Genetics and Neuroscience Biomarkers in Attention-deficit/hyperactivity disorder: Insights toward Precision Medicine, A Systematic Review

Ronak  Mesroghli; Seyed Mahmoud  Tabatabaei

doi:10.18502/jabs.v16i1.20131

Ronak Mesroghli Department of Psychology, Tabriz Branch, Islamic Azad University, Tabriz, Iran
Seyed Mahmoud Tabatabaei Department of Medical Physiology, TaMS.C., Islamic Azad University, Tabriz, Iran

DOI: https://doi.org/10.18502/jabs.v16i1.20131

Keywords: Attention Deficit Disorder with Hyperactivity, Genetic Markers, Precision Medicine, Neurofeedback, Electroencephalography

Abstract

Background & Objectives: Attention-deficit/hyperactivity disorder (ADHD) affects approximately 5 to 7% of children and 2 to 5% of adults worldwide, with heritability estimates of 70 to 80% reported in recent genome-wide association studies (GWAS) (1). The disorder arises from complex interactions among genetic, neurobiological, and environmental factors. This systematic review synthesizes recent advances in genetic and neuroscience-based biomarkers and evaluates their potential utility for precision medicine approaches in ADHD.

Materials & Methods: Study quality was assessed using the Cochrane Risk of Bias 2 (RoB 2) tool and the Newcastle–Ottawa Scale. A systematic review of the literature published up to October 2025 was conducted, encompassing GWAS, neuroimaging studies (functional magnetic resonance imaging and electroencephalography), and clinical trials. The analysis focused on key genetic variants involved in dopamine regulation, including dopamine receptor D4 (DRD4), dopamine transporter 1 (DAT1), and catechol-O-methyltransferase (COMT), neurophysiological markers such as the theta-to-beta ratio, and polygenic risk scores (PRS) for treatment response prediction. Data were retrieved from PubMed and Scopus databases.

Results: Genetic variants affecting dopaminergic signaling were associated with increased ADHD susceptibility and differential responses to stimulant medications. The incorporation of PRS improved the prediction of treatment response by increasing explained variance, for example, R² values rose from 0.05 to 0.28, representing an absolute increase of approximately 23%, although relative improvements varied between 15 and 25% across studies. Electroencephalography-based neurofeedback demonstrated small-to-moderate improvements in executive functioning among inattentive ADHD subtypes, with standardized mean differences ranging from 0.36 to 0.44, although ongoing debates suggest that a substantial proportion of observed effects may reflect placebo-related mechanisms (I² = 50 to 65%). Neuroimaging findings consistently revealed hypoactivation of the prefrontal cortex and dysconnectivity within the default mode network, facilitating subtype differentiation. Integrative approaches employing artificial intelligence showed promise for individualized treatment planning; however, financial constraints, limited accessibility, and methodological heterogeneity currently hinder widespread clinical implementation.

Conclusion: Genetic and neurobiological biomarkers provide a robust foundation for precision- oriented ADHD care, encompassing neurofeedback and pharmacogenomic strategies. Standardization of biomarker assessment tools and the strategic integration of artificial intelligence are essential to overcoming existing barriers and promoting equitable, outcome-optimized interventions.