Metabolic profiles and prediction of failure to thrive of citrin deficiency with normal liver function based on metabolomics and machine learning
Abstract 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 ca...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-05-01
|
| Series: | Nutrition & Metabolism |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12986-025-00928-x |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849726140364619776 |
|---|---|
| author | Peiyao Wang Duo Zhou Lingwei Hu Pingping Ge Ziyan Cen Zhenzhen Hu Qimin He Kejun Zhou Benqing Wu Xinwen Huang |
| author_facet | Peiyao Wang Duo Zhou Lingwei Hu Pingping Ge Ziyan Cen Zhenzhen Hu Qimin He Kejun Zhou Benqing Wu Xinwen Huang |
| author_sort | Peiyao Wang |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-0694adac77d44cc9aa80ffa08ef9b27b |
| institution | DOAJ |
| issn | 1743-7075 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
| record_format | Article |
| series | Nutrition & Metabolism |
| spelling | doaj-art-0694adac77d44cc9aa80ffa08ef9b27b2025-08-20T03:10:17ZengBMCNutrition & Metabolism1743-70752025-05-0122111210.1186/s12986-025-00928-xMetabolic profiles and prediction of failure to thrive of citrin deficiency with normal liver function based on metabolomics and machine learningPeiyao Wang0Duo Zhou1Lingwei Hu2Pingping Ge3Ziyan Cen4Zhenzhen Hu5Qimin He6Kejun Zhou7Benqing Wu8Xinwen Huang9Department of Genetics and Metabolism, Children’s Hospital of Zhejiang University School of Medicine, National Clinical Research Center for Child HealthDepartment of Genetics and Metabolism, Children’s Hospital of Zhejiang University School of Medicine, National Clinical Research Center for Child HealthDepartment of Genetics and Metabolism, Children’s Hospital of Zhejiang University School of Medicine, National Clinical Research Center for Child HealthDepartment of Genetics and Metabolism, Children’s Hospital of Zhejiang University School of Medicine, National Clinical Research Center for Child HealthDepartment of Genetics and Metabolism, Children’s Hospital of Zhejiang University School of Medicine, National Clinical Research Center for Child HealthDepartment of Genetics and Metabolism, Children’s Hospital of Zhejiang University School of Medicine, National Clinical Research Center for Child HealthSchool of Geography Science and Geomatics Engineering, Suzhou University of Science and TechnologyHuman Metabolomics Institute, Inc.Children’s Medical Center, Shenzhen Guangming District People’s HospitalDepartment of Genetics and Metabolism, Children’s Hospital of Zhejiang University School of Medicine, National Clinical Research Center for Child HealthAbstract 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.https://doi.org/10.1186/s12986-025-00928-xCitrin deficiencyMetabolomicsFailure to thriveAmino acidsLipids |
| spellingShingle | Peiyao Wang Duo Zhou Lingwei Hu Pingping Ge Ziyan Cen Zhenzhen Hu Qimin He Kejun Zhou Benqing Wu Xinwen Huang Metabolic profiles and prediction of failure to thrive of citrin deficiency with normal liver function based on metabolomics and machine learning Nutrition & Metabolism Citrin deficiency Metabolomics Failure to thrive Amino acids Lipids |
| title | Metabolic profiles and prediction of failure to thrive of citrin deficiency with normal liver function based on metabolomics and machine learning |
| title_full | Metabolic profiles and prediction of failure to thrive of citrin deficiency with normal liver function based on metabolomics and machine learning |
| title_fullStr | Metabolic profiles and prediction of failure to thrive of citrin deficiency with normal liver function based on metabolomics and machine learning |
| title_full_unstemmed | Metabolic profiles and prediction of failure to thrive of citrin deficiency with normal liver function based on metabolomics and machine learning |
| title_short | Metabolic profiles and prediction of failure to thrive of citrin deficiency with normal liver function based on metabolomics and machine learning |
| title_sort | metabolic profiles and prediction of failure to thrive of citrin deficiency with normal liver function based on metabolomics and machine learning |
| topic | Citrin deficiency Metabolomics Failure to thrive Amino acids Lipids |
| url | https://doi.org/10.1186/s12986-025-00928-x |
| work_keys_str_mv | AT peiyaowang metabolicprofilesandpredictionoffailuretothriveofcitrindeficiencywithnormalliverfunctionbasedonmetabolomicsandmachinelearning AT duozhou metabolicprofilesandpredictionoffailuretothriveofcitrindeficiencywithnormalliverfunctionbasedonmetabolomicsandmachinelearning AT lingweihu metabolicprofilesandpredictionoffailuretothriveofcitrindeficiencywithnormalliverfunctionbasedonmetabolomicsandmachinelearning AT pingpingge metabolicprofilesandpredictionoffailuretothriveofcitrindeficiencywithnormalliverfunctionbasedonmetabolomicsandmachinelearning AT ziyancen metabolicprofilesandpredictionoffailuretothriveofcitrindeficiencywithnormalliverfunctionbasedonmetabolomicsandmachinelearning AT zhenzhenhu metabolicprofilesandpredictionoffailuretothriveofcitrindeficiencywithnormalliverfunctionbasedonmetabolomicsandmachinelearning AT qiminhe metabolicprofilesandpredictionoffailuretothriveofcitrindeficiencywithnormalliverfunctionbasedonmetabolomicsandmachinelearning AT kejunzhou metabolicprofilesandpredictionoffailuretothriveofcitrindeficiencywithnormalliverfunctionbasedonmetabolomicsandmachinelearning AT benqingwu metabolicprofilesandpredictionoffailuretothriveofcitrindeficiencywithnormalliverfunctionbasedonmetabolomicsandmachinelearning AT xinwenhuang metabolicprofilesandpredictionoffailuretothriveofcitrindeficiencywithnormalliverfunctionbasedonmetabolomicsandmachinelearning |