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November 1, 2021
Both Taiwan and Sweden have universal single-payer health insurance systems that in effect track their entire national populations. With detailed health and other records on millions of individuals, with no significant exclusions, one can essentially eliminate sampling error, and also explore how associations vary by degree of familial/genetic relationship.
A Taiwanese research team used the Taiwan National Health Insurance Research Database to follow 708,517 family triads (father-mother-child) from 2001 through 2011. That's a total of over 2.1 million persons. The database covers over 99% of Taiwan's population.
Noting that previous studies had found links between maternal autoimmune diseases and ADHD in their offspring and that research on associations with paternal autoimmune diseases had been inconclusive, they were particularly interested in exploring the latter.
Children born from 2001 through 2008 were enrolled in the study. The investigators then noted the presence or absence of any autoimmune disease in their parents from 1996 through childbirth.
In Taiwan, expert panels review diagnostic information of severe systemic autoimmune diseases to confirm the diagnosis. Once confirmed, patient co-payments are waived. ADHD diagnoses are by board-certified psychiatrists.
To reduce the effect of confounding variables, adjustments were made for family demographic data (income level and residence), parental ages, parental mental disorders, and sex of children.
The presence of any maternal autoimmune diseases was associated with a 60% greater risk of ADHD in offspring. The risk was especially elevated for inflammatory bowel diseases (2.4 times the risk) and ankylosing spondylitis (twice the risk).
The presence of any paternal autoimmune diseases was also associated with an elevated risk of ADHD in offspring, although only about half as much as for maternal autoimmune diseases, with a 33% greater risk overall. The association was especially pronounced for psoriasis and ankylosing spondylitis, both doubling the risk of ADHD in offspring.
Meanwhile, half a world away, a joint Swedish, Norwegian, and U.S. team used the Swedish national registries to dig further into these associations. They did this by examining data not only from mothers and fathers, but from full siblings, aunts, uncles, and cousins as well, to probe genetic links.
The team used the Swedish registers to identify 5,178,225 individuals born in Sweden between 1960 and 2010 for whom the identity of the biological mother was known, excluding all who died or emigrated before age 10. They then used the registers to identify the aforementioned relatives.
The researchers only included autoimmune diseases with at least two thousand diagnosed individuals in the cohort, to avoid small sample effects.
They adjusted for sex and year of birth, but not "for another covariate that is often adjusted for (e.g. maternal education, family income, parental psychiatric disorder, parental AD [autoimmune disease] as these are likely not true confounders of the association between ADHD and ADD, but may rather represent either mediator between ADHD and AD's, or proxies of ADHD and/or AD risk or alternatively proxies for the associations we aim to measure."
The team found statistically significant associations between ADHD and autoimmune diseases in all categories of relatives. Mothers of children with ADHD were 29% more likely to have an autoimmune disease than those of typically developing children; fathers were 14% more likely to have an autoimmune disease; full siblings 19% more likely; aunts 12% more likely; uncles 7% more likely; and cousins 4% more likely.
Quantitative genetic modeling produced a significant genetic correlation, but no significant environmental correlation. Genetic correlation explained most, if not all, the covariance between ADHD and any autoimmune disease.
The authors concluded, "ADHD was to some degree more strongly associated with maternal than paternal AD's, but by using aunts and uncles in a genetically informative study design, we demonstrate that this difference cannot be readily explained by AD-mediated maternal effects. Quantitative genetic modeling further indicates that the familial co-aggregation of ADHD and ADs is partly due to shared genetic factors. In addition, biological aunts, uncles, and cousins must be assumed to share the little environment with the index individuals, in further support of shared genetic factors underlying the familial co-aggregation. Moreover, both epidemiological and molecular genetics studies have demonstrated positive genetic correlations between ADHD and ADs, in agreement with our findings."
The authors emphasize that these results do not warrant screening for autoimmune diseases among asymptomatic individuals with ADHD.
Tor-Arne Hegvik, Qi Chen, Ralf Kuja-Halkola, Kari Klungsøyr, Agnieszka Butwicka, Paul Lichtenstein, Catarina Almqvist, Stephen V Faraone, Jan Haavik, Henrik Larsson. "Familial co-aggregation of attention-deficit/hyperactivity disorder and autoimmune diseases: a cohort study based on Swedish population-wide registers," International Journal of epidemiology (2021), published online, https://doi.org/10.1093/ije/dyab151.
Hsuan Lee, Ju-Wei Hsu, Shih-Jen Tsai, Kai-Lin Huang, Ya-MeiBai, Tung-Png Su, Tzeng-Ji Chen, Mu-Hong Chen, "Risk of attention deficit hyperactivity and autism spectrum disorders among the children of parents with autoimmune diseases: a nationwide birth cohort study," European Child &Adolescent Psychiatry (2021), published online, https://doi.org/10.1007/s00787-021-01860-0.
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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