Untangling age and menopausal status reveals no effect of menopause on white matter hyperintensity volume
Bibliographic record
Abstract
Abstract Background and objectives White matter hyperintensities (WMHs) are radiological abnormalities indicative of cerebrovascular dysfunction associated with increased risk for cognitive decline and increase in prevalence in older age. However, there are known sex-differences as older females harbour higher WMH burden than males. Some have hypothesized that the increase in this dementia-related risk factor is related to the menopausal transition. Methods To untangle the effects of age and menopause, we leveraged a large sample from the UK Biobank (n = 10,519) to investigate differences in WMH volumes across the menopausal transition using a strict age-matching procedure. Results Surprisingly, we find increased WMH volumes in premenopausal women compared to postmenopausal women when simply correcting for age with linear models, but we find no effect in the age-matched sample. Menopause-related characteristics, such as age at menopause or hormone replacement therapy, did not replicate previous literature reporting an association with WMH volumes. Cardiovascular lifestyle variables, such as smoking and blood pressure, were significant predictors of WMH volume in the full sample without age-matching. These effects varied by menopausal status only for days of moderate activity. Discussion In sum, our findings in a well-powered study suggest that previous reports of menopause-related differences in WMH burden are potentially confounded by age. We further show that the effect of positive lifestyle factors on brain health, as indexed with WMH burden, generally does not change after menopause. Factors other than the menopausal transition are likely at play in explaining the difference in WMH burden between males and females in later life.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".