White Matter Hyperintensity Volume and Amyloid-PET Synergistically Impact Memory Independent of Tau-PET in Older Adults Without Dementia
Bibliographic record
Abstract
BACKGROUND: Alzheimer's disease (AD) and cerebrovascular disease are common, co-existing pathologies in older adults. Whether the effects of cerebrovascular disease and AD biomarkers on cognition are additive or synergistic remains unclear. OBJECTIVE: To examine whether white matter hyperintensity (WMH) volume moderates the independent association between each AD biomarker and cognition. METHODS: In 586 older adults without dementia, linear regressions tested the interaction between amyloid-β (Aβ) positron emission tomography (PET) and WMH volume on cognition, independent of tau-PET. We also tested the interaction between tau-PET and WMH volume on cognition, independent of Aβ-PET. RESULTS: Adjusting for tau-PET, the quadratic effect of WMH interacted with Aβ-PET to impact memory. There was no interaction between either the linear or quadratic effect of WMH and Aβ-PET on executive function. There was no interaction between WMH volume and tau-PET on either cognitive measure. CONCLUSION: Results suggest that cerebrovascular lesions act synergistically with Aβ to affect memory, independent of tau, highlighting the importance of incorporating vascular pathology into biomarker assessment of AD.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".