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This study aimed to explore whether NAFLD could be effectively detected using 341 volatile organic compounds (VOCs) via 10 machine learning (Mach-L) algorithms in a cohort of 1,501 individuals.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Participants were selected from the Taiwan MJ cohort, which includes comprehensive demographic, biochemical, lifestyle, and VOCs data. NAFLD was diagnosed by experienced gastroenterologists. Exhaled breath samples were collected using a 1.0-L aluminum bag (late expiratory fraction) and analyzed with selected-ion flow-tube mass spectrometry. Ten Mach-L techniques were employed to evaluate two predictive models: Model 1 (demographic, lifestyle, and biochemical data), and Model 2 (Model 1 + VOCs), assessed using area under the receiver operating characteristic curve (AUC).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Subjects with NAFLD had significantly higher values for age, BMI, blood pressure, and other biomedical markers, except for eGFR and HDL-C. Key predictors of NAFLD included BMI, triglycerides (TG), uric acid (UA), fasting plasma glucose (FPG), \u03b3-GT, gender, LDL-C, and sleep duration. The addition of VOCs to Model 1 improved the AUC from 0.722 \u00b1 0.149 to 0.770 \u00b1 0.264 (p &amp;lt; 0.001). Ten VOCs were identified as the most influential, in order of importance: 2-propanol, acetone, butyl 2-methylbutanoate, diethylethanolamine, urethane, \u03b2-caryophyllene, furfural, tridecane, 4-methyloctanoic acid, and (S)-2-methyl-1-butanol.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Incorporating VOCs into traditional demographic, biochemical, and lifestyle data significantly enhanced the model\u2019s predictive performance. This suggests that VOCs may be associated with the underlying pathophysiology of NAFLD.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fmolb.2025.1631265","type":"journal-article","created":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T07:39:27Z","timestamp":1754465967000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Using machine learning methods to investigate the role of volatile organic compounds in non-alcoholic fatty liver disease"],"prefix":"10.3389","volume":"12","author":[{"given":"Chih-Hao","family":"Shen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruei-Hao","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaw-Kuen","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ta-Wei","family":"Chu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dee","family":"Pei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,8,6]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1016\/j.plaphy.2020.03.030","article-title":"The AlRabring7 E3-Ub-ligase mediates AlRab7 ubiquitination and improves ionic and oxidative stress tolerance in Saccharomyces cerevisiae","volume":"151","author":"Agarwal","year":"2020","journal-title":"Plant Physiol. biochem."},{"key":"B2","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1042\/bj1680401","article-title":"Effects of analogles of ethanolamine and choline on phospholipid metabolism in rat hepatocytes","volume":"168","author":"Akesson","year":"1977","journal-title":"Biochem. 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