Causality of genetically determined blood metabolites on inflammatory bowel disease: a two-sample Mendelian...

Inflammatory bowel disease (IBD) is a chronic and recurrent inflammatory disease of the gastrointestinal tract, including two subtypes: Crohn's disease (CD) and ulcerative colitis (UC). Metabolic disorders are important factors in the development of IBD. However, the evidence for the causal relationship between blood metabolites and IBD remains limited. A two-sample MR analysis was applied to evaluate relationships between 486 blood metabolites and IBD. The inverse variance weighted method was chosen as the primary MR analysis method. False discovery rate correction was used to control for false positives in multiple testing. Following complementary and sensitivity analyses were conducted using methods such as weight median, MR-egger, weighted mode, simple mode, Cochran Q test, and MR-PRESSO. Moreover, we performed replication, meta-analysis, Steiger test, and linkage disequilibrium score regression to enhance the robustness of the results. Additionally, we performed metabolic pathway analysis to identify potential metabolic pathways. As a result, we identified four significant causal associations between four blood metabolites and two IBD subtypes. Specifically, one metabolite was identified as being associated with the development of CD (mannose: odds ratio (OR) = 0.19, 95% confidence interval (CI) 0.08-0.43, P = 8.54 × 10^-5). Three metabolites were identified as being associated with the development of UC (arachidonate (20:4n6): OR = 0.18, 95% CI 0.11-0.30, P = 2.09 × 10^-11; 1, 5-anhydroglucitol: OR = 2.21, 95% CI 1.47-3.34, P = 1.50 × 10^-4; 2-stearoylglycerophosphocholine: OR = 2.66, 95% CI 1.53-4.63, P = 5.30 × 10^-4). The findings of our study suggested that the identified metabolites and metabolic pathways can be considered as useful circulating metabolic biomarkers for the screening and prevention of IBD in clinical practice, as well as candidate molecules for future mechanism exploration and drug target selection.