Statins and cognition in late‐life bipolar disorder
Bibliographic record
Abstract
OBJECTIVES: Recent data suggests that statins have positive effects on cognition in older adults. Studies in patients with mood disorders have found contradicting positive and negative effects of statins on mood and cognition, with limited data in bipolar disorder (BD). The objective of this study was to assess the association between statin use and cognition in older adults with BD. METHODS: In a cross-sectional sample of 143 euthymic older adults with BD (age ≥ 50), statin users (n = 48) and nonusers (n = 95) were compared for cognitive outcomes: Global and cognitive domain z-scores were calculated from detailed neuropsychological batteries using normative data from healthy comparators (n = 87). RESULTS: The sample had a mean age of 64.3 (±8.9) years, 65.0% were female, with an average of 15.1 (±2.79) years of education. Statin users did not differ from nonusers on global (-0.60 [±0.69] vs -0.49 [±0.68], t[127] = 0.80, P = .42) or individual cognitive domains z-score. CONCLUSIONS: In older patients with BD, statin use is not independently associated with cognitive impairment. This suggests that in older BD patients, the cognitive dysfunction associated with BD trumps the potential cognitive benefit that is associated with statins in older adults without a psychiatric disorder. Further, statins do not seem to exacerbate this cognitive dysfunction. Future longitudinal studies are needed to confirm these findings.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".