Cargando…
Targeting the metabolic profile of amino acids to identify the key metabolic characteristics in cerebral palsy
BACKGROUND: Cerebral palsy (CP) is a neurodevelopmental disorder characterized by motor impairment. In this study, we aimed to describe the characteristics of amino acids (AA) in the plasma of children with CP and identify AA that could play a potential role in the auxiliary diagnosis and treatment...
Autores principales: | , , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10470834/ https://www.ncbi.nlm.nih.gov/pubmed/37664242 http://dx.doi.org/10.3389/fnmol.2023.1237745 |
_version_ | 1785099770249871360 |
---|---|
author | Wang, Dan Song, Juan Cheng, Ye Xu, Yiran Song, Lili Qiao, Yimeng Li, Bingbing Xia, Lei Li, Ming Zhang, Jin Su, Yu Wang, Ting Ding, Jian Wang, Xiaoyang Wang, Sujuan Zhu, Changlian Xing, Qinghe |
author_facet | Wang, Dan Song, Juan Cheng, Ye Xu, Yiran Song, Lili Qiao, Yimeng Li, Bingbing Xia, Lei Li, Ming Zhang, Jin Su, Yu Wang, Ting Ding, Jian Wang, Xiaoyang Wang, Sujuan Zhu, Changlian Xing, Qinghe |
author_sort | Wang, Dan |
collection | PubMed |
description | BACKGROUND: Cerebral palsy (CP) is a neurodevelopmental disorder characterized by motor impairment. In this study, we aimed to describe the characteristics of amino acids (AA) in the plasma of children with CP and identify AA that could play a potential role in the auxiliary diagnosis and treatment of CP. METHODS: Using high performance liquid chromatography, we performed metabolomics analysis of AA in plasma from 62 CP children and 60 healthy controls. Univariate and multivariate analyses were then applied to characterize different AA. AA markers associated with CP were then identified by machine learning based on the Lasso regression model for the validation of intra-sample interactions. Next, we calculated a discriminant formula and generated a receiver operating characteristic (ROC) curve based on the marker combination in the discriminant diagnostic model. RESULTS: A total of 33 AA were detected in the plasma of CP children and controls. Compared with controls, 5, 7, and 10 different AA were identified in total participants, premature infants, and full-term infants, respectively. Of these, β-amino-isobutyric acid [p = 2.9*10((−4)), Fold change (FC) = 0.76, Variable importance of protection (VIP) = 1.75], tryptophan [p = 5.4*10((−4)), FC = 0.87, VIP = 2.22], and asparagine [p = 3.6*10((−3)), FC = 0.82, VIP = 1.64], were significantly lower in the three groups of CP patients than that in controls. The combination of β-amino-isobutyric acid, tryptophan, and taurine, provided high levels of diagnostic classification and risk prediction efficacy for preterm children with an area under the curve (AUC) value of 0.8741 [95% confidence interval (CI): 0.7322–1.000]. The discriminant diagnostic formula for preterm infant with CP based on the potential marker combination was defined by p = 1/(1 + e(−(8.295–0.3848* BAIBA-0.1120*Trp + 0.0108*Tau))). CONCLUSION: Full-spectrum analysis of amino acid metabolomics revealed a distinct profile in CP, including reductions in the levels of β-amino-isobutyric acid, tryptophan, and taurine. Our findings shed new light on the pathogenesis and diagnosis of premature infants with CP. |
format | Online Article Text |
id | pubmed-10470834 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-104708342023-09-01 Targeting the metabolic profile of amino acids to identify the key metabolic characteristics in cerebral palsy Wang, Dan Song, Juan Cheng, Ye Xu, Yiran Song, Lili Qiao, Yimeng Li, Bingbing Xia, Lei Li, Ming Zhang, Jin Su, Yu Wang, Ting Ding, Jian Wang, Xiaoyang Wang, Sujuan Zhu, Changlian Xing, Qinghe Front Mol Neurosci Molecular Neuroscience BACKGROUND: Cerebral palsy (CP) is a neurodevelopmental disorder characterized by motor impairment. In this study, we aimed to describe the characteristics of amino acids (AA) in the plasma of children with CP and identify AA that could play a potential role in the auxiliary diagnosis and treatment of CP. METHODS: Using high performance liquid chromatography, we performed metabolomics analysis of AA in plasma from 62 CP children and 60 healthy controls. Univariate and multivariate analyses were then applied to characterize different AA. AA markers associated with CP were then identified by machine learning based on the Lasso regression model for the validation of intra-sample interactions. Next, we calculated a discriminant formula and generated a receiver operating characteristic (ROC) curve based on the marker combination in the discriminant diagnostic model. RESULTS: A total of 33 AA were detected in the plasma of CP children and controls. Compared with controls, 5, 7, and 10 different AA were identified in total participants, premature infants, and full-term infants, respectively. Of these, β-amino-isobutyric acid [p = 2.9*10((−4)), Fold change (FC) = 0.76, Variable importance of protection (VIP) = 1.75], tryptophan [p = 5.4*10((−4)), FC = 0.87, VIP = 2.22], and asparagine [p = 3.6*10((−3)), FC = 0.82, VIP = 1.64], were significantly lower in the three groups of CP patients than that in controls. The combination of β-amino-isobutyric acid, tryptophan, and taurine, provided high levels of diagnostic classification and risk prediction efficacy for preterm children with an area under the curve (AUC) value of 0.8741 [95% confidence interval (CI): 0.7322–1.000]. The discriminant diagnostic formula for preterm infant with CP based on the potential marker combination was defined by p = 1/(1 + e(−(8.295–0.3848* BAIBA-0.1120*Trp + 0.0108*Tau))). CONCLUSION: Full-spectrum analysis of amino acid metabolomics revealed a distinct profile in CP, including reductions in the levels of β-amino-isobutyric acid, tryptophan, and taurine. Our findings shed new light on the pathogenesis and diagnosis of premature infants with CP. Frontiers Media S.A. 2023-08-17 /pmc/articles/PMC10470834/ /pubmed/37664242 http://dx.doi.org/10.3389/fnmol.2023.1237745 Text en Copyright © 2023 Wang, Song, Cheng, Xu, Song, Qiao, Li, Xia, Li, Zhang, Su, Wang, Ding, Wang, Wang, Zhu and Xing. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Molecular Neuroscience Wang, Dan Song, Juan Cheng, Ye Xu, Yiran Song, Lili Qiao, Yimeng Li, Bingbing Xia, Lei Li, Ming Zhang, Jin Su, Yu Wang, Ting Ding, Jian Wang, Xiaoyang Wang, Sujuan Zhu, Changlian Xing, Qinghe Targeting the metabolic profile of amino acids to identify the key metabolic characteristics in cerebral palsy |
title | Targeting the metabolic profile of amino acids to identify the key metabolic characteristics in cerebral palsy |
title_full | Targeting the metabolic profile of amino acids to identify the key metabolic characteristics in cerebral palsy |
title_fullStr | Targeting the metabolic profile of amino acids to identify the key metabolic characteristics in cerebral palsy |
title_full_unstemmed | Targeting the metabolic profile of amino acids to identify the key metabolic characteristics in cerebral palsy |
title_short | Targeting the metabolic profile of amino acids to identify the key metabolic characteristics in cerebral palsy |
title_sort | targeting the metabolic profile of amino acids to identify the key metabolic characteristics in cerebral palsy |
topic | Molecular Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10470834/ https://www.ncbi.nlm.nih.gov/pubmed/37664242 http://dx.doi.org/10.3389/fnmol.2023.1237745 |
work_keys_str_mv | AT wangdan targetingthemetabolicprofileofaminoacidstoidentifythekeymetaboliccharacteristicsincerebralpalsy AT songjuan targetingthemetabolicprofileofaminoacidstoidentifythekeymetaboliccharacteristicsincerebralpalsy AT chengye targetingthemetabolicprofileofaminoacidstoidentifythekeymetaboliccharacteristicsincerebralpalsy AT xuyiran targetingthemetabolicprofileofaminoacidstoidentifythekeymetaboliccharacteristicsincerebralpalsy AT songlili targetingthemetabolicprofileofaminoacidstoidentifythekeymetaboliccharacteristicsincerebralpalsy AT qiaoyimeng targetingthemetabolicprofileofaminoacidstoidentifythekeymetaboliccharacteristicsincerebralpalsy AT libingbing targetingthemetabolicprofileofaminoacidstoidentifythekeymetaboliccharacteristicsincerebralpalsy AT xialei targetingthemetabolicprofileofaminoacidstoidentifythekeymetaboliccharacteristicsincerebralpalsy AT liming targetingthemetabolicprofileofaminoacidstoidentifythekeymetaboliccharacteristicsincerebralpalsy AT zhangjin targetingthemetabolicprofileofaminoacidstoidentifythekeymetaboliccharacteristicsincerebralpalsy AT suyu targetingthemetabolicprofileofaminoacidstoidentifythekeymetaboliccharacteristicsincerebralpalsy AT wangting targetingthemetabolicprofileofaminoacidstoidentifythekeymetaboliccharacteristicsincerebralpalsy AT dingjian targetingthemetabolicprofileofaminoacidstoidentifythekeymetaboliccharacteristicsincerebralpalsy AT wangxiaoyang targetingthemetabolicprofileofaminoacidstoidentifythekeymetaboliccharacteristicsincerebralpalsy AT wangsujuan targetingthemetabolicprofileofaminoacidstoidentifythekeymetaboliccharacteristicsincerebralpalsy AT zhuchanglian targetingthemetabolicprofileofaminoacidstoidentifythekeymetaboliccharacteristicsincerebralpalsy AT xingqinghe targetingthemetabolicprofileofaminoacidstoidentifythekeymetaboliccharacteristicsincerebralpalsy |