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

Abstract

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. A total of 331 school-aged overweight/obese 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 were adapted to build a set of variables to predict MAFLD in children. Then, the area under the curve (AUC) of multiple models and indices was compared to predict pediatric MAFLD. Compared with non-MAFLD children, children with MAFLD had more obvious metabolic disturbances, as they had higher anthropometric indicators, alanine aminotransferase, fasting plasma glucose, and inflammation indicators (white blood cell count, hemoglobin, neutrophil count) (all P < 0.05). The optimal variables for all subjects selected by random forest (RF) were alanine aminotransferase, uric acid, insulin, and BMI. The logistic regression (LR) model performed best, with AUC values of 0.758 for males and 0.642 for females in predicting MAFLD. LnAI-BMI, LnAI, and LnAL-WHtR were approving indices for predicting pediatric MAFLD in all participants, boys and girls individually.

Conclusions: This study developed LR models 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.

What is known: • Most of the indicators predicting pediatric MAFLD are derived from the predictive indicators for NAFLD, but the diagnostic criteria for MAFLD and NAFLD are not exactly the same. • The accuracy of predictors based on routine physical examination and blood biochemical indicators to diagnose MAFLD is limited.

What is new: • This study developed indicators based on routine examination parameters that have approving performance for MAFLD, with AUC values exceeding 0.70.

Keywords: MAFLD; Machine learning; Overweight/obese; Pediatrics; Random forest.