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• W4292297462 abstract "A quarter of the world's population is estimated to meet the criteria for metabolic syndrome (MetS), a cluster of cardiometabolic risk factors that promote development of coronary artery disease and type 2 diabetes, leading to increased risk of premature death and significant health costs. In this study we investigate whether the genetics associated with MetS components mirror their phenotypic clustering. A multivariate approach that leverages genetic correlations of fasting glucose, HDL cholesterol, systolic blood pressure, triglycerides, and waist circumference was used, which revealed that these genetic correlations are best captured by a genetic one factor model. The common genetic factor genome-wide association study (GWAS) detects 235 associated loci, 174 more than the largest GWAS on MetS to date. Of these loci, 53 (22.5%) overlap with loci identified for two or more MetS components, indicating that MetS is a complex, heterogeneous disorder. Associated loci harbor genes that show increased expression in the brain, especially in GABAergic and dopaminergic neurons. A polygenic risk score drafted from the MetS factor GWAS predicts 5.9% of the variance in MetS. These results provide mechanistic insights into the genetics of MetS and suggestions for drug targets, especially fenofibrate, which has the promise of tackling multiple MetS components." @default.
• W4292297462 created "2022-08-19" @default.
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• W4292297462 date "2022-08-19" @default.
• W4292297462 modified "2023-10-18" @default.
• W4292297462 title "Disentangling Genetic Risks for Metabolic Syndrome" @default.
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• W4292297462 doi "https://doi.org/10.2337/db22-0478" @default.
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