Vascular risk factors mediate the relationship between education and white matter hyperintensities
Bibliographic record
Abstract
Abstract INTRODUCTION Education can protect against cognitive decline and dementia through cognitive reserve and reduced vascular risk. This study examined whether vascular risk mediates the relationship between education and white matter hyperintensity (WMH) burden. METHODS Data from 1089 older adults from the National Alzheimer’s Coordinating Center were analyzed. A composite vascular score was created using diabetes, hypertension, hypercholesterolemia, smoking, alcohol abuse, body mass index, and blood pressure. Linear regressions and mediation analyses examined associations and indirect effects between education, vascular risk, and WMH, adjusting for age, sex, and diagnosis. RESULTS Higher education was associated with lower vascular risk ( p < .001) and WMH burden ( p = .01). Mediation analysis showed an indirect effect of education on WMH via vascular risk (a*b = −0.02, p = .004), accounting for 23% of the total effect. DISCUSSION Education influences cerebrovascular health via reducing vascular risk. Addressing vascular health may reduce WMH burden.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".