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December 8, 2025
The Background:
Myopia is a growing global health concern linked to conditions like macular degeneration, glaucoma, and retinal detachment. Its prevalence has surged in recent decades; by 2050, an estimated 5 billion people will have myopia. The increase is especially marked in Asia – a survey in Taiwan reports that 84% of students aged 15 to 18 are myopic, with 24% severely affected.
Dopamine is an important neurotransmitter in the retina, involved in eye development, visual signaling, and refractive changes. The dopamine hypothesis, suggesting that retinal dopamine release helps prevent myopia, has emerged as a leading theory of myopia control.
Most studies show ADHD is highly heritable, often involving dopamine system genes. ADHD is strongly associated with dopaminergic abnormalities, especially in dopamine transporter function and release dynamics.
Medications for ADHD, like methylphenidate, atomoxetine, and clonidine, help regulate dopamine to reduce symptoms.
The Study:
Given dopamine’s critical involvement in both ADHD and myopia, a Taiwanese research team hypothesized that medications for ADHD that influence dopaminergic pathways may have a significant effect on myopia risk.
To evaluate this hypothesis, the team conducted a nationwide cohort study using data from Taiwan’s National Health Insurance (NHI) program, which covers 99% of the nation’s 23 million residents and provides access to comprehensive eye care and screenings. Taiwan requires visual acuity screenings beginning at age four, with annual examinations for school-aged children to promote the early detection of visual anomalies such as myopia.
Furthermore, ADHD medication and diagnosis are tracked through compulsory diagnostic codes. This permits an accurate assessment of the effects of dopaminergic medications on myopia risk.
Propensity score allocation using a multivariable logistic regression model was applied to reduce bias from confounding influences, pairing cohorts based on similar scores.
The Results:
Comparing 133,945 individuals with ADHD with an equal number without ADHD, untreated ADHD was associated with a 22% greater risk of myopia.
However, after adjusting for covariates (gender, age, insured premium, comorbidities, location, and urbanization level), the ADHD cohort receiving medication treatment showed a 39% decreased risk of myopia relative to the untreated ADHD cohort.
Narrowing this further to the ADHD cohort receiving dopaminergic medications reduced the risk of myopia by more than half (52%) relative to the untreated ADHD cohort.
Treatment with two dopaminergic medications reduced the risk by well over two-thirds (72%) relative to the untreated ADHD cohort.
There were no significant differences between methylphenidate, atomoxetine, and clonidine. Each reduced risk by about 50%.
The team did not directly compare the ADHD cohort receiving dopaminergic medications with the non-ADHD cohort. But if there were 122 cases of myopia in the ADHD cohort for every 100 cases in the non-ADHD cohort, and dopaminergic medications halved the cases in the ADHD cohort to about 60, that would represent a roughly 40% reduction in myopia risk relative to the non-ADHD cohort.
The team concluded, “our research indicates that pharmacologically treated ADHD children have a reduced risk of myopia. Conversely, untreated ADHD children are at a heightened risk relative to those without ADHD. Moreover, the cumulative effects of ADHD medications were found to notably decrease myopia incidence, emphasizing the protective influence of dopaminergic modulation in these interventions.”
The Take-Away:
Children with untreated ADHD are more likely to develop myopia, but those receiving dopaminergic medications had a substantially lower risk. The findings suggest that ADHD medications may help protect against myopia by boosting dopamine signaling. More research is needed before firmly drawing this conclusion, but this research could open the door to new approaches for preventing myopia in at-risk children.
Yu-Te Huang, Jhih-Yi Lu, Peng-Tai Tien, Yih-Dih Cheng, Heng-Jun Lin, Yow-Wen Hsieh, Fuu-Jen Tsai, Lei Wan, and Hui-Ju Lin, “Dopaminergic medications as a preventive for myopia: insights derived from pediatric patients diagnosed with attention deficit hyperactivity disorder,” Postgraduate Medical Journal (2025) 101, 1201, 1127–1134, https://doi.org/10.1093/postmj/qgaf051.
The Background:
Down syndrome (DS) is a genetic disorder resulting from an extra copy of chromosome 21. It is associated with intellectual disability.
Three to five thousand children are born with Down syndrome each year. They have higher risks for conditions like hypothyroidism, sleep apnea, epilepsy, sensory issues, infections, and autoimmune diseases. Research on ADHD in patients with Down syndrome has been inconclusive.
The Study:
The National Health Interview Survey (NHIS) is a household survey conducted by the National Center for Health Statistics at the CDC.
Due to the low prevalence of Down syndrome, a Chinese research team used NHIS records from 1997 to 2018 to analyze data from 214,300 children aged 3 to 17, to obtain a sufficiently large and nationally representative sample to investigate any potential association with ADHD.
DS and ADHD were identified by asking, “Has a doctor or health professional ever diagnosed your child with Down syndrome, Attention Deficit Hyperactivity Disorder (ADHD), or Attention Deficit Disorder (ADD)?”
After adjusting for age, sex, and race/ethnicity, plus family highest education level, family income-to-poverty ratio, and geographic region, children and adolescents with Down syndrome had 70% greater odds of also having ADHD than children and adolescents without Down syndrome. There were no significant differences between males and females.
The Take-Away:
The team concluded, “in a nationwide population-based study of U.S. children, we found that a Down syndrome diagnosis was associated with a higher prevalence of ASD and ADHD. Our findings highlight the necessity of conducting early and routine screenings for ASD and ADHD in children with Down syndrome within clinical settings to improve the effectiveness of interventions.”
A Chinese research team performed two types of meta-analyses to compare the risk of suicide for ADHD patients taking ADHD medication as opposed to those not taking medication.
The first type of meta-analysis combined six large population studies with a total of over 4.7 million participants. These were located on three continents - Europe, Asia, and North America - and more specifically Sweden, England, Taiwan, and the United States.
The risk of suicide among those taking medication was found to be about a quarter less than for unmediated individuals, though the results were barely significant at the 95 percent confidence level (p = 0.49, just a sliver below the p = 0.5 cutoff point). There were no significant differences between males and females, except that looking only at males or females reduced sample size and made results non-significant.
Differentiating between patients receiving stimulant and non-stimulant medications produced divergent outcomes. A meta-analysis of four population studies covering almost 900,000 individuals found stimulant medications to be associated with a 28 percent reduced risk of suicide. On the other hand, a meta-analysis of three studies with over 62,000 individuals found no significant difference in suicide risk for non-stimulant medications. The benefit, therefore, seems limited to stimulant medication.
The second type of meta-analysis combined three within-individual studies with over 3.9 million persons in the United States, China, and Sweden. The risk of suicide among those taking medication was found to be almost a third less than for unmediated individuals, though the results were again barely significant at the 95 percent confidence level (p =0.49, just a sliver below the p = 0.5 cutoff point). Once again, there were no significant differences between males and females, except that looking only at males or females reduced the sample size and made results non-significant.
Differentiating between patients receiving stimulant and non-stimulant medications once again produced divergent outcomes. Meta-analysis of the same three studies found a 25 percent reduced risk of suicide among those taking stimulant medications. But as in the population studies, a meta-analysis of two studies with over 3.9 million persons found no reduction in risk among those taking non-stimulant medications.
A further meta-analysis of two studies with 3.9 million persons found no reduction in suicide risk among persons taking ADHD medications for 90 days or less, "revealing the importance of duration and adherence to medication in all individuals prescribed stimulants for ADHD."
The authors concluded, "exposure to non-stimulants is not associated with a higher risk of suicide attempts. However, a lower risk of suicide attempts was observed for stimulant drugs. However, the results must be interpreted with caution due to the evidence of heterogeneity ..."
Parents and teachers often ask: Does ADHD medication actually improve grades and school performance? The answer is: yes, but with important limitations. Medications are very effective at reducing inattention, hyperactivity, and impulsivity but their impact on long-term academic outcomes like grades and test scores is not as consistent.
In the Classroom
The medications for ADHD consistently: Improve attention, reduce classroom disruptions, increase time spent on-task and help children complete more schoolwork and homework. Medication can help children with ADHD access learning by improving the conditions for paying attention and persisting with work.
Does Medication Improve Test Scores and Grades?
This is where the picture gets more complicated. Medications have stronger effect on how much work is completed but a weaker effect on accuracy. Many studies show that children on medication attempt more problems in reading, math, and spelling, but the number of correct answers doesn’t always improve as much. Some studies find small but significant improvements in national exam scores and higher education entrance tests during periods when children with ADHD are medicated.
Grades improve, as well, but modestly. Large registry studies in Sweden show that students who consistently take medication earn higher grades than those who don’t. However, these gains usually do not close the achievement gap with peers who do not have ADHD.
Keep in mind that small improvements for a group as a whole mean that some children are benefiting greatly from medication and others not at all. We have no way of predicting which children will improve and which do not.
Medication Alone Isn’t Enough
Academic success depends on more than just reducing inattention, hyperactivity and impulsivity. Skills like organization, planning, studying, and managing long-term projects are also critical. Medication cannot teach these skills.
So, in addition to medication, the patient's treatment program should include educational support (tutoring, structured study skills programs), behavioral interventions (parent training, classroom management strategies), and accommodations at school (extra time, reduced distractions, organizational aids) Parents should discuss with their prescriber which of these methods would be appropriate.
Conclusions
ADHD medication is a powerful tool for reducing symptoms and supporting learning. It improves test scores and grades for some children, especially when taken consistently. But it is not a magic bullet for academic success. The best results come when medication is combined with educational and behavioral supports that help children build the skills they need to thrive in school and beyond.
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:
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:
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:
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.
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.
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