Sex Differences in the Association Between Vascular Risk Factors and Cognitive Decline
Bibliographic record
Abstract
Age-related cognitive decline is accelerated by vascular risk factors for cerebral small vessel disease. However, the association of vascular risk factors with cerebral small vessel disease contributing to the sex differences in cognitive decline remains unclear. The purpose of this study was to evaluate sex differences in cognitive decline and the association between vascular risk factors and cognitive decline by sex. We used data from the UK Biobank (>55 years of age; n = 19,067) to assess cognitive tests (executive function, processing speed, and memory) while adjusting for baseline measurements to examine how vascular risk factors affect cognition. A univariate regression analysis was used to assess sex differences at the first time point (2014). A repeated measure analysis with a mixed effect model was used to determine cognitive decline (between 2014 and 2019). Any significant interaction between vascular risk factors and sex was investigated. Females had lower scores in all three domains at the first cognitive tests (2014). We found a significant sex-by-time interaction over a 5-year period in matrix pattern completion (P = 0.03). After adjusting for vascular risk factors, this interaction was reduced (P = 0.08). High low-density lipoprotein, low education, and high blood pressure had a greater effect on the rate of cognitive decline in the executive function for females compared to males for the sex∗vascular risk factor interaction (P < 0.05). The rate of cognitive decline did not differ significantly between males and females. However, the impact of several vascular risk factors was greater in females than in males, possibly explaining observed sex differences in rates of 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".