Global Research Trends in Childhood Asthma and the Microbiome: A Bibliometric Analysis of 2000 to 2024
Bibliographic record
Abstract
Objective: This study systematically maps the global publications on childhood asthma and the microbiome from 2000 to 2024, quantifying publication output, collaboration networks, thematic evolution, and research gaps to guide future basic and translational work. Methods: On 30 March 2025, the Web of Science Core Collection was searched. English articles and reviews published between 2000 and 2024 were retained, yielding 2,537 records. Annual output was summarised with Microsoft Excel 2021, while VOSviewer 1.6.20, CiteSpace 6.4 R1, Scimago Graphica, and Charticulator were employed to visualise country, institution, author, and journal networks as well as keyword co-occurrence, bursts, and thematic clusters. Results: Annual publications rose exponentially, peaking at 225 papers in 2022; the United States led in volume (802 papers), citations (48,856), and H-index (105), partnering most closely with the United Kingdom, while China's fast growing output has yet to match Western citation impact. Copenhagen University, Ludwig-Maximilians-Universität Munich, and authors such as Erika von Mutius and Hans Bisgaard occupied central positions in collaboration and co-citation networks. The high frequency and centrality of the keywords "gut microbiota", "early life" and "regulatory T cells" highlight the pivotal role of the early-life gut-lung axis, while keyword burst analysis shows that research has shifted from the hygiene-hypothesis phase toward short-chain fatty acids, multi-omics integration and personalised micro-ecological interventions. Conclusion: Over the past quarter century, research on childhood asthma and the microbiome has progressed from macro level epidemiology to multi omics mechanism and is now entering a precision medicine phase. Future priorities include longitudinal birth cohort multi omics, targeted restoration of key taxa or metabolites, and expanded participation of low and middle income regions through strengthened international collaboration to reduce the global burden of childhood asthma.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.102 | 0.180 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".