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December 18, 2024
Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental condition that persists into adulthood for most individuals, affecting 60% to 90% of those diagnosed as children. However, understanding ADHD in older adults, particularly those over 50, remains limited. With the U.S. population aged 65+ projected to nearly double by 2050, this oversight has critical implications for healthcare.
A recent analysis of 20 studies (sample size: over 20 million) highlights ADHD prevalence in the elderly as 2.18% when community scales are used but only 0.23% when clinical diagnoses are reviewed in medical records. This discrepancy points to underdiagnosis and the need for clinician education. Furthermore, treatment rates are alarmingly low, with just 0.09% of elderly individuals receiving ADHD medications.
Current diagnostic criteria, still rooted in studies of youth, inadequately address age-specific symptoms. Barkley and Murphy’s screening tool is one step forward, but its moderate reliability signals the need for refinement. Diagnostic challenges grow more complex as clinicians must differentiate ADHD from cognitive changes due to aging, medical conditions, or psychiatric disorders like depression or dementia. The concurrent presence of conditions further complicates assessments and treatments.
Treatment hesitancy also hampers care. Concerns about cardiovascular risks, interactions with other medications, and lack of familiarity with ADHD medication dosing in older adults fuel clinician caution. While psychostimulants are generally safe when carefully managed, misconceptions about abuse and addiction persist, creating unnecessary barriers.
Addressing ADHD in older adults requires dedicated clinician training to overcome biases, refine diagnostic tools, and balance medical risks with the significant quality-of-life benefits ADHD treatment offers. With more research, improved clinical protocols, and better education, older adults with ADHD can receive accurate diagnoses and effective treatment. This will help them maintain cognitive function and independence, significantly enhancing their lives.
Goodman, D. W., Cortese, S., & Faraone, S. V. (2024). Why is ADHD so difficult to diagnose in older adults? Expert Review of Neurotherapeutics, 24(10), 941–944. https://doi.org/10.1080/14737175.2024.2385932
The current Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) requires evidence of symptom onset before age 12 to make a diagnosis of ADHD in adults.
A recently published clinical review questions the appropriateness of this criterion in older adults 50 years old and above. It sets forth several reasons:
On the other hand, the reason for the early onset criterion is to avoid any confusion with early neurodegenerative diseases such as Alzheimer's or Lewy body dementia, which have overlapping symptoms.
The authors suggest a possible fix:
It is unethical, the authors suggest, to deny care to older, presently undiagnosed adults, given the demonstrated poor outcomes associated with untreated ADHD.
The CDC recently reported that ADHD medication use in women ages 15 to 44 increased from 0.9 percent to 4 percent from 2003 to 2015. The most commonly used medications were formulations of amphetamine or methylphenidate.
This increase in treatment for ADHD suggests that educational programs such as adhdinadults.com have been effective in teaching clinicians how to identify and treat the disorder. The 4 percent rate reported by the CDC is encouraging because it is close to what Ron Kessler and colleagues reported as the prevalence of adult ADHD in the population. CDC correctly points out that little is known about the effects of ADHD medications on pregnancies. Thus, caution is warranted.
Oei et al.'s review of amphetamines concluded: "There is little evidence of amphetamine-induced neurotoxicity and long-term neurodevelopmental impact, as data is scarce and difficult to extricate from the influence of other factors associated with children living in households where one or more parent uses drugs in terms of poverty and neglect. ... We suggest that exposed children may be at risk of ongoing developmental and behavioral impediment, and recommend that efforts be made to improve early detection of perinatal exposure and to increase the provision of early intervention services for affected children and their families"
Bolea-Alamanac et al.'s review of methylphenidate effects concluded: "There is a paucity of data regarding the use of methylphenidate in pregnancy and further studies are required. Although the default medical position is to interrupt any non-essential pharmacological treatment during pregnancy and lactation, in ADHD this may present a significant risk. Doctors need to evaluate each case carefully before interrupting treatment." These words of caution should be heeded by clinicians caring for women of reproductive age.
Older adults are at greater risk for cardiovascular disease. Psychostimulants may contribute to that risk through side effects, such as elevation of systolic blood pressure, diastolic blood pressure, and heart rate.
On the other hand, smoking, substance abuse, obesity, and chronic sleep loss - all of which are associated with ADHD - are known to increase cardiovascular risk, and stimulant medications are an effective treatment for ADHD.
So how does this all shake out? A Dutch team of researchers sets out to explore this. Using electronic health records, they compared all 139 patients 55 years and older at PsyQ outpatient clinic, Program Adult ADHD, in The Hague. Because a principal aim of the study was to evaluate the effect of medication on cardiovascular functioning after first medication use, the 26 patients who had previously been prescribed ADHD medication were excluded from the study, leaving a sample size of 113.
The ages of participants ranged from 55 from 79, with a mean of 61. Slightly over half were women. At the outset, 13 percent had elevated systolic and/or diastolic blood pressure, 2 percent had an irregular heart rate, 15 percent had an abnormal electrocardiogram, and 29 percent had some combination of these (a "cardiovascular risk profile"), and 21 percent used antihypertensive medication.
Three out of four participants had at least e comorbid disorder. The most common are sleep disorders, affecting a quarter of participants, and unipolar mood disorders (depressive or more rarely manic episodes, but not both), also affecting a quarter of participants.
Twenty-four patients did not initiate pharmacological treatment. Of the 89 who received ADHD medication, 58 (65%) reported positive effects, and five experienced no effect. Thirty-eight (43%) discontinued ADHD medication while at the clinic due to lack of effect or to side effects. The most commonly reported positive effects were enhanced concentration, more overview, less restlessness, more stable mood, and having more energy. The principal reasons for discontinuing medication were anxiety/depression, cardiovascular complaints, and lack of effect.
Methylphenidate raised heart rate and lowered weight, but had no significant effect on systolic and diastolic blood pressure. Moreover, there was no significant correlation between methylphenidate dosage and any of these variables, nor between methylphenidate users taking hypertensive medication and those not taking such medication. There was no significant difference in systolic or diastolic blood pressure and heart rate before and after the use of methylphenidate among patients with the cardiovascular risk profiles.
Systolic blood pressure rose in ten out of 64 patients, with two experiencing an increase of at least 20 mmHg. It descended in five patients, with three having a decrease of at least 20 mmHg. Diastolic blood pressure rose by at least 10 mmHg in four patients, while dropping at least 10 mmHg in five others.
The authors concluded "that the use of a low dose of ADHD-medication is well tolerated and does not cause clinically significant cardiovascular changes among older adults with ADHD, even among those with an increased cardiovascular risk profile. Furthermore, our older patients experienced significant and clinically relevant improvement of their ADHD symptoms using stimulants, comparable with what is found among the younger age group," and that "the use of methylphenidate may be a relatively safe and effective treatment for older adults with ADHD, under the condition that all somatic complaints and especially cardiovascular parameters are monitored before and during pharmacological treatment."
Yet they cautioned that "due to the observational nature of the study and the lack of a control group, no firm conclusions can be drawn as to the effectiveness of the stimulants used. ... Important factors that were not systematically reported were the presence of other risk factors, such as smoking, substance (ab)use, aspirin use, and level of physical activity. In addition, the response to medication was not systematically measured"
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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