Circulating metabolites and coronary heart disease: a bidirectional Mendelian randomization

Numerous studies have established a link between coronary heart disease and metabolic disorders. Yet, causal evidence connecting metabolites and Coronary Heart Disease (CHD) remains scarce. To address this, we performed a bidirectional Mendelian Randomization (MR) analysis investigating the causal relationship between blood metabolites and CHD.Data were extracted from published genome-wide association studies (GWASs) on metabolite levels, focusing on 1,400 metabolite summary data as exposure measures. Primary analyses utilized the GWAS catalog database GCST90199698 (60,801 cases and 123,504 controls) and the FinnGen cohort (43,518 cases and 333,759 controls). The primary method used for causality analysis was random inverse variance weighting (IVW). Supplementary analyses included MR-Egger, weighted mode, and weighted median methods. Sensitivity analyses were conducted to evaluate heterogeneity and pleiotropy. Reverse MR analysis was employed to evaluate the direct impact of metabolites on coronary heart disease. Additionally, replication and meta-analysis were performed. We further conducted the Steiger test and colocalization analysis to reflect the causality deeply.This study identified eight metabolites associated with lipids, amino acids and metabolite ratios that may influence CHD risk. Findings include: 1-oleoyl-2-arachidonoyl-GPE (18:1/20:4) levels: OR = 1.08; 95% CI 1.04–1.12; P = 8.21E-06; 1-palmitoyl-2-arachidonoyl-GPE (16:0/20:4) levels: OR = 1.07; 95% CI 1.04–1.11; P = 9.01E-05; Linoleoyl-arachidonoyl-glycerol (18:2/20:4): OR = 1.08; 95% CI 1.04–1.22; P = 0.0001; Glycocholenate sulfate: OR = 0.93; 95% CI 0.90–0.97; P = 0.0002; 1-stearoyl-2-arachidonoyl-GPE (OR = 1.07; 95% CI 1.03–1.11; P = 0.0002); N-acetylasparagine (OR = 1.04; 95% CI 1.02–1.07; P = 0.0030); Octadecenedioate (C18:1-DC) (OR = 0.93; 95% CI 0.90–0.97; P = 0.0004); Phosphate to linoleoyl-arachidonoyl-glycerol (18:2–20:4) (1) ratio (OR = 0.92; 95% CI 0.88–0.97; P = 0.0005).The integration of genomics and metabolomics offers novel insights into the pathogenesis of CHD and holds significant importance for the screening and prevention of CHD.