Cookie Preferences
By clicking, you agree to store cookies on your device to enhance navigation, analyze usage, and support marketing. More Info
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
Counting umbilical cord vessels is standard in prenatal ultrasounds and confirmed at birth. Single umbilical artery (SUA) occurs in about 1 in 200 cases, with roughly 10% associated with anomalies, including central nervous system defects. Isolated SUA (iSUA) means one artery is missing without other structural issues.
Research on SUA, especially isolated iSUA, and childhood neurodevelopmental disorders (NDD) is limited and inconclusive. iSUA is linked to preterm birth and small-for-gestational age (SGA), both of which are NDD risk factors.
This Norwegian nationwide population study aimed to assess NDD risk in children with iSUA at birth, the influence of sex, and how preterm birth and SGA mediate this relationship.
The nation’s universal single-payer health insurance and comprehensive population registries made it possible to analyze all 858,397 single births occurring from 1999 to 2013, with follow-up continuing through 2019. Among these cases, 3,532 involved iSUA.
After adjusting for confounders such as parental age, education, and maternal health factors, no overall link was found between iSUA and later ADHD diagnosis. However, females with iSUA had about a 40% higher risk of subsequent ADHD compared to those without iSUA, even after adjustment.
The authors concluded, “The present study indicates that iSUA is weakly associated with ID [intellectual disability] and ADHD, and these associations are influenced by sex. This association is mediated negligibly through preterm birth and SGA. The associations were not clinically significant, and the absence of associations of iSUA with other NDD is reassuring. This finding can be useful in the counseling of expectant parents of fetuses diagnosed with iSUA.”
C. Ebbing, A. Halmoy, E. Jauniaux, A. Einum, S. Rasmussen, and D. Moster, “Association between isolated single umbilical artery and neurodevelopmental disorders: population-based study,” Ultrasound in Obstetrics & Gynecology (2026), https://doi.org/10.1002/uog.70164
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.
Roughly one in thirty adult women have ADHD. Research results indicate that psychostimulants (methylphenidate and amphetamines) offer the most effective course of treatment in most instances. But during pregnancy, such treatment also exposes the fetus to these drugs. Several studies have set out to determine whether such exposure is harmful.
The largest comparison was 5,571 infants exposed to amphetamines and 2,072 exposed to methylphenidate with unexposed infants. It found no increased risks for adverse outcomes due to amphetamine or methylphenidate exposures. Another study studied 3,331 infants exposed to amphetamines, 1,515 exposed to methylphenidate, and 453 to atomoxetine. Comparing these infants to unexposed infants, it found a slightly increased risk of preeclampsia, with an adjusted risk ratio of 1.29 (95% CI 1.11-1.49), but no statistically significant effect for placental abruption, small gestational age, and preterm birth. When assessing the two stimulants, amphetamine, and methylphenidate, together, it found a small increased risk of preterm birth, with an adjusted risk ratio of 1.3 (95% CI 1.10-1.55). There was a statistically significant effect for preeclampsia, placental abruption, or small gestational age. Atomoxetine use was free of any indication of increased risk.
Another study involving 1,591 infants exposed to ADHD medication (mostly methylphenidate) during pregnancy, reported increased risks associated with exposure. The adjusted odds ratio for admission to a neonatal intensive care unit was 1.5 (95% CI 1.3-1.7), and for the central nervous system, disorders were 1.9 (95% CI 1.1-3.1). There was no increased risk for congenital malformations or perinatal death.
Six studies focused on methylphenidate exposure. Two, with a combined total of 402 exposed infants, found no increased risk for malformations. Another, with 208 exposed infants, found a slightly greater risk of cardiovascular malformations, but it was not statistically significant. A fourth, with 186 exposed infants, found no increased risk of malformations but did find a higher rate of miscarriage, with an adjusted hazard ratio of 1.98(95% CI 1.23-3.20). A fifth, with 480 exposed infants, also found a higher rate of miscarriage, with an odds ratio of 2.07 (95% CI 1.51-2.84). But although the sixth, with 382 exposed infants, likewise found an increased risk of miscarriage (adjusted relative risk 1.55 with 95% CI1.03-2.06), it also found an identical risk for women with ADHD who were not on medication during their pregnancies (adjusted relative risk 1.56with 95% CI 1.11-2.20). That finding suggests that all women with ADHD have a higher risk of miscarriage, and that methylphenidate exposure is not the causal factor.
Summing up, while some studies have shown increased adverse effects among infants exposed to maternal ADHD medications, most have not. There are indications that higher rates of miscarriage are associated with maternal ADHD rather than fetal exposure to psychostimulant medications. One study did find a small increased risk of central nervous system disorders and admission to a neonatal intensive care unit. But, again, we do not know whether that was due to exposure to psychostimulant medication or associated with maternal ADHD. If there is a risk, it appears to be a small one.
The question then becomes how to balance that as yet uncertain risk against the disadvantage of discontinuing the effective psychostimulant medication. As the authors of this review conclude. It [ADHD] is associated with significant psychiatric comorbidities for women, including depression, anxiety, substance use disorders, driving safety impairment, and occupational impairment. The gold standard treatment includes behavioral therapy and stimulant medication, namely methylphenidate and amphetamine derivatives. Psychostimulant use during pregnancy continues to increase and has been associated with a small increased relative risk of a range of obstetric concerns. However, the absolute increases in risks are small, and many of the best studies to date are confounded by other medication use and medical comorbidities.
Thus, women with moderate-to-severe ADHD should not necessarily be counseled to suspend their ADHD treatment based on these findings. They advise that when functional impairment from ADHD is moderate to severe, the benefits of stimulant medications may outweigh the small known and unknown risks of medication exposure, and that "If a decision is made to take ADHD medication, women should be informed of the known risks and benefits of the medication use in pregnancy, and take the lowest therapeutic dose possible."
Noting “the incidence of parental obesity has been rising together with the prevalence of mental illness, suggesting a possible link between the two phenomena,” a Chinese study team performed a systematic search of the peer-reviewed literature on that topic.
Further noting that previous meta-analyses have suggested a link between maternal obesity and increased risk of ADHD in offspring, they set out to also look at paternal obesity.
Only two studies, however, probed the relationship between paternal overweight and obesity and offspring ADHD, making that meta-analysis impractical. A meta-analysis of six studies with a combined total of over a hundred thousand participants found no significant association between overweight or obsess fathers and offspring mental disease of any kind (with all such disorders lumped together). There was no indication of publication bias and little variability (heterogeneity) between studies.
Ten studies with a combined total of over 800,000 participants, however, examined the relationship between overweight and obese mothers and offspring ADHD. Overweight mothers were twenty percent more likely to have offspring with ADHD. Obese mothers were more than fifty percent more likely to have offspring with ADHD. There was absolutely no sign of publication bias in either case. Inter-study heterogeneity was negligible for overweight, and moderate for obesity.
The team concluded, “We found that the most recent evidence indicates the detrimental connections between parental pre-pregnancy overweight/obesity and offspring mental health.” That is perhaps a bit overstated, as the only clear sign was with maternal overweight or obesity.
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.
_logo%201.svg)
