Statins, cholesterol and cognition at the time of Alzheimer's disease diagnosis: A cross-sectional study from the Swedish registry for cognitive/dementia disorders
Bibliographic record
Abstract
Background: Evidence suggests statins may influence cognition in Alzheimer's disease (AD), but specific use patterns in AD patients remain unclear. Objective: To identify factors influencing statin use in AD and explore associations between statins, cholesterol, and cognition, evaluated with Mini-Mental State Examination (MMSE) at dementia diagnosis. Methods: A cross-sectional study using data from the Swedish Registry for Dementia and Cognitive Disorders (SveDem) and Stockholm Creatinine Measurements (SCREAM) from 2007 to 2018. Multivariable logistic regression examined associations between baseline characteristics and statin use, while linear regression analyzed relationships between statins, cholesterol levels, and MMSE scores. Results: We included 3074 AD patients (mean age 78.1 years; 59.4% women), of whom 1028 used statins (79.6% simvastatin, 20.4% atorvastatin). Patients with diabetes mellitus, ischemic heart disease, or stroke had greater odds of receiving statins. Older patients had slightly lower odds of receiving any statin at baseline (simvastatin use OR 0.98, 95% CI 0.97-0.99). Simvastatin users had 0.53 points higher MMSE on average at baseline compared to non-users of statins (se 0.23, p = 0.021). Higher low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC) and high-density lipoprotein cholesterol (HDL-C) levels were associated with higher MMSE in non-users of statins, but not in statin users. Conclusions: Younger AD patients and those with cardiovascular disease were more likely to use statins. Simvastatin use was linked to higher cognitive scores at diagnosis. In non-users, higher LDL-C, TC, and HDL-C levels correlated with better baseline cognitive scores. Longitudinal studies are needed to investigate the effects of statins on cognitive decline in AD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".