Metabolomic profiling of microbial disease etiology in community-acquired pneumonia

Diagnosis of microbial disease etiology in community-acquired pneumonia (CAP) remains challenging. We undertook a large-scale metabolomics study of serum samples in hospitalized CAP patients to determine if host-response associated metabolites can enable diagnosis of microbial etiology, with a specific focus on discrimination between the major CAP pathogen groups S. pneumoniae, atypical bacteria, and respiratory viruses. Targeted metabolomic profiling of serum samples was performed for three groups of hospitalized CAP patients with confirmed microbial etiologies: S. pneumoniae (n = 48), atypical bacteria (n = 47), or viral infections (n = 30). A wide range of 347 metabolites was targeted, including amines, acylcarnitines, organic acids, and lipids. Single discriminating metabolites were selected using Student's T-test and their predictive performance was analyzed using logistic regression. Elastic net regression models were employed to discover metabolite signatures with predictive value for discrimination between pathogen groups. Metabolites to discriminate S. pneumoniae or viral pathogens from the other groups showed poor predictive capability, whereas discrimination of atypical pathogens from the other groups was found to be possible. Classification of atypical pathogens using elastic net regression models was associated with a predictive performance of 61% sensitivity, 86% specificity, and an AUC of 0.81. Targeted profiling of the host metabolic response revealed metabolites that can support diagnosis of microbial etiology in CAP patients with atypical bacterial pathogens compared to patients with S. pneumoniae or viral infections.

Automated summary

Diagnosis of microbial disease etiology in hospitalized CAP patients can be supported by targeted metabolomic profiling, with the ability to differentiate between major pathogen groups such as S. pneumoniae, atypical bacteria, and respiratory viruses. Discrimination between atypical pathogens and other groups showed predictive capability, with an elastic net regression model achieving 61% sensitivity, 86% specificity, and an AUC of 0.81.