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July 21, 2025
New research has uncovered important links between certain blood metabolites and ADHD by using a genetic method called Mendelian randomization. This approach leverages natural genetic differences to help identify which metabolites might actually cause changes in ADHD risk, offering stronger clues than traditional observational studies.
Key Metabolic Pathways Involved:
The study found 42 plasma metabolites with a causal relationship to ADHD. Most fall into two major groups:
Since many metabolites come from dietary sources like proteins and fats this supports the idea that diet could influence metabolic pathways involved in ADHD. However, because the study focused on genetic influences on metabolite levels, it doesn’t directly prove that dietary changes will have the same effects.
Notable Metabolites:
Five metabolites showed bidirectional links with ADHD, meaning genetic risk for ADHD also affects their levels which suggests a complex interaction between brain function and metabolism.
Twelve ADHD-related metabolites are targets of existing drugs or supplements, including:
While these findings highlight biological pathways, they don’t prove that changing diet will directly alter ADHD symptoms. Metabolite levels are shaped by genetics plus environment, lifestyle, and health factors, which require further study.
Conclusion:
This research provides stronger evidence of metabolic pathways involved in ADHD and points to new possibilities for diagnosis and treatment. Future work could explore how diet or drugs might safely adjust these metabolites to help manage ADHD.
While this study strengthens the link between amino acid and fatty acid metabolism and ADHD risk, suggesting that diet could play a role, ultimately more research is still needed before experts could use this research to give specific nutritional advice.
Shi S, Baranova A, Cao H, Zhang F. Exploring causal associations between plasma metabolites and attention-deficit/hyperactivity disorder. BMC Psychiatry. 2025 May 16;25(1):498. doi: 10.1186/s12888-025-06951-9. PMID: 40380147; PMCID: PMC12084988.
A relatively new area of ADHD research has been examining the association between ADHD and eating disorders (i.e., anorexia nervosa, bulimia nervosa, and binge-eating disorder). Nazar and colleagues conducted a systematic review and meta-analysis of extant studies.
They found only twelve studies that assessed the presence of eating disorders among people with ADHD and five that examined the prevalence of ADHD among patients with eating disorders. Although there were few studies, the total number of people studied was large, with 4,013 ADHD cases and 29,404 controls for the first set of studies and 1,044 eating disorder cases and 11,292 controls for the second set of studies. The meta-analyses of these data found that ADHD people had a 3.8-fold increased risk for an eating disorder compared with non-ADHD controls. The level of risk was similar for each of the eating disorders. Consistent with this, their second meta-analysis found that people with eating disorders had a 2.6-fold increased risk for ADHD compared with controls who did not have an eating disorder. The risk for ADHD was highest for those with binge-eating disorder (5.8-fold increased risk compared with controls).
This bidirectional association between ADHD and eating disorders provides converging evidence that this association is real and, given its magnitude, clinically significant. The results were similar for males and females and pediatric and adult populations.
We cannot tell from these data why ADHD is associated with eating disorders. Nazar et al. note that other work implicates both impulsivity and inattention in promoting bulimic symptoms, whereas inattention and hyperactivity are associated with craving. The association may also be due to the neurocognitive deficits of ADHD, which could lead to a distorted sense of self-awareness and body image.
Given that ADHD is also associated with obesity, some obese ADHD patients may have an underlying eating disorder, such as binge-eating, which has been associated with obesity in prospective studies. Also, lisdexamfetamine is FDA-approved for treating both binge eating and ADHD, which suggests the possibility that the two conditions share an underlying etiology involving the dopamine system. We do not know if treating ADHD would reduce the risk for eating disorders, as that hypothesis has not yet been tested. But such an effect would seem likely if ADHD behaviors mediate the association between the two disorders.
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.
A Swedish-Danish-Dutch team used the Swedish Medical Birth Register to identify the almost 1.7 million individuals born in the country between 1980 and 1995. Then, using the Multi-Generation Register, they identified 341,066 pairs of full siblings and 46,142 pairs of maternal half-siblings, totaling 774,416 individuals.
The team used the National Patient Register to identify diagnoses of ADHD, as well as neurodevelopmental disorders (autism spectrum disorder, developmental disorders, intellectual disability, motor disorders), externalizing psychiatric disorders (oppositional defiant and related disorders, alcohol misuse, drug misuse), and internalizing psychiatric disorders (depression, anxiety disorder, phobias, stress disorders, obsessive-compulsive disorder).
The team found that ADHD was strongly correlated with general psychopathology overall (r =0.67), as well as with the neurodevelopmental (r = 0.75), externalizing (r =0.67), and internalizing (r = 0.67) sub factors.
To tease out the effects of heredity, shared environment, and non-shared environment, a multivariate correlation model was used. Genetic variables were estimated by fixing them to correlate between siblings at their expected average gene sharing (0.5for full siblings, 0.25 for half-siblings). Non-genetic environmental components shared by siblings (such as growing up in the same family) were estimated by fixing them to correlate at 1 across full and half-siblings. Finally, non-shared environmental variables were estimated by fixing them to correlate at zero across all siblings.
This model estimated the heritability of the general psychopathology factor at 49%, with the contribution of the shared environment at 7 percent and the non-shared environment at 44%. After adjusting for the general psychopathology factor, ADHD showed a significant and moderately strong phenotypic correlation with the neurodevelopmental-specific factor (r = 0.43), and a significantly smaller correlation with the externalizing-specific factor (r = 0.25).
For phenotypic correlation between ADHD and the general psychopathology factor, genetics explained 52% of the total correlation, the non-shared environment 39%, and the shared familial environment only 9%. For the phenotypic correlation between ADHD and the neurodevelopmental-specific factor, genetics explained the entire correlation because the other two factors had competing effects that canceled each other out. For the phenotypic correlation between ADHD and the externalizing-specific factor, genetics explained 23% of the correlation, shared environment 22%, and non-shared environment 55%.
The authors concluded that "ADHD is more phenotypically and genetically linked to neurodevelopmental disorders than to externalizing and internalizing disorders, after accounting for a general psychopathology factor. ... After accounting for the general psychopathology factor, the correlation between ADHD and the neurodevelopmental-specific factor remained moderately strong, and was largely genetic in origin, suggesting substantial unique sharing of biological mechanisms among disorders. In contrast, the correlation between ADHD and the externalizing-specific factor was much smaller and was largely explained by-shared environmental effects. Lastly, the correlation between ADHD and the internalizing subfactor was almost entirely explained by the general psychopathology factor. This finding suggests that the comorbidity of ADHD and internalizing disorders are largely due to shared genetic effects and non-shared environmental influences that have effects on general psychopathology."
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:
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:
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:
The Results: What Kind of Exercise Works Best?
Two factors stood out consistently across both gross and fine motor skills: session length and frequency.
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:
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.
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.
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