Global research landscape and advancements on the links between the gut microbiome and insulin resistance: hot issues, trends, future directions, and bibliometric analysis
Bibliographic record
Abstract
BACKGROUND: There is increasing evidence suggesting that the gut microbiota plays a key role in the development of insulin resistance (IR). Therefore, the present bibliometric study aimed to characterize the development trends and research hotspots of publications related to the gut microbiota and IR. METHODS: Publications on the gut microbiota and IR between 2015 and 2024 were retrieved from the Scopus database. Bibliometric analyses were conducted with the VOSviewer version 1.6.20 software program. RESULTS: The Scopus query (15 June 2025) retrieved 584 publications on the gut microbiota and IR. Most were research articles (n = 480, 82.19%), followed by reviews (n = 82, 14.04%). Output is highly skewed toward East Asia and North America, with China leading the list with 254 papers (43.49%), followed by the United States (96; 16.44%), Canada (44; 7.53%), and Germany (27; 4.62%). Term-cooccurrence mapping in VOSviewer (v1.6.20) of the 251 high-frequency keywords (≥ 15 occurrences) resolved three thematic clusters: Cluster 1 focused on the high-fat-diet gut-liver axis; Cluster 2 examined patient-centered epidemiology and clinical trials; and Cluster 3 investigated inflammatory and metabolic signalling. CONCLUSIONS: The annual number of publications on the gut microbiota and IR has increased rapidly in the past ten years, demonstrating that the gut microbiota and IR have the potential to be researched precisely and are attracting increasing attention. The findings of this study can help researchers explore new directions for future research in this area and could serve as a reference for future academic research.
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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 | 0.020 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.127 | 0.250 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".