American Diabetes Association
2 files

Disentangling genetic risks for metabolic syndrome

Version 5 2022-10-07, 18:10
Version 4 2022-09-28, 20:49
Version 3 2022-09-28, 19:53
Version 2 2022-09-01, 20:12
Version 1 2022-08-19, 11:22
posted on 2022-10-07, 18:10 authored by Eva S. van Walree, Iris E. Jansen, Nathaniel Y. Bell, Jeanne E. Savage, Christiaan de Leeuw, Max Nieuwdorp, Sophie van der Sluis, Danielle Posthuma

A quarter of the world’s population is estimated to meet the criteria for metabolic syndrome, a cluster of cardiometabolic risk factors that promote development of coronary artery disease and type II diabetes, leading to increased risk of premature death and significant health costs. In this study we investigate whether the genetics associated with metabolic syndrome components mirror their phenotypic clustering. A multivariate approach that leverages genetic correlations between fasting glucose, high-density lipoprotein 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 metabolic syndrome to date. Of these loci, 53 (22.5%) overlap with loci identified for two or more metabolic syndrome components, indicating that metabolic syndrome is a complex, heterogeneous disorder. Associated loci harbour genes that show increased expression in the brain, especially in GABAergic and dopaminergic neurons. A polygenic risk score drafted from the metabolic syndrome factor GWAS predicts 5.9% of the variance in metabolic syndrome. These results provide mechanistic insights in the genetics of metabolic syndrome and suggestions for drug targets, especially fenofibrate, which has the promise of tackling multiple metabolic syndrome components. 


CdL was funded by F. Hoffman-La Roche AG. MN is supported by a ZONMW VICI grant 2020 [09150182010020].


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