Identification of Biomarkers for Treatment Escalation of Ulcerative Colitis Based on Untargeted Metabolomics and...

Biomarkers to guide clinical decision making in active ulcerative colitis (UC) patients are urgently needed. This study aims to identify metabolites associated with UC treatment escalation and establish prediction models based on untargeted metabolomics and machine learning algorithms.

Liquid chromatography-mass spectrometry-based untargeted metabolomics analysis was performed on 88 plasma samples (44 active UC patients requiring treatment escalation and 44 active UC patients not requiring treatment escalation). Univariate and multivariate analyses were applied to identify metabolic biomarkers for UC treatment escalation. Metabolic pathway enrichment analysis was performed to reveal the disturbed metabolic pathways related to UC treatment escalation. Four machine learning algorithms, including Support Vector Machine, Random Forest, k-Nearest Neighbor, and logistic regression were used to build diagnostic models for UC treatment escalation.

Nine significantly differential metabolites were identified as the candidate biomarkers for UC treatment escalation. Pathway analysis revealed that phenylalanine metabolism and ether lipid metabolism are the disturbed metabolic pathways related to treatment escalation. The protein-metabolite interaction network identified 21 proteins are associated with 9 treatment escalation related metabolites. The areas under the receiver operating characteristic curve of the Support Vector Machine, Random Forest, k-Nearest Neighbor, and logistic regression models based on metabolic biomarkers were 0.923, 0.966, 0.897 and 0.803, respectively.

The plasma metabolome represents a promising source of biomarkers for the prediction of treatment escalation in active UC. Metabolic biomarkers, combined with machine learning algorithms, could be efficient for risk assessment and early identification of UC treatment escalation.