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June 10, 2025
This new meta-analysis confirms what other meta-analysis have already shown, i.e, that there exists in the population an association between maternal smoking during pregnancy and ADHD in their offspring. But reader beware, association does not mean causation.
The team identified 55 studies with quantitative data suitable for meta-analysis, including 11 case-control, 13 cross-sectional, and 31 retrospective/prospective cohort studies.
Altogether they combined more than four million persons in countries spanning six continents, including the United States, Finland, Sweden, Brazil, the Netherlands, Japan, the UK, Spain, China, Australia, New Zealand, Norway, Canada, France, Sweden, South Korea, Turkey, Romania, Bulgaria, Lithuania, Germany, Denmark, Egypt, and India.
Meta-analysis of all 55 studies found that offspring of mothers who smoked tobacco during pregnancy were about 70% more likely to develop ADHD than offspring of mothers who did not smoke during pregnancy.
Because variation in outcomes across studies was very high, the team performed subgroup analyses to explore potential sources of this heterogeneity.
Comparing study designs, cohort studies reported roughly 50% greater odds of ADHD among children of mothers who smoked during pregnancy, whereas case-control studies reported roughly 70% greater odds and cross-sectional studies 2.3-fold greater odds.
Studies using the most reliable method of determining ADHD – clinical interview/professional diagnosis – reported 90% greater odds, contrasting with 66% through medical records/databases and 58% through self-report by child/parent or through teacher report.
Good quality studies reported roughly 75% greater odds.
Studies with sample sizes above two thousand similarly found 70% greater odds.
There was no sign of publication bias using the more commonly used Egger’s test, but a marginal indication of publication bias using Begg’s test. Performing a standard correction reduced the effect size, indicating that the offspring of mothers who smoked tobacco during pregnancy were over 50% more likely to develop ADHD than the offspring of mothers who did not smoke during pregnancy.
The team concluded, “This systematic review and meta-analysis of 55 studies, encompassing over four million participants, provides compelling evidence that maternal tobacco smoking during pregnancy significantly increases the odds of ADHD in children … These findings underscore the critical need for public health interventions aimed at reducing tobacco smoking during pregnancy.”
However, we disagree with this conclusion; The authors ignore substantial evidence showing that maternal smoking during pregnancy is confounded by maternal ADHD. These mothers transmit ADHD via genetics, not via their smoking. This study should be seen not as "...[further evidence that smoking during pregnancy causes ADHD.] ", but as a lesson in how easy it can be to see correlation as causation.
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Mahdi Mohammadian, Lusine G. Khachatryan, Filipp V. Vadiyan, Mostafa Maleki, Fatemeh Fatahian, and Abdollah Mohammadian-Hafshejani, “The association between maternal tobacco smoking during pregnancy and the risk of attention-deficit/hyperactivity disorder (ADHD) in offspring: A systematic review and meta-analysis,” PLOS ONE (2025), 20(2): e0317112, https://doi.org/10.1371/journal.pone.0317112.
Our genes are very important for the development of mental disorders-including ADHD, where genetic factors capture up to 75% of the risk. Until now, the search for these genes had yet to deliver clear results. In the 1990s, many of us were searching for genes that increased the risk for ADHD because we know from twin studies that ADHD had a robust genetic component. Because I realized that solving this problem required many DNA samples from people with and without ADHD, I created the ADHD Molecular Genetics Network, funded by the US NIMH. We later joined forces with the Psychiatric Genomics Consortium (PTC) and the Danish psych group, which had access to many samples.
The result is a study of over 20,000 people with ADHD and 35,000 who do not suffer from it - finding twelve locations (loci) where people with a particular genetic variant have an increased risk of ADHD compared to those who do not have the variant. The results of the study have just been published in the scientific journal Nature Genetics, https://www.nature.com/articles/s41588-018-0269-7.
These genetic discoveries provide new insights into the biology behind developing ADHD. For example, some genes have significance for how brain cells communicate with each other, while others are important for cognitive functions such as language and learning.
Our study used the genome-wide association study (GWAS)methodology because it allowed us to discover genetic loci anywhere on the genome. The method assays DNA variants throughout the genome and determines which variants are more common among ADHDvs. control participants. It also allowed for the discovery of loci having very small effects. That feature was essential because prior work suggested that, except for very rare cases, ADHD risk loci would individually have small effects.
The main findings are:
A) we found 12 loci on the genome that we can be certain harbor DNA risk variants for ADHD. None of these loci were traditional candidate genes' for ADHD, i.e., genes involved in regulating neurotransmission systems that are affected by ADHD medications. Instead, these genes seem to be involved in the development of brain circuits.
B) we found a significant polygenic etiology in our data, which means that there must be many loci(perhaps thousands) having variants that increase the risk for ADHD. We will need to collect a much larger sample to find out which specific loci are involved;
We also compared the new results with those from a genetic study of continuous measures of ADHD symptoms in the general population. We found that the same genetic variants that give rise to an ADHD diagnosis also affect inattention and impulsivity in the general population. This supports prior clinical research suggesting that, like hypertension and hypercholesteremia, ADHD is a continuous trait in the population. These genetic data now show that the genetic susceptibility to ADHD is also a quantitative trait comprised of many, perhaps thousands, of DNA variants
The study also examined the genetic overlap with other disorders and traits in analyses that ask the questions: Do genetic risk variants for ADHD increase or decrease the likelihood a person will express other traits and disorders. These analyses found a strong negative genetic correlation between ADHD and education. This tells us that many of the genetic variants which increase the risk for ADHD also make it more likely that a person will perform poorly in educational settings. The study also found a positive correlation between ADHD and obesity, increased BMI, and type-2 diabetes, which is to say that variants that increase the risk of ADHD also increase the risk of overweight and type-2 diabetes in the population. This work has laid the foundation for future work that will clarify how genetic risks combine with environmental risks to cause ADHD. When the pieces of that puzzle come together, researchers will be able to improve the diagnosis and treatment of ADHD.
A Swedish-Danish-Dutch team used the Swedish Medical Birth Register to identify the almost 1.7 million individuals born in the country between 1980 and 1995. Then, using the Multi-Generation Register, they identified 341,066 pairs of full siblings and 46,142 pairs of maternal half-siblings, totaling 774,416 individuals.
The team used the National Patient Register to identify diagnoses of ADHD, as well as neurodevelopmental disorders (autism spectrum disorder, developmental disorders, intellectual disability, motor disorders), externalizing psychiatric disorders (oppositional defiant and related disorders, alcohol misuse, drug misuse), and internalizing psychiatric disorders (depression, anxiety disorder, phobias, stress disorders, obsessive-compulsive disorder).
The team found that ADHD was strongly correlated with general psychopathology overall (r =0.67), as well as with the neurodevelopmental (r = 0.75), externalizing (r =0.67), and internalizing (r = 0.67) sub factors.
To tease out the effects of heredity, shared environment, and non-shared environment, a multivariate correlation model was used. Genetic variables were estimated by fixing them to correlate between siblings at their expected average gene sharing (0.5for full siblings, 0.25 for half-siblings). Non-genetic environmental components shared by siblings (such as growing up in the same family) were estimated by fixing them to correlate at 1 across full and half-siblings. Finally, non-shared environmental variables were estimated by fixing them to correlate at zero across all siblings.
This model estimated the heritability of the general psychopathology factor at 49%, with the contribution of the shared environment at 7 percent and the non-shared environment at 44%. After adjusting for the general psychopathology factor, ADHD showed a significant and moderately strong phenotypic correlation with the neurodevelopmental-specific factor (r = 0.43), and a significantly smaller correlation with the externalizing-specific factor (r = 0.25).
For phenotypic correlation between ADHD and the general psychopathology factor, genetics explained 52% of the total correlation, the non-shared environment 39%, and the shared familial environment only 9%. For the phenotypic correlation between ADHD and the neurodevelopmental-specific factor, genetics explained the entire correlation because the other two factors had competing effects that canceled each other out. For the phenotypic correlation between ADHD and the externalizing-specific factor, genetics explained 23% of the correlation, shared environment 22%, and non-shared environment 55%.
The authors concluded that "ADHD is more phenotypically and genetically linked to neurodevelopmental disorders than to externalizing and internalizing disorders, after accounting for a general psychopathology factor. ... After accounting for the general psychopathology factor, the correlation between ADHD and the neurodevelopmental-specific factor remained moderately strong, and was largely genetic in origin, suggesting substantial unique sharing of biological mechanisms among disorders. In contrast, the correlation between ADHD and the externalizing-specific factor was much smaller and was largely explained by-shared environmental effects. Lastly, the correlation between ADHD and the internalizing subfactor was almost entirely explained by the general psychopathology factor. This finding suggests that the comorbidity of ADHD and internalizing disorders are largely due to shared genetic effects and non-shared environmental influences that have effects on general psychopathology."
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.
Stimulant medications have long been considered the default first-line treatment for attention-deficit/hyperactivity disorder (ADHD). Clinical guidelines, prescribing practices, and public narratives all reinforce the idea that stimulants should be tried first, with non-stimulants reserved for cases where stimulants fail or are poorly tolerated.
I recently partnered with leading ADHD researcher Jeffrey Newcorn for a Nature Mental Health commentary on the subject. We argue that this hierarchy deserves reexamination. It is important to note that our position is not anti-stimulant. Rather, we call into question whether the evidence truly supports treating non-stimulants as secondary options, and we propose that both classes should be considered equal first-line treatments.
Stimulants have earned their reputation as the go-to drug of choice for ADHD. They are among the most effective medications in psychiatry, reliably reducing core ADHD symptoms and improving daily functioning when properly titrated and monitored. However, when stimulant and non-stimulant medications are compared more closely, the gap between them appears smaller than commonly assumed.
Meta-analyses often report slightly higher average response rates for stimulants, but head-to-head trials where patients are directly randomized to one medication versus another frequently find no statistically significant differences in symptom improvement or tolerability. Network meta-analyses similarly show that while some stimulant formulations have modest advantages, these differences are small and inconsistent, particularly in adults.
When translated into clinical terms, the advantage of stimulants becomes even more modest. Based on existing data, approximately eight patients would need to be treated with a stimulant rather than a non-stimulant for one additional person to experience a meaningful benefit. This corresponds to only a 56% probability that a given patient will respond better to a stimulant than to a non-stimulant. This difference is not what we would refer to as “clinically significant.”
One reason non-stimulants may appear less effective is the way efficacy is typically reported. Most comparisons rely on standardized mean differences, a method of averages that may mask heterogeneity of treatment effects. In reality, ADHD medications do not work uniformly across patients.
For example, evidence suggests that response to some non-stimulants, such as atomoxetine, is bimodal: this means that many patients respond extremely well, while others respond poorly, with few in between. When this happens, average effect sizes can obscure the fact that a substantial subgroup benefits just as much as they would from a stimulant. In other words, non-stimulants are not necessarily less effective across the board, but that they are simply different in who they help.
In our commentary, we also highlight structural issues in ADHD research. Stimulant trials are particularly vulnerable to unblinding, as their immediate and observable physiological effects can reveal treatment assignment, potentially inflating perceived efficacy. Non-stimulants, with slower onset and subtler effects, are less prone to this bias.
Additionally, many randomized trials exclude patients with common psychiatric comorbidities such as anxiety, depression, or substance-use disorders. Using co-diagnoses as exclusion criteria for clinical trials on ADHD medications is nonviable when considering the large number of ADHD patients who also have other diagnoses. Real-world data suggest that a large proportion of individuals with ADHD would not qualify for typical trials, limiting how well results generalize to everyday clinical practice.
Standard evaluations of medication tolerability focus on side effects experienced by patients, but this narrow lens misses broader societal consequences. Stimulants are Schedule II controlled substances, which introduces logistical barriers, regulatory burdens, supply vulnerabilities, and administrative strain for both patients and clinicians.
When used as directed, stimulant medications do not increase risk of substance-use disorders (and, in fact, tend to reduce these rates); however, as ADHD awareness has spread and stimulants are more widely prescribed, non-medical use of prescription stimulants has become more widespread, particularly among adolescents and young adults. Non-stimulants do not carry these risks.
Non-stimulants are not without drawbacks themselves, however. They typically take longer to work and have higher non-response rates, making them less suitable in situations where rapid results are essential. These limitations, however, do not justify relegating them to second-line status across the board.
This is a call for abandoning a one-size-fits-all approach. Instead, future guidelines should present stimulant and non-stimulant medications as equally valid starting points, clearly outlining trade-offs related to onset, efficacy, misuse risk, and practical burden.
The evidence already supports this shift. The remaining challenge is aligning clinical practice and policy with what the data, and patient-centered care, are increasingly telling us.
Today, most treatment guidelines recommend starting ADHD treatment with stimulant medications. These medicines often work quickly and can be very effective, but they do not help every child, and they can have bothersome side effects, such as appetite loss, sleep problems, or mood changes. Families also worry about long-term effects, the possibility of misuse or abuse, as well as the recent nationwide stimulant shortages. Non-stimulant medications are available, but they are usually used only after stimulants have not been effective.
This stimulant-first approach means that many patients who would respond well to a non-stimulant will end up on a stimulant medication anyway. This study addresses this issue by testing two different ways of starting medication treatment for school-age children with attention-deficit/hyperactivity disorder (ADHD). We want to know whether beginning with a non-stimulant medicine can work as well as the “stimulant-first” approach, which is currently used by most prescribers.
From this study, we hope to learn:
Our goal is to give families and clinicians clear, practical evidence to support a truly shared decision: “Given this specific child, should we start with a stimulant or a non-stimulant?”
Who will be in the study?
We will enroll about 1,000 children and adolescents, ages 6 to 16, who:
We will include children with common co-occurring conditions (such as anxiety, depression, learning or developmental disorders) so that the results reflect the “real-world” children seen in clinics, not just highly selected research volunteers.
How will the treatments be assigned?
This is a randomized comparative effectiveness trial, which means:
Parents and clinicians will know which type of medicine the child is taking, as in usual care. However, the experts who rate how much each child has improved using our main outcome measure will not be told which treatment strategy the child received. This helps keep their ratings unbiased.
What will participants be asked to do?
Each family will be followed for 12 months. We will collect information at:
At these times:
We will also track:
Data will be entered into a secure, HIPAA-compliant research database. Study staff at each site will work closely with families to make participation as convenient as possible, including offering flexible visit schedules and electronic options for completing forms when feasible.
How will we analyze the results?
Using standard statistical methods, we will:
All analyses will follow the “intention-to-treat” principle, meaning we compare children based on the strategy they were originally assigned to, even if their medication is later changed. This mirrors real-world decision-making: once you choose a starting strategy, what tends to happen over time?
Why is this study necessary now?
This study addresses a critical, timely gap in ADHD care:
In short, this study is needed now to move ADHD medication decisions beyond “one-size-fits-all.” By rigorously comparing stimulant-first and non-stimulant-first strategies in real-world settings, and by focusing on what matters most to children and families overall functioning, side effects, and long-term well-being, we aim to give patients, parents, and clinicians the information they need to choose the best starting treatment for each child.
This project was conceived by Professor Stephen V. Faraone, PhD (SUNY Upstate Medical University, Department of Psychiatry, Syracuse, NY) and Professor Jeffrey H. Newcorn, MD (Icahn School of Medicine at Mount Sinai, Department of Psychiatry, New York, NY). It will be conducted at nine sites across the USA.
EBI-ADHD:
If you live with ADHD, treat ADHD, or write about ADHD, you’ve probably run into the same problem: there’s a ton of research on treatments, but it’s scattered across hundreds of papers that don’t talk to each other. The EBI-ADHD website fixes that.
EBI-ADHD (Evidence-Based Interventions for ADHD) is a free, interactive platform that pulls together the best available research on how ADHD treatments work and how safe they are. It’s built for clinicians, people with ADHD and their families, and guideline developers who need clear, comparable information rather than a pile of PDFs. EBI-ADHD Database The site is powered by 200+ meta-analyses covering 50,000+ participants and more than 30 different interventions. These include medications, psychological therapies, brain-stimulation approaches, and lifestyle or “complementary” options.
The heart of the site is an interactive dashboard. You can:
The dashboard then shows an evidence matrix: a table where each cell is a specific treatment–outcome–time-point combination. Each cell tells you two things at a glance:
Clicking a cell opens more detail: effect sizes, the underlying meta-analysis, and how the certainty rating was decided.
EBI-ADHD is not just a curated list of papers. It’s built on a formal umbrella review of ADHD interventions, published in The BMJ in 2025. That review re-analyzed 221 meta-analyses using a standardized statistical pipeline and rating system.
The platform was co-created with 100+ clinicians and 100+ people with lived ADHD experience from around 30 countries and follows the broader U-REACH framework for turning complex evidence into accessible digital tools.
Why it Matters
ADHD is one of the most studied conditions in mental health, yet decisions in everyday practice are still often driven by habit, marketing, or selective reading of the literature. EBI-ADHD offers something different: a transparent, continuously updated map of what we actually know about ADHD treatments and how sure we are about it.
In short, it’s a tool to move conversations about ADHD care from “I heard this works” to “Here’s what the best current evidence shows, and let’s decide together what matters most for you.”
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