Obesity: Dietary and Developmental Influences

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

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OBESITY Dietary and Developmental Influences

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

Boca Raton London New York

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

2005054943

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

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

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

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

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

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

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

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

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

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

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

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

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

FLORIDA DEPARTMENT OF HEALTH Obesity Prevention Program

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

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

CENTERS FOR DISEASE CONTROL AND PREVENTION

HARVARD UNIVERSITY

Division of Nutrition and Physical Activity

School of Public Health

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

Harvard Prevention Research Center

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

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

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

Sarah Levin Martin, Ph.D. Health Scientist

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

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

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

NORTH CAROLINA DEPARTMENT OF HEALTH AND HUMAN SERVICES

College of Natural Resources

Division of Public Health, Children and Youth Branch

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

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

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

ST. LOUIS UNIVERSITY School of Public Health Obesity Prevention Center

Center for Weight and Health

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

UNIVERSITY OF COLORADO

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

AMC Cancer Research Center

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

UNIVERSITY OF NEW MEXICO

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

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

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

TEXAS DEPARTMENT OF HEALTH Public Health Nutrition Program

Shirley Pareo, M.S. Research Scientist

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

UNIVERSITY OF NORTH CAROLINA

UNIVERSITY OF CALIFORNIA, BERKELEY

Center for Health Promotion and Disease Prevention

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

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

School of Public Health Center for Family and Community Health

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

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

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

UNIVERSITY OF OKLAHOMA

Roger Sargent, Ph.D. Professor

Health Sciences Center

Department of Exercise Science

College of Allied Health

Sara Wilcox, Ph.D. Associate Professor

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

UNIVERSITY OF WASHINGTON

College of Public Health

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

Center for Public Health Nutrition

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

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

UNIVERSITY OF SOUTH CAROLINA

WASHINGTON STATE DEPARTMENT OF TRANSPORTATION

Arnold School of Public Health Department of Promotion and Education

Highways and Local Transportation Program

Alexandra E. Evans, Ph.D. Assistant Professor

Charlotte Claybrooke, M.S. Bicycle and Pedestrian Coordinator

REVIEWERS UNIVERSITY OF CALIFORNIA, BERKELEY

UNIVERSITY OF SOUTH CAROLINA

Department of Nutritional Science and Toxicology

Arnold School of Public Health

Sharon Fleming, Ph.D.

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

Professor University of California, Berkeley

Center for Research in Nutrition and Health Disparities

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

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

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

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

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

1

Introduction THE OBESITY EPIDEMIC

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

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

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

2

Obesity: Dietary and Developmental Inﬂuences

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

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

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

How • Environment • Media/Marketing • Education

Where • School • Community • Workplace

The question of interest for this review

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

Introduction

3

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

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2

Methodology SEARCH PROCESS AND CRITERIA

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

6

Obesity: Dietary and Developmental Inﬂuences

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

Search Terms Dietary energy, Calorie intake Energy density Dietary fat

Carbohydrate2

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

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

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

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

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

Notes: 1

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

2

“Overweight” was also included as a search term.

3

“AND” was used as a Boolean operator.

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

Methodology

7

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

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

8

Obesity: Dietary and Developmental Inﬂuences

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

Article Citation Name of Study

Sample Size and Demographics

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

Control Variables (How Measured)

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

Results

Other comments:

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

Methodology

9

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

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

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

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

10

Obesity: Dietary and Developmental Inﬂuences

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

Strengths

1. Secular Trends

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

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

Food supply data

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

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

Dietary intake data

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

• Provides an average intake for age and gender groups.

2. Mechanistic/ Experimental Studies

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

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

3. Observational/ Epidemiological Studies

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

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

11

Limitations

Type of Evidence

Methodology

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

12

TABLE 2.4 (CONTINUED) 4. Prevention trials

• • • • •

•

• • •

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

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

Obesity: Dietary and Developmental Inﬂuences

3

Critical Periods INTRODUCTION TO CRITICAL PERIODS

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

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

14

Obesity: Dietary and Developmental Inﬂuences

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

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

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

Critical Periods

15

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

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

16

Obesity: Dietary and Developmental Inﬂuences

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

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

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

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

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

Critical Periods

17

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

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

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

18

Obesity: Dietary and Developmental Inﬂuences

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

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

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

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

Critical Periods

19

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

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

20

Obesity: Dietary and Developmental Inﬂuences

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

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

Critical Periods

21

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

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

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

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

22

Obesity: Dietary and Developmental Inﬂuences

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

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

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

Critical Periods

23

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

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

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

24

Obesity: Dietary and Developmental Inﬂuences

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

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

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

Critical Periods

25

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

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

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

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

26

Obesity: Dietary and Developmental Inﬂuences

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

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

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

Critical Periods

27

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

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

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

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

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Obesity: Dietary and Developmental Inﬂuences

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

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

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

Critical Periods

29

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

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4

Dietary Inﬂuences on Energy Balance TOTAL CALORIES

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

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

32

Obesity: Dietary and Developmental Inﬂuences

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

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

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

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

Dietary Inﬂuences on Energy Balance

33

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

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

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

34

Obesity: Dietary and Developmental Inﬂuences

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

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

Dietary Inﬂuences on Energy Balance

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

35

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Obesity: Dietary and Developmental Inﬂuences

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

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

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

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

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

Dietary Inﬂuences on Energy Balance

37

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

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

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

38

Obesity: Dietary and Developmental Inﬂuences

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

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

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

Study Name and/or Location

Study Population

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

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

Cox, 1999

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

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

Adiposity Measure1

Control Variables

Association2

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

Age, gender, race/ethnicity

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

Weight (kg) and BMI (continuous)

Age, gender

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

Underreporting, gender, age

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

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

Underreporting, matched for age

Dietary Inﬂuences on Energy Balance

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

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

Notes: 1

BMI based on measured weight and height unless otherwise noted.

2

39

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

40

Obesity: Dietary and Developmental Inﬂuences

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

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

Mechanisms Support Relationship?

(0)

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

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

Randomized Controlled Trials

Other

(0)

(0)

No studies

No studies

Conclusion: Consistency of Evidence Supporting Relationship

Adults No studies Inconcl.

Inconcl.

Yes

Yes

Inconcl. Children No studies

No studies

No studies

No studies

No studies

Notes: 1

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

2

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

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

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

Dietary Inﬂuences on Energy Balance

41

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

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

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

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

42

Obesity: Dietary and Developmental Inﬂuences

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

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

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

Dietary Inﬂuences on Energy Balance

43

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

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

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

44

Obesity: Dietary and Developmental Inﬂuences

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

Dietary Inﬂuences on Energy Balance

45

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

46

Obesity: Dietary and Developmental Inﬂuences

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

Study

Study Population

Control Variables

Measure of adiposity

Association1

Comments

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

Higher % fat was associated with higher kcal intakes.

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

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

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

2580 men 4567 women

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

Weight change compared to intake at baseline

Heitman (1995) Sweden 6-year study

361 women Ages 38–60

Total energy, smoking, PA, menopause

BMI change

Klesges (1992) Memphis 3-year study

294 adults Mostly middle class White

Gender

Change in BMI compared to baseline intakes

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

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

BMI change

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

Age Gender

Fat mass (4 skinfolds, ht, wt)

83 men 98 women Age 13 at baseline Nonobese at baseline

Dietary Inﬂuences on Energy Balance

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

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

0

47

48

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

1831 women Ages 19–50

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

0 + (when individuals with poor health status excluded)

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

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

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

BMI

–f (g, weak) 0m (g) +f (%) +m (%)

Trichopoulou (2002) EPIC study Greece

27,862 adults Healthy volunteers Ages 25–82

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

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

12,763 men Middle aged Diverse SES

Kcal

BMI and skinfolds Intercohort comparisons only

Bolton-Smith (1994) Scottish Health 1984–1986

5768 men 5858 women Ages 25–65

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

BMI

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

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

5837 men 5243 women Ages 25–74 Nationally representative

Age Gender

BMI

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

*Explained less than 1% of variance.

0

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

Obesity: Dietary and Developmental Inﬂuences

0 NR (BMI)

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

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

Macdiarmid (1998) 1986–1987

Lluch (2000) Stanislas Family Study (France)

Age

BMI (self-report)

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

BMI Skinfolds

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

BMI

Age Smoking Underreporting

BMI

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

LER; age, gender

BMI

Age

BMI Lorentz index (wt/ht categories)

+ (animal fat, g)

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

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

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

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

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

+m 0 (other groups)

49

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

Dietary Inﬂuences on Energy Balance

Appleby (1998) Oxford Vegetarian Study 1980–1984

50

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

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

Tucker (1992) Western U.S.

Nelson (1996) Utah

205 women Mostly white Average age 34.6

203 men Mostly white Average age 41

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

Gender

BMI at years 1, 2, and 3

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

Body fat categories based on skinfolds

Age, kcal, ﬁtness, PA

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

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

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

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

Obesity: Dietary and Developmental Inﬂuences

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

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

Anderson (1996) Gustaf Study Sweden

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

Age, sex, income, education

BMI categories

Age

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

Miller (1994)

46 men 32 women

Gender

Cox (1999) England

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

Underreporting, gender, age

Westerterp (1996) The Netherlands

Underreporting Matched for age

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

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

0

Only salty foods and energy density related to obesity.

Dietary Inﬂuences on Energy Balance

Alﬁeri (1995) Ontario, Canada

+

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

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

51

52

Obesity: Dietary and Developmental Inﬂuences

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

Study Name and/or Location

Adiposity Measure1

Study Population

Control Variables

Association2

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

0 m; 0 f Energy-adjusted fat

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

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

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

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

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

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

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

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

BMI.

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

—

Dietary Inﬂuences on Energy Balance

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

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

0 %

53

54

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

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

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

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

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

Scaglioni, 2000 Milan, Italy

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

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

0 m; 0 f %

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

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

Age, Gender.

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

Maffeis, 1998 Italy

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

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

Rolland-Cachera, 1995 Paris, France

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

BMI, Subscapular and Triceps skinfolds at age 8.

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

+f > 30%

0 %

Obesity: Dietary and Developmental Inﬂuences

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

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

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

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

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

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

BMI%, Overweight > 95th%.

Gender, Age.

0 m; 0 f %

+f % 0f Total Fat (grams)

0 m; 0 f %

55

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

Dietary Inﬂuences on Energy Balance

Carruth, 2001 Knoxville, Tennessee, U.S.

56

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

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

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

Race, Gender.

0 m; 0 f %

Rodriguez-Artalejo, 2002 Spain

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

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

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

Guillaume, 1998 Province de Luxembourg, Belgium

955 children Age 6–12 yr.

BMI.

Age, Gender.

+ m; 0 f %

Maffeis, 2000 Italy

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

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

Gender, Energy Intake/Basal Metabilic Rate ratio.

0 m; 0 f %

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

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

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

0 m; 0 f %

Garaulet, 2000 Spain

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

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

Gender.

+ m; 0 f %

Lluch, 2000 Stanislaus Family Study France

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

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

Gender, Age.

0 m; 0 f %

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

Obesity: Dietary and Developmental Inﬂuences

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

Body fat% (formula using skinfold measurement).

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

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

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

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

BMI (at baseline).

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

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

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

Dennison, 1997 New York, U.S.

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

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

+ m; + f %

Obesity: BMI > 85th%.

Analyses were stratiﬁed by Hispanic surname.

BMI (at baseline).

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

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

—

0 m; 0 f %

Dietary Inﬂuences on Energy Balance

Tucker, 1997 Utah, U.S.

0f > 30%

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

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

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

0 m; 0 f % at age 5 yr

57

58

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

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

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

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

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

Ricketts, 1997 Cincinnati, Ohio, U.S.

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

BMI, Triceps and Subscapular skinfold measures

Maffeis, 1996 Verona, Italy

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

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

RMR (indirect calorimetry).

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

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

% Body Fat (18O dilution).

Gender.

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

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

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

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

— .

0 m; 0 f %

0 % + %

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

Obesity: Dietary and Developmental Inﬂuences

McGloin, 2002 Northern Ireland, UK

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

BMI.

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

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

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

+ m; + f %

Koivisto, 1994 Sweden

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

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

0 m; 0 f %

Rocandio, 2001 Spain

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

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

Francis, 1999 Texas, U.S.

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

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

Bandini, 1999 Boston, Massachusetts, U.S.

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

% Body fat (18O dilution).

Fisher, 1995 Illinois, U.S.

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

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

Gender, Age.

—

Gender (examined, but no interaction).

—

+ %

Dietary Inﬂuences on Energy Balance

—

Ortega, 1995 Madrid, Spain

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

+ m; + f %

0 %

59

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

Obesity status (BMI > 85th%)

—

0 Total fat (grams)

60

Manos, 1993 Georgia, U.S.

Notes: 1

BMI based on measured weight and height unless otherwise speciﬁed.

2

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

3

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

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

Obesity: Dietary and Developmental Inﬂuences

Dietary Inﬂuences on Energy Balance

61

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

62

Obesity: Dietary and Developmental Inﬂuences

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

Dietary Inﬂuences on Energy Balance

63

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

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

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

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

64

Obesity: Dietary and Developmental Inﬂuences

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

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

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

Dietary Inﬂuences on Energy Balance

65

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

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

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Obesity: Dietary and Developmental Inﬂuences

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

Dietary Inﬂuences on Energy Balance

67

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

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

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

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

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

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Obesity: Dietary and Developmental Inﬂuences

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

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

Study Population

Control Variables

Measure of adiposity

Association1

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

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

BMI change

– (ﬁber)

BMI change

0 (CHO) 0 (sucrose)

83 men 98 women Age 13 at baseline Nonobese at baseline

Age Gender

Fat mass (4 skinfolds, ht, wt)

Age, dieting status

BMI (self-report ht and wt)

0 (% added sugars)

Gender

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

Age, ethnicity, smoking, alcohol, kcal, gender

BMI categories: Overweight vs. underweight

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

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

Comments

Dietary Inﬂuences on Energy Balance

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

0

69

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

70

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

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

–

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

0

12763 men Middle aged Diverse SES

Bolton-Smith (1994) Scottish Health 1984–1986

5768 men 5858 women Ages 25–65

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

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

5837 men 5243 women Ages 25–74 Nationally representative

Age Gender

BMI

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

PA and ﬁber explained 90% of the variance in skinfolds

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

Obesity: Dietary and Developmental Inﬂuences

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

*Explained less than 1% of variance

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

Age

Macdiarmid (1998) 1986–1987

Lluch (2000) Stanislas Family Study (France)

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

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

BMI (self-report)

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

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

BMI

Age Smoking Underreporting.

BMI

1239–1853 adults Healthy Nondieting Ages 16-64

Low energy reporters; age, gender

BMI

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

Age

BMI Lorentz index (wt/ht categories)

– (ﬁber, g)

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

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

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

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

Dietary Inﬂuences on Energy Balance

Appleby (1998) Oxford Vegetarian Study 1980–1984

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

71

72

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

330 men Ages 45–64

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

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

Age

BMI categories

Tucker (1992) Western U.S.

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

Body fat categories based on skinfolds

203 men Mostly white Average age 41

Age, kcal, ﬁtness, PA

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

Body fat based on skinfolds (continuous variable)

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

Fiber showed strongest association with body fat

Obesity: Dietary and Developmental Inﬂuences

Nelson (1996) Utah

205 women Mostly white Average age 34.6

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

150 adults Mostly women Ages 18-65

Age, sex, income, education BMI categories

Miller (1994)

46 men 32 women

Gender

Cox (1999) England

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

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

Westerterp (1996) The Netherlands

Underreporting Matched for age

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

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

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

Dietary Inﬂuences on Energy Balance

Alﬁeri (1995) Ontario, Canada

Only salty foods and energy density related to obesity.

–

Notes: 1

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

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

73

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Obesity: Dietary and Developmental Inﬂuences

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

Dietary Inﬂuences on Energy Balance

75

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

76

Obesity: Dietary and Developmental Inﬂuences

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

Study Name and/or Location

Adiposity Measure1

Study Population

Control Variables

Association2

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

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

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

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

BMI.

Lee, 2001 Pennsylvania, U.S.

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

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

BMI (at baseline).

Eck, 1992 Memphis, Tennessee, U.S.

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

Weight change.

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

– %

Scaglioni, 2000 Milan, Italy

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

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

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

0 m; 0 f %

—

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

Dietary Inﬂuences on Energy Balance

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

0 %

–f Total Carbohydrate (g)

77

78

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

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

Rolland-Cachera, 1995 Paris, France

112 children Age 2 to 8 yr; followed 6 yr

BMI, Subscapular and triceps skinfolds at age 8.

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

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

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

0 m; 0 f %

0 %

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

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

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

0 m; 0 f %

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

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

0 m; 0 f %

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

Race, Gender.

Obesity: Dietary and Developmental Inﬂuences

Maffeis, 1998 Italy

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

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

BMI.

Age, Gender.

Maffeis, 2000 Italy

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

Gender, EI/BMR ratio.

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

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

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

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

0 m; 0 f %

Garaulet, 2000 Spain

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

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

Gender.

– m; 0 f %

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

Gender, Age.

– m; – f %

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

– m; – f %

Guillaume, 1998 Province de Luxembourg, Belgium

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

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

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

79

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

Dietary Inﬂuences on Energy Balance

Rodriguez-Artalejo, 2002 Spain

80

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

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

Gender, Age.

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

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

BMI (at baseline).

Dennison, 1997 New York, U.S.

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

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

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

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

McGloin, 2002 Northern Ireland, UK

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

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

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

0 m; 0 f %

—

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

–f Total Carbohydrate (grams)

0 Total Carbohydrate (grams)

0 m; 0 f %

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

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

Obesity: Dietary and Developmental Inﬂuences

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

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

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

RMR (indirect calorimetry).

0 % 0 Total Carbohydrate (grams)

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

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

% Body Fat (18O dilution).

Gender.

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

Ortega, 1995 Madrid, Spain

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

BMI.

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

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

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

Koivisto, 1994 Sweden

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

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

Rocandio, 2001 Spain

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

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

Francis, 1999 Texas, U.S.

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

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

—

Gender, Age.

– %

Dietary Inﬂuences on Energy Balance

Maffeis, 1996 Verona, Italy

– m; – f %

0 m; 0 f Total Carbohydrate (grams)

—

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

81

82

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

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

% Body fat (18O dilution).

Gender (examined, but no interaction).

– m; – f %

Notes: 1

BMI based on measured weight and height unless otherwise speciﬁed.

2

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

Obesity: Dietary and Developmental Inﬂuences

Dietary Inﬂuences on Energy Balance

83

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

84

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

Garaulet, 2000 Spain

Tucker, 1997 Utah, U.S.

Adiposity Measure1

Control Variables

Association2

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

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

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

0 m; 0 f Energy-adjusted ﬁber

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

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

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

0 m; 0 f Total ﬁber (grams)

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

Gender.

0 m; – f Total ﬁber (grams)

Body fat% (formula using skinfold measurements).

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

0 m; 0 f Total ﬁber (grams)

Obesity: Dietary and Developmental Inﬂuences

Cross-Sectional Studies Rodriguez-Artalejo, 2002 Spain

Study Population

Rocandio, 2001 Spain

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

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

Gender, Age.

– m; – f Total Fiber (grams)

BMI.

—

0 Total Fiber (grams)

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

—

– Total Fiber (grams)

Dietary Inﬂuences on Energy Balance

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

Notes: 1

BMI based on measured weight and height unless otherwise speciﬁed.

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

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

85

86

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

Study Population

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

Koivisto, 1994 Sweden

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

Control Variables

BMI (reported height and weight).

Age, Dieting status.

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

Gender, Age.

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

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

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

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

0 m; 0 f % Total energy from sucrose

Notes: 1

2

BMI based on measured weight and height unless otherwise speciﬁed.

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

Obesity: Dietary and Developmental Inﬂuences

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

Adiposity Measure1

Dietary Inﬂuences on Energy Balance

87

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

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

88

Obesity: Dietary and Developmental Inﬂuences

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

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

Study Population

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

Lluch (2000) Stanislas Family Study (France)

Measure of Adiposity

Association1

176 men 289 women Prospective cohort

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

BMI change

0

83 men 98 women Age 13 at baseline Nonobese at baseline

Age Gender

Fat mass (4 skinfolds, ht, wt)

+

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

BMI

+

Age

BMI (self-report)

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

BMI Skinfolds

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

BMI

Age

BMI Lorentz index (wt/ht categories)

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

Control Variables

+ (meat eaters vs. nonmeat eaters)

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

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

+ (all groups)

89

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

Comments

Dietary Inﬂuences on Energy Balance

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

90

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

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

Age

BMI categories

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

Body fat categories based on skinfolds

0

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

0

86 obese men 61 normal weight men Case-control

Age

Cox (1999) England

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

Underreporting, gender, age

BMI categories Body fat (bioelectrical impedance)

Underreporting Matched for age

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

Westerterp (1996) The Netherlands

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

Notes: 1

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

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

Only salty foods and energy density related to obesity.

0

Obesity: Dietary and Developmental Inﬂuences

Anderson (1996) Gustaf Study Sweden

+ (g)

Dietary Inﬂuences on Energy Balance

91

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

92

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

Rolland-Cachera, 1995 Paris, France

Carruth, 2001 Knoxville, Tennessee, U.S.

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

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

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

BMI.

Control Variables

Association2

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

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

—

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

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

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

+ %

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

Obesity: Dietary and Developmental Inﬂuences

Maffeis, 1998 Italy

Adiposity Measure1

Study Population

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

0 m; 0 f %

Dietary Inﬂuences on Energy Balance

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

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

0 m; 0 f %

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

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

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

Race, Gender.

+ m; + f %

Rodriguez-Artalejo, 2002 Spain

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

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

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

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

Guillaume, 1998 Province de Luxembourg, Belgium

955 children Age 6–12 yr.

BMI.

Age, Gender.

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

93

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

94

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

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

Gender, EI/BMR ratio.

0 m; 0 f %

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

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

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

0 m; 0 f %

Garaulet, 2000 Spain

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

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

Gender.

0 m; 0 f %

Lluch, 2000 Stanislaus Family Study France

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

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

Gender, Age.

+ m; + f %

Tucker, 1997 Utah, U.S.

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

Body fat% (formula using skinfold measurement).

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

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

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

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

Gender, Age.

Dennison, 1997 New York, U.S.

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

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

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

0 m; 0 f %

0 Total Protein (grams)

Obesity: Dietary and Developmental Inﬂuences

Maffeis, 2000 Italy

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

McGloin, 2002 Northern Ireland, UK

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

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

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

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

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

Maffeis, 1996 Verona, Italy

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

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

RMR (indirect calorimetry).

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

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

% Body fat (18O dilution).

Gender.

Ortega, 1995 Madrid, Spain

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

BMI.

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

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

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

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

+ m; + f %

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

0 m; 0 f %

Dietary Inﬂuences on Energy Balance

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

0 %

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

+ %

0 m; 0 f %

95

96

TABLE 4.10 (CONTINUED) Koivisto, 1994 Sweden

Rocandio, 2001 Spain

Francis, 1999 Texas, U.S.

Bandini, 1999 Boston, Massachusetts, U.S.

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

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

Gender, Age.

0 m; 0 f %

—

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

% Body fat (18O dilution).

Gender (examined, but no interaction).

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

+ m; + f %

Notes: 1

BMI based on measured weight and height unless otherwise speciﬁed.

2

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

Obesity: Dietary and Developmental Inﬂuences

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

Dietary Inﬂuences on Energy Balance

97

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

98

Obesity: Dietary and Developmental Inﬂuences

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

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

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

Study Design and Strategies

Length of Intervention and Timing of F/U Measure

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

Relevant Target Behaviors Targeted

Reported Change among Subjects

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

Comments

Control Variables

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