Systemic and Airway Epigenetic Disruptions Are Associated with Health Status in COPD
Bibliographic record
Abstract
Epigenetic modifications are common in chronic obstructive pulmonary disease (COPD); however, their clinical relevance is largely unknown. We hypothesized that epigenetic disruptions are associated with symptoms and health status in COPD. We profiled the blood (n = 57) and airways (n = 62) of COPD patients for DNA methylation (n = 55 paired). The patients’ health status was assessed using the St. George’s Respiratory Questionnaire (SGRQ). We conducted differential methylation analyses and identified pathways characterized by epigenetic disruptions associated with SGRQ scores and its individual domains. 29,211 and 5044 differentially methylated positions (DMPs) were associated with total SGRQ scores in blood and airway samples, respectively. The activity, impact, and symptom domains were associated with 9161, 25,689 and 17,293 DMPs in blood, respectively; and 4674, 3730 and 5063 DMPs in airways, respectively. There was a substantial overlap of DMPs between airway and blood. DMPs were enriched for pathways related to common co-morbidities of COPD (e.g., ageing, cancer and neurological) in both tissues. Health status in COPD is associated with airway and systemic epigenetic changes especially in pathways related to co-morbidities of COPD. There are more blood DMPs than in the airways suggesting that blood epigenome is a promising source to discover biomarkers for clinical outcomes in COPD.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".