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Disordered eating (a broad category of persistent, harmful patterns in eating or weight control) affects between 5% and 22% of children and adolescents worldwide, with similar rates seen in the United States. The consequences are far-reaching: these conditions are linked to bone fractures, anemia, malnutrition, dental erosion, obesity, diabetes, hypertension, and elevated cholesterol and triglycerides. They also carry one of the highest mortality rates of any psychiatric illness.
Eating disorders rarely occur in isolation. They frequently arise alongside other psychiatric and neurological conditions. Yet, until now, no large-scale study had examined these co-occurrences in a nationally representative U.S. sample. A new study addresses that gap, focusing on children and adolescents aged 6–17 and the conditions most commonly associated with disordered eating, including ADHD.
The Study:
Researchers drew on data from the 2022–2023 National Survey of Children's Health (NSCH), a nationally representative, cross-sectional survey covering all 50 states and Washington, D.C. Households were selected using stratified, address-based sampling, and parents or guardians completed surveys about one randomly selected child per household. The final sample included 68,000 children and adolescents.
Results:
After accounting for factors including sex, age, race and ethnicity, household income, educational attainment, insurance status, and household language, children and adolescents with ADHD were 2.6 times more likely to have some form of disordered eating compared to their typically developing peers.
The elevated risk appeared across a range of specific behaviors:
A greater tendency toward using diet pills, laxatives, or diuretics was also observed in the ADHD group, though this finding did not reach statistical significance.
The Take-Away:
These findings underscore a need to improve both prevention and treatment strategies for disordered eating, particularly in children and adolescents who have ADHD. Clinicians working with this population are advised to screen for a wide spectrum of disordered eating behaviors.
Myriam Casseus, “Disordered Eating Behaviors Among U.S. Children With Neuropsychiatric Conditions: A Nationally Representative Study,” International Journal of Eating Disorders (2026), 0: 1-7, published online, https://doi.org/10.1002/eat.70100.
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.
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.
Background:
“Junk food” is a shorthand for highly processed foods and beverages with low nutritional value and dietary fiber that are typically high in added sugars, starches, and additives intended to boost flavor, color, texture, and shelf life.
Previous studies examining such an association have yielded contradictory or inconclusive results.
Method:
An Iranian research team therefore conducted a systematic search of the peer-reviewed medical literature to run a meta-analysis of data from studies published to date.
Meta-analysis of nine studies, with a combined total of over 58,000 persons, found that children and adolescents who consumed more junk foods were about 25% more likely to exhibit ADHD symptoms. Restricting this to the seven studies rated good or high quality (7+ out of 10 on the Newcastle–Ottawa Quality Assessment Scale) produced the same exact outcome.
The result was consistent across individual studies, with low heterogeneity. There was no sign of publication bias.
Breaking this down by subtype of junk food:
The team used data adjusted for confounders: “We used … adjusted OR [odds ratios] for the meta-analysis.”
This outcome, while suggestive, should be interpreted cautiously due to limitations:
Conclusion:
The team concluded, “This meta-analysis of observational studies adds strong evidence linking the consumption of junk foods, particularly, sweetened beverages/soft drinks and sweets/candies, with ADHD symptoms in children and adolescents. However, the study did not evaluate the causality of the relationship. So, to identify causality and the dose–response effects of junk food consumption on the development of ADHD, further studies are warranted.”
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Disordered eating (a broad category of persistent, harmful patterns in eating or weight control) affects between 5% and 22% of children and adolescents worldwide, with similar rates seen in the United States. The consequences are far-reaching: these conditions are linked to bone fractures, anemia, malnutrition, dental erosion, obesity, diabetes, hypertension, and elevated cholesterol and triglycerides. They also carry one of the highest mortality rates of any psychiatric illness.
Eating disorders rarely occur in isolation. They frequently arise alongside other psychiatric and neurological conditions. Yet, until now, no large-scale study had examined these co-occurrences in a nationally representative U.S. sample. A new study addresses that gap, focusing on children and adolescents aged 6–17 and the conditions most commonly associated with disordered eating, including ADHD.
The Study:
Researchers drew on data from the 2022–2023 National Survey of Children's Health (NSCH), a nationally representative, cross-sectional survey covering all 50 states and Washington, D.C. Households were selected using stratified, address-based sampling, and parents or guardians completed surveys about one randomly selected child per household. The final sample included 68,000 children and adolescents.
Results:
After accounting for factors including sex, age, race and ethnicity, household income, educational attainment, insurance status, and household language, children and adolescents with ADHD were 2.6 times more likely to have some form of disordered eating compared to their typically developing peers.
The elevated risk appeared across a range of specific behaviors:
A greater tendency toward using diet pills, laxatives, or diuretics was also observed in the ADHD group, though this finding did not reach statistical significance.
The Take-Away:
These findings underscore a need to improve both prevention and treatment strategies for disordered eating, particularly in children and adolescents who have ADHD. Clinicians working with this population are advised to screen for a wide spectrum of disordered eating behaviors.
For centuries, we’ve called the eyes the "windows to the soul," but for modern neurologists, they are quite literally a window into the brain. The retina and the central nervous system share the same embryonic origins, developing from the same neural tissue in the womb. Because of this deep biological connection, the back of your eye acts as a non-invasive map of your brain's health, displaying a complex web of nerves and blood vessels that can (theoretically!) mirror certain neurodevelopmental conditions.
Recently, a buzz rippled through the mental health community when a study published in partnership with Seoul National University Bundang Hospital claimed a massive breakthrough. Researchers developed an Artificial Intelligence (AI) model that could screen children for Attention-Deficit/Hyperactivity Disorder (ADHD) using nothing more than a simple retinal photograph. The study, which prospectively recruited children from Severance Hospital and Eunpyeong St. Mary’s Hospital, produced results that were staggering: the AI reportedly achieved an accuracy rate of 96.9%!
In the world of medical testing, scientists use a metric called AUROC (Area Under the Receiver Operating Characteristic) to measure how well a test works.
An AUROC of 96.9% is a near-perfect score, suggesting a tool is ready for immediate, real-world deployment. While headlines promised a revolution in mental health screening, a deeper look into this research and the study’s design has exposed that this 96.9% AUROC was more likely evidence of a flawed methodology rather than a biological reality.
To build their screening tool, researchers analyzed over 1,100 retinal images using a digital pipeline called AutoMorph and a machine-learning model known as XGBoost. The AI was trained to hunt for physical signals of the "Dopamine Connection." Dopamine is the primary neurotransmitter involved in ADHD, but it is also essential to the eye. It regulates synaptic formation, retinal blood flow, and vascular endothelial regulation. Because dopamine dysregulation influences how blood vessels grow and remodel, the study hypothesized that an ADHD brain would leave a unique "fingerprint" on the retinal vasculature, resulting in denser, thicker vessel structures.
On paper, the logic was sound: use AI to spot the subtle vascular remodeling caused by dopaminergic shifts. But a closer look at the investigation revealed that the AI wasn't just spotting ADHD; it was over-indexing on technical noise.
The most significant "smoking gun" flagged by critics is a massive temporal mismatch. In other words, there was a severe disparity in the timeframes and conditions under which the retinal images for the two comparison groups were collected. For an AI to learn a biological condition, it must compare groups under identical technical conditions. Instead, this study created a time-traveling dataset:
A scientific study is only as reliable as its control group. The control in any experiment acts as a baseline against which the study group is compared. In this case, the control group should be composed of children without any neurodevelopmental disorders, or of “typically developing” children.
In this study, the control group wasn't composed of healthy children from the community. Instead, they were patients visiting a tertiary ophthalmology clinic. Children visiting a specialist eye hospital are rarely "typical." They are there because they have symptomatic eye issues. This introduced a massive selection bias involving three major confounders:
When training AI, you must never allow the "test questions" to leak into the "study material." The researchers, however, committed a fundamental violation of machine learning hygiene known as Eye-to-Eye Data Leakage. The study split the data by the eye rather than by the participant.
Human eyes are highly correlated; the left eye is a near-mirror of the right. If a child's left eye was used for training and their right eye was used for testing, the AI was effectively "cheating." Instead of learning the general traits of ADHD, the model was potentially memorizing individuals. This error artificially balloons accuracy metrics.
The true test of medical AI is diagnostic specificity, or differential diagnosis. This refers to the ability to tell one condition apart from another. While the model claimed 96.9% accuracy against a flawed control group, its performance collapsed when faced with real-world complexity.
When the researchers asked the AI to differentiate between ADHD and Autism Spectrum Disorder (ASD), the accuracy plummeted to a poor 63% AUROC. In real-world clinical settings, an accuracy of 63% is dangerously close to a 50% coin flip. Since ADHD frequently co-occurs with ASD, anxiety, or intellectual disabilities, an AI that cannot handle these "clinical differentials" is functionally useless in a doctor's office. The failure at this stage proves the model was likely detecting technical quirks of the dataset rather than a unique biological marker for ADHD.
To move from the lab to the clinic, we must establish a foundation built on rigor rather than high-speed data scraping. Moving forward, we must demand these 3 Pillars of Trusted Medical AI :
The dream of a quick eye scan to diagnose ADHD is not dead, but it must be rescued from "fast science" shortcuts and buzzy headlines.
Background:
One of the more persistent concerns among parents of children with ADHD is whether stimulant medications will stunt their child's growth. A large Israeli cohort study now offers some of the most rigorous reassurance to date, and its methodology sets it apart from earlier research.
The question has long been complicated by a more fundamental uncertainty: do growth differences in children with ADHD stem from the condition itself, from stimulant treatment, or from factors present before any medication is ever prescribed? Without a clear answer, clinicians and families have faced a genuine dilemma when weighing the benefits of stimulant therapy against potential long-term physical costs.
Most previous studies compounded this difficulty by comparing group-average heights, which ignores the crucial variable of genetic potential. A child who is short relative to the general population may simply have short parents. Failing to account for this introduces systematic bias and can make medications appear more harmful than they are.
The Study:
The Israeli research team addressed this directly. Using health records from a nationwide provider, they assembled a retrospective cohort of children born between 1995 and 2003, following them through 2023. This amount of time was long enough for all participants to have reached adult stature (defined as 17 or older for females, 19 or older for males). Their sample included 5,671 children with untreated ADHD, 11,846 who received stimulant treatment, and 47,258 non-ADHD controls. Children who took stimulants for only one to two months, or who had chronic medical conditions requiring long-term medication, were excluded to avoid confounding the results.
Crucially, adult height was evaluated not against population norms but against each individual's expected height, calculated from parental heights using the Tanner-Goldstein-Whitehouse method, a standard approach for estimating genetic height potential via mid-parental height.
When the researchers compared adult heights across the three groups using analysis of variance (ANOVA), they did find statistically significant differences. But statistical significance, particularly in studies with tens of thousands of participants, does not automatically translate into clinical significance. The effect sizes were consistently very small, and the absolute differences were under one centimeter, which is a margin considered clinically negligible.
Their conclusion is measured but clear: after accounting for genetic growth potential, neither an ADHD diagnosis nor stimulant treatment was associated with meaningful reductions in adult height. The findings, they argue, support prioritizing behavioral and functional outcomes when making treatment decisions, since the risk of clinically significant height loss appears to be minimal.
The Take-Away:
For families navigating ADHD treatment, the practical implication is significant: concerns about permanent growth suppression, while understandable, should not be the primary driver of whether or how long a child receives stimulant therapy.
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