ArticleBMC microbiology2025
Interpretive prediction of hyperuricemia and gout patients via machine learning analysis of human gut microbiome.
Article in BMC microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Status, challenges, and prospects of artificial intelligence application in gout diagnosis and treatment, drug research and development, and disease monitoring.Frontiers in medicine · 2026Review
- The Progress of Gout Prediction Models Based on Multi-source Data.Current rheumatology reviews · 2026Review
- Research progress on the correlation between gut microbiota and the occurrence of hyperuricemia.Frontiers in microbiology · 2026Review
- The roles of gut microbiota and their metabolites in uric acid-related metabolic diseases: mechanisms and therapeutic targets.Frontiers in microbiology · 2026Review
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Authors and funding
3 authors.
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Abstract
Hyperuricemia (HUA) and gout result from imbalances in uric acid metabolism and are closely associated with the gut microbiota. Advanced analytical methods facilitate the exploration of microbiota complexity. In this study, 16S rRNA sequencing data from stool samples of 233 patients were thoroughly collected. Machine learning (ML) and Shapley Additive exPlanations (SHAP) interpretability algorithms were applied to identify core taxa and predict the metabolic functions. The results revealed that the high-contribution core taxa identified by SHAP in each group, such as Oscillospiraceae_UCG-005 and Rhodococcus provided the basis for ML prediction. Among the five classification models, Random Forest (RF) achieved the best diagnostic performance, with prediction accuracy ranging from 82 to 96%. Metabolic function predictions indicated that the purine metabolism pathway contributes the most to distinguishing gout from other groups. In sum, ML-based 16S rRNA sequencing reveals key gut microbiome biomarkers, aiding new diagnostic strategies for HUA and gout.
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