Body fat distribution -
Dual Energy X-ray Absorptiometry DEXA X-ray beams pass through different body tissues at different rates. Computerized Tomography CT and Magnetic Resonance Imaging MRI These two imaging techniques are now considered to be the most accurate methods for measuring tissue, organ, and whole-body fat mass as well as lean muscle mass and bone mass.
Is it healthier to carry excess weight than being too thin? References Centers for Disease Control and Prevention. Adult obesity facts. Guerreiro VA, Carvalho D, Freitas P. Obesity, Adipose Tissue, and Inflammation Answered in Questions.
Journal of Obesity. Lustig RH, Collier D, Kassotis C, Roepke TA, Kim MJ, Blanc E, Barouki R, Bansal A, Cave MC, Chatterjee S, Choudhury M. Obesity I: Overview and molecular and biochemical mechanisms.
Biochemical Pharmacology. Centers for Disease Control and Prevention. Body Mass Index: Considerations for practitioners. Kesztyüs D, Lampl J, Kesztyüs T. The weight problem: overview of the most common concepts for body mass and fat distribution and critical consideration of their usefulness for risk assessment and practice.
International Journal of Environmental Research and Public Health. World Health Organization. Body mass index — BMI. Berrington de Gonzalez A, Hartge P, Cerhan JR, Flint AJ, Hannan L, MacInnis RJ, Moore SC, Tobias GS, Anton-Culver H, Freeman LB, Beeson WL.
Body-mass index and mortality among 1. New England Journal of Medicine. Di Angelantonio E, Bhupathiraju SN, Wormser D, Gao P, Kaptoge S, de Gonzalez AB, Cairns BJ, Huxley R, Jackson CL, Joshy G, Lewington S. Body-mass index and all-cause mortality: individual-participant-data meta-analysis of prospective studies in four continents.
The Lancet. Willett W, Nutritional Epidemiology. Zhang C, Rexrode KM, Van Dam RM, Li TY, Hu FB. Abdominal obesity and the risk of all-cause, cardiovascular, and cancer mortality: sixteen years of follow-up in US women. Zhang X, Shu XO, Yang G, Li H, Cai H, Gao YT, Zheng W. Abdominal adiposity and mortality in Chinese women.
Archives of internal medicine. Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults—The Evidence Report. National Institutes of Health. Obesity Research. Willett WC, Dietz WH, Colditz GA.
Guidelines for healthy weight. Waist Circumference and Waist-Hip Ratio: Report of a WHO Expert Consultation. Gevenva, , December Ashwell M, Gibson S. BMJ open. Moosaie F, Abhari SM, Deravi N, Behnagh AK, Esteghamati S, Firouzabadi FD, Rabizadeh S, Nakhjavani M, Esteghamati A.
Waist-to-height ratio is a more accurate tool for predicting hypertension than waist-to-hip circumference and BMI in patients with type 2 diabetes: A prospective study.
Frontiers in Public Health. Measurements of Adiposity and Body Composition. In: Hu F, ed. Obesity Epidemiology. New York City: Oxford University Press, ; 53— Calle EE, Thun MJ, Petrelli JM, Rodriguez C, Heath Jr CW. Body-mass index and mortality in a prospective cohort of US adults.
Stevens J, Cai J, Pamuk ER, Williamson DF, Thun MJ, Wood JL. The effect of age on the association between body-mass index and mortality. Lee IM, Manson JE, Hennekens CH, Paffenbarger RS. Body weight and mortality: a year follow-up of middle-aged men.
Manson JE, Willett WC, Stampfer MJ, Colditz GA, Hunter DJ, Hankinson SE, Hennekens CH, Speizer FE. Body weight and mortality among women. Singh PN, Lindsted KD, Fraser GE. Body weight and mortality among adults who never smoked.
Other proxies that better represent distribution of body fat have also been utilized, such as waist circumference WC , hip circumference HC , and the waist-to-hip ratio WHR. Through genome-wide association studies GWAS , researchers have identified hundreds of loci to be associated with proximal measurements of body mass and body fat distribution such as BMI 9 , WHR 10 , 11 and hip and waist circumference Sex-stratified analyses have revealed sexual dimorphic effects at twenty WHR-associated loci and 19 of these loci displayed stronger effects in women Body fat mass has also been studied in GWAS by using bio-electrical impedance analysis BIA and dual energy X-ray absorptiometry DXA 13 , BIA measures the electrical impedance through the human body, which can be used to calculate an estimate of the total amount of adipose tissue.
The gold standard method for measurements of body fat distribution is computed tomography CT or magnetic resonance imaging MRI. However, these methods are costly. Developments in BIA technology has now allowed for cost-efficient segmental body composition scans that estimate of the fat content of the trunk, arms and legs 16 Fig.
In this study, we use segmental BIA sBIA data on , participants of the UK Biobank to study the genetic determinants of body fat distribution to the trunk, arms, and legs.
For this purpose, we perform GWAS on the proportion of body fat distributed to these compartments. We also perform sex-stratified analyses to identify effects that differ between males and females.
Segmental body impedance analyses. This method uses bio-electrical impedance to estimate body composition: fat mass, muscle mass, etc. In this study, adipose tissue mass was estimated using the Tanita BCMA body composition analyzer a.
This machine uses an eight-electrode method, which allows for five measurements of impedance. Electrical current is supplied to the front of both feet and the fingertips of both hands.
Voltage is measured on either heel or thenar portion of the palms. Body composition is derived from a regression formula for each body part. The formula is derived from regression analysis using height, weight, age and impedance for each body part as predictors for composition of each body part as assessed by DXA b.
GWAS for AFR, LFR, and TFR were conducted in the UK Biobank cohort and revealed associations with loci that have not previously been associated with standard anthropometric traits. c A manhattan plot with combined results for association studies of body fat ratios in combined and sex-stratified analyses.
Overall, independent associations with at least one of the body fat ratios were observed in the discovery analyses. Out of the initial associations, 98 replicated; of which 30 replicated for AFR, 44 for LFR and 66 for TFR.
We find 98 independent genetic signals to be associated with body fat distribution, as determined by sBIA, of which 29 have not previously been associated with any adiposity-related phenotype.
We also find that genetic associations strongly differ between the sexes, in particular for distribution of adipose tissue to the legs and trunk where effects are primarily observed in females. Tissue enrichment analyses with DEPICT reveal mesenchyme-derived tissues, as well as tissues related to female reproduction to be important for distribution of adipose tissue to the legs and trunk in females.
The proportions of body fat distributed to the arms—arm fat ratio AFR , the legs—leg fat ratio LFR , and the trunk—trunk fat ratio TFR were calculated by dividing the fat mass per compartment with the total body fat mass for each participant Fig.
We conducted a two-stage GWAS using data from the interim release of genotype data in UK Biobank as a discovery cohort. Another set of participants, for which genotype data were made available as part of the second release, was used for replication.
After removing non-Caucasians, genetic outliers and related individuals, , and , participants remained in the discovery and replication cohorts, respectively. Basic characteristics of the discovery and replication cohorts are presented in supplementary Table 1.
Females were found to have higher total sBIA-estimated fat mass compared to men in both the discovery and replication cohort. Males had higher average proportion of body fat located in the trunk compared to females While the total amount of adipose tissue in the arms was estimated to be higher in females compared to males, the fraction of adipose tissue distributed to the arms were similar.
Several smaller differences between the discovery and replication cohorts were present supplementary Table 1 , such as some slight differences in height and age between men and women in the discovery and replication cohorts.
These differences most likely represent the 50, participants for the UK Biobank Lung Exome Variant Evaluation UK BiLEVE project that were included in the first release of genotyping data for ~, participants, which were used as a discovery cohort in this study.
Selection for UK BiLEVE was conducted with specific consideration to lung function which may reflect the differences in baseline characteristics for this subset of the cohort. These participants were also genotyped on a separate but similar microarray and a batch variable was included in our association analyses to adjust for any effects related to the different genotyping arrays as well as having participated in the UK BiLEVE study see methods.
GWAS was performed for each of the three phenotypes AFR, LFR, and TFR in the discovery cohort sex-combined and when stratifying by sex males and females , while adjusting for covariates as described in the method section.
A total of 25,, imputed SNPs, with MAF of at least 0. LD score regression intercepts 17 ranged from 1. We used the --clump function in PLINK 18 , in combination with conditioning on the most significant SNP, to identify associations that were independent within each GWAS as well as between the GWAS for the three body fat ratios AFR, TFR, or LFR or between strata males, females or sex-combined; see methods.
For each independent association, the lead SNP, i. In total, independent associations were taken forward for replication of which 98 replicated supplementary Tables 3 — 5 , Supplementary Data 1.
Substantial overlap in associated loci was observed between LFR and TFR loci Fig. One locus in the vicinity of ADAMTSL3 was associated with all three phenotypes. Overlap and genetic correlation between body fat ratios and other anthropometric traits.
a The overlap between AFR-, LFR-, and TFR-associated loci is illustrated as a Venn diagram. The loci are denoted by the nearest gene or by the most likely target gene see methods section. The loci in bold type and larger font designate loci that have not previously been associated with an anthropometric trait.
The total number in each field is illustrated top right. b Genetic correlation between body fat ratios within, and between sexes. Genetic correlations were estimated by cross-trait LD-score regression The absolute values for each genetic correlation r g is included.
c Genetic correlations between body fat ratios and standard anthropometric traits. Sex-stratified summary statistics were generated for each trait by GWAS in the discovery cohort. Body fat ratio-associated SNPs were tested for overlap with associations from previous GWAS for anthropometric traits by determining LD with entries from the GWAS-catalog In total, we identified 29 body fat ratio-associated signals that have not previously been associated with an anthropometric trait Figure 1c , Table 1 , supplementary Figs.
For AFR, the strongest associations were observed at well known BMI and adiposity-associated loci such as: FTO , MC4R , TMEM18 , SEC16B, and TFAP2B supplementary Data 1.
We compared the direction of the effects for overlapping GWAS results by estimating the effects of lead body fat ratio-associated SNPs on the respective overlapping anthropometric traits.
The effects of TFR-associated SNPs were directionally consistent with effects on height and WHR adjusted for BMI WHRadjBMI , while the effects were the opposite for LFR. The direction of effects for AFR-associated SNPs were consistent with effects on BMI, WC, and WHR supplementary Table 6.
Among the loci that have not previously been associated with an adiposity-related anthropometric trait, five overlapped with cardiovascular and metabolic trait-associated loci from previous GWAS: near XKR6 , which is associated with carotid intima thickness 22 , triglycerides 23 , 24 , and systolic blood pressure 25 , 26 ; ZNF : coronary artery disease 27 and diastolic blood pressure 26 , 28 ; RPD Sex-heterogenous effects of associated variants were tested for using the GWAMA software.
This method utilizes summary statistics from sex-stratified GWAS to test for heterogeneity of allelic effects between males and females All replicated lead SNPs were included in these analyses.
SNPs were only tested for heterogenous effects on the traits that they were associated with, which corresponds to 30 variants that were tested for sex-heterogenous effects on AFR, 44 on LFR and 66 on TFR.
A striking heterogeneity in effects between males and females was observed Table 2 , supplementary Data 2. Two variants, near SLC12A2 and PLCE1 , were shown to have larger effects on AFR in males while 37 variants exhibited larger effects in females.
LD score regression LDSC was used to estimate the fraction of variance of body fat ratios that could be explained by SNPs, i. Phenotypic and genotypic correlations were assessed, in males and females separately. Phenotypic correlations were estimated by calculating squared semi-partial correlation coefficients with ANOVA of nested linear models that were adjusted for age and principal components while genetic correlations were estimated using cross-trait LD score regression 32 see methods.
Overall, the genetic and phenotypic correlations showed a large degree of similarity supplementary Tables 8 — 9 and the correlations between the anthropometric traits and body fat ratios were directionally consistent for phenotypic and genetic correlations for all phenotypes.
In females, BMI and WC was strongly correlated with AFR both with regards to phenotypic and genetic correlations Fig. Height contributed to a moderate degree in explaining the phenotypic variance in LFR and TFR in females In males, anthropometric traits contributed only to a small degree in explaining the phenotypic variance of body fat ratios supplementary Table 8.
Consistent with this result, genetic correlations between body fat ratios and anthropometric traits in males were also quite low Fig. LFR and TFR were inversely correlated, which agrees well with the large overlap in GWAS results for these phenotypes and the fact that the effect estimates from the GWAS was in the opposite direction for LFR and TFR supplementary Data 1.
In total, 31 body fat ratio-associated loci overlapped with an eQTL, and 11 lead SNPs were in LD with a potentially deleterious missense variant. Polyphen and SIFT-scores were used to assess the deleteriousness of the variants. These scores represent the probability for functional effects of missense variants and were estimated through sequence analyses 34 , Missense variants were found in ACAN , ADAMTS17 , FGFR4 and ADAMTS10 , where the lead SNPs were predicted to be damaging supplementary Table The missense variant rs, within FGFR4 , has also previously been shown to be associated with progression of cancer 36 , 37 and to affect insulin secretion in vitro To identify the functional roles of body fat ratio-associated variants and which tissues are mediating the genetic effects, we performed enrichment analyses with DEPICT Data-driven Expression Prioritized Integration for Complex Traits 39 , see method section.
In these analyses we used summary statistics from sex-stratified GWAS on the combined cohort , women and , men in order to maximize statistical power. Results from the enrichment analyses were compared with results from previous GWAS for height, BMI 9 and WHRadjBMI Enrichment analyses of genes at LFR and TFR-associated loci.
a Reconstituted gene-sets that were enriched for TFR- and LFR-associated genes in both males and females were compared to results from previous GWAS on WHRadjBMI 12 , BMI 9 , and height Tissue and cell type enrichment of b TFR- and c LFR-associated genes in females.
Tissue enrichment was observed for LFR and TFR-associated genes in females Fig. For TFR, DEPICT also revealed enrichment of genes associated with adipose tissue cells, female urogenital organs, endocrine organs as well as the arteries Fig.
Tissue enrichment was not seen for the other traits or strata. In the gene set analyses, enrichment was only detected for TFR- and LFR-associated genes in females as well as LFR-associated genes in males supplementary Data 4.
Gene sets related to bone morphology and skeletal development were among the most strongly associated with both LFR and TFR. We also find the TGFβ signaling pathway gene set to be enriched for genes within the TFR and LFR-associated loci in females, as well as SMAD1-, SMAD2-, SMAD3- and SMAD7 protein-protein interaction subnetworks supplementary Data 4 , which act as TGFβ downstream mediators.
There was a substantial overlap of enriched gene sets between TFR and LFR in females as well as moderate overlap with LFR-associated gene sets in males supplementary Fig. The large fraction of overlapping gene sets between LFR and TFR in females agrees well with the large overlap in GWAS signals.
In this study, we performed GWAS on distribution of body fat to different compartments of the human body and identified and replicated 98 independent associations of which 29 have not previously been associated with any adiposity-related phenotype.
In contrast to earlier studies, we have not addressed the total amount of fat but rather the fraction of the total body fat mass that is located in the arms, legs and trunk. Body fat distribution is well known to differ between males and females, which we also clearly show in our study.
We also show that the genetic effects that influence fat distribution are stronger in females compared to males. 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 10 , 40 , 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.
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.
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.
Body fat distribution you for visiting far. You are using a browser version dixtribution limited support for Athlete-friendly allergy management. To obtain the Boyd experience, Body fat distribution recommend you use a more up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. We Distributionn not appreciate body distribuyion, especially when it accumulates in specific areas like our bellies Body fat distribution thighs. Body fat distribution the Body cleanse for rejuvenation of body fat, also idstribution adipose tissue, there is distriburion only fat vistribution Body fat distribution nerve and immune cells and connective disribution. Macrophages, neutrophils, and eosinophils 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.
Ich meine, dass Sie nicht recht sind. Schreiben Sie mir in PM.