Meta-analysis of Two Nationwide Population Studies Finds No Harm to Offspring from Taking ADHD Medications During Pregnancy

ADHD is the most prevalent neurodevelopmental disorder. Nearly 1% of pregnant women in the Nordic countries and more than 1% in the United States are prescribed ADHD medications, ranking these among the most commonly used medications during pregnancy. However, the safety of exposing a fetus to ADHD medications is still uncertain, prompting many expectant mothers to stop using them out of fear for their unborn child’s well-being. 

The Study:

A European research team conducted a comprehensive nationwide study on the safety of ADHD medications during pregnancy using populations from Sweden and Denmark. The Swedish population was studied first, followed by inclusion of a separate study of the Danish population. Results were then combined through meta-analysis. Nordic countries, with their single-payer national health insurance systems and national population registers, facilitate the tracking of residents’ health from birth to death, thus providing robust data for such studies. 

The team accounted for various potential confounders, including maternal age, year of delivery, whether the mother was a first-time parent, self-reported smoking during pregnancy, and any psychiatric history. They also considered psychiatric inpatient or outpatient treatment received within two years before pregnancy, as well as the dispensing of other psychotropic medications during pregnancy, including antidepressants, antipsychotics, antiseizure medications, and anti-anxiety medications. Additionally, they examined the highest level of maternal education and civil status at delivery (married or cohabiting compared to single, divorced, or widowed). 

Out of 861,650 Swedish children, 2,257 were exposed to ADHD medications during pregnancy. Another 3,917 were born to mothers who discontinued ADHD medications before pregnancy.  

Children exposed to ADHD medications had lower rates of ADHD, autism spectrum disorder, and overall neurodevelopmental disorders; however, none of these differences were significant. 

Limiting the analysis to siblings to control for family environmental influences and genetics likewise found no significant differences.  

A meta-analysis combining the Swedish results with a separately conducted nationwide population study in neighboring Denmark similarly found no significant differences between children exposed to ADHD medications during pregnancy and children born to mothers who discontinued ADHD medications before pregnancy. 

Conclusion:

The team concluded, “Overall, our study provides reassuring evidence that continuing ADHD medication during pregnancy does not increase the risk of long-term NDDs [neurodevelopmental disorders] in offspring." 

Kathrine Bang Madsen, Henrik Larsson, Charlotte Skoglund, Xiaoqin Liu, Trine Munk-Olsen, Veerle Bergink, Jeffrey H. Newcorn, Samuele Cortese, Paul Lichtenstein, Ralf Kuja-Halkola, Zheng Chang, Brian D’Onofrio, Per Hove Thomsen, Kari Klungsøyr, Isabell Brikell, and Miguel Garcia-Argibay, “In utero exposure to methylphenidate, amphetamines and atomoxetine and offspring neurodevelopmental disorders – a population-based cohort study and meta-analysis,” Molecular Psychiatry (2025), https://doi.org/10.1038/s41380-025-02968-4

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ADHD and Acetaminophen use During Pregnancy

ADHD and Acetaminophen use During Pregnancy

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/24566677https://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.

March 16, 2021

Does Obesity Directly Contribute to Risk of ADHD in Offspring?

Does Acetaminophen use During Pregnancy Cause ADHD in Offspring?

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.

March 14, 2021

How To Best Manage ADHD During Pregnancy to Minimize Risk to Offspring

How can women best manage ADHD during pregnancy to minimize risk to their babies?

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."

June 18, 2021

The Retina as a Mirror: Decoding the ADHD AI "Breakthrough" and Its Fatal Flaws

The Background:

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.

  • 0.5  means the test is no better than a coin flip (pure luck).
  • 1.0  represents a perfect test with zero mistakes. 

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.

The Promise: How the AI "Sees" ADHD

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.

Flaw #1: Batch Effects

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:

  • The ADHD Group:  323 children recruited prospectively in a tight 6-month window in  2022 .
  • The Control Group:  323 children gathered retrospectively over a  17-year span  (2007 to 2024).This discrepancy triggers severe Batch Effects. This is a term scientists use to describe non-biological factors in an experiment that can cause inaccuracies in the data it produces. Fundus photography technology changed dramatically between 2007 and 2024. An investigation into the hardware uncovered shifts in camera models, lens optics, sensor degradation, and digital compression formats .Think of it this way: if you compare a selfie taken on the original 2007 iPhone with one from an iPhone 16, the AI doesn't need to look at your face to tell them apart; it just looks at the  2007 sensor noise  and pixel grain. The AI likely didn't learn to identify ADHD so much as it learned to distinguish between "old camera" and "new camera."

Flaw #2: Control Group

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:

  • Refractive Errors (Myopia/Nearsightedness):  Severe myopia physically stretches the retina. This stretching alters vessel density and optic disc size, which were the exact markers the AI was examining.
  • Strabismus:  Misaligned eyes.
  • Ocular Anomalies:  Physical eye defects.Because these conditions directly alter retinal architecture, the AI likely learned to distinguish between "kids with ADHD" and "kids with severe eye problems," rather than "kids with ADHD" and "typical kids."

Fatal Flaw #3: The "Mirror Image" Leakage

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: Differential Diagnosis 

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.

Conclusion:

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 :

  1. Prospective, Unified Hardware:  Data must be collected on identical camera systems with the same protocols to eliminate technical "batch effects."
  2. Healthy, Community-Based Controls:  Comparisons must be made against truly "typically developing" children, not patients from eye clinics with their own retinal anomalies.
  3. Rigorous External Validation:  AI models must be tested on independent datasets from entirely different hospital networks to ensure they aren't just "memorizing" one hospital's specific machinery.Artificial Intelligence holds immense potential, but we must demand detective-like scrutiny before these tools reach our children. In the search for the "window to the mind," we have to make sure we aren't just looking at a smudge on the glass.

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. 

June 17, 2026

Study Finds That ADHD Stimulants Have Negligible Effect on Adult Height

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. 

Meta-analysis: Cognitive Behavioral Therapy for Adult ADHD

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: 

  • Education: Adults with ADHD tend to have lower GPAs, use fewer effective study strategies, achieve less academically, and are more likely to drop out.  
  • Work: They are more likely to experience job instability, including underperformance, unemployment, being fired, or frequent job changes.  
  • Social life: They often report smaller social networks, fewer close relationships, greater loneliness, and difficulty maintaining friendships or intimacy. Importantly, stronger social networks can help buffer (reduce) the impact of ADHD symptoms on daily life.  
  • Quality of life: Overall well-being is typically lower, affecting not only individuals but also their families and close relationships.

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: 

  • Identify and challenge unhelpful thought patterns  
  • Reduce avoidance behaviors  
  • Build practical strategies for managing time, organization, and other executive functions (the mental skills used to plan, focus, and follow through)  

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: 

  • Randomized controlled trials (RCTs): studies comparing CBT to another treatment or to no treatment  
  • Within-subject studies: studies measuring change in the same individuals before and after CBT  

They focused specifically on outcomes beyond symptoms: 

  • Occupational functioning (work performance)  
  • Global functional impairment (overall daily functioning)  
  • Social relationships  
  • Academic functioning  
  • Quality of life  

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: 

  • CBT does improve real-world functioning, not just symptoms  
  • The strongest and most consistent benefits are in occupational (work) functioning  
  • Gains in social life, academics, and overall quality of life are more modest and variable  
  • Improvements in functioning do not always track directly with symptom reduction  

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. 

 

June 9, 2026