Category: Children

Genetics and fat distribution

Genetics and fat distribution

Third, this vat was Genetics and fat distribution based on population-based cohorts, Athlete diet plan participants of which are usually healthier than the general snd, and used analytical approaches that deliberately Advanced training periodization the influence eGnetics outliers, in this Personalized caloric needs people with extreme fat distribution. Subjects Fat metabolism Genome-wide association studies Obesity. Article PubMed PubMed Central CAS Google Scholar. Article Google Scholar Pi-Sunyer, F. Pi-Sunyer, F. Associations of the Genetic Variants With BMI-Adjusted WHR in GIANT and UK Biobank. Genes contribute to the causes of obesity in many ways, by affecting appetite, satiety the sense of fullnessmetabolism, food cravings, body-fat distribution, and the tendency to use eating as a way to cope with stress.

Genetics and fat distribution -

Children don't exercise as much in school, often because of cutbacks in physical education classes. Many people drive to work and spend much of the day sitting at a computer terminal.

Because we work long hours, we have trouble finding the time to go to the gym, play a sport, or exercise in other ways. Instead of walking to local shops and toting shopping bags, we drive to one-stop megastores, where we park close to the entrance, wheel our purchases in a shopping cart, and drive home.

The widespread use of vacuum cleaners, dishwashers, leaf blowers, and a host of other appliances takes nearly all the physical effort out of daily chores and can contribute as one of the causes of obesity. The average American watches about four hours of television per day, a habit that's been linked to overweight or obesity in a number of studies.

Data from the National Health and Nutrition Examination Survey, a long-term study monitoring the health of American adults, revealed that people with overweight and obesity spend more time watching television and playing video games than people of normal weight.

Watching television more than two hours a day also raises the risk of overweight in children, even in those as young as three years old. Part of the problem may be that people are watching television instead of exercising or doing other activities that burn more calories watching TV burns only slightly more calories than sleeping, and less than other sedentary pursuits such as sewing or reading.

But food advertisements also may play a significant role. The average hour-long TV show features about 11 food and beverage commercials, which encourage people to eat.

And studies show that eating food in front of the TV stimulates people to eat more calories, and particularly more calories from fat. In fact, a study that limited the amount of TV kids watched demonstrated that this practice helped them lose weight — but not because they became more active when they weren't watching TV.

The difference was that the children ate more snacks when they were watching television than when doing other activities, even sedentary ones. Obesity experts now believe that a number of different aspects of American society may conspire to promote weight gain.

Stress is a common thread intertwining these factors. For example, these days it's commonplace to work long hours and take shorter or less frequent vacations.

In many families, both parents work, which makes it harder to find time for families to shop, prepare, and eat healthy foods together. Round-the-clock TV news means we hear more frequent reports of child abductions and random violent acts.

This does more than increase stress levels; it also makes parents more reluctant to allow children to ride their bikes to the park to play. Parents end up driving kids to play dates and structured activities, which means less activity for the kids and more stress for parents.

Time pressures — whether for school, work, or family obligations — often lead people to eat on the run and to sacrifice sleep, both of which can contribute to weight gain. Some researchers also think that the very act of eating irregularly and on the run may be another one of the causes of obesity.

Neurological evidence indicates that the brain's biological clock — the pacemaker that controls numerous other daily rhythms in our bodies — may also help to regulate hunger and satiety signals. Ideally, these signals should keep our weight steady. They should prompt us to eat when our body fat falls below a certain level or when we need more body fat during pregnancy, for example , and they should tell us when we feel satiated and should stop eating.

Close connections between the brain's pacemaker and the appetite control center in the hypothalamus suggest that hunger and satiety are affected by temporal cues. Irregular eating patterns may disrupt the effectiveness of these cues in a way that promotes obesity.

Similarly, research shows that the less you sleep, the more likely you are to gain weight. Lack of sufficient sleep tends to disrupt hormones that control hunger and appetite and could be another one of the causes of obesity.

In a study of more than 1, volunteers, researchers found that people who slept less than eight hours a night had higher levels of body fat than those who slept more, and the people who slept the fewest hours weighed the most.

Stress and lack of sleep are closely connected to psychological well-being, which can also affect diet and appetite, as anyone who's ever gorged on cookies or potato chips when feeling anxious or sad can attest. Studies have demonstrated that some people eat more when affected by depression, anxiety, or other emotional disorders.

In turn, overweight and obesity themselves can promote emotional disorders: If you repeatedly try to lose weight and fail, or if you succeed in losing weight only to gain it all back, the struggle can cause tremendous frustration over time, which can cause or worsen anxiety and depression.

A cycle develops that leads to greater and greater obesity, associated with increasingly severe emotional difficulties. To find weight loss solutions that can be tailored to your needs, buy the Harvard Special Health Report Lose Weight and Keep It Off.

As a service to our readers, Harvard Health Publishing provides access to our library of archived content. Please note the date of last review or update on all articles. No content on this site, regardless of date, should ever be used as a substitute for direct medical advice from your doctor or other qualified clinician.

Successful weight loss depends largely on becoming more aware of your behaviors and starting to change them. Instead of relying on willpower, this process demands skill power. This Special Health Report, Lose Weight and Keep It Off , offers a range of solutions that have worked for many people and can be tailored to your needs.

Thanks for visiting. Don't miss your FREE gift. The Best Diets for Cognitive Fitness , is yours absolutely FREE when you sign up to receive Health Alerts from Harvard Medical School. Sign up to get tips for living a healthy lifestyle, with ways to fight inflammation and improve cognitive health , plus the latest advances in preventative medicine, diet and exercise , pain relief, blood pressure and cholesterol management, and more.

Get helpful tips and guidance for everything from fighting inflammation to finding the best diets for weight loss from exercises to build a stronger core to advice on treating cataracts.

PLUS, the latest news on medical advances and breakthroughs from Harvard Medical School experts. Sign up now and get a FREE copy of the Best Diets for Cognitive Fitness. Stay on top of latest health news from Harvard Medical School.

Recent Blog Articles. Flowers, chocolates, organ donation — are you in? What is a tongue-tie? 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.

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.

The researchers plan to continue their research into the effects of the gene variation. They also envision the possibility of creating drugs to block its harmful effects. The scientists have published their findings in the prestigious scientific journal Nature Genetics. The work was made possible by financial support from many generous backers, with Civelek receiving support from the National Institutes of Health, Award R00 HL Categories: All Releases.

April 17, Gene Affects How Some Women Store Fat — And Ups Their Diabetes Risk. The gene variation specifically affects tummy and hip fat, causing cells to become fewer but larger. In men, the effect on diabetes risk is much smaller.

A, Associations with Personalized caloric needs fat mass for the —genetic variants polygenic Quercetin and cancer prevention for higher Geneetics are distriibution. Associations are Dance fueling guidelines in clinical Geneitcs Genetics and fat distribution units Genetics and fat distribution continuous outcome per 1-SD increase Genetics and fat distribution body mass index Distributoin —adjusted WHR corresponding fxt 0. B, Associations with compartmental fat qnd for the waist- or hip-specific polygenic scores for higher WHR are shown. Associations are reported in clinical or standardized units of continuous outcome per 1-SD increase in BMI-adjusted WHR corresponding to 0. A, Associations with cardiometabolic risk factors for the waist- or hip-specific polygenic scores for higher WHR are shown. Data on blood pressure were from the UK Biobank 15 ; data on low-density lipoprotein LDL-C and triglyceride levels were from the Global Lipids Genetics Consortium 22 ; and data on fasting insulin and fasting glucose were from the Meta-analyses of Glucose and Insulin-Related Traits Consortium. Thank you for visiting nature. You Disribution using a browser version with distribjtion support Genetics and fat distribution CSS. To obtain the best experience, we recommend Flaxseed for menopause symptoms use a more up distgibution date browser Personalized caloric needs turn distribhtion Personalized caloric needs mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Obesity-associated morbidity is exacerbated by abdominal obesity, which can be measured as the waist-to-hip ratio adjusted for the body mass index WHRadjBMI. Here we identify genes associated with obesity and WHRadjBMI and characterize allele-sensitive enhancers that are predicted to regulate WHRadjBMI genes in women. We found that several waist-to-hip ratio-associated variants map within primate-specific Alu retrotransposons harboring a DNA motif associated with adipocyte differentiation.


Genetics and Obesity. Why Your Genes Drive Your Behaviors.

Genetics and fat distribution -

A natural variation of the gene KLF14 causes some women to store fat on their bellies and hips and puts them at significantly increased risk of type 2 diabetes , new research reveals. Cruelly, the gene is sex specific : Men with the same variation of the gene have a much less heightened diabetes risk.

And some women with the variation are spared as well, depending on whether they received the gene from their mothers or their fathers.

For reasons that we still do not understand, this gene is more active, or increases risk more, in women than in men. The discovery suggests that doctors may one day target the gene variation with drugs to reduce diabetes risk.

But the new finding reveals that fat, too, can be a culprit. And the gene variation has effects on hundreds of other genes as well, causing a cascade effect that the researchers have not yet fully charted.

The finding, the result of five years of work, speaks to the increasingly sophisticated ways scientists must think about human genetics. What if we look at it this way? The correlation is with the fat distribution. Those things really took time to figure out. The researchers plan to continue their research into the effects of the gene variation.

They also envision the possibility of creating drugs to block its harmful effects. Alexopoulos, N. Visceral adipose tissue as a source of inflammation and promoter of atherosclerosis. Atherosclerosis , — Article CAS PubMed Google Scholar. Canoy, D. Distribution of body fat and risk of coronary heart disease in men and women.

Article PubMed Google Scholar. Bergman, R. et al. Why visceral fat is bad: mechanisms of the metabolic syndrome. Obesity 14 , 16S—19S Censin, J. Causal relationships between obesity and the leading causes of death in women and men. PLoS Genet. Article PubMed PubMed Central Google Scholar.

Pulit, S. Yengo, L. Article CAS PubMed PubMed Central Google Scholar. Locke, A. Genetic studies of body mass index yield new insights for obesity biology. Nature , — Shungin, D. New genetic loci link adipose and insulin biology to body fat distribution.

Cannon, M. Open chromatin profiling in adipose tissue marks genomic regions with functional roles in cardiometabolic traits. G3 Bethesda 9 , — Finucane, H. Partitioning heritability by functional annotation using genome-wide association summary statistics.

Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Aran, D. xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol. Hoffman, G. CommonMind Consortium provides transcriptomic and epigenomic data for schizophrenia and bipolar disorder.

Data 6 , Merrick, D. Identification of a mesenchymal progenitor cell hierarchy in adipose tissue. Science , eaav Palmer, B. The sexual dimorphism of obesity. Chooi, Y. The epidemiology of obesity. Metabolism 92 , 6—10 Paeratakul, S. The relation of gender, race and socioeconomic status to obesity and obesity comorbidities in a sample of US adults.

Article CAS Google Scholar. Borgeraas, H. Bulik-Sullivan, B. An atlas of genetic correlations across human diseases and traits. Gusev, A. Integrative approaches for large-scale transcriptome-wide association studies.

Grarup, N. Loss-of-function variants in ADCY3 increase risk of obesity and type 2 diabetes. Dubern, B. Mutational analysis of melanocortin-4 receptor, agouti-related protein, and alpha-melanocyte-stimulating hormone genes in severely obese children. Fall, T. Genome-wide association studies of obesity and metabolic syndrome.

Justice, A. Protein-coding variants implicate novel genes related to lipid homeostasis contributing to body-fat distribution. Ulirsch, J.

Systematic functional dissection of common genetic variation affecting red blood cell traits. Cell , — Joslin, A. A functional genomics pipeline identifies pleiotropy and cross-tissue effects within obesity-associated GWAS loci.

Giambartolomei, C. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. Melnikov, A. Massively parallel reporter assays in cultured mammalian cells. Google Scholar. Xu, Z. The orphan nuclear receptor chicken ovalbumin upstream promoter-transcription factor II is a critical regulator of adipogenesis.

Natl Acad. USA , — Small, K. Regulatory variants at KLF14 influence type 2 diabetes risk via a female-specific effect on adipocyte size and body composition.

Seo, J. Activated liver X receptors stimulate adipocyte differentiation through induction of peroxisome proliferator-activated receptor γ expression. Lu, C. Thyroid hormone receptors regulate adipogenesis and carcinogenesis via crosstalk signaling with peroxisome proliferator-activated receptors.

Guo, L. Cao, J. Batchvarova, N. Inhibition of adipogenesis by the stress-induced protein CHOP Gadd EMBO J. Fox, K. Terrados, G. Genome-wide localization and expression profiling establish Sp2 as a sequence-specific transcription factor regulating vitally important genes.

Nucleic Acids Res. Huss, J. Estrogen-related receptor α directs peroxisome proliferator-activated receptor α signaling in the transcriptional control of energy metabolism in cardiac and skeletal muscle. Casado, M.

Essential role in vivo of upstream stimulatory factors for a normal dietary response of the fatty acid synthase gene in the liver. Yeo, C. SGBS cells as a model of human adipocyte browning: a comprehensive comparative study with primary human white subcutaneous adipocytes.

Yu, K. FEBS J. Xue, J. Distinct stages in adipogenesis revealed by retinoid inhibition of differentiation after induction of PPARgamma. Kundaje, A. Integrative analysis of reference human epigenomes. Fulco, C. Systematic mapping of functional enhancer—promoter connections with CRISPR interference.

Science , — Laber, S. Discovering cellular programs of intrinsic and extrinsic drivers of metabolic traits using LipocyteProfiler. Pontzer, H. Metabolic acceleration and the evolution of human brain size and life history. Lynch, V. Ancient transposable elements transformed the uterine regulatory landscape and transcriptome during the evolution of mammalian pregnancy.

Cell Rep. Chuong, E. Regulatory evolution of innate immunity through co-option of endogenous retroviruses. Khetan, S. Functional characterization of T2D-associated SNP effects on baseline and ER stress-responsive β cell transcriptional activation.

Qin, B. Sorting nexin 10 induces giant vacuoles in mammalian cells. Ye, L. Osteopetrorickets due to Snx10 deficiency in mice results from both failed osteoclast activity and loss of gastric acid-dependent calcium absorption.

Teriokhin, A. Worldwide variation in life-span sexual dimorphism and sex-specific environmental mortality rates. Mauvais-Jarvis, F. Sex and gender: modifiers of health, disease, and medicine. Lancet , — Oliva, M. The impact of sex on gene expression across human tissues.

aba Lonsdale, J. The Genotype-Tissue Expression GTEx project. Berrandou, T. LDAK-GBAT: fast and powerful gene-based association testing using summary statistics. Ongen, H. Fast and efficient QTL mapper for thousands of molecular phenotypes. Bioinformatics 32 , — Siva, N.

Xu, S. Use ggbreak to effectively utilize plotting space to deal with large datasets and outliers. Dobin, A.

STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29 , 15—21 McCarthy, D. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation.

Robinson, M. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26 , — Activity-by-contact model of enhancer—promoter regulation from thousands of CRISPR perturbations.

Sinnott-Armstrong, N. A regulatory variant at 3q Cell Metab. Eguchi, J. Transcriptional control of adipose lipid handling by IRF4. Download references. This work was supported by the Novo Nordisk Foundation challenge grant NNF18OC to M.

and training grant T32HL to A. J and the American Heart Association grant 20PRE to G. We thank A. Candles for support with the paper.

Department of Human Genetics, University of Chicago, Chicago, IL, USA. Grace T. Hansen, Débora R. Sobreira, Zachary T. Weber, Alexis G. Thornburg, Ivy Aneas, Li Zhang, Noboru J.

Sakabe, Amelia C. Joslin, Gabriela A. Haddad, Yang I. Pritzker School of Medicine, University of Chicago, Chicago, IL, USA. Broad Institute of MIT and Harvard, Boston, MA, USA. Sophie M.

Institute of Nutritional Medicine, School of Medicine, Technical University of Munich, Munich, Germany. Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK. Department of Rehabilitation Medicine, University of Minnesota, Minneapolis, MN, USA.

Department of Genetic Medicine, University of Chicago, Chicago, IL, USA. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Boston, MA, USA.

Massachussetts General Hospital, Harvard Medical School, Boston, MA, USA. Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA. Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA. Department of Medicine, Harvard Medical School, Boston, MA, USA.

Novo Nordisk Foundation Center for Genomic Mechanisms of Disease at the Broad Institute of MIT and Harvard, Boston, MA, USA. You can also search for this author in PubMed Google Scholar. and Y. conceived of the initial TWAS approach to finding genes associated with obesity and WHRadjBMI.

Gebetics many other medical conditions, obesity is Ginger health benefits result of faat interplay ane environmental distributioh Genetics and fat distribution factors. Polymorphisms in Genetics and fat distribution genes controlling appetite and metabolism predispose to obesity under certain dietary conditions. Personalized caloric needs is likely cistribution in each person a number of genes contribute to the likelihood of developing obesity in small part, with each gene increasing or decreasing the odds marginally, and together determining how an individual responds to the environmental factors. Although genetic deficiencies are currently considered rare, variations in these genes may predispose to common obesity. Several additional loci have been identified. Some studies have focused upon inheritance patterns without focusing upon specific genes.

Author: Nizahn

1 thoughts on “Genetics and fat distribution

Leave a comment

Yours email will be published. Important fields a marked *

Design by