Classification and regression tree methodology was used to construct a decision tree for predicting hepatic steatosis. Outcome measures were sensitivity, specificity, and positive predictive value (PPV) of BMI, WC, ALT, fasting insulin, and ethnicity as predictors of hepatic steatosis, individually and combined, in a risk assessment model. Hepatic steatosis was defined as MR-PDFF>5.5%. Hepatic fat was quantified using magnetic resonance imaging proton density fat fraction (MR-PDFF). The ethnic distribution was 27% Hispanic and 73% non-Hispanic the racial distribution was 64% Caucasian, 31% African-American, and 5% Asian, Fasting glucose, fasting insulin, alanine aminotransferase (ALT), body mass index (BMI), waist circumference (WC), and other metabolic markers were assessed.

This was a cross-sectional study of 134 adolescent and young adult females, age 11-22 years (mean 13.3☒ years) from a middle school and clinics in Madison, Wisconsin. To develop a risk assessment model for early detection of hepatic steatosis using common anthropometric and metabolic markers.

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