Testing Black-White disparities in biological aging in older adults in the United States: analysis of DNA-methylation and blood-chemistry methods
Bibliographic record
Abstract
ABSTRACT Biological aging is a proposed mechanism through which social determinants drive health disparities. We conducted proof-of-concept testing of eight DNA-methylation and blood-chemistry quantifications of biological aging as mediators of disparities in healthspan between Black and White participants in the United States Health and Retirement Study (HRS; n=9005). We quantified biological aging from four DNA-methylation “clocks” (Horvath, Hannum, PhenoAge, and GrimAge), a DNA-methylation Pace of Aging (DunedinPoAm), and three blood-chemistry measures (PhenoAge, Klemera-Doubal method Biological Age, and homeostatic dysregulation). We quantified Black-White disparities in healthspan from cross-sectional and longitudinal data on physical-performance tests, self-reported activities of daily living (ADL) limitations and physician-diagnosed chronic diseases, self-rated health, and survival. DNA-methylation and blood-chemistry quantifications of biological aging were moderately correlated (Pearson-r range 0.1-0.4). GrimAge, DunedinPoAm and all three blood-chemistry measures were associated with healthspan characteristics (e.g. mortality effect-size range 1.71-2.32) and showed evidence of more advanced/faster biological aging in Black compared with White participants (Cohen’s d=.4-.5). These measures accounted for 13-95% of Black-White differences in healthspan-related characteristics. Findings that Black Americans are biologically older and aging more rapidly than White Americans of the same chronological age suggest that eliminating disparities in the pace of aging can contribute building to aging health equity.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".