Vascular risk factors and neuropsychiatric symptoms in Alzheimer's disease: the Cache County Study
Bibliographic record
Abstract
OBJECTIVE: Knowledge of potentially modifiable risk factors for neuropsychiatric symptoms (NPS) in Alzheimer's disease (AD) is important. This study longitudinally explores modifiable vascular risk factors for NPS in AD. METHODS: Participants enrolled in the Cache County Study on Memory in Aging with no dementia at baseline were subsequently assessed over three additional waves, and those with incident (new onset) dementia were invited to join the Dementia Progression Study for longitudinal follow-up. A total of 327 participants with incident AD were identified and assessed for the following vascular factors: atrial fibrillation, hypertension, diabetes mellitus, angina, coronary artery bypass surgery, myocardial infarction, cerebrovascular accident, and use of antihypertensive or diabetes medicines. A vascular index (VI) was also calculated. NPS were assessed over time using the Neuropsychiatric Inventory (NPI). Affective and Psychotic symptom clusters were assessed separately. The association between vascular factors and change in NPI total score was analyzed using linear mixed model and in symptom clusters using a random effects model. RESULTS: No individual vascular risk factors or the VI significantly predicted change in any individual NPS. The use of antihypertensive medications more than four times per week was associated with higher total NPI and Affective cluster scores. CONCLUSIONS: Use of antihypertensive medication was associated with higher total NPI and Affective cluster scores. The results of this study do not otherwise support vascular risk factors as modifiers of longitudinal change in NPS in 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".