Can a Combined Epigenetic-Biomarker Score Supplant Chronologic Age as an Independent Determinant of Incident Death in Adults With Dysglycemia
Bibliographic record
Abstract
OBJECTIVES: Diabetes and prediabetes are associated with premature death and are recognized as conditions of accelerated biologic aging. To date, the best measurement of biologic age is chronologic age. Measures of biologic age that can replace chronologic age as a predictor of death can better approximate risk in affected individuals. METHODS: The relationship between 238 biomarkers, epigenetic age scores, and incident death was analyzed in 2,755 participants (mean age 63.7±8 years) in the Outcomes Reduction with an Initial Glargine Intervention (ORIGIN) trial. Independent biomarkers for death identified using Cox models with forward selection were used to derive a biomarker risk score, which, after validation, was combined with epigenetic scores. Hazards for death per standard deviation higher epigenetic-biomarker score, chronologic age, or the age after adjustment for the score were estimated, and the respective β coefficients were compared. RESULTS: Four hundred eighty-one participants died during a median follow-up of 6.2 years. Each standard deviation higher age increased the hazard of death 1.69-fold (95% confidence interval [CI] 1.58 to 1.81, β=0.53). When 11 independent death biomarkers were combined with 3 epigenetic risk scores to yield an epigenetic-biomarker score, each standard deviation higher score increased the hazard of death 3.27-fold (95% CI 2.90 to 3.68). Adding standardized age to this model yielded a β coefficient for age of 0.00 (p=0.93). C statistics for the epigenetic-biomarker score alone and age alone were 0.77 (95% CI 0.74 to 0.78) and 0.66 (95% CI 0.63 to 0.68), respectively (p<0.001 for the difference). CONCLUSION: An epigenetic-biomarker risk score is a better predictor of death than chronologic age.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".