Telomere length dynamics as a biomarker of biological aging and longevity in thoroughbred horses
Bibliographic record
Abstract
The Thoroughbred racing industry invests heavily in performance optimisation and career longevity, yet no reliable molecular biomarker currently predicts how rapidly an individual horse will age biologically or how long it will remain athletically competitive. Telomeres-repetitive nucleoprotein caps that protect chromosome ends from degradation-shorten with each cell division and are increasingly studied as integrative biomarkers of cumulative physiological stress and biological aging in humans and companion animals. This research investigated the relationship between peripheral blood leukocyte telomere length (LTL) and chronological age, racing career metrics, and longevity indicators in 120 Thoroughbred horses aged 2 to 24 years maintained at or retired from racing facilities in Ontario, Canada. Blood samples were collected between March and November 2024, and relative LTL was measured by monochrome multiplex quantitative PCR expressed as the telomere-to-single-copy gene ratio (T/S ratio). Horses were stratified into four age groups: 2-5 years (n = 32), 6-10 years (n = 38), 11-15 years (n = 30), and 16 years and older (n = 20). Mean T/S ratio declined significantly across age groups (1.42 ± 0.18, 1.18 ± 0.14, 0.89 ± 0.12, and 0.67 ± 0.11, respectively; p<0.001). After controlling for sex, body condition score, and training status, age remained the strongest predictor of LTL (? = ?0.041 per year; p<0.001). Number of career races was independently and negatively associated with LTL (? = ?0.0028 per race; p=0.009), whereas lifetime earnings and career wins showed weaker, non-significant associations once age was accounted for. Horses with above-median LTL for their age group were 2.4 times more likely to still be racing or in active training than those with below-median LTL (p=0.017). Resting heart rate and a plasma oxidative stress marker (8-hydroxy-2?-deoxyguanosine) both correlated positively with shorter telomeres (r = 0.53 and r = 0.61, respectively; p<0.01). These findings identify LTL as a promising biomarker of biological aging in Thoroughbreds that captures variation beyond chronological age alone, and suggest that cumulative race exposure may accelerate telomere attrition independently of the aging process.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".