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Noting that “evidence on the association between ADHD and a physical condition associated with obesity, namely type 2 diabetes mellitus (T2D), is sparse and has not been meta-analysed yet,” a European study team performed a systematic search of the peer-reviewed medical literature followed by a meta-analysis, and then a nationwide population study.
Unlike type 1 diabetes, which is an auto-immune disease, type 2 diabetes is believed to be primarily related to lifestyle, associated with insufficient exercise, overconsumption of highly processed foods, and especially with large amounts of refined sugar. This leads to insulin resistance and excessively high blood glucose levels that damage the body and greatly lower life expectancy.
Because difficulty with impulse control is a symptom of ADHD, one might hypothesize that individuals with ADHD would be more likely to develop type-2 diabetes.
The meta-analysis of four cohort studies encompassing more than 5.7 million persons of all ages spread over three continents (in the U.S., Taiwan, and Sweden) seemed to point in that direction. It found that individuals with ADHD had more than twice the odds of developing type 2 diabetes than normally developing peers. There was no sign of publication bias, but between-study variability (heterogeneity) was moderately high.
The nationwide population study of over 4.2 million Swedish adults came up with the same result when adjusting only for sex and birth year.
Within the Swedish cohort there were 1.3 million families with at least two full siblings. Comparisons among siblings with and without ADHD again showed those with ADHD having more than twice the odds of developing type 2 diabetes. That indicated there was little in the way of familial confounding.
However, further adjusting for education, psychiatric comorbidity, and antipsychotic drugs dropped those higher odds among those with ADHD in the overall population to negligible (13% higher) and barely significant levels.
The drops were particularly pronounced for psychiatric comorbidities, especially anxiety, depression, and substance use disorders, all of which had equal impacts.
The authors concluded, “This study revealed a significant association between ADHD and T2D [type 2 diabetes] that was largely due to psychiatric comorbidities, in particular SUD [substance use disorders], depression, and anxiety. Our findings suggest that clinicians need to be aware of the increased risk of developing T2D in individuals with ADHD and that psychiatric comorbidities may be the main driver of this association. Appropriate identification and treatment of these psychiatric comorbidities may reduce the risk for developing T2D in ADHD, together with efforts to intervene on other modifiable T2D risk factors (e.g., unhealthy lifestyle habits and use of antipsychotics, which are common in ADHD), and to devise individual programs to increase physical activity. Considering the significant economic burden of ADHD and T2D, a better understanding of this relationship is essential for targeted interventions or prevention programs with the potential for a positive impact on both public health and the lives of persons living with ADHD.”
Miguel Garcia-Argibay, Lin Li, Ebba Du Rietz, Le Zhang, Honghui Yao, Johan Jendle, Josep A. Ramos-Quiroga, Marta Ribasés, Zheng Chang, Isabell Brikell, Samuele Cortese, Henrik Larsson, “In utero exposure to ADHD medication and long-term offspring outcomes,” Neuroscience and Biobehavioral Reviews (2023), 147:105076, https://doi.org/10.1016/j.neubiorev.2023.105076.
Thanks to improvements in cancer treatment, there is a growing population of childhood and adolescent cancer survivors (CACSs). CACSs are at an increased risk of chronic physical, psychological, and social problems because of their cancer experiences and intensive cancer treatments. These include depression, anxiety, suicidal ideation, and post-traumatic stress disorder (PTSD).
To what extent, if at all, does this also apply to ADHD? Noting that “previous studies … have reported inconsistent findings,” a local research team took advantage of Taiwan’s mandatory single-payer National Health Insurance that covers over 99% of the island’s population. More specifically, the National Health Insurance Research Database (NHIRD) maintains data on the insured population available on formal request for study purposes.
Linking the catastrophic illness database, mental disorders database, and longitudinal health insurance database, they tracked children age younger than 10 years and adolescents aged 11-17 years who were diagnosed with any malignancy (cancer) between 2002 and 2011 with no history of major psychiatric disorders (including ADHD). Parental history of major psychiatric disorders was likewise controlled as a potential confounder.
The team identified 5,121 CACSs, which they matched one to ten with 51,210 age-, sex-, income-, and residence-matched cancer-free controls.
ADHD diagnoses were made by board-certified psychiatrists during the study follow-up period (from enrollment through 2011) based on a comprehensive clinical interview and clinical judgment.
Cancer survivors were diagnosed with ADHD at more than six times the rate of matched controls. Survival duration made no significant difference in this outcome.
Cancers of bone, connective tissue, skin, and breast were associated with a more than threefold increase in risk of an ADHD diagnosis. For cancers of the circulatory system, there was a more than sixfold increased risk of ADHD, and for those of the genitourinary organs, more than sevenfold increased risk.
For brain cancer survivors, the increased risk of ADHD was more than twelvefold. That may be at least in part because the brain itself was targeted for treatment in these instances, which plausibly could cause damage resulting in psychiatric disorders.
The team concluded, “we observed a comparatively higher risk of MPDs [major psychiatric disorders] among CACSs than among controls and likewise found that such risks varied across different cancer types. Survivors of both CNS [central nervous system] and non-CNS cancers have increased risks of MPD diagnoses. Among the enrolled CACSs, ASD [autism spectrum disorder] and ADHD were associated with most types/categories of cancers. Long-term care of this vulnerable population must include psychosocial interventions for patients and their families. Physicians need to be aware of early signs of mental health problems in this high-risk subpopulation and arrange early interventions accordingly.”
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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