The workshop was designed to review the evidence on the role of gene–nutrition and gene–physical activity interaction effects in the etiology of obesity and the growing prevalence of obesity. One important goal was to produce a set of recommendations that would be helpful to the research community and to the sponsors of the meeting. As evidenced by the papers in this publication, all contributors to the workshop made recommendations concerning future research, and these were discussed extensively during the meeting. Our goal herein is not to generate an exhaustive list of all suggestions made by the speakers at the workshop, but rather to focus on the key recommendations that can have a significant impact on the research agenda of the field. We are grouping them under five headings. However, one overarching issue needs to be raised first. The concept of gene–behavior interaction is one that is not always fully understood and appreciated. As was defined in the first paper of this supplement ((1)), one has to distinguish between the main effect of a gene from its potential contributions through interaction paths with behavioral or environmental factors or, perhaps more precisely, behavioral or environmental perturbations. The distinction is not always fully appreciated. For instance, some of the papers in this supplement deal with gene–behavior interaction effects only marginally. We will purposefully focus here only on recommendations that are relevant to the gene–behavior interaction research agenda. Most of the genetic studies reported to date for human obesity and related traits have reported on the main effects of genes. The genetic architecture of obesity is obviously more complex than suggested by the majority of studies published to date ((2)). For example, the FTO gene has been shown in a good number of studies with large sample sizes to have a significant effect on body mass index (BMI) (Loos and Bouchard, submitted). Homozygotes for the risk allele are on average about 3–4 kg heavier than homozygotes for the wild-type allele. However, it appears that there is a strong interaction between the FTO genotype and physical activity level. Indeed the carriers of the risk allele are normal weight if they are physically active ((3),(4)). It is only the sedentary individuals who are homozygotes for the risk allele who are heavier than the other genotypes. The ability to identify a gene–behavior effect is highly dependent on the study design. In this regard, a consensus emerged on several aspects of what productive study designs should include: . Measure a phenotype of interest with precision and have true control over the exposure to the behavior or the environmental agent of interest . Prioritize controlled interventions, since they offer the best opportunity to identify the genes and alleles causally related to changes in a phenotype . Among observational studies, favor cohorts followed prospectively, since they have a better chance of identifying gene–behavior interaction effects when compared to cross-sectional observation studies . Identify genes through genome-wide association studies from large cohorts and subsequently test them as candidate genes in well-controlled intervention studies with appropriate control over the exposure to behavioral change . Pursue the quantification and characterization of the individual differences in the response to behavioral changes with a view of generating hypotheses for subsequent research . Refine the phenotypes commonly used in studies focused on obesity by incorporating endophenotypes that will make it easier to identify specific gene–behavior interaction effects . Take advantage of findings on gene–nutrition or gene–exercise interaction effects to design human studies to verify whether the observations can be reproduced . Target candidate genes derived from animal studies for subsequent human testing . Compare gene–behavior interaction effects across ethnic groups . Design studies comparing obesity treatment responses across genotypes, using well-characterized genes associated with human variation in responsiveness to relevant behavioral exposure . Explore gene–macronutrients or –food groups interaction effects with respect to preference, overall consumption, and dietary restriction . Develop designs that will allow identification of the genes responsible for the genotype–overfeeding interaction effect in humans experiencing experimental weight gains . Identify the genes responsible for the differential responsiveness to weight loss protocols based on caloric restriction . Identify the genes responsible for the variation in weight loss and the changes in body composition in exercise-based protocols designed to induce caloric deficits . Explore whether human variation observed in weight loss in response to an exercise regimen results from gene–fitness interaction effects . Design studies to examine whether there are gene–motivation trait interactions influencing physical activity behavior . Standardize the behavioral challenge in gene–behavior interaction effect studies such that it can be implemented at other sites . Undertake studies to understand whether genotype–behavior interaction effects result from lasting in utero programming or epigenetic events influencing gene expression . Study gene–drug interaction effects in weight loss treatment . Study gene–bariatric surgery interaction effects in the weight loss following the surgery and the weight loss retention over time It should be apparent from the articles in this series that progress in the dissection of the gene–behavior interaction effects will necessitate combinations of informative animal models and human studies. We should continue to take advantage of existing large-scale population studies, which are generally characterized by more error variance in phenotype and exposure variable assessments. However, these population studies need to be complemented by smaller-scale experiments that offer an opportunity to measure phenotypes more precisely, to quantify the response to a standardized change in behavior, and to investigate mechanisms. Such small-scale experimental studies have the potential to define candidates that can be investigated further in larger cohorts. In the end, efforts to fully understand the genetic architecture of a trait such as human obesity will require very large sample sizes so that many genes with significant main effects (that are likely to be small) together with many potential gene–behavior interaction effects can be tested simultaneously. Replication in independent cohorts will also be a requirement. Even though the conditions that are necessary for such an undertaking to be successful have not been fully defined, it is obvious that issues such as optimal sample size, statistical power, and analytical tools will be of prime importance. Finally, a major challenge will remain as to how to translate advances in our understanding of gene–behavior interaction effects for public health consumption when promoting weight gain prevention and for clinical practice in the context of the treatment of obesity and its associated morbidities. C.B. has received honoraria from NCI, Weight Watchers International, and McCormick Institute. T.A.-C. declared no conflict of interest. This publication was sponsored by the National Cancer Institute (NCI) to present the talks from the “Gene–Nutrition and Gene–Physical Activity Interactions in the Etiology of Obesity” workshop held on 24–26 September 2007. The opinions or assertions contained herein are the views of the authors and are not to be considered as official or reflecting the views of the National Institutes of Health.
Editorial by Sorensen and Echwald Chronically elevated cortisol levels can increase body fat, as seen clearly in Cushing's syndrome. Subjects with abdominal obesity share many of the hormonal, metabolic, and circulatory characteristics of people with Cushing's syndrome. A dysfunctional glucocorticoid receptor may add to the adverse health effects of excessive cortisol concentrations. An Asn363Ser polymorphism in exon 2 of the glucocorticoid receptor gene (GRL) might be associated with overweight and an increased sensitivity to exogenous glucocorticoids.1 We therefore examined whether this variant was associated with altered sensitivity to glucocorticoids as well as obesity with its related metabolic and haemodynamic abnormalities in a cohort of Swedish men.2 Subjects (a total of 284 men) were randomly selected from a larger cohort of men born in Gothenburg, Sweden, in 1944. The design of the study has been described elsewhere.3 Measurements reported here were carried out in Gothenburg during …
We consider the consensus conference an outstanding success, the result of the contributions and hard work of many groups and individuals. The conference would not have been possible without the financial support of several public and private groups. M&M Mars provided a major grant to ACSM, which allowed us to commit to the conference and initiate the planning process. We are grateful for their substantial support and encouragement. The Robert Wood Johnson Foundation provided funds for publication of the review papers and consensus statement in this supplement of Medicine and Science in Sports and Exercise. Colleagues from the North American Society for the Study of Obesity provided financial support and scientific expertise in planning the program. Knoll Pharmaceutical Company and the International Food Information Council provided unrestricted educational grants to support the program. Several units of the U.S. Public Health Service provided additional financial resources without which the meeting would not have been possible. We are grateful for the support of the U.S. Centers for Disease Control and Prevention; the National Institute of Diabetes and Digestive and Kidney Diseases; the National Heart, Lung, and Blood Institute; and the National Institute of Child Health and Human Development. The National Coalition for Promoting Physical Activity also was a co-sponsor of the conference. We thank the Program Committee for their hard work and creative suggestions in developing the program. Outstanding scientists from the physical activity and obesity research community who served on the Program Committee were Drs. William Dietz, John Foreyt, James Hill, Gay Israel, and Rena Wing, with Drs. Steven Rizk and Barbara Campaigne as ex officio members. The Consensus Committee worked long hours before, during, and after the meeting, reviewing the evidence on physical activity and obesity, asking insightful and provocative questions, deliberating the issues, and writing the consensus report. We thank Dr. Scott Grundy for his leadership as Chair of the Consensus Committee and Drs. George Blackburn, Millicent Higgins, Ronald Lauer, Michael Perri, and Donna Ryan for their important contributions. Twenty-four outstanding and dedicated scientists from around the world accepted the charge of the Program Committee to thoroughly review the evidence of a specific aspect of physical activity and obesity. They met the deadlines of preparing their papers before the conference so that the Consensus Committee could review them before coming to the meeting, participated vigorously in the discussions and debates at the conference, and cheerfully (we think) revised their reports after the exchange of ideas in Indianapolis. Their collected works, along with the Consensus Statement, comprise the material assembled in this special issue of Medicine and Science in Sports and Exercise. We hope, and expect, that this collection of papers, along with the Consensus Statement, will represent a prime source of information for all those interested in the topic of physical activity and obesity and will provide direction for research and policy in physical activity and obesity for the next several years. Planning, organizing, and presenting this conference required enormous effort by the dedicated staff of the ACSM. We thank Jim Whitehead, ACSM Executive Vice-President, for his support and leadership throughout the planning process and during the meeting. Dr. Barbara Campaigne, formerly Director of Research at ACSM, was intimately involved in planning the program and provided excellent staff support to the Co-Chairs and the Program Committee. She was instrumental in keeping the process on schedule and in attending to countless details. Jane Gleason joined ACSM shortly before the meeting was held. She came into a challenging situation, in that Dr. Campaigne left ACSM about the time Jane joined our staff. Without missing a beat, Jane worked closely with the Co-Chairs in obtaining the review papers from participants, distributing them to the Consensus Committee, and coordinating many last minute details. Jane worked long hours with the Consensus Committee during and after the meeting as they developed their report. Amy Katzenberger and her staff did their usual outstanding job of handling logistical details for the meeting and in making the meeting a pleasure for the attendees. Other ACSM staff who provided the essential behind the scenes work without which no meeting is successful included Dave Brewer, Bev Brown, Claire Heister, Gail Hunt, Sandy Kuiper, Mark Robertson, and Amy Trobec. We extend our grateful thanks to them and applaud their high level of professionalism.
The recent cloning of a gene that codes for a novel uncoupling protein, UCP2, which is expressed in a wide range of adult human tissues, has raised the possibility that it may be involved in regulation of energy balance. To explore this concept we have investigated potential linkage relationships between three microsatellite markers which encompass the UCP2 gene location on 11q13 with resting metabolic rate (RMR), body mass index, percentage body fat (%FAT) and fat mass (FM) in 640 individuals from 155 pedigrees from the Québec Family Study. Using a linkage analysis strategy based on sibling, avuncular, grandparental and cousin pairs, strong evidence of linkage was found between the marker D11S911 (P = 0.000002) and RMR, with more moderate evidence for D11S916 (P = 0.006) and D11S1321 (P = 0.02). Suggestive evidence of linkage was also observed between D11S1321 and %FAT (P= 0.04) and FM (P= 0.02). It is concluded that the three markers encompassing the UCP2 locus and spanning a 5 cM region on 11q13 are linked to resting energy expenditure in adult humans. The evidence is strong enough to warrant a search for DNA sequence variation in the gene itself.
No abstract is provided for this article.
Correspondence among eight measures commonly used to identify the obese was investigated in 225 men and 212 women 18–59 years of age. Measures included weight, body mass index (BMI), relative weight, skinfold thicknesses at triceps and subscapular sites, the sum of four skinfolds, percentage body fat (%BF) determined hydrostatically, and total weight of fat. Intercorrelations within various measures of body mass and total fat are high (0.86–0.99), while correlations between the two skinfolds, and among measures of body mass, subcutaneous fatness, and total body fat are much lower (0.57–0.70). The amount of variation in %BF accounted for by correlations with the other individual measures of obesity (other than fat weight) ranges from 32% to 48%. When analyzed categorically, the upper quintiles of the various measures poorly identify the same individuals as the fattest. The median chance-adjusted correspondences were 0.57 and 0.64 for men and women, respectively, with single values as low as 0.40. Analyses of variance (ANOVA) indicate the misclassification occurring among upper quintiles is nonrandom with respect to mean levels of these obesity measures. We find a considerable lack of correspondence among measures commonly used to identify the obese. This lack of correspondence may lead to unanticipated dissimilarity among people variously classified as obese and to bias regarding risks of associated disease or mortality.
