Is menopause still evolving? Evidence from a longitudinal study of multiethnic populations and its relevance to women’s health
Bibliographic record
Abstract
BACKGROUND: To reflect on the impact of changing patterns of delayed marriage and reproduction and to seek evidence as to whether menopause is still evolving, characteristics of the menopause transition were investigated within and between ethnic populations in this study. METHODS: A cross-sectional analysis was conducted using data on 747 middle-aged women obtained from the Study of Women's Health Across the Nation (SWAN) from 1996 to 2008. The ethnic groups included: Afro-American, Chinese, Japanese, Caucasian, and Hispanic. Perimenopause age and duration, menopause age, and hormonal indicators of menopause were examined across five ethnicities. RESULTS: We found a similar window of menopause age within populations, but no significant difference in perimenopause and menopause age between populations. The rate of increase of follicle-stimulating hormone and testosterone differed significantly in Hispanics and African-Americans during the menopause transition period. CONCLUSIONS: The broad window of variation in age at menopause within the population and the absence of significant differences between populations, in combination with population variation in menopause symptoms, suggest that menopause is a relatively recently evolved and still evolving trait. Under the mate choice theory of menopause, menopause is the result of the accumulation of infertility mutations in older women due to men's preference for younger mates. We propose a shifting mate choice-shifting menopause model which posits that, as the age of mate choice/marriage shifts to older ages, so will the age at menopause, and that menopause is a transient phase of female fertility; it can de-evolve, be delayed, if not disappear completely. Integrated longitudinal menopausal studies linked with genomics and hormonal studies on diverse ethnic populations can provide valuable information bearing on women's health and personalized medicine.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".