Insulin Resistance Syndrome and Neuropsychiatric Disease (Medical Psychiatry)

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Psychiatry

The first book of its kind to tie the metabolic syndrome with psychiatric disorders, and the possibility that common antipsychotic treatments may be having an adverse effect on patients. Insulin Resistance Syndrome and Neuropsychiatric Disease describes: • insulin resistance syndrome • psychiatric and cognitive disorders • impact of treatment of psychiatric disorders on metabolic function • insulin resistance as a link between affective disorders and Alzheimer’s disease And also examines: • the metabolic syndrome, including its relationships with diseases of the central nervous system, as well as new treatments to help prevent metabolic complications among patients with neuropsychiatric illnesses Presenting a complete overview and the relationship between insulin resistance syndrome, and psychiatric and cognitive disorders, Insulin Resistance Syndrome and Neuropsychiatric Disease will be a welcome update to any psychiatrist’s, neurologist’s, endocrinologist’s, and research scientist’s library. about the editor... NATALIE L. RASGON is Professor, Departments of Psychiatry and Behavioral Sciences, Obstetrics and Gynecology, Stanford University, California, and she is Director of the Center for Neuroscience in Women’s Health at the Stanford School of Medicine and Stanford Neuroscience Institute. Dr. Rasgon received her M.D. and Ph.D. in Obstetrics and Gynecology and Pathological Physiology from the Central Institute of Postgraduate Medical Education, Central Institute of General Pathology and Pathological Physiology, Academy of Medical Sciences, Moscow, Russia. Dr. Rasgon is the author of over 124 peer-reviewed articles, more than 25 book chapters, and is a reviewer for more than 25 medical journals specific to psychiatry, neuropharmacology, and obstetrics and gynecology. Printed in the United States of America

InsulIn ResIstance syndRome and neuRopsychIatRIc dIsease

about the book…

InsulIn ResIstance syndRome and neuRopsychIatRIc dIsease Medical Psychiatry Series / 38

Edited by

Natalie L. Rasgon, M.D., Ph.D. Rasgon

$+

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Medical Psychiatry Series Editor Emeritus

William A. Frosch, M.D. Weill Medical College of Cornell University New York, New York, U.S.A. Advisory Board Jonathan E. Alpert, M.D., Ph.D.

Siegfried Kasper, M.D.

Massachusetts General Hospital and Harvard University School of Medicine Boston, Massachusetts, U.S.A.

Medical University of Vienna Vienna, Austria

Mark H. Rapaport, M.D. Bennett Leventhal, M.D. University of Chicago School of Medicine Chicago, Illinois, U.S.A.

Cedars-Sinai Medical Center Los Angeles, California, U.S.A.

1. Handbook of Depression and Anxiety: A Biological Approach, edited by Johan A. den Boer and J. M. Ad Sitsen 2. Anticonvulsants in Mood Disorders, edited by Russell T. Joffe and Joseph R. Calabrese 3. Serotonin in Antipsychotic Treatment: Mechanisms and Clinical Practice, edited by John M. Kane, H.-J. Mo¨ller, and Frans Awouters 4. Handbook of Functional Gastrointestinal Disorders, edited by Kevin W. Olden 5. Clinical Management of Anxiety, edited by Johan A. den Boer 6. Obsessive-Compulsive Disorders: Diagnosis . Etiology . Treatment, edited by Eric Hollander and Dan J. Stein 7. Bipolar Disorder: Biological Models and Their Clinical Application, edited by L. Trevor Young and Russell T. Joffe 8. Dual Diagnosis and Treatment: Substance Abuse and Comorbid Medical and Psychiatric Disorders, edited by Henry R. Kranzler and Bruce J. Rounsaville 9. Geriatric Psychopharmacology, edited by J. Craig Nelson 10. Panic Disorder and Its Treatment, edited by Jerrold F. Rosenbaum and Mark H. Pollack 11. Comorbidity in Affective Disorders, edited by Mauricio Tohen 12. Practical Management of the Side Effects of Psychotropic Drugs, edited by Richard Balon 13. Psychiatric Treatment of the Medically III, edited by Robert G. Robinson and William R. Yates

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14. Medical Management of the Violent Patient: Clinical Assessment and Therapy, edited by Kenneth Tardiff 15. Bipolar Disorders: Basic Mechanisms and Therapeutic Implications, edited by Jair C. Soares and Samuel Gershon 16. Schizophrenia: A New Guide for Clinicians, edited by John G. Csernansky 17. Polypharmacy in Psychiatry, edited by S. Nassir Ghaemi 18. Pharmacotherapy for Child and Adolescent Psychiatric Disorders: Second Edition, Revised and Expanded, David R. Rosenberg, Pablo A. Davanzo, and Samuel Gershon 19. Brain Imaging In Affective Disorders, edited by Jair C. Soares 20. Handbook of Medical Psychiatry, edited by Jair C. Soares and Samuel Gershon 21. Handbook of Depression and Anxiety: A Biological Approach, Second Edition, edited by Siegfried Kasper, Johan A. den Boer, and J. M. Ad Sitsen 22. Aggression: Psychiatric Assessment and Treatment, edited by Emil Coccaro 23. Depression in Later Life: A Multidisciplinary Psychiatric Approach, edited by James Ellison and Sumer Verma 24. Autism Spectrum Disorders, edited by Eric Hollander 25. Handbook of Chronic Depression: Diagnosis and Therapeutic Management, edited by Jonathan E. Alpert and Maurizio Fava 26. Clinical Handbook of Eating Disorders: An Integrated Approach, edited by Timothy D. Brewerton 27. Dual Diagnosis and Psychiatric Treatment: Substance Abuse and Comorbid Disorders: Second Edition, edited by Henry R. Kranzler and Joyce A. Tinsley 28. Atypical Antipsychotics: From Bench to Bedside, edited by John G. Csernansky and John Lauriello 29. Social Anxiety Disorder, edited by Borwin Bandelow and Dan J. Stein 30. Handbook of Sexual Dysfunction, edited by Richard Balon and R. Taylor Segraves 31. Borderline Personality Disorder, edited by Mary C. Zanarini 32. Handbook of Bipolar Disorder: Diagnosis and Therapeutic Approaches, edited by Siegfried Kasper and Robert M. A. Hirschfeld 33. Obesity and Mental Disorders, edited by Susan L. McElroy, David B. Allison, and George A. Bray 34. Depression: Treatment Strategies and Management, edited by Thomas L. Schwartz and Timothy J. Petersen 35. Bipolar Disorders: Basic Mechanisms and Therapeutic Implications, Second Edition, edited by Jair C. Soares and Allan H. Young 36. Neurogenetics of Psychiatric Disorders, edited by Akira Sawa and Melvin G. Mclnnis 37. Attention Deficit Hyperactivity Disorder: Concepts, Controversies, New Directions, edited by Keith McBurnett, Linda Pfiffner, Russell Schachar, Glen Raymond Elliot, and Joel Nigg 38. Insulin Resistance Syndrome and Neuropsychiatric Disease, edited by Natalie L. Rasgon

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Insulin Resistance Syndrome and Neuropsychiatric Disease

Edited by

Natalie L. Rasgon, M.D., Ph.D. Stanford University Stanford, California, USA

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Informa Healthcare USA, Inc. 52 Vanderbilt Avenue New York, NY 10017 # 2008 by Informa Healthcare USA, Inc. Informa Healthcare is an Informa business 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: 0-8493-8208-4 (Hardcover) International Standard Book Number-13: 978-0-8493-8208-6 (Hardcover) 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 consequence 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 Insulin resistance syndrome and neuropsychiatric disease / edited by Natalie L. Rasgon. p. ; cm. — (Medical psychiatry; 38) Includes bibliographical references and index. ISBN-13: 978-0-8493-8208-6 (hb : alk. paper) ISBN-10: 0-8493-8208-4 (hb : alk. paper) 1. Metabolic syndrome— Complications. 2. Mental illness—Complications. 3. Alzheimer’s disease— Complications. I. Rasgon, Natalie L. II. Series. [DNLM: 1. Metabolic Syndrome X—complications. 2. Alzheimer Disease— complications. 3. Mental Disorders—complications. W1 ME421SM v.38 2008/ WK 820 M5868 2008] RC662.4.M52 2008 616.30 99—dc22 2007045871 For Corporate Sales and Reprint Permissions call 212-520-2700 or write to: Sales Department, 52 Vanderbilt Avenue, 16th floor, New York, NY 10017. Visit the Informa Web site at www.informa.com and the Informa Healthcare Web site at www.informahealthcare.com

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Preface

A great deal has been written about current medical foes in our society. Obesity, diabetes, depression, and dementia are all critical health issues with ramifications beyond the physical health of our nation. In the United States, obesity and diabetes have reached epidemic levels, not only among adults, but also among children and adolescents. In 1990, the rate of depression was estimated at 11 million people, and the estimate of the annual cost of depression was $44 billion (1). The rate of depression in any given year is estimated to be 9.5% (2), and with an estimated population of 301,139,947 million, this estimate has enormous implications for the economy and the mental and physical health of our nation. The growing rates of depression in the United States are already reflected in the use of psychotropic medications, as antidepressants are the most often prescribed medications in the United States. Finally, as the population in the United States and the western world ages, the rate of dementia grows precipitously, doubling after age 85. These statistics only highlight the growing concern of the effects of physical and psychological illness and the critical need for new research and funding on the proper diagnosis and treatment of illnesses. It is estimated that approximately 50% of the population aged 50 years and older have a diagnosis of major depression. Four million adults in the United States alone carry a diagnosis of dementia and 16 million have diabetes. It is not a coincidence then that these three Ds (diabetes, depression, and dementia) are iii

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so prevalent in our society. There are numerous biological and psychosocial explanations to them, and this book is focused on the biological concepts of these diseases. The aim is to educate clinicians of all specialties on pathophysiology and interrelationship among the three Ds, in order to formulate optimal approaches to their diagnosis and treatments. As will be described in this book, bidirectional relationships exist between diabetes and depression and depression and dementia. There is also a notion of Alzheimer’s disease being a form of diabetes of the brain. Because of this degree of reciprocity between the brain and the body (soma), it is plausible to postulate that depressive illness is not just a psychiatric disease—as diabetes is not just an endocrine disorder, nor that dementia is just a neurodegenerative disease per se—but that all of these diseases represent complex psychoneuroendocrine conditions requiring a complex multisystem approach to their prevention and treatment. Though this concept may seem obvious, is not firmly established as a thinking paradigm in the clinical community. This book will thus serve as a resource and a guide to development of early diagnoses and interventions among afflicted individuals with one or more of these three Ds to protect them from damage and irreversible changes. Metabolic syndrome refers to a cluster of symptoms that increase the risk of morbidity and mortality from cardiovascular disease and diabetes (3). As such, this nosology has been a subject of intense debate with regard to its definitions causality or utility in clinical psychiatry. Part of the complexity regarding metabolic syndrome is the fact that there is not a single internationally agreed-upon definition. As such, we have included a chapter by Gerald Reaven, the father of Syndrome X, which provides an elegant overview of insulin resistance syndrome and an argument for its superior clinical relevance in lieu of metabolic syndrome. As mentioned above, the findings from several areas of research suggest that there is a link between depression and the risk of dementia. In retrospective studies of patients with Alzheimer’s disease, a history of depression has been found to be associated with late-onset Alzheimer’s disease (4–6). In prospective community sample studies, depressive symptoms at the baseline evaluation were associated with an increased risk of

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incident dementia (7,8). Likewise, a fair number of patients with depression have been found to develop dementia (9–12). Long-standing—especially, poorly controlled—diabetes has been shown to cause both diffuse and focal changes in the brain, which are manifested as cognitive decline. These effects are mediated by metabolic disturbances on neurons as well vascular disease and hypertension. In turn, diabetes is closely associated with depressive symptoms and depressive disorders, with comorbidity ranging between 40% and 70% (13). Cognitive declines are also commonly present among patients with primary affective disorders and are unrelated to psychosocial consequences of living with a chronic disease. The reciprocal links between the nervous system and endocrine systems underlie changes in the brain and body in both depressive illness and diabetes. Depressive disorder is associated with blunted central serotonin release (13), which, in turn, has been associated with metabolic dysfunction (14). If the metabolic dysfunction is associated with increased risk of developing cognitive impairment and, ultimately, dementia, then early identification and treatment of these conditions may offer avenues for primary prevention of neurodegenerative illness. Another possible mechanism for adverse consequence of insulin resistance in the central nervous system is a high level of inflammation. Specifically, inflammatory processes are widely implicated in the pathophysiology of diabetes and cardiovascular disease, as well as in cognitive impairment. Among the suggested explanations are the independent effects of atherosclerosis and associated inflammation on cognitive decline (15), although metabolic dysfunction and inflammation may have cumulative effect on vasculature, manifested by changes in periphery (cardiovascular disease) and central nervous system (cognitive decline). Our last chapter provides an overview and synthesis of the major connecting links between insulin resistance and other aspects of brain function. Several other mediators of the reciprocal interaction between the CNS and insulin resistance include glucocorticoids (cortisol), insulin, serotonin, and glutamate among others. The concept of a final common pathway can be applied to these interactions, as the most likely place of convergence in action of

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these biomarkers is the hippocampus. Specifically, the hippocampus is a central brain structure involved in regulation of mood and cognition and is specifically sensitive to cortisol, insulin, serotonin, and other neuromediators. A number of chapters in this monograph will address the anatomy and physiology of hippocampus with an emphasis on the integration of various influences in the three Ds. While insulin affects hippocampal structures involved in body weight regulation (16), it also it influences memory processing (17–25). There are central insulin receptors predominantly located in the hippocampus and adjacent limbic structures (26,27). Currently, the investigation of intranasal administration of insulin is promising, as it specifically targets hippocampal function. In fact, studies have suggested improvement in hippocampal–specific declarative memory upon intranasal administration of insulin. Though, it should be noted that such improvements may be the result of the effects of insulin on cortisol concentrations. In addition, cortisol is a well-known endotoxin with regard to hippocampal neurons, by binding to hippocampal glucocorticoid receptors, inhibiting synaptic longterm potentiation and decreasing hippocampal glutamate turn over (28). Therefore, a decrease in cortisol may be behind improving effects of insulin on hippocampal functioning (29). Finally, even as the neurodegeneration ensues, the impaired metabolic processes may be modulated to affect the disease course. In the chapter by Craft and colleagues, their current findings on the role of insulin resistance in patients with Alzheimer’s disease offer potential ways to modify this dysfunction. Certainly, lifestyle and other modifiable risk factors are attractive methods for disease prevention and overall wellness. Nevertheless, it is as important to be knowledgeable about the intricate links behind the disease formation, as it is to offer patients nonpharmacological interventions at an earlier stage when the impact is not as pronounced. With that, I highly recommend this volume and hope that the reader will find it not only informative, but provocative of new research directions in solving a fascinating puzzle of the three Ds. Natalie L. Rasgon

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References 1. Greenberg PE, Stiglin LE, Finkelstein SN, et al. The economic burden of depression in 1990. J Clin Psychiatry 1993; 54(11): 405–418. 2. Robins LN, Regier DA, eds. Psychiatric Disorders in America: The Epidemiologic Catchment Area Study. New York: The Free Press, 1990. 3. Lakka HM, Laaksonen DE, Lakka TA, et al. The metabolic syndrome and total and cardiovascular disease mortality in middleaged men. JAMA 2002; 288(21):2709–2716. 4. Jorm AF, van Duijn CM, Chandra V, et al. Psychiatric history and related exposures as risk factors for Alzheimer’s disease: a collaborative re-analysis of case-control studies. EURODEM Risk Factors Research Group. Int J Epidemiol 1991; 20(suppl 2):S43—S47. 5. Speck CE, Kukull WA, Brenner DE, et al. History of depression as a risk factor for Alzheimer’s disease. Epidemiology 1995; 6(4): 366–369. 6. Steffens DC, Plassman BL, Helms MJ, et al. A twin study of lateonset depression and apolipoprotein E epsilon 4 as risk factors for Alzheimer’s disease. Biol Psychiatry 1997; 41(8):851–856. 7. Devanand DP, Sano M, Tang MX, et al. Depressed mood and the incidence of Alzheimer’s disease in the elderly living in the community. Arch Gen Psychiatry 1996; 53(2):175–182. 8. Schmand B, Jonker C, Geerlings MI, et al. Subjective memory complaints in the elderly: depressive symptoms and future dementia. Br J Psychiatry 1997; 171:373–376. 9. Rabins PV, Merchant A, Nestadt G. Criteria for diagnosing reversible dementia caused by depression: validation by 2-year follow-up. Br J Psychiatry 1984; 144:488–492. 10. Alexopoulos GS, Meyers BS, Young RC, et al. The course of geriatric depression with ‘‘reversible dementia’’: a controlled study. Am J Psychiatry 1993; 150(11):1693–1699. 11. Stoudemire A, Hill CD, Morris R, et al. Long-term affective and cognitive outcome in depressed older adults. Am J Psychiatry 1993; 150(6):896–900.

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12. Stoudemire, Stoudemire A, Hill CD, et al. Improvement in depression-related cognitive dysfunction following ECT. J Neuropsychiatry Clin Neurosci 1995; 7(1):31–34. 13. Zhao W, Chen Y, Lin M, et al. Association between diabetes and depression: sex and age differences. Public Health 2006; 120(8): 696–704. 14. Ebmeier KP, Donaghey C, Steele JD. Recent developments and current controversies in depression. Lancet 2006; 367(9505): 153–167. (Review). 15. Yaffe K, Kanaya A, Lindquist K, et al. The metabolic syndrome, inflammation, and risk of cognitive decline. JAMA 2004; 292(18): 2237–2242. 16. Porte D Jr., Woods SC. Regulation of food intake and body weight in insulin. Diabetologia 1981; 20(suppl):274–280. 17. Marfaing P, Penicaud L, Broer Y, et al. Effects of hyperinsulinemia on local cerebral insulin binding and glucose utilization in normoglycemic awake rats. Neurosci Lett 1990; 115(2–3):279–285. 18. Craft S. Insulin resistance and Alzheimer’s disease pathogenesis: potential mechanisms and implications for treatment. Curr Alzheimer Res 2007; 4(2):147–152. 19. Craft S. Insulin resistance syndrome and Alzheimer disease: pathophysiologic mechanisms and therapeutic implications. Alzheimer Dis Assoc Disord 2006; 20(4):298–301. 20. Craft S. Insulin resistance syndrome and Alzheimer’s disease: ageand obesity-related effects on memory, amyloid, and inflammation. Neurobiol Aging 2005; 26(suppl 1):65–69. (Epub 2005 Nov 2). 21. Craft S, Asthana S, Cook DG, et al. Insulin dose-response effects on memory and plasma amyloid precursor protein in Alzheimer’s disease: interactions with apolipoprotein E genotype. Psychoneuroendocrinology 2003; 28(6):809–822. 22. Craft S, Asthana S, Schellenberg G, et al. Insulin effects on glucose metabolism, memory, and plasma amyloid precursor protein in Alzheimer’s disease differ according to apolipoprotein-E genotype. Ann N Y Acad Sci 2000; 903:222–228.

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23. Craft S, Asthana S, Newcomer JW, et al. Enhancement of memory in Alzheimer disease with insulin and somatostatin, but not glucose. Arch Gen Psychiatry 1999; 56(12):1135–1140. 24. Craft S, Asthana S, Schellenberg G, et al. Insulin metabolism in Alzheimer’s disease differs according to apolipoprotein E genotype and gender. Neuroendocrinology 1999; 70(2):146–152. 25. Unger JW, Livingston JN, Moss AM. Insulin receptors in the central nervous system: localization, signalling mechanisms and functional aspects. Prog Neurobiol 1991; 36(5):343–362. 26. Lannert H, Hoyer S. Intracerebroventricular administration of streptozotocin causes long-term diminutions in learning and memory abilities and in cerebral energy metabolism in adult rats. Behav Neurosci 1998; 112(5):1199–1208. 27. Sapolsky RM. Potential behavioral modification of glucocorticoid damage to the hippocampus. Behav Brain Res 1993; 57(2):175–182. (Review). 28. Benedict C, Hallschmid M, Hatke A, et al. Intranasal insulin improves memory in humans. Psychoneuroendocrinology 2004; 29(10):1326–1334.

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Contents

Preface . . . . . . . . . . . . . . . . iii Contributors . . . . . . . . . . . . xiii 1.

2.

3.

4.

The Metabolic Syndrome: Much Ado About (Almost) Nothing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gerald Reaven Insulin Resistance in Schizophrenia, Major Depression, and Bipolar Disorder . . . . . . . . . . . . . . . . . . . . . . . . . Steven E. Lindley, Carmen M. Schro¨ der, Ruth O’Hara, and Sergio Fazio Insulin Resistance Syndrome and Alzheimer’s Disease . . . . . . . . . . . . . . . . . . . . . . . . . . Kristoffer Rhoads and Suzanne Craft

1

49

87

Insulin Resistance Link Between Depressive Disorders and Alzheimer’s Disease . . . . . . . . . . . . . . . . . . . . . 103 Heather A. Kenna, Margaret F. Reynolds, Bowen Jiang, and Natalie L. Rasgon

Index . . . . 139

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Contributors

Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, Washington, U.S.A. Suzanne Craft

Division of Cardiovascular Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, U.S.A. Sergio Fazio

Bowen Jiang Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California, U.S.A. Heather A. Kenna Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California, U.S.A.

Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California, U.S.A.

Steven E. Lindley

Ruth O’Hara Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California, U.S.A.

Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California, U.S.A.

Natalie L. Rasgon

Gerald Reaven Department of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California, U.S.A. xiii

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Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California, U.S.A.

Margaret F. Reynolds

Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, Washington, U.S.A.

Kristoffer Rhoads

Carmen M. Schro¨der Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California, U.S.A.

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1 The Metabolic Syndrome: Much Ado About (Almost) Nothing GERALD REAVEN Department of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California, U.S.A.

1

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The Metabolic Syndrome: Much Ado About (Almost) Nothing

3

INTRODUCTION The past several years have witnessed a veritable frenzy of activity focused on a diagnostic category designated as the metabolic syndrome (MetS). At least three different sets of criteria have been published with which to make this diagnosis (1–3), and literally hundreds of papers have been published in the past few years using these definitions to describe the prevalence of the MetS in an almost infinite number of different populations and/or their relative ability to identify person at risk to develop cardiovascular disease (CVD) or type 2 diabetes mellitus (2DM) (4). Despite this publication onslaught, questions have been raised as to both the pedagogical utility of the various definitions, as well as the clinical relevance of deciding whether or not an individual meets the diagnostic criteria for the MetS (4–7). The goal of this presentation will be to address both of these issues.

THE METS AS A PATHOPHYSIOLOGICAL CONSTRUCT The three definitions of the MetS share many common attributes. Perhaps the most important one is that they are all the products of consensus conferences, in which a number of ‘‘thought leaders’’ have met, interacted, and produced definitions of a new diagnostic category. Thus, neither the criteria selected to serve as the basis of diagnosing the MetS nor the values of the cut points that constitute an abnormality are evidence based. The specific components that have been selected to serve as diagnostic criteria by the three versions of the MetS are also similar, but the manner in which they are used is quite different. For example, is there a biological connection that links the individual diagnostic criteria of the MetS to each other, and, if so, is the relationship hierarchical in nature? Conversely, are the individual components simply CVD risk factors that have no physiological relationship with each other, i.e., they cluster together for no discernible reason? A discussion of these issues will be the focus in this section.

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World Health Organization The World Health Organization (WHO) was the first major organization to propose a set of clinical criteria for the MetS, formalized and published (1) in a document titled ‘‘Definition, Diagnosis and Classification of Diabetes Mellitus and its Complications.’’ The primary purpose of this report was to update the classification and diagnostic criteria of diabetes mellitus. In this context, the WHO Consultation Group designated the MetS as a special classification for individuals with 2DM or with the potential for developing 2DM: manifested by having impaired glucose tolerance (IGT), impaired fasting glucose (IFG), or insulin resistance by hyperinsulinemic, euglycemic clamp. The WHO Consultation Group felt that once these individuals developed certain ‘‘CVD risk components,’’ they became a unique entity and qualified as having the MetS. Aside from glucose tolerance status and/or insulin resistance, risk components deemed useful to identify individuals with the MetS included obesity, dyslipidemia, hypertension, and microalbuminuria. It was the view of the WHO Consultation Group that each component conveyed increased CVD risk but as a combination became more ‘‘powerful.’’ Therefore, the primary goal of recognizing an individual as having the MetS was to identify persons at undue risk for CVD. Secondarily, by design, the diagnosis also helped identify individuals with high risk for diabetes if they did not already have it. Table 1 displays the criteria proposed by the WHO to make a diagnosis of the MetS. The components that make up the WHO definition of the MetS are hierarchical in that one component, insulin resistance, must be present to make this diagnosis. However, there are four ways in which this essential criterion can be satisfied. The most direct path to identify insulin resistance is to perform a hyperinsulinemic, euglycemic clamp study, an approach that has no clinical utility. Consequently, some form of glucose intolerance must be present to document insulin resistance: 2DM, IGT, or IFG. There is no doubt that the prevalence of insulin resistance is greatly increased in subjects with 2DM or IGT (8–14), but the need to measure plasma glucose concentration 120 minutes after an oral glucose load decreases the clinical utility of IGT to satisfy the essential WHO criterion for insulin resistance. Although the

Abbreviation: HDL-C, high-density lipoprotein–cholesterol.

Plus any two of following: l Waist/hip ratio > 0.9 in men, > 0.85 in women, and/or BMI > 30 kg/m2 l Triglycerides
1.7 mmol/L (150 mg/dL) and/or HDL-C <0.9 mmol/L (35 mg/dL) in men, <1.0 mmol/L (39 mg/dL) in women l Blood pressure
140/90 mmHg (revised from
160/90 mmHg) l Microalbuminuria (urinary albumin excretion rate
20 mg/min or albumin : creatinine ratio
30 mg/g)

Must have one of following: glucose concentration given in mmol/L (mg/dL) l Diabetes mellitus l Fasting plasma glucose
7 (126) l or 2-hr postglucose load
11.1 (200) l Impaired glucose tolerance l Fasting plasma glucose <7 (126) l and 2-hr postglucose load
7.8 (140) and <11.1 (200) l Impaired fasting glucose l Fasting plasma glucose
6.1 (110) and <7 (126) l and (if measured) 2-hr postglucose load <7.8 (140) l Insulin resistance l Glucose uptake below lowest quartile for background population under investigation under hyperinsulinemic, euglycemic conditions

Table 1 WHO Definition of the Metabolic Syndrome

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majority of individuals with IFG are certainly insulin resistant, only approximately 5% of an apparently healthy population meets the WHO definition of IFG (15). On the basis of the prevalence of 2DM in the population at large, it appears that these two forms of glucose intolerance will be equally important in identifying individuals meeting the WHO criterion of insulin resistance. Whether useful clinical information is gained by knowing that a patient with 2DM also has the MetS can be questioned, and this issue will be addressed subsequently. The ancillary criteria selected by the WHO to be considered in the diagnosis of the MetS are viewed as consequences of insulin resistance. However, there appear to be two exceptions. Most importantly, obesity increases the likelihood that an individual will be insulin resistant, but insulin resistance does not lead to obesity (16–21). The link between insulin resistance and microalbuminuria is also somewhat problematic (22–24), although there is no doubt that urinary albumin excretion is increased in prevalence in patients with 2DM or hypertension (25,26). With the two exceptions noted above, the individual criteria selected by the WHO to be considered in the diagnosis of the MetS are all closely associated with insulin resistance and compensatory hyperinsulinemia, and these relationships will be discussed in detail in a subsequent section. The Adult Treatment Panel III The Adult Treatment Panel III (ATP III) representing the National Cholesterol Education Program (NCEP) published their initial definition of the MetS in 2001 (2). As indicated in the ATP III document, its primary purpose was somewhat different from that of the WHO report in that its focus was not on diabetes, but instead to update clinical guidelines for cholesterol testing and management. In addition, a major thrust of this third report by the NCEP was to ‘‘focus on primary prevention in persons with multiple risk factors.’’ With these goals in mind, the ATP III introduced the MetS as ‘‘multiple, interrelated factors that raise CVD risk.’’ The panel believed that the MetS increased CVD risk at any given low-density lipoprotein cholesterol (LDL-C) concentration, and should be a secondary target of therapy in

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cholesterol management. Similar to the WHO, the ATP III goal for establishing criteria for the MetS was to identify individuals at special risk for CVD, and to institute intensified lifestyle changes to mitigate these risks. In contrast to the WHO, the ATP III did not consider direct evidence of insulin resistance necessary to make a diagnosis of the MetS. Although both the WHO and ATP III consider the MetS as conveying high risk for CVD, they view the underlying concept of the MetS somewhat differently. The WHO introduced the MetS in the context of classifying diabetes mellitus and impaired glucose regulation. They believed that having the MetS syndrome elevated the CVD risk profile of individuals who had diabetes, or who were at risk for diabetes, and that these individuals should be classified separately. This point of view has the potential of resulting in two separate diagnostic categories of patients with 2DM, those with or without the MetS. The ATP III agreed that having the MetS enhanced CVD risk, but in keeping with their organizational focus, they viewed the MetS, not in terms of diabetes, but as a special risk factor for CVD that was additive to other known risk factors. However, the fundamental goal of the two organizations was similar, a more effective way to prevent CVD in high-risk individuals. The ATP III criteria for diagnosing the MetS appear in Table 2, and although there are many similarities, fundamental differences exist between the WHO and ATP III definitions. The most

Table 2 ATP III Definition of the Metabolic Syndrome Any three of following: l l

l l

l

Fasting glucose Waist circumference l Men l Women Triglycerides HDL-C l Men l Women Blood pressure


6.1 mmol/L (110 mg/dL) > 102 cm (40 in.) > 88 cm (35 in.)
1.7 mmol/L (150 mg/dL) <1.036 mmol/L (40 mg/dL) <1.295 mmol/L (50 mg/dL)
130/85 mmHg

Abbreviations: ATP, Adult Treatment Panel; HDL-C, high-density lipoprotein–cholesterol.

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prominent difference is that the ATP III does not identify any one essential criterion, but proposes that an individual meeting any three of the five criteria in Table 2 has the MetS. Thus, not only is the presence of insulin resistance no longer required to make a diagnosis of the MetS, a person can be identified as having the ATP III version without any evidence of abnormal glucose tolerance. The two definitions also contain minor differences in the actual values needed to have an ‘‘abnormal’’ plasma triglyceride (TG) or highdensity lipoprotein cholesterol (HDL-C) concentration or blood pressure. However, there are two more substantive differences between the two organizations in that the ATP III no longer lists microalbuminuria as one of the possible diagnostic criteria, and abdominal obesity, as assessed by measuring waist circumference (WC), is the only acceptable index of excess adiposity. The International Diabetes Federation The International Diabetes Federation (IDF) is the most recent group to propose criteria with which to diagnose the MetS, and Table 3 lists the specific components they have chosen for this purpose (3). The IDF definition is philosophically similar to that of the WHO in that they have identified one essential criterion with which to make a diagnosis of the MetS. However, in contrast to the need to demonstrate the presence of glucose intolerance and/or insulin resistance, the diagnostic criterion that must be fulfilled is abdominal obesity as determined by measuring WC. Furthermore, they have provided ethnic-specific values for determining whether an individual has a WC that is large enough to satisfy the essential criterion for the IDF version of the MetS. The values of the two additional ancillary criteria that must be met for abdominally obese individuals to have the IDF version of the MetS are identical to those of the ATP III. Inspection of Tables 1–3 demonstrates that the individual components of the various definitions of the MetS do not differ a great deal, but their superficial similarities should not obscure appreciation of the profound philosophical differences that exist between them. The most obvious difference is a conceptual one, involving the pathophysiological relationship between the individual diagnostic criteria. Thus, in the case of the ATP III version,

Central obesity (defined as a waist circumference
94 cm for Europid men and
80 cm for Europid women, with ethnicity-specific values for other groups

Raised TG level:
150 mg/dL (1.7 mmol/L), or specific treatment for this abnormality Reduced HDL cholesterol: <40 mg/dL (1.03 mmol/L) in males and <50 mg/dL (1.29 mmol/L) in females, or specific treatment for this lipid abnormality Raised blood pressure: systolic BP
130 mmHg or diastolic BP
85 mmHg, or treatment of previously diagnosed hypertension Raised fasting plasma glucose (FPG)
100 mg/dL (5.6 mmol/L), or previously diagnosed type 2 diabetes. If FPG is above the values stated above, an oral glucose tolerance test is strongly recommended but is not necessary to define presence of the syndrome.

Abbreviations: IDF, International Diabetic Federation; BP, blood pressure; TG, triglyceride; HDL, high-density lipoprotein.

l

l

l

l

Plus any two of the following four factors:

l

In order for a person to be diagnosed with the metabolic syndrome, they must have:

Table 3 IDF Definition of the Metabolic Syndrome

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the five criteria represent separate, but apparently equal, CVD risks, and an abnormality in any three of them suffices to make a diagnosis of the MetS. In contrast, a diagnosis of the MetS with either the WHO or IDF version relies on a hierarchal ordering of the criteria, and in both instances, one essential ingredient must be satisfied: glucose intolerance and/or insulin resistance in the case of the WHO, whereas an abnormal WC must be present to satisfy IDF criteria for the MetS. The second substantive difference involves the role of excess adiposity in the diagnosis of the MetS; specifically, the clinical utility of assessing overall obesity, as measured by body mass index (BMI), versus abdominal obesity, quantified by WC or the ratio of waist/hip girth (WHR). Thus, excess adiposity, one of the several supplemental criteria in the WHO definition, measured as either BMI or WHR, remains a criterion with the ATP III definition, but can only be met by having an abnormal WC, whereas in the IDF version WC has become the essential criterion with which to diagnose the MetS. The implication of these two fundamental areas of disparity between the various definitions of the MetS deserves careful consideration. WHAT IS THE RELATIONSHIP BETWEEN THE METS DIAGNOSTIC CRITERIA: CASUAL OR CAUSAL? A recent joint report (27) from the American Heart Association/ National Heart, Lung, and Blood Institute (AHA/NHLBI) stated that the most widely recognized of the metabolic risk factors underlying the MetS are an ‘‘atherogenic dyslipidemia, elevated blood pressure, and elevated plasma glucose.’’ They further point out that ‘‘individuals with these characteristics commonly manifest a prothrombotic and proinflammatory state.’’ Although acknowledging that these changes represent ‘‘a grouping of ASCVD risk factors,’’ the cluster identified ‘‘probably has more than one cause.’’ This point of view is different from that expressed by either the WHO (1) or IDF (2,3) versions of the MetS, in that the former considers evidence of insulin resistance essential to make this diagnosis, whereas the IDF states that an increase in WC is the necessary ingredient.

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It is difficult to disagree with the conclusion of the AHA/ NHLBI that the cluster of abnormalities that make up the MetS ‘‘probably has more than one cause.’’ In fact it is obvious that there are multiple examples of why this is the case. For example, Ahrens and associates (28) indicated that there were at least two divergent causes of increase in plasma TG concentration: one related to the amount of carbohydrate ingested (carbohydrate-induced lipemia) and the other by the quantity of fat consumed (fat-induced lipemia). However, they further pointed out that carbohydrateinduced lipemia was by far the most common finding. Returning to the AHA/NHLBI version of the MetS, do the authors believe that their ‘‘grouping of ASCVD risk factors’’ is coincidental? Alternatively, is it possible that a common physiological event greatly increases the likelihood that an individual will develop the changes that make up their definition of the MetS? The proposed answer to this rhetorical question is that the abnormalities that comprise all three versions of the MetS do not ‘‘cluster’’ together by accident and that a defect in insulin action plays a fundamental role in the development of the CVD risk factors that comprise all versions of the MetS. The evidence in support of the formulation follows. Glucose Intolerance The prevalence of some degree of abnormal glucose tolerance and/or 2DM—one of the criteria in all three definitions of the MetS—is the abnormality most closely related to insulin resistance. Indeed, more than 60 years ago, Himsworth and Kerr (8) presented evidence that ‘‘a state of diabetes might result from inefficient action of insulin as well as from a lack of insulin,’’ and stated, ‘‘the diminished ability of the tissues to utilize glucose is referable either to a deficiency of insulin or to insensitivity to insulin, although it is possible that both factors may operate simultaneously.’’ In the same vein, in 1949, Himsworth suggested that ‘‘we should accustom ourselves to the idea that a primary deficiency of insulin is only one, and then not the commonest, cause of the diabetes syndrome’’ (9). The prescience of Himsworth’s observations is borne out by the fact that we now know that resistance to insulin-mediated

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glucose disposal is present in the great majority of individuals with 2DM (10–14). It is also clear that insulin resistance (or hyperinsulinemia as a surrogate estimate of insulin resistance) is a powerful and independent predictor of the development of 2DM (29–33). Finally, the greater the degree of insulin resistance, the higher the plasma glucose response to oral glucose is in individuals with normal oral glucose tolerance (34). Thus, there is an enormous amount of evidence documenting a very close relationship between insulin resistance and abnormal elevations in plasma glucose concentrations. Finally, it should be emphasized that nondiabetic individuals with relatively minor degrees of glucose tolerance also have higher blood pressures and the dyslipidemic changes—a high TG and a low HDL-C concentration—that comprise the remaining metabolic criteria of all three definitions of the MetS (35–38). Dyslipidemia It has been known for approximately 40 years that there is a highly significant relationship between insulin resistance, compensatory hyperinsulinemia, and hypertriglyceridemia (39,40). It is now apparent that the link between insulin resistance/hyperinsulinemia and dyslipidemia is not limited to an increase in plasma TG concentrations. Thus, although the various definitions of the MetS have selected the combination of a high plasma TG and a low HDL-C concentration as diagnostic criteria, it is clear that these changes are also associated with a decrease in LDL particle size (small, dense LDL) and the postprandial accumulation of TG-rich remnant lipoproteins (41). Not only are all of these changes significantly associated with insulin resistance/hyperinsulinemia (39–45), each one has been shown to increase risk of CVD (46–51). Plasma TG Concentration Table 4A depicts the relationship among insulin resistance, plasma insulin response, hepatic very low-density lipoprotein (VLDL)-TG synthesis and secretion, and plasma TG concentrations in nondiabetic individuals (40) whose baseline plasma TG concentrations range from 69 to 546 mg/dL, whereas Table 4B

Abbreviations: IMGU, insulin-mediated glucose uptake as quantified by the insulin suppression test; VLDL, very low-density lipoprotein; TG, triglyceride; conc, concentration. Source: From Refs. 28 and 40.

B. Triglyceride concentration (33–174 mg/dL) IMGU ?Insulin conc (r ¼ 0.81) ?VLDL-TG secretion rate (r ¼ 0.68) ?TG conc (r ¼ 0.87)

A. Triglyceride concentration (69–546 mg/dL) IMGU ?Insulin conc (r ¼ 0.74) ?VLDL-TG secretion rate (r ¼ 0.74) ?TG conc (r ¼ 0.88)

Table 4 Relationship Between Insulin Resistance and Plasma Triglyceride Concentration

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describes the same relationships in individuals with plasma TG concentrations less than 175 mg/dL (52). These findings provide the experimental basis for the conclusion that the major cause of elevated plasma TG concentration in nondiabetic individuals is an increase in hepatic VLDL-TG secretion rate, secondary to insulin resistance and the resultant hyperinsulinemia. Postprandial Lipemia The higher is the fasting TG concentration, the greater will be the postprandial accumulation of TG-rich lipoproteins (VLDL, chylomicron remnants, and VLDL remnants) in nondiabetic individuals (53). In addition to the relationship between fasting TG concentration and postprandial lipemia, the daylong increase in TGrich lipoproteins in nondiabetic individuals is significantly correlated with the magnitude of their insulin resistance/compensatory hyperinsulinemia (44,45,54). Although the postprandial elevation of TG-rich lipoproteins is related to the fasting TG concentration, it can also be demonstrated that postprandial lipemia is enhanced when insulin-resistant/hyperinsulinemic individuals are matched for degree of fasting hypertriglyceridemia with an insulin-sensitive population (55). These observations suggest that increases in postprandial lipemia are highly correlated to insulin resistance directly by decreasing the removal from plasma of TG-rich lipoproteins by mechanisms not clearly defined and indirectly by virtue of the role played by insulin resistance and/or compensatory hyperinsulinemia in stimulating hepatic VLDL-TG secretion and increasing fasting plasma TG concentration. HDL Cholesterol Increases in plasma VLDL-TG concentration are usually associated with low HDL-C concentrations, and it appears that insulin resistance/compensatory hyperinsulinemia are independently associated with both of these changes (30). In part, this is likely due to the transfer, catalyzed by cholesteryl ester transfer protein, of cholesterol from HDL to VLDL (56); the higher the VLDL pool size, the greater the transfer rate from HDL to VLDL, and the lower the ensuing HDL-C concentration. There is also evidence that the

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fractional catabolic rate (FCR) of apoprotein A-I is increased in patients with primary hypertriglyceridemia (57), hypertension (58), and 2DM (59). The greater the degree of hyperinsulinemia (59) and the higher the apoprotein A-I FCR (48), the lower is the HDL-C concentration (60), and both of these changes are associated with increases in plasma insulin concentrations. Thus, it appears that insulin resistance and hyperinsulinemia contribute to a low HDL-C concentration by increasing both the VLDL pool size and the FCR of apoprotein A-I. LDL Particle Diameter Analysis of LDL particle size distribution (47) has identified multiple distinct LDL subclasses, and it appears that LDL in most individuals can be characterized by a predominance of larger (diameter > 255 A˚, pattern A) or smaller LDL particles (<255 A˚, pattern B). Individuals with pattern B have higher plasma TG and lower HDL-C concentrations. Not surprisingly, healthy volunteers with small, dense LDL particles (pattern B) are relatively insulin resistant, glucose intolerant, hyperinsulinemic, hypertensive, and hypertriglyceridemic, with decreases in HDL-C concentration (43). Atherogenic Lipoproteins and Insulin Resistance Lipoprotein abnormalities that are part of all three definitions of the MetS are more likely to occur in insulin-resistant/hyperinsulinemic individuals. However, not all individuals with these abnormalities are insulin resistant. A high fasting plasma TG concentration and hyperchylomicronemia can occur (28,53) in individuals who have a fundamental defect in the catabolism of TG-rich lipoproteins (fat-induced lipemia). Similarly, a low HDL-C concentration can exist as a familial defect in lipoprotein metabolism (61), independent of any change in insulin sensitivity. Not all insulin-resistant individuals will develop the atherogenic lipoprotein profile associated with the defect in insulin action, but insulin resistance/hyperinsulinemia is the only fundamental physiological abnormality that can both account for the atherogenic lipoprotein profile discussed above and explain why it occurs more commonly in combination with an elevated plasma glucose concentration and blood pressure.

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Blood Pressure The blood pressure criteria suggested by the WHO for diagnosing the MetS have been lowered by both the ATP III and the IDF. However, since the objective basis of the values chosen by either organization is not clear, it is difficult to know which set of blood pressure criteria will be more useful. More importantly, the blood pressure link between insulin resistance on one hand, and CVD on the other, is more complicated than the relatively simplistic approach to this issue that characterizes all three definitions of the MetS. The following three sets of observations provide strong evidence linking insulin resistance/hyperinsulinemia to essential hypertension. Firstly, patients with essential hypertension, as a group, are insulin resistant and hyperinsulinemic (62–64). Secondly, normotensive first-degree relatives of patients with essential hypertension are relatively insulin resistant and hyperinsulinemic compared with a matched control group without a family history of hypertension (65–67). Thirdly, hyperinsulinemia, as a surrogate estimate of insulin resistance, has been shown in population-based studies to predict the eventual development of essential hypertension (68–71). These data provide substantial support that insulin resistance/hyperinsulinemia plays a role in the pathogenesis of essential hypertension. What is almost always overlooked in discussions of the relationship between insulin resistance and essential hypertension is the fact that probably no more than 50% of patients with essential hypertension are insulin resistant (72). However, although only approximately half the patients with essential hypertension are likely to be insulin-resistant/hyperinsulinemic, this subset has the other components of the various definitions of the MetS that render them at greatest CVD risk. For example, patients with essential hypertension and electrocardiographic evidence of myocardial ischemia are insulin resistant, somewhat glucose intolerant, hyperinsulinemic, and with a high TG and low HDL-C compared with either a normotensive control group or patients with essential hypertension whose electrocardiograms are entirely normal (73). The link between the dyslipidemia present in insulin-resistant/hyperinsulinemic patients with

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essential hypertension and CVD is consistent with findings from the Copenhagen Male Study (74), in which approximately 3000 participants were divided into three groups on the basis of their fasting plasma TG and HDL-C concentrations. Men, whose plasma TG and HDL-C concentrations were in the upper third or lower third, respectively, of the whole population, were assigned to the high TG-low HDL-C group, whereas a low TG-high HDL-C group was composed of those individuals whose plasma TG and HDL-C concentrations were in the lower third and upper third, respectively, of the study population for these two lipid measurements. The intermediate group consisted of those participants whose lipid values did not qualify them for either of the two extreme groups. The results of this prospective study indicated that CVD risk was not increased in patients with hypertension in the absence of a high TG and low HDL-C, and the group at greatest risk was that with a high blood pressure and a high TG and low HDL-C. In summary: (i) insulin-resistant/hyperinsulinemic individuals are more likely to develop essential hypertension; (ii) hypertension is a well-recognized CVD risk factor; (iii) patients with essential hypertension and a high TG and a low HDL-C are at greatest CVD risk; and (iv) the clustering of essential hypertension with glucose intolerance and dyslipidemia can only be accounted for by accepting a common pathophysiological role for insulin resistance/hyperinsulinemia Insulin Resistance and Procoagulant and Proinflammatory Factors All three definitions of the MetS comment on the fact that the cluster of components that make up the diagnostic category is also associated with a procoagulant and/or proinflammatory state. Although measures of the latter changes have not been elevated to become diagnostic criteria, there is no doubt that both of these changes are closely associated with insulin resistance. The association between insulin resistance/hyperinsulinemia, elevated concentrations of plasminogen activator inhibitor-1 (PAI-1), and CVD has been known for some time (75,76). Of greater relevance to this review are the data in Table 5 showing that PAI-1 concentration

0.42 0.39 0.15 0.06 0.62 0.65 0.32 0.69 0.22

r 0.02 0.03 0.49 0.77 <0.001 <0.001 0.07 <0.001 0.23

p — — 0.004 0.06 0.56 0.58 0.39 0.65 0.29

r

p — — 0.98 0.76 <0.001 <0.001 <0.05 <0.001 0.13

Partial correlation

Partial Correlations were calculated after adjustment for age and BMI. The SSPG during the last 30 minutes of a 180-minute infusion of octreotide (0.27 mg/m2/min), insulin (32 mU/m2/min), and glucose (267 mg/m2/min). Since the steady-state plasma insulin concentrations are comparable in all individuals, and the glucose infusion rate is identical, the resultant SSPG concentration provides a direct measure of the ability of insulin to mediate the disposal of a given glucose load; i.e., the higher the SSPG, the more insulin resistant the individual. Abbreviations: PAI-1, plasminogen activator inhibitor-1; BMI, body mass index; MAP, mean arterial pressure; WHR, waist to hip ratio; SSPG, steady-state plasma glucose concentration. Source: From Ref. 67.

Age (yr) BMI (kg/m2) Waist/Hip (WHR) MAP (mmHg) SSPG (mg/dL) Fasting plasma insulin (mU/mL) Triglyceride (mg/dL) HDL cholesterol (mg/dL) LDL cholesterol (mg/dL)

Variable

Simple correlation

Table 5 Simple and Partial Correlations Between PAI-1 and Other Relevant Variables in Normotensive Volunteers

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in a group of apparently healthy individuals was significantly correlated with degree of insulin resistance [as quantified by steadystate plasma glucose (SSPG) concentration during the insulin suppression test (IST)] and fasting plasma insulin, TG, and HDL-C concentrations (77). Thus, variations in PAI-1 concentrations cluster with insulin resistance/compensatory hyperinsulinemia and the dyslipidemia characteristic of the defect in insulin action. The proinflammatory factor currently attracting the most attention as indicating increased CVD risk is C-reactive protein (CRP), but there is a much longer history of a relationship between an increase in white blood cell (WBC) count and heart disease. Indeed, data from the Women’s Health Initiative Observational Study suggest that a high WBC count was comparable in magnitude as a predictor of CVD risk as increases in CRP concentration (78). Evidence published several years ago (79) of a relationship between WBC count and insulin resistance/ compensatory hyperinsulinemia indicated that the WBC count in apparently healthy individuals was significantly correlated with degree of insulin resistance (r ¼ 0.50, p > 0.001), the magnitude ( p < 0.001) of the plasma glucose (r ¼ 0.48) and insulin responses (r ¼ 0.50) to an oral glucose challenge, and higher TG (r ¼ 0.37) and lower HDL-C (r ¼ 0.38) concentrations ( p > 0.005). These observations provide evidence that the additional CVD risk factors considered to be present in patients diagnosed as having the MetS are significantly related to both insulin resistance/hyperinsulinemia as well as the other components of the MetS. As such, they provide additional evidence indicating that insulin resistance/hyperinsulinemia offers the only coherent explanation to account for how all of these individual variables cluster together in apparently healthy individuals and increase risk of CVD. EXCESS ADIPOSITY, INSULIN RESISTANCE, CVD, AND THE METS The use of an index of excess adiposity as a criterion with which to diagnose the MetS is qualitatively different from any of the other components listed in Tables 1–3.

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Dyslipidemia (a high TG and low HDL-C concentration), hyperglycemia, and hypertension are independent factors that directly increase risk of CVD (46,48,49,80,81). The relationship between excess adiposity and CVD risk is not the same. Substantial numbers of overweight/obese individuals do not display the components used to make a diagnosis of the MetS; being overweight/obese increases the probability that an individual will become glucose intolerant, dyslipidemic, and hypertensive, and the linchpin between excess adiposity and these abnormities is largely a consequence of the adverse effect of being overweight/ obese on insulin sensitivity (17–21). This point of view is consistent with the results of the recent study of Ninomiya et al. (82), showing that abdominal obesity, as defined by the ATP III, was the only one of their five variables not statistically associated with the development of either CVD or stroke in an analysis of the National Health and Nutrition Examination Survey (NHANES) III data. The authors suggested that this finding ‘‘may reflect an indirect effect of high WC through other components of the syndrome.’’ Consequently, this section will examine the relationship between excess adiposity, insulin resistance, and the diagnosis of the MetS. Obesity and Insulin-Mediated Glucose Uptake The most insightful study of the relationship between obesity and insulin-mediated glucose uptake (IMGU) is the report from the European Group for the Study of Insulin Resistance (83). On the basis of the results of euglycemic, hyperinsulinemic clamp studies in 1146 nondiabetic, normotensive volunteers, these investigators concluded that only approximately 25% of the obese volunteers were insulin resistant by the criteria they used. Parenthetically, these authors also pointed out that differences in WC were unrelated to insulin sensitivity after adjustments for age, gender, and BMI. We have published results similar to those of the European Group for the Study of Insulin Resistance, finding that the differences in degree of obesity account for approximately one-third of the variability of IMGU in apparently healthy individuals (17,18). Furthermore, these estimates did not take into account that overweight individuals tend to be more sedentary and that

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the more physically fit an individual, the more insulin sensitive he or she will be (84). Indeed, in a biethnic study, involving nondiabetic Pima Indians and individuals of European ancestry, it was shown that differences in degree of physical fitness are approximately as powerful as variations in adiposity in modulation of IMGU (85). Thus, the heavier an individual the more likely they are to be insulin resistant, but although differences in adiposity are an important modulator of insulin action, it is only one of the variables determining whether an individual is sufficiently insulin resistant to develop an adverse clinical outcome. WC Vs. BMI as Predictors of IMGU Measurements of BMI and WC in approximately 15,000 participants in the NHANES indicated that the correlation coefficient between the two indices of obesity was greater than 0.9 irrespective of the age, gender, and ethnicity of groups evaluated (86). Given this degree of correlation between BMI and WC, it is not immediately obvious why WC is considered to be a more useful index of metabolic abnormality associated with excess adiposity than is BMI. It is even less clear why it is considered to be the essential diagnostic criterion in the IDF version of the MetS (Table 3). Figure 1 displays the results of a study in which IMGU was quantified with the IST in 208 apparently healthy individuals and the relationship between these values and measurements of BMI and WC determined (87). The IST (88–90) is based upon determining the SSPG and steady-state plasma insulin (SSPI) concentrations during the last 30 minutes of a 180-minute infusion of octreotide (0.27 mg/m2/min), insulin (32 mU/m2/min), and glucose (267 mg/m2/min). Since the SSPI concentrations are comparable in all individuals, and the glucose infusion rate is identical, the resultant SSPG concentration provides a direct measure of the ability of insulin to mediate the uptake of a given glucose load (IMGU); i.e., the higher the SSPG, the more insulin resistant the individual. The results in men (upper two panels) and women (lower two panels) are shown separately. The fact that the correlation coefficients relationships (r values) between

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Figure 1 Relationship between degree of insulin resistance (SSPG concentration) and BMI or waist circumference in 208 apparently healthy volunteers The SSPG during the last 30 minutes of a 180-minute infusion of octreotide (0.27 mg/m2/min), insulin (32 mU/m2/min), and glucose (267 mg/m2/min). Since the steady-state plasma insulin concentrations are comparable in all individuals, and the glucose infusion rate is identical, the resultant SSPG concentration provides a direct measure of the ability of insulin to mediate the disposal of a given glucose load; i.e., the higher the SSPG, the more insulin resistant the individual. Abbreviations: SSPG, steady-state plasma glucose; BMI, body mass index. Source: From Ref. 80.

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the two indices of obesity and the SSPG concentration are essentially identical was not surprising in light of the NHANES data (86). However, it was surprising, and of considerable interest, to find that the magnitude of the correlation between the two indices of adiposity and the measure of IMGU was much greater in men (r & 0.7) than in women (r & 0.5). Consistent with the results of the NHANES study described above, BMI and WC were also highly correlated (r ¼ 0.9). Since there is substantial evidence that the relationship between IMGU and overall obesity (BMI) is no different from that between IMGU and abdominal obesity (WC), it seems that either index of adiposity is equally predictive of differences in insulin action. Relationship Between Adiposity, Insulin Resistance, and CVD Risk Rates of IMGU vary by more than sixfold in apparently healthy individuals, and the distribution of these values is continuous (91). Consequently, there is no objective way to select cut points that define individuals as being either insulin resistant or insulin sensitive. Obviously, this complicates any discussion of the relationship between excess adiposity, insulin resistance, and CVD. However, there are prospective studies that provide an operational definition with which to address this issue. For example, if the magnitude of the insulin response to oral glucose is used as a surrogate maker of insulin resistance, 25% of an apparently healthy population with the highest insulin concentrations is at statistically significant increased risk to develop CVD (92). On the basis of the results of two prospective studies in which the IST was used to quantify IMGU at baseline, the third of the population that was the most insulin resistant (those with the highest SSPG concentrations) was at significantly greater risk to develop CVD (93,94). Thus, for the purposes of this discussion, the third of the population at large with the highest SSPG concentrations will be operationally defined as being insulin resistant (IR), and those with SSPG concentrations in the lower third will be considered to be insulin sensitive (IS).

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Prevalence of Insulin Resistance as a Function of BMI The results shown in Table 6 come from a study of 465 apparently healthy individuals, divided into tertiles of IMGU on the basis of their BMI (20). Although the majority of normal weight individuals (BMI <25 kg/m2) are in the most IS third (70%), 30% of the most IS individuals are either overweight or obese. Furthermore, approximately two-thirds of those in IR third were either normal weight or overweight, and only approximately one-third of the most IR individuals were actually obese (BMI 30–35 kg/m2). These data provide further evidence that in general, the heavier the individuals, the more likely they are to be insulin resistant, but that obesity does not necessarily equal insulin resistance. Interaction Between BMI, Insulin Action and CVD Risk Factors The results displayed in Figure 2 illustrate the results of applying the operational definitions of IR and IS to 314 healthy, nondiabetic individuals (17). Each panel displays the best-fit line describing the relationship between BMI and a series of CVD risk factors, following the separation of the population into thirds on the basis of their SSPG concentration. Results in the two left panels indicate that the greater the BMI, the higher the total (upper left) and LDL-C (lower left) concentrations, but that these relationships do not vary as a function of degree of insulin resistance. In contrast, results in the middle panels of Figure 2 demonstrate that the relationships between BMI and plasma TG (upper middle) and HDL-C (lower middle) concentrations are quite different in IR compared with IS individuals; at any given BMI, the plasma concentrations of TG are higher and HDL-C lower in IR compared with IS individuals. Finally, the results in the right panels of Figure 2 highlight the untoward impact of being insulin resistant on the total integrated plasma glucose (upper right) and insulin (lower right) responses to a 75 g oral glucose challenge. In addition to documenting the enormous impact that being insulin resistant has on the plasma insulin response to oral glucose, the results in Figure 2 also emphasize that the plasma glucose response to oral glucose is

109 (70%) 39 (25%) 7 (5%) 155

Most insulin-sensitive third

Abbreviation: BMI, body mass index. Source: From Ref. 85.

<25 25.0–29.9 30.0–34.9 Total

BMI (kg/m2) 75 (48%) 54 (35%) 26 (17%) 155

Intermediate third

24 (15%) 75 (48%) 56 (36%) 155

Most insulin-resistant third

Table 6 Distribution of Body Mass Index (kg/m2) According to Degree of Insulin Resistance (Number and Percentage)

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Figure 2 Relationship between BMI and SSPG* tertile and several cardiovascular disease risk factors. The SSPG concentration during the last 30 minutes of a 180-minute infusion of octreotide (0.27 mg/m2/min), insulin (32 mU/m2/min), and glucose (267 mg/m2/min). Since the steady-state plasma insulin concentrations are comparable in all individuals, and the glucose infusion rate is identical, the resultant SSPG concentration provides a direct measure of the ability of insulin to mediate the disposal of a given glucose load; i.e., the higher the SSPG, the more insulin resistant the individual. Abbreviations: SSPG, steady-state plasma glucose; BMI, body mass index; HDL, high-density lipoprotein; LDL, lowdensity lipoprotein. Source: From Ref. 72.

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relatively well maintained despite increasing degrees of both obesity and insulin resistance. These latter comparisons emphasize the extraordinary ability of compensatory hyperinsulinemia to prevent gross decompensation of glucose homeostasis in insulin-resistant individuals. Obesity does not Necessarily Translate into Increased CVD Risk If insulin resistance/hyperinsulinemia increases CVD risk at any given BMI, and not all overweight/obese persons are insulin resistant, it seems clear that excess adiposity, per se, does not necessarily increase CVD risk. One way to look at this issue is to evaluate CVD risk factors in obese individuals selected to be either insulin resistant or insulin sensitive, with the IST as defined above. The results in Figure 3 compare daylong glucose, insulin, and free fatty acid (FFA) concentrations in response to breakfast and lunch in 20 IR and 18 IS obese individuals, matched for age, gender, BMI, and WC (18). In addition to having daylong increase in plasma glucose, insulin, and FFA concentrations, the CRP concentrations were also significantly higher in the IR subjects (0.39  0.08 vs. 0.12  0.03 mg/dL, p <0.005). WC IS NOT THE SAME AS VISCERAL OBESITY On the basis of the experimental data summarized earlier, it can be concluded that measurements of BMI and WC are highly correlated, associated with a specific measure of IMGU to an identical degree, and that CVD risk factors are increased primarily in those overweight/obese individuals who are also insulin resistant. It is apparent that this formulation is at odds with the views of the ATP III (2) and IDF (3) that abdominal obesity is the only relevant indicator of excess adiposity. A possible explanation for this discrepant view of the importance of abdominal obesity in the genesis of insulin resistance and its consequences is that measurements of WC only provide a surrogate estimate of visceral obesity, and it is visceral obesity that is responsible for the manifestations of the MetS that increase CVD.

Figure 3 Comparison of daylong plasma glucose, insulin, and FFA concentrations in insulin-resistant and insulinsensitive obese individuals. Test meals were consumed at 8 am and noon, and blood drawn before and at hourly intervals after the meals. Abbreviation: FFA, free fatty acid. Source: From Ref. 86.

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Visceral Obesity and Insulin Resistance Table 7 presents the results of 21 studies attempting to define the relative magnitude of the relationship between IMGU and various estimates of adiposity, including visceral fat (VF), in nondiabetic subjects (95–115). The studies are listed in chronological order, and several inclusion criteria were used to construct the table. In the first place, imaging techniques had to be used to determine the magnitude of the various fat depots. Secondly, IMGU had to be quantified with specific methods, and studies using surrogate estimates were not included. In addition, the actual experimental data had to be available prior to the use of arbitrary ‘‘adjustments’’ or multiple regression analysis. For example, following an ‘‘adjustment’’ for the relationship between differences in total body fat and IMGU, it is not clear how much one learns from now discerning a relationship between IMGU and VF. The omission of any published study that satisfied these two simple criteria was inadvertent, and no information was deliberately excluded. On the other hand, given the number of studies included and the diversity in the experimental populations represented, it is unlikely that the inclusion of additional reports would substantially alter the interpretation of these data. Space constraints prohibit a thoughtful discussion of possible differences in the imaging techniques used in individual studies, and the same considerations apply to the specific methods used to quantify IMGU. Finally, given the diversity of the participants enrolled in these studies, as well as the differences in experimental techniques used, it will not be possible to discuss each one thoroughly. Instead, an effort will be made to draw the general conclusions that seem to be both consistent with the data, as well as most relevant to the issue at hand. Perhaps the simplest conclusion to be drawn from the results in Table 7 is that correlation coefficients (r values) between VF and IMGU are almost always less than 0.6; the r values between IMGU and either BMI or WC that can be seen in Figure 1. Indeed, r values between IMGU and VF varied from 0.33 to 0.6 in 20 of the 25 measurements in Table 7, with differences in VF accounting for approximately 25% of the variability in IMGU in most instances.

39 men 60 subjects 26 OB subjects 54 subjects 20 SEA men 47 men 27 postM women 44 OB postM women 68 Cau children 51 AA children 55 postM women 48 subjects 24 subjects 89 OB males 40 OB preM women 174 subjects 32 Hispanic children 39 men 44 AA men 35 AA women 11 Thai women 11 Thai men 25 SEA subjects 40 SEA/Cau subjects 150 AA/Cau children

95 96 97 98 99 100 101 102 103

–0.46

–0.38 –0.80 –0.54 –0.46 –0.54

–0.06 –0.57 –0.46 –0.57 –0.67 –0.47 –0.45 –0.46 –0.45 –0.38

–0.68 –0.52

–0.30

–0.61

–0.61 –0.57 –0.54 –0.58 –0.56

TF

–0.61 –0.54 –0.53 –0.43 –0.17 –0.70 –0.47 –0.43 –0.41 0.47

–0.62 –0.50

SF

–0.56

–0.63

–0.52

–0.55

BMI

Abbreviations: IMGU, measurement of insulin-mediated glucose uptake; OB, obese; postM, postmenopausal; Cau, Caucasian; AA, AfricanAmerican; preM, premenopausal; SEA, South Asian; VF, visceral fat; SF, subcutaneous fat; TF, total fat; BMI, body mass index.

–0.51 –0.50 –0.56 –0.52 –0.59 –0.61 –0.39 –0.40 –0.59 –0.43 –0.49 –0.58 –0.55 –0.41 –0.34 –0.69 –0.44 –0.71 –0.57 –0.50 –0.60 –0.54 –0.55 –0.33 –0.33

VF

30

115

114

113

104 105 106 107 108 109 110 111 112

Population

Ref.

Table 7 Correlation Coefficients (r Values) Between IMGU and Body Fat Distribution

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Secondly, although the relationship between BMI and IMGU was only analyzed in four studies, the correlation coefficients were comparable in these instances to the values between VF and IMGU. More comparisons were made between the relationships of IMGU with VF as contrasted to total fat (TF), and it appears that either estimate of adiposity provided r values of similar magnitude. However, the emphasis in the studies listed in Table 7 was a comparison of the relationship between IMGU and subcutaneous abdominal fat (SF) with that between IMGU and VF. As before, the magnitude of the relationship with IMGU was reasonably comparable with either fat depot, but in this case there were two examples in which the values were quite discrepant (102,108). On the other hand, in the remaining 20 available comparisons, the r values between IMGU with VF or SF did not vary a great deal, being somewhat higher with VF (9 times), higher with SF (9 times), and identical on two occasions. Given the information in Table 7, it is not easy to understand the basis for the ‘‘conventional wisdom’’ that visceral obesity has a uniquely adverse effect on IMGU. One of the explanations may be the widespread use of multiple regression analysis to decide which variable is an ‘‘independent’’ predictor of an outcome, in this case IMGU. Although this approach can provide useful information, it is well recognized that it presents problems when very closely related variables are entered into the model being used. Since all measures of adiposity are highly correlated, the biological significance of the results of a multivariate analysis is not clear, indicating that only one of them is an ‘‘independent’’ predictor of IMGU. However, it is clear from the data in Table 7 that there is hardly overwhelming experimental support for the notion of a uniquely close relationship between VF and IMGU, in contrast to the relationship between IMGU and BMI, WC, SF, or TF. Indeed, this conclusion should not be too surprising in view of the results of a study showing that ‘‘independent of age and sex, the combination of BMI and WC explained a greater variance in nonabdominal, abdominal, subcutaneous, and visceral fat than either BMI or WC alone’’ (116).

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VF and Adverse Clinical Outcomes Although the data presented in Figure 2 and Table 7 do not identify a uniquely close relationship between either WC or VF and IMGU, measurements of abdominal obesity might still be the most effective way to identify individuals at increased risk of developing clinical syndromes related to insulin resistance. For example, many studies have been published emphasizing the relationship between abdominal obesity in general, or VF specifically, as predicting the development of the clinical syndromes related to insulin resistance (117–122). However, results of other studies have come to a somewhat different conclusion. For example, in Pima Indians, increases in visceral obesity did not correlate with decreases in IMGU (123), and BMI was the estimate of adiposity with the highest hazard ratio in the prediction of 2DM (124). Furthermore, adding WC to this study’s model did not improve its predictive ability. In a prospective study of Mexican-Americans (125), Haffner and colleagues reported somewhat similar results, illustrating those individuals with the highest baseline plasma glucose and insulin values were most likely to develop 2DM, independently of differences in age, BMI, or central obesity. In addition, a prospective study in a predominantly Caucasian population concluded that ‘‘both overall and abdominal adiposity strongly and independently predict risk of 2DM’’ (126). It has also been shown in studies of several ethnic groups that BMI is more strongly associated with blood pressure than is abdominal obesity (127–129). Finally, the clustering of dyslipidemia, hyperuricemia, diabetes, and hypertension described in both Whites and African-Americans was most strongly related to insulin concentration, although the magnitude decreased when adjusted for differences in BMI and abdominal obesity (130). In this latter instance, it was concluded that all three variables—insulin concentration, abdominal girth, and BMI—contributed to the adverse consequences related to insulin resistance. Thus, although WC may be a powerful predictor of clinical outcomes linked to insulin resistance, there is also considerable evidence that overall obesity, as estimated by BMI, not only contributes to insulin resistance, but also increases the likelihood that an individual will develop the clinical syndromes associated with the defect in insulin action.

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IS THERE CLINICAL UTILITY IN DIAGNOSING THE METS? Although the specific approaches to diagnose the MetS vary from version to version (Tables 1–3), the components listed in each of them are remarkably similar in that they are significantly associated with insulin resistance and increased CVD risk. It also seems reasonable that the more of these abnormalities that exist in an individual, the greater will be the risk of CVD. On the other hand, once the values of these measurements are known, is there any significant clinical benefit in knowing whether the number of arbitrary criteria exceeded qualifies an individual as having the MetS? For example, all three versions of the MetS include 2DM as one of the diagnostic criteria. Patients with 2DM are at increased risk of CVD and, in addition to being hyperglycemic, are often dyslipidemic and hypertensive, with a procoagulant and proinflammatory state. There are clinical guidelines (131) outlining the appropriate treatment paradigms for patients with 2DM. Once this diagnosis is made, the clinical problem is how best to control the hyperglycemia appropriately, and effectively address all remaining CVD risk factors, not deciding if the MetS is present or not. Rather than continue to describe a series of situations that question the clinical utility of diagnosing the MetS, it might be more informative to explore the clinical implications of not identifying patients at increased CVD risk who do not meet the requisite diagnostic criteria. Perhaps the simplest way to address this issue is to consider how the same individual would be classified by the three versions of the MetS. The patient in question is a man of European ancestry, with a WC of 93 cm, whose blood pressure is elevated (145/95 mmHg), with a high TG (155 mg/dL) and a low HDL-C (30 mg/dL) concentration. However, since his fasting plasma glucose concentration is only 105 mg/dL, he does not meet the diagnostic definition for the MetS by WHO criteria unless his physician is willing to perform either an oral glucose tolerance test or a euglycemic, hyperinsulinemic clamp study. Should the lack of a positive diagnosis of the MetS adversely affect the treatment plan for this patient? Would it make any substantive difference in the treatment if the patient’s fasting plasma glucose concentration had been 111 mg/dL, making him eligible for a diagnosis of the MetS?

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Alternatively, what if a second physician is willing to measure the patient’s glucose level 120 minutes after a 75 g oral glucose challenge, and it turns out to be 145 mg/dL. The patient would now have the MetS. Would this additional information make any substantive difference in the treatment program? The patient has hypertension and the dyslipidemia characteristic of insulin resistance, and there are well-established algorithms for treating both abnormalities. Parenthetically, approximately one-third of the apparently healthy, insulin-resistant individuals have neither IFG nor IGT (15). In contrast to the WHO version of the MetS, the patient described would meet ATP III criteria for this diagnosis, even if his fasting plasma glucose concentration was only 98 mg/dL. On the other hand, this would not be the case if his plasma TG concentration were 145 mg/dL, rather than 155 mg/dL. Is there any doubt that a hypertensive patient with a low HDL-C concentration is at increased CVD risk? Would use of ATP III criteria lead to a different treatment approach to a patient with hypertension and a low HDL-C concentration if his fasting plasma glucose and TG concentrations were 98 mg/dL and 145 mg/dL, compared with 103 mg/dL and 155 mg/dL, respectively? If not, what is the clinical utility of making, or not making, this diagnosis? Finally, since this patient did not meet the essential criterion of abdominal obesity (his WC was only 93 cm), he does not have the MetS by the IDF definition, and this is true despite the presence of hypertension (145/95 mmHg), a high TG (155 mg/dL) and low HDL-C (30 mg/dL) concentration, and IFG (105 mg/dL). Clearly, this prototypic patient is at considerable increased CVD risk, despite not having the IDF version of the MetS. Would his clinical status be any different if he now satisfied the essential criterion of abdominal obesity (WC
95 cm)? Has the CVD risk, or the appropriate therapeutic approach, changed because the abdominal girth has increased by 2 cm? The values of WC needed to diagnose the MetS shown in Table 3 are specific for ‘‘Europids,’’ and the IDF indicates that these values should vary with ethnicity. The requirement of ethnicspecific criteria for abdominal obesity raises additional questions concerning the clinical utility of the IDF criteria. As defined by the IDF, a normotensive man of Japanese ancestry will have the MetS

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if he has a WC of 88 cm and moderately increased fasting plasma concentrations of glucose (105 mg/dL) and TG (155 mg/dL). In contrast, a Chinese man with the same WC will not have the IDF version of the MetS, even if he is hypertensive (145/95 mmHg) and frankly diabetic (fasting plasma glucose concentration ¼ 150 mg/ dL) and has a plasma TG concentration of 220 mg/dL. Is the Chinese patient at decreased CVD risk because he does not qualify for the IDF version of the MetS? These examples discussed above were chosen purposefully to question the clinical utility of making a diagnosis of the MetS, irrespective of which organization’s definition is used. The point of this exercise is to emphasize that the specific components of the various definitions of the MetS are CVD risk factors and should be recognized as such, but there is not a great deal to be gained by deciding if any particular combination of them merits diagnosis of the MetS. This point of view is consistent with recent findings based on the Framingham data base (142), in which the authors used the ATP III criteria for the MetS, and concluded, ‘‘clusters of 3 traits do not substantially increase risk for outcomes over risk associated with clusters of 2 traits.’’ They further pointed out that these findings are ‘‘consistent with the hypothesis that even a modest degree of risk clustering reflects a global underlying insulin-resistant pathophysiology, and individual risk factors may contribute marginally to risk associated with the insulin-resistant phenotype.’’ CONCLUSION The ability of insulin to simulate glucose disposal varies six- to eightfold in apparently healthy individuals (91). Approximately one-third of the most insulin resistant of these individuals is at greatly increased risk to develop a number of abnormalities and clinical syndromes, only one of which is CVD (83,84). Approximately 50% of this extraordinary degree of variability in insulin action can be attributed to differences in degree of adiposity (25%) and level of physical fitness (25%), with the remaining 50% most likely related to genetic differences (85). Despite being composed of almost identical components, the three versions of the MetS

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differ profoundly in the philosophical basis underlying their approach to making a positive diagnosis. A number of issues have been raised in this review that questions both the clinical and the pedagogical utility of classifying an individual as having the MetS. In this context, it seems to me most reasonable to forget about making a clinical diagnosis of the MetS, irrespective of which version seems most appealing, and adhere to the following clinical advice from the joint report of the American Diabetes Association and the European Association for the Study of Diabetes (7). l

l

l

‘‘Providers should avoid labeling patients with the term metabolic syndrome’’ ‘‘Adults with any major CVD risk factor should be evaluated for the presence of other CVD risk factors’’ ‘‘All CVD risk factors should be individually and aggressively treated’’

The achievement of these goals will end the (i) need to make a diagnosis of the MetS, (ii) controversy over the best definition of the Mets, and (iii) confusion about the clinical approach to patients who, although they are at increased CVD risk, do not qualify for a diagnosis of the MetS. REFERENCES 1. Alberti KG, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet Med 1998; 15:539–553. 2. Executive Summary of Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). JAMA 2001; 285:2486–2497. 3. The IDF consensus worldwide definition of the metabolic syndrome. Part 1: Worldwide definition for use in clinical practice. Available at: www.idf.org. 4. Greenland P. Critical questions about the metabolic syndrome. Circulation 2005; 112; 3675–3676.

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2 Insulin Resistance in Schizophrenia, Major Depression, and Bipolar Disorder ¨ DER, and RUTH O’HARA STEVEN E. LINDLEY, CARMEN M. SCHRO Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California, U.S.A.

SERGIO FAZIO Division of Cardiovascular Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, U.S.A.

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INTRODUCTION The development of insulin resistance and its corollary of metabolic derangements is a common early event in the course of schizophrenia and chronic mood disorders. However, a broad spectrum of factors contributes to the association between insulin resistance and mental disorders, ranging from genetics to lifestyle characteristics and drug effects. Insulin resistance is implicated in many of the medical comorbidities commonly associated with these psychiatric disorders, including diabetes and atherosclerotic vascular disease. Lifestyle and behavioral characteristics of patients, such as obesity, poor diet, smoking, and lack of exercise offer an opportunity for early, targeted interventions aimed at reducing the personal and societal burden of these already devastating psychiatric illnesses. Significantly higher fasting glucose levels are observed in drug-naı¨ve schizophrenic patients, suggesting an integral role of insulin resistance in this disorder. Similarly, central insulin resistance has been proposed as a possible contributor to the pathophysiology of depression. Additionally, many atypical antipsychotic medications contribute to the development of obesity and insulin resistance. Finally, the hypothesis has been put forward that insulin resistance shares genetic risk factors with schizophrenia and mood disorders. Pathophysiological mechanisms, including stress-induced activation of the hypothalamic-pituitary-adrenal (HPA) axis also are hypothesized to contribute to insulin resistance in these psychiatric disorders. This chapter will review the current knowledge on insulin resistance in schizophrenia, major depression, and bipolar disorder and discuss the treatment options specific to these disorders. SCHIZOPHRENIA Epidemiology and Economics Individuals suffering from schizophrenia have a 20% reduced life expectancy compared to the general population, two-thirds of which is from cardiovascular disease (1). Many factors contribute to the elevated morbidity and mortality from cardiovascular

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disease, including higher rates of obesity, insulin resistance, the metabolic syndrome, and diabetes. Although the prevalence of diabetes in patients with schizophrenia has been recognized to be high for quite some time, in the last decade there has been an expanded awareness of the seriousness of insulin resistance as a health issue. This has been driven, in part, by the growth in the use of atypical antipsychotic medications and the epidemic of obesity, the metabolic syndrome, and diabetes in the general population. This increased concern has spawned research into the epidemiology, etiology, and treatment of insulin resistance and the metabolic syndrome in patients with schizophrenia. The extent of the problem of insulin resistance in patients with schizophrenia has become more apparent in recent years. In 2005, Sarri and colleagues published findings compiled from over 5000 subjects in the 1966 Finnish Birth Cohort study (2). In their analysis, 19% of individuals diagnosed with schizophrenia met the criteria for the metabolic syndrome by their mid 30s compared to just 6% of those never hospitalized for psychiatric conditions (3.7fold increased risk after controlling for gender). In a Swedish cohort of 269 patients with schizophrenia, the prevalence of the metabolic syndrome was 35% among all subjects studied and 48% among patients taking clozapine. Those with the metabolic syndrome had higher fasting plasma insulin levels, consistent with insulin resistance (3). In the United States, the recently completed Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) showed that 41% of the 1460 participants (and 52% of female participants) with schizophrenia met the criteria for the metabolic syndrome at baseline (4). Comparing the CATIE data to that from controls in the Third National Health and Nutrition Examination Survey found that males were 138%, and females 251%, more likely to have the metabolic syndrome than the comparison group. The presence of the metabolic syndrome in CATIE participants was highly associated with poor self-rating of physical health and an increased somatic preoccupation (5). These recent findings clearly demonstrate a significantly elevated rate of the metabolic syndrome in patients with schizophrenia. The accepted consensus is that the prevalence of insulin resistance in patients with schizophrenia has risen in recent years, but this has not been determined definitively. Data on

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diabetes demonstrate that the national epidemic extends to patients with schizophrenia. Specifically, diabetes in inpatient facilities operated by the New York State Office of Mental Health increased from a rate of 6.9% in 1997 to 14.5% in 2004 (6). The costs of insulin resistance and diabetes in patients with schizophrenia are difficult to estimate, but an analysis of data from the Medical Expenditure Panel Survey estimated that diabetes adds 77% and hypertension 46% more to the medical costs of treating patients with schizophrenia (7). Etiologies Despite the increased attention given to insulin resistance in schizophrenia, it has been very difficult to separate out the relative contribution of all the possible factors. There is no doubt that antipsychotics and other psychotropic medications contribute to the problem, but it seems clear from the available data that other factors are also involved and may be of preponderant value. Indeed, prior to the development of antipsychotic medications, there were case reports and natural history cohort studies suggesting that impaired insulin sensitivity and type 2 diabetes occurred at a higher frequency in schizophrenia. These included observations of the impaired effects of insulin on blood glucose concentrations in patients receiving insulin shock therapy (8). As reviewed below, factors contributing to insulin resistance and diabetes likely include: (1) pharmacological treatments for this disorder; (2) the biology of schizophrenia; (3) lifestyle characteristics of patients with schizophrenia; and (4) the exposure to stress that characterizes the disorder. Pharmacological Treatment Despite their overall improved side effect profile, it has been increasingly apparent that the atypical antipsychotics have the potential to cause weight gain, insulin resistance, disturbances of lipid metabolism, impaired glucose tolerance, and diabetes (9). All these changes greatly increase the risk of cardiovascular disease and other complications of insulin resistance and diabetes. The majority of the findings on the metabolic side effects of atypical antipsychotics relate to weight gain in patients on

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clozapine, olanzapine, and risperidone, given the large number of patients exposed to these medications and the relative ease of obtaining body weight measurements. With regard to weight gain, a study reported that treatment with clozapine for 16 weeks caused an 8.9% increase in body weight, with 67% of patients experiencing moderate-to-marked gains (10). Longer treatment is associated with an even greater incidence of weight gain; the cumulative incidence of substantial weight gain in patients treated with clozapine for up to three years exceeded 50% in one study. Clinical efficacy trials of atypical antipsychotics show weight gain in 50% to 80% of subjects (11). An expected consequence of this increase in weight would be a high risk of insulin resistance and diabetes. Patients treated with atypical antipsychotics are significantly more likely to develop insulin resistance and diabetes (12). Data from over 38,000 Veterans Health Administration patients indicated that the risk appeared to be greatest for patients under the age of 40 years (13), with an odds ratio (OR) of about 2 of having diabetes if treated with clozapine versus a typical antipsychotic. Although the subject of a great deal of debate, the relative potential for the various atypical antipsychotics to induce hyperglycemia appears to be so that clozapine ¼ olanzapine > quetiapine > risperidone > ziprasidone (14). The partial dopamine agonist, aripiprazole, appears to be associated with a very low risk for obesity and/or hyperglycemia. Recently, an examination of 36 nonobese individuals indicated that both clozapine and olanzapine were significantly more likely to be associated with insulin resistance than risperidone (15). Consistent with weight gain and insulin resistance, lipid abnormalities have also been reported with atypical antipsychotic treatment. In an investigation of 215 patients, clozapine and olanzapine were associated with significant increases in triglycerides, with over one-third of patients treated with any atypical antipsychotic having clinically meaningful triglyceride elevations (16). Whether the increase in insulin resistance and diabetes is solely the consequence of the associated weight gain is yet to be determined, but there is an indication that atypical antipsychotics may directly affect insulin secretion or actions (12). There are rare cases of new-onset diabetic ketoacidosis in patients on

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clozapine or olanzapine who were previously nondiabetic that resolved when the medication was stopped (17), but it is unclear if these idiosyncratic cases of diabetic ketoacidosis are in any way related to the more general phenomenon of insulin resistance and/or type 2 diabetes. The pharmacological mechanism(s) responsible for the metabolic abnormalities associated with atypical antipsychotics are also unclear (12,14). Atypical antipsychotics possess, to varying degrees, histaminergic H1, and adrenergic a1-receptor antagonism properties, which are known to affect appetite. Their affinities at 5-HT2c-receptors may be involved, as serotonin also regulates food intake. Baptista and colleagues (14) compared antipsychotics’ affinities for receptors associated with appetite control and found them to correlate with the propensity of the drug to cause body weight gain. In addition to antipsychotics, patients are also exposed to other psychotropic agents with their own associated risks of weight gain and insulin resistance including antidepressants, lithium, and third generation anticonvulsants. The risks of these agents are reviewed in the sections on Major Depression and Bipolar Disorder. Biology of Schizophrenia Although based on limited observational data, as mentioned earlier, insulin resistance and diabetes frequently occur in schizophrenia prior to the use of antipsychotic medications (8). Recent attempts to assess the extent of nonpharmacological factors have included investigations in first psychotic break patients who have not been exposed to antipsychotics. Ryan and coworkers (2003) (18) found that 4 of 26 first-episode, drug-naı¨ve schizophrenia patients had fasting plasma glucose concentrations in the impaired range (110 mg/mL and  125 mg/dL) compared to none of the age- and sex-matched healthy controls. Fasting insulin concentrations were also significantly higher in the patients. In contrast to this finding, a comparison of 50 younger antipsychotic-naı¨ve patients, 50 antipsychotic-free patients, and 50 healthy control subjects found that the antipsychotic-free patients had significantly higher insulin-resistance (as estimated

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by the homeostasis model assessment or HOMA) compared with the other two groups, covarying for age, body mass index (BMI), family history, and sex. There was no increased rate of insulin resistance in the antipsychotic-naı¨ve subjects (19). A limitation of this finding was the significant differences in BMI and ages among the groups at baseline. Further investigations of antipsychoticnaı¨ve subjects are needed to establish this issue, although the relative young age of first-episode schizophrenia subjects is a significant confounding factor in determining the innate component of insulin resistance in schizophrenia. The risk for developing either insulin resistance or schizophrenia is in part genetically determined, and the genetic risk for these two conditions could be linked. Consistent with a genetic association is the observation of higher than expected rates of diabetes in family members of patients with schizophrenia. Specifically, 31% of patients with schizophrenia had a positive family history (parent or grandparent) for diabetes (20). Lifestyle Characteristics Weight As the presence of obesity is strongly correlated with insulin resistance, increased obesity could be a factor in insulin resistance in schizophrenia. Obesity in patients with schizophrenia in the United States ranges 40% to 60% versus 20% to 30% of the general population (reviewed in Wirshing 2004)(16). Patients with schizophrenia generally have a significantly higher BMI than controls (21), although some studies have suggested a higher prevalence of both underweight and overweight groups. A recent investigation of 169 randomly selected psychiatric outpatients (diagnoses equally divided among schizophrenia, schizoaffective disorder, major depression, and bipolar disorder) found that the prevalence of BMI in the obese range (>30) was 50% and 41% among female and male patients, respectively, compared to 27% and 20% in a race- and age-matched comparison group (22). The distribution of fat has also been demonstrated to be altered in schizophrenia. In the CATIE trial, for example, 73% of the female subjects and 37% of the male subjects met the waist circumference criterion for the metabolic syndrome

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at baseline (4). In an investigation using CT scanning of fat distribution, visceral fat distribution in 15 subjects with schizophrenia compared to 15 age- and sex-matched controls revealed those with schizophrenia had three times as much intraabdominal fat than normal controls but did not differ in total body fat or subcutaneous fat (23). Approximately one-half of these subjects investigated were drug naı¨ve, suggesting the differences in fat distribution are not the result of antipsychotic use. Diet The diet of patients with schizophrenia has been reported to be unhealthy but not necessarily of higher calorie content. Brown and coworkers conducted semi-structured interviews of 102 middle-aged patients with schizophrenia and found patients ate a diet higher in fat and lower in fiber than the general English population (24). In a survey of the diets of patients with schizophrenia in Scotland, patients had diets lower in fruits and vegetables than the general population (25). An investigation of 146 patients in the United States demonstrated a diet higher in saturated fats compared to controls (26). The degree to which these diets are influenced by antipsychotic medications use is uncertain, although the study by Ryan and coworkers in drug-naı¨ve patients, indicated patients with schizophrenia have diets higher in saturated fat than their matched controls (18). Exercise and Resting Energy Expenditure It is well known that patients with schizophrenia have a sedentary lifestyle. For example, in the survey conducted by Brown and colleagues, in addition to the poor diets described above, the patients also reported less physical exercise than controls (24). Some dietary assessments have also hinted at a decrease in energy expenditure in schizophrenia. For example, a recent investigation of the dietary intake of 88 patients with schizophrenia reported BMI as significantly higher but caloric intake as significantly lower in subjects with schizophrenia compared to controls (27). In addition to reduced physical exercise, resting energy expenditure (REE) may be lower. An investigation using a doubly labeled water technique determined that total energy expenditure was significantly lower in patients taking clozapine

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(28). REE measured using indirect calorimetry was also lower in 30 schizophrenia patients compared to healthy controls (29). Some of this decreased energy expenditure has been attributed to the sedating effects of antipsychotic medications. In a rodent model, some of the weight gain due to atypical antipsychotics was mediated by reduced gross motor activity as well as by increased food intake and enhanced feeding efficiency (30). But studies in nonmedicated patients have suggested innate reduced energy expenditure associated with schizophrenia. Nilsson and colleagues found lower REE in a subgroup of 11 nonmedicated patients (11). Likewise, Gothelf and colleagues (31) reported low physical activity in 10 adolescent males before treatment with olanzapine. In the Gothelf study, caloric intake and BMI increased after four weeks of treatment with olanzapine, but there was no change in daily energy expenditure (31). Exposure to Stress Glucocorticoids decrease glucose utilization, increase hepatic glucose production (gluconeogenesis), impair insulin secretion from pancreatic b cells, and regulate insulin-responsive glucose transport in adipocytes among other actions to mobilize glucose (32). Prolonged exposure to corticosteroids induces insulin resistance and diabetes (33). Cushing’s disease, due to adrenocorticotropic hormone (ACTH)–secreting pituitary adenomas, and Cushing’s syndrome, due to excessive glucocorticoid exposure from any source, demonstrate the potential for glucocorticoids to induce insulin resistance and diabetes (34). Over 50% of patients with drug-induced Cushing’s syndrome from prolonged glucocorticoid agonist treatment demonstrate glucose abnormalities, which in many cases does not remit when the therapy is stopped (35). Cortisol secretion from the adrenal gland is under the control of the central nervous system. Corticotrophin-releasing hormone released from the hypothalamus stimulates ACTH release from the pituitary, which stimulates cortisol release from the adrenal. The normal circadian rhythm of the HPA axis

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has a physiologically significant role in regulating glucose metabolism (36). During exposure to psychological or physical stress, cortisol secretion is increased within minutes by activation of the HPA axis. This increase in cortisol during stress plays a vital role in responding to prolonged stressful situations. In individuals with adrenal insufficiency (either through disease or adrenal suppression by previous exogenous glucocorticoid administration), the absence of a cortisol response can result in death. On the other hand, prolonged elevations in cortisol that can occur with depression have been hypothesized to be involved in the development of obesity, insulin resistance, the metabolic syndrome, and diabetes (37). Some findings support major life events as being a factor in the acute onset of type 2 diabetes (38). As is the case with major depression, repeated exposure to stress-induced elevations in cortisol may contribute to insulin resistance in patients with schizophrenia (35). Shiloah and coworkers interpreted findings in acutely hospitalized patients to indicate that the stress accompanying a psychotic episode affects insulin sensitivity (39). A problem with the theory that elevated levels of cortisol contribute to insulin resistance in schizophrenia is that consistent, basal-activity abnormalities in HPA axis activity have not been found (40). Chronic schizophrenia is associated with numerous severely stressful events, including psychotic episodes, chaotic living environments, financial and other social stressors, and high rates of depression and suicide attempts (41). Drug-naı¨ve patients with schizophrenia have been reported to have higher area under the curve plasma cortisol concentrations during an acute psychotic episode compared to age- and sexmatched healthy controls (42). Thakore and colleagues reported elevated cortisol levels in drug-naı¨ve and drug-free patients with schizophrenia (23). In addition to some reports of cortisol elevations, increased pituitary volumes have been reported in schizophrenia and psychosis, consistent with HPA axis activation. For example, antipsychotic-free, first–psychotic break patients (12/18 antipsychotic naı¨ve) had a 15% larger pituitary volume on MRI than age- and gender-matched healthy controls (43).

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Antipsychotic-induced changes in HPA axis regulation have also been implicated as a potential mechanism through which antipsychotics induce metabolic abnormalities (14), although it is not clear how cortisol is affected by chronic administration of atypical antipsychotics. Increases, decreases, and no change in cortisol concentrations have been reported after patients were treated with atypical antipsychotics, and no one has correlated changes in cortisol with their propensity to induce metabolic abnormalities. It is hypothesized that the stress-reducing properties of atypical antipsychotics may actually decrease cortisol levels in many patients, thereby complicating the interpretation of the observed net effects of atypical antipsychotics on cortisol (14). Prevention and Treatment Prevention measures in part rely on a heightened vigilance for risk factors prior to treatment as well as on close monitoring during treatment for the possibility of the development of insulin resistance. Known risk factors, e.g., obesity, metabolic syndrome, or family history of diabetes should be considered when choosing a particular antipsychotic and encouraging preventive behavioral interventions. Recent findings from the CATIE trial demonstrating low rates of treatment for hypertension, dyslipidemia, and diabetes among patients with schizophrenia emphasize the need for enhanced monitoring (44). Proposed treatment interventions include behavioral programs aimed at improving diet and physical exercise, augmenting pharmacological treatments, and switching antipsychotics once weight gain or metabolic changes occur. Numerous pharmacological interventions to combat metabolic changes have been tried with mixed success. No treatment is in wide use clinically. In a double blind, randomized clinical trial (RCT), treatment with fluoxetine was not only ineffective in reducing weight gain associated with olanzapine but resulted in significantly less improvement in psychiatric symptoms in patients with schizophrenia (45). The use of amantadine has also been proposed (46), but its dopamine receptor occupancy seems likely to exacerbate the symptoms of schizophrenia in many patients. An RCT with topiramate demonstrated efficacy in reducing weight in

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obese schizophrenic subjects, but no additive effect of psychiatric symptoms was noted (47). An RCT investigation with metformin showed no differences in improvements in insulin resistance compared to placebo (48). Switching strategies from one atypical antipsychotic to another, e.g., olanzapine to risperidone, have shown some success in improving insulin resistance (5,49). The data on behavioral interventions are still limited but there is some controlled data indicating the success of behavioral programs on weight reduction but not on insulin resistance (50,51). Further controlled investigations of interventions specifically designed for patients with schizophrenia and insulin resistance are needed.

MOOD DISORDERS Major Depression Epidemiology and Economics As is the case for schizophrenia, depression is also associated with an increased risk of insulin resistance-related co-morbidities (52,53). Because depression is such a common disorder, and has been ranked by the World Bank as the number one contributor to the global burden of disease in adults aged 19 to 45 years in the developed world (54), any contribution of depression to medical disease is very significant. The economic burden of mood disorders alone is substantial, with estimates running as high as $51.5 billion a year in lost productivity; and an additional $26.1 billion in direct treatment costs (including inpatient care and antidepressant therapy) (55,56). As a large Canadian survey on 130,880 adults recently demonstrated, the combination of major depression with diabetes or heart disease was associated with a significantly increased likelihood of health care utilization and functional disability compared to the presence of diabetes or heart disease alone, even after adjustment for age, sex, education, income, alcohol dependence, and other chronic physical conditions (57). Indeed, depression is similarly associated with an overall increase of approximately 50% in medical costs of chronic medical illness, specifically diabetes and cardiovascular disease, even after controlling for severity of physical illness (58).

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Ciechanowski and coworkers (59) found that diabetic patients with higher levels of depressive symptoms had significantly higher expenses than patients without depression. Similar results were reported by Egede and coworkers (60) who observed that depressed patients with diabetes had health care costs that were 4.5 times higher than diabetics who were not depressed. As a result, the presence of major depression has been recognized to significantly increase disease burden in diabetes and heart disease. The data on the increased prevalence of insulin resistance in patients suffering from depression is less consistent than in schizophrenia. Investigations of depressed patients compared to controls or other psychiatric patient groups have found a higher prevalence of low glucose utilization and overall insulin resistance (52,61). Insulin resistance has been shown to normalize following recovery from depression but not in patients still depressed but on antidepressant treatment (62), suggesting that posttreatment improvement in insulin resistance is not the result of antidepressant treatment alone. Large population-based studies have presented conflicting findings on the association between depression or depressive symptoms and insulin resistance. In over 4000 older women (age range 60–79 years) participating in the British Women’s Heart and Health Study, the prevalence of depression linearly decreased with increasing insulin resistance (63), but was slightly increased in the subgroup of women suffering from diabetes. No association between baseline insulin resistance and depressive symptoms was observed in 2512 middle-aged men in the prospective Caerphilly Cohort study at any of four assessments that took place over a five-year period (64). In contrast, in a cross-sectional investigation on a younger male population of 2609 subjects in the Northern Finland 1966 Birth Cohort study (65), insulin resistance— as measured by quantitative insulin sensitivity check index (QUICKI) values—increased in parallel with severity of depressive symptoms, after adjusting for confounders such as socioeconomic status, BMI, smoking, alcohol consumption, serum C-Reactive protein (CRP), and cholesterol levels. Furthermore, insulin resistance was positively associated with current severe depressive symptoms, with ORs increasing to 2.18 and 3.15 in the

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highest quartile and the highest decile of QUICKI values, respectively. This was consistent with a previous report of an older study population (aged 61–63 years) that showed a positive association between insulin resistance and severity of depressive symptoms (66). The discrepancy among these population-based studies could be due to differences in the depression assessment method (e.g., self-report or structured interview), genetic background of the study population, differences in environmental interactions, and gender among other factors. Another approach to examining the relationship between insulin resistance and depression is to determine the prevalence of depression in populations of patients with insulin resistance. In adults with type 2 diabetes, the rates of depression are higher (67,68), with investigations observing an approximately twofold increase of depression in diabetic patients compared to controls [OR ¼ 2.0, 95% confidence interval (CI) 1.8–2.2]. Reports of the prevalence of depression in type 2 diabetes range from an average of 10.9% to 32.9%, depending on the instrument used for the assessment of depressive symptomatology. Furthermore, in a study on polycystic ovary syndrome (PCOS), characterized by hyperandrogenism and insulin resistance, about 50% of the participating women were depressed (69). However, there is currently a lack of prospective studies on incidence of major depression among a group of patients suffering from insulin resistance alone, independent of diabetes or other disorders. Gender, in particular female hormone status and oral contraception use, may be an important factor when considering the relationship between depression and insulin resistance (61,69,70). As mentioned, 50% of women with PCOS showed signs of major depression (69). Furthermore, while observing an overall higher risk for depression in type 2 diabetics independent of gender, the prevalence of concomitant depression was significantly higher in diabetic women (28%) compared to men (18%) (67). Another study reported prevalence of depression to be twice as high among diabetic women compared to diabetic men (68). It is not clear if these findings mirror the higher prevalence of depression in women compared to men, or if they reflect a specific interaction between gender, insulin resistance, and depression.

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Etiologies There is evidence to suggest a bidirectional relationship between depression and the metabolic syndrome (61,65), but there is a lack of prospective longitudinal studies to definitively address causality. In this section, we will summarize important findings in the current literature regarding the complex relationship between depression and insulin resistance. Pharmacological Treatment Antidepressants vary in their impact on insulin because of differences in their mechanisms of action. Certain antidepressants affect body weight, inducing either gains or losses. Tricyclic antidepressants increase appetite and carbohydrate craving as well as causing dry mouth and thirst, which can increase caloric liquid intake. All of this may result in significant weight gain, especially in the acute treatment phase (71). For example, Kazes and colleagues (72) reported consistent weight gain among different tricyclics (amitriptyline, imipramine, clomipramine, and maprotiline) over a four to six months observation period, resulting in a gain of more than 5 kg in 37% of patients and of more than 10 kg in 17% of patients. In contrast to tricyclics, most selective serotonin reuptake inhibitors (SSRIs) usually do not affect body weight, with the possible exception of paroxetine (73). Mirtazepine is in a unique position among the more recently developed antidepressant drugs as it increases weight relatively consistently. Increased appetite with food cravings has been reported in 11% to 24% of those treated with mirtazapine, with substantial weight gain occurs in about 10% of these patients (71). In contrast, venlafaxine does not increase weight and some groups have even reported slight weight loss (74). Bupropion is also thought to induce weight loss (75). Ramasubbu proposed that in addition to weight gain (61), some antidepressants may directly lead to hyperglycemia and decrease in insulin secretion through catecholamine stimulation of gluconeogenesis and inhibition of glucose-stimulated insulin release. In addition, many antidepressants, including tricyclics, have been shown to induce elevated serum prolactin levels under chronic conditions (76), which may in turn induce insulin resistance (77). In contrast, hyposerotonergic neuronal function may

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lead to insulin resistance (78), and, therefore, treatment for depression with SSRIs may have a positive impact on insulin sensitivity (62) and improve peripheral glucose utilization (79). In agreement with this hypothesis, fluoxetine has been shown to decrease insulin resistance by 20% in obese insulin-resistant subjects irrespective of its weight-reducing effect (80). Similarly, sertraline was found effective in reducing insulin requirements in diabetic patients with depression (81). However, citalopram did not lead to any changes in insulin sensitivity despite improvement of depressive symptoms in euglycemic, depressed women compared to nondepressed controls (82). Biology of Depression As has been hypothesized for schizophrenia, a shared genetic risk factor(s) for both depression and insulin resistance may exist, but there is limited data to investigate this issue. One twin study examining genetic risks for depression and cardiovascular disease jointly in 6903 male twins from the Vietnam Era Twin Registry demonstrated a genetic correlation between depressive symptoms and a composite measure of cardiovascular disease. Unfortunately, this study did not include data on metabolic measurements (83). Specific genetic polymorphisms related to insulin resistance have been examined. The association of a polymorphism in the gene coding for the rate-limiting enzyme in the synthesis of catecholamines, tyrosine hydroxylase, with insulin resistance and depression was examined in Japanese subjects (84). Allele 7 of this polymorphism [tyrosine hydroxylase (TH) HUMTH01], was associated with both depression and reduced insulin sensitivity. The His allele of the apolipoprotein A-IV gene (APOA-IV) Gln/His polymorphism was shown to be associated with a higher frequency of depression, obesity, and cerebrovascular disease, but not with diabetes in 383 older Brazilians (85). Ejchel and coworkers observed a threefold increase in the risk for depression and cerebrovascular disease, and a twofold increase in the risk for obesity in His allele carriers. Given that the APOA-IV polymorphism has been implicated in lipids modulation and dietary fat absorption, the authors have suggested that impaired fatty acid metabolism, particularly in the brain, may be responsible for

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the higher susceptibility for these disorders in the APOA-IV His isoform. Among possible candidate genes for depression, the serotonin transporter gene may impact the relationship between depression and insulin resistance. The modulation of central serotonergic neuronal activity is implicated in the pathophysiology of depression and may also influence insulin resistance by directly affecting hypothalamic appetite regulation centers and indirectly through effects on the HPA axis (79). HPA axisassociated polymorphisms may influence susceptibility to insulin resistance and depression as well. Van Rossum and coworkers (86) reported that a polymorphism in the glucocorticoid receptor gene was associated with decreased sensitivity to glucocorticoids and decreased insulin and cholesterol levels in 202 healthy, elderly subjects. In nondiabetic subjects, a single nucleotide polymorphism on exon 2 of the glucocorticoid receptor gene was associated with increased sensitivity to glucocorticoids and obesity (87). Effects of insulin on the central nervous system A component of the bidirectional relationship between depression and insulin resistance may involve the central effects of insulin. Several authors have suggested a ‘‘cerebral diabetes paradigm’’ related to unipolar depression (61,88,89) whereby insulin resistance in the central nervous system may lead to and/or maintain a unipolar depressive disorder. An initial suggestion for a central role of insulin in depression was the clinical use of insulin coma therapy for the treatment of depressive disorders (90). Additional clinical observations include suggestions that depressive symptoms are secondary to the metabolic syndrome (91,92), as well as a report of fluctuations of depressive symptoms in parallel with hyperglycemia (93). Also, positron emission tomography (PET) studies have demonstrated reduced glucose utilization in limbic structures in depressed patients (94) but conflicting results have been reported as to whether this hypometabolism is reversed with a return to euthymic state (95,96). Basic investigations have demonstrated a wide distribution of insulin receptors in the brain, and brain hyperinsulinemia may lead to increased glucose utilization, stimulate the autonomous nervous system, and affect food intake

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by direct action on the hypothalamus (97–99). The complex effects of insulin on brain glucose utilization, catecholamine release and reuptake, regional cerebral blood flow, neurotrophy and the HPA axis (100,101), all suggest central insulin resistance as a potential mechanism underlying neuronal changes in depression (102,103). Lifestyle and Behavioral Factors Weight and diet There may also be a bidirectional interaction between depression and obesity. Being overweight or obese has recently been reported as a risk factor for the development of depression in young adults (104). Data from the Finnish Birth Cohort indicated obesity in adolescence is associated with depression in young adulthood (105). Other investigators have described the potentially shared environmental and genetic factors underlying the interrelationship between obesity and depression (106). In turn, depressed patients exhibit reduced physical activity (107). Some investigators have provided evidence for a significant relationship between abnormalities of glucose regulation, weight gain, and depression (108,109). However, depression has also been independently associated with insulin resistance after controlling for physical activity (110), suggesting that decreased physical activity is not the only link between depression and insulin resistance. Because of its strong insulin-sensitizing effect and stimulation of neurotrophic factor expression and neurogenesis, exercise may improve both depression and insulin resistance (111,112). Similarly, dietary restriction has demonstrated neurotrophic and neurogenic effects in animal models (113), and specific foods, such as dietary antioxidants in fruits, seeds, or roots may decrease the brain’s vulnerability to inflammation and oxidative stress, and thus have a preventive effect for mood disorders (114). Both nonpharmacological interventions, exercise, and nutrition should be integral part of the treatment approach for depression and insulin resistance (see below). Alcohol abuse Alcohol abuse is common among depressed patients (115). While regular, moderate amounts of alcohol

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consumption seem to have a positive effect on insulin sensitivity (116) and result in an overall 20% to 45% reduction in the risk of developing coronary heart disease (117), this is not true for excess alcohol intake (118). The relationship between alcohol intake and the relative risk of cardiovascular disease demonstrates a J- or U-shaped dose response, where the risk is lower with light-to-moderate alcohol consumption but high when alcohol consumption is either high or absent altogether. In participants of the Framingham Study, higher incidences of diabetes were observed in those reporting former levels of alcohol intake in excess of 120 g/day to 150 g/day (119). In the same study, excess alcohol consumption resulted in a 2.4 times higher risk of stroke in men (119). Similarly increases in risk for heart disease, hypertension, and all-cause mortality are reported elsewhere (118). As the prevalence of alcohol abuse is higher among depressed patients (115), alcohol consumption may be another parameter to be closely monitored under therapy. Stress and Cortisol. Psychological stress both contributes to and results from major depression. Psychosocial stress also exerts independent negative effects on the cardiovascular system and metabolic outcomes (120). For example, the recent INTERHEART study reported that psychosocial stress of diverse origins significantly increased the risk of acute myocardial infarction among participants from 52 countries (121). The works of other investigators suggest that major life events are a factor in the acute onset of type 2 diabetes (38). As described, activation of the HPA axis is a component of the physiological response to both physical and psychological stress (122), and also one of the most frequently observed biological findings in patients with depressive disorders, normalizing with recovery (123,124). Therefore, prolonged elevations in cortisol occurring with depression are likely to impact the development of obesity, insulin resistance, the metabolic syndrome, and diabetes (37,106). Hypercortisolemia, whether associated with depression or not, has been linked to insulin resistance (125) and in turn, insulin resistance has been hypothesized to result in hypercortisolemia (126). This may be a primary mechanism underlying the bidirectional relationship between depression and insulin resistance.

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Prevention and Treatment As in schizophrenia, both primary and secondary prevention of insulin resistance are of utmost importance in patients suffering from depression. The reciprocal association between insulin resistance and major depression is not yet widely known by the general public. Nor is there overall public awareness that lifestyle changes may be able to modify the risk for depression (114). Public health initiatives to decrease childhood obesity via increased physical activity may decrease insulin resistance and the incidence of depression in younger adults. Exercise programs designed to treat insulin resistance associated with depression are likely to benefit both disorders. Enhanced attention to health habits such as diet, body weight management, alcohol use, and exercise is even more important in the depressed population where these behaviors are more prevalent. In patients with treatment-resistant chronic depression, a screening for insulin resistance and related cardiovascular risk factors is very appropriate. During pharmacological treatment of depression, particular attention needs first to be given to the possible metabolic side effects of antidepressants. Several SSRIs may have a favorable effect on insulin resistance while improving depression and may therefore qualify as first-line agents in treating patients exhibiting both. Tricyclic antidepressants as well as other psychotropic drugs that may induce weight gain should be avoided in these patients. As in schizophrenia, augmentation therapy with a variety of pharmacological agents has been reported with mixed results. The treatment plan should also include appropriate drug treatments for hypertension, dyslipidemia, and hyperglycemia. Treatment progress should be closely monitored, including regular assessments of weight, blood pressure, fasting glucose, hemoglobin A1c (in diabetics), and the lipid profile (127). In strong contrast to this ideal treatment approach, a recent controlled investigation showed that diabetes patients with serious mental illness (SMI), including major depression, were less likely than diabetics without SMI to receive prescriptions for statins, angiotensin-converting enzyme inhibitors, and angiotensin-receptor–blocking agents (128). These findings may

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underline the limits of our overspecialized medical services and call for a more translational approach for research and clinical practice, focusing on depression and insulin resistance and integrating the domains of psychiatry, endocrinology, and cardiology (129). Bipolar Disorder Epidemiology Less attention has been paid to insulin resistance in bipolar disorder compared to schizophrenia or major depression. Although there are many shared features with major depression, bipolar disorder is associated with a number of unique risks for insulin resistance (130). As is the case for major depression, patients with bipolar disorder suffer from high rates of general medical disorders (131), and, in fact, the rates may even be higher than for major depression. For example, a Swedish registry-based study of 15,386 bipolar and 39,182 unipolar patients found the standardized mortality rates for natural causes was 2.1 for bipolar versus 1.6 for unipolar depression in woman and 1.9 for bipolar versus 1.5 for unipolar depression in men (132). The degree to which this increased medical morbidity is associated with insulin resistance is unknown as there have been relatively few investigations directly addressing insulin sensitivity in bipolar disorder. Studies have indicated high rates of obesity (133,134), cardiovascular disease (135), and diabetes mellitus (136,137), although it is not entirely clear how much this differs from the prevalence in the general population. Recently the prevalence rate of the metabolic syndrome in 171 patients with bipolar disorder was demonstrated to be similar to the rates in the general population (which is already high). Thirty percent of the bipolar patients met the criteria for the metabolic syndrome and 8% had a fasting blood glucose greater than 110 mg/dL (138). The investigators found a higher prevalence of obesity (45%) than reported in the general population (30.5%). In another recent investigation of 125 bipolar patients in Turkey, 32% met the criteria for the metabolic syndrome (139).

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Etiologies Pharmacological Treatment Psychotropic medications are a significant risk factor for insulin resistance in bipolar disorder. Although patients were previously treated primarily with lithium or third generation anticonvulsants, patients with bipolar disorder are increasingly being exposed to atypical antipsychotics with their associated metabolic risks. In the Turkish study cited above, the use of atypical antipsychotics was associated with a significantly higher rate of the metabolic syndrome (139). In a nested case-controlled investigation of 283 Medicaid patients with bipolar disorder compared to 1134 matched controls, the use of atypical antipsychotics risperidone, olanzapine, and quetiapine were consistently associated with an increased risk for developing diabetes after adjustment for relevant factors (140). Lithium and some of the third-generation anticonvulsants used to treat bipolar disorder are also associated with significant weight gain and valproate is associated with a documented increased prevalence of insulin resistance (141,142). Comparatively, carbamazepine, and to even a greater degree lamotrigine, has a significantly lower risk for weight gain and insulin resistance (141,143). Biology of Bipolar Disorder Nonmedication-related biological factors, unique to bipolar disorder, may also play a role. The impact of elevated levels of cortisol on insulin and insulin sensitivity may play even more of a role than in schizophrenia and major depression. Although there have been relatively fewer studies on HPA activity in bipolar disorder, there is evidence suggesting that in addition to elevations of cortisol during depression episodes, cortisol may be elevated during manic episodes as well (44,145). Therefore the burden of elevated cortisol on insulin sensitivity could be worse. It is unknown whether the genetic factors contributing to bipolar disorder also contribute to insulin resistance. Lifestyle and Behavioral Factors As is the case for schizophrenia and depression, lifestyle issues such as lack of exercise

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and poor diet may increase the risk of insulin resistance (130). A comparison of self-reports from 89 bipolar patients to those from age- and sex-matched controls revealed higher total energy intake and fewer episodes of physical activity (146). Gender Women with bipolar disorder may be at a greater risk for insulin resistance than men, in part because of the association between bipolar disease, valproate, and polycystic ovarian syndrome (PCOS). PCOS is one of the more common endocrine conditions in woman. It is characterized by chronic anovulation and high levels of androgens that are not the result of specific diseases of the hypothalamus, pituitary, adrenal, or ovaries. Symptoms of PCOS include hirsutism, acne, male pattern baldness, and male body hair distribution. In addition, PCOS is associated with hyperinsulinism and elevated rates of insulin resistance (147). In a sample of 80 reproductive age women with bipolar disorder, 65% had menstrual abnormalities with 38% developing the abnormalities during treatment, 14 out of the 15 who were treated with valproate. The length of treatment with valproate was significantly correlated with free testosterone levels and 3 of the 50 women taking valproate met the criteria for PCOS (108). In another investigation of 230 bipolar women aged 18 to 25 years, participating in the Systematic Treatment Enhancement Program for Bipolar Disorder, 10.5% (9/86) on valproate versus 1.4% (2/144) treated with other anticonvulsants or lithium developed new-onset oligoamenorreha with hyperandrogenism (148). Given the association between PCOS and insulin resistance, treatment of women with bipolar disorder may significantly increase the risk for insulin resistance in a population already at risk. Prevention and Treatment Treatment options for patients with bipolar disorder and insulin resistance are similar to those with major depression with the exception of mood stabilizer selection. As indicated, lamotrigine may be the mood stabilizer with the lowest risk for obesity and insulin resistance.

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CONCLUSIONS Over the past two decades cumulating evidence has drawn increased attention to the important role of insulin resistance in schizophrenia, major depression, and most recently, bipolar disorder. A substantial literature now attests that insulin resistance is common in these psychiatric disorders. At the very least, insulin resistance represents a significant comorbid factor exacerbating the burden of these disorders and complicating treatment. Given the association of insulin resistance to lifestyle variables such as minimal exercise and poor diet, it already serves as an excellent target for interventions and preventative strategies that may improve symptoms and prognosis in schizophrenia, major depression, and bipolar disorder. However, increased understanding of the etiology of insulin resistance in these disorders could potentially yield even more effective treatments. Recent investigations have begun to examine the involvement of insulin resistance in the pathophysiology of these disorders, considering underlying mechanisms such as HPA function and the effects of central insulin. Attempts to identify shared genetic risk factors also aim to identify overlapping pathophysiology, but this exploratory work is at a very early stage. There is a significant need for prospective, longitudinal investigations that could more fully identify the complex relationships and mediational pathways among insulin resistance, schizophrenia, and depression. Although there are still many issues to be clarified, it is clear that insulin resistance represents a significant treatable health threat to patients suffering from these psychiatric disorders. The impact of schizophrenia and major depression on psychosocial function and the confound of medication use present unique challenges in the prevention and treatment of insulin resistance, but it is vitally important that solutions be found. REFERENCES 1. Hennekens CH, Hennekens AR, Hollar D, et al. Schizophrenia and increased risks of cardiovascular disease. Am Heart J 2005; 150:1115–1121.

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110. Winokur A, Maislin G, Phillips JL, et al. Insulin resistance after oral glucose tolerance testing in patients with major depression. Am J Psychiatry 1988; 145:325–330. 111. De Filippis E, Cusi K, Ocampo G, et al. Exercise-induced improvement in vasodilatory function accompanies increased insulin sensitivity in obesity and type 2 diabetes mellitus. J Clin Endocrinol Metab 2006; 91:4903–4910. 112. Ernst C, Olson AK, Pinel JP, et al. Antidepressant effects of exercise: evidence for an adult-neurogenesis hypothesis? J Psychiatry Neurosci 2006; 31:84–92. 113. Duman RS. Neurotrophic factors and regulation of mood: role of exercise, diet and metabolism. Neurobiol Aging 2005; 26 (suppl1):88–93. 114. Hendrickx H, Mcewen BS, Ouderaa F. Metabolism, mood and cognition in aging: the importance of lifestyle and dietary intervention. Neurobiol Aging 2005; 26(suppl1):1–5. 115. Alpert JE, Fava M, Uebelacker LA, et al. Patterns of axis I comorbidity in early-onset versus late-onset major depressive disorder. Biol Psychiatry 1999; 46:202–211. 116. Flanagan DE, Moore VM, Godsland IF, et al. Alcohol consumption and insulin resistance in young adults. Eur J Clin Invest 2000; 30:297–301. 117. Rimm EB, Williams P, Fosher K, et al. Moderate alcohol intake and lower risk of coronary heart disease: meta-analysis of effects on lipids and haemostatic factors. BMJ 1999; 319:1523–1528. 118. Lucas DL, Brown RA, Wassef M, et al. Alcohol and the cardiovascular system research challenges and opportunities. J Am Coll Cardiol 2005; 45:1916–1924. 119. Djousse L, Ellison RC, Beiser A, et al. Alcohol consumption and risk of ischemic stroke: The Framingham Study. Stroke 2002; 33:907–912. 120. Bjorntorp P. Visceral fat accumulation: the missing link between psychosocial factors and cardiovascular disease? J Intern Med 1991; 230:195–201. 121. Rosengren A, Hawken S, Ounpuu S, et al. Association of psychosocial risk factors with risk of acute myocardial

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133. Mcelroy SL, Frye MA, Suppes T, et al. Correlates of overweight and obesity in 644 patients with bipolar disorder. J Clin Psychiatry 2002; 63:207–213. 134. Fagiolini A, Kupfer DJ, Houck PR, et al. Obesity as a correlate of outcome in patients with bipolar I disorder. Am J Psychiatry 2003; 160:112–117. 135. Kilbourne AM, Cornelius JR, Han X, et al. Burden of general medical conditions among individuals with bipolar disorder. Bipolar Disord 2004; 6:368–373. 136. Cassidy F, Ahearn E, Carroll BJ. Elevated frequency of diabetes mellitus in hospitalized manic-depressive patients. Am J Psychiatry 1999; 156:1417–1420. 137. Regenold WT, Thapar RK, Marano C, et al. Increased prevalence of type 2 diabetes mellitus among psychiatric inpatients with bipolar I affective and schizoaffective disorders independent of psychotropic drug use. J Affect Disord 2002; 70:19–26. 138. Fagiolini A, Frank E, Scott JA, et al. Metabolic syndrome in bipolar disorder: findings from the Bipolar Disorder Center for Pennsylvanians. Bipolar Disord 2005; 7:424–430. 139. Yumru M, Savas HA, Kurt E, et al. Atypical antipsychotics related metabolic syndrome in bipolar patients. J Affect Disord 2007; 98:247–252. 140. Guo JJ, Keck PE Jr., Corey-Lisle PK, et al. Risk of diabetes mellitus associated with atypical antipsychotic use among Medicaid patients with bipolar disorder: a nested case-control study. Pharmacotherapy 2007; 27:27–35. 141. Isojarvi JI, Rattya J, Myllyla VV, et al. Valproate, lamotrigine, and insulin-mediated risks in women with epilepsy. Ann Neurol 1998; 43:446–451. 142. Pylvanen V, Knip M, Pakarinen AJ, et al. Fasting serum insulin and lipid levels in men with epilepsy. Neurology 2003; 60:571–574. 143. Akiskal HS, Fuller MA, Hirschfeld RM, et al. Reassessing carbamazepine in the treatment of bipolar disorder: clinical implications of new data. CNS Spectr 2005; 10:(suppl1):11 (discussion 12–3, quiz 14–15).

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144. Schmider J, Lammers CH, Gotthardt U, et al. Combined dexamethasone/corticotropin-releasing hormone test in acute and remitted manic patients, in acute depression, and in normal controls: I. Biol Psychiatry 1995; 38:797–802. 145. Cervantes P, Gelber S, Kin FN, et al. Circadian secretion of cortisol in bipolar disorder. J Psychiatry Neurosci 2001; 26:411–416. 146. Elmslie JL, Mann JI, Silverstone JT, et al. Determinants of overweight and obesity in patients with bipolar disorder. J Clin Psychiatry 2001; 62:486–491(quiz 492–3). 147. Dunaif A, Thomas A. Current concepts in the polycystic ovary syndrome. Annu Rev Med 2001; 52:401–419. 148. Joffe H, Cohen LS, Suppes T, et al. Valproate is associated with new-onset oligoamenorrhea with hyperandrogenism in women with bipolar disorder. Biol Psychiatry 2006; 59:1078–1086.

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3 Insulin Resistance Syndrome and Alzheimer’s Disease KRISTOFFER RHOADS and SUZANNE CRAFT Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, Washington, U.S.A.

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INTRODUCTION Insulin function and dysfunction continue to garner multidisciplinary research attention. This focus is of particular import as conditions related to insulin dysregulation, including obesity, diabetes, cardiovascular disease, and hypertension, reach pandemic proportions. Further, other disorders that were once thought to operate independently of insulin are now being conceptualized in light of insulin’s far-reaching role. Alzheimer’s disease (AD) has recently been proposed as a kind of ‘‘type-3 diabetes,’’ a unique disorder with intertwined cognitive and neuroendocrinological deficits that progress with disease severity (1). Our work extends this line of inquiry into the realm of cognition, focused on the mechanisms by which insulin abnormalities interact with the pathogenesis of AD and other disorders of aging. Data from a wide array of studies have corroborated the memory-facilitating properties of insulin, particularly when it is administered at optimal doses with sufficient levels of available basal glucose. Effects of insulin administration on brain function and cognitive performance have been demonstrated via direct intracerebroventricular administration of insulin in rodents (2), as well as via intravenous administration in humans (3), which initiates insulin transport across the blood-brain barrier (BBB) and into the central nervous system (CNS). Intranasal insulin administration, which allows insulin-like peptides direct access to the brain independent of perturbations in peripheral glucose or insulin levels, also yields improvements in memory, particularly in patients with AD (4). These insulin-mediated memory effects may be a function of several mechanisms, given insulin’s multiplicity of roles and effects. Although insulin does not appear to affect basal rates of brain glucose uptake, it does modulate glucose utilization in CNS (5). Insulin receptors are present in hippocampus, entorhinal cortex, and frontal cortex, all of which are key brain regions involved in memory and other higher order cognitive functions (6). Insulin may also exert its effect on cognition through modulation of critical neurotransmitter levels, including acetylcholine, norepinephrine, and dopamine. We have recently demonstrated that raising peripheral insulin levels leads

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to concomitant increases in CNS norepinephrine levels, which may then modulate cognitive function (7). Insulin also affects synaptic remodeling processes thought to underlie memory formation, exerting changes in membrane potentials, neuronal physiology, and long-term potentiation (6). Conversely, physiological and cognitive problems become manifest when normal insulin function is disrupted. The insulin resistance syndrome and associated conditions such as diabetes are the most common causes of insulin dysfunction. Characterized by diminished insulin function (i.e., mediation of glucose uptake into muscle) and persistently elevated levels of peripheral insulin, the insulin resistance syndrome also causes downregulation of insulin transport into brain, ultimately rendering a brain insulin–deficient state (8). Chronic hyperinsulinemia in the periphery also induces elevations in free fatty acids (FFAs) and inflammatory cytokines (9). This effect appears to be exacerbated by physiological variables such as adiposity and obesity, as well as age and ethnicity. This syndrome occurs at disproportionately high rates in older adult populations; current estimates indicate that approximately half of all adults older than 60 years are affected. This number increases dramatically across racial and ethnic groups, with rates of diabetes among AfricanAmerican and Hispanic groups almost double those of nonHispanic Caucasians (10). Thus, the memory-facilitating effects of insulin are largely diminished, if not completely abrogated, in a substantial proportion of older adults. In several of our studies, we have used a hyperinsulinemiceuglycemic clamp to model the acute effects of hyperinsulinemia on cognition in healthy older adults and in patients with AD. This technique consists of a continuous rate intravenous infusion of insulin designed to reach a predetermined level, coupled with a simultaneous variable rate dextrose administration to maintain euglycemia. For older adults without memory impairments, memory is facilitated with low doses of insulin. Higher insulin doses were required for a subgroup of patients with AD (who are most likely to have insulin resistance syndrome) to yield similar memory facilitation (3). Higher than optimal doses attenuated memory facilitation altogether. These data, taken together, provide support for insulin’s memory-facilitating effects at optimal

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doses and shed light on the physiological and cognitive variables that modulate this process. Potentially underlying these deleterious effects on cognition, hyperinsulinemia also induces a number of undesirable physiological effects. Of particular interest is the modulation of peptides critical to the pathology of AD, specifically the b-amyloid (Ab) peptide. Ab is characterized by a tendency to form oligomeric assemblies, leading to the peptide aggregations found in the senile plaques that are a hallmark of AD pathology. These assemblies may directly and negatively affect memory function, including acute effects on long-term potentiation (11,12). Other effects may be more indirect; increases in Ab levels, even to levels that do not affect the viability of cortical neurons, also yield suppression of cyclic adenosine monophosphate response element binding protein (CREB) phosphorylation and concomitant disruption of downstream events, including the activation of brain-derived neurotrophic factor (BDNF) (13). We have shown increases in levels of the Ab peptide in cerebrospinal fluid (CSF) as a result of acute hyperinsulinemia induced through insulin infusion. These synchronous increases in Ab, compared with a control condition in which saline was infused, were observed in an age-dependent manner (Fig. 1A) (14). Of additional interest, increases in hyperinsulinemia-induced levels of Ab were associated with decreases in insulin-related memory facilitation (Fig. 1B). Recently, we have presented data revealing elevations in insulin-provoked plasma Ab levels in the context of significantly reduced insulin clearance in patients with AD compared with healthy older adults (15). Given the substantial proportion of older adults with these Ab-provoking conditions, these findings are of particular relevance in that they support the connections between insulin resistance and peripheral hyperinsulinemia, agerelated memory impairment, and the development of AD. We have also provided the first data demonstrating that excessive peripheral insulin elevation increases inflammation in the CNS. In the periphery, low doses of insulin exert antiinflammatory effects, whereas higher levels incur proinflammatory effects (16). Through our acute hyperinsulinemia infusion paradigm, we demonstrated robust increases in CSF levels of proinflammatory cytokines [interleukin (IL)-1b, IL-6, and

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Figure 1 (A) Percent CSF Ab42 increase following insulin infusion relative to saline infusion was age dependent, with older adults showing larger increases (p < 0.01). (B) Insulin-induced Ab42 changes were negatively correlated with insulin-induced memory facilitation for older (age > 70 years) adults (r ¼ 0.95, p < 0.01). Abbreviations: CSF, cerebrospinal fluid; Ab, beta-amyloid. Source: From Ref. 14.

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tumor necrosis factor a (TNF-a)] (17). Increases in CSF TNF-a, a cytokine that inhibits Ab transport from brain to periphery, were greatest for participants with the highest body mass index. Infusion-induced hyperinsulinemia was also associated with increases in F2-isoprostane, an eicosonoid produced only by neurons and glia that is considered a marker of lipid peroxidation. Moreover, observed changes in Ab were positively correlated with the degree of increase from F2-isoprostane. These results suggest that the insulin resistance syndrome increases the risk for AD via a common and important pathway of synchronous hyperinsulinemia-induced increases in Ab and inflammation. A MODEL OF PERIPHERAL HYPERINSULINEMIA, INSULIN RESISTANCE, AND AD PATHOGENESIS Research continues to support the notion that high plasma insulin levels and peripheral insulin resistance modulate cognition, Ab42, and inflammation in the CNS. From this, a model emerges describing how this metabolic profile contributes to the pathogenesis of AD. It is likely that AD is not exempt from the principle of equifinality, with a myriad of etiologies resulting in the final common expression of AD pathology. The model we have constructed is hinged on one potential etiology, and as with any model under this principle, it may not apply equally to all patients. It is, however, a parsimonious model with relevance to a segment of our population that is growing in a multitude of dimensions. The model’s fundamental conditions, peripheral hyperinsulinemia and insulin resistance, are synergistic and also subject to the principle of etiological equifinality, arising from genetical vulnerability and/or environmental factors such as diet and inactivity. They are also increasingly ubiquitous; a partial function of insulin signaling pathway complexity and a partial function of pervasive changes in diet, exercise, and other sociocultural variables. The model from which we conceptualize the role of insulin resistance in the pathogenesis of AD is comprised of the effects of chronic peripheral hyperinsulinemia and insulin resistance

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on three interlocking components: brain insulin levels, clearance of Ab42 from the brain into the periphery, and inflammation in the brain and CNS vascular endothelium. Together, these three components, as modulated by hyperinsulinemia, highlight the dynamic interplay between a constellation of events that lead to the development of AD pathophysiology and the associated clinical sequelae. The first component of our model focuses on perturbations in brain insulin levels as a function of chronic peripheral hyperinsulinemia and insulin resistance. Peripheral hyperinsulinemia and insulin resistance downregulate brain insulin uptake at the BBB, yielding long-term reduction of brain insulin levels. Decreased brain insulin levels have been observed in diet-induced insulin-resistant transgenic 2576 (Tg 2576) mice, used to generate a rodent model of AD (G. Pasinetti, personal communication), and has been modeled in vivo in diet- and glucocorticoid-induced insulin-resistant dogs (8,18). In addition, our studies have shown that compared with healthy controls, AD patients have several congruent markers of this phenomenon, including higher plasma insulin levels, lower CSF insulin levels, and reduced CSF-to-plasma insulin ratios (19). This is of particular concern as deficiencies in brain insulin may contribute to elevated Ab levels through increased intraneuronal accumulation and/or decreased clearance, given insulin’s essential roles in the release of intracellular Ab (20) and the regulation of insulin-degrading enzyme (IDE) expression, a protease critical for the efficient clearance of Ab (21). Abnormally low levels of brain insulin have also been linked to disruption of long-term potentiation and decreased neurotransmitter and energy availability in critical brain areas. Thus, instead of facilitating memory processes and Ab regulation, an insulin-deficient state would intensify the memory impairment and Ab accumulation that are hallmarks of AD. The second area of interest in our model examines changes in the clearance of Ab42 from the brain into the periphery. Elevated levels of plasma Ab42 have been found in AD patients (22), even in prodromal and early disease stages. One potential mechanism contributing to excessive accumulations of brain Ab would be an obstruction of this peripheral ‘‘sink,’’ where Ab is no longer effectively transported out of the brain. It is possible that this

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transportation becomes blocked in cases of AD, or that clearance in peripheral sites is somehow impaired. Currently, there are a number of proposed sites for Ab clearance in the periphery, the most significant of which may be the liver (23). The Ab clearance process would likely be impeded as a function of hepatic insulin resistance, which has been associated with prolonged hyperinsulinemia and glucocorticoid elevations. IDE, which plays a key role in degrading Ab, may also be implicated as part of this system, as it is highly expressed in peripheral sites such as liver, kidneys, and muscle. High insulin levels, as seen in insulin resistance, may also serve to inhibit peripheral Ab clearance because of preferential affinity of IDE for insulin. IDE activity itself is also inhibited in cases of insulin resistance, thereby exacerbating problems with Ab clearance. Taken together, these insulin-related effects in key peripheral sites like liver collude to impede Ab clearance, including uptake and degradation. The effects of peripheral hyperinsulinemia and insulin resistance on inflammation in the brain and CNS vascular endothelium constitute the third component of our model. Griffin and others have proposed a synergistic ‘‘Ab-inflammation cycle,’’ comprised of a mutually reinforcing relationship between Ab and inflammation (24). Results from our studies suggest that peripheral hyperinsulinemia and insulin resistance may fuel or perpetuate this cycle. Our data indicate that acutely elevated insulin levels have pervasive effects on inflammation in the CNS, yielding striking increases in CSF levels of TNF-a, IL-1b, IL-6, and the lipid peroxidation marker F2-isoprostane (17). Furthermore, we have shown that hyperinsulinemia-induced elevations in CSF Ab42 are directly correlated with CSF IL-6 and F2-isoprostane levels, lending support to the ‘‘Ab-inflammation cycle’’ while implicating insulin as a key player in this interaction. The effects of insulin on inflammation are likely to be potentiated by obesity. Given the role of insulin in FFA release from adipocytes and insulin-induced elevations in TNF-a, it is entirely likely that this relationship would be potentiated by obesity. To summarize our model as shown in Figure 2, peripheral FFA and CNS inflammatory agents such as TNF-a levels rise as a function of peripheral insulin resistance. Ab uptake and clearance in liver and other peripheral sites is reduced in the context

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Figure 2 Model of the effects of peripheral insulin resistance and hyperinsulinemia on CNS insulin, IDE, and Ab levels. Abbreviations: IDE, insulin-degrading enzyme; Ab, beta-amyloid; CNS, central nervous system; FFA, free fatty acid; TNFa, tumor necrosis factor a. Source: From Ref. 27.

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of this syndrome, causing a rise in peripheral Ab levels. These effects on FFAs, TNF-a, and plasma Ab are exacerbated by increased adiposity and obesity. Ab levels in brain are elevated in relation to heightened plasma levels because of disruptions in clearance or transport between brain and periphery. Brain insulin levels are also diminished as a result of chronic peripheral insulin resistance and hyperinsulinemia. This serves to inhibit intraneuronal Ab release while lowering IDE levels; intraneuronal Ab accumulation is enhanced and compounded by inflammation in brain. This constellation of events and their resulting effects induce the hallmark memory impairment and pathophysiological characteristics of AD. IMPLICATIONS FOR THERAPY On the basis of the model we have constructed from the data, cognitive function in aging may be improved by lowering peripheral insulin levels and enhancing insulin sensitivity. Nonpharmacological lifestyle interventions used to manage insulin resistance, such as changes in diet and exercise, have potent insulin-sensitizing effects and may provide real benefit in this regard. We are currently examining the effect of these ecologically valid variables and their associated interventions on memory, insulin function, inflammation, and Ab levels in adults with amnestic mild cognitive impairment, widely believed to be a prodromal phase of AD. Therapeutic relief may also be found through pharmacological treatment with insulin-sensitizing compounds, such as the thiazolidinediones (TZD). This hope is bolstered by results from a recent study examining the effects of rosiglitazone, a TZD used for type 2 diabetes mellitus, as a treatment in a rodent model of AD. The glucocorticoid-lowering actions of rosiglitazone arrested decreases in IDE mRNA and activity while reducing Ab42 levels in Tg 2576 mice (25). We have also demonstrated that six months of treatment with rosiglitazone preserved memory function for patients with AD compared with a placebo-assigned group (26). We have also provided data that suggest it is possible to ameliorate the brain insulin deficiencies resulting from chronic peripheral insulin resistance.

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Memory was enhanced for a subgroup of AD patients by intranasal administration of insulin. This procedure has unique therapeutic promise in that it raises CNS insulin without affecting peripheral levels, which is particularly important for a population with disproportionately high rates of peripheral insulin resistance and hyperinsulinemia.

SUMMARY Insulin’s role in memory and other higher cognitive functioning is varied, yet critical. Disruptions in peripheral insulin function are not only most pronounced in cases of type 2 diabetes mellitus, but also exert their effects in many nondiabetic adults with conditions such as obesity, impaired glucose tolerance, cardiovascular disease, and hypertension. These disruptions in peripheral insulin function exert deleterious effects in the CNS, yielding cognitive impairments. Obesity and aging exacerbate these effects, particularly those related to Ab regulation and inflammation, two likely coconspirators in aging-related memory impairment and AD. Our work, in light of the pandemic of hyperinsulinemia-related conditions and a rapidly aging population, portends a difficult road ahead, marked by the potential for a dramatic increase in the prevalence of AD. However, we have also provided encouraging evidence related to treatments, both conventional and novel. Further identification of the mechanisms through which insulin resistance and hyperinsulinemia promote AD pathogenesis will likely increase our store of effective strategies for preventing, delaying, or treating these related conditions.

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2. Park CR, Seeley RJ, Craft S, et al. Intracerebroventricular insulin enhances memory in a passive-avoidance task. Physiology & Behavior 2000; 68(4):509–514. 3. Craft S, Asthana S, Cook DG, et al. Insulin dose-response effects on memory and plasma amyloid precursor protein in Alzheimer’s disease: interactions with apolipoprotein E genotype. Psychoneuroendocrinology 2003; 28(6):809–822. 4. Reger MA, Watson GS, Frey WH, et al. Effects of intranasal insulin on cognition in memory-impaired older adults: modulation by APOE genotype. Neurobiol Aging 2006; 27(3):451–458. 5. Bingham EM, Hopkins D, Smith D, et al. The role of insulin in human brain glucose metabolism: an 18fluoro-deoxyglucose positron emission tomography study. Diabetes 2002; 51(12):3384–3390. 6. Craft S, Watson GS. Insulin and neurodegenerative disease: shared and specific mechanisms. Lancet Neurol 2004; 3(3):169–178. 7. Watson GS, Bernhardt T, Reger MA, et al. Insulin effects on CSF norepinephrine and cognition in Alzheimer’s disease. Neurobiol Aging. 2006; 27(1):38–41. 8. Baura GD, Foster DM, Kaiyala K, et al. Insulin transport from plasma into the central nervous system is inhibited by dexamethasone in dogs. Diabetes 1996; 45(1):86–90. 9. Eckel RH, Grundy SM, Zimmet PZ. The metabolic syndrome. Lancet 2005; 365(9468):1415–1428. 10. McNeely MJ, Boyko EJ. Diabetes-related comorbidities in Asian Americans: results of a national health survey. J Diabetes Complications. 2005; 19(2):101–106. 11. Wang HW, Pasternak JF, Kuo H, et al. Soluble oligomers of beta amyloid (1-42) inhibit long-term potentiation but not long-term depression in rat dentate gyrus. Brain Res 2002; 924(2):133–140. 12. Westerman MA, Cooper-Blacketer D, Mariash A, et al. The relationship between Abeta and memory in the Tg2576 mouse model of Alzheimer’s disease. J Neurosci 2002; 22(5):1858–1867. 13. Tong L, Thornton PL, Balazs R, et al. Beta-amyloid-(1-42) impairs activity-dependent cAMP-response element-binding protein signaling in neurons at concentrations in which cell survival is not compromised. J Biol Chem 2001; 276(20):17301–17306.

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14. Watson GS, Peskind ER, Asthana S, et al. Insulin increases CSF Abeta 42 levels in normal older adults. Neurology 2003; 60(12): 1899–1903. 15. Kulstad JJ, Green PS, Cook DG, et al. Differential modulation of plasma beta-amyloid by insulin in patients with Alzheimer disease. Neurology 2006 May 23; 66(10):1506–1510. 16. Dandona P, Aljada A, Mohanty P. The anti-inflammatory and potential anti-atherogenic effect of insulin: a new paradigm. Diabetologia 2002; 45(6):924–930. 17. Fishel M, Montine T, Wang Q, et al. High insulin levels provoke synchronous increases in central and peripheral inflammation and beta amyloid in normal older adults. Arch Neurol. 2005; 62(10): 1539–1544. 18. Kaiyala KJ, Prigeon RL, Kahn SE, et al. Obesity induced by a highfat diet is associated with reduced brain insulin transport in dogs. Diabetes 2000; 49(9):1525–1533. 19. Craft S, Peskind E, Schwartz MW, et al. Cerebrospinal fluid and plasma insulin levels in Alzheimer’s disease: relationship to severity of dementia and apolipoprotein E genotype. Neurology 1998; 50(1):164–168. 20. Gasparini L, Gouras GK, Wang R, et al. Stimulation of betaamyloid precursor protein trafficking by insulin reduces intraneuronal beta-amyloid and requires mitogen-activated protein kinase signaling. J Neurosci 2001; 21(8):2561–2570. 21. Zhao L, Teter B, Morihara T, et al. Insulin-degrading enzyme as a downstream target of insulin receptor signaling cascade: implications for Alzheimer’s disease intervention. J Neurosci 2004; 24(49): 11120–11126. 22. Mayeux R, Honig LS, Tang MX, et al. Plasma A[beta]40 and A[beta]42 and Alzheimer’s disease: relation to age, mortality, and risk. Neurology 2003; 61(9):1185–1190. 23. Ghiso J, Shayo M, Calero M, et al. Systemic catabolism of Alzheimer’s Abeta40 and Abeta42. J Biol Chem 2004; 279(44): 45897–45908. 24. Griffin WS, Sheng JG, Royston MC, et al. Glial-neuronal interactions in Alzheimer’s disease: the potential role of a ‘cytokine cycle’ in disease progression. Brain Pathol 1998; 8(1):65–72.

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25. Pedersen WA, McMillan PJ, Kulstad JJ, et al. Rosiglitazone attenuates learning and memory deficits in Tg2576 Alzheimer mice. Exp Neurol 2006; 199(2):265–273. 26. Watson GS, Cholerton B, Reger MA, et al. Preserved cognition during rosiglitazone treatment in early Alzheimer’s disease. Am J Geriatr Psychiatry 2005; 13(11):950–958. 27. Craft, S. Neurobiology of Aging, 2005; Suppl 1:65–9.

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4 Insulin Resistance Link Between Depressive Disorders and Alzheimer’s Disease HEATHER A. KENNA, MARGARET F. REYNOLDS, BOWEN JIANG, and NATALIE L. RASGON Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California, U.S.A.

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INTRODUCTION Several lines of evidence demonstrate an association between depressive disorders (DD) and Alzheimer’s disease (AD), both conditions that are characterized by significant cognitive dysfunction. We previously suggested that insulin resistance (IR) is a primary link between DD and AD (1,2). Aside from its other deleterious effects, it is thought that IR causes inadequate glucose metabolism in the brain, resulting in cognitive dysfunction. As we postulated earlier, inadequate glucose utilization resulting from IR underlies neuronal changes in crucial brain regions observed among patients with DD (1,2). Further, such neuronal changes in glucose utilization, if unresolved, may result in treatment-resistant DD, cumulative cognitive impairment, and eventually, neurodegeneration and facilitation of AD onset. Herein, we review the lines of evidence demonstrating the importance of IR in DD and the development and progression of AD. Clinical Evidence of IR Link Between DD and AD As discussed in the chapter by Lindley and colleagues (see chap. 2), studies over the past 30 years have demonstrated significant glucose and insulin abnormalities, such as low glucose utilization rates and hyperinsulinemia, in patients with DD (3–18). Similarly, as discussed in the preceding chapter by Rhoads and Craft (chap. 3), such insulin-related abnormalities are now increasingly observed in AD populations (19). DD itself has been shown to be a significant risk factor for AD (20–26). Personal history of depression has been associated with earlier age of AD onset, and AD patients with history of depression perform significantly worse upon neuropsychological testing when compared to AD patients without history of depression (27). For the most part, studies have reported depression to be a prodromal sign of AD. Both case-control data (28,29) and longitudinal data (30–35) show that significant depressive symptoms are among the earliest signs of cognitive decline and AD. Still others have found depression to be both a prodromal symptom and risk factor (23,25). A limitation of the

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currently available data is that some studies have used self-report of depression, while others have used clinician assessments of depression. Due to the low agreement between these two measures, some argue that the association between DD and AD is difficult to ascertain (36). Yet, in light of the cognitive dysfunction observed in DD even in young and middle-aged adults (37), further investigation of the relationship between DD and AD and other dementias is warranted. Epidemiological data suggest a relationship between depression and AD, although study designs and results have been mixed (38). Complicating the investigation of depression as a risk factor for AD is the temporal onset of depression with respect to cognitive decline and AD. Case-control and longitudinal data show an increased risk of AD with history of depression (20–26,39–42), but the majority of these studies examined presence of depression in late life (60 years of age and older). One study reported depression to be a risk factor for AD even when the depression occurred more than 25 years prior to onset of AD (39), while another study found increasing risk of AD with increasing number of depressive episodes (43). However, at least one study did not find an association between AD and depression (44). Most recently, Owenby and colleagues conducted a metaanalysis of studies on contrasting patients with and without depression who did and did not subsequently develop AD, with results suggesting that rather than being a prodrome, depression may pose a risk factor for AD, as demonstrated by the long interval between diagnosis of depression and AD (38). Clinical evidence suggests a common pathophysiological mechanism linking DD and IR, given that a history of DD significantly increases the risk of type 2 diabetes, at all ages (45,46), even after controlling for potential confounding variables such as age, sex, and number of physician visits before diagnosis (45). Several prospective observational studies offer evidence to corroborate the claim that DD and depressive symptoms are risk factors for the development of type 2 diabetes, with relative risk estimates ranging from 1.3 to 3.0 (46–54). Further evidence of the IR-DD link comes from a number of investigations showing increased prevalence of DD in patients with metabolic syndromes in which IR is a primary characteristic, such as type 2

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diabetes, (55) polycystic ovary syndrome (PCOS) (56,57), Cushing’s disease (58,59), obesity (60), cardiovascular disease (61), and cerebrovascular disease (61). In our own research, we found a 50% rate of depression in women seeking treatment for PCOS (56). Notably, depression was significantly associated with greater IR ( p ¼ 0.02) and obesity ( p ¼ 0.05) (56). In turn, type 2 diabetes itself is a significant risk factor for AD (62–64). The Honolulu-Asia Aging Study found an increased risk for AD, dementia, and vascular dementia with type 2 diabetes (62). The Rotterdam and Mayo studies found increased risk of AD with type 2 diabetes, independent of vascular dementia (63,64). A longitudinal study by Arvanitakis and colleagues also found an increased risk of AD and cognitive decline with type 2 diabetes (65). Contrary to these results, a recent study found a negative association between type 2 diabetes and AD neuropathology in postmortem study with mean age of 84 years (66). Specifically, diabetics had significantly fewer neuritic plaques and neurofibrillary tangles (NFTs) in the cerebral cortex than did nondiabetics. However, these results raise the possibility that the varied associations observed between type 2 diabetes and AD may be specific to as yet ill-defined subgroups of dementia and diabetics patients or may be more characteristic of younger AD patients than those who survive to a mean age of 84 years (66). These findings are consistent with our postulate that persistent IR may lead not only to metabolic but also to cerebrovascular changes, further compounding the deficit in neuronal function (2). In fact, AD has recently been proposed to be a kind of ‘‘type 3 diabetes’’ because of its combination of cognitive and neuroendocrine abnormalities (67). Furthermore, longitudinal studies report an association of hyperinsulinemia with greater risk of AD and decline in memory (68,69). An association between IR and cognitive deficits in nondemented older adults has also been demonstrated in several large population-based studies that included persons with type 2 diabetes among the insulin resistant subjects (70,71), and in those studies that excluded or separately analyzed nondiabetics with indicators of IR (72–74). Presence of obesity may obscure findings on potential IR links between DD and AD, given that obesity is highly correlated

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with degree of IR. One large longitudinal study found a positive correlation between obesity and AD development later in life (75), while two other large longitudinal studies found no association between obesity at midlife and AD prevalence in late life (76,77). Research has shown that even lean individuals can have IR (78), and differences in IR within lean individuals appears to be due to the differences in postprandial skeletal muscle glycogen synthesis and net hepatic triglyceride synthesis (79). Neuroimaging and Neuropsychological Evidence of IR Link Between DD and AD Many neuroimaging studies lend support for IR as a link between DD and AD. Similar structural and functional abnormalities describing the same atrophy and dysfunction are observed in patients with DD, as well as in patients with AD and those considered at risk for AD. For example, research utilizing positron emission tomography (PET) data has shown decreased glucose metabolism in the cholinergic basal forebrain complex, including the limbic system (i.e., hippocampus, cingulate gyrus, and temporal regions) in both DD (80–89) and AD (90,91). In patients with DD, some of these abnormalities persist even after symptom remission (85,87–89). Interestingly, one study reported increased concentrations of sorbitol in a postmortem analysis of the parietal lobe in patients with unipolar depression, further suggesting disturbance of cerebral glucose metabolism in patients with DD (92). Deficits in glucose utilization are correlated with cognitive deficits in AD (93). While cognitive deficits in depression have not been investigated with respect to glucose utilization, one study reported a significant relationship between depressionassociated cognitive deficits and reduced cerebral blood flow upon PET examination (94). Interestingly, a number of abnormalities in glucose utilization globally and in the hippocampus, cingulate gyrus, and selective temporal and subcortical regions are observed in persons considered at risk for AD, as deemed by family history of AD or apoliprotein E (APOEe) genotype (95,96). Magnetic resonance imaging (MRI) data from patients with AD show extensive atrophy in numerous brain regions, most notably the hippocampus and amygdala (97). Volume reductions

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in these regions are also observed in persons at risk for AD (98–100). Similarly, patients with DD show these same volume reductions (101–105). Of note are findings by Caetano and colleagues that hippocampal and amygdala volumes were smaller in current compared to remitted depression patients, which were in turn smaller than healthy controls (101), suggesting that neurogeneration may occur with remission from depression. Hickie and colleagues reported reduced hippocampal volumes in patients with both early- and late-onset depression (106). This volume reduction was associated with deficits in both visual and verbal memory (106). A number of studies have found that the reduced hippocampal volumes appear strongly associated with measures of cognitive impairment in DD (102,106). There has been limited direct neuroimaging investigation of IR. No published data has examined the relationship between IR and structural or functional neuroimaging markers in nondiabetic populations or in patients with depression. Three neuroimaging studies in diabetic populations have reported significantly smaller hippocampal volume in patients with type 2 diabetes compared to nondiabetic subjects (107–109). In the first study, differences between diabetics and nondiabetics remained significant even after adjustment for age, sex, and peripheral vascular disease (107). In the second study, multivariate regression analysis of the associations between hippocampal atrophy and diabetes-related variables [hemoglobin A1C, body mass index (BMI), hypertension, and dyslididemia] showed that hemoglobin A1C was the only significant predictor of hippocampal volume, even when the diabetic group was considered alone (108). Finally, the third study, which was conducted in elderly men, found that hippocampal atrophy was related to duration of diabetes (109). Several other neuroimaging studies in diabetic populations have reported greater cortical and subcortical atrophy in diabetics compared to nondiabetics (110–117). A recent six-year follow-up MRI study of normal aging subjects found that increased circulatory glucose concentration, as evidence by elevated glycated hemoglobin A, was associated with greater rate of whole-brain atrophy (118). Further, a recent functional MRI study by Rotte and colleagues reported that insulin infusion during

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hyperinsulinemic-euglycemic clamp enhanced neuronal activity in the mediotemporal lobe and increased cognitive test performance (119). Taken together, the data to date suggests that insulin has direct structural and functional effects on the brain, effects that are in turn observed in patients with DD and AD. Additional data comes from studies of proton magnetic resonance spectroscopy (MRS) that suggest overlapping metabolic abnormalities between depression and diabetes. Recent findings have shown that in depressed patients with diabetes, concentrations of myo-inositol in frontal white matter were significantly increased compared to both nondepressed diabetic patients and healthy controls (120). Further neuropsychological evidence suggesting that DD and AD may be linked via IR is offered by the observation that the same types of cognitive impairment seen in AD, such as deficits in memory and executive functioning, accompany depressed mood in a significant proportion of patients with DD (37), and in some cases do not resolve upon depression treatment (41,121). Further, neuropsychological studies of patients with type 2 diabetes consistently show verbal memory impairment, as well as some evidence of impairment in executive functioning (122). Neurochemical Evidence of IR Link Between DD and AD Both DD and AD are associated with reduced serotonergic (5-HT) activity and hyperactivity of the hypothalamic-pituitary-adrenal (HPA) axis, (123–126) which are both in turn associated with insulin dysfunction (127,128) (Fig. 1). Insulin facilitates transport of the serotonin precursor, tryptophan, through the blood-brain barrier (129,130) thereby increasing synthesis of serotonin. Decreased concentrations of 5-HT and its major metabolite 5-hydroxyindoleacetic acid (5-HIAA) have been demonstrated in the central nervous system (CNS) of patients with DD and AD by postmortem brain studies, particularly in the temporal cortex (86,131–135) and cerebrospinal fluid (136). The 5-HT–IR connection could be mediated by the HPA axis. Activation of the HPA axis has been associated with impaired glycemic control and reported in both DD and AD (102,137). Numerous studies have documented increased HPA axis activity

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Figure 1 Neurochemical evidence of IR Link between DD and AD. Abbreviations: IR, insulin resistance; DD, depressive disorders; AD, Alzheimer’s disease.

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(e.g., hypercortisolemia) in association with IR (138), indicating that IR may trigger perpetual hypercortisolemia, and vice versa (139). As such, there appears to be a bimodal relationship between insulin and cortisol (140,141), and indeed within depression there are reports of a significant negative correlation between morning salivary cortisol and insulin sensitivity (142). As such, it may be that cortisol plays a role in the improvement of insulin sensitivity following improvement of depression. Insulin receptors are highly concentrated in the hippocampus (143), which represents a key area in the regulation of the HPA axis activity (144). IR has been shown to further promote cortisol neurotoxicity in the hippocampus (145), which may be the main mechanism by which changes in endocrine homeostasis affect both mood and cognition. Alternatively, based on our hypothesis, hypercortisolemia in DD may set the stage for IR, thereby propagating the metabolic and, possibly, cognitive manifestations of DD (10,146). This postulate is complementary to the glucocorticoid cascade hypothesis of aging (147). According to that hypothesis, ‘‘advancing age is associated with increasing HPA axis dysregulation, and this dysregulation is the result of hippocampal atrophy, itself accelerated by HPA axis hyperactivity.’’ As discussed in the preceding chapter by Rhoads and Craft (chap. 3), the collective evidence shows that IR is associated with cognitive impairment across its spectrum from that seen in normal aging to that seen in patients with AD. Improvement in IR in patients with type 2 diabetes through insulin sensitizers, diet, and exercise is associated with improvement in cognition and mood (122). Further, insulin administration improves cognition when administered intravenously or intranasally. It is not presently known what effect insulin administration has on mood in patients with DD. Craft and colleagues proposed a model of the association between IR and cognitive impairment, wherein IR and chronic peripheral hyperinsulinemia are key to ‘‘three interlocking components: brain insulin levels, clearance of Abeta42 from the brain into the periphery, and inflammation of the brain and CNS vascular endothelium.’’ We further postulate that patients with DD and IR suffer from similar cognitive impairment and neuronal damage due to low brain insulin and a prothrombotic,

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proinflammatory state, which then predisposes them to later dementia. Insulin and glucose effects on memory function may also be mediated through neurotransmitter systems involved in memory. Acetylcholine dysfunction has been implicated in DD and AD (148). The impairment in glucose utilization as a result of IR may lead to decreased acetylcholine synthesis and subsequent memory impairment. Cholinergic treatments have been shown to increase regional brain glucose uptake in rodents (149) and humans (150). Thus, by increasing brain glucose, insulin may increase acetylcholine synthesis and release, as well as increase memory performance (151). The role of excitatory amino acids (i.e., glutamate) has been increasingly recognized in DD and AD (152). Glutamate may mediate glucose and insulin effects on memory performance through its effects on the hippocampus (153). In turn, insulin may modulate glutamate actions through postsynaptic activity of NMDA (N-methyl-D-aspartate) receptors (153). Therefore, decreased glucose availability under conditions of IR may lead to NMDA-receptor hypofunction with implications for both DD and AD (26,152). In rodent models of diabetes, hippocampal long-term potentiation is impaired (154–159) and insulin appears to modify this effect (157,158). In light of the multiple neuroregulatory functions of insulin in the brain, it may be that CNS insulin deficits contribute to the evolution and progression of DD (2). Further Evidence: IR-Cardiovascular Consequences and Conditions Linking DD and AD As previously mentioned, cardiovascular diseases associated with IR provide additional evidence for IR as a link between DD and AD. Many studies have established a correlative relationship between depression and cardiovascular disease (CVD), and subsequently CVD has been linked with AD. Patients with depression have a two to fourfold increase in their risk for developing cardiovascular disease, while others report that persistent or new depression is comparable to that of major cardiac events, such as new cases or worsening of previous cases

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(160–162). Due to this association between depressive diseases and CVD, recent SADHART and ENRICHD drug trials have used antidepressants to treat patients with angina or myocardial infarction, documenting the beginning of a new treatment method (163). In contrast, some studies have produced negative findings on the link between depression and CVD. For example, in a large study by Gehi, no association was found between depression and heart rate variability in patients with stable coronary heart disease (164). DD may have a causal relationship with CVD through multiple shared risk factors for both diseases, such as smoking, hypertension, diabetes, and hypercholesterolemia (165). However, the presence of multiple risk factors has often been inadequately controlled in clinical studies. Thus, the examination of physiological and biochemical features of poor cardiovascular functioning as possible mechanisms of action has helped to elucidate the relationship between depression and CVD, with IR being a leading mechanism of action. One possible explanation of the physiological impact of DD and IR on CVD may be through HPA and sympathoadrenal (SA) activation. Abnormal HPA axis functioning is one of the most consistent findings in depression, resulting in elevated basal cortisol levels and impaired feedback suppression of endogenous cortisol secretion (166–168). HPA hyperactivity also promotes the development of atherosclerosis and hypertension while accelerating injury to vascular endothelial cells. The mechanism of action is through the upregulation of SA activity via central regulatory pathways. The resulting increase in plasma catecholamines leads to vasoconstriction, platelet activation, and elevated heart rate, all of which are damaging to the cardiovascular system (165,169). Numerous studies have documented increased HPA axis activity (e.g., hypercortisolemia) in association with IR (138), indicating that IR may trigger perpetual hypercortisolemia, and vice versa (139). Platelet activity is an important factor in the development of atherosclerosis, acute coronary syndromes, and thrombosis. The role of serotonin in IR, platelet function, and depression also provide suggestive evidence linking IR and depression (168). It is widely documented that there is central serotonin hypofunction

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in DD (170). A recent study has shown that diminishing brain serotonin 5-HT function mediates IR in DD (128). DD has also been found to be associated with abnormalities in platelet serotonin 5-HT2A receptors and abnormal platelet function (168). Further studies suggest that the peripheral serotonin disturbances, such as increased 5-HT2 binding sites associated with DD, may enhance platelet aggregation and vasoconstriction, thereby accelerating thrombogenesis and atherosclerosis with IR (171–173). Blood coagulation, anticoagulation, fibrinolysis, and platelet activity are crucial in the development and prognosis of CVD (174,175). When deregulated, a hypercoagulation state can result. The consequent promotion of fibrin deposition in the vasculature augments progression of CVD. Indeed, increased levels of coagulation promoting factors have been shown to predict angina, myocardial infarction, and sudden cardiac death in patients with CVD as well as in healthy individuals (165). In turn, these cardiovascular system damages have been shown to be risk factors for AD. Greater carotid wall thickness is associated with subclinical stroke and white matter disease, with each 0.2 mm increase positively correlated with increased risk for AD (64). In a recent study of 1498 Alzheimer patients, the history of stroke was associated with a fourfold increased risk for the AD, while systemic vascular disease was shown to significantly increase the rate of cognitive decline and increase the risk of AD in the setting of APOEe4 (176). The Cardiovascular Risk Factors Aging and Dementia Study found that high systolic blood pressure in midlife was significantly associated with prevalent AD in late life, independent of demographics and other cardiovascular risk factors (177–179). There have been many proposed mechanisms to explain the relationship between CVD and AD (180). Since DD predisposes individuals to CVD and CVD is a risk factor for AD, it is possible that CVD presents convincing evidence for IR being the missing link. It may be that vascular damage in the brain caused by CVD and IR could create neurodegeneration, or perhaps vascular factors could directly affect development of AD by causing neuronal death and accumulation of plaques and tangles (181). As discussed before, HPA and SA hyperactivity are observed in DD and IR, promoting the development of atherosclerosis and

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hypertension. The activation also results in elevated basal cortisol levels as well as impaired feedback suppression of endogenous cortisol secretion (166–168). Many studies believe that cortisol toxicity is one of the primary causes of AD; excess cortisol damages neurons in the hippocampus and inhibits neuronal uptake of glucose (2,182). Other studies have discovered elevated levels of cortisol in postmortem cerebrospinal fluid of Alzheimer’s patients (183). The elevated cortisol resulting from HPA hyperactivity supports the idea that IR could be the link between DD and AD. Both hypertension and hypercholesterolemia have been known to be closely linked to IR as risk factors for heart disease. High blood pressure can result in atherosclerosis by damaging the endothelium of the artery and leaving it susceptible to calcium and cholesterol deposits, which greatly augments the risk for cognition impairment and dementia. Other studies have indicated that high cholesterol may play a role in the pathogenesis of AD by being a significant risk factor for heart disease. Cholesterol appears to modulate production of b amyloid. In cell culture, the addition of cholesterol increased b amyloid 1-40 by up to twofold, and cholesterol was necessary for any secretion of b amyloid 1-42, the most toxic form (181,184). In a mouse model, hypercholesterolemia led to elevated b-amyloid levels in the CNS, as well as increased size and number of amyloid deposits in the brain (181,185). IR also induces small vessel diseases, such as atherosclerotic thickening. The result of these small vessel diseases is a loss of elasticity or ectasia (aortal dilation), which leads to the inability to compensate for lower blood pressure. This leads to chronic hypoperfusion, especially in the brain. Hypoperfusion results in neuron death, myelin breakdown, axonal fragmentation, as well as oligodendrocyte death (186). The result from these neuronal degeneration events is often Alzheimer’s disease. Besides these direct triggering factors, indirect inflammation processes have been seen in biological studies to be both a result of AD and a cause for AD. These inflammation processes, along with reactive isomorphic gliosis and release of vasoactive peptides result in ischemia. Along with hypoperfusion, ischemia will lead to further neuronal decline (179).

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Additionally, small vessel diseases result in the release of proinflammatory cytokines, such as IL-1, IL-6, and tumor necrosis factor alpha (TNF-a) (165,187). TNF-a, the ‘‘master regulator’’ of the immune response, is the key initiator of immune-mediated inflammation in multiple organ systems, including the brain (188). Recently, investigators identified a polymorphism in the promoter region of the TNF gene that is associated with greater risk for AD (189). Furthermore, amyloid beta has been shown to stimulate secretion of TNF-a (190). However, although there is biological evidence that inflammation may play a role in the development of AD pathology, epidemiologic literature on inflammation’s relationship with cognitive decline and dementia is not convincing. For example, among 1284 participants of the Longitudinal Aging Study Amsterdam there was no relation between IL-6 and decline on four cognitive tests (181,191). Similarly, in the Health, Aging and Body Composition Study composed of 3031 older subjects, the inflammatory markers IL-6, TNF-a, and C-Reactive protein (CRP) were shown to have little association with cognitive decline (192). More research is needed to investigate the differences between biological and epidemiological studies. Taken together, the specific mechanism linking CVD and AD has yet to be established, but the central position of IR in DD, AD, and CVD suggests that it may be the missing link. HPA hyperactivity and 5-HT2 abnormality are similar findings between DD and CVD that can be explained through IR. Additionally, CVD as a risk factor for Alzheimer’s can be established by examining the role IR plays in inflammation, ectasia, and hypercortisolemia. The contributions of CVD to AD and vascular dementia suggest that cardiovascular therapies might prove useful in treating or preventing dementia. For example, it has already been noted that antihypertensive medications appear to be beneficial in preventing vascular dementia. Statin drugs may also be beneficial in preventing the progression of dementia in subjects with AD (193). Studies examining these forms of crossover treatment and prevention will continue to elucidate our understanding of the role of IR and illuminate the importance of tracking measures of IR as part of the study design.

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Clinical Implications of IR Link Between DD and AD It is not known whether the mechanism by which patients with DD have increased risk of IR and type 2 diabetes is disease specific or iatrogenic. A thorough discussion of iatrogenically induced IR is provided in the chapter by Lindley and colleagues (chap. 2). Mood stabilizers and antipsychotics, particularly the atypical antipsychotics, may increase IR indirectly by promoting significant weight gain (194,195), with subsequent increased incidence of hyperglycemia, resulting in IR, glucose intolerance, and diabetes (196). It is not yet known whether such medications place patients at elevated risk for AD. IR-related effects of psychiatric medications may also be waged through dysregulation of the HPA and hypothalamus-pituitary-gonadal (HPG) axis, implicating hypercortisolinemia and hyperandrogenemia, which in turn are associated with AD. Taken as a whole, the data suggest that pharmacological treatments for DD have significant effects on insulin and glucose action, which may in turn have long-term consequences on brain functioning. Although findings are not conclusive, these observations play an important role in clinical management considerations. IR and type 2 diabetes within DD is especially prevalent in individuals with risk factors such as obesity, weight gain of more than 10% of BMI with psychotropic treatment, family history of diabetes, and hypertension (197). Thus, it is difficult to ascertain whether the association of DD with abnormalities of insulin and glucose regulation and weight gain is an inherent problem in DD patients or a side effect of treatment. There is evidence to suggest that the connection is at least partly separate from the influence of weight gain due to medications (17). Early identification of a subgroup of patients with DD and IR could lead to preventive measures targeting AD, as well as earlier diagnosis and intervention. These patients could be evaluated for IR, as well as cognitive performance, at the beginning of treatment and at intervals thereafter. As mentioned earlier, there is some evidence that IR improves with successful antidepressant treatment of DD, particularly with the use of serotonin reuptake inhibitors (3). On the other hand, one report suggests IR to be a trait symptom of DD that does not improve with depression remission (17). Either

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way, it is important to consider IR in patients with DD, given that patients with IR are subject to cerebral metabolic changes over prolonged periods of time, which may lead to currently irreversible structural brain changes. The reciprocal effect of direct improvement of IR (via use of insulin-sensitizing medication) on depressed mood is still not known. However, we published a case recounting the use of metformin in a woman with treatmentresistant depression and PCOS (198). Treatment with metformin resulted not only in improvement in the patient’s IR, but also in complete remission of her depression, without concomitant psychiatric medication. Pilot data from our group has shown significant improvement in depressive symptoms following 12 weeks of concomitant rosiglitazone treatment in major depressive disorder (MDD) patients who have symptoms of IR, in addition to significant improvement in glucose handling, insulin sensitivity, and blood lipid profiles (unpublished data). Rosiglitazone (a type of thiazolidinedione) has been investigated recently as a treatment in AD, with preliminary results showing preserved cognition in patients with early AD and methylchloroisothiazolinone (MCI) (199). Further, a more recent study showed significant improvement in cognition in APOEe4 noncarriers, but no improvement in APOEe4 carriers (200), suggesting that genetics may moderate the relationship between IR and cognitive dysfunction. Interestingly, rosiglitazone has been shown to attenuate learning and memory deficits in an AD mouse model (201). Addition of thiazolidinediones to treatment schedules of DD patients with IR might also be considered since thiazolidinediones are the only available antidiabetic drugs that enhance tissue sensitivity to insulin without causing a subsequent increase in the secretion of insulin. Currently thiazolidinediones are already being added for weight reduction to treatment of some patients with depression (202). By preventing hyperinsulinemia, thiazolidinediones may also protect DD patients against the development of dementia; however, long-term data is needed. Meanwhile, this approach should also be considered in the management of other diseases with IR. On a related note, additional evidence demonstrates decreased hippocampal volume and brain glucose metabolism in

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patients with chronic hypercortisolemia (203–207) and hippocampal volume loss may be reversible with normalization of cortisol levels (208). Therefore, successful treatment of IR may result in improved hippocampal functioning by virtue of an associated reduction in elevated cortisol levels.

CONCLUSIONS In conclusion, the role of IR in DD and AD is supported by a critical mass of clinical, neurochemical, and neuroimaging data to merit further biological study. We propose that these links are not mutually exclusive, but may represent crucial related pieces of the jigsaw puzzle of neuropsychiatric disorders. IR may be an important therapeutic target for effective management of DD, and hopefully, prevention of AD. Future clinical studies should monitor IR throughout the management of DD.

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Index

Ab42 clearance, from brain, 94–95 Ab–inflammation cycle, 95 Acetylcholine, 113 ACTH. See Adrenocorticotropic hormone AD. See Alzheimer’s disease (AD) Adiposity, 8, 10 BMI, 21–23, 24, 32 and CVD, 20 obesity. See Obesity waist circumference (WC), 21–23, 27 Adrenocorticotropic hormone (ACTH), 58 Adult Treatment Panel III (ATP III), diagnostic criteria of MetS by, 6–8 AHA/NHLBI. See American Heart Association/National Heart, Lung, and Blood Institute (AHA/NHLBI) Alcohol abuse, and depression, 67–68 Alzheimer’s disease (AD) acetylcholine in, 113 b–amyloid (Ab) peptide in, 91, 97 brain insulin levels in, 94 clearance of Ab42 from brain, 94–95 and CVD, 115–117 defined, 89

[Alzheimer’s disease (AD)] and depressive disorders (DD), 105–106 glutamate in, 113 inflammation in CNS vascular endothelium, 95 inflammation in the brain, 95 MRI in, 108–109 and obesity, 107–108 PET in, 108 serotonin in, 110 therapy for, 97–98 and type 2 diabetes, 107 American Heart Association/National Heart, Lung, and Blood Institute (AHA/NHLBI), 10, 11 Antidepressants, in major depression, 64–65 Aortal dilation. See Ectasia Apoprotein A-I, fractional catabolic rate (FCR) of, 15 ATP III. See Adult Treatment Panel III (ATP III)

b–amyloid (Ab) peptide, in Alzheimer’s disease (AD), 91, 97 clearance of Ab42 from brain, 94–95 139

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140 Bipolar disorder, insulin resistance in causes gender, 72 genetic factors, 71 lifestyle factors, 71–72 pharmacological treatment, 71 epidemiology of, 70 treatment of, 72 Blood pressure, 16–17 BMI. See Body mass index (BMI) Body mass index (BMI), 10, 24, 32 and CVD, 26, 27 and waist circumference (WC), 21–23 Brain clearance of Ab42 from, 94–95 inflammation in, 95 insulin level, in Alzheimer’s disease (AD), 94 insulin receptors in, 89

Cardiovascular disease (CVD) and adiposity, 20 and Alzheimer’s disease (AD), 115–117 and depressive disorders (DD), 113–115 and MetS AHA/NHLBI on, 10, 11 ATP III on, 6–7 dyslipidemia. See Dyslipidemia hypertension, 16–17 insulin resistance, 23–24 and obesity, 27 WHO on, 4 Central nervous system (CNS) vascular endothelium inflammation in hyperinsulinemia, effect of, 95 insulin resistance, effect of, 95 norepinephrine levels, 90 Cerebral diabetes paradigm, 66 Clozapine, 54–55 CNS. See Central nervous system Cognitive deficits, 108

Index Cortisol, 112 C-reactive protein (CRP), 19 CSF. See Cerebrospinal fluid (CSF) Cushing’s syndrome, 58 CVD. See Cardiovascular disease (CVD)

DD. See Depressive disorders (DD) Definition, Diagnosis and Classification of Diabetes Mellitus and its Complications, 4 Depression, insulin resistance in causes alcohol abuse, 67–68 antidepressants, 64–65 cortisol, 68 diet, 67 genetic factors, 65–66 insulin’s effect on CNS, 66–67 stress, 68 weight, 67 epidemiology of, 61–63 treatment of, 69–70 Depressive disorders (DD) acetylcholine in, 113 and Alzheimer’s disease (AD), 105–106 and CVD, 113–115 glutamate in, 113 hypercortisolemia in, 112 MRI in, 109 MRS in, 110 PET in, 108 serotonin in, 110, 114–115 and type 2 diabetes, 106 Diet, in schizophrenia, 57 2DM. See Type 2 diabetes mellitus Dyslipidemia HDL-C in, 14–15 LDL particle size distribution in, 15 plasma TG concentrations in, 12–14 postprandial lipemia in, 14

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Index

141

Ectasia, 116 Energy expenditure, in schizophrenia, 57–58

Hypertension, 16–17, 116–117 Hypothalamic-pituitary-adrenal (HPA) axis, 58–60, 66

F2-isoprostane, 93, 95 Fluoxetine, 60 Fractional catabolic rate (FCR), of apoprotein A-I, 15 Free fatty acids (FFA), 90, 95

IDF. See International Diabetes Federation (IDF) IMGU. See Insulin-mediated glucose uptake (IMGU) Impaired fasting glucose (IFG), 6, 34 Impaired glucose tolerance (IGT), 4, 34 Insulin-degrading enzyme (IDE), 94, 95 Insulin-mediated glucose uptake (IMGU). See also Insulin resistance and BMI and WC, 21–23 and obesity, 20–21 visceral, 29–31 Insulin receptors, in brain, 89 Insulin resistance in Alzheimer’s disease (AD) brain insulin levels, effects on, 94 clearance of Ab42 from brain, effects on, 94–95 clinical evidence, 107–108 and CVD, 115–117 inflammation in brain, effects on, 95 inflammation in CNS vascular endothelium, effects on, 95 neuroimaging evidence, 108–109 neuropsychological evidence, 110 in bipolar disorder causes of, 71–72 epidemiology of, 70 treatment of, 72 in depression causes of, 64–68 epidemiology of, 61–63 treatment of, 69–70 and depressive disorders (DD) clinical evidence, 106–107 and CVD, 113–115 neuroimaging evidence, 108, 109, 110 neuropsychological evidence, 110

Genetic factors in bipolar disorder, 71 in depression, 65–66 in schizophrenia, 56 Glucocorticoid cascade hypothesis of aging, 112 Glucose intolerance, 11–12 Glucose utilization in Alzheimer’s disease (AD), 108 in depressive disorders (DD), 108 Glutamate, 113

HDL-C. See High-density lipoprotein cholesterol (HDL-C) High-density lipoprotein cholesterol (HDL-C), 14–15 HPA axis, activation of, 110, 112 Hypercholesterolemia, 116 Hyperchylomicronemia, 15 Hypercortisolemia, 68 Hyperinsulinemia, 12. See also Insulin resistance in Alzheimer’s disease (AD), 90 brain insulin levels, effects on, 94 clearance of Ab42, effects on, 94–95 elevation in F2-isoprostane by, 93 inflammation in brain, effects on, 95 inflammation in CNS vascular endothelium, effects on, 95 modulation of peptides by, 91 and dyslipidemia. See Dyslipidemia and hypertension, 16, 17

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142 [Insulin resistance] in MetS and adiposity. See Adiposity ATP III on, 7, 8 in blood pressure, 16–17 and CVD, 23–24 and dyslipidemia. See Dyslipidemia glucose intolerance, 11–12 and microalbuminuria, 6 and PAI-1 concentration, 17–19 and proinflammatory factor, 19 and visceral obesity, 29–31 WHO on, 4–6 in schizophrenia. See Schizophrenia, insulin resistance in Insulin resistance syndrome, 90, 93. See also Insulin resistance, in Alzheimer’s disease (AD) International Diabetes Federation (IDF), diagnostic criteria of MetS by, 8–10 Intranasal insulin administration, 89

LDL. See Low-density lipoprotein (LDL) Lithium, for bipolar disorder, 71 Low-density lipoprotein (LDL) particle size distribution, 15

Magnetic resonance imaging (MRI) in Alzheimer’s disease (AD), 108–109 in depressive disorders (DD), 109 Magnetic resonance spectroscopy (MRS), 110 Major depression. See Depression Metabolic syndrome (MetS) diagnosis of, 33–35 diagnostic criteria by ATP III, 6–8 by IDF, 8–10 by WHO, 4–6

Index [Metabolic syndrome (MetS)] risk factors, 10–11 blood pressure, 15–17 CVD. See Cardiovascular disease (CVD) dyslipidemia, 12–15 glucose intolerance, 11–12 insulin resistance. See Insulin resistance Metformin, 61 MetS. See Metabolic syndrome (MetS) Microalbuminuria, 8 insulin resistance and, 6 Mirtazepine, 64 Mood disorders bipolar disorder. See Bipolar disorder major depression. See Depression MRI. See Magnetic resonance imaging (MRI) MRS. See Magnetic resonance spectroscopy (MRS)

Obesity and Alzheimer’s disease (AD), 107–108 in bipolar disorder, 70 and CVD, 27 in depression, 65, 67 and IMGU, 20–21 in schizophrenia, 56 visceral, 29–31 Olanzapine, 54–55

PAI-1. See Plasminogen activator inhibitor-1 (PAI-1) PCOS. See Polycystic ovarian syndrome (PCOS) PET. See Positron emission tomography (PET) Plasma triglyceride (TG) concentration, 12–14 Plasminogen activator inhibitor-1 (PAI-1)

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Index Polycystic ovarian syndrome (PCOS), 72 Positron emission tomography (PET), 108 Postprandial lipemia, 14

REE. See Resting energy expenditure (REE) Resting energy expenditure (REE), in schizophrenia, 57–58 Rosiglitazone, 97

Schizophrenia, insulin resistance in causes of atypical antipsychotics, 53–55 diet, 57 exposure to stress, 58–60 genetic factors, 56 nonpharmacological factors, 55–56 REE, 57–58 weight, 56–57 epidemiology of, 51–53 prevention of, 60 treatment of, 60–61 Selective serotonin reuptake inhibitors (SSRI), 64 Serotonin in Alzheimer’s disease (AD), 110 in depressive disorders (DD), 110, 114–115 Small vessel diseases, 116–117

143 Thiazolidinediones (TZD), 97 Topiramate, 60–61 Tricyclic antidepressants, 64 Tumor necrosis factor a (TNF-a), 117 CSF levels of, 93, 95 Type-3 diabetes. See Alzheimer’s disease (AD) Type 2 diabetes mellitus (2DM) and Alzheimer’s disease (AD), 107 and depressive disorders (DD), 106 and MetS, 32, 33 glucose intolerance, 11–12 WHO on, 4, 6 Visceral fat (VF). See also Visceral obesity and clinical syndromes related to insulin resistance, 32 Visceral obesity and insulin resistance, 29–31 vs. waist circumference (WC), 27 Waist circumference (WC), 8, 10 and BMI, 21–23 vs. visceral obesity, 27 Weight in depression, 67 in schizophrenia, 56–57 White blood cell (WBC) count, in CVD, 19 WHO. See World Health Organization (WHO) World Health Organization (WHO), diagnostic criteria of MetS by, 4–6

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Psychiatry

The first book of its kind to tie the metabolic syndrome with psychiatric disorders, and the possibility that common antipsychotic treatments may be having an adverse effect on patients. Insulin Resistance Syndrome and Neuropsychiatric Disease describes: • insulin resistance syndrome • psychiatric and cognitive disorders • impact of treatment of psychiatric disorders on metabolic function • insulin resistance as a link between affective disorders and Alzheimer’s disease And also examines: • the metabolic syndrome, including its relationships with diseases of the central nervous system, as well as new treatments to help prevent metabolic complications among patients with neuropsychiatric illnesses Presenting a complete overview and the relationship between insulin resistance syndrome, and psychiatric and cognitive disorders, Insulin Resistance Syndrome and Neuropsychiatric Disease will be a welcome update to any psychiatrist’s, neurologist’s, endocrinologist’s, and research scientist’s library. about the editor... NATALIE L. RASGON is Professor, Departments of Psychiatry and Behavioral Sciences, Obstetrics and Gynecology, Stanford University, California, and she is Director of the Center for Neuroscience in Women’s Health at the Stanford School of Medicine and Stanford Neuroscience Institute. Dr. Rasgon received her M.D. and Ph.D. in Obstetrics and Gynecology and Pathological Physiology from the Central Institute of Postgraduate Medical Education, Central Institute of General Pathology and Pathological Physiology, Academy of Medical Sciences, Moscow, Russia. Dr. Rasgon is the author of over 124 peer-reviewed articles, more than 25 book chapters, and is a reviewer for more than 25 medical journals specific to psychiatry, neuropharmacology, and obstetrics and gynecology. Printed in the United States of America

InsulIn ResIstance syndRome and neuRopsychIatRIc dIsease

about the book…

InsulIn ResIstance syndRome and neuRopsychIatRIc dIsease Medical Psychiatry Series / 38

Edited by

Natalie L. Rasgon, M.D., Ph.D. Rasgon

$+

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