Associations between statin use and risk of non‐Hodgkin lymphomas by subtype
Bibliographic record
Abstract
Non-Hodgkin lymphomas (NHL) are a group of cancers with highly heterogeneous biology and clinical features. Statins are increasingly prescribed to prevent cardiovascular diseases. Early evidence shows a preventive effect of statins for some cancers, but their effect on NHL risk is unclear. We conducted a population-based nested case-control study involving 5,541 NHL cases and 27,315 controls matched for gender, age, place of residence and length of period of available prescription drug data. We assessed the use of statins prior to diagnosis (excluding the 12 months prior to the index date). We used conditional logistic regression models to estimate odds ratio (OR) and 95% confidence interval (CI) for use of any statin, adjusting for medical conditions, number of family physician visits for 5 years prior to index date, healthcare utilization, income and use of other medications. Over one-quarter of cases and controls were prescribed statins. Ever-use of any statin was associated with lower risk of Total NHL (OR = 0.82, 95% CI 0.76-0.89) and of certain subtypes including diffuse large B-cell lymphomas (DLBCL, OR = 0.77, 95% CI 0.65-0.92), plasma cell neoplasms (PCN, OR = 0.76, 95% CI 0.63-0.91) and other B-cell NHL (0.75, 0.59-0.95). Analysis by statin type suggested that the association was limited to high potency statin and lipophilic statin users. No clear duration or dose-response relationships were observed. Our findings provide evidence that statin use can reduce the risk of DLBCL and plasma cell lymphomas, but not other NHL types. Further studies are warranted to verify these associations and to examine the biological mechanisms.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".