Small airway brush gene expression predicts chronic lung allograft dysfunction and mortality
Bibliographic record
Abstract
BACKGROUND: Chronic lung allograft dysfunction (CLAD) limits survival following lung transplant, but substantial lung damage occurs before diagnosis by traditional methods. We hypothesized that small airway gene expression patterns could identify CLAD risk before spirometric diagnosis and predict subsequent graft failure. METHODS: Candidate genes from 4 rejection-associated transcript sets were assessed for associations with CLAD or graft failure in a derivation cohort of 156 small airway brushes from 45 CLAD cases and 37 time-matched controls with >1-year stable lung function. Candidate genes not associated with CLAD and time to graft failure were excluded, yielding the Airway Inflammation 2 (AI2) gene set. Area under the receiver operating curve (AUC) for CLAD and competing risks of death or graft failure were assessed in an independent validation cohort of 37 CLAD cases and 37 controls. RESULTS: Thirty-two candidate genes were associated with CLAD and graft failure, comprising the AI2 score, which clustered into 3 subcomponents. The AI2 score identified CLAD before its onset, in early and late post-CLAD brushes, as well as in the validation cohort (AUC 0.69-0.88). The AI2 score association with CLAD was independent of positive microbiology, CLAD stage, or CLAD subtype. However, transcripts most associated with CLAD evolved over time from CLAD onset. The AI2 score predicted time to graft failure and retransplant-free survival in both cohorts (p ≤ 0.03). CONCLUSIONS: This airway inflammation gene score is associated with CLAD development, graft failure, and death. Future studies defining the molecular heterogeneity of airway inflammation could lead to endotype-targeted therapies.
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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 | 0.001 | 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".