Metabolomic Biomarker Discovery for ADHD Diagnosis Using Interpretable Machine Learning

TL;DR

This study develops an interpretable machine learning approach using urinary metabolomics to identify biochemical signatures for ADHD, achieving high accuracy and providing insights into underlying biological pathways.

Contribution

It introduces a novel Closest Resemblance classifier with embedded feature selection for ADHD diagnosis based on metabolomics data, outperforming traditional classifiers.

Findings

Achieved AUC > 0.97 with 14 metabolites

Identified key metabolites linked to dopaminergic and amino acid pathways

Demonstrated model's potential for clinical diagnostic integration

Abstract

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.