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. 2019 Apr:207:64-70.
doi: 10.1016/j.jpeds.2018.11.021. Epub 2018 Dec 14.

Prevalence of Nonalcoholic Fatty Liver Disease in Children with Obesity

Affiliations

Prevalence of Nonalcoholic Fatty Liver Disease in Children with Obesity

Elizabeth L Yu et al. J Pediatr. 2019 Apr.

Abstract

Objectives: To determine the prevalence of nonalcoholic fatty liver disease (NAFLD) in children with obesity because current estimates range from 1.7% to 85%. A second objective was to evaluate the diagnostic accuracy of alanine aminotransferase (ALT) for NAFLD in children with obesity.

Study design: We evaluated children aged 9-17 years with obesity for the presence of NAFLD. Diseases other than NAFLD were excluded by history and laboratories. Hepatic steatosis was measured by liver magnetic resonance imaging proton density fat fraction. The diagnostic accuracy of ALT for detecting NAFLD was evaluated.

Results: The study included 408 children with obesity that had a mean age of 13.2 years and mean body mass index percentile of 98.0. The study population had a mean ALT of 32 U/L and median hepatic magnetic resonance imaging proton density fat fraction of 3.7%. The estimated prevalence of NAFLD was 26.0% (95% CI 24.2%-27.7%), 29.4% in male patients (CI 26.1%-32.7%) and 22.6% in female patients (CI 16.0%-29.1%). Optimal ALT cut-point was 42 U/L (47.8% sensitivity, 93.2% specificity) for male and 30 U/L (52.1% sensitivity, 88.8% specificity) for female patients. The classification and regression tree model with sex, ALT, and insulin had 80% diagnostic accuracy for NAFLD.

Conclusions: NAFLD is common in children with obesity, but NAFLD and obesity are not concomitant. In children with obesity, NAFLD is present in nearly one-third of boys and one-fourth of girls.

Keywords: BMI; NAFLD; pediatric.

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Conflict of interest statement

Conflict of Interest: Michael Middleton, MD consults for Bracco, Kowa, Median, Merge Healthcare, Novo Nordisk, Quantitative Insights; is a stockholder with General Electric and Pfizer, and has grant funding from Gilead and Guerbet.

Claude B. Sirlin, MD has industry research support from Bayer, GE, Philips and Siemens; consults for AMRA, Boehringer and Guerbet; is on the speaker’s bureau for Resoundant and has lab service agreements with Gilead, ICON, Intercept, Shire and Synageva.

Figures

Figure 1
Figure 1
Shown are ROC curves for ALT as a diagnostic tool for hepatic steatosis in children with obesity. Panel A shows the ROC for all children with an AUROC of 0.78. Panel B shows the ROC for girls with an AUROC of 0.75. Panel C shows the ROC for boys with an AUROC of 0.81.
Figure 2
Figure 2
Decision tree algorithm for NAFLD in children with obesity developed by machine learning via classification and regression tree modeling. The tree separates by sex (nodes 1 and 2), then by ALT U/L (nodes 3–9) and then by fasting insulin uU/ml (nodes 10–14). Each node shows the separation of NAFLD and not NAFLD based upon the specific parameters by total number and percentage.

Comment in

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