Statin Use and Chronic Lymphocytic Leukemia Incidence: A Nested Case–Control Study in Manitoba, Canada
Bibliographic record
Abstract
BACKGROUND: Recent studies have reported reduced risk of chronic lymphocytic leukemia (CLL) among statin users. However, the possibility that the effect of statins may differ by their chemical or pharmacodynamic properties has not been investigated. METHODS: = 6,841). Using conditional logistic regression, statin use was analyzed by individual statins and groups: hydrophilic, low-potency lipophilic (fluvastatin and lovastatin), and high-potency lipophilic statins. RESULTS: Statin users constituted 27% and 28% of the CLL cases and controls, respectively. After adjusting for potential confounding by indication, patterns of healthcare utilization, and use of other drugs, CLL incidence was not associated with use of hydrophilic [odds ratio (OR) = 1.08; 95% confidence interval (CI), 0.86-1.34] or high-potency lipophilic (OR = 0.94; 95% CI, 0.79-1.11) statins. Low-potency lipophilic statins were associated with a lower risk of CLL (OR = 0.64; 95% CI, 0.45-0.92), with stronger association (OR = 0.44; 95% CI, 0.22-0.88) observed with more regular use (half to full standard dose on average). CONCLUSIONS: We found an association between low-potency lipophilic statin use and reduced CLL risk, with a possible dose-response effect. IMPACT: Although requiring replication in future studies, our findings suggest that the effect of statins on CLL risk may depend on their specific chemical or pharmacodynamic properties.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".