Research Hotspots and Trends of Interventions for Sarcopenic Obesity: A Bibliometric Analysis
Bibliographic record
Abstract
Sarcopenic obesity, characterized by both obesity and sarcopenia, significantly impacts health and independence of affected individuals. There is an urgent need to explore effective strategies for addressing or preventing sarcopenic obesity. An initial critical step is to promptly assess the impact of academic research in this field, considering factors such as geographical regions, authors, journals, and institutions. It is also essential to analyze current trends and identify potential areas that may inspire future researchers to conduct further studies, ultimately improving public health outcomes for individuals with sarcopenic obesity. To achieve this, bibliometric research was conducted using the Web of Science Core Collection database to identify English language articles and reviews focusing on sarcopenic obesity interventions published between January 1, 2004, and June 15, 2024, followed by a literature review. A total of 929 English-language articles were collected, consisting of 645 research articles and 284 reviews. Research output in the field has shown significant growth since 2017, reaching a peak of 139 papers in 2022. The United States leads in publication output with 234 papers and a total of 13,971 citations, highlighting substantial international collaboration. Both the United States and Europe are recognized as key academic hubs for sarcopenic obesity intervention research, characterized by robust academic interactions. Moreover, there has been a notable increase in publication volume from China, South Korea, and Japan. Noteworthy authors in this field include Boirie Y from Université Clermont Auvergne in France, Prado CM from the University of Alberta in Canada, Cruz-Jentoft AJ from Hospital Universitario Ramon y Cajal in Spain, and Prado CM from the University of Alberta, known for their high citation count. The University of Alberta leads in the number of publications, while the University of Verona in Italy leads in citation frequency. Journals with higher publication volumes in sarcopenic obesity intervention include Nutrients, Clinical Nutrition, and Journal of Cachexia Sarcopenia and Muscle. Among the top 20 keywords, the most relevant interventions for sarcopenic obesity are exercise, nutrition, resistance training, physical activity, and muscle strength. The primary evidence currently available suggests that resistance training is the most effective method for enhancing muscle strength in sarcopenic obesity patients. Additionally, combining protein supplementation with resistance exercise has shown encouraging results in reducing fat mass in these individuals. To progress in this field, it is crucial to foster collaboration among countries, regions, and academic institutions, promoting multidisciplinary partnerships.
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: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.017 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.153 | 0.211 |
| Science and technology studies | 0.002 | 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) | 0.004 | 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, 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".