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.

BackgroundPrevious observational studies suggested inconsistent insights on the associations between meat intake and the risk of digestive tract cancers (DCTs). The causal effect of meat intake on DCTs is unclear.MethodsTwo-sample Mendelian randomization (MR) was performed based on genome-wide association studies (GWAS) summary data from UK Biobank and FinnGen to evaluate the causal effect of meat intake [processed meat, red meat (pork, beef, and lamb), and white meat (poultry)] on DCTs (esophageal, stomach, liver, biliary tract, pancreatic, and colorectal cancers). The causal effects were estimated using a primary analysis that employed inverse-variance weighting (IVW) and complementary analysis that utilized MR-Egger weighted by the median. A sensitivity analysis was conducted using the Cochran Q statistic, a funnel plot, the MR-Egger intercept, and a leave-one-out approach. MR-PRESSO and Radial MR were performed to identify and remove outliers. To demonstrate direct causal effects, multivariable MR (MVMR) was applied. In addition, risk factors were introduced to explore potential mediators of the relationship between exposure and outcome.ResultsThe results of the univariable MR analysis indicated that genetically proxied processed meat intake was associated with an increased risk of colorectal cancer [IVW: odds ratio (OR) = 2.12, 95% confidence interval (CI) 1.07–4.19; P = 0.031]. The causal effect is consistent in MVMR (OR = 3.85, 95% CI 1.14–13.04; P = 0.030) after controlling for the influence of other types of exposure. The body mass index and total cholesterol did not mediate the causal effects described above. There was no evidence to support the causal effects of processed meat intake on other cancers, except for colorectal cancer. Similarly, there is no causal association between red meat, white meat intake, and DCTs.ConclusionsOur study reported that processed meat intake increases the risk of colorectal cancer rather than other DCTs. No causal relationship was observed between red and white meat intake and DCTs.

Background Nonalcoholic fatty liver disease (NAFLD) etiology involves a complex interaction of genetic and environmental factors. Previous observational studies have suggested that increased leptin levels may be associated with a low risk of developing NAFLD, but the causal relationship remains unclear. Due to advances in genome-wide association studies (GWAS) and the discovery of Mendelian randomization (MR), we aimed to investigate the causal effect of leptin and NAFLD using MR analysis. Methods We performed a two-sample Mendelian randomization analysis (TSMR) using summary GWAS data from leptin (up to 50,321 individuals) and NAFLD (894 cases and 217,898 controls) in a European population. Genetic instrumental variables (InstrumentalVariables, IVs) that satisfied the three core assumptions of Mendelian randomization were selected. TSMR analysis was performed using the Inverse Variance Weighted (IVW) method, MR-Egger regression method, and Weighted Median (WM) method. To ensure the accuracy and stability of the study results, heterogeneity tests, multiple validity tests, and sensitivity analyses were conducted. Results The results of TSMR correlation analysis between NAFLD and leptin were IVW (OR: 0.3032; 95% CI 0.1181–0.7783; P = 0.0131), WM method (OR: 0.2816; 95% CI 0.0931–0.8678; P = 0.0273), MR-Egger regression method (P = 0.6955), and Among them, the IVW method and WM method showed P > 0.05, and the results were statistically significant. In addition, TSMR correlation analysis between NAFLD and circulating leptin levels adjusted for Body Mass Index (BMI) resulted in IVW (OR: 0.4416; 95% CI 0.2807–0.6948; P = 0.0004), WM method (OR: 0.4194; 95% CI 0.2279–0.7720; P = 0.0052), MR-Egger regression method (OR: 0.2023; 95% CI 0.0541–0.7562; P = 0.0389), P > 0.05, and the results were statistically significant. It is further demonstrated that increased leptin is causally associated with reduced risk of NAFLD, and leptin may serve as a protective factor for NAFLD. Conclusions In this study, we explored the causal association between leptin and NAFLD from a genetic perspective based on the GWAS database using TSMR analysis. Further studies are needed to explain the underlying mechanisms.