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December 15, 2025
The Background:
Meta-analyses have previously suggested a link between maternal thyroid dysfunction and neurodevelopmental disorders (NDDs) in children, though some studies report no significant difference. Overweight and obesity are more common in children and adolescents with NDDs. Hypothyroidism is often associated with obesity, which may result from reduced energy expenditure or disrupted hormone signaling affecting growth and appetite. These hormone-related parameters could potentially serve as biomarkers for NDDs; however, research findings on these indicators vary.
The Study:
A Chinese research group recently released a meta-analysis examining the relationship between neurodevelopmental disorders (NDDs) and hormone levels – including thyroid, growth, and appetite hormones – in children and adolescents.
The analysis included peer-reviewed studies that compared hormone levels – such as thyroid hormones (FT3, FT4, TT3, TT4, TSH, TPO-Ab, or TG-Ab), growth hormones (IGF-1 or IGFBP-3), and appetite-related hormones (leptin, ghrelin, or adiponectin) – in children and adolescents with NDDs like ADHD, against matched healthy controls. To be included, NDD cases had to be first-diagnosis and medication-free, or have stopped medication before testing. Hormone measurements needed to come from blood, urine, or cerebrospinal fluid samples, and all studies were required to provide both means and standard deviations for these measurements.
Meta-analysis of nine studies encompassing over 5,700 participants reported a medium effect size increase in free triiodothyronine (FT3) in children and adolescents with ADHD relative to healthy controls. There was no indication of publication bias, but variation between individual study outcomes (heterogeneity) was very high. Further analysis showed FT3 was only significantly elevated in the predominantly inattentive form of ADHD (three studies), again with medium effect size, but not in the hyperactive/impulsive and combined forms.
Meta-analysis of two studies combining more than 4,800 participants found a small effect size increase in thyroid peroxidase antibody (TPO-Ab) in children and adolescents with ADHD relative to healthy controls. In this case, the two studies had consistent results. Because only two studies were involved, there was no way to evaluate publication bias.
The remaining thyroid hormone meta-analyses, involving 6 to 18 studies and over 5,000 participants in each instance, found no significant differences in levels between children and adolescents with ADHD and healthy controls.
Meta-analyses of six studies with 317 participants and two studies with 192 participants found no significant differences in growth hormone levels between children and adolescents with ADHD and healthy controls.
Finally, meta-analyses of nine studies with 333 participants, five studies with 311 participants, and three studies with 143 participants found no significant differences in appetite-related hormone levels between children and adolescents with ADHD and healthy controls.
The Conclusion:
The team concluded that FT3 and TPO-Ab might be useful biomarkers for predicting ADHD in youth. However, since FT3 was only linked to inattentive ADHD, and TPO-Ab’s evidence came from just two studies with small effects, this conclusion may overstate the meta-analysis results.
Our Take-Away:
Overall, this meta-analysis found only limited evidence that hormone differences are linked to ADHD. One thyroid hormone (FT3) was higher in children with ADHD—mainly in the inattentive presentation—but the findings varied widely across studies. Another marker, TPO-Ab, showed a small increase, but this came from only two studies, making the result less certain. For all other thyroid, growth, and appetite-related hormones, the researchers found no meaningful differences between children with ADHD and those without. While FT3 and TPO-Ab may be worth exploring in future research, the current evidence is not strong enough to consider them reliable biomarkers.
Hong Wang, Kun Huang, Lizhen Piao, and Xiaochen Xue, “Dysregulation of Thyroid, Growth, and Appetite Hormones in Children and Adolescents With Neurodevelopmental Disorders: A Meta-analysis,” Journal of Integrative Neuroscience (2025) 24(10), 39816, https://doi.org/10.31083/JIN39816.
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.
Noting that “evidence on the association between ADHD and a physical condition associated with obesity, namely type 2 diabetes mellitus (T2D), is sparse and has not been meta-analysed yet,” a European study team performed a systematic search of the peer-reviewed medical literature followed by a meta-analysis, and then a nationwide population study.
Unlike type 1 diabetes, which is an auto-immune disease, type 2 diabetes is believed to be primarily related to lifestyle, associated with insufficient exercise, overconsumption of highly processed foods, and especially with large amounts of refined sugar. This leads to insulin resistance and excessively high blood glucose levels that damage the body and greatly lower life expectancy.
Because difficulty with impulse control is a symptom of ADHD, one might hypothesize that individuals with ADHD would be more likely to develop type-2 diabetes.
The meta-analysis of four cohort studies encompassing more than 5.7 million persons of all ages spread over three continents (in the U.S., Taiwan, and Sweden) seemed to point in that direction. It found that individuals with ADHD had more than twice the odds of developing type 2 diabetes than normally developing peers. There was no sign of publication bias, but between-study variability (heterogeneity) was moderately high.
The nationwide population study of over 4.2 million Swedish adults came up with the same result when adjusting only for sex and birth year.
Within the Swedish cohort there were 1.3 million families with at least two full siblings. Comparisons among siblings with and without ADHD again showed those with ADHD having more than twice the odds of developing type 2 diabetes. That indicated there was little in the way of familial confounding.
However, further adjusting for education, psychiatric comorbidity, and antipsychotic drugs dropped those higher odds among those with ADHD in the overall population to negligible (13% higher) and barely significant levels.
The drops were particularly pronounced for psychiatric comorbidities, especially anxiety, depression, and substance use disorders, all of which had equal impacts.
The authors concluded, “This study revealed a significant association between ADHD and T2D [type 2 diabetes] that was largely due to psychiatric comorbidities, in particular SUD [substance use disorders], depression, and anxiety. Our findings suggest that clinicians need to be aware of the increased risk of developing T2D in individuals with ADHD and that psychiatric comorbidities may be the main driver of this association. Appropriate identification and treatment of these psychiatric comorbidities may reduce the risk for developing T2D in ADHD, together with efforts to intervene on other modifiable T2D risk factors (e.g., unhealthy lifestyle habits and use of antipsychotics, which are common in ADHD), and to devise individual programs to increase physical activity. Considering the significant economic burden of ADHD and T2D, a better understanding of this relationship is essential for targeted interventions or prevention programs with the potential for a positive impact on both public health and the lives of persons living with ADHD.”
Sweden has a national single-payer health insurance system that includes virtually the entire population. It also has a system of national registers that track every resident from birth to death. That makes it possible to conduct nationwide population studies with a very high degree of precision and reliability.
In addition, one of the national registers is the Swedish Twin Register. Tracking all twins in the population enables studies to evaluate the degree to which observed associations may be attributable to genetic influences and to familial confounding. The twin method relies on the different levels of genetic relatedness between monozygotic ("identical") twins, who are genetically identical, and dizygotic ("fraternal") twins, who share on average half of their genetic variation (as do ordinary full siblings).
A Swedish team of researchers identified 42,582 Swedish twins born between 1959 and 1985, and who were, therefore, adults by the time of the study (20-47 years old). Of these, 24,872 (three out of five) completed a web-based survey with 1,300 questions covering lifestyle and mental and physical health. Out of this group, 17,999 provided information on ADHD symptoms and food frequency.
Self-reported ADHD symptoms came from nine inattention components and nine hyperactivity/impulsivity components, covering the 18 DSM- IV symptoms of ADHD.
The food frequency questionnaire included 94 food items, with the following frequency categories: never, 1-3 times/month, 1-2 times/week, 3-4 times/week, 5-6 times/week, 1 time/day, 2 times/day, 3 times/day.
In the raw data, the two subtypes of ADHD exhibited very similar associations. Both had significant associations with unhealthy diets. Both were more likely to be eating foods high in added sugar, and neglecting fruits and vegetables while eating more meat and fats.
After adjusting for the degree of relatedness of twins (whether monozygotic or dizygotic) and controlling for the other ADHD subtype, the associations remained statistically significant for inattention, but diminished to negligible levels or became statistically non-significant for hyperactivity/impulsivity.
Even for persons with inattention symptoms, adjusted correlations were small (never exceeding r = 0.10), with the strongest associations being for overall unhealthy eating habits (r = 0.09), eating foods high in added sugar (r = 0.10) or high in fat (r = 0.05), and neglecting fruits and vegetables (r = 0.06). All other associations became statistically non-significant.
For persons with hyperactivity/impulsivity symptoms, the only associations that remained statistically significant - but at tiny effect sizes - were unhealthy dietary patterns (r = 0.04) and consumption of foods high in added sugar (r = 0.03).
The further genetic analysis, therefore, focused on the strongest associations, between ADHD subtypes on the one hand, and unhealthy dietary patterns and eating foods high in added sugar on the other hand. The heritability estimates (the fraction of phenotypic covariance explained by genetic influences) were 44%, 40%, and 37% for inattention and high-sugar food, inattention and unhealthy dietary patterns, and hyperactivity/impulsivity and high-sugar food, respectively.
When examining only differences between pairs of monozygotic("identical") twins, the correlations became stronger for inattention, rising to r = 0.12 for unhealthy eating habits and r = 0.13 for consumption of foods high in added sugar. For hyperactivity/impulsivity symptoms, the association with unhealthy eating habits was weaker, and the association with consumption of foods high in added sugar became statistically insignificant.
The authors concluded, "we identified positive associations between self-reported trait dimensions of ADHD and intake of seafood, high-fat food, high-sugar food, high-protein food, and an unhealthy dietary pattern, and negative associations with consumption of fruits, vegetables, and a healthy dietary pattern. However, all the associations are small in magnitude. These associations were stronger for inattention compared to hyperactivity/ impulsivity. This pattern of associations was also reflected at the etiological level, where we found a slightly stronger genetic correlation between inattention with dietary habits and hyperactivity/impulsivity with dietary habits. Non-shared environmental influences also contributed to the overlap between ADHD symptom dimensions and consumption of high-sugar food and unhealthy dietary pattern. However, shared environmental influences probably contributed relatively little to the associations between ADHD symptoms and dietary habits. ... significant MZ twin intraplate differences also provided support for a potential causal link between inattention and dietary habits.
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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