The Heterogeneity of Post-Menopausal Disease Risk: Could the Basis for Why Only Subsets of Females Are Affected Be Due to a Reversible Epigenetic Modification System Associated with Puberty, Menstrual Cycles, Pregnancy and Lactation, and, Ultimately, Menopause?
Bibliographic record
Abstract
For much of human evolution, the average lifespan was <40 years, due in part to disease, infant mortality, predators, food insecurity, and, for females, complications of childbirth. Thus, for much of evolution, many females did not reach the age of menopause (45–50 years of age) and it is mainly in the past several hundred years that the lifespan has been extended to >75 years, primarily due to public health advances, medical interventions, antibiotics, and nutrition. Therefore, the underlying biological mechanisms responsible for disease risk following menopause must have evolved during the complex processes leading to Homo sapiens to serve functions in the pre-menopausal state. Furthermore, as a primary function for the survival of the species is effective reproduction, it is likely that most of the advantages of having such post-menopausal risks relate to reproduction and the ability to address environmental stresses. This opinion/perspective will be discussed in the context of how such post-menopausal risks could enhance reproduction, with improved survival of offspring, and perhaps why such risks are preserved. Not all post-menopausal females exhibit risk for this set of diseases, and those who do develop such diseases do not have all of the conditions. The diseases of the post-menopausal state do not operate as a unified complex, but as independent variables, with the potential for some overlap. The how and why there would be such heterogeneity if the risk factors serve essential functions during the reproductive years is also discussed and the concept of sets of reversible epigenetic changes associated with puberty, pregnancy, and lactation is offered to explain the observations regarding the distribution of post-menopausal conditions and their potential roles in reproduction. While the involvement of an epigenetic system with a dynamic “modification-demodification-remodification” paradigm contributing to disease risk is a hypothesis at this point, validation of it could lead to a better understanding of post-menopausal disease risk in the context of reproduction with commonalities may also lead to future improved interventions to control such risk after menopause.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 0.001 |
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".