Biologic and epidemiologic evidence assessing if statins prevent prostate cancer.
Bibliographic record
Abstract
INTRODUCTION: During their lives, 1 in 8 men will be diagnosed with prostate cancer. Several drugs have been shown to decrease prostate cancer risk, but have not been widely used in prostate cancer prevention because of concerns about side-effects and cost-effectiveness. Statins are indicated for prevention of cardiovascular disease, have an excellent benefit to risk profile, and some studies suggest that statins may reduce the risk of prostate cancer. MATERIALS AND METHODS: We performed a systematic search of Medline (Ovid), EMBASE (Ovid), and PubMed. This search informed a narrative review of the biological rationale for why statins may reduce prostate cancer risk and an evaluation of the existing epidemiological evidence to determine whether further studies are needed to assess the true impact of statins on prostate cancer risk. RESULTS: Statins may help prevent the development of prostate cancer through inhibition of sustained proliferative signals (androgen and Ras/Rho), sensitizing potentially malignant cells to programmed cell death, minimizing inflammation, reducing angiogenesis, and impeding invasiveness by blocking adhesion molecules. The epidemiologic literature examining the effect of statin use on overall prostate cancer diagnosis is highly heterogeneous, with relative risks of 0.26 to 2.94. Out of 33 published studies, 5 show an increased risk of prostate cancer with statin use, 10 demonstrate a decreased risk, and 18 suggest no effect. CONCLUSION: There is a compelling pre-clinical rationale for statins as potential chemopreventive agents. However, large, population-based studies with long pre-diagnosis drug exposure data are needed to investigate the impact of statin exposure on prostate cancer incidence.
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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 | 0.013 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".