Stressful life events, social support, and epigenetic aging in the Women's Health Initiative
Bibliographic record
Abstract
BACKGROUND: Elevated psychosocial stress has been linked with accelerated biological aging, including composite DNA methylation (DNAm) markers that predict aging-related outcomes ("epigenetic age"). However, no study has examined whether stressful life events (SLEs) are associated with epigenetic age acceleration in postmenopausal women, an aging population characterized by increased stress burden and disease risk. METHODS: We leveraged the Women's Health Initiative, a large muti-ancestry cohort of postmenopausal women with available psychosocial stress measures over the past year and epigenomic data. SLEs and social support were ascertained via self-report questionnaires. Whole blood DNAm array (450 K) data were used to calculate five DNAm-based predictors of chronological age, health span and life span, and telomere length (HorvathAge, HannumAge, PhenoAge, GrimAge, DNAmTL). RESULTS: After controlling for potential confounders, higher SLE burden was significantly associated with accelerated epigenetic aging, as measured by GrimAge (β: 0.34, 95% CI: 0.08, 0.59) and DNAmTL (β: -0.016, 95% CI: -0.028, -0.004). Exploratory analyses showed that SLEs-GrimAge associations were stronger in Black women as compared to other races/ethnicities and in those with lower social support levels. In women with lower social support, SLEs-DNAmTL associations showed opposite association in Hispanic women as compared to other race/ethnicity groups. CONCLUSIONS: Our findings suggest that elevated stress burden is associated with accelerated epigenetic aging in postmenopausal women. Lower social support and/or self-reported race/ethnicity may modify the association of stress with epigenetic age acceleration. These findings advance understanding of how stress may contribute to aging-related outcomes and have important implications for disease prevention and treatment in aging women.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".