One size does not fit all: how type of menopause and hormone therapy matters for brain health
Bibliographic record
Abstract
BACKGROUND: Menopause is an inflection point in the ageing trajectory. Independent of chronological age, menopause is associated with the biological ageing of several body systems. In this review, we highlight the importance of considering the influence of menopause - its types, symptoms and interventions - on brain health. Supplementing the loss of ovarian hormones with menopausal hormone therapy (MHT) may be key for supporting the healthy brain ageing of females. MHT has been associated with reduced risk of several neurodegenerative diseases; however, its benefits are not always observed on brain health. AIMS: This narrative review highlights often overlooked MHT factors that influence its effects to produce positive or negative effects on brain health, cognition and neurodegenerative disease risk. These factors include the many varieties of MHT, including formulation, administration route and dosing schedule, as well as individual characteristics, particularly the presence of vasomotor symptoms and apolipoprotein ε4 (APOE4) genotype. METHOD: PubMed and Scopus were used to identify articles with relevant search terms. RESULTS: Menopause factors, including age, abruptness and symptoms, influence brain ageing. MHT influences brain health, with transdermal MHT showing more positive effects on brain ageing, but its effectiveness may depend on individual factors such as genotype, reproductive and lifestyle factors. CONCLUSIONS: To develop effective and individualised MHT treatments, further research is needed. Preclinical models must consider the type of human menopause and MHT. To achieve the greatest dementia prevention in females, more menopause education and care is needed that extends beyond 60 years of age, or 10 years postmenopause.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".