Incretin-based drugs decrease the incidence of prostate cancer in type 2 diabetics: A pooling-up analysis
Bibliographic record
Abstract
Incretin-based drugs, a class of Antidiabetic medications (ADMs) used in the treatment of type 2 diabetes, may affect the incidence of prostate cancer (PCa). But real-world evidence for this possible effect is lacking. Therefore, the aim of this study is to assess the effect of incretin-based drugs on the incidence of PCa, including glucagon-like peptide-1 (GLP-1) receptor agonists and dipeptidyl peptidase-4 (DPP-4) inhibitors. We searched PubMed, Embase, and Cochrane Library databases for eligible studies through September 2023. Two independent reviewers performed screening and data extraction. We used the Cochrane Handbook for Systematic Reviews and the Newcastle-Ottawa Scale (NOS) to assess the quality of included randomized controlled trials (RCTs) and cohort studies. We did a meta-analysis of available trial data to calculate overall risk ratios (RRs) for PCa. A total of 1238 articles were identified in our search. After screening for eligibility, 7 high-quality studies met the criteria for meta-analysis, including 2 RCTs and 5 cohort studies, with a total of 1165,738 patients. Compared with the control group, we found that incretin-based drugs reduced the relative risk of PCa by 35% (95% confidence interval (CI), 0.17-0.49; P = .0006). In subgroup analysis, the RR values for GLP-1 receptor agonists and DPP-4 inhibitors were 62% (95% CI, 0.45-0.85; P = .003) and 72% (95% CI, 0.46-1.12; P = .14), respectively. Incretin-based drugs are associated with lower incidence of prostate cancer and may have a preventive effect on prostate cancer in patients with type 2 diabetes.
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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 | 0.025 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.069 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".