Abstract
BACKGROUND: Definitions of nonalcoholic fatty liver disease (NAFLD), metabolic dysfunction-associated FLD (MAFLD), and steatotic liver disease (SLD) have been proposed to better guide clinical practice and epidemiological studies. The effects of the nomenclature on the incidence of FLD and its associations with obesity phenotypes, insulin resistance (IR), and liver fibrosis were examined in this study. METHODS: NAFLD, MAFLD, and metabolic dysfunction-associated steatotic liver disease (MASLD) were diagnosed on the basis of ultrasound examination and metabolic disorders among 6,718 community-dwelling individuals from southeast China. Six obesity phenotypes, seven IR surrogates, and the NAFLD Fibrosis Score (NFS) were applied to evaluate their association with FLDs through multivariable logistic regression models, restricted cubic splines, and receiver operating characteristic curves. RESULTS: The prevalence of FLD, NAFLD, MAFLD, and MASLD was 35.47%, 33.34%, 34.77%, and 32.79%, respectively. The associations of obesity-FLD, IR-FLD, and FLD-NFS were statistically significant across all the FLD definitions. Patients with MAFLD demonstrated slightly higher odds ratios (ORs) than those with FLD, NAFLD, and MASLD. However, alcoholic-FLD (AFLD), which is included in the MAFLD nomenclature, showed lower ORs with obesity and IR and lower NFS, significantly differently from other FLDs. Among all obesity and IR indices, triglyceride and glucose index body mass index (TyG-BMI), the TyG-waist height ratio (TyG-WHtR), and the TyG-waist circumstance (TyG-WC) were the best at predicting FLDs and ORs with respect to NFS. CONCLUSION: The nomenclature of MAFLD covers a wider range of FLD than NAFLD and MASLD do, but the heterogeneity of AFLD is nonnegligible. Compared with MASLD, NAFLD remains a practical and efficient definition for large-scale population screening, especially in resource-limited settings. TyG-BMI, TyG-WHtR, and TyG-WC could better predict FLD and associated fibrosis, affirming their potential as simple and cost-effective tools to support health monitoring and early intervention.