Metabolic profiles and prediction of failure to thrive of citrin deficiency with normal liver function based on metabolomics and machine learning.
Purpose: This study aimed to explore metabolite pathways and identify residual metabolites during the post-neonatal intrahepatic cholestasis caused by citrin deficiency (post-NICCD) phase, while developing a predictive model for failure to thrive (FTT) using selected metabolites. Method: A case-cont...
| Publicado en: | Nutrition & Metabolism Vol. 22; no. 1; pp. 1 - 13 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
BioMed Central
5/12/2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=185098769&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185098769 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17437075 1CYX jtl: Nutrition & Metabolism issn: 17437075 maglogo: N pubinfo: dt: 5/12/2025 vid: 22 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 185098769 185098769 185098769 10.1186/s12986-025-00928-x 185098769 ppf: 1 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Metabolic profiles and prediction of failure to thrive of citrin deficiency with normal liver function based on metabolomics and machine learning. aug: au: Wang, Peiyao Zhou, Duo Hu, Lingwei Ge, Pingping Cen, Ziyan Hu, Zhenzhen He, Qimin Zhou, Kejun Wu, Benqing Huang, Xinwen affil: https://ror.org/025fyfd20 Department of Genetics and Metabolism, Children's Hospital of Zhejiang University School of Medicine, National Clinical Research Center for Child Health, No. 3333 Binsheng Road, Binjiang District, 310052, Hangzhou City, Zhejiang Province, China sug: subj: Failure to Thrive Diagnosis Metabolomics Machine Learning Mutation Liver Physiology Prediction Models Cholestasis, Intrahepatic Diagnosis Postnatal Period Cholestasis, Intrahepatic Therapy Treatment Outcomes Human Case Control Studies Male Female Child, Preschool Child Random Forest Regression Biological Markers Blood ROC Curve Chromatography, Liquid Mass Spectrometry Amino Acids Blood Arginine Blood Alanine Blood Aspartic Acid Blood Descriptive Statistics Funding Source Child, Preschool: 2-5 years Child: 6-12 years Male Female ab: Purpose: This study aimed to explore metabolite pathways and identify residual metabolites during the post-neonatal intrahepatic cholestasis caused by citrin deficiency (post-NICCD) phase, while developing a predictive model for failure to thrive (FTT) using selected metabolites. Method: A case-control study was conducted from October 2020 to July 2024, including 16 NICCD patients, 31 NICCD-matched controls, 34 post-NICCD patients, and 70 post-NICCD-matched controls. Post-NICCD patients were further stratified into two groups based on growth outcomes. Biomarkers for FTT were identified using Lasso regression and random forest analysis. A non-invasive predictive model was developed, visualized as a nomogram, and internally validated using the enhanced bootstrap method. The model's performance was evaluated with receiver operating characteristic curves and calibration curves. Metabolite concentrations (amino acids, acylcarnitines, organic acids, and free fatty acids) were measured using liquid chromatography or ultra-performance liquid chromatography-tandem mass spectrometry. Results: The biosynthesis of unsaturated fatty acids was identified as the most significantly altered pathway in post-NICCD patients. Twelve residual metabolites altered during both NICCD and post-NICCD phases were identified, including: 2-hydroxyisovaleric acid, alpha-ketoisovaleric acid, C5:1, 3-methyl-2-oxovaleric acid, C18:1OH, C20:4, myristic acid, eicosapentaenoic acid, carnosine, hydroxylysine, phenylpyruvic acid, and 2-methylcitric acid. Lasso regression and random forest analysis identified kynurenine, arginine, alanine, and aspartate as the optimal biomarkers for predicting FTT in post-NICCD patients. The predictive model constructed with these four biomarkers demonstrated an AUC of 0.947. Conclusion: While post-NICCD patients recover clinically and biochemically, their metabolic profiles remain incompletely restored. The predictive model based on kynurenine, arginine, alanine, and aspartate provides robust diagnostic performance for detecting FTT in post-NICCD patients. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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