Total Cholesterol and Neuropsychiatric Symptoms in Alzheimer's Disease: The Impact of Total Cholesterol Level and Gender
Bibliographic record
Abstract
BACKGROUND: Neuropsychiatric symptoms (NPS) in Alzheimer's disease (AD) are a major factor in nursing home placement and a primary cause of stress for caregivers. Elevated cholesterol has been linked to psychiatric disorders and has been shown to be a risk factor for AD and to impact disease progression. The present study investigated the relationship between cholesterol and NPS in AD. METHODS: Data on cholesterol and NPS from 220 individuals (144 females, 76 males) with mild-to-moderate AD from the Texas Alzheimer's Research and Care Consortium (TARCC) cohort were analyzed. The total number of NPS and symptoms of hyperactivity, psychosis, affect and apathy were evaluated. Groups based on total cholesterol (TC; ≥200 vs. <200 mg/dl) were compared with regard to NPS. The impact of gender was also assessed. RESULTS: Individuals with high TC had lower MMSE scores as well as significantly more NPS and more symptoms of psychosis. When stratified by gender, males with high TC had significantly more NPS than females with high TC or than males or females with low TC. CONCLUSION: The role of elevated cholesterol in the occurrence of NPS in AD appears to be gender and symptom specific. A cross-validation of these findings will have implications for possible treatment interventions, especially for males with high TC.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".