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September 8, 2025
Background:
A previous meta-analysis found that children born to mothers with diabetes had a 34% higher risk of developing ADHD compared to those born to non-diabetic mothers.
However, previous studies suffered methodological limitations, such as small sample sizes, case-control or cross-sectional designs, and insufficient adjustment for key confounders such as maternal socio-economic status, mental health conditions, obesity, and substance use disorders.
Moreover, many studies relied on self-reported maternal diabetes, and on non-clinical ADHD assessments, such as parental reports or screening tools, which are prone to bias and inaccuracies.
Furthermore, the role of maternal antidiabetic medication use in relation to ADHD risk has rarely been examined. Antidiabetic medications are effective in controlling high blood sugar during pregnancy, but many can cross the placenta and the blood-brain barrier, raising concerns about potential effects on fetal brain development.
Study:
To address these gaps, an Australian study team used a large cohort of linked health administrative data from New South Wales to investigate both the association between maternal diabetes and the risk of ADHD and the independent effect of prenatal exposure to antidiabetic medications.
The study encompassed all mother-child pairs born from 2003 through 2005, with follow-up conducted through 2018 to monitor hospital admissions related to ADHD. That yielded a final cohort of almost 230,000 mother-child pairs.
The team adjusted for potential confounders including maternal age, socioeconomic status, previous children, pregnancy-related hypertension, caesarean delivery, birth order and plurality, maternal anxiety, depression, schizophrenia, bipolar disorder, substance use (alcohol, tobacco, stimulants, opioids, cannabis), and child factors such as Apgar score, sex, prematurity, and low birth weight.
Results:
For maternal diabetes overall, there was no significant association with offspring ADHD. That was also true when broken down into pre-existing maternal diabetes and gestational (pregnancy-induced) diabetes.
In a subset of 11,668 mother-child pairs, including 3,210 involving exposure to antidiabetic medications, there was likewise no significant association with offspring ADHD.
Conclusion:
The team concluded, “Our findings did not support the hypothesis that maternal diabetes increases the risk of ADHD in children. Additionally, maternal use of antidiabetic medication was not associated with ADHD.”
This study highlights the importance of high-quality research. A previous meta-analysis linking ADHD and maternal diabetes did not appropriately adjust for confounders and cited many small studies that may have included biased self-report scales. This large, registry-based cohort study of nearly 230,000 mother–child pairs found no evidence that maternal diabetes—whether pre-existing or gestational—or prenatal exposure to antidiabetic medications was associated with subsequent offspring ADHD as measured by hospital-recorded ADHD outcomes. The study’s strengths include its population scale, prolonged follow-up, and extensive adjustment for maternal and perinatal confounders (including maternal mental health and substance-use disorders), which address many limitations of earlier, smaller studies that reported elevated risks.
Yitayish Damtie, Kim Betts, Berihun Assefa Dachew, Getinet Ayanoa, and Rosa Alati, “The association between maternal diabetes, antidiabetic medication use, and severe ADHD requiring inpatient care: A registry-based cohort study,” Journal of Psychosomatic Research (2025), 195:112167, https://doi.org/10.1016/j.jpsychores.2025.112167.
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The report "Attention-Deficit/Hyperactivity Disorder Diagnosis, Treatment, and Telehealth Use in Adults" published in the CDC's Morbidity and Mortality Weekly Report provides a detailed examination of the prevalence and treatment of ADHD among U.S. adults based on data collected by the National Center for Health Statistics Rapid Surveys System during October–November 2023. This data is crucial as it offers updated estimates on the prevalence of ADHD in adults, a condition often regarded as primarily affecting children, and highlights the ongoing challenges in accessing ADHD-related treatments, including telehealth services and medication availability.
The methods used in this study involved the National Center for Health Statistics (NCHS) Rapid Surveys System (RSS), which gathers data to approximate the national representation of U.S. adults through two commercial survey panels: the AmeriSpeak Panel from NORC at the University of Chicago and Ipsos’s KnowledgePanel. The data were collected via online and telephone interviews from 7,046 adults. The responses were weighted to reflect the total U.S. adult population, ensuring that the results approximate national estimates. In identifying adults with current ADHD, respondents were asked if they had ever been diagnosed with ADHD and, if so, whether they currently had the condition. The study also collected data on treatment types (including stimulant and nonstimulant medications), telehealth use, and demographic variables such as age, education, race, and household income.
The results showed that approximately 6.0% of U.S. adults, or an estimated 15.5 million people, had a current ADHD diagnosis. Notably, more than half of the adults with ADHD reported receiving their diagnosis during adulthood (age ≥18 years), indicating that diagnosis can occur well beyond childhood. Analysis of demographics showed significant differences between adults with ADHD and those without; adults with ADHD were more likely to be younger, with 84.5% under the age of 50. Adults with ADHD were also less likely to have completed a bachelor's degree and more likely to have a household income below the federal poverty level compared to those without ADHD. Regarding treatment, the report found that approximately one-third of adults with ADHD were untreated, and around one-third received both medication and behavioral treatment. Among those receiving pharmacological treatment, 33.4% used stimulant medications, and 71.5% of these individuals reported difficulties in getting their prescriptions filled due to medication unavailability, reflecting recent stimulant shortages in the United States. Additionally, nearly half of adults with ADHD had used telehealth services for ADHD-related care, including obtaining prescriptions and receiving counseling or therapy.
The discussion emphasizes the public health implications of these findings. ADHD is often diagnosed late, with many individuals not receiving a diagnosis until adulthood, which underscores the need for improved awareness and early identification of ADHD symptoms across the life course. Moreover, the high prevalence of untreated ADHD and the barriers to accessing stimulant medications reveal significant gaps in the healthcare system's ability to support adults with ADHD. These gaps can contribute to poorer outcomes, such as increased risk of injury, substance use, and social impairment. The report also highlights the role of telehealth, which became more prominent during the COVID-19 pandemic. Telehealth appears to provide a viable solution for expanding access to ADHD diagnosis and treatment, though challenges remain regarding the quality of care and potential for misuse. The authors suggest that improved clinical care guidelines for adults with ADHD could help reduce delays in diagnosis and treatment access, thus improving long-term outcomes for affected individuals.
In conclusion, the study provides a comprehensive view of the prevalence, treatment, and telehealth use for ADHD among adults in the U.S. These data are crucial for guiding clinical care and shaping policies related to medication access and telehealth services. The findings underscore the importance of ensuring an adequate supply of stimulant medications and reducing barriers to ADHD care, ultimately enhancing the quality of life for adults with this condition. The good news is that many adults with ADHD are being diagnosed and treated. It is, however, concerning that many are not treated and that many of those treated with stimulants were impacted by the stimulant shortage.
For more details, see: https://www.cdc.gov/mmwr/volumes/73/wr/mm7340a1.htm
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.”
Our recent study, published in the Journal of Clinical Medicine, aims to shed light on an under-recognized challenge faced by many adults with Type 1 diabetes (T1D): attention-deficit/hyperactivity disorder (ADHD) symptoms.
We surveyed over 2,000 adults with T1D using the Adult Self-Report Scale (ASRS) for ADHD and analyzed their medical records. Of those who responded, nearly one-third met the criteria for ADHD symptoms—far higher than the general population average. Notably, only about 15% had a formal diagnosis or were receiving treatment.
The findings are striking: individuals with higher ADHD symptom scores had significantly worse blood sugar control, as indicated by higher HbA1c levels. Those flagged as "ASRS positive" were more than twice as likely to have poor glycemic control (HbA1c ≥ 8.0%). They also reported higher levels of depressive symptoms.
As expected, ADHD symptoms decreased with age but remained more common than in the general public. No strong links were found between ADHD symptoms and other cardiometabolic issues.
This study highlights a previously overlooked yet highly significant factor in diabetes management. ADHD-related difficulties—such as forgetfulness, inattention, or impulsivity—can make managing a complex condition like T1D more difficult. The researchers call for more screening and awareness of ADHD in adults with diabetes, which could lead to better mental health and improved blood sugar outcomes.
Takeaway: If you or a loved one with T1D struggles with focus, organization, or consistent self-care, it may be worth exploring whether ADHD could be part of the picture. Early identification and support are crucial to managing this common comorbidity.
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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