Targeted urinary metabolomics combined with machine learning to identify biomarkers related to central carbon metabolism for IBD

作者：Miao-Lin Lei, Guan-Wei Bi, Xiaolin Yin, Yue Wang, Zi-Ru Sun, Xinrui Guo, Huipeng Zhang, Xiao‐Han Zhao, Feng Li, Yanbo Yu · 发表于：Frontiers in Molecular Biosciences · 年份：2025 · DOI：10.3389/fmolb.2025.1615047 · 被引用次数：4 · 研究领域：Metabolomics and Mass Spectrometry Studies、Gut microbiota and health、Inflammatory Bowel Disease

Introduction: Inflammatory bowel disease (IBD), comprising Crohn's disease (CD) and ulcerative colitis (UC), is a chronic and relapsing inflammatory disorder of the gastrointestinal tract. Current diagnostic approaches are invasive, costly, and time-consuming, underscoring the need for non-invasive, accurate diagnostic methods. Methods: We conducted a targeted metabolomic analysis of 49 metabolites related to central carbon metabolism in urinary samples from individuals with IBD and control group. Diagnostic models were constructed using six machine learning algorithms, and their performance was evaluated by cross-validated area under the receiver operating characteristic curve (AUC). The SHAP (SHapley Additive exPlanations) method was used to interpret the models and identify key discriminatory features. Results: Six metabolites-xylose, isocitric acid, fructose, L-fucose, N-acetyl-D-glucosamine (GlcNAc), and glycolic acid-differentiated UC from control group, while three metabolites-xylose, L-fucose, and citric acid-distinguished CD from control group. The optimal diagnostic model achieved a mean AUC of 0.84 for UC and 0.93 for CD. These models retained high diagnostic accuracy even after adjusting for disease activity. SHAP analysis identified L-fucose, xylose, and GlcNAc as important features for UC, and citric acid and xylose for CD. Discussion: Our findings highlight distinct metabolic signatures in central carbon metabolism associated with IBD subtypes. The identified m...