<i>APOE</i> ε4, white matter hyperintensities, and cognition in Alzheimer and Lewy body dementia
Bibliographic record
Abstract
<h3>Objective</h3> To determine if <i>APOE</i> ε4 influences the association between white matter hyperintensities (WMH) and cognitive impairment in Alzheimer disease (AD) and dementia with Lewy bodies (DLB). <h3>Methods</h3> A total of 289 patients (AD = 239; DLB = 50) underwent volumetric MRI, neuropsychological testing, and <i>APOE</i> 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 <i>APOE</i> ε4 in the full sample, we tested associations of WMH with factor scores using linear regression models in <i>APOE</i> ε4 carriers (n = 167) and noncarriers (n = 122). We hypothesized that greater WMH volume would relate to worse cognition more strongly in <i>APOE</i> 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. <h3>Results</h3> A significant interaction was observed between WMH and <i>APOE</i> ε4 for language, but not for memory or executive functions. Separate analyses in <i>APOE</i> ε4 carriers and noncarriers showed that greater WMH volume was associated with worse attention/executive functions, learning/memory, and language in <i>APOE</i> ε4 carriers only. In ADNI-I, greater WMH burden was associated with worse attention/executive functions and language in <i>APOE</i> 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 <i>APOE</i> ε4 carriers only. <h3>Conclusion</h3> <i>APOE</i> 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".