Lipidomics-based investigation of its impact on the pathogenesis of coronary atherosclerosis: a Mendelian randomization study.

Abstract

Background: Considerable attention has been devoted to investigating the association between lipid metabolites and cardiovascular diseases, particularly coronary atherosclerosis.

Methods: A two-sample MR framework was used to investigate the relationship between lipid metabolites and the risk of coronary atherosclerosis. Two GWAS datasets were examined to take intersections of SNPs from 51,589 cases and 343,079 controls, and 14,334 cases and 346,860 controls to determine genetic susceptibility to coronary atherosclerosis. Random-effects inverse variance weighted (IVW) MR analyses were performed by a series of sensitivity assessments to measure the robustness of our findings and to detect any violations of MR assumptions.

Results: Through IVW, MR-Egger and weighted median regression methods, we inferred that these six lipid metabolites: cholesterol levels, sterol ester (27:1/18:2) levels, triacylglycerol (52:4) levels, triacylglycerol (52:5) levels, diacylglycerol (18:1_18.2) levels, triacylglycerol (53:4), could directly impact the development of atherosclerosis.

Conclusion: In conclusion, our study comprehensively illustrates a causal relationship between lipid metabolites and the risk of coronary atherosclerosis. Furthermore, cholesterol levels, sterol ester (27:1/18:2) levels, triacylglycerol (52:4) levels, triacylglycerol (52:5) levels, diacylglycerol (18:1_18.2) levels, and triacylglycerol (53:4) levels are positively correlated with the risk of coronary atherosclerosis. These six lipid metabolites have the potential as new predictors of the risk of atherosclerosis, providing new insights into the treatment and prevention of cardiovascular diseases.