Is Chronic Asthma Associated with Shorter Leukocyte Telomere Length at Midlife?
Bibliographic record
Abstract
RATIONALE: Asthma is prospectively associated with age-related chronic diseases and mortality, suggesting the hypothesis that asthma may relate to a general, multisystem phenotype of accelerated aging. OBJECTIVES: To test whether chronic asthma is associated with a proposed biomarker of accelerated aging, leukocyte telomere length. METHODS: Asthma was ascertained prospectively in the Dunedin Multidisciplinary Health and Development Study cohort (n = 1,037) at nine in-person assessments spanning ages 9-38 years. Leukocyte telomere length was measured at ages 26 and 38 years. Asthma was classified as life-course-persistent, childhood-onset not meeting criteria for persistence, and adolescent/adult-onset. We tested associations between asthma and leukocyte telomere length using regression models. We tested for confounding of asthma-leukocyte telomere length associations using covariate adjustment. We tested serum C-reactive protein and white blood cell counts as potential mediators of asthma-leukocyte telomere length associations. MEASUREMENTS AND MAIN RESULTS: Study members with life-course-persistent asthma had shorter leukocyte telomere length as compared with sex- and age-matched peers with no reported asthma. In contrast, leukocyte telomere length in study members with childhood-onset and adolescent/adult-onset asthma was not different from leukocyte telomere length in peers with no reported asthma. Adjustment for life histories of obesity and smoking did not change results. Study members with life-course-persistent asthma had elevated blood eosinophil counts. Blood eosinophil count mediated 29% of the life-course-persistent asthma-leukocyte telomere length association. CONCLUSIONS: Life-course-persistent asthma is related to a proposed biomarker of accelerated aging, possibly via systemic eosinophilic inflammation. Life histories of asthma can inform studies of aging.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".