New predictive models and indices for screening MAFLD in school-aged overweight/obese children

Abstract Aims & Background: Currently, most predictions of metabolic associated fatty liver disease (MAFLD) in school-aged children utilize indicators that usually predict nonalcoholic fatty liver disease (NAFLD). The present study aimed to develop new predictive models and predictors for children with MAFLD, which could enhance the feasibility of MAFLD screening programs in the future. Methods A total of 331 school-aged obese/overweight children were recruited from six primary schools in Ningbo city, China. Hepatic steatosis and fibrosis were detected with controlled attenuation parameter (CAP) and liver stiffness measurement (LSM), respectively. Machine learning methods to build a set of variables to predict MAFLD in children. Then, the areas under the curve (AUC) of multiple models were compared to predict pediatric MAFLD. Results Compared with non-MAFLD children, children with MAFLD had more obvious metabolic abnormalities as they had higher anthropometric indicators, alanine aminotransferase, fasting plasma glucose, and higher inflammation indicators (white blood cell count, hemoglobin, neutrophil) (all P < 0.05). The optimal variables for total subjects calculated by random forest (RF) were alanine aminotransferase, uric acid, insulin and BMI. RF performed best among the four models constructed to predict MAFLD in children, with an AUC value of 0.703. LnAI-WHR, LnAI and LnAL-WHtR were satisfactory indices for predicting pediatric MAFLD in all participants, boys and girls individually. Conclusions This study developed a RF model and sex-specific indices for predicting MAFLD in overweight/obese children that may be useful for widespread screening and identification of children at high risk of MAFLD for early treatment.