Intersecting vulnerabilities: Race, Depression, and White Matter Hyperintensity burden in Aging
Bibliographic record
Abstract
Abstract BACKGROUND White matter hyperintensities (WMHs) are markers of brain aging and are associated with cognitive decline and dementia. However, research regarding how race, ethnicity, and depression status influence WMHs remains mixed. This study examined the interactive effects of race/ethnicity and depression on WMHs and cognition in older adults. METHODS Data from the National Alzheimer’s Coordinating Center included 2,411 older adults (773 Whites with Depression, 1,360 Whites without Depression, 89 Blacks with depression, 189 Blacks without depression). Bootstrap sampling (1,000 iterations) was used to match the White and Black samples. Linear regressions were then used to i) assess WMH differences across race/ethnicity and depression groups, and ii) to examine whether the associations between WMH burden and cognition were different across these groups. RESULTS Black older adults with depression showed greater global as well as regional WMH burden than Black older adults without depression (median t = 0.68–1.67), and depression significantly influenced the relationship between WMH burden and cognitive impairment in this group (median t = 1.15–2.03). Similar results were observed for Hispanics with depression (median t = 1.47–2.87), while WMH burden did not differ in White older adults with and without depression. CONCLUSIONS These findings suggest that race and depression may jointly influence cerebrovascular disease burden as well as its associations with cognition in aging and 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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".