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Published on: December 8, 2014
Temporal Gut Microbial Changes Predict Recurrent Clostridiodes Difficile Infection in Patients With and Without
Allen A Lee1, Krishna Rao2, Julajak Limsrivilai3
1Division of Gastroenterology, Department of Internal Medicine, University of Michigan School of Medicine, Ann Arbor, MI.
Fecal microbial changes can predict the risk of recurrent Clostridiodes difficile infection (rCDI) and ulcerative colitis (UC) flares. These findings may help identify high-risk patients for targeted management strategies.
Area of Science:
- Microbiome research
- Gastroenterology
- Infectious disease
Background:
- Ulcerative colitis (UC) patients have a higher risk of Clostridiodes difficile infection (CDI), which can trigger UC flares.
- Specific alterations in fecal microbial composition are hypothesized to be linked to UC flare and recurrent CDI (rCDI).
Purpose of the Study:
- To investigate the association between fecal microbial profiles and the risk of rCDI and UC flare in patients with UC.
- To identify microbial biomarkers that can predict these adverse outcomes.
Main Methods:
- A prospective observational cohort study involving 57 patients with UC and/or CDI.
- 16S rRNA sequencing of stool samples collected at multiple time points (baseline, post-antibiotics, post-reconstitution).
- Logistic regression and Lasso models were used to analyze microbial data and predict outcomes.
Main Results:
- Patients with rCDI showed significant differences in gut microbial community structure compared to non-rCDI patients.
- Predictive models incorporating baseline and post-treatment microbial features, alongside clinical factors like female gender and UC hospitalization, accurately predicted rCDI risk (AuROC up to 0.94).
- A baseline model including UC hospitalization and increased Bacteroidetes predicted UC flare risk (AuROC 0.88).
Conclusions:
- Fecal microbial features before and after therapy are valuable predictors of rCDI risk in patients with and without UC.
- These microbial insights can aid in stratifying patients based on their risk for rCDI and UC flare, informing clinical management decisions.
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