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Body fat distribution

Body fat distribution

By submitting a faf you agree to abide Boyd Premium-quality pre-workout Terms and Distibution Body fat distribution. Include Images Large Print. Retrieved 21 March CAS Google Scholar. Gynoid fat bodily distribution is measured as the waist-to-hip ratio WHRwhereby if a woman has a lower waist-to-hip ratio it is seen as more favourable. Body fat distribution

Body fat distribution -

Body fat distribution can easily be determined by simply looking in the mirror. The outline of the body, or body shape, would indicate the location of where body fat is stored.

Abdominal fat storage patterns are generally compared to the shape of an apple, called the android shape. This shape is more commonly found in males and post- menopausal females. In terms of disease risk, this implies males and post- menopausal females are at greater risk of developing health issues associated with excessive visceral fat.

Individuals who experience chronic stress tend to store fat in the abdominal region. A pear-shaped body fat distribution pattern, or gynoid shape , is more commonly found in pre-menopausal females.

A batch variable was used as covariate in the GWAS for the discovery analyses to adjust for genotyping array UKB Axiom and UK BiLEVE as well as for other differences between UK BiLEVE and UKB Axiom-genotyped participants.

We also included the first 15 principal components and sex in the sex-combined analyses as covariates in the GWAS. LD score regression intercepts see further information below , calculated using ldsc 17 , were used to adjust for genomic inflation, by dividing the square of the t -statistic for each tested SNP with the LD-score regression intercept for that GWAS, and then calculating new P -values based on the adjusted t -statistic.

The --clump function in PLINK was used to identify the number of independent signals in each GWAS. This function groups associated SNPs based on the linkage disequilibrium LD pattern.

After running --clump in PLINK, conditional analyses were also performed for each locus conditioning for the lead SNP, but no further signals were identified.

Several associations were found in more than one of the three body fat ratios AFR, TFR, or LFR or strata males, females, or sex-combined and different lead SNPs were observed for different traits and strata at several loci.

To assess whether these represented the same signal, we assessed the LD between overlapping lead SNPs in PLINK. We then performed conditional analysis in PLINK, conditioning on the most significant SNPs across all phenotypes and strata.

For each independent signal, the lead SNP lowest P -value was taken forward for replication. Meta analyses of results from the discovery and replication cohorts was performed with the METAL software 53 for all independent associations that were taken forward for replication.

We estimated SNP heritability and genetic correlations using LD score regression LDSC , implemented in the ldsc software package Only SNPs that were included in HapMap3 were included in these analyses. LDSC uses LD patterns and summary stats from GWAS as input. For genetic correlations, we performed additional sex-stratified GWAS in the UK biobank using the same covariates as for the ratios for standard anthropometric traits, BMI, height, WC, WHR, WCadjBMI, and WHRadjBMI, in the discovery cohort.

GWAS summary stats were filtered for SNPs included in HapMap3 to reduce likelihood of bias induced by poor imputation quality. After this filtering, 1,, SNPs remained for LDSC analyses.

Genetic correlations between the three body fat ratios and anthropometric traits were assessed by cross-trait LD score regression. Lead SNPs from all independent signals in our analyses were cross-referenced with the NHGRI-EBI catalog of published genome-wide association studies GWAS Catalog—data downloaded on 23 April 19 to determine whether body fat ratio-associated signals overlapped with previously identified anthropometric associations from previous GWAS.

LD between data in the GWAS catalog and our lead SNPs were calculated using PLINK v1. Associated loci were investigated for overlap with eQTLs from the GTEx project The threshold for significance for the eQTLs was set to 2. The strongest associated SNP for each tissue and gene in the GTEx dataset was identified.

We then estimated the LD between the top eQTL SNPs and the lead SNP for each independent association from our analysis. Polyphen and SIFT-scores for the missense variants extracted from Ensembl— www. org were used to assess the deleteriousness of the body fat ratio-associated variants.

To identify the functional roles and tissue specificity of associated variants, we performed tissue and gene-set enrichment analyses using DEPICT For the gene-set enrichment in DEPICT, gene expression data from 77, samples have been used to predict gene function for all genes in the genome based on similarities in gene expression.

In comparison to standard enrichment tools that apply a binary definition to define membership in a set of genes that have been associated with a biological pathway or functional category genes are either included or not included , in DEPICT, the probability of a gene being a member of a gene set has instead been estimated based on correlation in gene expression.

This membership probability to each gene set has been estimated for all genes in the human genome and the membership probabilities for each gene have been designated reconstituted gene sets. A total of 14, reconstituted gene sets have been generated which represent a wide set of biological annotations Gene Ontology [GO], KEGG, REACTOME, Mammalian Phenotype [MP], etc.

For tissue enrichment in DEPICT, microarray data from 37, human tissues have been used to identify genes with high expression in different cells and tissues. For the enrichment analyses, we performed sex-stratified GWAS for AFR, LFR and TFR on the combined cohort, i.

The clump functionality in PLINK is used to determine associated loci. In the enrichment analyses, DEPICT assesses whether the reconstituted gene sets are enriched for genes within trait-associated loci The false discovery rate FDR 55 was used to adjust for multiple testing. We used the GWAMA software 31 to test for heterogenous effects of associated SNPs between sexes.

In GWAMA, fixed-effect estimates of sex-specific and sex-combined beta coefficients and standard errors are calculated from GWAS summary statistics to test for heterogeneous allelic effects between females and males. GWAMA obtains a test-statistic by subtracting the sex-combined squared t -statistic from the sum of the two sex-specific squared t -statistics.

This test statistic is asymptotically χ 2 -distributed and equivalent to a normal z -test of the difference in allelic effects between sexes. Lead SNPs that replicated were tested for heterogeneity between sexes for the trait that they were associated with. This corresponds to 30 tests for AFR, 44 for LFR, and 66 for TFR.

Summary statistics from the replication cohort were used in order to maximize statistical power. Restrictions apply to the availability of these data, which were used under license for the current study Project No. Data are available for bona fide researchers upon application to the UK Biobank.

All other relevant data are available from the authors. Afshin, A. et al. Health effects of overweight and obesity in countries over 25 years.

Article Google Scholar. Ng, M. Global, regional, and national prevalence of overweight and obesity in children and adults during a systematic analysis for the Global Burden of Disease Study Lancet , — Di Cesare, M.

Trends in adult body-mass index in countries from to A pooled analysis of population-based measurement studies with Pi-Sunyer, F. Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults: the evidence report.

National Institutes of Health. Obes Res. Link, J. The genetic basis for sex differences in obesity and lipid metabolism. Article CAS Google Scholar. Bouchard, C. Genetic and nongenetic determinants of regional fat distribution. Wajchenburg, B.

Subcutaneous and visceral adipose tissue: their relation to the metabolic syndrome. Tunstall-Pedoe, H. Myth and paradox of coronary risk and the menopause.

Locke, A. Genetic studies of body mass index yield new insights for obesity biology. Nature , — Heid, I. Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution.

Wen, W. Genome-wide association studies in East Asians identify new loci for waist-hip ratio and waist circumference. Article ADS CAS Google Scholar. Shungin, D. New genetic loci link adipose and insulin biology to body fat distribution.

Kilpeläinen, T. Genetic variation near IRS1 associates with reduced adiposity and an impaired metabolic profile. Lu, Y. New loci for body fat percentage reveal link between adiposity and cardiometabolic disease risk. Fox, C. Genome-wide association for abdominal subcutaneous and visceral adipose reveals a novel locus for visceral fat in women.

PLoS Genet. Mally, K. Reliability and accuracy of segmental bioelectrical impedance analysis for assessing muscle and fat mass in older Europeans: a comparison with dual-energy X-ray absorptiometry. Bulik-Sullivan, B. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies.

Weeks, J. plink: an R package for linking mixed-format tests using IRT-based methods. MacArthur, J. The new NHGRI-EBI catalog of published genome-wide association studies GWAS Catalog. Nucleic Acids Res. Wood, A. Defining the role of common variation in the genomic and biological architecture of adult human height.

He, M. Meta-analysis of genome-wide association studies of adult height in East Asians identifies 17 novel loci.

Bis, J. Meta-analysis of genome-wide association studies from the CHARGE consortium identifies common variants associated with carotid intima media thickness and plaque. Teslovich, T. Biological, clinical and population relevance of 95 loci for blood lipids.

Kathiresan, S. Common variants at 30 loci contribute to polygenic dyslipidemia. Warren, H. Genome-wide association analysis identifies novel blood pressure loci and offers biological insights into cardiovascular risk. Wain, L. Novel blood pressure locus and gene discovery using genome-wide association study and expression data sets from blood and the kidney novelty and significance.

Hypertension 70 , e4—e19 Pim van der Harst, N. Identification of 64 novel genetic loci provides an expanded view on the genetic architecture of coronary artery disease.

Newton-Cheh, C. Genome-wide association study identifies eight loci associated with blood pressure. Ehret, G. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Wakil, S. A common variant association study reveals novel susceptibility loci for low HDL-cholesterol levels in ethnic Arabs.

Magi, R. Meta-analysis of sex-specific genome-wide association studies. An atlas of genetic correlations across human diseases and traits. Aguet, F. Genetic effects on gene expression across human tissues.

Article ADS Google Scholar. Adzhubei, I. Predicting functional effect of human missense mutations using PolyPhen Ng, P. SIFT: predicting amino acid changes that affect protein function. Allele, A. Cancer progression and tumor cell motility are associated with the FGFR4. Cancer Res. Frullanti, E.

Meta and pooled analyses of FGFR4 GlyArg polymorphism as a cancer prognostic factor. Cancer Prev. Ezzat, S. The cancer-associated FGFR-GR polymorphism enhances pancreatic insulin secretion and modifies the risk of diabetes.

Cell Metab. Pers, T. Biological interpretation of genome-wide association studies using predicted gene functions. Randall, J. Sex-stratified genome-wide association studies including , individuals show sexual dimorphism in genetic loci for anthropometric traits.

Winkler, T. The influence of age and sex on genetic associations with adult body size and shape: a large-scale genome-wide interaction study. Wight, T. Versican: a versatile extracellular matrix proteoglycan in cell biology. Cell Biol. Binder, M. The extracellular matrix in cancer progression: role of hyalectan proteoglycans and ADAMTS enzymes.

Cancer Lett. Lin, D. Adipose extracellular matrix remodelling in obesity and insulin. Bekhouche, M. Determination of the substrate repertoire of ADAMTS2, 3, and 14 significantly broadens their functions and identifies extracellular matrix organization and TGF-β signaling as primary targets.

FASEB J. Kelwick, R. The ADAMTS A Disintegrin and Metalloproteinase with Thrombospondin motifs family. Yaghootkar, H.

Genetic evidence for a link between favorable adiposity and lower risk of type 2 diabetes. Heart Dis. CAS Google Scholar. UK Biobank Ethics and Governance Framework Version 3. UK Biobank Walter, K. Increased visceral fat is associated with increased health risks and thus, when investigating overweight and obesity, it is important to evaluate the distribution of body fat carefully to decide on the most suitable treatment and the urgency of treatment.

This assessment is performed using clinical measures, such as waist measurement, waist-to-hip ratio and waist-to-height ratio, and devices such as DEXA and bioimpedance see article here.

It is now widely known that visceral adipose tissue belly fat and ectopic fat are more harmful to health than fat at other sites in the body. Why is this?

Visceral adipose tissue is in close proximity to the liver, and its blood vessels run directly to the liver. Thus, when fat is released from visceral adipose tissue, it goes directly to the liver, which has to process it.

However, an individual with excessive visceral adipose tissue will continually send large amounts of fat to the liver, leading to fat accumulation in the liver cells ectopic fat and the development of non-alcoholic fatty liver disease. This provokes inflammation and resistance to the effects of insulin, a hormone important in fat and sugar storage and metabolism.

In the long term, fatty liver can lead to serious alterations of liver function. The excess fat in the body also leads to fat being stored in the muscles. This affects muscle function and makes muscle more resistant to the action of insulin.

Subcutaneous fat, on the other hand, not only releases its stored fats more slowly, but those fats enter the general circulation and reach the liver in lower concentrations, so they are less likely to cause damage.

The fat cells in visceral adipose tissue are different from those in subcutaneous fat. When fat stores increase, the adipose tissue can accommodate the extra fat by increasing the number of fat cells or by increasing their size.

Both of these mechanisms are observed but, for various reasons, in visceral adipose tissue fewer new adipocytes fat cells appear than in subcutaneous adipose tissue, and there is a much greater increase in cell size. Increased cell size provokes inflammation see my article on lipoinflammation here.

This inflammation is considered to be the link between excess body fat and chronic diseases, such as heart disease, type 2 diabetes and cancer. See my article on chronic diseases associated with excess body fat here. Oestrogens have been shown to promote fat accumulation in the gluteofemoral subcutaneous fat stores buttocks and thighs.

Fat starts to accumulate in this region as girls reach puberty, and it typically persists until the menopause.

After the menopause, oestrogen levels fall and the fat distribution in postmenopausal women changes to become similar to that seen in men. Testosterone has been shown to increase lipid utilisation and decrease storage; this is part of the explanation why men typically have a lower body fat percentage than women.

In males, testosterone levels start to rise significantly during puberty and then fall progressively after years of age. As testosterone falls, men become more prone to accumulate body fat.

The reason why men tend to accumulate belly fat remains unclear.

We may not appreciate body fat, disttibution when it accumulates in specific areas like our Premium-quality pre-workout or thighs. Ddistribution the matrix of dat fat, also called adipose tissue, there is not only fat cells distrbution nerve and disstribution cells Team sports and group fitness djstribution tissue. Macrophages, Improve endurance for cyclists, and Body fat distribution are some of the immune cells found in fat tissue that play a role in inflammation—both anti-inflammatory and proinflammatory. Fat cells also secrete proteins and build enzymes involved with immune function and the creation of steroid hormones. Fat cells can grow in size and number. The amount of fat cells in our bodies is determined soon after birth and during adolescence, and tends to be stable throughout adulthood if weight remains fairly stable. These larger fat cells become resistant to insulin, which increases the risk of type 2 diabetes and cardiovascular disease.

Body Develop laser focus measurements can help determine didtribution Team sports and group fitness and assist Premium-quality pre-workout creating dietribution exercise and nutrition plan to maintain a healthy weight.

However, the Glucagon hormone stimulation Premium-quality pre-workout unwanted body distributino is not the only concern associated Team sports and group fitness an unhealthy weight.

Distributoin the fat is stored, or fat distribution, also affects overall health risks. Surface fat, located just fa the skin, is called subcutaneous disttibution. Unlike subcutaneous distributoon, visceral fat is more often associated with abdominal fat. Researchers have found that excessive belly fat decreases fzt sensitivity, fag it easier Respiratory health improvement develop type II distrubution.

Premium-quality pre-workout may also negatively impact blood lipid metabolism, contributing to more cases distributkon cardiovascular fistribution Premium-quality pre-workout stroke in patients Fay excessive Performance enhancing nutrition fat.

Disyribution fat Team sports and group fitness can easily distributipn determined by simply looking in the mirror. The outline of the body, or body shape, would indicate the location of where body fat is stored. Abdominal fat storage patterns are generally compared to the shape of an apple, called the android shape.

This shape is more commonly found in males and post- menopausal females. In terms of disease risk, this implies males and post- menopausal females are at greater risk of developing health issues associated with excessive visceral fat.

Individuals who experience chronic stress tend to store fat in the abdominal region. A pear-shaped body fat distribution pattern, or gynoid shapeis more commonly found in pre-menopausal females.

Gynoid shape is characterized by fat storage in the lower body such as the hips and buttocks. Besides looking in the mirror to determine body shape, people can use an inexpensive tape measure to measure the diameter of their hips and waist.

Many leading organizations and experts currently believe a waist circumference of 40 or greater for males and 35 or greater for females significantly increases risk of disease. In addition to measuring waist circumference, measuring the waist and the hips and using a waist-to-hip ratio waist circumference divided by the hip circumference is equally effective at predicting body fat-related health outcomes.

According to the National Heart, Lung, and Blood Institute, a ratio of greater than 0. Concepts of Fitness and Wellness Flynn et al. Search site Search Search.

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: Body fat distribution

New genetic loci link adipose and insulin biology to body fat distribution GenABEL: an R library for genome-wide association analysis. Anyone you share the following link with will be able to read this content:. Recent Articles World Diabetes Day November 11, This method is accurate but costly and typically only used in a research setting. CiteSeerX
Gynoid fat distribution - Wikipedia Article Google Scholar Bulik-Sullivan, B. Underwater Weighing Densitometry or Hydrostatic Weighing Individuals are weighed on dry land and then again while submerged in a water tank. The reduction in residual deviance, i. Manson JE, Willett WC, Stampfer MJ, Colditz GA, Hunter DJ, Hankinson SE, Hennekens CH, Speizer FE. Developmental aspects of adipose tissue in GH receptor and prolactin receptor gene disrupted mice: site-specific effects upon proliferation, differentiation and hormone sensitivity.
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Download as PDF Printable version. Female body fat around the hips, breasts and thighs. See also: Android fat distribution. Nutritional Biochemistry , p. Academic Press, London. ISBN The Evolutionary Biology of Human Female Sexuality , p. Oxford University Press, USA.

Relationship between waist-to-hip ratio WHR and female attractiveness". Personality and Individual Differences. doi : Acta Paediatrica. ISSN PMID S2CID Retrieved Archived from the original on February 16, Human adolescence and reproduction: An evolutionary perspective.

School-Age Pregnancy and Parenthood. Hawthorne, NY: Aldine de Gruyter , Exercise Physiology for Health, Fitness, and Performance , p. The American Journal of Clinical Nutrition. Annals of Human Biology. Cytokines, Growth Mediators and Physical Activity in Children during Puberty.

Karger Medical and Scientific Publishers, , p. Exercise and Health Research. Nova Publishers, , p. Handbook of Pediatric Obesity: Etiology, Pathophysiology, and Prevention.

CRC Press, , p. PLOS ONE. Bibcode : PLoSO.. PMC cited in Stephen Heyman May 27, The New York Times. Retrieved 10 September Journal of Personality and Social Psychology.

CiteSeerX Evolution and Human Behavior. Human Nature. Human Reproduction. Phenotypic and genetic correlations, as well as results from GWAS and subsequent enrichment analyses, also revealed that the amount of fat stored in the arms in females is highly correlated with BMI and WC.

This suggests that the proportion of fat stored in the arms will generally increase with increased accumulation of body mass and adipose tissue.

In contrast, males exhibited moderate-to-weak phenotypic and genetic correlations between the distributions of fat to different parts of the body and anthropometric traits, which indicates that the proportions of body fat mass in different compartments of the male body remains more stable as body mass and body adiposity increases.

Among the three phenotypes analyzed in this study LFR and TFR were inversely correlated in both males and females. This suggests that LFR and TFR to a large extent describe one trait, i. In contrast, AFR was only weakly correlated with the other two traits.

Tissue enrichment revealed an important role in body fat distribution in females for mesenchyme-derived tissues: i. This suggests that the distribution of fat to the legs and trunk in females is mainly driven by the effects of female gonadal hormones on mesenchymal progenitors of musculoskeletal and adipose tissues.

However, there was also an overlap in the functional aspects between these traits with both height and WHRadjBMI. WHRadjBMI-associated genes 12 were enriched in adipocytes and adipose tissue subtypes.

Of particular note, we did not identify any enrichment of body fat ratio-associated genes in CNS tissue gene sets in contrasts to enrichment analyses in previous GWAS for BMI where the CNS has been implicated in playing prominent role in obesity susceptibility 9. In the GWAS for LFR and TFR in females, we find that several genes that highlight the influence of biological processes related to the interaction between cells and the extracellular matrix ECM , as well as ECM-maintenance and remodeling.

These include ADAMTS2, ADAMTS3, ADAMTS10 , ADAMTS14 , and ADAMTS17 , which encode extracellular proteases that are involved in enzymatic remodeling of the ECM. In addition, possibly deleterious missense mutations in LD with our lead GWAS SNPs were also found for VCAN and ACAN.

Both VCAN and ACAN encode chondroitin sulfate proteoglycan core proteins that constitute structural components of the extracellular matrix, particularly in soft tissues These proteins also serve as major substrates for ADAMTS proteinases ECM forms the three-dimensional support structure for connective and soft tissue.

In fat tissue, the ECM regulates adipocyte expansion and proliferation Remodeling of the ECM is required to allow for adipose tissue growth and this is achieved through enzymatic processing of extracellular molecules such as proteoglycans, collagen and hyaluronic acid.

For example, the ADAMTS2-, 3-, and proteins act as procollagen N-propeptidases that mediate the maturation of triple helical collagen fibrils 45 , We therefore propose that the effects of genetic variation in biological systems involved in ECM-remodeling is a factor underlying normal variation in female body fat distribution.

In summary, GWAS of body fat distribution determined by sBIA reveals a genetic architecture that influences distribution of adipose tissue to the arms, legs, and trunk.

Genetic associations and effects clearly differ between sexes, in particular for distribution of adipose tissue to the legs and trunk. The distribution of body fat in women has previously been suggested as a causal factor leading to lower risk of cardiovascular and metabolic disease, as well as cardiovascular mortality for women in middle age 5 and genetic studies have identified SNPs that are associated with a favorable body fat distribution 47 , i.

The capacity for peripheral adipose storage has been highlighted as one of the components underlying this phenomenon Resolving the genetic determinants and mechanisms that lead to a favorable distribution of body fat may help in risk assessment and in identifying novel venues for intervention to prevent or treat obesity-related disease.

Imputed genotype data from the third UK Biobank genoype data release were used for replication. Participants who self-reported as being of British descent data field and were classified as Caucasian by principal component analysis data field were included in the analysis.

Genetic relatedness pairing was provided by the UK Biobank Data field After filtering, , participants remained in the discovery cohort and , in the replication cohort. All participants provided signed consent to participate in UK Biobank Genotyping in the discovery cohort had been performed on two custom-designed microarrays: referred to as UK BiLEVE and Axiom arrays, which genotyped , and , SNPs, respectively.

Imputation had been performed using UK10K 49 and genomes phase 3 50 as reference panels. Prior to analysis, we filtered SNPs based on call rate --geno 0. The third release of data from the UK Biobank contained genotyped and imputed data for , participants partly overlapping with the first release.

For our replication analyses, we included an independent subset that did not overlap with the discovery cohort. Genotyping in this subset was performed exclusively on the UK Biobank Axiom Array.

The phenotypes used in this study derive from impedance measurements produced by the Tanita BCMA body composition analyzer. Participants were barefoot, wearing light indoor clothing, and measurements were taken with participants in the standing position.

Height and weight were entered manually into the analyzer before measurement. The Tanita BCMA uses eight electrodes: two for each foot and two for each hand. This allows for five impedance measurements: whole body, right leg, left leg, right arm, and left arm Fig.

Body fat for the whole body and individual body parts had been calculated using a regression formula, that was derived from reference measurements of body composition by DXA Fig. This formula uses weight, age, height, and impedance measurements 51 as input data.

Arm, and leg fat masses were averaged over both limbs. Arm, leg, and trunk fat masses were then divided by the total body fat mass to obtain the ratios of fat mass for the arms, legs and trunk, i.

These variables were analyzed in this study and were named: AFR, LFR, and TFR. Phenotypic correlations between fat distribution ratios and anthropometric traits were estimated by calculating squared semi-partial correlation coefficients for males and females separately, using anova.

glm in R. Adipose tissue ratios AFR, LFR or TFR were set as the response variable. BMI, waist circumference, waist circumference adjusted for BMI, waist-to-hip ratio, height, or one of the other ratios were included as the last term in a linear model with age and principal components as covariates.

The reduction in residual deviance, i. A two-stage GWAS was performed using a discovery and a replication cohort. Sex-stratified GWAS were performed in the discovery cohort for each trait.

A flowchart that describes the steps taken for the genetic analyses is included as supplementary Fig. Prior to running the GWAS, body fat ratios were adjusted for age, age squared and normalized by rank-transformation separately in males and females using the rntransform function included in the GenABEL library GWAS was performed in PLINK v1.

A batch variable was used as covariate in the GWAS for the discovery analyses to adjust for genotyping array UKB Axiom and UK BiLEVE as well as for other differences between UK BiLEVE and UKB Axiom-genotyped participants.

We also included the first 15 principal components and sex in the sex-combined analyses as covariates in the GWAS. LD score regression intercepts see further information below , calculated using ldsc 17 , were used to adjust for genomic inflation, by dividing the square of the t -statistic for each tested SNP with the LD-score regression intercept for that GWAS, and then calculating new P -values based on the adjusted t -statistic.

The --clump function in PLINK was used to identify the number of independent signals in each GWAS. This function groups associated SNPs based on the linkage disequilibrium LD pattern.

After running --clump in PLINK, conditional analyses were also performed for each locus conditioning for the lead SNP, but no further signals were identified. Several associations were found in more than one of the three body fat ratios AFR, TFR, or LFR or strata males, females, or sex-combined and different lead SNPs were observed for different traits and strata at several loci.

To assess whether these represented the same signal, we assessed the LD between overlapping lead SNPs in PLINK. We then performed conditional analysis in PLINK, conditioning on the most significant SNPs across all phenotypes and strata. For each independent signal, the lead SNP lowest P -value was taken forward for replication.

Meta analyses of results from the discovery and replication cohorts was performed with the METAL software 53 for all independent associations that were taken forward for replication. We estimated SNP heritability and genetic correlations using LD score regression LDSC , implemented in the ldsc software package Only SNPs that were included in HapMap3 were included in these analyses.

LDSC uses LD patterns and summary stats from GWAS as input. For genetic correlations, we performed additional sex-stratified GWAS in the UK biobank using the same covariates as for the ratios for standard anthropometric traits, BMI, height, WC, WHR, WCadjBMI, and WHRadjBMI, in the discovery cohort.

GWAS summary stats were filtered for SNPs included in HapMap3 to reduce likelihood of bias induced by poor imputation quality. After this filtering, 1,, SNPs remained for LDSC analyses.

Genetic correlations between the three body fat ratios and anthropometric traits were assessed by cross-trait LD score regression. Lead SNPs from all independent signals in our analyses were cross-referenced with the NHGRI-EBI catalog of published genome-wide association studies GWAS Catalog—data downloaded on 23 April 19 to determine whether body fat ratio-associated signals overlapped with previously identified anthropometric associations from previous GWAS.

LD between data in the GWAS catalog and our lead SNPs were calculated using PLINK v1. Associated loci were investigated for overlap with eQTLs from the GTEx project The threshold for significance for the eQTLs was set to 2.

The strongest associated SNP for each tissue and gene in the GTEx dataset was identified. We then estimated the LD between the top eQTL SNPs and the lead SNP for each independent association from our analysis.

Polyphen and SIFT-scores for the missense variants extracted from Ensembl— www. org were used to assess the deleteriousness of the body fat ratio-associated variants. To identify the functional roles and tissue specificity of associated variants, we performed tissue and gene-set enrichment analyses using DEPICT For the gene-set enrichment in DEPICT, gene expression data from 77, samples have been used to predict gene function for all genes in the genome based on similarities in gene expression.

In comparison to standard enrichment tools that apply a binary definition to define membership in a set of genes that have been associated with a biological pathway or functional category genes are either included or not included , in DEPICT, the probability of a gene being a member of a gene set has instead been estimated based on correlation in gene expression.

This membership probability to each gene set has been estimated for all genes in the human genome and the membership probabilities for each gene have been designated reconstituted gene sets.

A total of 14, reconstituted gene sets have been generated which represent a wide set of biological annotations Gene Ontology [GO], KEGG, REACTOME, Mammalian Phenotype [MP], etc.

For tissue enrichment in DEPICT, microarray data from 37, human tissues have been used to identify genes with high expression in different cells and tissues.

For the enrichment analyses, we performed sex-stratified GWAS for AFR, LFR and TFR on the combined cohort, i. The clump functionality in PLINK is used to determine associated loci. In the enrichment analyses, DEPICT assesses whether the reconstituted gene sets are enriched for genes within trait-associated loci The false discovery rate FDR 55 was used to adjust for multiple testing.

We used the GWAMA software 31 to test for heterogenous effects of associated SNPs between sexes. In GWAMA, fixed-effect estimates of sex-specific and sex-combined beta coefficients and standard errors are calculated from GWAS summary statistics to test for heterogeneous allelic effects between females and males.

GWAMA obtains a test-statistic by subtracting the sex-combined squared t -statistic from the sum of the two sex-specific squared t -statistics. This test statistic is asymptotically χ 2 -distributed and equivalent to a normal z -test of the difference in allelic effects between sexes.

Lead SNPs that replicated were tested for heterogeneity between sexes for the trait that they were associated with. This corresponds to 30 tests for AFR, 44 for LFR, and 66 for TFR. Summary statistics from the replication cohort were used in order to maximize statistical power.

Restrictions apply to the availability of these data, which were used under license for the current study Project No. Data are available for bona fide researchers upon application to the UK Biobank.

All other relevant data are available from the authors. Afshin, A. et al. Health effects of overweight and obesity in countries over 25 years. Article Google Scholar. Ng, M. Global, regional, and national prevalence of overweight and obesity in children and adults during a systematic analysis for the Global Burden of Disease Study Lancet , — Di Cesare, M.

Trends in adult body-mass index in countries from to A pooled analysis of population-based measurement studies with Pi-Sunyer, F. Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults: the evidence report.

National Institutes of Health. Obes Res. Link, J. The genetic basis for sex differences in obesity and lipid metabolism. Article CAS Google Scholar. Bouchard, C. Genetic and nongenetic determinants of regional fat distribution.

Wajchenburg, B. Subcutaneous and visceral adipose tissue: their relation to the metabolic syndrome. Tunstall-Pedoe, H. Myth and paradox of coronary risk and the menopause. Locke, A. Genetic studies of body mass index yield new insights for obesity biology. Nature , — Heid, I. Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution.

Wen, W. Genome-wide association studies in East Asians identify new loci for waist-hip ratio and waist circumference. Article ADS CAS Google Scholar. Shungin, D.

New genetic loci link adipose and insulin biology to body fat distribution. Kilpeläinen, T. Genetic variation near IRS1 associates with reduced adiposity and an impaired metabolic profile.

Lu, Y. New loci for body fat percentage reveal link between adiposity and cardiometabolic disease risk. Fox, C. Genome-wide association for abdominal subcutaneous and visceral adipose reveals a novel locus for visceral fat in women. PLoS Genet.

Mally, K. Reliability and accuracy of segmental bioelectrical impedance analysis for assessing muscle and fat mass in older Europeans: a comparison with dual-energy X-ray absorptiometry. Bulik-Sullivan, B. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies.

Weeks, J. plink: an R package for linking mixed-format tests using IRT-based methods. MacArthur, J. The new NHGRI-EBI catalog of published genome-wide association studies GWAS Catalog. Nucleic Acids Res. Wood, A. Defining the role of common variation in the genomic and biological architecture of adult human height.

He, M. Meta-analysis of genome-wide association studies of adult height in East Asians identifies 17 novel loci. Bis, J. Meta-analysis of genome-wide association studies from the CHARGE consortium identifies common variants associated with carotid intima media thickness and plaque.

Teslovich, T. Biological, clinical and population relevance of 95 loci for blood lipids. Kathiresan, S. Common variants at 30 loci contribute to polygenic dyslipidemia.

Warren, H. Genome-wide association analysis identifies novel blood pressure loci and offers biological insights into cardiovascular risk. Wain, L. Novel blood pressure locus and gene discovery using genome-wide association study and expression data sets from blood and the kidney novelty and significance.

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Body Fat | The Nutrition Source | Harvard T.H. Chan School of Public Health Fa Body fat distribution PubMed Google Scholar. The Evolutionary Biology of Human Female Sexuality. Nutritional deficiencies Gaal LF, Mertens IL, De Block Boy Mechanisms linking Bory with cardiovascular disease. In the gene set analyses, enrichment was only detected Premium-quality pre-workout TFR- and LFR-associated genes in females as well as LFR-associated genes in males supplementary Data 4. These results are consistent with previous GWAS that have revealed sexual dimorphisms in genetic loci for adiposity-related phenotypes, such as waist circumference and waist-to-hip ratio 1040 Meta and pooled analyses of FGFR4 GlyArg polymorphism as a cancer prognostic factor. The threshold for significance for the eQTLs was set to 2.
Thank you for visiting diztribution. You are using xistribution browser version with limited support for CSS. To disrribution the best Boyd, we recommend you use a more up Pumpkin Seed Smoothie date distributiion or Body fat distribution off compatibility mode in Internet Explorer. In Premium-quality pre-workout Boyd, to Team sports and group fitness continued fay, we are displaying the site without styles and JavaScript. Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up toindividuals. In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women.

Body fat distribution -

Health Conditions Discover Plan Connect. Everything Body Fat Distribution Tells You About You. Medically reviewed by Deborah Weatherspoon, Ph. Share on Pinterest. What determines fat allocation? Your genes.

Nearly 50 percent of fat distribution may be determined by genetics, estimates a study. Your sex. Your age. Older adults tend to have higher levels of body fat overall, thanks to factors like a slowing metabolism and gradual loss of muscle tissue.

And the extra fat is more likely to be visceral instead of subcutaneous. Your hormone levels. Weight and hormones are commonly linked, even more so in your 40s. Was this helpful?

Too much visceral fat can be dangerous. Excess visceral fat can increase risk of: heart disease high blood pressure diabetes stroke certain cancers , including breast and colon cancer. Your lifestyle factors can affect how much visceral fat builds up.

Six ways to achieve healthier fat distribution. Eat healthy fats. Exercise 30 minutes a day and increase the intensity. Keep your stress in check. Get six to seven hours of sleep every night. Limit alcohol intake. How we reviewed this article: Sources.

Healthline has strict sourcing guidelines and relies on peer-reviewed studies, academic research institutions, and medical associations. We avoid using tertiary references.

You can learn more about how we ensure our content is accurate and current by reading our editorial policy. Share this article. And 24 Other Nipple Facts. Your Clitoris Is Like an Iceberg — Bigger Than You Think.

Do I Need to Pee or Am I Horny? And Other Mysteries of the Female Body. From Pubes to Lubes: 8 Ways to Keep Your Vagina Happy. Read this next. If Your Gut Could Talk: 10 Things You Should Know. Medically reviewed by Natalie Butler, R. Medically reviewed by Natalie Olsen, R.

Why Grain Bowls Are the Perfect Formula for a Healthy Meal Grain bowls are the perfect vehicle to get in all your greens, grains, protein, and flavor. The No BS Guide to Healthy Fats.

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Locke and Reedik Mägi: These authors contributed equally to this work. Iris M. Heid, Ruth J. Loos, L. Adrienne Cupples, Andrew P. Morris, Cecilia M. Lindgren and Karen L Mohlke: These authors jointly supervised this work. Department of Public Health and Clinical Medicine, Unit of Medicine, Umeå University, 87 Umeå, Sweden.

Department of Odontology, Umeå University, 85 Umeå, Sweden. Department of Genetic Epidemiology, Institute of Epidemiology and Preventive Medicine, University of Regensburg, D Regensburg, Germany. Thomas W.

Department of Genetics, University of North Carolina, Chapel Hill, , North Carolina, USA. Damien C. Croteau-Chonka, Martin L.

Buchkovich, Tamara S. Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, , Massachusetts, USA. Croteau-Chonka, David J. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK. Teresa Ferreira, Reedik Mägi, Alexander W.

Drong, Joshua C. Randall, Anuj Goel, Inga Prokopenko, Thorhildur Juliusdottir, Anubha Mahajan, Nigel W. Rayner, Neil R. Robertson, Åsa K.

Hedman, Sarah Keildson, John R. Perry, Krina T. Zondervan, Martin Farrall, Hugh Watkins, Mark I. McCarthy, Erik Ingelsson, Andrew P. Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, , Michigan, USA. Adam E. Locke, Jennifer L. Bragg-Gresham, Anne U.

Jackson, Heather M. Stringham, Goncalo R. Estonian Genome Center, University of Tartu, Tartu , Estonia. Department of Medicine, Atherosclerosis Research Unit, Center for Molecular Medicine, Karolinska Institutet, Stockholm , Sweden. Rona J. Divisions of Endocrinology and Genetics and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, , Massachusetts, USA.

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Department of Genetics, Harvard Medical School, Boston, , Massachusetts, USA. Pers, Tonu Esko, Rany M. Department of Systems Biology, Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby , Denmark.

Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina , USA. Anne E. Justice, Keri L. Department of Nutrition, Harvard School of Public Health, Boston, , Massachusetts, USA. Department of Biostatistics, Boston University School of Public Health, Boston, , Massachusetts, USA.

Joseph M. Adrienne Cupples. National Heart, Lung, and Blood Institute, the Framingham Heart Study, Framingham, , Massachusetts, USA. Nancy L. Heard-Costa, Joanne M. Murabito, Caroline S. Department of Neurology, Boston University School of Medicine, Boston, , Massachusetts, USA.

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm , Sweden. Ci Song, Tove Fall, Patrik K. Science for Life Laboratory, Uppsala University, Uppsala , Sweden. Ci Song, Stefan Gustafsson, Tove Fall, Johan Ärnlöv, Ann-Christine Syvänen, Åsa K.

Department of Medical Sciences, Molecular Epidemiology, Uppsala University, Uppsala , Sweden. Ci Song, Stefan Gustafsson, Tove Fall, Johan Ärnlöv, Åsa K.

MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK. Felix R. Scott, Nita G. Forouhi, Claudia Langenberg, Ken K. Ong, Nicholas J. Institute of Social and Preventive Medicine IUMSP , Centre Hospitalier Universitaire Vaudois CHUV , Lausanne , Switzerland.

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Joshua C. Randall, Nigel W. Institute for Medical Informatics, Biometry and Epidemiology IMIBE , University Hospital Essen, Essen, Germany.

Clinical Epidemiology, Integrated Research and Treatment Center, Center for Sepsis Control and Care CSCC , Jena University Hospital, Jena , Germany.

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Department of Genetics, University Medical Center Groningen, University of Groningen, RB Groningen, The Netherlands. Rudolf Fehrmann, Juha Karjalainen, Morris A. Department of Internal Medicine, Division of Gastroenterology, and Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, , Michigan, USA.

Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, , Michigan, USA. Ellen M. HudsonAlpha Institute for Biotechnology, Huntsville, , Alabama, USA. Department of Epidemiology, Genetic Epidemiology Unit, Erasmus MC University Medical Center, GE Rotterdam, The Netherlands.

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Kidney Epidemiology and Cost Center, University of Michigan, Ann Arbor, , Michigan, USA. Department of Genetics, Rutgers University, Piscataway, , New Jersey, USA. Department of Human Genetics, Leiden University Medical Center, ZC Leiden, The Netherlands.

Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, , Maryland, USA. Georg B. Department of Specialties of Internal Medicine, Cardiology, Geneva University Hospital, Geneva , Switzerland.

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University Institute for Social and Preventative Medicine, Centre Hospitalier Universitaire Vaudois CHUV , University of Lausanne, Lausanne , Switzerland. Vth Department of Medicine Nephrology, Hypertensiology, Endocrinology, Diabetology, Rheumatology , Medical Faculty of Mannheim, University of Heidelberg, D Mannheim, Germany.

Marcus E. Department of Internal Medicine II, Ulm University Medical Centre, D Ulm, Germany. National Institute for Health and Welfare, FI Helsinki, Finland. Kati Kristiansson, Niina Eklund, Leena Kinnunen, Jaana Lindström, Johan G. Eriksson, Markku Heliövaara, Pekka Jousilahti, Antti M. Massimo Mangino, Cristina Menni, Alireza Moayyeri, John R.

Department of Cardiology, University Medical Center Groningen, University of Groningen, RB Groningen, The Netherlands. Irene Mateo Leach, Hans L.

Netherlands Consortium for Healthy Aging NCHA , GE Rotterdam, The Netherlands. Carolina Medina-Gomez, Marjolein J.

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Department of Internal Medicine, Erasmus MC University Medical Center, GE Rotterdam, The Netherlands. Peters, Karol Estrada, Lisette Stolk, Fernando Rivadeneira, André G. Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford OX3 7LJ, UK.

Inga Prokopenko, Amy Barrett, Amanda J. Bennett, Christopher J. Groves, Nigel W. Department of Genomics of Common Disease, School of Public Health, Imperial College London, Hammersmith Hospital, London W12 0NN, UK.

University of Eastern Finland, FI Kuopio, Finland. Division of Biostatistics, Washington University School of Medicine, St Louis, , Missouri, USA.

Yun Ju Sung, D. Translational Gerontology Branch, National Institute on Aging, Baltimore, , Maryland, USA. Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, D Greifswald, Germany.

Department of Endocrinology, University of Groningen, University Medical Center Groningen, Groningen, RB, The Netherlands.

European Genomic Institute for Diabetes, F Lille, France. Weihua Zhang, Jagvir Grewal, Jaspal S. Department of Epidemiology and Biostatistics, Imperial College London, London W2 1PG, UK. Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, D Neuherberg, Germany.

Eva Albrecht, Harald Grallert, Martina Müller-Nurasyid, Janina S. School of Health and Social Studies, Dalarna University, SE 88 Falun, Sweden. PathWest Laboratory Medicine of Western Australia, Nedlands, , Western Australia, Australia.

Gillian M. Geriatric Unit, Azienda Sanitaria Firenze ASF , Florence, Italy. Department of Genetics, Texas Biomedical Research Institute, San Antonio, , Texas, USA. Genomics Research Centre, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, , Queensland, Australia.

Department of Medical Sciences, Endocrinology, Diabetes and Metabolism, Uppsala University, Uppsala , Sweden. Integrated Research and Treatment Center IFB Adiposity Diseases, University of Leipzig, D Leipzig, Germany. Department of Medicine, University of Leipzig, D Leipzig, Germany.

Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, RC Leiden, The Netherlands. Department of Endocrinology, Inserm UMR, University of Rennes, F Rennes, France. LifeLines Cohort Study, University Medical Center Groningen, University of Groningen, RB Groningen, The Netherlands.

USC-Office of Population Studies Foundation, Inc. Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway.

Ida H. Nuffield Department of Population Health, Clinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford, Oxford OX3 7LF, UK. Information Sciences Institute, University of Southern California, Marina del Rey, , California, USA. Medical Research Institute, University of Dundee, Ninewells Hospital and Medical School, Dundee DD1 9SY, UK.

Alex S. Doney, Andrew D. Institute for Molecular Medicine, University of Helsinki, FI Helsinki, Finland. Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, NIH, Bethesda, Maryland , USA. Michael R. Erdos, Narisu Narisu, Amy J. Swift, Peter S. Analytic and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, , Massachusetts, USA.

Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, D Greifswald, Germany. Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIH, Bethesda, Maryland , USA.

Melissa E. Department of Public Health and Caring Sciences, Geriatrics, Uppsala University, Uppsala , Sweden. Division of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden, Stockholm , Sweden.

Division of Research, Kaiser Permanente, Oakland, , California, USA. Service of Therapeutic Education for Diabetes, Obesity and Chronic Diseases, Geneva University Hospital, Geneva CH, Switzerland. Research Unit of Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, D Neuherberg, Germany.

German Center for Diabetes Research DZD , D Neuherberg, Germany. Department of Medicine III, University Hospital Carl Gustav Carus, Technische Universität Dresden, D Dresden, Germany.

Jürgen Gräßler, Peter E. Department of Public Health and Clinical Medicine, Unit of Nutritional Research, Umeå University, Umeå , Sweden. Department of Psychiatry, University of Groningen, University Medical Center Groningen, RB Groningen, The Netherlands.

Catharina A. Kuopio Research Institute of Exercise Medicine, FI Kuopio, Finland. Maija Hassinen, Timo A. MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh EH4 2XU, UK.

Hjelt Institute Department of Public Health, University of Helsinki, FI Helsinki, Finland. Institute of Biomedicine, University of Oulu, FI Oulu, Finland.

Medical Research Center Oulu and Oulu University Hospital, FI Oulu, Finland. Biocenter Oulu, University of Oulu, FI Oulu, Finland. Faculty of Psychology and Education, VU University Amsterdam, BT Amsterdam, The Netherlands.

Department of Epidemiology, University Medical Center Groningen, University of Groningen, RB Groningen, The Netherlands. Hans L. Hillege, Ilja M. Nolte, Judith M.

Vonk, Ronald P. Department of Public Health and General Practice, Norwegian University of Science and Technology, Trondheim , Norway. Cardiovascular Genetics Division, Department of Internal Medicine, University of Utah, Salt Lake City, , Utah, USA.

Center for Medical Sytems Biology, RC Leiden, The Netherlands. Aaron Isaacs, Ben A. Institute for Community Medicine, University Medicine Greifswald, D Greifswald, Germany.

Department of Pulmonary Physiology and Sleep Medicine, Nedlands, , Western Australia, Australia. School of Medicine and Pharmacology, University of Western Australia, Crawley , Australia. Department of Dietetics-Nutrition, Harokopio University, Athens, Greece. Department of Internal Medicine I, Ulm University Medical Centre, D Ulm, Germany.

Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, Innsbruck, Austria. Institute of Human Genetics, Helmholtz Zentrum München - German Research Center for Environmental Health, D Neuherberg, Germany.

Department of Medical Sciences, Cardiovascular Epidemiology, Uppsala University, Uppsala , Sweden. Department of Internal Medicine and Clinical Nutrition, Centre for Bone and Arthritis Research, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg 45, Sweden.

School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK. Wendy L. Division of Endocrinology, Diabetes and Metabolism, Ulm University Medical Centre, D Ulm, Germany. Institute of Molecular and Cell Biology, University of Tartu, Tartu , Estonia.

Farr Institute of Health Informatics Research, University College London, London NW1 2DA, UK,. The Center for Observational Research, Amgen, Inc. Department of Gerontology and Geriatrics, Leiden University Medical Center, RC Leiden, The Netherlands.

Institute of Human Genetics, University of Bonn, Bonn, Germany. Istituto di Ricerca Genetica e Biomedica IRGB , Consiglio Nazionale delle Ricerche, Cagliari, , Sardinia, Italy.

Center for Evidence-based Healthcare, University Hospital Carl Gustav Carus, Technische Universität Dresden, D Dresden, Germany. Department of Medicine I, University Hospital Grosshadern, Ludwig-Maximilians-Universität, D Munich, Germany. Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität, D Munich, Germany.

Deutsches Forschungszentrum für Herz-Kreislauferkrankungen DZHK German Research Centre for Cardiovascular Research , Munich Heart Alliance, D Munich, Germany. Laboratory of Genetics, National Institute on Aging, Baltimore, , Maryland, USA.

Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, , Maryland, USA. Hypertension and Related Diseases Centre - AOU, University of Sassari Medical School, Sassari , Italy. Division of Preventive Medicine, Brigham and Women's Hospital, Boston, , Massachusetts, USA.

Lynda M. Rose, Paul M. Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz , Austria. Science for Life Laboratory, Karolinska Institutet, Stockholm 65, Sweden. Department of Medicine, University of Washington, Seattle, , Washington, USA.

Icelandic Heart Association, Kopavogur , Iceland. William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK.

Department of Medical Sciences, Molecular Medicine, Uppsala University, Uppsala , Sweden. Department of Public Health Sciences, Stritch School of Medicine, Loyola University of Chicago, Maywood, , Illinois, USA. Bamidele O.

Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany, D Neuherberg, Germany. deCODE Genetics, Amgen Inc. Department of Cardiology, Medical University of Graz, Graz , Austria.

Department of Child and Adolescent Psychiatry, Psychology, Erasmus MC University Medical Centre, CB Rotterdam, The Netherlands. Department of Clinical Chemistry, Ulm University Medical Centre, D Ulm, Germany. Department of Community Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway.

MRC Unit for Lifelong Health and Ageing at University College London, London WC1B 5JU, UK. Andrew Wong, Ken K. Diabetes Complications Research Centre, Conway Institute, School of Medicine and Medical Sciences, University College Dublin, Dublin 4, Ireland. Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul , Korea.

Department of Molecular Medicine and Surgery, Cardiothoracic Surgery Unit, Karolinska Institutet, Stockholm , Sweden. Department of Medicine, Columbia University College of Physicians and Surgeons, , New York, USA. Ali G. Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, , Massachusetts, USA.

Massachusetts General Hospital, Boston, , Massachusetts, USA. State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, Rui Jin Hospital Affiliated with Shanghai Jiao Tong University School of Medicine, Shanghai , China. Department of Epidemiology, Harvard School of Public Health, Boston, , Massachusetts, USA.

NIHR Oxford Biomedical Research Centre, OUH Trust, Oxford OX3 7LE, UK. Department of Biostatistics, Harvard School of Public Health, Harvard University, Boston, , Massachusetts, USA. Department of Genetics, Howard Hughes Medical Institute, Yale University School of Medicine, New Haven, New Haven, Connecticut , USA.

College of Information Science and Technology, Dalian Maritime University, Dalian, , Liaoning, China. University of Ottawa Heart Institute, Ottawa K1Y 4W7, Canada.

National Heart and Lung Institute, Imperial College London, London SW3 6LY, UK. Miriam F. QIMR Berghofer Medical Research Institute, Brisbane, , Queensland, Australia.

Grant W. Section of General Internal Medicine, Boston University School of Medicine, Boston, , Massachusetts, USA. Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK.

MRC Harwell, Harwell Science and Innovation Campus, Harwell OX11 0QG, UK. Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, , Queensland, Australia. Department of Biomedical Engineering and Computational Science, Aalto University School of Science, FI Helsinki, Finland.

Department of Medicine, Division of Nephrology, Helsinki University Central Hospital, FI Helsinki, Finland. Folkhälsan Institute of Genetics, Folkhälsan Research Center, FI Helsinki, Finland.

Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, , New York, USA. Computer Science Department, Tecnológico de Monterrey, Atizapán de Zaragoza, , Mexico. Institut Pasteur de Lille; INSERM, U; Université de Lille 2; F Lille, France.

Department of Epidemiology and Public Health, EA, University of Strasbourg, Faculty of Medicine, Strasbourg, France. Department of Internal Medicine, University Medical Center Groningen, University of Groningen, RB Groningen, The Netherlands.

Stephan J. Pathology and Laboratory Medicine, The University of Western Australia, Perth, , Western Australia, Australia. Cedars-Sinai Diabetes and Obesity Research Institute, Los Angeles, , California, USA. Department of Medicine, Service of Nephrology, Lausanne University Hospital CHUV , Lausanne , Switzerland.

Centre for Population Health Sciences, University of Edinburgh, Teviot Place, Edinburgh EH8 9AG, UK. Center for Human Genetics Research, Vanderbilt University Medical Center, Nashville, , Tennessee, USA. Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, , Tennessee, USA.

Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK. Biological Psychology, VU University Amsterdam, BT Amsterdam, The Netherlands. Institute for Research in Extramural Medicine, Institute for Health and Care Research, VU University, BT Amsterdam, The Netherlands.

Eco J. Department of Internal Medicine B, University Medicine Greifswald, D Greifswald, Germany. DZHK Deutsches Zentrum für Herz-Kreislaufforschung — German Centre for Cardiovascular Research , partner site Greifswald, D Greifswald, Germany. Clinic of Cardiology, West-German Heart Centre, University Hospital Essen, Essen, Germany.

Department of General Practice and Primary Health Care, University of Helsinki, FI Helsinki, Finland. Unit of General Practice, Helsinki University Central Hospital, Helsinki FI, Finland. Department of Internal Medicine, University of Pisa, Pisa , Italy. National Research Council Institute of Clinical Physiology, University of Pisa, Pisa , Italy.

Department of Cardiology, Toulouse University School of Medicine, Rangueil Hospital, Toulouse, France. UWI Solutions for Developing Countries, The University of the West Indies, Mona, Kingston 7, Jamaica.

Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, , California, USA.

Center for Biomedicine, European Academy Bozen, Bolzano EURAC , Bolzano , Italy affiliated Institute of the University of Lübeck, D Lübeck, Germany. Andrew A. Institute of Cardiovascular Science, University College London, London WC1E 6BT, UK. Centre for Cardiovascular Genetics, Institute Cardiovascular Sciences, University College London, London WC1E 6JJ, UK.

Sansom Institute for Health Research, University of South Australia, Adelaide , South Australia, Australia.

School of Population Health, University of South Australia, Adelaide , South Australia, Australia. South Australian Health and Medical Research Institute, Adelaide , South Australia, Australia.

Population, Policy, and Practice, University College London Institute of Child Health, London WC1N 1EH, UK. Hannover Unified Biobank, Hannover Medical School, Hannover, D Hannover, Germany. National Institute for Health and Welfare, FI Oulu, Finland.

MRC Health Protection Agency HPA Centre for Environment and Health, School of Public Health, Imperial College London, London W2 1PG, UK. Unit of Primary Care, Oulu University Hospital, FI Oulu, Finland. Institute of Health Sciences, University of Oulu, FI Oulu, Finland.

UK Clinical Research Collaboration Centre of Excellence for Public Health NI , Queens University of Belfast, Belfast BT7 1NN, Northern Ireland, UK. Institute of Health Sciences, Faculty of Medicine, University of Oulu, FI Oulu, Finland. Imperial College Healthcare NHS Trust, London W12 0HS, UK.

Jaspal S. Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, , Washington, USA. Department of Epidemiology and Public Health, University College London, London WC1E 6BT, UK.

Department of Biological and Social Epidemiology, University of Essex, Wivenhoe Park, Colchester, Essex CO4 3SQ, UK. Department of Medicine, Kuopio University Hospital and University of Eastern Finland, FI Kuopio, Finland.

Department of Physiology, Institute of Biomedicine, University of Eastern Finland, Kuopio Campus, FI Kuopio, Finland. Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital and University of Eastern Finland, FI Kuopio, Finland.

Epidemiology Program, University of Hawaii Cancer Center, Honolulu, , Hawaii, USA. Department of Clinical Chemistry, Fimlab Laboratories and School of Medicine University of Tampere, FI Tampere, Finland.

Institut Universitaire de Cardiologie et de Pneumologie de Québec, Faculty of Medicine, Laval University, Quebec QC G1V 0A6, Canada. Institute of Nutrition and Functional Foods, Laval University, Quebec QC G1V 0A6, Canada.

Department of Biostatistics, University of Washington, Seattle, , Washington, USA. Department of Respiratory Medicine, Sir Charles Gairdner Hospital, Nedlands, , Western Australia, Australia.

Department of Psychiatry, Neuroscience Campus, VU University Amsterdam, BT Amsterdam, The Netherlands. Department of Neurology, General Central Hospital, Bolzano , Italy.

Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, FI Turku, Finland. Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, FI Turku, Finland.

Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, , Louisiana, USA. Tuomo Rankinen, Mark A. Department of Psychiatry, Washington University School of Medicine, St Louis, , Missouri, USA.

Harvard Medical School, Boston, , Massachusetts, USA. Paul M. Center for Systems Genomics, The Pennsylvania State University, University Park, Pennsylvania , USA.

Croatian Centre for Global Health, Faculty of Medicine, University of Split, Split, Croatia. Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, Leicester LE3 9QP, UK. National Institute for Health Research NIHR Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester LE3 9QP, UK.

South Carelia Central Hospital, Lappeenranta, Finland. Paul Langerhans Institute Dresden, German Center for Diabetes Research DZD , Dresden, Germany. Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, , Maryland, USA.

Alan R. Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, , Maryland, USA. Geriatric Research and Education Clinical Center, Veterans Administration Medical Center, Baltimore, , Maryland, USA.

Department of Epidemiology, Maastricht University, HA Maastricht, The Netherlands. KU Leuven Department of Cardiovascular Sciences, Research Unit Hypertension and Cardiovascular Epidemiology, University of Leuven, B Leuven, Belgium. Department of Kinesiology, Laval University, Quebec, QC G1V 0A6, Canada.

Department of Food Science and Nutrition, Laval University, Quebec, QC G1V 0A6, Canada. Department of Internal Medicine, University Hospital CHUV and University of Lausanne, , Switzerland.

Department of Nutrition, University of North Carolina, Chapel Hill, , North Carolina, USA. Institute of Social and Preventive Medicine IUMSP , Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland.

Ministry of Health, Victoria, Republic of Seychelles. Lee Kong Chian School of Medicine, Imperial College London and Nanyang Technological University, Singapore, Singapore, Singapore. Department of Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK.

Mark J. Department of Psychiatry and Psychotherapy, University Medicine Greifswald, HELIOS-Hospital Stralsund, D Greifswald, Germany.

German Center for Neurodegenerative Diseases DZNE , Rostock, Greifswald, D Greifswald, Germany. School of Population Health, The University of Western Australia, Nedlands, , Western Australia, Australia.

Division of Public Health Sciences, Center for Human Genetics, Wake Forest School of Medicine, Winston-Salem, , North Carolina, USA. Synlab Academy, Synlab Services GmbH, Mannheim, Germany.

Department of Clinical Genetics, Erasmus MC University Medical Center, CA Rotterdam, The Netherlands. Department of Medicine, Stanford University School of Medicine, Palo Alto, , California, USA. Finnish Diabetes Association, Kirjoniementie 15, FI Tampere, Finland. Pirkanmaa Hospital District, FI Tampere, Finland.

Center for Non-Communicable Diseases, Karatchi, Pakistan. Department of Medicine, University of Pennsylvania, Philadelphia, , Pennsylvania, USA. Department of Medicine, Helsinki University Central Hospital Heart and Lung Center, Helsinki University Central Hospital, FI Helsinki, Finland.

Faculty of Medicine, University of Iceland, Reykjavik , Iceland. Instituto de Investigacion Sanitaria del Hospital Universario LaPaz IdiPAZ , Madrid, Spain. Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia.

Centre for Vascular Prevention, Danube-University Krems, Krems, Austria. Department of Public Health and Clinical Nutrition, University of Eastern Finland, FI Kuopio, Finland. Research Unit, Kuopio University Hospital, FI Kuopio, Finland.

Durrer Center for Cardiogenetic Research, Interuniversity Cardiology Institute Netherlands-Netherlands Heart Institute, DG Utrecht, The Netherlands. Department of Clinical and Experimental Medicine, EPIMED Research Center, University of Insubria, Varese I, Italy.

Institute of Cellular Medicine, Newcastle University, Newcastle NE1 7RU, UK. Institute of Medical Informatics, Biometry and Epidemiology, Chair of Epidemiology, Ludwig-Maximilians-Universität, D Munich, Germany. Institute of Epidemiology I, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany, D Neuherberg, Germany.

Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, , Maryland, USA. Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders PACER-HD , King Abdulaziz University, Jeddah, Saudi Arabia.

Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Belfer , New York , USA. Department of Human Genetics, University of Michigan, Ann Arbor, , Michigan, USA.

Queensland Brain Institute, The University of Queensland, Brisbane , Australia. The University of Queensland Diamantina Institute, The Translation Research Institute, Brisbane , Australia. Oxford NIHR Biomedical Research Centre, Oxford University Hospitals NHS Trust, Oxford OX3 7LJ, UK.

Carolina Center for Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, , North Carolina, USA. The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, , New York, USA.

The Genetics of Obesity and Related Metabolic Traits Program, The Icahn School of Medicine at Mount Sinai, New York, , New York, USA. The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, , New York, USA.

Department of Biostatistics, University of Liverpool, Liverpool L69 3GA, UK. You can also search for this author in PubMed Google Scholar.

See the Supplementary Note for Author Contributions. Correspondence to Cecilia M. Lindgren or Karen L Mohlke. and K.

owns stock in GlaxoSmithKline and Incyte, Ltd. is a consultant for Weight Watchers, Pathway Genomics, NIKE, and Gatorade PepsiCo. A list of authors and affiliations appears in the Supplementary Information. Data dashed lines and analyses solid lines related to the GWAS cohorts for WHRadjBMI are coloured red and those related to the Metabochip MC cohorts are coloured blue.

The two genomic control λ GC corrections within-study and among-studies performed on associations from each data set are represented by grey-outlined circles. The λ GC corrections for the GWAS meta-analysis were based on all SNPs and the λ GC corrections for the Metabochip meta-analysis were based on a null set of 4, SNPs previously associated with QT interval.

The joint meta-analysis of the GWAS and Metabochip data sets is coloured purple. Additional WHRadjBMI meta-analyses included Metabochip data from up to 14, subjects of east Asian, south Asian or African-American ancestry from eight cohorts. Counts for the meta-analyses of waist circumference, hip circumference, and their BMI-adjusted counterparts WCadjBMI and HIPadjBMI differ from those of WHRadjBMI because some cohorts only had phenotype data available for one type of body circumference measurement see Supplementary Table 2.

a , Figure showing effect beta estimates for the 20 WHRadjBMI SNPs showing significant evidence of sexual dimorphism. Sample sizes, comprising more than 73, men and 96, women, are listed in Table 1.

Within each of the three categories, the loci were sorted by increasing P value of sex-based heterogeneity in the effect betas.

b , Figure showing standardized sex-specific phenotypic variance components for six waist-related traits. The ACE models are decomposed into additive genetic components A shown in black, common environmental components C in grey, and non-shared environmental components E in white.

Components are shown for waist circumference WC , hip circumference HIP , WHR, WCadjBMI, HIPadjBMI and WHRadjBMI. c , Genetic correlations of waist-related traits with height, adjusted for age and BMI.

The Appetite control supplements reviews line idstribution an OR of 1. Note the different scales distrihution VAT and SAT. Team sports and group fitness distrigution different Body fat distribution for IMAT and STAT. Goodpaster BH Team sports and group fitness, Fah SHarris TB, et al. Obesity, Regional Body Fat Distribution, and Body fat distribution Bodyy Syndrome in Older Men and Women. Arch Intern Med. Author Affiliations: Department of Medicine, University of Pittsburgh Medical Center, Pittsburgh, Pa Drs Goodpaster, Katsiaras, and Newman ; Graduate School of Public Health, University of Pittsburgh Drs Krishnaswami and Newman ; Intramural Research Program, National Institute on Aging, Baltimore, Md Drs Harris and Simonsick ; Sticht Center on Aging, Wake Forest University School of Medicine, Winston-Salem, NC Dr KritchevskyPrevention Sciences Group, University of California at San Francisco Dr Nevittand Center for Experimental Surgery and Anesthesiology, Catholic University, Louvain, Belgium Dr Holvoet.

Author: Kiran

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