Identify Metabolites with Diagnostic Value by GC-MS in Attention Deficit Hyperactivity Disorder
Poster No:
260
Submission Type:
Abstract Submission
Authors:
Yi An Hung1, Susan Shur-Fen Gau1, Chi-Yung Shang1
Institutions:
1National Taiwan University Hospital, Taipei, Taiwan
First Author:
Co-Author(s):
Introduction:
Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by core symptoms of inattention, hyperactivity, and impulsivity. (Kieling & Rohde, 2012) It is one of the most prevalent psychiatric disorders in children and adolescents, (Chen et al., 2019; Drechsler et al., 2020) profoundly affecting their quality of life as well as that of their families. (Erskine et al., 2016) Despite its significant impact, the diagnosis of ADHD primarily relies on behavioral assessments, (Drechsler et al., 2020) which have been questioned for their consistency and accuracy. (Chen et al., 2023; Parlatini et al., 2024) Extensive and ongoing research in multi-omics, brain imaging, and electrophysiology has been dedicated to identifying reliable biomarkers and uncovering the underlying pathophysiological mechanisms, yet findings remain unsatisfactory. (Cortese et al., 2023; Michelini et al., 2022; Parlatini et al., 2024) Metabolomics is particularly promising given that it collects the end products from interactions between biological factors and the environment and reflects the dynamic course of a disorder. (Cortese et al., 2023; Predescu et al., 2024) This study seeks to identify biomarkers by metabolomics approach that could enhance diagnostic precision and provide insights into the potential pathophysiological mechanisms underlying ADHD.
Methods:
80 ADHD subjects and 80 healthy controls, aged 7-18 years and drug-naïve, were recruited from the Department of Child Psychiatry of National Taiwan University Hospital. Clinical symptoms were rated by the ADHD-Rating Scale-IV (SNAP-IV) and Clinical Global Impression (CGI). Peripheral blood samples from the participants were collected and processed by the gas chromatography–mass spectrometry (GC-MS). Data were cleansed and then analyzed by the Wilcoxon test and the Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) to identify significant metabolites. The receiver operating characteristic (ROC) curve with the area under curve (AUC) was calculated for diagnostic efficacy. Relationship between the metabolites and symptoms were explored by the Spearman correlation.
Results:
Overall, six metabolites, hypoxanthine, D-mannose, succinic acid, N-methylalanine, L-proline, and O-phosphocolamine, were identified as significantly different between ADHD and control group. Hypoxanthine and D-mannose were down-regulated, while the other four were up-regulated in ADHD, comparing to control group. Among the six metabolites, three showed good diagnostic efficacy: succinic acid (AUC: 0.75, 95% CI: 0.67-0.83), hypoxanthine (AUC: 0.84, 95% CI: 0.78-0.90), and D.mannose (AUC: 0.83, 95% CI: 0.76-0.89). Correlation between the metabolites with ADHD symptoms were evaluated, showing succinic acid with positive correlation (r=0.46~0.47, p<0.05) and hypoxanthine (r=-0.52~-0.53, p<0.05) and D.mannose (r=-0.59~-0.63, p<0.05) with negative correlation with scores in SNAP-IV and CGI.
·Figure 1. OPLS-DA score plot from MetaboAnalyst5.0.
Conclusions:
Our study found three metabolites with satisfactory diagnostic efficacy and correlation with ADHD symptoms. Future studies are needed to focus on the validation of the findings and the investigation on the roles of these metabolites in ADHD pathogenesis.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Modeling and Analysis Methods:
Other Methods
Physiology, Metabolism and Neurotransmission:
Physiology, Metabolism and Neurotransmission Other 2
Keywords:
Attention Deficit Disorder
Development
Informatics
Statistical Methods
Univariate
Other - mass spectrometry
1|2Indicates the priority used for review
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Erskine, H. E., Norman, R. E., Ferrari, A. J., Chan, G. C., Copeland, W. E., Whiteford, H. A., & Scott, J. G. (2016). Long-Term Outcomes of Attention-Deficit/Hyperactivity Disorder and Conduct Disorder: A Systematic Review and Meta-Analysis. J Am Acad Child Adolesc Psychiatry, 55(10), 841-850. https://doi.org/10.1016/j.jaac.2016.06.016
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Michelini, G., Norman, L. J., Shaw, P., & Loo, S. K. (2022). Treatment biomarkers for ADHD: Taking stock and moving forward. Transl Psychiatry, 12(1), 444. https://doi.org/10.1038/s41398-022-02207-2
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Predescu, E., Vaidean, T., Rapciuc, A. M., & Sipos, R. (2024). Metabolomic Markers in Attention-Deficit/Hyperactivity Disorder (ADHD) among Children and Adolescents-A Systematic Review. Int J Mol Sci, 25(8). https://doi.org/1