4.7 Article

The Underlying Changes in Serum Metabolic Profiles and Efficacy Prediction in Patients with Extensive Ulcerative Colitis Undergoing Fecal Microbiota Transplantation

Journal

NUTRIENTS
Volume 15, Issue 15, Pages -

Publisher

MDPI
DOI: 10.3390/nu15153340

Keywords

washed microbiota transplantation; inflammatory bowel disease; extensive UC; refractory ulcerative colitis; serum metabolomics; machine learning

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FMT is an effective treatment for UC, and the serum metabolites can be used as non-invasive predictors of FMT efficacy for UC.
(1) Background: Fecal microbiota transplantation (FMT) is an effective treatment for ulcerative colitis (UC). Metabolomic techniques would assist physicians in clinical decision-making. (2) Methods: Patients with active UC undergoing FMT were enrolled in the study and monitored for 3 months. We explored short-term changes in the serum metabolic signatures of groups and the association between baseline serum metabolomic profiles and patient outcomes. (3) Results: Forty-four eligible patients were included in the analysis. Of them, 50.0% and 29.5% achieved clinical response and clinical remission, respectively, 3 months post-FMT. The top two significantly altered pathways in the response group were vitamin B6 metabolism and aminoacyl-tRNA biosynthesis. Both the remission and response groups exhibited an altered and enriched pathway for the biosynthesis of primary bile acid. We found a clear separation between the remission and non-remission groups at baseline, characterized by the higher levels of glycerophosphocholines, glycerophospholipids, and glycerophosphoethanolamines in the remission group. A random forest (RF) classifier was constructed with 20 metabolic markers selected by the Boruta method to predict clinical remission 3 months post-FMT, with an area under the curve of 0.963. (4) Conclusions: FMT effectively induced a response in patients with active UC, with metabolites partially improving post-FMT in the responsive group. A promising role of serum metabolites in the non-invasive prediction of FMT efficacy for UC demonstrated the value of metabolome-informed FMT in managing UC.

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