Association Between Shortened Leukocyte Telomere Length and Cardiometabolic Outcomes
Bibliographic record
Abstract
BACKGROUND: Telomeres are repetitive, gene-poor regions that cap the ends of DNA and help maintain chromosomal integrity. Their shortening is caused by inflammation and oxidative stress within the cellular environment and ultimately leads to cellular senescence. Shortened leukocyte telomere length is hypothesized to be a novel biomarker for age and age-related diseases, yet reports on its association with cardiometabolic outcomes in the literature are conflicting. METHODS AND RESULTS: MEDLINE (1966 to present) and EMBASE (1980 to present) were last searched on September 9, 2013. Reference lists of retrieved citations were hand searched for relevant studies. No restrictions were placed on sample size, language, or publication type or date. Fifteen cohort and 12 case-control studies reporting the association between leukocyte telomere length and stroke, myocardial infarction, and type 2 diabetes mellitus were independently selected for inclusion by 2 reviewers. Data extraction and risk of bias assessment were completed independently by 2 reviewers using predefined criteria. Studies were pooled using the generic inverse variance method and both fixed and random effects models. A 1-SD decrease in leukocyte telomere length was significantly associated with stroke (odds ratio, 1.21; 95% confidence interval, 1.06-1.37; I(2)=61%), myocardial infarction (odds ratio, 1.24; 95% confidence interval, 1.04-1.47; I(2)=68%), and type 2 diabetes mellitus (odds ratio, 1.37; 95% confidence interval, 1.10-1.72; I(2)=91%). Stratification by measurement technique, study design, study size, and ethnicity explained heterogeneity in certain cardiometabolic outcomes. CONCLUSIONS: Shortened leukocyte telomere length demonstrates a significant association with stroke, myocardial infarction, and type 2 diabetes mellitus. Larger, well-designed studies are needed to confirm these findings and explore sources of heterogeneity.
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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 | 0.031 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".