Telomere Length and All-Cause Mortality: A Meta-analysis
Bibliographic record
Abstract
Telomere attrition is associated with increased morbidity and mortality of various age-related diseases. Reports of association between telomere length (TL) and all-cause mortality remain inconsistent. In the present study, a meta-analysis was performed using published cohort studies and un-published data from the Swedish Twin Registry (STR). Twenty-five studies were included: four STR cohorts (12,083 individuals with 2517 deaths) and 21 published studies. In the STR studies, one standard deviation (SD) decrement of leukocyte TL corresponded to 13% increased all-cause mortality risk (95% confidence interval [CI]: 7%-19%); individuals in the shortest TL quarter had 44% higher hazard (95% CI: 27%-63%) than those in the longest quarter. Meta-analysis of all eligible studies (121,749 individuals with 21,763 deaths) revealed one SD TL decrement-associated hazard ratio of 1.09 (95% CI: 1.06-1.13); those in the shortest TL quarter had 26% higher hazard (95% CI: 15%-38%) compared to the longest quarter, although between-study heterogeneity was observed. Analyses stratified by age indicated that the hazard ratio was smaller in individuals over 80 years old. In summary, short telomeres are associated with increased all-cause mortality risk in the general population. However, TL measurement techniques and age at measurement contribute to the heterogeneity of effect estimation.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.037 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 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".