Development of a Plasma Screening Panel for Pediatric Nonalcoholic Fatty Liver Disease Using Metabolomics

• Published in: Hepatology communications 2019-10, Vol.3 (10), p.1311-1321
• Main Authors: Khusial, Richard D., Cioffi, Catherine E., Caltharp, Shelley A., Krasinskas, Alyssa M., Alazraki, Adina, Knight‐Scott, Jack, Cleeton, Rebecca, Castillo‐Leon, Eduardo, Jones, Dean P., Pierpont, Bridget, Caprio, Sonia, Santoro, Nicola, Akil, Ayman, Vos, Miriam B.
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Chromatography; Fasting; Genotype & phenotype; Kidney diseases; Liver diseases; Machine learning; Mass spectrometry; Metabolism; Metabolites; NMR; Nuclear magnetic resonance; Obesity; Original; Pediatrics; Review boards; Scientific imaging; Studies; Teenagers
• ISSN: 2471-254X; 2471-254X
• DOI: 10.1002/hep4.1417

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. Currently, pediatric NAFLD is a diagnosis of exclusion and there is a need for cost‐effective screening tests to facilitate early detection. Here, we aimed to develop a pediatric NAFLD screening panel by applying machine learning to metabolomics and clinical data from a cross‐sectional cohort of 559 children and adolescents (2‐25 yrs). The resulting model consisted of 11 metabolite features and 3 clinic