Multi-Omics Research on ADHD

ADHD was first described over a century ago in children with high activity levels and poor attention, forming the basis for its classification as a neurodevelopmental disorder.1

Early descriptions of the condition evolved from Hyperkinetic Reaction of Childhood according to the Diagnostic and Statistical Manual of Mental Disorders (DSM)-2nd edition (DSM-II) in 1968, and later as Attention Deficit Disorder (DSM-III) in 1980. Ongoing research refined its definition, leading to the current DSM-5 criteria, which emphasize persistent patterns of inattention, hyperactivity, and impulsivity.1,2

ADHD is a common condition that predominantly affects children and often persists into adulthood, with a prevalence of around 5% in children and approximately 2% to 4% in adults, though global meta-analyses suggest rates of 5-7% in children and about 2.5% in adults3,4. The disorder has the potential to cause significant personal, academic, occupational, and social impairment.1,2

ADHD diagnosis typically relies on behavioral observation, clinical assessment, and standardized rating scales. The condition is increasingly recognized as complex, influenced by genetic, neurobiological, and environmental factors, highlighting the limitations of relying solely on behavioral criteria.1,2

While conventional assessment tools provide valuable insights into symptom expression, they remain subjective and largely dependent on clinician interpretation or self- and parent-reports. This subjectivity introduces variability, particularly when symptoms overlap with those of other neurodevelopmental or mental health conditions such as anxiety, depression, or learning disorders.1,2

The absence of validated biological or digital biomarkers further limits diagnostic precision and can lead to both under- and overdiagnosis, especially in individuals with subtle, late-onset, or gender-specific presentations that do not align with traditional diagnostic frameworks.2

Moreover, variations in diagnostic criteria, cultural perceptions, and assessment practices between regions challenge reliability, highlighting the need for objective, data-driven methods to improve accuracy, minimize bias, and promote a more personalized approach to ADHD in diverse populations.1,2

Efforts to identify biological markers for ADHD have focused primarily on genetics, neuroimaging, and neurophysiology.

Several genetic studies have reported associations with polymorphisms in the dopamine transporter (DAT1), receptor D4 (DRD4), and receptor D5 (DRD5) genes. Neuroimaging research has revealed structural and neural alterations in prefrontal, striatal, and cerebellar regions, such as reduced cortical thickness and altered connectivity patterns.5

Electrophysiological investigations, including EEG and event-related potentials (ERPs), have indicated differences in attention-related brain network activity and cortical arousal.1

However, no biomarker has yet demonstrated adequate validity or specificity for routine clinical application, prompting exploration of integrated and multimodal approaches that combine omic, imaging, and clinical data.2,5

Proteomic, metabolomic, and lipidomic studies have begun to reveal early evidence of molecular alterations in ADHD, involving neurotransmission, immune regulation, oxidative balance, and energy metabolism.6,7

Proteomic analyses of blood, cerebrospinal fluid, and brain tissue have identified distinct, but still preliminary alterations in proteins involved in synaptic function, intracellular signaling, and metabolic regulation, particularly within dopaminergic, noradrenergic, and serotonergic pathways.2,5,6,7

Recent proteome-wide association studies have implicated proteins such as LSM6, GMPPB, CISD2, and ICA1L in ADHD risk, providing new molecular leads that require replication in larger cohorts.8

This supports catecholaminergic models of the disorder, which propose that disrupted dopamine and noradrenaline signaling in frontostriatal and prefrontal circuits contributes to the attentional, motivational, and inhibitory control deficits characteristic of ADHD.2,6

Specific variations in DAT1 (SLC6A3) and catechol-O-methyltransferase (COMT) expression or activity have been hypothesized to indicate altered catecholamine turnover and disrupted dopaminergic signaling, while changes in tryptophan hydroxylase (TPH) may affect serotonergic neurotransmission, potentially contributing to the characteristic symptoms of ADHD.5

Changes in inflammatory and immune-related proteins have also been demonstrated, including C-reactive protein (CRP), interleukins (ILs), and complement factors (CFs), indicating the presence of low-grade systemic inflammation.6 Some studies show that individuals with ADHD have reported reduced levels of key antioxidant enzymes, including glutathione peroxidase (GPx) and superoxide dismutase (SOD), reflecting a diminished capacity to counteract oxidative stress.7,9 However, meta-analyses show variability across cohorts, highlighting the need for larger, standardized studies.9 These protein-level findings have yet to be consistently replicated across larger cohorts, and their diagnostic value remains to be validated.

These findings suggest that immune dysregulation and oxidative imbalance may contribute to the underlying pathophysiology of ADHD, influencing brain development and function.

Metabolomic studies reveal distinctive biochemical signatures associated with ADHD, with perturbed levels of amino acids and their derivatives directly influencing dopamine and serotonin synthesis.7

Altered glutamate and glutamine concentrations have been reported in some studies to correlate with ADHD severity, suggesting increased neural excitability. Nevertheless, evidence across metabolomic datasets remains mixed and requires further confirmation. Changes in tryptophan metabolism, particularly shifts in kynurenine and related metabolites, may disrupt serotonergic pathways and mood regulation, while alterations in homocysteine levels may impair dopaminergic neurotransmission. This may reflect broader disturbances in neurotransmitter metabolism observed in ADHD.7,10

Lipidomics research reveals potential imbalances in phosphatidylcholines and polyunsaturated fatty acids (PUFAs), reflecting alterations in membrane composition, signal transduction, and neuroinflammatory processes, consistent with clinical findings of increased oxidative stress and dysregulated lipid peroxidation in ADHD.7,10 These lipidomic findings remain preliminary and will require validation in larger, longitudinal cohorts.

Current integrative multi-omics approaches are beginning to be applied to contextualize molecular findings within broader biological networks. By combining proteomic and metabolomic profiles with genomic, transcriptomic, and epigenomic data, they enable mapping of cross-level interactions, providing a comprehensive view of ADHD’s molecular landscape that may underlie phenotypic variability and differential treatment response.5

For example, integrating proteomic and genomic data from DRD4, DAT1 (SLC6A3), and COMT has been proposed to reveal molecular expression patterns linked to genetic risk factors in dopamine signaling.5

Transcriptomic analyses have revealed alterations in pathways related to synaptic vesicle cycling, neurotrophic signaling, and immune modulation. These transcriptional changes potentially correspond with shifts in protein abundance and metabolite profiles, linking gene expression to downstream molecular effects.5

Epigenetic studies have shown methylation changes in genes involved in neurotransmission and energy metabolism, suggesting that environmental and developmental factors may influence ADHD risk by altering protein expression and metabolic pathways, connecting gene regulation to proteomic and metabolomic signatures.11 Emerging reviews indicate that such integrative multi-omics designs are increasingly being used across neurodevelopmental disorders, including ADHD.12

Although truly integrated multi-omics studies in ADHD are still scarce, combining these datasets holds promise for uncovering convergent biological pathways and molecular subtypes. This systems-level perspective facilitates the identification of distinct neurochemical and inflammatory molecular markers, while emphasizing the current need for replication and validation before clinical translation, guiding more precise diagnosis and the development of targeted, mechanism-based interventions.6,7

Advances in systems biology and multi-omics research may begin to translate molecular discoveries into potential biomarker candidates for ADHD, creating opportunities to guide diagnosis and treatment with greater accuracy and confidence. Ongoing progress in data harmonization, analytical methods, and computational modeling is enhancing knowledge of ADHD’s biological complexity and supporting precision medicine strategies that enable patient-specific assessment, targeted interventions, and optimized health outcomes.

Future research will require larger, longitudinal, and demographically diverse cohorts to establish reproducible omic signatures and ensure clinical applicability.

Cabral, M.D.I., Liu, S., & Soares, N. (2020). Attention-deficit/hyperactivity disorder: diagnostic criteria, epidemiology, risk factors and evaluation in youth. Translational Pediatrics, 9(Suppl 1):S104-S113. https://tp.amegroups.org/article/view/30808/28327

Rivas-Vazquez, R.A., Diaz, S.G., Visser, M.M., & Rivas-Vazquez, A.A. (2023). Adult ADHD: Underdiagnosis of a Treatable Condition. Journal of Health Service Psychology, 49(1):11-19. https://link.springer.com/article/10.1007/s42843-023-00077-w

Salari, N. et al. (2023). Global prevalence of ADHD: A systematic review and meta-analysis. BMC Psychiatry, 23, 275. https://pubmed.ncbi.nlm.nih.gov/37081447/

Faraone, S.V., & Banaschewski, T. (2024). Attention-deficit/hyperactivity disorder. Nature Reviews Disease Primers, 10, 28. https://www.nature.com/articles/s41572-024-00495-0

Yadav, S.K. et al. (2021). Genetic variations influence brain changes in patients with attention-deficit hyperactivity disorder. Translational Psychiatry, 11, 349. https://www.nature.com/articles/s41398-021-01473-w

Schnorr, I. et al. (2024). Inflammatory biotype of ADHD is linked to chronic stress: a data-driven analysis of the inflammatory proteome. Translational Psychiatry, 14, 37. https://www.nature.com/articles/s41398-023-02729-3

Predescu, E. et al. (2024). Metabolomic markers in ADHD among children and adolescents - A systematic review. International Journal of Molecular Sciences, 25(8), 4385. https://www.mdpi.com/1422-0067/25/8/4385

Liu, J. Proteome-wide association studies have predicted that the protein abundance of LSM6, GMPPB, ICA1L, and CISD2 is associated with attention-deficit/hyperactivity disorder. Eur Child Adolesc Psychiatry 34, 721–728 (2025). https://link.springer.com/article/10.1007/s00787-024-02517-4

Joseph, N., Zhang-James, Y., Perl, A. & Faraone, S. V. (2015) ‘Oxidative Stress and ADHD: A Meta-Analysis’, Journal of Attention Disorders, 19(11), pp. 915-924. https://pmc.ncbi.nlm.nih.gov/articles/PMC5293138/

Grzymala, B. et al. (2025). Metabolomic and lipidomic profiling reveals convergent pathways in ADHD therapeutics. The Journal of Pharmacology and Experimental Therapeutics, 392(4), 103403. https://jpet.aspetjournals.org/article/S0022-3565(25)39616-3/fulltext

Prasad, S., & Kumminimana, R. (2025). Attention-deficit/hyperactivity disorder: insights, advances and challenges in research and practice. Advances in Psychiatry & Neurology, 34(3):196-206. https://www.termedia.pl/Attention-deficit-hyperactivity-disorder-insights-advances-and-challenges-r-nin-research-and-practice,116,56582,0,1.html

Jiao, S., Bao, L., Lu, X. et al. Integrative multi-omics data from early development to identify the genes and cell types underlying attention-deficit/hyperactivity disorder. BMC Psychiatry 25, 741 (2025). https://bmcpsychiatry.biomedcentral.com/articles/10.1186/s12888-025-07209-0

Last Updated: Oct 22, 2025