Transcriptomic profiling of the airway epithelium in COPD links airway eosinophilia to type 2 inflammation and corticosteroid response
Bibliographic record
Abstract
BACKGROUND: A subset of COPD patients have high levels of eosinophils in the distal airways ("airway eosinophilia"). OBJECTIVES: To compare the gene expression of type 2 inflammation in airway epithelial brushings of COPD patients with and without airway eosinophilia and to investigate the changes after inhaled corticosteroids (ICS). METHODS: analyses of the DISARM randomised controlled trial investigated the expression of airway inflammation (type 1, 2 and 17), interleukin (IL)-13 and mast cell gene signatures at baseline and after 12-week ICS treatment. Gene signatures were generated from RNA sequencing of airway epithelial brushings. Airway eosinophilia was defined as eosinophils >1% of the total leukocyte count in bronchoalveolar lavage. Gene set enrichment analyses identified upregulated canonical pathways in airway eosinophilia. RESULTS: Among 58 COPD patients, 38% had airway eosinophilia at baseline. Patients with airway eosinophilia had more severe airflow obstruction and more radiographic emphysema than the non-eosinophilia group. Patients with airway eosinophilia showed a higher epithelial expression of type 2 airway inflammation and IL-13 and mast cell activation at baseline, but the expression of type 1 and type 17 airway inflammation was similar to patients without airway eosinophilia. The airway eosinophilia group showed an upregulation of canonical pathways related to type 2 immune response and asthma. Treatment with ICS for 12 weeks reduced the epithelial expression of type 2 inflammation and mast cell gene signatures in patients with airway eosinophilia, while this change was not significant in patients without airway eosinophilia. CONCLUSIONS: Airway eosinophilia marks a subset of COPD patients with increased airway epithelial expression of type 2 inflammation and a response to ICS treatment.
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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 | 0.000 | 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) | 0.001 | 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".