Clostridioides difficile (C.difficile) infection is the most common cause of healthcare-associated infection and an important cause of morbidity and mortality among hospitalized patients. A comprehensive understanding of C.difficile infection (CDI) pathogenesis is crucial for disease diagnosis, treatment, and prevention. Here, we characterized gut microbial compositions and a broad panel of innate and adaptive immunological markers in 243 well-characterized human subjects (including 187 subjects with both microbiota and immune marker data), who were divided into four phenotype groups: CDI, Asymptomatic Carriage, Non-CDI Diarrhea, and Control. We found that the interactions between gut microbiota and host immune markers are very sensitive to the status of C.difficile colonization and infection. We demonstrated that incorporating both gut microbiome and host immune marker data into classification models can better distinguish CDI from other groups than can either type of data alone. Our classification models display robust diagnostic performance to differentiate CDI from Asymptomatic carriage (AUC~0.916), Non-CDI Diarrhea (AUC~0.917), or Non-CDI that combines all other three groups (AUC~0.929). Finally, we performed symbolic classification using selected features to derive simple mathematic formulas that explicitly quantify the interactions between the gut microbiome and host immune markers. These findings support the potential roles of gut microbiota and host immune markers in the pathogenesis of CDI. Our study provides new insights for a microbiome-immune marker-derived signature to diagnose CDI and design therapeutic strategies for CDI.
Keywords: C. difficile infection; gut microbiome; host immune markers; machine learning.