Can transcriptomic profiles help classify ADHD subtypes?
Yes, emerging research suggests that ADHD transcriptomic profiles could one day help identify distinct ADHD subtypes, leading to more targeted diagnosis and treatment. Traditional classifications (like inattentive vs hyperactive-impulsive) rely on observable behaviour. But transcriptomics, the study of gene activity through RNA, offers a biological lens to explore what is happening beneath the surface.
How transcriptomic profiling works
Using tools like RNA sequencing, scientists can analyse which genes are “switched on” or “off” in people with ADHD. These expression patterns drawn from blood samples, saliva, or even brain tissue reveal real-time gene activity linked to brain development, neurotransmitter systems, and immune response.
What this means for ADHD subtypes
Biological fingerprints of symptoms
Individuals with primarily inattentive symptoms may show different RNA expression patterns than those with hyperactive-impulsive traits. For example, one subtype might show increased expression in dopamine-regulating genes, while another shows dysregulation in stress-response pathways.
Moving toward precision medicine
Rather than treating ADHD as a single disorder, transcriptomic data could allow clinicians to stratify patients into biologically meaningful subtypes, leading to more personalised and effective interventions.
Beyond behaviour
Transcriptomic signatures could help clarify ADHD presentations that do not neatly fit DSM categories, including co-occurring conditions like anxiety or learning difficulties.
The future of classification
While this research is still in the early stages, the potential is clear: RNA-based profiles could complement behavioural assessments, refine diagnoses, and even predict treatment response.
Visit providers like ADHD Certify for personal consultations that consider molecular and behavioural dimensions.
For a deeper dive into the science, diagnosis, and full treatment landscape, read our complete guide to Genetic studies and biomarkers.

