Identification of Biomarkers for Treatment Escalation of Ulcerative Colitis Based on Untargeted Metabolomics and Machine Learning Algorithms: A Prospective Cohort Study.

ABSTRACT:

Introduction

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.

Methods

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. Fou

SUBMITTER: Han M 

PROVIDER: S-EPMC12727375 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature