Diagnostic Performance of Hepatic Steatosis Algorithms in Korean Population with Metabolic-Associated Fatty Liver Disease
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Background: The new nomenclature of metabolic-associated fatty liver disease (MAFLD) has been proposed to describe fatty liver condition associated with metabolic dysfunction. Currently, hepatic steatosis indices for predicting MAFLD have not been extensively studied. The aims of this study were to validate the hepatic steatosis indices for predicting MAFLD in the Korean population and to investigate the effects of the subgroups on their diagnostic performance. Methods: Clinical and biochemical data were obtained from a total of 12,962 consecutive subjects visiting a health check-up center from January 2022 to December 2022. Hepatic steatosis algorithms such as the fatty liver index (FLI), hepatic steatosis index (HSI), non-alcoholic fatty liver disease liver fat score (NLFS), triglyceride glucose (TyG), triglyceride glucose–body mass index (TyG-BMI), and TyG–waist circumference (TyG-WC) were evaluated. Results: The TyG-BMI showed the highest area under the receiver operating characteristic curve (AUROC) for the MAFLD (0.877, 95% confidence interval: 0.871–0.882), followed by the FLI (0.872), TyG-WC (0.870), NLFS (0.847), TyG (0.769), and HSI (0.595). The AUROC of the hepatic steatosis algorithms tended to decrease in subgroups with advanced age, overweight/obesity, hypertension, diabetes, or metabolic syndrome. Conclusions: Hepatic steatosis algorithms can be useful for screening MAFLD in a general Korean population. Risk factors such as obesity, diabetes, or metabolic syndrome may affect the diagnostic performances of hepatic steatosis algorithms. MAFLD subgroups should be considered to optimize the hepatic steatosis assessments by these formulas.
Abstract
Background: The new nomenclature of metabolic-associated fatty liver disease (MAFLD) has been proposed to describe fatty liver condition associated with metabolic dysfunction. Currently, hepatic steatosis indices for predicting MAFLD have not been extensively studied. The aims of this study were to validate the hepatic steatosis indices for predicting MAFLD in the Korean population and to investigate the effects of the subgroups on their diagnostic performance. Methods: Clinical and biochemical data were obtained from a total of 12,962 consecutive subjects visiting a health check-up center from January 2022 to December 2022. Hepatic steatosis algorithms such as the fatty liver index (FLI), hepatic steatosis index (HSI), non-alcoholic fatty liver disease liver fat score (NLFS), triglyceride glucose (TyG), triglyceride glucose–body mass index (TyG-BMI), and TyG–waist circumference (TyG-WC) were evaluated. Results: The TyG-BMI showed the highest area under the receiver operating characteristic curve (AUROC) for the MAFLD (0.877, 95% confidence interval: 0.871–0.882), followed by the FLI (0.872), TyG-WC (0.870), NLFS (0.847), TyG (0.769), and HSI (0.595). The AUROC of the hepatic steatosis algorithms tended to decrease in subgroups with advanced age, overweight/obesity, hypertension, diabetes, or metabolic syndrome. Conclusions: Hepatic steatosis algorithms can be useful for screening MAFLD in a general Korean population. Risk factors such as obesity, diabetes, or metabolic syndrome may affect the diagnostic performances of hepatic steatosis algorithms. MAFLD subgroups should be considered to optimize the hepatic steatosis assessments by these formulas.
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