Updated Evidence on the Protective Role of Statins in Colorectal Cancer: A Systematic Review of Clinical and Mechanistic Insights
Bibliographic record
Abstract
Colorectal cancer (CRC) remains a leading cause of cancer-related mortality, highlighting the need for effective chemopreventive measures. Statins, commonly prescribed for cardiovascular disease, have demonstrated potential anti-cancer effects; however, epidemiological evidence remains inconsistent. This systematic review evaluates the relationship between statin usage and CRC risk, focusing on clinical outcomes. Following PRISMA guidelines, we conducted a thorough search through PubMed, Web of Science, Scopus, and Embase for relevant studies. Sixteen studies met our inclusion criteria, encompassing clinical and epidemiological research. Using the Newcastle-Ottawa Scale and the Cochrane Risk of Bias Tool, we synthesized the data and assessed bias risk. Most studies (11/16) indicated a protective effect of statins, with risk reductions between 12% to 24% (e.g., 95% adjusted odds ratio (AOR) of 0.87; CI: 0.83 to 0.91). However, conflicting findings were noted, including an increased risk of proximal CRC with long-term statin use (HR: 2.17) and neutral effects on metastatic CRC. Overall, statins show moderate chemopreventive effects against CRC, particularly in specific molecular subtypes. Discrepancies in outcomes may be attributed to differences in statin type, duration, and tumor biology. Future research should focus on biomarker-stratified randomized trials to refine statin-based prevention strategies.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".