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We investigated clustering analyses to decipher the complex landscape of SLD pathologies including the former nomenclature of nonalcoholic fatty liver disease (NAFLD) and metabolic dysfunction\u2010associated fatty liver disease (MAFLD).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Japanese individuals who received annual health checkups including abdominal ultrasonography (<jats:italic>n<\/jats:italic>\u00a0=\u00a015\u2009788, men\/women: 10\u2009250\/5538, mean age: 49\u00a0years) were recruited.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The numbers of individuals with SLD, MASLD, MetALD, ALD, NAFLD, and MAFLD were 5603 (35.5%), 4227 (26.8%), 795 (5.0%), 324 (2.1%), 3982 (25.8%), and 4946 (31.3%), respectively. Clustering analyses using t\u2010distributed stochastic neighbor embedding and K\u2010means to visually represent interconnections in SLDs uncovered five cluster formations. MASLD and NAFLD mainly shared three clusters including (i) low alcohol intake with relatively low\u2010grade obesity; (ii) obesity with dyslipidemia; and (iii) dysfunction of glucose metabolism. Both MetALD and ALD displayed one distinct cluster intertwined with alcohol consumption. MAFLD widely shared all of the five clusters. In machine learning\u2010based analyses using algorithms of random forest and extreme gradient boosting and receiver operating characteristic curve analyses, fatty liver index (FLI), calculated by body mass index, waist circumference, and levels of \u03b3\u2010glutamyl transferase and triglycerides, was selected as a useful feature for SLDs.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The new nomenclature of SLDs is useful for obtaining a better understanding of liver pathologies and for providing valuable insights into predictive factors and the dynamic interplay of diseases. FLI may be a noninvasive predictive marker for detection of SLDs.<\/jats:p><\/jats:sec>","DOI":"10.1111\/jgh.16552","type":"journal-article","created":{"date-parts":[[2024,4,17]],"date-time":"2024-04-17T11:45:34Z","timestamp":1713354334000},"page":"1382-1393","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Deciphering metabolic dysfunction\u2010associated steatotic liver disease: insights from predictive modeling and clustering analysis"],"prefix":"10.1111","volume":"39","author":[{"given":"Kazuma","family":"Mori","sequence":"first","affiliation":[{"name":"Department of Cardiovascular, Renal and Metabolic Medicine Sapporo Medical University School of Medicine  Sapporo Japan"},{"name":"Department of Immunology and Microbiology National Defense Medical College  Tokorozawa 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