Telomere Shortening, Regenerative Capacity, and Cardiovascular Outcomes
Bibliographic record
Abstract
Rationale: Leukocyte telomere length (LTL) is a biological marker of aging, and shorter LTL is associated with adverse cardiovascular outcomes. Reduced regenerative capacity has been proposed as a mechanism. Bone marrow–derived circulating progenitor cells are involved in tissue repair and regeneration. Objective: Main objective of this study was to examine the relationship between LTL and progenitor cells and their impact on adverse cardiovascular outcomes. Methods and Results: We measured LTL by quantitative polymerase chain reaction in 566 outpatients (age: 63±9 years; 76% men) with coronary artery disease. Circulating progenitor cells were enumerated by flow cytometry. After adjustment for age, sex, race, body mass index, smoking status, and previous myocardial infarction, a shorter LTL was associated with a lower CD34 + cell count: for each 10% shorter LTL, CD34 + levels were 5.2% lower ( P <0.001). After adjustment for the aforementioned factors, both short LTL ( Conclusions: Although shorter LTL is associated with decreased regenerative capacity, both LTL and circulating progenitor cell levels are independent and additive predictors of adverse cardiovascular outcomes in coronary artery disease patients. Our results suggest that both biological aging and reduced regenerative capacity contribute to cardiovascular events, independent of conventional risk factors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".