Racial and Ethnic Differences in White Matter Hyperintensity Burden: The Role of Vascular Risk Factors
Bibliographic record
Abstract
Abstract Background A critical pathological marker observed in the aging brain is white matter hyperintensities (WMHs). WMHs are associated with an increased risk of cognitive decline, progression to mild cognitive impairment, and development of dementia. Vascular risk factors such as hypertension, diabetes, and elevated body mass index are well known contributors to WMH burden and independently increase the risk of dementia. These risk factors are disproportionately prevalent in racially diverse populations. However, the role of vascular risk factors in explaining WMH differences is not well understood in racially and ethnically diverse populations. This study examined whether race and ethnicity influence WMH burden and whether vascular risk factors explain these differences. Method Clinical and MRI data from the National Alzheimer's Coordinating Center (NACC) included 7,132 Whites, 892 Blacks, 283 Asians, 8307 non‐Hispanics, and 661 Hispanics. Baseline and longitudinal WMHs (Figure 1) were examined using linear regression and mixed‐effects models across racial and ethnic groups, controlling for demographics and vascular risk factors. Result At baseline, significant WMH burden differences were found between Black vs White older adults in total, frontal, temporal, parietal and occipital regions ( t ranging between 2.07–5.29, p <0.001). After adjusting for vascular risk factors, WMH burden differences were reduced in total, frontal, parietal, and occipital regions ( t ranging between 3.58‐4.37, p <0.001) and eliminated in temporal regions ( t = 1.76, p = 0.08). In the longitudinal dataset, when comparing Hispanics to non‐Hispanics, significant differences in total WMH burden were found only after adjusting for vascular risk factors ( t = 2.00, p = 0.04). No significant WMH burden differences were observed between Asian and White participants at baseline or longitudinally ( p >0.05). See Figure 2 (total WMH volume) and 3 (total and regional WMH volume) for median t‐statistics from the WMH analyses. Conclusion Vascular risk factors contribute to some of the racial WMH burden differences between Black and White older adults. The reverse effect was observed in Hispanic vs non‐Hispanic populations, with addition of vascular risk factors revealing ethnic group differences. These findings highlight the importance of exploring how the interaction between risk factors and race/ethnicity contributes to brain changes in the aging population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.002 | 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".