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February 2, 2026
The Background:
Concerns remain about how ADHD and methylphenidate (MPH) use might affect children's health and growth, and especially how it may affect their adult height. While some studies suggest disrupted growth and a possible biological mechanism, the impact of ADHD prevalence and MPH use is still unclear. Children with ADHD may develop unhealthy habits – irregular eating, low physical activity, and poor sleep – that can contribute to obesity and reduced height. MPH’s appetite-suppressing effect can lead to skipped meals or overeating. Since growth hormone is mainly released during deep sleep, chronic sleep deprivation could plausibly slow growth and impair height development; however, a clear link between ADHD, MPH use, overweight, and shorter stature has never been firmly established.
The Study:
South Korea has a single payer health insurance system that covers more than 97% of its population. A Korean research team used the National Health Insurance Service database to perform a nationwide population study to explore this topic further.
The study involved 34,850 children, of whom 12,866 were diagnosed with ADHD. Of these children, 6,816 (53%) had received methylphenidate treatment, while 6,050 (47%) had not. Each patient with ADHD was precisely matched 1:1 by age, sex, and income level to a control participant without ADHD. The sex ratio was comparable in all groups.The team used Body Mass Index (BMI) as an indicator of overweight and obesity.
The Results:
The researchers found that being diagnosed with ADHD was associated with 50% greater odds of being overweight or obese as young adults, and over 70% greater odds of severe obesity (BMI > 30) compared to matched non-ADHD controls, regardless of whether or not they were medicated.
Those diagnosed with ADHD, but not on methylphenidate, had 40% greater odds of being overweight or obese, and over 55% greater odds of becoming severely obese, relative to matched non-ADHD controls.
Methylphenidate users had 60% greater odds of being overweight or obese, and over 85% greater odds of becoming severely obese, relative to matched non-ADHD controls.
There were signs of a dose-response effect. Less than a year’s exposure to methylphenidate was associated with roughly 75% greater odds of becoming severely obese, whereas exposure over a year or more raised the odds 2.3-fold, relative to matched non-ADHD controls. Using MPH increased the prevalence of overweight from 43.2% to 46.5%, with a greater prevalence among those using MPH for more than one year (50.5%).
It is important to note that most of this effect was from ADHD itself, with methylphenidate only assuming a predominant role in severe obesity among those with longer-term exposure to the medicine.
As for height, children with ADHD were no more likely to be short of stature than matched non-ADHD controls. Being prescribed methylphenidate was associated with slightly greater odds (7%) of being short of stature, but there was no dose-response relationship.
Conclusion:
The team concluded, “patients with ADHD, particularly those treated with MPH, had a higher BMI and shorter height at adulthood than individuals without ADHD. Although the observed height difference was clinically small in both sexes and age groups, the findings suggest that long-term MPH exposure may be associated with growth and body composition, highlighting the need for regular monitoring of growth.” They also point out that “Despite these findings, the clinical relevance should be interpreted with caution. In our cohort, the mean difference in height was less than 1 cm (eg, maximum −0.6 cm in females) below commonly accepted thresholds for clinical significance.” Likewise, increases in overweight/BMI were small.
One problem with interpreting the BMI/obesity results is that some of the genetic variants that cause ADHD also cause obesity. If that genetic load increases with severity of ADHD than the results from this study are confounded because those with more severe ADHD are more likely to be treated than those with less severe ADHD.
Due to these small effects along with the many study limitations noted by the authors, these results should be considered alongside the well-established benefits of methylphenidate treatment.
Jihun Song, Sun Jae Park, Jiwon Yu, Jina Chung, Seogsong Jeong, and Sang Min Park, “ADHD and Methylphenidate Use in Prepubertal Children and BMI and Height at Adulthood,” JAMA Network Open (2026), 9(1):e2552019, https://doi.org/10.1001/jamanetworkopen.2025.52019
Noting that “the association between adult ADHD and dementia risk remains a topic of interest because of inconsistent results,” an Israeli study team tracked 109,218 members of a nonprofit Israeli health maintenance organization born between 1933 and 1952 who entered the cohort on January 1, 2003, without an ADHD or dementia diagnosis and were followed up to February 28, 2020.
Israeli law forbids nonprofit HMOs from turning anyone away based on demographic factors, health conditions, or medication needs, thereby limiting sample selection bias.
The estimated prevalence of dementia in this HMO, as diagnosed by geriatricians, neurologists, or psychiatrists, is 6.6%. This closely matches estimates in Western Europe (6.9%) and the United States (6.5%).
The team considered, and adjusted for, numerous covariates: age, sex, socioeconomic status, smoking, depression, obesity, chronic obstructive pulmonary disease, hypertension, atrial fibrillation, heart failure, ischemic heart disease, cerebrovascular disease, diabetes, Parkinson’s disease, traumatic brain injury, migraine, mild cognitive impairment, psychostimulants.
With these adjustments, individuals diagnosed with ADHD were almost three times as likely to be subsequently diagnosed with dementia as those without ADHD. Men with ADHD were two and a half times more likely to be diagnosed with dementia, whereas women with ADHD were over three times more likely, than non-ADHD peers.
More concerning still, persons with ADHD were 5.5 times more likely to be subsequently diagnosed with early onset dementia, as opposed to 2.4 times more likely to be diagnosed with late onset dementia.
On the other hand, the team found no significant difference in rates of dementia between individuals with ADHD who were being treated with stimulant medications and individuals without ADHD. Those with untreated ADHD had three times the rate of dementia. The team nevertheless cautioned, “Due to the underdiagnosis of dementia as well as bidirectional misdiagnosis, this association requires further study before causal inference is plausible.”
Conclusions and Relevance:
This study reinforces existing evidence that adult ADHD is associated with an increased risk of dementia. Notably, the increased risk was not observed in individuals receiving psychostimulant medication, however the mechanism behind this association is not clear.
These findings underscore the importance of reliable ADHD assessment and management in adulthood. They also highlight the need for further study into the link between stimulant medications and the decreased risk of dementia.
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After noting that the association between ADHD and obesity has been called into question because of small sample sizes, wide age ranges, self-reported assessments, and inadequate attention to potential confounders, an Israeli study team set out "to assess the association between board-certified psychiatrist diagnoses of ADHD and measured adolescent BMI [body mass index] in a nationally represented sample of over one million adolescents who were medically evaluated before mandatory military service."
The team distinguished between severe and mild ADHD. It also focused on a single age group.
All Israelis are subject to compulsory military service. In preparation for that service, military physicians perform a thorough medical evaluation. Trained paramedics recorded every conscript's height and weight.
The study cohort was divided into five BMI percentile groups according to the U.S. Centers for Disease Control and Prevention's BMI percentiles for 17-year-olds, and further divided by sex: <5th percentile (underweight), 5th-49th percentile (low-normal), 50th-84th percentile (high normal), 85th-94th percentile (overweight) and ≥95th (obese). Low-normal was used as the reference group.
Adjustments were made for sex, birth year, age at examination, height, country of birth (Israeli or other), socioeconomic status, and education level.
In the fully adjusted results, those with severe ADHD were 32% more likely to be overweight and 84% more likely to be obese than their typically developing peers. Limiting results to Israeli-born conscripts made a no difference.
Male adolescents with mild ADHD were 24% more likely to be overweight, and 42% more likely to be obese. Females with mild ADHD are 33% more likely to be overweight, and 42% more likely to be obese. Again, the country of birth made no difference.
The authors concluded, that both severe and mild ADHD was associated with an increased risk for obesity in adolescents at the age of 17 years. The increasing recognition of the persistence of ADHD into adulthood suggests that this dual morbidity may have a significant impact on the long-term health of individuals with ADHD, thus early preventive measures should be taken.
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.
A recent meta-analysis examined how well cognitive behavioral therapy (CBT) improves not just symptoms, but everyday functioning and quality of life in adults with ADHD.
The Background:
ADHD in adults affects far more than attention or impulsivity. It often disrupts key areas of life:
These broad impacts highlight a key issue: reducing symptoms does not automatically translate into better day-to-day functioning.
CBT is a structured, skills-based therapy that helps people:
While both medication (especially stimulants) and CBT improve core ADHD symptoms, CBT is particularly aimed at improving real-world functioning.
The Study:
The researchers analyzed studies involving adults diagnosed with ADHD (or showing clinically significant symptoms). They included:
They focused specifically on outcomes beyond symptoms:
The Results:
1. Strongest Effects: Occupational functioning
CBT showed consistently strong improvements in work-related functioning compared to control groups, both immediately after treatment and at follow-up. This was the most robust finding across domains.
2. Moderate Improvement: Global Functional Impairment
CBT led to moderate improvements in overall daily functioning, with some evidence that gains persist over time. In studies tracking individuals over time, improvements were even stronger at follow-up.
3. Modest Gains: Social Relationships
CBT produced small to moderate improvements in social functioning. Benefits were present both after treatment and at follow-up, but were less pronounced than in work-related outcomes.
4. Limited Effects: Academic Functioning
There were moderate short-term gains when CBT was compared to control groups, but these did not persist at follow-up. Within-subject studies showed only small improvements overall.
5. Modest and Inconsistent Effects: Quality of Life
Improvements in quality of life were small when compared to control groups and often did not last. However, studies tracking individuals over time showed moderate improvements, suggesting some benefit that may not always show up clearly in between-group comparisons.
Overall, the findings suggest:
One notable nuance: CBT did not always outperform other active treatments (like medication or other therapies). This suggests that while CBT is effective, its benefits may partly overlap with broader therapeutic or support effects rather than relying on a single, unique mechanism.
The Take-Away:
CBT is a valuable, evidence-based treatment for adults with ADHD, especially for improving work functioning and overall daily life management. However, its impact on relationships, academic outcomes, and quality of life is more limited and less consistent, pointing to the need for more targeted or combined approaches in those areas.
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