ArticlePloS one2021
Metabolomic profiling of microbial disease etiology in community-acquired pneumonia.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed, 9 citations in OpenAlex.
- Pneumonia-specific plasma metabolite profiles among patients hospitalised with infection in Southeast Asia.ERJ open research · 2025Article
- Longitudinal metabolite profiling of Streptococcus pneumoniae-associated community-acquired pneumonia.Metabolomics : Official journal of the Metabolomic Society · 2024Article
- Differential metabolic host response to pathogens associated with community-acquired pneumonia.Metabolism open · 2023Article
- Metabolomic Profiling of Lungs from Mice Reveals the Variability of Metabolites inJournal of inflammation research · 2023Article
- Advances in diagnostic tools for respiratory tract infections: from tuberculosis to COVID-19 - changing paradigms?ERJ open research · 2022Review
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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