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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.
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.
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."
Treatment guidelines for childhood ADHD recommend medications as the first-line treatment for most youth with ADHD. Still, concerns about side effects and long-term outcomes have increased interest in non-pharmacological approaches. Researchers at Saudi Arabian Armed Forces hospitals recently conducted a network meta-analysis comparing several interventions, including mindfulness-based therapy, cognitive behavioral therapy, behavioral parent training, neurofeedback, yoga, virtual reality programs, and digital working memory training.
Although the authors aimed to “provide a rigorous methodological approach to combine evidence from multiple treatment comparisons,” the study illustrates several pitfalls that arise when network meta-analysis is applied to a thin and heterogeneous evidence base.
What Network Meta-analysis Can and Cannot Do:
Network meta-analysis extends conventional meta-analysis by combining:
When the evidence network is large and well-connected, this approach can provide useful estimates of comparative effectiveness among many treatments.
This method is not always best, however, as many networks are sparse. This is especially true in areas such as complementary or behavioral therapies. In sparse networks, estimates rely heavily on indirect comparisons, and single studies can exert disproportionate influence over the results.
Conventional meta-analysis focuses on heterogeneity, meaning differences in results across studies within the same comparison.
Network meta-analysis must additionally evaluate consistency, whether the direct and indirect evidence agree.
However, when comparisons are supported by only one or two studies and the network is weakly connected, statistical tests for heterogeneity and consistency have very little power. In practice, this means the analysis often cannot detect problems even if they are present.
Sparse networks also make publication bias difficult to evaluate. This concern is particularly relevant in fields dominated by small trials and emerging therapies.
Why Such Treatment Rankings Are Appealing, but Potentially Problematic:
Many network meta-analyses summarize results using SUCRA, which estimates the probability that each treatment ranks best.
SUCRA, or Surface Under the Cumulative Ranking, is a key statistical metric in network meta-analyses. It is used to rank treatments by efficacy or safety. This is achieved by summarizing the probabilities of a treatment's rank into a single percentage, where a higher SUCRA value indicates a superior treatment. Ultimately, SUCRA helps pinpoint the most effective intervention among the ones compared.
Again, in well-supported networks, SUCRA can provide a useful summary of comparative effectiveness. But in sparse networks, rankings can create an illusion of precision, because treatments supported by a single small study may appear highly ranked simply due to random variation.
What Did this New Network Meta-analysis Study?
The study includes 16 trials with a total of 806 participants. But the structure of the evidence network is far weaker than this headline number suggests.
Based on the underlying studies:
This produces a very thin network, in which several interventions rely entirely on single studies.
Another challenge is that the included trials measure different outcomes. Some evaluate ADHD symptom severity, while others measure parental stress.
When studies use different outcome scales, meta-analysis typically relies on standardized measures such as the standardized mean difference to allow comparisons across studies. However, the analysis reports only mean-average differences, making it difficult to interpret the relative effect sizes.
Study Issues (including Limited Evidence and Risk of Bias):
The intervention supported by the largest number of studies (family mindfulness-based therapy) was one of the two approaches reported as producing statistically significant results. The other was BrainFit, which is supported by only a single previous trial.
Despite this limited evidence base, the study ranks interventions using SUCRA:
Notably, none of the runner-up interventions demonstrated statistically significant efficacy.
The authors acknowledge methodological limitations in the included studies:
“Blinding of participants and personnel (performance bias) exhibited notable concerns, as blinding for active treatment was not applicable in most studies.”
Such limitations are common in behavioral intervention trials, but they further increase uncertainty in already small evidence networks.
Conclusions:
The study ultimately concludes:
“This network meta-analysis supports MBT and BPT as effective non-pharmacological treatments for ADHD.”
However, the evidence underlying these claims is limited. Some analyses rely on very small numbers of studies and participants, and the network structure depends heavily on indirect comparisons.
Network meta-analysis can be a powerful tool when applied to a large, consistent, and well-connected body of evidence. When the evidence base is sparse, however, the resulting rankings and comparisons may appear statistically sophisticated while resting on a fragile evidentiary foundation.
ADHD is one of the most common neurodevelopmental disorders in children, yet anyone familiar with this disorder, from clinicians and researchers to parents and patients, knows how differently it can manifest from one individual to the next. One person diagnosed with ADHD may primarily struggle with focus and staying on-task; another may find it nearly impossible to regulate their impulses or even start tasks; a third may frequently find themselves frozen with overwhelm and subject to emotional reactivity…
These are not just variations in severity; they may reflect genuinely different patterns of brain organization.
Our current diagnostic system groups all of these presentations under a single label (ADHD), with three behavioral subtypes (Hyperactive, Inattentive, and Combined) defined by symptom checklists. This framework has real clinical value of course, but it was built from behavioral observation rather than neurobiology, and may leave room for substantial heterogeneity to remain unexplained. In a new study, published in JAMA Psychiatry, researchers asked whether it’s possible to identify distinct neurobiologically subgroups within ADHD by analyzing patterns of brain structure, and whether those subgroups would map onto meaningful clinical differences.
How the Brain Was Analyzed
Researchers analyzed structural MRI scans from 446 children with ADHD and 708 typically-developing children across multiple research sites. From each scan, they constructed a morphometric similarity network; that is, a map of how different brain regions resemble one another in their structural properties. These networks reflect underlying biological organization, including shared patterns of cellular architecture and gene expression across brain regions.
From each individual's network, the research team calculated three properties that capture how each brain region functions within the broader network: how many connections it has, how efficiently it communicates with other regions, and how well it bridges different functional communities in the brain. Regions that score highly on these measures are sometimes called "hubs" and they play particularly influential roles in how information is integrated across the brain.
Rather than comparing the ADHD group to controls as a whole and looking for average differences, they used a normative modeling approach. This works similarly to a growth chart in pediatric medicine: instead of asking whether a child is above or below the group average, it asks how much a given child deviates from the expected range for their age and sex. This allows for individual variation across the ADHD group rather than flattening it into a single average profile.
The team then applied a data-driven clustering algorithm to these individual deviation profiles, allowing the data to reveal whether subgroups of children with ADHD shared similar patterns of brain network atypicality, without using any clinical symptom information to guide the clustering.
The Results:
Three stable, reproducible subtypes emerged from this analysis.
The first subtype was characterized by the most widespread differences from the normative range, particularly in regions connecting the medial prefrontal cortex to the pallidum (a deep brain structure involved in motivation and emotional regulation). Children in this group had the highest levels of both inattention and hyperactivity/impulsivity, and over a four-year follow-up period showed more persistent difficulties with emotional self-regulation than the other groups. They also had a higher rate of mood disorder comorbidity during follow-up, though this difference did not reach statistical significance given the sample size. The brain deviation patterns of this subtype showed correspondence with the spatial distributions of several neurotransmitter systems, including serotonin, dopamine, and acetylcholine, all of which have been previously implicated in ADHD pathophysiology.
The second subtype showed alterations concentrated in the anterior cingulate cortex and pallidum, a circuit involved in action control and response selection. This subtype had a predominantly hyperactive/impulsive profile, and its brain deviation patterns were associated with glutamate and cannabinoid receptor distributions.
The third subtype showed more focal differences in the superior frontal gyrus, a region involved in sustained attention. This subtype had a predominantly inattentive profile, with brain patterns linked to a specific serotonin receptor subtype.
A particularly important observation was that these brain-derived groupings aligned with clinically meaningful symptom differences, even though no symptom information was used in the clustering process. The fact that an analysis of brain structure alone arrived at groupings that correspond to recognizable clinical patterns is meaningful evidence that these subtypes reflect genuine neurobiological differences rather than statistical noise.
Replication in an Independent Sample
Scientific findings are only as trustworthy as their ability to replicate. The research team tested this clustering model in an entirely independent cohort of 554 children with ADHD from the Healthy Brain Network, a large, publicly available dataset collected under different conditions. The three subtypes were successfully identified in this new sample, with strong correlations between the brain deviation patterns observed in the original and validation cohorts. Differences in hyperactivity/impulsivity across subtypes were consistent with the discovery cohort, providing meaningful external validation of the approach.
What This Does and Doesn't Mean
It is important to be clear about what these findings do and do not imply. This study does not establish that these three subtypes are categorically distinct biological entities with sharp boundaries. They probably represent distinguishable regions along an underlying continuum of neurobiological variation. The neurochemical associations reported are exploratory and spatial in nature; they describe correspondences between brain deviation maps and neurotransmitter receptor density maps derived from separate imaging studies, and do not directly establish that any particular neurotransmitter system is altered in each subtype, nor do they currently inform treatment decisions.
The samples were not entirely medication-naive, and the strict comorbidity exclusion criteria may limit how well these findings generalize to typical clinical populations where comorbidities are the rule rather than the exception. All data came from research sites in the United States and China, and broader generalizability remains to be established.
What the study does demonstrate is that structured neurobiological heterogeneity exists within the ADHD diagnosis, that it can be reliably detected using brain imaging and data-driven methods, and that it aligns with meaningful clinical differences. The subtype defined by the most extensive brain network differences and the most severe, persistent clinical profile may be of particular importance, representing a group that could benefit most from early identification and targeted support.
The longer-term goal of this line of research is to move toward a more biologically grounded understanding of ADHD that complements existing diagnostic approaches and that may ultimately help guide more individualized treatment decisions. That goal, for now, remains a research ambition rather than a clinical reality, but this study takes a meaningful step in that direction.
Managing high blood pressure requires more than just getting a prescription; it means taking medication consistently, day after day, often for years. For people with ADHD, that kind of routine can be genuinely difficult. In our new study, published in BMC Medicine, we set out to understand just how much ADHD affects whether people stick with their blood pressure medication, and whether ADHD treatment itself might make a difference.
Why This Question Matters
Hypertension affects nearly a third of adults worldwide and is one of the leading drivers of heart disease and stroke. At the same time, ADHD, long thought of as a childhood disorder, affects around 2.5% of adults and is increasingly recognized as a risk factor for cardiovascular problems, including high blood pressure. Yet no large-scale study had ever examined whether having ADHD affects how well people follow through with their blood pressure treatment. We wanted to fill that gap.
What We Did
We analyzed health records from over 12 million adults across seven countries, Australia, Denmark, the Netherlands, Norway, Sweden, the UK, and the US, who had started antihypertensive (blood pressure-lowering) medication between 2010 and 2020. About 320,000 of them had ADHD. We tracked two things: whether they stopped their blood pressure medication entirely within five years, and whether they were taking it consistently enough (covering at least 80% of days) over one, two, and five years of follow-up.
What We Found
Across nearly all countries, adults with ADHD were more likely to stop their blood pressure medication and less likely to take it consistently. Overall, those with ADHD had about a 14% higher rate of discontinuing treatment within five years, and were 45% more likely to have poor adherence in the first year, a gap that widened to 64% by the five-year mark. These patterns were most pronounced in middle-aged and older adults.
Interestingly, young adults with ADHD were actually slightly less likely to discontinue treatment than their peers without ADHD, a finding we think may reflect the fact that younger people with ADHD are often more actively engaged with healthcare systems, especially given the cardiovascular monitoring that comes with ADHD medication use.
Perhaps the most encouraging finding was this: among people with ADHD who were also taking ADHD medication, adherence to blood pressure treatment was substantially better. Those on ADHD medication were about 38% less likely to have poor adherence at one year, and nearly 50% less likely at five years. While we can't establish causation from this type of study, one plausible explanation is that treating ADHD, reducing inattention and impulsivity, makes it easier to maintain the routines that consistent medication use requires. It's also possible that people on ADHD medication simply have more regular contact with healthcare providers, which keeps other health problems better monitored and managed.
What This Means in Practice
The core ADHD symptoms of inattention and poor organization are precisely the traits that make long-term medication adherence difficult. Add in the complexity of managing multiple disorders and medications, and it's easy to see why people with ADHD face extra challenges. Our findings suggest that clinicians treating adults with ADHD for cardiovascular disorders should be aware of these challenges and consider tailored support strategies, things like regular follow-up appointments, patient education, and tools that help with routine and organization.
There's also a broader message here about the potential ripple effects of treating ADHD well. Supporting someone in managing their ADHD may not just improve their attention and daily functioning; it may also help them take better care of their physical health, including disorders as serious as hypertension.
Future research should explore which specific support strategies are most effective, and whether these findings hold in lower- and middle-income countries where the data don't yet exist.
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