Shortened Telomere Length as a Risk Factor for Idiopathic Pulmonary Fibrosis: A Meta-Analysis
Bibliographic record
Abstract
Background: Idiopathic Pulmonary Fibrosis (IPF) is a progressive lung disease with limited life expectancy after diagnosis. The median survival time ranges from 2 to 4 years, indicating a poor prognosis. Multiple telomere-related genes that cause telomere shortening have been associated with a significant percentage of IPF cases. This review aims to analyze the association of short telomere length with IPF incidence. Methods: A systematic online search was conducted on PubMed, Scopus, and Cochrane. Articles that met the criteria were included. Quality of included literature was assessed using the Newcastle-Ottawa Scale (NOS). The pooled standard mean difference (SMD) with 95% confidence interval (CI) of telomere length was calculated using a random-effect model. Results: < 0.00001). Subgroup analysis showed that steeper telomere shortening was found in lung tissue compared to peripheral blood sample. The findings suggested that telomere length may be closely associated with the pathogenesis of pulmonary fibrosis. Discussion: Repeated cell divisions gradually shorten telomeres that lead to senescence and apoptosis. Premature senescence disrupts the balance of lung epithelial cells, potentially activating lung remodeling processes that result in fibrotic damage through senescence-associated secretory phenotype (SASP). Conclusion: This study shows significant shorter telomere lengths in IPF patients compared to healthy controls that suggest telomere as a risk factor for IPF occurrence. These findings highlight the value of telomere assessment not only for early detection but also as a potential predictive biomarker for clinical outcomes.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.036 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".