI at age 14 years was the most signiﬁcant predictor of BMI at 31 years (Laitinen, 2001). A second study, conducted among white males (n = 79) and females (n = 98) in the Netherlands, found that both boys and girls who matured earlier had a higher BMI and skinfold thickness 13 years later, compared to boys and girls who were later maturers (van Lenthe, 1996). Although the majority of studies of timing of puberty in relation to adiposity have been conducted in whites, several studies have noted ethnic differences. In a cohort of 2,379 9- and 10year-old black and white girls recruited from schools in California, Ohio, and Washington, D.C., black girls began puberty earlier than white girls. At age 9, 36% of black girls had begun puberty (11 were menstruating) compared to 16% of white girls (8 were menstruating); at age 10, corresponding numbers were 69% for black girls (94 were menstruating) compared to 35% for white girls (26 were menstruating). Black girls had a higher mean BMI and sum of skinfolds than white girls at both ages 9 and 10. However, racial differences in adiposity were not found when the comparison was restricted to prepubertal girls. Within each race, onset of pubertal maturation was associated with greater height, weight, BMI, and skinfold measurements, and the authors suggested that the earlier puberty in black girls could be a major factor in the increased adiposity of black compared to white women (Morrison, 1994). Kaplowitz et al. (2001) also examined the relationship of BMI, race, and age of onset of puberty. Among white girls, BMIs were higher in pubertal (measured by the appearance of pubic hair and/or breast development) versus prepubertal 6–9-yearolds. The same results were found for black girls; however, a smaller difference was detected, which was signiﬁcant only for the 9-year-old girls. Thus, obesity was signiﬁcantly related to early puberty in white girls and to a lesser extent in black girls. The authors concluded that factors other than obesity (e.g., genetic or environmental) are needed to explain the higher prevalence of early puberty in black versus white girls (Kalpowitz, 2001).

SUMMARY Evidence from secular trends, mechanistic research, and observational studies suggests that early onset of puberty is related to obesity later in life, at least in females. However, racial and gender

Critical Periods

21

differences between this relationship have been identiﬁed, and the direction of causality remains controversial. It is plausible that the relationship is bidirectional: higher adiposity prior to puberty may trigger an earlier onset of puberty, and children who go through puberty earlier may, due to hormonal inﬂuences or reduced levels of physical activity, gain more body fat than those who mature later. Regardless of directionality or exact mechanism, the predominance of evidence supports early puberty as a critical period for the development of obesity in girls.

PREGNANCY INTRODUCTION Pregnancy is a time of rapid physiological adjustment for women. In addition to the appropriate increase in weight and body fat experienced by most women, there are also changes in the way women perceive food and activity during pregnancy and in the ﬁrst months postpartum that may increase risk of obesity.

SECULAR TRENDS Recommended gestational weight gains have increased over the past 30 years based on optimal outcomes for the health of the infant. Before 1970, women were advised to gain 6.8 to 9 kg during pregnancy. In 1970, the Committee on Maternal Nutrition recommended a weight gain of 10.9 kg (National Research Council, 1970). In 1990, the Institute of Medicine (IOM) recommended a gestational weight gain of 11.4–15.9 kg for “normal” weight women (IOM, 1990). Recommendations were higher for underweight women and lower for overweight and obese women. The IOM recommendations were reviewed in 2000 and found to be predictive of the best outcomes of pregnancy (Abrams, 2000). However, weight gain in most pregnant women is not within the IOM recommended ranges. Actual gestational weight gain increased from an average of 10 kg in the 1960s to 15 kg by the late 1980s (IOM, 1990) and has continued to increase since that time in many settings (Abrams, 2000).

PLAUSIBLE MECHANISMS Several biological pathways have been proposed whereby pregnancy may pose a risk for the development of obesity. There is large individual variability in the metabolic response to pregnancy (Kopp-Hoolihan, 1999; Goldberg, 1993). The subset of women who gain and retain excessive weight at the time of pregnancy may have a “thrifty” phenotype. They may be programmed by their own intrauterine or early life experience or inherited genes to have a predisposition for excess fat accumulation, especially under the hormonal and metabolic conditions of pregnancy. Scholl and colleagues (2000) reported that leptin concentrations in pregnancy are associated with maternal adiposity. Insulin concentration at entry to prenatal care was recently reported to be associated with risk of excessive gestational weight gain and excess weight retention. The hormonal milieu of pregnancy may impact food intake and metabolic adaptation. Mothers who retain weight postpartum report more hunger and dietary disinhibition during pregnancy (Lovejoy, 1998). Excessive weight gain in adolescents during pregnancy appears to be a special concern. Young women who are still growing may have increased weight retention and adiposity as indicated by skinfold thickness at 6 months postpartum (Lenders, 2000). This effect may be mediated by leptin. Scholl and colleagues (2000) measured leptin concentrations in 162 pregnant girls and women both during pregnancy and postpartum. Adolescents who were still growing had higher leptin levels at each time point compared to nongrowing adolescents and adults. High leptin levels were associated with lower birth weight and a failure to mobilize maternal fat stores as an energy source for the fetus.

22

Obesity: Dietary and Developmental Inﬂuences

OBSERVATIONAL STUDIES Based on a review of studies reported up to the late 1990s, Gunderson and Abrams (2000) concluded that • The average body weight retained 1 to 1.5 years after delivery is less than 0.5 kg after adjusting for age and errors associated with self-report of prepregnancy weight. • The use of mean weight retention for the population hides the effect of pregnancy weight retention among a subset of women, because 15 to 20 percent of women sustain signiﬁcant weight retention associated with pregnancy. • The strongest inﬂuence on weight retention appears to be maternal weight gain during pregnancy. • Increased postpartum body weight is more likely following the ﬁrst pregnancy than subsequent pregnancies. • African-American women are at higher risk than white women for weight retention with pregnancy. Olson and colleagues (2003) recently reported on weight gain experiences of 540 healthy adult women in the U.S. Thirty-eight percent gained within the IOM guidelines. While mean sustained weight gain at 1 year postpartum was 1.51 kg, 25% of the women sustained a weight gain of 4.55 kg or more. Postpartum weight retention was associated with gestational weight gain, exercise frequency, change in food intake, and breastfeeding. Lower-income women who gained excessive weight were at the highest risk of sustained weight gain 1 year after delivery. The impact of parity on weight retention due to pregnancy appears to be similar to that of pregnancy itself. Parity does not substantially increase risk of obesity for the majority of women, but, for some women, weight incrementally increases with each pregnancy, and a higher weight is sustained. Linné and Rössner (2003) found that, at one year, postpartum weight was higher by 1.9 kg in the ﬁrst pregnancy, 1.8 kg in the second, 1.7 kg in the third, and 1.8 kg in the fourth. The length of time between pregnancies did not appear to impact weight retention. However, those women who gained more weight in the ﬁrst pregnancy were more likely to retain more weight in subsequent pregnancies. Wolfe and colleagues (1997) identiﬁed the following characteristics as increasing the risk of parity associated weight gain: being African-American, living in a rural area, not working outside the home, having fewer children, low income, low education, and being unmarried.

SUMMARY Evidence suggests that a subset of women is at increased risk for long-term weight retention associated with pregnancy. Excessive weight retention may be especially problematic for adolescents and women with lower socioeconomic status. The strongest predictor of weight retention following pregnancy appears to be maternal weight gain during pregnancy, which has increased at the population level during the time period that obesity prevalence has most rapidly risen.

POSTPARTUM INTRODUCTION The postpartum period is characterized by a variety of physiological (e.g., lactation), social, career, and environmental changes associated with motherhood that may impact a woman’s susceptibility to pregnancy weight retention and additional postpartum weight gain.

Critical Periods

23

SECULAR TRENDS There are interactions between lactation, exclusivity of lactation, and maternal employment that make it difﬁcult to fully understand the relationships between obesity trends and the postpartum experience. Both lactation and working outside the home are thought to be protective factors for maternal obesity. Lactation initiation rates have been increasing since 1970 (Li, 2002; Ryan, 2002). However, signiﬁcant postpartum weight loss associated with lactation is usually observed only with extended exclusive lactation that continues for at least 3 to 6 months, and recent reports indicate that only 8% of infants are exclusively breastfed at 6 months in the U.S. (Li, 2003). Exclusive breastfeeding is less likely among non-Hispanic Black women and those who were younger than 20 years, had lower education or income, smoked during pregnancy, or lived in the South (Li, 2002). Some studies have found that mothers who stay at home are more likely to gain weight or fail to lose weight during the postpartum period (Wolfe, 1997; Butte, 1998). Between 1969 and 1996, the number of working married women with children increased by 84 percent. By 1998, two thirds of all mothers in married-couple families in the U.S. were employed (Hayghe, 1999). In general, the trends toward increased rates of lactation and increased maternal employment do not offer support for the postpartum period as a critical period for intervention to reduce obesity prevalence in the population.

PLAUSIBLE MECHANISMS Postpartum lactation can affect energy balance by increasing maternal energy requirements for milk production. Some of that energy requirement can be met though mobilization of fat stores (Butte, 2001). However, maternal energy balance during lactation can be affected by a host of other mechanisms affecting both energy intake and expenditure (Butte, 1998). Postpartum weight gain has been associated with perceived increased food intake, greater access to food during the day, lower levels of exercise, and less social support. Women have reported changes in eating and cooking patterns including less time to prepare meals, eating more snacks, and ﬁnishing children’s meals. Furthermore, postpartum women have reported that they had reduced time, energy, and opportunity and motivation for regular exercise, and that they spent more time watching television (Harris, 1999).

OBSERVATIONAL STUDIES There have been few reported studies of changes in actual maternal adiposity after pregnancy. The ﬁndings have revealed substantial variation between individuals. Soltani and Fraser (2002) measured skinfold thickness at 5 sites in 77 women during pregnancy and postpartum. Obese (but not overweight or normal weight) women had increased suprailiac skinfolds 6 months postpartum despite having weight gain patterns that were similar to those of other women. Harris and colleagues (1999) interviewed 74 women who were part of a larger cohort study. At 2.6 years postpartum, the mean long-term weight gain associated with pregnancy was 0.50 kg. However, this experience was highly variable, and the range was from a 13.6 kg weight loss to a 17.7 kg weight gain. Long-term postpartum weight retention may be related to gestational weight gain. In two longterm studies [540 women assessed 8.5 years postpartum (Rooney, 2002); and 565 women assessed 15 years postpartum (Linné, 2003)], those who gained excessive weight in pregnancy continued to be at risk of excessive weight retention postpartum. While pregnancy weight gain appears to have some predictive value for long-term weight retention, many factors affect adult weight gain. In one study, women who gained more than the IOM recommendations were more likely to have higher weight gain or retention at 1 year postpartum regardless of prepregnancy weight status. However, other factors, including postpartum exercise frequency, food intake, and low income, were predictive of excessive weight retention during the postpartum period (Olson, 2003). In another study, a multivariate model that included weight gain in pregnancy, retained weight following pregnancy,

24

Obesity: Dietary and Developmental Inﬂuences

breastfeeding duration, and aerobic exercise explained only 14% of the variation in long-term weight change (Rooney, 2002). Harris and colleagues (1997) could account for only 12% of retained weight at about 3 years postpartum using a multivariate model that included a variety of maternal characteristics, gestational weight gain, and prepregnancy BMI. There are few studies that have examined the impact of lactation on long-term weight retention, and those that have examined weight loss during the ﬁrst year postpartum have had inconsistent ﬁndings. Butte (1998) reviewed body composition changes during lactation and also found changes to be highly variable. Many studies of the early postpartum period have not found increased weight loss in women during lactation, and one even reported weight gain (Lederman, 1993; Lovejoy, 1998; Gunderson, 2000). Variations in extent and duration of breastfeeding between women may have contributed in part to the variant ﬁndings. Exclusive breastfeeding that extends for several months postpartum may be associated with increased weight loss of about 2 kg in lactating women compared to those who are not lactating (Lovejoy, 1998; Dewey, 1993; Gunderson, 2000). Olson and colleagues (2003) recently reported on weight retention and weight gain in 540 postpartum women. About one fourth of the women were more than 4.55 kg heavier at 1 year postpartum than they were before pregnancy. Breastfeeding status was not associated with maternal weight gain or retention for most of the study period, but those who were still breastfeeding at 1 year postpartum were less likely to have weight retention. The variant ﬁndings for weight during lactation are not altogether surprising in light of the fact that postpartum infant feeding choices are not independent from other factors that inﬂuence risk of obesity in the mother and infant. Both obesity before pregnancy and inadequate weight gain during pregnancy have a negative impact on breastfeeding practice. For example, women who are obese before pregnancy or who gain inadequate weight during pregnancy are less likely to successfully initiate or sustain breastfeeding (Li, 2003). Women who choose to give their infants formula may be more likely to return to work, smoke cigarettes, and diet to lose weight. Women who provide both breast milk and formula to their infants may have a different metabolic experience from those who breastfeed exclusively. Failure to account for subtle differences may contribute to a lack of positive ﬁndings on weight loss with breastfeeding.

SUMMARY Weight retention after pregnancy is highly variable, and only a subset of women appears to be at increased risk for obesity postpartum. Identifying high-risk women is problematic, as a variety of maternal postpartum characteristics, including gestational weight gain, duration and extent of lactation, and postpartum changes in eating and physical activity behaviors, impact energy balance. Not surprisingly, studies to date have been able to account for relatively little of the variation in long-term weight retention after pregnancy. At present, it is not possible to conclude that postpartum is a critical period for the development of obesity.

MENOPAUSE INTRODUCTION The prevalence of obesity is higher among women than men (Flegal, 2002). Women have been thought to be uniquely predisposed to gaining and retaining excess body weight because of ﬂuctuations in reproductive hormones (Lovejoy, 1998). Natural menopause exempliﬁes a period during the life cycle characterized by ﬂuctuation in reproductive hormones. Menopause is most commonly deﬁned as 12 consecutive months of amenorrhea due to the natural cessation of estrogen production and loss of ovarian function without any other cause (Poehlman, 1998; Crawford, 2000). A multitude of other physiological and metabolic changes occur at the onset of menopause, including a rapid decline in bone mineral density (Toth, 2000), a rise in pituitary follicle-stimulating hormone (FSH), and a rise in cardiovascular disease risk factors (Ley, 1992). Based on the average age of menopause

Critical Periods

25

(50 years of age) and the current average life expectancy of women in the U.S., women can expect to spend more than one third of their lives in a postmenopausal stage (Poehlman, 1998).

SECULAR TRENDS Little study has been conducted to examine a secular trend in age of menopause (Flint, 1997). The data that are available (from a representative sample of several birth cohorts of Swedish women) show a consistent increase in the age of menopause over the last several decades. This trend was observed independent of a variety of potential confounders including socioeconomic status and smoking; women who had taken hormones or undergone surgical menopause were excluded from the analysis (Rodstrom, 2003). The implications of an increase in menopausal age on obesity are not known; however, based on discussion below, it would seem that a later menopause would delay menopause-associated changes in body composition.

PLAUSIBLE MECHANISMS Studies have not found signiﬁcant differences in energy and/or fat intake between pre- and postmenopausal women (Pasquali, 1994; Poehlman, 1995; Simkin-Silverman, 2000). Thus, it is possible that a positive energy balance during menopause is not a result of excess intake but rather due to decreased energy expenditure. Cross-sectional studies have suggested that the menopausal transition is characterized by an accelerated decline in resting metabolic rate (RMR), the energy required for basic body functions. Poehlman and colleagues (1998) found no age-related changes in RMR in healthy women up to the age of 48; however, after this age, a signiﬁcant decline (4–5% per decade) was noted. They estimated that such a decline in RMR with no compensatory adjustment in energy intake during the transition from pre- to postmenopausal status could account for a considerable increase in body fat (approximately 3–4 kg). The principal determinant of RMR is the amount of fat-free mass, the most metabolically active tissue in the body. Some observational studies have noted a menopause-related decrease in fat-free mass (see discussion below), which may help explain the reduction in RMR after menopause (Poehlman, 1995; Svendesen, 1995). A number of studies have suggested that depletion of estrogen may be associated with an increased deposition of body fat in the intra-abdominal region in postmenopausal women (Poehlman, 1998). Hormonal (estrogen-progesterone combination) replacement therapy (HRT), a practice commonly employed to offset the side effects associated with perimenopause, has been noted to slow weight gain and the accompanying increase in central adiposity in middle-aged women (Matthews, 2001; Poehlman, 1998). The physiologic basis for the shift in fat distribution after menopause appears to be a decreased lipoprotein lipase activity in femoral adipocytes and a loss of the high lipolytic responsiveness of abdominal and mammary adipocytes (Lovejoy, 1998; Pasquali, 1994).

OBSERVATIONAL STUDIES Observational studies that have examined the association between menopausal status and body weight, body composition, and/or body fat distribution have a number of potential limitations, which include failure to adjust for chronological age, the retrospective assessment of age at menopause, and the lack of separation and/or distinction between pre- and perimenopausal women (Crawford, 2000). In addition, many of these studies have been conducted predominantly among white women, when in fact the effects of the perimenopause may vary by ethnicity (Matthews, 2001). The results of the observational studies summarized below exclude HRT users. Cross-sectional studies that examined the association between menopausal status and body weight or BMI have found both signiﬁcant positive associations (Pasquali, 1994; Toth, 2000; Matthews, 2001) as well as nonsigniﬁcant associations (Davis, 1994). Age was controlled for in all of these studies. One study conducted among a large, multiethnic group (Matthews, 2001) found

26

Obesity: Dietary and Developmental Inﬂuences

that the menopausal transition effects on BMI were small relative to other inﬂuences, such as physical activity level and ethnicity. Longitudinal studies have been more conclusive, with all resulting in nonsigniﬁcant associations (Poehlman, 1995; Akahoshi, 1996; Crawford, 2000). In the Massachusetts Women’s Health Study (Crawford, 2000), chronological age and behavioral factors, such as alcohol consumption, physical activity, and smoking status, were more signiﬁcant than menopausal age in the prediction of disease risk as related to body size. Cross-sectional studies that examined the association between menopausal status and body composition consistently reported positive, signiﬁcant associations with body composition (Ley, 1992; Svendsen, 1995; Toth, 2000). In some cases, the signiﬁcant association referred to both the percentage of fat-free mass as well as fat mass (Svendsen, 1995) such that postmenopausal women had higher percentages of fat mass and lower fat-free mass than their premenopausal counterparts. In other cases, the signiﬁcance was only apparent with the percentage of fat mass tissue (Ley, 1992; Toth, 2000) such that postmenopausal women had higher percentages of fat mass tissue but no detectable differences in fat-free mass tissue. No difference in total body fat-free mass, regional fat-free mass, or appendicular skeletal muscle mass was found by Toth and colleagues (2000) until they statistically controlled for differences in body mass. Fat-free mass was then noted to be signiﬁcantly lower in postmenopausal women compared to premenopausal women. The increase in fat-free mass associated with weight gain likely masked the menopause-related loss of fat-free mass. Poehlman and colleagues (1995) studied the longitudinal association between menopausal status and body composition and also found a positive, signiﬁcant association with fat mass and an inverse, signiﬁcant association with fat-free tissue. While their sample size was fairly small (n = 35) and included only white women, they found that the women who experienced natural menopause over the 6-year period of observation not only had waist-to-hip ratios that increased more than the women who remained premenopausal, but the postmenopausal women had a greater decrease in RMR and physical activity during leisure time (Poehlman, 1995). Most cross-sectional studies that examined the association between menopausal status and upper body fat distribution also found a positive, signiﬁcant association (Svendsen, 1995; Ley, 1992) even after controlling for both age and total body mass (Toth, 2000). In one study, the Virgillio-Menopause Health Study (Pasquali, 1994), no signiﬁcant association was found. It should be noted, however, that body fat distribution in the Virgillio-Menopause Health Study was measured by a less precise, circumference-derived measurement than the DEXA or tomography techniques used in the other studies. Longitudinal studies found body fat distribution to be positively associated with menopausal status using both circumference-derived (Bjorkelund, 1996) and hydrodensitometry measurements (Poehlman, 1995).

SUMMARY Most studies have found no relationship between menopausal status and weight or BMI gain after controlling for chronological age, a ﬁnding that may be explained by hormonally triggered changes in body composition postmenopause (a loss of lean tissue coincident with a gain in fat tissue). A majority of studies have revealed that the postmenopausal period is characterized by an accumulation of body fat in the android pattern (in the abdominal or upper body region as opposed to the gynoid pattern characterized by fat stored in the trunk or lower body region). Therefore, it can be concluded that the postmenopausal period is a critical one for the development of adiposity.

ELDERLY INTRODUCTION The deﬁnition of when aging begins and what age constitutes “elderly” has not been well established. What is clear is that Americans are living longer than ever before, and that people over 65

Critical Periods

27

years of age represent a growing proportion of the population. In 2000, people 65 years of age and older represented 12.4% of the population; this age group is expected to grow to 20% by 2030 (www.aoa.gov, 2002).

SECULAR TRENDS There has been a dramatic rise in the prevalence of obesity in the aging population. In the United States, obesity among individuals over the age of 50 nearly doubled from 1982–1999; in 1999, 26.7% of the population surveyed were obese compared to the 14.4% who were obese in 1982 (AARP Report, 2002). Data from NHANES III indicate that, in the 65-to-74-year age range, approximately 34% of women and 44% of men are considered overweight and an additional 27% of women and 24% of men are considered obese (National Center for Health Statistics, 1999). Middle-aged and older adults have the highest overweight and obesity prevalence of any other age group (Flegal, 2002) and are considered at the highest risk for weight gain (Davy, 2001). Unfortunately, there is limited information on body weight guidelines, dietary recommendations, and obesity prevention strategies for the elderly population (Ritz, 2001). National body weight guidelines have traditionally been based on studies examining the association between body weight and mortality; however, such guidelines have been less clearly devised for this population.

PLAUSIBLE MECHANISMS Energy requirements are best estimated by determining total energy expenditure (TEE). TEE has been shown to decline with age; this decline is a result of both a reduction in RMR and physical activity (Ritz, 2001). RMR decreases between age 30 and 80 at a rate of 2–4% per decade. This decline is mainly attributed to the loss of fat-free mass (the most metabolically active tissue), which is a consequence of muscle mass loss, termed sarcopenia. Reductions in physical activity seen in older populations also contribute to the reduction in TEE. If individuals do not alter their energy intake to match the decrease in total energy expenditure, a positive energy balance results.

OBSERVATIONAL STUDIES The normal aging process is associated with a reduction in muscle mass and strength and an increase in fat mass mainly in the abdominal area (Ritz, 2001). BMI, therefore, is not the best predictor of chronic disease risk and/or mortality among the elderly population. A better marker is body fat distribution (Rossner, 2001; Kotz, 1999). Body fat distribution can be measured by waist circumference and/or waist-to-hip ratio. The association between BMI and waist-to-hip ratio declines with age (Stevens, 2000), and older adults are more likely to have less muscle mass than fat mass and more intra-abdominal fat than younger adults with the same BMI. There is increasing evidence that an accumulation of intra-abdominal fat plays a major role in age-related metabolic changes, particularly insulin resistance, which is a key factor in the development of type 2 diabetes and has been signiﬁcantly related to cardiovascular disease development (Ritz, 2001; Kotz, 1999). Weight history is likely to be an even better predictor of disease risk and mortality than body fat distribution in the elderly population (Rossner, 2001). Kotz and colleagues (1999) reported in their review of obesity and aging that obesity already existent throughout the life span may have more deleterious consequences than obesity that presents relatively late in life. The development of obesity late in life is relatively rare. Obesity is more likely to have been present at a more modest level throughout the life span. Thus, it may be more advantageous to base body weight recommendations for elderly populations on weight history and body fat distribution rather than current BMI or body weight. Future research to assess risk of disease and/or mortality among older populations should assess weight history and body fat distribution in addition to or as opposed to BMI.

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Obesity: Dietary and Developmental Inﬂuences

On average, BMI increases by roughly one unit value every 10 years after 40 years of age (Ritz, 2001). While there is increased risk of becoming obese with age (Kotz, 1999), most crosssectional studies ﬁnd that the mean body weight increases with age only up to the age of 60, at which time it appears to level off (Rossner, 2001; Kotz, 1999). Fat mass also increases until age 60 and then levels off, with decreases occurring after age 80 years (Kotz, 1999). Contrary to what most observational studies have found in other age groups, some crosssectional studies have actually suggested that there is a protective effect of being overweight among elderly individuals (Rossner, 2001; Stevens, 2000). Data from a large cohort of men and women above the age of 85 found a consistent reduction in mortality with increased BMI, after adjusting for age and sex (Rossner, 2001). In the Build Study, Andres and colleagues found that the BMI associated with the lowest mortality was higher at older compared to younger ages in both men and women. In the oldest age group (60–69 years of age), the BMI associated with the lowest mortality was at a level considered overweight by current standards: BMI = 26.6 and 27.3 in men and women, respectively (Stevens, 2000). Data from both of the Cancer Prevention Studies (CPS I & II) support the current guidelines that the BMI associated with the lowest mortality is between 18.5 and 24.9 in men and women between the ages of 30 and 64. The more current data from CPS II indicate that, after age 75, the lowest mortality was associated with BMI at the higher end of this recommended range (Stevens, 2000). The mechanism by which overweight is protective against mortality is unclear. Whether the effect of body size on morbidity is increased or diminished by age is dependent on the outcome measured (Stevens, 2000). For instance, it is clinically well accepted that being elderly and overweight impairs physical activity (Rossner, 2001); therefore, it is strongly suggested that older individuals avoid excess body weight so as to reduce risk of disability. However, whether overweight prevention in the elderly reduces cardiovascular disease and cancer risk is unclear (Ritz, 2001). Weight loss or maintenance through increased physical activity among obese elderly subjects has been shown to have positive effects on weight-related cardiovascular risk factors, such as blood pressure, diabetes mellitus, and hyperlipoproteinemia (Rossner, 2001). Physical activity is also likely to be effective in preventing obesity among individuals over 55 years of age. In a roundtable consensus statement entitled Physical Activity in the Prevention and Treatment of Obesity and its Comorbidities, a panel of experts concluded that aerobic training reduces fat mass without changing fat-free mass, and resistance training reduces fat mass as well as increases fat free mass. This conclusion was based on studies performed among persons over the age of 55 years (Rossner, 2001).

SUMMARY Aging is associated with increased overweight until about the age of 60, whereupon weight tends to remain stable until around 80 years of age, when it begins to decline. There seems to be little evidence to support weight reduction after age 65 in men and women to prolong life (Rossner, 2001); however, weight reduction may be beneﬁcial for other reasons, such as reducing the complications associated with overweight (e.g., sleep apnea, physical activity impairment, poor blood pressure/blood sugar control, reduced self-esteem). Implementing overweight prevention strategies in adults in their 60s may be beneﬁcial, since a large portion of the aging population will still be living 20 years later. Strategies should be implemented to preserve and/or increase muscle mass and strength with advancing age. Future research needs to focus on more speciﬁc body weight guidelines and obesity prevention strategies for the elderly population, particularly as the number of elderly who are overweight continues to grow.

SUMMARY OF CRITICAL PERIODS Increased risk of obesity can emerge as early as in utero, with the cumulative evidence suggesting a relationship between birth weight, particularly low birth weight, and obesity later in childhood

Critical Periods

29

and into adulthood. Regardless of birth weight, obesity in childhood is a risk factor for later obesity: an overweight child is more likely than a child of normal weight to be obese as an adult, and this relationship gets stronger as a child grows older (Whitaker, 1997). In addition to tracking of weight, certain stages and patterns of maturation during childhood may be critical. Rapid weight gain in infancy and early childhood appears to add to the cumulative risk of obesity in later childhood, although the likelihood of excess weight gained during infancy persisting into adulthood has not been established. Interrelationships between growth patterns complicate identiﬁcation of early childhood as a critical period. For example, young children who are growing more rapidly than their peers tend to mature earlier, and early maturation is associated with early adiposity rebound and early puberty. Early adiposity rebound and early puberty, in turn, are associated with increased risk of adult obesity. In this regard, all of childhood can be considered important in the development of obesity, and consequently for obesity prevention. For women, several life stages associated with hormonal ﬂuctuations, namely pregnancy and menopause, have been associated with increased obesity risk. Although individual experiences are highly variable, some women are especially susceptible to weight retention associated with pregnancy and the postpartum period, most notably adolescents who are still growing and AfricanAmerican women. Increases in body fat, particularly in the abdominal region, are cause for concern during menopause. Aging is associated with weight gain until the seventh decade of life, and then, beginning around age 80, weight loss predominates. Even in the absence of weight change, age-related body composition changes are characterized by a relative gain of body fat and loss of lean tissue. The health impact of obesity in the elderly remains controversial. Studies of overweight in the elderly underscore the importance of weight gain prevention early in life.

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4

Dietary Inﬂuences on Energy Balance TOTAL CALORIES

INTRODUCTION There is no doubt that weight gain occurs when energy intake exceeds energy expenditure, that weight loss occurs when energy expenditure exceeds intake, and that weight maintenance results from a balance between energy intake and expenditure. Calorie intake is by deﬁnition a determinant of energy balance and, by deﬁnition, when a person is in energy balance, weight is maintained. It is not the purpose of this chapter to question whether calorie intake is part of the energy balance equation and therefore a determinant of energy balance. There is no scientiﬁc basis or empirical evidence to suggest otherwise. Rather, the purpose of this chapter is to determine what dietary composition, dietary behaviors, and other lifestyle factors promote energy balance by facilitating the individual’s ability to balance calorie intake with energy expenditure. Given that the question as to whether calories matter is answered by the law of thermodynamics, the more interesting question, and the one addressed in this section, is: what is the relative contribution of energy intake to the recent rise in obesity rate?

SECULAR TRENDS There has been considerable debate concerning the relative contribution of increases in calorie intake to the rises in the rates of obesity since the mid-1970s in the U.S. Some have argued that calorie intake has remained fairly constant and that changes in energy expenditure account for most of the increases in the rates of overweight. However, national food supply data from the USDA and data from two nationally representative surveys of food intake demonstrate that calorie intake has increased since the mid-1970s. According to the USDA (Putnam, 2002), the amount of calories available for human consumption has increased steadily since the early 1970s (Figure 4.1). In fact, the amount of calories has increased by more than 500 per day per capita even after adjusting for spoilage, cooking, plate waste, and other losses. If energy expenditure remained constant, an extra 500 calories per day would lead to a weight gain of approximately 52 lb per year. It appears, therefore, that the increase in the availability of calories is more than enough to explain the current obesity epidemic. Two national surveys that monitor food intake conﬁrm the upward trend in calorie intake, at least among adults. Data collected from the National Health and Nutrition Examination Survey (NHANES) suggest that, among adults, the per capita intake of calories has increased by over 200 per day between the late 1970s and early 1990s (Figure 4.2) (Troiano, 2000; McDermott, 2003). Data from the USDA’s Continuing Survey of the Intake of Individuals/National Food Consumption Survey (CSFII/NFCS) also reveal an increase in calorie intake of over 200 calories during roughly the same time period (Figure 4.3) (Nielson, 2002). The situation among children is less clear. Although NHANES failed to detect any increase in calorie intake among children (Figure 4.2) (Troiano, 2000; McDermott, 2003), CSFII/NFCS data suggest that children have increased their calorie intake by almost 200 calories per day, but only between 1989–1991 and 1994–1996, and 31

32

Obesity: Dietary and Developmental Inﬂuences

FIGURE 4.1 Calories from the U.S. per Capita Food Supply, Adjusted for Losses. (Source: USDA/ERS(a), 2003.)

FIGURE 4.2 Trend in Energy Intake (NHANES). (Sources: Troiano, 2000; McDowell, 2003.)

not prior to that (Figure 4.3) (Nielson, 2002). As mentioned previously, of course, food intake data is highly subject to reporting error (Lichtman, 1992; Briefel, 1997), regardless of the methodology (food record, recall, or frequency) (Kortizinger, 1997). These errors are even more acute when reporting the intake of children. However, the consistency in ﬁndings from different national data sets over the same time period that obesity rates have risen most steeply provides strong support that adults, at least, have increased their calorie intake considerably and that this increase could explain the rising rates of overweight.

PLAUSIBLE MECHANISMS Experimental studies conﬁrm that variations in energy balance tend to be due largely to the intake side of the equation. Thousands of calories can be consumed in very short period of time, while there is a much lower upper limit to energy expenditure given the time needed to expend energy

Dietary Inﬂuences on Energy Balance

33

FIGURE 4.3 Trend in Energy Intake (NFCS and CSFII). (Source: Neilsen et al., 2002.)

and the ﬁtness required to sustain it. As shown in Figure 4.4, for example, a Big Mac® can easily be consumed in a few minutes, but to “burn” those calories off requires somewhere between 82 and 141 minutes, depending on the activity. Indeed, daily variations in food intake have been reported to have coefﬁcient of variations as large as ±23% (Bingham, 1995), whereas daily variations in energy expenditure have been reported to be consistently smaller—only about ±2% in subjects spending several days in a respiratory chamber (Jequier, 1993). Such differences suggest that total calorie intake may be more signiﬁcant in determining changes in energy homeostasis. Clinical trials clearly demonstrate that total caloric restriction, when followed, is effective in inducing temporary weight loss in obese subjects (Rolls, 2000). Two systematic reviews of weight loss intervention trials found that weight loss was largely a function of reduced caloric intake (Kennedy, 2001; Bravata, 2003). Regardless of dietary composition, the degree of weight loss reﬂected the extent to which the diets were effective in reducing calorie intake and not due to some independent effect of variations in dietary composition. Unfortunately, in most weight loss trials, compliance (at least to calorie-counting) has been poor, and the relapse rate is remarkably high, with 50% of all patients regaining or exceeding their pretreatment weight at 12 months’ follow-up (Wadden, 1989; Wadden, 1993). Five-year relapse rates are even higher; it has been documented that 95–97% of persons losing weight by dietary restriction and exercise regain this weight within 5 years (Barner, 1991). Prolonged periods of low-energy intake may require that the subject be hungry and uncomfortable and therefore may be too difﬁcult to sustain (Rolls, 2000). Thus, small or no weight loss seen in interventions designed to reduce total caloric intake can be attributed to a lack of compliance with the dietary intervention — reduction of total caloric intake — and are

FIGURE 4.4 Minutes of exercise required to burn a Big Mac. (Source: Courtesy of Dan Nemet, Child Health and Sports Center, Meir General Hospital, Tel-Aviv University, Israel.)

34

Obesity: Dietary and Developmental Inﬂuences

not a result of a nonsigniﬁcant association between total caloric intake and weight gain (Rolls, 2000). Despite the overriding inﬂuence of total calorie intake (as opposed to diet composition) on weight loss, one cannot assume that the body acts like a bomb calorimeter. The energy value of foods as determined by a bomb calorimeter does not take into account variables such as digestibility, rate of absorption, excretion, the thermic effect of food (TEF), or satiety — all of which vary between individuals and are inﬂuenced by macronutrient composition and other characteristics of the foods consumed (Dairy Council of California. Macronutrient Composition of the Diet, Health Connections 4, 1, 2004.) Therefore, although the bottom line for the dietary side of the energy balance equation is caloric intake, the composition of the diet, as well as eating styles and patterns, have the potential to inﬂuence both calorie intake and utilization. It must also be kept in mind that the amount of caloric restriction necessary to lose or maintain weight varies widely between individuals and is dependent upon the individual’s height, physical activity level, and other metabolic parameters. The observed increases in calorie intake in recent decades are easily explained by increases in the availability (convenience and price), variety, and palatability of foods in recent decades. Consumers can now acquire a wide variety of food products that are concentrated in calories, easy to prepare, and relatively inexpensive. The number and variety of different food products, especially those that are concentrated in calories, has increased dramatically in recent decades (Nestle, 2002). The number of fast food restaurants that can provide high-calorie meals in minutes and at relatively low prices doubled between the 1970s and 1990s (Chou, 2002). Millions of dollars each year are invested in creating food products that are increasingly convenient and highly palatable. In the mid1980s, only 6,000 new food products were introduced annually on average, whereas, in the peak year of 1995, 16,900 new food and beverage products were introduced (Nestle, 2002). These new products tend to be high in energy density (McCrory, 1999) and low in cost per calorie (Rolls and Barnett, 1999; ERS/USDA Food Review, 2002). Price, convenience, variety and palatability are all powerful determinants of food intake (Nasser, 2001; Raynor, 2001; Darmon, 2002), and therefore changes in these factors likely explain the increases in calorie intake demonstrated by national surveys.

OBSERVATIONAL STUDIES A wealth of observational studies have examined the association between total caloric intake and some measure of adiposity with inconsistent results. Cross-sectional and longitudinal studies have shown total caloric intake to be positively [Slattery, 1992 (in women only); Klesges, 1992 (in women); Nelson, 1996; Parker, 1997; Holmes, 1998], negatively [Klesges, 1992 (in men); Tavani, 1994 (in women); Lluch, 2000], and nonsigniﬁcantly [Miller, 1994; Tavani, 1994 (in men); Jorgensen, 1995; Andersson, 1996; Wamala, 1997; Katz, 1998; deGonzague, 1999] associated with some measure of adiposity in adult populations. Similarly conﬂicting results have been noted in populations of children and adolescents, some concluding with positive (Berkey, 2000; Gills, 2002) or negative (Kemper, 1999; Rocandio, 2001) associations and others with nonsigniﬁcant ones (Manos, 1993; McGloin, 2002). Stunkard and colleagues (1999) studied this association longitudinally in a sample of infants born to obese and nonobese mothers in their ﬁrst year of life. They examined the role of energy expenditure (sleeping energy expenditure measured by indirect calorimetry plus total energy expenditure measured by the doubly-labeled water method) and total caloric intake (derived from nutritive suckling behaviors and food intake) in the prediction of body size. They found that energy intake predicted all the measures of body size and composition at 12 months, and energy expenditure and parental obesity predicted none of these measures (Stunkard, 1999). These conﬂicting results are not entirely unexpected. Observational studies are not the most appropriate methodology for addressing the question of interest for this section of the chapter regarding the contribution of increases in energy intake to the rising rates of obesity. This is due to the methodological issues summarized here:

Dietary Inﬂuences on Energy Balance

• Currently available methods for measuring total caloric intake and total energy expenditure outside of the laboratory setting are not precise enough to detect the relatively small imbalances between these two factors that can cause obesity given sufﬁcient time (Roberts, 1998). If energy intake exceeds energy expenditure by as little as 5% a day, a 5-kg gain in fat mass will occur over a 1-year period; yet a difference of 5% between energy intake and energy expenditure is hardly detectable with current assessment tools. • Underreporting of intake often occurs as a result of conscious underreporting, difﬁculty quantifying food portions, or mere lapses in memory. Food consumption has been shown to be underreported by as much as 47% (Lichtman, 1992). Considerable efforts have been made to improve dietary data collection so as to minimize recall bias since the earlier NHANES; these efforts included the inclusion of weekend days and strict quality control procedures. Unfortunately, underreporting continues to occur, regardless of these efforts (Briefel, 1997). In Kortzinger and colleagues’ analysis (1997), signiﬁcant underreporting was noted using both 7-day dietary recorders and a diet history in nonobese subjects, indicating that subjects consistently displayed the tendency to underreport, even when different dietary assessment tools are used. • Underreporting most often occurs in women, overweight subjects, and weight-conscious subjects or restrained eaters (Briefel, 1995; Price, 1997; Pomerleau, 1999; Kretsch, 1999; Ballard-Barbash, 1996; Pomerleau, 1999). Weight status has been shown to be the largest independent predictor of underreporting, and weight loss attempts (persons who reported they were dieting to lose weight) were also found to be an independent predictor of underreporting (Briefel, 1997). Underreporting is also more common among subjects with lower mean values of energy intakes (Lindross, 1999) and lower energy expenditure (Lindross, 1999; Ballard-Barbash, 1996). These directional biases would tend to obscure the relationship between calorie intake and adiposity. • Overweight individuals are more likely to be dieting or restricting food intake at any given point in time than are individuals who are not overweight. In fact, it was estimated that the proportion of the British population who are reducing normal food intake at any time to lose weight could be as high as 25–40% (Price, 1997). Studies that assess energy intake at a single point in time or over a very short time interval may capture intake during a period of energy restriction or excess that may not reﬂect long-term dietary patterns and caloric intake. Ballard-Barbash (1996) used CSFII data (from women only) to determine the contribution of dieting to the inverse association between caloric intake and BMI and found that overweight women were more likely to be dieting and eating less than 1000 calories on any one of the 4 days of data collection than were women who were not overweight. Furthermore, controlling for low-energy dieting alone reduced the inverse association between caloric intake and BMI by 20% (BallardBarbash, 1996). • Observations that overweight individuals consume less energy than normal or underweight individuals may be explained by the fact that overweight subjects may be less active compared to their nonoverweight counterparts (Ballard-Barbash, 1996). Lichtman and colleagues (1992) found that obese subjects from their study sample signiﬁcantly overestimated their energy expended in physical activities by an average of 51%. Such ﬁndings reinforce the need to collect adequate assessments of energy expenditure to correctly interpret the association of energy intake and body weight. • There may be other differences between overweight individuals and their counterparts that inﬂuence caloric requirements. In fact, these differences might explain the susceptibility of certain individuals to excess weight gain. For example, it has been found that, after weight loss, previously overweight individuals involuntarily reduce all components of energy expenditure even after controlling for lean body mass and adiposity (Leibel, 1995).

35

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Obesity: Dietary and Developmental Inﬂuences

In conclusion, the conﬂicting results from observational studies that examine the relationship between calorie intake and adiposity are largely a result of methodological weakness.

INTERVENTION TRIALS No overweight prevention trials were identiﬁed that targeted speciﬁc levels of calorie intake. It appears that prevention trials tend to focus on dietary composition, and may encourage reduced intake overall, but do not target speciﬁc calorie intakes or require that participants monitor their calorie intakes.

CONCLUSION While the observational ﬁndings supporting the association between total caloric intake and energy imbalance are ﬂawed by methodological limitations, national surveys indicate that per capita calorie intake has increased dramatically and is sufﬁcient to explain observed increases in adiposity over recent decades. The scientiﬁc basis for the role of energy intake in the development of obesity is incontrovertible and increases in the availability of calories may explain why consumers are eating more calories than ever. Given the dramatic increase in per capita calorie intake, it is doubtful that overweight prevention efforts will be successful if they focus exclusively on physical activity. To be successful, overweight prevention interventions need to also impact behaviors that result in reductions in per capita calorie intake. Therefore, the remainder of the of the dietary section of this document will explore which of these target dietary behaviors hold the most promise for promoting energy balance.

ENERGY DENSITY INTRODUCTION Energy density is deﬁned as the total metabolized energy from the macronutrients, protein, carbohydrates, fat, and alcohol, divided by the total weight of the food including water (energy density = calories/gram). Thus, the energy density of a food is, in part, a function of its macronutrient composition (Rolls, 2000). Both dietary proteins and carbohydrates have a relatively low energy density, providing only four calories per gram; whereas, dietary fat is much more energy dense, providing nine calories per gram. Alcohol is also fairly energy dense, providing seven calories per gram. The two most signiﬁcant determinants of dietary energy density are dietary fat and water content, as illustrated in Figure 4.5 (Yao, 2001). Dietary fat is a major determinant of energy density as a result of its high caloric density compared to protein and/or carbohydrate and its wide variation in the proportion in which it is found in commonly consumed foods. Water’s major inﬂuence on energy density is a result of its contribution to the food’s weight without the addition of calories and its wide variation in the proportion in which it is found in commonly consumed foods (Rolls, 1999; Yao, 2001). Dietary ﬁber also has the potential to inﬂuence energy density because of its minimal energy content (Yao, 2001). However, in actuality, the inﬂuence of dietary ﬁber on energy density is only modest, as the ﬁber content of foods does not widely vary, and there is an upper limit to the amount of ﬁber found in foods typically consumed by humans. As seen in Figure 4.5 (Yao, 2001), dietary ﬁber content was not signiﬁcantly related to energy density when studied in 200 commonly consumed foods. Therefore, although ﬁber can contribute to a reduction in the energy density, its effect is dwarfed by the much larger impact of water and fat on dietary energy density.

SECULAR TRENDS The current food supply is ﬂooded with high energy-dense foods. Because there is such a widerange of foods that are high in energy density, it would be difﬁcult to estimate the overall energy

Dietary Inﬂuences on Energy Balance

37

FIGURE 4.5 The association of energy density (kcal/100 g) with fat, water, and ﬁber contents (g/100 g) of 200 common foods randomly selected from the Fred Hutchinson Cancer Research Center Food Frequency Questionnaire (FHCRC/Block FFQ, version 06.10.88). Nutrient contents of the foods were calculated using standard food composition tables (Minnesota Nutrition Data System, software developed by the Nutrition Coordination Center, University of Minnesota, Minneapolis. Food Database version 11A; Nutrient Database version 26, 1996). Permission for use was granted by the International Life Sciences Institute. (Source: Yao, 2001.)

density of the American diet, as high energy-dense foods include not only high-fat foods but some newly engineered low-fat foods that are not necessarily any lower in energy density than their fullfat counterparts (Kant, 2000; Stubbs, 2001; Bell, 1998). Kant (2000) examined the contribution of energy-dense, nutrient-poor (EDNP) foods to daily energy and macronutrient intakes in a nationally representative sample of the adult American population (NHANES III, 1988–1994) and found such foods to supply ~27% of energy intake, with alcohol providing an additional 4%. This estimate was comparable with that reported in NHANES II, 1976–1980, where EDNP foods (including alcohol) provided one third of the total daily energy intake for American adults. The total reported energy intakes in the NHANES III population were, however, higher, and thus it is likely that the NHANES III data represent an increase in absolute intake of EDNP over the same time that overweight and obesity have been on the rise.

PLAUSIBLE MECHANISMS Energy density is an important determinant of energy intake and, ultimately, energy balance (Yao, 2001; Westerterp-Plantenga, 2001; McCrory, 2000; Rolls, 1999; Bell 1998; Stubbs, 1995). Cuco and colleagues (2001) found both men and women from a representative sample of the adult Mediterranean population who ingested high energy-dense diets to have a signiﬁcantly higher consumption of total energy, protein, fat, and saturated fat than those who consumed low energydense diets. Kant (2000) also found energy-dense, nutrient-poor (EDNP) food intake (those foods that constitute the tip of the food guide pyramid) to be positively associated with energy intake among a large U.S. nationally representative sample from NHANES III. In addition, experimental studies have demonstrated that total caloric intake is markedly affected by manipulations in energy density, independent of the percentages of energy from macronutrients (Bell, 2001; Rolls, 1999; Bell, 1998; Stubbs, 1993). It has been hypothesized that people tend to eat a constant weight of food despite manipulations in diet composition (Rolls, 2000; Yao, 2000). Based on this concept, consumption of foods with high energy density will encourage consumption of excess energy because of the low weight (or volume) in relation to energy content (Yao, 2001). The high palatability of energy-dense foods may also contribute to overconsumption (Drewnowski, 1999; McCrory, 2000). Bell and colleagues (1998) demonstrated this among 18 healthy, normalweight women by creating 3 variations of an entrée, differing only in their energy density (low, medium, and high energy-density conditions). These women consumed a similar amount of food

38

Obesity: Dietary and Developmental Inﬂuences

(by weight) across the three conditions of energy density, and thus signiﬁcantly more energy was consumed in the high energy-density condition than in the medium or low energy-density condition. Such results suggest that the overconsumption of high-fat foods (previously termed hyperphagia) is likely to be a result of their high energy-density nature rather than a consequence of the fat content per se (Poppitt, 1996). Several studies have suggested that incorporating foods that are low in energy density into a diet can help reduce energy intake while controlling hunger (Rolls, 2000); while a diet rich in high energy-dense foods can lead to a positive energy balance.

OBSERVATIONAL STUDIES Few observational studies have examined the direct association between energy density and adiposity (see Table 4.1). Kant (2000) studied the relationship between the consumption of energy-dense, nutrient-poor (EDNP) foods and adiposity among adults from the third NHANES study and found that the relative odds of having a high BMI or high waist circumference were not different in the three tertiles of percentages of daily energy from EDNP foods. Cuco and colleagues (2001) also examined this association and found no differences between the absolute weight and BMI across three groups, men and women consuming diets of low, medium, and high energy density. In this study, total daily energy ingested (kJ) was divided by the food volume consumed (cm3) to quantify the energy density of the diet. The study population was then classiﬁed into three groups (diets of low, medium, and high energy density) according to the tertiles of the residual of energy density corrected for age in each sex. Cox and colleagues (1999) studied the relationship among weight status and energy density in a sample of 41 lean and 35 obese nondieting healthy subjects and found the obese group to have a signiﬁcantly higher mean dietary energy density than the lean group (6.4 calories/gram compared to 5.9 calories/gram, p < 0.05). Furthermore, these investigators found a signiﬁcant correlation between percent body fat assessed by bioelectrical impedance and dietary energy density (calculated with nonalcoholic beverages and water excluded). Westerterp-Plantenga and colleagues (1996) also examined energy intake in relation to energy density of food in obese and nonobese women. They found the obese women in their sample to consume larger portions of foods with a high energy density than the nonobese women in their sample. They also found that the obese women consumed smaller portions of foods with lower energy density in comparison to their nonobese counterparts who were matched on age. Energy density in this study was based on three energy density food categories: 0–7.5 kJ/g, 7.5–15.0 kJ/g, and 15.0–22.5 kJ/g. The literature makes comparisons of such energy density results from different studies difﬁcult because of differing or poorly deﬁned measures of energy density. All four of these studies were cross-sectional in design and therefore limited by their inability to establish causation. Longitudinal and intervention studies are needed to determine if, in fact, energy density is causing an increase in adiposity.

CONCLUSIONS A low energy-dense diet tends to be low in fat and include foods that are high in water content such as fruits and vegetables. Assuming that people consume a constant weight of food each day, eating foods with lower energy density (fewer calories per unit weight) should reduce overall energy intakes and the prevalence of overweight and obesity. Indeed, short-term experimental studies consistently ﬁnd that decreasing the energy density of the diet enhances satiation, and the termination of eating is reached on the consumption of a lower energy intake. The high palatability of energy-dense foods may also contribute to passive overconsumption. However, two large observational studies, one of which was large and nationally representative, found no relationship between energy density and adiposity, although they did ﬁnd a positive association with calorie intake. The two observational studies that did ﬁnd a positive association between energy density and adiposity were very small case control studies. Given the absence of prevention trials that evaluate the impact of energy density on adiposity, the limited number and inconclusive ﬁndings from the observational

Study Name and/or Location

Study Population

U.S. Nationally Representative Cross-Sectional Studies Kant, 15,611 adults (7470 men and 8141 women) from 2000 a nationally representative sample of U.S. adults NHANES III, ≥ age 20 yr 1988–1994

Other Cross-Sectional Studies Cuco, 2001 572 adults (267 men and 305 women) aged 25–65 yr from Reus, Spain

Cox, 1999

41 lean and 35 obese nondieting, healthy males and females aged 18–65 yr from Reading (southeastern England)

Case-Control Studies Westerterp34 obese and 34 nonobese women matched for Plantenga, age (20–50 yr) from Limburg, Netherlands 1996

Adiposity Measure1

Control Variables

Association2

High BMI (BMI > 24.9). High waist circumference (>88 cm for women; >102 cm for men)

Age, gender, race/ethnicity

0 (all foods and beverages) 3 tertiles of % of daily energy from energy dense/nutrient poor foods (0–16.5, 16.6-31.3 and >31.3%)

Weight (kg) and BMI (continuous)

Age, gender

Weight status: Lean (BMI = 20–25) or obese (BMI > 30). Percent body fat (bioelectrical impedance)

Underreporting, gender, age

0 (all foods and beverages except water, adjustment factor applied to the volume of some beverages and foods) 3 tertiles of energy density (diets of low, medium, and high energy density) + Energy density (excluding nonalcoholic beverages and water)

Weight status: Nonobese (BMI < 25) or Overweight-obese (BMI > 25)

Underreporting, matched for age

Dietary Inﬂuences on Energy Balance

TABLE 4.1 Observational Studies Examining the Association between Dietary Energy Density and Adiposity

+ (all foods and beverages except water) Energy density (based on 3 energy density food categories: 0–7.5 kJ/g, 7.5–15.0 kJ/g, and 15.0–22.5 kJ/g)

Notes: 1

BMI based on measured weight and height unless otherwise noted.

2

39

Plus (+) sign indicates signiﬁcant direct relationship, negative (–) indicates signiﬁcant inverse relationship, and zero (0) indicates nonsigniﬁcant relationship between dietary factor and measure of adiposity.

40

Obesity: Dietary and Developmental Inﬂuences

studies (evaluating the impact on adiposity), and lack of secular trend data, it is not possible to come to a ﬁrm conclusion regarding the role of the energy density of the diet on adiposity. However, the substantive experimental and observational data supporting the notion that low energy-dense diets increase satiety and decrease calorie intake suggests that this is a subject to be given priority for future research (Table 4.2).

TABLE 4.2 Does the Preponderance of Evidence Support a Relationship between Energy Density and Higher Adiposity?1 Observational Studies2 Support Relationship? (number of studies) Longitudinal Secular Trends Support Relationship?

Mechanisms Support Relationship?

(0)

U.S. Nationally Representative Cross-Sectional (1 in adults, 0 in children)

Prevention Trials2 Support Relationship? (number of studies ) Other CrossSectional or Case-Control (3 in adults, 0 in children)

Randomized Controlled Trials

Other

(0)

(0)

No studies

No studies

Conclusion: Consistency of Evidence Supporting Relationship

Adults No studies Inconcl.

Inconcl.

Yes

Yes

Inconcl. Children No studies

No studies

No studies

No studies

No studies

Notes: 1

Description of criteria used for summary table is located in the methods section.

2

Numbers in parentheses indicate the number of relevant studies identiﬁed and examined for each study type.

MACRONUTRIENT INTAKE INTRODUCTION The role of the macronutrient composition of the diet in the development of obesity has been a subject of intensive study for decades. Recent studies that have focused on determining the optimal macronutrient composition for weight loss have generated much debate. Research designed to determine the role of macronutrients in the prevention of weight gain has received much less attention, despite the fact that prevention of weight gain and maintenance of weight loss have posed the greater challenge. This section will focus on the role of dietary macronutrient composition in energy balance and hence the prevention of excess weight gain. Three of the four lines of evidence, described previously (secular trends, mechanisms, and observational studies), will be discussed for each of the following macronutrients individually: dietary fat, total carbohydrate, dietary ﬁber, sugars/simple carbohydrates, and protein. The fourth line of evidence, prevention trials, will be discussed for all of the macronutrients at the end of the section.

MACRONUTRIENT INTAKE: FAT Secular Trends — Dietary Fat Nationally representative surveys (NFCS and CSFII) of the food intake of Americans suggest that the percent of calories consumed as fat has declined steadily over the 30-year period from 1965

Dietary Inﬂuences on Energy Balance

41

to 1995, approximately the same period of time when obesity has been rising most steeply (Kennedy, 1999). This trend is a observed among men, women, and children (Figures 4.6 and 4.7). According to the same report, from 1965 to 1989–1991, the absolute number of grams of fat consumed also declined initially over the same time period but then increased between 1989–1991 and 1995 along with total calories. Results from another national survey, NHANES I, II, III (Figures 4.8 and 4.9) indicate a smaller reduction in fat intake among children: from 36–37% of total calories in the 1970s to 33–34% of calories in 1988–1994 (Troiano, Briefel, 2000). However, data from NHANES suggest that adults have increased their total fat intake since the early 1970s and have only begun

FIGURE 4.6 Trend in fat intake as proportion of energy intake (NFCS and CSFII). (Source: USDA/ERS(b), 2003. With permission.)

FIGURE 4.7 Trend in fat intake (NFCS and CSFII). (Source: USDA/ERS(b), 2003. With permission.)

FIGURE 4.8 Trend in fat intake as proportion of energy intake (NHANES). (Sources: Troiano, 2000; McDowell, 2003. With permission.)

42

Obesity: Dietary and Developmental Inﬂuences

FIGURE 4.9 Trend in fat intake (NHANES). (Sources: Troiano, 2000; McDowell, 2003.)

decreasing the proportion of calories from fat since 1976–1980 (McDowell, 2003). It is important to keep in mind that, regardless of reductions in the proportion of calories from fat, most Americans are still consuming large proportions of their calories from fat (Kennedy, 1999, Troiano, Briefel, 2000). Only about 1 in 3 Americans age 6–50 years consumes less than 30% of calories from dietary fat (Kennedy, 1999). Food disappearance data also indicate that the percent of calories available from fat has decreased between 1970 and 1994 (from 42 to 38%), but the absolute amount of available fat has increased along with calories (Figures 4.10 and 4.11). The availability of added fats and oils in particular has increased by 21.8% between 1970 and 1995 (Figure 4.12) (Harnack, 2000). Specifically, the availability of shortening and salad and cooking oils increased signiﬁcantly. A major reason for this trend is the increase in the number of fast food restaurants and other eating establishments that use deep fat frying oil, about half of which is discarded. But even taking into account these losses, it appears that the availability of added fats and oils for the American diet has steadily increased in recent years. As described previously, there are many limitations associated with data gained from these national surveys. Dietary intake data are notoriously inaccurate, with large margins of error due to the individual person’s inability to accurately recall the number of servings and serving sizes consumed (Black, 1993; Worsley, 1984). These limitations may be particularly acute with regard

FIGURE 4.10 Percent of energy in U.S. food supply. (Source: Bente, 2003.)

Dietary Inﬂuences on Energy Balance

43

FIGURE 4.11 Energy sources in U.S. food supply. (Source: Bente, 2003.)

FIGURE 4.12 Foods on the rise. (Source: Putnam, 2002.)

to dietary fat. Over the time period that the data referred to was collected, major national campaigns were conducted to reduce fat intake. Therefore, the social desirability effect would tend to lead to a progressively larger underestimation of fat intake over time. Furthermore, when merely examining population trends, we have no idea which individuals increased or decreased their fat intake. It could be that the individuals who remained within the normal weight range were those who reduced their fat intake, and those who became overweight increased, or vice versa. In fact, the food intake data indicate that higher fat intakes are consistently associated with higher calorie intakes (Kennedy, 1999). Fat intake may also be a marker for what is generally considered a healthier diet. For example, lower-fat diets are associated with higher intakes of fruit, grains, and skim milk, and higher fat diets are associated with higher intakes of fried potatoes (Kennedy, 1999). Therefore, although secular trends in fat intake cannot establish or refute the relationship between fat intake and obesity, we can conclude the following. Small reductions in the percent of calories from fat at the population level have not stemmed the rising tide of obesity. On the other hand, increases in the availability of added fats and oils, especially in fast food and other food establishments, could be a contributor to the recent rise in obesity. Although higher intakes of dietary fat are associated with higher calorie intakes, fat is not the only contributor to the recent rise in caloric availability and consumption. As a population, we have not yet met the dietary recommendations for fat intake and therefore cannot conclude based on these data whether dietary intakes at the recommended levels or lower would be protective against obesity.

44

Obesity: Dietary and Developmental Inﬂuences

Mechanisms — Dietary Fat Energy Density, Palatability, and Passive Overconsumption Dietary fat has been implicated for decades as a contributor to obesity. Diets that contain 30% or more of energy from fat reliably produce obesity in rats, mice, dogs, and primates, at least in part as a result of increased energy intake and efﬁciency of storage (Hill, 2000). Most studies with humans as subjects report lower caloric intakes on low-fat compared to high-fat diets and lower BMI’s in persons consuming low-fat than high-fat diets (Ballard-Barbash, 1996). This observed relationship is thought to be largely due to a phenomenon referred to as “passive overconsumption” or the unintentional ingestion of excess calories when signals for satiation do not function effectively to control meal size. Energy density and palatability of foods, in turn, are thought to be the main factors that contribute to passive overconsumption. Dietary fat is the most energy dense of the macronutrients (9 cal/g vs. 4 cal/g for carbohydrate and protein). Therefore, foods with high fat contents tend to be concentrated in calories. In fact, a recent study found that water and fat content explained most of the variance in the energy density of 200 randomly selected, commonly eaten foods in the American diet (Yao, 2001). It is not surprising, then, that higher intakes of dietary fat tend to lead to passive overconsumption among humans (Astrup, 2001; Grundy 1998; Rolls, 1997) and animals (Grundy, 1998). Foods with high energy density tend to lead to passive overconsumption for a variety of reasons, including rapid ingestion before satiety mechanisms kick in and lack of volume, and therefore stomach distension, which contributes to a sense of fullness. Studies have shown that, when lean subjects are allowed to eat from a range of high-fat or high-sucrose foods, passive overconsumption occurred only when high-fat foods were consumed (Green 1994, Rolls, 1995). However some studies show that, when energy density is controlled, this distinction is eliminated (Astrup, 2001; Raben, 2003; McCrory, 2000). Other studies suggest that factors in addition to energy density, notably palatability, contribute to passive overconsumption of high-fat foods (Stubbs, 2001; Stubbs, 2000; Johnstone, 1996; Blundell, 1997). Palatability is highly associated with energy density, by an r = 0.46 according to McCrory (2000). Energy-dense foods (including high-fat foods) tend to receive higher palatability ratings than less energy-dense foods (Nasser, 2001; Drewnowski, 1998). Both energy density and palatability are positively associated with energy intake (r = 0.56 and r = 0.73) (McCrory, 2000). It has been proposed that fat has the lowest inter- and intrameal satiety value of the macronutrients (Stubbs, 2001; Lawton, 1998), but it remains to be seen if this is due primarily to the palatability and density of high-fat foods. Although there is ample evidence that lower-fat diets consumed ad libitum result in lower calorie intakes (Raben, 1997; Thomas, 1992; Kennedy, 2001; Lissner, 1995), evidence does not support the notion, however, that low-fat diets that are not lower in energy density can be consumed in unlimited quantities and not result in weight gain (Rolls, 1997; Lawton, 1998). Several other factors have been identiﬁed that tend to stimulate energy intake. De Castro (1999) found that eating with others, later in the day, when more hungry, when more elated, and on the weekends all tend to increase fat intake. In general, factors that stimulate higher levels of fat intake in particular tend to stimulate energy intake. Utilization and Storage In addition to facilitating overconsumption, several other properties of fat may lead to weight gain. First, fecal energy losses have been reported to be higher when a high-carbohydrate, rather than a high-fat, diet is consumed (Lammert, 2000), suggesting that overall energy absorption may be higher for high-fat diets. Secondly, metabolic efﬁciency is higher for fat than for either carbohydrate or protein, leading to a lower thermic effect following consumption of a high-fat meal (Maffeis et al., 2001) and perhaps more efﬁcient energy storage in the adipose tissue. Thirdly, fat overfeeding

Dietary Inﬂuences on Energy Balance

45

has minimal effects on total energy expenditure in humans, whereas carbohydrate overfeeding produces progressive increases in total energy expenditure (Horton, 1995). Thus, consumption of a high-fat diet may inﬂuence both sides of the energy balance equation. High-fat diets have also been shown to favor fat storage in the adipose. When consumed as part of a mixed food diet, the fat component is directed via insulin to the adipose tissue. Storage costs for fat are lower (4%) than for carbohydrate (12% for glycogenesis and 23% for de novo lipogenesis) (Golay, 1997). In one study, postprandial fat storage was eight-fold higher after a highfat meal compared to a low-fat meal (Maffeis, 2001). High levels of physical activity may protect against some of the obesigenic effects of a highfat diet. Physical activity tends to increase overall energy expenditure and to increase fat oxidation, allowing the individual to oxidize the dietary fat that was directed to the adipose tissue in the immediate postprandial period and, thus, maintain body homeostasis despite the higher levels of fat intake (Bray, 2002; Schrauwen, 2000; Magarey, 2001; Shepard, 2001). Conversely, inactivity, even occasional inactivity, has been shown to increase susceptibility to positive energy balance of those on a high-fat diet. A high-fat compared to low-fat diet produced a net increase in fat balance of 443 kcal/day or 50 g among individuals with an occasional day of physical inactivity (Shepard, 2001). The type of fatty acid may also be an important factor in exercise-induced oxidation. Exercise has been shown to increase the oxidation of monosaturated fat, but not saturated fat, after a subsequent meal (Votruba, 2002). Genetic Susceptibility There appears to be a genetic component (susceptibility) in humans to weight gain with exposure to high-fat diets (Gibney, 1995). This phenomenon has also been observed in studies with mice (Grundy, 1998). Among “normal” weight subjects, fat intake and oxidation tend to be related; when more fat is eaten, the oxidation of fat increases. Among obese subjects, however, there is some discrepancy. Whereas some studies have shown fat oxidation to correlate with fat ingestion in the obese (Maffeis, 1995; Maffeis, 1999), others (Thomas, 1992) have indicated that fat oxidation does not increase with increased fat intakes, suggesting that obese subjects may be especially vulnerable to weight gain on higher-fat diets Furthermore, animal studies demonstrate that those strains that are more susceptible to obesity are less responsive to satiety signals normally triggered by fat intake (Bray, 2002). Overweight subjects also seem to have a greater preference for fat compared to their lean counterparts (Anderson, 1995; Cox, 1999; Reed, 1997). A preference for fat-rich foods is positively associated with obesity. One study found that a liking for fat-rich foods explained 7–9% of the variation in body fatness, considerably more than was explained by other lifestyle variables such as exercise, snacking habits, smoking, or drinking (Nakamura, 2001). Threshold Effect Both observational studies and secular trends suggest that very low-fat diets promote leanness, but past some undetermined level, the percentage calories from fat has little impact on body fatness (McCarty, 2000). Conversely, there may be a threshold of dietary fat below which obesity does not develop. In one study conducted with rats, the threshold below which obesity does not develop was approximately 28% of calories from fat (Bray, 2002). Results among humans consistently show that more calories are consumed when consuming high-fat diets (>40% of total energy) as opposed to low fat diets (20% or less from fat) (Hill, 2000). However, the effects of dietary fat intake within the 20–40% range are less clear. Not All Fatty Acids Are Alike Research has shown that, when long-chain triglycerides are replaced by medium-chained triglycerides (MCTs), energy expenditure increases (Delany, 2000), and there is a depression in food intake and a lowering of body fat mass (St-Onge, 2002). This may be explained by the fact that

46

Obesity: Dietary and Developmental Inﬂuences

MCTs are absorbed directly into the portal circulation and transported to the liver for rapid oxidation rather than being directed to the adipose tissue for storage. Saturated fats may also be more obesigenic than polyunsaturated. Animal and human studies conducted in metabolic chambers have shown that polyunsaturated fatty acids oxidize more rapidly than saturated fatty acids (Gonzalez, 2000; Van Marken, 1997; Delany, 2000). Conclusion — Mechanisms There are numerous mechanisms to explain the observed relationship between high dietary fat intakes and obesity. These include the high energy density and palatability of high-fat foods, which tend to promote passive overconsumption, as well as the efﬁcient storage and utilization of fats compared to other macronutrients. However, not all humans react equally to dietary fat. Both genetics and lifestyle factors such as physical activity appear to determine whether the individual will be susceptible to weight gain at higher levels of fat intake. Furthermore, different types of dietary fat may vary in the extent to which they promote obesity. Although the evidence explaining why high intakes of dietary fat lead to obesity is compelling, it remains unclear the exact levels of dietary fat at which individuals face the optimal protection from, and highest risk for, the development of obesity. It appears that above a certain minimum and below a certain maximum, variations in dietary fat intake may have a relatively limited inﬂuence on energy balance. Determining these levels would be a priority for future research. Observational Studies — Dietary Fat Longitudinal Studies — Adults Five longitudinal studies (see Table 4.3) were identiﬁed that examined the relationship between dietary fat intake and weight gain in adults. Two of the studies were conducted in Europe and the remainder in the U.S. The largest and nationally representative study by Kant et al. (1995) included numerous control variables. This rigorous study found that fat intake was predictive of weight gain in healthy men of all ages, was negatively associated with weight gain in women under 50 years of age, and was not signiﬁcantly associated with weight gain in unhealthy men and older women. Higher fat intake was, however, consistently associated with higher calorie intakes. This study compared intakes in 1971 with weight change from 1971 to 1982–1984, therefore the results cannot necessarily be extrapolated to reﬂect the more recent increases in BMI in the U.S. The other studies listed in Table 4.3 found similarly mixed results, i.e., either a positive or no signiﬁcant association with weight gain. The two most vigorously controlled of these studies (Parker, 1997; Heitman, 1995) found no signiﬁcant association between dietary fat and weight gain except among women with obese parents. These results suggest (as described in the mechanisms section above) that individuals at risk for obesity may be particularly susceptible to weight gain at high intakes of dietary fat. Heitman (1995) also reported that the diets of weight gainers were characterized by a particularly high intake of fat (40–45% of total calories). The longest longitudinal study (15 years), followed 181 children from age 13 and found no association between fat intake and accumulation of fat mass. Given that this study did not include individuals who were overweight at the start of the study, the individuals at higher risk for weight gain were excluded. This may explain the lack of association. Cross-Sectional Studies — Adults Twenty-three cross-sectional studies (see Table 4.3) that examined the relationship between adiposity and dietary fat intake among adults were identiﬁed, four of which were small case-control studies. Twelve of these 22 studies were conducted in European countries, 1 in Canada, and 1 in a combination of countries including the U.S. The remainder were conducted exclusively in the

Study

Study Population

Control Variables

Measure of adiposity

Association1

Comments

–f (<50 yr) +m (healthy, all ages) 0 (other groups)

Higher % fat was associated with higher kcal intakes.

+f (with obese parents) 0f (lean) 0f (obese with lean parents)

Weight gainers ate 40–45% of kcal from fat.

Longitudinal Studies Kant (1995) NHANES I (1971) and NHEFS (1982–1984)

2580 men 4567 women

Race, education, smoking, length of follow-up, PA, kcal, alcohol, baseline BMI, special diet, parity

Weight change compared to intake at baseline

Heitman (1995) Sweden 6-year study

361 women Ages 38–60

Total energy, smoking, PA, menopause

BMI change

Klesges (1992) Memphis 3-year study

294 adults Mostly middle class White

Gender

Change in BMI compared to baseline intakes

Parker (1997) 176 men Pawtucket Heart Health Study 289 women (New England) Prospective cohort 4-year study 1986–87 and 91–92

Age, kcal, smoking, BMI, aerobic activity, gender

BMI change

Kemper (1999) Amsterdam Growth and Health Longitudinal study 15-year study

Age Gender

Fat mass (4 skinfolds, ht, wt)

83 men 98 women Age 13 at baseline Nonobese at baseline

Dietary Inﬂuences on Energy Balance

TABLE 4.3 Observational Studies of the Association of Dietary Fat Intake with a Measure of Adiposity in Adults

+f (years 2 and 3) +m (all years) 0 (total, animal, vegetable)

0

47

48

TABLE 4.3 (CONTINUED) U.S. Nationally Representative Cross-Sectional Studies Ballard-Barbash (1996) 1985–86 CSFII

1831 women Ages 19–50

Kcal, age, education, PA, BMI (self-report) weight smoking, low energy intake categories: overweight = BMI > 27.3

0 + (when individuals with poor health status excluded)

Other Cross-Sectional or Case-Control Studies Gonzalez (2000) EPIC study, Spain 3–4 year study

14,374 men 23,289 women Ages 29–69 Varying SES

Age, PA (housework, sport, leisure, work), smoking, education

BMI

–f (g, weak) 0m (g) +f (%) +m (%)

Trichopoulou (2002) EPIC study Greece

27,862 adults Healthy volunteers Ages 25–82

Adjusted for other nutrients. BMI Kcal, age smoking, education, energy expenditure

Kromhout (2001) Seven Countries Study (Europe, Japan and U.S.) 1958 and 1964

12,763 men Middle aged Diverse SES

Kcal

BMI and skinfolds Intercohort comparisons only

Bolton-Smith (1994) Scottish Health 1984–1986

5768 men 5858 women Ages 25–65

Age, sex, alcohol, kcal, PA, smoking, menopause

BMI

+ m/f (fat: extrinsic sugar ratio) +f (fat: total sugar)

Stam-Moraga (1999) Belgian Interuniversity Research on Nutrition and Health 1979–1984

5837 men 5243 women Ages 25–74 Nationally representative

Age Gender

BMI

+m 0f + m/f (high fat/sugar ratio)

*Explained less than 1% of variance.

0

PA and ﬁber explained 90% of the variance in skinfolds.

Obesity: Dietary and Developmental Inﬂuences

0 NR (BMI)

Slattery (1992) CARDIA 5 U.S. cities Ludwig (1999) CARDIA 5 U.S. cities

Gibson (1996) Dietary and Nutritional Survey of British Adults 1986–1987

Macdiarmid (1998) 1986–1987

Lluch (2000) Stanislas Family Study (France)

Age

BMI (self-report)

Gender, race, age education, smoking, alcohol and PA

BMI Skinfolds

Race/ethnicity Age, sex, education, city, smoking, PA, vitamin supplements, kcal, alcohol, baseline adiposity

BMI

Age Smoking Underreporting

BMI

1239–1853 adults (with and without LER) healthy Nondieting Ages 16–64 387 families 1320 family members 379 men 381 women 270 boys 290 girls Ages 11–65

LER; age, gender

BMI

Age

BMI Lorentz index (wt/ht categories)

+ (animal fat, g)

Did not measure protein per se, only compared meat eaters vs. nonmeat eaters.

– white f (BMI) 0 (skinfolds) 0 (other groups) 0 (whites) + (blacks)

–m (high sugar/low fat) 0f (high sugar/low fat) – (weak, high sugar and fat foods) – (sugar among high fat consumers) + m/f combined + m only

Sugar and fat appear to have opposing not synergistic impacts on BMI.

The direction and signiﬁcance of the relationships did not change with inclusion/ exclusion of LER.

+m 0 (other groups)

49

1914 men 3378 women Nonsmokers About half nonmeat eaters 5115 adults Black and white Ages 18–30 Diverse SES 2909 adults Healthy Black and white Ages 18–30 (endpoint of 10-year longitudinal) 1087 men 1110 women Ages 16–64

Dietary Inﬂuences on Energy Balance

Appleby (1998) Oxford Vegetarian Study 1980–1984

50

TABLE 4.3 (CONTINUED) Katz (1998) RENO Diet-Heart Study 5-year study

384 adults Mostly upper SES Mostly married Mostly white Cross-sectional analysis of longitudinal study

Tucker (1992) Western U.S.

Nelson (1996) Utah

205 women Mostly white Average age 34.6

203 men Mostly white Average age 41

Age, PA, education, smoking, BMI dieting, total energy, low energy records Age BMI categories

Gender

BMI at years 1, 2, and 3

Age, smoking, other macronutrients, PA, total energy intake

Body fat categories based on skinfolds

Age, kcal, ﬁtness, PA

Body fat based on skinfolds (continuous variable) Body fat categories based on skinfolds Body fat based on skinfolds (continuous variable)

+m (BMI) +m (ht/wt status, yr 1 only) 0f BMI and ht/wt status, all years) + (BMI, multivariate, all subjects) + (g and%)

Change in kcal intake was positively associated with fat intake in all groups except lean men; all groups had high fat intakes; the multivariate model explained very little of the variance in BMI (R2 = 0.075).

0 (g) +f (yr 2 and 3) 0f (yr 1) +m (all years) + (g and%) + (g and%, R2 = 0.033–0.0016) + Fiber showed strongest association with body fat 0 (after controls)

Obesity: Dietary and Developmental Inﬂuences

Ruidavets (2002) 330 men Toulouse, France Ages 45–64 1996–1997 Wamala (1997) 300 women Stockholm Healthy Female Coronary Risk Study Ages 30–65 Klesges (1992) 294 adults Memphis Mostly middle class 3-year study Mostly white

Multivariate analysis: gender, BMI, age, education, marital Ht/wt status (lean vs. status, smoking, sport overweight) activity, calorie intake

Anderson (1996) Gustaf Study Sweden

150 adults Mostly women Ages 18–65 86 obese men 61 normal weight men Case-control

Age, sex, income, education

BMI categories

Age

BMI categories (obese vs. control) (3 categories of obesity) Obese vs. lean (25% vs. 15% body fat by hydrostatic weighing) BMI categories Body fat (bioelectrical impedance)

Miller (1994)

46 men 32 women

Gender

Cox (1999) England

41 lean adults 35 obese adults Nondieting Healthy Case-control 34 obese women 34 nonobese women Ages 20–50 Case-control

Underreporting, gender, age

Westerterp (1996) The Netherlands

Underreporting Matched for age

Obese (average BMI = 30) vs. non obese (average BMI = 22)

+ (% and g) 0 (obese vs. control) + (between obese categories) +

0

Only salty foods and energy density related to obesity.

Dietary Inﬂuences on Energy Balance

Alﬁeri (1995) Ontario, Canada

+

Notes: NR = not reported; PA = physical activity, HEI = Healthy Eating Index; CHO = total carbohydrate; LER = low energy reporters; SES = socioeconomic status. f = female; m = male; % = % of calories; g = grams (absolute intake); kcal = kilocalories; ht = height; wt = weight. BMI was a continuous variable unless otherwise indicated. Macronutrient intake was deﬁned in terms of % calories unless otherwise indicated. 1

Plus (+) indicates a signiﬁcant direct association, negative (–) indicates a signiﬁcant inverse association, and zero (0) indicates nonsigniﬁcant association.

51

52

Obesity: Dietary and Developmental Inﬂuences

U.S. The majority of the studies (17) found a positive association between fat intake and adiposity among at least some groups. Seven found no signiﬁcant association between dietary fat and adiposity among at least some groups studied. Only three found a negative association between fat intake and adiposity. One of these only found the negative association to hold true when fat intake was deﬁned in absolute terms (the association was positive when deﬁned as percent calories from fat). Another found the negative association only among white females (not among white males or blacks of either gender). The third study to ﬁnd a negative association did not examine total or percent intake of diet fat but rather examined intake of “high-fat foods” (Gibson, 1996). Of those studies that controlled for physical activity levels, most still found a positive association between fat intake and adiposity among most groups (Gonzalez, 2000; Bolton-Smith, 1994; Slattery, 1992; Ludwig, 1999; Wamala, 1997; Tucker, 1992; Nelson, 1996), but the association tended to be weak or nonsigniﬁcant, depending on the subpopulation analyzed and how the dependent variable was categorized. No other consistent trends in results were noted when considering the nature of the control variables, categorization of the independent variable, or characteristics of the study population. Only one study examined a nationally representative sample (Ballard-Barbash, 1996) and found a negative but nonsigniﬁcant association between fat intake and adiposity after adjustment for confounding variables (Beta = 0.049, P = 0.06). However, after exclusion of subjects with poor health status, the association became signiﬁcant. The main weaknesses of this otherwise rigorous study were that BMI was based on self-reported height and weight and the study used relatively “old” data from the 1985–1986 CSFII. Conclusions — Observational Studies in Adults The cross-sectional studies among adults were fairly consistent in demonstrating that dietary fat is usually associated with higher levels of adiposity in most groups. Some studies, though, found no association and very few found a negative association between adiposity and dietary fat intake. The longitudinal studies met with more mixed results. However, the majority of studies found a positive association among dietary fat and adiposity among at least some groups. The lack of a signiﬁcant association between dietary fat intake and adiposity in many of the studies may be due in part to methodological limitations but also suggests that dietary fat is only one of many factors inﬂuencing adiposity. Therefore, although this data provides fairly strong support for the hypothesis that higher intakes of dietary fat increase the risk for obesity among adults, dietary fat is only one of many factors contributing to adiposity and may put only certain (susceptible) groups at risk for excess weight gain. Longitudinal Studies — Children Twelve longitudinal studies were identiﬁed from 11 different study cohorts of children age 3 months to 18 years that examined the association between dietary fat intake and change in adiposity (Table 4.4). Two of these studies found higher dietary fat intake associated with an increase in adiposity (Lee, 2001 and Davison, 2001; Klesges, 1995 and Eck, 1992). Three studies reported mixed results depending on the measure of adiposity used in the analysis (Magarey, 2001), the method of analysis (Carruth, 2001), or whether dietary fat was measured in absolute terms (grams) or as a percentage of total energy intake (Robertson, 1999). Finally, seven studies found no statistically signiﬁcant association between dietary fat intake and adiposity (Berkey, 2000; Scaglioni, 2000; Alexy, 1999; Maffeis, 1998; Boulton, 1995; Rolland-Cachera, 1995; Shea, 1993). None of the longitudinal studies found signiﬁcant, inverse associations between dietary fat intake and adiposity in children. The majority of studies involved a fairly small number of children (100 to 200) and were of relatively short duration (1 to 4 years), limiting the ability to detect small differences that may be physiologically relevant over longer periods of time. The largest study (n = 10,769) lasted only 1 year and was further limited by reliance on self-reported heights and weights (Berkey, 2000). This

Study Name and/or Location

Adiposity Measure1

Study Population

Control Variables

Association2

Gender, Race/ethnic group, Baseline BMI, Annual change in height, Menstrual history in females, Tanner stage and age. One-year change in BMI was adjusted for time lag between the two returned questionnaires; fat and ﬁber intakes were energy-adjusted.

0 m; 0 f Energy-adjusted fat

Longitudinal Studies Berkey, 2000 10,759 children (4620 males, 6149 Change in BMI Growing Up Today Study (offspring females) (ht and wt self-reported by child). of Nurses’ Health Study II Age 9–14 yr (at baseline) participants) 94.7% non-Hispanic white, 0.9% U.S. black (not Hispanic), 1.5% Hispanic, 1.5% Asian, and 1.4% other (including Native American) Followed 1 yr Magarey, 20013 Adelaide Nutrition Study (ANS) Adelaide, Australia

243 children From a representative birth cohort Followed from age 2 to 15 yr.

BMI, Triceps (TC) and Subscapular Previous corresponding measure of (SS) Skinfolds, expressed as body fatness, sex and parental standard deviation scores at each BMI, TC or SS skinfolds. age.

Shea, 1993 Columbia University Study of Childhood Activity and Nutrition New York City, U.S.

215 children (105 males, 110 females) Age 3–4 yr (at baseline) Predominantly Hispanic, lowincome Followed mean of 25 months

Growth (change during follow-up in ht, wt and BMI).

Alexy,1999 Dortmund Nutritional and Anthropometrical Longitudinally Designed (DONALD) Study Dortmund, Germany

205 children (105 males, 100 females) Age 3 yr (at baseline) Followed 2 yr

BMI.

Age in months at ﬁrst 24-hour recall, Gender, race/ethnicity, total energy intake, baseline ht, wt and BMI.

—

Dietary Inﬂuences on Energy Balance

TABLE 4.4 Observational Studies of the Association of Dietary Fat Intake with a Measure of Adiposity in Children

+ m; + f % SS Skinfold 0 m; 0 f % BMI and TC skinfold 0 m; 0 f %

0 %

53

54

TABLE 4.4 (CONTINUED) 192 females (and their mothers) Change in BMI, (Lee, 2001), 142 females (Davison, Skinfold thickness. 2001) Age 5 yr (at baseline) White, middle class Followed 2 yr.

BMI (at baseline) (Lee, 2001); Also family income, Parental education status (Davison, 2001)

Eck, 1992 Klesges, 1995 Memphis, Tennessee, U.S.

187 children for 1-yr follow-up (92 Weight change. high-risk with 1 or 2 overweight Change in BMI. parents and 95 low-risk); 147 for 2-yr follow-up Age 3 yr (at baseline) Predominately white, middle-class Followed 1 yr, 2 yr.

Cohort separated into high- and low- + m; + f risk groups based on parental % overweight. OR cohort combined and controlled for Gender, Weight and Length at birth and at 1 yr of age, Parental age.

Scaglioni, 2000 Milan, Italy

147 children (80 males, 67 females) Overweight status at age 5 Age 1 yr (at baseline) (Overweight: BMI > 90th %). Followed 4 yr.

Gender, Weight and Length at birth and at 1 yr of age, Parental age.

0 m; 0 f %

Boulton, 19953 Adelaide Nutrition Study (ANS) Adelaide, Australia

140 children Fatness (sum of four skinfold Randomly selected by birth order. thickness measurements and White length/height and weight). Followed from age 3 months to 8 yr.

Age, Gender.

0 m; 0 f % at age 2, 4 and 8 years

Maffeis, 1998 Italy

112 children Mean age 8.7 ± 1.1 yr (at baseline), 12.3 ± 1.0 (at follow-up). White Followed 4 yr.

Obesity status at baseline (Obesity = Age, Gender, Puberty development 0 m; 0 f rel BMI > 120%; rel BMI = (clinically assessed), Parents’ BMI. % BMI/BMI at 50th% for age and gender).

Rolland-Cachera, 1995 Paris, France

112 children Age 10 months to 8 yr Followed 6 yr.

BMI, Subscapular and Triceps skinfolds at age 8.

Baseline energy intake (at age 2 years), Baseline BMI (at age 2 years), Social class (father’s occupation), Parental BMI (selfreported by parents).

+f > 30%

0 %

Obesity: Dietary and Developmental Inﬂuences

Lee, 2001 Davison, 2001 Pennsylvania, U.S.

Robertson, 1999 Studies of Child Activity and Nutrition (SCAN) Texas, U.S.

53 children White, middle and upper socioeconomic status. Followed from age 2 to 96 months

+ Mean longitudinal intake of Total Fat (g) [unadjusted for other control variables] 0 (in multivariate model) 15 children Adiposity Take-off, Matched on age, ethnicity and 0 m; 0 f Identiﬁed from a larger study with Sum of 7 skinfolds increased by 1.5 gender for the same year of data % in year prior to adiposity take-off “adiposity take off” (children standard deviations or more in any collection and three were randomly and average intake over study whose adiposity increased 1.5 year of the study. selected as control subjects per duration standard deviations or more above case. + m; + f the mean from the previous year) Total Fat (g) intake in year prior to and 33 matched control subject; 20 adiposity take-off and average intake white, 12 Mexican-American and over study duration 16 African-American children. Case-control. Followed from age 3 to 7 yr (3-year duration).

U.S. Nationally Representative Cross-Sectional Study Troiano, 2000 10,372 children and adolescents Third National Health and Nutrition Ages 2–19 yr. Examination Survey(1988–1994)

Total Body Fat% and Total Body Fat Gender, BMI at 70 months, dairy (g). intake, dietary protein, DEXA measurement at 70 months. monounsaturated fat

BMI%, Overweight > 95th%.

Gender, Age.

0 m; 0 f %

+f % 0f Total Fat (grams)

0 m; 0 f %

55

Other Cross-Sectional or Case-Control Studies Obarzanek, 1994 2147 females (1044 black, 1103 BMI, Race/ethnicity, Age, Income, Level NHLBI Growth and Health Study white) Sum of Skinfold measures. of education. (NGHS) Age 9–10 yr Berkeley, California, Cincinnati, Diverse socioeconomic Ohio and Rockville, Maryland, backgrounds. U.S. Davies, 1997 1444 children BMI standard deviation scores (age- Age, Gender. National Diet and Nutrition Survey UK nationally representative sample and sex- adjusted). (NDNS) Ages 1.5–4.5 yr. UK

Dietary Inﬂuences on Energy Balance

Carruth, 2001 Knoxville, Tennessee, U.S.

56

TABLE 4.4 (CONTINUED) Bao, 1996 Bogalusa Heart Study Louisiana, U.S.

1419 children Age 10 yr 35% black, 65% white.

Fat-free Mass and Body Fat (estimated from wt, ht and triceps skinfold measures) Ponderal index.

Race, Gender.

0 m; 0 f %

Rodriguez-Artalejo, 2002 Spain

1112 children (557 males, 555 females) Age 6–7 yr From four Spanish cities (two with relatively high IHD mortality and two with relatively low IHD mortality).

Ponderal index BMI Overweight = BMI > 17.6; Obesity = BMI > 20.1.

Birth weight. Children were selected through random cluster-sampling of schools, stratiﬁed by sex and socioeconomic level.

Guillaume, 1998 Province de Luxembourg, Belgium

955 children Age 6–12 yr.

BMI.

Age, Gender.

+ m; 0 f %

Maffeis, 2000 Italy

530 children (278 males, 252 females) Age 7–11 yr.

Relative Body Fat Mass (FM%): Fat-free mass and Body fat mass (Lohman’s formulae based on triceps and subscapular skinfold thickness measurements).

Gender, Energy Intake/Basal Metabilic Rate ratio.

0 m; 0 f %

Stewart, 1999 FRESH (Food Reeducation for Elementary School Health) Baltimore, Maryland, U.S.

468 preadolescent children Grades 2 BMI through 5 (mean age 8.9 yr) Sum of triceps and subscapular Working class families; 87% white, skinfolds. 12% AA, and 3% other.

Because there were no substantial differences between males and females and among the racial groups, their data were combined.

0 m; 0 f %

Garaulet, 2000 Spain

331 adolescents (139 males, 192 females) Ages 14–18 yr Representative sample of all socioeconomic levels of speciﬁed area.

BMI (Overweight: BMI ≥ 23; Normal-weight: BMI < 23).

Gender.

+ m; 0 f %

Lluch, 2000 Stanislaus Family Study France

270 males (mean age 15.2 yr) and 290 females (mean age 15.8 yr).

Relative weight (calculated using Lorentz’s index (ideal weight), taking into account gender).

Gender, Age.

0 m; 0 f %

0 m; 0 f % + m; + f Total Fat (grams)

Obesity: Dietary and Developmental Inﬂuences

253 children Age 9–10 yr Race/ethnicity not speciﬁed.

Body fat% (formula using skinfold measurement).

Hanley, 2000 Sandy Lake Health and Diabetes Project. Sandy Lake First Nation, Canada Muecke, 1992 Texas, U.S.

242 adolescents (94 males, 148 females) Age 10–19 yr Native Canadian. 237 preadolescents Age 8–10 yr (3rd grade) Representative of a range of socioeconomic and ethnic heritages. 215 children (105 males, 110 females) Age 3–4 yr (at baseline) Predominantly Hispanic, lowincome.

Overweight status (Overweight: BMI > 85th%).

192 females (and their mothers) Age 5 yr (at baseline) White.

BMI (at baseline).

181 children Age 4–16 yr Mostly middle-class, Divided into two groups: obese and nonobese. 168 children (94 2–yr old children, 74 5-yr old children) Predominantly white, low-middle class. 147 children (80 males, 67 females) Age 5 yr (at baseline) White.

% BMI (actual BMI/median BMI for age and sex) Obese = BMI > 95th%; Nonobese = BMI < 75th%. BMI and Ponderal Index (Obese = BMI > 75th% age and sexspeciﬁc %; Ponderal index > 90th% age-speciﬁed) Overweight status (at baseline) (Overweight: BMI > 90th%).

Shea, 1993 (Cross-sectional results from longitudinal study) Columbia University Study of Childhood Activity and Nutrition. New York City, U.S. Lee, 2001 (Cross-sectional results from longitudinal study) Pennsylvania, U.S. Gills, 2002 Ontario, Canada

Dennison, 1997 New York, U.S.

Scaglioni, 2000 (Cross-sectional results from longitudinal study) Milan, Italy

Gender, Total energy intake, Physical ﬁtness (run/walk test), Parental body mass (self-reported ht and wt). Gender, Age.

+ m; + f %

Obesity: BMI > 85th%.

Analyses were stratiﬁed by Hispanic surname.

BMI (at baseline).

Age in months at ﬁrst 24-hour recall, Gender, Race/ethnicity, Total energy intake, Baseline ht, wt and BMI.

0 Number of High-Fat Foods over 3–day period, high-fat food deﬁned as any food with ≥ 50% calories from fat – m; – f < 30% (3 Semi-quantitative FFQ) 0 m; 0 f < 30% (4, 24-hour recalls)

—

0 m; 0 f %

Dietary Inﬂuences on Energy Balance

Tucker, 1997 Utah, U.S.

0f > 30%

Gender, Age. Nonobese subjects were recruited by stratifying age, gender and socioeconomic background. Age.

0 m; 0 f % + m; + f Total Fat (grams) 0 Total Fat (grams)

Gender, Weight and Length at birth and at 1 yr of age, Parental age.

0 m; 0 f % at age 5 yr

57

58

TABLE 4.4 (CONTINUED) 114 children (66 males, 48 females) Age 6–8 yr Divided into three groups based on their risk for obesity or their own obesity status Predominantly white European population with mixed socioeconomic background.

Obesity status (BMI > 95th%), Gender. low-risk (LR) (child with two lean parents) or high-risk (children with at least one biological parent with BMI > 29.5), Body fatness (difference between body weight and lean mass).

+ m; + f % or Total Fat (grams) Obese vs. LR + m; 0 f % Body Fatness

Maffeis, 1998 (Cross-sectional results from longitudinal study) Italy

112 children Rel BMI at baseline (rel BMI = Mean age 8.7 ± 1.1 yr (at baseline) BMI/BMI at 50th% for age and White. gender).

Ricketts, 1997 Cincinnati, Ohio, U.S.

85 children Age 9–12 yr Race/ethnicity not speciﬁed.

BMI, Triceps and Subscapular skinfold measures

Maffeis, 1996 Verona, Italy

82 prepubertal children (30 obese, 52 nonobese) Age 7.5–11.5 yr White.

BMI, Obesity: BMI > 97th% of reference values for age and sex.

RMR (indirect calorimetry).

Atkin, 2000 Feasibility Study for National Diet and Nutrition Survey of Children. UK

77 children (39 males, 38 females) Age 1.5–4.5 yr.

% Body Fat (18O dilution).

Gender.

Nguyen, 1996 Burlington, Vermont and Akwesasne, New York, U.S.

71 children (36 males, 35 females) Age 4–7 yr White and Mohawk.

Fat Mass Gender, Ethnicity, Physical activity (bioelectrical impedance, wt, ht, and energy expenditure (difference skinfold measures). between total energy expenditure — measured over 14 days by the doubly labeled water method and postprandial resting energy expenditure — measured by indirect calorimetry)

Age, Gender, Puberty development (clinically assessed), Parents’ BMI.

— .

0 m; 0 f %

0 % + %

0 m; 0 f % 0 m; 0 f Total Fat (grams) + m; 0 f %

Obesity: Dietary and Developmental Inﬂuences

McGloin, 2002 Northern Ireland, UK

64 adolescents (37 males, 27 females) Obese and normal-weight Age 15–17 yr Medium socioeconomic level.

BMI.

Gazzaniga, 1993 Muscatine Coronary Risk Factors Project. Iowa, U.S.

48 children (23 males, 25 females) 30 nonobese children, 18 obese children; Case-control Age 9–11 yr White.

% Body Fat (estimated by taking Gender, REE, Energy expended for average of two skinfold thickness physical activity, Age, Body measurements, triceps and weight. subscapular and subject’s sex, age, ht and wt).

+ m; + f %

Koivisto, 1994 Sweden

39 children 15 overweight, 24 normal weight; Case-control Age 3–7 yr

Weight-length index (WLI), (overweight: WLI > 109).

0 m; 0 f %

Rocandio, 2001 Spain

32 children (16 males, 16 females) Age 11 yr Divided into overweight and nonoverweight groups.

Weight-for-height (overweight > 90th% compared with 1985 Spanish reference values).

Francis, 1999 Texas, U.S.

12 pairs of prepubertal, nonobese children (12 with an obese mother (BMI > 30), 12 with a nonobese mother (BMI 20–25)) matched for age, gender and weight White.

% Abdominal fat and Fat-free mass Children were matched for age, (Dual-energy x-ray absorptiometry). gender, weight.

Bandini, 1999 Boston, Massachusetts, U.S.

21 obese adolescents (10 males, 11 females); 22 nonobese (10 males, 12 females); Case-control Age 12–18 yr Race/ethnicity not speciﬁed.

% Body fat (18O dilution).

Fisher, 1995 Illinois, U.S.

18 children (8 males, 10 females Age 36–57 months Race/ethnicity not speciﬁed.

Weight-for-stature percentile scores, Triceps and Subscapular skinfolds measurements.

Gender, Age.

—

Gender (examined, but no interaction).

—

+ %

Dietary Inﬂuences on Energy Balance

—

Ortega, 1995 Madrid, Spain

0 % 0 Total Fat (grams) 0 m; 0 f %

+ m; + f %

0 %

59

13 females 8 obese, 5 nonobese; Case-control Age 7–10 yr African-American

Obesity status (BMI > 85th%)

—

0 Total fat (grams)

60

Manos, 1993 Georgia, U.S.

Notes: 1

BMI based on measured weight and height unless otherwise speciﬁed.

2

Plus (+) indicates signiﬁcant direct relationship, negative (–) indicates signiﬁcant inverse relationship, and zero (0) indicates nonsigniﬁcant relationship between dietary factor and measure of adiposity; results presented by gender as available, in which case f indicates female, m indicates male; % indicates % total energy from fat.

3

Margarey, 2001, and Boulton, 1995, are analyses of subsets of the same study cohort measured at different lengths of follow-up; therefore, viewed as separate studies.

IHD = ischemic heart disease; EI/BMR = energy intake/basal metabolic rate; FFQ = food frequency questionnaire; ht = height; wt = weight, REE = resting energy expenditure; RMR = resting metabolic rate.

Obesity: Dietary and Developmental Inﬂuences

Dietary Inﬂuences on Energy Balance

61

study (Berkey, 2000), as well as the majority of the other studies, examined adiposity in relation to the percent energy consumed as fat rather than the absolute amount of fat. The two longitudinal studies that examined adiposity in relation to the absolute amount of fat both detected a signiﬁcant positive association (Robertson, 1999; Carruth, 2001) that did not persist when the percentage of calories consumed from fat was used or when adjusted for other control variables. This is not surprising given that, when all else is equal, larger children would tend to consume more overall. However, if the main mechanism whereby fat contributes to obesity is by increasing calorie intake, controlling for energy intake could provide misleading results. Interestingly, the study of longest duration, 13 years, did detect a signiﬁcant positive association between fat intake and subscapular skinfolds (Magarey, 2001), but did not ﬁnd a signiﬁcant association in an earlier 8-year study involving a subset of the same cohort of children (Boulton, 1995). This suggests that dietary fat’s contribution to weight gain may be small in most cases and therefore requires a long period of time to detect. Most of the longitudinal studies were conducted among predominantly non-Hispanic white children, and therefore these results may not be applicable to other populations of children. The study by Shea and colleagues (1993) was one of two exceptions. This study sample consisted predominately of Hispanic children with a small percentage of African-Americans and found no signiﬁcant association between dietary fat and change in adiposity. The other study (Robertson, 1999) included Mexican-American and African-American preschool children and found a signiﬁcant positive association between adiposity and dietary fat only when dietary fat was measured in absolute terms. This study also suffered from several other methodological limitations including very small sample size (15 cases, 30 controls) and a dietary assessment methodology that is subject to high intra-individual variation. Two of the longitudinal analyses, which appear to be subsets of the same cohort, were conducted among girls only (Lee, 2001; Davison, 2001), and both found signiﬁcant, positive associations. However, of the remaining studies, all of which were conducted among populations of both boys and girls, none detected gender differences. Furthermore, no apparent trend in the results was observed when the longitudinal studies were ordered chronologically by the baseline age of the sample children. Cross-Sectional Studies — Children Only one U.S. nationally representative study that examined the association between dietary fat intake and adiposity in children was identiﬁed (Troiano, 2000). This study involved children age 2–19 years from the Third National Health and Nutrition Examination Survey (1988–1994). Intakes of total dietary fat and saturated fatty acids did not differ signiﬁcantly between overweight and nonoverweight children and adolescents in this study. This very large study (over 10,000 subjects) relied on measured heights and weights but failed to control for many potential confounding variables such as race/ethnicity and physical activity. Thirty-one other cross-sectional or case-control studies were identiﬁed (four of these were cross-sectional results from longitudinal studies discussed above). Six found signiﬁcant, positive associations between dietary fat intake and adiposity (Bandini, 1999; Tucker, 1997; Maffeis, 1996; Ortega, 1995; Obarzanek, 1994; Gazzaniga, 1993). Seven reported mixed ﬁndings depending on the dietary assessment methodology (Shea, 1993), measure of adiposity (McGloin, 2002), gender of the subjects (Garaulet, 2000; Guillaume, 1998; Nguyen, 1996), and whether dietary fat intake was measured in absolute terms (total fat grams) or as a percentage of total energy (RodriguezArtalejo, 2002; Gills, 2002). Finally, 18 of the 31 studies found no statistically signiﬁcant association between dietary fat intake and adiposity (Lee, 2001; Rocandio, 2001; Maffeis, 2000; Lluch, 2000; Hanley, 2000; Scaglioni, 2000; Atkin, 2000; Stewart, 1999; Francis, 1999; Maffeis, 1998; Davies, 1997; Dennison, 1997; Ricketts, 1997; Bao, 1996; Fisher, 1995; Koivisto, 1994; Manos, 1993; Muecke, 1992).

62

Obesity: Dietary and Developmental Inﬂuences

Small sample size may explain some of the discrepancies; very few provided details on statistical power in order to assess if sample size was adequate. Variations in intake and growth over time may contribute to variability in the dietary and adiposity measures, an inherent difﬁculty in studying determinants of adiposity in children and one that may contribute to a lack of signiﬁcant ﬁndings in comparison to studies of adults. A majority of the studies with a sample size less than 100 children found no statistically signiﬁcant association between dietary fat intake and adiposity (Rocandio, 2001; Atkins, 2000; Francis, 1999; Ricketts 1997; Fisher, 1995; Koivisto, 1994; Manos, 1993), supporting the conclusion from other studies that the effect of dietary fat on adiposity is probably small. Muecke and colleagues (1992) reported a provocative ﬁnding. They did not ﬁnd an increased risk of overweight associated with either intake of high-fat foods or low levels of physical activity when examined alone in their sample of preadolescent children, but the results did indicate a possible synergistic effect between the two exposures when both were present in the same child. Unfortunately, few of the studies of any design controlled for physical activity level or ﬁtness. However, all four that did (one longitudinal study [Klesges, 1995] and three cross-sectional studies [Tucker, 1997; Nguyen, 1996; Gazzaniga, 1993]), found a positive relationship between fat intake and some measure of adiposity. Therefore, failure to adjust for potential confounders such as physical activity may account for the lack of association between dietary fat intake and adiposity in some studies. The cross-sectional analyses were conducted among populations that were more racially/ethnically diverse than the samples studied in the longitudinal analyses. In addition to non-Hispanic, white populations, this association was examined in African-American (Stewart, 1999; Bao, 1996; Obarzanek, 1994), Latino (Shea, 1993), and Mohawk (Nguyen, 1996) children. In a large, rigorously conducted study of African-American and white girls, in the ﬁnal regression model total dietary fat was related to adiposity in whites, while saturated fat was related in blacks (Obarzanek, 1994). Differences by ethnicity were not reported in the other smaller studies. However the studies that included 12% and 35% African-American children, respectively (Stewart, 1999; Bao, 1996), did not ﬁnd an association between dietary fat and adiposity, and the former did not ﬁnd substantial differences between the racial groups in this regard. The study (Shea, 1993) that included predominately low-income Hispanic children found a negative association between dietary fat and adiposity when using a food frequency questionnaire but not when using dietary recall. The study by Nguyen et al. (1996) that included Mohawk children found a positive association between dietary fat and adiposity among the boys but not the girls. No obvious trends were evident when the results of the studies were organized by the subject’s age. Atkins and colleagues (2000) concluded that the relation between dietary fat intake and body fat may develop over time and may not be evident in preschool children. Other study samples consisting of preschool-aged children also found no statistically signiﬁcant association (Davies, 1997; Dennison, 1997; Fisher 1995; Koivisto, 1994; Shea, 1993). Two studies included girls only (Lee, 2001; Obarzanek, 1994), and both found signiﬁcant, positive associations between dietary fat and adiposity. The other studies examined the association among populations consisting of both boys and girls. Four studies found gender differences in the association (McGloin, 2002; Garaulet, 2000; Guillaume, 1998; Nguyen, 1996); all of these studies found a signiﬁcant, positive association among the boys and no statistically signiﬁcant association among the girls. These gender-speciﬁc ﬁndings may be a result of a methodological or study design limitations (e.g., macronutrient-speciﬁc underreporting, small sample size). Underreporting has been shown to occur more often among females than males (Briefel, 1995) and among mothers reporting their daughter’s dietary intake (Nguyen, 1996). Nguyen and colleagues (1996) concluded that the absence of a relation between fat mass and dietary fat intake in the girls was most likely complicated by the presence of maternal bias when mothers reported their daughter’s intake.

Dietary Inﬂuences on Energy Balance

63

Conclusions — Observational Studies in Children Nearly all observational studies among children detected either a statistically signiﬁcant, positive association between dietary fat intake and a measure of adiposity or no statistically signiﬁcant association. However, the large number of insigniﬁcant ﬁndings from observational studies suggest that dietary fat is just one of many determinants of adiposity with a small independent effect. There is no support for the hypothesis that dietary fat intake is protective against adiposity. Prevention Trials — Dietary Fat Prevention trials involving dietary fat are discussed beginning on page 98 for adults and page 105 for children.

MACRONUTRIENT INTAKE: CARBOHYDRATE Secular Trends — Carbohydrate It is fairly well established that Americans have increased their average per capita intake of carbohydrates at the same time that obesity rates have risen most steeply. Both national food supply and intake data suggest that total carbohydrate intake has increased since the 1970s (Figure 4.13, Figure 4.14, and Figure 4.15). Total carbohydrate intake has increased in both absolute terms (grams) and as a proportion of total energy intake (Figure 4.14 and Figure 4.16). According to data from the National Food Consumption Survey (NFCS) and the Continuing Survey of Food Intake

FIGURE 4.13 Energy sources in U.S. food supply. (Source: Bente, 2003.)

FIGURE 4.14 Trend in carbohydrate intake among adults (NFCS and CSFII). (Source: Enns, 1997.)

64

Obesity: Dietary and Developmental Inﬂuences

FIGURE 4.15 Percent of energy in U.S. food supply. (Source: Bente, 2003.)

FIGURE 4.16 Per capita annual average of total caloric sweeteners. (Source: USDA 2003.)

by Individuals (CSFII), the percent of calories consumed as carbohydrates has increased among adults from about 40% in the late 1970s to about 50% in the mid-1990s (Enns, 1997). Over the same time period, among adolescents, carbohydrate intake has increased from 47 to 54% of calories (Siega-Riz, 2000). However, according to food supply data the increase in the percent of calories from carbohydrate was initially less dramatic, recently leveled off, and even decreased slightly in recent years (Figure 4.15). Of course, total carbohydrate is composed of various nutrients that would be expected to have quite different and even opposite effects on energy intake. Of particular interest are sugar, other reﬁned carbohydrates, and ﬁber. Food supply data suggest that, on the average, Americans have increased their intake of all of these types of carbohydrates with ﬂour and cereal products leading the group. Since the early 1970s, added sugars in the food supply have increased by 20% (Figure 4.16), ﬁber has increased by 5 grams or 26% (Figure 4.17), and total ﬂour and cereal products have increased by 48% (Putnum, 2002). Food intake data from NFCS and CSFII substantiate these trends (Enns, 1997; Krebs-Smith, 2001). Needless to say, a very small proportion of the total ﬂour and grain intake is in the form of whole grains. In the mid-1990s, two thirds of the U.S. population over 2 years of age ate less than one serving of whole grains per day, whereas Americans average more than ten servings per day of grain products (Putnam, 2002). Whereas the intake of foods that are classiﬁed as sugars and sweets have increased about 12% since the late 1970s, sugar intake from sweetened beverages has increased even more dramatically (see section titled “Sweetened

Dietary Inﬂuences on Energy Balance

65

FIGURE 4.17 Fiber in U.S. food supply. (Source: USDA, 2003.)

Beverages and Fruit Juice”). Americans get 43% of their added sugars from soft drinks and fruit drinks (Johnson, 2001). Secular trends in nutrient intake therefore tend to point toward carbohydrate, and all types of carbohydrate, as possible culprits in the recent steep rise in obesity rates, with reﬁned ﬂour products and liquid forms of sugar leading the trend. Mechanisms — Carbohydrate Although the secular trends described above tend to implicate carbohydrate intake as a contributor to the current obesity epidemic, the scientiﬁc literature abounds with evidence that suggests the opposite. Dietary Fat: Carbohydrate Ratio There is a high degree of intercorrelation between the percentage of energy derived from dietary fat and carbohydrate in the diet. Observational studies have identiﬁed this signiﬁcant inverse correlation in a number of populations (Tucker, 1992; Dreon, 1988). Intervention trials have also illustrated this inverse relationship whereby reductions in dietary fat intake are typically accompanied by increases in the percentage of energy derived from carbohydrates while the percentage of energy derived from protein remains rather stable (Lissner, 1995). The reciprocal relationship between the percentages of energy from fat and carbohydrate has been coined the fat-sugar seesaw (Stubbs, 2001). A high dietary fat:carbohydrate ratio has been associated with greater total energy intake and greater body weight (Drewnowski, 1997; Lissner, 1995, review). The dominant mechanism by which this ratio inﬂuences positive energy imbalance is through properties of dietary fat (described previously) that lead to excess calorie intake and not through properties of dietary carbohydrates. Dietary fat’s energy density leads to passive overconsumption (Green, 1994), and reductions in dietary fat have been accompanied by decreases in total energy intake (Lissner, 1995, review). Therefore, it can be concluded that a low carbohydrate diet may increase risk of positive energy imbalances or overweight development by way of its correlation with a high dietary fat intake. Properties of Carbohydrates that Tend to Protect against Excess Energy Intake Several properties of carbohydrates appear to protect against overconsumption of food and positive energy balance. As discussed previously, carbohydrate has a lower energy density than fat (and the same density as protein) and therefore would theoretically be less likely to lead to passive overconsumption. Indeed, many experimental studies suggest that ad libitum calorie intake tends to be less on a high-carbohydrate (especially complex carbohydrate) diet compared to higher-fat diets (Raben, 1997; Thomas, 1992; Kennedy, 2001; Ballard-Barbash, 2000). There is also some limited evidence that weight loss maintenance is more successful with high-carbohydrate diets (Kennedy, 2000; Dreon, 1988; King, 1989).

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Obesity: Dietary and Developmental Inﬂuences

There is an apparent satiety hierarchy among the macronutrients: protein > carbohydrate > fat (Rolls, 1995; Johnstone, 1996; Blundell, 1997; Stubbs, 2000). This hierarchy is due primarily to differences in energy density, ﬁber content, and palatability (Raben, 2003; Stubbs, 2001; Nasser, 2001; Drewnowski, 1998; Holt 1995) although other, as of yet unidentiﬁed, factors may be involved. While most research has suggested that protein has the most potent action on satiety, there is less clear consensus regarding the relative satiety values of carbohydrates and fats (de Castro, 1999; Johnstone, 1996; Blundell, 1997; Marmonier, 2000; Bray, 1998). However, at least one study (Green, 1994) found that, when lean subjects were allowed to eat from a range of high-fat or highsucrose foods, passive overconsumption only occurred when high-fat foods were consumed (Green, 1994; Rolls, 1995). Conversion of carbohydrate to body fat is more energetically expensive than conversion from fat (Bolton-Smith, 1996; Schwartz, 1985; Schutz, 1989). Although excessive intakes of carbohydrate are converted to fat stores, sustained and excess intakes are required for this mechanism to “kick in.” De novo lipogenesis, the conversion of carbohydrate (or protein) to fat, occurs when the body’s total glycogen stores are considerably raised from their usual 4–6 g/kg body weight to >8–10 g/kg body weight. This requires deliberate and sustained overconsumption of large amounts of carbohydrates for ≤2–3 days (Flatt, 1995). Lean people and animals seem to have a preference for sweet and less preference for fat compared to their overweight counterparts (Anderson, 1995; Cox, 1999; Reed, 1997; Drewnowski, 1991; Rissanen, 2002). A preference for both sweet and fat tastes also has been associated with regain of weight in obese subjects (Drewnowski, 1991). These taste preferences seem to have both genetic and acquired components (Reed, 1997; Rissanen, 2002). Simple and Reﬁned Carbohydrates Not all of the experimental evidence, however, support the contention that carbohydrates protect against overconsumption. Experimental evidence implicating carbohydrate as a contributor to positive energy balance, and hence obesity, tends to focus on reﬁned and simple carbohydrates. Some high-carbohydrate foods that are low in both water and dietary ﬁber can be just as energy dense and palatable as high-fat foods and, therefore, could similarly be conducive to excess weight gain. Experimental studies have conﬁrmed this hypothesis (Stubbs, 2001; Howard 2002). As discussed previously, energy density may be a factor that inﬂuences energy intakes independently of fat content (Rolls, 1997; Lawton, 1998). High glycemic index foods (generally characterized by high content of reﬁned and simple carbohydrates) elicit higher insulin levels, which may promote weight gain by preferentially directing dietary fat toward storage in the adipose tissue (Ludwig, 2000). High sugar intake has been shown to lead to insulin resistance, which reduces glucose uptake by peripheral tissues. This glucose then becomes available for de novo hepatic fatty acid synthesis (Minehira et al., 2003; Schwarz et al., 2003; Parks review, 2002; McDevitt et al., 2001; MarquesLopes et al., 2001), which may promote visceral fat deposition in particular (Hill, 1995). Highly reﬁned and simple carbohydrates are absorbed more rapidly into the bloodstream than fats, protein, or complex carbohydrates. This rapid entry of glucose into the blood can trigger a release of insulin in excess of needs, resulting in a rapid and steep decline in blood sugar and hence, theoretically, a more rapid and intense resumption of hunger. In a review of 16 studies, all but one found that intake of lower glycemic index foods leads to increased inter- and intrameal satiety and decreased calorie intake (Ludwig, 2000). However, others (Vermunt, 2003; Raben, 2002) concluded that studies that have investigated the association between glycemic index, appetite, and long-term body weight maintenance have had inconsistent results, and many are methodologically ﬂawed. Furthermore, studies that have examined the relationship between glycemic index and energy imbalance have been limited by their short-term duration. Long-term clinical trials are necessary to understand the effects of glycemic index on body weight regulation. Given that glycemic index does not merely reﬂect content in the food of carbohydrate, reﬁned carbohydrate,

Dietary Inﬂuences on Energy Balance

67

or sugar, it is not possible at this time to translate results from these studies into recommendations regarding carbohydrate intake. As described previously, experimental studies have not consistently shown foods high in simple carbohydrates to have a lower satiety value than high-fat foods. Sugar preloads in children have been shown to decrease appetite in subsequent meals, whereas limited data among adults suggest that sugar preloads fail to have this effect and therefore could lead to excess intake (Anderson, 1995). Sugar in liquid forms may have a particularly weak appetite suppressant effect and, therefore, can result in excess calorie intake (Mattes, 1996). See the section on sweetened beverages for a more detailed discussion of this phenomenon. Dietary Fiber Findings from almost all mechanistic and short-term experimental studies suggest that ﬁber is protective against obesity: Energy Density

As only about 40% of ﬁber is fermentable, its caloric value is only 1–2 kcal/g. The water binding capacity of ﬁber leads to additional lowering of the energy-to-weight ratio in foods (Howarth, 2001). Fiber does not, however, appear to have a very signiﬁcant affect on the energy density of commonly consumed foods (Yao, 2001). This lack of impact is due primarily to the low variability in the ﬁber content of foods and to the fact that there is an upper limit to the ﬁber content of foods typically consumed by humans. Satiety

Fiber enhances satiety in various ways, including increased stomach distention, delayed gastric emptying, slowed rate of nutrient absorption, and increased gut hormones. These gut hormones include cholecystokinin, glucagon-like pepetide-1, peptide YY, and neurotensin that may alter glucose homeostasis or act independently of glycemic response as satiety factors (Howarth, 2001; Pereira, 2001; Saltman, 1997). The increased chewing that ﬁber requires also inhibits intake and distends the stomach (as a result of increased gastric juice and saliva secretion), leading to increased satiety. The low palatability of high-ﬁber foods also contributes to reduced intake (Stubbs, 2001). It is likely that dietary ﬁber’s unique ability to promote satiety and satiation through a number of different mechanisms may be inﬂuential in preventing excess calorie intake, positive energy balance, and overweight. A review of studies that examined the effects of ﬁber on hunger and satiety found consistent evidence of an increase (sometimes signiﬁcant and sometimes not) in satiety and/or reduction in hunger with high ﬁber intakes. This effect was observed for both soluble and insoluble ﬁbers and was independent of calorie intake, dietary fat, and energy density (Howarth, 2001). Similarly, a majority of studies that examined the relationship between ﬁber intake and calorie intake found decreases in calorie intake on a high-ﬁber regimen, even when controlling for dietary fat, food form, variety, palatability, and energy density (Howarth 2001). Nutrient Absorption

Fiber, especially soluble ﬁber, reduces and/or slows the intestinal absorption of fat and protein, probably by blocking absorption sites (Howarth, 2001; Saltman, 1997). Therefore, ﬁber could reduce the caloric availability of other nutrients. Fiber may also increase fecal energy losses (Saltman, 1997). Glycemic Response

Fiber substantively attenuates the glycemic response to carbohydrate intake and reduces insulin secretion by slowing the rate of nutrient absorption (Ludwig 1999; Pereira, 2001). As described previously, reduced insulin secretion may improve satiety and reduce fat deposition. Therefore, ﬁber may alter metabolic fuel partitioning to favor oxidation of fats (Pereria, 2001). Higher dietary ﬁber content is often associated with low GI in foods and/or meals. It is often difﬁcult to assess if

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Obesity: Dietary and Developmental Inﬂuences

the positive metabolic effects are a result of the high dietary ﬁber content or low-glycemic index nature of a meal, as these two characteristics are often present together in foods (Bjorck, 2003). In summary, not all carbohydrates are alike. When considered as a whole, carbohydrates would appear to be protective from obesity, because the properties of carbohydrate per se tend to contribute to lower calorie intakes. But the properties of short chain (simple) carbohydrates and high glycemic index foods tend to promote excess intake compared to less reﬁned carbohydrates. The properties of ﬁber, on the other hand, tend to lead to lower calorie intake and utilization. Observational Studies — Carbohydrate Longitudinal Studies — Adults Three longitudinal studies that examined the relationship between carbohydrate intake and adiposity among adults were identiﬁed in the literature published within the last decade. Only two of these (Parker, 1997; Kemper, 1999) looked at total carbohydrate intake (as a percent of total calorie intake). Neither of these studies found a signiﬁcant relationship between total carbohydrate intake and adiposity. However, both suffered from methodological weaknesses. Both were relatively small studies with subjects from limited geographic areas. The study by Kemper (1999) excluded overweight youths (13 years old at baseline) and therefore may have excluded those at highest risk for excess gain. Furthermore, Kemper only controlled for age and gender in this analysis. Given the limitations of these studies, it is not possible to arrive at a conclusion regarding the association between total carbohydrate intake and adiposity. One longitudinal study reported on dietary ﬁber intake and its relation to adiposity (Ludwig, 1999). The CARDIA study (Ludwig, 1999) that followed black and white adults in ﬁve U.S. cities for 10 years found a signiﬁcant, negative relationship between ﬁber intake and change in BMI after controlling for a number of possible confounders. This rigorous long-term study provides strong evidence for the protective effect of ﬁber intake for the prevention of obesity among black and whites in the U.S. However, these results would need to be replicated to arrive at a ﬁrm conclusion regarding the association between ﬁber intake and adiposity. Sucrose intake was not found to be associated with weight change in adults in the one study that examined this relationship (Parker, 1997). Unfortunately, this study included only sucrose and not all forms of sugar in the analysis. The lack of observed association may be due to the fact that one of the main sources of extrinsic sugar in the U.S. diet comes from high-fructose corn syrup used in sweetened beverages, a source that would not have been included in this analysis. Furthermore, this study included a relatively small sample from New England; therefore, it is not possible to extrapolate the results to other populations. More studies would need to be conducted before arriving at a conclusion regarding the impact of sucrose intake in particular, and extrinsic sugar intake in general, on adiposity. Unfortunately, given the limited number of longitudinal studies examining the relationship between the intake of various types of carbohydrate and adiposity, it is not possible to arrive at any conclusions except that ﬁber appears to have a protective effect. Additional studies are needed. Cross-Sectional Studies — Adults Cross-sectional studies that examined the relationship between carbohydrate intake and adiposity in adults are both much more numerous and consistent in their results. We identiﬁed 21 such studies published since 1991, as summarized in Table 4.5. Sixteen of these reported on total carbohydrate intake, seven reported on ﬁber intake and nine reported on sugars or simple versus complex carbohydrates. Thirteen of the 17 studies (76%) that reported on the association between total carbohydrate intake and BMI or some other measure of adiposity found a signiﬁcant negative association (Kennedy, 2001; Yang, 2003; Gonzalez, 2000; Ludwig, 1999; Slattery, 1992; Stam-Moraga, 1999;

Study Longitudinal Studies Ludwig (1999) CARDIA 5 U.S. cities 10-year study Parker (1997) Pawtucket Heart Health Study (New England) 4-year study 1986–1987 and 1991–1992 Kemper (1999) Amsterdam Growth and Health Longitudinal study 15 year study

Study Population

Control Variables

Measure of adiposity

Association1

2909 adults Healthy Black and white Ages 18–30 176 men 289 women Prospective cohort

Race/ethnicity Age, sex, education, city, smoking, PA, fasting insulin Age, kcal, smoking, BMI, aerobic activity, gender

BMI change

– (ﬁber)

BMI change

0 (CHO) 0 (sucrose)

83 men 98 women Age 13 at baseline Nonobese at baseline

Age Gender

Fat mass (4 skinfolds, ht, wt)

Age, dieting status

BMI (self-report ht and wt)

0 (% added sugars)

Gender

Mean BMI (self-report ht and wt) % with BMI <25

Age, ethnicity, smoking, alcohol, kcal, gender

BMI categories: Overweight vs. underweight

– (mean BMI) – (% BMI >25) – all groups (controlling for sugar enhanced the association)

U.S. Nationally Representative Cross-Sectional Studies Lewis (1992) 30,770 adults Nationwide Food Children over 4 Consumption Survey Nationally representative (NFCS) 1977–1978 Kennedy (2001) 9786 adults Continuous Survey of Intake of Individuals (CSFII) 1994–1996 Yang (2003) 3754 men NHANES III 4074 women 1998–1994 Age 25–64 Nationally representative

Comments

Dietary Inﬂuences on Energy Balance

TABLE 4.5 Observational Studies that Examine the Association between the Intake of Various Types of Carbohydrate (CHO) and Adiposity in Adults

0

69

Vegetarians have lower BMIs, kcal, fat intakes. High CHO diets have higher HEI, less kcal, 5 × more sugar. Positive association between CHO and ﬁber and sugar; negative association between fat and total CHO; positive association between kcal and total CHO.

70

TABLE 4.5 (CONTINUED) Other Cross-Sectional Studies or Case-Control Studies Gonzalez (2000) 14,374 men EPIC study, Spain 23,289 women 3–4 year study Ages 29-69 Varying SES Trichopoulou (2002) 27,862 adults EPIC study Greece Healthy volunteers Ages 25–82

Age, PA (housework, sport, BMI leisure, work) smoking education

–

Adjusted for other nutrients. BMI Kcal, age smoking, education, energy expenditure Kcal BMI and skinfolds Inter cohort comparisons only

0

12763 men Middle aged Diverse SES

Bolton-Smith (1994) Scottish Health 1984–1986

5768 men 5858 women Ages 25–65

Age, sex, alcohol, kcal, PA, BMI smoking, menopause

Stam-Moraga (1999) Belgian Interuniversity Research on Nutrition and Health 1979–1984

5837 men 5243 women Ages 25–74 Nationally representative

Age Gender

BMI

– (ﬁber) (skinfolds) NR (BMI) – (CHO) (p-value NR) – (total sugar) – (extrinsic sugar) 0 (intrinsic sugar) 0 (lactose) 0 (starch) – (CHO) – (sugar)

PA and ﬁber explained 90% of the variance in skinfolds

Sugar had the strongest association with adiposity of the nutrients analyzed.

Obesity: Dietary and Developmental Inﬂuences

Kromhout (2001) Seven Countries Study (Europe, Japan and U.S.) 1958 and 1964

*Explained less than 1% of variance

1914 men 3378 women Nonsmokers About half nonmeat eaters 5115 adults Black and white Ages 18–30 Diverse SES 2909 adults Healthy Black and white Ages 18–30 (endpoint of 10 year longitudinal) 1087 men 1110 women Ages 16–64

Age

Macdiarmid (1998) 1986–1987

Lluch (2000) Stanislas Family Study (France)

Slattery (1992) CARDIA 5 U.S. cities Ludwig (1999) CARDIA 5 U.S. cities

Gibson (1996) Dietary and Nutritional Survey of British Adults 1986–1987

BMI (self-report)

Gender, race, age education, BMI smoking, alcohol, and PA Skinfolds

Race/ethnicity Age, sex, education, city, smoking, PA, vitamin suppl., kcal, alcohol, baseline adiposity

BMI

Age Smoking Underreporting.

BMI

1239–1853 adults Healthy Nondieting Ages 16-64

Low energy reporters; age, gender

BMI

387 families 1320 family members 379 men 381 women 270 boys 290 girls Ages 11–65

Age

BMI Lorentz index (wt/ht categories)

– (ﬁber, g)

–m (BMI) 0 (skinfolds) – (CHO) – (ﬁber)

–m (high sugar/low fat) – (weak, high sugar and fat foods) – (sugar among high fat consumers) –m (sugar) –m (high fat sweet foods) +f (high fat sweet foods) – (all groups)

Did not measure protein per se, only compared meat eaters vs. nonmeat eaters.

Of the nutrients analyzed CHO had the weakest signiﬁcant association and ﬁber had the strongest association with adiposity.

Dietary Inﬂuences on Energy Balance

Appleby (1998) Oxford Vegetarian Study 1980–1984

Sugar and fat appear to have opposing not synergistic impacts on BMI.

71

72

TABLE 4.5 (CONTINUED) Ruidavets (2002) Toulouse, France 1996–1997

330 men Ages 45–64

Age, PA, education, BMI smoking, dieting, total energy, low energy records

Wamala (1997) 300 women Stockholm Healthy Female Coronary Risk Study Ages 30–65

Age

BMI categories

Tucker (1992) Western U.S.

Age, smoking, other macronutrients, PA, total energy intake

Body fat categories based on skinfolds

203 men Mostly white Average age 41

Age, kcal, ﬁtness, PA

Body fat based on skinfolds (continuous variable) Body fat categories based on skinfolds

Body fat based on skinfolds (continuous variable)

0 – (CHO) – (complex) – (ﬁber) 0 (simple CHO) – (% and g CHO) – (% and g complex CHO) – (ﬁber) 0 (simple CHO)

Fiber showed strongest association with body fat

Obesity: Dietary and Developmental Inﬂuences

Nelson (1996) Utah

205 women Mostly white Average age 34.6

– (g and%) – (polysaccharides, g and%) 0 (oligosaccharides, g and%) + (CHO, g) + (sucrose, g) + (ﬁber, g) –

150 adults Mostly women Ages 18-65

Age, sex, income, education BMI categories

Miller (1994)

46 men 32 women

Gender

Cox (1999) England

41 lean adults 35 obese adults Nondieting Healthy Case-control 34 obese women 34 nonobese women Ages 20–50 Case-control

Underreporting, gender, age BMI categories Body fat (bioelectrical impedance)

Westerterp (1996) The Netherlands

Underreporting Matched for age

Obese vs. lean (25% v 15% body fat by hydrostatic weighing)

– (CHO, % and g) – (ﬁber, g and %, R2 = 17%) – (CHO) 0 (total sugars) + (% added sugar of total sugars) – (ﬁber g) 0

Obese (average BMI = 30) vs. nonobese (average BMI = 22)

Dietary Inﬂuences on Energy Balance

Alﬁeri (1995) Ontario, Canada

Only salty foods and energy density related to obesity.

–

Notes: 1

Plus (+) indicates a signiﬁcant direct association; negative (–) indicates signiﬁcant inverse association; and zero (0) indicates nonsigniﬁcant association.

NR = not reported; PA = physical activity; HEI = Healthy Eating Index; CHO = total carbohydrate; SES = socioeconomic status. f = female; m = male, % = % of calories; g = grams (absolute intake); kcal = kilocalories; ht = height; wt = weight. BMI was a continuous variable unless otherwise indicated. BMI was based on measured height and weight unless otherwise indicated. Macronutrient intake was determined in terms of % of calories unless otherwise indicated.

73

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Obesity: Dietary and Developmental Inﬂuences

Bolton-Smith, 1994; Kennedy, 2001; Alﬁeri, 1995; Wamala, 1997; Tucker, 1996; Nelson, 1996, Miller, 1994; Westerterp, 1996; Lluch, 2000; Gazzaniga, 1993; Rudiavets, 2002). Of the remaining four studies, two reported no signiﬁcant association (Trichopulou, 2002; Cox, 1999), one study reported mixed results (Tucker, 1996), and one study in Sweden (Wamala, 1997) reported a positive association with BMI. Two of the studies (Yang, 2003; Kennedy, 2001) that reported signiﬁcant negative associations between BMI and total carbohydrate studied nationally representative data sets (NHANES and CSFII). Ten of the studies examined populations outside the U.S. (in Europe, Japan and Canada). Of the seven studies conducted in the U.S., all found a signiﬁcant negative association between total carbohydrate intake and adiposity, although one study (Tucker, 1992) found no association when the dependent variable was continuous (as opposed to categorical). The studies varied widely in terms of control of potential confounding variables, but several did control for an array of potential confounders and still reported signiﬁcant and negative associations. The only study to ﬁnd a positive relationship between carbohydrate intake and BMI examined the relationship with regard to absolute intake of carbohydrate, not as percentage calorie intake (Wamala, 1997). Taken as a whole, these results establish that intakes of relatively larger percent of calories from carbohydrates are consistently associated with lower BMI among adults. However, Ludwig (1999) reported that the association between total carbohydrate intake and adiposity was weak and substantially attenuated when adjusted for ﬁber, suggesting that ﬁber may explain most of the association among the cohort studied. Fewer studies have examined different types of carbohydrates and their association with some measure of adiposity. We identiﬁed seven cross-sectional studies that reported on the association between dietary ﬁber and adiposity. Six of the seven reported a signiﬁcant negative association (Ludwig, 1999; Kromhout, 2001; Appleby, 1998; Alﬁeri, 1995; Nelson; 1996; Miller, 1994). Only one study (conducted in Sweden) reported a signiﬁcant positive association (Wamala, 1997). As mentioned previously, this study failed to control for energy intake, hence almost all dietary factors were positively associated with BMI. These seven studies were conducted in various countries in Europe as well as the U.S., Canada, and Japan. Outcome measures varied among these studies and included both measured and self-reported BMI, height for weight based on underwater weighing, and skinfolds. The number and nature of the controls varied widely, but several studies controlled for numerous potential confounders. No study included a nationally representative sample from the U.S., but the CARDIA study (Slattery, 1992; Ludwig, 1999) did include blacks and whites from ﬁve U.S. cities. One study in Canada (Alﬁeri, 1995) found that ﬁber accounted for 17% of the variance in BMI. Ludwig et al. (1999) found that dietary ﬁber accounted for a larger proportion of the variation in BMI than total carbohydrate, fat, or protein. These studies provide strong evidence that relatively higher ﬁber intake is consistently associated with lower adiposity in adults. Three studies analyzed the relationship between starch (Bolton-Smith, 1994), complex carbohydrates (Nelson, 1996) or polysaccharides (Ruidavets, 2002) and adiposity. The ﬁrst found no signiﬁcant association, whereas the other two found signiﬁcant negative associations, suggesting that relatively higher intakes of complex carbohydrates may be associated with lower adiposity. The study by Nelson however was limited to mostly white males and the study by Ruidavets included only men ages 45–64 in France, hence limiting the generalizability of these well conducted studies to other population groups. Six cross-sectional studies were identiﬁed that examined the relationship between total sugar and/or “simple” carbohydrates and adiposity (Stam-Moraga, 1999; Bolton-Smith, 1994; Macdiarmid, 1998; Nelson, 1996; Miller, 1994; Rudiavets, 2002). Of those that looked at the relationship between total sugar or “simple” carbohydrate, two found signiﬁcant negative associations with adiposity (Stam-Moraga, 1999; Bolton-Smith, 1994), three found no signiﬁcant association (Nelson, 1996; Miller, 1994; Rudiavets, 2002), and one found a negative association for males but none for females (Macdiarmid, 1998). One of the studies to ﬁnd no signiﬁcant association was a very small case-control study (Miller, 1994); the other two examined what was described as “simple” carbo-

Dietary Inﬂuences on Energy Balance

75

hydrates or “oligosaccharides” and therefore may not be equivalent to total sugar (Nelson, 1996; Rudiavets, 2002). It is notable that Stam-Moraga found that total sugar intake (in terms of percent calorie intake) had the strongest (negative) association with adiposity of the nutrients examined. Therefore, as with total carbohydrate, and perhaps as a consequence of its association with total carbohydrate intake, total sugar intake tends to be associated with lower adiposity in adults. Although several of the studies found no signiﬁcant relationship between adiposity and simple carbohydrate intake, there is no epidemiological evidence to suggest that total sugar intake is associated with increased adiposity in adults. Added sugar, however, is generally thought to be a more likely culprit in the development of obesity than total sugar, which includes naturally occurring sugars often found in low energy dense and otherwise “healthy” foods such as fruits and vegetables. Of the four studies that looked at added sugars or sucrose, one found a negative association (Bolton-Smith, 1994;), two found a positive association (Wamala, 1997; Miller, 1994), and one found no signiﬁcant association with adiposity (Lewis, 1992). The study by Lewis was a very large, nationally representative study, but it controlled for relatively few potential confounders, used self-reported height and weight to determine BMI, and examined a cohort from 1977–1978. Therefore, the results would need to be interpreted with caution, especially when applied to the current U.S. population. The two studies to ﬁnd a positive association between added sugar intake and adiposity also suffered from methodological limitations. The study by Miller (1994) was a very small case-control study, and the study by Wamala (1997) was conducted in Sweden, failed to control for calorie intake, and looked only at sucrose intake, which would not include many other types of added sugars such as fructose found in the numerous products made from high-fructose corn syrup. The study by Bolton-Smith included a large sample, measured height and weight, and numerous control variables. However, it was conducted in Scotland on cohort from 1984–1986. Given the limited number of studies, their methodological limitations, and the conﬂicting results, no conclusion can be drawn regarding the association between added sugars and adiposity. In an effort to help clarify the relationship between sugar intake and adiposity, Gibson (1996) looked at the relationship between the intake of foods characterized by varying levels of fat and sugar. This study showed that high sugar/low fat foods were associated with lower adiposity among males, high sugar/high fat foods were weakly associated with lower adiposity among males and females, and that sugar intake was associated with lower adiposity among high fat consumers. The author concludes that sugar and fat do not have synergistic but rather appear to have opposing inﬂuences on adiposity. Macdiarmid (1998) also found that high-fat/high-sweet foods were negatively associated with adiposity in males but positively associated with adiposity in females. These studies suggest that the relationship between sugar intake and adiposity is a complex one, is not independent of the relative intake of other nutrients, and may have distinct impacts on adiposity among males and females. Only one study looked at intrinsic sugar (sugar naturally present in foods as opposed to being added during processing or preparation) and found no signiﬁcant relationship with adiposity (Bolton Smith, 1994). When interpreting these results, it must be kept in mind that underreporting of intake can be particularly problematic with foods that are high in sugar (or fat), given that these foods are considered less desirable from a nutritional standpoint (Lafay, 2000). Macdiarmid (1998), for example, found that an observed negative association between the intake of high fat and sweet products was reversed (and became signiﬁcant) when low-energy reporters were excluded from the analysis. Overweight individuals may be the most likely to underreport these “forbidden” foods (Fogelholm, 1996). This may explain in part the conﬂicting results when examining the association between sugar intake and adiposity. In summary, the ﬁndings from the cross-sectional studies provide strong evidence of the protective effect, with regard to adiposity, of relatively higher intakes of total carbohydrate and ﬁber. The cross-sectional studies also suggest that greater intakes of total sugar/simple carbohydrates

76

Obesity: Dietary and Developmental Inﬂuences

and complex carbohydrates may be protective, and no conclusion can be made regarding added or intrinsic sugars and their relation to adiposity in adults. Conclusions — Observational Studies in Adults When taken as a whole, the evidence from observational studies is fairly compelling that higher levels of carbohydrate intake are protective against the development of overweight in adults. The cross-sectional data clearly establishes that higher intakes of total carbohydrate (percent of total calories) are associated with lower adiposity in all age and ethnic groups studied. The lack of signiﬁcant ﬁndings among longitudinal studies, however, suggests that the protective effect of total carbohydrate intake may be fairly limited. Fiber appears to account for some, but not all, of the protective effect of total carbohydrate intake, as ﬁber intake is quite consistently associated with lower adiposity in both the one longitudinal and several cross-sectional studies. Given the more limited number of studies relating ﬁber intake to adiposity, this is a promising area for further study. Despite commonly held concerns regarding the intake of added sugar, the evidence relating sugar or simple carbohydrate intake to obesity is conﬂicting and therefore inconclusive. More studies, especially of a longitudinal nature, that examine the impact of different “types” of carbohydrate on obesity are clearly merited. Observational Studies — Total Carbohydrate — Children Eight longitudinal studies were identiﬁed that examined the association between total carbohydrate intake (measured either as a percentage of total calories or in absolute terms) and adiposity in children (Table 4.6). Two of the eight studies found a signiﬁcant negative association (Lee, 2001; Eck, 1992), suggesting that, as the percentage of calories from carbohydrate increases, adiposity decreases. Another study (Magarey, 2001) found a signiﬁcant inverse association when adiposity was determined by subscapular skinfold but not when determined by triceps skinfold. The remaining ﬁve studies (Scaglioni, 2000; Alexy, 1999; Robertson, 1999; Maffeis, 1998; Rolland-Cachera, 1995) found no signiﬁcant associations between total carbohydrate intake and adiposity. None of the studies found a signiﬁcant positive association between total carbohydrate intake and adiposity in children. While all of the studies involved relatively small numbers of children, those with smaller sample sizes were less likely to ﬁnd a signiﬁcant association. The rigor of studies in terms of controlling for potential confounding variables varied, but no consistent pattern was evident in terms of control variables and study ﬁndings. No U.S. nationally representative studies were identiﬁed that examined the association between total carbohydrate intake and adiposity in children. Twenty-three cross-sectional other studies were identiﬁed. Three were cross-sectional results from longitudinal studies discussed previously (Lee, 2001; Scaglioni, 2000; Maffeis, 1998). Eight of the 23 studies found signiﬁcant inverse associations between the two variables of interest when carbohydrate was expressed in terms of percent calories (Lee, 2001; Rocandio, 2001; Lluch, 2000; Bandini, 1999; Guillaume, 1998; Tucker, 1997; Ortega, 1995; Gazzaniga, 1993). Only a few studies analyzed carbohydrate as an absolute value, and all but one of these also reported either an inverse or insigniﬁcant association with adiposity. Eleven of the cross-sectional studies found no signiﬁcant association between dietary carbohydrate intake and adiposity (Hanley, 2000; Scaglioni, 2000; Atkin, 2000; Stewart, 1999; Francis, 1999; Maffeis, 1998; Davies, 1997; Dennison, 1997; Bao, 1996; Maffeis, 1996; Koivisto, 1994). The only cross-sectional study (Rodriguez-Artalejo, 2002) to detect a signiﬁcant positive association between carbohydrate intake and adiposity did so only when carbohydrate was expressed in absolute terms. Furthermore, this study did not directly investigate this association but, rather, compared children from cities with contrasting mortality rates from ischemic heart disease that were also characterized by contrasting adiposity levels and prevalences overweight. Both Maffeis and colleagues (2000) and Garaulet and colleagues (2000) found signiﬁcant negative associations in their samples among boys but not girls. However, none of the other studies

Study Name and/or Location

Adiposity Measure1

Study Population

Control Variables

Association2

Longitudinal Studies Magarey, 2001 Adelaide Nutrition Study (ANS) Adelaide, Australia

243 children BMI, Triceps (TC), and Subscapular Previous corresponding measure of From a representative birth cohort of (SS) skinfolds, expressed as body fatness, Sex, and Parental healthy children. standard deviation scores at each BMI, TC or SS skinfolds. Followed from age 2 to 15 yr age.

Alexy, 1999 Dortmund Nutritional and Anthropometrical Longitudinally Designed (DONALD) Study Dortmund, Germany

205 children (105 males, 100 females) Age 3 yr (at baseline) Followed 2 yr

BMI.

Lee, 2001 Pennsylvania, U.S.

192 females (and their mothers) Age 5 yr (at baseline) White Followed 2 yr

Change in BMI, Skinfold thickness from 5–7 years of age.

BMI (at baseline).

Eck, 1992 Memphis, Tennessee, U.S.

187 children (92 high-risk with one or two overweight parents and 95 low-risk) Age 3 yr (at baseline); Predominantly white, middle-class Followed 1 yr

Weight change.

Cohort separated into high and lowrisk groups based on parental overweight.

– %

Scaglioni, 2000 Milan, Italy

147 Caucasian children (80 males, 67 females), age 1 year at baseline and followed up through age 5 yr Followed 4 yr

Overweight status at age 5 (Overweight: BMI > 90th%).

Gender, Weight, and Length at birth and at 1 yr of age, Parental age.

0 m; 0 f %

—

– m; – f % SS Skinfold 0 m; 0 f % BMI and TC Skinfold

Dietary Inﬂuences on Energy Balance

TABLE 4.6 Observational Studies of the Association of Total Carbohydrate Intake with a Measure of Adiposity in Children

0 %

–f Total Carbohydrate (g)

77

78

TABLE 4.6 (CONTINUED) 112 children Mean age 8.7+1.1 yr (at baseline), 12.3+1.0 (at follow-up) White Followed 4 yr

Obesity status at baseline (Obesity = Age, Gender, Puberty development rel BMI > 120%; rel BMI = (clinically assessed), Parents’ BMI. BMI/BMI at 50th% for age and gender).

Rolland-Cachera, 1995 Paris, France

112 children Age 2 to 8 yr; followed 6 yr

BMI, Subscapular and triceps skinfolds at age 8.

Robertson, 1999 Studies of Child Activity and Nutrition (SCAN) Texas, U.S.

15 children Adiposity Take-off, Matched on age, ethnicity and Identiﬁed from a larger study with Sum of 7 skinfolds increased by 1.5 gender for the same year of data “adiposity take off” (children standard deviations or more in any collection and 3 were randomly whose adiposity increased 1.5 year of the study. selected as control subjects per standard deviations or more above case. the mean from the previous year) and 33 matched control subjects 20 white, 12 Mexican-American, 16 African-American children. Followed from age 3 to 7 yr (3-year duration)

Baseline energy intake (at age 2 years), Baseline BMI (at age 2 years), Social class (father’s occupation), Parental BMI (selfreported by parents).

0 m; 0 f %

0 %

0 m; 0 f % in year prior to adiposity take-off and average intake over study duration

Other Cross-Sectional or Case-Control Studies Davies, 1997 National Diet and Nutrition Survey (NDNS) UK

1444 children BMI standard deviation scores (age- Age, Gender. UK nationally representative sample and sex-adjusted) Age 1.5–4.5 yr

0 m; 0 f %

Bao, 1996 Bogalusa Heart Study. Louisiana, U.S.

1419 children Age 10 yr 35% black, 65% white

0 m; 0 f %

Fat-free mass and Body fat (estimated from wt, ht, and triceps skinfold measures) Ponderal index.

Race, Gender.

Obesity: Dietary and Developmental Inﬂuences

Maffeis, 1998 Italy

Ponderal index BMI Overweight = BMI > 17.6; Obesity = BMI > 20.1.

Birth weight. Children were selected through random cluster-sampling of schools, stratiﬁed by sex and socioeconomic level.

BMI.

Age, Gender.

Maffeis, 2000 Italy

530 children (278 males, 252 females) Age 7–11 yr

Gender, EI/BMR ratio.

Stewart, 1999 FRESH (Food Reeducation for Elementary School Health) Baltimore, Maryland, U.S.

468 preadolescent children Grades 2–5 (mean age 8.9 yr) Working class families 87% white, 12% African-American, 3% other

Relative body fat mass (FM%): Fat-free mass and Body fat mass (Lohman’s formulae based on triceps and subscapular skinfold thickness measurements). BMI, Sum of triceps and subscapular skinfolds sites combined.

Because there were no substantial differences between males and females and among the racial groups, their data were combined.

0 m; 0 f %

Garaulet, 2000 Spain

331 adolescents (139 males, 192 females) Ages 14–18 yr Representative sample of all socioeconomic levels of speciﬁed area 270 males (mean age 15.2 yr) and 290 females (mean age 15.8 yr)

BMI (Overweight: BMI ≥ 23; Normal-weight: BMI < 23).

Gender.

– m; 0 f %

Relative weight, (calculated using Lorentz’s index [ideal weight], taking into account gender). Body fat% (formula using skinfold measurements).

Gender, Age.

– m; – f %

Gender, Total energy intake, Physical ﬁtness (run/walk test), Parental body mass (self-reported ht and wt).

– m; – f %

Guillaume, 1998 Province de Luxembourg, Belgium

Lluch, 2000 Stanislaus Family Study France Tucker, 1997 Utah, U.S.

253 children Age 9–10 yr Race/ethnicity not speciﬁed

0 m; 0 f % Cadiz and Murcia children vs. Madrid and Orense children + m; + f Total carbohydrate (grams) – m; – f % – m; – f Total Carbohydrate (grams) – m; 0 f %

79

1112 children (557 males, 555 females) Age 6–7 yr From 4 Spanish cities (two with relatively high IHD mortality [Cadiz and Murcia] and two with relatively low IHD mortality [Madrid and Orense]) 955 children Age 6–12 yr

Dietary Inﬂuences on Energy Balance

Rodriguez-Artalejo, 2002 Spain

80

TABLE 4.6 (CONTINUED) 242 adolescents (94 males, 148 females) Age 10–19 yr Native Canadian

Overweight status (Overweight: BMI > 85th%).

Gender, Age.

Lee, 2001 (Cross-sectional results from longitudinal study) Pennsylvania, U.S.

192 females (and their mothers) Age 5 yr (at baseline)

BMI (at baseline).

Dennison, 1997 New York, U.S.

168 children (94 2 yr old children, 74 5 yr old children) Predominantly white, low-middle class

BMI and Ponderal Index (Obese = Age. BMI > 75th% age and sexspeciﬁc%; Ponderal index > 90th% age-speciﬁed)

Scaglioni, 2000 (Cross-sectional results from longitudinal study) Milan, Italy

147 children (80 males, 67 females) Overweight status (at baseline) Age 5 yr (Overweight: BMI > 90th%). White

McGloin, 2002 Northern Ireland, UK

114 children (66 males, 48 females) Obesity status (BMI > 95th% Gender. Age 6–8 yr according to British growth Divided into three groups based on standards), low-risk (LR) (child their risk for obesity or their own with two lean parents) or high-risk obesity status (children with at least one Predominantly white with mixed biological parent with BMI > 29.5), socioeconomic background Body fatness (difference between body weight and lean mass).

Maffeis, 1998 (Cross-sectional results from longitudinal study) Italy

Rel BMI at baseline, 112 children Mean age 8.7 + 1.1 yr (at baseline) measured ht and wt White (rel BMI = BMI/BMI at 50th% for age and gender).

0 m; 0 f %

—

Gender, Weight, and Length at birth and at 1 yr of age, Parental age.

–f Total Carbohydrate (grams)

0 Total Carbohydrate (grams)

0 m; 0 f %

– m; – f % Obese vs. LR 0 m; 0 f Total Carbohydrate (grams) Obese vs. LR 0 m; 0 f % Body Fatness

Age, Gender, Puberty development 0 m; 0 f (clinically assessed), Parents’ BMI. % Total Energy from Carbohydrate

Obesity: Dietary and Developmental Inﬂuences

Hanley, 2000 Sandy Lake Health and Diabetes Project. Sandy Lake First Nation, Canada

82 prepubertal children (30 obese, 52 nonobese), Age 7.5–11.5 yr White

BMI, measured ht and wt Obesity: BMI > 97th% of reference values for age and sex.

RMR (indirect calorimetry).

0 % 0 Total Carbohydrate (grams)

Atkin, 2000 Feasibility Study for National Diet and Nutrition Survey of Children Great Britain, UK

77 children (39 males, 38 females) Age 1.5–4.5 yr

% Body Fat (18O dilution).

Gender.

0 m; 0 f % 0 m; 0 f Total Carbohydrate (grams)

Ortega, 1995 Madrid, Spain

64 adolescents (37 males, 27 females) Obese and normal-weight Age 15–17 yr Medium socioeconomic level

BMI.

Gazzaniga, 1993 Muscatine Coronary Risk Factors Project. Iowa, U.S.

48 children (23 males, 25 females) 30 nonobese children, 18 obese children Case-control Age 9–11 yr White

% Body Fat (estimated by taking Gender, REE, Energy expended for average of two skinfold thickness physical activity, Age, Body measurements, triceps and weight. subscapular and subject’s sex, age, ht and wt).

Koivisto, 1994 Sweden

39 children 15 overweight, 24 normal weight Case-control Age 3–7 yr

Weight-length index (WLI), (overweight: WLI > 109)

Rocandio, 2001 Spain

32 children (16 males, 16 females) Age 11 yr Divided into overweight and nonoverweight groups

Weight-for-height (overweight > 90th% compared with 1985 Spanish reference values).

Francis, 1999 Texas, U.S.

12 pairs of prepubertal, nonobese children [12 with an obese mother (BMI > 30), 12 with a nonobese mother (BMI 20–25)] matched for age, gender, and weight White

% Abdominal fat and fat-free mass Children were matched for age, (Dual-energy x-ray absorptiometry). gender, weight.

—

Gender, Age.

– %

Dietary Inﬂuences on Energy Balance

Maffeis, 1996 Verona, Italy

– m; – f %

0 m; 0 f Total Carbohydrate (grams)

—

– % – Total Carbohydrate (grams) 0 m; 0 f %

81

82

TABLE 4.6 (CONTINUED) Bandini, 1999 Boston, Massachusetts, U.S.

21 obese adolescents (10 males, 11 females); 22 nonobese (10 males, 12 females) Case-control Age 12–18 yr Race/ethnicity not speciﬁed

% Body fat (18O dilution).

Gender (examined, but no interaction).

– m; – f %

Notes: 1

BMI based on measured weight and height unless otherwise speciﬁed.

2

Plus (+) indicates signiﬁcant direct relationship, negative (–) indicates signiﬁcant inverse relationship, and zero (0) indicates nonsigniﬁcant relationship between dietary factor and measure of adiposity; results presented by gender are available, in which case f indicates female, m indicates male; % indicates % total energy from carbohydrate. IHD = ischemic heart disease; EI/BMR = energy intake/basal metabolic rate; ht = height; wt = weight; REE = resting energy expenditure; RMR = resting metabolic rate.

Obesity: Dietary and Developmental Inﬂuences

Dietary Inﬂuences on Energy Balance

83

that compared males and females found a gender difference, suggesting methodological reasons rather than a biological cause in the former studies. In general, the observational studies were of fairly good quality, controlling for a number of potentially confounding variables with no obvious biases introduced. However, a minority adjusted for physical activity. Interestingly, the two that did (Gazzaniga, 1993, controlled for physical activity; Tucker, 1997, controlled for physical ﬁtness) both reported signiﬁcant inverse associations between carbohydrate intake and adiposity, reinforcing the hypothesis that a high dietary carbohydrate intake is protective against overweight. In conclusion, increased dietary carbohydrate intake does not appear to be associated with adiposity but, rather, is likely to be protective against adiposity in children. The number of studies that failed to ﬁnd a signiﬁcant association suggests that carbohydrate may be only one of many components inﬂuencing adiposity, the independent impact on adiposity may be relatively weak, and/or only certain components of total carbohydrate intake may inﬂuence adiposity. Observational Studies — Fiber — Children Only one longitudinal study was identiﬁed that examined the association between dietary ﬁber intake and change in adiposity in children (Table 4.7). Berkey and colleagues (2000) studied this association among a relatively large sample of children and adolescents from the Growing Up Today cohort and found that, after controlling for a variety of potential confounding variables, including dietary energy, dietary ﬁber was not related to adiposity. It is important to note, however, that the children’s heights and weights were not measured but based on self-report. No U.S. nationally representative cross-sectional studies that examined the association between dietary ﬁber intake and adiposity in children were identiﬁed. Three of the six other cross-sectional studies that were identiﬁed found signiﬁcant inverse associations (Rocandio, 2001; Hanley, 2000; Tucker, 1997), indicating that ﬁber intake is protective against adiposity. One study found a signiﬁcant negative association only in girls and no signiﬁcant association in boys (Garaulet, 2000). The remaining two studies found no signiﬁcant association between these two variables (RodriguezArtalejo, 2002; Ortega, 1995). None of the studies found a signiﬁcant positive association, indicating that increased dietary ﬁber intake is not likely to contribute to overweight. Most of these studies were of relatively good quality, although several did not control for potentially confounding variables such as physical activity. Furthermore, all but one of these studies (Tucker, 1997) were conducted outside of the U.S., the majority in Spain. Therefore, extrapolation of results to the U.S. population must be done with caution. Based on the results of the limited number of observational studies, it appears that dietary ﬁber has no effect or is protective against overweight in children. However, additional studies, particularly longitudinal and nationally representative ones, are needed to conﬁrm the nature and determine the strength of this association. Observational Studies — Sugar — Children No longitudinal studies that examined the association between dietary sugar intake and adiposity were identiﬁed (Table 4.8). In the single U.S. nationally representative cross-sectional study, children were classiﬁed as high, moderate, or low consumers of added sugars (sugars added to foods by processors or consumers) (Lewis, 1992). High consumers of added sugars weighed less than low or moderate consumers (P <,0.01). However, a weakness of this study is that BMI was based on reported rather than measured height and weight. Four other cross-sectional studies were identiﬁed. Gillis and colleagues (2002) found that obese subjects consumed signiﬁcantly more sugar (167 g) than their nonobese counterparts (134 g). However, sugar intake was not reported to be included in their step-wise multiple regression model, and thus it is unknown if sugar was a signiﬁcant predictor of percentage body fat after controlling for gender, age, and socioeconomic status. The remaining three studies (McGloin, 2002; Hanley, 2000; Koivisto, 1994) found no signiﬁcant association between a measure of dietary sugar intake (percent of total energy from sugar, simple sugar, or sucrose) and adiposity in children. Because

84

TABLE 4.7 Observational Studies of the Association of Dietary Fiber Intake with a Measure of Adiposity in Children Study Name and/or Location Longitudinal Study Berkey, 2000 Growing Up Today Study (offspring of Nurses’ Health Study II participants) U.S.

Garaulet, 2000 Spain

Tucker, 1997 Utah, U.S.

Adiposity Measure1

Control Variables

Association2

10,769 children (4620 males, 6149 females) Age 9–14 yr (at baseline) 94.7% non-Hispanic white, 0.9% black (not Hispanic), 1.5% Hispanic, 1.5% Asian, and 1.4% other (including Native American). Followed 1 yr.

Change in BMI (ht and wt self-reported by child).

Race/ethnic group, Baseline BMI, Annual change in height, Menstrual history in girls, Tanner stage, and Age. One year change in BMI was adjusted for time lag between the 2 returned questionnaires; fat and ﬁber intakes were energy-adjusted. All analysis performed separately by gender.

0 m; 0 f Energy-adjusted ﬁber

1112 children (557 males, 555 females) Age 6–7 yr From 4 Spanish cities (two with relatively high IHD mortality and two with relatively low IHD mortality). 331 adolescents (139 males, 192 females) Ages 14–18 yr Representative sample of all socioeconomic levels of speciﬁed area. 253 children Age 9–10 yr Race/ethnicity not speciﬁed.

Ponderal index BMI Overweight = BMI > 17.6; Obesity = BMI > 20.1.

Birth weight. Children were selected through random clustersampling of schools, stratiﬁed by sex and socioeconomic level.

0 m; 0 f Total ﬁber (grams)

BMI (Overweight: BMI > 23; Normal-weight: BMI < 23).

Gender.

0 m; – f Total ﬁber (grams)

Body fat% (formula using skinfold measurements).

Gender, Total energy intake, Physical ﬁtness (run/walk test), Parental body mass (self-reported ht and wt).

0 m; 0 f Total ﬁber (grams)

Obesity: Dietary and Developmental Inﬂuences

Cross-Sectional Studies Rodriguez-Artalejo, 2002 Spain

Study Population

Rocandio, 2001 Spain

242 adolescents (94 males, 148 females) Age 10–19 yr Native Canadian. 64 adolescents (37 males, 27 females) Obese and normal-weight Age 15–17 yr Medium socioeconomic level. 32 children (16 males, 16 females) Age 11 yr Divided into overweight and nonoverweight groups.

Overweight status (Overweight: BMI > 85th%).

Gender, Age.

– m; – f Total Fiber (grams)

BMI.

—

0 Total Fiber (grams)

Weight-for-height (overweight > 90th% compared with 1985 Spanish reference values).

—

– Total Fiber (grams)

Dietary Inﬂuences on Energy Balance

Hanley, 2000 Sandy Lake Health and Diabetes Project. Sandy Lake First Nation, Canada Ortega, 1995 Madrid, Spain

Notes: 1

BMI based on measured weight and height unless otherwise speciﬁed.

2Plus (+) indicates signiﬁcant direct relationship, negative (–) indicates signiﬁcant inverse relationship, and zero (0) indicates nonsigniﬁcant relationship between dietary factor and measure of adiposity; results presented by gender are available, in which case f indicates female, m indicates male.

IHD = ischemic heart disease; ht = heigh; wt = weight.

85

86

TABLE 4.8 Observational Studies of Association of Sugar Intake with a Measure of Adiposity in Children Study Name and/or Location

Study Population

U.S. Nationally Representative Study Lewis, 1992 4,682 children USDA 1977–1978 Nationwide Food Age 4–10 yr Consumption Survey (NFCS) Divided into high, moderate, and low consumers of added sugars.

Koivisto, 1994 Sweden

39 children 15 overweight, 24 normal weight; Case-control Age 3–7 yr.

Control Variables

BMI (reported height and weight).

Age, Dieting status.

Overweight status (Overweight: BMI > 85th%).

Gender, Age.

% BMI (actual BMI/median BMI Gender, Age. for age and sex) Nonobese subjects were recruited by Obese = BMI > 95th%; Nonobese = stratifying age, gender, and BMI < 75th%. socioeconomic background. Obesity status (BMI > 95th% Gender. according to British growth standards), low-risk (LR) (child with two lean parents) or high-risk (children with at least one biological parent with BMI > 29.5), Body fatness (difference between body weight and lean mass). Weight-length index (WLI), Gender, Age. (overweight: WLI > 109)

Association2 – Grams of added sugars consumed per kilogram of body weight (g/kg)

0 m; 0 f % Total energy from Simple Sugar + m; + f Total sugar (grams)

0 m; 0 f % Total energy from total sugar Body fatness

0 m; 0 f % Total energy from sucrose

Notes: 1

2

BMI based on measured weight and height unless otherwise speciﬁed.

Plus (+) indicates signiﬁcant direct relationship, negative (–) indicates signiﬁcant inverse relationship, and zero (0) indicates nonsigniﬁcant relationship between dietary factor and measure of adiposity; results presented by gender are available, in which case f indicates female, m indicates male.

Obesity: Dietary and Developmental Inﬂuences

Other Cross-Sectional or Case-Control Studies Hanley, 2000 242 adolescents (94 males, 148 Sandy Lake Health and Diabetes females) Project. Age 10–19 yr Sandy Lake First Nation, Canada Native Canadian. Gillis, 2002 181 children Ontario, Canada Age 4–16 yr Predominantly middle-class Divided into two groups: obese and nonobese. McGloin, 2002 114 children (66 males, 48 females) Northern Ireland, UK Age 6–8 yr Divided into three groups based on their risk for obesity or their own obesity status. Predominantly white with mixed socioeconomic background.

Adiposity Measure1

Dietary Inﬂuences on Energy Balance

87

it is not clear how sugar, simple sugar, and sucrose were deﬁned in these studies, it is not clear if the results are comparable. Also, in general, few variables were controlled for in these studies. Given the methodological limitations and the small number of studies, no ﬁrm conclusions can be drawn with respect to the relationship between sugar intake and adiposity in children. Prevention Trials — Dietary Fat Prevention trials involving dietary carbohydrate are discussed beginning on p. 98 for adults and p. 105 for children.

MACRONUTRIENT INTAKE: PROTEIN Secular Trends — Protein The per capita availability of protein in the U.S. diet increased about 16% between 1983 and 1999, from 95 to 111 g per day per person (see Figure 4.11). Prior to that (between 1970 and 1983), the availability of protein was relatively stable. During the same time period that protein availability increased, so did calories, which explains why the percent of calories available from protein has remained amazingly constant at 11.6%. Siega-Riz and colleagues (2000) also found that protein intake varied little between 1965 and 1994–1996, at least among adolescents 11–18 years of age. They examined data from nationally representative USDA food consumption surveys and found that protein as a percent of calories declined slightly from 16.1% to 14.2% during this time period among adolescents. Furthermore, protein intake (expressed as a percentage of kilocalories) did not change among adults between 1987 and 1992 according to the National Health Interview Surveys (Norris, 1997). Therefore, secular trend data suggest that a change in percent of calories from protein has not contributed to the current rise in overweight. Of course, protein is found in many different foods. Of the high-protein foods, the availability of red meats (beef, pork, veal, and lamb), eggs, and milk decreased, whereas the availability of poultry, seafood, and cheese increased. The greatest increases were seen for poultry (up 84.5%) and cheese (up 111.6%). Despite the decrease in red meat consumption over time, red meat still accounts for 49% of “meat-group” servings, compared to 27% from poultry; 24% from seafood; 7% from eggs; 14% from dried beans, peas, and lentils; and 2% from peanuts and peanut butter (Putnam, 2000). The availability of meat-group servings is roughly equivalent to food pyramid recommendations (Putnam, 2000) despite a total protein intake that greatly exceeds the recommended intake (St. Jeor, 2001). On the other hand, the number of servings of dairy available for consumption are less than food pyramid recommendations (1.6 vs. 2 servings). Cheese accounts for over two ﬁfths of the total dairy servings consumed by Americans (USDA food supply website, 2003; Harnack, 2000). Therefore, of the protein-rich foods, increases in cheese intake would be of particular interest as a possible contributor to the recent rises in obesity. About one third of Americans’ intake of protein is from vegetable sources, and about two thirds is from animal sources. The percent of protein from animal sources has decreased slightly since the 1970s and, conversely, the percent from plant sources has increased slightly (form 33% in 1970 to 37% in 1999 (USDA food supply website). Therefore, the origin of the protein source (plant vs. animal) is unlikely to have impacted rates of overweight since 1970. Plausible Mechanisms — Protein It is generally acknowledged that protein has the highest satiety value of the three macronutrients (de Castro, 1999; Johnstone, 1996; Blundell,1997; Marmonier, 2000; Mikkelsen, 2000). Therefore, theoretically, higher protein intakes could contribute to energy balance by enhancing the sense of fullness and delaying return of hunger. It has been found that it is the amount of protein (not carbohydrate or fat) remaining in the stomach that tends to reduce the size of the next meal (de Castro, 1999). However, this is a short-term effect that accounts for only a small proportion of the variance in meal intake and does not account for the larger variation in day-to-day intake (de

88

Obesity: Dietary and Developmental Inﬂuences

Castro, 1999). There are also numerous reports that dieters on high-protein regimens experience less hunger compared to those on low-protein regimens (St. Jeor, 2001). This may explain the lower dropout rates that have been observed in some of these studies (Brehm, 2003). Studies among domestic animals have found that protein intake is associated with lower rates of fat accretion (Metges, 2001). Protein has also been found to produce a greater thermogenic effect after a meal than carbohydrate and therefore may result in increased energy expenditure (Mikkelson, 2000). Despite the high satiety value of protein, others have found that protein may contribute to obesity. High-protein diets have been shown to induce overeating in rats (Rolland-Cachera, 1995). Among humans, Rolland-Cachera (1995) found that high protein intakes early in life are associated with early adiposity rebound, accelerated growth, and an increased risk of childhood obesity. Dorosty et al. (2000), on the other hand, did not ﬁnd a signiﬁcant association between the timing of adiposity rebound and protein intake. Nevertheless, Rolland-Cachera and others theorize that high-protein diets in early childhood may precociously increase IGF-1 (insulin-like growth factors) levels and/or trigger adipocyte multiplication. This accelerated growth may precipitate earlier adiposity rebound (de Castro, 1999; Metges, 2001; Scaglioni, 2000). Metges (2001) and Scaglioni (2000) point out that breastfeeding may protect against obesity, at least in part because of its lower protein content compared to infant formulas. Furthermore, factors (hunger, socialization, mood, time of day, etc.) that promote increased intake tend to promote fat and protein intake but not carbohydrate intake (de Castro, 1999). Both animal and human studies have produced mixed results in the investigations of prenatal maternal protein intake and birth weight. However, the results from two were consistent in that they both identiﬁed higher birth weights when protein intake was lowest in early pregnancy (Metges, 2001). Puberty is another developmental stage when protein may play a role in the development of obesity. DeRiddler (1991) found that girls whose diets had lower concentration of vegetable products had lower concentrations of gonadrotropin and estradiol, both of which are related to pubertal development. Therefore, relatively high consumption of animal products could, theoretically, lead to early puberty. In conclusion, the high satiety of value of protein suggests that increasing protein intake might help reduce calorie intake and, therefore, excess weight gain. Yet animal studies have produced conﬂicting results regarding the impact of protein on calorie intake, and human studies suggest that, at least during critical periods of development, excess protein intake may increase the risk of obesity, theoretically due to hormonal inﬂuences. Unfortunately, research into the impact of protein on the development of adiposity is scarce, given that most researchers have focused on carbohydrate and fat. The intriguing results presented here, however, suggest that protein may play an important role in the development and therefore prevention of obesity by virtue of its impact on satiety and growth-related hormones. Further research is merited. Observational Studies — Protein Longitudinal Studies — Adults Two longitudinal studies were identiﬁed that examined the relationship between protein intake and adiposity in adults (see Table 4.9). One found a signiﬁcant and positive relationship between protein and increase in BMI (Kemper, 1999) whereas the other (Parker, 1997) detected no signiﬁcant relationship between these two variables. The study by Kemper et al. (1999) is notable in that it followed adolescents starting at age 13 into adulthood for 15 years. The study, however, suffers from several methodological limitations: none of the subjects were obese at start of the study, thereby eliminating the adolescents at highest risk for adult obesity, the sample size was relatively small, and few control variables were included in the analysis. In this study, conducted in Amsterdam, protein was the only macronutrient that was signiﬁcantly associated with obesity (with an odds ratio of 1.5), while total energy was strongly negatively associated with obesity. The study

Study Longitudinal Studies Parker (1997) Pawtucket Heart Health Study (New England) 4-year study 1986–1987 and 1991–1992 Kemper (1999) Amsterdam Growth and Health Longitudinal study 15-year study

Study Population

Slattery (1992) CARDIA 5 U.S. cities Ludwig (1999) CARDIA 5 U.S. cities

Lluch (2000) Stanislas Family Study (France)

Measure of Adiposity

Association1

176 men 289 women Prospective cohort

Age, kcal, smoking, BMI, aerobic activity, gender

BMI change

0

83 men 98 women Age 13 at baseline Nonobese at baseline

Age Gender

Fat mass (4 skinfolds, ht, wt)

+

Adjusted for other nutrients. Kcal, age smoking, education, energy expenditure

BMI

+

Age

BMI (self-report)

Gender, race, age education, smoking, alcohol, and PA

BMI Skinfolds

Race/ethnicity Age, sex, education, city, smoking, PA, vitamin suppl., kcal, alcohol, baseline adiposity

BMI

Age

BMI Lorentz index (wt/ht categories)

Other Cross-Sectional or Case-Control Studies Trichopoulou (2002) 27,862 adults EPIC study Greece Healthy volunteers Ages 25–82 Appleby (1998) Oxford Vegetarian Study 1980–1984

Control Variables

+ (meat eaters vs. nonmeat eaters)

The association between protein intake and BMI was the strongest of the 3 macronutrients. Did not measure protein per se, only compared meat eaters vs. nonmeat eaters

+ (BMI, g) 0 (skinfolds) + (whites)

+ (all groups)

89

1914 men 3378 women Nonsmokers About half nonmeat eaters 5115 adults Black and white Ages 18–30 Diverse SES 2909 adults Healthy Black and white Ages 18–30 (endpoint of 10-year longitudinal) 387 families 1320 family members 379 men 381 women 270 boys 290 girls Ages 11–65

Comments

Dietary Inﬂuences on Energy Balance

TABLE 4.9 Observational Studies that Examined the Relationship between Protein Intake and Adiposity in Adults

90

TABLE 4.9 (CONTINUED) Wamala (1997) Stockholm Female Coronary Risk Study Tucker (1992) Western U.S.

300 women Healthy Ages 30–65 205 women Mostly white Average age 34.6

Age

BMI categories

Age, smoking, other macronutrients, PA, total energy intake

Body fat categories based on skinfolds

0

Body fat based on skinfolds (continuous variable) BMI categories (obese vs. control) (3 categories of obesity)

0

86 obese men 61 normal weight men Case-control

Age

Cox (1999) England

41 lean adults 35 obese adults Nondieting Healthy Case-control 34 obese women 34 nonobese women Ages 20–50 Case-control

Underreporting, gender, age

BMI categories Body fat (bioelectrical impedance)

Underreporting Matched for age

Obese (average BMI = 30) vs. nonobese (average BMI = 22)

Westerterp (1996) The Netherlands

+ (obese vs. control) (3 obese categories) 0

Notes: 1

Plus (+) indicates a signiﬁcant direct association; negative (–) indicates a signiﬁcant inverse association; zero (0) indicates nonsigniﬁcant association.

NR = not reported, PA = physical activity, HEI = Healthy Eating Index, CHO = total carbohydrate, SES = socioeconomic status. f = female, m = male, % = % of calories, g = grams (absolute intake), kcal = kilocalories, ht = height, wt = weight. BMI was a continuous variable unless otherwise indicated. BMI was based on measured height add weight unless otherwise indicated. Macronutrient intake was deﬁned in terms of % of calories unless otherwise indicated.

Only salty foods and energy density related to obesity.

0

Obesity: Dietary and Developmental Inﬂuences

Anderson (1996) Gustaf Study Sweden

+ (g)

Dietary Inﬂuences on Energy Balance

91

by Parker (1997) included several important control variables and followed 465 adults in New England for 4 years. Given the limited number of studies, the lack of generalizability to the overall U.S. population, and the conﬂicting results, it is not possible to arrive at a conclusion regarding the association between protein and adiposity in adults based on the available longitudinal studies. Cross-Sectional Studies — Adults Nine cross-sectional studies were identiﬁed that examined the relationship between protein intake and adiposity in adults (Table 4.9). An additional study compared meat eaters to vegetarians (Appleby, 1998) but did not measure protein intake per se. Most of the studies were conducted in Europe. Only three were in the U.S. Two thirds (six) of the studies found a signiﬁcant and positive association between protein intake and adiposity. One of the studies (Ludwig, 1992) found the association to only hold true for whites, and another (Anderson, 1996) found the relationship to hold true when comparing obese subjects to controls but not when comparing different degrees of obesity; the a third found the relationship to hold true for BMI but not skinfolds (Slattery, 1992). BMI was also positively associated with being a meat eater in England (Appleby, 1998). Three of the studies [(Cox, 1999; Westerterp, 1996) and one conducted in the western U.S. (Tucker, 1992)] found no signiﬁcant association between protein intake and obesity. Most of the studies did not include adequate control variables. But of those that did (Trichopoulou, 2002; Ludwig, 1999; Slattery, 1992; and Tucker, 1996), all but one (Tucker, 1996) found a positive association between protein intake and adiposity among at least some groups. None of the studies was nationally representative, although a few included large cohorts from several different locations within the country where the study was located (Ludwig, 1999; Slattery, 1992; Trichopoulou, 2002). Two of the three studies that failed to detect a positive association between obesity were very small casecontrol studies (Cox, 1999; Westerterp, 1996). The very large, rigorously conducted study in Greece (Trichopoulou, 2002) found that, of the three macronutrients, protein intake had the strongest association with adiposity. It is also interesting to note that, although one might think that protein would be a marker for high fat intake, half of the studies (Trichopulou, 2002; Anderson, 1996; Slattery, 1992) demonstrating a signiﬁcant relationship between BMI and protein intake found that dietary fat was either not signiﬁcantly associated or signiﬁcantly negatively associated with adiposity. Two others (Ludwig, 1999; Lluch, 2002) found that dietary fat was only positively associated with adiposity in some groups (blacks and females, respectively). In conclusion, although the evidence from cross-sectional studies is strongly supportive of the notion that higher relative intakes of protein are positively associated with adiposity, the limited number of studies conducted in the U.S. suggests that these results should be applied U.S. populations with caution. Conclusions — Observational Studies in Adults The observational studies taken as a whole suggest a positive relationship between protein intake and adiposity among adults. The studies, however, varied greatly in terms of characteristics of the study populations (age and geographic location) as well as the number and nature of the control variables. Of primary concern, most of these studies had not identiﬁed protein intake as a primary variable of interest. Most were looking at the relationship between carbohydrate and/or fat and adiposity. Therefore, there may have been a bias in the reporting of positive results, because those studies that found no relationship between protein intake and adiposity may not have reported on these “secondary” ﬁndings and/or may not have included “protein” in their list of key words. Therefore, although we consider the epidemiologic evidence supporting a positive relationship between protein intake to be quite strong, additional studies (longitudinal and cross-sectional) with nationally representative samples and rigorous controls are needed. Longitudinal Studies — Children Seven longitudinal studies of children were identiﬁed that examined the association between dietary protein intake and adiposity (Table 4.10). Three found signiﬁcant positive associations between

92

TABLE 4.10 Observational Studies of the Association of Protein Intake with a Measure of Adiposity in Children Study Name and/or Location Longitudinal Studies Magarey, 2001 Adelaide Nutrition Study (ANS) Adelaide, Australia Alexy,1999 Dortmund Nutritional and Anthropometrical Longitudinally Designed (DONALD) Study Dortmund, Germany Scaglioni, 2000 Milan, Italy

Rolland-Cachera, 1995 Paris, France

Carruth, 2001 Knoxville, Tennessee, U.S.

243 children From a representative birth cohort Followed from age 2–15 yr.

BMI, Triceps and subscapular skinfolds, expressed as standard deviation scores at each age.

205 children (105 males, 100 females) Age 3 yr (at baseline) Followed 2 yr.

BMI.

Control Variables

Association2

Previous corresponding measure of body fatness, Sex, and Parental BMI, triceps (TC) skinfold or subscapular skinfold (SS).

0 m; 0 f % (3- and 4-Day Food Records)

—

Gender, Weight, and Length at birth 147 children (80 males, 67 females) Overweight status at age 5 (Overweight: BMI > 90th%). and at 1 yr of age, Parental age. Age 1 yr (at baseline) Followed 4 yr. Obesity status at baseline (Obesity = Age, Gender, Puberty development 112 children (clinically assessed), Parents’ BMI. rel BMI > 120%; rel BMI = Mean age 8.7 ± 1.1 yr (at baseline), BMI/BMI at 50th% for age and 12.3 ± 1.0 (at follow-up). gender). White Followed 4 yr. 112 children BMI, Subscapular and triceps Baseline energy intake (at age 2 Age 2 to 8 yr; Followed 6 yr. skinfolds at age 8. years), Baseline BMI (at age 2 years), Social class (father’s occupation), Parental BMI (selfreported by parents). Total body fat % and Total body fat Gender, BMI at 70 months, dairy 53 children intake, dietary fat, (g), DEXA measurement at 70 White, middle and upper monounsaturated fat. months. socioeconomic status. Followed from age 2 to 96 months

0 % (3-day weighed food records completed by child’s parents)

+ m; + f % 0 m; 0 f %

+ %

+ Mean longitudinal intake Total Protein (g) [unadjusted for other control variables] + % Body fat [in multivariate model] 0 Body fat (g) [in multivariate model]

Obesity: Dietary and Developmental Inﬂuences

Maffeis, 1998 Italy

Adiposity Measure1

Study Population

Adiposity Take-off, 15 children Matched on age, ethnicity, and Identiﬁed from a larger study with Sum of 7 skinfolds increased by 1.5 gender for the same year of data standard deviations or more in any collection and 3 were randomly “adiposity take off” (children year of the study. whose adiposity increased 1.5 selected as control subjects per standard deviations or more above case. the mean from the previous year) and 33 matched control subject; 20 white, 12 Mexican-American and 16 African-American children. Case-control. Followed from age 3 to 7 yr (3-year duration).

0 m; 0 f %

Dietary Inﬂuences on Energy Balance

Robertson, 1999 Studies of Child Activity and Nutrition (SCAN) Texas, U.S.

Other Cross-Sectional or Case-Control Studies 1444 children BMI standard deviation scores (age- Age, Gender. UK nationally representative sample and sex-adjusted) Ages 1.5–4.5 yr.

0 m; 0 f %

Bao, 1996 Bogalusa Heart Study Louisiana, U.S.

1419 children Age 10 yr 35% black, 65% white.

Fat-free Mass and Body Fat (estimated from wt, ht, and triceps skinfold measures) Ponderal index.

Race, Gender.

+ m; + f %

Rodriguez-Artalejo, 2002 Spain

1112 children (557 males, 555 females) Age 6–7 yr From 4 Spanish cities (two with relatively high IHD mortality and two with relatively low IHD mortality).

Ponderal index BMI Overweight = BMI > 17.6; Obesity = BMI > 20.1.

Birth weight. Children were selected through random cluster-sampling of schools, stratiﬁed by sex and socioeconomic level.

0 m; 0 f % + m; + f Total Protein (grams)

Guillaume, 1998 Province de Luxembourg, Belgium

955 children Age 6–12 yr.

BMI.

Age, Gender.

0 m; 0 f % + m; + f Total Protein (grams)

93

Davies, 1997 National Diet and Nutrition Survey (NDNS) UK

94

TABLE 4.10 (CONTINUED) 530 children (278 males, 252 females) Age 7–11 yr.

Relative Body Fat Mass (FM%): Fat-free mass and Body fat mass (Lohman’s formulae based on triceps and subscapular skinfold thickness measurements).

Gender, EI/BMR ratio.

0 m; 0 f %

Stewart, 1999 FRESH (Food Reeducation for Elementary School Health) Baltimore, Maryland, U.S.

468 preadolescent children grades 2 BMI through 5 (mean age 8.9 yr) Sum of triceps and subscapular Working class families; 87% white, skinfolds. 12% Africann-American, and 3% other.

Because there were no substantial differences between males and females and among the racial groups, their data were combined.

0 m; 0 f %

Garaulet, 2000 Spain

331 adolescents (139 males, 192 females) Ages 14–18 yr Representative sample of all socioeconomic levels of speciﬁed area.

BMI (Overweight: BMI ≥ 23; Normal-weight: BMI < 23).

Gender.

0 m; 0 f %

Lluch, 2000 Stanislaus Family Study France

270 males (mean age 15.2 yr) and 290 females (mean age 15.8 yr).

Relative weight (calculated using Lorentz’s index (ideal weight), taking into account gender).

Gender, Age.

+ m; + f %

Tucker, 1997 Utah, U.S.

253 children Age 9–10 yr Race/ethnicity not speciﬁed.

Body fat% (formula using skinfold measurement).

Gender, Total energy intake, Physical ﬁtness (run/walk test), Parental body mass (self-reported ht and wt).

Hanley, 2000 Sandy Lake Health and Diabetes Project. Sandy Lake First Nation, Canada

242 adolescents (94 males, 148 females) Age 10–19 yr Native Canadian.

Overweight status (Overweight: BMI ≥ 85th%).

Gender, Age.

Dennison, 1997 New York, U.S.

168 children (94 2–yr old children, 74 5-yr old children) Predominantly white, low-middle class.

BMI and Ponderal Index Age. (Obese = BMI ≥ 75th% age and sexspeciﬁc%; Ponderal index ≥ 90th% age-speciﬁed)

0 m; 0 f % and Total Protein (grams)

0 m; 0 f %

0 Total Protein (grams)

Obesity: Dietary and Developmental Inﬂuences

Maffeis, 2000 Italy

147 children (80 males, 67 females) Overweight status (at baseline) Age 5 yr (at baseline) (Overweight: BMI > 90th%). White.

McGloin, 2002 Northern Ireland, UK

114 children (66 males, 48 females) Obesity status (BMI > 95th%), Gender. Age 6 ≥ 8 yr low-risk (LR) (child with two lean Divided into three groups based on parents) or high-risk (children with their risk for obesity or their own at least one biological parent with obesity status BMI > 29.5), Predominantly white European Body fatness (difference between population with mixed body weight and lean mass). socioeconomic background.

Maffeis, 1998 (Cross-sectional results from longitudinal study) Italy

112 children Mean age 8.7 ± 1.1 years (at baseline) White.

Rel BMI at baseline (rel BMI = BMI/BMI at 50th% for age and gender).

Age, Gender, Puberty development (clinically assessed), Parents’ BMI.

Maffeis, 1996 Verona, Italy

82 prepubertal children (30 obese, 52 nonobese) Age 7.5–11.5 yr White.

BMI, Obesity: BMI > 97th% of reference values for age and sex.

RMR (indirect calorimetry).

Atkin, 2000 Feasibility Study for National Diet and Nutrition Survey of Children. UK

77 children (39 males, 38 females) Age 1.5–4.5 yr.

% Body fat (18O dilution).

Gender.

Ortega, 1995 Madrid, Spain

64 adolescents (37 males, 27 females) Obese and normal-weight Age 15–17 yr Medium socioeconomic level.

BMI.

Gazzaniga, 1993 Muscatine Coronary Risk Factors Project. Iowa, U.S.

48 children (23 males, 25 females) 30 nonobese children, 18 obese children; Case-control Age 9–11 yr White.

% Body fat (estimated by taking Gender, REE, Energy expended for average of two skinfold thickness physical activity, Age, Body measurements, Triceps and weight. subscapular, and subject’s sex, age, ht, and wt).

Gender, Weight, and Length at birth and at 1 yr of age, Parental age.

+ m; + f %

0 m; 0 f % or Total protein (grams) Obese vs. LR 0 m; 0 f % Body fatness

0 m; 0 f %

Dietary Inﬂuences on Energy Balance

Scaglioni, 2000 (Cross-sectional results from longitudinal study) Milan, Italy

0 %

0 m; 0 f % 0 m; 0 f Total protein (grams) —

+ %

0 m; 0 f %

95

96

TABLE 4.10 (CONTINUED) Koivisto, 1994 Sweden

Rocandio, 2001 Spain

Francis, 1999 Texas, U.S.

Bandini, 1999 Boston, Massachusetts, U.S.

Weight-length index (WLI), (overweight: WLI > 109).

Weight-for-height (overweight > 90th% compared with 1985 Spanish reference values).

Gender, Age.

0 m; 0 f %

—

% Abdominal fat and fat-free mass Children were matched for age, (Dual-energy x-ray absorptiometry). gender, weight.

% Body fat (18O dilution).

Gender (examined, but no interaction).

0 % 0 Total Protein (grams) 0 m; 0 f %

+ m; + f %

Notes: 1

BMI based on measured weight and height unless otherwise speciﬁed.

2

Plus (+) indicates signiﬁcant direct relationship, negative (–) indicates signiﬁcant inverse relationship, and zero (0) indicates nonsigniﬁcant relationship between dietary factor and measure of adiposity; results presented by gender are available, in which case f indicates female, m indicates male; % indicates % total energy from protein. IHD = ischemic heart disease, EI/BMR = energy intake/basal metabolic rate, ht = height, wt = weight, REE = resting energy expenditure, RMR = resting metabolic rate.

Obesity: Dietary and Developmental Inﬂuences

39 children 15 overweight, 24 normal weight; Case-control Age 3–7 yr 32 children (16 males, 16 females) Age 11 yr Divided into overweight and nonoverweight groups. 12 pairs of prepubertal, nonobese children (12 with an obese mother (BMI > 30), 12 with a nonobese mother (BMI 20–25)) matched for age, gender and weight White. 21 obese adolescents (10 males, 11 females); 22 nonobese (10 males, 12 females); Case-control Age 12–18 yr Race/ethnicity not speciﬁed.

Dietary Inﬂuences on Energy Balance

97

dietary protein intake and adiposity (Scaglioni, 2000; Rolland-Cachera, 1995; Carruth, 2001). Scaglioni and colleagues (2000) found that, among the macronutrients, only protein intake (expressed as a percentage of energy intake) at 1 year of age was associated with overweight at 5 years of age (p = 0.05). In an additional logistic analysis, which was performed only among the subjects who were not overweight at baseline, the adjusted association of the protein intake as a percentage of energy at 1 year with overweight at 5 years was even more marked. The study by Rolland-Cachera and colleagues (1995) was also a rigorous study that controlled for numerous potential confounders. The study by Carruth, however, only reported ﬁndings for absolute amount and not percentage of calories from protein, a study limitation. A higher absolute intake of protein among fatter children is not unexpected; all other things being equal but their larger size, larger children would consume more dietary energy and other nutrients, including protein, than their smaller peers. Four studies found no statistically signiﬁcant association between dietary protein intake and adiposity (Magarey, 2001; Alexy, 1999; Robertson, 1999; Maffeis, 1998). These four studies varied substantially in sample size (48 to 243 subjects) and the length of study duration (2–13 years study duration). The largest and longest longitudinal study (Margarey, 2001), a rigorous one in terms of multiple control variables and measures of adiposity, found no relation between protein intake and adiposity. This study was conducted in Australia. Indeed, only the two smallest studies were conducted in the U.S. Furthermore, only one longitudinal study was conducted among a multiethnic sample; however, this study consisted of a small sample size and utilized a dietary assessment tool with relatively high intraindividual variability, which may have limited their ability to detect a signiﬁcant association (Robertson, 1999). Therefore, caution must be used in extrapolating these results to diverse populations. There were no longitudinal studies that found an inverse association between these two variables. Cross-Sectional Studies — Children No U.S. nationally representative studies that examined the association between dietary protein intake and adiposity in children were identiﬁed. Twenty-two other cross-sectional studies were examined (two of these studies were cross-sectional results from longitudinal studies discussed previously). Five found signiﬁcant positive associations between dietary protein intake and adiposity in children. These results were observed among a fairly large study sample (N = 560) in France (Lluch, 2000), baseline data from a longitudinal cohort in Italy (Scaglioni, 2000), and in a smaller case-control study (N = 33) conducted in Boston (Bandini, 1999). Bao and colleagues (1996) found that the percent of energy from protein among their study sample from Louisiana was the only signiﬁcant but weak correlate of body fat detected (r = 0.06; p = 0.04). In a somewhat smaller study sample (N = 64), Ortega and colleagues (1995) found overweight subjects in their Spanish sample derived a greater proportion of their energy from proteins (19.8% vs. 16.4%; p < 0.05). Two of the 22 cross-sectional studies found mixed results (Rodriguez-Artalejo, 2002; Guillaume, 1998). In a relatively large study sample, Rodriguez-Artalejo and colleagues (2002) found absolute protein intake (measured in grams) to be positively and signiﬁcantly associated with overweight but, when protein intake was measured as a percent of energy, no signiﬁcant association was detected. Guillaume and colleagues (1998) concluded with the same mixed ﬁnding: a signiﬁcant positive association when protein intake was measured in absolute terms (grams) and no signiﬁcant association when protein intake was measured as a percent of total energy. This is not an unexpected result (see discussion above) and does not provide strong evidence that protein is a determinant of adiposity. Finally, 15 of the 22 cross-sectional studies found no statistically signiﬁcant association between dietary protein intake and adiposity in children (McGloin, 2002; Rocandio, 2001; Maffeis, 2000; Garaulet, 2000; Hanley, 2000; Atkin, 2000; Stewart, 1999; Francis, 1999; Maffeis, 1998; Davies, 1997; Dennison, 1997; Tucker, 1997; Maffeis, 1996; Koivisto, 1994; Gazzaniga, 1993). These

98

Obesity: Dietary and Developmental Inﬂuences

studies varied substantially in terms of sample size (N = 24 to 530) and study location, but no obvious trend in ﬁndings according to these factors was evident. McGloin and colleagues (2002) found the obese subjects in their sample did not consume more protein in absolute terms or as a percentage of total energy; however, there was evidence of underreporting among the obese children that may have inﬂuenced the results. Tucker and colleagues (1997) found both absolute protein (measured in total grams) and the percent energy from protein to be positively associated with body fat percentage only when gender and energy were controlled for, but not when gender, energy, physical ﬁtness level, and parental adiposity were controlled for. These ﬁndings underline the importance of adjusting for potential confounders (e.g., physical activity), something that was rarely done in these studies. There did not appear to be an age-dependent effect, although relatively fewer studies were done in younger children than older children. No other differences in study merit were noted between studies that found signiﬁcant associations and studies that did not. None of the cross-sectional studies found a negative (inverse) association between protein intake and adiposity in children. Given the large number of insigniﬁcant ﬁndings, it is not possible to arrive at a ﬁrm conclusion regarding the association of protein intake with adiposity in children. The observational studies described herein, however, do suggest that relatively higher protein intakes do not protect against obesity. Prevention Trials — Macronutrients — Adults Intervention Design Twelve “prevention” trials conducted among adults were identiﬁed for the purposes of this review (Table 4.11). To be included, the trial had to target at least one of the behaviors of interest to this review, and include some measure of adiposity, but did not have to target adiposity as a primary outcome. As a result, the majority of the studies aimed to reduce the risk of chronic diseases (cancer, heart disease, etc.) rather than prevent weight gain per se. Only ﬁve of the studies mentioned weight as a targeted outcome, and all of these targeted weight maintenance rather than weight loss. For reasons described previously, studies that explicitly targeted weight loss as a primary outcome were excluded. Although not a targeted outcome, weight loss may have occurred as a result of behavior changes. All the identiﬁed studies were randomized controlled trials, even though this was not a required criterion for inclusion. See the methods section for a more detailed discussion of exclusion/inclusion criteria. All of the interventions involved counseling and education delivered to groups or individuals in a variety of settings including worksite, clinic, home, and community. Some mentioned the use of goal setting, self-monitoring and evaluation, risk assessment, newsletters, and group activities. None of the interventions mentioned the use of other environmental or mass media strategies, or institutional changes. Most targeted multiple (2–3) dietary changes. The most common dietary behavior targeted was a reduction in dietary fat (Lanza, 2001; Kristal, 2000; Rock, 2001; Jeffery, 1999and1997; Bhargava, 2002; Boyd, 1997; Simon, 1997; Djuric, 2001; Leermakers, 1998), followed by an increase in fruit and vegetable intake (Lanza, 2001; Kristal, 2000; Rock, 2001; Jeffery, 1997 and 1999; Bhargava, 2002; Smith-Warner, 2000; Cox, 1998, Djuric 2002;) and/or an increase in ﬁber or complex carbohydrates (Lanza, 2001; Rock, 2001; Boyd, 1997). One study targeted a reduction in sodas, desserts, and breads (Djuric, 2002). Only three studies targeted an increase in physical activity in addition to dietary changes (Jeffrey, 1997 and1999; Gomel, 1993; Leermarkers, 1998). Study Length and Impact on Adiposity Studies varied in length from 8 weeks to 4 years. Three of the studies included follow-up measurements at 12 months post intervention (Kristal, 2000; Gomel, 1993; Simon, 1997). These interventions were 1 year, 12 weeks, and 3 months in duration, respectively. All but four of the

Author, Year, Study Name, Location Lanza, 2001 Polyp Prevention Trial U.S.

Study Population, Sample Size, Age, Ethnicity 2079 adults with large bowel adenomatous polyps, 35–89 y.o., 88–91% white.

Study Design and Strategies

Length of Intervention and Timing of F/U Measure

Randomized controlled trial; 4 years Individualized instruction and counseling program to prevent the recurrence of adenomatous polyps.

Relevant Target Behaviors Targeted

Reported Change among Subjects

Results: Impact on Measure of Adiposity Direction of Change Relative to Controls1

Comments

Control Variables

↓ Dietary fat, ↑ Intake of fruit and vegetables, ↑ Dietary ﬁber.

↓ Dietary fat (% and g/d), ↓ ↑ Intake of fruit and vegetables (Weight) (svgs/MJ and g/d), ↑ Fiber (g/MJ), 0 Change in total calories, ↑ Carbohydrates (%), ↑ Protein (%; men), ↑ Calcium (men), ↑ Whole grains (g/d), ↓ High fat foods (g/d; red/processed meats, high-fat dairy and desserts), ↑ Low-fat alternatives (g/d).

Gender.

No signiﬁcant differences at baseline between groups for: gender, age, minority race, education, marital status, BMI, current smoking, current aspirin use, or vigorous and moderate physical activity.

1-year intervention and 12-month follow-up

↓ Dietary fat, ↑ Intake of fruit and vegetables.

↓ Dietary fat (fat-related diet 0 habits based on score for 21- (Self-reported BMI) item scale), ↑ Intake of fruit and vegetables (svg/d).

Baseline Value, Gender, Age Trend toward ↓ BMI. No Group, Ethnicity, BMI status, signiﬁcant differences at Household income (Others baseline b/t groups for gender, tested but not included). age, race, income or BMI.

Rock, 2001 1010 women, 18–70 y.o., 86% Randomized controlled trial; Women’s Healthy non-Hispanic white. Individualized dietary Eating and counseling. Living (WHEL) Tested effect of low-fat, highStudy vegetable diet on patients at CA, AZ, TX and risk for breast cancer OR, U.S. recurrence.

1 year

↑ Fruit and vegetable intake, ↑ Fiber intake, ↓ Percentage of calories from dietary fat.

↑ Fruit and vegetable intake (servings/1000 kcal), ↑ Fiber intake (g/1000 kcal), ↓ Percentage of calories from dietary fat, ↑ Percentage of calories from carbohydrates.

0 (Weight)

Age, Stage of cancer at initial No signiﬁcant differences in diagnosis, Overweight status, age, ethnicity, education level, Menopausal status. stage at diagnosis or BMI between intervention and comparison groups at baseline. * Change in vegetable intake was inversely associated with weight change

0 (weight change at 1 year) 0 (weight change at 3 years)

Age, Participant type (men, None of the comparisons high-income women, lowbetween treatment groups income women), Ethnicity, were sig. different at baseline; Smoking, Baseline values for baseline BMI, physical physical activity, prior activity and prior participation participation in a weight loss in formal weight loss program program and BMI – controlled approached signiﬁcance and for in 1-year analysis. were controlled for in the Baseline value for BMI, analysis. Participant type (men, high- In sample with 3-year results, income women, low-income mean BMI of education-only women), Smoking status at group was higher than other each year, marital status at two groups and higher % of each and ethnicity – controlled whites in intervention groups. for in 3-year analysis.

Jeffery, 1997 and 985 men and high- and lowRandomized controlled trial; 1 year and 1999 income women, 20-45 y.o., Community-based (educational 3 years Pound of predominantly white and behavioral messages Prevention population at 1 year AND delivered via a monthly (POP) 809 men and high- low-income newsletter). Minneapolis/St. women at 3 years. Participants were randomized to Paul, Minnesota, education + incentive, U.S. education only or control group. Weight gain prevention intervention.

↑ Numbers of ↑ Frequency of weighing at 1 times weighing year and 3 years, yourself to at Smaller decrease in frequency least 1×/week, of healthy weight loss ↑ Fruit and practices in intervention than vegetable intake, control group, ↓ Intake of high fat 0 Signiﬁcant change in total foods, energy intake, percentage of ↑ Frequency of calories from fat, exercise or walking at least unhealthy weight loss 20 minutes to 3 practices at 3 years. ×/week.

99

Kristal, 2000 1205 adults, 18-69 y.o., 87% Randomized controlled trial; Puget Sound white, 4% African-American, Self-help education program Eating Patterns 5% Asian. to promote lower fat and Trial higher fruit and vegetable Washington, U.S. consumption.

Dietary Inﬂuences on Energy Balance

TABLE 4.11 Prevention Trials among Adults that Reported Impact on Adiposity

100

TABLE 4.11 (CONTINUED) Bhargava, 2002 926 postmenopausal women, Randomized controlled trial; Women’s Health 50–70 y.o., multiethnic Group dietary counseling. Trial: Feasibility population (28% black, 16% Weight gain prevention Study in Hispanic and 54% white). intervention. Minority Populations GA, AL, and FL, U.S.

1 year

↓ Energy intakes ↓ Fat intake (g) (saturated fat, from fat to ~20% monounsaturated fat, and calories, polyunsaturated fat), ↓ Intake of ↓ Energy intake, saturated fat, ↑ % Total calories from ↑ Consumption of carbohydrate. fruits, grain products, and vegetables.

Ø (Weight) Ø (Waist Circumference) Ø (Hip Circumference)

Baseline values were similar across groups. * In both intervention and control groups, weight change was explained by changes in carbohydrate and saturated, monounsaturated, and polyunsaturated fats.

↓ Intake of dietary ↓ Mean percentage of calories Ø fat to 15% of derived from fat (fell from (Weight) total calories, 33% to 21%), ↑ Intake of Protein intake as a percent of complex total calories was unchanged, carbohydrates. ↑ Carbohydrate intake (rose from 50 to 61% of calories), ↑ Intake of total dietary ﬁber (from 17.2 to 20.3 g/day).

Baseline characteristics were similar across (intervention and control) groups.

Gomel, 1993 431 adults (84% completed 12Work-site mo f/u), mean age 31–33 y.o. cardiovascular risk reduction Sydney metropolitan area, state of New South Wales, Australia

↓ Overweight, 0 Change in aerobic capacity Ø (speciﬁc dietary (increased at 3 or 6 months for (BMI; in Groups 3+4 vs. behaviors not all groups and then returned to 1+2, but BMI increased in described), baseline). all groups) ↑ Physical activity. 0 (% body fat from sum of 4 SF at 12 mo, but ↓ in 6 mo measure in Groups 3+4 vs. 1+2)

Education and counseling only offered only to those with identiﬁed CVD risk factors. No signiﬁcant differences at baseline b/t groups for: gender, marital status, or educational level, and major outcome variables (except systolic blood pressure). There was also a signiﬁcant baseline difference b/t groups for age and job description. Age was not used as a covariate because diffs small and unlikely to interact with intervention over time.

Randomizedcontrolled trial; 12-week Education and counseling at intervention work-site to reduce CVD risk and 1-year Group 1: Health risk assessment follow-up (control) Group 2: + Risk factor education Group 3: + Behavioral counseling Group 4: + Behavioral counseling + Incentives

Smith-Warner, 201 adults with adenomatous Randomized controlled trial; 1 year 2000 large bowel polyps, 30–74 y.o, Individual diet counseling to Minnesota Cancer 99% white. prevent colon cancer. Prevention Research Unit Intervention Study Minneapolis, MN, U.S.

↑ Intake of fruit and vegetables.

↑ Intake of fruit/vegetable (svgs/d), 0 Change energy intake or protein (%), ↓ Dietary fat (%), ↑ Carbohydrate (%), ↑ Fiber (g/d).

0 (weight) 0 (BMI)

Baseline value, Gender.

No signiﬁcant differences at baseline b/t groups for age, gender, household income, education, marital status, employment, ethnicity, smoking, BMI, alcohol intake, or use of nutrient supplements.

Obesity: Dietary and Developmental Inﬂuences

Boyd, 1997 786 women with Randomized controlled trial; 2 years Canadian Diet and mammographic dysplasia, age Intensive individual dietary Breast Cancer 30–65 yr; race/ethnicity not counseling to determine Prevention Study speciﬁed. whether adoption of low-fat, Group high-carbohydrate diet would Toronto, reduce the area of Hamilton, radiologically dense breast London and tissue. Windsor in Ontario, Canada

Cox, 1998 Reading and Glasgow, UK

125 adults, 16–65 y.o.

Djuric, 2002 Nutrition and Breast Health Study Detroit, MI, U.S.

97 women with a family history Randomized controlled trial; of breast cancer, 21–50 y.o, Individual counseling 75% white. program to decrease cancer risk Group1: ↓ fat Group 2: ↑ fruit + vegetables Group 3: ↓ fat, ↑ fruit + vegetables Group 4: control.

No p-values were reported for dietary changes but they appeared to be signiﬁcant. Women in the Low Fat Diet Group lost an average of 3 lb and women in NonIntervention Group lost an average of 5 lb. % body fat ↓ slightly for both groups.

↑ Intake of Fruit and Vegetables.

↑ Intake of fruit and vegetables (g/d; attenuated but remained after 1 year follow-up), 0 Difference in calories (↓ in both intervention and control groups), fat (%; except signiﬁcant ↓ in subgroup with baseline fat >35%), or starch (%) ↑ CHO (%) and Total Sugars (%).

0 (weight) (both intervention and control group gained weight)

—

No signiﬁcant differences at baseline between groups for age, gender, occupation, household income, employment status, or BMI.

1 year

↓ Fat, ↑ Fruit and Vegetable intake, ↑ Fruit and Vegetables Variety, ↓ Soda, Desserts, Breads, 0 Change in Total Calories.

Groups 1 and 3: ↓ Fat (%), ↓ Calories, Groups 2 and 3: ↑ Fruit and Vegetables (svg/d), Group 2: ↑ Calories, Slight ↓ Fat (%) Group 4: 0 change.

↓ (weight and % body fat in Group 1) ↑ (weight and % body fat in Group 2) 0 (Weight and % body fat in Groups 3 and 4)

—

Study designed to maintain energy intake. No comparison of demographics at baseline between groups.

4 months

↓ Dietary Fat, ↑ Aerobic Exercise.

No comparison of fat intake between intervention and controls provided, 0 Change in aerobic ﬁtness, 0 Change in physical activity.

↓ (weight) 0 (% body fat for H + C groups vs. control, but ↓ in H vs. control and ↓ in overweight vs. nonoverweight) 0 (relation between changes in physical activity and weight)

—

Trend toward ↑ physical activity. No signiﬁcant differences at baseline between groups for: age, marital status, education, weight, BMI, % body fat, or physical activity. No relation between changes in PA and weight.

Randomized controlled trial; 8 weeks Education to promote fruit and vegetable consumption.

Leermakers, 1998 62 men, 15–40 y.o., 80% white. Randomized controlled trial; Pittsburgh, PA, Home vs. clinic-based U.S. instruction and activities to prevent weight gain.

0 Study site. (weight and percent body fat)

Dietary Inﬂuences on Energy Balance

Simon, 1997 133 women at high risk for Randomized controlled trial; 3-month ↓ Dietary fat ↓ Mean percent caloric intake Detroit, Michigan developing breast cancer, age Combination of education, intensive intake to 15% of from fat (from 36 to 18%), this and Wichita, 18–67 yr, 89% Caucasian, 9% goal setting, evaluation, intervention, 12 total calories. change was maintained at 12 Kansas, U.S. African-American, 2% feedback and participant self- months followmonths, Hispanic. monitoring included both up ↓ Mean caloric intake at 12 intensive individual measurements months compared to baseline, counseling sessions and group ↑ Dietary ﬁber intake at 3 meetings – weight reduction months, increase was was not encouraged. maintained at 12 months.

Notes: ↑ = increase, ↓ = decrease, 0 = no signiﬁcant change relative to control group.

1

y.o. = years old; g = grams; svg = serving; mJ = millijoules; CVD = cardiovascular disease.

101

102

Obesity: Dietary and Developmental Inﬂuences

interventions were at least 1 year in duration and therefore, presumably, of sufﬁcient duration for an observed impact. Two of the studies (Gomel, 1993; Leermarkers, 1998), with interventions less than a year in duration (12 weeks and 4 months, respectively) still resulted in signiﬁcant reductions in BMI relative to controls. There was no apparent trend regarding the length of the intervention and impact on adiposity. Intensity of the intervention may be a more important predictor of impact on adiposity than duration. Study Sample Size and Impact on Adiposity Study populations varied in size from 62 to 2079. Only two of the studies included less than 100 subjects (Leermarkers, 1998; Djuric, 2002), and both of these smaller studies observed signiﬁcant reductions in BMI relative to controls. Therefore, although adiposity was not necessarily the primary outcome of interest for most of these studies, it appears that the studies had sufﬁcient power to detect reasonable changes in adiposity. Study Sample Demographics and Impact on Adiposity Study populations varied with regard to age, ethnicity, and gender. Four of the studies included adults ranging in age from their late teens to their late 60s or early 70s (Kristal, 2000; Rock, 2001; Simon, 1997; Cox, 1998). Three studies included only adults over 30 (Lanza, 2001; Boyd, 1997; Smith-Warner, 1993), and three included only adults under 50 (Jeffery, 1997 and1999; Djuric, 2002; Leermakers, 1998). One included adults in the 50 to 70 age range (Bhargava, 2002), and one included only adults aged 31–33 years (Gomel; 1993). The only apparent trend between age of the target population and impact on adiposity is that none of the studies that targeted the widest age range (late teens or early 20s to mid 60s or early 70s) had an impact on adiposity, whereas at least some of the studies in all the other age categories listed above did impact adiposity. Perhaps agespeciﬁc interventions are more effective than those that target broader age ranges. Half of the studies included adults of both genders, ﬁve included only women, and one included only men. Three of the ﬁve that included only women, two of the six that included both genders, and the one that included only men had an impact on adiposity. Therefore, there tends to be a slight trend toward higher success rates with studies that target one gender only. All of the study populations were predominantly white. The lowest reported percentage of white subjects was 54% (Bhargava, 2002). Half (six) of the study populations were over 85% white, and three studies did not report on the ethnicity of the participants. African-Americans and Hispanics were the most commonly included ethnic groups after whites. Therefore, the conclusions drawn here may not apply to nonwhite populations. In summary, of the 12 identiﬁed prevention intervention trials, one half (6) resulted in a reduction in body weight relative to controls (Lanza, 2001; Bhargava, 2002; Boyd, 1997; Gomel, 1993; Djuric, 2002; Leermakers, 1998). No trends were observed with regard to the success of these interventions in terms of sample size or length of intervention. Interventions that targeted a single gender or a narrower age range tended to be more effective in reducing adiposity. The nature of the interventions was very similar: all involved primarily education and counseling, and all were randomized controlled trials. The interventions did vary in terms of setting — clinic, community, home, and worksite. Setting may have accounted for differences in impact on adiposity as well as other aspects of the intervention quality and intensity that are not addressed here. Macronutrient Intake and Impact on Adiposity The purpose of this section of the review is to determine the extent to which intervention trials help to clarify the relationship between speciﬁc nutrients and energy balance or adiposity. Of the 12 studies identiﬁed, 10 targeted and/or reported on dietary fat, 6 reported on intakes of carbohydrate, 5 targeted and/or reported on ﬁber intake, 1 reported on sugar intake, and 1 reported on protein intake. No prevention intervention trials among adults were identiﬁed that reported on energy density. Trials that examined the intake of vitamins, minerals, and dairy are addressed in their respective sections and therefore are not addressed here.

Dietary Inﬂuences on Energy Balance

103

Dietary Fat

Of the 12 identiﬁed prevention trials, all but 2 included a reduction in dietary fat as one of the targeted behaviors. One of these (Gomel, 1993) did not identify the nature of the recommended diet. However, reducing dietary fat may have been a part of the intervention, as the purpose of the study was cardiovascular disease reduction. A reduction in dietary fat was observed in nearly all of the studies that targeted this behavior. One study did not measure the change in fat intake (Leermakers,1998), and another (Jeffery, 1997 and1999) did not detect a change in fat intake as a result of the intervention. Five of the ten studies that aimed to decrease fat intake observed a decrease in weight in subjects relative to controls regardless of whether weight maintenance was a stated objective. Those interventions that were successful in reducing weight included a 4-year trial involving over 2000 adults that was designed to reduce the recurrence of bowel polyps by also increasing fruits, vegetables, and dietary ﬁber (Lanza, 2001); a 1-year weight gain prevention trial with over 900 multiethnic women that aimed to also increase consumption of fruits, vegetables, and grains (Bhargava, 2002); a 2-year trial designed to reduce the area of radiologically dense breast tissue in 786 women with mammographic dysplasia that aimed to also increase complex carbohydrate intake (Boyd, 1997); a 1-year trial that aimed to reduce breast cancer risk by also decreasing soda, desserts, and breads (Djuric, 2002); and, ﬁnally, a 4-month study of 62 men designed to prevent weight gain by also increasing aerobic exercise (Leermarkers, 1998). It is interesting to note that, in the study by Djuric (2002), the intervention was not successful at impacting weight when an increase in fruits and vegetables was added to the targeted dietary behavior of reducing fat intake. Of the studies that targeted a reduction in dietary fat but did not observe a reduction in weight or BMI relative to controls, one (Kristal, 2000) observed a nonsigniﬁcant trend toward reduced BMI, one (Jeffery, 1997 and1999) failed to observe a signiﬁcant reduction in fat intake, and one (Simon, 1997) reported weight loss in both controls (5 lb) and intervention subjects (3 lb) that were not statistically different. An additional study that failed to observe a reduction in adiposity (Smith-Warner, 2000) aimed only to increase fruit and vegetable consumption but observed a decrease in dietary fat. Given that most of the studies were successful in reducing fat intake, approximately half of those were successful at reducing weight relative to controls, and none of the studies found that decreasing fat intake increased weight relative to controls, it can be concluded that reduction of dietary fat can be part of a successful strategy to prevent weight gain. Although the majority of interventions targeted behaviors in addition to dietary fat reduction, it appears that dietary fat is one of the components responsible for the favorable impact on weight. Most studies reported targeting a fairly limited number of dietary behaviors (two to three) and at least one (Djuric, 2002) found that decreased dietary fat was the behavior (as opposed to increased fruit and vegetable consumption) that was associated with the positive impact on weight. Only two of the studies targeted physical activity (Jeffery, 1997 and 1999; Leermarkers, 1998); the former did not impact weight, whereas the latter impacted weight but did not observe a change in physical activity or ﬁtness. Therefore, a physical activity component does not appear to account for observed program affects, although spontaneous changes in physical activity cannot be ruled out and were not controlled for in most analyses. The fact that one study (Simon, 1997) was successful in reducing fat intake substantively (from 36 to 18%) but did not observe an impact on weight, and another (Smith-Warner, 2000) incidentally resulted in a reduced fat intake but also did not impact weight, suggests that reductions in fat intake do not always favorably impact adiposity. It is worth repeating, however, that since the aim of many of these studies was to reduce fat intake for the purposes of reducing cardiovascular disease and cancer risk, the education components may not have encouraged and may even have discouraged a reduction in calorie intake. Indeed, one study (Smith-Warner, 2000) that was effective in reducing fat intake may not have impacted adiposity, because calorie intake was not affected. Therefore, the large number of studies that favorably impacted adiposity by reducing fat intake, even in the absence of weight control as an expressed goal, suggests that reducing dietary fat intake can spontaneously facilitate weight control.

104

Obesity: Dietary and Developmental Inﬂuences

Carbohydrate

None of the identiﬁed intervention studies targeted total carbohydrate intake, although one did aim to increase grain consumption (Bhargava, 2002), and another (Boyd, 2002) aimed to increase the intake of complex carbohydrates. Both of these studies observed reductions in the weight of subjects relative to controls. Several, although they did not speciﬁcally target carbohydrate intake, did measure and detect increases in percent of calories from carbohydrate relative to controls (Lanza, 2001; Rock, 2001; Bhargava, 2002; Boyd, 1997; Smith-Warner, 2000; Cox, 1998). Three of these six studies detected a signiﬁcant decrease in the weight of subjects relative to controls (Lanza, 2001; Rock, 2001; Bhargava, 2002). The three interventions that were successful in impacting weight all targeted a reduction in dietary fat in addition to an increase in ﬁber or complex carbohydrates. Two of them also targeted an increase in fruits and vegetables (Lanza, 2001; Bhargava, 2002). The two interventions (Smith-Warner, 2000; Cox, 1998) that only targeted fruit and vegetable intake (thereby resulting in increased percent carbohydrate intake), however, did not have a signiﬁcant impact on weight. It appears therefore that increases in percent calories from carbohydrate can, but do not always, have a protective effect on weight gain. This effect may be larger when the increase in percent calories from carbohydrate is a result of an increase in whole grains and complex carbohydrates, as opposed to fruits and vegetables. Sugar

Only one study examined sugar intake (Cox, 1998). This study, which aimed to increase fruit and vegetable intake, resulted in an increase in total sugar intake and did not have a signiﬁcant impact on the weight of subjects relative to controls. Based on this limited data, no conclusions can be drawn regarding sugar intake among adults and the prevention of weight gain. These studies do not, however, support the notion that an increase in percent calories from carbohydrate increases the risk of excess weight gain. Fiber

Five prevention trials among adults were identiﬁed that resulted in a signiﬁcant increase in ﬁber intake relative to controls (Lanza, 2001; Rock, 2001; Boyd, 1997; Smith-Warner, 2000; Simon, 1997). Two (Lanza, 2001; Boyd, 1997) detected a decrease in weight or BMI relative to controls. The others had no signiﬁcant impact. However, only two aimed to increase ﬁber intake per se (Lanza, 2001; Rock, 2001), one of which favorably impacted weight. Another aimed to increase complex carbohydrates (Boyd, 1997) and also favorably impacted weight. The one that aimed only to increase fruit and vegetable intake (Smith-Warner, 2000) and the one that only targeted a decrease in dietary fat (Simon, 1997) failed to signiﬁcantly impact weight relative to controls. It is difﬁcult to draw conclusions regarding the independent impact of dietary ﬁber on adiposity given the limited number of studies and the variability in the combination of target behaviors in each intervention. However, it appears that dietary ﬁber is sometimes but not always effective in preventing weight gain. Interventions may be more effective when they explicitly target ﬁber intake from a variety of sources, including whole grains. Protein

None of the prevention intervention trials identiﬁed for the purposes of this review aimed to inﬂuence protein intake. However, one study (Lanza, 2001) reported a signiﬁcant increase in protein intake among male subjects only (relative to controls). This study, which aimed to reduce recurrence of bowel polyps, lasted 4 years, included a large sample size of over 2000 individuals, and targeted reduction in dietary fat as well as increases in fruit, vegetable, and ﬁber intake. The intervention succeeded in increasing the intake of fruits, vegetables, ﬁber, carbohydrate, protein (in men only), calcium, whole grains, and low-fat alternatives while decreasing the intake of fat, high fat foods, red/processed meats, high-fat dairy, and desserts. Therefore, it is not possible to discern the independent effect of the change in protein intake. Although subjects experienced reductions in

Dietary Inﬂuences on Energy Balance

105

weight relative to controls, no conclusion can be drawn regarding the impact of protein on prevention of weight gain. Conclusions: The Impact of Changes in Macronutrient Intake on Adiposity in Adults Prevention trials that involve the promotion and support of a variety of changes in dietary behaviors and physical activity are not ideal for determining the independent effect of single behaviors on adiposity. None of the adult studies that were examined conducted analyses to distinguish the independent impact of the behavior changes on adiposity. Furthermore, many of these studies did not identify a reduction or maintenance of weight as a primary outcome targeted by the intervention; therefore, a spontaneous impact on weight might be less likely than if weight per se was addressed in the intervention design. Furthermore, these studies were relatively short term and therefore shed little light regarding whether the dietary changes and the impact on adiposity can be maintained in the long run. In contrast to the studies among children, the adult studies were successful, for the most part, in effecting the desired dietary changes. These desired changes were consistent with a heart healthy “prudent” diet in all the studies examined, although each study targeted distinct combinations of dietary changes including primarily reductions in dietary fat, increases in dietary ﬁber, and/or increases in fruit and vegetable intake. Those studies that were the most effective in impacting adiposity were those that targeted dietary fat and those that resulted in increased ﬁber, especially from complex carbohydrates such as whole grains. Not all studies, however, that achieved these changes had an impact on adiposity, suggesting that these dietary changes facilitate but do not guarantee prevention of weight gain. Given that none of these studies reported an increase in adiposity relative to controls, it can be concluded that targeted dietary behaviors (i.e., decreases in dietary fat, increases in fruits and vegetables, and increases in dietary ﬁber) do not increase the risk of weight gain. Prevention Trials — Macronutrients — Children Overall Study Characteristics and Outcomes Fifteen prevention trials involving children were identiﬁed that met the inclusion criteria (see methods section) (Table 4.12). As described previously, prevention trials are deﬁned as interventions whose intent is to prevent increases in adiposity and/or prevent nutrition or physical-activity related chronic disease. Any trial that explicitly stated weight reduction as a goal or imposed calorie limits at levels clearly intended to invoke weight loss was excluded. Therefore, only 5 (Muller, 2001; Teufel, 1998; Sahota, 2001; Robinson 1999; Epstein, 2001) of the 15 studies mentioned healthy weight as a targeted outcome. At least one (Luepker, 1996; Webber, 1996; Nader, 1999), the CATCH program, was speciﬁcally designed not to impact adiposity. All the others aimed to improve health but did not speciﬁcally mention adiposity as a primary outcome of interest. Therefore, when interpreting the results of these studies, it should be kept in mind that they may not have been adequately designed to control weight gain. Furthermore, the investigators may not have calculated their sample size to ensure adequate power to detect differences in changes in adiposity. Although not a criterion for inclusion, all but two of the studies were randomized controlled trials. One was not randomized, nor did it include a control group (Teufel, 1998), and the other was randomized but did not include a control group (Epstein, 2001). Most of the programs were school based. The two that were not included a clinic-based, family-oriented counseling program (DISC Writing Group, 1995) and a parent-focused behavioral intervention (Epstein, 2001). About half (8) of the 15 interventions had a positive impact on some measure of adiposity relative to controls. Four of the interventions favorably impacted either BMI and skinfolds or adiposity based on a combined measure of BMI and skinfolds, although two detected the effect

106

TABLE 4.12 Prevention Trials among Children that Reported Impact on Adiposity. Author, Year, Study Name, Location

Study Population, Sample Size, Age, Ethnicity

Study Design and Strategies

Length of Intervention and Timing of F/U Measure

CHILDREN 3 school years Randomized controlled 5106 children (school Luepker, 1996 trial; School-based Child and Adolescent Trial level data), 4019 children (individual level multicomponent CVD for Cardiovascular risk reduction program data), grades 3–5; 69% Health (CATCH) (Education, PE, School San Diego, CA; Houston, white, 13% Africanlunch, and Home American, 14% TX; New Orleans, LA, programs, for half of Hispanic. Minneapolis, MN, U.S. families).

Targeted ↓ Dietary fat ↑ Physical activity.

↓ Dietary fat ↑ Physical activity.

Reported Change among Subjects School level: ↓ Fat (%) in school lunch menus ↓ Dietary energy in school lunch menus ↑ Physical activity intensity in PE Individual level: ↓ Rise in dietary energy (MJ/d) ↓ Dietary fat (%), ↑ CHO intake (%), 0 Change in Protein intake (%) ↑ Vigorous physical activity. ↓ Dietary fat ↑ Vigorous physical activity. (Note: details not provided in this paper on diet and PA.)

↓ Dietary fat ↑ Physical activity.

↓ Dietary fat (%) ↑ CHO (%) 0 Change protein (%) ↑ Vigorous physical activity (Note: ↓ dietary energy at end of 3-yr intervention gone by 3-yr f/u.)

↓ TV viewing, ↓ Intake of high-fat foods, ↑ Fruit and vegetable intake ↑ Moderate and vigorous physical activity.

↓ TV (h/d) ↑ Fruit and vegetable intake (svgs/d; girls only) ↓ Energy increment (J/d; girls only) 0 Change in fat intake (%) 0 Change in moderate/vigorous physical activity.

Results: Impact on Measure of Adiposity Direction of Change Relative to Controls1

Control Variables

Comments

No signiﬁcant differences at School level: baseline between groups at Observation days within semester and lessons within school level for: observation days, Location of environmental, behavioral, psychosocial, and risk factor the lesson, Specialty of the data. teacher Not explicitly stated in paper, Individual level: but prevention of obesity was Baseline value, Gender, Ethnicity, CATCH ﬁeld site, not a goal of CATCH, but Random effect of school with rather aimed to avoid growth retardation. site and intervention group. Not explicitly stated in paper, Baseline value, Gender, 0 but prevention of obesity was (BMI for white and Ethnicity, age (for BMI), not a goal of CATCH, but Hispanic children) CATCH ﬁeld site, Random effect of school with site and rather aimed to avoid growth ↑ retardation. intervention group, (BMI in AfricanAmerican children) Interaction. 0 (TSF or SSF) 0 Age, Site, Gender, Ethnicity, Not explicitly stated in paper, (BMI) Intraclass correlation within but prevention of obesity was 0 school and student, Gender x not a goal of CATCH, but (TSF or SSF) rather aimed to avoid growth ethnicity interaction. retardation. 0 (BMI) 0 (TSF or SSF)

↓ (Obesity based on BMI + TSF >85th percentile, girls only)

Baseline value of behavioral No signiﬁcant differences at variable, Intervention status, baseline between groups for: age, anthropometric, diet, PA, Randomization pairs, or sociodemographic Gender, Age, Ethnicity, characteristics; higher Baseline obesity, BMI and African-American girls and TSF (Other variables tested Hispanic boys in control but not included in ﬁnal schools. model). Only TV viewing associated with reduction of obesity.

Obesity: Dietary and Developmental Inﬂuences

2.5 years 4019 children, grades 3-5, Randomized controlled Webber, 1996 trial; School-based 69% white, 13% CATCH San Diego, CA; Houston, African-American, 14% multi-component CVD risk reduction program Hispanic. TX; New Orleans, LA, (Education, PE, School Minneapolis, MN, U.S. lunch, and Home programs, for half of families). 3 years follow-up 3714 children, grades 6–8 Randomized controlled Nader, 1999 (after 3-yr (73% of original cohort). trial; School-based CATCH intervention) multicomponent CVD San Diego, CA; Houston, TX; New Orleans, LA, risk reduction program (Education, PE, School Minneapolis, MN, U.S. lunch, and Home programs, for half of families). 2 school years 1295 children, grades 6–8, Randomized controlled Gortmaker, 1999 63-69% white, 11–15% trial; School-based Planet Health health education African-American, Boston, MA, U.S. program. 11–16% Hispanic.

Relevant Targeted Behaviors

8 weeks Randomized controlled trial; School-based CVD risk reduction education program. Subsample: Group 1: Populationbased, taught to all children in classroom; Group 2: High-risk-based intervention, taught to high-risk children in small groups.

Heart healthy eating (↓ fat) ↑ Physical activity.

1,209 children, 6–13 y.o., Nonrandomized controlled 1.5-year intervention ↑ Health knowledge, Resnicow, 1992 ↑ Fiber and ↓ fat content and predominantly Hispanic trial; School-based Know Your Body of foods served in comprehensive school 3-year f/u measure population. comprehensive school school, health education health education ↑ Vegetable and heart program (classroom program healthy indices. curriculum, school-wide New York, New York, U.S. health activities and environmental modiﬁcations).

Sallis, 2003 Middle-School Physical Activity and Nutrition (M-SPAN) study San Diego County, CA, U.S.

2 school years Mean of 1109 students per Randomized controlled trial; School-based school; individual multicomponent surveys to random program (School food samples at baseline (1678) and end of study service changes, (1434), grades 6–8, 55% Improved daily PE quality and promotion of white, 45% nonwhite. physical activity throughout school day, Parent education and social marketing).

↓ Dietary fat ↑ Physical activity

↑ Knowledge on heart healthy eating, but not physical activity ↑ Physical activity score in school, but not individual level analysis ↑ Aerobic power in individual, but not school level analysis Subsample: ↑ Knowledge on heart healthy eating in Gps 1 and 2 vs. control (school and individual level analysis) ↑ Knowledge on physical activity in Gp 1 vs. Gp 2 and control (school and individual level analysis) 0 Change in eating a high-fat diet ↑ Physical activity in Gp 1 vs. control (individual, but not school-level analysis).

0 Signiﬁcant differences in dietary indices, ↑ Health knowledge at 3 year f/u, ↑ Number of servings of vegetables and heart healthy foods, ↓ Number of servings of meat and desserts.

School level: 0 Change in fat intake (g) at school ↑ Physical activity at school (boys only) Individual level: 0 Change in fatty foods (tally/d of 32 common high-fat foods/beverages) 0 Change in moderate to vigorous physical activity 0 Change in sedentary activity.

Region of state, Urban or rural No signiﬁcant differences at 0 baseline between groups for: locale, Gender, Ethnicity, (BMI) Parental education, Grade in gender, ethnicity, grade, age, ↓ and parental education. school, Baseline value (TSF and SSF in Signiﬁcantly fewer white (except for knowledge). individual, but not children were in the control school-level group than Groups 1 and 2. A analysis) greater proportion of children ↓ in the coastal area were in (Obesity based on Group 1 while the majority of BMI + TSF ≥ 85th children in the piedmont percentile, girls only region were in Group 2 or the in subsample) control.

0 (BMI)

Age, Ethnicity, Gender, Baseline values.

Gender. ↓ (self-reported BMI; boys only)

Dietary Inﬂuences on Energy Balance

1274 children, grades 3–4 Harrell, 1996, 1998 (7–11 y.o.), 74% white, Cardiovascular Health in 20% African-American. Children (CHIC) study Subsample of 422 students North Carolina, U.S. with ≥ 2 CVD risk factors (low aerobic power + high serum cholesterol or obesity), 65–76% white, 20–25% African-American

At baseline, intervention students did not signiﬁcantly differ with regard to sex, total cholesterol, systolic blood pressure, BMI, health attitudes, and self-efﬁcacy. They were signiﬁcantly younger, more likely to be Hispanic, and had signiﬁcantly lower health knowledge and fruit intake scores than control students.

No signiﬁcant differences between groups at baseline on school characteristics or outcome variables.

107

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TABLE 4.12 (CONTINUED) 9 months

↑ Consumption of fruit, vegetables, whole-grain bread and cereal relative to other foods, ↓ Consumption of fatty, sugary and salty foods relative to other foods.

Vandongen, 1995 West Australia, AU

869 children, 10–12 y.o.; Randomized controlled trial; School-based considered to be representative sample of nutrition and ﬁtness program to improve the socioeconomic mix cardiovascular risk of the community in factors. West Australia; race/ethnicity was not speciﬁed.

Burke, 1998 Western Australia, AU

Randomized Controlled 20-week (2 school ↑ Duration and frequency 720 children, of physical activity, 11 y.o; race/ethnicity not Trial; School and home- term) intervention and 6 months f/u ↓ Consumption of fat, based physical speciﬁed. sugar and salt, enrichment program for measure ↑ Fiber intake. children at higher risk of cardiovascular disease.

Gender, Baseline values. ↓ (Triceps skinfolds, boys and girls) 0 (Subscapular skinfolds, boys and girls) 0 (% Body Fat, boys and girls) 0 (BMI, boys and girls)

Gender, Baseline values. 0 ↑ Physical ﬁtness (boys and (BMI, boys and girls) girls), ↓ 0 Change in physical activity (Subscapular (boys and girls), Skinfolds, girls only) ↓ TV watching (boys only), ↓ 0 Dietary change (boys and girls). (Triceps Skinfolds, boys and girls)

↓ Total fat (%) ↓ Dietary fat Energy and other nutrients ↓ Total energy (kJ/d) at RDA ↑ Protein (%) ↑ Carbohydrate (%) 0 Change in diet Ca, Zn, Fe vitamins A, C 0 change in serum ferritin, Zn, retinol.

Gender, Baseline value. 0 (BMI) 0 (Weight) 0 (Sum TSF, SSF, and Suprailiac skinfold) 0 (WHR)

At baseline, there were no signiﬁcant differences in dietary variables, time spent in leisure-time physical activity or hours of TV watching.

No signiﬁcant differences at baseline between groups for: age, gender (?; appears so, but not explicitly stated), anthropometry, blood lipid levels, and blood pressure. Small differences in dietary intake, with intervention group having slightly lower % energy from PUFA and slightly higher intakes of vitamin B6 and Zn. Intervention group had slightly higher proportion with household income <$20K.

Obesity: Dietary and Developmental Inﬂuences

3 years The Writing Group for the 663 children with elevated Randomized controlled trial; Clinic-based LDL cholesterol, 8–10 DISC Collaborative yr, race/ethnicity not personalized, familyResearch Group, 1995 speciﬁed. oriented, dietary The Dietary Intervention counseling program to Study in Children reduce LDL cholesterol. Baltimore, MD; Chicago, IL; Iowa City, IA; Newark, NJ; New Orleans, LA; Portland, OR, U.S.

↓ % Total energy from sugar (boys only), ↓ % Total energy from fat (girls only), ↓ % Total energy from saturated fat (girls only), 0 Change in total energy (boys and girls), ↑ % Total energy from protein (boys only), ↑ Fiber intake - g/d (boys and girls).

↑ Fruit and vegetable Nonrandomized controlled 3 month and 1 year ↑ Fruit and vegetable 297 children, consumption, consumption, 5–7 y.o.; race/ethnicity not trial; Combined school- f/u measure ↓ Intake of high fat foods, ↑ Consumption of low-fat foods, based and family-based speciﬁed. ↑ Physical activity to at ↑ Physical activity, which was program aimed to associated with ↓ TV-watching. least 1 h/d, prevent weight gain ↓ TV consumption to less and/or reduce body that 1 h/d. weight.

↓ (Tricep Skinfold Thickness after 1 year)

Schools matched for sociodemographic variables.

— 292 children, grades 9-12, Nonrandomized, nonTeufel, 1998 Native American. controlled trial; and School-based Ritenbaugh multidimensional 2003 diabetes risk factor Zuni Diabetes Program reduction program Zuni Indian Reservation, (Student education, New Mexico, U.S. School food service changes, School Wellness Center, Supportive social networks).

3 school years

12 months Randomized controlled trial; School-based, multidisciplinary intervention to reduce risk factors for obesity.

Sahota, 2001 Active Programme Promoting Lifestyle in Schools (APPLES) Leeds, UK

203–303 children, 8–10 y.o., some ethnic minority children in sample.

Robinson, 1999 San Jose, CA, U.S.

1 school year Randomized controlled 192 3rd and 4th grade trial; School-based students; race/ethnicity educational curriculum not speciﬁed. to assess effects of reducing television, video tape and video game use on changes in adiposity, physical activity and dietary intake (also used electronic television time manager).

↓ Sweetened beverages ↓ Sweetened beverages (% ↑ Fiber by increasing fruit beverage intake) 0 Change in ﬁber intake (g/d) and vegetables ↑ Physical ﬁtness. ↑ Physical ﬁtness.

Inﬂuence diet and physical activity behaviors, ↑ Consumption of fruits and vegetables, ↓ Consumption of foods high in fat, ↓ Consumption of foods and drinks high in sugars.

↑ Vegetable intake, ↓ Fruit intake in obese children, ↑ Intake of foods and drinks high in sugar in overweight children, ↑ Sedentary behavior in the overweight children.

↓ Television, videotape and video game use.

↓ Television viewing, ↓ Frequency of children eating meals in a room with a television turned on, 0 Signiﬁcant change in servings of high-fat foods, 0 Effect on reports of children’s physical activity levels or performance.

0 (BMI)

0 (BMI)

No change in BMI conﬁrmed in ﬁnal publication (Ritenbaugh 2003), which included 200 11th–12th grade children.

Gender, Age, Baseline BMI.

Schools matched on ↓ sociodemographics and (BMI, Triceps Skinfold Thickness, scholastics. Waist Circumference and Hip Circumference)

Dietary Inﬂuences on Energy Balance

Muller, 2001 Kiel Obesity Prevention Study (KOPS) Kiel, Germany

No signiﬁcant differences were found between the intervention and comparison pupils for any of the measures at baseline.

Intervention and control participants were comparable in age, sex, mean number of TV in home, mean no. of video game players, % of children with TV in bedroom and all anthropometric measurements.

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110

TABLE 4.12 (CONTINUED) 3 school years Randomized controlled 76 children from intervention schools and trial; School-based 63 children from control multicomponent schools; children were in program (teacherdelivered health and ﬁrst grade at the nutrition component, beginning of the physical ﬁtness and intervention; activity components and race/ethnicity not parental involvement). speciﬁed.

Donnelly, 1996 rural Nebraska, U.S.

44 intervention students Nonrandomized controlled 2 school years and 64 controls students trial; School-based multicomponent from grades 3–5, program (nutrition subsample of 11 intervention subjects and education, modiﬁed 25 control subjects with school lunches and enhanced physical ≥22% body fat; activity program). race/ethnicity not speciﬁed.

Dietary target behaviors School level: 0 signiﬁcant change in intake of not speciﬁed. total energy, total fat (g), ↑ Leisure-time moderate monounsaturated fat (g), to vigorous physical polyunsaturated fat (g), activity, ↑ Physical ﬁtness indices, saturated fat (g), trans fat (g), ↑ Children’s and parent’s cholesterol (mg), protein (g), carbohydrate (g) or ﬁber (g), health knowledge. ↑ Leisure-time moderate-tovigorous physical activity (h/week), ↑ physical ﬁtness indices, ↑ children’s health knowledge and 0 change in parent’s health knowledge. 0 Signiﬁcant change in total ↓ Intake of fat, energy, % kcal from fat, ↓ Intake of cholesterol, carbohydrate, protein, or ﬁber, ↓ Intake of sodium, ↓ Intake of sodium. ↑ Intake of ﬁber, ↑ Physical ﬁtness, and increase knowledge and awareness of diet in health.

↑ Fruit and vegetable intake 6-month intervention ↑ Fruit and vegetable Epstein, 2001 27 intervention families Randomized, (svgs/d) and 1-year follow- intake Childhood Weight Control (at least 1 obesity [BMI noncontrolled trial; ↓ High-fat and high-sugar ↓ High-fat and high-sugar foods up measure and Prevention Program >85th percentile] parent Parent-focused (svgs/d). food intake. behavioral intervention Buffalo, NY, U.S. + a non-obesity [BMI on parent and child <85th percentile] child eating changes, and 6–11 y.o. weight control treatment for parents.

Change in height, Sex, Parental At baseline, intervention group ↓ had signiﬁcantly higher (BMI and Suprailiac educational group, and average values of TC and LDL Baseline values. Skinfold) and lower mean 20-meter shuttle run test measurement, health knowledge scores and parental health scores compared to control group.

Schools were matched for 0 ethnicity/SES and baseline (Weight, BMI and Body Fat % [under characteristics (grade, ht, wt, BMI, mile run time). water weighing]) 0 (Attenuation of Obesity [subjects with body fat of ≥22%]) 0 (Percentage of overweight)

No signiﬁcant differences at baseline between groups for: gender, age, weight, % OW, family history of obesity and chronic diseases (except for more hypertension in families in fat/sugar group), food habits, child feeding practices, conﬁdence in making choices. Correlations showed no differences by age or gender in outcomes.

Notes: ↑ = increase, ↓ = decrease, 0 = no signiﬁcant change relative to control group.

1

PA = physical activity; CHO = carbohydrate; CVD = cardiovascular disease; TSF = triceps skinfolds; SSF = subscapular skinfolds; TC = total cholesterol; LDL = low density cholesterol; PE = physical education; OW = overweight; SES = socioeconomic status. f/u = follow-up; d = day; g = grams; svg = serving; gp = group; h = hour; ht = height; wt = weight; y.o. = years old; J = joules; mJ = millijoules.

Obesity: Dietary and Developmental Inﬂuences

Manios, 1999 Know Your Body Crete, Greece

Dietary Inﬂuences on Energy Balance

111

among girls only. Another program (Sallis, 2003) reported a favorable impact on self-reported BMI among boys only. The other three programs only detected a favorable impact on skinfolds. Therefore, although more than half detected some impact, the results were mixed, depending on the measure of adiposity, and often applied to only one gender. Intervention Design and Impact on Adiposity Neither of the interventions that were not school based impacted adiposity among the children studied. Given the limited number (two) that were not school based, no conclusions can be drawn regarding effectiveness based on program venue. Most (ten) of the school-based programs were multicomponent, including not only student education but also food service changes, changes in PE, parent/home-based interventions, and/or other environmental changes (Luepker, 1996; Webber, 1996; Nader, 1999; Resnicow, 1992; Sallis, 2003; Burke, 1998; Muller, 2001; Teufel, 1998; Manios, 1999; Donnelly 1996; Vandongen, 1995; Sahota, 2001). Three of the school-based programs involved only education and behavior change activities (Gortmaker, 1999; Harrel, 1996 and 1998; Robinson 1999). The programs that impacted BMI, or adiposity based on BMI and skinfolds, were nearly equally divided between education-only and multicomponent; three were education-only interventions, and two were multicomponent. Of the three programs that impacted skinfolds only, all were multicomponent school-based interventions. Therefore, it appears that both multicomponent and educationonly school-based programs can be effective in preventing increases in adiposity; education-only interventions may be more likely to impact BMI. However, despite these trends, there are not enough comparable studies in any given category to draw deﬁnitive conclusions regarding the impact of intervention design on outcome. Study Length and Impact on Adiposity The studies varied in length from 8 weeks to 3 years. Several included follow-up measures taken anywhere from 6 months to 3 years postintervention. The studies that impacted BMI or adiposity based on a combined BMI-skinfold calculation varied from 8 weeks to 3 years in length, but only one was less than 1 year in length. On the other hand, those that impacted only skinfolds tended to be shorter (3–9 months in length). None of those that impacted BMI or adiposity based on BMI and skinfolds reported on postintervention follow-up measures, and most of those that only impacted triceps skinfolds did. Overall, there did not appear to be a trend in terms of study impact on adiposity and length of the program. Age of Study Population and Impact on Adiposity The age of the children studied varied within the elementary to middle school age ranges (i.e., 5–14 years of age). None of the studies included preschool or high school age children. Impact on adiposity was not limited to any age group. Those studies detecting an impact on BMI or adiposity based on BMI and skinfolds targeted children in approximately the 6–9, 8–10, and 11–14 year age ranges. Those that impacted skinfolds only did so among 5–7, 10–12, and 11-year-olds. It is interesting to note that the two studies to target the widest age ranges (6–11 years and 6–13 years) did not observe any signiﬁcant impact on adiposity. Nutrient Intake and Impact on Adiposity Most of the interventions targeted multiple behaviors. Seven targeted changes in the intake of multiple nutrients or foods and physical activity. Four targeted only dietary changes. Three targeted only dietary fat and physical activity, and one targeted only a reduction in TV viewing. Only one, however, conducted an analysis to determine the differential impact of changes in these behaviors on adiposity (and found it was due exclusively to a reduction in TV viewing). Given the variety of behavioral changes that were targeted, in addition to the variety of ages, intervention designs,

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and study durations, it is not possible to discern which of these factors, much less which target behavior, was responsible for the impact (or lack thereof) on adiposity. However, when viewed as a whole, these studies provide some evidence as to whether changes in speciﬁc nutrients appear to inﬂuence a program’s impact. Dietary Fat

All but two of the studies aimed to reduce the intake of dietary fat, but most were not successful in effecting a detectable change in fat intake. Of these 13 studies, only 4 actually detected a change in dietary fat (percent of calories) intake during the course of the intervention, 2 did not measure fat intake, and the rest did not detect a signiﬁcant change. Of the 13 studies that targeted fat intake, 7 impacted some measure of adiposity, suggesting that reducing fat intake may contribute to a positive program outcome. But of the four that detected lower fat intakes among the intervention groups, only one also observed a reduction in adiposity, suggesting that a reduction in fat intake does not necessarily prevent excess weight gain. Likewise, most of the studies that favorably impacted adiposity failed to detect a change in dietary intake of fat. The one study that investigated the independent effect of different program components found that all of the impact on adiposity was a result of reductions in TV viewing. Given that almost all of the studies included dietary fat reduction as part of their intervention but few were successful in detecting a change in fat intake, interpretation of these results is problematic. One can only conclude that either most programs are not successful in reducing fat intake or that the dietary instruments are unable to detect the changes that occur — and a reduction in fat intake does not always result in the prevention of increases in adiposity. It is worth mentioning, however, that these studies support the notion that decreases in fat intake (as a percentage of calories) either protects against obesity or has no effect. The evidence does not support the notion that reducing fat intake increases the risk of obesity. Carbohydrate

As might be expected, none of the studies speciﬁcally aimed to change the intake of total carbohydrate, although a few interventions aimed to decrease certain high-carbohydrate foods. Generally speaking, however, when fat intake decreases (as a percentage of calories), carbohydrate intake increases; therefore, our conclusions regarding the impact of carbohydrate intake could be the reverse of those regarding fat intake: a relative increase in carbohydrate intake (as a percentage of calories) is sometimes, but not always, associated with lower adiposity. However, the only three studies that actually measured and detected a change in carbohydrate intake relative to controls (Luepker, 1996; Nader, 1999; DISC Writing Group, 1995) did not observe any impact of the intervention on any measure of adiposity. Again, there are many possible reasons why these interventions were not effective. Therefore, it is not possible to come to a conclusion regarding the independent impact of carbohydrate intake among children based on these results. Fiber

Five programs speciﬁcally targeted an increase in ﬁber intake (Resnicow, 1992; Vandongen, 1995; Burke, 1998; Donnelly; Teufel). Only one, however, measured and detected an increase in ﬁber intake among subjects compared to controls (Vandongen, 1995). Therefore, once again, it is hard to arrive at a conclusion regarding the impact of a reduction in ﬁber intake, when most of the studies were unable to produce a detectable change. Although two of these ﬁve studies did observe an impact on adiposity (Vandogen, 1995; Burke, 1998), this impact was observed only with regard to skinfolds and not BMI. It is also notable that the only intervention to attempt an increase in ﬁber intake solely through an increase in fruit and vegetable intake failed to have an impact on adiposity. Half of those that attempted increases in ﬁber through multiple food groups, and the only one to speciﬁcally mention an increase in whole grains, did have an impact on adiposity. All of these programs aimed to reduce fat and/or sugar as well as increasing ﬁber, and two of them

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113

also targeted physical activity (Burke, 1998; Donnelly, 1996), again making it difﬁcult to attribute program impact (or lack thereof) to any one of the targeted behaviors. Therefore, we can only conclude that increases in ﬁber intake may be part of an effective strategy to prevent overweight, and it may be more effective to increase ﬁber through multiple food groups and not solely through an increase in fruits and vegetables. Sugar

Four studies were identiﬁed that speciﬁcally targeted the reduction of sugar or foods high in sugar among children (Vandongen, 1995; Burke, 1998; Sahota, 2001; Epstein 2001). Three of these were school-based randomized controlled trials, and the other (Epstein, 2001) was a family-focused behavior intervention and did not include a control group. Half of these studies demonstrated an impact on adiposity, but only with regard to skinfolds and not BMI. The only study that detected a decrease in the intake high sugar foods did not observe any impact on adiposity (Epstein, 2001). Another study (Sahota, 2001) actually detected an increase in the intake of high-sugar foods and beverages relative to controls and observed no impact on BMI. All of these interventions targeted multiple dietary changes, and some also included physical activity, making it hard to determine the independent effect of sugar intake on adiposity. Again, it appears that a reduction in sugar intake can be part of an effective strategy to prevent increases in adiposity, but strategies reported on to date have been largely ineffective in reducing sugar intake. Protein

Not surprisingly, none of the identiﬁed studies targeted protein intake, however two studies reported that protein intake increased in subjects relative to controls (Vandongen, 1995; DISC Writing Group, 1995). The study by Vandongen (1995) observed this effect only among boys and was a schoolbased randomized controlled trial that also aimed to increase the intake of fruits, vegetables, and whole grains and decrease intake of sugary, fatty, and salty foods. The other was a clinic-based counseling program that primarily targeted a reduction in fat intake. Both aimed to reduce cardiovascular risk factors. The study by Vandongen (1995) observed a decrease in triceps skinfolds among subject relative to controls but observed no affect on BMI. The study by the DISC Writing Group (1995) observed no impact on any measure of adiposity. Based on the limitations of these studies, discussed previously, for determining the independent effect of any given dietary change, no conclusions can be drawn. However, these limited results suggest that protein intake has either a neutral or protective effect in preventing increases in adiposity among children. Conclusions: The Impact of Changes in Macronutrient Intake on Adiposity in Children Unfortunately, the prevention interventions identiﬁed for the purposes of this review shed little light on the role of changes in nutrient intake on adiposity. Given the multiple dietary changes targeted, the different intervention designs employed, and age groups studied, it is not possible to separate out the effect of the speciﬁc nutrient changes. Furthermore, the interventions described herein were largely ineffective in producing detectable changes in the intake of the nutrients described above. All of the studies targeted changes in nutrient intake that are consistent with a heart-healthy “prudent” diet. For example, almost all attempted to reduce the intake of fat; many attempted to reduce sugar and salt intake and/or increase the intake of fruits, vegetables, and whole grains. Given that about half the programs observed decreases in adiposity of subjects relative to controls, and only one subpopulation in one study experienced an increase in adiposity relative to controls, it appears that the “prudent” diet can protect against increases in adiposity among children. However, only 5 of 15 studies detected a positive impact when BMI, or adiposity based on BMI, was the measure employed. The others only detected the impact based on skinfolds. Since most of the studies were school based, it may be that school-based programs are an important component but

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not sufﬁcient to protect against increases in adiposity. There may be other aspects of the intervention designs that limit their effectiveness in preventing excess weight gain, such as behavior change models employed, and intensity and/or quality of the intervention. Interventions that speciﬁcally target adiposity may also be more effective. Speciﬁcally, interventions that include a reduction in TV viewing and/or increases in physical activity appear to be the most effective in impacting adiposity in children (Gortmaker, 1999; Harrel, 1996 and 1998; Sallis, 2003; Robinson, 1999; Manios 1999). Given the enormity of the problem, and the limited resources available, it should be a priority for future prevention trials to determine which program components are most critical for success.

MACRONUTRIENTS: SUMMARY

AND

CONCLUSIONS

When examining the evidence to determine the role of macronutrient composition of the diet in energy imbalance, a paradox becomes evident. It appears that absolute intakes of both carbohydrate and fat have increased since the mid 1970s, when obesity rates began to rise steeply, but the increase in carbohydrate intake has been more dramatic. Speciﬁcally, the percentage of calories from carbohydrate has increased, and the percentage of calories from fat has decreased over this time period. On the other hand, no observational studies meeting our criteria were identiﬁed that found negative associations between fat intake and adiposity or positive associations between total carbohydrate intake and adiposity. To the contrary, nearly all of the studies conducted among adults, and nearly half of the studies conducted among children, found that total carbohydrate intake was negatively associated with adiposity. The studies that examined the association between dietary fat intake and adiposity are somewhat less consistent but, nevertheless, more than half of the studies among adults, and about 25% of those among children, found positive associations between these two variables. Likewise, the preponderance of experimental ﬁndings would suggest that fat is more likely to be obesigenic than carbohydrate by nature of its contribution to energy density and palatability, as well as its efﬁcient utilization and storage. Experts have recently argued that carbohydrate is not a homogenous category, and therefore the different components of carbohydrate should be examined separately. Fiber, complex carbohydrates, and sugars have been of particular interest. However, along with total carbohydrate, the per capita intake of each these categories of carbohydrate has also risen. Fiber intake actually increased to a greater degree than did total sugar intake since the mid-1970s. Observational studies, on the other hand, showed that ﬁber was consistently associated with lower adiposity among adults and to a somewhat lesser degree of consistency among children. Experimental studies also provide consistent support for the hypothesis that higher intakes of ﬁber would be protective. The story with sugars is more complex. Although experimental evidence suggests that simple carbohydrates are more readily overconsumed than complex carbohydrates, the evidence is more controversial when comparing sugars to fats. The preponderance of evidence indicates that fats are more likely to lead to passive overconsumption than sugars. Furthermore, observational studies examining the association between sugar intake and adiposity are limited and difﬁcult to compare given the variable deﬁnitions of sugar used. However, the limited data available to date suggest that total sugar intake is either not associated or negatively associated with adiposity. The ﬁndings from observational studies relating added sugar intake to adiposity are so limited and mixed that no conclusion can be drawn. The glycemic index or glycemic load offers another option for the classiﬁcation of carbohydratecontaining foods that may inﬂuence satiety and energy balance. However, these indices are not merely a function of total carbohydrate or the relative levels of ﬁber, sugar, or complex carbohydrate content of a food and cannot be used as a proxy for such. For example, some sugars, such as fructose, have a lower glycemic index than some foods with high complex carbohydrate content, such as potatoes. Data relating glycemic index or load to obesity are largely limited to short-term

Dietary Inﬂuences on Energy Balance

115

experimental and weight loss studies. Furthermore, dietary intakes compared in these studies often differ in several aspects, including ﬁber and protein content. Although the impact of glycemic index on energy balance was not thoroughly reviewed for the purposes of this document, this is an important area for future research. The intervention trials examined for the purposes of this chapter failed to shed much light on the controversy regarding the relative inﬂuence of fat and carbohydrate intake on the prevention of obesity. All of the prevention trials evaluated the impact of diets consistent with the “prudent” diet, most attempted to reduce fat intake among subjects, and none attempted to increase fat or protein intake. The results of these trials suggest that reducing dietary fat, increasing percent total carbohydrate, increasing dietary ﬁber and complex carbohydrate, as well as decreasing added sugar intake can be components of successful weight gain prevention strategies, but these dietary changes do not always favorably impact adiposity. Although some of the trials failed to have an impact on adiposity, none of the trials examined (except for one school-based program) resulted in higher gains in adiposity relative to the control group. Protein is also perplexing in relation to obesity. Absolute intakes of protein have increased during the same time period as the recent steep rise in prevalence of obesity. But the percentage of calories from protein has remained constant. The sources of protein in the diets of Americans have also varied considerably in recent decades, as evidenced by large increases in poultry and cheese intake and reductions in the intake of red meats. Although protein has the highest satiety value of the macronutrients, it is not consistently associated with lower calorie intakes and may be related to undesirably accelerated growth in early childhood and perhaps during puberty. Although observational studies have generally found a positive relationship between adiposity and protein intake, the insufﬁcient number and quality of studies and the inconsistency of design preclude making a ﬁrm conclusion. Although weight loss studies often intentionally manipulate protein intake (with mixed results), no prevention studies were identiﬁed that targeted protein intake. The few studies that compared the protein intake of controls and subjects in prevention trials found that protein intakes either did not differ signiﬁcantly or were higher among the group that gained less weight. The role of protein in the development of obesity remains inconclusive. The studies examined here suggest that the role of this macronutrient may be signiﬁcant, albeit complex, and, therefore, further study is merited. In summary, the only line of evidence suggesting the higher relative intakes of fat and lower relative intakes of carbohydrate are protective against the development of obesity come from secular trends in the consumption of these nutrients (Table 4.13). The preponderance of the other lines of evidence suggest that higher relative intakes of carbohydrate and lower intakes of fat are either protective or have no impact on adiposity. The evidence supporting the protective intake of ﬁber is remarkably consistent at least among adults, and the evidence with respect to intake of sugars, especially added sugars, remains inconclusive. Protein intake may have a dual effect: a minimum level may be required to promote satiety, but excessive intakes may increase the risk of obesity. Given that secular trend data are the farthest removed from causation of the four lines of evidence examined and based on the preponderance of evidence available to date, the authors conclude that current dietary guidelines recommending moderation in dietary fat intake, higher relative intakes of dietary ﬁber and complex carbohydrates, and adequate protein intake are consistent with the prevention of obesity. This should not be misinterpreted to suggest that absolute intake of carbohydrate should be increased. Rather, efforts should focus on increasing the proportion of high-ﬁber and high-complex-carbohydrate foods relative to high-fat alternatives.

MINERALS AND VITAMINS Despite public health efforts, the prevalence of obesity among children continues to rise (Troiano, 1998), indicating the need for new and innovative interventions. Calorie intake and macronutrient

116

TABLE 4.13 Does the Preponderance of Evidence Suggest a Relationship between Macronutrient Intake and Lower Adiposity? Nutrient/ Target Behavior1 (Total number of observational studies/ prevention trials)2 ↓ Dietary Fat 28/10 Adults 44/13 Children

↑ Dietary Fiber 8/5 Adults 7/5 Children ↑ Complex Carbohydrate 4/1 Adults 0/0 Children

Prevention Trials Support Relationship? Randomized Controlled Trials

Other

Yes

No studies

Conclusion: Consistency of Evidence Supporting Relationship

Adults Inconclusive No

Inconclusive

Yes

Yes

Moderate Children Inconclusive

Inconclusive

Inconclusive

Inconclusive

Inconclusive

Yes

No studies

Adults Inconclusive No

Yes

Yes

Yes

Moderate

Children Inconclusive

No studies

Inconclusive

Inconclusive

No studies

Yes

No studies

Adults Yes No

No studies

Yes

Yes

Moderate Children Inconclusive

No studies

Yes

Inconclusive

Inconclusive

Adults No studies No

No studies

Yes

Inconclusive

No studies

Yes

Inconclusive Children No studies

No studies

No studies

No studies

No studies

Obesity: Dietary and Developmental Inﬂuences

↑ Total % Carbohydrate 21/6 Adults 31/3 Children

Observational Studies Support Relationship? Secular Trends Mechanisms Longitudinal U.S. Nationally Other Support Support Representative Cross-Sectional Relationship? Relationship? Cross-Sectional or Case-Control

↓ Added Sugar/ Sucrose 5/0 Adult 2/4 Children ↓ Intrinsic Sugar 1/0 Adults 0/0 Children ↓ Dietary Protein 12/1 Adults 29/2 Children

Adults No studies Yes

No studies

No

Inconclusive

No studies Inconclusive

Yes Children No studies

No studies

Inconclusive

Inconclusive

Inconclusive

Inconclusive

No studies

No studies

No studies

No studies

Adults Yes

Yes

Inconclusive Children No studies

Inconclusive

No studies

Inconclusive

Inconclusive

No studies

No studies

Adults No studies None

No studies

Inconclusive

Yes

Dietary Inﬂuences on Energy Balance

↓ Total Sugar/Simple Carbohydrate 6/1 Adult 3/0 Children

Inconclusive Children No studies

No studies

No studies

No studies

No studies

Inconclusive

No Studies

Adults Inconclusive No

No Studies

Yes

Inconclusive

Inconclusive Children Inconclusive

No Studies

Inconclusive

Inconclusive

No Studies

Notes: 1

Description of criteria used for summary table is located in the methods section

2

Indicates the number of relevant studies identiﬁed and examined for each study type.

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Obesity: Dietary and Developmental Inﬂuences

composition have been the focus of most previous research on the role of diet in the etiology of obesity, oftentimes failing to produce the expected results. Whether micronutrient components of the diet are related to body fatness has been less thoroughly investigated. Herein are documented ﬁndings with respect to the following minerals: calcium (including a brief discussion of vitamin D), iron, zinc; and antioxidant vitamins: vitamin C, vitamin E, vitamin A, and carotenoids. Fewer studies have been published relating other minerals [e.g., magnesium (Singh, 1998)] and vitamins [e.g., B vitamins (de Burgos, 1992)] to adiposity.

CALCIUM (AND DAIRY) Secular Trends Calcium and dairy intake are discussed together, as dairy products are the primary source of calcium in the U.S. food supply, and it is frequently difﬁcult in studies to distinguish effects of calcium versus other nutrients found in dairy products (e.g., vitamin D, magnesium, potassium). Food disappearance data suggest that per capita calcium intake has increased from an estimated 890 mg/day per person in 1975 to 990 mg in 1999 (USDA, 2003) (Figure 4.18). This increase in the dietary calcium supply has occurred despite a drop in ﬂuid milk consumption from 30 gal per capita annually in 1972–1976 to 24 gal in 1997 (Figure 4.19), and can primarily be attributed to a large increase in consumption of cheese from 14 lb per capita annually in 1972–1976 to 28 lb in 1997 (Putnam, 1999) (Figure 4.20), as well as an increase in nondairy foods fortiﬁed with calcium (e.g., grains, fruit drinks). Figure 4.21 illustrates the change in total daily servings of dairy observed in recent years. However, this national trend in calcium and dairy intake has not been observed among all population groups. Speciﬁcally, the secular increase in pediatric overweight has occurred at a time during which dietary calcium has declined among youth (Albertson, 1997; Morton, 1998). For example, data from national surveys of 12- to 19-year-old females between the late 1970s and the mid-1990s indicate that milk intake decreased by 36% (whereas that of soda and fruit drinks nearly doubled) (Bowman, 2002). Between the 1970s and 1999/2000, mean calcium intake also declined in children ages 3 to 11, in adolescent boys, and in girls ages 12 to 15 (Briefel, 2004). Plausible Mechanisms Several treatment studies have found that increasing calcium and/or dairy intake have beneﬁcial effects on weight loss while consuming a hypocaloric diet. Summerbell et al. (1998), in a random-

FIGURE 4.18 Per capita calcium in the U.S. food supply. (Source: USDA, 2003.)

Dietary Inﬂuences on Energy Balance

119

FIGURE 4.19 Per capita milk, yogurt, and cream in the U.S. food supply. (Source: USDA/ERS, 2003.)

FIGURE 4.20 Per capita cheese, cottage cheese, and frozen dairy products in the U.S. food supply. (Source: USDA/ERS, 2003.)

FIGURE 4.21 Per capita servings of total dairy products in the U.S. food supply. (Source: Putnam, 2002.)

ized, controlled clinical trial, found the mean weight loss after 16 weeks to be signiﬁcantly greater on a milk-only diet (9.4 kg) compared to an isocaloric control diet (1.7 kg). Barr et al. (2000) found that the weight gain (0.6 kg more than the control group) after 12 weeks of increased milk consumption was less than predicted from the added energy from milk. Zemel et al. reported that increasing dietary calcium of obese adults resulted in signiﬁcant weight loss over a 1-year (Zemel,

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Obesity: Dietary and Developmental Inﬂuences

2000) or 24-week (Zemel, 2002b) period. In comparison to a control group on a low-calcium (400–500 mg/d) hypocaloric diet, weight loss was 26% and 70% greater among the calcium and dairy supplemented groups (1200–1300 mg/d), respectively (Zemel, 2002b). Body fat loss (via DEXA) also showed a similar trend. However, in another randomized controlled clinical trial of energy restriction in obese women, no signiﬁcant difference in weight change over a 3-month period was observed between a high-calcium group (1800 mg/d) versus a lower-calcium group (800 mg/d) (Jensen, 2001). The reason for the discrepancy in results is not obvious, but perhaps length of supplementation plays a role. It has also been suggested that there may be a threshold value above which additional calcium intake has an inconsequential effect (Parikh, 2003). It appears that there may be several mechanisms involved in the antiobesity effect of calcium and dairy, some calcium dependent and others linked to dairy consumption independent of calcium (for review see Zemel 2002a, 2003). Speciﬁcally, it has been shown that increasing adipocyte intracellular calcium (2+) in vitro results in a simultaneous stimulation of lipogenesis and inhibition of lipolysis (Zemel, 2000). It has also been demonstrated that higher acute calcium intake is associated with higher rates of whole-body fat oxidation in humans (Melanson, 2003) and higher rates of postprandial thermogenesis (Soares, 2004). Studies in rats suggest that calcium may reduce metabolic efﬁciency and increase thermogenesis via actions on uncoupling protein expression (Zemel, 2002a). The calcitropic hormones, parathyroid hormone (PTH), and 1,25-(OH)2 vitamin D, can both inﬂuence intracellular calcium (2+) concentration (Shi, 2001). Obese individuals have been reported to have higher levels of circulating PTH and 1,25-(OH)2 vitamin D than nonobese controls (Kerstetter, 1991; Jorde, 2000). 1,25-(OH)2vitamin D has been shown to stimulate the inﬂux of calcium into adipocytes via a nongenomic adipocyte membrane vitamin D receptor pathway (Shi, 2001). This increase in intracellular calcium, in turn, simultaneously inhibits lipolysis via a calcium-dependent mechanism and stimulates lipogenesis by increasing the expression of fatty acid synthase (Zemel, 2002a). It has therefore been suggested that dietary calcium could reduce the size of adipose storage and thus obesity risk by suppressing 1,25-(OH)2 vitamin D production (Zemel, 2000). Another effect of high calcium intake, particularly when dietary fat is concurrently high, is to reduce the digestibility of fat and increase the fecal loss of energy via the formation of insoluble calcium-fatty acid “soaps” (Papakonstantinou, 2003; for review, see Parikh, 2003). It remains to be established whether it is solely the calcium content of dairy products or whether other bioactive components in dairy products are involved in affecting weight status. As the bioavailability of calcium from dairy and nondairy foods is similar in most cases (an exception being spinach and a few other high-oxalate or high-phytate plant foods) (Gueguen, 2000), other components in dairy (such as vitamin D or milk proteins) may act synergistically or independently with calcium. In rats and humans, dairy seems to exert a slightly greater effect than calcium alone (Zemel, 2002a). In a study of children, an association was observed with dairy intake, but not with calcium intake, in multivariate models (Tanasescu, 2000). In a study of adults, blood 25-hydroxy vitamin D concentration, an indicator of vitamin D nutritional status, was inversely related to BMI (Rock, 1999). Finally, in a weight loss treatment trial, weight and fat losses were greater among the obese adults on the high-dairy diet compared to the supplemental calcium diet, even though the total calcium intakes for the dairy and calcium supplemented groups were the same (1200–1300 mg/day) (Zemel, 2002b). Observational Studies As early as 1984, in an analysis of nationally representative data (NHANES I), it was observed that dietary calcium was negatively correlated with BMI in adults (McCarron, 1984). Such an epidemiological ﬁnding did not generate much interest and could be explained by the fact that individuals concerned about their weight reportedly avoid dairy products (Klesges, 1999). However, relatively recently, once convincing evidence for a plausible mechanism was put forward, additional

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121

reports have been published relating dietary calcium to adiposity in animals (for review see Parikh, 2003) and humans (Tables 4.14 and 4.15). The majority of longitudinal studies have supported the hypothesis that a low intake of calcium and/or dairy was predictive of overweight. Besides the study by French et al. (1994) that examined mostly high-fat, high-calorie dairy (e.g., whole milk, cheese, dairy desserts) and found both crosssectionally (at baseline) and longitudinally (after two years of follow-up), a positive association was observed in women (longitudinally) and men (cross-sectionally), and one by Parker et al. (1997) that found no association, the majority of longitudinal prospective studies (ranging in duration from 2–10 years), one of preschool children (Carruth, 2001), and the others in adults (Lin, 2000; Pereira, 2002; Schulz, 2002) found that higher longitudinal intakes of calcium and dairy products were associated with lower body fat and/or weight loss. In most cases, these associations were signiﬁcant even after controlling for energy intake and some measure of physical activity. It should be noted, however, that in the study by Lin et al. (2000), there was an interaction between dietary energy and calcium intakes such that calcium intake predicted changes in body weight only at lower energy intakes. It may be that the effect of calcium and/or dairy on body weight is overwhelmed by high energy intakes (e.g., from high-fat dairy products or other sources). The effect of calcium and/or dairy may also differ according to weight status. In the majority of longitudinal studies, baseline BMI or weight status was controlled for or was similar between subjects. However, in the study by Pereira et al. (2002), the development of obesity with lower dairy consumption was observed only among individuals who were overweight at baseline (p < 0.001), but not among adults who were not (p = 0.19) (Figure 4.22). These observations merit additional investigation. In an analysis of nationally representative data from NHANES III, a signiﬁcant negative association was reported for both men and women. Speciﬁcally, the risk of being in the highest quartile of body fat was reduced by 84% for those who consumed the highest quartile of calcium and dairy intake compared to those consuming the lowest quartile (Zemel, 2000). Likewise, in a nationally representative sample of U.S. youth, milk was inversely related to BMI, but only signiﬁcantly among girls (Forshee, 2003). The ﬁndings from other cross-sectional studies among adults have been more mixed, a result that is not surprising given the methodological differences between studies. The two largest studies were both conducted among northern European populations and found positive associations. It should be noted that, in the Finnish study, only milk and not other dairy or total calcium was assessed (Lahti-Koski, 2002). In the large Norwegian study, calcium intake was positively related to overweight in men but not in women (Kamycheva, 2003). The researchers acknowledged concern, however, with respect to the food frequency instrument used to assess dietary intake and the comparatively low intakes of calcium recorded. Interestingly, vitamin D intake was signiﬁcantly and consistently inversely related to obesity: the lowest quartile of vitamin D intake (vs. the highest

FIGURE 4.22 Ten-year cumulative development of obesity (BMI > 30 kg/m2) by categories of total dairy intake according to weight status at baseline. (Source: Pereira, 2002.)

122

TABLE 4.14 Observational Studies of the Association of Calcium and/or Dairy Intake with a Measure of Adiposity in Adults Study Name and/or Location

Study Population1

Adiposity Measure2

Control Variables

Association3

5 weight change classiﬁcations based on weight change/yr

Gender, Baseline age, weight and height, Education, Reported dietary change, Prior weight change, Leisure activity, Life and health satisfaction, Medication affecting body weight, Prevalent disease, Menopausal status

– (dairy)

French, 1994 3,552 adults Health Worker Project, Minnesota Mean 38 yr Ethnicity not speciﬁed 2-yr follow-up

Weight

Gender (separate analysis), Age, Smoking status, + (mostly high-fat dairy; Education, Occupation, Marital status, Treatment women only in longitudinal group, Baseline body weight (Physical activity, dieting analysis, men only in crosshistory also included in model) sectional analysis at baseline)

Pereira, 2002 Coronary Artery Risk Development in Young Adults, Alabama, Illinois and California

3,157 adults 18–30 yr Black and White 10-yr follow-up

Overweight (BMI ≥ 25)

Overweight status (separate analysis), Age, Race, Energy intake, Study center

Parker, 1997 Pawtucket Heart Health Program, New England

465 adults 18–64 yr 94% White 4-yr follow-up

Weight change

Gender, Age, BMI, Smoking status, Physical activity, Energy intake (all baseline measures?)

Lin, 2000 Indiana

54 women 18–31 yr Ethnicity not speciﬁed 2-yr follow-up

Body weight Body fat

Calories (denominator with diet variable); No differences by exercise group

– (dairy) – (calcium)

Body fat

Gender (separate analysis), Age, Caloric intake, Race/ethnicity, Physical activity

– (dairy) – (calcium)

Longitudinal Studies Schulz, 2002 European Prospective Investigation into Cancer and Nutrition, Potsdam, Germany

17,269 adults 19–70 yr Ethnicity not speciﬁed 2.2-yr follow-up

0 (dairy)

Obesity: Dietary and Developmental Inﬂuences

U.S. Nationally Representative Cross-Sectional Studies Zemel, 2000 7,494 adults (95% men) NHANES III Mean 29–44 yr Ethnically diverse

– (dairy; overweight only)

Lovejoy, 2001 Healthy Transitions Study, Louisiana Ortega, 1995a Spain Buchowski, 2002 Texas

149 women Mean 47 yr 65% white, 35% black 122 adults 65–95 yr Spanish 50 women (24 lactose intolerant, 26 lactose tolerant) 34–37y Black

Obesity (BMI ≥ 30)

Age, Education

+ (milk)

Obesity (BMI > 30)

Gender (separate analysis), Age, Coffee intake, Alcohol intake, Physical activity, Smoking, Vitamin D intake

+ (calcium; men only)

BMI category (<22, 22–29, ≥30)

Gender, Age, Ethnicity, Special diet use

+ (beverage milk, cheese, yogurt)

BMI category (<25, 25–30, >30)

Gender (separate analysis), Age, Smoking status?

– (dairy)

BMI

Age, Gender (separate analysis), Low energy reporting (separate analysis)

0 (dairy)

BMI Body fat Waist circumference Abdominal adipose tissue area BMI Body fat

Gender (separate analysis), Age, Energy intake, Dietary fat (% energy), Dietary protein, SES variables

– (calcium; women only)

Race (separate analysis), Energy intake

– (calcium; whites only)

Obese (BMI ≥ 25)

Gender, Place of residence, Age

0 (dairy) 0 (calcium)

BMI (unclear if self-reported)

Age, Lactose intolerance, Energy intake (by analyzing mg Ca/1000 kcal)

– (calcium)

Dietary Inﬂuences on Energy Balance

Other Cross-Sectional or Case-Control Studies Lahti-Koski, 2002 24,604 adults 1982-1997 FINRISK Studies, 15–64 yr Finland Ethnicity not speciﬁed Kamycheva, 2003 18,914 adults 1994–1995 Tromsø study, Norway 25–69 yr Ethnicity not speciﬁed Kumanyika, 1994 4,643 adults Cardiovascular Health Study, ≥65 yr Pennsylvania, North Carolina, 95% White, 5% Black California, Maryland Heseker, 1995 2,066 adults VERA subgroup of the German 18–88 yr Nutrition Survey, representative Ethnicity not speciﬁed of West Germany Macdiarmid, 1998 1,239 adults 1986-1987 Dietary and Nutrition 16–64 yr Survey of British Adults, England Ethnicity not speciﬁed Jacqmain, 2003 470 adults Québec Family Study, Canada 20–65 yr Ethnicity not speciﬁed

Notes: 1

Based on convenience samples except for Parker, 1997; Zemel, 2000.

2

BMI based on measured weight and height unless otherwise speciﬁed.

3

123

Plus (+) indicates signiﬁcant direct relationship, negative (–) indicates signiﬁcant inverse relationship, and zero (0) indicates nonsigniﬁcant relationship between dietary factor and measure of adiposity.

124

TABLE 4.15 Observational Studies of the Association of Calcium and/or Dairy Intake with a Measure of Adiposity in Children Study Name and/or Location

Study Population1

Adiposity Measure2

Control Variables

Association3

Longitudinal Studies Carruth, 2001 Tennessee

53 children 2–6 yr Ethnicity white 3-yr follow-up

Body fat

Gender, Baseline BMI, Parent’s BMI

– (calcium)

U.S. Nationally Representative Cross-Sectional Studies Forshee, 2003 CSFII 1994–1996, 1998

Reported BMI (continuous) Gender (separate analysis), Age, Ethnicity, Family income BMI ≥ 85th percentile (dichotomous)

– (milk; girls only)

Other Cross-Sectional or Case-Control Studies Rockett, 2001 Growing Up Today Study (children of women in Nurses Health Study II), U.S.

16,281 children and adolescents 9–14 yr 96% white

Overweight (BMI [self-report] > 85th percentile NHANES I)

Black, 2002 New Zealand

70 children (50 milk avoiders, 20 controls) 3–10 yr White

Weight BMI Body fat

Ortega, 1995b Spain

64 adolescents 15–17 yr Spanish

Overweight (BMI ≥ 23.0)

Age (for dairy), Dietary energy (for calcium)

– (dairy) – (calcium)

– (dairy)

0 (dairy) 0 (calcium)

Obesity: Dietary and Developmental Inﬂuences

3,311 children and adolescents 6–19 yr Ethnically diverse

53 children (29 obese; 24 control) 7–11 yr Puerto Rican

Obesity (BMI > 85th percentile)

Maternal BMI, Maternal marital status, TV viewing, Fruit juice intake (others tested, [i.e., food insecurity, physical activity, infant feeding practices, maternal age, education and parity, paternal characteristics, SES, food group and nutrient intakes], but not included in ﬁnal model)

– (dairy) 0 (calcium)

Notes: 1

Based on convenience samples.

2

BMI based on measured weight and height unless otherwise speciﬁed.

3

Plus (+) indicates signiﬁcant direct relationship, negative (–) indicates signiﬁcant inverse relationship, and zero (0) indicates non-signiﬁcant relationship between dietary factor and measure of adiposity.

Dietary Inﬂuences on Energy Balance

Tanasescu, 2000 Connecticut

125

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quartile) was associated with odds ratios of 2.24 (95% conﬁdence interval 1.80 to 2.80) and 1.51 (95% conﬁdence interval 1.23–1.85) in men and women, respectively. Furthermore, the fact that dairy products are not fortiﬁed with vitamin D in Norway as they are in the U.S. and many other countries, and differential sun exposure (and therefore contribution of vitamin D from skin synthesis), limit extrapolation of these ﬁndings to other populations. One smaller study in the U.S. also found a positive relationship between adiposity and intake of milk consumed as a beverage, cheese, and yogurt. Unfortunately, total dairy or calcium intake was not assessed in this study (Kumanyika, 1994). Two smaller studies, both conducted in Europe, found no signiﬁcant association between dairy or calcium intake and adiposity (Macdiarmid, 1998; Ortega, 1995a). The four remaining studies (only two of which were conducted in the U.S.) found inverse associations with some caveats (Heseker, 1995; Jacqmain, 2003; Lovejoy, 2001; Buchowski, 2002). In a Canadian study, a lower intake of calcium was associated with increased BMI and body fat, but only among women; dairy intake was not reported (Jacqmain, 2003). In a study of black and white women in Louisiana, calcium intake was inversely associated with BMI and percent body fat, but only in white women. Furthermore, calcium did not remain signiﬁcant after multiple other factors were added to the model (Lovejoy, 2001). It may be that the type of dairy product assessed (e.g., low/nonfat vs. whole; beverage vs. total) and failure to account for the inﬂuence of dieting on dairy intake may have contributed to discrepant results. The relationship between vitamin D intake and adiposity, independent and in relation to calcium intake, also merits further study. Other cross-sectional studies among youth have found an inverse association between calcium or dairy intake and adiposity more consistently. In a cross-sectional study of primarily white youth, intake of calcium (after controlling for dietary energy) and intake of dairy foods (after controlling for age) were lower among overweight than nonoverweight 9- to 14-year-olds (Rockett, 2001). In two case-control studies [milk avoiders (Black, 2002); overweight (Tanasescu, 2000)], lower dairy product intake was associated with a measure of adiposity. Calcium intake was also signiﬁcantly lower in obese girls, but not boys, but this difference no longer remained in the multivariate modeling (Tanasescu, 2000). Prevention Trials Trials originally intended to examine bone mineral outcomes have also had mixed results. Davies et al. (2000), in a meta-analysis, reevaluated ﬁve studies of a total of 780 adult women (one doubleblind, placebo-controlled randomized trial and four observational studies, two of which were longitudinal) and found a signiﬁcant negative association between calcium intake and weight in all age groups (third, ﬁfth, and eighth decades of life). Speciﬁcally, an increase of 1000 mg in calcium intake was associated with an 8-kg reduction in body weight. Approximately 3% of the interindividual variation in body weight was explained by differences in calcium intake. Heaney et al. (2002) added several additional clinical studies and found the same result for adults: an increase of 1000 mg in calcium intake was associated with a 8–10 kg reduction in body weight. Among children, for each 300 mg increment in calcium intake, body fat was approximately 1 kg less (Table 4.16). A relatively small amount (i.e., less than 10%) of the interindividual variation in body weight was explained by differences in calcium intake. However, based on an analysis of data from 564 of the women reported by Davies et al. (2000), Heaney (2003) estimated that increasing the calcium intake of the population to be consistent with current recommendations could reduce obesity in women by 60–80%. However, in a recent analysis of all randomized, controlled trials conducted between 1966 and 2001 as identiﬁed from a search of the Medline database, little support was found for an effect of calcium or dairy supplementation in reducing body weight or fatness (Barr, 2003). In only 1 of 17 calcium supplementation trials was a greater weight loss observed (Table 4.17), and in 2 of 9 dairy supplementation studies, greater gains of body weight or fat were observed in treatment compared to control groups (Table 4.18). Barr cautioned, however, that none of the studies reviewed were

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127

TABLE 4.16 Summary from Several Studies of the Estimated Effect of 300 mg/Day of Calcium on Body Measures

Study [4] [4] [4] [1] [7] [8] [11] [5]

Cohort Young women Middle-aged women Elderly women Adult women African-American male hypertensives Children Young women Young women

Body Fat (kg)

Body Fat (%BW)

Effect Variable ∆ Body Fat (yr–1)

Wt (kg) –2.5

∆ Wt (kg * yr–1) –0.11 –0.16

–3 –4.9 –1.0

–15% –20% –1.2 kg –1.1%

–1.3

Notes: Key to studies: [4] = Davies; 2000, [1] = Zemel; 2000; [7] = Zemel, 1990; [8] = Carruth, 2001; [11] = Summerbell, 1998; [5] = Berger-Lux, 2001. Source: Heaney et al., 2002.

designed or powered to address the effect of calcium or dairy product consumption on body weight or adiposity; rather, effects on bone mineral density were of primary interest. Furthermore, in the absence of changes in energy balance (e.g., energy intake and/or expenditure), calcium and/or dairy intake might not affect body weight. Although not explicitly stated, it is likely that, in the majority of these supplementation trials, changes in energy intake and weight were discouraged to minimize the possibility of confounding effects of weight change on bone mass. Large prevention trials designed speciﬁcally to study prevention of overweight are clearly needed. Conclusion The data suggest a potential role for calcium and dairy in the etiology of obesity and the potential for preventing obesity by improving the calcium and dairy intake of the U.S. population. Several comprehensive reviews have been published recently which come to the same conclusion (Zemel, 2002a; Parikh, 2003; Teegarden, 2003). There is some evidence to suggest that the effects may be greater for dairy as compared to nondairy sources of calcium and that low-fat dairy may be more beneﬁcial than full-fat varieties. Although differences in calcium or dairy intake may explain only a relatively small percentage of the variance in body weight, effects of this magnitude could be substantial over time (as reviewed by Parikh, 2003). However, the lack of a dramatic drop in calcium and total dairy intake in recent decades suggests that other factors may be more important in contributing to the obesity epidemic (Tables 4.19 and 4.20). In summary, the cumulative evidence is moderately consistent in supporting a relationship between calcium and/or dairy intake and adiposity.

ZINC

AND IRON

Secular Trends Per capita disappearance data indicate a nationwide increase in intake of iron (15.4 mg/day per person in 1970 to 21.2 mg in 1994) and zinc (from 12.2 mg/day per person in 1970 to 13.2 mg in 1994) in recent decades (Putnam, 1999). According to nationally representative data on the intake of individuals, mean iron intake increased for most gender and age groups of children, the exception

128

TABLE 4.17 Calcium Supplementation Trials as Reviewed by Barr (2003) Author

Subjects

Study Design

Results

1-yr trial of 1000 mg (5 d/wk) CaCO3 No signiﬁcant differences between groups in gains in wt, ht, triceps skinfold thickness, and midvs. placebo upper arm circumference.

Dibba (22)

80 boys and 80 girls aged 8.3–11.9 yr in rural Gambia

Johnston (23)

45 pairs of identical U.S. twins 3-yr trial of 1000 mg/d calcium aged 6–14 yr citrate malate

Increases in wt and ht similar in both those receiving calcium and those receiving placebo.

Lee (24)

84 Hong Kong children aged 7 yr

Calcium and placebo groups had similar gains in wt (24.4 vs. 25.6%, P = 0.46) and ht (8.4 vs. 8.4%, P = 0.92).

18-mo trial of 800 mg/d CaCO3

162 Chinese children aged 7 yr 18-mo trial of 300 mg/d CaCO3

Calcium and placebo groups had similar gains in wt (17.2 vs. 17.4%) and ht (7.2 vs. 7.2%).

94 American girls aged 11.9 yr 18-mo trial of 500 mg/d calcium citrate malate

Calcium and placebo groups had similar gains in wt (P = 0.53), ht (P = 0.86), BMI (P = 0.80), and body fat % (P = 0.86).

Bonjour (27)

149 Swiss girls aged 7.9 yr

Nowson (28)

42 female Australian twin pairs 18-mo trial of 1000 mg/d CaCO3/Ca Calcium and placebo groups had similar gains in wt (≈8 vs. ≈7.9 kg and ht (≈7.3 vs. ≈7.8 cm). lactate gluconate vs. placebo aged 10–17 yr

Riggs (34)

236 postmenopausal American women

4-yr trial of 1600 mg/d Ca citrate or placebo

Ricci (29)

31 postmenopausal American women with BMI 28–42 kg/m2

6-mo weight loss program, subjects Calcium and placebo groups had similar changes in wt (–9.0 vs. –8.8 kg), BMI (–3.3 vs. –3.3 randomized to 1 g/d calcium citrate kg/m2), fat mass (–7.3 vs. –7.3 kg) and lean mass (–1.0 vs. –0.7 kg). malate or placebo

Storm (16)

60 postmenopausal American women

2-yr trial of 1 g/d CaCO3, ≈250 mL/d Calcium and placebo groups had similar changes in wt and BMI (Rosen, C. J., unpublished milk, or placebo results, personal communication, March 2002).

DawsonHughes (35)

176 men and 213 women aged ≥65 yr

3-yr trial of 500 mg/d calcium plus 700 IU/d vitamin D, or placebo

Recker (30), Davies (3)

197 postmenopausal women aged >60 yr

≈4.3-yr trial of 1200 mg/d CaCO3 or Calcium group lost more wt than the placebo group (–0.67 vs. –0.32 kg/yr, P < 0.025). placebo

1-yr trial of food products with Ca+ + Calcium and placebo groups had similar gains in ht (5.4 vs. 5.0 cm), wt (3.4 vs. 3.7 kg) and (850 mg/d) from milk extract, or BMI (0.6 vs. 0.8 kg/m2). placebo

Calcium and placebo groups had similar changes in wt (P = 0.47) and fat mass (P = 0.59), but lean mass was lower in the calcium group (P = 0.006) (Riggs, B. L., unpublished results, personal communication, April 2002).

Calcium/vitamin D and placebo groups had similar changes in wt and body composition (Dawson-Hughes, B., unpublished results, personal communication, March 2002).

Obesity: Dietary and Developmental Inﬂuences

Lee (25) Lloyd (26)

98 postmenopausal Spanish women with rapid bone loss

1-yr open trial of 1 g/d calcium, hormone treatment, eelcatonin, or control

Kalkwarf (36)

327 American women studied during lactation and weaning

6-mo trial of 1 g/d CaCO3 vs. placebo Calcium and placebo groups had similar wt loss during lactation (–2.34 vs. –2.66 kg, P = 0.57) and weaning (–0.54 vs. –0.46 kg, P = 0.84) (Kalkwarf, H., unpublished results, personal communication, March 2002).

Prentice (37)

60 Gambian women studied during lactation

12-mo trial of ≈700 mg/d calcium vs. Calcium and placebo groups lost similar amounts of weight during the 1-yr study (–1.2 vs. –0.9 placebo kg). There was no effect of supplement group on change in wt (P = 0.85) (Prentice, A. and Jarjou, L.M.A., unpublished results, personal communication, April 2002).

Elders (32)

214 perimenopausal Dutch women

3-yr trial of supplementation with 1 g/d calcium, 2 g/d calcium or no calcium

Jensen (33)

52 obese Danish women (14 postmenopausal)

3-mo trial of supplementation with During the 3 mo of calcium supplementation and wt loss, the calcium group and untreated 1 g/d calcium during 4.2 MJ wt loss controls groups lost similar amounts of wt (5.7 and 6.6%, respectively). diet

Changes in wt, ht and BMI were similar among the three treatment groups. On average, subjects gained 1.8 kg wt, lost 0.2 cm height and increased BMI by 0.4 kg/m2.

Dietary Inﬂuences on Energy Balance

No signiﬁcant change in body wt in either the calcium or the control group (wt was also unchanged in the calcitonin and hormone treatment groups).

Perez-Jaraiz, (31)

Notes: g = grams; d = day; ht = height; wt = weight; mo = month.

129

130

TABLE 4.18 Dairy Supplementation Trials as Reviewed by Barr (2003) Author Chan (10)

Cadogan (11)

Subjects Design 48 U.S. girls, initially 11 yr 1-yr randomized trial of dairy product old supplementation (to total 1200 mg/d) vs. usual diet 82 British girls, initially 18-mo randomized trial of addition of 12.2 yr old 568 ml/d milk vs. usual diet

Merrilees (12) 91 New Zealand girls, initially age 15–16 Baran (13) 37 U.S. women aged 30–42 yr 168 Australian women, >10 yr postmenopause

Storm (16)

60 U.S. women >65 yr

Lau (17)

2-yr randomized trial of dairy foods (1000 mg Ca/d) or usual diet 3-yr randomized trial of increased dietary calcium (+610 mg/d) vs. usual diet 2-yr randomized trial of (1) placebo, (2) No signiﬁcant change in BMI during the intervention in either the milk-powder group or the Ca tablet milk powder (1 g Ca/d), (3) Ca tablets group (raw data not reported). Change in BMI not reported for placebo group or Ca tablet plus (1 g/d), (4) Ca tablets + exercise exercise group. 2-yr randomized trial of addition of milk No signiﬁcant differences in change in weight or body composition among treatment groups (Rosen, (≈250 mL/d), 1 g/d CaCO3 or placebo C. J., personal communication, March 2002). 2-yr randomized trial of addition of 50 Wt change: controls –0.26 kg; dairy +0.52 kg (P < 0.001). Fat change: controls –0.14 kg; dairy +0.42 g/d high calcium, low fat milk powder kg. Lean change: controls +0.2 kg; dairy +0.3 kg.

185 Chinese women aged 55–59, >5 yr postmenopause Barr (18) 200 U.S. adults aged 12-wk randomized trial of addition of ≈2 Wt gain (women): controls +0.4 kg; dairy +1.9 kg. (men): controls +1.0 kg; dairy +1.6 kg. Gain was 55–85 (129 women, 71 cups/d low fat milk vs. usual diet signiﬁcantly greater in dairy group (P < 0.005). men) Cleghorn (19) 115 Australian women, <5 2-yr, open crossover of 3 L/wk CaWt change: 0.06 kg difference between on/off added dairy products (95% CI: –0.71 to 0.83 kg, NS). yr postmenopause fortiﬁed milk (1 yr on milk, 1 yr off) Notes: d = day; g = grams; mo = month; wk = week; ht = height; wt = weight.

Obesity: Dietary and Developmental Inﬂuences

Prince (14), Devine (15)

Results Wt gain: controls +7.2 kg; dairy +6.4 kg. Ht gain: controls +7.4 cm, dairy +7.8 cm. Fat gain: controls +2.62 kg; dairy +2.59 kg. Lean gain: controls +3.8 kg; dairy +4.3 kg. All comparisons not signiﬁcant (NS). Wt gain: controls +7.2 kg; dairy +8.0 kg Ht gain: controls +8.4 cm; dairy +9.1 cm Fat gain: controls +1.8 kg; dairy +1.9 kg. Lean gain: controls +5.2 kg; dairy +5.6 kg. All comparisons NS (absolute data approximated from graph showing % changes). Wt gain: controls +4 kg; dairy +4.6 kg. Ht gain: controls +1.6 cm; dairy +1.3 cm. Fat gain: controls +2556 g; dairy +2616 g. Lean gain: controls +121 g; dairy +720 g. All comparisons NS. Wt gain: controls +3.4 kg; dairy +4.2 kg (signiﬁcance not reported).

Observational Studies2 Support Relationship? (number of studies) Longitudinal Secular Trends Support Relationship?

Mechanisms Support Relationship?

(1 in adults 1 in children)

U.S. Nationally Representative CrossSectional (1 in adults 0 in children)

Yes

Yes

Yes

Other CrossSectional or CaseControl (5 in adults 3 in children)

Prevention Trials2 Support Relationship? (number of studies) Randomized Other Controlled Trials (13 in adults 7 in children)

(1 in adults 0 in children)

Inconclusive

Yes

Conclusion: Consistency of Evidence Supporting Relationship

Dietary Inﬂuences on Energy Balance

TABLE 4.19 Does the Preponderance of Evidence Support a Relationship between Calcium Intake and Lower Adiposity?1

Adults

No — adults

Inconclusive

Moderate

Yes — children

Children Yes

No studies

Yes

Inconclusive

No studies

Notes: 1

Description of criteria used for summary table is located in the methods section.

2Numbers in parentheses indicate the number of relevant studies identiﬁed and examined for each study type; the ﬁve studies included in the meta-analysis by Davies (2000) are included individually in the above count by study type.

131

132

TABLE 4.20 Does the Preponderance of Evidence Support a Relationship between Dairy Intake and Lower Adiposity?1 Observational Studies2 Support Relationship? (number of studies) Longitudinal Secular Trends Support Relationship?

Mechanisms Support Relationship?

(5 in adults 0 in children)

U.S. Nationally Representative CrossSectional (1 in adults 1 in children)

Yes

Yes

Yes

Other CrossSectional or CaseControl (5 in adults 4 in children)

Prevention Trials2 Support Relationship? (number of studies) Randomized Other Controlled Trials (6 in adults 3 in children)

(1 in adults 0 in children)

Inconclusive

Yes

Conclusion: Consistency of Evidence Supporting Relationship

Adults Inconclusive

Moderate

Yes — milk

Children No studies

Yes

Yes

Notes: 1

Description of criteria used for summary table is located in the methods section.

2

Numbers in parentheses indicate the number of relevant studies identiﬁed and examined for each study type.

Inconclusive

No studies

Obesity: Dietary and Developmental Inﬂuences

No — total dairy

Dietary Inﬂuences on Energy Balance

133

being adolescent girls. Mean intake for all men was 17.8 mg in 1988–1994 and 17.2 mg in 1999–2000; analogous values for women were 12.8 mg and 13.4 mg, respectively (Briefel, 2004). National changes in zinc intake by individuals between 1998–1994 and 1999–2000 have not been consistent: increases were observed for children and adult females, no change was observed for adolescent females, and decreases were observed for adolescent and adult males (Briefel, 2004). Any observed increases have largely been attributed to increased intake of grains, sources of less bioavailable iron and zinc than meat, poultry, and ﬁsh intake, food sources of more bioavailable iron and zinc that have declined in this same time period (Johnson-Down, 1997; Popkin, 1998; USDA/ARS, 1999; PAHO/WHO, 2000). Data on trends in bioavailable zinc and iron were not identiﬁed. Between 1976–1980 (NHANES II) and 1988–1994 (NHANES III), the prevalence of iron deﬁciency (deﬁned as an abnormal value on at least two of the following indicators: serum ferritin, transferrin saturation, and free erythrocyte protoporphyrin) was fairly constant at below 5% for most age and gender groups (Briefel, 2004). Between 1988–1994 and the 1999–2000 NHANES, the prevalence of iron deﬁciency increased very slightly among most age and gender groups (Looker, 2002). The increase was signiﬁcant among males 12–15 years, males 16–69 years, and females 50–69 years. However, the changes in prevalence were relatively small (e.g., from 1% in 1988–1994 to 2% in 1999–2000 among males 16–69 years old) and, among select vulnerable groups (e.g., toddlers and menstruating females), the prevalence of iron deﬁciency anemia (deﬁned as iron deﬁciency in addition to a low hemoglobin level) did not change signiﬁcantly. It must be also noted that iron deﬁciency anemia remains rare in the U.S. (e.g., prevalence <5% among vulnerable groups). Plausible Mechanisms There is evidence that obese adults have reduced plasma zinc (Singh, 1998). Based on a rat model, zinc administration is protective against the obesity normally induced by sucrose and water feedings (Chen, 2000a). Zinc-deﬁcient adipose cells produce less leptin (Mangian, 1998; Mantzoros, 1998; Chen, 2000b, 2000c), a hormone integral to the regulation of adipose storage. There is evidence that spontaneous physical activity is reduced with iron deﬁciency (Pinero, 2001), possibly because iron deﬁciency affects muscle tissue energy metabolism and nervous system neurotransmitters (Youdim, 2000). A reduction in physical activity could translate into weight gain if energy intake is not reduced correspondingly. There is also preliminary evidence that warrants additional research that obesity in adults is associated with low iron status resulting from low iron utilization, rather than low iron stores (i.e., low serum iron and transferrin concentrations [indicators of low iron in circulation] simultaneous with high serum ferritin and erythrocyte protoporphyrin saturation [indicators of high iron storage]). Furthermore, iron deﬁciency is more frequent among young children who gain more weight than in children with lower but normal weight (Al-O, 1999; Sherriff, 1999). Much of this greater weight is adipose tissue, which may alter iron utilization. Observational Studies In a longitudinal study of 54 young adult women, no relation was observed between zinc (or iron) intake and body weight or body fat (Lin, 2000). Furthermore, in two cross-sectional studies conducted in Spain, one of 122 elderly adults (Ortega, 1995a) and one of 64 adolescents (Ortega, 1995b), no relationship was observed between dietary intake of zinc (or iron) and obesity. Prevention Trials No relevant prevention trials were identiﬁed.

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Obesity: Dietary and Developmental Inﬂuences

Conclusion Inadequate evidence is currently available to suggest that iron or zinc play a major role in the development of excessive weight gain and overweight (Table 4.21).

ANTIOXIDANT VITAMINS Secular Trends Both food supply data and national surveys of individual intakes indicate that fruit and vegetable intake has increased slightly over the past several decades, suggesting that intakes of antioxidant vitamins have increased as well (Krebs-Smith, 2001). Based on U.S. food supply data, per capita per day intake of vitamin C increased from 114 mg in 1975 to 132 mg in 1999. Likewise, during the same time period, vitamin A increased from 1590 to 1780 µg retinol equivalents, carotene increased from 590 to 800 µg retinol equivalents, and vitamin E increased from 14.0 to 17.8 mg alpha-tocopherol equivalents (USDA, 2003). Based on nationally representative surveys of food intake by individuals, mean intake of vitamins C and A was higher in 1999–2000 versus 1971–1974 for all age and gender groups except for 12- to 15-year-old boys (Briefel, 2004). Plausible Mechanisms Because none of the observational studies (described below) was conducted prospectively in a way to demonstrate cause and effect relationships, it is not clear whether lower concentrations of certain vitamins in the blood is a cause or result of adiposity. It has been proposed that metabolic or endocrinological changes due to excessive weight gain and adipose tissue storage may inﬂuence vitamin status (de Burgos, 1992). Since a predominant storage site of most fatsoluble vitamins is adipose tissue, a higher body adipose may result in increased distribution of these compounds from blood to fat tissue (Wallström, 2001). For example, adipose tissue can sequester vitamin E, making it not readily available to other tissues. In contrast to this hypothesis, in one study in which adipose tissue concentrations of antioxidant vitamins were measured, those with the highest visceral adiposity had the lowest subcutaneous adipose levels of α-carotene, βcarotene, retinol, and lycopene (both men and women) (Virtanen, 1996a), and α-tocopherol and γ-tocopherol (men only) (Virtanen, 1996b). Furthermore, not all fat-soluble vitamins (i.e., vitamin K, retinol) appear to circulate in lower blood concentrations in individuals with increased adiposity (Rock, 1999), while circulating concentrations of a water-soluble antioxidant (i.e., vitamin C) does appear to be lower in more obese individuals (de Burgos, 1992; Drewnowski, 1997; Singh, 1998). However, in a depletion-repletion study in which subjects were provided the same amounts of vitamin C, body weight was the strongest predictor of variation in plasma ascorbic acid concentration. The only other variable that was predictive, out of a variety examined (e.g., age, energy intake, physical activity, illness, stress, medications, passive smoke exposure) was prior plasma concentration (Block, 1999). It is possible that some of the differences between circulating vitamins in relation to adiposity can be explained by different distribution properties (e.g., relatively large amounts of vitamin E are “stored” in the adipose tissue while relatively little vitamin K is distributed in adipose). It has also been hypothesized that obesity may be consequent to higher fat intake and lower fruit and vegetable intake (for review see dietary fat section and fruit and vegetable section), which would presumably result in obese individuals having a lower intake of antioxidant vitamins rich in fruits and vegetables. However, when diet was assessed in conjunction with blood concentrations of antioxidants the results were not consistent; compared to individuals with a lower BMI, individuals with a higher BMI have been

Dietary Inﬂuences on Energy Balance

TABLE 4.21 Does the Preponderance of Evidence Support a Relationship between Iron or Zinc Intake and Lower Adiposity?1

Longitudinal Secular Trends Support Relationship? Inconclusive

Mechanisms Support Relationship? Inconclusive

(1) Inconclusive

Observational Studies2 Support Relationship? (number of studies) U.S. Nationally Representative CrossSectional (0) No studies

Other CrossSectional (2) Inconclusive

Prevention Trials2 Support Relationship? (number of studies) Randomized Other Controlled Trials (0) No studies

(0) No studies

Conclusion: Consistency of Evidence Supporting Relationship Inconclusive

Notes: 1

Description of criteria used for summary table is located in the methods section.

2

Numbers in parentheses indicate the number of relevant studies identiﬁed and examined for each study type; two studies were conducted in adults, one other cross-sectional study was conducted in adolescents.

135

136

Obesity: Dietary and Developmental Inﬂuences

reported to have higher intakes (Drewnowski, 1997), similar intakes (Kuno, 1998; Strauss, 1999), and reduced intakes (Neuhouser, 2001) of fruits and vegetables. Because oxidation of lipoproteins may be critical in the etiology of atherosclerosis, reduced levels of antioxidants in circulation may in part explain why adiposity is a risk factor for cardiovascular disease (Ohrvall, 1993; Kuno, 1998). Indeed, obesity has been associated with increased oxidative stress (Keaney, 2003). However, the mechanism whereby circulating concentrations of these vitamins are reduced remains unexplained, as does the nature of the relationship — cause, effect, or coincidental — between antioxidant vitamins and adiposity. Identifying the mechanisms involved in the consistent ﬁnding (discussed below) of reduced concentrations of antioxidant vitamins in overweight and obese individuals, whether due to tissue uptake and sequestering (e.g., by adipose tissue) or differences in intake, warrants further study. Observational Studies Numerous analyses of cross-sectional data have examined blood concentrations of antioxidant vitamins and measures of adiposity, primarily BMI (Table 4.22). The bulk of the analyses have been on vitamin E (either α-tocopherol or total vitamin E) and the carotenoids (primarily α- and β-carotene). Less often have vitamin C and retinol and other carotenoids been investigated. These studies have included children (Decsi, 1997; Kuno, 1998; Strauss, 1999), adolescents (Strauss, 1999; Neuhouser, 2001), and younger and older adults of both genders (Ascherio, 1992; de Burgos, 1992; Ohrvall, 1993; Sinha, 1993; Campbell, 1994; Hebert, 1994; Brady, 1996; Goodman, 1996; Drewnowski, 1997; Vogel, 1997; Singh, 1998; Block, 1999; Rock, 1999; Wallström, 2001; White 2001). The results are remarkably comparable with the majority of studies demonstrating a lower blood concentration of most antioxidant vitamins, particularly vitamin C, vitamin E (either total or in the form of α-tocopherol), and carotenoids (particularly α- and β-carotene) with increasing weight or BMI. Circulating levels of antioxidant vitamins may not accurately reﬂect dietary intake or nutrient status. Unfortunately, no longitudinal studies of the relation between antioxidant vitamin blood concentrations and adiposity were identiﬁed that could help shed light on whether low levels of circulating antioxidant vitamins preceded or proceeded weight gain or the development of obesity. Few studies comparing dietary intake of antioxidant vitamins and a measure of adiposity were identiﬁed: two longitudinal (both in adults) and two cross-sectional (one in adults and one in adolescents). In a 2-year longitudinal study of adults, vitamin A intake was positively associated (p < 0.05) with weight gain (Lin, 2000; see Table 4.14 for additional study details). Vitamin A intake was not related to intake of total energy or any of the macronutrients but was related to intake of fruits and vegetables. The investigators were at a loss to explain this unexpected result. However, in another longitudinal study, this one ten years in duration, vitamin E self-supplementation was inversely associated with change in BMI in both men (N = 35,166; p = 0.009) and women (N = 44,080; p = 0.008) after controlling for numerous variables (e.g., age, education, region of the country, baseline BMI, slope of BMI from 18 years of age to baseline, change in marital status, smoking, total calorie intake, meat and vegetable intake, alcohol intake, physical activity, menopausal status, estrogen use and parity in women). Vitamin E self-supplementation was also associated with less likelihood of gain at the waist among men, but not women (Kahn, 1997). In two relatively small cross-sectional studies conducted in Spain, one of 122 elderly adults (Ortega, 1995a) and the other of 64 adolescents (Ortega, 1995b), no relationship was observed between dietary intake of vitamins C, A, or E and obesity (Table 4.23). Prevention Trials No relevant prevention trials were identiﬁed.

Vitamin E Study Name and/or Location

Vitamin C Ascorbic Acid

α-tocopherol

Study Population

Adiposity Measure

1,182 men 51–63 yr

BMI

+

1, 047 women 50–79 yr

BMI WHR

– 0

Ohrvall, 1993 Sweden

894 adults 19–66 yr

BMI, sagittal diameter WHC

–

Drewnowski, 1997 Val-de-Marne Study, France

837 adults 18–94 yr

BMI

Vogel, 1997 Framingham Heart Study, Massachusetts

638 adults 67–96 yr

BMI

0

Wallström, 2001 Malmö Diet and Cancer Study, Sweden

529 adults 46–67 yr

BMI % body fat WC WHR

– (M) 0 + +

Brady, 1996 Nutritional Factors in Eye Disease Study, Wisconsin

400 adults 50–75+ yr

BMI

307 adults Mean 53–56 yr

BMI

– (F only)

Hebert, 1994 Working Well Study, Massachusetts

167 adults 19–73 yr

BMI

0

de Burgos, 1992 Poland

135 women (OW/OB, control) 16–63 yr

BMI

–

68 men 30–59 yr

Weight

–

β-tocopherol

Carotenoids lutein/ γ-toco- Vitamin A lycozeβ-cryptoRetinol pherol α-carotene β-carotene pene xanthin xanthin

Adults Goodman, 1996 Carotene and Retinol Efﬁcacy Trial, U.S. White, 2001 Women’s Health Initiative, U.S.

Ascherio, 1992 Health Professionals Follow-up Study, Nurses’ Health Study, Boston

–

– 0 0

0

–

–

– (total)

0

–

–

?

– (F only)

– (F only)

–

– – – –

– (M only)

0

–

–

0

–

– (M only)

– (M only)

0

0/– (F only)

0

– – (total)

137

Block, 1999 Maryland

0

Dietary Inﬂuences on Energy Balance

TABLE 4.22 Observational Studies of the Association of Blood Concentrations of Antioxidant Vitamins with a Measure of Adiposity in Adults or Children1

138

TABLE 4.22 (CONTINUED) 65 men 30–59 yr

BMI

Strauss, 19992 NHANES III, U.S.

4,771 children and adolescents 6–19 yr

BMI

– (adjusted for TG, CHOL)

Neuhouser, 2001 Olestra Post-Marketing Surveillance Survey, U.S.

285 adolescents 12–17 yr

BMI

–

Kuno, 1998 Japan

24 girls (OW and Control) 10–11 yr

BMI

– (total)

– (in LDL)

Decsi, 1997 Hungary

23 boys (OW and Control) Mean 13–15 yr

BMI % body fat

–

–

Sinha, 1993 Maryland

– (nonsupplementers)

0

Children –

0

–

–

0

0

–

Notes: Key to abbreviations: F, female; M, male; BMI, body mass index; CHOL, cholesterol; CL, control; LDL, low-density lipoprotein; OB, obese; OW, overweight; TG, triglycerides; WC, waist circumference; WHR, waist-to-hip ratio; yr, years old. Plus (+) indicates signiﬁcant direct relationship, negative (–) indicates signiﬁcant inverse relationship, and zero (0) indicates nonsigniﬁcant relationship between dietary factor and measure of adiposity.

2

Nationally representative study; all other studies are nonlongitudinal, nonnationally representative cross-sectional or case-control studies.

Obesity: Dietary and Developmental Inﬂuences

1

Dietary Inﬂuences on Energy Balance

TABLE 4.23 Does the Preponderance of Evidence Support a Relationship between Antioxidant Vitamin Intake and Lower Adiposity?1 Observational Studies2 Support Relationship? (number of studies)

Secular Trends Support Relationship? No

Mechanisms Support Relationship? No

Longitudinal (2 — intake) Inconclusive (intake) No studies (blood)

U.S. Nationally Representative CrossSectional (1 — blood concentrations) No studies (intake) Yes3 (blood)

Other CrossSectional or CaseControl (2 — intake; 15 — blood concentrations) Inconclusive (intake) Yes3 (blood)

Prevention Trials2 Support Relationship? (number of studies) Randomized Other Controlled Trials

(0) No studies

(0) No studies

Conclusion: Consistency of Evidence Supporting Relationship Inconclusive

Notes: 1

Description of criteria used for summary table is located in the methods section.

2

Numbers in parentheses indicate the number of relevant studies identiﬁed and examined for each study type and according to whether dietary intake or blood concentration was measured; with the exception of one nationally representative study and four of the other cross-sectional or case-control studies, all were conducted in adults.

3

Blood concentrations may not accurately reﬂect dietary intake or nutrient status.

139

140

Obesity: Dietary and Developmental Inﬂuences

VEGETABLES AND FRUITS INTRODUCTION Recently, the U.S. General Accounting Ofﬁce released a report that concluded that intake of vegetables and fruits should be increased to improve the health of the American population (GAO, 2002). Leading public health ofﬁcials have promoted vegetable and fruit consumption as part of low-fat and high-ﬁber diets for a variety of health reasons. Vegetables and fruits have been studied for their role in reducing the risk of chronic diseases such as cancer, coronary artery disease, stroke, hypertension, diverticulosis, and chronic obstructive pulmonary disease (Van Duyn, 2000). The speciﬁc relationship between vegetable and fruit intake and energy balance and obesity prevention, however, has not been ﬁrmly established.

SECULAR TRENDS According to national food supply data, between 1970–1974 and 2000 total fruit availability (including juice) increased by 17%, and total vegetable availability (including juice) increased by 27% (Figures 4.23 and 4.24). Data from the nationally representative Continuing Survey of Food Intake by Individuals (CSFII) likewise indicated that daily vegetable and fruit intake increased by nearly half a serving between 1989–1991 and 1994–1996 (GAO, 2002). Furthermore, Americans appear to be increasingly aware of the “5 A Day” message (Stables, 2002). Despite these positive trends, it is currently estimated that only one quarter of Americans eat ﬁve or more servings of vegetables and fruits daily (approximately 23% eating the recommended two to four daily servings of fruits and 41% eating the recommended three to ﬁve daily servings of vegetables) (GAO, 2002; Stables, 2002). Furthermore, the variety and nutritional quality of vegetables and fruits available and consumed remain limited. More than a third of the total daily vegetables in the U.S. food supply consisted of iceberg lettuce, frozen potatoes (mostly french fries), and potato chips (Putman, 2000). Potatoes alone accounted for about one third of vegetables consumed according to 1994–1996 CSFII data (Krebs-Smith, 2001). Of the more than 60 fruit products assessed in the national food supply in 2000, six (bananas, fresh apples, fresh grapes, watermelon, and apple and orange juice) accounted for just over half of total fruit (Putnam, 2000). Finally, because older

FIGURE 4.23 Availability of common fruits in the U.S. food supply, 1970–2000. (Source: USDA/ERS, 2003.)

Dietary Inﬂuences on Energy Balance

141

FIGURE 4.24 Availability of common vegetables in the U.S. food supply, 1970–2000. (Source: USDA/ERS, 2003.)

individuals, nonsmokers, and persons with higher education and/or income tend to eat more vegetables and fruits, the increases in intake may be explained in large part by shifts in demographics rather than a dramatic change in individual behaviors (Krebs-Smith, 2001). In a comparison of data from the 1990 and 1996 Behavioral Risk Factor Surveillance System, the proportion of adults reporting the consumption of vegetables and fruits at least ﬁve times a day increased among those who were physically active and normal weight but remained unchanged among those who were physically inactive and actually decreased among the obese (Li, 2000). In contrast to food supply data, a study developed by the Produce for Better Health Foundation used commercially collected National Eating Trends information to examine trends in frequency of eating fruits and vegetables. It identiﬁed a 10-year decline of 14% from 874 annual times consumed per capita in 1992 to 754 in 2002 (NPD, 2002). Vegetables accounted for 72% of the decline. However, it must be noted that actual quantity, i.e., measured servings, of consumption was not measured — only the number of times each fruit or vegetable was eaten.

PLAUSIBLE MECHANISMS On average, vegetables and fruits are low in energy density and fat and high in ﬁber content, dietary properties hypothesized to displace other more energy dense foods and allow the body to optimally regulate energy balance (see sections on energy density and ﬁber for further detail). Energy intakes, for example, have been shown to be relatively low for individuals consuming vegetarian diets, which tend to be higher in vegetables and fruits in comparison to most other omnivorous dietary patterns (Kennedy, 2001). Alternatively, as previously noted, there is evidence that people who eat more vegetables and fruits adopt other health-promoting habits as well (e.g., increased physical activity) that may reduce the risk of overweight (Serdula, 1996; Lowry, 2000; Stables, 2002).

OBSERVATIONAL STUDIES The observational studies are approximately equally distributed between those showing an inverse association (Kumanyika, 1994; Ortega, 1995a/b; Neumark-Sztainer, 1996; Kahn, 1997; Appleby, 1998; Trudeau, 1998; McCrory, 1999, 2000; Mascarinec, 2000; Kennedy, 2001; Rockett, 2001;

142

Obesity: Dietary and Developmental Inﬂuences

Bazzano, 2002; Flood, 2002; Lahti-Koski, 2002; Lin, 2002; Perez, 2002; Wardle, 2002) and those showing no relation (Wolfe, 1994; Serdula, 1996; Parker, 1997; Macdiarmid, 1998; Bandini, 1999; Heitmann, 1999; Hanley, 2000; Liu, 2000; Tanasescu, 2000; Terry, 2001; Boutelle, 2002; Schulz, 2002; Rissanen, 2003) between fruit and/or vegetable intake and a measure of body adiposity (Tables 4.24 and 4.25). Only one (Gillman, 1995) reported an increase in BMI with increased intake of vegetables and fruits. However, among the studies ﬁnding an inverse association, the results have not always been consistent by gender, age group, or type of produce (i.e., vegetable and fruit). For example, in the nationally representative analysis of weight status in children and adolescents by Lin (2002), a lower prevalence of overweight was associated with increased fruit intake in all age groups and both genders, but the relationship with vegetable intake was less consistent. Conversely, in a study of twin children, those with overweight or obese parents reported a lower liking for vegetables, but no difference in preference for fruits, in comparison to those with normalweight or lean parents (Wardle, 2002). In the study by Mascarinec et al. (2000), a vegetable-rich dietary pattern was related to a lower BMI among Chinese and Japanese women but not for white or native Hawaiian women. For white and native Hawaiian women, BMI was lower for those consuming a dietary pattern rich in fruit and fruit juice, although the ﬁnding was not signiﬁcant for the small sample of native Hawaiians. Results from three of the four longitudinal studies examined showed no relationship between weight change or BMI at follow-up and fruit or vegetable intake at baseline (Parker, 1997; Heitmann, 1999; Schulz, 2002). Only one study (Kahn, 1997) that evaluated change in vegetable intake found an inverse relationship. However, all four longitudinal studies examined baseline fruit or vegetable intake rather than change in intake over the observational period during which weight change was assessed. Few observational studies controlled for potential confounders such as physical activity (controlled for only in Kahn, 1997, Parker, 1997, Appleby, 1998, Heitmann, 1999, Perez, 2002 and Schulz, 2002) and dietary energy (controlled for only in Kahn, 1997, Parker, 1997, Appleby, 1998, Bandini, 1999 and Mascarinec, 2000). Four of the six studies (Gillman, 1995; Liu, 2000; Terry, 2001; Rissanen, 2003) designed speciﬁcally to test the association of vegetable and fruit intake on chronic disease outcome other than obesity failed to ﬁnd the expected inverse association with BMI. These studies may attract participants who are more health conscious than the population at large, so even control participants may have been more attuned to developing positive eating habits in response to factors that were prevalent in the environment and could not be controlled for, such as the National Cancer Institute’s national 5 A Day campaign. It is feasible that the relationship of vegetable and fruit intake with weight status varies according to type of produce predominantly consumed. Certain produce, for example, may typically be consumed with added calories from fat, such as when potatoes are fried. In the nationally representative study by Lin (2002), there was no relationship between total vegetable intake and weight status among adults; however, there was a positive relationship between weight status and potato intake. Clearly, this issue merits additional investigation.

PREVENTION TRIALS Numerous interventions have been designed to promote vegetable and fruit intake (e.g., Jeffery, 1997, 1998; Harris, 1998; Nicklas, 1998; Perry, 1998; Trevino, 1998; Reynolds, 2000; Marcus, 2001; Bhargava, 2002; Bernstein, 2002; Stevens, 2002; Baranowski, 2003; Beech, 2003; Steptoe, 2003; Story, 2003), but relatively few have assessed concurrently the change in fruit and/or vegetable intake and a measure of adiposity as an outcome variable (Table 4.6). Furthermore, because most interventions included program components in addition to promotion of vegetable and fruit consumption (such as other diet and physical activity prescriptions), in most cases, it is not possible to determine the extent to which an increase in fruit and/or vegetable intake was responsible for preventing overweight or weight gain when it occurred.

Study Name and/or Location

Study Population1

Longitudinal Studies Kahn, 1997 79,236 adults Cancer Prevention Study II, 21 50–64 yr at baseline States, U.S. White 10-year follow-up

Adiposity Measure2

BMI (self-report) Gain in waist circumference (selfreport)

Control Variables

Association3

Age, Education, Change in marital status, Country region, Baseline BMI, Slope of BMI from age 18 yr, Smoking, Energy intake at follow-up, Meat intake, Vitamin E supplementation, Alcohol intake, 10 physical activities, Reproductive characteristics for women

– (V) (baseline and followup V)

Schulz, 2002 Germany

17,369 adults 19–70 yr at baseline ethnicity not speciﬁed 2-year follow-up

Weight change per year used to classify Gender, Baseline age, weight and height, Education, subjects into ﬁve categories: Dietary change, Previous weight changes, Leisure Stable, small loss, large loss, small physical activity, Life and health satisfaction, gain, large gain (measured at baseline; Medications affecting body weight, Prevalent disease, self-report at follow-up) Menopausal status

0 (F) 0 (V) (baseline V/F)

Heitmann, 1999 Swedish Twin Registry, Sweden

548 adult twins 25–59 yr at baseline Ethnicity not speciﬁed 11-year follow-up

BMI (self-report)

Age, Zygosity, Baseline leisure physical activity, smoking and BMI

0 (F) 0 (V) (baseline V/F)

Weight change

Gender, Age, BMI, Smoking status, Physical activity, Total energy intake

0 (F) 0 (V) (baseline V/F?)

Parker, 1997 465 adults Pawtucket Heart Health 18–64 yr at baseline 94% White Program, New England, U.S. 4-year follow-up

– (vegetarian diet)

BMI [self-report]

BMI

4

– (V/F)

143

U.S. Nationally Representative Cross-Sectional Studies Kennedy, 2001 9,786 adults CSFII 1994–1996, U.S. ≥19 yr Ethnicity not speciﬁed Bazzano, 2002 9,608 adults First National Health and 25–74 yr Nutrition Examination Survey Ethnicity not speciﬁed Epidemiologic Follow-up Study, U.S.

Dietary Inﬂuences on Energy Balance

TABLE 4.24 Observational Studies of the Association of Vegetable and/or Fruit Intake with a Measure of Adiposity in Adults

144

TABLE 4.24 (CONTINUED) Lin, 2002 CSFII 1994–1996, U.S.

9,117 adults Age not speciﬁed Ethnicity not speciﬁed

Other Cross-Sectional Studies Terry, 2001 61,463 women The Swedish Mammography 40–74 yr Screening Cohort, Sweden Ethnicity not speciﬁed Perez, 2002 46,866 adults Canadian Community Health >20 yr Survey, Cycle 1.1; Canada Ethnicity not speciﬁed

Lahti-Koski, 2002 FINRISK Studies, Finland

45,490 women Age not speciﬁed Ethnicity not speciﬁed 39,876 women 45–75+ yr Ethnicity not speciﬁed 24,604 adults 25–64 yr Ethnicity not speciﬁed

Serdula, 1996 21,892 adults Behavioral Risk Factor ≥18 yr Surveillance System, 1990, 16 92% White, 8% Black States, U.S.

Age group, Gender (separate analysis)

BMI (self-report?)

4

0 (V/F)

BMI (self-report)

Gender (separate analysis), Physical activity, Smoking status, Alcohol-dependency, Chronic conditions, Selfperceived health, Age, Household income, Education, Marital status

– (V/F)

BMI (self-report)

4

– (F) – (V)

BMI (self-report?)

4

0 (F) 0 (V)

Obesity (BMI > 30)

Age, Education, Perceived health status

For men and women respectively: Age, Gender, Ethnicity, Education Underweight (BMI [self-report] ≤20.7, ≤19.1), Normal weight (>20.7–27.8, >19.1–27.3), Overweight (>27.8–31.1, >27.3–32.3), Very overweight (>31.1, >32.3)

– (F) 0 (V) except + for potatoes and + for vegetables in OW women)

– (V) but 0 (V) when controlled for other diet variables and smoking 0 (V/F)

Obesity: Dietary and Developmental Inﬂuences

Flood, 2002 Breast Cancer Detection Project, U.S. Liu, 2000 Women’s Health Study, U.S.

Overweight (BMI 25–29.9), Obese (BMI ≥ 30) (self-report)

6,631 adults ≥65 yr 95% white, 5% black

BMI category (<22, 22–29, ≥30)

Age, Gender Race/ethnicity, Special diet use

0 (F) – (V using one assessment method, but not another)

Appleby, 1998 Oxford Vegetarian Study, England

5,292 adults 20–89 yr Ethnicity not speciﬁed

BMI

Age, Gender, Social class, Smoking, Alcohol intake, Other diet variables, Physical activity, Parity

– (nonmeat eaters vs. meat eaters)

Rissanen, 2003 Kuopio Ischaemic Heart Disease Risk Factor Study, Finland

2,642 men 42–60 yr Ethnicity not speciﬁed

Obesity (BMI [measured?] > 30)

4

Trudeau, 1998 Washington State Cancer Risk Behaviors Survey, 1995–96

1,450 adults >18 yr Ethnicity not speciﬁed

Obesity (BMI (self-report) > 32.2 in women > 31.1 in men)

Age, Gender (separate analysis), Education, Marital status

Macdiarmid, 1998 1986–1987 Dietary and Nutrition survey of British Adults, England

1,239 adults 16–64 yr Ethnicity not speciﬁed

BMI

Age, Gender (separate analysis), Low energy reporting (separate analysis)

Gillman, 1995 Framingham Study, Massachusetts

832 men 45–65 yr Ethnicity not speciﬁed

BMI

4

Mascarinec, 2000 Hawaii

514 women Mean 54 yr White, native Hawaiian, Chinese/Japanese

BMI (self-report?)

Ethnicity (separate analysis), Energy intake

Ortega, 1995a Madrid, Spain

122 adults 65–89 yr Spanish

Obese (BMI ≥ 25)

Age, Gender, type of residence (institutionalized or freeliving)

0 (V/F)

Dietary Inﬂuences on Energy Balance

Kumanyika, 1994 The Cardiovascular Health Study; Pennsylvania, North Carolina, California, Maryland

– (F women only) 0 (V)

0 (F)

+ (V/F)

– (V Chinese/ Japanese only) – (F white only) dietary pattern analysis – (F) 0 (V)

145

146

TABLE 4.24 (CONTINUED) McCrory, 1999/2000 Massachusetts

71 adults 20–79 yr Ethnicity not speciﬁed

% Body fat (cases obese)

Age, Gender

– (V based on dietary variety)

Notes: 1

Based on convenience samples except for Ortega, 1995a; Serdula, 1996; Parker, 1997; Kennedy, 2001; Bazzano; 2002; Perez, 2002.

2

BMI based on measured weight and height unless otherwise speciﬁed.

3

Plus (+) indicates signiﬁcant direct relationship, negative (–) indicates signiﬁcant inverse relationship, and zero (0) indicates nonsigniﬁcant relationship between dietary factor and measure of adiposity.

4

Study and analysis not designed to test relationship between vegetable and fruit intake and BMI, so control for potential confounders was not undertaken.

Key to abbreviations: F, fruits; V/F, vegetables and fruits combined; PA, physical activity; V, vegetables.

Obesity: Dietary and Developmental Inﬂuences

Study Name and/or Location

Study Population1

U.S. Nationally Representative Cross-Sectional Studies Lin, 2002 2,181 children CSFII 1994–1996 and Supp. 5–12 yr CSFII Children’s Survey 883 adolescents 1998, U.S. 13–18 yr Ethnicity not speciﬁed Other Cross-Sectional Studies Neumark-Sztainer, 1996 36,284 adolescents Minnesota Adolescent Health 12–20 yr Survey, Minnesota 86% white; 8% black Rockett, 2001 16,281 children and adolescents Growing Up Today Study, U.S. 9–14 yr 96% White Boutelle, 2002 8,330 adolescents The Voice of Connecticut 7th, 9th, and 11th grades 74% Youth Survey white, 9% black, 7% Hispanic 1,797 children 6–12 yr Mostly White

Hanley, 2000 Sandy Lake Health and Diabetes Project, Canada Wardle, 2001 Sample from the Twins Early Development Study, England and Wales

242 adolescents 10–19 yr Native Canadians 428 twin children (214 families) 4–5 yr Ethnicity not speciﬁed

Overweight (BMI [self-report] ≥ 95th percentile CDC/NCHS)

Control Variables

Age group, Gender (separate analysis)

Overweight (BMI [self-report] >23.8) Overweight (BMI [self-report] > 85th percentile NHANES I) Weight status: (BMI [self-report] normal [15th–85th percentile], overweight [85th–95th], obese [>95th]) BMI Triceps skinfold Arm fat area Overweight (BMI > 85th percentile NHANES III) Parental weight status (overweight/obese parents vs. normal weight/lean parents [selfreport BMI for fathers, measured BMI for mothers])

–

Age

Association3

– (F) – (V; boys only)

– (F) 0 (V) – (V/F)

Grade, Gender, Ethnicity, School, Parent socioeconomic status

0 (V) 0 (F)

Grade, Age in grade, Ethnicity, Height, SES, Family structure, School lunch, Number of siblings, Maternal employment, Breakfast skipping, Food diversity, Number of snacks, Food group pattern Age, Gender

0 (no V other than potatoes or tomato sauce)

–

0 (V)

– (V preference) 0 (F preference)

147

Wolfe, 1994 New York

Adiposity Measure2

Dietary Inﬂuences on Energy Balance

TABLE 4.25 Observational Studies of the Association of Vegetable and/or Fruit Intake with a Measure of Adiposity in Children

148

TABLE 4.25 (CONTINUED) Ortega, 1995b Spain Tanasescu, 2000 Connecticut Bandini, 1999 Boston

64 adolescents 15–17 yr Spanish 53 children (29 obese, 24 control) 7–11 yr Puerto Rican 42 adolescents (21 obese, 21 control) 12–18 yr Ethnicity not speciﬁed

Overweight (BMI >23.0)

–

Obesity (BMI > 85th percentile)

Gender (many others tested, but not included in ﬁnal model)

% Body fat (cases obese)

Gender, Underreporting energy intake

0 (F) 0 (V) 0 (V) 0 (F, excluding juice) 0 (V, excluding potatoes) 0 (F, excluding juice)

Notes: 1

Based on convenience samples except for Lin, 2002.

2

BMI based on measured weight and height unless otherwise speciﬁed.

3

4

Study and analysis not designed to test relationship between vegetable and fruit intake and BMI, so control for potential confounders was not undertaken.

Key to abbreviations: F, fruits; V/F, vegetables and fruits combined; PA, physical activity; V, vegetables.

Obesity: Dietary and Developmental Inﬂuences

Plus (+) indicates signiﬁcant direct relationship, negative (–) indicates signiﬁcant inverse relationship, and zero (0) indicates nonsigniﬁcant relationship between dietary factor and measure of adiposity.

Author, Year, Name and/or Location

Study Population

Study Design and Strategies

Adults Kristal, 2000 Puget Sound Eating Patterns Trial, Washington State

1,205 adults 18–69 yr 87% white, 4% black, 5% Asian

Randomized Controlled Trial; Self-help education program to promote lower fat and higher fruit and vegetable consumption.

Cox, 1998 Reading and Glasgow, U.K.

125 adults Randomized Controlled Trial; 16–65 yr Education to promote fruit Ethnicity not speciﬁed and vegetable consumption.

Djuric, 2002 97 women (with a Nutrition and Breast family history of Health Study, breast cancer) Detroit, Michigan 21–50 yr 75% white

Children Gortmaker, 1999 Planet Health, Boston, Massachusetts

1,295 children 6th–8th grades 63–69% white, 11–15% black, 11–16% Hispanic

Length of Intervention and Timing of F/U Measure

Relevant Targeted Behaviors

Targeted

Reported Change

1-year intervention and 12-mo f/u

↓ Dietary Fat, ↑ Intake of Fruit and Vegetables.

↓ Dietary Fat, ↑ Intake of Fruit and Vegetables.

8 weeks

↑ Intake of Fruit and Vegetables.

↑ Intake of Fruit and Vegetables, 0 Difference in calories (↓ in both Intervention and Control groups), ↑ CHO, Total Sugars. Groups 1 and 3: ↓ Fat, ↓ Calories, Groups 2 and 3: ↑ Fruit and Vegetables, Group 2: ↑ Calories, Slight ↓ Fat Group 4: 0 Change.

Randomized Controlled Trial; Individual counseling program to decrease cancer risk Group1: ↓ fat Group 2: ↑ fruit + vegetables Group 3: ↓ fat, ↑ fruit + vegetables Group 4: control.

1 year

↓ Intake of Fat, ↑ Intake of Fruit and Vegetables, ↑ Fruit and Vegetable Variety, ↓ Intake of Soda, Desserts, Breads, 0 Change in Total Calories.

Randomized Controlled Trial; School-based health education program.

2 school years

↓ TV viewing, ↓ Intake of highfat foods, ↑ Fruit and vegetable intake ↑ Moderate and vigorous physical activity.

↓ TV ↑ Fruit and vegetable intake (girls only) ↓ Energy increment (girls only) 0 Change in high-fat food intake 0 Change in physical activity.

Results: Impact on Measure of Adiposity Direction of Change Relative to Controls Control Variables

Comments

0 Baseline value, (Self-reported BMI) Gender, Age, Ethnicity, BMI Status, Household income (Others tested but not included). 0 (Weight)

Trend toward ↓ BMI.

↓ (Weight and % body fat in Group 1) ↑ (Weight and % body fat in Group 2) 0 (Weight and % body fat in Groups 3 and 4)

Study designed to maintain energy intake.

↓ (Obesity based on BMI + TSF >85th percent-tile, Girls only)

Only TV viewing associated with reduction of obesity.

149

Baseline value of behavioral variable, Intervention status, Randomization pairs, Gender, Age, Ethnicity, Baseline obesity, BMI and TSF (Other variables tested but not included in ﬁnal model).

Dietary Inﬂuences on Energy Balance

TABLE 4.26 Prevention Intervention Studies of Effects of Vegetable and/or Fruit Intake on a Measure of Adiposity in Adults or Children

150

TABLE 4.26 (CONTINUED) Resnicow, 1992 Know Your Body comprehensive school health education program, New York, New York

1,209 children 6–13 yr Mostly Hispanic

203–303 children 8–10 yr Some ethnic minority children

Muller, 2001 Kiel Obesity Prevention Study (KOPS), Kiel, Germany

297 children Nonrandomized controlled 5–7 yr trial; Combined schoolEthnicity not speciﬁed based and family-based program aimed at prevent in weight gain and/or reducing body weight.

3-month and 1-year f/u measure

Epstein, 2001 Childhood Weight Control and Prevention Program, Buffalo, New York

27 intervention families (at least 1 obese [BMI >85th percentile] parent + a nonobese [BMI <85th percentile] child 6–11 yr Mostly white

6-mo intervention and 1-year f/u measure

Randomized, noncontrolled trial; Parent-focused behavioral intervention on parent and child eating changes, and weight control treatment for parents.

12 months

↑ Health knowledge, ↑ Vegetable and heart healthy indices.

↑ Health Knowledge at 3-yr f/u, ↑ Consumption of vegetables and hearthealthy foods, ↓ Consumption of meat and desserts.

0 (BMI)

Age, Ethnicity, Gender, Baseline values.

Inﬂuence diet and physical activity behaviors, ↑ Consumption of fruits and vegetables, ↓ Consumption of foods high in fat, ↓ Consumption of foods and drinks high in sugars. ↑ Fruit and vegetable consumption, ↓ Intake of highfat foods, ↑ Physical activity to at least 1 h/d, ↓ TV viewing to less that 1 h/d. ↑ Fruit and vegetable intake ↓ High-fat and high-sugar food intake.

↑ Vegetable intake, ↓ Fruit intake in obese children in intervention, ↑ Intake of foods and drinks high in sugar in overweight children in intervention, ↑ Sedentary behavior in the overweight children in intervention

0 (BMI)

Gender, Age, Baseline BMI.

↑ Fruit and vegetable consumption, ↑ Consumption of lowfat foods, ↑ Physical activity, which was associated with ↓ TV viewing.

↓ (Body fat)

↑ Fruit and vegetable intake ↓ High-fat and highsugar foods.

Notes: Key to abbreviations: ↓, decrease; ↑ increase; 0, no change; f/u, follow-up; CHO, carbohydrate; TSF, triceps skinfolds.

0 (Percentage of overweight)

Schools matched for sociodemographic variables.

Correlations showed no differences by age or gender.

Obesity: Dietary and Developmental Inﬂuences

1.5-year intervention and 3-year f/u measure

Sahota, 2001 Active Programme Promoting Lifestyle in Schools (APPLES), Leeds, U.K.

Nonrandomized controlled trial; School-based multicomponent health education program (Classroom curriculum, School-wide health activities and Environmental modiﬁcations). Randomized controlled trial; School-based, multicomponent program to reduce risk factors for obesity.

Dietary Inﬂuences on Energy Balance

151

In none of the three identiﬁed and relevant studies in adults (Cox, 1998; Kristal, 2000; Djuric, 2002) was an increase in vegetable and fruit intake associated with a decrease in a measure of adiposity. All three studies were randomized, controlled trials, and two were one year in duration, presumably providing enough time for changes in weight to occur. It should be noted, however, that one trial was designed to reduce cancer risk, in which case prevention of weight gain was not a goal and indeed the study aimed to not change energy intake. Interestingly, the group that increased vegetable and fruit intake without promoting other dietary changes reported an increase in energy intake (despite counseling participants to maintain their calorie consumption at baseline levels) and a concomitant gain in body weight and fat (Djuric, 2002). Of the ﬁve identiﬁed prevention interventions in children, the majority of which were multicomponent school-based trials, and only two of which were both randomized and controlled, only two reported a reduction in adiposity, despite measured increases in vegetable and fruit intake (Gortmaker, 1999; Müller, 2001). In the randomized, controlled 2-year school-based Planet Health study of 1295 ethnically diverse children in grades 6–8, vegetable and fruit intake increased and overweight decreased after controlling for baseline weight status among girls, but not among boys, in the intervention compared to control schools. However, further analysis of the data revealed that the reduction of overweight in girls was associated only with a decrease in TV viewing and not with an increase in vegetable and fruit intake (Gortmaker, 1999). In the 3-month school- and familybased trial of nearly 300 5- to 7-year-old children in Kiel, Germany, a signiﬁcant decrease in body fat was achieved. However, it was not reported which of the numerous changes in targeted behaviors was signiﬁcantly associated with this decrease, and separate analyses were not conducted by gender (Muller, 2001). In a parent-focused comprehensive 6-month weight-control program involving 30 families (27 with complete data) with at least one obese parent and one nonobese 6- to 11-yearold child, half were randomly assigned to decrease their fat and sugar intake, and half counseled to increase their vegetable and fruit intake. Only adults were instructed to consume a hypocaloric diet. In this study, parents in the vegetable and fruit group had signiﬁcantly greater intake of vegetables and fruits and weight loss than parents in the other group (Epstein, 2001). However, no signiﬁcant change in weight status was observed among the children, despite a signiﬁcant increase in vegetable and fruit intake. Likewise, in randomized, controlled (Sahota, 2001) or noncontrolled (Resnicow, 1992) multicomponent school-based trials, no change in adiposity was detected in children, despite reported increases in vegetable and fruit intake. It must be acknowledged that the majority of interventions resulted in relatively small increases in vegetable and fruit intake (on the order of less than or equal to a serving per day). It is possible that a larger increase in intake of vegetables and fruits is necessary before any change in body weight occurs. It is logical to expect that, in the face of a substantial substitution of vegetables and fruits for other foods and no change in energy expenditure, energy balance will be maintained, and no change in weight status will occur unless a decline in total energy intake is simultaneously achieved. Examination of the relationship of increases in certain types of vegetables and fruits with BMI and other adiposity measures is also needed.

CONCLUSION Based on secular trend data, it does not appear that vegetable and fruit consumption (or lack thereof) can be implicated as a major factor in the recent secular increase in overweight and obesity that has been observed in the national population. However, mechanistic data and a sizeable number of observational studies tend to support that vegetable and fruit intake is a determinant of adiposity. Results from prevention trials are not very compelling; however, these results are limited by generally small changes in vegetable and fruit intake in the absence of a decrease in energy consumption, in which case measurable changes in weight status would not be anticipated. In all, the cumulative data on fruit and vegetable intake are moderately consistent in supporting a relation to decreasing adiposity (Table 4.27).

152

TABLE 4.27 Does the Preponderance of Evidence Support a Relationship between Vegetable and Fruit Intake and Lower Adiposity?1 Observational Studies2 Support Relationship? (number of studies) Longitudinal Secular Trends Support Relationship?

Mechanisms Support Relationship?

(4 in adults 0 in children)

U.S. Nationally Representative CrossSectional (3 in adults 1 in children)

Inconclusive

Yes

Other CrossSectional or CaseControl (15 in adults 9 in children)

Prevention Trials2 Support Relationship? (number of studies) Randomized Other Controlled Trials (3 in adults 2 in children)

(0 in adults 3 in children)

Inconclusive

Inconclusive

Conclusion: Consistency of Evidence Supporting Relationship

Adults Yes

Moderate

Yes Children No studies

Yes

Inconclusive

Notes: 1

Description of criteria used for summary table is located in the methods section.

2

Numbers in parentheses indicate the number of relevant studies identiﬁed and examined for each study type.

Inconclusive

Inconclusive

Obesity: Dietary and Developmental Inﬂuences

No

Dietary Inﬂuences on Energy Balance

153

SWEETENED BEVERAGES AND FRUIT JUICE INTRODUCTION The absence of strong evidence implicating dietary sugar as a determinant of energy imbalance (see carbohydrate section) does not preclude the possibility that certain sources of dietary sugar may be more “obesigenic” than others. Beverage sources of sugar have been commonly criticized in both the popular press and public health arenas in the “battle against the bulge.” The term sweetened beverages is used herein to describe those beverages to which sugar is added, such as regular (nondiet) soft drinks, fruit drinks, fruitades, and sports drinks. Fruit juices connote beverages that contain only water and fruit juice and no added sugar (i.e., only sugars contained naturally in fruit).

SECULAR TRENDS Soft drink consumption parallels the secular increase in overweight. Data from national surveys indicate that, in recent decades, dramatic increases have occurred in nationwide production (Jacobson, 2001) and consumption of soft drinks (Borrud, 1996; Gerrior, 1998; Morton, 1998; Cavadini, 2000) (Figures 4.25 and 4.26). Per capita soft drink consumption in the U.S. has increased by nearly 100% since the 1970s. In 1997, over three quarters of soft drinks consumed were sugar sweetened, while a relatively smaller percentage was consumed as diet drinks (Putnam, 1999). Portion sizes have also increased dramatically from an average serving size of 6.5 ﬂ oz (88 calories) in the 1950s to the 12 ﬂ oz (160 calories) and 20 ﬂ oz (266 calories) servings common today (Jacobson, 2001; Nielsen, 2003). While the average fast food restaurant serving of soda in 2002 approximated 23 ﬂ oz, chains commonly have available sizes ranging from 32 to 64 ﬂ oz (Young, 2003). Among children, soft drink consumption increased by 23%, while ﬂuid milk consumption decreased by 16% between the late 1970s and early 1990s (USDA, 1996). Figure 4.27 shows the increases in beverage intake by 2- to 17-year-olds between 1989 and 1995 alone. Of all beverages examined, the most dramatic increase has occurred for soft drinks; sugar-sweetened beverages have become the primary source of added sugars in the diets of U.S. children (Morton, 1998). Data from the USDA’s National Food

FIGURE 4.25 Annual soft drink production in the U.S. (12-oz cans per person). (Source: USDA/ERS, 2003.)

154

Obesity: Dietary and Developmental Inﬂuences

FIGURE 4.26 Per capita carbonated soft drink and milk consumption in the U.S. (Source: USDA/ERS(c), 2003.)

FIGURE 4.27 Changes in beverage intake among 2- to 17-year-olds, from the Continuing Survey of Food Intake by Individuals 1989–1991 vs. 1994–1995. (Source: Morton, 1998.)

Consumption Survey show that consumption of nondiet soft drinks by 12- to 19-year-olds has more than doubled between the late 1970s and late 1990s (Jacobson, 2001, Bowman, 2002; Tables 4.28 and 4.29). Media spending in the U.S. for the promotion of soft drinks has likewise increased: in 1997, major beverage companies spent $631 million on advertising, up from $381 million in 1986 (Beverage Digest, 1998). Consumption of regular (nondiet) soft drinks is particularly high among children. In the 1994–1995 Continuing Survey of Food Intake by Individuals (CSFII) carbonated soft drink consumption represented 55% (lowest income group) to 64% (highest income group) of total beverages consumed by children (Kennedy, 1997). The majority of children were nonconsumers of diet soft drinks (89% of school aged children, 86% of adolescents). According to data from NHANES III,

Dietary Inﬂuences on Energy Balance

155

TABLE 4.28 Consumption of Regular (Nondiet) Soft Drinks by 12- to 19-Year-Olds, Including Nondrinkers, from the NFCS 1977–1978, 1987–1988, 1994–1996 Mean oz per Day Boys Girls 7 6 12 7 19 12

Year 1977–1978 1987–1988 1994–1996

Mean Percent of Calories Boys Girls 3 4 6 5 9 8

Source: Jacobson, 2001.

TABLE 4.29 Consumption of Regular and Diet Soft Drinks by 12- to 19-Year-Olds, Excluding Nondrinkers, from the NFCS 1977–1978, 1987–1988, 1994–1996 Mean oz per Day Year 1977–1978 1987–1988 1994–1996

Boys 16 23 28

Girls 15 18 21

Source: Jacobson, 2001.

non-Hispanic white adolescents drink less regular soda but more of other sweetened beverages than black and Hispanic adolescents (Figures 4.28–4.30). Adolescent and young adult males were the most avid consumers of soft drinks. Among boys 12 to 19 years old who consumed soft drinks, the average intake was nearly 2.5 12-oz sodas (28 oz) per day (Jacobson, 2001). Adolescence represents a time during which soft drink consumption increases dramatically (Morton, 1998; Harnack, 1999; Guthrie, 2000) (Table 4.30). In the 1994–1996 CSFII, 53% of 2- to 5-year-olds reported consuming soft drinks at least once in two days, a number that increased to 62 and 81% among 6- to 11- and 12- to 19-year-olds, respectively. Average portion size also increased from 7.2 ﬂ oz per serving in 2- to 5-year-olds to 10.6 ﬂ oz and 15.1 ﬂ oz in 6- to 11- and 12- to 19-year-olds, respectively (Smiciklas-Wright, 2002). According

FIGURE 4.28 Consumption (times per month) of regular (nondiet) soda by black, Hispanic, and non-Hispanic white adolescents (13–17 years of age), from NHANES III.

156

Obesity: Dietary and Developmental Inﬂuences

FIGURE 4.29 Consumption (times per month) of all sweetened beverages (fortiﬁed with vitamin C) by black, Hispanic, and non-Hispanic white adolescents (13–17 years of age), from NHANES III.

FIGURE 4.30 Consumption (times per month) of diet soda by black, Hispanic, and non-Hispanic white adolescents (13–17 years of age), from NHANES III.

TABLE 4.30 Proportion of Children Consuming Soft Drinks, by Age Category School Age (n = 810) (Age 6–12) Nonconsumers Less than 9 oz 9 or more oz

35.9% 31.8% 32.3% Adolescents (n = 423) (Age 13–18)

Nonconsumers Less than 13 oz 13–25.9 oz 26 or more oz

17.5% 32.2% 28.1% 22.2%

Source: Harnack, 1999.

to the study by Ludwig et al. (2001), a majority (57%) of children (mean age of 11.7 years at baseline) increased their intake of sweetened beverages over the 1.5-year study period, with a quarter drinking more than one additional serving daily. In a 4-year longitudinal study of 2- to 6year-olds, intake of carbonated and other sweetened beverages increased signiﬁcantly while intake

Dietary Inﬂuences on Energy Balance

157

of 100% fruit juice declined (Skinner, 2001). Lytle et al. (2000) reported that, as students moved from elementary to junior high and middle school, the proportion of beverages consumed that were soft drinks more than tripled while milk and 100% fruit juice consumption plummeted. High schools are a signiﬁcantly greater source of soft drinks than elementary or middle schools. For example, nationally, 25% of elementary school children, 62% of middle/junior high school children, and 92% of high school students have access to soda from vending machines or other a la carte venues (Wechsler, 2001). In a study of 19 junior and senior high schools in Minnesota, soda and fruit drinks ranked second only to cookies as the highest category of a la carte food sales (Harnack, 2000). Soda and other sweetened beverage vending have increased dramatically in schools. According to the Center for Commercial Free Public Education, exclusive soda contracts with school districts rose nationally from 46 to 240 between 1998 and 2000 (Oakland Tribune, 2000). Although the focus of seemingly less attention, there has been a marked increase in per capita consumption of other sweetened beverages in addition to soda. For example, the average portion size of fruit drinks consumed by Americans has increased from 11 oz/day in 1977–1978 to 15 oz/day in 1994–1996 (Nielsen, 2003). In addition, sports drinks have become increasingly popular in the past decade and are currently estimated as the sixth largest beverage category (Sﬁligoj, 1998). Estimated 1997 per capita consumption was 6.1 liters (Beverage Digest Company, 1998). Coffees and teas, many of which are presweetened or have sugar or fat calories added by the consumer, have also become one of the fastest growing beverage markets. For example, in 1997 per capita ready-to-drink tea consumption was over 9.3 liters (Sﬁligoj, 1998). Secular trends in 100% fruit juice consumption based on food disappearance data are illustrated in Figure 4.31. It appears that there has been a slight increase in fruit juice consumption in the last several decades, particularly among noncitrus juices. According to the 1994 CSFII, mean daily intake for total fruit juice was 5.1 oz among 2- to 3-year-olds and 3.5 oz/day among 4- to 5-yearolds (Riddick, 1994).

PLAUSIBLE MECHANISMS An increase in total energy intake without an increase in physical activity will result in increased BMI or overweight. In the 1980s (NHANES II data), soft drinks were the seventh largest contributor of energy to the diet of U.S. adults, contributing nearly 5% of total calories (Block, 1985). By 1996 (CSFII data), the contribution of soft drinks had increased to nearly 7% of total calories among

FIGURE 4.31 Average annual per capita consumption of 100% fruit juice in the U.S. (Source: Putnam, 1999.)

158

Obesity: Dietary and Developmental Inﬂuences

adults (from DiMeglio, 2000). According to the 1989–1991 CSFII, soft drinks were the sixth leading food source of energy and the second leading food source of carbohydrate among U.S. children (Subar, 1998). According to the 1994–1996 CSFII, soft drinks were the leading caloric beverage of choice (USDA, 1997). Soft drinks were the major beverage contributor to energy intake among adolescents, providing 8% of total calories (Troiano, 2000). Furthermore, high consumption of soft drinks has been associated with higher intake of carbohydrate (Morton, 1998) and total energy (Guenther, 1986; Harnack, 1999) (Figure 4.32). Adolescents drinking an average of 265 mL or more of soft drinks daily consumed almost 835 kJ more total energy every day than those drinking other types of beverages (Harnack, 1999). Children (grades 4–6) who consumed the largest amounts of sweetened drinks took in 330 daily calories more and 62% less fruits than children with the lowest intake of sweetened beverages (Cullen, 2002). Furthermore, a greater consumption of soft drinks had been shown to account for a large percentage of the increase in energy intakes that has occurred in the past decade (Chanmugam, 2000). On a per capita basis, during 1994–1996, Americans 2 years and older consumed 16% of total energy intake from added sweeteners, by far the largest source of which was from sweetened beverages (regular soft drinks, 33%; fruit drinks, 10%) (Guthrie, 2000; Krebs-Smith, 2001). While there is no clear scientiﬁc evidence that consumption of sugar per se contributes to weight gain (Hill, 1995; also see section on carbohydrates), a large body of evidence suggests that “liquid sugar” or energy consumed as a liquid may be less well regulated than energy consumed in a solid form (Mattes, 1996). Based on a review of the literature over the past 25 years (which included over 40 published studies in humans), Mattes (1996) found that a mean of 64% of an energy challenge (i.e., an additional source of food energy) as a solid was offset by dietary adjustment, while only 9% of a liquid challenge was compensated for by subsequent changes in energy intake. This poor compensation for energy provided in ﬂuid form has been documented for a variety of beverages (e.g., coffee, alcohol, soda, fruit juice, milk). Furthermore, it has been shown to occur in acute studies as well as during longer, more nutritionally relevant time periods. For example in a 3 week study, relative to subjects ingesting 1.12 L/day of diet soda, subjects ingesting the same volume of regular cola gained weight corresponding very closely to the energy contributed by the high fructose corn syrup (Tordoff, 1990). Even when liquid and solid loads were matched for carbohydrate content, subjects failed to compensate for the energy in the liquid load (DiMeglio, 2000). Furthermore, when beverages (sweetened and unsweetened) were provided with the same ﬂavoring agent, sweetened beverages were more highly consumed than unsweetened ones (BeridotTherond, 1998). Although the mechanism(s) responsible for this lack of caloric compensation have not been determined, several have been proposed (Mattes, 1996; DiMeglio, 2000). Beverages, because they typically are quickly consumed and absorbed by the gastrointestinal tract, may not stimulate satiety

FIGURE 4.32 Mean energy intake by level of soda consumption for adolescents (adjusted for age, race, and gender; CSFII 1994). (Source: Harnack, 1999.)

Dietary Inﬂuences on Energy Balance

159

signals to the same extent as solids. Furthermore, there is evidence that ﬂuids may not be as effective in eliciting the cephalic phase of insulin release (Teff, 1994). In rats, ﬂuid diets have been shown to enhance food intake and weight gain, independent of an inﬂuence on diet palatability (Ramirez, 1987, 1988). The metabolic rate may be acutely higher after ingestion of solids than liquids, resulting over time in weight gain with chronic consumption of liquid calories (Habas, 1998). It is also possible that thirst and hunger mechanisms are not fully integrated, particularly if beverages are calorically rich (i.e., beverages may be consumed to satisfy thirst, even if in so doing more energy is consumed than required for energy balance). Having presumably evolved on a diet where water was the primary beverage, humans may not have fully developed mechanisms to compensate for consumption of energy-dense beverages. Moreover, the dietary response to beverages may have a cognitive basis; because beverages are typically less calorically dense than solids, through experience, we may not “learn” to compensate as adequately. Even when the energy density of a liquid and solid meal has been matched, intake was higher after the liquid meal (Tournier, 1991). Furthermore, although sweetened beverages are classiﬁed according to the food guide pyramid as “desserts” or “extras,” it is not clear whether beverages such as soft drinks are treated as such by consumers. In the Forbidden Food Study, adding sugar to foods or beverages was reported to be a “forbidden” practice by nearly 30% of 6- to 19-year-old children interviewed. However, consumption of soft drinks (which compositionally are mainly sweeteners added to water) was reported as “never or almost never allowed” by less than 10% of children (O’Dea, 1999). In national survey data of teens (NFCS, 1977–1978), soft drinks were as likely to be consumed at meals as at snack time (Guenther, 1996). Because sweetened beverages taste good, they may be more likely to be consumed in excess than unsweetened beverages. For example, in one study, preschoolers drank more chocolate milk than plain milk, resulting in increased energy intake during meals in which sweetened milk was served (Wilson, 2000). The nature of the sweetener in sweetened beverages may also be inﬂuential. During the past several decades, there has been a dramatic rise in the use of high fructose corn syrup in the U.S. (Figure 4.33). According to the U.S. Department of Agriculture Food Consumption Surveys of 1987–1998, 39 g (or approximately 40%) of the sugar consumed per capita was fructose, which represents approximately 9% of the average energy intake (Gibney, 1995; Sigman-Grant, 1997).

FIGURE 4.33 Per capita food disappearance data for sweeteners in the U.S. HFCS = high-fructose corn syrup; others include glucose syrups, dextrose, honey, and other edible syrups. (Source: USDA/ERS(c), 2003.)

160

Obesity: Dietary and Developmental Inﬂuences

About one third of dietary fructose came from natural sources (primarily fruits and vegetables), and two thirds was added to beverages and foods (Park, 1993). Soft drinks and fruit-ﬂavored drinks accounted for about half of the daily intake of added fructose among adolescents (Park, 1993). Although fructose has been shown to stimulate less insulin secretion and leptin production in comparison to other simple sugars (Teff, 2001), the metabolic effects of fructose remain controversial (Glinsman, 1993; Hellerstein; 1996; Daly, 1997; Bantle, 2000). While there is evidence that increased fructose consumption results in reduced fat oxidation and increased lipogenesis compared to other carbohydrates (Tittelbach, 2000; Van Gaal, 1999), other studies have failed to ﬁnd any adverse effects of dietary fructose (McDevitt, 2000). It has been suggested that certain groups of individuals may be adversely affected by high intake of fructose (Glinsman, 1993; Daly, 1997). It is also possible that consumption of soda and other sweetened beverages may affect weight status by displacing nutrients critical for proper regulation of energy balance. Speciﬁcally, dietary calcium has been related to adiposity (please see section on minerals). The secular increase in adiposity among children, for example, has occurred at a time during which dietary calcium intake has declined, a change related primarily to a drop in ﬂuid milk consumption (Albertson, 1997) and replacement with soda and other sweetened beverages. The odds of children (Harnack, 1999) and adults (Guthrie, 1996) consuming adequate calcium decreases signiﬁcantly as soda consumption increases (Harnack, 1999).

OBSERVATIONAL STUDIES Numerous observational studies, the majority of which have been conducted in children, suggest that consumption of excessive soft drink or other sweetened beverages is related to overweight (Tables 4.31 and 4.32). For each additional serving of a sweetened beverage consumed daily over a 1.5-year period among school-aged children in a prospective study, the risk of becoming overweight increased 60% after controlling for other potentially confounding variables (Ludwig, 2001). Furthermore, based on national survey data, beverage consumption in general, and soft drink consumption in particular, were generally higher among overweight than nonoverweight children and adolescents (Troiano, 2000). Among California 9- to 11-year-old children, overweight or atrisk children drank more soft drinks and fruit drinks compared to children who were not at risk of overweight (1.4 vs. 1.1 servings/day), although these results were not adjusted for differences in ethnicity or socioeconomic status (Public Health Institute, 2001). In several studies of adults, dietary patterns characterized by high consumption of sweetened beverages have been associated with overweight (Wirfalt, 1997; Millen, 2001; Sichieri, 2002) (see dietary patterns sections for additional information). In an analysis of the 1986–1987 Dietary and Nutrition Survey of British Adults that focused on sugar and fat intake, sugar intake was not related to BMI among women when soft drinks were excluded from the sugar products (p = 0.70) but was nearly signiﬁcantly related to a higher BMI when soft drinks were included (p = 0.06). Interestingly, lower sugar intakes (whether soft drinks were included or not) were signiﬁcantly associated with higher BMIs among men in this study (Macdiarmid, 1998). Not all observational studies have found a signiﬁcant relationship between sweetened beverage intake and adiposity. In a small (n = 21), short-duration (4–8 weeks) study, weight gain was not signiﬁcantly related to consumption of sweetened beverages (Mrdjenovic, 2003). Likewise, no difference was seen in soft drink consumption in two small case-control studies comparing obese and nonobese children (Bandini, 1999; Tanasesucu, 2000). Likewise, in a fairly homogeneous sample of primarily white 9- to 14-year-old youth, overweight boys (p = 0.06) and girls (p = 0.05) consumed more drinks than nonoverweight youth. However, the only speciﬁc beverages that were consumed signiﬁcantly more by overweight than nonoverweight youth were diet soda (for boys and girls) and sugared iced tea (for girls) (Rockett, 2001). Forshee and Storey (2003) also found that overweight youth consumed more diet soda, but not regular soda or regular fruit drinks.

Dietary Inﬂuences on Energy Balance

161

TABLE 4.31 Observational Studies of the Association of Sweetened Beverage Intake with a Measure of Adiposity in Adults Study Name and/or Location

Study Population1

Longitudinal Studies French, 1994 3,552 adults Health Worker Mean 38 yr Project, Minnesota 2-yr follow-up

Adiposity Measure2

Weight

Control Variables

Association3

Gender (separate analysis), Age, Smoking status, Education, Occupation, marital status, Treatment group, Baseline body weight, (Physical activity, dieting history also examined in model)

0 (servings of nondiet soft drinks) (Note: +at baseline in women only)

Notes: 1

Based on convenience sample.

2

BMI based on measured weight and height unless otherwise speciﬁed.

3

Plus (+) indicates signiﬁcant direct relationship, negative (–) indicates signiﬁcant inverse relationship, and zero (0) indicates nonsigniﬁcant relationship between dietary factor and measure of adiposity.

However, overweight was assessed in this study on the basis of reported (rather than measured) height and weight, and sweetened beverages were analyzed separately rather than combined together, which conceivably could have altered the results. In a study conducted of children in Spain, no association was observed between soft drink consumption and BMI (Rodríguez-Artalejo, 2003). The extent to which differences in beverage classiﬁcation may have contributed to disparate ﬁndings is not clear. In a large longitudinal study of German adults (Schulz, 2002), which reported no relation between “soft drink” intake and weight change over a 2-year period, the term soft drinks was used to include mineral and tap water, fruit juices, lemonade, cola (presumably diet and regular), and multivitamin drinks. Calorie-containing beverages would presumably have different affects on risk of weight gain from noncaloric ones. Furthermore, several of the studies assessed soft drink intake but did not include other sweetened beverages. In conclusion, the studies that have failed to ﬁnd a relationship between sweetened beverage consumption and overweight have generally been of weaker design than those that have found a relationship. Compared to sweetened beverages, there is less evidence from observational studies that 100% fruit juice can be implicated in the development of overweight (Table 4.33). Dennison et al. (1997) and Tanasescu et al. (2000) found that high intakes of fruit juice were associated with childhood overweight, while others (Riddick, 1997; Skinner, 1999; Alexy, 1999; Kloeblen-Tarver, 2001) reported no relation between 100% fruit juice and overweight among preschool children. One limitation of the Tanasescu study was that what was reported as fruit juice may have included artiﬁcially ﬂavored beverages with little or no fruit juice. However, this does not appear to have been a weakness of the Dennison study. Dennison et al. (1997) found that preschool children who consumed more than 12 oz/day (n = 19) of 100% fruit juice were at increased risk of short stature and overweight. In a further analysis, Dennison et al. (1999) found that, among the different types of fruit juices, only apple juice was signiﬁcantly related to child BMI. Skinner et al. (2001) was unable to replicate these ﬁndings in a recent study of preschool-aged children. Unlike previous studies, Skinner followed the children longitudinally (from 24 to 72 months of age). No relation was seen between 100% juice intake and anthropometric measures. One criticism of the Skinner study is that only three children in this study consumed ≥12 oz/day over time, and only one of these had ≥12 oz/day at all seven dietary interviews. However, when consumption of fruit juice was examined as a continuous variable using

162

TABLE 4.32 Observational Studies of the Association of Sweetened Beverage Intake with a Measure of Adiposity in Children Study Name and/or Location

Study Population1

Adiposity Measure2

Control Variables

Association3

Longitudinal Studies Ludwig, 2001 Planet Health subsample, Massachusetts

548 children 11–12 yr 64% White, 15% Hispanic, 14% Black, 8% Asian, 8% other 2 year follow-up

BMI Obesity (BMI and TSF ≥ 85th percentile)

Baseline anthropometry, Demographics, Dietary variables, Physical activity

+ (servings of sweetened beverages)

Mrdjenovic, 2003 New York

21 children and adolescents 6–13 yr 76% White 4–8 week follow-up

BMI

Age, Gender, Energy intake

0 (servings of sweetened beverages)

U.S. Nationally Representative Cross-Sectional Studies 10,371 children and adolescents 2–19 yr Ethnically diverse

Overweight (BMI >95th percentile)

Age, Gender, Ethnicity

Forshee, 2003 CSFII 1994-96, 1998

3,311 children and adolescents 6–19 yr Ethnically diverse

BMI (self-report; continuous) BMI >85th percentile (self-report; dichotomous)

Age, Gender, Ethnicity, Family income

Rockett, 2001 16,882 children and adolescents Growing Up Today Study, 9–14 yr U.S. (baseline data of 96% White longitudinal study)

Overweight (BMI [self-report] ≥ 85th percentile)

Age

Rodrguez-Artalejo, 2003 Four Provinces Study, Spain

BMI

Energy intake

+ (% energy from soft drinks) 0 (regular soda) 0 (regular fruit drinks)

Other Cross-Sectional or Case-Control Studies

1,112 children 6–7 yr Hispanic

0 (servings of sweetened beverages except + for sugared iced tea for girls) 0 (sweetened soft drinks)

Obesity: Dietary and Developmental Inﬂuences

Troiano, 2000 NHANES III

814 children 9–11 yr Ethnically diverse

Overweight/at risk (BMI [self-report] ≥ 85th percentile)

Tanasescu, 2000 Connecticut

53 children (29 obese, 24 control) 7–10 yr Puerto Rican

Obesity (BMI ≥ 85th percentile)

Bandini, 1999 Massachusetts

43 adolescents (21 obese, 22 control) 12–18 yr Ethnicity not speciﬁed

Obesity (based on % body fat; 21% nonobese; 43% obese)

–

Gender (many tested, but not included in ﬁnal model) –

+ (servings of soft drinks and fruit drinks)

0 (soft drinks) 0 (energy intake from soft drinks)

Dietary Inﬂuences on Energy Balance

Public Health Institute, 2001 California Children’s Healthy Eating and Exercise Practices Survey

Notes: 1

Based on convenience samples except for Troiano, 2000.

2

BMI based on measured weight and height unless otherwise speciﬁed.

3

Plus (+) indicates signiﬁcant direct relationship, negative (–) indicates signiﬁcant inverse relationship, and zero (0) indicates nonsigniﬁcant relationship between dietary factor and measure of adiposity.

163

164

TABLE 4.33 Observational Studies of the Association of 100% Fruit Juice Intake with a Measure of Adiposity in Children Study Name and/or Location Longitudinal Studies Alexy, 1999 Dortmund Nutritional and Anthropometrical Longitudinal Designed Study, Germany Skinner, 1999 Tennessee

Skinner, 2001 Tennessee (same population as above)

Study Population1

U.S. Nationally Representative Cross-Sectional Studies Forshee, 2003 3,311 children and adolescents CSFII 1994-96, 1998 6–19 yr Ethnically diverse Riddick, 1997 830 children CSFII 1994 2–5 yr Ethnically diverse Other Cross-Sectional or Case-Control Studies Dennison, 1999 223 children New York (same population as below) 2 and 5 yr Mostly white Dennison, 1997 168 children New York 2 and 5 yr Mostly white

BMI Weight gain (g/day)

Control Variables

Association3

–

0

BMI Ponderal index

Age, Gender, Maternal height

BMI Ponderal index At risk (BMI ≥ 85th percentile) Overweight (BMI ≥ 95th percentile)

Gender, Height at 24 months, Longitudinal energy intake, Parents’ heights (or BMI)

Reported BMI (continuous) BMI ≥ 85th percentile (dichotomous)

Age, Gender, Ethnicity, Family income

BMI (self-report)

–

0

0 (BMI) – (Ponderal index)

0

0

BMI Ponderal index

Age, Gender, Energy intake (except juice), Maternal height

+ (apple juice only)

Obesity (BMI ≥ 90th percentile; Ponderal index ≥ 90th percentile)

Age, Gender, Maternal height, Child agegender interaction

+ (≥12 ﬂ oz/day)

Obesity: Dietary and Developmental Inﬂuences

205 children 3–5 yr Ethnicity not speciﬁed 3-year follow-up 105 children 2–3 yr at baseline White 4-mo. to 1-year follow-up 72 children 2–3 yr at baseline White 4-year follow-up

Adiposity Measure2

Tanasescu, 2000 Connecticut

87 children 1–5 yr 95% Black 53 children (29 obese, 24 control) 7–10 yr Puerto Rican

Overweight (BMI ≥ 95th percentile)

Obesity (BMI ≥ 85th percentile)

–

Maternal BMI, Maternal marital status, TV viewing, Dairy intake, Gender (many others tested, but not included in ﬁnal model)

0

+

Notes: 1

Based on convenience samples except for Riddick, 1997.

2

3

BMI based on measured weight and height unless otherwise speciﬁed.

Plus (+) indicates signiﬁcant direct relationship, negative (–) indicates signiﬁcant inverse relationship, and zero (0) indicates nonsigniﬁcant relationship between dietary factor and measure of adiposity.

Dietary Inﬂuences on Energy Balance

Kloeblen-Tarver, 2001 Georgia

165

166

Obesity: Dietary and Developmental Inﬂuences

some of the same data, there was still not a signiﬁcant association between intake and BMI. In fact, children with a higher intake of fruit juice were more likely to have a lower ponderal index (an indicator of weight status analogous to BMI but calculated as weight divided by height to the third power) (Skinner, 2001). Furthermore, in a USDA study (based on data from the 1994 CSFII) with 78 (out of a total sample size of 830) 2- to 5-year-olds in the sample who reported 12 oz or more of 100% juice daily for 2 days, no relation was seen between fruit juice consumption of 12 or more ﬂuid ounces per day and BMI (Riddick, 1997). Likewise, in a study of older children from the national CSFII surveys, no relation between fruit juice consumption and overweight was observed (Forshee, 2003). Finally, in a study of preschoolers enrolled in WIC, 79% of whom reportedly consumed ≥12 ﬂ oz of fruit juice daily, no relation was observed between 100% fruit juice consumption and BMI (Kloeblen-Tarver, 2001). All of the studies have been conducted in young children or adolescents; no studies of adults were located. This has been justiﬁed on the basis of consumption patterns of fruit juice, though it may be an important future consideration to assess the patterns of caloric beverage intake in adults as they are consuming a large amount of soft drinks, fruitades, and sport drinks. According to national surveys, by age ﬁve, mean intake of fruit drinks and fruitades (as well as soft drinks) exceeded the intake of 100% fruit juice (Rampersaud, 2003). Thereafter, fruit juice declines and is replaced by soft drinks and other beverages (Skinner, 2001). The American Academy of Pediatrics recently recommended that fruit juice consumption be limited to 4–5 oz/day for children 1–6 years of age and 8–12 oz/day for children 7–18 years of age (AAP, 2001). According to recent national surveys, average intake of fruit juice is within these recommendations (4.6 oz/day for children 6 months–6 years old, 3.4 oz/day for children 7–18 years old) (Rampersaud, 2003). These recommendations, however, were based on consideration of nutrient and gastrointestinal problems; more research was viewed necessary before overweight could be considered a consequence of excess fruit juice consumption (Conlon, 2001). The USDA has stressed the important contribution to nutrient intake that 100% fruit juices can make and advise that fruit juice consumed in quantities consistent with the Dietary Guidelines for Americans is advantageous for healthy children. For example, among 2- to 5-year-old U.S. children, 31–38% of daily vitamin C, 7–8% of daily folate, and 8–12% of daily potassium was provided by fruit juice, while only 3–5% of daily calories came from fruit juice (Riddick, 1997). Furthermore, restricting 100% fruit juice consumption may result in increased consumption of less nutritious beverages. While fruit juice consumption tends to decrease with age in children, consumption of soft drinks and other sweetened beverages tends to increase (Skinner, 2001). In the analysis of the CSFII data, compared to children consuming ≥12 oz of fruit juice daily, children consuming lower levels of fruit juice tended to consume less milk and more fruit drinks and soft drinks (Riddick, 1997).

PREVENTION TRIALS Few intervention studies have been performed to test the hypothesis that reducing the consumption of “liquid sweets” reduces or prevents overweight (Table 4.34). To date, only two interventions have been conducted, both only in children, primarily school-based, and aiming to inﬂuence numerous factors in addition to consumption of sweetened beverages (Teufel, 1998; Sahota, 2001). One component of the Zuni Diabetes Prevention Program designed to reduce diabetes risk factors among high school age youth was to reduce availability of soft drinks in vending machines and provide water coolers at schools (Teufel, 1998). At the midpoint of this 4-year study, there was a signiﬁcant reduction in soft drink consumption and a nonsigniﬁcant decrease in overweight. However, this program also included other components (e.g., an integrated health and nutrition curriculum, a school wellness facility for promotion of physical activity, development of supportive social networks, and modiﬁcation to the school lunch program) such that the independent effects of soft drink consumption cannot be determined. In another multicomponent school-based trial, despite

Study Name and/or Location

Study Population

Study Design and Strategies

Relevant Targeted Behaviors

Length of Intervention and Timing of F/U Measure

Targeted

Reported Change

Results: Impact on Measure of Adiposity Direction of Change Relative Control Variables to Controls1

Teufel, 292 children Nonrandomized, 3 school years ↓ Sweetened beverages ↓ Sweetened beverage intake 1998 9th–12th grade noncontrolled trial; (midpoint intake 0 Change in ﬁber intake Zuni Diabetes Native School-based, evaluation of 4- ↑ Fiber intake by ↑ Physical ﬁtness. Program, Zuni American multicomponent diabetes year study) increasing fruit and Indian risk factor reduction vegetables Reservation, New program (Student ↑ Physical ﬁtness. Mexico, U.S. education, School food service changes, School Wellness Center, Supportive social networks).

0 (BMI)

↑ Vegetable intake, ↓ Fruit intake in obese children from the intervention schools, ↑ Intake of foods and drinks high in sugar in overweight children in the intervention School, ↑ Sedentary behavior in the overweight children in the intervention school.

0 (BMI)

Sahota, 203–303 Randomized controlled 2001 children 8–10 trial; School-based, Active Programme yr multicomponent Promoting Some ethnic intervention to reduce Lifestyle in minority risk factors for obesity. Schools children in (APPLES), Leeds, sample U.K.

12 months

Inﬂuence diet and physical activity behaviors, ↑ Consumption of fruits and vegetables, ↓ Consumption of foods high in fat, ↓ Consumption of foods and drinks high in sugars.

Comments Trend toward reduction in BMI.

Dietary Inﬂuences on Energy Balance

TABLE 4.34 Prevention Intervention Studies of Effects of Sweetened Beverage Intake on a Measure of Adiposity in Children

Gender, Age, Baseline BMI.

Notes: 1

167

↑ = increase; ↓ = decrease; 0 = no signiﬁcant change or association; f/u = follow-up.

168

Obesity: Dietary and Developmental Inﬂuences

efforts to reduce consumption of foods and drinks high in sugar, there was an unexpected increase in intake in such foods among overweight children in the intervention group compared to overweight children in the control group. The extent to which, if any, the increase in consumption of sweetened beverages and other sweets contributed to the overweight status of the children in the intervention is not clear (Sahota, 2001). According to 1994–1995 CSFII data, among females 12–19 years of age, 55% of sodas consumed were purchased from grocery stores, 25% from fast-food and other restaurants, and 9% from school cafeterias and vending machines (Bowman, 2002), suggesting that successful interventions must target home as well as school meal purchases. In a pilot family-based intervention designed to promote consumption of water in the place of sweetened beverages in 8–10-year-old African-American girls, despite a decrease in consumption of sodas and similar beverages, no change in adiposity was detected. No interventions involving restriction of 100% fruit juice consumption were identiﬁed.

CONCLUSION Excessive consumption (i.e., in excess of energy needs) of any caloric beverage could, presumably via the same “liquid sugar” mechanism, lead to weight gain that, if continued over time, would result in the development of overweight. However, the evidence that 100% fruit juice contributes to excessive weight gain or the recent rise in adiposity among U.S. children is conﬂicting, based on observational studies. It is possible that certain types of juices (e.g., apple juice) are more conducive to excessive consumption than others. Because of the potential nutritional beneﬁts and the lack of data showing a deleterious effect of consuming 100% fruit juice, it is premature to suggest that fruit juice should be curtailed on a population-wide basis to prevent overweight. It appears more likely that consumption of sweetened beverages such as soda and fruit drinks, which are much more widely consumed and have little nutritional value, contributes to increased energy intake and thereby excessive weight gain and may be a contributing factor to the recent rise in adiposity in the U.S. population. Secular trend data, mechanistic studies, and observational studies of strong design among children all support that the intake of sweetened beverages is an important determinant of adiposity. In summary, the cumulative evidence suggests that sweetened beverage intake is moderately consistent in relation to adiposity, while the evidence is inconclusive with respect to the relationship between 100% fruit juice consumption and overweight (Tables 4.35 and 4.36).

RESTAURANT-PREPARED FOODS INTRODUCTION Considerable interest has been recently generated on the relationship between obesity and eating restaurant-prepared foods, especially at fast food restaurants (i.e., quick serve establishments that feature order counters or windows, and self seating and service). The best-selling book, Fast Food Nation (Schlosser, 2001), brought much negative attention to the industry, implicating fast food as a primary culprit in the rise of obesity in the nation. Economists have blamed the obesity epidemic on the rising reliance on fast foods and convenience foods due to the increased number of adults (particularly women) working away from home, leaving less time for the labor-intensive activity of food preparation (Chou, 2002). More recently, The National Alliance for Nutrition and Activity distributed their report, From Wallet to Waistline (NANA, 2002), which looked at the role of “value” marketing (i.e., providing a cost savings for larger purchases) in the fast food industry that might encourage overeating and obesity. There are occasional calls for a special tax on fast food or warning labels on foods as a result of such negative attention.

Observational Studies2 Support Relationship? (number of studies) Longitudinal Secular Trends Support Relationship?

Mechanisms Support Relationship?

(1 in adults 2 in children)

U.S. Nationally Representative CrossSectional (0 in adults 2 in children)

Inconclusive

No studies

Other CrossSectional or CaseControl (0 in adults 5 in children)

Prevention Trials2 Support Relationship? (number of studies) Randomized Other Controlled Trials (0 in adults 1 in children)

(0 in adults 1 in children)

No studies

No studies

Conclusion: Consistency of Evidence Supporting Relationship

Dietary Inﬂuences on Energy Balance

TABLE 4.35 Does the Preponderance of Evidence Support a Relationship between Sweetened Beverage Intake and Higher Adiposity?1

Adults Yes

No studies

Moderate

Yes Children Yes

Yes

Inconclusive

Inconclusive

Inconclusive

Notes: 1

Description of criteria used for summary table is located in the methods section.

2Numbers in parentheses indicate the number of relevant studies identiﬁed and examined for each study type; the ﬁve studies included in the meta-analysis by Davies (2000) are included individually in the above count by study type.

169

170

TABLE 4.36 Does the Preponderance of Evidence Support a Relationship between 100% Fruit Juice Intake and Higher Adiposity?1 Observational Studies2 Support Relationship? (number of studies) Longitudinal Secular Trends Support Relationship?

Mechanisms Support Relationship?

(0 in adults 3 in children)

U.S. Nationally Representative CrossSectional (0 in adults 2 in children)

No studies

No studies

Other CrossSectional or CaseControl (0 in adults 4 in children)

Prevention Trials2 Support Relationship? (number of studies) Randomized Other Controlled Trials (0 in adults 0 in children)

(0 in adults 0 in children)

No studies

No studies

Conclusion: Consistency of Evidence Supporting Relationship

Adults No studies

Inconclusive

Yes Children No

No

Yes

Notes: 1

Description of criteria used for summary table is located in the methods section.

2

Numbers in parentheses indicate the number of relevant studies identiﬁed and examined for each study type.

No studies

No studies

Obesity: Dietary and Developmental Inﬂuences

No

Dietary Inﬂuences on Energy Balance

171

SECULAR TRENDS There is a clear trend in the U.S. that more food is prepared and consumed away from home. Between the 1970s and 1990s, the energy consumed from fast food by adults increased over threefold, from approximately 60 kcals/day to over 200 kcals/day (Cutler, 2003). Analogously, during this same time period, the amount of their food dollar that households spent on foods eaten away from home nearly doubled (Figure 4.34), percentage of meals and snacks eaten away from home increased from 16 to 27% (Harnack, 2000), and percentage of total calories consumed as food prepared away from home increased from 18 to 32% (Guthrie, 2002). At the same time, the number of per capita fast food restaurants has doubled, and the number of full service restaurants has increased by 35% (Chou, 2002). Between 1970 and 2003, restaurant sales grew at a rate of 7.2% per year. In 2003, restaurant food sales were expected to reach a record $426.1 billion, representing the 12th consecutive year of industry growth (National Restaurant Association, 2003). Fast food restaurants represent an increasing percentage of away-from-home food purchases; by the 1990s, fast food comprised 34% of total sales of food outside of the home, a dramatic increase from only 4% in the 1950s (Putnam, 1996). In 2000, the average annual household expenditure for food away from home was $2,137, equivalent to $855 per person, a number projected to grow (National Restaurant Association, 2003). In 2001, advertising for the top nine brands of fast food was estimated at $3.5 billion (Welch, 2003).

PLAUSIBLE MECHANISMS As reviewed by McCrory et al. (1999), people are eating more food away from home, and that food is of lower nutritional quality (higher in fat and lower in ﬁber) than food eaten at home. In a study of 7- to 17-year-old children, restaurant meals were higher in fat and calories than meals consumed at home (Zoumas-Morse, 2001). In an analysis of adolescent eating habits from two national surveys, eating fast food and eating breakfast or lunch away from home or school was associated with consuming foods low in nutrient density and high in fat (Dausch, 1995). According to data from 17,370 adults and children in the 1994–1996 and 1998 CSFII, compared to those not eating at fast food establishments, those who reported eating fast food had higher intakes of energy, fat, and carbonated soft drinks, and lower intakes of dietary ﬁber, fruits and vegetables, and milk (Paeratakul, 2003; Bowman, 2004). In one study, women who ate out more often consumed nearly 300 more calories per day than those who ate out less often (Clemens, 1999). In another study,

FIGURE 4.34 Proportion of food dollars spent for foods eaten away from home, by U.S. households. (Source: Schwenk, 1995.)

172

Obesity: Dietary and Developmental Inﬂuences

children ate over 300 more calories at a meal consumed out compared to at home (Zoumas-Morse, 2001). High-fat, low-ﬁber diets have been implicated in obesity development due to increased energy density and thus passive overeating (Bray, 1998). “Value” marketing that encourages purchase of higher calorie meals and large portion sizes may contribute to the larger calorie intake of restaurant meals compared to those served at home. Portion sizes served at restaurants commonly exceed standard serving sizes (Center for Science in the Public Interest, 2003), and being served a larger portion at a restaurant has been shown to increase the amount consumed (Diliberti, 2004). Increased palatability and variety, possibly being more important determinants of caloric intake than fat content, might also increase food intake of restaurant food compared to food served at home (McCrory, 1998, 2000).

OBSERVATIONAL STUDIES Two longitudinal studies, one nationally representative cross-sectional study, and ten other crosssectional studies of eating out in relation to a measure of adiposity were identiﬁed (Tables 4.37 and 4.38). In both of the longitudinal studies and in the national representative study, a positive association between fast food consumption and adiposity was observed. The results from the other cross-sectional studies were approximately equally mixed between those showing a positive association, no association, or an inverse association. In the longitudinal study by French (2000), change in body weight was assessed annually over a 3-year period. This analysis was performed on the same cohort of women that was assessed crosssectionally at entry into the Pound of Prevention intervention study by Jeffery and French (1998). Although this cohort included women from both higher and lower income strata, the ethnic diversity was limited. All subjects were recruited for a weight gain prevention program (the average BMI was 26–28), so one can conclude that this group was generally overweight and looking for ways to lose weight. Since the obese tend to underreport intake generally, and might underreport fast food intake especially, the association might be even stronger than reported in this study. In support of other ﬁndings, increased frequency of fast food restaurant patronage was associated with longitudinal increases in energy intake and proportion of energy from dietary fat. Interestingly, increased fast food intake over time was additionally associated with decreased physical activity. In the longitudinal Australian study, self-reported body weight and height were assessed in young women after 4 years and not during the interim period (Ball, 2002). Women who reported occasionally (once a week) eating “takeaway” food, which is roughly equivalent to fast food, were 15% more likely to gain weight than women who rarely or never ate such food (OR 0.85; 95% CI = 0.75–0.96, p = 0.008). Interestingly, women who frequently (>once a week) ate fast food were not signiﬁcantly more likely to be weight gainers (though the trend was in the expected direction: OR 0.88; 95%CI 0.76–1.02, p = 0.09). Binkley et al. (2000) looked at the Continuing Survey of Food Intake by Individuals (CSFII) data to determine if restaurant and fast food intake was associated with BMI. The CSFII is a national survey of 16,103 adults in the U.S., collecting 24-h recalls by in-person interviews. As part of the survey, individuals were asked the source of each food item purchased, such as grocery store or restaurant. There was a positive association between eating out at fast food restaurants and BMI in both males and females. Furthermore, there was a signiﬁcant association between eating at any type of restaurant and higher BMI in males. The authors estimated that if you compared two males of 1.77 m height, one who had eaten at a restaurant during the previous 24 h and one that had not, the one that had eaten at the restaurant would weigh 1 kg more than the other. In a similar comparison of two females, 1.63 m in height, who have or have not eaten at a fast food restaurant, the one that had eaten at a fast food restaurant in the past 24 h would weigh 1 kg more. The percent fat in the diet had little impact on BMI. The authors judged that the impact of eating away from home had about the same effect on BMI as low physical activity.

Study Name and/or Location Longitudinal Studies Ball, 2002 Australian Longitudinal Study on Women’s Health French, 2000 Pound of Prevention Study, Minnesota

Study Population1

Adiposity Measure2

8,726 women “Weight maintainers” (≤5% Occupation, Student status, Parity, New mothers 18–23 yr BMI change) vs. ‘Weight (only those variables identiﬁed in univariate analysis to be Ethnicity not speciﬁed gainers’ (>5% increase in important) 4 years follow-up BMI) 891 women Weight Age, Income, Ethnicity, Marital status, Treatment, Baseline 20–45 yr BMI, Baseline frequency of fast food restaurant use 85% white 3 years follow-up

U.S. Nationally Representative Cross-Sectional Studies Binkley, 2000 16,103 adults BMI (self-report) CSFII 1994-1996

Age, Gender (separate analysis), Education, Income, Ethnicity, Smoking, Geographic location, Rural/urban, Physical activity habits, Special diets

10,863 adults BMI category (underweight Age, Gender, Income >18 yr (<18.5), acceptable weight Ethnicity not speciﬁed (18.5–<25), overweight (25–<30), obese (>30) 4,643 adults BMI category (<22, 22–29, Age, Gender, Ethnicity, Special diet use >65 yr ≥30) 95% white, 5% black 499 adults 20–70 yr 88% white

Obesity (BMI ≥30)

Age, Gender, Total physical activity, total energy intake, educational level (others tested but not included in ﬁnal model)

Association3

+ (fast food eaten “occasionally,” nonsigniﬁcant trend for fast food eaten “frequently”) + (fast food)

+ (fast food) + males only (restaurant food)

0 (proportion of energy from foods prepared outside the home) + (speciﬁed restaurant/fast foods) 0 (any fast food) + (breakfast or dinner eaten away from home) – (lunch eaten away from home)

173

Other Cross-Sectional Studies Burns, 2002 1995 National Nutrition Survey, Australia (nationally representative) Kumanyika, 1994 The Cardiovascular Health Study, Penn., N. Carolina, Calif., Maryland Ma, 2003 Seasonal Variation of Blood Cholesterol Study, Massachusetts

Control Variables

Dietary Inﬂuences on Energy Balance

TABLE 4.37 Observational Studies of the Association of Eating Restaurant-Prepared Foods with a Measure of Adiposity in Adults

174

TABLE 4.37 (CONTINUED) Jeffery, 1998 Pound of Prevention Study, Minnesota Clemens, 1999 U.S. McCrory, 1999 Massachusetts

198 men, 861 women BMI 20–45 yr Mostly white 129 women BMI (not speciﬁed) Premenopausal 65% white, 29% black 73 adults Body fat (measured by 19–80 yr hydrostatic weighing) Ethnicity not speciﬁed

Age, Gender (separate analysis), Education, Ethnicity, Marital status, Current smoking status Income, Education

Age, Gender, Education, Smoking status, Alcohol intake, Leisure physical activity

+ females only, especially lowincome (fast food) 0 (eating out) + (restaurant food)

Notes: 1

Based on convenience samples except for Binkley, 2000 and Burns, 2002.

2

BMI based on measured weight and height unless otherwise speciﬁed.

3

Plus (+) indicates signiﬁcant direct relationship, negative (–) indicates signiﬁcant inverse relationship, and zero (0) indicates nonsigniﬁcant relationship between dietary factor and measure of adiposity.

Obesity: Dietary and Developmental Inﬂuences

Study Name and/or Location Other Cross-Sectional Studies Mikkila,2002 School Health Promotion Study, Finland French, 2001 Project EAT, Minnesota

Public Health Institute, 2001 California Children’s Healthy Eating and Exercise Practices Survey Pastore, 1996 Health Screening Week Project, New York City

Study Population1

60,252 adolescents 14–16 yr Ethnicity not speciﬁed

Adiposity Measure2

Overweight (relative weight [selfreport] > 120%)

Socioeconomic status, School performance, Physical exercise (boys only), Use of alcohol (girls only), School lunch (boys only), Evening meals at home (girls only) BMI Grade, Gender (separate analysis), Ethnicity, Overweight (BMI ≥ 95th percentile) School, Socioeconomic status

4,710 adolescents 11–18 yr 49% white, 20% black, 19% Asian, 6% Hispanic, 4% native American 914 children Overweight/ 9–11 yr at risk (BMI [self-report] ≥ 85th Ethnically diverse percentile) 753 adolescents Mean 16 yr 66% African-American, 23% Hispanic, 8% white

Control Variables

Overweight (ideal body weight ≥ 120%), Overweight (IBW 110–119%), Normal weight (IBW 86–109%), Underweight (IBW ≤ 85%)

–

Gender (separate analysis)

Association3

– (fast food)

0 (fast food and OW) – (fast food and BMI, males only) + (fast food)

Dietary Inﬂuences on Energy Balance

TABLE 4.38 Observational Studies of the Association of Eating Restaurant-Prepared Foods with a Measure of Adiposity in Children

0 (fast food)

Notes: 1

Based on convenience samples.

2

BMI based on measured weight and height unless otherwise speciﬁed.

3

Plus (+) indicates signiﬁcant direct relationship, negative (–) indicates signiﬁcant inverse relationship, and zero (0) indicates nonsigniﬁcant relationship between dietary factor and measure of adiposity.

175

176

Obesity: Dietary and Developmental Inﬂuences

Jeffery and French (1998) reported that fast food intake was positively associated with body mass index (BMI) in women, especially low-income women, but not in men. The sample size was smaller for the men than the women in this study. The vast majority of the men were also white and well educated. The authors found that the frequency of fast food intake was positively associated with energy and fat intake in both men and women, which would explain the increased BMI seen with increased fast food intake. However, since TV watching was also positively correlated with BMI in women, low physical activity could be a mediator of the association of fast food consumption and increased BMI. McCrory et al. (1999) found a positive association between restaurant food intake and body fatness, independent of educational level, smoking and alcohol habits, and physical activity. The group studied had an average BMI of 24–25 and were primarily women of average age 52 years. The study looked at seven types of restaurants (fried chicken, burger, pizza, Chinese, Mexican, fried ﬁsh, and “other”) to determine if some restaurant types were more or less associated with obesity incidence. Consuming food at burger and fried chicken restaurants was associated with increased body fatness. The authors also found that frequency of restaurant intake was positively associated with energy and fat intake, and negatively associated with ﬁber intake, reinforcing other ﬁndings. Likewise, Kumanyika et al. (1994), in a study of older adults, found that increased BMI was associated with frequent consumption (one or more times per month) of certain restaurant foods (e.g., burgers, fried chicken, and Chinese food). Interestingly, in the latter study, even though certain restaurant foods were associated with obesity, there was no signiﬁcant association between BMI and simply “any fast food” patronage. It may be the case that consumption of certain foods at restaurants increases risk of weight gain. Ma et al. (2003) found that eating breakfast or dinner away from home was positively associated with obesity in adults, although the trend was not linear, particularly for the dinner meal. Meals eaten away from home were also found to be signiﬁcantly higher in energy and fat then meals eaten at home. For example, in comparison to breakfasts consumed at home, breakfasts eaten away from home were 105 kcals and 7% of kcals from fat higher. Adjustment for eating other meals from home did not change the relation of each meal with obesity risk, suggesting that the effect of each meal location is independent of the others. Interestingly, even though lunches eaten away from home tended to be higher in energy content than those consumed at home, the risk of obesity was reduced among those regularly consuming lunches away from home in comparison to in-home consumers. The authors suggest that this ﬁnding may in part be explained by the fact that subjects were not asked to distinguish whether meals were brought from home or purchased at a restaurant, a substantial limitation of this study in terms of assessing the effect of restaurant-purchased foods on weight status. Clemens et al. (1999) investigated the eating habits of 129 premenopausal women enrolled in a study looking at smoking and energy balance. Nutrient intake and frequency of eating out was assessed using a 7-day diet record. Subjects were categorized as either a “low eating out” group (deﬁned as ≤5 times per week) or a “high eating out” group (deﬁned as 6–13 times per week). The authors found no signiﬁcant difference in BMI between the low (mean BMI of 24.4) and high (mean BMI of 26.2) eating out group. However, the high eating out group was found to have a higher energy and fat intake. Likewise, Burns et al. (2002) recently reported no relationship between the proportion of energy consumed from foods prepared outside the home and BMI in a nationally representative sample of Australian adults, even though away from home food intake was associated with increased fat consumption and decreased dietary ﬁber and numerous micronutrients. Only four studies of the relationship between fast food and overweight in youth were identiﬁed, all of which were cross-sectional in design. French et al. (2001) examined fast food restaurant use among an ethnically diverse sample of 4,710 adolescents, grades 7 through 12, enrolled in 31 junior and senior high schools in a large metropolitan area of Minnesota. Students were asked about their frequency of fast food restaurant use in the past week. Fast food intake was similar between overweight and nonoverweight youth of both genders. However, BMI (examined as a

Dietary Inﬂuences on Energy Balance

177

continuous variable) was lower for those males who reported eating at fast food establishments three or more times per week compared to two or fewer times per week. Among females, BMI was not associated with reported fast food restaurant patronage. Consistent with other studies, frequency of fast food restaurant use was positively associated with intake of total energy, percent energy from fat, daily servings of soft drinks, cheeseburgers, french fries, and pizza, and was inversely associated with daily servings of fruit, vegetables, and milk, suggesting that teens who frequent fast food establishments consume a less nutritious diet. Another study of adolescents, this one of predominantly minority high school students in New York City, also found no relationship between reported fast food consumption (expressed in terms of the number reporting fast food intake more than three times in the prior week) and weight status (Pastore, 1996). In contrast, a study of younger children (elementary school age) in California found intake of fast food to be associated with overweight, though controlling for potential confounding variables was not done and weight status was based on self-reported weights and heights, which are of questionable validity in this age group (Public Health Institute, 2001). Based on a large sample of Finnish adolescents, fast food consumption was inversely associated with weight status (based on selfreported weights and heights) as well as with weight dissatisfaction (controlled for weight status), suggesting that overweight teens concerned about their weight may avoid (or underreport) fast food consumption (Mikkila, 2002). The extent to which these ﬁndings on fast food restaurants in Finland apply to the U.S. is not clear. In summary, observational studies support that frequently eating restaurant-prepared food is associated with adiposity in adults. The evidence, most notably from the longitudinal and nationally representative cross-sectional studies, is more compelling for eating fast food than eating out in general. Whether frequently patronizing other types of restaurants is related to weight status merits additional research. Also limited in terms of quantity of studies and strength of design (i.e., no longitudinal or nationally representative studies were identiﬁed) are analyses in children. The existing observational data in youth are much less compelling than the observational evidence for adults. A question that merits additional research is the extent to which people become obese because they eat away from home. Both of the prospective (longitudinal) studies that were evaluated included only women. Prospective studies, following weight gain over a long period of time in a large and varied cohort of people including youth, are needed to fully address this question.

PREVENTION TRIALS Despite an extensive search of the literature, no intervention studies looking at the effect of reducing fast food or restaurant intake on weight maintenance or prevention of weight gain were found. While considerable circumstantial evidence suggests that if people reduce intake from fast food and other restaurants their caloric intake will go down, we will not know if it is an effective obesity prevention strategy until it is properly tested.

CONCLUSION There is no question that people are eating away from home more often than in the past. Increases in portion size, variety, palatability, energy density, and dietary fat have all been implicated as potential causes of increased caloric intake that might lead to obesity. Since most of these variables are associated with intake at both fast food and other restaurants, it is difﬁcult at this time to determine whether fast food restaurants have any greater relationship with obesity development than eating at other types of restaurants. However, several observational studies agree that people who consume more food away from home, particularly from fast food establishments, tend to have a higher adiposity. Data suggest that the association is especially true for adults; less often has the

178

Obesity: Dietary and Developmental Inﬂuences

relationship been studied in children, and less consistent have been the results. The longitudinal and nationally representative studies are particularly compelling in implicating fast food restaurants, but there is little data in the other cross-sectional studies, with the exception of a study done in Finland, where restaurant access may be very different from that in the U.S., that contradicts this conclusion. Examining the evidence altogether suggests that eating out is moderately consistent in relation to adiposity (Table 4.39).

DIETARY PATTERNS INTRODUCTION While countless studies have examined the relationship between intake of individual nutrients or foods and risk of energy imbalance and obesity (topics that are reviewed in other sections of this paper), relatively few have systematically evaluated the relation of overall dietary patterns to this risk. Foods are eaten in distinct and identiﬁable patterns, which can be used to classify individuals (Millen, 1996). Examination of overall dietary patterns is conceptually appealing in that it mimics the way in which people eat, consuming meals and snacks consisting of a variety of foods, rather than isolated nutrients (Hu, 2000). Both factor and cluster analyses are valid methods for assessing dietary patterns. The major difference between the two approaches is that factor analysis is used to establish dietary patterns on the basis of which food groups correlate with each other, while, with cluster analysis, subjects are grouped together based on similarity with respect to a speciﬁc set of food groups. The dietary pattern methodology has been validated in studies of adults by establishing consistency between patterns of dietary behavior assessed by food frequency questionnaire and (1) nutrient intake determined by food records, and (2) biological and behavioral risk factors (Hu, 1999; Millen, 2001). The following reviews studies that examined if the pattern of what people eat is related to adiposity.

SECULAR TRENDS Most studies of dietary patterns have been conducted relatively recently (i.e., in the last several years), precluding systematic examination of secular trends over the last several decades. However, based on secular trends in speciﬁc food groups (e.g., soda consumption) and dietary behaviors (e.g., consumption of meals outside of the home), shifts in dietary patterns appear to have occurred during the period of the rise in the population weight status (please see other sections on food and beverage intake). Furthermore, a rapid replacement of traditional diets with a “Western” dietary pattern has occurred in recent decades in numerous countries outside of the U.S. (Mendoza, 1980; O’Dea, 1991; Bjerregaard, 1997; Yamada, 1997; Hsu, 2002; Noor, 2002; Rivera, 2002; Shetty, 2002).

PLAUSIBLE MECHANISMS The dietary patterns approach has successfully been applied to the study of cancer (Randall, 1992; Slattery, 1998) and coronary heart disease (Nicklas, 1989; Huifbregts, 1995; Hu, 2000), showing that a “Western” type diet (characterized by a relatively high intake of red meat, high-fat dairy, and reﬁned grains and a relatively low intake of fruits, vegetables, and whole grains) was associated with health risk. The Mediterranean diet pattern has been associated with low rates of a number of chronic diseases (Helsing, 1995). Furthermore, intervening to modify the consumption of multiple food components of the diet (i.e., increasing consumption of fruits, vegetables, and low-fat dairy) was used in the Dietary Approaches to Stop Hypertension (DASH) trial to successfully

Observational Studies2 Support Relationship? (number of studies) Longitudinal Secular Trends Support Relationship?

Mechanisms Support Relationship?

(2 in adults 0 in children)

U.S. Nationally Representative CrossSectional (1 in adults 0 in children)

Other CrossSectional or CaseControl (6 in adults 4 in children)

Prevention Trials2 Support Relationship? (number of studies) Randomized Other Controlled Trials

(0)

(0)

No studies

No studies

Conclusion: Consistency of Evidence Supporting Relationship

Dietary Inﬂuences on Energy Balance

TABLE 4.39 Does the Preponderance of Evidence Support a Relationship between eating Restaurant-Prepared Food and Higher Adiposity?1

Adults Yes Yes

Yes

Yes

Moderate

Yes Children No studies

No studies

Inconclusive

No studies

No studies

Notes: 1

Description of criteria used for summary table is located in the methods section.

2

Numbers in parentheses indicate the number of relevant studies identiﬁed and examined for each study type.

179

180

Obesity: Dietary and Developmental Inﬂuences

reduce blood pressure (Appel, 1997). The dietary pattern approach has also been effective in interventions to reduce other cardiovascular risk factors (Metz, 1997; McCarron, 1998). Alterations in patterns of food intake may affect energy balance through changes in calorie and macronutrient content, alterations in satiety, or a combination of mechanisms. The speciﬁc mechanisms whereby alterations in diet composition and food intake may affect adiposity are detailed in other sections.

OBSERVATIONAL STUDIES Analysis of food groups consumed to determine predominant dietary patterns have been applied to the study of obesity in numerous studies published in the past decade among adults (Table 4.40). Based on a comprehensive review of the literature, Togo et al. (2001) found that out of 18 studies using factor or cluster analysis, ﬁve (Randall, 1991; Hulshof, 1992; Slattery, 1998; Hu, 2000; Pryer, 2001b) reported a positive association between BMI or obesity and dietary patterns categorized as fatty, sweet, or energy dense, while similar patterns in three other studies (Gex-Fabry, 1988; Barker, 1990; Fraser, 2000) were negatively associated with BMI. The remainder found an association with a different pattern and BMI or obesity (Farchi, 1989; Tucker, 1992; Wirfalt, 1997; Gittelsohn, 1998; Greenwood, 2000) or no association (Huijbregts, 1995; Wolff, 1995; Schroll, 1996; Beaudry, 1998; Fung, 2001). In one study that examined dietary patterns longitudinally (at baseline and then again at two 4-year intervals), the prospective relationship between dietary pattern and body fatness was not assessed (Fung, 2001). In a prospective assessment of the relationship between dietary patterns and overweight, the crude risk of becoming overweight over a 12-year period was 41% for women in the “empty calorie” cluster compared to 24% for women in the “heart healthy” cluster. This translates into a 40% increase in overweight risk over baseline for women who consumed a diet rich in sweetened beverages, high-fat sweets, and empty-calorie snack foods and low in fruits, vegetables, and ﬁber-rich foods, compared to women whose diets more closely resembled current dietary recommendations (although this risk was attenuated after adjusting for multiple covariates) (Quatromoni, 2002). A longitudinal study with a 2-year mean follow-up also found that a “healthy” dietary pattern (characterized by a diet high in fruit, vegetables, reduced-fat dairy, and whole grains and low in red and processed meat, fast food, and soda) was associated with smaller gains in BMI and waist circumference (Newby, 2003). Furthermore, a 2-year longitudinal analysis on the basis of 24 food groups (analyzed as separate groups rather than as dietary patterns using factor or cluster analysis, which was incidentally mentioned as an appropriate goal) among a German cohort showed that intake of high-energy, high-fat food groups (e.g., fats, meats, and sauces) among women and intake of high-energy, high-sugar foods (e.g., soft drinks and sweets) among men was predictive of large weight gain, even after controlling for potential confounding factors (Schulz, 2002). To maximize comparability between results, only those studies using factor or cluster analysis have been emphasized in this section. Unfortunately, most dietary pattern studies have been cross-sectional in design. It is possible that failure of some studies to detect differences in BMI according to diet pattern and some of the differences between studies can be attributed to changes in diet adopted by overweight individuals attempting to lose weight. In the study by Tseng and DeVelles (2001), for example, no signiﬁcant difference in BMI was detected between individuals with a “Western” dietary pattern and a “prudent” dietary pattern. However, more individuals reporting attempted weight loss followed the “prudent” pattern than the “Western” pattern. One limitation of the present dietary pattern literature is that the focus of most studies has been older Caucasian adults. None of the studies published speciﬁcally examined dietary patterns of children or adolescents. The only study that included children, and incidentally one of the few studies of nonwhite subjects (Gittelsohn, 1998), combined data from the 10- to 20-year-olds with that of the over 20-year-olds, precluding distinct conclusions with respect to age group. Also notably lacking are longitudinal and nationally representative studies. Furthermore, numerous studies were

Study Name and/or Location

Study Population1

Diet Assessment2

Adiposity Measure3

Pattern Analysis Method

Control Variables

Dietary Patterns Identiﬁed

Pattern(s) Associated with Higher Weight Status

Pattern(s) Associated with Lower Weight Status

Longitudinal Studies Quatromoni, 2002 Framingham OffspringSpouse Cohort Study, U.S.

737 women Mean age 45 yr Mostly white 12-yr follow-up

FFQ (145 items)

Newby, 459 adults 2003 Mean age 59 yr Baltimore Longitudinal 95% white Study of Aging, 2-yr mean followMaryland up

Weight status (BMI < 24, 24.9–24.9, 25–29.9, ≥30)

Cluster (42 food categories)

Age, Energy intake, Physical activity, Smoking, Menopausal status, Intentional dieting, Weight pattern

“heart healthy” “light eating” “wine and moderate eating” “high fat” “empty calorie”

“empty calorie” “heart healthy” (12-year development of “light eating” overweight, unadjusted “wine and moderate for covariates) eating” “high fat”

BMI Waist circumference

Cluster (41 food categories)

Age, Gender, Ethnicity, Physical activity, Smoking, Education, Vitamin supplement use, Energy intake

“healthy” “white bread” “alcohol” “sweets” “meat and potatoes”

“meat and potatoes” (BMI) “white bread” (waist circumference)

“healthy”

Factor (? food categories)

Age, Gender, Geographic region

“red meat-starch” “vegetable-fruit”

No association

No association (but attempted weight loss associated with “vegetable-fruit”)

Dietary Inﬂuences on Energy Balance

TABLE 4.40 Observational Studies of the Association of Dietary Patterns with a Measure of Adiposity in Adults

U.S. Nationally Representative Cross-Sectional Studies Tseng, 2001 NHANES III

5,794 adults 20–74 yr White

FFQ (62 items)

Weight status (BMI < 25, 25–<30, ≥ 30)

Hulshof, 1992 3,781 adults Dutch National Food 19–85 yr Consumption Survey, White The Netherlands

2-day diet records

Weight status Cluster (BMI [self(26 food categories) report] 25–29.9, ≥ 30)

–

“Cluster 1” (high fat, low “Cluster 1” (high fat, low alcohol) alcohol) “Cluster 2” (high kcal/fat/alcohol) “Cluster 7” (moderate fat, low alcohol, high ﬁber/micronutrients) (8 clusters total)

BMI (self-report)

–

“Western” “prudent”

Other Cross-Sectional and Case-Control Studies 44,875 men 40–75 yr Mostly white

FFQ (131 items)

Factor (40 food categories)

“Western”

“prudent”

181

Hu, 2000 Health Professionals Follow-Up Study, U.S.4

182

TABLE 4.40 (CONTINUED) –

FFQ (217 items)

BMI, Obesity (BMI [method not stated] ≥ 30)

Cluster (74 food categories)

Sánchez-Villegas, 2003 3,847 adults Mean 34–39 yr Seguimiento Universidad de Navarra, Spain4

FFQ (136 items)

Obesity (BMI [self-report] ≥ 30)

Factor (30 food categories)

Hulshof, 1992 3,781 adults Dutch National Food 19–85 yr Consumption Survey, White The Netherlands

2-day diet records

Weight status Cluster (BMI [self(26 food categories) report] 25–29.9, ≥ 30)

Slattery, 1998 2,410 adults Western U.S. (controls Median 67 yr in cancer study) White, black, and Hispanic

Diet history

BMI (self-report)

Factor (35 food categories)

Age, Gender

Sichieri, 2002 Brazil

FFQ (80 items)

Weight status (BMI < 18.5, 18.5–24.9, 25–29.9, ≥ 30)

Factor (16 food categories)

Age, Dieting, Leisure “Western” activity, Occupation “mixed” “traditional”

2,266 adults 20–60 yr Mostly Hispanic or mixed Hispanic and black

“monotonous low-quantity omnivores” “health conscious” “traditional meat chips and pudding eaters” “higher-diversity, traditional omnivores” “conservative omnivores” “low-diversity vegetarians” “high-diversity vegetarians”

Age, Gender, Marital “Western” status, Education, “Spanish-Mediterranean” Health-related profession, Leisure activity, Smoking, Disease history –

“monotonous lowquantity omnivores” “traditional meat chips and pudding eaters” “higher-diversity, traditional omnivores” “conservative omnivores”

“low-diversity vegetarian” high-diversity vegetarian” (not clear if differences signiﬁcant)

“Western” (p = 0.01 for women, p = 0.08 for men)

“SpanishMediterranean” (p = 0.07 for women, p = 0.04 for men)

–

“Cluster 1” (high fat, low “Cluster 1” (high fat, low alcohol) alcohol) “Cluster 2” (high kcals/fat/alcohol) “Cluster 7” (moderate fat, low alcohol, high ﬁber/micronutrients) (8 clusters total) “Western” “prudent” “high fat/sugar dairy” “substituters” “drinkers”

“Western”

“prudent”

“Western” “mixed”

“traditional”

Obesity: Dietary and Developmental Inﬂuences

Greenwood, 2000 33,971 women Women’s Cohort Study, 35–69 yr U.K.4

7-day weighed diet records

Beaudry, 1998 Provincial Nutrition Survey, Québec, Canada

2,118 adults 13–74 yr

24-hour recall

Haveman-Nies, 2001 Framingham Study, U.S. and SENECA, Europe

2,110 adults (828 U.S., 1,282 Europe) 70–77 yr Mostly white 1,968 adults 45–74 yr

Fraser, 2000 East Anglia cohort of the European Prospective Investigation of Cancer, U.K.4 Schroll, 1996 SENECA Follow-Up Study, Europe

1,357 adults 74–79 yr

BMI

Weight status (BMI [method not stated] < 20, 20–25, >25–27, > 27) FFQ (126 items BMI in FramingWaist ham); Diet circumference history in SENECA) FFQ BMI (method not (130 items) stated)

Cluster (51 food categories)

–

Factor (30 food categories)

–

Cluster (12 food categories)

Cluster (32 food categories)

–

Cluster (10 food categories)

–

BMI

1,265 women Millen, 2001 Framingham Offspring- Mean 46–51 yr Spouse Study, U.S.4 Mostly white

FFQ (145 items)

Normal weight Cluster (BMI 18.5–24.9) (42 food categories)

Pryer, 2001a National Diet and Nutrition Survey, U.K. Tucker, 1992 Boston

1,097 adults > 65 yr Mostly white

4-day weighed diet records

BMI

680 adults Elderly Mostly white

3-day diet records

Cluster (27 food categories)

Cluster (16 food categories)

Age

–

Age, Gender

“traditional”

–

No association

No association

“sugar and sugar products” “ﬁsh and grain” “meat, eggs and fat” “milk and fruit” “alcohol” “vegetarian” “low calorie” “alcohol, nuts, meat” “healthy eating” “traditional”

“meat, eggs and fat” “alcohol”

“sugar” “ﬁsh and grain”

“lean and green” “gourmands” “milk drinkers” “small eaters” “modest eating” (women only) “empty calorie” “high fat” “wine and moderate eating” “light eating” “heart healthy” “traditional” “healthy” “mixed”

No association

No association

“empty calorie”

“wine and moderate eating”

F: “mixed” “healthy” M: No association

“traditional”

“alcohol” “milk, cereals, and fruits” “bread and poultry” “meat and potatoes”

“meat and potatoes” “bread and poultry”

“milk, cereals and fruits” “alcohol”

“low calorie”

–

M: No association

183

Diet history

BMI

Age, Gender, Smoking, Physical activity, Country

Female groups: “traditional” “healthier cosmopolitan” “convenience” “healthier sweet” Male groups: “beer convenience” “traditional” “mixed sweet” “healthier” “high-energy density” “traditional” “health-conscious”

Dietary Inﬂuences on Energy Balance

Pryer, 2001b 2,197 adults Dietary and Nutritional 16–64 yr Survey of British Adults, U.K.

184

TABLE 4.40 (CONTINUED) Wolff, 1995 HHANES, U.S.

549 women Mean 24 yr Mexican American

BMI

Factor (18 food categories)

Wirfalt, 1997 526 adults Minneapolis and Mean 37 yr Pittsburgh (data Mostly white pooled from 3 studies)

FFQ (60 items)

BMI

Cluster (38 food categories)

Huijbregts, 1995 518 men Zutphen Study Follow- 70–89 yr Up, The Netherlands Mostly white?

Dietary history

BMI

Cluster (17 food categories)

–

Maskarinec, 2000 Hawaii

514 women Mean 54 yr white and Asian

FFQ (≈200 items)

BMI

Factor (23 food categories)

Diet energy

Gittelsohn, 1998 Ontario, Canada

478 adults Mean 37 yr 243 children 10–20 yr Native Canadian

FFQ (34 items)

BMI (measured)

Factor (28 food categories)

Age, Gender

Fung, 2001 Health Professionals Follow-Up Study, U.S.4

466 men 40–75 yr Mostly white

FFQ (≈130 items)

BMI Leptin

Factor (42 food categories)

Age, Diet energy

Notes: 1

Based on convenience samples except for Tseng, 2001, Pryer, 2001b.

2

FFQ, food frequency questionnaire.

3

BMI based on measured weight and height unless otherwise speciﬁed.

4

Cross-sectional analysis of longitudinal cohort.

–

Age, Substudy, Diet energy, Physical activity

“nutrient dense” “traditional” “transitional” “nutrient dilute” “protein rich” “high fat dairy” “mixed dishes” “soft drinks” “pastry” “skim milk” “meat” “meat-cheese” “white bread” “alcohol” “meat” “reﬁned sugars” “healthy diet” “meat” “vegetable” “bean” “cold foods” “vegetables” “junk foods” “bush foods” “breakfast foods” “hot meal foods” “tea foods” “bread and butter” “Western” “prudent”

No association

No association

F: No association M: “soft drinks”

F: No association M: “skim milk” and “meat-cheese”

No association

No association

“meat”

“vegetable” “bean” “cold foods”

“bush foods”

No association with BMI No association with BMI Higher leptin for “Western”

Lower leptin for “prudent”

Obesity: Dietary and Developmental Inﬂuences

FFQ (57 items)

Dietary Inﬂuences on Energy Balance

185

not designed speciﬁcally to assess the relationship between dietary patterns and adiposity and frequently relied on self-reported height and weight. Clearly, additional well conducted prospective studies including youth and individuals of a variety of ethnic backgrounds are needed. A lack of consistent methodology in identifying and establishing food groups may be responsible for some of the heterogeneity in identiﬁed dietary patterns and resultant relation with BMI (Togo, 2001). Given that distinct dietary patterns have been variously deﬁned and identiﬁed on the basis of diverse foods and food groupings measured using a variety of dietary intake tools, and that in most cases the identiﬁed dietary patterns explained a relatively small percentage of the variation in food intake, it is perhaps not surprising that the results have not been completely consistent. Nonetheless a “Western” type diet has been associated with overweight in numerous studies (Table 4.41), while a diet relatively high in grains, dairy, fruits, and vegetables has been associated with a lower BMI (Table 4.42). In most cases, signiﬁcant differences in BMI on the basis of dietary pattern occurred even after controlling for total energy intake.

PREVENTION TRIALS Although no prevention trials were identiﬁed that assessed changes achieved in patterns of food intake using factor or cluster analysis, numerous overweight prevention trials have aimed to alter such patterns. Because prevention trials are reviewed in detail in other dietary sections (e.g., see sections on macronutrients, and vegetables and fruits) they will not be described again here. The majority of trials have focused on reducing the intake of high-fat foods and/or increasing vegetable and fruit intake. Most trials in adults have been moderately successful in preventing weight gain. Studies in children have been less fruitful, probably due to inherent difﬁculties both in intervening in children and in assessing the diet of children. Nevertheless, the cumulative evidence from prevention trials suggests that altering dietary patterns is an effective strategy in the prevention of overweight.

CONCLUSION Reﬁnement of the analytical techniques and inclusion of study subjects who are more diverse in terms of ethnic group and age are warranted before it can be concluded deﬁnitively that a “Western” dietary pattern is a determinant of energy imbalance. Despite study limitations, however, there is enough cumulative evidence in support of a dietary pattern characterized by a predominance of high-fat foods (i.e., high-fat meat and dairy, fats, and oils), reﬁned grains, and sweetened drinks and other added sugars is related to increased adiposity. Conversely, evidence is moderately consistent that a dietary pattern characterized by a high intake of lower-fat foods, whole grains, fruits, vegetables, and legumes appears protective against overweight (Table 4.43).

VARIETY OF FOODS INTRODUCTION Food variety has been deﬁned as the inclusion of many foods in the diet that differ on the basis of at least one sensory quality such as color, taste, or texture (Raynor, 2001). Variety among food groups has long been considered critical to achieving nutrient adequacy and a health-promoting diet (Kant, 1993; Westenhoefer, 2001). Exposure to a variety of ﬂavors in young children has been touted as a means of improving acceptance of novel foods (Gerrish, 2001). Furthermore, “eat a variety of foods” has long been a recommendation of the Dietary Guidelines. However, one reason the Dietary Guidelines Advisory Committee revised this statement in 2000 to “let the Pyramid guide your food choices” was a concern that variety within some food groups might lead to excessive energy intake and implicit weight gain (Dixon, 2001).

186

TABLE 4.41 Food Groups Characteristic of Dietary Patterns Associated with Increased Adiposity

Slattery, 1998

“Western”

Greenwood, 2000

Several “omnivore” groups “Western”

Study Hulshof, 1992 Tucker, 1992 Tucker, 1992

Hu, 2000

Maskarinec, 2000 Haveman-Nies, 2001 Haveman-Nies, 2001 Millen, 2001 Pryer, 2001a

“meat” “meat, eggs and fat” “alcohol” “empty calorie” “healthy”

Meat X X

Poultry

Eggs

Cheese

X X

X X (wild game high in added fat) X (processed, red, fast food) X

Potatoes X X

Added Sugar/ Sweets

Fats/Oils

Other

X (males only) X

X

X (chips)

X

X

X (snacks, condiments) X (condiments)

X X

X (duck high in added fat) X

X (reﬁned)

X (females only)

X (reﬁned)

X (processed, red) X (processed, red) X

Grains

Soda/ Sweetened Drinks

X

X (and ﬁsh) X

X (high-fat dairy)

X (reﬁned)

X (and fries)

X

X

X

X X (alcohol) X X

X (whole)

X X (low-fat spreads)

Obesity: Dietary and Developmental Inﬂuences

Wirfalt, 1997 Gittelsohn, 1998

Name of Pattern “Cluster 1” “meat and potatoes” “bread and poultry” “soft drinks” “bush foods”

“mixed”

Pryer, 2001b

“traditional”

Sichieri, 2002

“Western”

Quatromoni, 2002

“empty calorie”

Newby, 2003

“meat and potatoes” and “white bread” “Western”

SánchezVillegas, 2003

X (high-fat dairy; only females) X (and other dairy; only males)

X (reﬁned) X (reﬁned)

X

X (red and processed) X (red)

X

X (high-fat dairy)

X (reﬁned)

X

X (high-fat dairy)

(low on whole wheat bread)

X (french fries)

X

X

X

X (only males)

X

X (and diet beverages) X (soda and fast food)

X

X (animal and ﬁrm vegetable)

X (processed pastries)

X (tea; only males)

Dietary Inﬂuences on Energy Balance

Pryer, 2001a

X (fast food, processed meals, sauces)

187

188

TABLE 4.42 Food Groups Characteristic of Dietary Patterns Associated with Reduced Adiposity Name of Pattern “milk, cereal, fruit”

Tucker, 1992

“alcohol”

Wirfalt, 1997 Slattery, 1998

“skim milk” “meat-cheese” “prudent”

Greenwood, 2000

“vegetarian”

Hu, 2000

“prudent”

Maskarinec, 2000 Maskarinec, 2000

“vegetable” “bean” “cold foods”

Millen, 2001

“wine and moderate eating”

Pryer, 2001a

“traditional”

Sichieri, 2002

“traditional”

Quatromoni, 2002

“heart healthy”

Newby, 2003

“healthy”

Sánchez-Villegas, 2003

“Spanish-Mediterranean”

Protein

Dairy X

Grains X (breakfast cereals)

Fruit X

Vegetables

Legumes

Other

X (alcohol) X (meat)

X (nonfat)

X (soy) X (poultry, ﬁsh)

X (whole grains) X (whole grains)

X (fresh) X

X

X

X

X

X

X (and nuts) X

X

X (eggs, organ meats)

X (high-fat) X (whole)

X (breakfast cereals) X (reﬁned) X

X (and juice)

X (rice) X (low-fat) X (low-fat) X (poultry, seafood, meat products)

X (whole)

X (and soy)

X X

X

X

X

X

X (and potatoes)

X (and nuts)

X (wine, snacks) X (sweets) X (coffee) X (snacks)

X (olive oil)

Obesity: Dietary and Developmental Inﬂuences

Study Tucker, 1992

Observational Studies2 Support Relationship? (number of studies) Longitudinal Secular Trends Support Relationship?

Mechanisms Support Relationship?

(2 in adults 0 in children)

U.S. Nationally Representative CrossSectional (1 in adults 0 in children)

Yes

Inconclusive

Other CrossSectional or CaseControl (20 in adults 0 in children)

Prevention Trials2 Support Relationship? (number of studies) Randomized Other Controlled Trials (see other sections)

(see other sections)

Yes

Yes

Conclusion: Consistency of Evidence Supporting Relationship

Dietary Inﬂuences on Energy Balance

TABLE 4.43 Does the Preponderance of Evidence Support a Relationship between Dietary Patterns and Adiposity?1

Adults Yes

Yes

Moderate

Yes Children No studies

No studies

No studies

Inconclusive

Inconclusive

Notes: 1

Description of criteria used for summary table is located in the methods section.

2

Numbers in parentheses indicate the number of relevant studies identiﬁed and examined for each study type.

189

190

Obesity: Dietary and Developmental Inﬂuences

SECULAR TRENDS The variety of foods available in the U.S. has increased dramatically over the past two decades. In 1970, the number of total food products introduced was 1,045. By 1996, the number of new annual food products on the market increased nearly 13-fold to over 13,000 (Gallo, 1997). The rise has been particularly evident for nutrient-poor, energy-dense foods. In contrast, the increase in new fruit and vegetable products, which are relatively nutrient dense, has been more modest (McCrory, 1999) (Figure 4.35). National food supply data suggest that inadequate variety of fruits and vegetables are currently available for consumption (Putnam, 2002). According to national intake data of individuals, the variety of all food groups (assessed as the number of different foods consumed as at least 0.5 servings from each of the ﬁve food groups (grains, vegetables, fruits, milk, and meat) consumed increased from 1989 (CSFII) to 1999–2000 (NHANES). Out of a maximum score of 10, variety of food group selections improved from approximately 6.5 to 7.7 over this time period (Briefel, 2004).

PLAUSIBLE MECHANISMS One hypothesized mechanism whereby food variety may inﬂuence intake and ultimately weight gain involves what has been termed “sensory-speciﬁc satiety” (Le Magnen, 1971). In short, the pleasurable rating of a food decreases when it is eaten to satiation in comparison to a food consumed in smaller quantities. Therefore, a person is likely to ﬁnd a greater variety of sensorially distinct foods more palatable than a single food or a limited variety of foods and less likely to become habituated or “tired” of eating a variety of distinct foods. Relatively short-term clinical studies in

FIGURE 4.35 The number of new food products introduced in the U.S. food market in comparison to the prevalence of overweight among adults between the 1970s and 1990s. (Source: McCrory, 1999.)

Dietary Inﬂuences on Energy Balance

191

both humans and laboratory animals suggest that the differential response to varied versus constant diets is not greatly inﬂuenced by the energy density or macronutrient composition of foods and is not very sensitive to postingestive feedback mechanisms (for review, see Raynor, 2001). In an observational study conducted in France, food variety based on the number of food groups, but not the number of foods, was associated with a higher energy intake (Drewnowski, 1996). Likewise, in the majority of clinical trials in humans, all of which have been short-term (e.g., a single meal) and in controlled settings, greater variety resulted in greater food intake (Table 4.44). Even when nutrient composition was controlled (e.g., similar energy density and/or macronutrient composition), varying

TABLE 4.44 Clinical Trials that Have Examined Effects of Food Variety on Energy Regulation in Adults

Study Bellisle, 1980 Pliner, 1980

Bellisle, 1981

Rolls, 1981a Rolls, 1981b

Beatty, 1982 Rolls, 1982a Rolls, 1982b

Rolls, 1984 Berry, 1985

Hetherington, 1989 Spiegel, 1990

Sample Size 7 3 24 24 24 7 6 3 2 22 36 12 12 12 10 24 24 24 24 24 24 24 61 65

31 9 9 9

Subject Characteristics1 NW F adults NW M adults Obese M adults NW, dieting M adults ND, NW M adults NW F adults Obese F adults NW M adults NW F adults NW M adults ND F adults NW F adults NW M adults Nonobese F adults Nonobese M adults ND F adults ND F children ND, NW F and M adults ND, NW F and M adults ND F and M ND, NW F adults ND, NW M adults F adults, no history of eating disorder M adults, no history of eating disorder NW, unrestrained F adults Underweight F adults NW F adults Obese F adults

Variety Design2 Free selection Free selection

No. Controlled Flavors Nutrient or Foods Composition Effect of Variety 5 No Greater food intake 3

No

Greater food intake

Free selection

5

No

Greater food intake

Sequential courses Sequential courses

2

No

Greater energy intake

4 3 3 3

No

Greater food intake

Yes

Greater food intake No effect on intake No effect on intake

Free selection Sequential courses Sequential courses Sequential courses Free selection

Sequential courses Free selection

3 4 3 3 4

No Yes Yes

No

No effect on intake Greater energy intake Greater energy intake Greater energy intake

3

Yes

Greater food intake

2

No

No effect on intake

3

No

Greater food intake

Notes: 1

2

Key to abbreviations: F, female; M, male; NW, normal weight; ND, not dieting.

“Free selection” design refers to studies comparing a meal of one food versus a varied meal eaten ad libitum; “Sequential courses” design refers to studies comparing a meal served as courses of a single food vs. a meal in which each course is composed of a new food. Source: Adapted from Raynor, 2001.

192

Obesity: Dietary and Developmental Inﬂuences

the ﬂavors, colors, and/or textures of foods resulted in increased intake. However, if the sensory qualities of the foods were too similar (e.g., three berry ﬂavors of yogurt or three different colored but otherwise similar chocolate candies), enhanced intake did not occur (Rolls, 1982a/b). It has been suggested that eating a variety of foods offered early humans an evolutionary advantage by providing a balanced diet and a reduced risk of nutrient deﬁciencies (Rolls, E.T., 1981; McCrory, 1999). However, in modern times, the biological drive to eat a variety of foods may be disadvantageous for weight maintenance, since that variety is largely provided by energydense commercial foods (McCrory, 1999). Whether some individuals are more responsive to variety of foods and therefore more at risk of weight gain remains to be established, but it has been shown that obese adults (Cox, 1999) and some ethnic groups with a relatively high prevalence of overweight (Schiffman, 2000) may be more responsive and exemplify less habituation to certain tastes. Also not clear from mechanistic studies is the relative effects of consuming a variety of nutrient-dense foods (e.g., fruits and vegetables) vs. empty-calories foods (e.g., sweetened drinks and other sweets). The former would presumably be more beneﬁcial in terms of weight control and the prevention of other nutrition-related diseases than the latter.

OBSERVATIONAL STUDIES Most epidemiological studies of food variety have focused on variety within and among healthful foods. Furthermore, most have not examined inﬂuences on weight status (McCrory, 2002). The few studies identiﬁed have not had consistent results (Tables 4.45 and 4.46). Using nationally representative data, variety based on the number of foods consumed per day was not related to body weight in 1- to 2-year-old children (McConahy, 2002). Likewise, no relationship was observed between the number of foods consumed and several measures of adiposity in a cross-sectional study of 6- to 12-year-old children (Wolfe, 1994). In a cross-sectional study of 71 adults (20–80 yr) consuming a variety of highly energy-dense foods (e.g., the number of different sweets, snacks) was positively associated with calorie intake and BMI, whereas consuming a variety of low energydense foods (e.g., the number of different vegetables) was inversely associated with BMI after controlling for age and gender (McCrory, 1999). However, in two studies conducted in Spain, one of the breakfast and daily dietary habits of 122 elderly Madrid residents (Ortega, 1995a, 1996) and one of the daily dietary habits of 64 adolescents (Ortega, 1995b), overweight and obese subjects ate a less varied diet than their nonobese peers. These results suggest that the inﬂuence of food variety on energy balance and adiposity is dependent on how diversity is deﬁned and the classiﬁcation of foods being considered.

PREVENTION TRIALS Laboratory animals fed a more varied diet gain more weight and body fatness than rats fed the same food every day (for review, see Raynor, 2001). Only one short-term intervention study in humans was identiﬁed that examined weight as an outcome (Stubbs, 2001). The aim of this randomized cross-over study was to compare the effect of sensorial variety (but nutritional homogeneity in terms of energy density and macronutrient composition) on food intake and body weight between 12 lean and overweight men. Unintentionally, the two groups of men also differed significantly by age; the lean men averaged 27 years of age, while the overweight men averaged 40 years of age. After a 2-day maintenance diet, for 7 days subjects were allowed to select ad libitum from a menu consisting of a low variety (5 foods), medium variety (10 foods), or high variety (15 foods). Although body weight did not differ by treatment over the course of the short-term study, food and energy intake did increase signiﬁcantly, but only for the lean men. The overweight men tended to lose weight on all treatments. The authors suggested that the overweight men may have cognitively restrained their food intake during the study or, alternatively but less likely, the difference in response was due to the average 13-year age differential between the lean and overweight groups. While caution must be used in extrapolating the results of this relatively short-term study to longer periods

Study Name and/or Location

Study Population1

Other Cross-Sectional Studies Ortega, 1995a/1996 122 adults Spain 65–95 yr Spanish McCrory, 1999 Massachusetts

71 adults 20–79 yr Ethnicity not speciﬁed

Food Variety Measure

Adiposity Measure2

Number of foods Number of food groups (5-day consecutive weighed food records)

Obese (BMI ≥ 25)

Percent of food types within 10 food groups (modiﬁed Block FFQ of usual intake over preceding 6 months)

% Body fat

Control Variables

Gender, Place of residence, Disease, Medications, Vitamin-mineral supplements, Tobacco, Alcohol (tested for differences between obese and nonobese) Age, Gender

Association3

– (breakfast and daily intake)

Dietary Inﬂuences on Energy Balance

TABLE 4.45 Observational Studies of the Association of Variety of Foods with a Measure of Adiposity in Adults

+ (for high energy-dense foods) – (for low energy-dense foods)

Notes: 1

Based on convenience samples except for Ortega, 1995a/1996.

2

BMI based on measured weight and height unless otherwise speciﬁed.

3

Plus (+) indicates signiﬁcant direct relationship, negative (–) indicates signiﬁcant inverse relationship, and zero (0) indicates nonsigniﬁcant relationship between dietary factor and measure of adiposity. FFQ = food frequency questionnaire.

193

194

TABLE 4.46 Observational Studies of the Association of Variety of Foods with a Measure of Adiposity in Children Study Name and/or Location

Study Population1

Food Variety Measure

Adiposity Measure2

U.S. Nationally Representative Cross-Sectional Studies McConahy, 2002 1,039 children Number of foods Weight status 1994–1996, 1998 CSFI 1–2 yr (two consecutive 24-hour recalls) (<15th, 15–85th, >85th 65% White, 14% black, percentiles for body 15%, Hispanic weight)

Ortega, 1995b Spain

64 adolescents 15–17 yr Spanish

Number of foods (24-hour recall) BMI Triceps skinfold Arm fat area

Number of foods (5-day consecutive weighed food records)

Overweight (BMI ≥ 23)

Association3

–

0

Grade, Age in grade, Ethnicity, Height, SES, Family structure, school lunch, Number of siblings, Maternal employment, Breakfast skipping, No consumption of vegetables, Number of snacks, Food group pattern –

0

–

Notes: 1

Based on convenience samples except for Ortega, 1995a/1996.

2

BMI based on measured weight and height unless otherwise speciﬁed.

3

Plus (+) indicates signiﬁcant direct relationship, negative (–) indicates signiﬁcant inverse relationship, and zero (0) indicates nonsigniﬁcant relationship between dietary factor and measure of adiposity. SES = socioeconomic status.

Obesity: Dietary and Developmental Inﬂuences

Other Cross-Sectional Studies Wolfe, 1994 1,797 children New York 6–12 yr Mostly white

Control Variables

Dietary Inﬂuences on Energy Balance

195

of time, the authors concluded that increasing the variety of nutritionally similar but sensorially distinct foods signiﬁcantly increases energy intake, at least among young lean men.

CONCLUSION It is common practice for dietitians to offer a variety of energy-dense, highly palatable foods to encourage patients to eat for the promotion of weight gain. It is logical that the converse, restricting the variety of energy-dense, nutrient-poor foods offered, would be a promising strategy for weight management. Alternatively, increasing the variety of low energy density foods may be a potential weight management strategy. However, additional experimental research on the effects of the variety of foods consumed on the long-term regulation of energy balance and weight in humans is needed before such recommendations can be decisively adopted. The optimal means of evaluating and deﬁning variety in the diet must also be established. In summary, the nascent state of research in this area precludes us from being able to make a deﬁnitive statement with respect to the relationship of variety of foods and adiposity (Table 4.47).

REDUCED-FAT FOOD PRODUCTS INTRODUCTION In recent years, the general public has demanded reduced-fat food products to assist in reducing both dietary fat and caloric intake. According to a recent national survey, 47% of those using reduced-fat products did so to maintain their current weight, and 38% did so to reduce their weight (Calorie Control Council, 2003). According to the Food Marketing Institute (1999), buying food products labeled as “low-fat” was the most common strategy used by consumers to improve their diet. Two broad categories of food products have been developed and introduced that are low-fat alternatives: (1) those that have fat content reduced during processing or production and (2) those that contain fat substitutes. Methods commonly used to reduce fat in foods and examples of food product are found in Table 4.48. Most notable among the fat substitutes is sucrose polyester or olestra, which is indigestible and therefore provides no calories but retains many of the hedonic properties of fat (Drewnowski, 1997). It was approved by the FDA in 1998 for use in savory snack foods. In addition to olestra, numerous fat substitutes, some of which are carbohydrate or protein based, are currently used by the food industry (Table 4.49). Some are natural components isolated from food sources, while others have been synthesized in food laboratories. While all are designed to mimic one or more of the roles of fat in food without as many calories from fat, most do provide some calories. While it has been argued that reduced-fat food products may help with both the prevention and treatment of obesity by allowing diets to be more palatable (Astrup, 1997), it has also been cautioned that overconsumption of such products may be contributing to the obesity epidemic (Allred, 1995; Astrup, 1998). Of particular concern have been those that remain calorie dense because of the addition of caloric sweeteners (e.g., some snack and dessert products). This review brieﬂy summarizes the impact on energy balance of consumption of manufactured reduced-fat food products — those with fat substitutes as well as others with the fat content lowered by other means. Studies involving olestra were emphasized in the “fat substitute” category in this review, because it has been the most extensively studied and would presumably have a larger impact than calorie-containing fat substitutes. Discussions of foods naturally low in fat (e.g., vegetables and fruits) are found elsewhere.

SECULAR TRENDS One of the goals of Healthy People 2000 was to increase the availability of processed reduced-fat products to ≥5000. In 1986, the number of reduced-fat processed foods was approximately 2,500.

196

TABLE 4.47 Does the Preponderance of Evidence Support a Relationship between Variety of Food and Higher Adiposity?1 Observational Studies2 Support Relationship? (number of studies) Longitudinal Secular Trends Support Relationship?

Mechanisms Support Relationship?

(0)

U.S. Nationally Representative CrossSectional (0 in adults 1 in children)

Other CrossSectional or CaseControl (2 in adults 2 in children)

Prevention Trials2 Support Relationship? (number of studies) Other Randomized Controlled Trials (1 in adults 0 in children)

(0)

Inconclusive

No studies

Conclusion: Consistency of Evidence Supporting Relationship

Adults No studies

Inconclusive

Inconclusive

Yes Children No studies

Inconclusive

Inconclusive

Notes: 1

Description of criteria used for summary table is located in the methods section.

2

Numbers in parentheses indicate the number of relevant studies identiﬁed and examined for each study type.

No studies

No studies

Obesity: Dietary and Developmental Inﬂuences

No studies Yes

Dietary Inﬂuences on Energy Balance

197

TABLE 4.48 Examples of Foods that Have Their Fat Content Reduced (Besides Using Fat Substitutes) Method of Fat Reduction Removing fat during processing Incorporating additional water or air into a product during processing Lowering the fat content during production

Examples Low-fat/skim milk Reduced-fat ground and luncheon meats Reduced-fat margarine and other spreads Changing animal feed to reduce the fat content of meat or poultry

TABLE 4.49 Examples of Fat Substitutes Commonly Used in Foods Generic Category

Brand Name(s)

Fat-Based Emulsiﬁers

Dur-Lo

Salatrim Olestra

Benefat Olean

Carbohydrate-Based Cellulose Avicel, Solka-Floc Dextrins Amylum, N-Oil Gums Kelcogel, Keltrol Polydextrose Litesse, Sta-Lite Protein-Based Microparticulated Protein Modiﬁed whey protein concentrate

Energy Density (kcal/g)

Typical Food Uses

9 Baked goods, dairy (but less used than traditional fats) 5 Confections, baked goods, dairy 0 Salty snacks, crackers

0–4

Dairy, sauces, salad dressings, frozen desserts

4

Salad dressings, puddings, spreads, dairy, frozen desserts

0

Salad dressings, desserts, processed meats

1

Baked goods, confections, salad dressings, dairy

Simplesse

1–2

Dairy-Lo

4

Dairy, salad dressing, margarine, sauces, mayonnaise, baked goods Dairy, baked goods, frostings, salad dressing, mayonnaise

Sources: Mattes, 1998; Calorie Control Council, 2003.

In 1990 alone, more than 200 new “light” dairy products were introduced into the food market, accounting for nearly 40% of all reduced-fat products introduced that year (White, 1993). By 1991, the number of reduced-fat products had already increased to 5,618 (NCHS, 1999). In 1995, food manufacturers introduced 1,914 new reduced-fat products, and in 1996 this number grew to 2,076. By the late 1990s, however, introductions declined. In 1999, only 481 new reduced-fat products were introduced (Allshouse, 2002). Between 1997 and 2000, a little more than 3,000 new reducedfat products were introduced (GNPD Staff, 2000). Between 1987–1988 and 1994, use of reduced-fat food products increased from 31 to 43% of consumers, according to CSFII data (Sigman-Grant, 1997). By 1996, approximately 46% of American adults were low users (>0 and ≤2/day), an additional 11% were high users (≥3/day) of reducedfat food products, and approximately 10% of the 7,000 foods reported were reduced in fat (Kennedy,

198

Obesity: Dietary and Developmental Inﬂuences

2001). A national survey conducted by Good Research Services for the Calorie Control Council found that, most recently, 79% of the adult U.S. population reported consuming reduced-fat foods and beverages. The most popular reduced-fat products included (1) low-fat or skim milk (consumed by approximately 66% of the U.S. population); (2) salad dressings, sauces, or mayonnaise (60%); (3) cheese and other dairy products such as yogurt and sour cream (53%); (4) margarine (50%); and (5) chips and other snack foods (43%) (Calorie Control Council, 1996). Meat products and ice cream and other frozen desserts are also popular (Calorie Control Council, 2003). Trends among types of reduced-fat foods vary. Figure 4.36 illustrates reported national consumer use of several common reduced-fat products between 1991 and 1996. Based on reported intakes, reduced-fat chip and snack foods appear to have gained in popularity (Calorie Control Council, 1996). However, based on retail scanner data, purchase of reduced-fat potato chips, pretzels, and microwave popcorn have declined in recent years (e.g., by 6% between 1995 and 1999, compared to an 11% increase in sales of regular fat versions) (Allshouse, 2002). Overreporting behaviors considered desirable (e.g., consumption of reduced-fat snacks) may be one reason for the discrepancy between reported usage and documented purchasing patterns. Figure 4.37 illustrates the trend among U.S. adult consumers of increased consumption of low-fat and skim milk and decreased consumption of whole milk. Consumption of leaner cuts of meat has increased as well. Compared to 1970, Americans in 1997 consumed, on average, 21 lb less red meat (mostly beef and other traditionally higher fat meats), while consuming 31 lb more poultry, and 3 lb more ﬁsh and shellﬁsh, which tend to be lower in fat content. Furthermore, consumer concerns about total fat, saturated fat, and cholesterol intake prompted cattle ranchers to shift from higher-fat breeds to leaner varieties, a trend that began in the 1960s. Other lower-fat market developments include the promotion of leaner cuts of meat, the trimming of visible fat during butchering, and the production of lower-fat ground and processed meats (Putnam, 1998). Little data is available about trends in reduced-fat food use speciﬁc to children. According to the 1989–1991 CSFII data, use of reduced-fat food products is lower among children than adults (Peterson, 1997, 1999). Furthermore, food disappearance and consumption data indicate a population-wide trend in recent decades toward a lower-fat diet in terms of percentage of calories

FIGURE 4.36 Trend for use of common reduced-fat food products according to U.S. national survey data. Note: “cheese/other dairy” does not include milk. (Source: Calorie Control Council, 1996.)

Dietary Inﬂuences on Energy Balance

199

FIGURE 4.37 Trend for whole and low-fat/skim milk consumption according to U.S. food supply data. (Source: Putnam, 1999.)

consumed as fat (see dietary fat section). Therefore, even though total fat intake may have increased in the last several decades, this increase is concurrent with increases in total energy intake, and selection of lower-fat choices appears widespread among both children and adults.

PLAUSIBLE MECHANISMS A number of controlled studies have been conducted to examine the effect of reduced-fat food products on fat and energy intake, and extensive reviews have been written elsewhere (Miller, 1996; Lawton, 1998; Stubbs, 2001; Wylie-Rosett, 2002). What follows is a brief description of some study highlights and the main conclusions that can be drawn from the evidence to date. Foods with Fat Reduced during Production or Processing A majority of intervention studies of free-living subjects (Mattes, 1993; Gatenby, 1995; Rodriguez, 2000) and observational studies (Lee, 1998; Peterson, 1999; Bellisle, 2001; Kennedy, 2001; Wosje, 2001) have reported a reduction in total fat intake among groups consuming reduced-fat food products. However, most studies have found no signiﬁcant change in total energy intake because of compensatory increases in protein and/or carbohydrate consumption (Mattes, 1993; Gatenby, 1995, 1997; Westerterp, 1996; Rodriguez, 2000). In an observational study of children (n = 50) and teens and adults (n = 130) in Denmark, portion sizes of four food items (milk, sauces, sliced cold meats, and pork) consumed were generally larger for the reduced-fat than the regular-fat versions, resulting in similar total energy intake for the consumption of all except one food group (sliced cold meats) (Matthiessen, 2003). In U.S. national survey data (1989–1991 CSFII) for over 15,000 individuals 2 years and older, the degree of energy compensation among reduced-fat milk drinkers varied by age and gender but generally was more complete for males than females and for younger children and adults than for adolescents. Furthermore, when compensation occurred, it was largely due to an increase in carbohydrate intake (Lee, 1998). However, in another analysis of 1989–1991 CSFII data (for over 7000 adults), use of any of 3 reduced-fat strategies (consumption of skim milk, lean meats, or other reduced-fat products) was associated with reduced energy intake (Peterson, 1999). In yet another analysis of national data (1996 CSFII), adults who were high users (≥3/day) of reduced-fat food products tended to have higher total energy from fat (Kennedy, 2001). Other observational studies have found inconsistent results (Bellisle, 1994, 2001; Peterson, 1997) or no difference (Wosje, 2001) in total energy intake with respect to reduced-fat product consumption and total energy intake. In conclusion, it appears that compensation for the reduced calorie density of reduced-fat food products frequently occurs. When compensation occurs, it does not typically involve increased consumption of fat-rich foods but, rather, increased consumption of reduced-fat foods or increasing consumption of other (i.e., carbohydrate and/or protein rich) foods.

200

Obesity: Dietary and Developmental Inﬂuences

Foods with Fat Substitutes A large number of studies have looked at olestra use in the diet and its impact on fat and caloric intake. While fat intake tends to decrease, some compensation in the form of energy from carbohydrate and protein may occur (Lawton, 1998). Of the 24 studies of olestra reviewed by Stubbs (2001), 22 reported decreased caloric intake; an average energy compensation of only 27% was seen when combining all results. There was considerable variation in the amount of compensation seen in subjects, from as little as 3% compensation to as much as 46% compensation. In the two studies where caloric compensation was most complete, olestra was used at breakfast. Effects of olestra on eating behavior were generally the same between lean and obese individuals and between men and women. Most of the 24 studies with olestra were of short duration, lasting two weeks or less. Because of this, it is unclear what the long-term effects of olestra substitution have on caloric intake. Based on 1-year follow-up data from the Olestra Post-Marketing Surveillance Study, adults with the highest consumption of olestra (≥2 g/d) did not signiﬁcantly reduce total energy intake, despite reducing the percentage of calories from fat by nearly 3% (Patterson, 2000). An additional limitation is that most of the studies used covert measures to add olestra to the diet. This complicates interpretation of the data since free-living people may eat reduced-fat food products overtly. A concern voiced by many (Rolls, 1997; Wylie-Rosett, 2002; Straily, 2003) is that use of reduced-fat products by the general public may encourage overconsumption, because consumers may not be aware that reduced-fat food product are not necessarily low in calories and may feel that they can “get away with” eating more. In a large study of French adults, use of reduced-fat food products was associated with an increased intake of simple sugars (Bellisle, 2001). Furthermore, several (though not all) clinical trials have found that, when people knew they were eating reduced-fat food products, they tended to eat more of that food (see review by Miller, 1996). This has been shown to occur even when the foods provided were perceived to be low in fat but were, in fact, not low in fat (Caputo, 1993). For example, in one study, women ate more of a yogurt identiﬁed as low-fat than a yogurt labeled high-fat (even though the two had identical fat and calorie content) and maintained a caloric surplus through a subsequent lunch and dinner (Rolls, 1992). Some evidence suggests that increased consumption of reduced-fat food products may be dependent on the food type. For example, in a study that involved free access to overtly identiﬁed reduced-fat or full-fat versions of salad dressings, mayonnaise, and soft-serve frozen yogurt, women did not consume increased amounts of fat free mayonnaise and salad dressing compared to the full-fat versions but did consume larger amounts of fat-free frozen yogurt (Lyle, 1993). Response may also vary by individual. For example, in a study of overtly identiﬁed fat-free (olestra-containing) potato chips, restrained eaters ate more fat-free chips than their full fat versions, while nonrestrained eaters did not. This effect was observed regardless of weight status (i.e., in both obese and nonobese adults), although the increase in chip intake did not offset the reduction in overall caloric intake (Miller, 1995, 1998). When asked in a national survey, a minority of respondents (13%) reported eating more of a reduced-fat food than the regular variety, while 71% said they eat about the same amount. Furthermore, only 10% said they eat more of other foods when consuming reduced-fat foods, while 84% said that they usually do not allow themselves to eat more of other foods (Calorie Control Council, 1996). Subjects who wish to lose weight or are concerned about their weight may be less likely to eat more than those who are not interested in weight loss (Stubbs, 2001). Energy compensation may also vary as a function of degree of energy manipulation. For example, in one study, little energy compensation was found when the manipulated energy intake was higher than subjects’ habitual intake, but signiﬁcant compensation was found when energy intake was lower than habitual intake (Caputo, 1992). Compensation appears more likely to occur when limited amounts and types of reduced-fat food products are consumed as opposed to when reduced-fat food products are widespread (as reviewed by Rolls, 1997). According to 1996 CSFII data, total intake of sugars and sweets was not signiﬁcantly different between high, low, and nonusers of reduced-fat foods (Kennedy, 2001). Furthermore, several studies have found that users of reduced-

Dietary Inﬂuences on Energy Balance

201

fat products tend to have healthier diets in terms of higher intakes of micronutrients, dietary ﬁber, fruits and vegetables, and dairy products (Arsenault, 2000; Bellisle, 2001; Kennedy, 2001). Thus, while energy compensation does appear to occur to some extent when individuals consume reducedfat food products, there is not compelling evidence to date that such consumption has widespread effects opposite to those intended; that is, in increasing consumption of total calories.

OBSERVATIONAL STUDIES The results of studies examining the relationship between consumption of reduced-fat food products and adiposity have had mixed results, with studies showing positive, negative and no association (Tables 4.50 and 4.51). It should be noted, however, that many of these analyses did not control for other potential confounding variables. Use of reduced-fat food products has been found to vary by ethnicity (more among whites), income (more among higher income), education (more among higher years of education) (Peterson, 1997, 1999), and level of physical activity (more among those more active) (Arsenault, 2000). Further complicating interpretation of epidemiological results is the fact that individuals trying to lose weight may be more likely to adopt reduced-fat food products. Fat substitutes were not distinguished from other reduced-fat foods, and reduced-fat food products were often combined in analyses with other reduced-calorie foods. Reduced-fat food products that are low in calories (either because of using a fat substitute or incorporating air or water into the product) may have different effects on energy intake and balance from a reduced-fat food that is relatively energy dense (for example, because of replacing fat with sugar). Furthermore, not all reduced-fat food products may be identiﬁed as such (e.g., meat with excess fat trimmed by the butcher) by researchers. Interpreting the results of these studies is also complicated by the fact that individuals may increase their intake of a food when it is reduced in fat. If the product is low in calories, this increase may not negate the reduction the total reduction in energy intake. However, if the product is energy dense, an increase in intake of the product could result in excessive energy intake. Finally, the consequences on intake and resultant weight status may be different when a consumer is aware vs. unaware of the fact that a food consumed is reduced in fat.

PREVENTION TRIALS As reviewed by Gatenby (1995), most clinical studies of the effects of reduced-fat food products on dietary intake and body adiposity have involved small numbers of subjects for short periods of time and have required subjects to consume prepared and provided foods in a controlled setting that would not occur in the typical free-living situation. For this reason, in the discussion that follows, emphasis was placed on studies involving free-living subjects who were counseled to use reduced-fat food products, with the caveat that even the dietary guidance and special attention involved in study participation would not be typically experienced by a majority of consumers. Foods with Fat Reduced during Production or Processing There have been a number of intervention trials in nonobese free-living adults in which reducedfat foods were used, but energy intake was otherwise not restricted (Table 4.52). The largest (n > 200) and longest (6 months) study was a multicenter intervention trial involving men and women who ate either full-fat or reduced-fat foods (Westerterp, 1996; de Graaf, 1997). Those consuming the low-fat alternatives reduced their fat intake from 35 to 33% of calories. There was no change in total energy intake or body weight over the study period in the reduced-fat group, while those consuming the full-fat foods increased energy intake and gained weight. Further analysis by level of restraint indicated that restrained eaters on the reduced-fat diet lost weight while those on the high-fat diet maintained weight, as compared to their unrestrained counterparts who maintained or gained weight, respectively (Westerterp-Plantenga, 1998). A similar result was achieved in two shorter (10–12 week) studies: fat intake was reduced, while no signiﬁcant change in total

202

TABLE 4.50 Observational Studies of the Association of Reduced-Fat Food Product (RFFP) Intake with a Measure of Adiposity in Adults Study Name and/or Location

Study Population1

U.S. Nationally Representative Cross-Sectional Studies Kennedy, 2001 1,731 adults 1996 CSFII 19–50 yr Ethnically diverse Other Cross-Sectional Studies Bellisle, 2001 France (nationally representative)

Control Variables

Association3

BMI (self-report)

Gender, ≤30% vs. >30% energy from fat

0 (non, low vs. high users of RFFP)

Body weight BMI Waist-to-hip ratio Body weight BMI Waist, thigh, and hip circumference Waist-to-hip ratio (WHR) (all self-report)

Age, Gender (separate analysis)

Gender (separate analysis), Age

+ (women) 0 (men) (RFFP users vs. nonusers) + (women, except for no relation with WHR) 0 (men, except – for hip circumference) (low-fat butter, milk, yogurt, and soft white cheese)

Notes: 1

Based on convenience samples with the exception of Boutelle, 1994, 2001, and Kennedy, 2001.

2

BMI based on measured weight and height unless otherwise speciﬁed.

3

Plus (+) indicates signiﬁcant direct relationship, negative (–) indicates signiﬁcant inverse relationship, and zero (0) indicates nonsigniﬁcant relationship between dietary factor and measure of adiposity.

Obesity: Dietary and Developmental Inﬂuences

4,278 adults 45–60 yr Ethnicity not speciﬁed Bellisle, 1994 741 adults Val de Marne, Paris (representative) 18–65 yr Ethnicity not speciﬁed

Adiposity Measure2

Study Name and/or Location Longitudinal Studies Wosje, 2001 Cincinnati

Other Cross-Sectional Boutelle, 2002 The Voice of Connecticut Youth Survey

Study Population1

51 children 12 months (at baseline) 94% White 1-year follow-up Studies 8,330 adolescents 7th, 9th and 11th grades 74% white, 9% black, 7% Hispanic

Adiposity Measure2

Body weight % Body fat (DXA)

Control Variables

Association3

Socioeconomic status, anthropometry not different between groups at 12 months

0 (2% vs. whole milk drinkers)

Weight status: (BMI [self-report] normal [15th- Grade, Gender, School, Parent 85th percentile], overweight [85th-95th], obese socioeconomic status, Ethnicity [>95th])

Dietary Inﬂuences on Energy Balance

TABLE 4.51 Observational Studies of the Association of Reduced-Fat Food Product (RFFP) Intake with a Measure of Adiposity in Children

0 (low-fat dairy)

Notes: 1

Based on convenience samples with the exception of Boutelle, 2002.

2

BMI based on measured weight and height unless otherwise speciﬁed.

3

Plus (+) indicates signiﬁcant direct relationship, negative (–) indicates signiﬁcant inverse relationship, and zero (0) indicates nonsigniﬁcant relationship between dietary factor and measure of adiposity.

203

204

TABLE 4.52 Prevention Intervention Studies of Effects of Intake of Reduced-Fat Food Products (RFFPs) on a Measure of Adiposity in Adults Study

Study Population

Randomized Controlled Clinical Trials Westerterp, 1996 217 men and women Mean age 35–36 yr Mean BMI 25

Intervention Groups

Outcome Measure1

Intervention Effect2

6-month trial

1. RFFP 2. full fat control

Body weight Body fat

↓

2-wk baseline 10-wk trial

1. RFFP 2. full fat control

Body weight BMI

0

2-wk baseline 6-wk trial

1. RFFP 2. full fat control

Body weight BMI

↓

1-wk baseline 12-wk trial 12-wk follow-up

1. no discretionary fat, limited high-fat foods 2. same as above + RFFP 3. full fat control

Body weight Body fat

0

Notes: 1

Outcome measures based on actual measurements in all studies.

2

Zero (0) indicates no difference in intervention vs. control group; down arrow (↓) indicates outcome measure was signiﬁcantly lower in intervention vs. control group.

Obesity: Dietary and Developmental Inﬂuences

Nonrandomized Controlled Clinical Trials Gatenby, 1997 30 women Mean age 35–37 yr Mean BMI 23–24 Gatenby, 1995 29 men and women Mean age 40 yr Mean BMI 23–26 Mattes, 1993 27 men and women Mean age 27–30 yr Mean BMI 23–26

Study Duration

Dietary Inﬂuences on Energy Balance

205

energy intake or body weight or BMI was detected (Mattes, 1993; Gatenby, 1997). Only in one short-term trial was a signiﬁcant loss in weight achieved by consumers of reduced-fat food products. After 4 weeks of intervention, consumers lost 1.1 kg, while the control group gained 0.4 kg (Gatenby, 1995). It is not clear why the results of this study differed from the others, including another similar study by the same research group. Data are lacking on the effects of reduced-fat food consumption in children. It has been suggested that restricting dietary fat in early life may result in growth retardation (Lifshitz, 1996). However, studies of fat reduction in children have failed to conﬁrm this concern (Shea, 1993; Lagstrom, 1999; Obarzanek, 2001; Wosje, 2001). Foods with Fat Substitutes Fat substitutes were not clearly distinguished from other reduced-fat food products in the studies of free-living subjects discussed in Table 4.52. Two of the four studies reported a reduction in weight gain when reduced-fat foods were consumed. Two additional studies (not tabled) aimed to decrease the consumption of dietary fat, resulting in an increase intake of reduced-fat alternatives. One randomized, controlled trial study involved 2,079 adults (25–89 years of age) with large bowel adenomatous polyps and also included counseling to increase fruit, vegetable, and ﬁber intake (Lanza, 2001). The other was a nonrandomized, controlled school-based trial involving nearly 300 children 5–7 years of age, which also promoted fruit and vegetable intake as well as increased physical activity and reduced TV viewing (Miller, 2001). In both cases a reduction in adiposity was observed, but in neither case was it possible to distinguish between the effects of adopting reduced-fat foods and other intended behaviors. In Table 4.53, only studies involving olestra-containing foods are included. One 3-month intervention trial showed no difference in caloric intake between a group given olestra and the placebo control group (Kelly, 1998). However, those subjects consuming the full-fat placebo foods gained weight, while those consuming the olestra did not. In all of the other studies, signiﬁcant weight loss occurred (de Graaf, 1996; Bray, 2002; Roy, 2002). In one study that included overweight women, the group that had one-third of dietary fat replaced by olestra lost 5.0 kg of weight from the baseline measure 10 weeks earlier (Roy, 2002). In one study, participants lost weight regardless of whether they were aware they were eating olestra-containing foods (de Graaf, 1996). In the study of longest duration, which included only overweight or obese men, after 9 months, those on the olestra diet lost 6.7% of their body weight, 2.5 kg of body fat more than the control group, and 4.2 kg more than the low-fat diet group (Bray, 2002). Signiﬁcantly greater losses were also observed for abdominal fat. In all cases, the olestra-containing diets were well tolerated with minimal gastrointestinal side effects. It should be noted, however, that in these studies, subjects were provided with all foods eaten, as opposed to the studies reviewed in Table 4.52, in which subjects were freeliving. In summary, use of olestra as a fat substitute is likely to reduce fat and caloric intake over the short term. This appears to be true in both men and women and in both lean and obese individuals. However, all studies to date have involved adults and have been clinical trials in which subjects were provided all food. The effect on free-living subjects of varying ages of fat substitutes such as olestra over longer periods of time remains to be established.

CONCLUSIONS If reduced-fat food products were substituted for their higher-fat counterparts without any other changes, weight loss would be expected. However, if reduced fat (but calorie-containing) foods are added to the diet without any reduction in other foods, weight gain would presumably occur (Sigman-Grant, 1997). It is logical that reduced-fat food products that are also low in calories may be more beneﬁcial than reduced-fat food products that are relatively calorie dense (e.g., due to added sugar). Unfortunately, it is not possible to prove these suppositions based on the current state

206

TABLE 4.53 Prevention Intervention Studies on Effects of Intake of Olestra-Containing Foods on a Measure of Adiposity in Adults Study

Study Population

Randomized Controlled Clinical Trials Kelly, 1998 76 men and women Mean age 36 yr (double-blind; within subject design) Bray, 2002 36 men Mean age 37 yr Mean BMI 31 15 women Roy, 20023 Mean age 24–25 yr Lean and overweight (single-blind; within subject design)

Intervention Groups

Outcome Measure1

Intervention Effect2

3-month control 3-month trial

1. 20–40 g olestra 2. control

Body weight

↓

9-month trial

1. 2. 3. 1. 2. 3.

Body weight Body fat (DXA) Abdominal fat (CT) Body weight Body fat (DXA)

↓

6-day control 10–12 week trial

olestra (25% fat) low fat (25% fat) control (33% fat) olestra (31% fat) low fat (31% fat) control (40% fat)

↓

12 day

1. 52 g olestra 2. control

Body weight

↓

8-day control 14-day trial

1. olestra (31% fat) 2. control (40% fat)

Body weight

↓

Notes: 1

Outcome measures based on actual measurements in all studies.

2

Zero (0) indicates no difference in intervention vs. control group; down arrow (↓) indicates outcome measure was signiﬁcantly lower in intervention vs. control group.

3

Separate studies reported in same paper.

Obesity: Dietary and Developmental Inﬂuences

Nonrandomized Controlled Clinical Trials de Graaf, 1996 95 men and women Mean age 22 yr Normal weight 10 men Roy, 20023 Mean age 25 yr Lean (single-blind; within subject design)

Study Duration

Dietary Inﬂuences on Energy Balance

207

of the evidence. The evidence that is available would suggest that reduced-fat food products might be more effective at preventing weight gain than causing weight loss. It may be that a signiﬁcant reduction in calorie intake is achieved with reduced-fat food product consumption only when a conscientious effort is made to overcome the physiologic regulation of energy balance. Thus, substitution of low-fat for full-fat products may not be effective in the prevention of weight gain if other strategies for weight control are not implemented. However, few studies have been conducted to test this hypothesis, particularly among children. Presently, a deﬁnitive recommendation cannot be made for the use of fat substitutes and other reduced-fat food products as a determinant of adiposity. However, there is little evidence that the use of reduced-fat food products has contributed to weight gain and the obesity epidemic (Table 4.54).

PORTION SIZE INTRODUCTION The large portion sizes served at restaurants and produced by manufacturers have been commonly blamed as one of the etiologic factors in the rising prevalence of obesity (Hill, 1998; Goran, 2001). Recently, the American Institute for Cancer Research, the Consumer Federation of America, and the Center for Science in the Public Interest recommended that Americans avoid “supersizing” their body weight by requesting smaller portion sizes and sharing larger portions (Fox, 2002).

SECULAR TRENDS Portion sizes for manufactured and restaurant foods in the U.S. appear to have increased concurrently with obesity prevalence; they began to rise in the 1970s, increased dramatically in the 1980s, and have continued to grow gradually since (Figure 4.38) (NANA, 2002; Young, 2002). Currently, portion sizes of french fries, hamburgers, and sodas are two to ﬁve times larger than when originally offered in fast food restaurants. The average fast food burger, which weighed approximately 1 oz in 1957, weighs up to 6 oz now; the typical serving of soda, which was 8 ﬂ oz in 1957, is now 32 to 64 ﬂ oz; and the average theatre serving of popcorn, which was 3 cups in 1957, is now 16 cups (Nicklas, 2001). According to a survey by the National Restaurant Association, which collected menus from the same 66 restaurants in 1988 and again in 1993, the number of menus offering more than one portion size, such as “super” sizes, increased by 12% (NRA, 1993). “Value” marketing, the practice of offering more food for a small additional cost (or even lower cost), encourages that more food be purchased in a meal. For instance, in a recent look at pricing at fast food restaurants, it cost 8 cents more to purchase a meal containing 890 calories than to purchase

FIGURE 4.38 Introduction of new, larger portion sizes in the U.S. (Source: Young, 2002.)

208

TABLE 4.54 Does the Preponderance of Evidence Support a Relationship between Intake of Reduced-Fat Food and Higher Adiposity?1 Observational Studies2 Support Relationship? (number of studies) Longitudinal Secular Trends Support Relationship?

Mechanisms Support Relationship?

No studies

Inconclusive

Other CrossSectional or CaseControl (2 in adults 1 in children)

(4 in adults 0 in children)

(5 in adults 0 in children)

No

No

Conclusion: Consistency of Evidence Supporting Relationship

Adults Yes

Inconclusive

Inconclusive

No Children Inconclusive

No studies

Inconclusive

Notes: 1

Description of criteria used for summary table is located in the methods section.

2

Numbers in parentheses indicate the number of relevant studies identiﬁed and examined for each study type.

No studies

No studies

Obesity: Dietary and Developmental Inﬂuences

(0 in adults 1 in children)

U.S. Nationally Representative CrossSectional (1 in adults 0 in children)

Prevention Trials2 Support Relationship? (number of studies) Randomized Other Controlled Trials

Dietary Inﬂuences on Energy Balance

209

a “value meal” containing 1380 calories (NANA, 2002). Labeling of portion sizes has changed as well (Young, 2002). What is called “large” fries today was called “supersize” fries in 1998. Furthermore, although other countries have reported recent increases in portion sizes of commercial foods (Matthiessen, 2003), portion sizes in fast food restaurants in the U.S. are often larger than portion sizes offered in other countries (Young, 2002). The trend toward larger portion sizes appears to be occurring for foods consumed at home as well as away from home, at least among most age groups. Young and Nestle (2002, 2003) reported that, except for white bread, all portion sizes in restaurants (fast food and family-type) and foods sold by manufacturers as single servings examined exceeded USDA and FDA portion size deﬁnitions. Cookies were 700% of USDA standards, while cooked pasta was 480% of standards. Although there is evidence that portion sizes remained similar for the vast majority of foods consumed by 1- to 2-year-old children in the U.S. between the 1970s and 1990s (McConahy, 2002), recent comparisons in older persons (2 years and above) indicated that portion sizes for numerous commonly consumed foods had increased. Smiciklas-Wright (2003) found that Americans reported consuming larger portion sizes for the majority of foods with signiﬁcant size differences between the 1989–1991 and 1994–1996 CSFII surveys (Figure 4.39). Nielsen and Popkin (2003) examined changes in speciﬁc food groups (salty snacks, desserts, soft drinks, fruit drinks, french fries, hamburgers, cheeseburgers, pizza, and Mexican food) between the 1977–1978 Nationwide Food Consumption Survey and the 1989–1991 and 1994–1996/1998 CSFII surveys. With the exception of pizza, portion sizes of all selected food groups increased signiﬁcantly. For example, the average reported portion size of salty snacks increased by 0.6 oz (93 kcal), for soft drinks by 6.8 oz (49 kcal), for hamburgers by 1.3 oz (97 kcal), for french fries by 0.5 oz (68 kcal), and for Mexican dishes by 1.7 oz (133 kcal). In most cases, these increases were signiﬁcant, regardless of whether the food was consumed at home or purchased from a fast food restaurant or other type of restaurant. Indeed, for several foods (e.g., desserts, hamburgers, cheeseburgers), the portion sizes of foods eaten at home were larger than those eaten out.

PLAUSIBLE MECHANISMS Energy content increases with portion size. Larger portion sizes could equate to increased calorie consumption and weight gain, unless energy expenditure is altered or reduced amounts of other foods are eaten. Unfortunately, few studies have empirically evaluated the effect of portion size on overall energy intake, and those performed have been short-term and had conﬂicting results. Many (Booth, 1981; Engell, 1995; McConahy, 2002; Rolls, 2002), but not all (Stunkard, 1980; Edelman, 1986), have shown that more calories were consumed when larger portion sizes were offered (McCrory, 2000). For example, in a study of 3- to 5-year-old children, energy intake increased when portion size of an entrée was doubled, a ﬁnding that remained even after controlling for age (Fisher, 2003). However, in another study of preschool age children conducted by the same research group, increasing portion size resulted in increased food intake among 5-year-olds, but had no inﬂuence on amount consumed by 3-year-olds (Rolls, 2000). The inﬂuence of portion size on intake may vary by age. It is also possible that the inﬂuence of portion size varies by weight status. The “supersized” and “value-sized” portions used in fast food and other food service settings may tempt consumers as a means of stretching the food dollar and getting more for their money. Sales would appear to indicate that these are effective marketing strategies to promote consumption (Anon, 2003). Indeed, pricing may inﬂuence consumer behavior more than messages about health (Guo, 1999; Horgen, 2002). Furthermore, there is evidence that consumers are not very perceptive about the importance of portion size and are not aware that portion sizes have increased over time. In several recent national surveys conducted by the American Institute for Cancer Research of approximately 1000 adults each, less than 40% thought that portions served in restaurants were larger compared to 10 years ago, and approximately 20% reported that portions they ate at home were the same or smaller. The

210 Obesity: Dietary and Developmental Inﬂuences

FIGURE 4.39 Percent increases from 1989–1991 of select foods consumed in the U.S. (Source: Smiciklas-Wright, 2003.)

Dietary Inﬂuences on Energy Balance

211

majority (67% in 2000, 69% in 2003) reported always or almost always “cleaning their plate;” 73% of those who regularly did so further indicated that portion sizes served at restaurants were “just right.” Only a minority was able to correctly estimate standard serving sizes of common foods as deﬁned by the USDA Food Pyramid. Approximately 30% of Americans indicated that they typically based the amount of food they consumed on the amount they were served, up 4% from previous surveys. Furthermore, a majority of Americans do not appear to be concerned about portion size or to be aware of the implications of total energy intake, as 78% indicated that eating certain types of food (e.g., “healthy foods”) was more important to weight management efforts than eating less food (AICR, 2000, 2001, 2003). Preschool-age children have been shown not to appreciably notice when the portion size of a lunch entrée was doubled in size (Fisher, 2003). Regardless of portion size, adults tend to view the amount they typically consume as being medium or average (Smith, 1991). Recent evidence suggests that the inﬂuence of portion size may be dependent, at least in part, on the caloric density of the food consumed. For example, in a 7-week controlled feeding study of 33 women, eating a large portion of a low energy-dense salad before the lunch meal resulted in a 10% decrease in energy intake at lunch. However, consuming a large portion of a high energydense salad resulted in a 17% increase in total energy intake (Rolls, 2003). In a 6-week feeding study of 39 women, subjects consumed 56% more energy when served the largest portion size of a high energy-dense meal in comparison to when served the smallest portion size of a low energydense meal (Kral, 2003).

OBSERVATIONAL STUDIES Empirical evidence to demonstrate a relationship between portion size and energy balance is lacking (Table 4.55). In a cross-sectional analysis of CSFII data for 1039 1- to 2-year-old children, portion size was positively related to body weight (McConahy, 2002). Unfortunately, control for potential confounding variables (i.e., gender, ethnicity, income) was not possible because of small sample size among subcategories. It was noted that portion size was higher among black compared to white children (Hispanic children did not differ from either group) and among lower-income groups compared to children from higher-income families. In a smaller (n = 55) longitudinal study of children followed from 12 to 18 months of age, portion size increased as children grew older and larger, a ﬁnding which was not unexpected (McConahy, 2002). However, an analysis of risk of excessive weight gain in relation to portion size over time was not provided. In one short-term clinical trial involving 3- to 5-year-old preschoolers, total energy intake at lunch increased by an average of 15% when children were served an entrée twice the size of the reference age-appropriate portion. This response was not related to BMI. Greater responsiveness, however, was observed for those children who also demonstrated eating in the absence of hunger, suggesting that individuals with a weak response to satiety may be relatively more inﬂuenced by portion size differences (Fisher, 2003). Whether all age groups are affected by increases in portion size and whether effects are sustained over the long term remain to be determined. Likewise, factors responsible for intrinsic differences in response need to be investigated.

PREVENTION TRIALS While portion control may be employed in overweight interventions, no studies were identiﬁed that would allow for assessment of the independent effect of this strategy.

CONCLUSION While substantial secular trend data and some plausible mechanism evidence exists in support of reducing portion size as a target behavior for prevention of overweight, observational and intervention studies are notably lacking. Only one observational study was identiﬁed that was conducted

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TABLE 4.55 Observational Studies of the Association of Portion Size with a Measure of Adiposity in Children Study Name and/or Location

Study Population1

Adiposity Measure2

U.S. Nationally Representative Cross-Sectional Study McConahy, 2002 1039 children Weight status 1994–1996, 1998 1–2 yr (<15th, 15–85th, >85th CSFII 65% white, 14% black, 15% percentiles for body weight) Hispanic

Control Variables

Association3

–

+

Notes: 1

Based on random sample.

2

Based on reported weight.

3

Plus (+) sign indicates signiﬁcant direct relationship.

in young children, and no prevention trials were identiﬁed. Despite the seemingly apparent rationality of reducing portion size to prevent overweight and the popularity of attributing our population’s fattening to enlarged portion sizes, the merit of this behavioral strategy has yet to be adequately tested. In particular, the interaction between energy density and portion size is of interest. It may be that consuming larger portion sizes of lower energy-dense foods or meals and smaller portion sizes of higher energy-dense foods or meals may be protective strategies. In summary, it is presently not possible to make a conclusive statement with respect to portion size as a determinant of overweight (Table 4.56).

MEAL AND SNACK PATTERNS INTRODUCTION The literature on meal and snack patterns is complicated by the fact that there is no consensus on what constitutes a snack, a snack food, a meal, or even an eating occasion. Most studies rely on the respondent ﬁlling out the survey or being interviewed to identify meals and snacks. Respondents are not asked to clarify whether they feel that a snack is any food eaten between or in place of “regular” meals, or if a snack is any small amount of food eaten, or some other variation of those deﬁnitions. Some researchers use arbitrary deﬁnitions. For example, in one study, an eating occasion was classiﬁed as any event that provided at least 50 kcal with a minimum time interval of at least 15 minutes between episodes (Ma, 2003). In another study, a snacking occasion was deﬁned as food eaten within a 15-minute period when a snack is consumed (Zizza, 2001). Some have equated “frequency of eating” with “snacking,” but these are likely separate issues that need to be addressed, and probably are terms that should not be used interchangeably. Some use the term “nibbler” to identify someone who eats frequently and have related nibbling to snacking regardless of whether the eating occasions were meals or snacks (Drummond, 1996). In other cases, researchers deﬁned meals by the time of day. For example, in one study, breakfast was deﬁned as any intake between 6:00 and 9:49 in the morning (Forslund, 2002). In another study, breakfast was deﬁned as the ﬁrst meal of the day, regardless of the time it was consumed (Summerbell, 1996). Differences in methodology impede making comparisons between studies and may confound results. In most studies, we must assume that a respondent is reﬂecting the societal view of what a snack and meal are at the time. Since eating habits have changed over the past several decades, it seems reasonable to suggest that deﬁnitions of snack and meal patterns might have also changed, but this issue is

Observational Studies2 Support Relationship? (number of studies) Longitudinal Secular Trends Support Relationship?

Mechanisms Support Relationship?

(0)

U.S. Nationally Representative CrossSectional (0 in adults 1 in children)

Other CrossSectional or CaseControl (0)

Prevention Trials2 Support Relationship? (number of studies) Randomized Other Controlled Trials

(0)

(0)

No studies

No studies

Conclusion: Consistency of Evidence Supporting Relationship

Dietary Inﬂuences on Energy Balance

TABLE 4.56 Does the Preponderance of Evidence Support a Relationship between Portion Size and Higher Adiposity?1

Adults No studies Yes

No studies

No studies

Inconclusive

Inconclusive Children No studies

Inconclusive

No studies

No studies

No studies

Notes: 1

Description of criteria used for summary table is located in the methods section.

2

Numbers in parentheses indicate the number of relevant studies identiﬁed and examined for each study type.

213

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Obesity: Dietary and Developmental Inﬂuences

not clearly addressed in the literature. Nor can we be certain that the deﬁnitions of snack and meal patterns in one country or culture are the same as the deﬁnition in another. It would be informative to know whether meal and snack frequency had independent effects on adiposity and whether skipping of particular meals was associated with a larger impact than others. Unfortunately, because of the limitations in the way the data on eating frequency have been deﬁned, collected, and analyzed, it is difﬁcult to tease apart these independent issues. For example, many of the studies on eating frequency assessed meals and snacks combined, while others examined snacks only. The strategy adopted in this manuscript was to group studies into three main categories identiﬁed in the literature: (1) those that assessed frequency of total eating episodes or meals, (2) those that assessed snacking frequency or intake, and (3) those that assessed breakfast skipping.

SECULAR TRENDS Eating Frequency Using nationally representative data from adults, Kant et al. (1995b) looked at the change in eating frequency over a 10-year period using data collected for the NHANES I in 1971–1975 and for the NHANES I Epidemiologic Follow-up Study in 1982–1984. At baseline, data from 24-h recalls were used to estimate eating frequency. At follow-up, respondents were asked how many meals and between-meal snacks they consumed to determine eating frequency. Over the time period, eating frequency decreased (from 5.3 to 3.6 daily eating occasions for men and from 4.9 to 3.6 for women). However, it is difﬁcult to interpret this data, since the method of estimating eating frequency differed between the two collection periods, and the time frame of comparison is relatively short. Based on cross-sectional CSFII data compared between the 1970s and the 1990s, the frequency of eating has increased for both men and women (Cutler, 2003) (Figure 4.40). During this time period, although total energy intake increased (see section on total calories), because of the increased eating frequency, the energy consumed per meal decreased (from 573 to 566 kcals among men; from 422 to 408 kcals among women). Interestingly, according to this data the change in energy intake during breakfast and lunch have risen only very modestly, while the intake from dinner has actually decreased. The most dramatic change in proportion of calorie intake has come from snacks, as will be discussed further in the next section.

FIGURE 4.40 Trends in eating frequency among U.S. adults from 1977–1978 and 1994–1996 based on CSFII data. (Source: Cutler, 2003.)

Dietary Inﬂuences on Energy Balance

215

Snacking Snacking frequency has increased in recent years. Based on combined data of 10,000 children ages 0 to 9 from a special 1998 nationwide USDA survey of children and the 1994–96 CSFII, 83% of children snacked on the day surveyed, up from 65% in 1977–1978. Snacks contributed an average of 20% of daily energy intake among children 9 years and under in the most recent surveys (ARS, 2000). According to nationally representative data, while the average size of snacks and the energy per snack remained relatively constant, the frequency of self-deﬁned snacking increased from 1977 to 1996 among children in all age groups between 2 to 18 years (Kennedy, 1997; Jahns, 2001). Similar trends have been observed from other analyses of national food consumption data. Nielsen (2002) compared data from 63,380 individuals 2 years and older from the 1977–1978 NFCS and the 1989–1991, 1994–1996, and 1998 (for children 9 and younger) CSFII. Increased snacking was observed among all age groups. For example, in 1977 approximately 11% of the average American’s energy intake was derived from snacks. This number increased by more than 50% to nearly 18% by 1996. Among adolescents, between one fourth and one third of the energy intake has been reported to be derived from snacks (Dausch, 1995). Zizza et al. (2001) compared snacking data on nationally representative samples of young adults, aged 19–29 years, from 1977 to 1996. Data from over 4400 respondents from the USDA’s 1977–1978 NFCS, over 2300 respondents from the 1989–1991 CSFII, and over 1600 respondents from the 1994–1996 CSFII were collected. Respondents were asked to recall or keep a record of foods eaten and to identify the eating occasion, such as a snack. The prevalence of snacking increased from 77% of adults in 1977–1978 to 84% in 1994–1996; the majority of that increase was seen between 1989–1991 and 1994–1996. Intake from snacks increased from 20 to 23% of total calories over this time period. The energy density of foods consumed as snacks increased by 30%, while in contrast there was no change in the energy density of nonsnack foods consumed. According to CSFII data, the energy intake from snacks increased by 90% among men and by 112% among women between the 1970s and the 1990s (Cutler, 2003) (Figure 4.41). The top snacks selected by teens from the 1976–1980 NHANES II and the 1987 National Adolescent Student Health Survey included potato chips, ice cream, candy, cookies, breakfast cereal, popcorn, crackers, soup, cake, and carbonated beverages. Fruits and vegetables were infrequent choices (Jahns, 2001). According to national surveys, among adults, the top contributors to

FIGURE 4.41 Trends in energy intake from snacks among U.S. adults from 1977–1978 and 1994–1996 based on CSFII data. (Source: Cutler, 2003.)

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Obesity: Dietary and Developmental Inﬂuences

energy intake from snacks were desserts (15.7%), sweetened beverages (15.7%), alcoholic beverages (12.5%), and salty snacks (11.5%) (Zizza, 2001). The contribution from salty snacks increased the most between 1977–1978 and 1994–1996, while the contribution from desserts decreased. In 1999, Americans purchased more than 1.6 billion pounds of potato chips, pretzels, and microwave popcorn, according to retail scanner data, an 11% increase in regular-fat versions from 1995 (Allshouse, 2002). Interestingly, according to CSFII intake data for adults, the increase in snacking that has been observed over the last several decades has been due more to snacks consumed at home than to snacks purchased at restaurants and other food-selling establishments (Cutler, 2003). Breakfast Skipping Population-based surveys have revealed that many children, particularly adolescents, skip breakfast and other meals and eat more food later in the day, and that this pattern has increased in recent years (Singleton, 1982; Siega-Riz, 1998b) (Figure 4.42). Multivariate analysis indicated that the decline in reported consumption of breakfast (deﬁned as any food or beverage consumed between 5:00 and 10:00 a.m.) was due to a behavior change rather than a change in the nation’s demographics (Siega-Riz, 1998b). Breakfast consumption has also declined among U.S. adults: 14% reported skipping breakfast in 1975, while 25% reported doing so in 1991 (Haines, 1996). In the NHANES III cohort measured in 1988–1994, 20% of adults reported skipping breakfast (Cho, 2003).

PLAUSIBLE MECHANISMS Eating Frequency A mechanism relating eating frequency to obesity has not been clearly established, but numerous hypotheses have been put forward. Studies have found that in comparison to eating more frequently, eating infrequently is associated with higher 24-hour insulin concentrations (Young, 1972; Wadhwa, 1973; Jenkins, 1989). It has been hypothesized that meal frequency may affect body weight, since insulin is related to fat storage (e.g., insulin inhibits lipase activity and increases fat deposition). Eating more frequently may increase the thermic effect of food (TEF) and decrease the efﬁciency of energy utilization, although such an effect, if it exists, has proven difﬁcult to detect and therefore may be too small to be practically important in the control of body weight (Drummond, 1996). It has also been suggested that eating small, frequent meals may suppress hunger and reduce overeating (Lawton, 1998; Ma, 2003). Furthermore, results from several short-term studies suggest that

FIGURE 4.42 Trends in breakfast consumption among U.S. children from 1965 to 1991. Weighted results are from the NFCS of 1965 and 1977 and the CSFII of 1989–1991. (Source: Siega-Riz, 1998b.

Dietary Inﬂuences on Energy Balance

217

frequent eaters more accurately regulate energy intake in response to an energy challenge (i.e., extra energy intake) than do those who eat less often during the day (Westerterp-Plantenga, 1994, 2002; Speechly, 1999). On the other hand, eating more frequently has been advocated as a way to increase food intake for weight gain and has been associated with higher energy intakes in some subjects (Edelstein, 1992; Kant, 1995b; Drummond, 1998; Forslund, 2002). Short-term clinical trials in adults have reported conﬂicting effects of extra eating episodes or energy challenges on total daily energy intake and the ability of individuals to compensate for the extra calories (Marmonier, 2000, 2002; Johnstone, 2000). There is some evidence to suggest that obese individuals may be more likely to overeat in response to an energy challenge than lean individuals, so frequent eating may be a risk factor for overeating in some (Spiegel, 1989). Snacking Separate from affecting eating frequency, snacking may impact energy regulation due to the composition of the foods selected and the amount of calories consumed. It is common for people to associate snacking with obesity development, and many conventional weight loss programs include the avoidance of snacks in their program to minimize overeating. In some individuals, snacks tend to be higher in energy density and fat content than meals (Jahns, 2001), and high snack consumption has been associated with high intakes of fat and sugar (Cusatis, 1996). Snacking data on more than 4400 respondents from the USDA’s 1977–1978 NFCS, on more than 2300 respondents from the 1989–1991 CSFII, and on more than 1600 respondents from the 1994–1996 CSFII showed that those who consumed snacks had a higher energy, fat, and carbohydrate intake (Zizza, 2001). In a relatively small study of snacking habits in 47 obese people in the Netherlands, investigators found that those who overconsumed snacks (5 or more snacks/d) had a higher intake of calories, carbohydrate, and fat (Drent, 1995). The extent to which recall bias (i.e., higher energy intake reported by those who admitted eating more snacks and lower energy intake by those who underreported snacks) may have contributed to this ﬁnding is not clear. In a study of 273 obese French women, snackers had a higher total daily energy intake than nonsnackers (Basdevant, 1993). In a study of 83 obese Swedish women, a positive correlation was observed between snacking and energy intake (Gorslund, 2002). Furthermore, among girls, those who frequently bought snacks were more likely to have higher energy intakes than girls of comparable weight who did not report this behavior (McNutt, 1997) Breakfast Skipping It has been suggested that eating breakfast reduces fat intake, limits snacking, and reduces hunger over the remainder of the day (Schlundt, 1992). Research in animals suggests that food deprivation can lead to overeating when food is again available (Hunt, 1990). In one human study, energy intakes tended to be higher on days when subjects skipped breakfast (Ma, 2003). However, in other observational studies of adults, breakfast skipping was associated with lower intakes of energy and other nutrients (Morgan, 1986; Nicklas, 1998; Cho, 2003). Another hypothesis is that eating smaller amounts early in the day and more food later in the day is a risk factor for weight gain, because individuals tend to be more sedentary later in the day (Maffeis, 2000). Thermic effect of food (TEF) has been shown to be higher when a meal is followed by exercise (Segal, 1985). TEF may also follow a circadian rhythm. In one study, TEF was lower after a night meal than isocaloric meals eaten earlier in the day (Romon, 1993). It has also been suggested that hormonal regulation of metabolism in response to carbohydrate consumption in the late evening may be related to obesity (Ma, 2003). A hypothesized hormonal basis involves the secretion of insulin in response to eating breakfast (AHA, 2003). However, because not all studies have found a relationship between TEF and exercise (Van Zant, 1992) and time of day (Zwiauer, 1992), these mechanisms remain in question. Based on nationally representative data, breakfast skippers have been reported to have

218

Obesity: Dietary and Developmental Inﬂuences

lower energy intakes than most other types of breakfast eaters (Cho, 2003). Skipping breakfast has also been reported as a behavior adopted by subjects who are concerned about excess body weight (Bellisle, 1995). Therefore, it is possible that breakfast skipping may be a consequence rather than a cause of overweight.

OBSERVATIONAL STUDIES Eating Frequency The literature on the relation between eating frequency and weight status is mixed (Tables 4.57 and 4.58). Drummond et al. (1996) summarized a number of studies looking at the issue of eating frequency and obesity and found no consensus in the data. Three observational studies in adults found an inverse relationship between eating frequency and measure of body adiposity (Fabry, 1964; Metzner, 1977; Drummond, 1996, unpublished observations). Four other studies in adults, however, reported no relationship (Young, 1971; Garrow, 1981; Morgan, 1983; Dreon, 1988; Edelstein, 1992; Ruxton, 1994). Sixteen additional studies are reviewed here that were not included in the paper by Drummond. Eight of these studies, cross-sectional, case-control, and longitudinal in design, also suggest that eating frequency has no relation with body adiposity (Kant, 1995b; Andersson, 1996; Summerbell, 1996; Crawley,1997; Drummond, 1998; Siega-Riz, 1998a; Amosa, 2001; McConahy, 2002). In a cross-sectional study of 220 subjects ranging in age between adolescence through the elderly, “nibbling” was initially associated with a lower BMI than “gorging,” but only among adolescents. However, when dietary underreporters were excluded from the analysis (based on diet records with unreasonably low energy intakes), this relationship was no longer signiﬁcant. The authors suggest that failure to account for underreporting in free-living subjects, particularly by individuals who are overweight or obese, may account for the fact that some studies have found fewer snacks and/or eating occasions to be associated with a greater BMI (Summerbell, 1996). Similarly, in a Swedish study of men, obese subjects ate signiﬁcantly less often compared to controls, 5.3 vs. 5.6 episodes per day. However, this difference disappeared when underreporters were excluded from the analysis (it should be noted that the statistical power of the study simultaneously decreased as 80 of the 147 subjects [73% of the obese and 28% of the nonobese] were classiﬁed as underreporters) (Andersson, 1996). In another study of adolescents, BMI was found initially to be inversely related to eating frequency, even after underreporters were excluded from analysis. However, when dieting overweight boys and normal-weight girls who considered themselves overweight were removed from the analysis, the association was no longer signiﬁcant (Crawley, 1997). In a study that excluded obese subjects and dieters from recruitment and excluded underreporters from analysis, a negative relationship was observed between body weight and eating frequency in men, but no relationship was observed between eating frequency and BMI or percent body fat in either men or women (Drummond, 1998). In a cross-sectional study of 80 New Zealand women, the number of total eating occasions did not differ according to BMI (Amosa, 2001). In a nationally representative study of 1039 children 1–2 years of age, no relationship was observed between number of daily meals and weight status (McConahy, 2002). Frequency of eating occasions was compared longitudinally in 7,147 adults with weight change in the NHANES I Epidemiologic Follow-Up Study (Kant, 1995b). BMI and subscapular skinfold thickness at baseline were inversely related to the number of eating occasions in both men and women. However, no association between weight change and frequency of eating at baseline or follow-up was observed after controlling for confounding variables. The authors suggested that a high BMI was likely to predict a low frequency of meals, since dieters were more likely to consume two or less daily meals. Other studies have also reported that meal skipping is a common behavior adopted for weight loss. In a study of 470 Australian adolescents, skipping snacks and meals was a commonly used weight loss strategy, and not eating between meals was more common among overweight than nonoverweight teens (O’Dea,

Study Name and/or Location

Study Population1

Longitudinal and U.S. Nationally Representative Kant, 1995b4 7,147 adults Number of eating occasions NHANES I, 1971–75 and 25–74 yr (24-hour recall at baseline; NHEFS, 1982–84 Ethnicity not speciﬁed questions on number of meals 8–10 years duration and snacks at follow-up) Other Cross-Sectional or Case-Control Studies Titan, 2001 14,666 adults European prospective 45–75 yr Ethnicity not investigation into cancer, speciﬁed Norfolk cohort 7,147 adults Kant, 1995b4 NHANES I, 1971–75 and 25–74 yr NHEFS, 1982–84 Ethnicity not speciﬁed 8–10 years duration Kumanyika, 1994 4,643 adults Cardiovascular Health Study, ≥65 yr Tennessee, California, 95% white, 5% black Maryland, North Carolina Kumanyika, 1993 500 women Washington, DC 25–64 yr Black 499 adults 20–70 yr 88% White

Number of eating episodes (Question)

Number of eating episodes (24-hour recalls)

Association3

Weight change

Age and BMI at baseline, Gender, Ethnicity, Education, Smoking, Length of follow-up, Energy intake, Alcohol intake, Special diet status, Parity, Physical activity, Morbidity

0

BMI WHR

Gender (separate analysis), Age, Smoking, Physical activity, Intake of calories, alcohol, fat, protein and carbohydrate

– (BMI for men only) – (WHR for women only) – (at baseline)

Number of eating occasions BMI (24-hour recall at baseline; Subscapular skinfold questions on number of meals and snacks at follow-up) Number of meals BMI (<22, 22–29, (Food frequency questionnaire) ≥30)

Number of meals (Question on meal skipping)

Control Variables

Age and BMI at baseline, Gender, Ethnicity, Education, Smoking, Length of follow-up, Energy intake, Alcohol intake, Special diet status, Parity, Physical activity, Morbidity Age, Gender, Ethnicity, Use of a special diet

BMI (15.1–21.9, – 22.0–27.2, 27.3–32.2, 32.3–60.4) (selfreport) Obesity (BMI ≥ 30) Age, Gender, Total physical activity, Total energy intake, Educational level (others tested, but not included in ﬁnal model)

330 men Number of total eating episodes BMI 45–64 yr (3-day food diary) WHR Ethnicity not speciﬁed

Age, Educational level, Smoking habits, Total energy intake or macronutrients, Physical activity, Restrained dieting, Underreporting

–

+

–

-

219

Ma, 2003 Seasonal Variation of Blood Cholesterol Study, Massachusetts Ruidavets, 2002 France

Adiposity Measure2

Eating Frequency Measure

Dietary Inﬂuences on Energy Balance

TABLE 4.57 Observational Studies of the Association of Eating Frequency with a Measure of Adiposity in Adults

220

TABLE 4.57 (CONTINUED) Wahlqvist, 1999 International Union of Nutritional Sciences study, Greece and Australia Forslund, 2002 Swedish Obese Subjects study and Mölndal study Summerbell, 1996 England

Andersson, 1996 The “Gustaf” study, Sweden

Amosa, 2001 New Zealand

Number of eating occasions (Diet history)

BMI % Body fat

177 women (83 obese, 94 control) 37–60 yr Ethnicity not speciﬁed 54 working age (17–60 yr) 28 middle-aged (3959 yr) 78 elderly (65–91 yr) Ethnicity not speciﬁed 142 men (81 obese, 61 controls) 20–60 yr Ethnicity not speciﬁed 95 adults 20–55 yr Ethnicity not speciﬁed 80 women 18–27 yr White, Polynesian

Number of meals (Meal pattern questionnaire)

BMI

Number of eating occasions (7-day weighed food records)

BMI

Number of eating occasions (Twelve 24-hour recalls)

BMI (not clear if measured)

Number of eating occasions (7- BMI day food diary) % Body fat Number of meals BMI Number of total eating episodes (not clear if measured) (7-day food diary)

Country of residence

(Underreporters excluded from analysis)

(Underreporters excluded from analysis)

Gender (Underreporters excluded from analysis)

– (only for % body fat among Australian Greeks) +

0

0

0

0

Notes: 1

Based on convenience samples.

2

BMI based on measured weight and height unless otherwise speciﬁed.

3

Plus (+) indicates signiﬁcant direct relationship, negative (–) indicates signiﬁcant inverse relationship, and zero (0) indicates nonsigniﬁcant relationship between dietary factor and measure of adiposity.

4

Different analyses of data from same study.

WHR = waist-to-hip ratio.

Obesity: Dietary and Developmental Inﬂuences

Drummond, 1998 England

293 adults 70–80 yr Greek

Study Name and/or Location

Study Population1

Eating Frequency Measure

Adiposity Measure2

Control Variables

U.S. Nationally Representative Cross-Sectional Studies Siega-Riz, 1998a 1,310 children and Consistency of meal pattern BMI (self-report) Age, Gender, Ethnicity, SES, School 1989-1991 CSFII adolescents (One 24-hour recall and two 1attendance, School meal 11–18 yr day food records) participation (others tested, but not Ethnically diverse included in ﬁnal model) McConahy, 2002 1,039 children Number of meals Weight status 1994-1996, 1998 CSFII 1–2 yr (Two 24-hour recalls) (<15th, 15-84th, 65% white, 14% black, >85th percentiles 15% Hispanic for body weight) Other Cross-Sectional or Case-Control Studies Pastore, 753 adolescents Eating 3 meals per day more 1996 Mean 16 yr than 3 times in prior week Health Screening Week 66% African American, (questionnaire) Project, New York City 23% Hispanic, 8% White Crawley, 1997 731 adolescents Number of eating occasions 1970 Longitudinal Birth 16–17 yr Ethnicity not (4-day food diary) Cohort Study, England speciﬁed

Summerbell, 1996 England

27 adolescents 13–14 yr Ethnicity not speciﬁed

Number of eating occasions (7-day weighed food records)

Obese (ideal body weight ≥ 120%) vs. underweight (IBW ≥ 85%) BMI

BMI

Gender (separate analysis)

(Underreporters, dieting overweight males, and normal weight females who perceived themselves as overweight excluded from analysis) (Underreporters excluded from analysis)

Association3 0

0

Dietary Inﬂuences on Energy Balance

TABLE 4.58 Observational Studies of the Association of Eating Frequency with a Measure of Adiposity in Children

–

– (with all subjects included or with underreporters excluded) 0 (with dieting overweight males and nonoverweight females with perceived overweight excluded) 0

Notes: 1

Based on convenience samples with the exception of McConahy, 2002.

2

BMI based on measured weight and height unless otherwise speciﬁed.

3

Plus (+) indicates signiﬁcant direct relationship, negative (–) indicates signiﬁcant inverse relationship, and zero (0) indicates nonsigniﬁcant relationship between dietary factor and measure of adiposity. Different analyses of data from same study.

221

4

222

Obesity: Dietary and Developmental Inﬂuences

1996). In the nationally representative data from the 1989–1991 CSFII, there were twice as many adolescents on a weight reducing diet in the inconsistent meal group (one meal, with or without snacks, or snacks only, on all 3 days that diet intake was recorded) than in the more consistent groups (Siega-Riz, 1998a). Any observed relationship between eating frequency and adiposity may therefore simply be an artifact of underreporting, dieting and/or dietary restraint. Six other studies have found fewer eating episodes to be associated with a greater measure of adiposity in at least some cases (Kumanyika, 1994; Pastore, 1996; Wahlqvist, 1999; Titan, 2001; Ruidavets, 2002; Ma, 2003). In a cross-sectional study of almost 300 elderly Greek adults living in Australia and Greece, those who ate less frequently had a higher body fat and BMI (Wahlqvist, 1999). In older adults, Kumanyika et al. (1994) also found that obese adults were more likely to skip meals and eat after-dinner snacks than their leaner counterparts. Likewise, based on an average of 13 dietary records collected from each of nearly 500 adults over a 1-year period, a greater number of eating episodes each day was associated with a lower risk of obesity (OR = 0.55 for ≥4 eating episodes vs. ≤3; 95% CI = 0.33–0.91), after controlling for important covariates such as physical activity and energy intake (Ma, 2003). In a study of an ethnically diverse sample of high school age youth in New York City, 32% of obese adolescents (with obesity deﬁned as 120% of ideal body weight) reported regular consumption of three meals per day, compared to 59% of underweight adolescents (IBW ≤ 85%, which occurred in approximately 5% of the sample) (Pastore, 1996). In only one of the studies that reported a negative association for eating frequency with BMI and waist-to-hip ratio (this using data collected from 330 middle-aged French men) was control for underreporting and dieting done. Even after controlling for additional important confounder such as energy intake and expenditure, those who ate ﬁve times a day or more had, on average, a 13% lower BMI and 4% lower WHR than those who ate once or twice a day (Ruidavets, 2002). In contrast, two studies found a positive relationship between eating frequency and adiposity. In a cross-sectional study of 500 25- to 65-year-old black women using self-reported weight and height overweight women had a higher meal frequency compared to nonoverweight women (Kumanyika, 1993). Likewise, in a Swedish study, obese women ate signiﬁcantly more often compared to control women, 6.1 vs. 5.2 episodes per day (Forslund, 2002). Snacking Results on snacking are also equivocal: seven studies reported a positive relationship (Basdevant, 1993; Kumanyika, 1993, 1994; Coakley, 1998; Andersson, 1996; Public Health Institute, 2001; Forslund, 2002), ten studies reported no relationship (Locard, 1992; Wolfe, 1994; Heseker, 1995; Bandini, 1999; Takahashi, 1999; Maffeis, 2000; Neuhouser, 2000; Tanasescu, 2000; Amosa, 2001; Dwyer, 2001; Sekine, 2002), and three studies reported a negative relationship (O’Dea, 1996; Hanley, 2000; Rockett, 2001) between snacking and a measure of adiposity (Tables 4.59 and 4.60). Drummond et al. (1996) also reviewed two studies in children that reported no relationship between snacking and adiposity (Morgan, 1983; Ruxton, 1994). Interestingly, the studies in adults tend to be more suggestive of a direct association between snacking and obesity than the studies in children. All three studies that found snackers to be less likely to be overweight were performed in children. It is not clear whether this possible discrepancy between adults and children may be due to physiological differences or differences in reporting (e.g., underreporting) between older and younger populations or methodological differences between studies (e.g., many of the studies in adults and those that reported a positive association were based on self-reported height and weight). In a longitudinal study of 19,478 U.S. male health professionals aged 40–75 years at baseline, eating between meals at follow-up was associated with gain in self-reported weight over a 4-year period, after adjusting for numerous potential confounding variables (Coakley, 1998). In a crosssectional study of 500 25- to 65-year-old black women, overweight women were more likely to eat snacks compared to nonoverweight women, based on self-reported weight and height (Kumanyika, 1993), a ﬁnding replicated by the same research team based on a larger and more diverse

Study Name and/or Location Longitudinal Studies Coakley, 1998 Health Professionals Follow-up Study, U.S.

Study Population1

19,478 men 40–75 yr at baseline Ethnicity not speciﬁed 4-yr follow-up

Other Cross-Sectional or Case-Control Studies Kumanyika, 1994 4,643 adults Cardiovascular Health Study; ≥65 yr Tennessee, California, Maryland, 95% white, 5% black North Carolina Heseker, 1995 2,066 adults VERA subgroup of the German 18–88 yr Nutrition Survey, representative of Ethnicity not speciﬁed West Germany Neuhouser, 2000 961 adults Olestra Postmarketing Surveillance 18–55+ yr 81% white, 19% black Study, Indiana Kumanyika, 1993 500 women Washington, DC 25–64 yr Black Basdevant, 1993 273 obese women France 18–65 yr Ethnicity not speciﬁed

177 women (83 obese, 94 control) 37–60 yr Ethnicity not speciﬁed

Control Variables

Association3

Eating between meals (Question)

Weight gain (self-report)

Age, Hypertension, Hypercholesterolemia (at baseline)

+

After-dinner snacks (Food frequency questionnaire)

BMI category (<22, 22–29, ≥30)

Age, Gender, Ethnicity, Use of a special diet

+

Energy intake from snacks (7-day dietary record)

BMI category (<25, 25–30, >30)

Age?, Gender?

0

Adjusted to be representative of the Marion County, Indiana population of adults –

0

Number of high, medium or low- BMI category (<24.9, fat snack foods (Food frequency 25–29.9, ≥30) (self-report) questionnaire) Eating between meals (question) BMI category (15.1–21.9, 22.0–27.2, 27.3–32.2, 32.3–60.4) (self-report) Eating between meals (≥15% BMI daily energy intake, diet history) Weight loss (self-report)

Number of afternoon or evening snacks (Meal pattern questionnaire)

BMI

–

–

+

0 (current BMI) + (history of weight gain) +

223

Forslund, 2002 Swedish Obese Subjects study and Mölndal study

Adiposity Measure2

Snacking Measure

Dietary Inﬂuences on Energy Balance

TABLE 4.59 Observational Studies of the Association of Snacking with a Measure of Adiposity in Adults

224

TABLE 4.59 (CONTINUED) Andersson, 1996 The “Gustaf” study, Sweden

Amosa, 2001 New Zealand

142 men (81 obese, 61 control) 20–60 yr Ethnicity not speciﬁed 80 women 18–27 yr White, Polynesian

Size of snacks (energy per serving) Number of snacks (Twelve 24-hour recalls) Number of snacks (7-day food diary)

BMI (not clear if measured) (Underreporters excluded from analysis)

BMI (not clear if measured)

+ (snack size) 0 (snack frequency) 0

Notes: 1

Based on convenience samples.

2

BMI based on measured weight and height unless otherwise speciﬁed.

3

Plus (+) indicates signiﬁcant direct relationship, negative (–) indicates signiﬁcant inverse relationship, and zero (0) indicates nonsigniﬁcant relationship between dietary factor and measure of adiposity.

4

Different analyses of data from same study.

Obesity: Dietary and Developmental Inﬂuences

Study Name and/or Location Longitudinal Studies Francis, 2003 Pennsylvania

Study Population1

173 girls 5 yr at baseline White 4-yr follow-up

Other Cross-Sectional or Case-Control Studies Rockett, 2001 16,281 children and Growing Up Today Study, U.S. adolescents 9–14 yr 96% white 8,941 children Sekine, 20024 Toyoma Birth Cohort Study, 2.5–4.3 yr Japan Ethnicity not speciﬁed

Wolfe, 1994 New York

1,797 children 6–12 yr Mostly White

Adiposity Measure2

Eating between meals, fat intake BMI change from energy-dense snacks, snacking frequency in front of TV (dietary recall)

Control Variables

Association3

Weight status of parents (separate analysis), child BMI (at baseline), family income (at baseline)

0 (bivariate analysis) + (fat from energy-dense snacks; multivariate analysis; girls with overweight parents only)

Snack intake (FFQ)

Overweight (BMI [self- Age report] >85th percentile)

–

Snacking irregularity Number of snacks (questionnaire)

Obesity (BMI >17.55–18.13 for males, >17.28–17.76 for females; values corresponding to adult BMI of 25) BMI Triceps skinfold Arm fat area

0

Number of snack foods (“snack foods” not deﬁned) (24-hour recall)

Self-deﬁned snacking (24-hour recall)

Age, Gender

Grade, Age in grade, Ethnicity, Height, SES, Family structure, School lunch, Number of siblings, Maternal employment, No vegetable intake, Food diversity, Breakfast skipping, Food group pattern Overweight (BMI >85th Gender, Ethnicity, Site, CATCH percentile) intervention group, Random interschool variation

0

0

225

Dwyer, 2001 1,493 adolescents CATCH, California, Louisiana, 13–16 yr Minnesota, Texas 70% white, 12% black, 14% Hispanic

Snacking Measure

Dietary Inﬂuences on Energy Balance

TABLE 4.60 Observational Studies of the Association of Snacking with a Measure of Adiposity in Children

226

TABLE 4.60 (CONTINUED) Takahashi, 19994 Toyoma study, Japan

1,281 children (427 obese, 854 control) 3 yr Japanese

Locard, 1992 France

1,031 children Snacks between meals, Snacks (327 obese, 704 control) during TV viewing Mean 5.4 yr (Questionnaire) Ethnicity not speciﬁed

Obesity (weight for Parental overweight height Z score > 2 SDs above mean)

0

Public Health Institute, 2001 California Children’s Healthy Eating and Exercise Practices Survey

814 children 9–11 yr 43% white, 7% black, 38% Hispanic

Number of high-fat snacks (2day diet diary)

At risk or overweight (BMI [self-report] ≥85th or ≥95th percentiles, respectively)

+

Maffeis, 2000 Italy

530 children 7–11 yr Ethnicity not speciﬁed

Size of snacks (% total energy intake) (Diet history)

% Body fat BMI Relative body wt status (overweight 110–120%, obese >120%)

O’Dea, 1996 Australia

470 children and adolescents 11–14 yr 72% White

Number of snacks (single question)

Overweight (standard body weight [actual/expected] >110%)

Hanley, 2000 Sandy Lake Health and Diabetes Project, Canada

242 children and adolescents 10–19 yr Native Canadian

Intake of “junk foods” (chips, Overweight (BMI > fries, soda, candy, cookies, 85th percentile) cake, processed luncheon meat, canned fruit) (food frequency questionnaire)

Snacking irregularity Number of snacks (questionnaire)

BMI

Child’s weight and BMI at birth, physical 0 activity, duration of outdoor playtime, (snacking frequency) seasoning of food, caretaker, sleeping time, + bedtime, wake-up time, kindergarten (snacking irregularity) attendance, obesity of mother and father, mother’s employment

0

–

Age, Gender

–

Obesity: Dietary and Developmental Inﬂuences

Gender, Parents’ BMI, Energy intake/BMR ratio, Fat intake (others tested but not included in ﬁnal model)

Bandini, 1999 Boston

53 children (29 obese, 24 control) 7–11 yr Puerto Rican 42 adolescents (21 obese, 21 control) 12–18 yr Ethnicity not speciﬁed

Intake of snack foods (chips, peanuts, peanut butter, popcorn) (food frequency questionnaire) Intake of “junk foods” (chips, soda, candy, baked goods, ice cream) (14-day measured food record)

Obesity (BMI > 85th percentile)

Gender (others tested but not included in ﬁnal model)

0

% Body fat

Gender, Underreporting

0

Notes: 1

Based on convenience samples.

2

BMI based on measured weight and height unless otherwise speciﬁed.

3

Plus (+) indicates signiﬁcant direct relationship, negative (–) indicates signiﬁcant inverse relationship, and zero (0) indicates nonsigniﬁcant relationship between dietary factor and measure of adiposity.

4

Dietary Inﬂuences on Energy Balance

Tanasescu, 2000 Connecticut

Different analyses of data from same study.

BMR = basal metabolic rate; SD = standard deviation; SES = socioeconomic status.

227

228

Obesity: Dietary and Developmental Inﬂuences

subject population and measured BMI (Kumanyika, 1994). However, in most other studies that have reported a signiﬁcant relationship between snacking and adiposity, there are important caveats to consider. For example, in a study of obese French women, while current BMI did not differ between snackers and nonsnackers, subjects reporting a history of gaining weight had higher energy intake from snacks, while those who reported losing weight were more likely to be nonsnackers. It should be noted, however, that energy consumption during meals was also higher in snackers (Basdevant, 1993). In a study of Swedish adults, the obese ate a fewer number of snacks when all subjects were considered, and there was no statistically signiﬁcant difference in energy intake per snack. Interestingly, when underreporters were excluded from the analysis, there was no longer any difference in the number of snacks consumed, but the obese subjects ate more calories per snack than the nonobese controls (Andersson, 1996). In a case-control study of a relatively small number of women, obese subjects reported eating 2.2 snack meals per day vs. 1.6 for nonobese subjects, but strict deﬁnitions of the meal type provided on the questionnaire (main meal, light meal/breakfast, snack meal, and drink meal) were not provided (Forslund, 2002). In a study of 3year-old Japanese children, snacking frequency was not related to BMI as a continuous variable, but children who “snacked irregularly” (a pattern that is not speciﬁcally deﬁned by the authors) were 30% more likely to be obese (BMI ≥ 18) than children whose snacking pattern was reported as being “regular” (Takahashi, 1999). However, in a later reanalysis of data from the same study (e.g., using a larger sample size and a different deﬁnition of overweight), neither frequency nor regularity of snacking was related to child BMI (Sekine, 2002). It is a popular perception that eating a great deal of “snack” or “junk” foods contributes to weight gain and obesity, whether such foods are consumed between meals or during meals. However, ﬁndings from the few studies that examined the relationship between weight status and “snack foods,” regardless of when they were eaten, are not compelling (Wolfe, 1994; Bandini, 1999; Hanley, 2000; Neuhouser 2000; Tanasescu, 2000; Public Health Institute, 2001; Francis, 2003). Limitations of most of these studies include small numbers of subjects, and varying and seemingly arbitrary deﬁnitions of what constitutes a snack food. In a longitudinal study of white girls followed from ages ﬁve to nine, an increased snacking frequency was related to an increased fat intake from energy-dense snacks foods (i.e., cookies/pastries, crackers/chips, and sweets/confectioneries), which, in turn, was related to an increase in BMI. However, this relationship was found only for girls with overweight parents; it was not signiﬁcant for girls with nonoverweight families (Francis, 2003). High-fat snacking, unadjusted for race/ethnicity or SES factors, was associated with at risk for overweight or overweight (based on self-reported weight and height) among children who completed the California Children’s Healthy Eating and Exercise Practices Survey; 87% of at risk or overweight children reported eating a high-fat snack on a survey day, compared to 79% of their normal weight peers. However, what constituted a high-fat snack was not deﬁned, and results on frequency of consuming other types of snack foods were not reported (Public Health Institute, 2001). Intake of snack foods, deﬁned as chips, peanuts, peanut butter, and popcorn, was higher in 29 overweight compared to 24 control prepubertal Puerto Rican children in a case-control study in bivariate analysis, but snack food consumption was no longer related to weight status in multivariate modeling (Tanasescu, 2000). In a cross-sectional study, intake of chips, fries, soda, candy, cookies, processed luncheon meat, and canned fruit was lower in overweight than nonoverweight native American children and adolescents (Hanley, 2000). Two other studies in children, one cross-sectional (Wolfe, 1994) and one case-control (Bandini, 1999), and one crosssectional study in adults (Neuhouser, 2000) found no relationship between type of snack food intake and measures of adiposity. Breakfast Skipping Overweight children, adolescents, and adults have been shown in most (Kumanyika, 1994; Wolfe, 1994; Pastore, 1996; Monneuse, 1997; Ortega, 1998; Siega-Riz, 1998b; Amosa, 2001; Dwyer, 2001;

Dietary Inﬂuences on Energy Balance

229

O’Dea, 2001; Boutelle, 2002; Cho, 2003; Ma, 2003), but not all (Sampson, 1995; Ortega, 1996; Forslund, 2002; Sekine, 2002), studies to be more likely to skip breakfast and consume a few large meals per day than their leaner counterparts who are more likely to consume smaller frequent meals (Tables 4.61 and 4.62). It should be noted, however, that very few observational studies controlled for SES and other potential confounding variables. SES has been found in several studies to be related to meal patterns, with lower-income groups more likely to skip breakfast and other meals (O’Dea, 1994; Kant, 1995b; Hulshof, 2003). Another limitation of existing studies is that few controlled for dieting. As studies have reported more breakfast skipping among dieters (Bellisle, 1995; Shaw, 1998), skipping the morning meal may more often result from overweight and the desire to lose weight rather than being a major contributing factor to excessive weight gain. On the other hand, a signiﬁcant positive association was reported in the two studies that did attempt to take dieting into account (Kumanyika, 1994; Monneuse, 1997). Furthermore, the larger and wellcontrolled studies, including the two nationally representative ones (Siega-Riz, 1998b; Cho, 2003), have tended to report a positive relationship between breakfast skipping and a measure of adiposity. Meal size and distribution may also impact adiposity. Overweight children and adults have been reported to eat smaller breakfasts than their nonoverweight peers in some (Bellisle, 1988; Ortega, 1996, 1998), but not in all (Andersson, 1996; Bari, 2002) studies. In a study in England, eating a smaller breakfast was positively related to BMI among adolescents (13–14 yr) but not among adults (17–91 yr) (Summerbell, 1996). In comparison to normal-weight subjects, obese elderly subjects have been reported to spend less time eating breakfast, to eat a fewer number of foods and food groups at breakfast (Ortega, 1996), and to eat breakfast later in the morning (e.g., 9:00 vs. 7:00 a.m.) (Wahlqvist, 1999). In a study of 7- to 11-year-old children, Maffeis and colleagues (2000) found that, while diet composition was not related to adiposity, the distribution of energy intake among the different meals was. Speciﬁcally, a smaller breakfast was associated with adiposity in bivariate, but not multivariate analyses, while a larger dinner remained signiﬁcantly related to overweight in children after adjusting for covariates. No relation was observed for adults. Two Swedish case-control studies, one involving 177 women (Forslund, 2002) and the other 67 men (Andersson, 1996), also found that the obese consumed a larger number of meals or calories later in the day than the nonobese. In a study of elderly Greek adults, those who consumed their main meal at dinner as opposed to at lunch or at lunch and dinner, had a higher BMI (Wahlqvist, 1999). However, in a study of 2134 women from the 1985–1986 CSFII, percent of energy consumed in the evening (deﬁned as after 5:00 or 8:00 p.m.) was not related (either in bivariate or multivariate models) to self-reported BMI (Kant, 1995a). Furthermore, in the longitudinal NHANES I Epidemiologic Follow-Up Study (1982–1984) involving 2,580 men and 4,567 women aged 25–74 years at baseline (1975–1975), evening eating (expressed as a percent of energy intake after 5:00 p.m.) was not a signiﬁcant predictor of ten-year weight change (Kant, 1997).

PREVENTION TRIALS Eating Frequency The few meal and snack pattern interventions identiﬁed in the literature focused on treatment of overweight subjects rather than on prevention. Bellisle et al. (1997) summarized the results of several studies that looked at meal frequency and weight loss. They reported ﬁnding only one study with data showing a beneﬁt of increasing the number of meals consumed per day on weight loss. Six other studies, including one conducted in a metabolic ward, showed no beneﬁt. Only one prevention intervention study (nonrandomized, controlled) was examined, and this was one performed in hearing impaired children (n = 226) in boarding schools in Czechoslovakia in the 1960s (Fabry, 1966). In this study, an increased eating frequency (seven or ﬁve versus three times per day) resulted in less gain of weight and skinfold thickness in older (boys 11–16 years old, girls 10–16 years old), but not younger (boys 6–11 years old, girls 6–10 years old), children after one

230

TABLE 4.61 Observational Studies of the Association of Breakfast Skipping with a Measure of Adiposity in Adults Study Name and/or Location

Study Population1

Adiposity Measure2

Control Variables

Association3

U.S. Nationally Representative Cross-Sectional Studies Cho, 2003 NHANES III

BMI

Age, Gender, Ethnicity, Socioeconomic status, Smoking, Alcohol intake, Physical activity

+

4,643 adults ≥65 yr 95% white, 5% black

BMI (<22, 22–29, ≥30)

Age, Gender, Ethnicity, Use of a special diet

+

656 adults 18–30 yr Ethnicity not speciﬁed

BMI (self-report) status: below and above 90th percentile of the sample distribution (≥23 for women, ≥24.5 for men) Obesity (BMI ≥30)

Gender, Self-perception of body size, Trying to lose weight

+

Age, Gender, Total physical activity, Total energy intake, Educational level (others tested but not included in ﬁnal model)

+

16,452 adults 18–80+ yr 79% white, 11% black, 10% Hispanic

Other Cross-Sectional and Case-Control Studies Kumanyika, 1994 Cardiovascular Health Study Tennessee, California, Maryland, North Carolina Monneuse, 1997 Part of European Health and Behavior study, France

499 adults 20–70 yr 88% white 177 women (83 obese, 94 control) 37–60 yr Ethnicity not speciﬁed 122 adults 65–95 yr Ethnicity not speciﬁed

Amosa, 2001 New Zealand

80 women 18–27 yr White, Polynesian

0

BMI

Obese (BMI ≥25)

BMI (measured?)

Gender, Place of residence, Disease, Medications, Vitamin-mineral supplements, Tobacco, Alcohol (tested for differences between obese and nonobese) Ethnicity (separate analysis)

0

+ (European New Zealanders) 0 (Polynesian New Zealanders)

Notes: 1

Based on convenience samples.

2

BMI based on measured weight and height unless otherwise speciﬁed.

3

Plus (+) indicates signiﬁcant direct relationship, negative (–) indicates signiﬁcant inverse relationship, and zero (0) indicates nonsigniﬁcant relationship between dietary factor and measure of adiposity.

Obesity: Dietary and Developmental Inﬂuences

Ma, 2003 Seasonal Variation of Blood Cholesterol Study, Massachusetts Forslund, 2002 Swedish Obese Subjects study and Mölndal study Ortega, 1996 Spain

Study Name and/or Location

Study Population1

U.S. Nationally Representative Cross-Sectional Studies Siega-Riz, 1998b 24,363 children and adolescents NFCS 1965, 1977, 1–18 yr CSFII 1989–1991 Ethnically diverse

Other Cross-Sectional and Case-Control Studies Sekine, 2002 8,941 children Toyoma Birth Cohort 2.5–4.3 yr Study, Japan Ethnicity not speciﬁed

Adiposity Measure2

Control Variables

Association3

BMI (self-report)

Age, Gender, Ethnicity, Supplement use, SES, Location of residence, Family size, Participation in School Breakfast Program and WIC (others tested but not included in ﬁnal model)

+ (adolescents) 0 (children)

Obesity (BMI >17.55–18.13 for males, >17.28–17.76 for females; values corresponding to adult BMI of 25) Overweight (BMI [self-report] 85th –95th percentile) Obese (BMI >95th percentile) BMI Triceps skinfold Arm fat area

Age, Gender

0

Grade, Gender, Ethnicity, School, Parents’ SES

+

Grade, Age in grade, Ethnicity, Height, SES, Family structure, School lunch, Number of siblings, Maternal employment, No vegetable intake, Food diversity, Number of snacks, Food group pattern Gender, Ethnicity, Site, CATCH intervention group, Random inter-school variation

+

– (but all low income)

0

Gender, Age, SES

+

Boutelle, 2002 The Voice of Connecticut Youth Survey Wolfe, 1994 New York

8,330 adolescents 7th, 9th, and 11th grade 74% white, 9% black, 7% Hispanic 1,797 children 6–12 yr Mostly white

Dwyer, 2001 CATCH; California, Louisiana, Minnesota, Texas Sampson, 1995 New Jersey

1,493 adolescents 13–16 yr 70% white, 12% black, 14% Hispanic

Overweight (BMI >85th percentile)

1,151 children 7–12 yr 97% black 1,126 children and adolescents 6–19 yr Ethnicity not speciﬁed

Weight-for-age Obese (BMI > 85th percentile) Superobese (BMI > 95th percentile) Overweight (BMI ≥ 85th percentile)

O’Dea,2001 New South Wales, Australia

Dietary Inﬂuences on Energy Balance

TABLE 4.62 Observational Studies of the Association of Breakfast Skipping with a Measure of Adiposity in Children

+

231

232

TABLE 4.62 (CONTINUED) Pastore,1996 753 adolescents Health Screening Week Mean 16 yr Project, New York City 66% African-American, 23% Hispanic, 8% white Ortega, 1998 200 children and adolescents Spain 9–13 yr Ethnicity not speciﬁed

Obese (ideal body weight ≥ 120%) vs. Gender (separate analysis) underweight (IBW ≤ 85%)

+

Overweight/obese (BMI >75th percentile)

+

Gender, Under-/overreporting

Notes: 1

Based on convenience sample with the exception of Boutelle, 2002, Ortega, 1998; Siega-Riz, 1998b.

2

BMI based on measured weight and height unless otherwise speciﬁed.

3

Plus (+) indicates signiﬁcant direct relationship, negative (–) indicates signiﬁcant inverse relationship, and zero (0) indicates nonsigniﬁcant relationship between dietary factor and measure of adiposity. SES = socioeconomic status; IBW = ideal body weight.

Obesity: Dietary and Developmental Inﬂuences

Dietary Inﬂuences on Energy Balance

233

year. The effect of changing eating frequency was particularly marked among the older girls. The authors hypothesized that puberty may be a critical period for the inﬂuence of eating frequency. However, no study was identiﬁed that has replicated these ﬁndings. Snacking In a repeated measures study, 36 habitual snackers were encouraged to consume at least 25% of their daily energy intake from 1 of 4 different types of test snacks for 3 weeks. At the onset of the study, the BMI of subjects ranged between 18 and 30 kg/m2, and 94% reported normally consuming three to four or more snacks per day. Even though subjects consumed more daily calories from snacks when provided with those that were sweet or high in fat (vs. nonsweet or low-fat snacks), there was no signiﬁcant change in total daily energy intake or body weight over the course of the study (Lawton, 1998). Increased snacking may not lead to weight gain if energy consumed at main meals is reduced. However, no studies were identiﬁed that examined the effect of snacking over a long enough time period in a diverse sample so as to make conclusions with regards to obesity prevention. Breakfast Skipping Numerous studies have examined the effect of breakfast skipping and breakfast size in weight loss trials, but the results have been equivocal (Keim, 1997; Schlundt, 1992; Verboeket-van de Venne, 1993). No studies were identiﬁed that examined the inﬂuence of breakfast skipping on prevention of overweight.

CONCLUSION With the exception of breakfast skipping, the literature on snacking and meal frequency is minimal and conﬂicting. Without the beneﬁt of standardized deﬁnitions and analytical techniques, ﬁndings from the currently available studies on meal and snack patterns in relation to adiposity must be interpreted with caution. Furthermore, many studies have failed to take into account factors related to energy expenditure, particularly dieting for weight loss and selective underreporting of eating occasions such as snacking. Finally, the extent to which the composition and amount of foods may override any effect of timing of consumption is unclear. Given the many qualiﬁcations, only a few things can be summarized with some certainty regarding meal and snack patterns and obesity. The secular trend data available suggest that snacking has increased while meal eating has decreased. Those who consumed snacks tended to consume more calories, fat, and carbohydrate. In observational studies, breakfast skipping was consistently and positively related to adiposity among both children and adults, though longitudinal studies are lacking. In summary, based on examination of the cumulative evidence, breakfast skipping appears to be moderately consistently related to adiposity, but no conclusive statement can be made at present with respect to the relationship between eating frequency or snacking and overweight (Tables 4.63–4.65).

PARENTING INFLUENCES INTRODUCTION Parents and child care providers are likely to be inﬂuential in the emergence of characteristics that place children at risk of overweight. Looking to parents as a potential determinant of obesity does not stop at merely the passing of genes that may be implicated in obesity. There are a number of

234

TABLE 4.63 Does the Preponderance of Evidence Support a Relationship between Eating Frequency and Higher Adiposity?1 Observational Studies2 Support Relationship? (number of studies) Longitudinal Secular Trends Support Relationship?

Mechanisms Support Relationship?

(1 in adults3 0 in children)

U.S. Nationally Representative CrossSectional (1 in adults3 2 in children)

Other CrossSectional or CaseControl (11 in adults 3 in children)

Prevention Trials2 Support Relationship? (number of studies) Randomized Other Controlled Trials

(0)

(0 in adults 1 in children)

No studies

No studies

Conclusion: Consistency of Evidence Supporting Relationship

Adults Inconclusive

Inconclusive

Inconclusive

Inconclusive Children No studies

Inconclusive

Inconclusive

Notes: 1

Description of criteria used for summary table is located in the methods section.

2

Numbers in parentheses indicate the number of relevant studies identiﬁed and examined for each study type.

3

Longitudinal and nationally representative study and therefore was counted twice.

4

One study included both adults and adolescents and therefore was counted twice.

No studies

Inconclusive

Obesity: Dietary and Developmental Inﬂuences

Inconclusive Yes

Observational Studies2 Support Relationship? (number of studies) Longitudinal Secular Trends Support Relationship?

Mechanisms Support Relationship?

(1 in adults 1 in children)

U.S. Nationally Representative CrossSectional (0)

Other CrossSectional or CaseControl (8 in adults 12 in children)

Prevention Trials2 Support Relationship? (number of studies) Randomized Other Controlled Trials

(0)

(1 in adults 0 in children)

No studies

Inconclusive

Conclusion: Consistency of Evidence Supporting Relationship

Dietary Inﬂuences on Energy Balance

TABLE 4.64 Does the Preponderance of Evidence Support a Relationship between Snacking and Higher Adiposity?1

Adults Yes Yes

No studies

Yes

Inconclusive

Yes Children Inconclusive

No studies

Inconclusive

No studies

No studies

Notes: 1

Description of criteria used for summary table is located in the methods section.

2

Numbers in parentheses indicate the number of relevant studies identiﬁed and examined for each study type.

235

236

TABLE 4.65 Does the Preponderance of Evidence Support a Relationship between Breakfast Skipping and Higher Adiposity?1 Observational Studies2 Support Relationship? (number of studies) Longitudinal Secular Trends Support Relationship?

Mechanisms Support Relationship?

(0)

U.S. Nationally Representative CrossSectional (1 in adults 1 in children)

Other CrossSectional or CaseControl (6 in adults 8 in children)

Prevention Trials2 Support Relationship? (number of studies) Randomized Other Controlled Trials

(0)

(0)

No studies

No studies

Conclusion: Consistency of Evidence Supporting Relationship

Adults Yes

Yes

Moderate

Inconclusive Children No studies

Yes

Yes

Notes: 1

Description of criteria used for summary table is located in the methods section.

2

Numbers in parentheses indicate the number of relevant studies identiﬁed and examined for each study type.

No studies

No studies

Obesity: Dietary and Developmental Inﬂuences

No studies Yes

Dietary Inﬂuences on Energy Balance

237

nongenetic ways in which the family environment and parents may inﬂuence children’s eating practices, which could be linked to children’s adiposity. For the purposes of this review, we have focused on the following parental behaviors and attitudes, which have been the primary areas of investigation into parental inﬂuences on child adiposity: • Parental child-feeding practices o Parental control over child’s dietary intake o Parental restriction of highly palatable foods o Parental encouragement/pressure to eat o Instrumental (using food as a reward) and emotional feeding • Parental dietary self-restraint and disinhibition • Parental concern about child’s weight status • Family functioning

PARENTING INFLUENCES: CHILD-FEEDING PRACTICES Plausible Mechanisms Translating dietary recommendations into child-feeding practices that actually promote healthy eating patterns constitutes a considerable challenge for most parents and child care providers. There is consistent data to support that in noncontrolling, noncoercive conditions where children have access to a variety of healthy foods, children have the ability to self-regulate the amount of food and energy consumed (Johnson, 2000). Parents can negatively inﬂuence children’s eating and their ability to self-regulate dietary intake by either applying excessive external control or failing to offer healthy options. Ideally, the control over children’s eating is to be shared by parents and children according to their unique capabilities and responsibilities (Satter, 1987). It has been proposed that that parental control over what the child is offered is appropriate but that controlling if the child eats and how much could lead to disruption in the child’s health and spontaneous self-regulation of intake. There are a number of inappropriate child-feeding practices that have been suggested to undermine children’s ability to self-regulate their dietary intake (Satter, 1987). Parents often restrict children’s intake, especially of highly palatable foods (foods that are both high in fat and sugar), in an attempt to protect their children from the development of overweight. However, it has been suggested that a high degree of parental control over a child’s intake can disrupt natural systems of self-regulation and that limiting highly palatable foods (high in fat and sugar) may actually promote the children’s desire for such foods, causing dysregulation of caloric intake, overeating, and ultimately weight gain in children. Such parental control prevents children from learning to regulate their own dietary consumption (Johnson, 1994). It has also been suggested that instrumental feeding,* or using food as a reward, increases a child’s preference for that food, whereas pressuring or prompting a child to eat in order to obtain a reward (e.g., making a child ﬁnish her vegetables before she can be excused to play) tends to decrease a child’s preference for that food (Birch, 1999). The child-feeding practice known as emotional feeding (e.g., “I give my child something to eat to make him feel better when he is upset.”) has also been studied in association with children’s dietary overconsumption (Wardle, 2002). Assuming that parental child-feeding practices are modiﬁable and are directly related to childhood overweight, they present potentially important avenues for interventions to prevent childhood overweight (Spruijt-Metz, 2002). * “I offer sweets (candy, ice cream, cake, pastries) to my child as a reward for good behavior.” [Adapted from Birch and colleagues Child Feeding Questionnaire (CFQ) (Birch, 2001)].

238

Obesity: Dietary and Developmental Inﬂuences

Observational Studies Child-Feeding Practice: Parental Control over Child’s Dietary Intake The ﬁndings of the cross-sectional studies that have examined the association between parental control over a child’s dietary intake and child adiposity almost consistently found no signiﬁcant association (Wardle, 2002; Baughcum, 2001; Robinson, 2001; Saelens, 2000; Sherman, 1995; Koivisto, 1994). However, one study (Johnson, 1994) did ﬁnd a positive association between parental control over the child’s feeding practices and anthropometric measures, and one study (Robinson, 2001) found an inverse association between parental control over the child’s intake and BMI and triceps skinfold thickness in girls only (see Table 4.66). Robinson and colleagues’ study (2001), which found both no association among the sampled boys and an inverse association among the sampled girls, was conducted among a diverse ethnic and socioeconomic study sample. While the results of this study were weak, the exploratory analysis revealed that parents who controlled their daughter’s intake were more likely to view them as underweight, not overweight. Thus, the relationship between parents’ use of restriction and child adiposity may be bidirectional; parents may impose restrictions in response to the child’s eating behaviors or weight status, and parental restriction may also promote behaviors that contribute to childhood overweight. Unfortunately, no longitudinal data that examined the association between these two variables were identiﬁed, and of the cross-sectional studies that exist, the preponderance of the data do not indicate that this child-feeding practice is either a risk factor for adiposity development or a target for prevention efforts. Child-Feeding Practice: Parental Restriction of Highly Palatable Foods Several cross-sectional studies examined the association between the parental child-feeding practice: restriction of highly palatable foods and some measure of adiposity in children. In most cases, parental restriction of highly palatable foods was measured by using the Restriction Subscale from the Child-Feeding Questionnaire (CFQ), and thus this parental child-feeding practice may be considered a speciﬁc type of parental control over child’s dietary intake. Of the cross-sectional studies that examined this association, many of them found positive associations between these two variables of interest (Fisher 2002; Lee 2001; Spruijt-Metz, 2002; Birch, 2000; Fisher, 1999a; Fisher, 1999b), and two found no signiﬁcant association (Davison, 2001a; Francis, 2001). Most of these studies were conducted among the same non-Hispanic, white study population, and therefore the results may not be applicable to other populations. Spruijt-Metz and colleagues (2002), however, examined this association among a black and white population of boys and girls and found this association to be positive after controlling for a number of potentially confounding variables. However, because of the cross-sectional nature of these studies, no causality can be determined. It is unclear if these restrictive feeding practices caused the adiposity in these children or if it is the child’s adiposity that causes the parent to enforce restrictive feeding practices. It is also possible, and likely, that the association between parental restriction of highly palatable foods and child’s adiposity is bidirectional. Based on these observational studies, the data do support the hypothesis that high levels of parental restriction increase risk of adiposity in children; however, the data are weak, and the direction of this association is unknown. Additional longitudinal studies need to be conducted among multiethnic populations to conﬁrm the direction of this association. Until such research is conducted, it is suggested that parents be encouraged to avoid excessive restriction of highly palatable foods. (See Table 4.67.) Child-Feeding Practice: Parental Pressure/Encouragement to Eat While the child-feeding practice of parental pressure/encouragement of child’s intake may also be considered a type of parental control over child’s intake, a number of studies have directly examined this speciﬁc parental child-feeding practice as it relates to child adiposity (see Table 4.68). The results of the cross-sectional studies that have examined this association have been mixed, with some showing no association (Wardle, 2002; Koivisto, 1994) and others showing an inverse

Author, Year, Location

Study Population

Adiposity Measure1

Control Variables

Statistical Analysis

Association2

Cross-Sectional Studies Robinson 2001 USA

792 third-grade boys and girls with parent’s participation from diverse ethnic and socioeconomic backgrounds

BMI and Tricep skinfold thickness

Gender, Household education level, Spearman Correlation Parents’ perception of their own Coefﬁcients weight, Children’s age

– Parental control over child’s intake (in girls only)

Baughcum 2001 USA

453 white, black, Hispanic, and Asian children (age 11–23 months) and their mothers

Weight for height % (Overweight ≥90th %)

Family income, Maternal obesity Factor analysis (based on self-reported ht and wt)

0 Maternal control over childfeeding practices

Sherman 1995 USA

377 mother-child pairs recruited from WIC program; 189 Mexican-American, 188 white (age 3–5 yr)

Triceps and subscapular skinfold thickness and weight for height z score

Ethnicity

Multiple Linear Regression and Logistic Regression Methods

0 Maternal control over child feeding practices

Wardle 2002 Twins Early Development Study (TEDS) UK

214 predominantly white families with Child’s risk of obesity (measured Child’s age, Gender, and Zygosity (mono- or di-), Mother’s BMI (in same sex-twins: 100 families in which both by parental weight status), Child’s BMI and % body fat the second analysis — outcome parents were overweight or obese and 114 variable: child’s weight), Social in which both parents were normal weight or lean (child’s age ~4 yr). Case control. class

Pearson Correlation Coefﬁcients and Partial Correlations

0 Parental control over child’s eating

Johnson 1994 USA

77 preschoolers (age 3–5 yr) predominantly Weight for height white (5 black and 9 Asian), middle-class population

Gender

Pearson Correlation Coefﬁcients

+ Parental control of child’s feeding practices

Koivisto 1994 Sweden

50 children from Swedish families (age 3–7 yr)

Age, Gender

Test for Correlation

0 Parental control over child’s intake

Saelens 2000 USA

18 white, middle-to-upper-middle- class BMI % (Obesity: BMI > 85th %) Parental weight, Socioeconomic families with obese and nonobese children status and Food storage and (age 7–12 yr) preparation, Gender, Age

Intraclass Correlational Analysis

0 Maternal control over childfeeding

Weight-length index (Overweight >109)

Dietary Inﬂuences on Energy Balance

TABLE 4.66 Observational Studies Conducted among Children to Examine the Association between the Child-Feeding Practice: Parental Control over Child’s Dietary Intake and Child Adiposity

Notes: 1

Anthropometric measurements are based on actual measurements except where otherwise noted. Plus (+) indicates signiﬁcant direct relationship, negative (–) indicates signiﬁcant inverse relationship, and zero (0) indicates nonsigniﬁcant relationship between factor of interest and measure of adiposity.

239

2

Author, Year, Location

Study Population

Cross-Sectional Studies Fisher 191 non-Hispanic, white girls (age 5) 2002 and both of their parents

Adiposity Measure1

Control Variables

Statistical Analysis

Overweight (BMI ≥85th %) at both age 5 and 7

Logistic Regression

Pearson Correlation Analysis

Same study population, n = 192

BMI at age 5 and 7 yr

Same study population, n = 197

Weight for height % (Overweight ≥85th %)

Francis 2001 Birch 2000

Same study population, n = 196

BMI at age 5 yr

Same study population, n = 156

Weight for height z score

Maternal education, Income, Depression, General parental control

Same as above

Spruijt-Metz 2002

120 white and Black boys and girls (age 7–14 yr) and their mothers

Correlations and Multiple Regression Analysis

Total lean mass, Gender, Ethnicity, SES, Energy intake, Energy intake from sources other than fat

Body composition: total fat mass.

Fisher 1999a

56 children (age 3–6 yr) and their parents; predominantly white population

Pearson’s Rank-Order Correlations and Multiple Regression Analysis

Gender, Parental adiposity (selfreported ht and wt)

Child’s weight for height % and skinfold measurement %.

Fisher 1999b

Pearson’s Product-Moment 32 healthy, normal-weight children (age 4–6 yr) and their parents; sample Correlation consisted of 80% white, 15% Asian, 4% black and 1% other race/ethnicity

Gender

Weight for height %.

General parental control — not speciﬁc to domain of child-feeding practices.

Multiple Regression Analysis

Structural Equation Model

Association2

+ Eating in absence of hunger which was related to Parental restriction + Maternal restriction 0 Parental restriction 0 Maternal restriction + Maternal restriction + Mother’s concern for her child’s weight, which correlated w/ Restrictive feeding practices + Mothers’ reports of restricting children’s access to 10 snack foods + Parental restriction of access to snack foods at home

Notes: 1

BMI based on measured weight and height unless otherwise speciﬁed.

2

Plus (+) indicates signiﬁcant direct relationship, negative (–) indicates signiﬁcant inverse relationship, and zero (0) indicates nonsigniﬁcant relationship between factor of interest and measure of adiposity.

Obesity: Dietary and Developmental Inﬂuences

Lee 2001 Davison 2001a

240

TABLE 4.67 Observational Studies Conducted among Children to Examine the Association between the Child-Feeding Practice: Restriction of Highly Palatable Foods and Child Adiposity

Author, Year, Location

Adiposity Measure1

Study Population

Control Variables

Statistical Analysis

Association2

Cross-Sectional Studies Wardle 214 predominantly white families with same 2002 sex-twins: 100 families in which both parents Twins Early were overweight or obese and 114 in which Development Study both parents were normal weight or lean (TEDS) (child’s age ~4 yr). Case control.

Child’s risk of obesity Child’s age, Gender, and Zygosity Pearson Correlation (measured by parental (mono- or di-), Mother’s BMI (in the Coefﬁcients and Partial weight status), Child’s BMI second analysis — outcome Correlations and % body fat variable: child’s weight), Social class

Francis 2001

196 non-Hispanic, white girls (age 5 yr) and their mothers.

BMI and Triceps and subscapular skinfold measurements.

Lee 2001

Same study population; n=192.

BMI at age 5 and 7 years.

Spruijt-Metz 2002

120 white and black boys and girls (age 7–14 yr) and their mothers

Body composition: total fat mass

Koivisto 1994

50 children from Swedish families (age 3–7 yr) Weight-length index (Overweight > 109)

Maternal education, Income, Structural Equation Model Depression, General parental control

0 Parental encouragement to eat

Dietary Inﬂuences on Energy Balance

TABLE 4.68 Observational Studies Conducted among Children to Examine the Association between the Child-Feeding Practices: Parental Encouragement/Pressure to Eat and Child Adiposity

– Maternal pressure to eat

Pearson Correlation Analysis

– Maternal pressure to eat

Total lean mass, Gender, Ethnicity, socioeconomic status, Energy intake, Energy intake from sources other than fat

Correlations and Multiple Regression Analysis

– Maternal pressure to eat

Age, Gender

Test for Correlation

0 Parental encouragement

Notes: 1

BMI based on measured weight and height unless otherwise speciﬁed.

2

Plus (+) indicates signiﬁcant direct relationship, negative (–) indicates signiﬁcant inverse relationship, and zero (0) indicates nonsigniﬁcant relationship between factor of interest and measure of adiposity.

241

242

Obesity: Dietary and Developmental Inﬂuences

association (Francis, 2001 and Lee, 2001; Spruijt-Metz, 2002). Only one of these studies examined this association among children from a population other than a non-Hispanic, white one (SpruijtMatz, 2002). This study found a negative association between this child-feeding practice and total fat mass among their sample of black and white children (Spruijt-Metz, 2002), suggesting that mothers pressure their thinner children to eat. It is likely that the negative associations can be explained, as the Spruijt-Metz study suggests, by the fact that parents are more likely to encourage dietary intake in response to their children’s underweight status. Based on these observational studies, few conclusions can be drawn in regard to overweight prevention. Longitudinal studies conducted among multiethnic populations may be beneﬁcial to draw further conclusions in regards to overweight prevention. Child-Feeding Practice: Instrumental Feeding (Using Food as a Reward) and Emotional Feeding While the child-feeding practices (instrumental feeding and emotional feeding) may again be considered a speciﬁc type of parental control over child’s dietary intake, these feeding practices were directly examined in a case-control study as they relate to child adiposity (see Table 4.69). Wardle and colleagues (2002) found no evidence for differential parental feeding styles in the obese subgroup compared to their normal weight counterparts. More research is necessary to determine the role of this speciﬁc parental child-feeding practice in the development and prevention of overweight.

PARENTAL INFLUENCES: DIETARY SELF-RESTRAINT

AND

DISINHIBITION

Plausible Mechanism How parents handle their own eating and weight issues is often as important as how they feed their child. A parent who consistently diets is providing a model that his child may emulate. In the same way, a mother who makes negative comments about her own body is setting an unhealthful example for her child. Costanzo and Woody (1985) suggest that parents are likely to exert control over their children’s behavior in areas that are important and potentially problematic for them. Speciﬁcally, parent’s attitudes toward their own dietary intake have been suggested to increase risk of offspring overweight. These parental attitudes have been assessed by measuring parents’ levels of dietary restraint and dietary disinhibition. Dietary restraint is typically deﬁned as the conscious restriction of food to control intake and ultimately body weight; dietary disinhibition is deﬁned as a lack of control over food intake in response to external cues, such as emotional distress (Cutting, 1999; Hood, 2000). It is thought that the higher the degree of parental dietary restraint and disinhibition, the more likely the child is to emulate these behaviors thereby externalizing control of dietary intake and ignoring internal cues. The resultant disruption in natural self-regulation of intake puts the child at risk for overweight. Observational Studies Parental attitudes toward their own dietary intake are typically identiﬁed by measuring parental dietary restraint and disinhibition. Dietary restraint and disinhibition are often measured using Stunkard and Messick’s Eating Inventory Questionnaire. Both cross-sectional and longitudinal studies have been conducted to examine the association between parental dietary restraint and disinhibition and child adiposity (see Table 4.70). Cross-sectional studies that examined the association between maternal and parental disinhibition and childhood overweight consistently found positive associations (Cutting, 1999; Johnson, 1994); however, these studies were conducted among predominantly white populations. In regard to dietary restraint, the cross-sectional study results were mixed. Cutting and colleagues (1999) found no signiﬁcant association between children’s

Author, Year, Location

Study Population

Adiposity Measure1

Control Variables

Statistical Analysis

Association2

Dietary Inﬂuences on Energy Balance

TABLE 4.69 Observational Studies Conducted among Children to Examine the Association between Parental Instrumental and Emotional Feeding and Child Adiposity

Cross-Sectional Study: Case Control Wardle 214 predominantly white families with same 2002 sex-twins: 100 families in which both parents Twins Early were overweight or obese and 114 in which Development Study both parents were normal weight or lean (TEDS) (child’s age ~4 yr). Case control. UK

Child’s risk of obesity Child’s age, Gender and Zygosity Pearson Correlation (measured by parental (mono- or di-), Mother’s BMI (in the Coefﬁcients and Partial weight status), Child’s BMI second analysis — outcome Correlations and % body fat variable: child’s weight), Social class

0 Instrumental and emotional feeding

Notes: 1

BMI based on measured weight and height unless otherwise speciﬁed.

2

Plus (+) indicates signiﬁcant direct relationship, negative (–) indicates signiﬁcant inverse relationship, and zero (0) indicates nonsigniﬁcant relationship between factor of interest and measure of adiposity.

243

244

TABLE 4.70 Observational Studies Conducted among Children to Examine the Association between Parental Attitudes toward Their Own Dietary Intake and Child Adiposity Author, Year, Location

Study Population

Adiposity Measure1

Longitudinal Studies Davison 192 non-Hispanic, white girls (age 5 Change in BMI and 2002 yr at start of the study) and both of Skinfold thickness USA their parents studied over 2 yr Hood 92 predominantly white children Change in BMI and 2000 (age 3–5) and their parents studied Skinfold thickness The Framingham over 6 yr Children’s Study USA

Cutting 1999 USA

75 predominantly white children (ages 3–6 yr) and their parents

Statistical Analysis

Association2

Family income, Parental body weight, Parental Cluster Analysis dietary intake adjusted for parental body weight Gender, Baseline values for age, height, Analysis of caltract count/ hr, total energy intake and % Covariance Models kcal from fat, Baseline BMI, Baseline sum of skinfolds, Parent’s education level, Parental adiposity

0 Parental dietary disinhibition

+ Parental dietary disinhibition + males 0 females Maternal dietary restraint 0 males + females Maternal dietary disinhibition 0 Maternal dietary restraint

Weight for Height

Gender

Pearson Correlation Coefﬁcients

Weight for Height

Gender

Correlational Analysis and Multiple Regression Analysis

+ Parental dietary disinhibition + Parental dietary restraint

Notes: 1

BMI based on measured weight and height unless otherwise speciﬁed.

2Plus (+) indicates signiﬁcant direct relationship, negative (–) indicates signiﬁcant inverse relationship, and zero (0) indicates nonsigniﬁcant relationship between factor of interest and measure of adiposity.

Obesity: Dietary and Developmental Inﬂuences

Cross-Sectional Studies Johnson 77 preschoolers (age 3–5 yr) 1994 predominantly white (5 black and USA 9 Asian), middle-class population

Control Variables

Dietary Inﬂuences on Energy Balance

245

weight for height and maternal restraint, whereas Johnson and Birch (1994) found a positive association between maternal restraint and weight/height in their sons only. Two longitudinal studies were identiﬁed that examined this association prospectively; the results of these studies were also inconclusive (Davison, 2002; Hood, 2000). Davison and Birch (2002) studied this association in young, white girls over a 2-year period and found no association between parental disinhibition and change in BMI and/or skinfold thickness after controlling for a number of potential confounding variables. Hood and colleagues (2000) studied this association over a 6-year period in the Framingham Children’s Study and found a positive association between these two variables. Both of these observational studies were conducted among non-Hispanic, white populations only. Therefore, additional studies are needed to establish existence and nature of these associations in other ethnic groups. Intervention Studies Although no intervention studies were identiﬁed that measured adiposity as an outcome, Johnson (2000) conducted an intervention study to investigate whether children could be taught to focus on internal cues of hunger and satiety and consequently improve their self-regulation of energy intake. The results of this 6-week intervention showed that children were in fact able to improve their focus on internal cues of hunger and satiety. The intervention also seemed to alter the negative relationship between mothers’ and daughters’ eating styles that was previously identiﬁed. Mothers who had difﬁculty controlling their own food intake (high maternal disinhibition) prior to the intervention had children who do not show evidence of good self-regulation; however, after the intervention, the relations between mothers’ and children’s eating patterns were no longer signiﬁcant. The impact, if any, on the child’s BMI was not reported.

PARENTAL INFLUENCES: CONCERN

ABOUT

CHILD’S WEIGHT STATUS

Plausible Mechanism The inﬂuence of our weight-conscious society on child’s body size and dietary intake cannot be underestimated. Because weight issues and eating problems are more prevalent in girls and women, parents of girls often monitor and evaluate weight and eating behaviors more closely than parents of boys (Fisher, 1999). Mothers’ perceptions of their daughters’ risk of overweight and mothers’ child-feeding practices may represent important nonshared environmental inﬂuences on daughters’ eating and relative weight (Birch, 2000). Parents who wish to spare their children the negative consequences of overweight often impose limits on their children’s access to food. Thus, it is likely that the association between parental concern about a child’s weight status and childhood overweight may be mediated by restrictive child-feeding practices that result from parents’ concern about their children’s body weight (Spruijt-Metz, 2002). Observational Studies The results of two cross-sectional studies that have examined the association between parental concern about child’s weight status and child adiposity were consistent, reporting positive associations between these two measures (Davison, 2001; Spruijt-Metz, 2002; Davison, 2002) (see Table 4.71). Because of these studies’ cross-sectional design, the causality of their association is unknown; parents may become concerned about their child’s weight status in response to their child’s overweight status, or parents who are concerned their child may become overweight may practice a restrictive feeding style that may directly result in overweight development. These few studies have been conducted among predominantly non-Hispanic, white populations only; therefore, future studies should examine this association among large, multiethnic populations. Longitudinal

246

TABLE 4.71 Observational Studies Conducted among Children to Examine the Association between Parental Concern about Child’s Weight Status and Child Adiposity Author, Year, Location

Study Population

Adiposity Measure1

Control Variables

Statistical Analysis

Association2

Cross-Sectional Studies 197 white girls (age 5 yr) and both of their parents

Weight for height % (Overweight ≥85%; Nonoverweight < 85%)

General parental control — not speciﬁc to the domain of childfeeding practices

Multiple Regression Analysis

+ Parental concern about their daughter’s weight status

Davidson 2002

182 white girls (age 5 yr) and both their parents

BMI % at age 5 and 7

Family income, Parental educational status, General parental control

Logistic Regression Analysis

+ Parent criticism (parent’s reaction to child’s weight status)

Spruijt-Metz

120 white and black boys and girls (ages 7–14) Multiple Regression and their mothers Analysis

Total lean mass, Gender, Ethnicity, Socioeconomic status, Energy intake, Energy intake from sources other than fat

Body composition: total fat mass

2002

+ Mother’s concern about her child’s weight status

Notes: 1

2

BMI based on measured weight and height unless otherwise speciﬁed.

Plus (+) indicates signiﬁcant direct relationship, negative (–) indicates signiﬁcant inverse relationship, and zero (0) indicates nonsigniﬁcant relationship between factor of interest and measure of adiposity.

Obesity: Dietary and Developmental Inﬂuences

Davidson 2001a

Dietary Inﬂuences on Energy Balance

247

studies would also provide additional, valuable information, allowing us to further assess if this parental behavior would be an effective target for childhood overweight prevention.

PARENTAL INFLUENCES: FAMILY FUNCTIONING Plausible Mechanism It has been generally accepted that characteristics of family life are closely linked to the development and maintenance of obesity in children (Lissau, 1994). A number of methods have been used to identify and measure family functioning (family functioning as it pertains to family styles and relationships). A healthy family environment is thought to be protective against childhood overweight development (Mendelson, 1995; Strauss, 1999). There have been a number of suggestive mechanisms by which this can occur. For instance, it is likely that a child who does not receive adequate parental support lacks parental promotion of healthful eating patterns. These children may, therefore, be more likely to consume foods that lead to overconsumption and ultimately weight gain. Observational Studies There have been a number of observational studies conducted to examine the association between the family functioning style and child adiposity (see Table 4.72). Mendelson and colleagues (1995) explored this association in a cross-sectional study design by using Bloom’s Self-Report Measure of Family Functioning and found obese girls rated their families lower on cohesion, expressiveness, and democratic style. In a smaller case-control analysis, Valtolina and colleagues (1998) tested the hypothesis that parent-adolescent communication and support are different in families with obese adolescents than those with normal-weight adolescents but found no differences. In a longitudinal analysis, Lissau and colleagues (1993) measured parental support by assessing maternal attitude and reports of offspring’s sweet-eating habits and found more than a fourfold increased risk of overweight in young adults whose mothers claimed not to know about the offspring’s sweet-eating habits as a child than young adults whose mothers did know about the offspring’s sweet-eating habits as a child. In an analysis conducted among the same longitudinal cohort, children receiving no parental support as perceived by the child’s teacher were at signiﬁcantly higher risk of becoming obese than children who were raised in a supportive environment (Lissau, 1994). Finally, in a U.S. nationally representative sample of children, Strauss and colleagues (1999) found only the Home Observation for Measurement of the Environment (HOME) score for cognitive stimulation of the child’s environment and family income to be signiﬁcant predictors of childhood obesity after controlling for a number of potentially confounding variables. The HOME score for emotional relationships between mother and child was not found to be a predictor of childhood obesity after controlling for these same potentially confounding variables. Based on these observational studies, it appears that positive aspects of family functioning such as cohesion, expressiveness, democratic style, parental support, and cognitive stimulation at home may be protective against childhood overweight, while other negative aspects of family functioning, such as mother’s lack of support, may be associated with overweight in children.

CONCLUSION Although there is consensus that parents play an important role in inﬂuencing their children’s dietary behaviors, there is a surprising lack of consistent evidence relating speciﬁc parental behaviors to child adiposity. Of the behaviors examined in this chapter, there is weak support for the hypothesis that high levels of parent restriction and control over their child’s intake may increase the risk of obesity in the child, and there is no epidemiological support for the contention that pressure to eat or use of food as a reward increases the risk of obesity. However, there is fairly

248

TABLE 4.72 Observational Studies Conducted among Children to Examine the Association between Family Functioning Style and Child Adiposity Author, Year, Location

Study Population

Adiposity Measure1

Control Variables

Statistical Analysis

Association2

Nationally Representative Longitudinal Study Strauss 1999 National Longitudinal Survey of Youth USA

2913 ethnically diverse, normal BMI % weight boys and girls at (Obesity: BMI > 95th % for age baseline (aged 0–8 yr) and gender) Followed for 6 yr

Maternal obesity (self-reported ht and Multivariate wt), Initial weight-for-height zLogistic Regression score, Gender, Socioeconomic Analysis factors, Race and marital status.

– Cognitive stimulation at home 0 Family emotional environment

– Parental support 0 Overprotective parental support

Longitudinal Studies 756 third grade children living in Copenhagen municipality followed up with 10 yr later (ages 9–10 yr)

Childhood: BMI % Young adults: BMI % with selfreported ht and wt (Overweight: BMI = 90–95th %; Obese: BMI > 95th %)

Age, BMI in 1974, Gender, Social background

Lissau 1993

522 third grade children living in Copenhagen municipality followed up with 10 yr later (ages 9–10 yr)

Same as above

Gender, Parental social factors Same as above (school education of both parents, house-holder’s occupational status and rearing area), BMI in childhood

Denmark

Logistic Regression Analysis

+ Lack of knowledge about offspring’s sweet eating habits

Cross-Sectional Studies Mendelson 1995

Canada

572 adolescents in grades 9–11 (M age = 15.7 yr), from lower to upper middle class, ethnically diverse families

Gender Relative weight2 (Underweight > 90% expected wt; Normal weight 90–110%; Overweight 111–125%; Obese > 125%)

Analyses of Variance (ANOVA)

0 males – females Cohesion, expressiveness and democratic style among girls’ families

Obesity: Dietary and Developmental Inﬂuences

Lissau 1994

Italy

30 family triads with a normal- BMI weight child — controls and 30 (Obese: BMI > 35; Normal wt: families with an obese child — BMI< 25) case (ages 16–19 yr) Case Control

Age, Gender, Socioeconomic status

Multivariate Analysis of Variance

0 Quality of parent-child relationship: communication and support

Notes: 1

BMI based on measured weight and height unless otherwise speciﬁed.

2

Plus (+) indicates signiﬁcant direct relationship, negative (–) indicates signiﬁcant inverse relationship, and zero (0) indicates nonsigniﬁcant relationship between factor of interest and measure of adiposity.

3

Lissau 1994 and Lissau 1993 are analyses of subsets of the same study cohort.

4

Relative weight calculation = [(actual weight – average weight)/average weight] × 100.

Dietary Inﬂuences on Energy Balance

Valtolina 1998

249

250

Obesity: Dietary and Developmental Inﬂuences

consistent evidence that maternal restraint and/or disinhibition and maternal concern about her child’s weight are associated with overweight in the child. Given the lack of evidence regarding the direction of these associations, however, it is hard to arrive at ﬁrm conclusions regarding causation. Findings also suggest that healthy family functioning in terms of appropriate levels of parental involvement and support may also confer some protection against development of overweight in the child. Although clearly more studies, especially longitudinal studies, in the area of parental inﬂuences are needed, the ﬁndings presented here suggest that a focus on healthy parental attitudes toward their own weight and eating practices, avoidance of preoccupation with the child’s weight, and healthy levels of parental involvement/support appear to be the most promising target behaviors for prevention of overweight in children. Although avoidance of parental pressure to eat and instrumental feeding may have other beneﬁts, the ﬁndings presented suggest that it would be premature to focus on these behaviors for prevention of overweight.

BREASTFEEDING INTRODUCTION Numerous health beneﬁts to the infant are associated with breastfeeding. These include the provision of optimal nutrients, protection from infection, and psychological beneﬁts. Breastfeeding has also been examined in relation to the development of obesity.

SECULAR

TRENDS

Based on 1998 national data, 64% of mothers initiated breastfeeding, and 29% of mothers reported they were still breastfeeding at 6 months (DHHS, 2000). More recently, the 2001 Ross Laboratories Mothers Survey, a large national survey designed to determine patterns of milk feeding during infancy, found the prevalence of breastfeeding initiation and the continuation of breastfeeding at 6 months of age was 69.5 and 32.5%, respectively (Ryan, 2002). The National Immunization Survey exclusive breastfeeding data from the third quarter of 2001 showed nearly 60% of U.S. infants were exclusively breastfed in the early postpartum period and just 8% were exclusively breastfed by 6 months (Li, 2003). While these proportions continue to be less than the Healthy People 2010 objectives of 75% in the early postpartum period, 50% at 6 months, and 25% at 1 year (USDHHS, 2000), rates are on the rise and are higher than they were in the 1970s, when only 25% of women reported to have initiated breastfeeding (Gillman, 2002). As breastfeeding rates have increased during the time period that overweight has risen, the secular trend data do not support the hypothesis that breastfeeding is protective against obesity.

PLAUSIBLE MECHANISMS The mechanism by which breastfeeding may protect against the development of obesity has not been established. However, a number of metabolic and behavioral explanations have been proposed. Human milk contains bioactive substances that may inﬂuence adipocyte differentiation and proliferation (Butte, 2001). It also contains epidermal growth factor and tumor necrosis factor alpha (TNFα), both of which are known to inhibit adipocyte differentiation in vitro. Formula-fed infants, on the other hand, exhibit signiﬁcantly higher plasma insulin levels and a prolonged insulin response, which would be expected to stimulate fat deposition and thereby affect the early development of adipocytes (Lucas, 1980; Oakley, 1977). Differences in macronutrient composition between breast milk and formula may also play a speciﬁc role in breastfeeding’s protective effect against overweight development. The total energy and protein intakes of breastfed children are signiﬁcantly less than those found in formula-fed infants (Dewey, 1992). These early differences in macronutrient supply may have long-term effects on substrate metabolism. Furthermore, it has been proposed that a higher protein intake during

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251

early childhood (as in formula-fed babies) may predispose one to obesity later in life (RollandCachera, 1995), but results of studies of the effects of protein on childhood growth patterns have been inconsistent (Dorosty, 2000) (see protein section under dietary determinants of energy imbalance for further details). One of the most commonly proposed behavioral mechanisms by which breastfeeding is thought to be protective against the development of overweight is seen in infant’s ability to regulate intake. Breast milk composition changes during feeding to meet infant’s needs and provides satiety signals to tell the infant to stop suckling. The regulation of intake in formula-fed infants is often governed by the judgment of the individual feeding the infant and not by the infant’s physiological internal cues (i.e., satiety signals); the infant may be prompted to consume excessive formula based on caregivers’ perception of what is an appropriate amount/volume. Therefore, breastfed infants are likely to acquire more control over their feeding behavior than formula-fed children (Taveras, 2005). Other recent studies have suggested alternative mechanisms. Mennella and colleagues (2001) hypothesized that experience with a ﬂavor in amniotic ﬂuid or breast milk modiﬁes infants’ acceptance and enjoyment of similarly ﬂavored foods at weaning. They tested this hypothesis by exposing mothers prenatally and postnatally to carrot juice. Based on the results of the study, the authors concluded that prenatal and early postnatal exposure to a ﬂavor enhanced the infants’ enjoyment of that ﬂavor in solid foods during weaning. Therefore, in theory, an infant who is breastfed by a mother consuming a well-balanced diet with a variety of fruit and vegetables may be more likely to subsequently accept and enjoy a diet with a wide variety of fruits and vegetables — a dietary factor associated with reduced risk of chronic disease and obesity development later in life (see section on fruits and vegetables).

OBSERVATIONAL STUDIES Several published reviews have examined the effect of breastfeeding on the development of overweight/obesity later in life. Parsons and colleagues (1999), in a systematic review of longitudinal studies that have examined adiposity in children aged 2–7 years and lasted at least 1 year in length, found no consistent pattern to emerge from the relationship between mode of infant feeding or duration of breastfeeding and later risk of obesity. Butte (2001) came to a similar conclusion — the protective effect of breastfeeding on later obesity remains controversial. In a more recent review, however, Dewey (2003) concluded from recent studies that breastfeeding reduces the risk of child obesity to a moderate extent, but that the effect is probably small compared to other factors that inﬂuence child obesity (Table 4.73).

TABLE 4.73 Published Reviews of Studies Examining the Association of Breastfeeding with Measure of Adiposity First Author, Year Parson 1999 Buthe 2001

Dewey 2003

Number Publication Years of of Studies Studies Inclusion Criteria Conclusions 12 1966–1998 for literature Longitudinal studies of at least No consistent pattern search; 1977–1998 for 1 yr duration studies 18 1976–1999 All studies that examined effect Protective effect of of breastfeeding on breastfeeding on later obesity development of childhood remains controversial obesity 11 Emphasis 1999–2002; Children > 3 yr old; Sample size 8 studies showed protective only 2 of 11 before ≥ 100/feeding group effects, and 3 studies lacked 1999 signiﬁcant information

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When the 27 individual studies reviewed (some being included in more than one of the review articles) are examined in more detail, it is not surprising that the review authors came to different conclusions, as only Dewey (2003) limited her review to studies with a relatively larger sample size. About half of the studies reported an inverse association between breastfeeding and adiposity, while the other half found no relationship, with a few even ﬁnding a positive relationship. However, when the studies included in the three reviews are ranked according to sample size (Table 4.74), a clearer pattern emerges: the larger studies tended to ﬁnd an inverse association between breastfeeding and adiposity, while the smaller ones tended to ﬁnd no association, or a positive association.

TABLE 4.74 Summary of Studies Included in Review Articles on the Association of Breastfeeding with Measure of Adiposity Study1

Age at Outcome

Sample Size

Association with Breastfeeding2

2 yr 5 yr 3–26 yr 17–18 yr 7 yr 7 yr 5 yr 8 yr 5 yr 32 yr 3 yr 2 yr 6 yr 2–4 yr 7 yr

382,347 4,062 1,037 781 562 545 476 432 203 172 170 89 54 20 NA

– 0 – – 0 0 0 0 0 + (men); 0 (women) 0 –; +; 0 (depending on measure) + (BMI); 0 (SF) + –

3–5 yr

2,685

– (at risk); 0 (overweight)

33,768 32,200 15,341 9,357 3,731 2,108 427 366 270 246 136

– – – – 0 – – 0 0 0 0

Longitudinal Studies Kramer, 1985a/b;1986a,b O’Callaghan, 1997a,b,c Poulton, 2001c Tulldahl, 1999b,c Birkbeck, 1985a,b Wilson, 1998a,b Dine, 1979a,b Fomon, 1984b Poskitt, 1977a Marmot, 1980a,b Vobecky, 1983a,b Dewey, 1993; Heinig, 1993a Agras, 1987; 1990a,b Wells, 1998a Strbak, 1991b U.S. Nationally Representative Study Hediger, 2001c

Other Cross-Sectional or Case-Control Studies Toschke, 2002c 6–14 yr Armstrong, 2002c 3–4 yr 9–14 yr Gillman, 2001c von Kries, 1999b,c 5–6 yr Wadsworth, 1999b,c 6 yr Liese, 2001c 9–10 yr Kramer, 1981b,c 12–18 yr Charney, 1976b 20–30 yr Zive, 1992a,b 4 yr Baranowski, 1990b 3–4 yr Elliott, 1997b 15–16 yr Notes: 1

“a” indicates inclusion in review by Parsons et al (1999), “b” by Butte (2001), and “c” by Dewey (2003), for a total of 27 separate studies. Many of the studies were included in more than one review and several studies were described in more than one published paper as indicated. 2

Plus (+) indicates signiﬁcant direct relationship, negative (–) indicates signiﬁcant inverse relationship, and zero (0) indicates nonsigniﬁcant relationship between breastfeeding and measure of adiposity.

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253

That a large sample size is needed to detect a difference in adiposity supports Dewey’s (2003) conclusion that breastfeeding is protective against subsequent adiposity, but that the effect size is relatively small. Identiﬁed studies that have been published since these reviews are shown in Table 4.75. Two (von Kries, 2000; Bergmann, 2003) of the three, one of which involved prospective data collection (Bergmann, 2003), found an inverse association between breastfeeding and subsequent adiposity in childhood. The third study, which involved retrospective data collection, found no relationship between having ever been breastfed and childhood overweight (von Kries, 2002). All involved measures of adiposity in children; none measured adults. It should be noted that one strength of the literature is that all of the studies identiﬁed were, in a sense, longitudinal, because breastfeeding in early life was assessed in relation to subsequent growth. However, some studies used a prospective method of data collection (i.e., infants were followed into childhood or adulthood when adiposity was measured) and were therefore labeled longitudinal, while others collected data retrospectively (i.e., information on method of infant feeding collected at time of adiposity measure in childhood or adulthood) and were labeled crosssectional. The studies that used a prospective data collection are of stronger design, because they are not subject to memory bias; however, as is the case with longitudinal studies in general, they tend to include a relatively smaller sample size than cross-sectional studies that involve retrospective data collection. Both sample size and method of data collection need to be considered when interpreting the breastfeeding literature. Unfortunately, few studies have examined this association between breastfeeding and adiposity over a period greater than 7 years. Only three relatively small studies, one using retrospective data collection (Charney, 1976) and two prospective data collection (Marmot, 1980; Poulton, 2001) included adults, and the results were mixed. Any impact of breastfeeding that persists into childhood may not be detectable later in life, given that adiposity can be affected by numerous factors over one’s lifetime and that the effect of breastfeeding is presumed to be small. Many of the studies were also limited by failure to gather data on potential confounding variables such as socioeconomic status, birth weight, gender, and maternal obesity — factors that have been previously identiﬁed to have their own independent inﬂuence on risk of overweight/obesity. Hediger and colleague’s 1988–1994 NHANES III analysis of 2,685 U.S.-born children between the ages of 3 and 5 years found a reduction in becoming at risk for overweight, BMI between the 85th and the 94th percentiles, among the children who were ever breastfed compared with the children who were never breastfed, adjusted odds ratio 0.63 (0.41–0.96). However, there was no signiﬁcant reduced risk of being overweight, BMI ≥ 95th percentile, adjusted odds ratio 0.84 (0.62–1.13). The strongest predictor of child overweight status in this analysis was maternal overweight status (Hediger, 2001). Other studies have noted that maternal obesity is associated with a decreased likelihood of successful breastfeeding initiation and increased discontinuation rates (Liese, 2001; Li, 2003). In addition to the genetic risk that maternal obesity carries, maternal weight status may introduce substantial bias in studies of breastfeeding. An additional weakness is that many studies relied upon “ever breastfed” as a measure of breastfeeding. The degree of breastfeeding can vary by duration (e.g., weeks vs. months or years) as well as exclusivity (e.g., exclusively breastfed vs. supplemental formula feeds). The mode of feeding (e.g., breastmilk fed in a bottle vs. nursing only) may also be important in relation to adiposity (see discussion of plausible mechanisms above). Several studies have found the degree of breastfeeding important. For example, Liese and colleagues (2001) studied over 2,000 children from Germany to assess if breastfeeding and its duration were associated with a reduced prevalence of overweight in preadolescent children aged 9–10 years. Not only did they ﬁnd breastfed children to have a statistically signiﬁcant reduced risk of overweight development compared to the nonbreastfed children, odds ratio 0.66 (0.52–0.87), but when they excluded all the nonbreastfed children from the analysis, they found a marked dose-response pattern with the lowest likelihood of overweight being found among those children who were exclusively breastfed for the longest duration.

254

TABLE 4.75 Observational Studies of the Association of Breastfeeding with a Measure of Adiposity in Children Study Name and/or Location

Study Population

Adiposity Measure1

Control Variables

Association2

BMI > 90th and 97th percentiles; skinfolds (OR not shown)

Mother’s overweight status, Maternal smoking during pregnancy, Social status

– OR: 0.53 (0.31–0.89)

Cross-Sectional Studies von Kries, 2000 9,206 children 5-6 yr Germany

BMI > 90th and 97th percentiles

von Kries, 2002 Germany

BMI > 90th and 97th percentiles

Parental education (marker of social class), Maternal smoking during pregnancy, Low birth weight, Child’s own bedroom, Frequent consumption of butter Maternal smoking during pregnancy, High level of parental education (≥10 years), BMI of either parent of ≥30, Birth weight above 90th percentile, Watching TV or playing video games > 1 hour/day, Eating snacks while watching television

– OR: 0.79 (0.68–0.93) 0 OR: 0.84 for BMI > 90th percentile (0.66–1.06); OR: 0.91 for BMI > 97th percentile (0.60–1.38)

6,483 children 5-6.99 yr

Notes: 1

BMI based on measured weight and height unless otherwise speciﬁed.

2

Plus (+) indicates signiﬁcant direct relationship, negative (–) indicates signiﬁcant inverse relationship, and zero (0) indicates nonsigniﬁcant relationship between factor of interest and measure of adiposity. OR = odds ratio.

Obesity: Dietary and Developmental Inﬂuences

Longitudinal Studies Bergmann, 2003 480 children followed German Multicenter from birth through 6 yr Atopy Study

Dietary Inﬂuences on Energy Balance

255

Von Kries and colleagues (2000) also found a clear dose dependent, protective effect of breastfeeding on obesity and overweight in their analysis, conducted among over 9,000 Bavarian school children participating in an obligatory health examination at the time of school entry. They found 3–5 months of exclusive breastfeeding was associated with a 35% reduction in obesity at the age of 5–6 years. In a longitudinal birth cohort analysis, Bergmann and colleagues (2003) also found breastfeeding for 3 months or longer lowered the risk for overweight and obesity development at the age of 6 years, after adjusting for the potential confounding effects of mother’s overweight, smoking status during pregnancy, and social status.

PREVENTION TRIALS No trials were identiﬁed that aimed to intervene upon breastfeeding and measure impact on subsequent adiposity. It is unlikely that any randomized, controlled trials of breastfeeding will be conducted, because of ethical concerns.

CONCLUSION Despite some inconsistencies in the results from observational studies, and the fact that breastfeeding has increased over the time period that obesity has risen, breastfeeding appears to have some protective effect against the development of overweight in childhood. Studies of the relationship of breastfeeding to obesity in adulthood are far fewer in number and less conclusive. Although the effect of breastfeeding may be small compared to other factors, the promotion and support of breastfeeding initiation and duration are a low-cost method of providing many healthful beneﬁts, one of which is moderately likely to include reducing obesity risk in children (Table 4.76).

FOOD INSECURITY INTRODUCTION The U.S. Department of Agriculture (USDA) has deﬁned food insecurity as the “limited or uncertain availability of nutritionally adequate and safe foods or limited or uncertain ability to acquire acceptable food in socially acceptable ways” (Hamilton, 1997). Based on the responses to an 18item questionnaire, USDA’s Food Security Survey Module, food security status is categorized into three levels, with hunger being the most severe form of food insecurity (Carlson, 1999) (Figure 4.43). Food insufﬁciency, deﬁned as inadequacy in the amount of food intake because of a lack of money or resources to access enough food (Briefel, 1992), is a term often used interchangeably with food insecurity (Casey, 2001). Conceptually, it has been suggested that food insufﬁciency is closer to the phenomenon of food insecurity with hunger (Alaimo, 2001). Severe food shortage (i.e., food insecurity with hunger), if experienced chronically, can contribute to weight loss and the development of underweight. Food insecurity, however, does not necessarily imply inadequate energy supply but also includes conditions in which choices are limited due to economic constraints and changes in eating habits are made due to fear of running out of food (Sarlio-Lhteenkorva, 2001). Under these conditions of mild or moderate food insecurity (i.e., food insecurity without hunger), the risk of overweight may be increased.

SECULAR TRENDS Ascertaining national trends in food insecurity is hampered by both lack of data (national surveys of food insecurity were not systematically undertaken until the mid 1990s) and the use of different

256

TABLE 4.76 Does the Preponderance of Evidence Support a Relationship between Breastfeeding and Lower Adiposity?1 Observational Studies2 Support Relationship? (number of studies) Longitudinal Secular Trends Support Relationship?

Mechanisms Support Relationship?

(1 in adults 14 in children)

U.S. Nationally Representative CrossSectional (0 in adults 1 in children)

Inconclusive

No studies

Other CrossSectional or CaseControl (2 in adults 13 in children)

Prevention Trials2 Support Relationship? (number of studies) Randomized Other Controlled Trials

(0)

(0)

No studies

No studies

Conclusion: Consistency of Evidence Supporting Relationship

Adults Inconclusive

Moderate

Yes Children Yes

Inconclusive

Yes

Notes: 1

Description of criteria used for summary table is located in the methods section.

2

Numbers in parentheses indicate the number of relevant studies identiﬁed and examined for each study type.

No studies

No studies

Obesity: Dietary and Developmental Inﬂuences

No

Dietary Inﬂuences on Energy Balance

257

FIGURE 4.43 Ranking of food security status according to the USDA’s Food Security Survey Module. (Source: adapted from Guthrie, 2002.)

methods of quantiﬁcation and deﬁnitions between surveys. According to the most recent national data collected using the USDA’s Food Security Survey Module (supplemented to the U.S. Census Bureau’s Current Population Survey), 10.7% of households (11.5 million) experienced food insecurity sometime during the 2001 calendar year (Figure 4.44). Although the prevalence of household food insecurity increased slightly from 10.1% in 1999, it appears that food insecurity has remained fairly stable over the last half decade, declining slightly between 1995 and 2001 (Figure 4.45). The prevalence of food insecurity was highest for families headed by a single women (32%), black and Hispanic households (21% and 22%, respectively), and households living below the federal poverty level (37%). In 2001, twice as many households with children under 18 years of age (16%) experienced food insecurity compared to households without children (8%). On average, in 1998 and 1999, based on the 8 questions pertaining to children in the Food Security Survey Module, approximately 613,000 children lived in households where children’s hunger occurred (Nord, 2002b). Child hunger in the U.S. has appeared to decrease in recent years (Figure 4.46). It should be noted that food insecurity statistics are based on selfreport about the condition experienced at any time during the previous year, even if only once. Therefore, on any given day, food insecurity, with and without hunger, is substantially less than the annual average. Furthermore, not all persons living in a food insecure household are necessarily food insecure (Hampl, 2002).

FIGURE 4.44 Food security status of U.S. households in 2001. (Source: Nord, 2002a.)

258

Obesity: Dietary and Developmental Inﬂuences

FIGURE 4.45 Trends in the prevalence of food insecurity in U.S. households, 1995–2001. (Source: USDA/ERS, 2003.)

FIGURE 4.46 Trends in the prevalence of hunger among U.S. children, 1995–1999. (Source: USDA/ERS, 2003.)

Dietary Inﬂuences on Energy Balance

259

PLAUSIBLE MECHANISMS At least two mechanisms have been proposed to explain the relationship between mild to moderate food insecurity and overweight risk: (1) changes in quantity of food eaten and (2) changes in quality of foods eaten. Involuntary food restriction may result in preoccupation with food, overeating and/or disordered eating, and weight gain (analogous to the binge eating behavior that may result from self-imposed dieting) (Kendall, 1996; Frongillo, 1997; Townsend, 2001). Studies in adults (Lavery, 1993; Polivy, 1994, 1996; Raynor, 2003) and children (Dietz, 1995, Fisher, 1999a/b) suggest that food deprivation (either voluntary, such as with dieting, or involuntary, such as with food-restrictive parents) may induce binge eating behavior and overeating when food is plentiful. Overeating when food is available has been reported by nutrition educators as a common food-management practice for participants in the Expanded Food and Nutrition Education Program (EFNEP) and Food Stamp Nutrition Education Program (FSNEP) (Kempson, 2002). Food constraints may be intermittent or cyclical, occurring when money runs out before payday or food stamps run out before the end of the month (Kempson, 2002). In a study of preschoolers, monthly duration of food stamps of less than 4 weeks was a predictor of household food insecurity (Prez-Escamilla, 2000). Furthermore, a decrease in the number of weekly food servings has been observed during the last week of the month among low-income households, particularly among those with children (Taren, 1990). Finally, in a study of Hispanic school age children a drop in total energy and meat consumption was observed before payday in children from food-insecure households, a signiﬁcant difference from the steadier intake pattern of children from food-secure households (Matheson, 2002). It has also been hypothesized that cyclical food shortages may increase reliance on high-fat or empty-calorie foods (Dietz, 1995; Olson, 1999). An increased reliance on high-fat or empty-calorie foods may be adopted as a strategy to cope with constrained ﬁnances, as such foods are often a less-expensive, yet satisfying, source of calories. In the 1989–1991 and 1994–1995 Continuing Survey of Food Intake by Individuals (CSFII), while total dietary energy decreased for children from food-insecure compared to food-secure households, the distribution of calories from fat and saturated fat increased (Kennedy, 1997). Household food insecurity has also been associated with a low intake among adults of nutrient-dense, low-kilocalorie foods such as fruits and vegetables (Cristofar, 1992; Kendall, 1996; Neumark-Sztainer, 1996; Dixon, 2001). In several studies, food insecurity, at least among adult women, has been associated with not only a lower intake of energy but also of a number of other nutrients as well (Cristofar, 1992; Rose, 1997; Tarasuk, 1999; Dixon, 2001). Children in households experiencing severe food insecurity have also been shown to be less likely to eat the recommended number of Food Guide Pyramid servings than other children, particularly for the milk group (Kaiser, 2002). In another study, preschool children classiﬁed as “hungry” or “at risk of hunger” consumed more empty calories by drinking more fruit drinks than nonhungry children (Cutts, 1998).

OBSERVATIONAL STUDIES Adults Despite differences in tools used to assess food insecurity and different BMI cutoffs used to classify weight status, the results from studies among adults are remarkably consistent. All but one (Vozoris, 2003) of the numerous cross-sectional studies examined found a positive association between mild to moderate household food insecurity and overweight in adult women, an association that remained after controlling for SES and several other potentially confounding variables (Frongillo, 1997; Sarlio-Lhteenkorva, 2001; Townsend, 2001; Crawford, 2002; Adams, 2003). Food insecurity and overweight in men has been less thoroughly studied, and the results are not as consistent. According to nationally representative data, women experiencing mild food insecurity were 30% more likely to be overweight than food-secure women (Townsend, 2001). It has been hypothesized that the

260

Obesity: Dietary and Developmental Inﬂuences

failure to observe a signiﬁcant relationship in the men in this study may be because women are more susceptible to overeating in response to food deprivation, just as they are more conscious of social pressures to be thin. It is also possible that food-insecure women, who were more frequently heads of household with children compared to food-insecure men, who more frequently lived alone, are apt to make sacriﬁces in their own diet so as to provide food for their children (and in the case of married women, possibly also their husband) (Townsend, 2001). It is also possible that the single question used in the CSFII did not accurately predict food insecurity among men. In the large Canadian study, men in food-insufﬁcient households were less likely to be overweight than men living in food-sufﬁcient households (Vozoris, 2003). In one other study, this one in Finland (SarlioLhteenkorva, 2001), both women and men from food insecure households were likely to have an increased BMI (see Table 4.77). Children Interestingly, in a separate analysis of the 1994–1996 CSFII data for children [the same survey in which Townsend (2001) found a signiﬁcant association for women], low-income households included more overweight children than higher-income food-secure households, but there was no difference in overweight between low-income food-secure vs. low-income food-insecure groups of children (Casey, 2001) (Table 4.78). Furthermore, in a cross-sectional study of Latino children in California, a signiﬁcant association was observed between food insecurity and overweight among mothers, but not among their children (Crawford, 2002). Likewise, in several other cross-sectional studies of children, although some trends were observed, no consistent association was observed in weight status on the basis of food insecurity among children (Cutts, 1998; Alaimo, 2001; Kaiser, 2002; Matheson, 2002). Furthermore, in a case-control study, this one involving U.S. children of Puerto Rican descent, prevalence of food insecurity did not differ between overweight and control children (Tanasescu, 2000). These data suggest that food-insecure children may be largely spared the adiposity effects experienced by their mothers. In the face of inadequate resources, mothers may focus on feeding their children ﬁrst, sacriﬁcing their own dietary intakes if necessary (Campbell, 1989). Indeed, this coping strategy has been reported in several studies (Tarasuk, 1990; Kempson, 2002; McIntyre, 2003). Interestingly, in the Crawford study (2002), overweight was higher among children of mothers who experienced food insufﬁciency as a child, suggesting that parental inﬂuences may override concurrent food insecurity status (see section on parental inﬂuences for review of this topic). Unfortunately, none of the present studies utilized USDA’s Food Security Module as modiﬁed for children (Nord, 2002b), which may more accurately measure food insecurity experienced by children in a household. Furthermore, potentially confounding variables were not accounted for in the majority of studies.

PREVENTION TRIALS No relevant overweight prevention trials were identiﬁed. Because of ethical issues, it is unlikely that randomized, controlled studies of the longitudinal effects of food insecurity on the weight status of children and adults will be conducted.

CONCLUSION The preponderance of evidence from observational studies (all cross-sectional — no longitudinal and few nationally representative) suggests that food insecurity is associated with overweight among adults (particularly women), but not conclusively so among children (Table 4.79). It is noteworthy that, in the vast majority of studies in adults, food insecurity was positively related to BMI, even after controlling for income, suggesting that food insecurity is not merely a proxy indicator of economic status. Inasmuch as food insecurity among children, regardless of impact on weight status, has been associated with health, academic, and psychological problems (Wehler, 1995;

Study Name and/or Location

Study Population1

Measure of Food Insecurity2

Adiposity Measure3

U.S. Nationally Representative Cross-Sectional Studies Townsend, 2001 9,479 adults Mild BMI (self-reported weight and CSFII 1994–1996 >20 yr Moderate height adjusted for Ethnically diverse (75% (single question; similar to underreporting of weight and white) Alaimo, 2001; Casey, 2001) overreporting of height) Mixed SES

Association4

Gender (separate analysis), Age, Ethnicity, Education, Income, Food stamps, Vigorous exercise, TV/video viewing

+ (women) 0 (men)

Food insufﬁciency (3-item questionnaire)

BMI (self-report) status: Gender (separate analysis), Age Underweight (<19.0), Normal group, Education level, Income, weight (19.0–24.9), Overweight Functional health (25.0–29.9), Obese (30.0–34.9), Morbidly obese (≥35.0)

0 (women) – (men; overweight group only)

Mild (no hunger) Moderate (hunger) (4 items from USDA Household Food Security Module) Food insecurity (5-item Edmonton Food Policy Council’s survey)

Obesity (BMI ≥30)

Age, Ethnicity, Income, Education, Country of birth, General health status, Walking

+ (white, mild women) 0 (white, moderate women) + (other ethnicities, mild and moderate)

Thin (BMI<20) Normal (20–24.9) Overweight (25–29.9) Obese (≥30) (self-report)

Age, Gender (separate analysis), Education

+ (women) + (men) (also associated with thinness)

Mild (no hunger) Moderate (adult) Severe (child) (18-item USDA Household Food Security Module)

Overweight (BMI ≥ 25)

Age, Education, Income, Acculturation variables, Breastfeeding

+

261

Other Cross-Sectional Studies Vozoris, 2003 50,215 adults 1996/1997 National 20–64 yr Population Health Ethnicity and SES not Survey, Canada speciﬁed (nationally representative) Adams, 2003 8,169 women 1998 and 1999 California >18 yr Women’s Health Survey 62% white, 21% Hispanic, 10% Asian, 7% black Mixed SES Sarlio-Lähteen-korva, 6,506 adults 2001 25–64 yr Survey on Living Mixed SES Conditions, Finland (nationally representative) Crawford, 2002 561 women California ≥18 yr Hispanic Low SES

Control Variables

Dietary Inﬂuences on Energy Balance

TABLE 4.77 Observational Studies of the Association of Food Insecurity with a Measure of Adiposity in Adults

262

TABLE 4.77 (CONTINUED) Frongillo, 1997 (as reported in Olson, 1999) Rural New York

193 women 20–39 yr Ethnicity and SES not speciﬁed

Mild (household) Moderate (caregiver) Severe (child) (Radimer/Cornell hunger scale)

BMI

Height, Income, Education, Single parent status, Employment

0 (Note: + association p = 0.06+

Notes: 1

Based on random samples except for Crawford, 2002.

2

BMI based on measured weight and height unless otherwise speciﬁed.

3

Includes comparison group that was food secure (i.e., no food insecurity, insufﬁciency, or hunger).

4

Plus (+) indicates signiﬁcant direct relationship, negative (–) indicates signiﬁcant inverse relationship, and zero (0) indicates nonsigniﬁcant relationship between dietary factor and measure of adiposity. SES = socioeconomic status.

Obesity: Dietary and Developmental Inﬂuences

Study Name and/or Location

Study Population1

Measure of Food Insecurity2

Adiposity Measure3

Control Variables

Association4

U.S. Nationally Representative Cross-Sectional Alaimo, 2001 NHANES III

9,196 children and Food insufﬁciency Risk of overweight (BMI Gender (separate analysis), Ethnicity (8–16 yr), Age, – (2–7 yr girls; p<.10) adolescents (similar/same question as > 85th percentile) Height, Birth weight, Mother’s height and weight, + (8–16 yr white 2–16 yr Casey, 2001; Townsend, 2001) Father’s height and weight, Poverty-income ratio, girls; p<.10) White, black and MexicanHousehold size, Family head education, employment 0 (others) American and marital status, Metropolitan location, Health Mixed SES insurance coverage, Regular source of health care, Smoke exposure during pregnancy (2–7 yr), Birth complications (2–7 yr), TV viewing (8–16 yr), Exercise (8–16 yr)

5,669 children and Casey, adolescents 2001 CSFII 1994–1996 0–17 yr Ethnically diverse Mixed SES

Food insufﬁciency (single question; similar to Alaimo, 2001; Townsend, 2001)

Overweight (BMI cutoff not speciﬁed; selfreport)

0

0

Dietary Inﬂuences on Energy Balance

TABLE 4.78 Observational Studies of the Association of Food Insecurity with a Measure of Adiposity in Children

Other Cross-Sectional or Case-Control Studies 2,578 children <5 yr 55–62% black, 18–26% white Low SES

At risk for hunger Hunger (8-item Hunger scale)

Weight for height percentile

Crawford, 2002 California

561 children 3–6 yr Hispanic Low SES

Mild (no hunger) Moderate (adult) Severe (child) (18-item USDA Household Food Security Module)

Children: At risk for overweight (BMI ≥ 85th percentile)

Maternal income, education, acculturation and BMI, Breastfed, Birth weight

0

263

Cutts, 1998 Minnesota

264

TABLE 4.78 (CONTINUED) Kaiser, 2002 California

211 children 3–6 yr Mexican-American Low SES

Mild (household) Moderate (caregiver) Severe (child) (12-item Radimer/Cornell hunger scale)

Weight for height Z Maternal acculturation score; Overweight (Z ≥ 85th percentile); Obese (Z ≥ 95th percentile)

0

Matheson, 2002 California

124 children 9.9–12.5 yr Mostly Hispanic Low SES

Mild (no hunger) Moderate (adult) Severe (child) (18-item USDA Household Food Security Module)

BMI

–

Tanasescu, 2000 Connecticut

53 children (29 obese; 24 control) 7–11 yr Puerto Rican Low SES

Mild (household) Moderate (caregiver) Severe (child) (10-item Radimer/Cornell hunger scale)

Obesity (BMI ≥ 85th percentile)

Many tested (i.e., physical activity, TV viewing, infant feeding practices, maternal and paternal characteristics, SES, food group and nutrient intakes), but food insecurity not included in ﬁnal multivariate model

0

1

Based on convenience samples except for Alaimo, 2001, and Casey, 2001.

2

BMI based on measured weight and height unless otherwise speciﬁed.

3

Includes comparison group that was food secure (i.e., no food insecurity, insufﬁciency, or hunger).

4

Plus (+) indicates signiﬁcant direct relationship, negative (–) indicates signiﬁcant inverse relationship, and zero (0) indicates nonsigniﬁcant relationship between dietary factor and measure of adiposity. SES = socioeconomic status.

Obesity: Dietary and Developmental Inﬂuences

Notes:

Dietary Inﬂuences on Energy Balance

TABLE 4.79 Does the Preponderance of Evidence Support a Relationship between Food Insecurity and Higher Adiposity?1 Observational Studies2 Support Relationship? (number of studies) Longitudinal Secular Trends Support Relationship?

Mechanisms Support Relationship?

(0)

U.S. Nationally Representative CrossSectional (1 in adults 2 in children)

No studies

Yes (women only)

Other CrossSectional or CaseControl (5 in adults 5 in children)

Prevention Trials2 Support Relationship? (number of studies) Randomized Other Controlled Trials

(0)

(0)

No studies

No studies

Conclusion: Consistency of Evidence Supporting Relationship

Adults No

Yes

Inconclusive

Inconclusive Children No studies

Inconclusive

Inconclusive

No studies

No studies

Notes: 1

Description of criteria used for summary table is located in the methods section.

2

Numbers in parentheses indicate the number of relevant studies identiﬁed and examined for each study type.

265

266

Obesity: Dietary and Developmental Inﬂuences

Kleinman, 1998; Murphy, 1998), food security for all U.S. households is a requisite public health goal. However, given that the prevalence of food insecurity appears to have decreased during the time in which overweight and obesity have increased, and that a relatively small number of households experience food insecurity, it is not likely that food insecurity is a major contributor to the rapid rise in adiposity observed in recent years in the U.S. Given the lack of longitudinal studies to clarify the direction of causality (e.g., obesity may contribute to poverty and food insecurity), it is premature to make a conclusive statement regarding the relationship between food insecurity and adiposity.

5

Conclusions WHEN TO INTERVENE: CRITICAL PERIODS IN THE DEVELOPMENT OF OBESITY

THE INTRAUTERINE ENVIRONMENT The risk of obesity is cumulative for each individual, and exposure begins at conception. Women who are obese, still growing, or diabetic provide an intrauterine environment for their fetus that differs from women who do not have these characteristics. The intrauterine environment appears to inﬂuence later risk for obesity through various mechanisms, often referred to as “fetal programming,” which include changes in the central nervous system, in ratio of fat to lean body mass, in insulin metabolism, and in other hormonal factors. High birth weight (HBW) and exposure to diabetes in utero are both consistently associated with higher BMI later in life (attained BMI). However, when adjusted for confounders such as parental BMI, gestational age, and socioeconomic status, the relationship between birth weight and attained BMI is less robust, and recent studies suggest that the greater attained BMI may reﬂect greater lean body mass rather than fat mass. Low birth weight (LBW) is more consistently associated with attained BMI and has been implicated as a risk factor for central adiposity and risk of chronic disease. LBW followed by rapid “catch-up” growth in childhood appears to pose an additional risk for adult obesity. Furthermore, rates of LBW have risen while rates of HBW have fallen between 1980 and 2001. Taken as a whole, these ﬁnding suggest that the intrauterine environment plays an important, albeit complex, role in the etiology of obesity.

INFANCY

AND

CHILDHOOD

Growth patterns and feeding practices in infancy and early childhood add to the cumulative risk of obesity. A rapid rate of weight gain in infancy and early childhood (more commonly associated with formula than breastfeeding) may increase risk of obesity. Rate of weight gain in the ﬁrst four months of life has been reported to be associated with overweight at age seven even after adjusting for birth weight and weight attained at one year, and children who gain weight for age faster than height for age in the ﬁrst two years of life have higher BMI, body fat, and waist circumference at age ﬁve. The timing of adiposity rebound (the point in early childhood, usually between 4–8 years of age, when BMI starts to climb again after reaching a nadir) may be a particularly critical period. A trend toward earlier adiposity rebound has been reported over the last few decades, and children who experience an earlier adiposity rebound are at an increased risk for becoming overweight. It has been hypothesized that dietary and other lifestyle factors, including formula feeding, that stimulate rapid growth and early maturation may contribute to early adiposity rebound, but the extent to which the timing of adiposity rebound can be altered is unknown. Because overweight tends to track over time for children, an overweight child is more likely than a child of normal weight to be obese as an adult. This relationship gets stronger as children grow older. In this regard, all of childhood might be considered as a critical period for the development of adult weight status.

PUBERTY Children who are growing more rapidly than their peers tend to mature earlier as well. Various studies suggest trends over the last several decades toward early onset of puberty and menarche 267

268

Obesity: Dietary and Developmental Inﬂuences

among girls of all racial groups studied. Observational studies consistently ﬁnd an association between early puberty and higher adiposity. The relationship between puberty and adiposity is probably bidirectional. Early puberty may contribute to increased adiposity by nature of increased levels of hormones that stimulate the accumulation of body fat. Also, puberty is frequently associated with reduced levels of physical activity. Conversely, higher adiposity may stimulate hormones that trigger early maturation. It appears, therefore, that early inﬂuences set the stage for rapid growth and early maturation that tend to “snowball” as the child grows. Factors favoring increased adiposity at any stage, and the increase in adiposity itself, tend to predispose to continuing risk in subsequent stages of development.

PREGNANCY

AND THE

POSTPARTUM PERIOD

Adult women experience particular life course factors that have been implicated as contributors to increased adiposity, namely pregnancy. Weight gained during pregnancy has increased during the last several decades. Although most women do not retain substantial weight due to pregnancies, a subset of women appears to be especially susceptible to postpartum weight retention. Women who gain more during pregnancy, lower-income women, adolescents who are still growing, and African-American women are at higher risk for weight retention with pregnancy. Despite the signiﬁcant energy expenditure involved in producing breast milk, neither secular trends nor observational studies suggest a relationship between lactation and adiposity. Studies, however, are limited and confounded by many factors, including the probability that women who choose to formula feed are more likely to return to work, smoke cigarettes, and diet to lose weight.

MENOPAUSE Weight gain during menopause is also highly variable but, on the whole, observational studies fairly consistently ﬁnd an association between menopausal status and increased body fat, particularly abdominal fat. These associations are thought to be due to hormonal changes that favor the central deposition of body fat as well as an accelerated decline in resting metabolic rate. Accumulation of body fat can be exacerbated by declines in physical activity associated with menopause.

THE ELDERLY The rates of overweight experienced by older Americans appear to be due to the cumulative life time risk of becoming overweight rather than to a distinctly rapid rate of gain during the later years of life. Indeed, rates of overweight and obesity tend to level off during the seventh decade of life (60–69 years) and start to decline in the eighth decade (70+ years). It appears that weight history is a better predictor of disease than current weight among the elderly, and fat acquired late in life may not be as harmful as excess adiposity experienced over a lifetime. In fact, overweight may have a protective effect for the elderly, and the healthy weight range may therefore be somewhat higher than for other age groups. This should not be misinterpreted to suggest excess body weight is of no concern to the elderly. At a certain level, excess weight can impede physical activity, increase insulin resistance, and increase the risk for disability. Given that changes in body fat distribution, loss of muscle mass, and decreases in total energy expenditure are particular concerns for the elderly, a focus on maintaining a physically active lifestyle is particularly important for this age group.

Conclusions

269

IMPLICATIONS The literature concerning the inﬂuences of life cycle on the development of obesity strongly point to the need for early prevention efforts. To be effective at preventing obesity, interventions should begin with preconception and continue through pregnancy to decrease the likelihood of fetal obesity programming. Early childhood is equally critical for preventing rapid growth and metabolic sequelae that increase the risk of higher adiposity, as well as early puberty, which, in turn, creates additional risk for excess weight gain. Tracking of overweight through childhood and into adulthood provides further evidence that prevention must begin early. With cumulative exposure through childhood, whereby one insult then increases the risk for the next insult and so on, the course may be hard to reverse by the time a child reaches puberty. Therefore, prevention efforts must not only begin preconception but must continue throughout childhood. The biggest increases in rates of overweight and obesity among adults occur during early adulthood and tend to level off as the population ages. Therefore prevention efforts would probably have the biggest impact among younger adults. Pregnancy may be the most important discrete life cycle factor during adulthood, especially for women who are susceptible to excessive weight gain during pregnancy. The effects of the mother’s weight gain and health on the health of the fetus provide additional rationale for a focus on pregnancy and the postpartum period. It is not yet established if lactation provides protection against overweight for the mother; however, other beneﬁts to the mother and child are indisputable. Menopause poses an additional risk for women. Maintaining an active lifestyle is particularly important for menopausal women, because women at this stage of life are at increased risk for increases in abdominal fat mass, decreases in lean body mass, and decreases in resting metabolic rate, all of which can be improved with increased physical activity. The elderly tend to have the lowest risk for becoming obese, and the healthy weight range among the elderly may be higher than for younger age groups. As with menopausal women, a focus on physical activity is particularly important for this age group; prevention of weight gain is probably less of a concern. Weight loss may be beneﬁcial for older adults whose weight is associated with comorbidities. However, the merit of weight loss for overweight elderly was not addressed by this review.

WHAT TO RECOMMEND: DIETARY INFLUENCES ON ENERGY BALANCE This review examined the evidence relating 28 different dietary factors to adiposity with the intent of determining the dietary composition and eating behaviors that would be most conducive to the prevention of overweight. These dietary factors are listed in Tables 5.1 and 5.2, which summarize the consistency of evidence from the various types of studies examined (see Chapter 3). Eleven of these factors (Table 5.1) were found to have moderately consistent evidence supporting a relationship with adiposity. These include 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.

dietary fat total carbohydrate dietary ﬁber fruits and vegetables sweetened beverages calcium dairy dietary patterns restaurant-prepared/fast foods breakfast skipping breastfeeding

Observational Studies Support Relationship? Nutrient/ Target Behavior1 (Total number of observational studies/prevention trials) ↓ Dietary fat 28/10 Adults 47/14 Children

↑ Dietary ﬁber 8/5 Adults 7/5 Children ↑ Calcium intake 7/142 Adults 4/72 Children ↑ Dairy intake 11/7 Adults 5/3 Children

No

No

No — adults Yes — children

No — total dairy Yes— milk

Mechanisms Support Relationship? Yes

Longitudinal

U.S. Nationally Representative Cross-Sectional

Inconclusive

Inconclusive

Inconclusive

Inconclusive

Other Cross-Sectional or Case-Control Adults Yes Children Inconclusive

Randomized Controlled Trials

Other

Yes

No studies

Inconclusive

Inconclusive

Yes

Conclusion: Consistency of Evidence Supporting Relationship Moderate

Moderate Inconclusive

Yes

Inconclusive

No studies

Adults Yes Children Inconclusive

Yes

No studies

Inconclusive

No studies

Yes

Moderate Yes

No studies

Adults Yes

Inconclusive

No studies

Children Yes

Yes

No studies

Inconclusive

Inconclusive

Yes

Moderate Adults Inconclusive

Inconclusive

Yes

Children Yes

Inconclusive

No studies

Yes

Yes

Yes

No studies

Yes

Yes

Adults Inconclusive

Inconclusive

Yes

No studies

Yes

Children Yes

Inconclusive

No studies

Yes Moderate

Obesity: Dietary and Developmental Inﬂuences

↑ Total% carbohydrate 20/6 Adults 32/3 Children

Secular Trends Support Relationship? No

Prevention Trials Support Relationship?

270

TABLE 5.1 Summary of the Nature and Consistency of Evidence Supporting the Relationship between Various Dietary Factors and Lower Adiposity: Factors for Which Support Was Moderate

No

↓ Sweetened beverage intake 1/0 Adults 9/2 Children

Yes

↓ Eating restaurant-prepared food 9/0 Adults 4/0 Children

Yes

∆ Dietary patterns 23/* Adults 0/* Children

Yes

↓ Breakfast skipping 7/0 Adults 9/0 Children

Yes

↑ Breastfeeding 3/0 Adults 28/0 Children

No

Yes

Adults Yes

Moderate

Inconclusive

Yes

No studies

Yes

Children Inconclusive

Inconclusive

No studies

Adults No studies

No studies

No studies

Yes

Yes

Children Inconclusive

Inconclusive

Inconclusive

Yes

Yes Yes

Yes

No studies

No studies

Yes

Yes

Inconclusive

No studies

No studies

No studies

Yes

No studies

Yes

Inconclusive

Yes Inconclusive

No studies

Yes

Inconclusive

Adults Yes Children Inconclusive Adults Yes

Inconclusive

Inconclusive

Inconclusive

Inconclusive Moderate

Moderate No studies

No studies

No studies

No studies Moderate

1

3

Yes

Yes

Inconclusive3

Inconclusive3

Adults Yes

No studies

No studies

Children Yes

No studies

No studies

Children No studies

Conclusions

↑ Vegetable and fruit intake 22/3 Adults 10/5 Children

Moderate

Moderate

Adults Inconclusive

No studies

No studies

Children Yes

No studies

No studies

Notes: 1

Description of criteria used for summary table is located in the methods section.

2

The ﬁve studies included in the meta-analysis by Davies (2000) are included individually in the above count by study type.

3

Based on trials reviewed in other sections of this document.

271

Observational Studies Support Relationship? Nutrient/ Target Behavior1 (Total number of observational studies/prevention trials) ↓ Energy density 4/0 Adults 0 Children

↓ Total sugar/simple CHO 8/1 Adult 3/0 Children ↓ Added sugar/sucrose 5/0 Adult 2/3 Children ↓ Intrinsic sugar 1/0 Adults 0/0 Children

No

Yes

Yes

None

Mechanisms Support Relationship? Yes

Longitudinal

U.S. Nationally Representative Cross-Sectional

No studies

Inconclusive

No studies

No studies

Other CrossSectional or Case-Control Adults Yes Children No studies

Randomized Controlled Trials

Other

No studies

No studies

No studies

No studies

Yes

Conclusion: Consistency of Evidence Supporting Relationship Inconclusive

Inconclusive No studies

No studies

No studies

No studies

Adults Yes Children No studies

Inconclusive

No studies

No studies

No studies

Yes

Inconclusive Adults No

No studies

No studies

Inconclusive

No studies

No studies

No studies

Children Inconclusive

No studies

No studies

Inconclusive

Inconclusive

Adults Inconclusive

No studies

No studies

No studies

Inconclusive

Children No studies

Inconclusive

Inconclusive

No studies

No studies

Adults Inconclusive

No studies

No studies

No studies

No studies

Children No studies

No studies

No studies

Yes

Inconclusive

Yes

Inconclusive

Obesity: Dietary and Developmental Inﬂuences

↑ Complex CHO 4/1 Adults 0/0 Children

Secular Trends Support Relationship? Inconclusive

Prevention Trials Support Relationship?

272

TABLE 5.2 Summary of the Nature and Consistency of Evidence Supporting the Relationship between Various Dietary Factors and Lower Adiposity: Factors for Which Evidence Was Inconclusive

↑ Iron or zinc intake 2/0 Adults 1/0 Children ↑ Antioxidant vitamin intake 4/0 Intake 16/0 Blood ↓ 100% fruit juice intake 0/0 Adult 9/0 Children ↓ Variety of food intake 2/1 Adults 3/0 Children ↓ Reduced fat food product intake 3/9 Adults 2/0 Children

No

Inconclusive

Inconclusive Adults Inconclusive

No studies

Yes

Inconclusive

No studies

Inconclusive

Inconclusive

No studies

Inconclusive

No studies

Children Inconclusive

Conclusions

↓ Dietary protein 12/1 Adults 30/2 Children

Inconclusive

Inconclusive Adults Inconclusive

No studies

Inconclusive

No studies

No studies

Inconclusive

No studies

No studies

No studies

No studies

Children No

No

Inconclusive Intake Inconclusive

No studies

Inconclusive

No studies

Yes2

Yes2

No studies

No studies

No studies

No studies

Blood No

Yes

Inconclusive Adults No studies

No studies

No

No

No studies

No studies

No studies

No studies

No studies

Children Yes

Yes

Inconclusive

Inconclusive Adults No studies

No studies

Inconclusive

No studies

Inconclusive

Inconclusive

Inconclusive

No studies

No studies

No studies

Children Yes

No

Inconclusive Adults No studies

Inconclusive

Inconclusive

Inconclusive

No studies

Inconclusive

No

No

No studies

No studies

Children

273

274

TABLE 5.2 (CONTINUED) ↓ Portion size 0/0 Adults 1/0 Children ↓ Snacking3 9/1 Adults 13/0 Children ↓ Eating frequency 12/0 Adults 5/1 Children

Inconclusive

Inconclusive Adults No studies

No studies

No studies

No studies

Inconclusive

No studies

No studies

No studies

No studies

No studies

Children Yes

Yes

Inconclusive Adults Yes

No studies

Yes

Inconclusive

No studies

Inconclusive

No studies

Inconclusive

No studies

No studies

Children Yes

Inconclusive

Inconclusive Adults Inconclusive

Inconclusive

Inconclusive

No studies

Inconclusive

Inconclusive

No studies

No studies

No studies

Inconclusive

Children No

Inconclusive

Inconclusive Adults No studies

Yes (women only)

Yes

No studies

Inconclusive

Inconclusive

No studies

No studies

No studies

No studies

Children

Notes: 1

Description of criteria used for summary table is located in the methods section.

2

Blood concentrations may not accurately reﬂect dietary intake or nutrient status.

3

Due to inconsistencies in deﬁning and accessing snacking across studies, no conclusion can be drawn with respect to which aspect of snacking (e.g., timing, frequency, amount, or food type) may be related to adiposity.

Obesity: Dietary and Developmental Inﬂuences

↓ Food insecurity 6/0 Adults 7/0 Children

Yes

Conclusions

275

The others factors (Table 5.2) were found to have insufﬁcient and/or conﬂicting evidence, and therefore their role in the development of obesity remains inconclusive. This should not be misinterpreted to imply that these factors do not play a role in the development of obesity. Indeed, with additional research, some of these factors may prove to be critical dietary determinants of adiposity. However, it is not possible to arrive at a conclusion based on the currently available ﬁndings. The evidence for studies conducted among children tended to be less conclusive than that for adults for most of the dietary factors examined; however, the direction of the associations tend to be the same as for adults. For example, with regard to dietary fat, all observational studies among both adults and children tend to show either a positive or nonsigniﬁcant relationship with adiposity, but among children, the proportion of studies showing a signiﬁcant relationship is smaller. This should not be misinterpreted to mean that dietary factors play a smaller role in the development of overweight in children. This trend toward less conclusive results for children may be the result of methodological problems in assessing dietary intake data during a period characterized by highly variable growth. Obtaining accurate intake data from adults is challenging; problems are only ampliﬁed when working with children, who have more difﬁculty recalling or recording their intake and who often receive food from various caregivers throughout the day. Therefore, the ultimate conclusion regarding each factor’s relation to overweight is made for all age groups as a whole and not differentiated for children and adults. Given that dietary habits are established during childhood, and individuals become increasingly overweight as adults, it would be confusing and counterproductive to suggest a separate set of guidelines for children and adults unless there was compelling evidence to do so. No single dietary factor stood out as a having a predominant inﬂuence but, rather, the 11 dietary factors identiﬁed in this review appear to have a combined effect on the maintenance of energy balance. These eleven dietary factors are the foundation for the following nine dietary recommendations for the maintenance of energy balance.

CALORIES COUNT Calorie intake has increased dramatically in recent decades. Calories are cheaper than ever before. The availability of a variety of affordable and palatable energy-dense foods has increased dramatically. Because it is so easy to consume large quantities of calories in a short period of time, it is unlikely that increases in energy expenditure alone can reverse the current trends in overweight. Adjusting calorie intake in balance with energy expenditure is the ultimate goal of any dietary strategy to prevent excess adiposity. Counting calories, however, has not proven to be a successful approach. Therefore, it is important to determine which dietary behaviors are most conducive to the prevention of calorie consumption in excess of energy needs. The remainder of these recommendations address strategies to achieve this goal.

REDUCE INTAKE

OF

DIETARY FAT

There was considerable evidence in all categories of studies, with the exception of secular trends, to support that higher relative intakes of dietary fat contribute to higher levels of adiposity. There was even more consistent data among adults relating a higher proportion of calories from carbohydrate to lower levels of adiposity. Secular trend data support the notion that added fats and oils in particular may have contributed to the recent increase in rates of overweight. Furthermore, observational studies that examine patterns of dietary intake suggest that higher intakes of highfat protein sources such as red meats, processed meats, and cheese, as well as other sources of fats and oils, are associated with higher adiposity. The lack of secular trend data supporting this conclusion and the numerous observational studies and prevention trials that failed to ﬁnd a signiﬁcant association with adiposity suggest that the proportion of calories from fat vs. carbohydrates in the diet is only one of many factors inﬂuencing energy balance and that changes in the

276

Obesity: Dietary and Developmental Inﬂuences

proportion of calories from fat alone is not sufﬁcient to ensure energy balance. A reduction in saturated and trans fats in particular is recommended for the prevention of heart disease, and limited data from laboratory studies and dietary pattern studies suggest saturated fat may preferentially contribute to obesity. These results should not be misinterpreted as a recommendation to increase total carbohydrate intake. A change in the macronutrient composition of the diet will assist with energy balance only if calorie intake is affected as a result. Therefore, a reduction in fat intake that results in a reduction of total calories and hence an increase in the percent of calories (but not absolute amount) from carbohydrate would be the most prudent recommendation.

REPLACE REFINED CARBOHYDRATES

WITH

HIGH-FIBER FOODS

All lines of evidence, except secular trends, very consistently support that higher intakes of dietary ﬁber contribute to the maintenance of energy balance. The ﬁndings from observational studies were especially compelling, with nearly all studies in adults ﬁnding a signiﬁcant and negative association with adiposity. Studies of dietary patterns conﬁrm that diets characterized by higher intakes of whole grains, legumes, fruits, and vegetables were associated with lower adiposity. Although the number of intervention trials that targeted dietary ﬁber was few, the results showed a favorable impact. The ﬁndings also suggest that to favor energy balance one should choose sources of ﬁber such as whole grains and legumes in addition to fruits and vegetables. To avoid an increase in total calorie intake, whole grains should replace reﬁned grain products rather than be added to the diet.

INCREASE

THE INTAKE OF

VEGETABLES

AND

FRUITS

Fruits and vegetables are high in ﬁber and low in energy density and therefore have the potential to increase satiety and reduce overall energy intake. Cross-sectional observational studies fairly consistently support that fruits and vegetables play a role in obesity prevention. Furthermore, observational studies that examined patterns of dietary intake found that diets characterized by high intakes of fruits and/or vegetables were associated with lower adiposity. However, most of the available longitudinal data failed to ﬁnd a signiﬁcant relationship between fruit and/or vegetable intake and adiposity, and the limited number of prevention trials were inconclusive when taken as a whole. Secular trends data also fail to support a role for fruits and vegetables, given that fruit and vegetable intake, although still below recommended levels, has increased along with total energy intake and rates of obesity. Therefore, it appears that higher intakes of fruits and vegetables can contribute to energy balance but that increases in fruits and vegetable should be accompanied by other dietary measures to prevent excess calorie intake.

ENSURE ADEQUATE INTAKE

OF

CALCIUM

AND

LOW-FAT DAIRY

Secular trend data suggest that milk intake (but not total dairy) and calcium intake among children (but not adults) have been on the decline, and cheese intake has risen dramatically over roughly the same time period that obesity rates have risen most rapidly in the U.S. There are possible mechanisms by which calcium and/or dairy may affect energy balance independently of calorie intake. Furthermore, the majority of longitudinal studies and cross-sectional nationally representative studies support the hypothesis that calcium and/or dairy protect against overweight. Results were more mixed for other cross-sectional studies due in large part to different measures of calcium and dairy intake and lack of adequate control variables. At least one study suggests that high-fat dairy may not have the same protective effect as low-fat dairy. Findings from studies of dietary patterns also suggest that diets characterized by low-fat dairy are associated with lower adiposity, whereas cheese intake is associated with higher adiposity. Prevention trials have had mixed results, and few were speciﬁcally designed to evaluate the impact of calcium or dairy on adiposity. The evidence therefore is moderately consistent in supporting a relationship between calcium and/or dairy intake and adiposity. It would be prudent to obtain adequate calcium intake from low-fat dairy as part of a strategy to ensure energy balance and the prevention of excess weight gain.

Conclusions

REDUCE INTAKE

277 OF

SWEETENED BEVERAGES

Sweetened beverage intake has increased dramatically along with the rates of obesity in the U.S. Experimental studies conﬁrm that sugar taken in liquid form tends to result in increased energy intake. Longitudinal and nationally representative studies among children ﬁnd a consistent association of sweetened beverage intake; however, studies among adults are scant. Studies of dietary patterns also ﬁnd that diets characterized by high intakes of sweetened beverages are associated with higher adiposity. Prevention trials are lacking, although at least one study published after the time period covered for this review found that reducing sweetened beverage intake had a favorable impact on adiposity in children. Given the compelling secular trend, mechanistic, and observational data, a reduction in the intake of sweetened beverages appears to be a promising component of a diet to prevent excess weight gain.

REDUCE CONSUMPTION WHEN DOING SO)

OF

RESTAURANT-PREPARED FOODS (OR CHOOSE WISELY

All the lines of evidence for which data were available support that the consumption of foods prepared away from home contributes to higher levels of adiposity. There is no question that people are eating food that is prepared away from the home more often than in the past. Increases in portion size, variety, palatability, energy density, and dietary fat have all been implicated as potential causes of increased caloric intake from restaurant-prepared foods. Furthermore, there are several observational studies agreeing that people who consume more food prepared away from home, particularly from fast food establishments, tend to have a higher adiposity. Data suggest that the association is especially true for adults; studies among children are fewer and less consistent. The longitudinal and nationally representative studies are particularly compelling in implicating fast food restaurants. Therefore, it seems reasonable to recommend a reduction in the consumption of restaurant-prepared foods in general, and fast foods in particular, as part of a strategy to prevent excess weight gain. Choosing foods from these establishments that are otherwise consistent with the recommendations above is a logical, though untested, alternative strategy but would require that these establishments provide more options that meet these recommendations.

EAT BREAKFAST

ON A

REGULAR BASIS

Skipping breakfast has become more common in recent decades among both adults and children. One ﬁfth to one quarter of adults skip breakfast on any given day. Studies examining the impact of breakfast on energy intake and metabolism have had inconsistent ﬁndings. However, the ﬁndings from observations studies among adults and children, including one longitudinal study, have consistently found an association between skipping breakfast and higher adiposity. Weight loss trials have had equivocal results and prevention trials are lacking. Given the compelling secular trend and observational study data, breakfast skipping is believed to be moderately associated with higher adiposity, and the recommendation to eat breakfast on a regular basis as part of strategy to prevent excess weight gain is therefore merited.

BREASTFEED Over the period of time that breastfeeding rates have been on the rise, obesity rates have not declined; on the contrary, they have risen, suggesting that breastfeeding has not helped to lower the risk for obesity. Recent observational studies suggest that breastfeeding provides a small but protective inﬂuence against development of overweight among children. Fewer studies have measured adiposity outcome in adulthood and, among those studies, the results are less conclusive. This relationship is complicated by the observation that obese women are less likely to breastfeed their infants. Breastfeeding is thought to protect against the development of obesity through various

278

Obesity: Dietary and Developmental Inﬂuences

mechanisms, including bioactive substances in breast milk that inﬂuence adipocyte differentiation and proliferation; changes in milk composition during a feed that help signal satiety; higher and prolonged insulin response to formula; and higher protein content in formula, which may stimulate accelerated growth in childhood. Breastfeeding is also thought to facilitate the infant’s ability to self-regulate intake, because breast milk intake is driven by internal cues and infant demand rather than the caregiver’s perception of the appropriate amount of formula that should be consumed. Prevention trials to assess the relationship between breastfeeding and subsequent risk of obesity are lacking, primarily because it would not be ethical to randomize or assign mothers to treatment and control groups. Given the fairly consistent observational data and compelling mechanistic basis, breastfeeding is believed to be moderately related to lower levels of adiposity.

STUDIES OF DIETARY PATTERNS PROVIDE FURTHER SUPPORT RECOMMENDATIONS ABOVE

FOR THE

Observational studies that examine the relationship between dietary patterns and adiposity have found that dietary patterns characterized by high intakes of meat (especially red meats and processed meats), cheese, reﬁned grains, potatoes (particularly French fries), sweetened drinks, sweets/added sugars, and added fats/oils are associated with higher adiposity, whereas dietary patterns characterized by higher intakes of lower fat protein sources, dairy (especially low fat), breakfast cereals and whole grains, fruits, vegetables, and legumes are associated with lower adiposity. These ﬁndings are also supported by secular trend data for speciﬁc foods and nutrients described above. The transition from traditional diets to westernized diets in many countries, and among immigrants to the U.S. that have observed increases in rates of overweight, provides further support for this association. Findings from mechanistic studies and prevention trials described previously for speciﬁc foods and nutrients also support the relationship of these dietary patterns with adiposity.

TELEVISION VIEWING Television viewing was reviewed and found to be the behavior most consistently related to adiposity. The evidence is not described here but is covered in a separate document. It is worth mentioning that television viewing is not merely a sedentary behavior that has the potential for reducing physical activity, but it also exposes viewers, especially children, to advertising for foods that are inconsistent with the recommendations described above. Furthermore, time watching television is an opportunity for snacking on foods that are equally inconsistent with these recommendations. Therefore, targeting reduction in television viewing is a promising means of achieving some of the desired changes in dietary behaviors as described above.

IMPLICATIONS This review was conducted to determine those dietary behaviors that are most likely to protect against the development of obesity. Although evidence was limited, and studies suffered from methodological limitations, when viewed as a whole, consistent trends in ﬁndings were observed. No single factor stood out as having an overwhelming inﬂuence on energy balance; rather, a dietary pattern for obesity prevention emerged and was also supported by studies that explicitly examined dietary patterns. Findings for each dietary factor were generally consistent with, and supported by, the ﬁnding for other factors. For example, ﬁndings consistently supported the notion that a higher proportion of calories from carbohydrate, and higher intakes of dietary ﬁber in particular, are protective against obesity. These ﬁndings in turn are consistent with ﬁndings that lower levels of dietary fat and higher intakes of fruits and vegetables are associated with lower levels of adiposity. Other examples are the ﬁndings associating fast food and other restaurant-prepared food with adiposity. Fast food and other restaurant-prepared meals tend to be high in fat, low in ﬁber, low in

Conclusions

279

fruits and vegetables, and high in sweetened beverages, all of which were consistently related to an increased risk of overweight. In this review, we examined the evidence concerning more than 25 dietary factors and were able to reduce and synthesize the ﬁndings from hundreds of studies into 9 actionable recommendations that include not only nutrients but also eating behaviors such as eating breakfast, and speciﬁc foods and beverages such as restaurant-prepared foods. These recommendations may be considered as target behaviors from which to choose. It would seem imprudent to focus efforts on other dietary behaviors for which there is no base of evidence. Furthermore, the nine recommendations are consistent with guidelines for the prevention of other chronic diseases such as heart disease and cancer. Therefore, recommendations to prevent obesity promote health in a multitude of ways. Other factors not covered by this review, in particular physical activity, may be equally important to address. It should be noted that these recommendations represent target behaviors for change and not behavior change messages per se. How to best achieve these changes is not addressed in this review. Converting these recommendations to effective messages requires formative research with the target population. Other strategies that do not involve messaging, especially environmental and policy strategies targeting these behaviors, should also be considered. Therefore, this review is the ﬁrst step toward designing effective programs to prevent overweight by helping to identify the behaviors that should be targeted for intervention. Some may argue that these recommendations are too consistent with dietary guidelines that have not been effective at preventing obesity. However, it should be noted that actual U.S. dietary intake patterns are not, and have not been, consistent with recommendations by health ofﬁcials. This lack of compliance is probably due to challenges with messaging as well as lack of environmental support for recommended behavior changes. Therefore, our ﬁndings suggest that major changes to current guidelines are not needed, but rather there is a need to focus on more speciﬁc behaviors (i.e., reducing consumption of sweetened beverages) and ﬁne tuning of the current guidelines (i.e., focusing on high-ﬁber sources of carbohydrates), coupled with more focused and effective strategies to promote and support these changes.

PRIORITIES FOR FURTHER RESEARCH Given limited research dollars, it is important to focus obesity-related research efforts in directions that are most likely to have an impact on public health. Since there are many dietary factors that could feasibly affect energy balance, it is important to identify those that are deemed most likely to have an impact on energy balance or for which clariﬁcation is needed due to scientiﬁc debate or public perception. Therefore, the criteria used for assigning highest research priority were based on the following: • Available data is limited but promising in terms of an association with adiposity. • The factor is currently incorporated into commonly used dietary recommendations, guidelines, or practices, but there is limited scientiﬁc support relating the factor to adiposity. • The factor is a topic of frequent discourse, policy, or scientiﬁc debate. Most of the 12 factors placed in the highest priority category (Table 5.3) have been studied to a limited degree or were lacking in data from one or more of the lines of evidence. Interestingly, even in the absence of convincing evidence, in every case, these factors are already assumed or suspected of contributing to obesity by leading scientists and/or the public. Although ﬁve of these factors have already been studied to a fair extent, the studies were either deemed to have been of inadequate quality or were limited to only one or a few research methodologies (i.e., observational studies but not intervention trials). Five of these (breakfast skipping, dietary ﬁber, restaurantprepared foods, calcium/dairy, and sweetened beverages) were, as described previously, considered

280

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to have moderately consistent support for their role in contributing to and protecting against development of obesity and therefore might be considered a lower priority than the others for additional research. However, the lack of prevention trials and rigorous longitudinal observational studies is a concern even in these cases. The six factors in Table 5.4 that were judged to be of somewhat less priority for research were those that had been studied to a greater degree and for which the preliminary evidence is already fairly conclusive and/or were unlikely to beneﬁt from additional study. Furthermore, some of these factors are not of great concern or controversy among scientists or the public. For example, although the role of protein in energy balance remains inconclusive, a fair amount of data is available, the observational study results are quite consistent, and there is less interest in the role of protein for prevention of obesity compared to some of the other factors. Likewise, with regard to fruits and vegetables, many studies have been conducted, and although results tend to be mixed, the mild protective effect is fairly conclusive. Additional studies with regard to both of these factors, as well as the others in Table 5.4 are certainly merited but are not as high of a priority as factors that have been studied less and/or for which results are even more inconclusive or the controversy greater. With regard to nearly all 18 factors listed in Table 5.3 and Table 5.4, it will not be helpful to conduct additional small cross-sectional studies among white populations that employ few control variables. Rather, the following are needed with regard to all factors listed: • • • • •

More prevention trials More studies with adequate control for confounding variables A more diverse subject pool More longitudinal studies as opposed to cross-sectional observational studies Isolation of the impact of speciﬁc dietary behaviors in multicomponent intervention trials

In most of the cases, a more clear deﬁnition of the factor is also needed. Snacking provides a good example of this concern. There is no consensus about what constitutes a snack, so deﬁnitions from study to study vary. Furthermore, the concept of snacking can be broken down into several distinct variables. The impact of snacking on adiposity may vary depending on which aspect of snacking is measured — timing, frequency, composition, or size. Because of deﬁnitional inconsistencies between studies, the evidence regarding snacking was not considered strong enough at present to make a recommendation with respect to adiposity. Secular trend data were also unavailable for many factors. For surveillance purposes as well as “determinants” research, it would be important to deﬁne these factors and establish a means of tracking their prevalence over time. Seven factors (Table 5.5) were deemed to be of low priority for further research. In two instances (100% fruit juice and reduced-fat food products), currently available evidence suggests that these factors are not major contributors to obesity. Likewise, with regard to total and intrinsic sugar, preliminary evidence suggests that these are not related to obesity, and there is stronger theoretical basis for implicating added sugars. With regard to antioxidant vitamins, the currently available ﬁndings suggest that adiposity affects blood levels rather than low intake affecting adiposity. Although the lack of an established role of these variables in the development of obesity could be clariﬁed by additional research, these factors tended to lack compelling secular trend, mechanistic data, or preliminary observational ﬁndings to merit the investment in more rigorous observational studies or intervention trials. There is some basis for a relationship with obesity for all of the dietary factors identiﬁed, and in no case was the relationship with obesity either ﬁrmly established or refuted. Therefore, additional research in the area of dietary determinants of obesity is warranted. However some factors are of more interest because of the promising nature of currently available ﬁndings and/or their high proﬁle in the public or scientiﬁc debate.

Conclusions

TABLE 5.3 Dietary Factors Identiﬁed as Highest Priority for Research Rationale for Recommending Additional Studies A Subject of Policy Initial and/or Dietary Limited No. Findings Scientiﬁc Factor of Studies Very Promising Debate Energy density x x x Added sugars x x Dietary variety x x Portion size x x x

Parental inﬂuences Snacking Breakfast skipping Dietary ﬁber Restaurantprepared food Calcium/dairy

x

Sweetened beverages Dietary patterns

x

x

Need Mechanisms for Following Secular Trends x x x

Recommended Directions for Future Research Need to More Need More Need More Clearly and/or Longitudinal Cross-Sectional Need More Consistently Observational Observational Prevention Deﬁne the Studies Studies Trials Factor x x x x x x x x x x x x x x x

x

x

x

x

x

x

x

x

x x

x1

x x

x x

x x

x x2

x1 x

x x

x x

x

x

x2

x

x

x

x

x

x

x

x

x2

x

x

x

Other/Comments

Need to deﬁne portions in terms of which types of food and/or in relation to calories per portion.

Need to distinguish calcium from dairy and low-fat vs. higher-fat dairy

x

Notes: Additional studies are needed primarily that are nationally representative.

2

Additional studies are needed primarily among children.

281

1

282

TABLE 5.4 Dietary Factors Identiﬁed as Moderate Priority for Additional Study

Dietary Factor Protein Fat Complex carbohydrates

Rationale for Recommending Additional Studies A Subject of Policy Initial and/or Limited No. Findings Scientiﬁc of Studies Very Promising Debate

Need Mechanisms for Following Secular Trends

x x

x

Eating frequency Food insecurity

x x

x2

x

x

Notes: 1

Additional studies are needed primarily that are nationally representative.

2

Additional studies are needed primarily among children.

x x

x1

x

x

x

x

x

Other/Comments

Fiber content appears to be a more important factor than saccharide chain length Need to exclude certain high fat, high sugar and/or high calorie fruits and vegetables

Obesity: Dietary and Developmental Inﬂuences

Vegetables and fruits

Recommended Directions for Future Research Need to More Need More Need More Clearly and/or Longitudinal Cross-Sectional Need More Consistently Observational Observational Prevention Deﬁne the Studies Studies Trials Factor x x x1 x x x1 x x x x

Conclusions

TABLE 5.5 Factors Identiﬁed as Low Priority for Additional Study

Dietary Factor 100% fruit juice Reduced fat food products Total carbohydrate Total sugar Intrinsic sugar Antioxidant vitamins Iron and zinc

Rationale for Low Priority for Additional Studies Currently Available Lack of Policy Evidence Suggests Interest and/or Lack of Relation Compelling Abundance of with Adiposity Scientiﬁc Rationale Evidence Exists x x x x x x x x x x

Other/Comments Prevalence of excessive 100% fruit juice intake is low. May help reduce fat and energy intake. Evidence suggests that type of carbohydrate is a more promising direction for study. Evidence suggests that added sugar is a more promising direction for study. Evidence suggests that added sugar is a more promising direction for study. Lack of rationale for relationship with adiposity. Lack of rationale for relationship with adiposity.

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Index A Adiposity rebound, 16–18, 28–29 observational studies, 17–18 plausible mechanisms, 17–18 Aging, 26–29, 268 observational studies, 27–28 plausible mechanisms, 27–28 secular trends, 27–28 Antioxidant vitamins, 134–139 observational studies, 136 plausible mechanisms, 134–136 prevention trials, 136 secular trends, 134 Article abstraction, 7–9 article review table, instructions, 8 manuscript format, 7–9 article citation, 8 article review table, instructions, 8 control variables, 8 criteria, question answers, 9 demographics, 8 dependent variables, 8 design, 8 design strengths, weaknesses, 8 duration, 8 hypothesis, 9 independent variables, 8 instructions, 8 mechanisms, 9 methodological strengths, weaknesses, 8 methodology, 8 observational studies, 9 overweight prevention trials, 9 results, 8 sample size, 8 sampling strategy, 8 secular trends, 9 statistical analyses, 8 summary table criteria, 9 summary table criteria, 9 B Beverages, sweetened, 153–168, 277 observational studies, 160–166, 168 plausible mechanisms, 157–160, 168 prevention trials, 166–168 secular trends, 153–157, 168

Big mac, exercise required to burn, 33 Birth weight, 13–22, 267 observational studies, 14–16 plausible mechanisms, 14–16 secular trends, 14–16 Breakfast, 277 Breakfast skipping, 216–218, 228–229, 233 Breastfeeding, 6, 250–256, 277–278 observational studies, 251–255 plausible mechanisms, 250–251, 255 prevention trials, 255 secular trends, 250, 255 C Calcium, 118–127, 276 observational studies, 120–127 plausible mechanisms, 118–120, 127 prevention trials, 126–127 secular trends, 118, 127 Calories, 31–36, 275 exercise, 33 intervention trials, 36 observational studies, 34–36 plausible mechanisms, 32–34, 36 secular trends, 31–32, 36 trend in energy intake, 32–33 from U.S. per capita food supply, 32 Carbohydrates, 63–87, 104–105, 112–115, 276 caloric sweeteners, 64 cross-sectional studies, adults, 68–76 dietary fat, ratio, 65 energy density, 67 excess energy intake, protection against, 65–66 ﬁber, 67–68 U.S. food supply, 65 glycemic response, 67–68 longitudinal studies, adults, 68 mechanisms, 65–68 nutrient absorption, 67 observational studies, 68–87 adults, 76 children, 76–83 ﬁber, 83 sugar, 83–87 satiety, 67 secular trends, 63–65 331

332

simple carbohydrates, 66–67 starch, 74 sugars, added, 75 U.S. food supply energy sources, 63 percent of energy in, 64 Cardiovascular disease, 13–22, 267 Child-feeding practices, 237–242, 247–250 observational studies, 238–242, 247–250 plausible mechanisms, 237, 247–250 Childhood, 16, 28–29, 267 Cost of prevention activities, 2 Critical periods, 13–29, 267–269 adiposity rebound, 16–18, 28–29 observational studies, 17–18 plausible mechanisms, 17–18 birth weight, 13–22, 267 observational studies, 14–16 plausible mechanisms, 14–16 secular trends, 14–16 childhood, 16, 28–29, 267 early puberty, 18–21, 28–29, 267–268 observational studies, 19–21 plausible mechanisms, 19–21 secular trends, 18, 20–21 elderly, 26–29, 268 observational studies, 27–28 plausible mechanisms, 27–28 secular trends, 27–28 infancy, 16, 28–29, 267 observational studies, 16 intrauterine environment, 13–22, 267 intrauterine growth, 13–22, 267 observational studies, 14–16 plausible mechanisms, 14–16 secular trends, 14–16 menopause, 24–26, 28–29, 268 observational studies, 25–26 plausible mechanisms, 25–26 secular trends, 25–26 overview, 13 postpartum period, 21–22, 28–29, 268 postpartum weight retention, 22–24, 28–29, 268 observational studies, 23–24 plausible mechanisms, 23–24 secular trends, 23–24 pregnancy, 21–22, 28–29, 268 observational studies, 22 plausible mechanisms, 21–22 secular trends, 21–22 puberty, 18–21, 28–29, 267–268

Obesity: Developmental and Dietary Inﬂuences

D Dairy, 118–127, 276 observational studies, 120–127 plausible mechanisms, 118–120, 127 prevention trials, 126–127 secular trends, 118, 127 Diabetes, 13–22, 267 Dietary fat, 40–63, 103, 105, 112–115, 275–276 cross-sectional studies adults, 46–52, 63 children, 61–63 energy density, 44, 46 fatty acids, 45–46 foods on rise, 43 genetic susceptibility, 45–46 longitudinal studies adults, 46, 63 children, 52–61, 63 mechanisms, 44–46 observational studies, 46–63 adults, 52, 63 children, 53–60, 63 prevention trials, 63 as proportion of energy intake, 41 secular trends, 40–46 threshold effect, 45–46 U.S. food supply energy sources in, 43 percent of energy in, 42 utilization, 44–46 Dietary intake data, examination of, methodology, 11 Dietary patterns, 178–185 observational studies, 180–185 plausible mechanisms, 178–180, 185 prevention trials, 185 secular trends, 178, 185 Dietary self-restraint of parents, effect of, 242–245, 247–250 intervention studies, 245, 247–250 observational studies, 242–245, 247–250 plausible mechanism, 242, 247–250 Diets, unsustainability of, 2 E Early puberty, 18–21, 28–29, 267–268 observational studies, 19–21 plausible mechanisms, 19–21 secular trends, 18, 20–21 Eating frequency, 216–222, 229–233 Elderly, 26–29, 268 observational studies, 27–28

Index

plausible mechanisms, 27–28 secular trends, 27–28 Emotional feeding, 242, 247–250 Energy balance, dietary inﬂuences on, 269–278 Energy density, 36–40 dietary fat, association with, 37 ﬁber, association with, 37 observational studies, 38–40 plausible mechanisms, 37–40 secular trends, 36–40 water, association with, 37 Energy imbalance, dietary inﬂuences, 31–266, 269–278 antioxidant vitamins, 134–139 breastfeeding, 6, 250–256, 277–278 calcium, 118–127 carbohydrate, 63–87, 114–115 dairy, 118–127 dietary fat, 40–63, 114–115 dietary patterns, 178–185 energy density, 36–40 food insecurity, 255–266 fruit juice, 153–168 fruits, 140–152 iron, 127–134 macronutrient intake, 40–115 meal patterns, 212–233 minerals, 115–139 parenting inﬂuences, 233–250 portion size, 207–212 poverty, 255–266 protein, 87–115 reduced-fat food products, 195–207 restaurant-prepared foods, 168–178 snack patterns, 212–233 sweetened beverages, 153–168 total calories, 31–36 variety of foods, 185–195 vegetables, 140–152 vitamins, 115–139 zinc, 127–134 Epidemiological studies, examination of, methodology, 11 Evidence examination lines, 9–12 dietary intake data, 11 epidemiological studies, 11 experimental studies, 11 food supply data, 11 limitations, 11–12 strengths of, 11–12 mechanistic studies, 11 observational studies, 11 prevention trials, 11

333

secular trends-, 11 strengths, 11–12 types of evidence, 11 Experimental studies, examination of, methodology, 11 F Family functioning, 247–250 observational studies, 247 plausible mechanism, 247 Fat. See Dietary fat Fetal development, 13–22, 267 observational studies, 14–16 plausible mechanisms, 14–16 secular trends, 14–16 Fiber, 104–105, 112–114, 276 Food insecurity, 255–266 adults, 259–265 children, 260–265 observational studies, 259–265 plausible mechanisms, 259–265 prevention trials, 260 secular trends, 255–258, 260–265 Food supply data, examination of, methodology, 11 Fruit juice, 153–168 observational studies, 160–166, 168 plausible mechanisms, 157–160, 168 prevention trials, 166–168 secular trends, 153–157, 168 Fruits, 140–152, 276 observational studies, 141–142, 151–152 plausible mechanisms, 141, 151–152 prevention trials, 142–152 secular trends, 140–141, 151–152 Further research, priorities for, 279–283 H Healthy lifestyles, beneﬁts of, 2 Hunger. See Food insecurity I Infancy, 16, 28–29, 267 observational studies, 16 Intrauterine environment, 13–22, 267 Intrauterine growth, 13–22, 267 observational studies, 14–16 plausible mechanisms, 14–16 secular trends, 14–16 Iron, 127–134 observational studies, 133–134 plausible mechanisms, 133–134 prevention trials, 133–134 secular trends, 127–134

334

J Juice, fruit, 153–168 observational studies, 160–166, 168 plausible mechanisms, 157–160, 168 prevention trials, 166–168 secular trends, 153–157, 168 L Lack of success with weight loss programs, 2 Lifestyle, healthy, beneﬁts of, 2 M Macronutrient intake, 40–115 Manuscript format, 7–9 article abstraction, 7–9 article citation, 8 article review table, instructions, 8 control variables, 8 criteria, question answers, 9 demographics, 8 dependent variables, 8 design, 8 strengths, weaknesses, 8 duration, 8 hypothesis, 9 independent variables, 8 instructions, 8 mechanisms, 9 methodological strengths, weaknesses, 8 methodology, 8 observational studies, 9 overweight prevention trials, 9 results, 8 sample size, 8 sampling strategy, 8 secular trends, 9 statistical analyses, 8 summary table criteria, 9 Meal patterns, 212–233 observational studies, 218–229, 233 breakfast skipping, 228–229, 233 eating frequency, 218–222, 233 snacking, 222–228, 233 plausible mechanisms, 216–218, 233 breakfast skipping, 217–218, 233 eating frequency, 216–217, 233 snacking, 217, 233 prevention trials, 229–233 breakfast skipping, 233 eating frequency, 229–233 snacking, 233 secular trends, 214–216, 233 breakfast skipping, 216, 233 eating frequency, 214, 233

Obesity: Developmental and Dietary Inﬂuences

snacking, 215–216, 233 Mechanistic studies, examination of, methodology, 11 Menopause, 24–26, 28–29, 268 observational studies, 25–26 plausible mechanisms, 25–26 secular trends, 25–26 Methodology, 5–12 article abstraction, 7–9 article review table, instructions, 8 summary table criteria, 9 evidence examination lines, 9–12 limitations, strengths of, 11–12 types of evidence, 11 manuscript format, 7–9 search process, criteria, 5–7 dietary factor terms, 6 exclusion criteria, 5 exclusion terms, 6 inclusion criteria, 5 search terms, 6 Minerals, 115–139 vitamins, 115–139 antioxidant vitamins, 134–139 observational studies, 136 plausible mechanisms, 134–136 prevention trials, 136 secular trends, 134 calcium, 118–127 observational studies, 120–127 plausible mechanisms, 118–120, 127 prevention trials, 126–127 secular trends, 118, 127 dairy, 118–127 observational studies, 120–127 plausible mechanisms, 118–120, 127 prevention trials, 126–127 secular trends, 118, 127 iron, 127–134 observational studies, 133–134 plausible mechanisms, 133–134 prevention trials, 133–134 secular trends, 127–134 zinc, 127–134 observational studies, 133–134 plausible mechanisms, 133–134 prevention trials, 133–134 secular trends, 127–134 O Observational studies, examination of, methodology, 11

Index

P Parenting inﬂuences, 233–250 child-feeding practices, 237–242, 247–250 observational studies, 238–242, 247–250 plausible mechanisms, 237, 247–250 dietary self-restraint, disinhibition, 242–245, 247–250 intervention studies, 245, 247–250 observational studies, 242–245, 247–250 plausible mechanism, 242, 247–250 emotional feeding, 242, 247–250 family functioning, 247–250 observational studies, 247 plausible mechanism, 247 food as reward, 242, 247–250 parental control, 238, 247–250 parental pressure, 238–242, 247–250 parental restriction, 238, 247–250 weight status of child, concern about, 245–250 observational studies, 245–250 plausible mechanism, 245, 247–250 Portion size, 207–212 observational studies, 211–212 plausible mechanisms, 209–212 prevention trials, 211–212 secular trends, 207–209, 211–212 Postpartum weight retention, 21–24, 28–29, 268 observational studies, 23–24 plausible mechanisms, 23–24 secular trends, 23–24 Poverty, 255–266. See also Food insecurity Pregnancy, 21–22, 28–29, 268 observational studies, 22 plausible mechanisms, 21–22 secular trends, 21–22 Prevention trials children, 105–114 examination of, methodology, 11 macronutrients, adults, 98–105, 114–115 Proportion of U.S. population, risk for obesity, 2 Protein, 87–115 cross-sectional studies adults, 91 children, 97–98 longitudinal studies adults, 88–91 children, 91–97 observational studies, 88–98 adults, 91

335

plausible mechanisms, 87–88 secular trends, 87 Puberty, 18–21, 28–29, 267–268 early, 18–21, 28–29, 267–268 observational studies, 19–21 plausible mechanisms, 19–21 secular trends, 18, 20–21 Q Questions, research, 2 R Reduced-fat food products, 195–207 dietary fat substitutes, 200–201, 205–207 fat reduced during production, processing, 199, 201–207 observational studies, 201, 205–207 plausible mechanisms, 199, 205–207 prevention trials, 201, 205–107 secular trends, 195–199, 205–207 Research, priorities for, 279–283 Research questions, 2 Restaurant-prepared foods, 168–178, 277 observational studies, 172–178 plausible mechanisms, 171–172, 177–178 prevention trials, 177–178 secular trends, 171, 177–178 Reward, food as, 242, 247–250 parental control, 238, 247–250 parental pressure, 238–242, 247–250 parental restriction, 238, 247–250 S Search process, criteria, 5–7 breastfeeding, as search term, 6 carbohydrate, as search term, 6 dietary factor terms, 6 dietary fat, as search term, 6 dietary patterns, as search term, 6 dietary variety, as search term, 6 energy intake/energy density, as search term, 6 exclusion criteria, 5 exclusion terms, 6 food insecurity, as search term, 6 fruit juice, as search term, 6 fruits, as search term, 6 inclusion criteria, 5 meal, as search term, 6 mineral, as search term, 6 parental inﬂuences, as search term, 6 portion size, as search term, 6 protein, as search term, 6

336

reduced dietary fat food products, as search term, 6 restaurant-prepared foods, as search term, 6 search terms, 6 snack patterns, as search term, 6 sweetened beverages, as search term, 6 vegetables, as search term, 6 vitamins, as search term, 6 Secular trends, examination of, methodology, 11 Snack patterns, 212–233 Sugar, 104–105, 113–114 Sweetened beverages, 153–168, 277 observational studies, 160–166, 168 plausible mechanisms, 157–160, 168 prevention trials, 166–168 secular trends, 153–157, 168 T Target behaviors, deﬁning of, 2–3 Television viewing, 278 Total calories, 31–36, 275 exercise, 33 intervention trials, 36 observational studies, 34–36 plausible mechanisms, 32–34, 36 secular trends, 31–32, 36 trend in energy intake, 32–33 from U.S. per capita food supply, 32 U Unsustainability of diets, 2 U.S. population, proportion at risk for obesity, 2 V Variety of foods, 185–195 observational studies, 192, 195 plausible mechanisms, 190–192, 195 prevention trials, 192–195 secular trends, 190, 195 Vegetables, 140–152, 276 observational studies, 141–142, 151–152 plausible mechanisms, 141, 151–152

Obesity: Developmental and Dietary Inﬂuences

prevention trials, 142–152 secular trends, 140–141, 151–152 Vitamins, 115–139 minerals, 115–139 antioxidant vitamins, 134–139 observational studies, 136 plausible mechanisms, 134–136 prevention trials, 136 secular trends, 134 calcium, 118–127 observational studies, 120–127 plausible mechanisms, 118–120, 127 prevention trials, 126–127 secular trends, 118, 127 dairy, 118–127 observational studies, 120–127 plausible mechanisms, 118–120, 127 prevention trials, 126–127 secular trends, 118, 127 iron, 127–134 observational studies, 133–134 plausible mechanisms, 133–134 prevention trials, 133–134 secular trends, 127–134 zinc, 127–134 observational studies, 133–134 plausible mechanisms, 133–134 prevention trials, 133–134 secular trends, 127–134 W Weight loss programs, lack of success with, 2 Weight status of child, parental concern about, 245–250 observational studies, 245–250 plausible mechanism, 245, 247–250 Z Zinc, 127–134 observational studies, 133–134 plausible mechanisms, 133–134 prevention trials, 133–134 secular trends, 127–134