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.1–3 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, 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.
Drug repurposing may provide a solution to the substantial challenges facing de novo drug development. Given that 66% of FDA-approved drugs in 2021 were supported by human genetic evidence, drug repurposing methods based on genome-wide association studies (GWAS), such as drug gene-set analysis, may prove an efficient way to identify new treatments. However, to our knowledge, drug gene-set analysis has not been tested in non-psychiatric phenotypes, and previous implementations may have contained statistical biases when testing groups of drugs. Here, 1201 drugs were tested for association with hypercholesterolemia, type 2 diabetes, coronary artery disease, asthma, schizophrenia, bipolar disorder, Alzheimer's disease, and Parkinson's disease. We show that drug gene-set analysis can identify clinically relevant drugs (e.g., simvastatin for hypercholesterolemia [p = 2.82E-06]; mitiglinide for type 2 diabetes [p = 2.66E-07]) and drug groups (e.g., C10A for coronary artery disease [p = 2.31E-05]; insulin secretagogues for type 2 diabetes [p = 1.09E-11]) for non-psychiatric phenotypes. Additionally, we demonstrate that when the overlap of genes between drug-gene sets is considered we find no groups containing approved drugs for the psychiatric phenotypes tested. However, several drug groups were identified for psychiatric phenotypes that may contain possible repurposing candidates, such as ATC codes J02A (p = 2.99E-09) and N07B (p = 0.0001) for schizophrenia. Our results demonstrate that clinically relevant drugs and groups of drugs can be identified using drug gene-set analysis for a number of phenotypes. These findings have implications for quickly identifying novel treatments based on the genetic mechanisms underlying diseases.
<p>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. </p>
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