Causal effects between blood metabolites and myocardial infarction: A 2-sample Mendelian randomization study

Observational studies have reported associations between circulating blood metabolite features and myocardial infarction (MI), but whether these associations are causal remains uncertain. Mendelian randomization (MR) can strengthen causal inference by using genetic variants as instrumental variables. We therefore conducted a 2-sample MR study, followed by pathway enrichment analysis, to evaluate the potential causal effects of genetically predicted blood metabolite features on MI. Genetic association data for 452 blood metabolite features were obtained from a published metabolomics GWAS including 7824 participants of European ancestry. The metabolite panel comprised annotated small molecules and lipids as well as a limited number of peptide/partially annotated or unknown metabolite signatures defined in the source GWAS. Summary statistics for MI were obtained from FinnGen (n = 369,139) and the IEU Open GWAS project (n = 461,823), and a discovery-replication design was applied. The inverse-variance weighted method was the primary MR analysis, complemented by weighted median, MR-Egger, weighted mode, and simple mode. Sensitivity analyses included Cochran Q test, MR-Egger intercept, funnel plots, and leave-one-out analyses. Significant findings were further evaluated using pathway enrichment analysis. In FinnGen, 12 annotated metabolite features were associated with a lower MI risk, and 8 annotated metabolite features were associated with a higher MI risk after excluding unknown signatures from the descriptive summary. In the IEU Open GWAS dataset, 10 annotated metabolite features were protective, and 7 were risk-associated. Glycine, N-acetylglycine, deoxycholate, propionylcarnitine, and margarate (17:0) showed directionally consistent associations across both MI datasets, supporting the robustness of these findings. This 2-sample MR analysis supports potential causal roles of several circulating metabolite features in MI. These findings expand the evidence base for metabolic mechanisms underlying MI and may help prioritize biomarkers and pathways for future validation.