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.