Inflammation and Not Cardiovascular Risk Factors Is Associated With Short Leukocyte Telomere Length in 13- to 16-Year-Old Adolescents
Bibliographic record
Abstract
OBJECTIVE: Short leukocyte telomere length (LTL) is associated with cardiovascular (CV) disease in adulthood. However, the biological basis of this association remains unclear. We sought to define early determinants of the association between CV disease and LTL in an adolescent population. METHODS AND RESULTS: One thousand eighty adolescents, aged 13 to 16 years and participating in the Ten Towns Heart Health Study, provided blood samples for DNA extraction and measurement of a range of CV risk factors. LTL was measured by real-time polymerase chain reaction. LTL was inversely associated with age (P=0.04), longer in females than in males (P=0.03), and longer in South Asians than in white Europeans (P=0.01). No associations were found between LTL and traditional CV risk factors. There was a significant and inverse association between LTL and inflammatory markers, including C-reactive protein (P<0.001) and fibrinogen (P=0.001). The associations between LTL and inflammatory markers were not affected by multiple adjustments for behavioral and metabolic factors. CONCLUSIONS: High levels of inflammation are associated with shorter LTL from early adolescence; traditional CV risk factors have little association with LTL in adolescence. Inflammation in early life may play a causal role in the adult association between short LTL and CV disease.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".