Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder with limited objective diagnostic tools, highlighting the urgent need for objective, biology-based diagnostic frameworks in precision psychiatry. We integrate urinary metabolomics with an interpretable machine learning framework to identify biochemical signatures associated with ADHD. Targeted metabolomic profiles from 52 ADHD and 46 control participants were analyzed using a Closest Resemblance (CR) classifier with embedded feature selection. The CR model outperformed Random Forest and K-Nearest Neighbor classifiers, achieving an AUC > 0.97 based on a reduced panel of 14 metabolites. These metabolites including dopamine 4-sulfate, N-acetylaspartylglutamic acid, and citrulline map to dopaminergic neurotransmission and amino acid metabolism pathways, offering mechanistic insight into ADHD pathophysiology. The CR classifier's transparent decision boundaries and low computational cost support integration into targeted metabolomic assays and future point of care diagnostic platforms. Overall, this work demonstrates a translational framework combining metabolomics and interpretable machine learning to advance objective, biologically informed diagnostic strategies for ADHD.

翻译：注意缺陷多动障碍（ADHD）是一种普遍存在的神经发育障碍，其客观诊断工具有限，这凸显了在精准精神病学领域亟需建立客观的、基于生物学的诊断框架。本研究将尿液代谢组学与可解释机器学习框架相结合，以识别与ADHD相关的生化特征谱。我们使用嵌入特征选择的最相似度分类器，分析了来自52名ADHD参与者和46名对照参与者的靶向代谢组学数据。该CR模型的表现优于随机森林和K近邻分类器，基于一组精简的14种代谢物实现了AUC > 0.97。这些代谢物包括多巴胺-4-硫酸盐、N-乙酰天冬氨酰谷氨酸和瓜氨酸，它们映射到多巴胺能神经传递和氨基酸代谢通路，为ADHD的病理生理学提供了机制性见解。CR分类器透明的决策边界和较低的计算成本，有利于其整合到靶向代谢组学检测及未来的即时诊断平台中。总之，这项工作展示了一个结合代谢组学与可解释机器学习的转化框架，以推进针对ADHD的客观、基于生物学的诊断策略。