Glucagon-like peptide-1 agonists in cardiovascular diseases: a bibliometric analysis from inception to 2023
Bibliographic record
Abstract
Background: In recent years, glucagon-like peptide-1 (GLP-1) agonists have garnered increasing attention for their potential cardiovascular benefits beyond glycemic control in patients with diabetes. Understanding the research landscape surrounding GLP-1 agonists and cardiovascular diseases (CVDs) is crucial for informing clinical practice and guiding future research endeavors. This bibliometric analysis aimed to comprehensively assess the scholarly output and trends in this field, shedding light on the evolving landscape of GLP-1 agonists' role in cardiovascular health. Methods: The publications concerning GLP-1 agonists in CVDs were gathered from the Web of Science Core Collection, and visualizations were created utilizing Excel 2019, Cite Space, and VOS viewer software. Results and Conclusion: Using bibliometric and visual methods, the research hotspots and trends regarding GLP-1 agonists in cardiovascular diseases were pinpointed. Additionally, a thriving interest in GLP-1 agonists research within cardiovascular medicine was observed, with a notable surge in publications from 2016 onwards. The analysis revealed that the United States and China are the leading contributors, accounting for over 50% of the total publications. The University of Copenhagen and the University of Toronto emerged as the most prolific institutions in this field. Co-citation analysis highlighted the influential role of landmark clinical trials, such as the LEADER, ELIXA, and EXSCEL. Keyword trend analysis identified the emergence of newer GLP-1 agonists, such as tirzepatide and semaglutide, as well as a growing focus on topics like 'healthy obesity' and chronic kidney disease. These findings suggest that the research landscape is evolving, with a focus on expanding the therapeutic applications of GLP-1 agonists beyond glycemic control. Overall, this bibliometric analysis provided insights into the current state and future directions of research on GLP-1 agonists and their impact on cardiovascular health, guiding future research endeavors, and informing clinical practice.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.147 | 0.210 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".