Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease in children, but diagnosis is challenging due to limited availability of noninvasive biomarkers. Machine learning applied to high‐resolution metabolomics and clinical phenotype data offers a novel framework for developing a NAFLD screening panel in youth. Here, untargeted metabolomics by liquid chromatography–mass spectrometry was performed on plasma samples from a combined cross‐sectional sample of children and adolescents ages 2‐25 years old with NAFLD (n = 222) and without NAFLD (n = 337), confirmed by liver biopsy or magnetic resonance imaging. Anthropometrics, blood lipids, liver enzymes, and glucose and insulin metabolism were also assessed. A machine learning approach was applied to the metabolomics and clinical phenotype data sets, which were split into training and test sets, and included dimension reduction, feature selection, and classification model development. The selected metabolite features were the amino acids serine, leucine/isoleucine, and tryptophan; three putatively annotated compounds (dihydrothymine and two phospholipids); and two unknowns. The selected clinical phenotype variables were waist circumference, whole‐body insulin sensitivity index (WBISI) based on the oral glucose tolerance test, and blood triglycerides. The highest performing classification model was random forest, which had an area under the receiver operating characteristic curve (AUROC) of 0.94, sensitivity of 73%, and specificity of 97% for detecting NAFLD cases. A second classification model was developed using the homeostasis model assessment of insulin resistance substituted for the WBISI. Similarly, the highest performing classification model was random forest, which had an AUROC of 0.92, sensitivity of 73%, and specificity of 94%. Conclusion: The identified screening panel consisting of both metabolomics and clinical features has promising potential for screening for NAFLD in youth. Further development of this panel and independent validation testing in other cohorts are warranted.

Arrhythmogenic right ventricular cardiomyopathy/dysplasia (ARVC/D) is a heritable cardiomyopathy characterized by fibro-fatty replacement of right ventricular myocardium. Diagnostic criteria, established in 1994 and modified in 2010, are based on predominately adult manifestations of ARVC/D. The goal of this paper is to review a single-center experience with pediatric ARVC/D and propose modifications of current diagnostic criteria to appropriately include pediatric ARVC/D. We identified 16 pediatric cases of ARVC/D from our tertiary care center. Patient demographics, presentation, course, genetic testing, and family history were reviewed. Sixteen patients were diagnosed with ARVC/D through the modified diagnostic criteria, genetic testing, and pathology. Five patients had positive family histories. Five patients presented with cardiac arrest, and six were found to have ventricular tachycardia. Two patients presented with heart failure. Six autopsies, six explanted hearts, and three biopsies found massive fibro-fatty infiltration of the right ventricular wall. Six patients underwent heart transplantation, and two have received automatic implantable cardioverter defibrillator. Two patients had identifiable genetic mutations previously noted in the literature. One patient had a novel mutation of a known ARVC/D gene. Many pediatric patients do not meet the current ARVC/D diagnostic criteria, resulting in delays in diagnosis and treatment. The current criteria need further revision to encompass pediatric manifestations of ARVC/D. In our opinion, pathological and clinical findings alone are sufficient for accurate diagnosis of pediatric ARVC/D. Creating modified pediatric criteria would facilitate prompt diagnosis and management of ARVC/D and facilitate structured research with the goal of improving outcomes.

The tested non-invasive fibrosis scoring systems, some of which were originally designed for adult populations, did not adequately predict fibrosis in a paediatric cohort. Further development of risk prediction scores in children are needed for the management of paediatric patients and will likely need to be developed within a large paediatric data set in order to improve specificity and sensitivity.

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