Affiliation:
1. Department of Cardiovascular, Renal and Metabolic Medicine Sapporo Medical University School of Medicine Sapporo Japan
2. Department of Immunology and Microbiology National Defense Medical College Tokorozawa Japan
3. Department of Neurosurgery Sapporo Medical University School of Medicine Sapporo Japan
4. Department of Cellular Physiology and Signal Transduction Sapporo Medical University School of Medicine Sapporo Japan
5. Department of Cardiovascular Surgery Sapporo Medical University School of Medicine Sapporo Japan
6. Department of Health Checkup and Promotion Keijinkai Maruyama Clinic Sapporo Japan
7. Department of Gastroenterology and Hepatology Hokkaido University Faculty of Medicine and Graduate School of Medicine Sapporo Japan
Abstract
AbstractBackground and AimNew nomenclature of steatotic liver disease (SLD) including metabolic dysfunction‐associated SLD (MASLD), MASLD and increased alcohol intake (MetALD), and alcohol‐associated liver disease (ALD) has recently been proposed. 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‐associated fatty liver disease (MAFLD).MethodsJapanese individuals who received annual health checkups including abdominal ultrasonography (n = 15 788, men/women: 10 250/5538, mean age: 49 years) were recruited.ResultsThe 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‐distributed stochastic neighbor embedding and K‐means 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‐grade 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‐based 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 γ‐glutamyl transferase and triglycerides, was selected as a useful feature for SLDs.ConclusionsThe 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.
Funder
Japan Society for the Promotion of Science