用代谢组学和可解释模型发现注意力缺陷诊断生物标志物
Metabolomic Biomarker Discovery for ADHD Diagnosis Using Interpretable Machine Learning
- 基于14种尿液代谢物构建可解释分类器，精准识别ADHD
- 模型AUC超0.97，优于随机森林与K近邻方法
- 结果揭示多巴胺与氨基酸代谢通路，适合临床转化
注意缺陷多动障碍（ADHD）是一种高发的神经发育障碍，缺乏客观的诊断工具，凸显了在精准精神病学中建立生物学基础诊断框架的迫切需求。本研究将尿液代谢组学与可解释机器学习框架结合，识别与ADHD相关的生化特征。对52名ADHD患者和46名健康对照者的靶向代谢组数据，采用嵌入式特征选择的最近相似性（CR）分类器进行分析。CR模型在14种代谢物组成的简化面板上表现优异，AUC超过0.97，优于随机森林和K-近邻分类器。这些代谢物包括多巴胺4-硫酸盐、乙酰天冬氨谷氨酸和瓜氨酸，均关联于多巴胺能神经传递与氨基酸代谢通路，为ADHD病理生理机制提供机理解释。CR分类器具有透明决策边界和低计算成本，支持集成到靶向代谢组检测及未来诊疗平台。本研究展示了一种结合代谢组学与可解释机器学习的转化框架，推动了客观、生物驱动的ADHD诊断策略发展。
原文摘要 · Abstract (English)
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.
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