Effect of sex and estrogen therapy on the aging brain
Bibliographic record
Abstract
OBJECTIVE: It is still a matter of debate whether estrogen can have a protective effect on brain integrity and against age- and Alzheimer-related assaults. Evidence points toward selective sparing of gray matter (GM) in postmenopausal women using hormone therapy. In the current study, the effect of sex and estrogen therapy (ET) exposure on GM density using voxel-based morphometry was assessed. METHODS: High-resolution structural magnetic resonance imaging scans of 46 healthy participants were analyzed using voxel-based morphometry. A total of 15 men and 31 healthy postmenopausal women were included: 15 ET-naive women (never users) and 16 current ET users with an average duration of use of 11 years. RESULTS: Sex differences were found in fronto-temporo-parietal areas, with postmenopausal women having greater GM concentration in the medial prefrontal cortex, temporal cortices, angular gyrus, and precuneus, whereas the men had greater GM density in the superior frontal, inferior temporal gyri, and inferior parietal lobules. ET users compared with never users had greater GM density in the superior frontal gyrus and less GM density in the posterior part of the hippocampus and parahippocampal gyrus, posterior cingulate, and angular gyri. In the group of ET users, a negative association was found between duration of ET use and posterior hippocampus and parahippocampal GM density, whereas a positive association was found in the hypothalamus, striatum, precunei, and inferior parietal lobules. CONCLUSIONS: These results point toward a potential regional- and duration-dependent estrogen exposure effect on cerebral areas known to be involved in age-related cognitive functions and Alzheimer and Parkinson diseases.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".