Racial and Ethnic Differences in White Matter Hyperintensity Burden: The Role of Vascular Risk Factors
Bibliographic record
Abstract
Abstract INTRODUCTION White matter hyperintensities (WMHs) are markers of cerebrovascular pathology associated with cognitive decline. Reports of racial and ethnic differences in WMH have been inconsistent across studies. This study examined whether race and ethnicity influence WMH burden and whether vascular risk factors explain these differences. METHODS Data from the National Alzheimer’s Coordinating Center included 7,132 Whites, 892 Blacks, 283 Asians, and 661 Hispanics. Baseline and longitudinal WMHs were examined using linear regression and mixed-effects models across racial and ethnic groups, controlling for demographics and vascular risk factors. RESULTS Adjusting for vascular risk factors reduced WMH burden differences and eliminated differences in temporal regions in Black vs White older adults. For Hispanics, differences became significant after adjusting for vascular risk factors. DISCUSSION While some racial and ethnic WMH disparities are influenced by vascular risk factors, others persist, highlighting the need for multidimensional approaches when targeting WMHs in diverse populations. Highlights Current research is inconsistent as to whether there are racial differences in WMHs Blacks exhibit higher WMH burden than Whites, mediated by vascular factors In Hispanics, WMH differences emerged only after adjusting for vascular risk factors
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".