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

Abstract

Enhancing the understanding of attention-deficit/hyperactivity disorder (ADHD) by linking biological processes with behavioral manifestations is a primary objective of the Research Domain Criteria (RDoC) framework, which aims to transcend traditional diagnostic categories and enable a more precise understanding of mental disorders. This study aimed to replicate five data-driven profiles of attentional control in school-aged children and, for the first time, to explore associated metabolic biomarkers. Understanding these profiles and their biological underpinnings can become critical for improving ADHD diagnosis and developing new targeted interventions. A clinically well-characterized sample of 83 children with (n = 37) and without (n = 46) diagnosed ADHD completed a virtual reality continuous performance test (VR-CPT) and provided urine samples for analysis. Clustering analyses of VR-CPT data identified and replicated five distinct attentional control subgroups, two of which-ADHD-IMP and ADHD-SP-exhibited clinically significant impairments in attention and hyperactivity but opposite performance profiles in response inhibition and latency of response. NMR-based metabolomics further revealed that children in the ADHD-IMP subgroup exhibited a distinct urinary metabolic signature, with alterations in metabolites such as 3-indoxylsulfate, N-phenylacetylglycine, 3-methyl-2-oxovalerate, creatine, creatinine, pseudouridine, and trigonelline. These compounds are potentially linked to microbial activity, energy metabolism, and oxidative stress, biological pathways increasingly recognized in ADHD pathophysiology. Although no direct association emerged between these metabolites and behavioral clusters, combining both data types using machine learning, particularly Logistic Regression, substantially improved classification accuracy compared to using behavioral data alone. These findings highlight the potential of integrating behavioral and molecular markers to refine ADHD characterization and move toward more individualized approaches.

Keywords: ADHD; NMR; RDoC; VR‐CPT; cluster analysis; diagnosis; metabolomics; urine.

FIGURE 1

Attentional control profiles measured by the virtual CPT AULA according to the five‐cluster solution. (A) 10% trimmed mean values of AULA main indices (t scores): omission errors, the standard deviation of reaction time (SDRT), time deviating the attentional focus from the blackboard, mean RT, commission errors, and total head movements. Error bars represent the 10% trimmed SEM. t scores ≥ 61 represent a clinically low performance. Dashed lines indicate cut‐offs for risk of attention problems (> 60 = at risk; > 70 = high risk). (B) Percentage distribution of each cluster in ADHD and control groups.

FIGURE 2

Representative ¹H NMR spectra of a urine sample. Assignments—(1) 3‐methyl‐2‐oxovalerate; (2) leucine; (3) isoleucine; (4) valine; (5) 3‐hydroxyisobutyrate; (6) isopropanol; (7) 3‐hydroxybutyrate; (8) fucose; (9) methylmalonate; (10) 3‐hydroxyisovalerate; (11) threonine; (12) lactate; (13) 2‐hydroxyisobutyrate; (14) 2‐phenylpropionate; (15) alanine; (16) lysine; (17) arginine; (18) acetate; (19) glutamate; (20) methionine; (21) glutamine; (22) acetone; (23) succinate; (24) pyruvate; (25) citrate; (26) DMA (dimethylamine); (27) TMA (trimethylamine); (28) creatinine; (29) creatine; (30) carnitine; (31) TMAO (trimethylamine‐N‐oxide); (32) taurine; (33) betaine; (34) phenylalanine; (35) methanol; (36) 4‐hydroxyphenylacetate; (37) glycine; (38) N‐phenylacetylglicine; (39) 3‐methylhistidine; (40) arabinose; (41) pseudouridine; (42) trigonelline; (43) cis‐aconitate; (44) urea; (45) tyrosine; (46) histidine; (47) 3‐indoxylsulfate; (48) tryptophan, (49) hippurate; (50) hypoxanthine; (51) formate.

FIGURE 3

PCA scores plot applied to the urine ¹H NMR dataset, scaled to Unit Variance. Different data‐driven profile groups were illustrated (sluggish: green; ADHD‐IMP: dark blue; high: red; average: yellow; ADHD‐SP: light blue), as well as the traditional ADHD diagnosis (ADHD: Δ, control: Ο).

FIGURE 4

Multiclass ROC curves for the eight machine learning models generated using combined metabolite and behavioral markers as input data. The respective AUC values for each model are also shown, illustrating their effectiveness in class discrimination.