ArticleThe Journal of infectious diseases2021
Microbiome Data Enhances Predictive Models of Lung Function in People With Cystic Fibrosis.
Article in The Journal of infectious diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Bacteria of the lung microbiome and health biomarkers in chronic airway disease: a systematic review and meta-analysis.NPJ biofilms and microbiomes · 2026Pooled it
- Differential Metabolite Production Underlies Disruption of the Cystic Fibrosis Airway Microbiota by Pathogens.bioRxiv : the preprint server for biology · 2026Article
- Article
- Antibiofilm activity ofBiofilm · 2024Article
- Single nucleotide variants inMicrobial genomics · 2023Article
- Antibiotics Drive Expansion of Rare Pathogens in a Chronic Infection Microbiome Model.mSphere · 2022Article
- Exploring the Cystic Fibrosis Lung Microbiome: Making the Most of a Sticky Situation.Journal of the Pediatric Infectious Diseases Society · 2022Review
- Mucosal immunology of the ocular surface.Mucosal immunology · 2022Review
- Article
- The State of Microbiome Science at the Intersection of Infectious Diseases and Antimicrobial Resistance.The Journal of infectious diseases · 2021Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundMicrobiome 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).
methodsWe 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.
resultsModels 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.
conclusionsThese results support a reconsideration of clinical microbiology pipelines to ensure the provision of informative data to guide clinical practice.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.