期刊
JOURNAL OF INFECTIOUS DISEASES
卷 223, 期 -, 页码 S246-S256出版社
OXFORD UNIV PRESS INC
DOI: 10.1093/infdis/jiaa655
关键词
microbiome; machine learning; cystic fibrosis
资金
- Centers for Disease Control and Prevention [BAA 2016-N-17812, BAA 2017-OADS-01]
- National Institutes of Health [HR56L142857, R21AI143296]
- Cystic Fibrosis Foundation [BROWN19I0, BROWN19P0]
- Emory and Children's Center for Cystic Fibrosis and Airways Disease
The study found that models trained on whole-microbiome quantitation outperformed models trained only on pathogen quantitation in lung infections in people with cystic fibrosis. The most accurate models retained key pathogens and nonpathogen taxa as important predictors of lung health.
Background. Microbiome sequencing has brought increasing attention to the polymicrobial context of chronic infections. However, clinical microbiology continues to focus on canonical human pathogens, which may overlook informative, but nonpathogenic, biomarkers. We address this disconnect in lung infections in people with cystic fibrosis (CF). Methods. We collected health information (lung function, age, and body mass index [BMI]) and sputum samples from a cohort of 77 children and adults with CF. Samples were collected during a period of clinical stability and 16S rDNA sequenced for airway microbiome compositions. We use ElasticNet regularization to train linear models predicting lung function and extract the most informative features. Results. Models trained on whole-microbiome quantitation outperformed models trained on pathogen quantitation alone, with or without the inclusion of patient metadata. Our most accurate models retained key pathogens as negative predictors (Pseudomonas, Achromobacter) along with established correlates of CF disease state (age, BMI, CF-related diabetes). In addition, our models selected nonpathogen taxa (Fusobacterium, Rothia) as positive predictors of lung health. Conclusions. These results support a reconsideration of clinical microbiology pipelines to ensure the provision of informative data to guide clinical practice.
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