Influence of White Matter Hyperintensities on Baseline and Longitudinal Amyloid-β in Cognitively Normal Individuals
Bibliographic record
Abstract
BACKGROUND: The associations between small vessel disease (SVD) and cerebrospinal amyloid-β1-42 (Aβ1-42) pathology have not been well-elucidated. OBJECTIVE: Baseline (BL) white matter hyperintensities (WMH) were examined for associations with month-24 (M24) and longitudinal Aβ1-42 change in cognitively normal (CN) subjects. The interaction of WMH and Aβ1-42 on memory and executive function were also examined. METHODS: This study included 72 subjects from the Alzheimer's Disease Neuroimaging Initiative. Multivariable linear regression models evaluated associations between baseline WMH/intracranial volume ratio, M24 and change in Aβ1-42 over two years. Linear mixed effects models evaluated interactions between BL WMH/ICV and Aβ1-42 on memory and executive function. RESULTS: Mean age of the subjects (Nmales = 36) = 73.80 years, SD = 6.73; mean education years = 17.1, SD = 2.4. BL WMH was significantly associated with M24 Aβ1-42 (p = 0.008) and two-year change in Aβ1-42 (p = 0.006). Interaction between higher WMH and lower Aβ1-42 at baseline was significantly associated with worse memory at baseline and M24 (p = 0.003). CONCLUSION: BL WMH was associated with M24 and longitudinal Aβ1-42 change in CN. The interaction between higher WMH and lower Aβ1-42 was associated with poorer memory. Since SVD is associated with longitudinal Aβ1-42 pathology, and the interaction of both factors is linked to poorer cognitive outcomes, the mitigation of SVD may be correlated with reduced amyloid pathology and milder cognitive deterioration in Alzheimer's disease.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".