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March 31, 2026
ADHD is one of the most common neurodevelopmental disorders in children, yet anyone familiar with this disorder, from clinicians and researchers to parents and patients, knows how differently it can manifest from one individual to the next. One person diagnosed with ADHD may primarily struggle with focus and staying on-task; another may find it nearly impossible to regulate their impulses or even start tasks; a third may frequently find themselves frozen with overwhelm and subject to emotional reactivity…
These are not just variations in severity; they may reflect genuinely different patterns of brain organization.
Our current diagnostic system groups all of these presentations under a single label (ADHD), with three behavioral subtypes (Hyperactive, Inattentive, and Combined) defined by symptom checklists. This framework has real clinical value of course, but it was built from behavioral observation rather than neurobiology, and may leave room for substantial heterogeneity to remain unexplained. In a new study, published in JAMA Psychiatry, researchers asked whether it’s possible to identify distinct neurobiologically subgroups within ADHD by analyzing patterns of brain structure, and whether those subgroups would map onto meaningful clinical differences.
How the Brain Was Analyzed
Researchers analyzed structural MRI scans from 446 children with ADHD and 708 typically-developing children across multiple research sites. From each scan, they constructed a morphometric similarity network; that is, a map of how different brain regions resemble one another in their structural properties. These networks reflect underlying biological organization, including shared patterns of cellular architecture and gene expression across brain regions.
From each individual's network, the research team calculated three properties that capture how each brain region functions within the broader network: how many connections it has, how efficiently it communicates with other regions, and how well it bridges different functional communities in the brain. Regions that score highly on these measures are sometimes called "hubs" and they play particularly influential roles in how information is integrated across the brain.
Rather than comparing the ADHD group to controls as a whole and looking for average differences, they used a normative modeling approach. This works similarly to a growth chart in pediatric medicine: instead of asking whether a child is above or below the group average, it asks how much a given child deviates from the expected range for their age and sex. This allows for individual variation across the ADHD group rather than flattening it into a single average profile.
The team then applied a data-driven clustering algorithm to these individual deviation profiles, allowing the data to reveal whether subgroups of children with ADHD shared similar patterns of brain network atypicality, without using any clinical symptom information to guide the clustering.
The Results:
Three stable, reproducible subtypes emerged from this analysis.
The first subtype was characterized by the most widespread differences from the normative range, particularly in regions connecting the medial prefrontal cortex to the pallidum (a deep brain structure involved in motivation and emotional regulation). Children in this group had the highest levels of both inattention and hyperactivity/impulsivity, and over a four-year follow-up period showed more persistent difficulties with emotional self-regulation than the other groups. They also had a higher rate of mood disorder comorbidity during follow-up, though this difference did not reach statistical significance given the sample size. The brain deviation patterns of this subtype showed correspondence with the spatial distributions of several neurotransmitter systems, including serotonin, dopamine, and acetylcholine, all of which have been previously implicated in ADHD pathophysiology.
The second subtype showed alterations concentrated in the anterior cingulate cortex and pallidum, a circuit involved in action control and response selection. This subtype had a predominantly hyperactive/impulsive profile, and its brain deviation patterns were associated with glutamate and cannabinoid receptor distributions.
The third subtype showed more focal differences in the superior frontal gyrus, a region involved in sustained attention. This subtype had a predominantly inattentive profile, with brain patterns linked to a specific serotonin receptor subtype.
A particularly important observation was that these brain-derived groupings aligned with clinically meaningful symptom differences, even though no symptom information was used in the clustering process. The fact that an analysis of brain structure alone arrived at groupings that correspond to recognizable clinical patterns is meaningful evidence that these subtypes reflect genuine neurobiological differences rather than statistical noise.
Replication in an Independent Sample
Scientific findings are only as trustworthy as their ability to replicate. The research team tested this clustering model in an entirely independent cohort of 554 children with ADHD from the Healthy Brain Network, a large, publicly available dataset collected under different conditions. The three subtypes were successfully identified in this new sample, with strong correlations between the brain deviation patterns observed in the original and validation cohorts. Differences in hyperactivity/impulsivity across subtypes were consistent with the discovery cohort, providing meaningful external validation of the approach.
What This Does and Doesn't Mean
It is important to be clear about what these findings do and do not imply. This study does not establish that these three subtypes are categorically distinct biological entities with sharp boundaries. They probably represent distinguishable regions along an underlying continuum of neurobiological variation. The neurochemical associations reported are exploratory and spatial in nature; they describe correspondences between brain deviation maps and neurotransmitter receptor density maps derived from separate imaging studies, and do not directly establish that any particular neurotransmitter system is altered in each subtype, nor do they currently inform treatment decisions.
The samples were not entirely medication-naive, and the strict comorbidity exclusion criteria may limit how well these findings generalize to typical clinical populations where comorbidities are the rule rather than the exception. All data came from research sites in the United States and China, and broader generalizability remains to be established.
What the study does demonstrate is that structured neurobiological heterogeneity exists within the ADHD diagnosis, that it can be reliably detected using brain imaging and data-driven methods, and that it aligns with meaningful clinical differences. The subtype defined by the most extensive brain network differences and the most severe, persistent clinical profile may be of particular importance, representing a group that could benefit most from early identification and targeted support.
The longer-term goal of this line of research is to move toward a more biologically grounded understanding of ADHD that complements existing diagnostic approaches and that may ultimately help guide more individualized treatment decisions. That goal, for now, remains a research ambition rather than a clinical reality, but this study takes a meaningful step in that direction.
Pan N, Long Y, Qin K, et al. Mapping ADHD Heterogeneity and Biotypes by Topological Deviations in Morphometric Similarity Networks. JAMA Psychiatry. Published online February 25, 2026. doi:10.1001/jamapsychiatry.2026.0001
Centanafadine, which is currently under investigation as a treatment for ADHD, will be the first triple reuptake inhibitor for the disorder if it is approved by the FDA. It improves norepinephrine, dopamine and serotonin levels. This new medication is not a stimulant, but due to the dopamine component, it has a stimulant-like effect in patients. In adults, two phase 3 trials and a year-long extension have shown sustained benefits and a tolerable safety profile, laying the groundwork for pediatric research.
Based on this study, improvement was already noticeable after the first week and held steady through week 6. The lower dose (164.4 mg) didn’t separate from placebo, reminding us that getting the dose right will be critical. The effect size was smaller than what is seen for stimulants but 50% of patients had excellent outcomes as indicated by reductions in the ADHD-RS of 50% or more.
Side effect patterns look familiar to anyone who prescribes ADHD medications; loss of appetite, nausea and headaches topped the list. About half of teens on the higher dose reported at least one treatment-emergent adverse event, compared with a quarter of those on placebo. Severe reactions were rare but did include isolated liver enzyme spikes, rash, and a few reports of aggression or somnolence. For everyday practice, that translates to routine growth checks, a look at baseline liver function, and clear guidance to families about reporting rashes or mood changes promptly.
The researchers noted that the study had certain limitations, including limited generalizability to adolescents beyond North America, the exclusion of teacher ratings on the ADHD-RS-5 scale and the study’s short duration. They added that future studies should explore long-term treatment outcomes and efficacy compared with other ADHD treatments, as well as its effect on treating ADHD with comorbid conditions.
First, speed. Centanafadine separated from placebo within a week. In this regard, it might be closer to stimulants than to the multi-week ramp-up we expect from current non-stimulants. Second, it offers another option when stimulants are contraindicated or poorly tolerated, or when they raise diversion concerns. Its mechanism also makes it intriguing for patients who need both norepinephrine and dopamine coverage but prefer to avoid schedule II drugs. Because it also improves serotonergic transmission, it may be useful for some of ADHD’s comorbidities (see our new article for evidence about serotonin’s role in these disorders).
Keep in mind that centanafadine for ADHD is still investigational, so participation in clinical trials remains the only access route.
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This New York Times article, “5 Takeaways from New Research about ADHD”, earns a poor grade for accuracy. Let’s break down their (often misleading and frequently inaccurate) claims about ADHD.
The Claim: A.D.H.D. is hard to define/ No ADHD Biomarkers exist
The Reality: The claim that ADHD is hard to define “because scientists haven’t found a single biological marker” is misleading at best. While it is true that no biomarker exists, decades of rigorous research using structured clinical interviews and standardized rating scales show that ADHD is reliably diagnosed. Decades of validation research consistently show that ADHD is indeed a biologically-based disorder. One does not need a biomarker to draw that conclusion and recent research about ADHD has not changed that conclusion.
Additionally, research has in fact confirmed that genetics do play a role in the development of ADHD and several genes associated with ADHD have been identified.
The Claim: The efficacy of medication wanes over time
The Reality: The article’s statement that medications like Adderall or Ritalin only provide short-term benefits that fade over time is wrong. It relies almost entirely on one study—the Multimodal Treatment Study of ADHD (MTA). In the MTA study, the relative advantage of medication over behavioral treatments diminished after 36 months. This was largely because many patients who had not initially been given medication stopped taking it and many who had only been treated with behavior therapy suddenly began taking medication. The MTA shows that patients frequently switched treatments. It does not overturn other data documenting that these medications are highly effective. Moreover, many longitudinal studies clearly demonstrate sustained benefits of ADHD medications in reducing core symptoms, psychiatric comorbidity, substance abuse, and serious negative outcomes, including accidents, and school dropout rates. A study of nearly 150,000 people with ADHD in Sweden concluded “Among individuals diagnosed with ADHD, medication initiation was associated with significantly lower all-cause mortality, particularly for death due to unnatural causes”. The NY Times’ claim that medications lose their beneficial effects over time ignores compelling evidence to the contrary.
The Claim: Medications don’t help children with ADHD learn
The Reality: ADHD medications are proven to reliably improve attention, increase time spent on tasks, and reduce disruptive behavior, all critical factors directly linked to better academic performance.The article’s assertion that ADHD medications improve only classroom behavior and do not actually help students learn also oversimplifies and misunderstands the research evidence. While medication alone might not boost IQ or cognitive ability in a direct sense, extensive research confirms significant objective improvements in academic productivity and educational success—contrary to the claim made in the article that the medication’s effect is merely emotional or perceptual, rather than genuinely educational.
For example, a study of students with ADHD who were using medication intermittingly concluded “Individuals with ADHD had higher scores on the higher education entrance tests during periods they were taking ADHD medication vs non-medicated periods. These findings suggest that ADHD medications may help ameliorate educationally relevant outcomes in individuals with ADHD.”
The Claim: Changing a child’s environment can change his or her symptoms.
The Reality: The Times article asserts that ADHD symptoms are influenced by environmental fluctuations and thus might not have their roots in neurobiology. We have known for many years that the symptoms of ADHD fluctuate with environmental demands. The interpretation of this given by the NY Times is misleading because it confuses symptom variability with underlying causes. Many disorders with well-established biological origins are sensitive to environmental factors, yet their biology remains undisputed.
For example, hypertension is unquestionably a biologically based condition involving genetic and physiological factors. However, it is also well-known that environmental stressors, dietary
habits, and lifestyle factors can significantly worsen or improve hypertension. Similarly, asthma is biologically rooted in inflammation and airway hyper-reactivity, but environmental triggers such as allergens, pollution, or even emotional stress clearly impact symptom severity. Just as these environmental influences on hypertension or asthma do not negate their biological basis, the responsiveness of ADHD symptoms to environmental fluctuations (e.g., improvements in classroom structure, supportive home life) does not imply that ADHD lacks neurobiological roots. Rather, it underscores that ADHD, like many medical conditions, emerges from the interplay between underlying biological vulnerabilities and environmental influences.
Claim: There is no clear dividing line between those who have A.D.H.D. and those who don’t.
The Reality: This is absolutely and resoundingly false. The article’s suggestion that ADHD diagnosis is arbitrary because ADHD symptoms exist on a continuum rather than as a clear-cut, binary condition is misleading. Although it is true that ADHD symptoms—like inattention, hyperactivity, and impulsivity—do vary continuously across the population, the existence of this continuum does not make the diagnosis arbitrary or invalidate the disorder’s biological basis. Many well-established medical conditions show the same pattern. For instance, hypertension (high blood pressure) and hypercholesterolemia (high cholesterol) both involve measures that are continuously distributed. Blood pressure and cholesterol levels exist along a continuum, yet clear diagnostic thresholds have been carefully established through decades of clinical research. Their continuous distribution does not lead clinicians to question whether these conditions have biological origins or whether diagnosing an individual with hypertension or hypercholesterolemia is arbitrary. Rather, it underscores that clinical decisions and diagnostic thresholds are established using evidence about what levels lead to meaningful impairment or increased risk of negative health outcomes. Similarly, the diagnosis of ADHD has been meticulously defined and refined over many decades using extensive empirical research, structured clinical interviews, and validated rating scales. The diagnostic criteria developed by experts carefully delineate the point at which symptoms become severe enough to cause significant impairment in an individual’s daily functioning. Far from being arbitrary, these thresholds reflect robust scientific evidence that individuals meeting these criteria face increased risks for the serious impairments in life including accidents, suicide and premature death.
The existence of milder forms of ADHD does not undermine the validity of the diagnosis; rather, it emphasizes the clinical reality that people experience varying degrees of symptom severity.
Moreover, acknowledging variability in severity has always been a core principle in medicine. Clinicians routinely adjust treatments to meet individual patient needs. Not everyone diagnosed with hypertension receives identical medication regimens, nor does everyone with elevated cholesterol get prescribed the same intervention. Similarly, people with ADHD receive personalized treatment plans tailored to the severity of their symptoms, their specific impairments, and their individual circumstances. This personalization is not evidence of arbitrariness; it is precisely how evidence-based medicine is practiced. In sum, the continuous nature of ADHD symptoms is fully compatible with a biologically-based diagnosis that has substantial evidence for validity, and acknowledging symptom variability does not render diagnosis arbitrary or diminish its clinical importance.
In sum, readers seeking a balanced, evidence-based understanding of ADHD deserve clearer, more careful reporting. By overstating diagnostic uncertainty, selectively interpreting research about medication efficacy, and inaccurately portraying the educational benefits of medication, this article presents an overly simplistic, misleading picture of ADHD.
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The Neuroeconomic Perspective
Neuroeconomics combines neuroscience, psychology, and economics to understand how people make decisions. Neuroeconomic studies suggest that brain regions responsible for evaluating risk and reward, including the prefrontal cortex and dopamine pathways, function differently in individuals with ADHD. These insights are crucial for developing more tailored interventions. For example, understanding how ADHD affects reward processing might inform strategies that help individuals resist impulsive choices or increase motivation for delayed rewards.
Understanding Decision-Making in ADHD
We know that decision-making is a sophisticated process involving various cognitive procedures. It’s not just about choosing between options but also about how to weigh risks, rewards, and potential future outcomes; Attention, motivation, and cognitive control are core to this process. For individuals with ADHD, however, this neural framework is affected by impairments in attention and impulse control, often resulting in “delay discounting”—the tendency to prefer smaller, immediate rewards over larger, delayed ones.
This propensity for impulsive decisions is more than a personal challenge; it has broader societal and economic implications. Previous studies have shown that these tendencies in ADHD can lead to issues in academics, work, finances, and personal relationships, emphasizing the need for targeted support and interventions.
Implications and Future Directions
This review highlights a need for continued research to bridge the gaps in understanding how ADHD-specific cognitive deficits influence decision-making. Viewing ADHD through a neuroeconomic lens clarifies how cognitive and neural differences affect decision-making, often leading to impulsive choices with economic and social impacts. This perspective opens doors to more effective interventions, improving decision-making for individuals with ADHD. Future policies informed by this approach could enhance support and reduce associated societal costs.
ADHD is one of the most common neurodevelopmental disorders in children, yet anyone familiar with this disorder, from clinicians and researchers to parents and patients, knows how differently it can manifest from one individual to the next. One person diagnosed with ADHD may primarily struggle with focus and staying on-task; another may find it nearly impossible to regulate their impulses or even start tasks; a third may frequently find themselves frozen with overwhelm and subject to emotional reactivity…
These are not just variations in severity; they may reflect genuinely different patterns of brain organization.
Our current diagnostic system groups all of these presentations under a single label (ADHD), with three behavioral subtypes (Hyperactive, Inattentive, and Combined) defined by symptom checklists. This framework has real clinical value of course, but it was built from behavioral observation rather than neurobiology, and may leave room for substantial heterogeneity to remain unexplained. In a new study, published in JAMA Psychiatry, researchers asked whether it’s possible to identify distinct neurobiologically subgroups within ADHD by analyzing patterns of brain structure, and whether those subgroups would map onto meaningful clinical differences.
How the Brain Was Analyzed
Researchers analyzed structural MRI scans from 446 children with ADHD and 708 typically-developing children across multiple research sites. From each scan, they constructed a morphometric similarity network; that is, a map of how different brain regions resemble one another in their structural properties. These networks reflect underlying biological organization, including shared patterns of cellular architecture and gene expression across brain regions.
From each individual's network, the research team calculated three properties that capture how each brain region functions within the broader network: how many connections it has, how efficiently it communicates with other regions, and how well it bridges different functional communities in the brain. Regions that score highly on these measures are sometimes called "hubs" and they play particularly influential roles in how information is integrated across the brain.
Rather than comparing the ADHD group to controls as a whole and looking for average differences, they used a normative modeling approach. This works similarly to a growth chart in pediatric medicine: instead of asking whether a child is above or below the group average, it asks how much a given child deviates from the expected range for their age and sex. This allows for individual variation across the ADHD group rather than flattening it into a single average profile.
The team then applied a data-driven clustering algorithm to these individual deviation profiles, allowing the data to reveal whether subgroups of children with ADHD shared similar patterns of brain network atypicality, without using any clinical symptom information to guide the clustering.
The Results:
Three stable, reproducible subtypes emerged from this analysis.
The first subtype was characterized by the most widespread differences from the normative range, particularly in regions connecting the medial prefrontal cortex to the pallidum (a deep brain structure involved in motivation and emotional regulation). Children in this group had the highest levels of both inattention and hyperactivity/impulsivity, and over a four-year follow-up period showed more persistent difficulties with emotional self-regulation than the other groups. They also had a higher rate of mood disorder comorbidity during follow-up, though this difference did not reach statistical significance given the sample size. The brain deviation patterns of this subtype showed correspondence with the spatial distributions of several neurotransmitter systems, including serotonin, dopamine, and acetylcholine, all of which have been previously implicated in ADHD pathophysiology.
The second subtype showed alterations concentrated in the anterior cingulate cortex and pallidum, a circuit involved in action control and response selection. This subtype had a predominantly hyperactive/impulsive profile, and its brain deviation patterns were associated with glutamate and cannabinoid receptor distributions.
The third subtype showed more focal differences in the superior frontal gyrus, a region involved in sustained attention. This subtype had a predominantly inattentive profile, with brain patterns linked to a specific serotonin receptor subtype.
A particularly important observation was that these brain-derived groupings aligned with clinically meaningful symptom differences, even though no symptom information was used in the clustering process. The fact that an analysis of brain structure alone arrived at groupings that correspond to recognizable clinical patterns is meaningful evidence that these subtypes reflect genuine neurobiological differences rather than statistical noise.
Replication in an Independent Sample
Scientific findings are only as trustworthy as their ability to replicate. The research team tested this clustering model in an entirely independent cohort of 554 children with ADHD from the Healthy Brain Network, a large, publicly available dataset collected under different conditions. The three subtypes were successfully identified in this new sample, with strong correlations between the brain deviation patterns observed in the original and validation cohorts. Differences in hyperactivity/impulsivity across subtypes were consistent with the discovery cohort, providing meaningful external validation of the approach.
What This Does and Doesn't Mean
It is important to be clear about what these findings do and do not imply. This study does not establish that these three subtypes are categorically distinct biological entities with sharp boundaries. They probably represent distinguishable regions along an underlying continuum of neurobiological variation. The neurochemical associations reported are exploratory and spatial in nature; they describe correspondences between brain deviation maps and neurotransmitter receptor density maps derived from separate imaging studies, and do not directly establish that any particular neurotransmitter system is altered in each subtype, nor do they currently inform treatment decisions.
The samples were not entirely medication-naive, and the strict comorbidity exclusion criteria may limit how well these findings generalize to typical clinical populations where comorbidities are the rule rather than the exception. All data came from research sites in the United States and China, and broader generalizability remains to be established.
What the study does demonstrate is that structured neurobiological heterogeneity exists within the ADHD diagnosis, that it can be reliably detected using brain imaging and data-driven methods, and that it aligns with meaningful clinical differences. The subtype defined by the most extensive brain network differences and the most severe, persistent clinical profile may be of particular importance, representing a group that could benefit most from early identification and targeted support.
The longer-term goal of this line of research is to move toward a more biologically grounded understanding of ADHD that complements existing diagnostic approaches and that may ultimately help guide more individualized treatment decisions. That goal, for now, remains a research ambition rather than a clinical reality, but this study takes a meaningful step in that direction.
Managing high blood pressure requires more than just getting a prescription; it means taking medication consistently, day after day, often for years. For people with ADHD, that kind of routine can be genuinely difficult. In our new study, published in BMC Medicine, we set out to understand just how much ADHD affects whether people stick with their blood pressure medication, and whether ADHD treatment itself might make a difference.
Why This Question Matters
Hypertension affects nearly a third of adults worldwide and is one of the leading drivers of heart disease and stroke. At the same time, ADHD, long thought of as a childhood disorder, affects around 2.5% of adults and is increasingly recognized as a risk factor for cardiovascular problems, including high blood pressure. Yet no large-scale study had ever examined whether having ADHD affects how well people follow through with their blood pressure treatment. We wanted to fill that gap.
What We Did
We analyzed health records from over 12 million adults across seven countries, Australia, Denmark, the Netherlands, Norway, Sweden, the UK, and the US, who had started antihypertensive (blood pressure-lowering) medication between 2010 and 2020. About 320,000 of them had ADHD. We tracked two things: whether they stopped their blood pressure medication entirely within five years, and whether they were taking it consistently enough (covering at least 80% of days) over one, two, and five years of follow-up.
What We Found
Across nearly all countries, adults with ADHD were more likely to stop their blood pressure medication and less likely to take it consistently. Overall, those with ADHD had about a 14% higher rate of discontinuing treatment within five years, and were 45% more likely to have poor adherence in the first year, a gap that widened to 64% by the five-year mark. These patterns were most pronounced in middle-aged and older adults.
Interestingly, young adults with ADHD were actually slightly less likely to discontinue treatment than their peers without ADHD, a finding we think may reflect the fact that younger people with ADHD are often more actively engaged with healthcare systems, especially given the cardiovascular monitoring that comes with ADHD medication use.
Perhaps the most encouraging finding was this: among people with ADHD who were also taking ADHD medication, adherence to blood pressure treatment was substantially better. Those on ADHD medication were about 38% less likely to have poor adherence at one year, and nearly 50% less likely at five years. While we can't establish causation from this type of study, one plausible explanation is that treating ADHD, reducing inattention and impulsivity, makes it easier to maintain the routines that consistent medication use requires. It's also possible that people on ADHD medication simply have more regular contact with healthcare providers, which keeps other health problems better monitored and managed.
What This Means in Practice
The core ADHD symptoms of inattention and poor organization are precisely the traits that make long-term medication adherence difficult. Add in the complexity of managing multiple disorders and medications, and it's easy to see why people with ADHD face extra challenges. Our findings suggest that clinicians treating adults with ADHD for cardiovascular disorders should be aware of these challenges and consider tailored support strategies, things like regular follow-up appointments, patient education, and tools that help with routine and organization.
There's also a broader message here about the potential ripple effects of treating ADHD well. Supporting someone in managing their ADHD may not just improve their attention and daily functioning; it may also help them take better care of their physical health, including disorders as serious as hypertension.
Future research should explore which specific support strategies are most effective, and whether these findings hold in lower- and middle-income countries where the data don't yet exist.
If you or someone you know has ADHD, you may be familiar with the challenge of staying on medication. Stimulants like methylphenidate (Ritalin) are the most common and effective treatment for ADHD, but a surprisingly large number of people stop taking them within the first year. In our new study, published in Translational Psychiatry, we sought to determine whether a person's genetic makeup plays a role in the development of the disorder.
What We Did
We analyzed data from over 18,000 people with ADHD in Denmark, all of whom had started stimulant medication. We tracked whether they stopped treatment within the first year, defined as going more than six months without filling a prescription. Nearly 4 in 10 (39%) had discontinued by that point. We then looked at their genetic data to see whether DNA differences could help explain who was more likely to stop.
What We Found
The short answer is: genetics does play a role, but it's modest. No single gene had a dramatic effect. Instead, we found that a collection of small genetic influences—distributed across the genome—contributed to the likelihood of stopping treatment early.
One of the most consistent findings was that people with a higher genetic predisposition for psychiatric disorders like schizophrenia, depression, or general mental health difficulties were more likely to discontinue their medication. This was true across all age groups. Interestingly, having a higher genetic risk for ADHD itself was not associated with stopping treatment, suggesting that the genetics of having ADHD and the genetics of staying on medication are quite different things.
We also found that the genetic picture looks different depending on age. In children under 16, body weight genetics (BMI) played a surprising role, children with a genetic tendency toward higher weight were actually less likely to stop, possibly because stimulant-related appetite suppression is less of a problem for them. In older adolescents and adults, higher genetic potential for educational attainment and IQ was linked to staying on treatment, possibly reflecting better access to information and healthcare support.
On the rare variant side, we found a tentative signal that people who stopped treatment had fewer disruptive variants in genes involved in dopamine, the brain chemical that stimulants work on. This might mean that those who continue on medication genuinely have more disruption in their dopamine system and benefit more from stimulant treatment.
What This Means
Our findings suggest that stopping ADHD medication early isn't simply a matter of willpower or forgetting to take a pill. Biology matters. A person's broader genetic vulnerabilities, particularly for other psychiatric disorders, may make it harder to stay on treatment, perhaps because of side effects, poor response, or the complexity of managing multiple mental health challenges at once.
We're still far from being able to use genetics to predict who will stop their medication, the effects we found are real but small, and much of the variation in treatment persistence remains unexplained. But this work is a step toward understanding the biological foundations of treatment challenges in ADHD, and hopefully toward more personalized approaches to care in the future.
Larger studies and research that can distinguish why people stop (side effects versus poor response versus practical barriers), will be the next steps.
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