Relationship between inflammatory status and microbial composition in severe asthma and during exacerbation
Bibliographic record
Abstract
Abstract Background In T2‐mediated severe asthma, biologic therapies, such as mepolizumab, are increasingly used to control disease. Current biomarkers can indicate adequate suppression of T2 inflammation, but it is unclear whether they provide information about airway microbial composition. We investigated the relationships between current T2 biomarkers and microbial profiles, characteristics associated with a Proteobacteria HIGH microbial profile and the effects of mepolizumab on airway ecology. Methods Microbiota sequencing was performed on sputum samples obtained at stable and exacerbation state from 140 subjects with severe asthma participating in two clinical trials. Inflammatory subgroups were compared on the basis of biomarkers, including FeNO and sputum and blood eosinophils. Proteobacteria HIGH subjects were identified by Proteobacteria to Firmicutes ratio ≥0.485. Where paired sputum from stable visits was available, we compared microbial composition at baseline and following ≥12 weeks of mepolizumab. Results Microbial composition was not related to inflammatory subgroup based on sputum or blood eosinophils. FeNO ≥50 ppb when stable and at exacerbation indicated a group with less dispersed microbial profiles characterised by high alpha‐diversity and low Proteobacteria. Proteobacteria HIGH subjects were neutrophilic and had a longer time from asthma diagnosis than Proteobacteria LOW subjects. In those studied, mepolizumab did not alter airway bacterial load or lead to increased Proteobacteria. Conclusion High FeNO could indicate a subgroup of severe asthma less likely to benefit from antimicrobial strategies at exacerbation or in the context of poor control. Where FeNO is <50 ppb, biomarkers of microbial composition are required to identify those likely to respond to microbiome‐directed strategies. We found no evidence that mepolizumab alters airway microbial composition.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".