ArticleJournal of Crohn's & colitis2025
Machine learning and metabolomics identify biomarkers associated with the disease extent of ulcerative colitis.
Article in Journal of Crohn's & colitis, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Metabolomics and metabolites in cancer diagnosis and treatment.Molecular biomedicine · 2025Pooled it
- Review
- Oxidative-Stress-Associated Molecular Signatures in Immune-Mediated Diseases: A Systematic Review Integrating Machine Learning and Systems Biology Approaches.Antioxidants (Basel, Switzerland) · 2026Review
- Integrated metabolomics and gut microbiota analyses reveal the protective effects of matrine in ulcerative colitis.Frontiers in chemistry · 2026Article
- Integrated Bioinformatic Identification and Experimental Validation Reveal That Aging Exacerbates ARDS Through MAPK14/ADM/MAPK8 Axis.Journal of inflammation research · 2026Article
- Integration of localized microbiome, metabolome, and clinical datasets predicts healing in chronic wounds among veterans.bioRxiv : the preprint server for biology · 2025Article
- Article
- Multi-omics integration analysis based on plasma circulating proteins reveals potential therapeutic targets for ulcerative colitis.Frontiers in molecular biosciences · 2025Article
- Early prediction of sepsis-induced coagulopathy in the ICU using interpretable machine learning: a multi-center retrospective cohort study.Frontiers in medicine · 2025Article
- TRIM29 drives ulcerative colitis by disrupting lipid metabolism via lysosomal dysfunction: a multi-omics and experimental study.Frontiers in immunology · 2025Article
- Fecal microbiota transplantation: application scenarios, efficacy prediction, and factors impacting donor-recipient interplay.Frontiers in microbiology · 2025Review
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Authors and funding
9 authors.
Funding
Abstract
BACKGROUND AND
aimsUlcerative colitis (UC) is a metabolism-related chronic intestinal inflammatory disease. Disease extent is a key parameter of UC. Using serum metabolic profiling to identify noninvasive biomarkers of disease extent may inform therapeutic decisions and risk stratification.
methodsThe orthogonal partial least squares-discriminant analysis (OPLS-DA) was performed to identify the metabolites. Least absolute shrinkage and selection operator regression, random forest-recursive feature elimination, and support vector machine-recursive feature elimination algorithms were used to screen metabolites. Five machine learning algorithms (eXtreme Gradient Boosting, K-NearestNeighbor, Naive Bayes, random forest [RF], and SVM) were used to construct the prediction model.
resultsA total of 220 differential metabolites between the patients with UC and healthy controls (HCs) were confirmed by the OPLS-DA model. Machine learning screened 8 essential metabolites for distinguishing patients with UC from HCs. A total of 23, 6, and 6 differential metabolites were obtained through machine learning between groups E1 and E2, E1 and E3, and E2 and E3. The RF model had a prediction accuracy of up to 100% in all 3 training sets. The serum levels of tridecanoic acid were significantly lower, and pelargonic acid was significantly higher in patients with extensive colitis than in the other groups. The serum level of asparaginyl valine in patients with rectal UC was significantly lower than that in the E2 and E3 groups.
conclusionsOur findings revealed the metabolic landscape of UC and identified biomarkers for different disease extents, confirming the value of metabolites in predicting the occurrence and progression of UC.
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