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April 7, 2021
There is a growing interest (and controversy) in 'adult-onset ADHD. No current diagnostic system allows for the diagnosis of ADHD in adulthood, yet clinicians sometimes face adults who meet all criteria for ADHD, except for age at onset. Although many of these clinically referred adult-onset cases may reflect poor recall, several recent longitudinal population studies have claimed to detect cases of adult-onset ADHD that showed no signs of ADHD as a youth (Agnew-Blais, Polanczyk et al. 2016, Caye, Rocha, et al. 2016). They conclude, not only that ADHD can onset in adulthood, but that childhood-onset and adult-onset ADHD may be distinct syndromes(Moffitt, Houts, et al. 2015)
In each study, the prevalence of adult-onset ADHD was much larger than the prevalence of childhood-onset adult ADHD). These estimates should be viewed with caution. The adults in two of the studies were 18-19 years old. That is too small a slice of adulthood to draw firm conclusions. As discussed elsewhere (Faraone and Biederman 2016), the claims for adult-onset ADHD are all based on population as opposed to clinical studies.
Population studies are plagued by the "false positive paradox", which states that, even when false positive rates are low, many or even most diagnoses in a population study can be false.
Another problem is that the false positive rate is sensitive to the method of diagnosis. The child diagnoses in the studies claiming the existence of adult-onset ADHDused reports from parents and/or teachers but the adult diagnoses were based on self-report. Self-reports of ADHD in adults are less reliable than informant reports, which raises concerns about measurement error. Another longitudinal study found that current symptoms of ADHD were under-reported by adults who had had ADHD in childhood and over-reported by adults who did not have ADHD in childhood(Sibley, Pelham, et al. 2012). These issues strongly suggest that the studies claiming the existence of adult-onset ADHD underestimated the prevalence of persistent ADHD and overestimated the prevalence of adult-onset ADHD. Thus, we cannot yet accept the conclusion that most adults referred to clinicians with ADHD symptoms will not have a history of ADHD in youth.
The new papers conclude that child and adult ADHD are "distinct syndromes", "that adult ADHD is more complex than a straightforward continuation of the childhood disorder" and that adult ADHD is "not a neurodevelopmental disorder". These conclusions are provocative, suggesting a paradigm shift in how we view adulthood and childhood ADHD. Yet they seem premature. In these studies, people were categorized as adult-onset ADHD if full-threshold add had not been diagnosed in childhood. Yet, in all of these population studies, there was substantial evidence that the adult-onset cases were not neurotypical in adulthood (Faraone and Biederman 2016). Notably, in a study of referred cases, one-third of late adolescent and adult-onset cases had childhood histories of ODD, CD, and school failure(Chandra, Biederman, et al. 2016). Thus, many of the "adult onsets" of ADHD appear to have had neurodevelopmental roots.
Looking through a more parsimonious lens, Faraone and Biederman(2016)proposed that the putative cases of adult-onset ADHD reflect the existence of subthreshold childhood ADHD that emerges with full threshold diagnostic criteria in adulthood. Other work shows that subthreshold ADHD in childhood predicts onsets of full-threshold ADHD in adolescence(Lecendreux, Konofal, et al. 2015). Why is onset delayed in subthreshold cases? One possibility is that intellectual and social supports help subthreshold ADHD youth compensate in early life, with decompensation occurring when supports are removed in adulthood or the challenges of life increase. A related possibility is that the subthreshold cases are at the lower end of a dimensional liability spectrum that indexes risk for onset of ADHD symptoms and impairments. This is consistent with the idea that ADHD is an extreme form of a dimensional trait, which is supported by twin and molecular genetic studies(Larsson, Anckarsater, et al. 2012, Lee, Ripke, et al. 2013). These data suggest that disorders emerge when risk factors accumulate over time to exceed a threshold. Those with lower levels of risk at birth will take longer to accumulate sufficient risk factors and longer to onset.
In conclusion, it is premature to accept the idea that there exists an adult-onset form of ADHD that does not have its roots in neurodevelopment and is not expressed in childhood. It is, however, the right time to carefully study apparent cases of adult-onset ADHD to test the idea that they are late manifestations of a subthreshold childhood condition.
Agnew-Blais, J. C., G.V. Polanczyk, A. Danese, J. Wertz, T. E. Moffitt and L. Arseneault (2016)."Persistence, Remission and Emergence of ADHD in Young Adulthood:Resultsfrom a Longitudinal, Prospective Population-Based Cohort." JAMA.Caye, A., T. B.-M. Rocha, L. Luciana Anselmi, J. Murray, A. M.B. Menezes, F. C. Barros, H. Gonçalves, F. Wehrmeister, C. M. Jensen, H.-C.Steinhausen, J. M. Swanson, C. Kieling and L. A. Rohde (2016). "ADHD doesnot always begin in childhood: E 1 vidence from a large birth cohort." JAMA.
Chandra, S., J. Biederman and S. V. Faraone (2016)."Assessing the Validity of the Ageat Onset Criterion for Diagnosing ADHD in DSM-5." J Atten Disord.
Faraone, S. V. and J. Biederman (2016). "CanAttention-Deficit/Hyperactivity Disorder Onset Occur in Adulthood?" JAMAPsychiatry.
Larsson, H., H. Anckarsater, M. Rastam, Z. Chang and P.Lichtenstein (2012). "Childhood attention-deficit hyperactivity disorderas an extreme of a continuous trait: a quantitative genetic study of 8,500 twinpairs." J Child Psychol Psychiatry53(1): 73-80.
Lecendreux, M., E. Konofal, S. Cortese and S. V. Faraone(2015). "A 4-year follow-up of attention-deficit/hyperactivity disorder ina population sample." J Clin Psychiatry76(6): 712-719.
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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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