Interpretive prediction of hyperuricemia and gout patients via machine learning analysis of human gut microbiome
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 th...
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| Language: | English |
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BMC
2025-07-01
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| Series: | BMC Microbiology |
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| Online Access: | https://doi.org/10.1186/s12866-025-04125-x |
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| author | Jia-Wei Tang Alfred Chin Yen Tay Liang Wang |
| author_facet | Jia-Wei Tang Alfred Chin Yen Tay Liang Wang |
| author_sort | Jia-Wei Tang |
| collection | DOAJ |
| description | 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. Graphical Abstract |
| format | Article |
| id | doaj-art-ea4f921ec72643c69201861e8cd374ed |
| institution | Kabale University |
| issn | 1471-2180 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Microbiology |
| spelling | doaj-art-ea4f921ec72643c69201861e8cd374ed2025-08-20T03:42:23ZengBMCBMC Microbiology1471-21802025-07-0125111210.1186/s12866-025-04125-xInterpretive prediction of hyperuricemia and gout patients via machine learning analysis of human gut microbiomeJia-Wei Tang0Alfred Chin Yen Tay1Liang Wang2The Marshall Centre for Infectious Diseases Research and Training, The University of Western AustraliaThe Marshall Centre for Infectious Diseases Research and Training, The University of Western AustraliaDepartment of Laboratory Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Southern Medical UniversityAbstract 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. Graphical Abstracthttps://doi.org/10.1186/s12866-025-04125-xUric acidHyperuricemiaGoutMachine learningPurine metabolism |
| spellingShingle | Jia-Wei Tang Alfred Chin Yen Tay Liang Wang Interpretive prediction of hyperuricemia and gout patients via machine learning analysis of human gut microbiome BMC Microbiology Uric acid Hyperuricemia Gout Machine learning Purine metabolism |
| title | Interpretive prediction of hyperuricemia and gout patients via machine learning analysis of human gut microbiome |
| title_full | Interpretive prediction of hyperuricemia and gout patients via machine learning analysis of human gut microbiome |
| title_fullStr | Interpretive prediction of hyperuricemia and gout patients via machine learning analysis of human gut microbiome |
| title_full_unstemmed | Interpretive prediction of hyperuricemia and gout patients via machine learning analysis of human gut microbiome |
| title_short | Interpretive prediction of hyperuricemia and gout patients via machine learning analysis of human gut microbiome |
| title_sort | interpretive prediction of hyperuricemia and gout patients via machine learning analysis of human gut microbiome |
| topic | Uric acid Hyperuricemia Gout Machine learning Purine metabolism |
| url | https://doi.org/10.1186/s12866-025-04125-x |
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