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Record W4411947914 · doi:10.3389/fendo.2025.1621932

Association between gestational diabetes mellitus and risk of breast cancer: a systematic review and meta-analysis

2025· review· en· W4411947914 on OpenAlexaboutno aff
Jing Li, Jinzhu Li, Jie Jin, Ruiqin Zhang, Rong Li, Xian Xu, Yu Wang, Xinghe Hu, Lu Wang, Siyuan Yu

Bibliographic record

VenueFrontiers in Endocrinology · 2025
Typereview
Languageen
FieldMedicine
TopicCancer Risks and Factors
Canadian institutionsnot available
Fundersnot available
KeywordsGestational diabetesMedicineBreast cancerMeta-analysisPublication biasSubgroup analysisHazard ratioOncologyInternal medicineCochrane LibraryFunnel plotConfidence intervalObstetricsGynecologyCancerPregnancyGestationBiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.028
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0180.031
Bibliometrics0.0080.010
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.028
GPT teacher head0.329
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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