P1‐431: VASCULAR MEDICAL TREATMENTS INFLUENCE THE ASSOCIATION BETWEEN VASCULAR BURDEN AND AMYLOID PATHOLOGY IN ASYMPTOMATIC INDIVIDUALS AT RISK FOR ALZHEIMER'S DISEASE
Bibliographic record
Abstract
The influence of vascular risk factors (VRF) on Alzheimer's disease (AD) pathophysiology remains inconclusive. This study aims to examine the associations of lipids, blood pressure and combined VRF scores with Aß and tau pathology in the preclinical disease stage, considering the moderating impact of vascular drug treatment. Cognitively healthy individuals with family history of AD from the PREVENT-AD cohort were included (mean age: 62 years). Aß-PET [F-NAV-4694] and tau-PET [Flortaucipir] scans were obtained from 120 individuals to examine the association between lipids [total cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL) cholesterol], blood pressure [systolic and diastolic blood pressure, pulse pressure], combined VRF scores [CAIDE, FCRP, FHS-CVD (see Figure1 legend)] and global Aß and entorhinal tau SUVR. Individuals were binarized for vascular medication (dyslipidemia and/or hypertensive drugs) to examine interaction effects, using linear regression models. Subsequently, we tested for within-group effects. All models were corrected for age, sex and time difference between VRF and PET measurements, while secondary models also included correction for apolipoproteinE ε4 (APOEε4) status. The analyses were repeated using CSF Aβ1-42 and p-tau biomarkers in 162 PREVENT-AD individuals (67 also included in the PET analyses). In most analyses, we found interactions between VRF and vascular medical treatment on Aß brain deposition (Figure1). In non-treated participants, higher levels of total cholesterol, LDL, systolic blood pressure, pulse pressure and all combined VRF scores were associated with higher Aß-PET deposition (all pnon-treated≤0.04). Similarly, total cholesterol, LDL and the CAIDE risk score were related to lower Aß1-42 in the CSF in non-treated participants only (all pnon-treated≤0.02). While PET results remained almost identical, CSF results were diminished after correction for APOEε4. No associations were found between VRF and tau.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".