Targeting the metabolic profile of amino acids to identify the key metabolic characteristics in cerebral palsy

Dan Wang; Juan Song; Ye Cheng; Yiran Xu; Lili Song; Yimeng Qiao; Bingbing Li; Lei Xia; Ming Li; Jin Zhang; Yu Su; Ting Wang; Jian Ding; Xiaoyang Wang; Sujuan Wang; Changlian Zhu; Qinghe Xing

doi:10.3389/fnmol.2023.1237745

Targeting the metabolic profile of amino acids to identify the key metabolic characteristics in cerebral palsy

Front Mol Neurosci. 2023 Aug 17:16:1237745. doi: 10.3389/fnmol.2023.1237745. eCollection 2023.

Authors

Dan Wang¹, Juan Song², Ye Cheng¹, Yiran Xu², Lili Song³, Yimeng Qiao^{1

2}, Bingbing Li², Lei Xia², Ming Li², Jin Zhang¹, Yu Su¹, Ting Wang¹, Jian Ding¹, Xiaoyang Wang^{2

4}, Sujuan Wang¹, Changlian Zhu^{2

5}, Qinghe Xing^{1

6}

Affiliations

¹ Children's Hospital of Fudan University and Institutes of Biomedical Sciences of Fudan University, Shanghai, China.
² Henan Key Laboratory of Child Brain Injury and Henan Pediatric Clinical Research Center, Department of Pediatrics, The 3rd Affiliated Hospital and Institute of Neuroscience, Zhengzhou University, Zhengzhou, China.
³ Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China.
⁴ Centre of Perinatal Medicine and Health, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
⁵ Center for Brain Repair and Rehabilitation, Institute of Neuroscience and Physiology, University of Gothenburg, Gothenburg, Sweden.
⁶ Shanghai Center for Women and Children's Health, Shanghai, China.

Abstract

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.

Keywords: amino acid metabolism; biomarker; cerebral palsy; diagnosis; preterm birth; targeted quantification; tryptophan; β-amino-isobutyric acid.