An Investigation on Modifiable and Nonmodifiable Estrogen Exposure and Gray Matter Volume in Healthy Older Women
Bibliographic record
Abstract
Introduction: It is projected that the global population of adults above age 60 years will surpass 2 billion by 2050. Age-related cognitive decline represents a prevalent issue and research has demonstrated that women are at greater risk than men. Changes in cognitive function with age are influenced by many factors and may include lifetime exposure to estrogen and the transition to menopause. While the exact relationship between estrogen and the aging brain is unclear, the hormonal changes in menopause have been associated with a decline in gray matter volume. However, some studies have demonstrated that the use of hormone therapy may mitigate some of the effects of cognitive decline. Methods: The current study used magnetic resonance imaging and voxel-based morphometry to examine the relationship between gray matter volume and endogenous lifetime estrogen exposure ( e.g., reproductive period length or age of menopause − age of menarche in years) and differences in gray matter volume between women who used hormone therapy ( N = 62, M age = 70.97 [2.97], M edu = 12.43 [3.22]) and those who did not ( N = 62, M age = 70.14 [2.62], M edu = 12.81 [3.75]). It was hypothesized that higher lifetime estrogen exposure and use of hormone therapy would be correlated to greater gray matter volume. The data were retrieved from the Women’s Healthy Ageing Project. Results: Results demonstrated no significant correlations between whole brain gray matter volume and lifetime estrogen exposure. There were no significant differences between groups based on hormone therapy use. However, there was a nonsignificant relationship that suggested that women who did not use hormone therapy had greater gray matter volume than those who did use it. Discussion: As the aging population continues to grow globally, it is essential to better understand the variables that influence trajectories of aging; especially for women, who are particularly at risk for age-related cognitive decline.
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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 | 0.002 |
| 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".