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August 26, 2024
Most previous studies of suicide and self-harm risk among persons with ADHD have focused on adolescents and adults. They’ve also tended to be cross-sectional, analyzing data from a population at a specific point in time.
An Australian study team took a different approach, conducting a before-and-after study through the birth cohort of the Longitudinal Study of Australian Children (LSAC), comprising 5,107 children who have been followed up every two years since birth.
The diagnosis of ADHD was based on parents reporting that their child had received a diagnosis of ADHD at or before age ten.
Suicide and self-harm were defined as children’s self-report at age 14 of any thought or attempt of suicide and self-harm respectively over the past year.
The team adjusted for the following confounders: socioeconomic status, birth weight, ADHD medication history, maternal education level, maternal age at birth, experience in bullying victimization at age 12, and depression score based on Short Mood and Feelings Questionnaire (SMFQ).
Of the 5,107 participants, 3,696 had all the valid data required for analysis and were included in the final cohort. Of these, 3.6% were diagnosed with ADHD by age 10.
With a diagnosis of ADHD at age 10 and all other factors held constant:
Both depression and exposure to bullying were statistically significant mediators for the relationship. Nevertheless, depression and exposure to bullying each accounted for well under 10% of the overall effect.
Neither socioeconomic status nor maternal factors had any significant mediating effect on outcomes.
The authors concluded, “This study provides compelling evidence that children diagnosed with ADHD at the age of 10 years face significantly elevated risks of experiencing suicidal thoughts, planning, or attempts, as well as self-harm, by the age of 14 years, which underscores the critical importance of recognizing and addressing these heightened risks in children with ADHD.”
Ping-I Lin, Weng Tong Wu, Enoch Kordjo Azasu, and Tsz Ying Wong, “Pathway from attention-deficit/hyperactivity disorder to suicide/self-harm,” Psychiatry Research (2024), 337:115936, https://doi.org/10.1016/j.psychres.2024.115936.
In the general population, most mothers experience mood disturbances right after childbirth, commonly known as postpartum blues, baby blues, or maternity blues. Yet only about one in six develop symptoms with a duration and magnitude that require treatment for depressive disorder, and one in ten for anxiety disorder.
To what extent does ADHD contribute to the risk of such disorders following childbirth? A Swedish study team used the country’s single-payer health insurance database and other national registers to conduct the first nationwide population study to explore this question.
They used the medical birth register to identify all 420,513 women above 15 years of age who gave birth to their first child, and all 352,534 who gave birth to their second child, between 2005 and 2013. They excluded miscarriages. They then looked for diagnoses of depression and/or anxiety disorders up to a year following childbirth.
In the study population, 3,515 mothers had been diagnosed with ADHD, and the other 769,532 had no such diagnosis.
Following childbirth, depression disorders were five times more prevalent among mothers with ADHD than among their non-ADHD peers. Excluding individuals with a prior history of depression made little difference, lowering the prevalence ratio to just under 5. Among women under 25, the prevalence ratio was still above 3, while for those 25 and older it was above 6.
Similarly, anxiety disorders were over five times more prevalent among mothers with ADHD than among their non-ADHD peers. Once again, excluding individuals with a prior history of depression made little difference, lowering the prevalence ratio to just under 5. Among women under 25, the prevalence ratio was still above 3, while for those 25 and older it was above 6.
The team cautioned, “There is a potential risk of surveillance bias as women diagnosed with ADHD are more likely to have repeated visits to psychiatric care and might have an enhanced likelihood of also being diagnosed with depression and anxiety disorders postpartum, compared to women without ADHD.”
Nevertheless, they concluded, “ADHD is an important risk factor for both depression and anxiety disorders in the postpartum period and should be considered in the post- pregnancy maternal care, regardless of sociodemographic factors and the presence of other psychiatric disorders. Parental education prior to conception, psychological surveillance during, and social support after childbirth should be provided to women diagnosed with ADHD.”
Suicide is one of the most feared outcomes of any psychiatric condition. Although its association with depression is well known, a small but growing research literature shows that ADHD is also a risk factor for suicidality. Suicide is difficult to study. Because it is relatively rare, large samples of patients are needed to make definitive statements.
Studies of suicide and ADHD must also consider the possibility that medications might elevate that risk. For example, the FDA placed a black box warning on atomoxetine because that ADHD medication had been shown to increase suicidal risk in youth. A recent study of 37,936 patients with ADHD now provides much insight into these issues (Chen, Q., Sjolander, A., Runeson, B., D'Onofrio, B. M., Lichtenstein, P. & Larsson, H. (2014). Drug treatment for attention-deficit/hyperactivity disorder and suicidal behavior: a register-based study. BMJ 348, g3769.). In Sweden, such large studies are possible because researchers have computerized medical registers that describe the disorders and treatments of all people in Sweden. Among 37,936 patients with ADHD, 7019 suicide attempts or completed suicides occurred during 150,721 person-years of follow-up. This indicates that, in any given year, the risk for a suicidal event is about 5%. For ADHD patients, the risk for a suicide event is about 30% greater than for non-ADHD patients. Among the ADHD patients who attempted or completed suicide, the risk was increased for those who had also been diagnosed with a mood disorder, conduct disorder, substance abuse, or borderline personality. This is not surprising; the most serious and complicated cases of ADHD are those that have the greatest risk for suicidal events. The effects of the medication were less clear. The risk for suicide events was greater for ADHD patients who had been treated with non-stimulant medication compared with those who had not been treated with non-stimulant medication. A similar comparison showed no effect of stimulant medications. This first analysis suffers from the fact that the probability of receiving medication increases with the severity of the disorder. To address this problem, the researchers limited the analyses to ADHD patients who had some medication treatment and then compared suicidal risk between periods of medication treatment and periods of no medication treatment. This analysis found no increased risk for suicide from non-stimulant medications and, more importantly, found that for patients treated with stimulants, the risk for suicide was lower when they were taking stimulant medications. This protective effect of stimulant medication provides further evidence of the long-term effects of stimulant medications, which have also been shown to lower the risks for traffic accidents, criminality, smoking, and other substance use disorders.
A Chinese research team performed two types of meta-analyses to compare the risk of suicide for ADHD patients taking ADHD medication as opposed to those not taking medication.
The first type of meta-analysis combined six large population studies with a total of over 4.7 million participants. These were located on three continents - Europe, Asia, and North America - and more specifically Sweden, England, Taiwan, and the United States.
The risk of suicide among those taking medication was found to be about a quarter less than for unmediated individuals, though the results were barely significant at the 95 percent confidence level (p = 0.49, just a sliver below the p = 0.5 cutoff point). There were no significant differences between males and females, except that looking only at males or females reduced sample size and made results non-significant.
Differentiating between patients receiving stimulant and non-stimulant medications produced divergent outcomes. A meta-analysis of four population studies covering almost 900,000 individuals found stimulant medications to be associated with a 28 percent reduced risk of suicide. On the other hand, a meta-analysis of three studies with over 62,000 individuals found no significant difference in suicide risk for non-stimulant medications. The benefit, therefore, seems limited to stimulant medication.
The second type of meta-analysis combined three within-individual studies with over 3.9 million persons in the United States, China, and Sweden. The risk of suicide among those taking medication was found to be almost a third less than for unmediated individuals, though the results were again barely significant at the 95 percent confidence level (p =0.49, just a sliver below the p = 0.5 cutoff point). Once again, there were no significant differences between males and females, except that looking only at males or females reduced the sample size and made results non-significant.
Differentiating between patients receiving stimulant and non-stimulant medications once again produced divergent outcomes. Meta-analysis of the same three studies found a 25 percent reduced risk of suicide among those taking stimulant medications. But as in the population studies, a meta-analysis of two studies with over 3.9 million persons found no reduction in risk among those taking non-stimulant medications.
A further meta-analysis of two studies with 3.9 million persons found no reduction in suicide risk among persons taking ADHD medications for 90 days or less, "revealing the importance of duration and adherence to medication in all individuals prescribed stimulants for ADHD."
The authors concluded, "exposure to non-stimulants is not associated with a higher risk of suicide attempts. However, a lower risk of suicide attempts was observed for stimulant drugs. However, the results must be interpreted with caution due to the evidence of heterogeneity ..."
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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