Genetics and Neuroscience Biomarkers in Attention-deficit/hyperactivity disorder: Insights toward Precision Medicine, A Systematic Review
Bibliographic record
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".