The Effect of Metformin on Bladder Cancer Incidence and Outcomes: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Effective oral treatment options for urothelial bladder cancer (BC) are lacking. Metformin, the most frequently used oral drug in type II diabetes mellitus, has putative anticancer properties and could, therefore, influence BC incidence and treatment outcomes. We systematically reviewed the current literature regarding the effect of metformin on BC incidence and oncological outcomes in non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC). METHODS: This review was conducted according to the PRISMA guidelines. Literature was gathered through a systematic search in PubMed/Medline, EMBASE and the Cochrane library. Risk of bias was determined using the Cochrane risk-of-bias tool for randomized trials and the Newcastle-Ottawa Scale for non-randomized trials. Hazard ratios (HRs) were extracted and pooled in a random-effects meta-analysis. RESULTS: We reviewed 13 studies, including 3,315,320 patients, considering the risk of developing BC after metformin exposure and 9 studies, including 4,006 patients, on oncological outcomes of patients with BC. Metformin did not affect BC incidence (HR 0.97, 95% CI 0.87 -1.09) or oncological outcomes for NMIBC but did show a reduced risk of recurrence (HR 0.52, 95% CI 0.32 -0.84), cancer-specific mortality (HR 0.58, 95% CI 0.43 -0.78) and overall mortality (HR 0.66, 95% CI 0.47 -0.92) in MIBC. CONCLUSIONS: The role of metformin in the prevention and treatment of BC in patients remains unclear. Although a beneficial effect of metformin on treatment outcomes of certain stages of BC may exist, a definitive conclusion cannot be drawn. Prospective clinical trials are needed to assess the efficacy of metformin for BC treatment.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.039 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".