Diabetes mellitus and the risk of ovarian cancer: an updated systematic review and meta-analysis of cohort and case–control studies
Bibliographic record
Abstract
OBJECTIVE: The goal of this study is to evaluate the association between diabetes mellitus and the risk of ovarian cancer development through an updated systematic review and meta-analysis. METHODS: A comprehensive search was conducted across multiple databases from their inception through April 2025, identifying observational studies that provided quantitative risk estimates for the correlation between diabetes mellitus and ovarian cancer. Pooled relative risks and corresponding confidence intervals were determined via random-effects models. Subgroup analyses were performed based on diabetes subtype, study design, age, geographic region, adjustment status and study quality which was evaluated through the Newcastle-Ottawa Scale. RESULTS: The pooled relative risk (RR) was found to be 1.14 (95% CI, 1.02-1.27); indicating a modest though statistically significant association between diabetes mellitus and ovarian cancer development. In total, 43 studies (30 cohort, 13 case-control) contributed 46 effect sizes, and between-study heterogeneity was found to be high (I² = 88.35%). According to subgroup analyses, diabetes subtypes (type 1 RR: 1.46; type 2 RR: 1.12; gestational RR: 1.09), study design or adjustment status did not reach to statistical significance regarding the increase in risk. Furthermore, the Egger's test showed no evidence of publication bias (p = 0.24), alongside sensitivity analyses confirming the robustness of the pooled estimate. CONCLUSION: The present study, being an updated meta-analysis, implies a modest, statistically significant increase in the risk of ovarian cancer development which is associated with diabetes mellitus; although the overall calculated effect size remains small. While the potential biological mechanisms linking diabetes mellitus to ovarian cancer development are plausible, diabetes mellitus does not appear to be a significant risk factor independently. Future prospective studies that incorporate comprehensive phenotyping, treatment exposure plus molecular tumor characteristics are of most necessity to further elucidate this association and enhance risk stratification.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".