Fat mass index as a screening tool for the assessment of non-alcoholic fatty liver disease

Abstract

Abstract Non-alcoholic fatty liver disease (NAFLD) is replacing hepatitis B as the leading cause of chronic liver disease in China. The purpose of this study is to select good tools to identify NAFLD from the body composition, anthropometry and related routine clinical parameters. A total of 5076 steelworkers, aged 22–60 years, was included in this study. Body fat mass was measured via bioelectrical impedance analysis (BIA) and fat mass index (FMI) was derived. Ultrasonography method was used to detect hepatic steatosis. Random forest classifier and best subset regression were used to select useful parameters or models that can accurately identify NAFLD. Receiver operating characteristic (ROC) curves were used to describe and compare the performance of different diagnostic indicators and algorithms including fatty liver index (FLI) and hepatic steatosis index (HSI) in NAFLD screening. ROC analysis indicated that FMI can be used with high accuracy to identify heavy steatosis as determined by ultrasonography in male workers [area under the curve (AUC) 0.95, 95% CI 0.93–0.98, sensitivity 89.0%, specificity 91.4%]. The ability of single FMI to identify NAFLD is no less than that of combination panels, even better than the combination panel of HSI. The best subset regression model that including FMI, waist circumference, and serum levels of triglyceride and alanine aminotransferase has moderate accuracy in diagnosing overall NAFLD (AUC 0.83). FMI and the NAFLD best subset (BIC) score seem to be good tools to identify NAFLD in Chinese steelworkers.