Exploring the role of gut microbiota dysbiosis in gout pathogenesis: a systematic review
Bibliographic record
Abstract
Abstract Objective Different mechanisms play role in the pathogenesis of gout and gut microbiota is believed to be one of these factors. The main goal of this systematic review is to summarize evidence regarding changes in gut microbiota composition in gout disease and uncover underlying mechanisms. Methods A comprehensive search was conducted on PubMed, Web of Science and Scopus databases up to October 2021. Animal studies and human observational studies including case-control, cross-sectional, and cohorts assessing associations between the gut microbiota composition and gout were included. The quality of the included human and animal studies has been evaluated using the Newcastle–Ottawa Quality Assessment scale (NOS) and the SYRCLE's risk of bias tool, respectively. Results 15 studies from 274 recorded studies were included in this systematic review. 10 studies on human and 5 on animals. Increase in frequency of Alistipes and decreased Enterobacteriaceae lead to changes of enzyme level in purine metabolism and aggravates gout condition. Moreover, rise of Phascolarctobacterium and Bacteroides play role in gout through enzyme modulation. Butyrate-producing bacteria such as Faecalibacterium, prausnitzii, Oscillibacter, Butyricicoccus and Bifidobacterium revealed an increase in healthy controls compared to gout patients which points to the possible underlying role of short-chain fatty acids (SCFAs) leading to both anti-inflammatory advantages and promoting intestinal barrier for host. Lipopolysaccharides (LPS)-releasing bacteria, Enterobacteriaceae, Prevotella and Bacteroides, also impact on gout disease by stimulating the innate immune system. Conclusion Exploring gut microbiota dysbiosis in gout disease and the underlying mechanisms could make a novel insight for microbiota-modulating therapies.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".