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April 9, 2024
In recent years, there has been growing interest in understanding the connection between our gut microbiota (the community of microorganisms in our digestive system) and various neurodevelopmental disorders like autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD). A new study by Shunya Kurokawa and colleagues dives deeper into this area, comparing dietary diversity and gut microbial diversity among children with ASD, ADHD, their normally-developing siblings, and unrelated volunteer controls. Let's unpack what they found and what it means.
The Study Setup
The researchers recruited children aged 6-12 years diagnosed with ASD and/or ADHD, along with their non-ASD/ADHD siblings and the unrelated non-ASD/ADHD volunteers. The diagnoses were confirmed using standardized assessments like the Autism Diagnostic Observation Schedule-2 (ADOS-2). The study looked at gut microbial diversity using advanced DNA extraction and sequencing techniques, comparing alpha-diversity indices (which reflect the variety and evenness of microbial species within each gut sample) across different groups. They also assessed dietary diversity through standardized questionnaires.
Key Findings
The study included 98 subjects, comprising children with ASD, ADHD, both ASD and ADHD, their non-ASD/ADHD siblings, and the unrelated controls. Here's what they discovered:
Gut Microbial Diversity: The researchers found significant differences in alpha-diversity indices (like Chao 1 and Shannon index) among the groups. Notably, children with ASD had lower gut microbial diversity compared to unrelated neurotypical controls. This suggests disorder-specific differences in gut microbiota, particularly in children with ASD.
Dietary Diversity: Surprisingly, dietary diversity (assessed using the Shannon index) did not differ significantly among the groups. This finding implies that while gut microbial diversity showed disorder-specific patterns, diet diversity itself might not be the primary factor driving these differences.
What Does This Mean?
The study highlights intriguing connections between gut microbiota and neurodevelopmental disorders like ASD and ADHD. The lower gut microbial diversity observed in children with ASD points towards potential links between gut health and the pathophysiology of ASD. Understanding these connections is crucial for developing targeted therapeutic interventions.
Implications and Future Directions
This research underscores the importance of considering gut microbiota in the context of neurodevelopmental disorders. Moving forward, future studies should account for factors like co-occurrence of ASD and ADHD, as well as carefully control for dietary influences. This will help unravel the complex interplay between gut microbiota, diet, and neurodevelopmental disorders, paving the way for innovative treatments and interventions.
In summary, studies like this shed light on the intricate relationship between our gut health, diet, and brain function. By unraveling these connections, researchers are opening new avenues for understanding and potentially treating conditions like ASD and ADHD.
Kurokawa S, Nomura K, Sanada K, Miyaho K, Ishii C, Fukuda S, Iwamoto C, Naraoka M, Yoneda S, Imafuku M, Matsuzaki J, Saito Y, Mimura M, Kishimoto T. A comparative study on dietary diversity and gut microbial diversity in children with autism spectrum disorder, attention-deficit hyperactivity disorder, their neurotypical siblings, and non-related neurotypical volunteers: a cross-sectional study. J Child Psychol Psychiatry. 2024 Apr 2. doi: 10.1111/jcpp.13962. Epub ahead of print. PMID: 38562118.
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Our research team conducted a study, published in the Journal of the American Academy of Child & Adolescent Psychiatry, to understand how COVID-19 (SARS-CoV-2) affects the mental health of young people. We used a method called Kaplan-Meier survival analysis to figure out how likely kids were to develop new mental health problems, including suicidal thoughts, within two years after being infected. We looked at medical records of 7.5 million children and 5.3 million teenagers who were part of the TriNetX Research Network. Importantly, we focused only on those who didn’t have any mental health issues before.
Of these young people, almost 300,000 children and over 220,000 teens had tested positive for COVID-19. The results were significant: children who had COVID-19 had a 15% chance of being diagnosed with a new mental health condition, compared to just 2.6% for children who didn’t get COVID-19. For teens, the chance was 19% for those infected and 5% for those not infected.
We found that the risk of developing new mental health issues was six times higher in children and four times higher in teens who had COVID-19. This shows that younger kids are more strongly affected.
The study also highlighted that COVID-19 was linked to higher rates of various mental health problems, especially in children. This means it’s really important to screen for mental health issues in young people after they’ve had COVID-19, particularly for those who had severe cases.
Overall, our findings point to the need for special support for kids and teens who may be more vulnerable after the pandemic. It’s clear that the mental health effects of COVID-19 go beyond just physical health, and it’s crucial that doctors and policymakers include mental health care in plans to help young people recover.
Neurodevelopmental conditions often coexist, creating a complex web of challenges for affected individuals. A recent study by Hollingdale et al. delves into the cumulative effects of attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and intellectual disability (ID) on young people’s behavioral and socio-emotional well-being, as well as their overall functioning as rated by clinicians.
The researchers conducted a cross-sectional analysis of 2768 young individuals aged 3-17 years, with a mean age of approximately 11.5 years. Diagnostic information along with caregiver-rated behavioral and socio-emotional data, and clinician-rated functioning scores, were collected from electronic patient records at the point of initial diagnosis.
The study aimed to understand whether the number of neurodevelopmental conditions—ranging from one to three—correlates with more pronounced behavioral and socio-emotional issues, and lower levels of clinician-rated functioning. The behavioral and socio-emotional aspects were assessed using the Strengths and Difficulties Questionnaire, while the Children's Global Assessment Scale was used to evaluate clinician-rated functioning.
The findings revealed that young people with multiple neurodevelopmental conditions tend to exhibit higher levels of inattention and hyperactivity, greater peer-related problems, reduced prosocial behaviors, and poorer overall functioning. Interestingly, this cumulative impact was more evident in males compared to females, with females only showing significant cumulative effects in clinician-rated functioning.
This research underscores the importance of recognizing the compounded difficulties faced by young people with multiple neurodevelopmental conditions. It highlights the need for tailored interventions that address the unique and overlapping challenges presented by ADHD, ASD, and ID. For practitioners, understanding these cumulative effects is crucial for developing effective treatment plans that can better support the holistic development and well-being of these young individuals.
In conclusion, the presence of multiple neurodevelopmental conditions can significantly affect various domains of a young person’s life, with notable differences between males and females. This study provides a critical insight into the intricate nature of these conditions and calls for a more nuanced approach in both research and clinical practice.
Noting that "Growing evidence shows that moderate physical activity (PA) can improve psychological health through enhancement of neurotransmitter systems," and "PA may play a physiological role similar to stimulant medications by increasing dopamine and norepinephrine neurotransmitters, thereby alleviating the symptoms of ADHD," a Chinese team of researchers performed a comprehensive search of the peer-reviewed journal literature for studies exploring the effects of physical activity on ADHD symptoms.
They found nine before-after studies with a total of 232 participants, and fourteen two-group control studies with a total of 303 participants, that met the criteria for meta-analysis.
The meta-analysis of before-after studies found moderate reductions in inattention and moderate-to-strong reductions in hyperactivity/impulsivity. It also reported moderate reductions in emotional problems and small-to-moderate reductions in behavioral problems.
The effect was even stronger among unmediated participants. There was a very strong reduction in inattention and a strong reduction in hyperactivity/impulsivity.
The meta-analysis of two-group control studies found strong reductions in inattention, but no effect on hyperactivity/impulsivity. It also found no significant effect on emotional and behavioral problems.
There was no sign of publication bias in any of the meta-analyses.
The authors concluded, "Our results suggest that PA intervention could improve ADHD-related symptoms, especially inattention symptoms. However, due to a lot of confounders, such as age, gender, ADHD subtypes, the lack of rigorous double-blinded randomized-control studies, and the inconsistency of the PA program, our results still need to be interpreted with caution."
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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