Cardiovascular contributions to dementia: Examining sex differences and female‐specific factors
Bibliographic record
Abstract
Growing evidence underscores the importance of cardiovascular contributions to Alzheimer's disease and related dementias (AD/ADRD). While sex differences in cardiovascular disease (CVD) risk factors and outcomes are well established, the question of whether vascular contributions to AD/ADRD vary by sex has only recently garnered attention. In this narrative review, we discuss sex differences in conventional CVD risk factors (e.g., hypertension, dyslipidemia, diabetes), as well as underrecognized female-specific (e.g., menopause history, polycystic ovary syndrome, adverse pregnancy outcomes) and female-predominant (e.g., autoimmune conditions, breast cancer) CVD risk factors. Despite their relevance, these sex-specific considerations are rarely incorporated into current approaches to quantify CVD risk in AD/ADRD research. We offer recommendations to address these gaps and promote the use of sex-informed methods for studying cardiovascular contributions to AD/ADRD in women, which is essential for developing precision strategies to improve outcomes for all individuals at risk of dementia. HIGHLIGHTS: There are extensive sex differences in cardiovascular risk, dementia risk, and their interrelationships. Many cardiovascular risk factors confer greater risk for dementia in women than men. Existing approaches to quantifying cardiovascular risk often overlook sex differences and female-specific factors. Sex-informed approaches are essential for an accurate understanding of cardiovascular contributions to dementia.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.004 | 0.001 |
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".