Genetic Investigation Of ANthropometric Traits Research Articles

Body mass index and meniscal tears: Evidence from meta-analysis of observational studies and Mendelian randomization.

Obesity is a potential risk factor for meniscal tear (MT). We utilized meta-analysis of observational studies and Mendelian randomization (MR) analyses to elucidate the association between body mass index (BMI) and MT. In meta-analysis, a search was performed on June 27, 2022, using PubMed and Embase databases. Odds ratios and 95% confidence intervals were extracted from included studies. In MR analyses, the research utilized summary-level data on BMI and MT obtained from Genetic Investigation of Anthropometric Traits and the FinnGen Consortium, respectively. In meta-analysis, four studies comprising 826,383 participants were included. The pooled odds ratio of MT in the high BMI group was 1.32 (95% confidence interval, 0.83-2.09), compared with the nonhigh BMI group. The pooled odds ratio in the under 30 group was 1.76 (95% confidence interval, 0.61-5.03). In MR analyses, one standard deviation increase in genetically predicted BMI was associated with meniscus derangement as a chronic subtype of MT (odds ratio, 1.36; 95% confidential interval, 1.17-1.59). We found that a high BMI was not associated with an increased likelihood of MT based on meta-analysis of observational studies; however, by complementing MR analyses, we elucidated the causality of BMI increase on meniscus derangement as a chronic subtype of MT.

Estimating causality between smoking and abdominal obesity by Mendelian randomization.

Smokers tend to have a lower body weight than non-smokers, but also more abdominal fat. It remains unclear whether or not the relationship between smoking and abdominal obesity is causal. Previous Mendelian randomization (MR) studies have investigated this relationship by relying upon a single genetic variant for smoking heaviness. This approach is sensitive to pleiotropic effects and may produce imprecise causal estimates. We aimed to estimate causality between smoking and abdominal obesity using multiple genetic instruments. MR study using causal analysis using summary effect estimates (CAUSE) and latent heritable confounder MR (LHC-MR) methods that instrument smoking using genome-wide data, and also two-sample MR (2SMR) methods. Genome-wide association studies (GWAS) summary statistics from participants of European ancestry, obtainedfrom the GWAS and Sequencing Consortium of Alcohol and Nicotine use (GSCAN), Genetic Investigation of Anthropometric Traits (GIANT) Consortium and the UK Biobank. We used GWAS results for smoking initiation (n = 1 232 091), life-time smoking (n = 462 690) and smoking heaviness (n = 337 334) as exposure traits, and waist-hip ratio (WHR) and waist and hip circumferences (WC and HC) (n up to 697 734), with and without adjustment for body mass index (adjBMI), as outcome traits. Smoking initiation, life-time smoking, smoking heaviness, WHR, WC, HC, WHRadjBMI, WCadjBMI and HCadjBMI. Both CAUSE and LHC-MR indicated a positive causal effect of smoking initiation on WHR (0.13 [95% confidence interval (CI) = 0.10, 0.16 and 0.49 (0.41, 0.57), respectively] and WHRadjBMI (0.07 (0.03, 0.10) and 0.31 (0.26, 0.37). Similarly, they indicated a positive causal effect of life-time smoking on WHR [0.35 (0.29, 0.41) and 0.44 (0.38, 0.51)] and WHRadjBMI [0.18 (0.13, 0.24) and 0.26 (0.20, 0.31)]. In follow-up analyses, smoking particularly increased visceral fat. There was no evidence of a mediating role by cortisol or sex hormones. Smoking initiation and higher life-time smoking may lead to increased abdominal fat distribution. The increase in abdominal fat due to smoking is characterized by an increase in visceral fat. Thus, efforts to prevent and cease smoking can have the added benefit of reducing abdominal fat.

Links between the genetic determinants of morning plasma cortisol and body shape: a two-sample Mendelian randomisation study

High cortisol production in Cushing’s syndrome leads to fat centralisation. The influence of modest cortisol variations on body shape, however, is less clear. We examined potentially causal associations between morning plasma cortisol and body shape and obesity with inverse-variance weighted random-effects models in a two-sample Mendelian randomisation analysis. We used publicly available summary statistics from the CORtisol NETwork (CORNET) consortium, UK Biobank, and the Genetic Investigation of Anthropometric Traits (GIANT) consortium. Only in women, morning plasma cortisol (proxied by ten genetic polymorphisms) was associated positively with waist size reflected in waist-to-hip index (WHI, 0.035 standard deviation (SD) units change per one SD cortisol increase; 95% confidence interval (0.002–0.067); p = 0.036) and “a body shape index” (ABSI; 0.039 (0.006–0.071); p = 0.021). There was no evidence for associations with hip index (HI) or body mass index (BMI). Among individual polymorphisms, rs7450600 stood out (chromosome 6; Long Intergenic Non-Protein-Coding RNA 473 gene, LINC00473). Morning plasma cortisol proxied by rs7450600 was associated positively with WHI and inversely with HI and BMI in women and men. Our findings support a causal association of higher morning plasma cortisol with larger waist size in women and highlight LINC00473 as a genetic link between morning plasma cortisol and body shape.

Genetic causal association of adiposity, body fat distribution with incident heart failure in a European cohort: A linear and non-linear Mendelian randomisation analysis

Abstract Background Excess adiposity has been associated with higher risk of heart failure in observational studies. However, these observational studies are prone to residual confounding and the shape and strength of the independent associations of adiposity measures with heart failure is uncertain. Purpose This study aimed to compare the associations of usual and genetically determined general and central adiposity measures on incident heart failure in a European population cohort. Methods The MR analysis was performed in a cohort of 326,300 adults (mean age 55.9 years, 56% women) of European ancestry with complete genetic data and no prior cardiac and vascular disease recruited from UK general population in 2006-2010. Resurvey on a subset of 20,346 participants was conducted in 2012-13. Regression dilution ratios (RDR) of the different adiposity measures were calculated using Pearson’s correlation method to obtain usual levels of each adiposity measure. Participants were followed up by linkage to electronic health records for incident heart failure events. Externally weighted genetic risk scores (GRS) for body mass index (BMI) and waist circumference (WC) adjusted for BMI (WCadjBMI) were constructed using single nucleotide polymorphisms (SNPs) that were genome-wide significant for BMI and WC as reported by the Genetic Investigation of ANthropometric Traits (GIANT) consortium. Associations of usual measured adiposity measures and genetically predicted usual adiposity (76-SNP BMI-GRS [pleiotropic for other adiposity measures], 36-SNP BMI-GRS [non-pleiotropic for other adiposity measures] and 68-SNP WCadjBMI-GRS) with incident HF subtypes were determined using Cox proportional hazards regression models. The shapes of the associations of genetically predicted adiposity with heart failure at different levels of adiposity measures were examined using localized average causal effects within strata of residual adiposity measures after adjustment for GRS and correction for regression dilution. Hazard ratios (HRs) are presented as per equivalent 5 usual BMI units higher adiposity measure. Results Over a median follow-up of 12.8 years, there were 6,570 first-ever incident HF events. A positive non-linear association (J-shape) was observed between genetically predicted BMI and heart failure with while positive log-linear association was observed for genetically predicted WCadjBMI. Consistent with the observational analyses, the hazard ratio per 5 BMI units equivalent higher genetically predicted WCadjBMI were notably stronger (68-SNP WCadjBMI GRS: HR 2.03, 95% CI1.04-3.97) than for genetically predicted BMI (36-SNP GRS-BMI: HR 1.78, 95% CI 1.12-2.81 and 76-SNP GRS-BMI: HR 1.99, 95% CI 1.62-2.45). Conclusion The findings provide evidence for increased heart failure risk at extremes of BMI and also confirm the stronger causal role of central adiposity over general adiposity in heart failure across the whole range of waist circumference.Figure 1Figure 2

Evidence of causality of low body mass index on risk of adolescent idiopathic scoliosis: a Mendelian randomization study.

Adolescent idiopathic scoliosis (AIS) is a disorder with a three-dimensional spinal deformity and is a common disease affecting 1-5% of adolescents. AIS is also known as a complex disease involved in environmental and genetic factors. A relation between AIS and body mass index (BMI) has been epidemiologically and genetically suggested. However, the causal relationship between AIS and BMI remains to be elucidated. Mendelian randomization (MR) analysis was performed using summary statistics from genome-wide association studies (GWASs) of AIS (Japanese cohort, 5,327 cases, 73,884 controls; US cohort: 1,468 cases, 20,158 controls) and BMI (Biobank Japan: 173430 individual; meta-analysis of genetic investigation of anthropometric traits and UK Biobank: 806334 individuals; European Children cohort: 39620 individuals; Population Architecture using Genomics and Epidemiology: 49335 individuals). In MR analyses evaluating the effect of BMI on AIS, the association between BMI and AIS summary statistics was evaluated using the inverse-variance weighted (IVW) method, weighted median method, and Egger regression (MR-Egger) methods in Japanese. Significant causality of genetically decreased BMI on risk of AIS was estimated: IVW method (Estimate (beta) [SE] = -0.56 [0.16], p = 1.8 × 10-3), weighted median method (beta = -0.56 [0.18], p = 8.5 × 10-3) and MR-Egger method (beta = -1.50 [0.43], p = 4.7 × 10-3), respectively. Consistent results were also observed when using the US AIS summary statistic in three MR methods; however, no significant causality was observed when evaluating the effect of AIS on BMI. Our Mendelian randomization analysis using large studies of AIS and GWAS for BMI summary statistics revealed that genetic variants contributing to low BMI have a causal effect on the onset of AIS. This result was consistent with those of epidemiological studies and would contribute to the early detection of AIS.

Genetic Insights into Obesity and Brain: Combine Mendelian Randomization Study and Gene Expression Analysis.

As a major public-health concern, obesity is imposing an increasing social burden around the world. The link between obesity and brain-health problems has been reported, but controversy remains. To investigate the relationship among obesity, brain-structure changes and diseases, a two-stage analysis was performed. At first, we used the Mendelian-randomization (MR) approach to identify the causal relationship between obesity and cerebral structure. Obesity-related data were retrieved from the Genetic Investigation of ANthropometric Traits (GIANT) consortium and the UK Biobank, whereas the cortical morphological data were from the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium. Further, we extracted region-specific expressed genes according to the Allen Human Brian Atlas (AHBA) and carried out a series of bioinformatics analyses to find the potential mechanism of obesity and diseases. In the univariable MR, a higher body mass index (BMI) or larger visceral adipose tissue (VAT) was associated with a smaller global cortical thickness (pBMI = 0.006, pVAT = 1.34 × 10-4). Regional associations were found between obesity and specific gyrus regions, mainly in the fusiform gyrus and inferior parietal gyrus. Multivariable MR results showed that a greater body fat percentage was linked to a smaller fusiform-gyrus thickness (p = 0.029) and precuneus surface area (p = 0.035). As for the gene analysis, region-related genes were enriched to several neurobiological processes, such as compound transport, neuropeptide-signaling pathway, and neuroactive ligand-receptor interaction. These genes contained a strong relationship with some neuropsychiatric diseases, such as Alzheimer's disease, epilepsy, and other disorders. Our results reveal a causal relationship between obesity and brain abnormalities and suggest a pathway from obesity to brain-structure abnormalities to neuropsychiatric diseases.

Establishing causal relationships between sleep and adiposity traits using Mendelian randomization.

The aim of this study was to systematically evaluate the direction of any potential causal effect between sleep and adiposity traits. Two-sample Mendelian randomization was used to assess the association of genetically predicted sleep traits with adiposity and vice versa. Using data from UK Biobank and 23andMe, the sleep traits explored weremorning preference (chronotype; N=697,828), insomnia (N=1,331,010), sleep duration (N=446,118), napping (N=452,633), and daytime sleepiness (N=452,071). Using data from the Genetic Investigation of ANthropometric Traits (GIANT) and Early Growth Genetics (EGG) consortia, the adiposity traits explored were adult BMI, hip circumference (HC), waist circumference (WC), waist-hip ratio (WHR; N=322,154), and childhood BMI (N=35,668). This study found evidence that insomnia symptoms increased mean WC, BMI, and WHR (difference in means, WC=0.39 SD [95% CI: 0.13-0.64], BMI=0.47 SD [95% CI: 0.22-0.73], and WHR=0.34 SD [95% CI: 0.16-0.52]). Napping increased mean WHR (0.23 SD [95% CI: 0.08-0.39]). Higher HC, WC, and adult BMI increased odds of daytime sleepiness (HC=0.02 SD [95% CI: 0.01-0.04], WC=0.04 SD [95% CI: 0.01-0.06], and BMI 0.02 SD [95% CI: 0.00-0.04]). This study also found that higher mean childhood BMI resulted in lower odds of napping (-0.01 SD [95% CI: 0.02-0.00]). The effects of insomnia on adiposity and of adiposity on daytime sleepiness suggest that poor sleep and weight gain may contribute to a feedback loop that could be detrimental to overall health.

Causal effect of central obesity on left ventricular structure and function in preserved EF population: A Mendelian randomization study.

Observational studies have shown that central obesity is associated with adverse cardiac structure and function. However, causal association between central obesity and left ventricular (LV) structure and function in preserved ejection fraction (EF) population is still uncertain. Genome-wide association studies summary data of waist circumference adjusted for body mass index (WCadjBMI) and waist-to-hip ratio adjusted for body mass index (WHRadjBMI) were selected as instrumental variables from the Genetic Investigation of Anthropometric Traits (GIANT) Consortium (n = 224,459). Outcome datasets for LV parameters including LV end-diastolic volume (LVEDV), LV end-systolic volume (LVESV), LV ejection fraction (LVEF), LV mass (LVM), and LV mass-to-end-diastolic volume ratio (LVMVR) were obtained from the participants without prevalent myocardial infarction or heart failure (LVEF ≥ 50%) in UK Biobank Cardiovascular Magnetic Resonance sub-study (n = 16,923). Two-sample Mendelian randomization (MR) was performed with the inverse-variance weighted (IVW) method as the primary estimate and with the weighted median and MR-Egger as the supplemental estimates. Sensitivity analysis was used to assess the heterogeneity and pleiotropic bias in the MR results. In the IVW analysis, every 1-standard deviation (SD) higher WHRadjBMI was significantly associated with higher LVMVR (β = 0.4583; 95% confidence interval [CI]: 0.2921 to 0.6244; P = 6.418 × 10-8) and lower LVEDV (β = -0.2395; 95% CI: -0.3984 to -0.0807; P = 0.0031) after Bonferroni adjustment. No heterogeneity and horizontal pleiotropy were detected in the analysis. No association of WCadjBMI was found with LVEF, LVEDV, LVESV, LVM, or LVMVR. Our findings provide evidence of significant causal association between WHRadjBMI and adverse changes in LV structure and function in preserved EF population.

Discrimination Exposure and Polygenic Risk for Obesity in Adulthood: Testing Gene-Environment Correlations and Interactions

Introduction: Exposure to discrimination has emerged as a risk factor for obesity. It remains unclear, however, whether the genotype of the individual can modulate the sensitivity or response to discrimination exposure (gene × environment interaction) or increase the likelihood of experiencing discrimination (gene-environment correlation). Methods: This was an observational study of 4,102 white/European Americans in the Health and Retirement Study with self-reported, biological assessments, and genotyped data from 2006 to 2014. Discrimination was operationalized using the average of nine Everyday Discrimination Scale items. Polygenic risk scores (PRSs) for body mass index (BMI) and waist circumference (WC) were calculated using the weighted sum of risk alleles based on studies conducted by the Genetic Investigation of Anthropometric Traits (GIANT) consortium. Results: We found that greater PRS-BMI was significantly associated with more reports of discrimination (β = 0.04 ± 0.02; p = 0.037). Further analysis showed that measured BMI partially mediated the association between PRS-BMI and discrimination. There was no evidence that the association between discrimination and BMI, or the association between discrimination and WC, differed by PRS-BMI or PRS-WC, respectively. Conclusion: Our findings suggest that individuals with genetic liability for obesity may experience greater discrimination in their lifetime, consistent with a gene-environment correlation hypothesis. There was no evidence of a gene-environment interaction. More genome-wide association studies in diverse populations are needed to improve generalizability of study findings. In the meantime, prevention and clinical intervention efforts that seek to reduce exposure to all forms of discrimination may help reduce obesity at the population level.

Body shape and risk of glaucoma: A Mendelian randomization.

BackgroundBody size (BS) is one of the risk factors for the development of many clinical diseases, but the relationship between BS and glaucoma is controversial. Herein, we try to use Mendelian randomization (MR) method to study BS causal association with glaucoma risk from the genetic level.MethodsThe Body Size was determined through anthropometric traits (ATs), such as body mass index (BMI), waist-to-hip ratio adjusted by body mass index (WHRadjBMI), waist-to-hip ratio (WHR), and waist circumference (WC). Association of single nucleotide polymorphisms (SNPs) with each AT and glaucoma were determined individually from the aggregated data of the Genetic Investigation of Anthropometric Traits (GIANT) consortium and the FinnGen study summary data (8,591 cases with glaucoma and 210,201 controls). To explore the role of BS and glaucoma, a two-sample MR analysis was performed on genome-wide association study (GWAS) data. Besides, three MR methods [inverse variance weighted (IVW), Weighted median, and MR-Egger regression] were used to get the whole causal estimate for multiple instrumental SNPs.ResultsBMI (OR = 1.20; 95% CI = 1.02–1.41; P = 0.03) and WC (OR = 1.32; 95% CI =1.04–1.69; P = 0.03) were associated with a risk of glaucoma. Besides, genetically predicted WHRadjBMI (OR = 1.10; 95% CI = 0.88–1.35; P = 0.43) and WHR (OR = 1.22; 95% CI = 0.93–1,572; P = 0.14) were not associated with glaucoma. No heterogeneity and directional pleiotropy were detected.ConclusionThe data of this study revealed that increased BMI and WC are potential risk factors for glaucoma, and WHRadjBMI and WHR are not associated with the occurrence of glaucoma.

The weight of childhood adversity: evidence that childhood adversity moderates the impact of genetic risk on waist circumference in adulthood.

The present study tested the interactive effects of childhood adversity and polygenic risk scores for waist circumference (PRS-WC) on waist circumference (WC). Consistent with a diathesis-stress model, we hypothesize that the relationship between PRS-WC and WC will be magnified by increasing levels of childhood adversity. Observational study of 7976 adults (6347 European Americans and 1629 African Americans) in the Health and Retirement Study with genotyped data. PRS-WC were calculated by the HRS administrative core using the weighted sum of risk alleles based on a genome-wide association study conducted by the Genetic Investigation of Anthropometric Traits (GIANT) consortium. Childhood adversity was operationalized using a sum score of three traumatic events that occurred before the age of 18 years. There was a statistically significant interaction between PRS-WC and childhood adversity for European Americans, whereby the magnitude of PRS-WC predicting WC increased as the number of adverse events increased. This study supports the idea of the interactive effects of genetic risks and childhood adversity on obesity. More epidemiological studies, particularly with understudied populations, are needed to better understand the roles that genetics and childhood adversity play on the development and progression of obesity.

Comparison of Methods Utilizing Sex-Specific PRSs Derived From GWAS Summary Statistics.

The polygenic risk score (PRS) is calculated as the weighted sum of an individual’s genotypes and their estimated effect sizes, which is often used to estimate an individual’s genetic susceptibility to complex traits and disorders. It is well known that some complex human traits or disorders have sex differences in trait distributions, disease onset, progression, and treatment response, although the underlying mechanisms causing these sex differences remain largely unknown. PRSs for these traits are often based on Genome-Wide Association Studies (GWAS) data with both male and female samples included, ignoring sex differences. In this study, we present a benchmark study using both simulations with various combinations of genetic correlation and sample size ratios between sexes and real data to investigate whether combining sex-specific PRSs can outperform sex-agnostic PRSs on traits showing sex differences. We consider two types of PRS models in our study: single-population PRS models (PRScs, LDpred2) and multiple-population PRS models (PRScsx). For each trait or disorder, the candidate PRSs were calculated based on sex-specific GWAS data and sex-agnostic GWAS data. The simulation results show that applying LDpred2 or PRScsx to sex-specific GWAS data and then combining sex-specific PRSs leads to the highest prediction accuracy when the genetic correlation between sexes is low and the sample sizes for both sexes are balanced and large. Otherwise, the PRS generated by applying LDpred2 or PRScs to sex-agnostic GWAS data is more appropriate. If the sample sizes between sexes are not too small and very unbalanced, combining LDpred2-based sex-specific PRSs to predict on the sex with a larger sample size and combining PRScsx-based sex-specific PRSs to predict on the sex with a smaller size are the preferred strategies. For real data, we considered 19 traits from Genetic Investigation of ANthropometric Traits (GIANT) consortium studies and UK Biobank with both sex-specific GWAS data and sex-agnostic GWAS data. We found that for waist-to-hip ratio (WHR) related traits, accounting for sex differences and incorporating information from the opposite sex could help improve PRS prediction accuracy. Taken together, our findings in this study provide guidance on how to calculate the best PRS for sex-differentiated traits or disorders, especially as the sample size of GWASs grows in the future.

Body Mass Index and the Risk of Atrial Fibrillation: A Mendelian Randomization Study.

Although observational studies have shown positive associations between body mass index (BMI) and the risk of atrial fibrillation (AF), the causal relationship is still uncertain owing to the susceptibility to confounding and reverse causation. This study aimed to examine the potential causality of BMI on AF by conducting a two-sample Mendelian randomization (TSMR) study. Methods: The independent genetic variants associated with BMI (n = 303) at the genome-wide significant level were derived as instrumental variables (IV) from the Genetic Investigation of Anthropometric Traits (GIANT) consortium consisting of 681,275 individuals of European ancestry. We then derived the outcome data from a GWAS meta-analysis comprised of 60,620 cases and 970,216 controls of European ancestry. The TSMR analyses were performed in five methods, namely inverse variance weighted (IVW) method, MR-Egger regression, the weighted median estimator (WME), the generalized summary data-based Mendelian randomization (GSMR), and the robust adjusted profile score (RAPS), to investigate whether BMI was causally associated with the risk of AF. Results: We found a genetically determined 1–standard deviation (SD) increment of BMI causally increased a 42.5% risk of AF (OR = 1.425; 95% CI, 1.346 to 1.509) based on the IVW method, which was consistent with the results of MR-Egger regression, WME, GSMR, as well as RAPS. The Mendelian randomization assumptions did not seem to be violated. Conclusion: This study provides evidence that higher BMI causally increased the risk of AF, suggesting control of BMI and obesity for prevention of AF.

Smoking-Interaction Loci Affect Obesity Traits: A Gene-Smoking Stratified Meta-Analysis of 545,131 Europeans

Introduction: Although many studies have investigated the association between smoking and obesity, very few have analyzed how obesity traits are affected by interactions between genetic factors and smoking. Here, we aimed to identify the loci that affect obesity traits via smoking status-related interactions in European samples. Methods: We performed stratified analysis based on the smoking status using both the UK Biobank (UKB) data (N = 334,808) and the Genetic Investigation of ANthropometric Traits (GIANT) data (N = 210,323) to identify gene-smoking interaction for obesity traits. We divided the UKB subjects into two groups, current smokers and nonsmokers, based on the smoking status, and performed genome-wide association study (GWAS) for body mass index (BMI), waist circumference adjusted for BMI (WCadjBMI), and waist-hip ratio adjusted for BMI (WHRadjBMI) in each group. And then we carried out the meta-analysis using both GWAS summary statistics of UKB and GIANT for BMI, WCadjBMI, and WHRadjBMI and computed the stratified p values (p_stratified) based on the differences between meta-analyzed estimated beta coefficients with standard errors in each group. Results: We identified four genome-wide significant loci in interactions with the smoking status (p_stratified < 5 × 10⁻⁸): rs336396 (INPP4B) and rs12899135 (near CHRNB4) for BMI, and rs998584 (near VEGFA) and rs6916318 (near RSPO3) for WHRadjBMI. Moreover, we annotated the biological functions of the SNPs using expression quantitative trait loci (eQTL) and GWAS databases, along with publications, which revealed possible mechanisms underlying the association between the smoking status-related genetic variants and obesity. Conclusions: Our findings suggest that obesity traits can be modified by the smoking status via interactions with genetic variants through various biological pathways.

Obesity increases heart failure incidence and mortality: observational and Mendelian randomization studies totalling over 1 million individuals.

Whether high body mass index (BMI) causally influences development and prognosis of heart failure has implications for clinical practice. We tested the hypotheses that high BMI causally influences heart failure incidence and mortality. Using observational and Mendelian randomization causal, genetic analyses, we studied 106 121 individuals from the Copenhagen General Population Study, 18 407 from the Copenhagen City Heart Study, and 977 323 from publicly available databases. In observational analyses in the Copenhagen studies with 10 years of median follow-up, multivariable adjusted hazard ratios per 1 kg/m2 increment of BMI were 1.06 (95% confidence interval: 1.05-1.07; P < 0.001; n = 124 528; events = 6589) for heart failure incidence, 1.04 (1.03-1.06; P < 0.001; n = 124 528; events = 1237) for heart failure mortality, and 1.01 (1.00-1.01; P < 0.001; n = 124 528; events = 24 144) for all-cause mortality. In genetic analyses in the Copenhagen studies, the age and sex adjusted causal risk ratios per 1 kg/m2 increment of BMI were 1.19 (1.05-1.36; P = 0.008; n = 118 200; events = 6541) for heart failure incidence, 1.27 (0.82-1.98; P = 0.28; n = 118 200; events = 889) for heart failure mortality, and 1.11 (1.02-1.22; P = 0.022; n = 118 200; events = 16 814) for all-cause mortality. Finally, combining genetic data from the Copenhagen studies, the Genetic Investigation of ANthropometric Traits, the Heart Failure Molecular Epidemiology for Therapeutic Targets, and the UK Biobank, the unadjusted causal risk ratios per 1 kg/m2 increment of BMI were 1.39 (1.27-1.52; P < 0.001; n = 1 095 523; events = 53 850) for heart failure incidence, 1.18 (1.00-1.38; P = 0.05; n = 576 853; events = 2373) for heart failure mortality, and 1.02 (1.00-1.04; P = 0.03; n = 576 853; events = 44 734) for all-cause mortality. High BMI causally increases the risk of both heart failure incidence and mortality.

Obesity and Kidney Function: A Two-Sample Mendelian Randomization Study.

Obesity and type 2 diabetes (T2D) are correlated risk factors for chronic kidney disease (CKD). Using summary data from GIANT (Genetic Investigation of Anthropometric Traits), DIAGRAM (DIAbetes Genetics Replication And Meta-analysis), and CKDGen (CKD Genetics), we examined causality and directionality of the association between obesity and kidney function. Bidirectional 2-sample Mendelian randomization (MR) estimated the total causal effects of body mass index (BMI) and waist-to-hip ratio (WHR) on kidney function, and vice versa. Effects of adverse obesity and T2D were examined by stratifying BMI variants by their association with WHR and T2D. Multivariable MR estimated the direct causal effects of BMI and WHR on kidney function. The inverse variance weighted random-effects MR for Europeans was the main analysis, accompanied by several sensitivity MR analyses. One standard deviation (SD ≈ 4.8 kg/m2) genetically higher BMI was associated with decreased estimated glomerular filtration rate (eGFR) [β=-0.032 (95% confidence intervals: -0.036, -0.027) log[eGFR], P = 1 × 10-43], increased blood urea nitrogen (BUN) [β = 0.010 (0.005, 0.015) log[BUN], P = 3 × 10-6], increased urinary albumin-to-creatinine ratio [β = 0.199 (0.067, 0.332) log[urinary albumin-to-creatinine ratio (UACR)], P = 0.003] in individuals with diabetes, and increased risk of microalbuminuria [odds ratios (OR) = 1.15 [1.04-1.28], P = 0.009] and CKD [1.13 (1.07-1.19), P = 3 × 10-6]. Corresponding estimates for WHR and for trans-ethnic populations were overall similar. The associations were driven by adverse obesity, and for microalbuminuria additionally by T2D. While genetically high BMI, unlike WHR, was directly associated with eGFR, BUN, and CKD, the pathway to albuminuria was likely through T2D. Genetically predicted kidney function was not associated with BMI or WHR. Genetically high BMI is associated with impaired kidney function, driven by adverse obesity, and for albuminuria additionally by T2D.

Deciphering the Irregular Risk of Stroke Increased by Obesity Classes: A Stratified Mendelian Randomization Study

BackgroundTo investigate the relationship between different classes of obesity and stroke, we conducted a stratified Mendelian randomization (MR) study.MethodsThe body mass index (BMI) data of 263,407 Europeans with three classes of obesity (obesity class I, 30 kg/m2 ≤ BMI < 35 kg/m2; obesity class II, 35 kg/m2 ≤ BMI < 40 kg/m2; obesity class III, 40 kg/m2 ≤ BMI) were extracted from the Genetic Investigation of ANthropometric Traits (GIANT) consortium. Summary-level data of stroke and its subtypes [ischemic stroke (IS) and intracerebral hemorrhage (ICH)] were obtained from the genome-wide association study (GWAS) meta-analysis, which was performed by the MEGASTROKE consortium. MR methods were used to identify the causal relationships.ResultsThe MR analysis revealed that both obesity class I [odds ratio (OR) = 1.08, 95% CI: 1.05–1.12, p = 1.0 × 10-5] and obesity class II (OR = 1.06, 95% CI: 1.03–1.09, p = 1 × 10-4) were significantly positively related to IS, while obesity class III was not (OR = 1.01, 95% CI: 0.96–1.06, p = 0.65). In contrast to IS, there was no class of obesity associated with ICH risk. Further examination of the relationship between obesity classification and IS subtypes revealed that certain degrees of obesity were related to large artery stroke (LAS) (OR = 1.14, 95% CI: 1.04–1.24, p = 2.8 × 10-3 for class I; OR = 1.08, 95% CI: 1.01–1.16, p = 0.002 for class II) and cardioembolic stroke (CES) (OR = 1.11, 95% CI: 1.02–1.20, p = 0.02 for class I; OR = 1.08, 95% CI: 1.02–1.15, p = 0.007 for class II).ConclusionsA higher risk of IS, but not ICH, could be linked to obesity classes I and II. A strong association between LAS and CES and obesity was observed among all IS subtypes in the obese population.

Abstract 12326: Obesity Increases Heart Failure Incidence and Mortality:Observational and Mendelian Randomisation Studies Totalling Over 1 Million Individuals

Introduction: Whether high body mass index (BMI) causally influences development and prognosis of heart failure has implications for clinical practice. Hypothesis: We tested the hypotheses that high BMI causally influences heart failure incidence and mortality. Methods: Using observational and Mendelian randomization causal, genetic analyses, we studied 106,121 individuals from the Copenhagen General Population Study, 18,407 from the Copenhagen City Heart Study, and 977,323 from publicly available databases. Results: In observational analyses in the Copenhagen studies with 10 years of median follow-up, multivariable adjusted hazard ratios per 1 kg/m 2 increment of BMI were 1.06 (95% confidence interval: 1.05-1.07; p&lt;0.001; n=124,528; events=6,589) for heart failure incidence, 1.04 (1.03-1.06; p&lt;0.001; n=124,528; events=1,237) for heart failure mortality, and 1.01 (1.00-1.01; p&lt;0.001; n=124,528; events=24,144) for all-cause mortality. In genetic analyses in the Copenhagen studies, the age and sex adjusted causal risk ratios per 1 kg/m 2 increment of BMI were 1.19 (1.05-1.36; p=0.008; n=118,200; events=6,541) for heart failure incidence, 1.27 (0.82-1.98; p=0.28; n=118,200; events=889) for heart failure mortality, and 1.11 (1.02-1.22; p=0.022; n=118,200; events=16,814) for all-cause mortality. Finally, combining genetic data from the Copenhagen studies, the Genetic Investigation of ANthropometric Traits (GIANT), the Heart Failure Molecular Epidemiology for Therapeutic Targets (HERMES), and the UK Biobank, the unadjusted causal risk ratios per 1 kg/m 2 increment of BMI were 1.39 (1.27-1.52; p&lt;0.001; n=1,095,523; events=53,850) for heart failure incidence, 1.18 (1.00-1.38; p=0.05; n=576,853; events=2,373) for heart failure mortality, and 1.02 (1.00-1.04; p=0.03; n=576,853; events=44,734) for all-cause mortality. Conclusions: High body mass index causally increases the risk of both heart failure incidence and mortality. Obesity should be recognised as a causal factor for development of heart failure, heart failure mortality, and all-cause mortality in treatment guidelines.

Genetically predicted obesity and risk of deep vein thrombosis

Aims/hypothesisAn association between obesity and deep vein thrombosis (DVT) has been revealed by observational studies, but it is not clear if the observed associations are causal, caused by confounding bias or reverse causation. MethodsWe performed a two-sample Mendelian Randomization (MR) study by obtaining exposure and outcome data from separate published studies. We utilized data from Genetic Investigation of Anthropometric Traits (GIANT, 339,224 participants) consortium and FinnGen project (FinnGen, 1785 DVT case and 84,462 control participants) to determine the causal effect of BMI on DVT. ResultsAll three MR methods provided a positive association between BMI and DVT. Using IVW, we found evidence of causal relationships between BMI and DVT. BMI is positively associated with DVT (IVW odds ratio [OR] per SD increase in BMI = 1.67 [95% CI, 1.16–2.40]; P = 0.006). MR Egger and weighted median regression also showed directionally similar estimates (MR-Egger OR per SD increase in BMI, 2.50 [95% CI, 1.07–5.84], P = 0.034; weighted median OR per SD increase in BMI, 2.02 [95% CI, 1.10–3.71], P = 0.023). Both funnel plots and MR-Egger intercepts suggest no directional pleiotropic effects observed between BMI and DVT. Conclusions/interpretationOur findings provide evidence of significant causal association between BMI and DVT in agreement with observational studies. Taking measures to reduce the proportion of obesity may help reduce the incidence of DVT.

Causal Associations of Obesity With Chronic Kidney Disease and Arterial Stiffness: A Mendelian Randomization Study.

Observational studies have been associated obesity with chronic kidney disease (CKD) and arterial stiffness, but the causality remains unclear. We aimed to investigate the causality of obesity with CKD and arterial stiffness using mendelian randomization (MR) analysis. We genotyped 14 body mass index (BMI)-associated variants validated in East Asians in 11 384 Chinese adults. A genetic risk score based on the 14 variants and the 14 individual single-nucleotide variations (SNVs, formerly single-nucleotide polymorphisms [SNPs]) were respectively used as instrumental variables (IVs). CKD was defined as estimated glomerular filtration rate less than 60 mL/min/1.73 m2. Arterial stiffness was defined as brachial-ankle pulse wave velocity greater than 1550 cm/s. Using the genetic risk score as the IV, we demonstrated causal relations of each 1-SD increment in BMI with CKD (odds ratio [OR]: 2.36; 95% CI, 1.11-5.00) and arterial stiffness (OR: 1.71; 95% CI, 1.22-2.39). Using the 14 SNVs individually as IVs, each 1-SD increment in BMI was casually associated with CKD (OR: 2.58; 95% CI, 1.39-4.79) and arterial stiffness (OR: 1.87; 95% CI, 1.24-2.81) in the inverse-variance weighted analysis, and MR-Egger regression revealed no evidence of horizontal pleiotropy (both P for intercept ≥ .34). The causality between obesity and CKD was validated in 2-sample MR analysis among Europeans (681 275 of Genetic Investigation of ANthropometric Traits and 133 413 of CKD Genetics). This study provided novel insights into the causality of obesity with CKD and arterial stiffness, highlighting the importance of weight management for primary prevention and control of subclinical vascular diseases.