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A new study from Japan suggests that infants born with craniosynostosis are significantly more likely to be diagnosed with ADHD later in childhood. Craniosynostosis is a condition in which the bony plates of the skull fuse prematurely, leading to increased intracranial pressure.
The Background:
Craniosynostosis affects roughly one in every 2,000 births. When the skull’s natural seams close prematurely, it can restrict brain growth and increase intracranial pressure, potentially reducing blood flow to the brain. Because the condition is relatively rare, it has been difficult to study at scale until now.
The Study:
To overcome this, researchers tapped into a large Japanese insurance database compiled by JMDC, Inc., which holds records on around 20 million people, or about 15% of Japan’s population. Drawing on two decades of data, the team tracked over 338,000 mother-child pairs. Children with related genetic syndromes or chromosomal conditions such as Down syndrome were excluded to keep the focus on craniosynostosis itself.
Of the children studied, around 1,145 had craniosynostosis, and 7,325 were diagnosed with ADHD. After accounting for factors like sex, birth year, maternal age, mental health history, pregnancy infections, and birth complications, children with craniosynostosis were found to have roughly 2.4 times the risk of a subsequent ADHD diagnosis compared to those without it.
To test whether shared family genetics or home environment might be driving the association rather than the skull condition itself, the researchers conducted a separate analysis among siblings. The elevated risk remained at 2.2 times. The consistency of the finding across both analyses strengthens the case for a genuine biological link.
The Results:
The results point to raised intracranial pressure and restricted cerebral blood flow as plausible mechanisms, though the study’s observational design means causation cannot be confirmed. Ultimately, these findings highlight the need for proactive, long-term care strategies for those born with craniosynostosis. By establishing a solid link between premature skull fusion and a significantly higher risk of ADHD, the research demonstrates that medical care for this condition should not end once the skull's physical structure is addressed.
The Takeaway:
Pediatricians, neurologists, and parents can use this data to implement early, routine behavioral and developmental screening for these children as they grow. This additional support would ensure that those who do develop ADHD can receive timely interventions, educational aids, and therapies, ultimately improving their long-term developmental outcomes.
Takanori Yanai, Satomi Yoshida, and Koji Kawakami, “Association between craniosynostosis and neurodevelopmental disorders: a nationwide claims database,” BMC Pediatrics (2026), https://doi.org/10.1186/s12887-026-06726-5.
The Background:
Down syndrome (DS) is a genetic disorder resulting from an extra copy of chromosome 21. It is associated with intellectual disability.
Three to five thousand children are born with Down syndrome each year. They have higher risks for conditions like hypothyroidism, sleep apnea, epilepsy, sensory issues, infections, and autoimmune diseases. Research on ADHD in patients with Down syndrome has been inconclusive.
The Study:
The National Health Interview Survey (NHIS) is a household survey conducted by the National Center for Health Statistics at the CDC.
Due to the low prevalence of Down syndrome, a Chinese research team used NHIS records from 1997 to 2018 to analyze data from 214,300 children aged 3 to 17, to obtain a sufficiently large and nationally representative sample to investigate any potential association with ADHD.
DS and ADHD were identified by asking, “Has a doctor or health professional ever diagnosed your child with Down syndrome, Attention Deficit Hyperactivity Disorder (ADHD), or Attention Deficit Disorder (ADD)?”
After adjusting for age, sex, and race/ethnicity, plus family highest education level, family income-to-poverty ratio, and geographic region, children and adolescents with Down syndrome had 70% greater odds of also having ADHD than children and adolescents without Down syndrome. There were no significant differences between males and females.
The Take-Away:
The team concluded, “in a nationwide population-based study of U.S. children, we found that a Down syndrome diagnosis was associated with a higher prevalence of ASD and ADHD. Our findings highlight the necessity of conducting early and routine screenings for ASD and ADHD in children with Down syndrome within clinical settings to improve the effectiveness of interventions.”
A recent CNN report, http://tinyurl.com/yannlfd6, highlighted a paper published in Pediatrics, which reported that pregnant women who use acetaminophen during pregnancy put their unborn child at two-fold increased risk for attention deficit hyperactivity disorder (ADHD). In that study, acetaminophen use during pregnancy was common; nearly half of women surveyed used the painkiller during pregnancy. Other studies have reported similar associations of acetaminophen, also known as paracetamol with ADHD or with other problems in childhood (e.g., https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5300094/, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4177119/, https://www.ncbi.nlm.nih.gov/pubmed/24566677, https://www.ncbi.nlm.nih.gov/pubmed/24163279). Given these prior findings, it seems unlikely that the new report is a chance finding. But does it make any biological sense? One answer to that question came from an epigenetic study. Such studies figure out if assaults from the environment change the genetic code. One epigenetic study found that prenatal exposure changes the fetal genome via a process called methylation. Such genomic changes could increase the risk for ADHD (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5540511/). Because all of these studies are observational studies, one cannot assert with certainty that there is a causal link between acetaminophen use during pregnancy.
The observed association could be due to some unmeasured third factor. Although the researchers did a respectable job ruling out some third factors, we must acknowledge some uncertainty in the finding. That said, what should pregnant women do if they need acetaminophen. I suggest you bring this information to your physician and ask if there is a suitable alternative.
Many media outlets have reported on a study suggesting that mothers who use acetaminophen during pregnancy may put their unborn child at risk for ADHD. Given that acetaminophen is used in many over-the-counter painkillers, correctly reporting such information is crucial. As usual, rather than relying on one study, looking at the big picture using all available studies is best. Because it is not possible to examine this issue with a randomized trial, we must rely on naturalistic studies.
One registry study (http://www.ncbi.nlm.nih.gov/pubmed/24566677)reported that fetal exposure to acetaminophen predicted an increased risk of ADHD with a risk ratio of 1.37. The risk was dose-dependent, in the sense that it increased with increased maternal use of acetaminophen. Of particular note, the authors made sure that their results were not accounted for by potential confounds (e.g., maternal fever, inflammation, and infection). Similar results were reported by another group (http://www.ncbi.nlm.nih.gov/pubmed/25251831), which also showed that the risk for ADHD was not predicted by maternal use of aspirin, antacids, or antibiotics. But that study only found an increased risk at age 7 (risk ratio = 2.0) not at age 11. In a Spanish study, (http://www.ncbi.nlm.nih.gov/pubmed/27353198), children exposed prenatally to acetaminophen were more likely to show symptoms of hyperactivity and impulsivity later in life. The risk ratio was small (1.1) but it increased with the frequency of prenatal acetaminophen use by their mothers.
We can draw a few conclusions from these studies. There does seem to be aweak, yet real, the association between maternal use of acetaminophen while pregnant and subsequent ADHD or ADHD symptoms in the exposed child. The association is weak in several ways: there are not many studies, they are all naturalistic, and the risk ratios are small. So mothers that have used acetaminophen during pregnancy and have an ADHD child should not conclude that their acetaminophen usecausedtheir child's ADHD. On the other hand, pregnant women who are considering the use of acetaminophen for fever or pain should discuss other options with their physician. As with many medical decisions, one must balance competing for risks to make an informed decision.
Find more evidence-based blogs at www.adhdinaduls.com.
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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