Association of Metabolic Signatures with Nonalcoholic Fatty Liver Disease in Pediatric Population
Several adult omics studies have been conducted to understand the pathophysiology of nonalcoholic fatty liver disease (NAFLD). However, the histological features of children are different from those of adults, and the onset and progression of pediatric NAFLD are not fully understood. In this study,...
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MDPI AG
2022-09-01
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| Series: | Metabolites |
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| Online Access: | https://www.mdpi.com/2218-1989/12/9/881 |
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| author | Woori Chae Kyung Jae Lee Ki Young Huh Jin Soo Moon Jae Sung Ko Joo-Youn Cho |
| author_facet | Woori Chae Kyung Jae Lee Ki Young Huh Jin Soo Moon Jae Sung Ko Joo-Youn Cho |
| author_sort | Woori Chae |
| collection | DOAJ |
| description | Several adult omics studies have been conducted to understand the pathophysiology of nonalcoholic fatty liver disease (NAFLD). However, the histological features of children are different from those of adults, and the onset and progression of pediatric NAFLD are not fully understood. In this study, we aimed to evaluate the metabolome profile and metabolic pathway changes associated with pediatric NAFLD to elucidate its pathophysiology and to develop machine learning-based NAFLD diagnostic models. We analyzed the metabolic profiles of healthy control, lean NAFLD, overweight control, and overweight NAFLD groups of children and adolescent participants (<i>N</i> = 165) by assessing plasma samples. Additionally, we constructed diagnostic models by applying three machine learning methods (ElasticNet, random forest, and XGBoost) and multiple logistic regression by using NAFLD-specific metabolic features, genetic variants, and clinical data. We identified 18 NAFLD-specific metabolic features and metabolic changes in lipid, glutathione-related amino acid, and branched-chain amino acid metabolism by comparing the control and NAFLD groups in the overweight pediatric population. Additionally, we successfully developed and cross-validated diagnostic models that showed excellent diagnostic performance (ElasticNet and random forest model: area under the receiver operating characteristic curve, 0.95). Metabolome changes in the plasma of pediatric patients with NAFLD are associated with the pathophysiology of the disease and can be utilized as a less-invasive approach to diagnosing the disease. |
| first_indexed | 2024-03-09T23:10:54Z |
| format | Article |
| id | doaj.art-9738f68c09604573ac1d06ae38b3be82 |
| institution | Directory Open Access Journal |
| issn | 2218-1989 |
| language | English |
| last_indexed | 2024-03-09T23:10:54Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metabolites |
| spelling | doaj.art-9738f68c09604573ac1d06ae38b3be822023-11-23T17:45:20ZengMDPI AGMetabolites2218-19892022-09-0112988110.3390/metabo12090881Association of Metabolic Signatures with Nonalcoholic Fatty Liver Disease in Pediatric PopulationWoori Chae0Kyung Jae Lee1Ki Young Huh2Jin Soo Moon3Jae Sung Ko4Joo-Youn Cho5Department of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Hospital, Seoul 03080, KoreaDepartment of Pediatrics, Hallym University Sacred Heart Hospital, Anyang 14068, KoreaDepartment of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Hospital, Seoul 03080, KoreaDepartment of Pediatrics, Seoul National University College of Medicine, Seoul 03080, KoreaDepartment of Pediatrics, Seoul National University College of Medicine, Seoul 03080, KoreaDepartment of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Hospital, Seoul 03080, KoreaSeveral adult omics studies have been conducted to understand the pathophysiology of nonalcoholic fatty liver disease (NAFLD). However, the histological features of children are different from those of adults, and the onset and progression of pediatric NAFLD are not fully understood. In this study, we aimed to evaluate the metabolome profile and metabolic pathway changes associated with pediatric NAFLD to elucidate its pathophysiology and to develop machine learning-based NAFLD diagnostic models. We analyzed the metabolic profiles of healthy control, lean NAFLD, overweight control, and overweight NAFLD groups of children and adolescent participants (<i>N</i> = 165) by assessing plasma samples. Additionally, we constructed diagnostic models by applying three machine learning methods (ElasticNet, random forest, and XGBoost) and multiple logistic regression by using NAFLD-specific metabolic features, genetic variants, and clinical data. We identified 18 NAFLD-specific metabolic features and metabolic changes in lipid, glutathione-related amino acid, and branched-chain amino acid metabolism by comparing the control and NAFLD groups in the overweight pediatric population. Additionally, we successfully developed and cross-validated diagnostic models that showed excellent diagnostic performance (ElasticNet and random forest model: area under the receiver operating characteristic curve, 0.95). Metabolome changes in the plasma of pediatric patients with NAFLD are associated with the pathophysiology of the disease and can be utilized as a less-invasive approach to diagnosing the disease.https://www.mdpi.com/2218-1989/12/9/881nonalcoholic fatty liver diseasehepatic steatosispediatric obesityplasma metabolomicsmachine learning |
| spellingShingle | Woori Chae Kyung Jae Lee Ki Young Huh Jin Soo Moon Jae Sung Ko Joo-Youn Cho Association of Metabolic Signatures with Nonalcoholic Fatty Liver Disease in Pediatric Population Metabolites nonalcoholic fatty liver disease hepatic steatosis pediatric obesity plasma metabolomics machine learning |
| title | Association of Metabolic Signatures with Nonalcoholic Fatty Liver Disease in Pediatric Population |
| title_full | Association of Metabolic Signatures with Nonalcoholic Fatty Liver Disease in Pediatric Population |
| title_fullStr | Association of Metabolic Signatures with Nonalcoholic Fatty Liver Disease in Pediatric Population |
| title_full_unstemmed | Association of Metabolic Signatures with Nonalcoholic Fatty Liver Disease in Pediatric Population |
| title_short | Association of Metabolic Signatures with Nonalcoholic Fatty Liver Disease in Pediatric Population |
| title_sort | association of metabolic signatures with nonalcoholic fatty liver disease in pediatric population |
| topic | nonalcoholic fatty liver disease hepatic steatosis pediatric obesity plasma metabolomics machine learning |
| url | https://www.mdpi.com/2218-1989/12/9/881 |
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