Global research trends and hotspots in gestational diabetes and long-term cardiovascular health: A bibliometric analysis
Bibliographic record
Abstract
To identify emerging trends and hotspots in research regarding cardiovascular disease (CVD) risk linked to gestational diabetes mellitus (GDM). A systematic bibliometric review of the literature on the risk of long-term CVD in patients with GDM between 1990 and 2022 from the Web of Science Core Collection (WoSCC) was performed by using Citespace and VOSviewer. This analysis gathered a total of 1185 articles, with 77 publications in 2019 and 119 in 2022, demonstrating a steady growth in the amount of research on the relationship between GDM and CVD in recent years. The United States of America (USA) led in national publications, followed by the United Kingdom (UK) and Canada. Key institutions included Harvard University, the University of Toronto, and the University of Oslo, with Prof. Ravi Retnakaran and Prof. Jane W. Rich-Edwards being prominent figures. The most productive journal was the Journal of Clinical Endocrinology &Metabolism , while Diabetes Care was the most influential and most co-cited journal. Common terms over the last 20 years included “risk,” “type 2 diabetes,” “cardiovascular disease,” and “gestational diabetes,” with recent focus shifting towards “prevention,” “gene expression,” and “DNA methylation”. This is the first bibliometric analysis linking CVD and GDM. Future research should investigate pathways between GDM and CVD, emphasizing gene expression and inflammation, while advocating for collaborative prevention strategies. • This study is the first to assess the future cardiovascular disease risk in women with gestational diabetes mellitus via bibliometric analysis. • Utilizing Citespace and VOSviewer, we find that "prevention," "prediction," "management," "gene expression," and "DNA methylation" have increasingly gained attention and might emerge as research hotspots in the coming years. • Future research should prioritize potential predictors, treatments, dietary modifications, and shared mechanisms in gestational diabetes mellitus and cardiovascular disease.
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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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.171 | 0.239 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".