Sputum microbiota is predictive of long-term clinical outcomes in young adults with cystic fibrosis
Bibliographic record
Abstract
Background Complex polymicrobial communities infect cystic fibrosis (CF) lower airways. Generally, communities with low diversity, dominated by classical CF pathogens, associate with worsened patient status at sample collection. However, it is not known if the microbiome can predict future outcomes. We sought to determine if the microbiome could be adapted as a biomarker for patient prognostication. Methods We retrospectively assessed prospectively collected sputum from a cohort of 104 individuals aged 18–22 to determine factors associated with progression to early end-stage lung disease (eESLD; death/transplantation <25 years) and rapid pulmonary function decline (>−3%/year FEV 1 over the ensuing 5 years). Illumina MiSeq paired-end sequencing of the V3-V4 region of the 16S rRNA was used to define the airway microbiome. Results Based on the primary outcome analysed, 17 individuals (16%) subsequently progressed to eESLD. They were more likely to have sputum with low alpha diversity, dominated by specific pathogens including Pseudomonas . Communities with abundant Streptococcus were observed to be protective. Microbial communities clustered together by baseline lung disease stage and subsequent progression to eESLD. Multivariable analysis identified baseline lung function and alpha diversity as independent predictors of eESLD. For the secondary outcomes, 58 and 47 patients were classified as rapid progressors based on absolute and relative definitions of lung function decline, respectively. Patients with low alpha diversity were similarly more likely to be classified as experiencing rapid lung function decline over the ensuing 5 years when adjusted for baseline lung function. Conclusions We observed that the diversity of microbial communities in CF airways is predictive of progression to eESLD and disproportionate lung function decline and may therefore represent a novel biomarker.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".