Mild catalytic defects of tert rs61748181 polymorphism affect the clinical presentation of chronic obstructive pulmonary disease
Bibliographic record
Abstract
Chronic obstructive pulmonary disease (COPD) is a disorder of accelerated lung aging. Multiple pieces of evidence support that the aging biomarker short telomeres, which can be caused by mutations in telomerase reverse transcriptase (TERT), contribute to COPD pathogenesis. We hypothesized that short telomere risk-associated single nucleotide polymorphisms (SNPs) in TERT, while not able to drive COPD development, nonetheless modify the disease presentation. We set out to test the SNP carrying status in a longitudinal study of smokers with COPD and found that rapid decline of FEV1 in lung function was associated with the minor allele of rs61748181 (adjusted odds ratio 2.49, p = 0.038). Biochemical evaluation of ex vivo engineered human cell models revealed that primary cells expressing the minor allele of rs61748181 had suboptimal telomere length maintenance due to reduced telomerase catalytic activity, despite having comparable cell growth kinetics as WT-TERT expressing cells. This ex vivo observation translated clinically in that shorter telomeres were found in minor allele carriers in a sub-population of COPD patients with non-declining lung function, over the 5-year period of the longitudinal study. Collectively, our data suggest that functional TERT SNPs with mild catalytic defects are nonetheless implicated in the clinical presentation of 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 | 0.000 |
| 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) | 0.002 | 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".