Genetically Predicted 1400 Blood Metabolites in Relation to Risk of Prostate Cancer: A Mendelian Randomization Study - Xiaojin Lu; Yongming Chen; Yuxiao Jiang; Jiaxin Ning; Shengjie Liu; Zhengtong Lv; Miao Wang; Huiming Hou; Ming Liu (Aging Medicine, 2025)
ABSTRACT Objectives Metabolic dysregulation is common in cancer, yet evidence linking circulating metabolites to causal relationships in prostate cancer (PCa) is lacking. We performed a Mendelian randomization analysis utilizing 1400 blood metabolites to evaluate their roles in PCa. Methods Exposure data from genome‐wide association studies (GWAS) was extracted from metabolite level GWAS involving 462,933 individuals of European descent. GWAS data for PCa were obtained from the UK Biobank (UKB) database (79,148 cases, 61,106 controls) for a two‐sample Mendelian randomization (MR) preliminary analysis, where we investigated potential causal relationships between 1400 metabolites and PCa. Inverse variance weighting (IVW) was the primary method for causal analysis, with MR‐Egger and weighted median as supplementary analyses to enhance robustness. Sensitivity analyses including Cochran Q test, MR‐Egger intercept test, MR‐PRESSO, and leave‐one‐out analysis were employed to evaluate the robustness of MR results. For significant associations, an additional independent PCa dataset was utilized for validation analysis and meta‐analysis. Results Our findings revealed significant associations between two metabolites and prostate cancer: Cysteinylglycine disulfide levels (OR: 0.999, 95% CI: 0.998–0.999, p = 0.004). Validation analyses showed a similar trend, and sensitivity analyses supported the robustness of MR estimates. Conclusions Our results suggest that Cysteinylglycine disulfide levels may have a causal relationship with increased PCa risk.