Global research landscape and advancements on the links between the gut microbiome and insulin resistance: hot issues, trends, future directions, and bibliometric analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.025 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it