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June 13, 2025
The Spanish National Health Survey tracks health care outcomes through representative samples of the Spanish population.
A Spanish research team used survey data to explore the relationship between ADHD symptoms and dental and gum health in a representative sample of 3,402 Spanish children aged 6 to 14.
While previous studies have found associations between ADHD and poor dental health, they have not fully accounted for such important determinants of poor oral health as socioeconomic status, dental hygiene, or diet.
The team therefore adjusted for sociodemographic factors, lifestyle variables, and oral hygiene behaviors. More specifically, they adjusted for sex, age, social class, parental education, exposure to tobacco smoke, consumption of sweets, consumption of sugary drinks, use of asthma or allergy medication, adequate oral hygiene behavior of children, adherence to regular dental visits, parental adequate oral hygiene behavior, and parental adherence to regular dental visits.
With those adjustments, children with ADHD symptoms had over twice the incidence of dental caries (cavities) as their counterparts without ADHD symptoms.
Tooth extractions and dental restorations also occurred with over 40% greater frequency in children with ADHD symptoms.
Gum bleeding, a sign of gum disease, was more than 60% more common among children with ADHD symptoms than among their non-ADHD peers.
Importantly, excluding children with daily sugar consumption, which left 1,693 children in the sample, made no difference in the outcome for cavities.
Excluding children with poor oral hygiene habits, which left 1,657 children in the sample, those with ADHD had 2.5-fold more caries than their non-ADHD counterparts.
Excluding children of low social class, which left 1,827 children in the sample, those with ADHD had 2.6-fold more caries than their non-ADHD counterparts.
Turning to a different method to address potential confounding factors, the team used nearest-neighbor propensity score matching to create virtual controls. This compared 461 children with ADHD to 461 carefully matched children without ADHD.
This time, children with ADHD symptoms had just under twice the incidence of cavities as their counterparts without ADHD symptoms, but 60% more tooth extractions and about 75% more dental restorations. The difference in gum bleeding became nonsignificant.
Noting that “The increased risk of caries was maintained when the analyses were restricted to middle/high social class families and children with low sugar intake, good oral hygiene behaviors and regular dental visits,” the team concluded, “Children with ADHD symptoms in Spain had worse oral health indicators than those without ADHD symptoms. Our results suggest that the association of ADHD symptoms with caries was independent of socioeconomic level, cariogenic diet, frequency of toothbrushing, and dental visits.”
Lucía Fernández-Arce, José Manuel Martínez-Pérez, Miguel García-Villarino, María Del Mar Fernández-Álvarez, Rubén Martín-Payo, and Alberto Lana, “Symptoms of Attention Deficit Hyperactivity Disorder and Oral Health Problems among Children in Spain,” Caries Research (2025), 59(1):35-45, https://doi.org/10.1159/000541013.
In recent years, there has been growing interest in understanding the connection between our gut microbiota (the community of microorganisms in our digestive system) and various neurodevelopmental disorders like autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD). A new study by Shunya Kurokawa and colleagues dives deeper into this area, comparing dietary diversity and gut microbial diversity among children with ASD, ADHD, their normally-developing siblings, and unrelated volunteer controls. Let's unpack what they found and what it means.
The Study Setup
The researchers recruited children aged 6-12 years diagnosed with ASD and/or ADHD, along with their non-ASD/ADHD siblings and the unrelated non-ASD/ADHD volunteers. The diagnoses were confirmed using standardized assessments like the Autism Diagnostic Observation Schedule-2 (ADOS-2). The study looked at gut microbial diversity using advanced DNA extraction and sequencing techniques, comparing alpha-diversity indices (which reflect the variety and evenness of microbial species within each gut sample) across different groups. They also assessed dietary diversity through standardized questionnaires.
Key Findings
The study included 98 subjects, comprising children with ASD, ADHD, both ASD and ADHD, their non-ASD/ADHD siblings, and the unrelated controls. Here's what they discovered:
Gut Microbial Diversity: The researchers found significant differences in alpha-diversity indices (like Chao 1 and Shannon index) among the groups. Notably, children with ASD had lower gut microbial diversity compared to unrelated neurotypical controls. This suggests disorder-specific differences in gut microbiota, particularly in children with ASD.
Dietary Diversity: Surprisingly, dietary diversity (assessed using the Shannon index) did not differ significantly among the groups. This finding implies that while gut microbial diversity showed disorder-specific patterns, diet diversity itself might not be the primary factor driving these differences.
What Does This Mean?
The study highlights intriguing connections between gut microbiota and neurodevelopmental disorders like ASD and ADHD. The lower gut microbial diversity observed in children with ASD points towards potential links between gut health and the pathophysiology of ASD. Understanding these connections is crucial for developing targeted therapeutic interventions.
Implications and Future Directions
This research underscores the importance of considering gut microbiota in the context of neurodevelopmental disorders. Moving forward, future studies should account for factors like co-occurrence of ASD and ADHD, as well as carefully control for dietary influences. This will help unravel the complex interplay between gut microbiota, diet, and neurodevelopmental disorders, paving the way for innovative treatments and interventions.
In summary, studies like this shed light on the intricate relationship between our gut health, diet, and brain function. By unraveling these connections, researchers are opening new avenues for understanding and potentially treating conditions like ASD and ADHD.
In December 2016, the U.S. Food and Drug Administration (FDA) warned “that repeated or lengthy use of general anesthetic and sedation drugs during surgeries or procedures in children younger than 3 years or in pregnant women during their third trimester may affect the development of children’s brains.” The FDA adds, “Health care professionals should balance the benefits of appropriate anesthesia against the potential risks, especially for procedures lasting longer than 3 hours or if multiple procedures are required in children under 3 years,” and “Studies in pregnant and young animals have shown that using these drugs for more than 3 hours caused widespread loss of brain nerve cells.”
That raises a concern that such exposure could lead to increased risk of psychiatric disorders, including ADHD.
Noting “There are inconsistent reports regarding the association between general anesthesia and adverse neurodevelopmental and behavioral disorders in children,” a South Korean study team conducted a nationwide population study to explore possible associations through the country’s single-payer health insurance database that covers roughly 97% of all residents.
The team looked at the cohort of all children born in Korea between 2008 and 2009, and followed them until December 31, 2017. They identified 93,717 children in this cohort who during surgery received general anesthesia with endotracheal intubation (a tube inserted down the trachea), and matched them with an equal number of children who were not exposed to general anesthesia.
The team matched the unexposed group with the exposed group by age, sex, birth weight, residential area at birth, and economic status.
They then assessed both groups for subsequent diagnoses of ADHD.
In general, children exposed to general anesthesia were found to have a 40% greater risk of subsequently being diagnosed with ADHD than their unexposed peers.
This effect was found to be dose dependent by several measures:
All three measures were highly significant.
The authors concluded, “exposure to general anesthesia with ETI [endotracheal intubation] in children is associated with an increased risk of ADHD … We must recognize the possible neurodevelopmental risk resulting from general anesthesia exposure, inform patients and parents regarding this risk, and emphasize the importance of close monitoring of mental health. However, the risk from anesthesia exposure is not superior to the importance of medical procedures. Specific research is needed for the development of safer anesthetic drugs and doses.”
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The U.S. government released a sweeping document titled The MAHA Report: Making Our Children Healthy Again, developed by the President’s “Make America Healthy Again” Commission. Chaired by public figures and physicians with ties to the current administration, the report presents a broad diagnosis of what it calls a national health crisis among children. It cites rising rates of obesity, diabetes, allergies, mental illness, neurodevelopmental disorders, and chronic disease as signs of a generation at risk.
The report's overarching goal is to shift U.S. health policy away from reactive, pharmaceutical-based care and toward prevention, resilience, and long-term well-being. It emphasizes reforming the food system, reducing environmental chemical exposure, addressing lifestyle factors like physical inactivity and screen overuse, and rethinking what it calls the “overmedicalization” of American children.
While some of the report’s arguments are steeped in political rhetoric and controversial claims—particularly around vaccines and mental health diagnoses—others are rooted in well-established public health science. This blog aims to highlight where the MAHA Report gets the science right, especially as it relates to childhood health and ADHD.
Although the MAHA Report contains several debatable assertions, it also outlines six key public health priorities that are well-supported by decades of research. If implemented thoughtfully, these recommendations might make a meaningful difference in the health of American children:
Reduce Ultra-Processed Food (UPF) Consumption
UPFs now make up nearly 70% of children’s daily calories. These foods are high in added sugars, refined starches, unhealthy fats, and chemical additives, but low in nutrients. Studies—including a 2019 NIH-controlled feeding study—show that UPFs promote weight gain, overeating, and metabolic dysfunction. What can help: Tax incentives for fresh food retailers, improved school meals, front-of-pack labeling, and food industry regulation.
Promote Physical Activity and Limiting Sedentary Time
Most American children don’t get the recommended 60 minutes of physical activity per day. This contributes to obesity, cardiovascular risk, and even mental health issues. Physical activity is known to improve attention, mood, sleep, and self-regulation. What can help: Mandatory daily PE, school recess policies, walkable community infrastructure, and screen-time education.
Addressing Sleep Deprivation
Teens today sleep less than they did a decade ago, in part due to screen use and early school start times. Sleep loss is linked to depression, suicide risk, poor academic performance, and metabolic problems. What can help: Later school start times, family education about sleep hygiene, and limits on evening screen exposure.
Improving Maternal and Early Childhood Nutrition
The report indirectly supports actions that are backed by strong evidence: encouraging breastfeeding, supporting maternal whole-food diets, and improving infant nutrition. These are known to reduce chronic disease risk later in life.
ADHD is one of the most discussed neurodevelopmental disorders in the MAHA Report, but many of its claims about ADHD are misleading, oversimplified, or inconsistent with decades of scientific evidence, much of which is described in the International Consensus Statement on ADHD, and other references given below.
This is true. Diagnosis rates have risen over the past two decades, due in part to better recognition, broadened diagnostic criteria, and changes in healthcare access. Diagnosis rates in some parts of the country are too high, but we don’t know why. That should be addressed and investigated. MAHA attributes increasing diagnoses to ‘overmedicalization’. That is a hypothesis worth testing but not a conclusion we can draw from available data.
These have been associated with ADHD but have not been documented as causes. ADHD is highly heritable, with genetic factors accounting for 70–80% of the risk. Unlike genetic studies, environmental risk studies are compromised by confounding variables. There are good reasons to address these issues but doing so is unlikely to reduce diagnostic rates of ADHD.
❌ Inaccurate: ADHD medications don’t work long-term.
The report criticizes stimulant use but fails to note that ADHD medications are among the most effective psychiatric treatments, especially when consistently used. They cite the MTA study’s long term outcome study of kids assigned to medication vs. placebo as showing medications don’t work in the long term. But that comparison is flawed because during the follow-up period, many kids on medication stopped taking them and many on placebo started taking medications. Many studies document that medications for ADHD protect against many real-world outcomes such as accidental injuries, substance abuse and even premature death.
Despite the issues discussed above, the MAHA Report can indirectly help children and adults with ADHD by pushing for systemic changes that reduce ultra-processed food consumption, increase physical activity, and motivate better sleep practices.
In other words, you don’t need to reject the diagnosis of ADHD to support broader changes in how we feed, educate, and care for children. A more supportive, less toxic environment benefits everyone—including those with ADHD.
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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