Vascular Risk and <b>β</b>‐Amyloid Are Synergistically Associated with Cortical Tau
Bibliographic record
Abstract
OBJECTIVE: Neuropathological studies have demonstrated that cerebrovascular disease and Alzheimer disease (AD) pathology frequently co-occur in older adults. The extent to which cerebrovascular disease influences the progression of AD pathology remains unclear. Leveraging newly available positron emission tomography (PET) imaging, we examined whether a well-validated measure of systemic vascular risk and β-amyloid (Aβ) burden have an interactive association with regional tau burden. METHODS: F-flortaucipir) PET imaging on the same participants. Aβ PET was performed at baseline; tau PET was acquired on average 2.98 ± 1.1 years later. Tau was measured in the entorhinal cortex (EC), an early site of tau deposition, and in the inferior temporal cortex (ITC), an early site of neocortical tau accumulation associated with AD. Linear regression models examined FHS-CVD and Aβ as interactive predictors of tau deposition, adjusting for age, sex, APOE ε4 status, and the time interval between baseline and the tau PET scan. RESULTS: We observed a significant interaction between FHS-CVD and Aβ burden on subsequently measured ITC tau (p < 0.001), whereby combined higher FHS-CVD and elevated Aβ burden was associated with increased tau. The interaction was not significant for EC tau (p = 0.16). INTERPRETATION: Elevated vascular risk may influence tau burden when coupled with high Aβ burden. These results suggest a potential link between vascular risk and tau pathology in preclinical AD. Ann Neurol 2019; 1-8 ANN NEUROL 2019;85:272-279.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".