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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...

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Autores principales: 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
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
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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.
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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
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