Accelerated epigenetic ageing worsens survival and mediates environmental stressors in fibrotic interstitial lung disease
Bibliographic record
Abstract
Background The role of epigenetic ageing in the environmental pathogenesis and prognosis of fibrotic interstitial lung disease (fILD) is unclear. We evaluated whether ambient particulate matter with diameter ≤2.5 μm (PM2.5) and neighbourhood disadvantage exposures are associated with accelerated epigenetic ageing, and whether epigenetic age is associated with adverse clinical outcomes in patients with fILD. Methods This multicentre, international, cohort study included patients with fILD from the University of Pittsburgh (UPitt, n=306) and University of British Columbia (UBC, n=170). 5-year PM2.5exposures were estimated using satellite-derived hybrid models. Neighbourhood disadvantage was calculated using US and Canadian census-based metrics. Epigenetic age difference (EAD=epigenetic age−chronological age) was calculated using GrimAge analysis of blood DNA methylation data. Linear models assessed associations of exposures with EAD. Cox models assessed associations of EAD with transplant-free survival. Causal mediation analysis evaluated EAD mediation of exposure–survival relationships. Results Median epigenetic age was 11.7 years older than chronological age in patients with fILD. In combined cohort analysis, each interquartile range (IQR) increase in PM2.5was associated with 2.88 years (95% CI 1.39–4.38; p<0.001) increased EAD. In UPitt, each IQR neighbourhood disadvantage increase was associated with 1.16 years (95% CI 0.22–2.09; p=0.02) increased EAD. Increased EAD was associated with worse transplant-free survival (hazard ratio 1.17 per 1-year increase in EAD, 95% CI 1.10–1.24; p<0.001), with EAD mediating 40% of the PM2.5–survival relationship and 59% of the neighbourhood disadvantage–survival relationship. Epigenetic age was also more strongly associated with transplant-free survival than chronological age. Conclusions Epigenetic age acceleration is associated with worse survival and mediates adverse exposure impacts in fILD.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".