DNAmFitAge: Biological Age Indicator Incorporating Physical Fitness
Bibliographic record
Abstract
Physical fitness is a well-known correlate of health and the aging process. DNA methylation (DNAm) data lend themselves for estimating chronological and biological age through epigenetic clocks. However, current epigenetic clocks did not yet use measures of mobility, strength, lung, or endurance physical fitness parameters in their construction. Here, we develop blood DNAm biomarkers for fitness parameters gait speed (walking speed), hand grip strength, forced expiratory volume in one second (FEV1), and maximal oxygen uptake (VO2max). We then use these DNAm biomarkers to construct DNAmFitAge, a new biological age indicator that incorporates physical fitness with epigenetic mortality risk estimators. Adjusting DNAmFitAge for chronological age generates a novel measure of epigenetic age acceleration, FitAgeAcceleration, which is informative for physical activity level (p=1.2E-12), mortality risk (p=5.9E-13), coronary heart disease risk (p=0.0051), comorbidities (p=9.0E-9), and disease-free status (p=1.1E-6) across several large validation datasets. These newly constructed DNAm biomarkers and DNAmFitAge provide researchers and physicians a new method to incorporate physical fitness into epigenetic clocks and emphasizes the effect of lifestyle on 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".