Presented by: Jennifer Dawkins
Clostridioides difficile infection (CDI) is the most common hospital acquired infection in the U.S., causing ~450,000 cases and 29,000 deaths annually. CDI recurrence in patients is high: ~25% for the first recurrence and increasing with each episode. The initial development of CDI and CDI recurrence are mechanistically tied to disruption of the normal gut microbiota. Metabolites reflect functional activities of the microbiome and pathways common to multiple bacterial species, and thus may provide a clearer picture of the gut microbiota than microbial compositional data alone. We aim to predict CDI recurrence and better understand its pathogenesis by analyzing the gut microbiota and gut metabolites present in participants recently diagnosed with initial CDI.
We used 16S rRNA amplicon sequencing and liquid-chromatography/mass-spectrometry (LC/MS) untargeted metabolomics to analyze stool samples from 53 participants at diagnosis of CDI, directly after cessation of treatment, and weekly or bi-weekly for 4-6 weeks or until recurrence occurred. Using lasso-penalized logistic regression on these data, we developed predictors of CDI recurrence.
Our predictor achieved a median cross-validated area-under-the-curve (AUC) of 0.788 (0.733, 0.788) when using only metabolome data, compared to a median 0.645 (0.623, 0.695) AUC when using only microbial composition data. The combined data achieved an AUC of 0.781 (0.780, 0.781) and moreover selected only metabolite covariates, suggesting no gain in predictive capability from the microbial composition data. We found several metabolites that predict recurrence, including a host inflammatory biomarker, a metabolite reported to affect permeability of the intestinal lumen, and a metabolite highly associated with microbial-host co-metabolism. We also found a metabolite that predicts protection against CDI recurrence; this metabolite has been implicated in antimicrobial activity and cell cycle regulation.
These results suggest that gut-metabolites may provide mechanistic insights into CDI and moreover can accurately predict recurrence, a challenging clinical problem that if solved could enable prompt, targeted treatments to short-circuit the vicious cycle of recurrence. Because CDI is so prevalent in hospitals, preventing CDI recurrence would make hospital stays safer and shorter for those admitted for any condition.
Jennifer Dawkins – Poster Description (Audio Clip)
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