Additive Effects of White Matter Hyperintensity and <scp>APOE</scp> ε4 Status on Risk of Incident Dementia in Two Large Longitudinal Cohorts
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether white matter hyperintensities (WMH) and apolipoprotein E (APOE) ε4 status have an additive or multiplicative effect on the risk of incident all-cause dementia. METHODS: We conducted a prospective cohort study in the Atherosclerosis Risk in Communities (ARIC) study and confirmed findings in the UK Biobank (UKB). The exposures were APOE ε4 status (0 vs. ≥1 allele) and WMH on magnetic resonance imaging (MRI). The primary outcome was incident all-cause dementia. After confirming an additive interaction, we created combined exposure groups: WMH-/ε4-, WMH+/ε4-, WMH-/ε4+, and WMH+/ε4+. Cox proportional hazards models were adjusted for age, sex, race, education, cognition (ARIC only), hypertension, diabetes, and prior stroke. RESULTS: In ARIC (n = 1,736, mean age 63, 58.8% female, 48.7% non-Hispanic White individuals, median follow-up 18.6 years), the dementia incidence rate was 10.4 (95% CI, 9.2-11.6) per 1,000 person-years. Compared to WMH-/ε4-, adjusted hazard ratios (HRs) for dementia were: WMH-/ε4+, 1.5 (95% CI, 1.1-2.1); WMH+/ε4-, 2.0 (95% CI, 1.4-2.7); and WMH+/ε4+, 3.2 (95% CI, 2.2-4.6). In UKB (n = 40,307, mean age 55, 52.7% female, 97.1% non-Hispanic White individuals, median follow-up 3.2 years), the dementia incidence rate was 0.42 (95% CI, 0.32-0.55) per 1,000 person-years. Adjusted HRs were: WMH-/ε4+, 2.3 (95% CI, 1.2-4.5); WMH+/ε4-, 2.1 (95% CI, 1.0-4.6); and WMH+/ε4+, 6.7 (95% CI, 3.2-13.9). INTERPRETATION: WMH burden and APOE ε4 status additively increase dementia risk. These findings support the potential benefit of vascular risk management to reduce WMH and delay dementia onset, even among genetically at-risk individuals. ANN NEUROL 2026;99:656-667.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".