#AI reads Urine# Urinary Metabolic Biomarkers of Attentional Control in Children With Attention-­Deficit/Hyperactivity Disorder

This article investigates the urinary metabolic biomarkers of attentional control in children with attention-deficit/hyperactivity disorder. By conducting a virtual reality continuous performance test on 83 school-aged children (37 with attention-deficit/hyperactivity disorder and 46 typical controls) and analyzing their urine samples, the study identifies five data-driven attentional control subgroups. Among them, the attention-deficit/hyperactivity disorder-impulsive and attention-deficit/hyperactivity disorder-slow processing subgroups show distinct behavioral differences. The former exhibits abnormalities in urinary metabolites such as 3-indoxylsulfate and N-phenylacetylglycine. Additionally, machine learning models combining metabolic and behavioral data demonstrate higher classification accuracy than those using behavioral data alone. These findings provide new directions for the precise diagnosis and personalized intervention of attention-deficit/hyperactivity disorder.

NMR Biomed. 2025 Aug;38(8):e70088. doi: 10.1002/nbm.70088.

Urinary Metabolic Biomarkers of Attentional Control in Children With Attention-Deficit/Hyperactivity Disorder: A Dimensional Approach Through 1H NMR-Based Metabolomics

Youhe Gao

Statement: During the preparation of this work the author(s) used Doubao / AI reading for summarizing the content. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.

For earlier AI Reads Urine articles：

https://www.keaipublishing.com/en/journals/advances-in-biomarker-sciences-and-technology/ai-reads-urine/