<i>APOE</i> ε4, white matter hyperintensities, and cognition in Alzheimer and Lewy body dementia
Bibliographic record
Abstract
Objective To determine if APOE ε4 influences the association between white matter hyperintensities (WMH) and cognitive impairment in Alzheimer disease (AD) and dementia with Lewy bodies (DLB). Methods A total of 289 patients (AD = 239; DLB = 50) underwent volumetric MRI, neuropsychological testing, and APOE ε4 genotyping. Total WMH volumes were quantified. Neuropsychological test scores were included in a confirmatory factor analysis to identify cognitive domains encompassing attention/executive functions, learning/memory, and language, and factor scores for each domain were calculated per participant. After testing interactions between WMH and APOE ε4 in the full sample, we tested associations of WMH with factor scores using linear regression models in APOE ε4 carriers (n = 167) and noncarriers (n = 122). We hypothesized that greater WMH volume would relate to worse cognition more strongly in APOE ε4 carriers. Findings were replicated in 198 patients with AD from the Alzheimer9s Disease Neuroimaging Initiative (ADNI-I), and estimates from both samples were meta-analyzed. Results A significant interaction was observed between WMH and APOE ε4 for language, but not for memory or executive functions. Separate analyses in APOE ε4 carriers and noncarriers showed that greater WMH volume was associated with worse attention/executive functions, learning/memory, and language in APOE ε4 carriers only. In ADNI-I, greater WMH burden was associated with worse attention/executive functions and language in APOE ε4 carriers only. No significant associations were observed in noncarriers. Meta-analyses showed that greater WMH volume was associated with worse performance on all cognitive domains in APOE ε4 carriers only. Conclusion APOE ε4 may influence the association between WMH and cognitive performance in AD and DLB.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".