Genetics and fat distribution -
Experts say it's a combination of increased availability, bigger portions, and more high-calorie foods. Practically everywhere we go — shopping centers, sports stadiums, movie theaters — food is readily available. You can buy snacks or meals at roadside rest stops, hour convenience stores, even gyms and health clubs.
In the s, fast-food restaurants offered one portion size. Today, portion sizes have ballooned, a trend that has spilled over into many other foods, from cookies and popcorn to sandwiches and steaks.
A typical serving of French fries from McDonald's contains three times more calories than when the franchise began.
A single "super-sized" meal may contain 1,—2, calories — all the calories that most people need for an entire day.
And research shows that people will often eat what's in front of them, even if they're already full. Not surprisingly, we're also eating more high-calorie foods especially salty snacks, soft drinks, and pizza , which are much more readily available than lower-calorie choices like salads and whole fruits.
Fat isn't necessarily the problem; in fact, research shows that the fat content of our diet has actually gone down since the early s.
But many low-fat foods are very high in calories because they contain large amounts of sugar to improve their taste and palatability.
In fact, many low-fat foods are actually higher in calories than foods that are not low fat. The government's current recommendations for exercise call for an hour of moderate to vigorous exercise a day. Our daily lives don't offer many opportunities for activity.
Children don't exercise as much in school, often because of cutbacks in physical education classes. Many people drive to work and spend much of the day sitting at a computer terminal. Because we work long hours, we have trouble finding the time to go to the gym, play a sport, or exercise in other ways.
Instead of walking to local shops and toting shopping bags, we drive to one-stop megastores, where we park close to the entrance, wheel our purchases in a shopping cart, and drive home.
The widespread use of vacuum cleaners, dishwashers, leaf blowers, and a host of other appliances takes nearly all the physical effort out of daily chores and can contribute as one of the causes of obesity.
The average American watches about four hours of television per day, a habit that's been linked to overweight or obesity in a number of studies. Data from the National Health and Nutrition Examination Survey, a long-term study monitoring the health of American adults, revealed that people with overweight and obesity spend more time watching television and playing video games than people of normal weight.
Watching television more than two hours a day also raises the risk of overweight in children, even in those as young as three years old.
Part of the problem may be that people are watching television instead of exercising or doing other activities that burn more calories watching TV burns only slightly more calories than sleeping, and less than other sedentary pursuits such as sewing or reading.
But food advertisements also may play a significant role. The average hour-long TV show features about 11 food and beverage commercials, which encourage people to eat. And studies show that eating food in front of the TV stimulates people to eat more calories, and particularly more calories from fat.
In fact, a study that limited the amount of TV kids watched demonstrated that this practice helped them lose weight — but not because they became more active when they weren't watching TV.
The difference was that the children ate more snacks when they were watching television than when doing other activities, even sedentary ones.
Obesity experts now believe that a number of different aspects of American society may conspire to promote weight gain. Stress is a common thread intertwining these factors. For example, these days it's commonplace to work long hours and take shorter or less frequent vacations.
In many families, both parents work, which makes it harder to find time for families to shop, prepare, and eat healthy foods together. Round-the-clock TV news means we hear more frequent reports of child abductions and random violent acts.
This does more than increase stress levels; it also makes parents more reluctant to allow children to ride their bikes to the park to play.
Parents end up driving kids to play dates and structured activities, which means less activity for the kids and more stress for parents.
Time pressures — whether for school, work, or family obligations — often lead people to eat on the run and to sacrifice sleep, both of which can contribute to weight gain.
Some researchers also think that the very act of eating irregularly and on the run may be another one of the causes of obesity. Neurological evidence indicates that the brain's biological clock — the pacemaker that controls numerous other daily rhythms in our bodies — may also help to regulate hunger and satiety signals.
Ideally, these signals should keep our weight steady. They should prompt us to eat when our body fat falls below a certain level or when we need more body fat during pregnancy, for example , and they should tell us when we feel satiated and should stop eating.
Close connections between the brain's pacemaker and the appetite control center in the hypothalamus suggest that hunger and satiety are affected by temporal cues.
Irregular eating patterns may disrupt the effectiveness of these cues in a way that promotes obesity. Similarly, research shows that the less you sleep, the more likely you are to gain weight.
Lack of sufficient sleep tends to disrupt hormones that control hunger and appetite and could be another one of the causes of obesity. In a study of more than 1, volunteers, researchers found that people who slept less than eight hours a night had higher levels of body fat than those who slept more, and the people who slept the fewest hours weighed the most.
Stress and lack of sleep are closely connected to psychological well-being, which can also affect diet and appetite, as anyone who's ever gorged on cookies or potato chips when feeling anxious or sad can attest. Studies have demonstrated that some people eat more when affected by depression, anxiety, or other emotional disorders.
In turn, overweight and obesity themselves can promote emotional disorders: If you repeatedly try to lose weight and fail, or if you succeed in losing weight only to gain it all back, the struggle can cause tremendous frustration over time, which can cause or worsen anxiety and depression.
A cycle develops that leads to greater and greater obesity, associated with increasingly severe emotional difficulties. To find weight loss solutions that can be tailored to your needs, buy the Harvard Special Health Report Lose Weight and Keep It Off. As a service to our readers, Harvard Health Publishing provides access to our library of archived content.
Please note the date of last review or update on all articles. No content on this site, regardless of date, should ever be used as a substitute for direct medical advice from your doctor or other qualified clinician. Successful weight loss depends largely on becoming more aware of your behaviors and starting to change them.
Bonetti, L. Stuppia, S. Paolacci, A. Dautaj, M. Bertelli EBTNA-LAB, Rovereto TN , Italy. paolacci assomagi. Adipose tissue distribution usually varies among men and women. In men, adipose tissue is known to accumulate in the abdominal region surrounding the visceral organs android fat distribution whereas, in women, the accumulation of adipose tissue generally occurs in the gluteal-femoral regions gynoid fat distribution.
In some cases, however, android distribution can be found in women and gynoid distribution can be found in men. The abdominal region was defined as the area between the ribs and the pelvis, and was enclosed by the trunk region.
The leg region included all of the area below the lines that form the lower borders of the trunk. The gluteofemoral region included the hips and upper thighs, and overlapped both leg and trunk regions.
The upper demarcation of this region was below the top of the iliac crest at a distance of 1. DEXA CoreScan software GE Healthcare was used to determine visceral abdominal fat mass within the abdominal region. In stage 4, the risk factors included systolic and diastolic blood pressures, defined as the values of arterial blood pressure in mm Hg measured using an Omron monitor during the systolic and diastolic phases of the heart cycle.
For disease outcomes analyses in the UK Biobank in stage 4, binary definitions of prevalent disease status and a case-control analytical design were used in line with previous work.
Controls were participants who 1 did not self-report a diagnosis of diabetes of any type, 2 did not take any diabetes medications, and 3 did not have an electronic health record of diabetes of any type. In EPIC-InterAct, the outcome was incident type 2 diabetes.
Incident type 2 diabetes case status was defined on the basis of evidence of type 2 diabetes from self-report, primary care registers, drug registers medication use , hospital record, or mortality data.
Participants with prevalent diabetes at study baseline were excluded from EPIC-InterAct. Controls were participants who did not meet any of these criteria. In stage 1, GWAS analyses were performed in the UK Biobank using BOLT-LMM, 27 which fits linear mixed models accounting for relatedness between individuals using a genomic kinship matrix.
Restriction to individuals of European ancestry, use of linear mixed models UK Biobank , and adjustment for genetic principal components and genomic inflation factor GIANT were used to minimize type I error.
Quality measures of genuine genetic association signal vs possible confounding by population stratification or relatedness included the mean χ 2 statistic, the linkage-disequilibrium score LDSC regression intercept, and its attenuation ratio eMethods 2 in the Supplement , as recommended for genetic studies of this size using linear mixed model estimates.
A forward-selection process was used to select independent genetic variants for stage 2. Full details about genetic analyses are in eMethods 2 in the Supplement.
In stage 2, polygenic scores capturing genetic predisposition to higher WHR were derived by combining the independent genetic variants from stage 1 or subsets of the variants as described below , weighted by their association with BMI-adjusted WHR in stage 1.
A general polygenic score for higher WHR was derived by combining all genetic variants. A fourth polygenic score was derived by combining genetic variants not included in the waist- or hip-specific polygenic scores.
The statistical performance of these polygenic scores was assessed by estimating the proportion of the variance in BMI-adjusted WHR accounted for by the score variance explained and by the F statistic eMethods 4 in the Supplement.
The F statistic is a measure of the ability of the polygenic score to predict the independent variable BMI-adjusted WHR. Values of F statistic greater than 10 have been considered to provide evidence of a statistically robust polygenic score.
In stages 3 and 4, associations of polygenic scores with DEXA phenotypes, cardiometabolic risk factors, and outcomes were estimated in each study separately and results were combined using fixed-effect inverse-variance weighted meta-analysis.
In individual-level data analyses, polygenic scores were calculated for each study participant by adding the number of copies of each contributing genetic variant weighted by its association estimate in SD units of BMI-adjusted WHR per allele from stage 1.
Association of polygenic scores with outcomes were estimated using linear, logistic, or Cox regression models as appropriate for outcome type and study design. Regression models were adjusted for age, sex, and genetic principal components or a genomic kinship matrix to minimize genetic confounding.
In UK Biobank disease outcomes analyses, prevalent disease status was defined as a binary variable and logistic regression was used to estimate the odds ratio OR of disease per 1-SD increase in BMI-adjusted WHR due to a given polygenic score. In EPIC-InterAct, Cox regression weighted for case-cohort design was used to estimate the hazard ratio of incident type 2 diabetes per 1-SD increase in BMI-adjusted WHR due to a given polygenic score.
In summary statistics analyses, estimates equivalent to those of individual-level analyses were obtained using inverse-variance weighted meta-analysis of the association of each genetic variant in the polygenic score with the outcome, divided by the association of that genetic variant with BMI-adjusted WHR.
They also assume a linear relationship of the polygenic score with continuous outcomes linear regression , with the log-odds of binary outcomes logistic regression , or with the log-hazard of incident disease Cox regression.
All of these assumptions were largely met in this study eMethods 5, eTable 4, and eFigures in the Supplement. Meta-analyses of log-ORs and log—hazard ratios of disease assumed that these estimates are similar, an assumption that was shown to be reasonable in a sensitivity analysis conducted in EPIC-InterAct eMethods 5 and eFigure 7 in the Supplement.
In stages 3 and 4, associations with continuous outcomes were expressed in standardized or clinical units of outcome per 1-SD increase in BMI-adjusted WHR corresponding to 0.
Associations with disease outcomes were expressed as ORs for outcome per 1-SD increase in BMI-adjusted WHR due to a given polygenic score.
Absolute risk increases ARIs for disease outcomes were estimated using the estimated ORs and the incidence of type 2 diabetes or coronary disease in the United States eMethods 5 in the Supplement. All reported P values were from 2-tailed statistical tests. In addition to deriving specific polygenic scores, the independent association of gluteofemoral or abdominal fat distribution with outcomes was studied using multivariable genetic association analyses adjusting for either of these 2 components of body fat distribution eMethods 6 and eFigure 8 in the Supplement.
Adjusting for abdominal fat distribution measures was used as a way of estimating the residual association of the polygenic score with outcomes via gluteofemoral fat distribution, while adjusting for gluteofemoral fat distribution measures as a way of estimating the residual association via abdominal fat distribution eFigure 8 in the Supplement.
To obtain adjusted association estimates, multivariable-weighted regression models were fitted in which the association of the variant general polygenic score exposure with cardiometabolic risk factors or diseases outcomes was estimated while adjusting for a polygenic score comprising the same genetic variants but weighted for measures of abdominal fat distribution or measures of gluteofemoral fat distribution covariates.
This method was also used to conduct a post hoc exploratory analysis of the association of the hip-specific polygenic score with cardiometabolic disease outcomes after adjusting for visceral abdominal fat mass estimates. Statistical analyses were performed using Stata version These genetic variants were used to derive polygenic scores for higher WHR Table 1.
The general variant and variant polygenic scores were associated with higher visceral abdominal and lower gluteofemoral fat mass Figure 1 A; eFigure 15 in the Supplement.
The waist-specific polygenic score for higher WHR was associated with higher abdominal fat mass, but not with gluteofemoral or leg fat mass Figure 1 B. The hip-specific polygenic score for higher WHR was associated with lower gluteofemoral and leg fat mass, but did not show statistically significant associations with abdominal fat mass Figure 1 B.
Participants with higher values of the hip-specific polygenic score had numerically higher visceral abdominal fat mass, but the difference was not statistically significant when accounting for multiple tests Figure 1 B.
Both hip-specific and waist-specific polygenic scores for higher WHR were associated with higher systolic and diastolic blood pressure and triglyceride level, with similar association estimates for a 1-SD increase in BMI-adjusted WHR Figure 2 A.
While the hip-specific polygenic score was associated with higher fasting insulin and higher LDL-C levels, the waist-specific polygenic score did not have statistically significant associations with these traits Figure 2 A.
Both the hip-specific and waist-specific polygenic scores were associated with higher odds of type 2 diabetes and coronary disease, similarly in men and women Figure 2 B and eTable 9 in the Supplement.
The hip-specific polygenic score had a statistically larger association estimate for diabetes than the waist-specific polygenic score per 1-SD increase in BMI-adjusted WHR OR, 2. In a post-hoc multivariable analysis adjusting for visceral abdominal fat mass estimates, the hip-specific polygenic score showed a statistically significant association with higher odds of type 2 diabetes and coronary disease OR for diabetes per 1-SD increase in BMI-adjusted WHR due to the hip-specific polygenic score, 2.
The variant polygenic score showed associations with risk factors and disease outcomes similar to those observed for the variant general polygenic score eFigure 15 in the Supplement. Sensitivity analyses supported the robustness of the main analysis to sex-specific associations, associations with height, or the possibility of false-positive associations in stage 1 or stage 2 eMethods 7 and eTables in the Supplement.
In multivariable analyses adjusting for hip circumference estimates, the variant polygenic score had a pattern of association with compartmental fat mass, cardiometabolic risk factors, and disease outcomes, which was similar to that of the waist-specific polygenic score eFigures 8D and 17 in the Supplement.
The variant polygenic score remained associated with higher risk of type 2 diabetes and coronary disease even when adjusting for hip circumference and leg fat mass in the same model eTable 12 in the Supplement.
In multivariable analyses adjusting for waist circumference estimates, the variant polygenic score had a pattern of association with compartmental fat mass, cardiometabolic risk factors, and disease outcomes, which was similar to that of the hip-specific polygenic score eFigures 8C and 17 in the Supplement.
The variant polygenic score remained associated with higher risk of type 2 diabetes and coronary disease even when adjusting for waist circumference and visceral abdominal fat mass in the same model eTable 12 in the Supplement.
In multivariable analyses adjusting for both waist and hip circumference estimates, the variant polygenic score was not associated with risk of type 2 diabetes or coronary disease eFigure 8B and eTable 12 in the Supplement.
This large study identified distinct genetic variants associated with a higher WHR via specific associations with lower gluteofemoral or higher abdominal fat distribution. Both of these distinct sets of genetic variants were associated with higher levels of cardiometabolic risk factors and a higher risk of type 2 diabetes and coronary disease.
While this study supports the theory that an enhanced accumulation of fat in the abdominal cavity may be a cause of cardiovascular and metabolic disease, it also provides novel evidence of a possible independent role of the relative inability to expand the gluteofemoral fat compartment.
Previous studies of approximately 50 genomic regions associated with BMI-adjusted WHR 16 have shown an association between genetic predisposition to higher WHR and higher risk of cardiometabolic disease, 26 , 35 mirroring the well-established BMI-independent association of a higher WHR with incident cardiovascular and metabolic disease in large-scale observational studies.
The results of this study support the hypothesis that an impaired ability to preferentially deposit excess calories in the gluteofemoral fat compartment leads to higher cardiometabolic risk in the general population. This is consistent with observations in severe forms of partial lipodystrophy 6 , 7 and with the emerging evidence of a shared genetic background between extreme lipodystrophies and fat distribution in the general population.
These associations may perhaps reflect the secondary deposition within ectopic fat depots, such as liver, cardiac and skeletal muscle, and pancreas, of excess calories that cannot be accommodated in gluteofemoral fat.
It has been hypothesized that the association between fat distribution and cardiometabolic risk is due to an enhanced deposition of intra-abdominal fat generating a molecular milieu that fosters abdominal organ insulin resistance.
This study has several limitations. First, as this is an observational study, it cannot establish causality. Second, the discovery and characterization of genetic variants was conducted in a large data set but was limited to individuals of European ancestry.
While the genetic determinants of anthropometric phenotypes may be partly shared across different ethnicities, 16 , 39 , 40 further investigations in other populations and ethnicities will be required for a complete understanding of the genetic relationships between body shape and cardiometabolic risk.
Third, this study was largely based on population-based cohorts, the participants of which are usually healthier than the general population, and used analytical approaches that deliberately minimized the influence of outliers, in this case people with extreme fat distribution.
Genetic studies in people with extreme fat distribution may help broaden understanding of the genetic basis of this risk factor. Fifth, absolute risk increase estimates are based on incidence rates and ORs calculated in different populations and therefore assume that these populations are similar.
Seventh, this analysis focused on common genetic variants captured in both UK Biobank and GIANT and, by design, did not investigate the role of rare genetic variation or of other variants captured by dense imputation in the UK Biobank.
Eighth, there was a statistically significant difference in the association of hip- vs waist-specific polygenic scores with diabetes risk, with greater estimated magnitude of association for the hip-specific polygenic score.
However, given that the difference in absolute risk was small, this observation does not necessarily represent a strong signal of mechanistic difference or differential clinical importance in the relationship between the gluteofemoral vs abdominal components of fat distribution and diabetes risk.
Distinct genetic mechanisms may be linked to gluteofemoral and abdominal fat distribution that are the basis for the calculation of the waist-to-hip ratio. Corresponding Authors: Claudia Langenberg, MD, PhD claudia. langenberg mrc-epid. uk , and Luca A. Lotta, MD, PhD luca.
lotta mrc-epid. uk , MRC Epidemiology Unit, University of Cambridge, Cambridge CB20QQ, United Kingdom. Author Contributions: Dr Lotta had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Acquisition, analysis, or interpretation of data: Lotta, Wittemans, Zuber, Stewart, Sharp, Luan, Day, Li, Bowker, Cai, De Lucia Rolfe, Khaw, Perry, Scott, Burgess, Wareham, Langenberg.
Critical revision of the manuscript for important intellectual content: Lotta, Wittemans, Zuber, Stewart, Sharp, Luan, Day, Li, Bowker, Cai, De Lucia Rolfe, Khaw, Scott, Burgess, Wareham, Langenberg.
Statistical analysis: Lotta, Wittemans, Zuber, Stewart, Sharp, Luan, Day, Li, Bowker, Cai, De Lucia Rolfe, Perry, Burgess, Langenberg.
Obtained funding: Khaw, Wareham, Langenberg. Administrative, technical, or material support: De Lucia Rolfe, Khaw, Wareham, Langenberg. Supervision: Lotta, Wareham, Langenberg. Dr Scott is an employee and shareholder in GlaxoSmithKline. No other disclosures were reported.
Additional Contributions: This research was conducted using the UK Biobank resource and data from the EPIC-InterAct, Fenland, and EPIC-Norfolk studies. We gratefully acknowledge the help of the MRC Epidemiology Unit Support Teams, including the field, laboratory, and data management teams.
full text icon Full Text. Download PDF Top of Article Key Points Abstract Introduction Methods Results Discussion Conclusions Article Information References.
Figure 1. Associations With Compartmental Fat Mass of Polygenic Scores for Higher Waist-to-Hip Ratio WHR. View Large Download. Figure 2. Associations With Cardiometabolic Risk Factors and Disease Outcomes of Waist- or Hip-Specific Polygenic Scores for Higher Waist-to-Hip Ratio WHR.
Table 1. Summary of the Study Design. Table 2. Participants of the UK Biobank Included in This Study a. Data Sources, Study Design, Measurements, and Phenotype Definitions eMethods 2. Genetic Association Analyses eMethods 3. Selection of Subsets of Genetic Variants Associated With Higher WHR via a Specific Association With Higher Waist Circumference, or via a Specific Association With Lower Hip Circumference eMethods 4.
Assessment of Performance and Statistical Power of Polygenic Scores for Higher WHR eMethods 5. Assumptions and Interpretation of Association Analyses Between Polygenic Scores for Higher WHR and Outcome Traits eMethods 6.
Multivariable Genetic Association Analyses eMethods 7. Secondary and Sensitivity Analyses eTable 1. Participating Studies eTable 2. and UK Biobank Studies Who Underwent Detailed Anthropometric Measurements by Dual-Energy X-ray Absorptiometry eTable 3. Characteristics of Participants of the EPIC-InterAct Study Included in the Analysis eTable 4.
Difference in Age-, Sex- and BMI-Residualized WHR at Different Levels of the Distribution of Standardized BMI-Adjusted WHR Following the Inverse-Rank Normal Transformation eTable 5.
Standard Deviation Values Used to Convert Estimates Between Clinical and Standardized Units and Their Source eTable 6. List of the Independent Lead Genetic Variants Identified in Stage 1 Which Were Used to Derive Polygenic Scores for Higher WHR eTable 7.
Associations of Polygenic Scores for Higher WHR With Additional Continuous Phenotypes in Secondary Analyses eTable 8.
Associations of Polygenic Scores for Higher WHR With Nondiabetic Hyperglycemia eTable 9. Association of Polygenic Scores for Higher WHR With Risk of Type 2 Diabetes and Coronary Artery Disease in Men and Women From the UK Biobank Study eTable Results of Sensitivity Analyses eTable Associations of the Variant Polygenic Score for Higher WHR With Cardiometabolic Disease Outcomes in Multivariable Genetic Association Analyses Adjusting for Height eTable Associations of the Genetic Variants With Risk of Cardiometabolic Disease Outcomes in Multivariable Genetic Analyses eFigure 1.
Compartmental Body Fat Mass Measurement by Dual-Energy X-ray Absorptiometry eFigure 2. Statistical Power Calculations eFigure 3. Distribution of the Values of Polygenic Scores for Higher WHR in UK Biobank eFigure 4.
Distribution of the Values of Standardized Systolic and Diastolic Blood Pressure Outcome Variables in UK Biobank eFigure 5.
Genetics and fat distribution you for visiting nature. You are using a browser Genetucs with limited support for Distributiin. To obtain Enhances cognitive function and performance best experience, we Renewable energy guides you use faat more up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Obesity-associated morbidity is exacerbated by abdominal obesity, which can be measured as the waist-to-hip ratio adjusted for the body mass index WHRadjBMI. Camilleri, A. Kiani, K. Herbst, J. Kaftalli, A. Bernini, K. Dhuli, Xistribution.
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