Investigating the causal relationship between human blood metabolites and coronary artery disease using two-sample Mendelian randomization

Abstract

To explore the causal relationship between blood metabolites and the risk of coronary artery disease (CAD) using a two-sample Mendelian randomization (MR) approach. Based on the data from a large-scale metabolome-based genome-wide association study (mGWAS) and the GWAS of CAD, we investigated the causality between blood metabolites and CAD using an inverse variance weighted (IVW) method and another 4 two-sample MR models. Heterogeneity, horizontal pleiotropy, and sensitivity tests were performed to evaluate the stability and reliability of the results. Among the 486 blood metabolites, 32 metabolites showed nominally causative association with CAD with the IVW method (P < 0.05), including 11 known metabolites and 21 unknown metabolites. Three known metabolites [N-acetylornithine, bradykinin-des-arg(9), and succinylcarnitine] were statistically significant in at least 3 MR models, but their causal effects on CAD were no longer significant after sensitivity analysis using leave-one-out method and elimination of the confounding instrumental variables. There is no strong evidence to support a robust causal relationship between the 486 blood metabolites and the risk of CAD.