Diabetes Mellitus and Prostate Cancer Risk—A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction: Prostate cancer is the second most commonly diagnosed malignant tumor worldwide and poses a significant challenge to public health. This systematic review and meta-analysis aims to investigate the association between diabetes mellitus and the risk of developing prostate cancer. Methods: We conducted a search of PubMed, Embase, and the Cochrane Library from 1998 to 2024. The risk of bias within the included studies was assessed using the Newcastle–Ottawa Scale. The DerSimonian–Laird random-effect model was employed for the meta-analysis. Heterogeneity was evaluated using a forest plot and statistically assessed via the Q test, I2 index, and p-values. Results: Forty-three studies involving a total of 3,746,769 patients were included. Both case–control (pOR = 0.68, 95% CI: 0.61–0.97; I2 = 92.24%) and cohort studies (pRR = 0.71, 95% CI: 0.59–0.99; I2 = 85.41%) suggest that diabetes mellitus is associated with a reduced risk of prostate cancer, though with significant heterogeneity (p < 0.05). Subgroup analysis revealed that the risk of developing prostate cancer was significantly higher in patients with a family history of prostate cancer (pRR = 1.25, 95% CI: 1.16–1.35; I2 = 69.51%). Conclusions: Our meta-analysis of recent observational studies indicates that diabetes mellitus is associated with a reduced risk of developing prostate cancer.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.040 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".