[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-291052-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-291052-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","machine-learning-derived-gut-microbiome-signature-predicts-fatty-liver-disease-in-the-presence-of-insulin-resistance","Machine learning-derived gut microbiome signature predicts fatty liver disease in the presence of insulin resistance","","A supervised machine learning classifier was developed to predict fatty liver disease among individuals with or without insulin resistance using fecal 16S rDNA sequencing data. Using the Kangbuk Samsung Hospital cohort (n=777), a random forest model was evaluated with accuracy, AUROC, kappa, and F1-score. Performance improved for insulin-resistant individuals (AUROC=0.77). Genetic algorithm optimization yielded an insulin-resistance classifier based on often ten microbial genera with higher AUROC (0.93), comparable or superior to the fatty liver index.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/machine-learning-derived-gut-microbiome-signature-predicts-fatty-liver-disease-in-the-presence-of-insulin-resistance/291052/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/machine-learning-derived-gut-microbiome-signature-predicts-fatty-liver-disease-in-the-presence-of-insulin-resistance/291052.png","ImageObject",300,407,{"name":42,"@type":43},"Genevieve","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-19","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",9,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What data source was used to build the fatty liver disease classifier?","Question",{"text":63,"@type":64},"The classifier was trained using fecal 16S rDNA sequencing data.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How was model performance evaluated?",{"text":68,"@type":64},"Performance was assessed using accuracy, AUROC, kappa, and F1-score.",{"name":70,"@type":61,"acceptedAnswer":71},"How did optimization affect prediction in insulin-resistant individuals?",{"text":72,"@type":64},"Genetic algorithm optimization produced an improved classifier for insulin resistance with AUROC=0.93 based on a core set of often ten microbial genera.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},291052,1789643321,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,115,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":112,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":113,"slug":114},8,30,"research-report",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":112,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":139,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":80,"read_time":113,"preview_page_count":4},1374391974585,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nMachine learning‑derived gut microbiome signature predicts fatty liver disease in the presence of insulin resistance  \nBaeki E. Kang1,12, Aron Park2,12, Hyekyung Yang3, Yunju Jo1, Tae Gyu Oh4, Seung Min Jeong1,5, Yosep Ji5, Hyung‐Lae Kim6, Han‐Na Kim3,7, Johan Auwerx8, Seungyoon Nam2,9*, Cheol‑Young Park10,11* & Dongryeol Ryu1,11*  \nA simple predictive biomarker for fatty liver disease is required for individuals with insulin resistance. Here, we developed a supervised machine learning‑based classifier for fatty liver disease using fecal 16S rDNA sequencing data. Based on the Kangbuk Samsung Hospital cohort (n = 777), we generated a random forest classifier to predict fatty liver diseases in individuals with or without insulin resistance (n = 166 and n = 611, respectively). The model performance was evaluated based on metrics, including accuracy, area under receiver operating curve (AUROC), kappa, and F1‑score. The developed classifier for fatty liver diseases performed better in individuals with insulin resistance (AUROC = 0.77). We further optimized the classifiers using genetic algorithm. The improved classifier for insulin resistance, consisting often microbial genera, presented an advanced classification (AUROC = 0.93), whereas the improved classifier for insulin‑sensitive individuals failed to distinguish participants with fatty liver diseases from the healthy. The classifier for individuals with insulin resistance was comparable or superior to previous methods predicting fatty liver diseases (accuracy = 0.83, kappa = 0.50, F1‑score = 0.89), such as the fatty liver index. We identified the ten genera as a core set from the human gut microbiome, which could be a diagnostic biomarker of fatty liver diseases for insulin resistant individuals. Collectively, these findings indicate that the machine learning classifier for fatty liver diseases in the presence of insulin resistance is comparable or superior to commonly used methods.  \nFatty liver disease (FL), or hepatic steatosis, is diagnosed when a liver has at least 5% hepatocytes containing fat1. Depending on its etiology, it can be categorized into alcoholic fatty liver disease (AFLD) or nonalcoholic FL disease (NAFLD). The international prevalence of NAFLD was estimated to be 25.2%2. However, it was estimated to be 55.5% in participants with type 2 diabetes mellitus (T2DM)3. Insulin resistance (IR), a major pathological  \n1Department of Molecular Cell Biology, Sungkyunkwan University School of Medicine, 2066, Seobu-Ro, Suwon 16419, Republic of Korea. 2Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology, Gachon University, Incheon 21999, Republic of Korea. 3Medical Research Institute, School of Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University, Seoul 03181, Republic of Korea. 4Gene Expression Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA. 5HEM Inc., 404, Ace Gwanggyo Tower 3, Suwon 16229, Republic of Korea. 6Department of Biochemistry, College of Medicine, Ewha Womans University, Seoul 07985, Republic of Korea. 7Department of Clinical Research Design and Evaluation, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul 06355, Republic of Korea. 8Institute of Bioengineering, Faculty of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland. 9Department of Genome Medicine and Science, AI Convergence, Center for Medical Science, Gachon Institute of Genome Medicine and Science, Gachon University Gil Medical Centre, Gachon University College of Medicine, 38-13, Dokjeom-Ro 3Beon-Gil, Incheon 21999, Republic of Korea. 10Division of Endocrinology and Metabolism, Department of Internal Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29, Saemunan-Ro, Jongno-Gu, Seoul 03181, Republic of Korea. 11Biomedical Instit","cbCaivpYc8XfXo8a","https://ap.wps.com/l/cbCaivpYc8XfXo8a","pdf",3248572,12,"English","# Background\n## Fatty liver disease and insulin resistance\n## Current diagnostic approaches and limitations\n# Methods\n## Supervised machine learning classifier\n## Random forest model and evaluation metrics\n## Genetic algorithm optimization\n# Results\n## Classifier performance in insulin-resistant vs insulin-sensitive groups\n## Comparison with prior methods (fatty liver index)\n## Core microbial genera as a biomarker set","[{\"question\":\"What data source was used to build the fatty liver disease classifier?\",\"answer\":\"The classifier was trained using fecal 16S rDNA sequencing data.\"},{\"question\":\"How was model performance evaluated?\",\"answer\":\"Performance was assessed using accuracy, AUROC, kappa, and F1-score.\"},{\"question\":\"How did optimization affect prediction in insulin-resistant individuals?\",\"answer\":\"Genetic algorithm optimization produced an improved classifier for insulin resistance with AUROC=0.93 based on a core set of often ten microbial genera.\"}]","Machine learning-derived gut microbiome signature predicts fatty liver disease in the presence of insulin resistance | PDF"]