Education is associated with vascular risk factors and cerebrovascular disease burden in racially diverse individuals on the Alzheimer's disease spectrum
Bibliographic record
Abstract
BACKGROUND: Education has been shown to mitigate the risk of Alzheimer's dementia (AD). While the link between education and cognitive reserve has been investigated, another potential pathway through which education can impact cognitive decline could be through its influence on vascular risk factors which in turn lead to cerebrovascular pathology such as white matter hyperintensities (WMHs) and infarcts. The prevalence of these factors varies across racial groups, highlighting the importance of understanding disparities in education and health outcomes in these populations. METHOD: This study analyzed data from the National Alzheimer's Coordinating Center (NACC), including 42,668 participants aged 55 and older from diverse racial backgrounds: White (n = 31,232), Black (n = 6,676), Asian (n = 3,573), and Hispanic (n = 1,187) (Table 1). Linear mixed-effects models were employed to investigate the relationship between education and vascular risk factors (i.e., diabetes, hypertension, hypercholesterolemia, systolic and diastolic blood pressure (BP), smoking, alcohol consumption, and body mass index; BMI), WMHs, and lacunar infarcts. Age, sex, and diagnostic status (cognitively normal, mild cognitive impairment, and AD) were added as covariates in the models. RESULT: Higher education was significantly associated with lower rates of diabetes, hypertension, hypercholesterolemia, and smoking in most racial groups (p < .0001). In White and Hispanic groups, strong negative associations were seen for all risk factors (p < .0001), except diastolic BP and alcohol consumption. Among Black individuals, significant relationships were observed for all risk factors (p < .0001), except for systolic and diastolic BP. In Asian individuals, education was associated with all vascular risk factors (p < .05) except BMI and diabetes (Table 2). Furthermore, higher education was significantly associated with a lower WMH burden in White (p = 0.03) and Black (p = 0.02) individuals (Table 3). No significant relationship was observed between education and lacunar infarcts in any race. CONCLUSION: While higher education was overall negatively associated with vascular risk factors in all racial groups, certain risk factors showed different associations with education across different groups. Racial disparities were also found in the relationship between education and cerebrovascular disease markers, suggesting that while education is generally associated with better health outcomes, its impact varies across racial groups.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".