Microbiome Data Enhances Predictive Models of Lung Function in People With Cystic Fibrosis

被引:8
|
作者
Zhao, Conan Y. [1 ,2 ,3 ,6 ]
Hao, Yiqi [2 ]
Wang, Yifei [2 ,3 ,4 ,6 ]
Varga, John J. [2 ,3 ,5 ,6 ]
Stecenko, Arlene A. [5 ,6 ]
Goldberg, Joanna B. [5 ,6 ]
Brown, Sam P. [2 ,3 ,6 ]
机构
[1] Georgia Inst Technol, Interdisciplinary Grad Program Quantitat Biosci, Atlanta, GA 30332 USA
[2] Georgia Inst Technol, Sch Biol Sci, Atlanta, GA 30332 USA
[3] Georgia Inst Technol, Ctr Microbial Dynam & Infect, Atlanta, GA 30332 USA
[4] Georgia Inst Technol, Inst Data Engn & Sci IDEaS, Atlanta, GA 30332 USA
[5] Emory Univ, Sch Med, Dept Pediat, Div Pulm Allergy Immunol Cyst Fibrosis & Sleep, Atlanta, GA USA
[6] Emory Childrens Ctr Cyst Fibrosis & Airway Dis Re, Atlanta, GA USA
来源
JOURNAL OF INFECTIOUS DISEASES | 2021年 / 223卷
基金
美国国家卫生研究院;
关键词
microbiome; machine learning; cystic fibrosis; AIRWAY MICROBIOTA; PATHOGENS;
D O I
10.1093/infdis/jiaa655
中图分类号
R392 [医学免疫学]; Q939.91 [免疫学];
学科分类号
100102 ;
摘要
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.
引用
收藏
页码:S246 / S256
页数:11
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