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.

The aim of this study was to evaluate the associations of rotating night shift work with body fat percentage (BF%) and fat mass index (FMI). A cross-sectional study was conducted among 435 female steelworkers, aged 26–57 years in Tangshan, China. BF% was assessed via bioelectrical impedance analysis and FMI was calculated. Different exposure metrics of night shift work were used to examine the effects of night shift work on BF% and FMI. The duration (years), cumulative number (nights), and cumulative length of night shifts (hours) were positively correlated with FMI and BF%, and these relationships were independent of body mass index (BMI). Compared with day workers, night shift workers with an average frequency of night shifts >7 nights/month (odds ratio (OR) 2.50, 95% confidence interval (CI) 1.17 to 5.35) and percentage of hours on night shifts >30% (OR 2.55, 95% CI 1.21 to 5.39) had elevated odds of obesity (BF% ≥ 35.0%). Nonobese night shift workers by the BMI criterion should also be alert to the risk of the excess accumulation of body fat, which is actually responsible for most obesity-associated adverse health consequences. Health interventions for related populations need to be improved, which is currently more focused on overall weight control.