Predicting hepatic steatosis in a racially and ethnically diverse cohort of adolescent girls

Abstract

Objective: To develop a risk assessment model for early detection of hepatic steatosis using common anthropometric and metabolic markers.

Study design: This was a cross-sectional study of 134 adolescent and young adult females, age 11-22 years (mean 13.3±2 years) from a middle school and clinics in Madison, Wisconsin. 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. Hepatic fat was quantified using magnetic resonance imaging proton density fat fraction (MR-PDFF). Hepatic steatosis was defined as MR-PDFF>5.5%. 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. Classification and regression tree methodology was used to construct a decision tree for predicting hepatic steatosis.

Results: MR-PDFF revealed hepatic steatosis in 16% of subjects (27% overweight, 3% nonoverweight). Hispanic ethnicity conferred an OR of 4.26 (95% CI, 1.65-11.04; P=.003) for hepatic steatosis. BMI and ALT did not independently predict hepatic steatosis. A BMI>85% combined with ALT>65 U/L had 9% sensitivity, 100% specificity, and 100% PPV. Lowering the ALT value to 24 U/L increased the sensitivity to 68%, but reduced the PPV to 47%. A risk assessment model incorporating fasting insulin, total cholesterol, WC, and ethnicity increased sensitivity to 64%, specificity to 99% and PPV to 93%.

Conclusion: A risk assessment model can increase specificity, sensitivity, and PPV for identifying the risk of hepatic steatosis and guide the efficient use of biopsy or imaging for early detection and intervention.

Figure 1

Risk assessment strategy incorporating readably available clinical measures improves prediction of hepatic steatosis risk. Sensitivity = 64%, Specificity = 99%, Positive predictive value = 93%, Negative predictive value = 95% *This is equivalent to a fasting insulin value 2 standard deviations above the mean.