Reduced Epigenetic Age in Older Adults With High Sense of Purpose in Life
Bibliographic record
Abstract
Psychosocial risk factors have been linked with accelerated epigenetic aging, but little is known about whether psychosocial resilience factors (eg, Sense of Purpose in Life) might reduce epigenetic age acceleration. In this study, we tested if older adults who experience high levels of Purpose might show reduced epigenetic age acceleration. We evaluated the relationship between Purpose and epigenetic age acceleration as measured by 13 DNA methylation (DNAm) "epigenetic clocks" assessed in 1 572 older adults from the Health and Retirement Study (mean age 70 years). We quantified the total association between Purpose and DNAm age acceleration as well as the extent to which that total association might be attributable to demographic factors, chronic disease, other psychosocial variables (eg, positive affect), and health-related behaviors (heavy drinking, smoking, physical activity, and body mass index [BMI]). Purpose in Life was associated with reduced epigenetic age acceleration across 4 "second-generation" DNAm clocks optimized for predicting health and longevity (false discovery rate [FDR] q < 0.0001: PhenoAge, GrimAge, Zhang epigenetic mortality index; FDR q < 0.05: DunedinPoAm). These associations were independent of demographic and psychosocial factors, but substantially attenuated after adjusting for health-related behaviors (drinking, smoking, physical activity, and BMI). Purpose showed no significant association with 9 "first-generation" DNAm epigenetic clocks trained on chronological age. Older adults with greater Purpose in Life show "younger" DNAm epigenetic age acceleration. These results may be due in part to associated differences in health-related behaviors. Results suggest new opportunities to reduce biological age acceleration by enhancing Purpose and its behavioral sequelae in late adulthood.
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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 | 0.000 | 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) | 0.001 | 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".