Urinary Metabolomic Study in a Healthy Children Population and Metabolic Biomarker Discovery of Attention-Deficit/Hyperactivity Disorder (ADHD) - Tian, Xiaoyi; Liu, Xiaoyan; Wang, Yan; Liu, Ying; Ma, Jie; Sun, Haidan; Li, Jing; Tang, Xiaoyue; Guo, Zhengguang; Sun, Wei; Zhang, Jishui; Song, Wenqi (Frontiers in Psychiatry, 2022)
Objectives: The knowledge of urinary metabolomic profiles of healthy children and adolescents plays a promising role in the fields of pediatrics. Metabolomics also has been used to diagnose disease, discover novel biomarkers and elucidate pathophysiological pathways. Attention deficit/hyperactivity disorder (ADHD) is one of the most common psychiatric disorders in childhood. However, large-sample urinary metabolomic studies in children with ADHD are relatively few. Here we aimed to find specific biomarkers for ADHD disease diagnosis in children and adolescents by urinary metabolomic profiling. Methods: We explored urine metabolome in 363 healthy children aging from 1~18 years and 76 ADHD patients by high-resolution mass spectrometry. Results: Metabolic pathways, such as arachidonic acid metabolism, steroid hormone biosynthesis and catecholamine biosynthesis, were found to be related to gender and age in healthy children. Urinary metabolites displaying the largest differences between ADHD patients and healthy controls belonged to the tyrosine, leucine and fatty acid metabolic pathways. A metabolite panel consisting of FAPy-adenine, 3-methylazelaic acid and phenylacetylglutamine was discovered to have good predictive ability for ADHD with the receiver operating characteristic–area under the curve (ROC–AUC) of 0.918. A panel of FAPy-adenine, N-acetylaspartylglutamic acid, dopamine 4-sulfate, aminocaproic acid and asparaginyl-leucine was used to establish a robust model for ADHD comorbid tic disorders and controls distinction with AUC of 0.918. Conclusions: Our data established a reference urinary metabolome resource in healthy children on the global age profile (aged 1-18 years old), which provided a critical foundation for future work to define the utility of metabolic profiles to predict the impact of environmental and other exposures on child health. Furthermore, we had identified potential biomarker candidates which were possibly able to aid clinicians in diagnosing childhood ADHD.