Shared genetic etiology and causality between body fat percentage and cardiovascular diseases: a large-scale genome-wide cross-trait analysis.
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ABSTRACT: Background
Accumulating evidences have suggested that high body fat percentage (BF%) often occurs in parallel with cardiovascular diseases (CVDs), implying a common etiology between them. However, the shared genetic etiology underlying BF% and CVDs remains unclear.Methods
Using large-scale genome-wide association study (GWAS) data, we investigated shared genetics between BF% (N = 100,716) and 10 CVD-related traits (n = 6968-977,323) with linkage disequilibrium score regression, multi-trait analysis of GWAS, and transcriptome-wide association analysis, and evaluated causal associations using Mendelian randomization.Results
We found strong positive genetic correlations between BF% and heart failure (HF) (Rg = 0.47, P = 1.27 × 10- 22) and coronary artery disease (CAD) (Rg = 0.22, P = 3.26 × 10- 07). We identified 5 loci and 32 gene-tissue pairs shared between BF% and HF, as well as 16 loci and 28 gene-tissue pairs shared between BF% and CAD. The loci were enriched in blood vessels and brain tissues, while the gene-tissue pairs were enriched in the nervous, cardiovascular, and exo-/endocrine system. In addition, we observed that BF% was causally related with a higher risk of HF (odds ratio 1.63 per 1-SD increase in BF%, P = 4.16 × 10-04) using a MR approach.Conclusions
Our findings suggest that BF% and CVDs have shared genetic etiology and targeted reduction of BF% may improve cardiovascular outcomes. This work advances our understanding of the genetic basis underlying co-morbid obesity and CVDs and opens up a new way for early prevention of CVDs.
SUBMITTER: Zhuang Z
PROVIDER: S-EPMC8082910 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectivesIt has long been discussed whether fitness or fatness is a more important determinant of health status. If the same genetic factors that promote body fat percentage (body fat%) are related to cardiorespiratory fitness (CRF), part of the concurrent associations with health outcomes could reflect a common genetic origin. In this study we aimed to 1) examine genetic correlations between body fat% and CRF; 2) determine whether CRF can be attributed to a genetic risk score (GRS) based on known body fat% increasing loci; and 3) examine whether the fat mass and obesity associated (FTO) locus associates with CRF.MethodsGenetic correlations based on pedigree information were examined in a family based cohort (n = 230 from 55 families). For the genetic association analyses, we examined two Danish population-based cohorts (ntotal = 3206). The body fat% GRS was created by summing the alleles of twelve independent risk variants known to associate with body fat%. We assessed CRF as maximal oxygen uptake expressed in millilitres of oxygen uptake per kg of body mass (VO2max), per kg fat-free mass (VO2maxFFM), or per kg fat mass (VO2maxFM). All analyses were adjusted for age and sex, and when relevant, for body composition.ResultsWe found a significant negative genetic correlation between VO2max and body fat% (ρG = -0.72 (SE ±0.13)). The body fat% GRS associated with decreased VO2max (β = -0.15 mL/kg/min per allele, p = 0.0034, age and sex adjusted). The body fat%-increasing FTO allele was associated with a 0.42 mL/kg/min unit decrease in VO2max per allele (p = 0.0092, age and sex adjusted). Both associations were abolished after additional adjustment for body fat%. The fat% increasing GRS and FTO risk allele were associated with decreased VO2maxFM but not with VO2maxFFM.ConclusionsOur findings suggest a shared genetic etiology between whole body fat% and CRF.
| S-EPMC5112859 | biostudies-literature
Project description:Background: Accumulating evidence has suggested that there is a positive association between asthma and cardiovascular diseases (CVDs), implying a common architecture between them. However, the shared genetic architecture and causality of asthma and CVDs remain unclear. Methods: Based on the genome-wide association study (GWAS) summary statistics of recently published studies, our study examined the genetic correlation, shared genetic variants, and causal relationship between asthma (N = 127,669) and CVDs (N = 86,995-521,612). Statistical methods included high-definition likelihood (HDL), cross-trait meta-analyses of large-scale GWAS, transcriptome-wide association studies (TWAS), and Mendelian randomization (MR). Results: First, we observed a significant genetic correlation between asthma and heart failure (HF) (Rg = 0.278, P = 5 × 10-4). Through cross-trait analyses, we identified a total of 145 shared loci between asthma and HF. Fifteen novel loci were not previously reported for association with either asthma or HF. Second, we mapped these 145 loci to a total of 99 genes whose expressions are enriched in a broad spectrum of tissues, including the seminal vesicle, tonsil, appendix, spleen, skin, lymph nodes, breast, cervix and uterus, skeletal muscle, small intestine, lung, prostate, cardiac muscle, and liver. TWAS analysis identified five significant genes shared between asthma and HF in tissues from the hemic and immune system, digestive system, integumentary system, and nervous system. GSDMA, GSDMB, and ORMDL3 are statistically independent genetic effects from all shared TWAS genes between asthma and HF. Third, through MR analysis, genetic liability to asthma was significantly associated with heart failure at the Bonferroni-corrected significance level. The odds ratio (OR) is 1.07 [95% confidence interval (CI): 1.03-1.12; p = 1.31 × 10-3] per one-unit increase in loge odds of asthma. Conclusion: These findings provide strong evidence of genetic correlations and causal relationship between asthma and HF, suggesting a shared genetic architecture for these two diseases.
| S-EPMC8811262 | biostudies-literature
Project description:Coffee or caffeine consumption has been associated with neuropsychiatric disorders, implying a shared etiology. However, whether these associations reflect causality remains largely unknown. To understand the genetic structure of the association between decaffeinated coffee consumption (DCC) and neuropsychiatric traits, we examined the genetic correlation, causality, and shared genetic structure between DCC and neuropsychiatric traits using linkage disequilibrium score regression, bidirectional Mendelian randomization (MR), and genome-wide cross-trait meta-analysis in large GWAS Consortia for coffee consumption (N = 329,671) and 13 neuropsychiatric traits (sample size ranges from 36,052 to 500,199). We found strong positive genetic correlations between DCC and lifetime cannabis use (LCU; Rg = 0.48, P = 8.40 × 10-19), alcohol use disorder identification test (AUDIT) total score (AUDIT_T; Rg = 0.40, P = 4.63 × 10-13), AUDIT_C score (alcohol consumption component of the AUDIT; Rg = 0.40, P = 5.26 × 10-11), AUDIT_P score (dependence and hazardous-use component of the AUDIT; Rg = 0.28, P = 1.36 × 10-05), and strong negative genetic correlations between DCC and neuroticism (Rg = -0.15, P = 7.27 × 10-05), major depressed diseases (MDD; Rg = -0.15, P = 0.0010), and insomnia (Rg= -0.15, P = 0.0007). In the cross-trait meta-analysis, we identified 6, 5, 1, 1, 2, 31, and 27 shared loci between DCC and Insomnia, LCU, AUDIT_T, AUDIT_C, AUDIT_P, neuroticism, and MDD, respectively, which were mainly enriched in bone marrow, lymph node, cervix, uterine, lung, and thyroid gland tissues, T cell receptor signaling pathway, antigen receptor-mediated signaling pathway, and epigenetic pathways. A large of TWAS-significant associations were identified in tissues that are part of the nervous system, digestive system, and exo-/endocrine system. Our findings further indicated a causal influence of liability to DCC on LCU and low risk of MDD (odds ratio: 0.90, P = 9.06 × 10-5 and 1.27, P = 7.63 × 10-4 respectively). We also observed that AUDIT_T and AUDIT_C were causally related to DCC (odds ratio: 1.83 per 1-SD increase in AUDIT_T, P = 1.67 × 10-05, 1.80 per 1-SD increase in AUDIT_C, P = 5.09 × 10-04). Meanwhile, insomnia and MDD had a causal negative influence on DCC (OR: 0.91, 95% CI: 0.86-0.95, P = 1.51 × 10-04 for Insomnia; OR: 0.93, 95% CI: 0.89-0.99, P = 6.02 × 10-04 for MDD). These findings provided evidence for the shared genetic basis and causality between DCC and neuropsychiatric diseases, and advance our understanding of the shared genetic mechanisms underlying their associations, as well as assisting with making recommendations for clinical works or health education.
| S-EPMC9309364 | biostudies-literature
Project description:Venous thromboembolism occurs in up to one-third of patients with COVID-19. Venous thromboembolism and COVID-19 may share a common genetic architecture, which has not been clarified. To fill this gap, we leverage summary-level genetic data from the latest COVID-19 host genetics consortium and UK Biobank and examine the shared genetic etiology and causal relationship between COVID-19 and venous thromboembolism. The cross-trait and co-localization analyses identify 2, 3, and 4 shared loci between venous thromboembolism and severe COVID-19, COVID-19 hospitalization, SARS-CoV-2 infection respectively, which are mapped to ABO, ADAMTS13, FUT2 genes involved in coagulation functions. Enrichment analysis supports shared biological processes between COVID-19 and venous thromboembolism related to coagulation and immunity. Bi-directional Mendelian randomization suggests that venous thromboembolism was associated with higher risk of three COVID-19 traits, and SARS-CoV-2 infection was associated with a higher risk of venous thromboembolism. Our study provides timely evidence for the genetic etiology between COVID-19 and venous thromboembolism (VTE). Our findings contribute to the understanding of COVID-19 and VTE etiology and provide insights into the prevention and comorbidity management of COVID-19.
| S-EPMC10120502 | biostudies-literature
Project description:BackgroundLimited studies have compared the association between various physical measurements and the risk of cancer or cardiovascular disease (CVD). We aim to explore the best-individualized indicators of cancer and CVD risk assessment.MethodsFrom May 2004 to December 2017, a community-based cohort in China involving 100 280 participants were enrolled. BMI, height, body surface area (BSA), and body fat percentage (BFP) were compared in parallel about cancer and CVD risk with the multivariable-adjusted Cox proportional hazard regression model.ResultsWithin the follow-up period, 3107 (3.10%) were diagnosed with cancer and 3721 (3.71%) had CVD. Per-level increased (in tertile: T1, T2, and T3 level) BSA, height, and BFP was positively associated with the risk of overall cancer [HR (95% CI): 1.10 (1.05-1.15), 1.12 (1.07-1.18), and 1.10 (1.03-1.16), respectively], whereas BMI was insignificant. Compared with the reference group (T2), the highest BSA level (T3) was positively associated with overall cancer incidence for both male [HR (95% CI): 1.28 (1.13-1.45)] and female [HR (95% CI): 1.13 (1.00-1.28)]. The BSA, height, and BFP also significantly associated with some site-specific cancers including thyroid, stomach, breast, urinary system, and skin cancer. Meanwhile, BFP presented a strong positive association with overall CVD [HR (95% CI): 1.22 (1.15-1.30) in trend] in both gender and associated with nearly all CVD subtypes especially the myocardial infarction and heart failure.ConclusionBSA, height, and BFP have more sensitivity in assessing cancer risk and BFP shows the largest hazard ratios for CVD incident. We provided valuable evidence for the application of height, BSA, and BFP in routine healthcare practice. These encouraging findings should be tested in more well-defined studies for risk prediction.
| S-EPMC7300397 | biostudies-literature
Project description:BackgroundAccurate obesity classification is important so that appropriate intervention can be instituted to modify metabolic risk factors. Commonly utilized body mass index (BMI) and percentage body fat (PBF) are influenced by lean mass whereas fat mass index (FMI) measures only body fat. This study compares the prevalence of obesity and metabolic risk factors with FMI, BMI and PBF using DXA (dual-energy x-ray absorptiometry).Methods489 women randomly recruited from the electoral roll were stratified into 4 age groups; 40-49, 50-59, 60-69 and 70-79 years from 2000 to 2001. Clinical data and DXA body composition were obtained. Statistical analyses were performed using Medcalc v15 (Ostend, Belgium) with significance level at p = 0.05 (two-tailed).ResultsThere was higher prevalence of obesity using PBF compared to BMI and FMI (p<0.001). This difference was greater from age 50-59 (p<0.05) which may be explained by age-related lean mass loss. PBF over-classified obesity in over 35% of normal and 95% of overweight categories compared to FMI and BMI. BMI has a sensitivity of 78.9% and specificity of 98.3% for obesity using FMI as the standard. BMI under-classified obesity in the overweight category by 14.9% compared to FMI. There was no difference in diabetes, dyslipidemia, hypertension and metabolic syndrome prevalence within the BMI-obesity and FMI-obesity categories (p>0.05).ConclusionPBF classified more obesity than BMI and FMI because of its low pre-determined threshold. The greater difference with PBF compared to BMI and FMI from the 50-59 decade onwards can be attributed to age-related lean mass loss. BMI had the lowest sensitivity for obesity diagnosis. BMI under-classified obesity in the overweight category compared to FMI due to its inability to differentiate lean mass. However, there was no significant difference in the prevalence of metabolic risk factors between BMI and FMI-obesity categories indicating that fat location may influence metabolic dysregulation.
| S-EPMC7808627 | biostudies-literature
Project description:Obesity is a prevalent childhood condition and the degree of adiposity appears likely to be an important covariate in the pharmacokinetics (PKs) of many drugs. We undertook these studies to facilitate the evaluation and, where appropriate, quantification of the covariate effect of body fat percentage (BF%) on PK parameters in children. We examined two large databases to determine the values and variabilities of BF% in children with healthy body weights and in those with obesity, comparing the accuracy and precision of BF% estimation by both clinical methods and demographically derived techniques. Additionally, we conducted simulation studies to evaluate the utility of the several methods for application in clinical trials. BF% was correlated with body mass index (BMI), but was highly variable among both children with healthy body weights and those with obesity. Bio-impedance and several demographically derived techniques produced mean estimates of BF% that differed from dual x-ray absorptiometry by < 1% (accuracy) and a SD of 5% or less (precision). Simulation studies confirmed that when the differences in precision among the several methods were small compared with unexplained between-subject variability of a PK parameter, the techniques were of similar value in assessing the contribution of BF%, if any, as a covariate for that PK parameter. The combination of sex and obesity stage explained 68% of the variance of BF% with BMI. The estimation of BF% from sex and obesity stage can routinely be applied to PK clinical trials to evaluate the contribution of BF% as a potential covariate.
| S-EPMC7993323 | biostudies-literature
Project description:IntroductionBiological sex influences both overall adiposity and fat distribution. Further, testosterone and sex hormone binding globulin (SHBG) influence adiposity and metabolic function, with differential effects of testosterone in men and women. Here, we aimed to perform sex-stratified genome-wide association studies (GWAS) of body fat percentage (BFPAdj) (adjusting for testosterone and sex hormone binding globulin (SHBG)) to increase statistical power.MethodsGWAS were performed in white British individuals from the UK Biobank (157,937 males and 154,337 females). To avoid collider bias, loci associated with SHBG or testosterone were excluded. We investigated association of BFPAdj loci with high density cholesterol (HDL), triglyceride (TG), type 2 diabetes (T2D), coronary artery disease (CAD), and MRI-derived abdominal subcutaneous adipose tissue (ASAT), visceral adipose tissue (VAT) and gluteofemoral adipose tissue (GFAT) using publicly available data from large GWAS. We also performed 2-sample Mendelian Randomization (MR) using identified BFPAdj variants as instruments to investigate causal effect of BFPAdj on HDL, TG, T2D and CAD in males and females separately.ResultsWe identified 195 and 174 loci explaining 3.35% and 2.60% of the variation in BFPAdj in males and females, respectively at genome-wide significance (GWS, p<5x10-8). Although the direction of effect at these loci was generally concordant in males and females, only 38 loci were common to both sexes at GWS. Seven loci in males and ten loci in females have not been associated with any adiposity/cardiometabolic traits previously. BFPAdj loci generally did not associate with cardiometabolic traits; several had paradoxically beneficial cardiometabolic effects with favourable fat distribution. MR analyses did not find convincing supportive evidence that increased BFPAdj has deleterious cardiometabolic effects in either sex with highly significant heterogeneity.ConclusionsThere was limited genetic overlap between BFPAdj in males and females at GWS. BFPAdj loci generally did not have adverse cardiometabolic effects which may reflect the effects of favourable fat distribution and cardiometabolic risk modulation by testosterone and SHBG.
| S-EPMC10585153 | biostudies-literature
Project description:People with coronavirus disease 2019 (COVID-19) have different mortality or severity, and this clinical outcome is thought to be mainly attributed to comorbid cardiovascular diseases. However, genetic loci jointly influencing COVID-19 and cardiovascular disorders remain largely unknown. To identify shared genetic loci between COVID-19 and cardiac traits, we conducted a genome-wide cross-trait meta-analysis. Firstly, from eight cardiovascular disorders, we found positive genetic correlations between COVID-19 and coronary artery disease (CAD, R g = 0.4075, P = 0.0031), type 2 diabetes (T2D, R g = 0.2320, P = 0.0043), obesity (OBE, R g = 0.3451, P = 0.0061), as well as hypertension (HTN, R g = 0.233, P = 0.0026). Secondly, we detected 10 shared genetic loci between COVID-19 and CAD, 3 loci between COVID-19 and T2D, 5 loci between COVID-19 and OBE, and 21 loci between COVID-19 and HTN, respectively. These shared genetic loci were enriched in signaling pathways and secretion pathways. In addition, Mendelian randomization analysis revealed significant causal effect of COVID-19 on CAD, OBE and HTN. Our results have revealed the genetic architecture shared by COVID-19 and CVD, and will help to shed light on the molecular mechanisms underlying the associations between COVID-19 and cardiac traits.
| S-EPMC9520490 | biostudies-literature
Project description:AimTo ascertain to what extent it is possible to stop being obese (to normalize body mass index [BMI], waist circumference [WC] and/or body fat percentage [BFP]).DesignLongitudinal observational and retrospective study. SITE: Eleven Spanish health centers.ParticipantsMen and women with BMI≥30kg/m2 (n=1246) or general obesity (GO), with WC>102cm and >88cm, respectively (n=2122) or abdominal obesity (AO) and with BFP>25% and >35%, respectively (n=2436) or excess body fat (EBF), from the PEPAF Study cohort of 4927 participants aged 20-80years.Main measurementsData from the PEPAF study at baseline and at 6, 12 and 24months: gender, age, diagnoses of diabetes, hypertension and dyslipidemia, smoking, levels of and compliance with physical activity recommendations, maximum oxygen consumption, weigh, height, WC and three skin-folds (thoracic, umbilical and anterior thigh for men and triceps, suprailiac and anterior thigh for women).ResultsOf 2054 participants with any type of obesity at baseline and valid data at 2years, 240 (11.6%) had normalized all of their obesity diagnostic indexes. 19.5% (95% confidence interval (95%CI: 17.6-21.4) ceased to have EBF, 12.0% (95%CI: 10.4-13.7) ceased to have AO and 10.5% (95%CI: 8.5-12.7) ceased to have GO.ConclusionsObesity differs from other chronic diseases in that it can be «cured» by normalizing the amount of body fat.
| S-EPMC9813526 | biostudies-literature