Sputum microbiota in adults with CF associates with response to inhaled tobramycin
Bibliographic record
Abstract
Background Inhaled tobramycin powder/solution (TIP/S) use has resulted in improved clinical outcomes in patients with cystic fibrosis (CF) with chronic Pseudomonas aeruginosa. However, TIP/S effect on the CF sputum microbiome has not been explored. We hypothesised that TIP/S has additional ‘off-target’ effects beyond merely P. aeruginosa and that baseline microbiome prior to initiation of therapy is associated with subsequent patient response. Methods We drew sputum samples from a prospectively collected biobank. Patients were included if they had one sputum sample in the 18 months before and after TIP/S. Bacterial 16S rRNA gene profiling was used to characterise the sputum microbiome. Results Forty-one patients met our inclusion criteria and 151 sputum samples were assessed. At baseline, median age was 30.4 years (IQR 24.2–35.2) and forced expiratory volume in 1 (FEV1) second was 57% predicted (IQR 44–74). Nineteen patients were defined a priori as responders having no net decrease in FEV1 in the year following TIP/S. No significant changes were observed in key microbiome metrics of alpha (within-sample) or beta (between-sample) diversity for samples collected before and after TIP/S. However, significant beta-diversity (Bray-Curtis) differences were noted at baseline between patients based on response status. Notably, responders were observed to have a higher abundance of Staphylococcus in pretherapy baseline samples. Conclusions Our longitudinal study demonstrates that the sputum microbiome of patients with CF is relatively stable following inhaled tobramycin over many months. Intriguingly, our findings suggest that baseline microbiome may associate with patient response to TIP/S—suggesting the sputum microbiome could be used to personalise therapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".