Disentangling Genetic Risks for Metabolic Syndrome

Abstract

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.

Genetic evidence for the causal relations between metabolic syndrome and psychiatric disorders: a Mendelian randomization study

“…1 . The MetS GWAS was conducted using the genetic single MetS factor, which is derived from the genomic structural equation modeling [ 11 ]. The MetS factor captured and summarized genetic correlations and shared genetic variance among five metabolic components.…”

“…In addition, a replication study was conducted using the same analytical strategy as described for the primary MR analysis. The MetS GWAS data were obtained from van Walree ES’s study, and PDs GWASs data were sourced from the FinnGen R9 database, to investigate the association between MetS and PDs [ 11 , 34 ]. Detailed information, including the exact number of cases and controls, the online depository of the PD GWASs summary statistics can be found in Supplementary Table 15 .…”

Genetic prediction of the relationship between metabolic syndrome and colorectal cancer risk: a Mendelian randomization study

“…The GWAS data on MetS were sourced from the CNCR database ( https://ctg.cncr.nl/ ). van Walree et al [ 15 ], selectively included individuals of European ancestry, ultimately using an effective population size of 461,920, and identified 235 genomic risk loci correlated with MetS. Genetic data on FG were obtained from the MAGIC ( https://magicinvestigators.org/ ).…”

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

“…In human brain, the metabolic syndrome cluster was significantly enriched for regions of open chromatin in cell types including IT projecting neurons and SST + GABAergic inhibitory neurons. IT projecting neurons are a type of glutamatergic excitatory pyramidal neuron in the cerebral cortex, and metabolic syndrome was previously associated with pyramidal neurons and GABAergic neurons in cell-type specificity analyses in a GWAS that examined genetic factors in metabolic syndrome 35 . We observed no significant enrichments in the body fat cluster or in the liver and lipid metabolism cluster.…”