Global research trends in the relationship between diabetic cardiomyopathy and mitochondria: a bibliometric analysis
Bibliographic record
Abstract
BACKGROUND: Diabetic Cardiomyopathy (DCM) is a distinct form of heart disease whose pathogenesis remains largely elusive. Recent studies have shed light on the significant role of mitochondria in the development of DCM, emphasizing their critical involvement. Despite these advancements, a bibliometric analysis focusing on the nexus between mitochondria and DCM has not been conducted, leaving a gap in a holistic understanding of research trends in this field. METHODS: This study extracted publications addressing the role of mitochondria in DCM from the Web of Science Core Collection, spanning from 1988 to 2024. A detailed bibliometric analysis was undertaken using tools like CiteSpace, VOSviewer, Microsoft Excel, and Tableau Public to assess the data. RESULTS: The analysis encompassed 440 publications involving 2705 researchers from 1457 institutions across 175 countries/regions. These studies were disseminated across 202 journals. China was the most prolific country with 192 publications, followed by the United States with 156, and Canada with 26. E. Dale Abel emerged as the most prolific author in this area. Key journals contributing to this research included the American Journal of Physiology-Heart and Circulatory Physiology and the Journal of Molecular and Cellular Cardiology. The future research direction is likely to focus deeper into the mechanisms of mitochondrial dysfunction in the diabetic heart and to identify molecular and cellular targets for therapeutic intervention. CONCLUSION: This report presents the first detailed bibliometric review of the intersection between mitochondrial research and DCM. It offers critical insights and guidance for researchers aiming to navigate and contribute to this evolving area of study.
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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 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.157 | 0.255 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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, 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".