Bibliographic record
Abstract
PURPOSE OF REVIEW: The purpose of the study is to explore the evidence linking telomere length with atherosclerotic ischemic disease. RECENT FINDINGS: There has been a recent expansion in strategies for measuring telomere length, including analyzing genome sequence data and capitalizing on genomic loci that associate with telomere length. These, together with more established approaches, have been used to generate a more complete picture of telomere length relationships with ischemic disease. Whereas earlier meta-analyses suggested an association between short leukocyte telomeres and ischemic disease, several recent large population studies now provide particularly compelling data, including an association with cardiovascular mortality. In addition, whether short leukocyte telomeres might be causally related to ischemic disease has been interrogated using Mendelian randomization strategies, which point to shorter leukocyte telomeres as a determining risk factor. Importantly however, the wide, interindividual variability in telomere length still means that a single assessment of leukocyte telomere length in an individual does not reliably report on a biological aging process. In this regard, recent multi-tissue analyses of telomere length dynamics are providing both new mechanistic insights into how telomere length and shortening rates may participate in atherogenesis and risk prediction opportunities. The balance of evidence indicates that short leukocyte telomeres confer a risk for atherosclerotic cardiovascular disease. Moreover, an integrated analysis of telomere lengths in leukocytes and other tissues may provide a window into individualized telomere dynamics, raising new prospects for risk management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".