GLP-1 analogues and prostate cancer incidence: A systematic review and meta-analysis.
Bibliographic record
Abstract
374 Background: Preliminary evidence from preclinical and observational data suggests that GLP-1 receptor agonists (GLP-1RAs) may reduce prostate cancer (PCa) incidence. We aimed to assess the effects of GLP-1RAs on PCa risk. Methods: Embase, Medline, Web of Science, and Cochrane databases were searched from inception to identify phase III randomized controlled trials (RCTs) comparing a GLP-1 RA with placebo/non-GLP-1 RA regimens. The primary outcome was incidence of PCa. A Mantel-Haenszel random-effect meta-analysis was carried out to estimate the pooled relative risk (RR) and 95% confidence interval (CI) for the occurrence of PCa among male participants. Results: Of the 27,103 abstracts identified, 56 trials including 59,334 male participants were included. Eligibility included type 2 diabetes mellitus in 48 studies, obesity in 7 studies, and heart failure in 1 study. The GLP-1 RA’s studied included liraglutide (18), semaglutide (13), dulaglutide (6), albiglutide (6), lixisenatide (5), exenatide (4), and others (5). One study included both liraglutide and semaglutide as interventions. Follow-up durations ranged from 12 weeks to 260 weeks (5 years), with a mean of 72.5 weeks (standard deviation = 50.85). The pooled incidence of PCa was 0.55% (150 of 27,126) for the control group and 0.46% (148 of 32,208) for the GLP-1 intervention group. The pooled RR of PCa incidence with all GLP-1 RAs compared to controls was 0.86 (95% CI: 0.69-1.06, p=0.16). Conclusions: There were proportionally fewer incident PCa cases among GLP-1 RA recipients as compared with control. However, this did not achieve statistical significance. Trials were limited in duration and by the lack of pre-specification of this outcome. Further research is needed to evaluate whether GLP-1 RAs have activity against established PCa.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.034 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".