Short telomeres are a risk factor for idiopathic pulmonary fibrosis
Bibliographic record
Abstract
Idiopathic interstitial pneumonias (IIPs) have a progressive and often fatal course, and their enigmatic etiology has complicated approaches to effective therapies. Idiopathic pulmonary fibrosis (IPF) is the most common of IIPs and shares with IIPs an increased incidence with age and unexplained scarring in the lung. Short telomeres limit tissue renewal capacity in the lung and germ-line mutations in telomerase components, hTERT and hTR, underlie inheritance in a subset of families with IPF. To examine the hypothesis that short telomeres contribute to disease risk in sporadic IIPs, we recruited patients who have no family history and examined telomere length in leukocytes and in alveolar cells. To screen for mutations, we sequenced hTERT and hTR. We also reviewed the cases for features of a telomere syndrome. IIP patients had shorter leukocyte telomeres than age-matched controls (P < 0.0001). In a subset (10%), IIP patients had telomere lengths below the first percentile for their age. Similar to familial cases with mutations, IPF patients had short telomeres in alveolar epithelial cells (P < 0.0001). Although telomerase mutations were rare, detected in 1 of 100 patients, we identified a cluster of individuals (3%) with IPF and cryptogenic liver cirrhosis, another feature of a telomere syndrome. Short telomeres are thus a signature in IIPs and likely play a role in their age-related onset. The clustering of cryptogenic liver cirrhosis with IPF suggests that the telomere shortening we identify has consequences and can contribute to what appears clinically as idiopathic progressive organ failure in the lung and the liver.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".