Association Between Gestational Diabetes Mellitus and Risk of Overall and Site-Specific Cancers (Pancreatic, Liver, Thyroid, Lung): A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Gestational diabetes mellitus (GDM) is a common endocrine and metabolic disorder during pregnancy. However, current studies have not reached a consensus on the correlation between GDM and the risk of developing cancers. Objective: This systematic review and meta-analysis aims to comprehensively evaluate the association between GDM and the risk of overall cancer and cancers at specific sites (pancreatic cancer, thyroid cancer, liver cancer, lung cancer). Methods: A systematic search was conducted in PubMed, Web of Science, Scopus, EMBASE, and Cochrane Library databases from the establishment of the databases to 16 January 2025. Two researchers independently assessed the quality of the included studies using the Newcastle-Ottawa Scale and extracted relevant data. Data were analyzed using STATA Version 17.0. Results: This systematic review and meta-analysis included a total of 8 studies involving 1,936,836 participants. We calculated the pooled hazard ratio (HR) to evaluate the association, and the results showed that the pooled HR for overall cancer risk was 1.16 (95%CI: 1.04–1.28), indicating a significant increase in the risk of overall malignancies among patients with GDM. GDM was also significantly associated with the risk of pancreatic cancer (HR = 2.80; 95%CI: 1.20–6.55), thyroid cancer (HR = 1.21; 95%CI: 1.08–1.36), and liver cancer (HR = 1.33; 95%CI: 1.10–1.61). Additionally, the association between GDM and lung cancer was close to being statistically significant (HR = 1.19; 95%CI: 0.98–1.44). Conclusion: Our study suggests that GDM is associated with an increased risk of overall cancer, as well as pancreatic cancer, thyroid cancer, and liver 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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.043 |
| Bibliometrics | 0.007 | 0.008 |
| 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".