Global research trends on DPP-4 inhibitors and cardiovascular outcomes: a comprehensive bibliometric analysis
Bibliographic record
Abstract
Background: Dipeptidyl peptidase-4 (DPP-4) inhibitors are oral antihyperglycemic agents commonly prescribed for type 2 diabetes (T2DM). Due to the intricate relationship between glucose regulation and cardiovascular diseases (CVDs), DPP-4 inhibitors have attracted attention for their cardiovascular safety and efficacy. This bibliometric analysis aims to provide insights into the global research landscape on DPP-4 inhibitors and cardiovascular outcomes (CVOs). Methods: A bibliometric analysis was performed, using the Web of Science Core Collection. Data were analyzed using VOSviewer, CiteSpace, and Biblioshiny. Results: The United States led in publication output, followed by Japan and China. Harvard University and the University of Toronto were the leading institutions. The most influential journals were Cardiovascular Diabetology and Diabetes Obesity & Metabolism. Darren K. McGuire was the most prolific author followed by Rury R. Holman. The most commonly occurring keyword was heart failure. Cluster analysis revealed key thematic areas in the field, including "incretin-based therapy," "dipeptidyl peptidase-4 inhibition," and "cardiovascular safety." Emerging clusters, such as "atrial fibrillation," have gained attention in recent years, highlighting evolving areas of investigation. Conclusion: This study underscores the importance of CVOs in the research on DPP-4 inhibitors. The high frequency of keywords such as "heart failure," along with recent terms like "mortality" and "risk," highlights a strong focus on cardiovascular safety and complications in the literature. Our analysis reflected that most studies address these critical aspects of cardiovascular health, discussing the potential role of DPP-4 inhibitors in mitigating adverse outcomes, particularly in patients with T2DM.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.168 | 0.267 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".