ArticleNMR in biomedicine2025
Urinary Metabolic Biomarkers of Attentional Control in Children With Attention-Deficit/Hyperactivity Disorder: A Dimensional Approach Through
Article in NMR in biomedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Challenging the prescientific frameworks of criminal justice: neurobiology and criminolytic interventions in the legalome era.Frontiers in psychology · 2026Review
- Urinary Metabolic Biomarkers of Attentional Control in Children With Attention-Deficit/Hyperactivity Disorder: A Dimensional Approach ThroughNMR in biomedicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.