Incretin-Based Drugs and the Incidence of Prostate Cancer Among Patients With Type 2 Diabetes
Bibliographic record
Abstract
BACKGROUND: There is some evidence that glucagon-like peptide 1 (GLP-1) receptor agonists and dipeptidyl peptidase-4 (DPP-4) inhibitors have chemopreventive effects on prostate cancer cells but real-world evidence for this possible effect is lacking. Thus, the objective of this study was to estimate whether use of GLP-1 receptor agonists and DPP-4 inhibitors, separately, is associated with a decreased risk of prostate cancer among patients with type 2 diabetes. METHODS: We assembled two new-user, active-comparator cohorts using the UK Clinical Practice Research Datalink (2007 to 2019). The first cohort included 5,063 initiators of GLP-1 receptor agonists and 112,955 of sulfonylureas. The second cohort included 53,529 initiators of DPP-4 inhibitors and 114,417 of sulfonylureas. We fit Cox proportional hazards models to estimate adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for prostate cancer. We weighted the models using propensity score fine stratification, which considered over 50 potential confounders. RESULTS: GLP-1 receptor agonists were associated with a decreased risk of prostate cancer when compared with sulfonylureas (incidence rates = 156.4 vs. 232.0 per 100,000 person-years, respectively; HR = 0.65; 95% CI = 0.43, 0.99). DPP-4 inhibitors were also associated with a decreased risk of prostate cancer when compared with sulfonylureas (incidence rates = 316.2 vs. 350.5 events per 100,000 person-years, respectively; HR = 0.90; 95% CI = 0.81, 1.00). CONCLUSIONS: The results of this study are consistent with the hypothesis that the use of GLP-1 receptor agonists and DPP-4 inhibitors, separately, may decrease the risk of prostate cancer when compared with the use of sulfonylureas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".