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A new study from Japan suggests that infants born with craniosynostosis are significantly more likely to be diagnosed with ADHD later in childhood. Craniosynostosis is a condition in which the bony plates of the skull fuse prematurely, leading to increased intracranial pressure. 

The Background:

Craniosynostosis affects roughly one in every 2,000 births. When the skull’s natural seams close prematurely, it can restrict brain growth and increase intracranial pressure, potentially reducing blood flow to the brain. Because the condition is relatively rare, it has been difficult to study at scale until now. 

The Study:

To overcome this, researchers tapped into a large Japanese insurance database compiled by JMDC, Inc., which holds records on around 20 million people, or about 15% of Japan’s population. Drawing on two decades of data, the team tracked over 338,000 mother-child pairs. Children with related genetic syndromes or chromosomal conditions such as Down syndrome were excluded to keep the focus on craniosynostosis itself. 

Of the children studied, around 1,145 had craniosynostosis, and 7,325 were diagnosed with ADHD. After accounting for factors like sex, birth year, maternal age, mental health history, pregnancy infections, and birth complications, children with craniosynostosis were found to have roughly 2.4 times the risk of a subsequent ADHD diagnosis compared to those without it. 

To test whether shared family genetics or home environment might be driving the association rather than the skull condition itself, the researchers conducted a separate analysis among siblings. The elevated risk remained at 2.2 times. The consistency of the finding across both analyses strengthens the case for a genuine biological link. 

The Results:

The results point to raised intracranial pressure and restricted cerebral blood flow as plausible mechanisms, though the study’s observational design means causation cannot be confirmed. Ultimately, these findings highlight the need for proactive, long-term care strategies for those born with craniosynostosis. By establishing a solid link between premature skull fusion and a significantly higher risk of ADHD, the research demonstrates that medical care for this condition should not end once the skull's physical structure is addressed.

The Takeaway:

Pediatricians, neurologists, and parents can use this data to implement early, routine behavioral and developmental screening for these children as they grow. This additional support would ensure that those who do develop ADHD can receive timely interventions, educational aids, and therapies, ultimately improving their long-term developmental outcomes.

Children and adolescents with ADHD come into contact with child welfare services (CWS) far more often than their peers. There are many contributing factors to consider, including the fact that hyperactivity and impulsivity frequently lead to behaviors that are considered disruptive and cause academic and social difficulties. Many of these children are also growing up in households marked by parental conflict and/or single-parent arrangements.  All of these circumstances can compound vulnerability and, historically, increase the likelihood of CWS involvement.

Background: 

In Norway, Child Welfare Services operate at the municipal level and are legally required in every local authority. Their scope spans investigation, family support, and, where necessary, out-of-home placement and ongoing monitoring. Grounds for intervention include abuse, neglect, behavioral or psychosocial difficulties, and inadequate care-giving. Norwegian CWS works closely with health, education, and social services and places a strong emphasis on keeping families together. Compared with systems in countries such as the United States, Poland, Romania, and the Czech Republic, the Norwegian approach sets a lower bar for intervention and leans toward home-based support, while setting a higher bar for out-of-home placements. This model is shared by other Nordic countries, as well as Germany and the United Kingdom. 

Research into whether ADHD medication affects child welfare caseloads is remarkably sparse. A single Danish study previously found that medication treatment accounted for much of an observed decline in foster care cases, but no study had examined medication’s broader impact on CWS involvement, covering both supportive interventions and out-of-home placements. 

Norway’s universal single-payer health system and comprehensive national registers make population-wide research of this kind feasible. Drawing on these resources, a Norwegian research team set out to test whether ADHD medication reduces children’s contact with CWS and their need for out-of-home placement. 

The Study:

This study included all 5,930 children and adolescents aged 5 to 14 who received a clinical ADHD diagnosis from Child and Adolescent Mental Health Services between 2009 and 2011. Each was followed for up to 4 years post-diagnosis, the upper age limit being 18, at which point CWS jurisdiction ends. This group was compared with more than 53,000 peers who had no CWS contact during the same period. 

The results showed a meaningful, though not dramatic, association between medication and reduced CWS contact. At one year, treated children had approximately 7% fewer contacts with CWS; by two years, that figure had risen to around 12%. The effect then narrowed, settling at roughly 7–8% reductions at the three- and four-year marks. 

The picture for out-of-home placements is considerably less convincing. The research team highlighted a 3% reduction at two-year follow-up, but this finding barely crossed the threshold of statistical significance, and no effect was observed at the one-, three-, or four-year follow-up points. 

The Take-Away:

The authors concluded that pharmacological treatment for ADHD is associated with reductions in both supportive CWS services and out-of-home placements among children affected by clinicians’ prescribing decisions in Norway. A more cautious reading of the same data, however, would emphasize an overall reduction in CWS contact of roughly 8%, while treating the out-of-home placement finding as, at best, inconclusive. 

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Stimulant medications, such as methylphenidate (Ritalin) and amphetamines (Adderall),  are among the most widely prescribed drugs in the world. In the United States alone, prescription rates have climbed more than 50% over the past decade, driven largely by growing awareness of ADHD in both children and adults. Yet stimulants also have a long history of non-medical use, and concerns about their psychological risks persist among patients, families, and clinicians alike. 

Two major studies now offer the clearest picture yet of what that risk actually looks like, and who it may affect.

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The Background: 

Before turning to the research, it helps to understand the landscape. A notable share of stimulant users misuse their medication: roughly one in four takes it in ways other than prescribed, and about one in eleven meets criteria for Prescription Stimulant Use Disorder (PSUD). Counterintuitively, most people with PSUD aren’t obtaining drugs illicitly — they’re misusing their own prescriptions. 

This distinction between therapeutic and non-therapeutic use turns out to be critical when evaluating psychosis risk. 

The Study: 

A comprehensive meta-analysis by Jangra and colleagues pooled data across more than a dozen studies to compare psychotic outcomes in people using stimulants therapeutically versus non-therapeutically. The contrast was striking. 

Among therapeutic users  (more than 220,000 individuals taking stimulants at prescribed doses under medical supervision), psychotic episodes occurred in roughly one in five hundred people. When symptoms did appear, they typically emerged after prolonged treatment or in individuals with pre-existing psychiatric vulnerabilities, and they usually resolved when the medication was stopped. 

Among non-therapeutic users  (over 8,000 participants across twelve studies, many using methamphetamine or high-dose amphetamines), nearly one in three experienced psychotic symptoms. These episodes tended to be more severe, involving persecutory delusions and hallucinations, with faster onset and a greater likelihood of recurrence or persistence. 

The biology underlying this difference is well understood. When stimulants are taken orally at guideline-recommended doses, they produce moderate, gradual changes in neurotransmitter activity central to attention and executive functions. The brain tolerates these changes relatively well. Non-therapeutic use, by contrast, often involves much higher doses that are frequently delivered through non-oral routes such as injection or smoking. This produces a rapid, excessive surge in dopamine activity, which is precisely the neurochemical pattern associated with psychotic symptoms. 

The takeaway here is not that therapeutic stimulant use is risk-free, but that risk is strongly modulated by dose, route of administration, and individual psychiatric history. Clinicians are advised to monitor patients with pre-existing mood or psychotic disorders, particularly carefully. 

A Nationwide Study Focuses on Methylphenidate Specifically:

Where the meta-analysis cast a wide net, a large-scale population study by Healy and colleagues drilled into a specific and clinically pressing question: does methylphenidate (the most commonly prescribed ADHD medication, also known as Ritalin) increase the risk of developing a psychotic disorder? 

To find out, the researchers analyzed Finland's national health insurance database, tracking nearly 700,000 individuals diagnosed with ADHD. Finland's single-payer system made this kind of comprehensive, long-term tracking possible in a way that fragmented healthcare systems rarely allow. 

Critically, the team adjusted for a range of confounding factors that have clouded previous research, including sex, parental education, parental history of psychosis, and the number of psychiatric visits and diagnoses prior to the ADHD diagnosis itself (a proxy for illness severity). After these adjustments, they found no significant difference in the risk of schizophrenia or non-affective psychosis between patients treated with methylphenidate and those who remained unmedicated. This held true even among patients with four or more years of continuous methylphenidate use. 

The Take-Away: 

When considered together, these studies offer meaningful reassurance without encouraging complacency. 

For patients and families weighing ADHD treatment, the evidence suggests that methylphenidate used as prescribed does not increase psychosis risk, even over years of use. The rare cases of stimulant-associated psychosis in therapeutic settings are typically linked to high doses, pre-existing vulnerabilities, or both, and tend to resolve with discontinuation. 

For clinicians, the findings reinforce the importance of baseline psychiatric assessment before initiating stimulant therapy, ongoing monitoring in patients with mood or psychotic disorder histories, and clear patient education about the risks of dose escalation or non-oral use. 

The picture that emerges is one of a meaningful distinction between a medication used carefully within its therapeutic window and a drug misused outside of it. This distinction matters enormously when communicating risk to patients, policymakers, and the public. 

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ADHD is commonly treated with medication, but these treatments frequently cause side effects such as reduced appetite and disrupted sleep. Psychological and behavioral therapies exist as alternatives, but they tend to be expensive, hard to scale, and generally do little to address the motor difficulties that many children with ADHD experience — things like clumsy movement, poor handwriting, or difficulty with coordination. 

Physical exercise has attracted attention as a more accessible option. But research findings have been mixed, partly because studies vary so widely in how exercise is delivered and what outcomes they measure. This meta-analysis, drawing on 21 studies involving 850 children and adolescents aged 5–20 with a clinical ADHD diagnosis, tries to cut through that noise. 

Two types of motor skills 

The researchers separated motor skills into two broad categories: 

• Gross motor skills — movements involving large muscle groups, such as running, jumping, throwing, and maintaining balance 

• Fine motor skills — precise, controlled movements, typically of the hands and fingers, such as handwriting and manual dexterity (the ability to handle objects skillfully) 

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The Data: 

Gross motor skills (16 studies, 613 participants) 

Overall, exercise produced medium-to-large improvements in gross motor skills. The strongest gains were in: 

• Object control (e.g., throwing, kicking) — large improvement 

• Locomotion (e.g., running, swimming), body coordination, and strength — medium improvements 

No significant gains were found in balance or flexibility. 

Fine motor skills (13 studies, 553 participants):

Exercise also produced medium-to-large improvements in fine motor skills, specifically: 

• Handwriting: large improvement 

• Manual dexterity: medium-to-large improvement 

• Hand-eye coordination: moderate improvement 

The Results: What Kind of Exercise Works Best? 

Two factors stood out consistently across both gross and fine motor skills: session length and frequency. 

• Sessions longer than 45 minutes produced roughly twice the benefit of shorter sessions 

• Three or more sessions per week outperformed less frequent programs for gross motor gains 

The type of exercise mattered; structured programs with clear motor-skill components (rather than unstructured physical activity) yielded stronger results. 

These results are not without caveats, however. The authors urge caution in interpreting these findings. A few key limitations include: 

• Potential Publication Bias:  Studies showing positive results are more likely to be published, which can inflate apparent benefits. For gross motor skills, adjusting for this bias reduced the effect size from medium-to-large,  to medium. 

• Active vs. Passive Controls: When exercise was compared against doing nothing (a passive control), improvements looked significant. When compared against regular school activities (an active control), the gains were no longer statistically significant. This is a meaningful distinction: it suggests exercise may be beneficial, but not dramatically more so than simply being physically active in a structured school setting. 

• Medication status: Most participants were taking ADHD medication, so it’s unclear how well these findings apply to unmedicated children who might stand the most to benefit from structured exercise. 

• Study quality: Many studies lacked proper randomization, weakening confidence in the conclusions. 

The Bottom Line 

This meta-analysis provides tentative moderate evidence that structured physical exercise can meaningfully support motor skill development in children and adolescents with ADHD — particularly when sessions run longer than 45 minutes and occur at least three times a week. The benefits appear most robust for object control, locomotion, handwriting, and manual dexterity. 

That said, the evidence base still has real gaps. The authors call for better-designed, fully randomized controlled trials with consistent methods, standardized ways of measuring exercise intensity, and greater inclusion of children and adolescents who are not on medication — all of which would help clarify when, how, and for whom exercise works best. 

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Treatment guidelines for childhood ADHD recommend medications as the first-line treatment for most youth with ADHD. Still, concerns about side effects and long-term outcomes have increased interest in non-pharmacological approaches. Researchers at Saudi Arabian Armed Forces hospitals recently conducted a network meta-analysis comparing several interventions, including mindfulness-based therapy, cognitive behavioral therapy, behavioral parent training, neurofeedback, yoga, virtual reality programs, and digital working memory training. 

Although the authors aimed to “provide a rigorous methodological approach to combine evidence from multiple treatment comparisons,” the study illustrates several pitfalls that arise when network meta-analysis is applied to a thin and heterogeneous evidence base. 

What Network Meta-analysis Can and Cannot Do:

Network meta-analysis extends conventional meta-analysis by combining: 

• Direct comparisons (treatment A vs. treatment B tested in clinical trials), and 

• Indirect comparisons (A vs. B inferred through a common comparator such as placebo or usual care). 

When the evidence network is large and well-connected, this approach can provide useful estimates of comparative effectiveness among many treatments. 

This method is not always best, however, as many networks are sparse. This is especially true in areas such as complementary or behavioral therapies. In sparse networks, estimates rely heavily on indirect comparisons, and single studies can exert disproportionate influence over the results. 

Conventional meta-analysis focuses on heterogeneity, meaning differences in results across studies within the same comparison. 

Network meta-analysis must additionally evaluate consistency, whether the direct and indirect evidence agree. 

However, when comparisons are supported by only one or two studies and the network is weakly connected, statistical tests for heterogeneity and consistency have very little power. In practice, this means the analysis often cannot detect problems even if they are present. 

Sparse networks also make publication bias difficult to evaluate. This concern is particularly relevant in fields dominated by small trials and emerging therapies. 

Why Such Treatment Rankings Are Appealing, but Potentially Problematic:

Many network meta-analyses summarize results using SUCRA, which estimates the probability that each treatment ranks best. 

SUCRA, or Surface Under the Cumulative Ranking, is a key statistical metric in network meta-analyses. It is used to rank treatments by efficacy or safety. This is achieved by summarizing the probabilities of a treatment's rank into a single percentage, where a higher SUCRA value indicates a superior treatment. Ultimately, SUCRA helps pinpoint the most effective intervention among the ones compared. 

Again, in well-supported networks, SUCRA can provide a useful summary of comparative effectiveness. But in sparse networks, rankings can create an illusion of precision, because treatments supported by a single small study may appear highly ranked simply due to random variation. 

What Did this New Network Meta-analysis Study?

The study includes 16 trials with a total of 806 participants. But the structure of the evidence network is far weaker than this headline number suggests. 

Based on the underlying studies: 

• Six interventions are supported by a single trial each (digital cognitive mindfulness training, BrainFit, neurofeedback, online mindfulness-based program, cognitive behavioral therapy, and working-memory training) 

• Three interventions are supported by two trials each 

• Only one intervention is supported by three trials (family mindfulness-based therapy) 

This produces a very thin network, in which several interventions rely entirely on single studies. 

Another challenge is that the included trials measure different outcomes. Some evaluate ADHD symptom severity, while others measure parental stress. 

When studies use different outcome scales, meta-analysis typically relies on standardized measures such as the standardized mean difference to allow comparisons across studies. However, the analysis reports only mean-average differences, making it difficult to interpret the relative effect sizes. 

Study Issues (including Limited Evidence and Risk of Bias): 

The intervention supported by the largest number of studies (family mindfulness-based therapy) was one of the two approaches reported as producing statistically significant results. The other was BrainFit, which is supported by only a single previous trial. 

Despite this limited evidence base, the study ranks interventions using SUCRA: 

• Family MBT: 92% probability of being best 

• Behavioral parent training (BPT): 65% 

• Online mindfulness program: 49% 

• Cognitive behavioral therapy: 48% 

• Yoga: 39% 

Notably, none of the runner-up interventions demonstrated statistically significant efficacy. 

The authors acknowledge methodological limitations in the included studies: 

“Blinding of participants and personnel (performance bias) exhibited notable concerns, as blinding for active treatment was not applicable in most studies.” 

Such limitations are common in behavioral intervention trials, but they further increase uncertainty in already small evidence networks. 

Conclusions:

The study ultimately concludes: 

“This network meta-analysis supports MBT and BPT as effective non-pharmacological treatments for ADHD.” 

However, the evidence underlying these claims is limited. Some analyses rely on very small numbers of studies and participants, and the network structure depends heavily on indirect comparisons. 

Network meta-analysis can be a powerful tool when applied to a large, consistent, and well-connected body of evidence. When the evidence base is sparse, however, the resulting rankings and comparisons may appear statistically sophisticated while resting on a fragile evidentiary foundation.

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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.    

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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.

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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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ADHD affects both individuals and society in many ways. Children and adolescents with ADHD often struggle with focusing, controlling impulses, and staying organized, which leads to problems with schoolwork, learning, and taking tests. These challenges can cause academic failure and make it harder for them to stay in school. 

ADHD symptoms often continue into adulthood, affecting jobs, relationships, and increasing risks for substance abuse and legal problems. 

Families of children and adolescents with ADHD face extra stress, with parents more likely to experience depression, anxiety, and relationship difficulties. The economic impact is also large, with billions spent each year on medical care, special education, lost productivity, and other related costs. 

Current treatments for ADHD mostly include medication, behavioral therapy, and educational support. While medications like stimulants can help control ADHD symptoms in the short term, they often cause side effects such as loss of appetite, trouble sleeping, slowed growth, cardiovascular risks, and potential substance dependence. These issues can make it hard for children and adolescents to stay on their medication, and about a third either don’t respond well or can’t tolerate the side effects. Once medication is stopped, the benefits fade quickly and do not lead to lasting improvements in executive functions (thinking skills). 

Behavioral therapy and parent training can help with behavior problems, but have limited effects on core mental skills like planning and self-control. These approaches also tend to be expensive, require a lot of support from parents and teachers, and are hard to use widely in schools and communities that lack resources.

Recently, exercise interventions have attracted growing interest as a non-pharmacological option. They provide several benefits: no drug-related side effects, easy accessibility, low cost, simple implementation in schools and communities, and enhanced physical and mental health. 

Previous meta-analyses examining how exercise interventions affect children and adolescents with ADHD have used traditional univariate models, which treat each study as if it only offers one independent effect size. In contrast, this study used multilevel meta-analysis — a more advanced statistical method modelling both between-study and within-study effects. This approach results in more accurate estimates and more dependable conclusions. 

Eligible studies were randomized controlled trials (RCTs) with usual care, no intervention, or waitlist controls, involving children and adolescents aged 5–18 diagnosed with ADHD by internationally recognized diagnostic criteria, and reporting inhibitory control outcomes. 

Eleven studies combining 512 children and adolescents met these inclusion standards. 

The analysis between experimental and control groups indicated that the exercise intervention group had significantly improved inhibitory control performance compared to the control group, with a medium-to-large effect size. There was very little variation (heterogeneity) in outcome between the studies, and no sign of publication bias.  

Within-group analyses showed that experimental groups had significant improvements after the intervention compared to baseline, with large effect sizes and moderate heterogeneity. 

By comparison, analyzing control groups over the same period revealed no significant differences, indicating that inhibitory control abilities in these groups remained largely unchanged throughout the observation period. There was little heterogeneity.  

Nevertheless, only one of the studies was rated low risk of bias, nine had some concerns, and two were rated high risk of bias. The greatest shortcomings were a lack of blinding and preregistration. 

The study authors therefore concluded that the overall evidence quality of this meta-analysis is low, limiting confidence in the results. While exercise interventions seem to improve inhibitory control abilities in children and adolescents with ADHD, significant methodological limitations create uncertainty about the effect size. These require more rigorous future studies to clarify these effects. Despite these caveats, they noted that all included studies reported statistically significant, consistent benefits from exercise interventions, offering preliminary support for their use as an adjunctive approach. 

Takeaway

This study lands in the same conversation as the adult ADHD exercise meta-analysis, and together they start to form a coherent picture: exercise appears to support attention and impulse control across the lifespan for people with ADHD, not just in one age group. The honest caveat is that the research quality in this field is still catching up to the enthusiasm — most studies have design weaknesses that limit confidence in the exact size of the effect. But the consistency of findings across studies, age groups, and now two separate meta-analyses is hard to dismiss.  

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A new study in the respected journal PLOS One analyzes data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) to examine trends in the incidence, prevalence, and disability-adjusted life-years associated with ADHD among adolescents and young adults aged 10 to 24 years between 1990 and 2021.  

The GBD 2021, released by the Institute for Health Metrics and Evaluation (U.S.), is a comprehensive global analysis of 371 diseases, injuries, and risk factors – such as ADHD – across 204 countries from 1990 to 2021. Its open-source data are publicly available. 

First, a distinction. Incidence measures the number of new cases of a disease that develop in a specific population each year. Prevalence measures the total number of existing cases – both new and pre-existing – in a population each year.   

The estimated global incidence of ADHD declined marginally from 12.61 per 100,000 population in 1990 to 11.89 per 100,000 population in 2021, representing an average annual decrease of 0.6% in age-standardized incidence. The rates observed were comparable between males and females. 

Regional trends varied: Western Europe had the highest rise in ADHD incidence (0.5% annually), while North Africa and the Middle East saw the largest drop (0.7% annually). Overall, a higher Socio-Demographic Index (SDI) is linked to a greater incidence, although it is far from a perfect fit. Nationally, showed the highest increase in ADHD incidence (1.15% annually), while Qatar showed the largest decrease with an annualized reduction of 1.77%. 

The estimated global prevalence of ADHD declined marginally from 2.38% in 1990 to 2.17% in 2021. Again, the decline was similar for males and females, and across all age groups (10-14, 15-19, 20-24). Higher SDI was associated with higher prevalence, but inconsistently. 

Disability-adjusted life-years (DALYs) combine years lost from early death and years lived with disability to measure disease burden. Globally, the age-standardized DALYs rate for ADHD decreased slightly from 30.3 per 100,000 population to 26.6 per 100,000 population, for an average annual decline of 0.6%. The decline occurred across age groups and was similar between males and females.  

The authors concluded that ADHD rates and related health burdens have generally declined over the past quarter century, though recent patterns are less consistent due to factors like socioeconomic changes and evolving diagnostic standards. Continued research is needed to improve the accuracy and accessibility of ADHD diagnosis and treatment to further reduce its global impact. 

 Take-Away:

The broader takeaway is one of cautious reassurance. Despite rising public awareness and diagnosis rates in many Western countries, the global picture over 25 years shows a gentle decline in ADHD burden among young people as opposed to a crisis of escalating proportions as social media may make one think. That said, the variation between regions suggests that access to diagnosis, cultural factors, and reporting standards are shaping the numbers as much as underlying biology. Progress is real but uneven, and the work of improving equitable access to diagnosis and care is far from finished.