Ethnic Differences in the Association between Vascular Risk and Brain MRI Markers of Dementia: Findings from the CAMERA Study
Bibliographic record
Abstract
BACKGROUND: Vascular contributions to Alzheimer's disease and related dementias (ADRD) are well established. Interestingly, South Asian individuals tend to have a higher vascular burden compared to White individuals, while Chinese individuals generally exhibit a lower vascular burden. However, epidemiological data on dementia incidence among Asian populations are limited and often contradictory. Some studies report a higher incidence of dementia in South Asians compared to Chinese and White individuals, whereas others suggest a lower risk in both South Asians and Chinese populations relative to White individuals. This study investigated whether ethnicity moderates the association between vascular risk factors and brain magnetic resonance imaging (MRI) markers of ADRD among South Asian, Chinese, and White adults. METHOD: Participants included 113 cognitively unimpaired adults (mean age = 67.1 ± 6.6 years, 70.8% female) of South Asian (n=36), Chinese (n=39), and White (n=38) descent, aged 55-85, from the Canadian Multi-Ethnic Research on Aging (CAMERA) study. Vascular risk burden was assessed using the INTERHEART Risk Score (IHRS). Brain imaging markers included log-transformed white matter hyperintensities (WMH) derived from FreeSurfer software (v.8), as well as whole-atlas fractional anisotropy (FA) and free water (FW) diffusion obtained using UKF tractography-based analyses. Linear regression models tested for interactions between IHRS and ethnicity on WMH, FA, and FW. We adjusted for age, sex, years of education, income, intracranial volume, and white matter hyperintensities where relevant. RESULT: Ethnicity moderated the associations between IHRS and brain markers (Table 1). Higher IHRS was associated with greater WMH burden, lower FA, and higher FW in White participants. Chinese participants exhibited a similar pattern to White individuals. South Asian participants showed a counterintuitive pattern, where higher IHRS scores were associated with lower WMH burden, higher FA, and lower FW. CONCLUSION: These findings suggest that the impact of vascular burden on brain aging may differ across ethnic groups. However, the small sample size and preliminary nature of the data warrant cautious interpretation. Further research with larger samples is needed to replicate these findings, clarify the underlying mechanisms, and inform equitable healthcare strategies.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 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".