The prospective association of cellular markers of biological aging with menopause in the Coronary Artery Risk Development in Young Adults Study
Bibliographic record
Abstract
OBJECTIVE: Evidence from cross-sectional studies mainly among postmenopausal women suggests that biological aging is associated with reproductive senescence. We evaluated the prospective association of cellular markers of biological aging measured during the premenopausal period, and changes in these markers, with age at menopause. METHODS: We studied 583 premenopausal women (39% Black) from the Coronary Artery Risk Development in Young Adults Study who had data on biological aging markers in 2000-2001 and reached menopause by 2020-2021. Linear regression models were used to evaluate the association of telomere length, mitochondrial DNA copy number, intrinsic or extrinsic epigenetic age acceleration, and PhenoAge or GrimAge acceleration with age at menopause. RESULTS: The mean age at baseline was 41.2 ± 3.3 years, with the mean age at menopause being 49.1 (median, 50) years. About one in five women had surgical menopause. In chronological age-adjusted models, only baseline GrimAge acceleration was associated with age at menopause; women whose epigenetic age was older than their chronological age reached menopause at 0.12 years (~6 weeks) earlier compared with women with equal epigenetic and chronological age ( β = -0.123; 95% CI, -0.224 to -0.022; P = 0.018). However, this association was not statistically significant after adjustment for sociodemographic, behavior/lifestyle, and metabolic factors. Similar results were observed when changes in these biological aging markers were evaluated. The same associations were observed in analyses limited to women who reached natural menopause. CONCLUSIONS: Sociodemographic, behavior/lifestyle, and metabolic factors remain comparable, if not more robust predictors of the age at menopause compared with cellular measures of biological 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".