Detection of Unspecified Inflammatory Bowel Disease, Crohn’s Disease and Ulcerative Colitis via Metabolite Analysis and Machine Learning

The current standard of inflammatory bowel disease (IBD), especially, Crohn’s disease (CD), diagnosis is set through an invasive endoscopy procedure. However, serum metabolites hold potential as useful biomarkers for non-invasive diagnosis and treatment of IBDs. The goal of this research was to elucidate the biomarkers including metabolites and genes related to IBDs, to show their distinguishing and common features, and to create a machine-learning (ML) model for recognition of each disease. We explored metabolic pathways and gene–metabolite networks related to unspecified-IBD (uIBD), Crohn’s diseaseand ulcerative colitis (UC). P38 MAPK, ERK1/2, AMPK, and proinsulin were found to be closely related to the pathology of IBDs. The best performing ML model, trained on filtered disease-specific metabolite datasets, was able to predict metabolite class with 92.17% accuracy. Through examination of IBD-related serum, significant relationships between the inputted metabolites and certain metabolic and signaling pathways were found, which can be pinpointed and used to increase accuracy of disease diagnoses. Development of a ML model including metabolites and their chemical descriptors made it possible to achieve considerable accuracy of prediction of the IBDs. Our results elucidate a large variety of metabolites, genes, and pathways that could be used for better understanding of IBDs’ molecular mechanisms.