Association between gestational diabetes mellitus and risk of breast cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Gestational diabetes mellitus (GDM), a prevalent metabolic complication during pregnancy, has a global prevalence of approximately 14%. Its onset is closely associated with insulin resistance, insufficient compensatory function of β - cells, and abnormal placental function. Epidemiological studies have indicated that type 2 diabetes is an independent risk factor for breast cancer. However, the association between GDM and the risk of breast cancer remains controversial. Objective: This systematic review and meta-analysis aim to comprehensively evaluate the association between GDM and the risk of breast cancer and explore its underlying mechanisms. Methods: This study systematically searched PubMed, Web of Science, Scopus, EMBASE, and the Cochrane Library databases, covering the period from establishing each database until April 14, 2025. Two researchers extracted relevant data and assessed the quality of included studies using the Newcastle-Ottawa Scale. The study evaluated inter-study heterogeneity using the I² statistic. Based on the magnitude of heterogeneity, fixed-effect or random-effect models were employed to calculate the pooled hazard ratio (HR) and its corresponding 95% confidence interval (CI). Additionally, subgroup analyses, sensitivity analyses, funnel plot analyses, and publication bias assessments were performed. All data analyses were conducted using STATA 17 software. Results: The overall analysis revealed no significant association between GDM and breast cancer risk (HR=1.03, 95%CI: 0.92-1.15). However, subgroup analysis revealed significant regional heterogeneity: within the regional subgroups, North American results showed an association between GDM and a reduced breast cancer risk (HR=0.89, 95%CI: 0.84-0.95), whereas Asian findings suggested an association with an increased risk (HR=1.23, 95%CI: 1.15-1.31). No significant associations were observed in subgroups based on study design (cohort/case-control) or follow-up duration (short-term/long-term). Sensitivity analysis demonstrated robust results, and there was no publication bias in this study. Conclusion: In summary, there is no significant association between GDM and breast cancer risk overall. However, notable regional heterogeneity exists: in the North American subgroup, GDM is associated with a reduced risk of breast cancer, while in the Asian subgroup, GDM is significantly associated with an increased risk of breast cancer. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/, identifier CRD420251032589.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".