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Background Metabolic disorders are a hallmark feature of cancer. However, the evidence for the causality of circulating metabolites to promote or prevent colorectal cancer (CRC) is still lacking. We performed a two‐sample Mendelian randomization (MR) analysis to assess the causality from genetically proxied 486 blood metabolites to CRC. Methods Genome‐wide association study (GWAS) data for exposures were extracted from 7824 Europeans GWAS on metabolite levels. GWAS data for CRC from the GWAS catalog database GCST012879 were used for the preliminary analysis. The random inverse variance weighted (IVW) is the primary analysis for causality analysis while MR‐Egger and weighted median as complementary analyses. Cochran Q test, MR‐Egger intercept test, MR‐PRESSO, Radial MR, and leave‐one‐out analysis were used for sensitivity analyses. For significant associations, additional independent CRC GWAS data GCST012880 were used for replication analysis and meta‐analysis. For the final identification of metabolites, Steiger test, linkage disequilibrium score regression, and colocalization analysis were performed for further evaluation. Multivariable MR was performed to assess the direct effect of metabolites on CRC. Results The results of this study indicated significant associations between six metabolites pyruvate (odds ratio [OR]: 0.49, 95% confidence interval [CI]: 0.32–0.77, p = 0.002), 1,6‐anhydroglucose (OR: 1.33, 95% CI: 1.11–1.59, p = 0.002), nonadecanoate (19:0) (OR: 0.40, 95% C I:0.4–0.68, p = 0.0008), 1‐linoleoylglycerophosphoethanolamine (OR: 0.47, 95% CI: 0.30–0.75, p = 0.001), 2‐hydroxystearate (OR: 0.39, 95% CI: 0.23–0.67, p = 0.0007), gamma‐glutamylthreonine (OR: 2.14, 95% CI: 1.02–4.50, p = 0.040) and CRC. MVMR analysis revealed that genetically predicted pyruvate, 1‐linoleoylglycerophosphoethanolamine and gamma‐glutamylthreonine can directly influence CRC independently of other metabolites. Conclusion The current work provides evidence to support the causality of the six circulating metabolites on CRC and a new perspective on the exploration of the biological mechanisms of CRC by combining genomics and metabolomics. These findings contribute to the screening, prevention and treatment of CRC.

Background: Non-alcoholic fatty liver disease (NAFLD) is a liver disease associated with obesity, insulin resistance, type 2 diabetes mellitus (T2DM), and metabolic syndrome. The risk factors for NAFLD have not been identified. Metabolic dysfunction has been found to be an important factor in the pathogenesis and progression of NAFLD. However, the causal impact of blood metabolites on NAFLD is unclear.Methods: We performed a two-sample Mendelian randomization (MR) study. A genome-wide association study (GWAS) with 7824 participants provided data on 486 human blood metabolites. Outcome information was obtained from a large-scale GWAS meta-analysis of NAFLD, which contained 8,434 cases and 770,180 controls of Europeans. The inverse variance weighted (IVW) model was chosen as the primary two-sample MR analysis approach, followed by sensitivity analyses such as the heterogeneity test, horizontal pleiotropy test, and leave-one-out analysis. In addition, we performed replication, meta-analysis, and metabolic pathway analysis. We further conducted colocalization analysis to deeply reflect the causality.Results: After rigorous genetic variant selection, IVW, sensitivity analysis, replication, and meta-analysis, two known metabolites were identified as being associated with the development of NAFLD [biliverdin: OR = 1.45; 95% CI 1.20-1.75; p = 0.0001; myristoleate: OR = 0.57; 95% CI 0.39-0.83; p = 0.0030].Conclusion: By combining genomics with metabolomics, our findings provide a new perspective on the underlying mechanisms of NAFLD and have important implications for the screening and prevention of NAFLD.