Gene-Environment Interaction Mendelian Randomization Reveals Statin-Dependent Associations Between Metabolites and Lipid Traits.

Introduction/Background: Observational links between circulating metabolites and lipid traits are often confounded by environmental factors. This study applied Mendelian randomization (MR) and gene-environment interaction MR (MR-G×E) to assess the average causal effects of genetically predicted metabolites on lipid traits and their modification by statin use. Methods: Participants (n = 2810) from Qatar Biobank were analyzed including genetics, metabolomics and lipid profiles. The cohort was divided into discovery (n = 1968) and validation (n = 842). One-sample MR using two-stage least squares regression was applied to estimate the average causal effect of metabolites on LDL, HDL, and Triglycerides, while MR-G×E analysis was also applied to investigate whether genetically predicted metabolite-LDL/HDL associations differ by statin use. Genetic variants associated with metabolites were used to generate a polygenic risk score (PRS). The interaction term between the PRS and statin use was incorporated in MR-G×E, while adjusting for confounders. Results: A total of 167 metabolites were commonly associated with LDL, HDL, and Triglycerides. One-sample MR showed no significant average causal effect of genetically predicted metabolites on lipid traits in the overall population. However, MR-G×E analysis revealed significant statin-dependent effects. Genetically predicted 2-amino-octanoate and cysteine-glutathione disulfide demonstrated strong and reproducible positive interaction effects with LDL levels in statin users in both discovery and validation cohorts. While no overall average causal effects were observed, MR-G×E revealed that genetically predicted metabolites can influence LDL levels in the context of statin use. Conclusions: These findings highlight the importance of incorporating pharmacological exposures in MR analyses and support the role of drug-metabolite interactions in precision lipid management.