Host and Microbial Biomarkers Related to the Development of Complicated Clostridium difficile Infection

University Of Michigan At Ann Arbor, Ann Arbor MI

Investigators

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Abstract

? DESCRIPTION (provided by applicant): Clostridium difficile infection (CDI) is implicated in nearly 3 million cases of diarrhea and colitis in the U.S. each year. CDI, which disproportionately affects older adults, can result in fulminant, life-threatening colitis and lead to multiple recurrnt episodes. There is an urgent need for validated biomarker-based clinical decision-making tools to predict which patients will experience adverse outcomes from CDI. There is a gap in knowledge regarding which known and yet to-be-discovered biomarkers, derived from the host, microbiome and pathogen, will best predict complications and recurrence. Our long-term goal is to develop and validate accurate risk-prediction models for adverse outcomes following CDI that can be used to guide therapy. The next step in pursuit of that goal, and our overall objective for the proposed research, is to discover new candidate biomarkers and determine which ones together best predict complicated CDI in an adjusted model. Our central hypothesis is that a biomarker-based model will better predict complicated CDI compared to models based on clinical variables alone. To address this hypothesis we propose three specific aims: 1) validate host biomarkers previously shown to associate with complicated CDI; 2) discover novel microbial biomarkers for complicated CDI; and 3) develop a candidate biomarker-based predictive model for complicated CDI. We will collect sera and stool prospectively in a cohort of older adults to determine if characteristics of the microbiome, microbial features of C. difficile, levels of antitoxin antibodies, and/or inflammatory mediators associate with complications. Multivariable models will be constructed using linear regression and machine learning techniques and model diagnostics will be used to generate a final portable, biomarker-based predictive model for use in future studies.