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June 22, 2021
Several meta-analyses have assessed this question by computing the Standardized Mean Difference or SMD statistic. The SMD is a measure that allows us to compare different studies. For context, the effect of stimulant medication for treating ADHD is about 0.9. SMDs less than 0.3 are considered low, between 0.3 to 0.6 medium, and anything greater than high.
A 2004 meta-analysis combined the results of fifteen studies with a total of 219 participants and found a small association(SMD = .28, 95% CI .08-.49) between consumption of artificial food colors by children and increased hyperactivity. Excluding the smallest and lowest quality studies further reduced the SMD to .21, and a lower confidence limit of .007 also made it barely statistically significant. Publication bias was indicated by an asymmetric funnel plot. No effort was made to correct the bias.
A 2012 meta-analysis by Nigg et al. combined twenty studies with a total of 794 participants and again found a small effect size (SMD =.18, 95% CI .08-.29). It likewise found evidence of publication bias. Correcting for the bias led to a tiny effect size at the outer margin of statistical significance (SMD = .12, 95% CI .01-.23). Restricting the pool to eleven high-quality studies with 619 participants led to a similarly tiny effect size that fell just outside the 95% confidence interval (SMD = .13, CI =0-.25, p = .053). The authors concluded, "Overall, a mixed conclusion must be drawn. Although the evidence is too weak to justify action recommendations absent a strong precautionary stance, it is too substantial to dismiss."
In 2013 a European ADHD Guidelines Group consisting of 21 researchers (Sonuga-Barke et al.) performed a systematic review and meta-analysis that examined the efficacy of excluding artificial colors from the diets of children and adolescents as a treatment for ADHD. While many interventions showed benefits in unblinded assessments, only artificial food color exclusion and, to a lesser extent, free fatty acid supplementation remained effective under blinded conditions. The findings suggest that eliminating artificial food dyes may meaningfully reduce ADHD symptoms in some children, though it should be noted that the positive results were mostly seen in children with other food sensitivities.
The research to date does suggest a small effect of artificial food colors in aggravating symptoms of hyperactivity in children, and a potential beneficial effect of excluding these substances from the diets of children and adolescents, but the evidence is not very robust. More studies with greater numbers of participants, and better control for the effects of ADHD medications, will be required for a more definitive finding.
In the meantime, given that artificial food colors are not an essential part of the diet, parents could consider excluding them from their children's meals, since doing so is risk-free, and the cost (reading labels) is negligible.
Joel T. Nigg, Kara Lewis, Tracy Edinger, Michael Falk, “Meta-Analysis of Attention-Deficit/Hyperactivity Disorder attention-Deficit/Hyperactivity Disorder Symptoms, Restriction Diet, and synthetic Food Color Additives,” Journal of The American Academy of Child & Adolescent Psychiatry (2012), Vol.51, No. 1, 86-97.David W. Schab and Nhi-Ha T. Trinh, “Do Artificial FoodColors Promote Hyperactivity in Children with Hyperactive Syndromes? Aneta-Analysis of Double-Blind Placebo-Controlled Trials,” Developmental and behavioral Pediatrics(2004), Vol. 25, No. 6, 423-434.Edmund J.S. Sonuga-Barke et al., “NonpharmacologicalInterventions for ADHD: Systematic Review and Meta-Analyses of RandomizedControlled Trials of Dietary and Psychological Treatments,” American Journal of Psychiatry(2013), 170:275-289.
If we are to read what we believe on the Internet, dieting can cure many of the ills faced by humans. Much of what is written is true. Changes in dieting can be good for heart disease, diabetes, high blood pressure, and kidney stones to name just a few examples. But what about ADHD? Food elimination diets have been extensively studied for their ability to treat ADHD. They are based on the very reasonable idea that allergies or toxic reactions to foods can have effects on the brain and could lead to ADHD symptoms.
Although the idea is reasonable, it is not such an easy task to figure out what foods might cause allergic reactions that could lead to ADHD symptoms. Some proponents of elimination diets have proposed eliminating a single food, others include multiple foods, and some go as far as to allow only a few foods to be eaten to avoid all potential allergies. Most readers will wonder if such restrictive diets, even if they did work, are feasible. That is certainly a concern for very restrictive diets.
Perhaps the most well-known ADHD diet is the Feingold diet(named after its creator). This diet eliminates artificial food colorings and preservatives that have become so common in the western diet. Some have claimed that the increasing use of colorings and preservatives explains why the prevalence of ADHD is greater in Western countries and has been increasing over time. But those people have it wrong. The prevalence of ADHD is similar around the world and has not been increasing over time. That has been well documented but details must wait for another blog.
The Feingold and other elimination diets have been studied by meta-analysis. This means that someone analyzed several well-controlled trials published by other people. Passing the test of meta-analysis is the strongest test of any treatment effect. When this test is applied to the best studies available, there is evidence that the exclusion of fool colorings helps reduce ADHD symptoms. But more restrictive diets are not effective. So removing artificial food colors seems like a good idea that will help reduce ADHD symptoms. But although such diets ‘work’, they do network very well. On a scale of one to 10where 10 is the best effect, drug therapy scores 9 to 10 but eliminating food colorings scores only 3 or 4. Some patients or parents of patients might want this diet change first in the hopes that it will work well for them. That is a possibility, but if that is your choice, you should not delay the more effective drug treatments for too long in the likely event that eliminating food colorings is not sufficient. You can learn more about elimination diets from Nigg, J. T., and K.Holton (2014). "Restriction and elimination diets in ADHD treatment."Child Adolesc Psychiatr Clin N Am 23(4): 937-953.
Keep in mind that the treatment guidelines from professional organizations point to ADHD drugs as the first-line treatment for ADHD. The only exception is for preschool children where medication is only the first-line treatment for severe ADHD; the guidelines recommend that other preschoolers with ADHD be treated with non-pharmacologic treatments, when available. You can learn more about non-pharmacologic treatments for ADHD from a book I recently edited: Faraone, S. V. &Antshel, K. M. (2014). ADHD: Non-Pharmacologic Interventions. Child AdolescPsychiatr Clin N Am 23, xiii-xiv.
Mindfulness has been defined as “intentionally directing attention to present moment experiences with an attitude of curiosity and acceptance.” Mindfulness-based interventions (MBIs) aim to improve mindfulness skills.
A newly-published meta-analysis of randomized controlled trials (RCTs) by a team of British neurologists and psychiatrists explores the effectiveness of MBIs in treating a variety of mental health conditions in children and adolescents. Among those conditions is the attention deficit component of ADHD.
A comprehensive literature search identified studies that met the following criteria:
1) The effects of mindfulness were compared against a control condition – either no contact, waitlist, active, or attention placebo. The waitlist means the control group receives the same treatment after the study concludes. Active control means that a known, effective treatment (as opposed to a placebo) is compared to an experimental treatment. Attention placebo means that controls receive a treatment that mimics the time and attention received by the treatment group but is believed not to have a specific effect on the subjects. Participants were randomly assigned to the control condition.
2) The MBI was delivered in more than one session by a trained mindfulness teacher, involved sustained meditation practice, and it was not mixed in with another activity such as yoga.
Eight studies evaluating attention deficit symptoms, with a combined total of 1,158 participants, met inclusion criteria. The standardized mean difference (SMD) was 0.19, with a 95% confidence range of 0.04 to 0.34 (p = .02). That indicates a small effect size for MBIs in reducing attention deficit symptoms. Heterogeneity was low (I2 = 35, p =.15), and the Egger test showed little sign of publication bias (p = 0.42).
When looking only at studies with active controls, five studies with a total of 787 participants yielded an SMD of 0.13, with a 95% confidence interval of -0.01 to 0.28 (p = .06), indicating a tiny effect size that failed to reach significance. Active controls most commonly received health education, with a few receiving social responsibility training or Hatha yoga.
Overall, this meta-analysis suggests limited effectiveness, especially when compared with active controls. If MBIs are effective for ADHD, their effect on symptoms is very small. Thus, such treatments should not be used in place of the many well-validated, evidenced-based therapies available. Whether longer periods of MBI (training times varied between 2 and 18 hours spread out over 2 to 24 weeks) might result in greater effect sizes remains unexplored
After noting that the association between ADHD and obesity has been called into question because of small sample sizes, wide age ranges, self-reported assessments, and inadequate attention to potential confounders, an Israeli study team set out "to assess the association between board-certified psychiatrist diagnoses of ADHD and measured adolescent BMI [body mass index] in a nationally represented sample of over one million adolescents who were medically evaluated before mandatory military service."
The team distinguished between severe and mild ADHD. It also focused on a single age group.
All Israelis are subject to compulsory military service. In preparation for that service, military physicians perform a thorough medical evaluation. Trained paramedics recorded every conscript's height and weight.
The study cohort was divided into five BMI percentile groups according to the U.S. Centers for Disease Control and Prevention's BMI percentiles for 17-year-olds, and further divided by sex: <5th percentile (underweight), 5th-49th percentile (low-normal), 50th-84th percentile (high normal), 85th-94th percentile (overweight) and ≥95th (obese). Low-normal was used as the reference group.
Adjustments were made for sex, birth year, age at examination, height, country of birth (Israeli or other), socioeconomic status, and education level.
In the fully adjusted results, those with severe ADHD were 32% more likely to be overweight and 84% more likely to be obese than their typically developing peers. Limiting results to Israeli-born conscripts made a no difference.
Male adolescents with mild ADHD were 24% more likely to be overweight, and 42% more likely to be obese. Females with mild ADHD are 33% more likely to be overweight, and 42% more likely to be obese. Again, the country of birth made no difference.
The authors concluded, that both severe and mild ADHD was associated with an increased risk for obesity in adolescents at the age of 17 years. The increasing recognition of the persistence of ADHD into adulthood suggests that this dual morbidity may have a significant impact on the long-term health of individuals with ADHD, thus early preventive measures should be taken.
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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