[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125416-en":3,"doc-seo-125416-105":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},125416,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Harnessing serum VOCs and machine learning for the early detection of MAFLD","Metabolic dysfunction-associated fatty liver disease (MAFLD) remains difficult to diagnose at early stages, as ultrasound lacks sufficient sensitivity. This study evaluates serum volatile organic compound (VOC) profiling using GC-IMS and machine learning in a preliminary single-center cohort of MAFLD patients and healthy controls. Seven discriminative VOCs support model performance, with random forest yielding a test AUC of 0.941, enabling non-invasive distinction and potential stage stratification via 2-pentylfuran, pending larger multi-center validation.","TYPE Original Research PUBLISHED 18 November 2025 DOI 10.3389/fendo.2025.1691853  \nOPEN ACCESS  \nEDITED BY  \nTong Wang,  \nUniversity of Connecticut, United States  \nREVIEWED BY  \nYuan Gao,  \nGenentech Inc., United States He Gao,  \nUniversity of California, San Diego, United States  \n*CORRESPONDENCE  \nXuewei Zhuang  \n [zhuangxuewei@sdu.edu.cn](zhuangxuewei@sdu.edu.cn)  \nRECEIVED 24 August 2025  \nACCEPTED 29 October 2025  \nPUBLISHED 18 November 2025  \nCITATION  \nLi X, Zhao X, Zhang R and Zhuang X (2025) Harnessing serum VOCs and machine learning for the early detection of MAFLD. Front. Endocrinol. 16:1691853 .  \ndoi: 10.3389/fendo.2025.1691853  \nCOPYRIGHT  \n© 2025 Li, Zhao, Zhang and Zhuang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nHarnessing serum VOCs and machine learning for the early detection of MAFLD  \nXin Li 1, Xiaoyue Zhao 1, Ruonan Zhang 1 and Xuewei Zhuang 2* 1Shandong University of Traditional Chinese Medicine, Jinan, China, 2Shandong University Affiliated Shandong Provincial Third Hospital Department of Clinical Laboratory, Jinan, China  \nIntroduction: Metabolic dysfunction-associated fatty liver disease (MAFLD) is a complex metabolic disorder and one of the leading causes of chronic liver disease worldwide. Current diagnostic tools, such as ultrasound, lack sufficient sensitivity for detecting early-stage disease, emphasizing the urgent need for novel and non-invasive diagnostic strategies. Metabolomics, particularly the profiling of volatile organic compounds (VOCs) in biofluids, has emerged as a promising approach for biomarker discovery in metabolic diseases.  \nMethods: In this preliminary single-center study, serum samples were collected from 199 participants, including 110 MAFLD patients and 89 healthy controls. Volatile organic compounds were analyzed using gas chromatography–ion mobility spectrometry (GC-IMS) . Machine learning algorithms, including random forest, were applied to construct diagnostic models and identify key discriminatory metabolites. Clinical and biochemical parameters such as age, body mass index, liver function, and lipid profiles were also compared between groups.  \nResults: A total of 79 serum VOCs were detected, among which 54 showed significant differences between MAFLD patients and controls (29 identified and 25 unidentified) . The random forest model exhibited the best diagnostic performance, achieving a test AUC of 0.941, with 86.7% sensitivity and 88.5% specificity. Seven key VOCs were identified as important contributors to the model, including two upregulated compounds (2-Butoxyethanol and Cyclopentanone-D) and five downregulated compounds ((E)-3-hexenoic acid, 2-Ethylbutanal, 2-Propyl acetate, Benzaldehyde-M, and Furaneol) . Notably, 2-pentylfuran displayed significant variation across different pathological grades of MAFLD, suggesting potential as a stage-specific biomarker.  \nDiscussion: This study demonstrates that serum VOC profiling using GC-IMS combined with machine learning can effectively distinguish MAFLD patients from healthy individuals. The identified VOC signatures, particularly 2-pentylfuran, may serve as non-invasive biomarkers for MAFLD diagnosis and staging. However, due to the limited sample size and single-center design, these findings require validation in larger, multi-center, and longitudinal studies to confirm their clinical applicability, especially for early disease detection.  \nKEYWORDS  \nmetabolic dysfunction-associated fatty liver disease, volatile organic compounds, gas chromatography–ion mobility spectrometry, machine learning, biomarker, early diagnosis  \nFronti","cbCaijkaVXKlfMkH","https://ap.wps.com/l/cbCaijkaVXKlfMkH","pdf",4326463,1,13,"English","en",105,"# Introduction\n# Methods\n# Results\n# Discussion","[{\"question\":\"Why is early detection of MAFLD challenging?\",\"answer\":\"Early-stage MAFLD is often asymptomatic, and ultrasound may not detect disease reliably due to limited sensitivity.\"},{\"question\":\"How were serum VOCs measured in this study?\",\"answer\":\"Serum VOCs were analyzed using gas chromatography–ion mobility spectrometry (GC-IMS).\"},{\"question\":\"Which model and biomarkers showed the best diagnostic value?\",\"answer\":\"The random forest model achieved a test AUC of 0.941, identifying seven key VOCs; 2-pentylfuran varied across pathological grades and may act as a stage-specific biomarker.\"}]","Harnessing serum VOCs and machine learning for the early detection of MAFLD | PDF",1785898796,33,{"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},"harnessing-serum-vocs-and-machine-learning-for-the-early-detection-of-mafld","",{"@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/harnessing-serum-vocs-and-machine-learning-for-the-early-detection-of-mafld/125416/",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},"Why is early detection of MAFLD challenging?","Question",{"text":75,"@type":76},"Early-stage MAFLD is often asymptomatic, and ultrasound may not detect disease reliably due to limited sensitivity.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were serum VOCs measured in this study?",{"text":80,"@type":76},"Serum VOCs were analyzed using gas chromatography–ion mobility spectrometry (GC-IMS).",{"name":82,"@type":73,"acceptedAnswer":83},"Which model and biomarkers showed the best diagnostic value?",{"text":84,"@type":76},"The random forest model achieved a test AUC of 0.941, identifying seven key VOCs; 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