Anxiety disorders and the gut microbiota: a bibliometric and visual analysis
Bibliographic record
Abstract
Introduction: Anxiety disorder is a common mental illness, yet its specific mechanisms remain unclear. Recent research has revealed a connection between gut microbiota and anxiety disorders. This study aims to assess the current global research landscape, highlight current topics of interest, and explore future research directions in the field of anxiety disorders and gut microbiota. Methods: We extracted research review articles related to anxiety and gut microbiota from the Web of Science, covering the period from 2004 to 2023. We used VOSviewer 1.6.18.0, Scimago Graphica, and CiteSpace 6.2. R2 to visualize the contributions of countries, institutions, journals, authors, citations, and keywords in this field. Result: A total of 1198 articles were included in this bibliometric analysis. Over the past two decades, both publications and citations have shown a steady increase. China, the United States, and Canada were the top three countries in terms of publication output. John Cryan from University College Cork had the highest number of publications and citation impact in this area of research. The journal Nutrients had the highest number of publications, while Brain Behavior and Immunity had the most citations. Key research themes in recent years have included anxiety, gut microbiota, depression, stress, gut-brain axis, and probiotics, all of which are likely to be important future research directions. Conclusion: This analysis has key research areas and emerging trends, including risk factors, stressors, inflammatory responses, the gut-brain axis, and probiotics. These insights can guide researchers towards a more comprehensive understanding of recent advancements in this field, help shape future research directions and facilitate the identification of new therapeutic targets for anxiety disorder, ultimately improving clinical outcomes.
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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 | 0.010 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.212 | 0.217 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".