Abstract
OBJECTIVE: This study aimed to explore how serum metabolites affect the risk of substance use disorders (SUD). METHODS: In the initial stage, Mendelian randomization was applied to assess the relationship between 1,400 serum metabolites and SUD. Inverse variance weighting, the Wald ratio odds ratio, and 95% confidence intervals were primarily used to evaluate causal relationships, and the false discovery rate was used for multiple comparison corrections. Sensitivity analysis was conducted via Cochran's Q test and MR-PRESSO. The MR-Steiger test was used to examine reverse causality. In the validation stage, we sought additional GWAS data on SUD to verify the initial results. Furthermore, the pathway enrichment analysis was conducted for known metabolites that exhibited causal relationships with SUD in both phases. RESULTS: In the initial phase, we analysis suggests that these 77 metabolites may have potential causal associations with SUD, including 14 metabolite ratios and 63 metabolites (49 known and 14 unknown). In the validation phase, for 57 metabolites (38 known, 6 unknown, 13 ratios), confirmed associations may indicate causal effects on SUD incidence. The synthesis analysis results indicated that the overall effect of the combined metabolites was consistent with the primary analysis with two identified as risk factors and four as protective factors for SUD. Specifically, Erythronate levels, 1-(1-enyl-stearoyl)-2-oleoyl-GPE (P-18:0/18:1) levels, aspartate to citrulline ratios, and cis-4-decenoate (10:1n6) levels were negatively correlated with SUD, whereas gamma-glutamyl-alpha-lysine and ethyl alpha-glucopyranoside levels were positively correlated with disease incidence. The metabolites linked to the risk of SUD in both phases were primarily enriched in several metabolic pathways, including pantothenate and CoA biosynthesis; pyrimidine metabolism; biosynthesis of valine, leucine, and isoleucine; taurine and hypotaurine metabolism; histidine metabolism; and glycerolipid metabolism. CONCLUSION: Circulating metabolites may have a causal relationship with the risk of SUD. "Specific metabolites may be potential biomarkers for SUD, contributing to risk prediction and the development of personalized treatment strategies".