[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127291-en":3,"doc-seo-127291-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127291,2336475104736,"Quinn","https://ap-avatar.wpscdn.com/avatar/22000c4c5e0e5b17e70?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786591360781797222",8,"Research & Report","Interpretive prediction of hyperuricemia and gout patients via machine learning analysis of human gut microbiome","Hyperuricemia (HUA) and gout are driven by dysregulated uric acid metabolism and are tightly linked to gut microbiota composition. Using 16S rRNA sequencing from stool samples of 233 patients, machine learning combined with SHAP interpretability identified core taxa and predicted metabolic functions. SHAP-informed key taxa supported model prediction, while Random Forest produced the best diagnostic performance (82–96%). Purine metabolism contributed most to distinguishing gout from other groups. The study supports ML-based biomarkers to improve HUA and gout diagnosis.","Edith Cowan University  \nResearch Online  \nResearch outputs 2022 to 2026  \n12-1-2025  \nInterpretive prediction of hyperuricemia and gout patients via machine learning analysis of human gut microbiome  \nJia Wei Tang  \nAlfred Chin Yen Tay  \nLiang Wang  \nEdith Cowan University  \nFollow this and additional works at: [https://ro.ecu.edu.au/ecuworks2022-2026](https://ro.ecu.edu.au/ecuworks2022-2026)  \n Part of the Nutritional and Metabolic Diseases Commons  \nTang, J., Tay, A. C. Y., & Wang, L. (2025) . Interpretive prediction of hyperuricemia and gout patients via machine learning analysis of human gut microbiome. BMC Microbiology, 25. [https://doi.org/10.1186/s12866-025-04125-x](https://doi.org/10.1186/s12866-025-04125-x)[ ](https://doi.org/10.1186/s12866-025-04125-x)[This Journal Article is posted at Research Online.](This Journal Article is posted at Research Online.)  \n[https://ro.ecu.edu.au/ecuworks2022-2026/6656](https://ro.ecu.edu.au/ecuworks2022-2026/6656)  \nTang et al. BMC Microbiology (2025) 25:429 [https://doi.org/10.1186/s12866-025-04125-x](https://doi.org/10.1186/s12866-025-04125-x)  \nBMC Microbiology  \nRESEARCH Open Access  \nInterpretive prediction of hyperuricemia and gout patients via machine learning analysis of human gut microbiome  \nJia-Wei Tang 1, Alfred Chin Yen Tay 1,2,3,4* and Liang Wang5,6,7,8,9*  \nAbstract  \nHyperuricemia (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, 16SrRNA 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.  \nHighlights  \n• 1. Healthy gut microbiome diversity is significantly reduced in HUA and gout.  \n• 2. Interpretive machine learning models were developed for HUA and gout prediction.  \n• 3. Key bacteria were identified in the human gut microbiome for HUA and gout.  \nKeywords Uric acid, Hyperuricemia, Gout, Machine learning, Purine metabolism  \nIntroduction  \nGout is an inflammatory and metabolic rheumatic disease caused by abnormal increases in uric acid (UA) and the deposition of monosodium urate (MSU) crystals in joints or surrounding tissues [1]. It is characterized by severe pain and erosive arthritis in the context of hyperuricemia (HUA) [2]. However, it is worth mentioning  \n*Correspondence:  \nAlfred Chin Yen Tay [alfred.tay@uwa.edu.au](alfred.tay@uwa.edu.au)[ ](alfred.tay@uwa.edu.au)Liang Wang [liang.wang@uwa.edu.au](liang.wang@uwa.edu.au)  \nFull list of author information is available at the end of the article  \nthat although HUA is an important risk factor for gout, only approximately 10% of HUA patients will eventually develop gout [3]. In recent years, the global prevalence of gout has ranged from 0.6 to 10%, while incidences range from 0.3 to 6 cases in every 1000 person-years [4]. Predictive models estimate that the number of gout patients will increase by 70% by 2050, reaching 95.8 million [5]. As age increases, gout is increasingly associated with comorbidities such as cardiovascular disease, diabetes, and chronic kidney disease [6], making it a significant global public health concern. In healthy individuals, approximately two-thirds of UA is","cbCaipokWjXhgldi","https://ap.wps.com/l/cbCaipokWjXhgldi","pdf",6931103,1,13,"English","en",105,"# Abstract\n# Highlights\n# Keywords\n# Introduction\n# Graphical Abstract","[{\"question\":\"What data and methods were used to study hyperuricemia and gout?\",\"answer\":\"The study collected 16S rRNA sequencing data from stool samples of 233 patients. Machine learning models were combined with SHAP interpretability to identify core taxa and predict metabolic functions.\"},{\"question\":\"Which model performed best for diagnostic prediction?\",\"answer\":\"Among five classification models, Random Forest achieved the best diagnostic performance, with prediction accuracy ranging from 82% to 96%.\"},{\"question\":\"Which metabolic pathway most distinguished gout from other groups?\",\"answer\":\"Metabolic function predictions indicated that purine metabolism was the most important pathway for distinguishing gout from other groups.\"}]","Interpretive prediction of hyperuricemia and gout patients via machine learning analysis of human gut microbiome | PDF",1785938132,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"interpretive-prediction-of-hyperuricemia-and-gout-patients-via-machine-learning-analysis-of-human-gut-microbiome","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/interpretive-prediction-of-hyperuricemia-and-gout-patients-via-machine-learning-analysis-of-human-gut-microbiome/127291/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What data and methods were used to study hyperuricemia and gout?","Question",{"text":75,"@type":76},"The study collected 16S rRNA sequencing data from stool samples of 233 patients. Machine learning models were combined with SHAP interpretability to identify core taxa and predict metabolic functions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which model performed best for diagnostic prediction?",{"text":80,"@type":76},"Among five classification models, Random Forest achieved the best diagnostic performance, with prediction accuracy ranging from 82% to 96%.",{"name":82,"@type":73,"acceptedAnswer":83},"Which metabolic pathway most distinguished gout from other groups?",{"text":84,"@type":76},"Metabolic function predictions indicated that purine metabolism was the most important pathway for distinguishing gout from other groups.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]