Background: Inflammatory Bowel Disease (IBD), comprising Crohn's Disease (CD) and Ulcerative Colitis (UC), is a complex condition with diverse manifestations; recent advances in multi-omics technologies are helping researchers unravel its molecular characteristics to develop targeted treatments.
Objective: In this work, we explored one of the largest multi-omics cohorts in Inflammatory Bowel Disease, the Study of a Prospective Adult Research Cohort (SPARC IBD), with the goal of identifying predictive biomarkers for CD and UC and elucidating patient subtypes.
Design: We analyzed genomics, transcriptomics (gut biopsy samples), and proteomics (blood plasma) from hundreds of patients from SPARC IBD. We trained a machine learning model that classifies UC vs. CD samples. In parallel, we integrated multi-omics data to unveil patient subgroups in each of the two indications independently and analyzed the molecular phenotypes of these patient subpopulations.
Results: The high performance of the model showed that multi-omics signatures are able to discriminate between the two indications. The most predictive features of the model, both known and novel omics signatures for IBD, can potentially be used as diagnostic biomarkers. Patient subgroup analysis in each indication uncovered omics features associated with disease severity in UC patients, and with tissue inflammation in CD patients. This culminates with the observation of two CD subpopulations characterized by distinct inflammation profiles.
Conclusion: Our work unveiled potential biomarkers to discriminate between CD and UC and to stratify each population into well-defined subgroups, offering promising avenues for the application of precision medicine strategies.
Keywords: Crohn’s Disease; Inflammatory Bowel Disease; Machine learning; Multi-omics; Precision medicine; Ulcerative Colitis.
© The Author(s) 2025. Published by Oxford University Press on behalf of European Crohn’s and Colitis Organisation.