Sex-dimorphic genetic effects and novel loci for fasting glucose and insulin variability

Abstract

Differences between sexes contribute to variation in the levels of fasting glucose and insulin. Epidemiological studies established a higher prevalence of impaired fasting glucose in men and impaired glucose tolerance in women, however, the genetic component underlying this phenomenon is not established. We assess sex-dimorphic (73,089/50,404 women and 67,506/47,806 men) and sex-combined (151,188/105,056 individuals) fasting glucose/fasting insulin genetic effects via genome-wide association study meta-analyses in individuals of European descent without diabetes. Here we report sex dimorphism in allelic effects on fasting insulin at IRS1 and ZNF12 loci, the latter showing higher RNA expression in whole blood in women compared to men. We also observe sex-homogeneous effects on fasting glucose at seven novel loci. Fasting insulin in women shows stronger genetic correlations than in men with waist-to-hip ratio and anorexia nervosa. Furthermore, waist-to-hip ratio is causally related to insulin resistance in women, but not in men. These results position dissection of metabolic and glycemic health sex dimorphism as a steppingstone for understanding differences in genetic effects between women and men in related phenotypes.

Telomere length and metabolic syndrome traits: A Mendelian randomisation study

“…These were discovered in a recent GWAS meta‐analysis of LTL from 78,592 subjects of European descent and collectively explained 2.93% of the total proportion of LTL variance (Li et al, 2020 ). Summary‐level data for the different components of the MetS were obtained from the largest publicly available GWAS meta‐analyses for anthropometric ( n = up to 694,649 subjects) (Pulit et al, 2019 ), glycaemic ( n = up to 151,188) (Lagou et al, 2021 ), lipid ( n = up to 188,577 subjects) (Willer et al, 2013 ), and blood pressure (BP) ( n = up to 757,601 subjects) (Evangelou et al, 2018 ) traits conducted in Europeans. In the case of lipid traits, we only had access to metadata from a multi‐ancestry GWAS, which nonetheless comprised over 95% European subjects.…”

“…Genetic instruments for TL were selected from a genome‐wide meta‐analysis for LTL (Table S1 , (Li et al, 2020 ), n = 52 instruments, FDR<0.05; GWAS significant (p < 5 × 10 −8 ), n = 21 instruments, based on N of up to 78,592 individuals of European descent). For outcome data, we used summary‐level results from meta‐analyses of GWAS for obesity and body fat distribution in the UKBB and GIANT (BMI and WHRadjBMI, based on N of up to 694,649 individuals of European ancestry ( https://github.com/lindgrengroup/fatdistnGWAS ) (Pulit et al, 2019 ), and GWAS summary statistics from the GIANT consortium (waist circumference adjusted for BMI, based on N of up to 231,353 individuals of European descent) (MRBase [app.mrbase.org], ieu‐a‐67) (Shungin et al, 2015 ); the European‐based analyses of the Meta‐Analyses of Glucose and Insulin‐related traits Consortium (MAGIC) (fasting glucose and fasting insulin, based on N of up to 151,188 individuals of European ancestry without diabetes mellitus) ( https://magicinvestigators.org/ downloads/) (Lagou et al, 2021 ); the Global Lipids Genetics Consortium (GLGC; fasting lipid traits, based on N of up to 188,577 subjects of European [95%], East Asian, South Asian and African ancestry) (MRBase, ieu‐a‐299, ieu‐a‐302) (Willer et al, 2013 ); and the International Consortium for Blood Pressure (BP phenotypes, based on data from N = 757,601 individuals of European descent) (MRBase, ieu‐b‐38, ieu‐b‐39) (Evangelou et al, 2018 ). For the MetS as defined by the harmonised NCEP criteria (Alberti et al, 2009 ), we used publicly available GWAS summary statistics from the UKBB ( https://www.ukbiobank.ac.uk ) (based on N = 291,107 individuals of British descent and European ancestry).…”

What factors explain the much higher diabetes prevalence in Russia compared with Norway? Major sex differences in the contribution of adiposity

“…The higher diabetes prevalence in men is usually explained by diverse biological, cultural, lifestyle, and environmental factors. [26][27][28][29] Explanations for the pattern observed in Russia require further research. Certain cultural factors in Russia may be considered distal, that is, influencing behavior, and modify the biologically lower predisposition of women to develop diabetes.…”

Multi-trait genome-wide association study identifies novel endometrial cancer risk loci that are associated with obesity or female testosterone levels

“…Thus, we cannot say whether these variants are transferrable to other ethnic groups, a limitation of this study. The lack of availability of sex-stratified GWAS summary statistics for some traits may fail to detect some risk factors and consequently risk loci of endometrial cancer due to the existence of sex dimorphism for some traits 12,[50][51][52][53][54][55] . As the UK Biobank, FinnGen, and Japanese Biobank did not include histology information of endometrial cancer cases, we were unable to validate the identified risk loci associated with endometrioid endometrial cancer.…”