Evidence map›Paper›PMID 42507172›Full record

ArticleJournal of neural transmission (Vienna, Austria : 1996)2026

Transdiagnostic monoamine-based subtyping for attention-deficit/hyperactivity disorder and autism spectrum disorder via unsupervised machine learning.

Masatoshi Yamashita, Qiulu Shou, Sayo Hamatani, Hideo Matsuzaki, Masanori Fujieda, Yoshiyuki Hirano, Kuriko Kagitani-Shimono, Hidehiko Okazawa, Yoshifumi Mizuno

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Article in Journal of neural transmission (Vienna, Austria : 1996), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

9 authors.

Masatoshi YamashitaResearch Center for Child Mental Development, University of Fukui, Fukui, Japan. ymasa09@hama-med.ac.jp.
Qiulu ShouResearch Center for Child Mental Development, University of Fukui, Fukui, Japan.
Sayo HamataniResearch Center for Child Mental Development, University of Fukui, Fukui, Japan.
Hideo MatsuzakiResearch Center for Child Mental Development, University of Fukui, Fukui, Japan.
Masanori FujiedaResearch Center for Child Mental Development, University of Fukui, Fukui, Japan.
Yoshiyuki HiranoUnited Graduate School of Child Development, The University of Osaka, Kanazawa University, Hamamatsu University School of Medicine, Chiba University, University of Fukui, Osaka, Japan.
Kuriko Kagitani-ShimonoUnited Graduate School of Child Development, The University of Osaka, Kanazawa University, Hamamatsu University School of Medicine, Chiba University, University of Fukui, Osaka, Japan.
Hidehiko OkazawaUnited Graduate School of Child Development, The University of Osaka, Kanazawa University, Hamamatsu University School of Medicine, Chiba University, University of Fukui, Osaka, Japan.
Yoshifumi MizunoResearch Center for Child Mental Development, University of Fukui, Fukui, Japan. mizunoy@u-fukui.ac.jp.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neuroimaging and molecular studies have examined the etiology of attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). However, their findings remain inconsistent because of within-disorder heterogeneity and cross-disorder phenotypic overlap. We sought to identify monoamine-based subtypes across ADHD and ASD and clarify their brain structural characteristics. In 83 children with ADHD and/or ASD, we applied unsupervised machine learning (NbClust with K-means) to identify neurodevelopmental disorder (NDD) phenotypes using urinary monoamine metabolite (MM) profiles. Behavioral symptoms, cognitive performance, cortical surface area, and gray matter volume (GMV) were evaluated for each NDD phenotype and for 83 typically developing (TD) children as controls. Clustering identified two urinary MM-defined NDD phenotypes: NDD-A (n = 18, including 5 ADHD, 2 ASD, and 11 ADHD + ASD cases), characterized by high levels of 4-hydroxy-3-methoxyphenylglycol, 5-hydroxyindoleacetic acid, and homovanillic acid, and NDD-B (n = 65, including 16 ADHD, 19 ASD, and 30 ADHD + ASD cases), characterized by low levels of these metabolites. Urinary 4-hydroxy-3-methoxyphenylglycol levels correlated positively with social communication difficulties in NDD-A. NDD-B showed significantly lower cognitive control, cognitive flexibility, and inhibitory control than TD. Structurally, compared with TD, NDD-A showed significant surface area enlargement in the isthmus cingulate gyrus, whereas NDD-B exhibited significant GMV reductions primarily in fronto-opercular/orbitofrontal regions, with additional reductions in the superior parietal lobule and supramarginal gyrus. These findings suggest that urinary MM-defined NDD-A and NDD-B phenotypes are associated with distinct cognitive and brain structural characteristics. Such phenotype specificity may provide a novel framework for understanding within-disorder heterogeneity and cross-disorder phenotypic overlaps.

Indexed as

Attention Deficit Disorder with HyperactivityAutism Spectrum DisorderBiogenic MonoaminesUnsupervised Machine LearningAdolescentChildFemaleGray MatterHumansMagnetic Resonance ImagingMalePhenotypeBiogenic MonoaminesBrain structureCross-disorder phenotypic overlapMonoamine metaboliteNeurodevelopmental disorderUnsupervised machine learning

Identifiers

PMID42507172
PMCPMC13545128

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