Global Trends and Hotspots in Dulaglutide in the Fields of Diabetes, Obesity, and Cardiovascular Research: A Bibliometric Analysis
Bibliographic record
Abstract
Aims: Dulaglutide, a glucagon-like peptide-1 (GLP-1) receptor agonist, has captured significant attention in the fields of diabetes, obesity, and cardiovascular research. This scientometric analysis was to identify global trends and hotspots in Dulaglutide research (DGR). Methods: A comprehensive search of the Scopus database was conducted to retrieve Englishlanguage data-driven studies published from the inception of the DGR from 2010 to December 2023. The collected data were subsequently analyzed using VOSviewer and Bibliometrix software. This study sheds light on the intellectual structure of the DGR, which includes identifying key research areas, influential authors, and collaborations, as well as the conceptual structure comprising the identified themes and trends within DGR. Results: The study identified a significant growth in DGR, with the United States, China, and the United Kingdom leading in research output. Canada exhibited strong international collaboration. A small group of highly productive authors contributed disproportionately to the literature, consistent with Lotka’s law. Research trends have evolved from broad themes in cardiovascular health to more specialized studies focusing on the drug’s mechanisms, comparative effectiveness, and emerging applications, such as non-alcoholic fatty liver disease. Citation analysis revealed cardiovascular outcomes, real-world effectiveness, and GLP-1 receptor interactions are among the most researched areas. Conclusion: DGR is a rapidly expanding field with shifting priorities from general diabetes management to specific pharmacological and clinical outcomes. The findings underscore the need for more diverse geographic representation in research and highlight knowledge gaps that future studies should address. This bibliometric analysis provides valuable insights into the intellectual landscape of Dulaglutide research, aiding future investigations and clinical applications.
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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 | 0.014 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.185 | 0.263 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".