Interactive Effects of Pulse Pressure and Tau Imaging on Longitudinal Cognition
Bibliographic record
Abstract
BACKGROUND: Studies have demonstrated that both tau and cardiovascular risk are associated with cognitive decline, but the possible synergistic effects of these pathologic markers remain unclear. OBJECTIVE: To explore the interaction of AD biomarkers with a specific vascular risk marker (pulse pressure) on longitudinal cognition. METHODS: Participants included 139 older adults from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Biomarkers of tau, amyloid-β (Aβ), and vascular risk (pulse pressure) were assessed. Neuropsychological assessment provided memory, language, and executive function domain composite scores at baseline and 1-year follow-up. Multiple linear regression examined interactive effects of pulse pressure with tau PET independent of Aβ PET and Aβ PET independent of tau PET on baseline and 1-year cognitive outcomes. RESULTS: The interaction between pulse pressure and tau PET significantly predicted 1-year memory performance such that the combined effect of high pulse pressure and high tau PET levels was associated with lower memory at follow-up but not at baseline. In contrast, Aβ PET did not significantly interact with pulse pressure to predict baseline or 1-year outcomes in any cognitive domain. Main effects revealed a significant effect of tau PET on memory, and no significant effects of Aβ PET or pulse pressure on any cognitive domain. CONCLUSION: Results indicate that tau and an indirect marker of arterial stiffening (pulse pressure) may synergistically contribute to memory decline, whereas Aβ may have a lesser role in predicting cognitive progression. Tau and vascular pathology (particularly in combination) may represent valuable targets for interventions intended to slow cognitive decline.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".