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Take ONE Teaspoon of This Spice to Fix Your DiabetesFat distribution and diabetes -
Distribution of adipose tissue and risk of cardiovascular disease and death: A year follow-up of participants in the population study of women in Gothenburg.
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Design, Setting, and Participants Genome-wide association studies GWAS for WHR combined data from the UK Biobank cohort and summary statistics from previous GWAS data collection: Specific polygenic scores for higher WHR via lower gluteofemoral or via higher abdominal fat distribution were derived using WHR-associated genetic variants showing specific association with hip or waist circumference.
Associations of polygenic scores with outcomes were estimated in 3 population-based cohorts, a case-cohort study, and summary statistics from 6 GWAS data collection: Exposures More than 2.
Main Outcomes and Measures BMI-adjusted WHR and unadjusted WHR GWAS ; compartmental fat mass measured by dual-energy x-ray absorptiometry, systolic and diastolic blood pressure, low-density lipoprotein cholesterol, triglycerides, fasting glucose, fasting insulin, type 2 diabetes, and coronary disease risk follow-up analyses.
Conclusions and Relevance Distinct genetic mechanisms may be linked to gluteofemoral and abdominal fat distribution that are the basis for the calculation of the WHR. These findings may improve risk assessment and treatment of diabetes and coronary disease. The distribution of body fat is associated with the propensity of overweight individuals to manifest insulin resistance and its associated metabolic and cardiovascular complications.
While many studies support this assertion and plausible mechanisms have been proposed, WHR can also be increased by a reduction in its denominator, the hip circumference. Evidence from several different forms of partial lipodystrophy 6 , 7 and functional studies of peripheral adipose storage compartments 8 - 10 suggests that a primary inability to expand gluteofemoral or hip fat can also underpin subsequent cardiometabolic disease risk.
Emerging evidence from the analysis of common genetic variants associated with greater insulin resistance but lower levels of hip fat suggests that similar mechanisms may also be relevant to the general population.
In this study, large-scale human genetic data were used to investigate whether genetic variants related to body fat distribution via lower levels of gluteofemoral hip fat or via higher levels of abdominal waist fat are associated with type 2 diabetes or coronary disease risk. A multistage approach was adopted Table 1.
In stage 1, genome-wide association studies GWAS of WHR with and without adjustment for BMI were performed to identify genetic variants associated with fat distribution. Stage 1 included data from participants of European ancestry in the UK Biobank study and summary statistics from previously published GWAS of the Genetic Investigation of Anthropometric Traits GIANT Consortium.
Stage 2 included data from participants of European ancestry in the UK Biobank and summary statistics from GIANT.
In stage 3, associations of polygenic scores with compartmental fat mass measured by dual-energy x-ray absorptiometry DEXA were estimated in participants of European ancestry in the UK Biobank, Fenland, and EPIC-Norfolk studies.
In stage 4, associations of polygenic scores with 6 cardiometabolic risk factors and with risk of type 2 diabetes and coronary artery disease were estimated using data from participants of European ancestry in the UK Biobank, the EPIC-InterAct case-cohort study, and summary statistics from 6 previously published GWAS.
All studies were approved by local institutional review boards and ethics committees, and participants gave written informed consent. The UK Biobank data collection: is a prospective population-based cohort study of people aged 40 to 69 years who were recruited from to from 22 centers located in urban and rural areas across the United Kingdom.
Fenland data collection: is a prospective population-based cohort study of people born from to and recruited from to from outpatient primary care clinics in Cambridge, Ely, and Wisbech United Kingdom. EPIC-Norfolk data collection: is a prospective population-based cohort study of individuals aged 40 to 79 years and living in Norfolk County rural areas, market towns, and the city of Norwich in the United Kingdom at recruitment from outpatient primary care clinics in to EPIC-InterAct data collection: is a case-cohort study nested within the European Prospective Investigation into Cancer and Nutrition EPIC , a prospective cohort study.
Summary statistics from 11 GWAS published by research consortia between and were used in the different stages of the study eMethods 1 and eTable 1 in the Supplement.
Detailed descriptions of study design, sources of data, and participants in each stage are in Table 1 and Table 2 , and eMethods 1 and eTables in the Supplement.
Outcomes of the study were WHR stages 1 and 2b , hip and waist circumference stage 2a , compartmental body fat masses stage 3 , 6 cardiometabolic risk factors systolic and diastolic blood pressure, fasting glucose, fasting insulin, triglycerides, and LDL-C; stage 4 , and 2 disease outcomes type 2 diabetes and coronary disease; stage 4.
In stages 1 and 2, WHR was defined as the ratio of the circumference of the waist to that of the hip, both of which were estimated in centimeters using a Seca cm tape measure. BMI-adjusted WHR was obtained by calculating the residuals for a linear regression model of WHR on age, sex, and BMI.
In stage 3, compartmental fat masses were measured in grams by DEXA, a whole-body, low-intensity x-ray scan that precisely quantifies fat mass in different body regions.
In the UK Biobank, DEXA measures were obtained using a GE-Lunar iDXA instrument. In the Fenland and EPIC-Norfolk studies, DEXA scans were performed using a Lunar Prodigy advanced fan beam scanner GE Healthcare. Participants were scanned by trained operators using standard imaging and positioning protocols.
All the images were manually processed by one trained researcher, who corrected DEXA demarcations according to a standardized procedure as illustrated and described in eFigure 1 and eMethods 1, respectively, in the Supplement.
In brief, the arm region included the arm and shoulder area. The trunk region included the neck, chest, and abdominal and pelvic areas. 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.
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Es ist schade, dass ich mich jetzt nicht aussprechen kann - ich beeile mich auf die Arbeit. Aber ich werde befreit werden - unbedingt werde ich schreiben dass ich in dieser Frage denke.
die Ausgezeichnete Mitteilung, ich beglückwünsche)))))