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ADHD is usually framed as a dopamine-and-norepinephrine condition, but recent studies have revealed that serotonin may also play a significant role. To delve deeper into this, we conducted a systematic literature review of studies looking at serotonin, its receptors, and the serotonin transporter (SERT) in relation to ADHD. The result: serotonin appears to be an important piece of the puzzle, but the overall picture is quite complex.
An ADHD & Serotonin Literature Review:
The authors searched the literature without time limits and screened thousands of records to end up with 95 relevant publications. Those included animal/basic-science work, neuroimaging, pharmacodynamics, a couple of large genetic/transcriptomic studies (GWAS and a cortico-striatal TWAS), and a few clinical reports. Each paper was graded for quality: 17 high, 59 medium, and 19 low.
As the study points out, the idea that serotonin may play a role in the neurobiology of ADHD is not new, but this literature review “identified multiple individual strands of evidence gathered over several decades and brought them into a more coherent focus”. It concludes that serotonergic neurotransmission is implicated in ADHD. This doesn’t mean variations in serotonin levels cause ADHD, but that serotonin may be a plausible target for future treatments and research.
ADHD is polygenic and multi-systemic. For now, clinicians and patients should view serotonin as part of a complex network that may contribute to ADHD symptoms. More research is needed before making treatment decisions based on these findings.
Faraone, S. V., Ward, C. L., Boucher, M., Elbekai, R., & Brunner, E. (2025). Role of serotonin in the neurobiology of attention-deficit/hyperactivity disorder: a systematic literature review. Expert Opinion on Therapeutic Targets, 1–18. https://doi.org/10.1080/14728222.2025.2552324
Although there has been much research documenting that ADHD adults are at risk for other psychiatric and substance use disorders, relatively little is known about whether ADHD puts adults at risk specifically for somatic medical disorders.
Given that people with ADHD tend toward being disorganized and inattentive, and that they tend to favor short-term over long-term rewards, it seems logical that they should be at higher risk for adverse medical outcomes. But what does the data say?
In a systematic review of the literature, Instances and colleagues have provided a thorough overview of this issue. Although they found 126 studies, most were small and were of "modest quality". Thus, their results must be considered to be suggestive, not definitive for most of the somatic conditions they studied.
Also, they excluded articles about traumatic injuries because the association between ADHD and such injuries is well established. Using qualitative review methods, they classified associations as being a) well-established; b) tentative, or c) lacking sufficient data.
Only three conditions met their criteria for being a well-established association: asthma, sleep disorders, and obesity.
They found tentative evidence implicating ADHD as a risk factor for three conditions: migraine headaches, celiac disease, and diseases of the circulatory system.
These data are intriguing, but cannot tell us why ADHD people are at increased risk for somatic conditions. One possibility is that suffering from ADHD symptoms can lead to an unhealthy lifestyle, which leads to increased medical risk. Another possibility is that the biological systems that are dysregulated in ADHD are also dysregulated in some medical disorders. For example, we know that there is some overlap between the genes that increase the risk for ADHD and those that increase the risk for obesity. We also know that the dopamine system has been implicated in both disorders.
Instances and colleagues also point out that some medical conditions might lead to symptoms that mimic ADHD. They give sleep-disordered breathing as an example of a condition that can lead to the symptom of inattention.
But this seems to be the exception, not the rule. Other medical conditions co-occurring with ADHD seem to be true comorbidities, rather than the case of one disorder causing the other. Thus, primary care clinicians should be alert to the fact that many of their patients with obesity, asthma, or sleep disorders might also have ADHD.
By screening such patients for ADHD and treating that disorder, you may improve their medical outcomes indirectly via increased compliance with your treatment regime and an improvement in health behaviors. We don't yet have data to confirm these latter ideas, as the relevant studies have not yet been done.
Serotonin is a key chemical in the body that helps regulate mood, behavior, and also many physical functions such as sleep and digestion. It has also been linked to how ADHD (attention-deficit/hyperactivity disorder) develops in the brain. This study looks at how serotonin may be involved in both the mental health and physical health conditions that often occur alongside ADHD.
It is well-established that ADHD is more than just trouble focusing or staying still. For many, it brings along a host of other physical and mental health challenges. It is very common for those with ADHD to also have other diagnosed disorders. For example, those with ADHD are often also diagnosed with depression, anxiety, or sleep disorders. When these issues overlap, they are called comorbidities.
A new comprehensive review, led by Dr. Stephen V. Faraone and colleagues, delves into how serotonin (5-HT), a major brain chemical, may be at the heart of many of these common comorbidities.
Serotonin is a neurotransmitter most often linked to mood, but its role in regulating the body has much broader implications. It regulates sleep, digestion, metabolism, hormonal balance, and even immune responses. Although ADHD has long been associated with dopamine and norepinephrine dysregulation, this review suggests that serotonin also plays a central role, especially when it comes to comorbid conditions.
This research suggests that serotonin dysregulation could explain the diverse and sometimes puzzling range of symptoms seen in ADHD patients. It supports a more integrative model of ADHD—one that goes beyond the brain’s attention, reward and executive control circuits and considers broader physiological and psychological health.
future research into the role of serotonin could help develop more tailored interventions, especially for patients who don't respond well to stimulant medications. Future studies may focus on serotonin’s role in early ADHD development and how it interacts with environmental and genetic factors.
This study is a strong reminder that ADHD is a complex, multifaceted condition. Differential diagnosis is crucial to properly diagnosing and treating ADHD. Clinicians' understanding of the underlying link between ADHD and its common comorbidities may help future ADHD patients receive the individualized care they need. By shedding light on serotonin’s wide-reaching influence, this study may provide a valuable roadmap for improving how we diagnose and treat those with complex comorbidities in the future.
Taiwan's National Health Insurance program is a single-payer system that covers 99.6% of the island's 23 million residents. It includes family relationships.
This enabled a Taiwanese study team to examine the comorbidity of psychiatric disorders among close relatives in the entire population over eleven years, beginning at the start of 2001 and concluding at the end of2011.
For greater certainty of diagnosis, only persons twice diagnosed with the same psychiatric disorder were included as index individuals. There were 431,887 index patients, 152,443 of whom were ADHD index patients.
These index patients were then compared with all of their first-degree relatives (FDRs): parents, children, siblings, and twins. This produced 1,017,430 patient-FDR pairs, of which 401,301 were ADHD patient-FDR pairs.
Next, four controls were matched by age, gender, and type relative to each case, resulting in 4,069,720 control pairs.
After adjusting for age, gender, urbanization, and income level, ADHD patients were seven times more likely than controls to have first-degree relatives with ADHD. They were also seven times more likely to have FDRs with major depressive disorder, four times more likely to have FDRs with autism spectrum disorder, twice as likely to have FDRs with bipolar disorder, and 80%more likely to have FDRs with schizophrenia.
For centuries, we’ve called the eyes the "windows to the soul," but for modern neurologists, they are quite literally a window into the brain. The retina and the central nervous system share the same embryonic origins, developing from the same neural tissue in the womb. Because of this deep biological connection, the back of your eye acts as a non-invasive map of your brain's health, displaying a complex web of nerves and blood vessels that can (theoretically!) mirror certain neurodevelopmental conditions.
Recently, a buzz rippled through the mental health community when a study published in partnership with Seoul National University Bundang Hospital claimed a massive breakthrough. Researchers developed an Artificial Intelligence (AI) model that could screen children for Attention-Deficit/Hyperactivity Disorder (ADHD) using nothing more than a simple retinal photograph. The study, which prospectively recruited children from Severance Hospital and Eunpyeong St. Mary’s Hospital, produced results that were staggering: the AI reportedly achieved an accuracy rate of 96.9%!
In the world of medical testing, scientists use a metric called AUROC (Area Under the Receiver Operating Characteristic) to measure how well a test works.
An AUROC of 96.9% is a near-perfect score, suggesting a tool is ready for immediate, real-world deployment. While headlines promised a revolution in mental health screening, a deeper look into this research and the study’s design has exposed that this 96.9% AUROC was more likely evidence of a flawed methodology rather than a biological reality.
To build their screening tool, researchers analyzed over 1,100 retinal images using a digital pipeline called AutoMorph and a machine-learning model known as XGBoost. The AI was trained to hunt for physical signals of the "Dopamine Connection." Dopamine is the primary neurotransmitter involved in ADHD, but it is also essential to the eye. It regulates synaptic formation, retinal blood flow, and vascular endothelial regulation. Because dopamine dysregulation influences how blood vessels grow and remodel, the study hypothesized that an ADHD brain would leave a unique "fingerprint" on the retinal vasculature, resulting in denser, thicker vessel structures.
On paper, the logic was sound: use AI to spot the subtle vascular remodeling caused by dopaminergic shifts. But a closer look at the investigation revealed that the AI wasn't just spotting ADHD; it was over-indexing on technical noise.
The most significant "smoking gun" flagged by critics is a massive temporal mismatch. In other words, there was a severe disparity in the timeframes and conditions under which the retinal images for the two comparison groups were collected. For an AI to learn a biological condition, it must compare groups under identical technical conditions. Instead, this study created a time-traveling dataset:
A scientific study is only as reliable as its control group. The control in any experiment acts as a baseline against which the study group is compared. In this case, the control group should be composed of children without any neurodevelopmental disorders, or of “typically developing” children.
In this study, the control group wasn't composed of healthy children from the community. Instead, they were patients visiting a tertiary ophthalmology clinic. Children visiting a specialist eye hospital are rarely "typical." They are there because they have symptomatic eye issues. This introduced a massive selection bias involving three major confounders:
When training AI, you must never allow the "test questions" to leak into the "study material." The researchers, however, committed a fundamental violation of machine learning hygiene known as Eye-to-Eye Data Leakage. The study split the data by the eye rather than by the participant.
Human eyes are highly correlated; the left eye is a near-mirror of the right. If a child's left eye was used for training and their right eye was used for testing, the AI was effectively "cheating." Instead of learning the general traits of ADHD, the model was potentially memorizing individuals. This error artificially balloons accuracy metrics.
The true test of medical AI is diagnostic specificity, or differential diagnosis. This refers to the ability to tell one condition apart from another. While the model claimed 96.9% accuracy against a flawed control group, its performance collapsed when faced with real-world complexity.
When the researchers asked the AI to differentiate between ADHD and Autism Spectrum Disorder (ASD), the accuracy plummeted to a poor 63% AUROC. In real-world clinical settings, an accuracy of 63% is dangerously close to a 50% coin flip. Since ADHD frequently co-occurs with ASD, anxiety, or intellectual disabilities, an AI that cannot handle these "clinical differentials" is functionally useless in a doctor's office. The failure at this stage proves the model was likely detecting technical quirks of the dataset rather than a unique biological marker for ADHD.
To move from the lab to the clinic, we must establish a foundation built on rigor rather than high-speed data scraping. Moving forward, we must demand these 3 Pillars of Trusted Medical AI :
The dream of a quick eye scan to diagnose ADHD is not dead, but it must be rescued from "fast science" shortcuts and buzzy headlines.
Background:
One of the more persistent concerns among parents of children with ADHD is whether stimulant medications will stunt their child's growth. A large Israeli cohort study now offers some of the most rigorous reassurance to date, and its methodology sets it apart from earlier research.
The question has long been complicated by a more fundamental uncertainty: do growth differences in children with ADHD stem from the condition itself, from stimulant treatment, or from factors present before any medication is ever prescribed? Without a clear answer, clinicians and families have faced a genuine dilemma when weighing the benefits of stimulant therapy against potential long-term physical costs.
Most previous studies compounded this difficulty by comparing group-average heights, which ignores the crucial variable of genetic potential. A child who is short relative to the general population may simply have short parents. Failing to account for this introduces systematic bias and can make medications appear more harmful than they are.
The Study:
The Israeli research team addressed this directly. Using health records from a nationwide provider, they assembled a retrospective cohort of children born between 1995 and 2003, following them through 2023. This amount of time was long enough for all participants to have reached adult stature (defined as 17 or older for females, 19 or older for males). Their sample included 5,671 children with untreated ADHD, 11,846 who received stimulant treatment, and 47,258 non-ADHD controls. Children who took stimulants for only one to two months, or who had chronic medical conditions requiring long-term medication, were excluded to avoid confounding the results.
Crucially, adult height was evaluated not against population norms but against each individual's expected height, calculated from parental heights using the Tanner-Goldstein-Whitehouse method, a standard approach for estimating genetic height potential via mid-parental height.
When the researchers compared adult heights across the three groups using analysis of variance (ANOVA), they did find statistically significant differences. But statistical significance, particularly in studies with tens of thousands of participants, does not automatically translate into clinical significance. The effect sizes were consistently very small, and the absolute differences were under one centimeter, which is a margin considered clinically negligible.
Their conclusion is measured but clear: after accounting for genetic growth potential, neither an ADHD diagnosis nor stimulant treatment was associated with meaningful reductions in adult height. The findings, they argue, support prioritizing behavioral and functional outcomes when making treatment decisions, since the risk of clinically significant height loss appears to be minimal.
The Take-Away:
For families navigating ADHD treatment, the practical implication is significant: concerns about permanent growth suppression, while understandable, should not be the primary driver of whether or how long a child receives stimulant therapy.
A recent meta-analysis examined how well cognitive behavioral therapy (CBT) improves not just symptoms, but everyday functioning and quality of life in adults with ADHD.
The Background:
ADHD in adults affects far more than attention or impulsivity. It often disrupts key areas of life:
These broad impacts highlight a key issue: reducing symptoms does not automatically translate into better day-to-day functioning.
CBT is a structured, skills-based therapy that helps people:
While both medication (especially stimulants) and CBT improve core ADHD symptoms, CBT is particularly aimed at improving real-world functioning.
The Study:
The researchers analyzed studies involving adults diagnosed with ADHD (or showing clinically significant symptoms). They included:
They focused specifically on outcomes beyond symptoms:
The Results:
1. Strongest Effects: Occupational functioning
CBT showed consistently strong improvements in work-related functioning compared to control groups, both immediately after treatment and at follow-up. This was the most robust finding across domains.
2. Moderate Improvement: Global Functional Impairment
CBT led to moderate improvements in overall daily functioning, with some evidence that gains persist over time. In studies tracking individuals over time, improvements were even stronger at follow-up.
3. Modest Gains: Social Relationships
CBT produced small to moderate improvements in social functioning. Benefits were present both after treatment and at follow-up, but were less pronounced than in work-related outcomes.
4. Limited Effects: Academic Functioning
There were moderate short-term gains when CBT was compared to control groups, but these did not persist at follow-up. Within-subject studies showed only small improvements overall.
5. Modest and Inconsistent Effects: Quality of Life
Improvements in quality of life were small when compared to control groups and often did not last. However, studies tracking individuals over time showed moderate improvements, suggesting some benefit that may not always show up clearly in between-group comparisons.
Overall, the findings suggest:
One notable nuance: CBT did not always outperform other active treatments (like medication or other therapies). This suggests that while CBT is effective, its benefits may partly overlap with broader therapeutic or support effects rather than relying on a single, unique mechanism.
The Take-Away:
CBT is a valuable, evidence-based treatment for adults with ADHD, especially for improving work functioning and overall daily life management. However, its impact on relationships, academic outcomes, and quality of life is more limited and less consistent, pointing to the need for more targeted or combined approaches in those areas.
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