The Role of Estrogen Therapy as a Protective Factor for Alzheimer’s Disease and Dementia in Postmenopausal Women: A Comprehensive Review of the Literature
Bibliographic record
Abstract
The complete cessation of menstruation for 12 months with associated vasomotor symptoms is termed menopause. Apart from playing a role in reproduction, estrogen significantly affects the central nervous system (CNS). Population-based studies highlighted a substantial difference in the prevalence of dementia between men and women, with Alzheimer-associated dementia being more prevalent in women, indicating that estrogen deficiency might be a risk factor for neurodegenerative diseases. Patients with dementia experience a progressive decline in neurocognitive function, beginning with short-term memory loss that progresses to long-term memory loss and the inability to perform everyday activities, leading ultimately to death. There is currently no cure for dementia, so preventing or slowing the disease's progression is paramount. Accordingly, researchers have widely studied the role of estrogen as a neuroprotective agent. Estrogen prevents dementia by augmenting Hippocampal and prefrontal cortex function, reducing neuroinflammation, preventing degradation of estrogen receptors, decreasing oxidative damage to the brain, and increasing cholinergic and serotonergic function. According to the window phase hypothesis, estrogen's effect on preventing dementia is more pronounced if therapy is started early, during the first five years of menopause. Other studies like The Woman's Health Initiative Memory Study (WHIMS) showed unfavorable effects of estrogen on the brain. This review aims to establish an understanding of the currently available data on estrogen's effect on neurodegeneration, namely, dementia and Alzheimer's disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| 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".