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November 16, 2023
The US Center for Disease Control's (CDC)review of ADHD starts with the statement: "Attention-deficit/hyperactivity disorder (ADHD) is a serious public health problem affecting many children and adults" (http://www.cdc.gov/ncbddd/adhd/research.html). My colleagues and I recently reviewed the ADHD literature. That let us describe ADHD as "... a seriously impairing, often persistent neurobiological disorder of high prevalence..." (Faraone et al., 2015). The figure 1, which comes from that paper, provides an overview of the lifetime trajectory of ADHD-associated morbidity.
Especially compelling data about ADHD and injuries comes from a recent paper, in Lancet Psychiatry, which used the Danish national registers to follow a cohort of 710,120 children (Dalsgaard et al., 2015a). Compared with children not having ADHD, those with ADHD were 30% more likely to sustain injuries than other children. Pharmacotherapy for ADHD reduced the risk for injuries by 32% from 5 to 10 years of age. Pharmacotherapy for ADHD reduced emergency room visits by 28.2% at age 10and 45.7% at age 12.
These results are shown in Figure 2, taken from the publication.
Especially compelling data about ADHD and injuries comes from a recent paper, in Lancet Psychiatry, which used the Danish national registers to follow a cohort of 710,120 children (Dalsgaard et al., 2015a). Compared with children not having ADHD, those with ADHD were 30% more likely to sustain injuries than other children. Pharmacotherapy for ADHD reduced the risk for injuries by 32% from 5 to 10 years of age. Pharmacotherapy for ADHD reduced emergency room visits by 28.2%at age 10and 45.7% at age 12.
These results are shown in Figure2, taken from the publication. The Figure compares the prevalence of injuries among three groups. ADHD children treated with medication, ADHD children not treated with medication, and children without ADHD. The Figure shows how ADHD risk for injuries occurs for all age groups. It also shows how the risk for injuries drops with treatment so that by age 12, the prevalence of injuries among treated ADHD children is the same as the prevalence of injuries for children without ADHD.
Documented examples of ADHD-associated injuries which impact day-to-day functioning include severe burns (Fritz and Butz, 2007), dental injuries (Sabuncuoglu, 2007), penetrating eye injuries (Bayar et al., 2015), the hospital treated injuries (Hurtig et al., 2013), and head injuries (DiScala et al., 1998). In one study (DiScala et al., 1998), when compared to other children admitted to the hospital for injuries, ADHD children were more likely to sustain injuries in multiple body regions (57.1% vs 43%), sustain head injuries (53% vs 41%), and to be severely injured as measured by the Injury Severity Score (12.5% vs5.4%) and the Glasgow Coma Scale (7.5% vs 3.4%).
Injuries are a substantial cause of ADHD-associated premature death. This assertion comes from the work of Dalsgaard et al. (2015b)based on the same Danish registry discussed above. In this second study, ADHD was associated with an increased risk for premature death and 53% of those deaths were due to injuries. They reported the risk for premature death in three age groups: 1-5, 6-17, and >17. For all three age groups, they found a greater risk for death in the ADHD group. For ages 6 to 17 and greater than 17. The ADHD-associated risk for mortality remained significant after excluding individuals with antisocial or substance use disorders.
There are currently no data about the effect of ADHD treatment on ADHD-associated premature death. We do, however, know from the data reviewed above that ADHD treatment reduces injuries and that half the deaths in the ADHD group were due to injuries. From this, we infer that ADHD treatments could reduce the risk of ADHD-associated premature death.
Two other ADHD-associated mobilities, obesity and cigarette smoking, have clear medical consequences. In a meta-analysis of 42 cross-sectional studies comprising 48,161 people with ADHD and 679,975 controls, my colleagues and I reported that the pooled prevalence of obesity was increased by about 40% in ADHD children compared with non-ADHD children and by about 70% in ADHD adults compared with non-ADHD adults(Cortese et al.,2015). The association between ADHD and obesity was significant for ADHD medication-naïve subjects but not for those medicated for ADHD, which suggests that medication reduces the risk for obesity.
Likewise, a meta-analysis of 27 longitudinal studies assessed the risk for several addictive disorders with sample sizes ranging from 4142 to 4175 for ADHD and 6835 to 6880 for non-ADHD controls (Lee et al., 2011). Children with ADHD were at higher risk for disorders of abuse or dependence on nicotine, alcohol, marijuana, cocaine, and other unspecified substances. Another meta-analysis (42 studies totaling, 2360 participants) showed that medications for ADHD reduced the ADHD-associated risk for smoking (Schoenfelder et al., 2014). The authors concluded that, for ADHD patients, "Consistent stimulant treatment for ADHD may reduce the risk of smoking". This finding is especially notable given that, for ADHD youth, cigarette smoking is a gateway drug to more serious addictions (Biederman et al., 2006).
Yes, ADHD is a serious disorder. Although most ADHD people will be spared the worst of these outcomes, they must be considered by parents and patients when weighing the pros and cons of treatment options.
Bayar, H., Coskun, E., Oner, V., Gokcen,C., Aksoy, U., Okumus, S. & Erbagci, I. (2015). Association between penetrating eye injuries and attention deficit hyperactivity disorder in children.Br J Ophthalmol99, 1109-11.
Biederman, J., Monuteaux, M., Mick, E., Wilens, T., Fontanella, J.,Poetzl, K. M., Kirk, T., Masse, J. & Faraone, S. V. (2006). Is cigarette smoking a gateway drug to subsequent alcohol and illicit drug use disorders? A controlled study of youths with and without ADHD. Biol Psychiatry59, 258-64.
Cortese, S., Moreira-Maia, C. R., St Fleur, D., Morcillo-Penalver, C.,Rohde, L. A. & Faraone, S. V. (2015). Association Between ADHD and Obesity: A Systematic Review and Meta-Analysis. Am J Psychiatry, appiajp201515020266.
Dalsgaard, S., Leckman, J. F., Mortensen, P. B., Nielsen, H. S. &Simonsen, M. (2015a). Effect of drugs on the risk of injuries in children with attention deficit hyperactivity disorder: a prospective cohort study. Lancet Psychiatry2, 702-9.
Dalsgaard, S., Ostergaard, S. D., Leckman, J. F., Mortensen, P. B.& Pedersen, M. G. (2015b). Mortality in children, adolescents, and adults with attention deficit hyperactivity disorder: a nationwide cohortstudy. Lancet385, 2190-6.
DiScala, C., Lescohier, I., Barthel, M. & Li, G. (1998).Injuries to children with attention deficit hyperactivity disorder. Pediatrics102, 1415-21.
Faraone, S. V., Asherson, P., Banaschewski, T., Biederman, J.,Buitelaar, J. K., Ramos-Quiroga, J. A., Rohde, L. A., Sonuga-Barke, E. J. S.,Tannock, R. & Franke, B. (2015). Attention deficit hyperactivitydisorder. In Nature Reviews: DiseasePrimers.
Fritz, K. M. & Butz, C. (2007). Attention Deficit/Hyperactivity Disorder and pediatric burn injury: important considerations regarding premorbid risk. Curr Opin Pediatr19, 565-9.
Hurtig, T., Ebeling, H., Jokelainen, J., Koivumaa-Honkanen, H. &Taanila, A. (2013). The Association Between Hospital-Treated Injuries and ADHD Symptoms in Childhood and Adolescence: A Follow-Up Study in the Northern Finland Birth Cohort 1986. J Atten Disord.
Lee, S. S., Humphreys, K. L., Flory, K., Liu, R. & Glass, K. (2011).Prospective association of childhood attention-deficit/hyperactivity disorder(ADHD) and substance use and abuse/dependence: a meta-analytic review. Clin Psychol Rev31, 328-41.
Sabuncuoglu, O. (2007). Traumatic dental injuries and attention-deficit/hyperactivity disorder: is there a link? Dent Traumatol23,137-42.
Schoenfelder, E. N., Faraone, S. V. & Kollins, S. H. (2014).Stimulant treatment of ADHD and cigarette smoking: a meta-analysis. Pediatrics133, 1070-1080.
ADHD is a neurodevelopmental condition rooted in delayed or atypical maturation of the prefrontal cortex (the brain region that governs self-regulation). This maturational lag underlies the hallmark difficulties with attention, hyperactivity, and impulsivity, and also impairs what researchers call executive function: the cognitive toolkit we rely on for working memory, impulse control, mental flexibility, emotional regulation, and the ability to tolerate delays in reward.
The Background:
Standard treatments work through two main routes. Stimulant and non-stimulant medications are considered very safe and effective treatments, but are not without risk of side effects and are not appropriate for every ADHD patient. Behavioral and psychosocial interventions can improve self-regulation and social functioning, but they require sustained effort and produce variable results. These limitations have kept the search for better alternatives active.
One candidate that has drawn growing attention is transcranial direct current stimulation (tDCS). The technique is appealingly simple: a weak electrical current is applied to the scalp through small electrodes, modulating the excitability of neurons in the underlying cortex without requiring surgery, anesthesia, or significant discomfort. Its safety profile and ease of use have made it attractive to researchers.
The Study:
A newly published meta-analysis set out to give the technique its most rigorous test yet, pooling results from randomized controlled trials, including crossover designs, that compared active tDCS against sham stimulation in people with ADHD across all age groups.
The Results:
The findings were consistently null. Across seven trials enrolling 303 participants, tDCS produced no significant reduction in overall ADHD symptom severity compared with sham. Breaking symptoms into their components made no difference: neither hyperactivity/impulsivity nor inattention improved. Turning to executive function, 18 studies with 872 participants found no meaningful gain in inhibitory control, and 12 studies with 506 participants found the same for working memory. Smaller bodies of evidence, including three studies on cognitive flexibility (122 participants) and two on hot executive function, the motivational and emotional dimension of self-regulation (86 participants), similarly came up empty. Variation in outcomes across studies was small to moderate, and there was no evidence of publication bias skewing the picture.
The authors’ conclusion was succinct: tDCS was well tolerated but “did not demonstrate significant overall efficacy for core ADHD symptoms or executive functions.”
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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.
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