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November 29, 2021
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.
Lin Li, Mark J. Taylor, Katarina Bälter, Ralf Kuja-Halkola, Qi Chen, Tor-Arne Hegvik, Ashley E. Tate, Zheng Chang, Alejandro Arias-Vásquez, Catharina A. Hartman, Henrik Larsson, "Attentiondeficit/hyperactivity disorder symptoms and dietary habits in adulthood: A large populationbased twin study in Sweden," American Journal of Medical Genetics (2020) Vol.183, Issue 8, 475-485, https://doi.org/10.1002/ajmg.b.32825.
A recent Wall Street Journal article raised alarms by concluding that many children who start medication for ADHD will later end up on several psychiatric drugs. It’s an emotional topic that will make many parents, teachers, and even doctors worry: “Are we putting kids on a conveyor belt of medications?”
The article seeks to shine a light on the use of more than one psychiatric medication for children with ADHD. My biggest worry about the article is that it presents itself as a scientific study because they analyzed a database. It is not a scientific study. It is a journalistic investigation that does not meet the standards of a scientific report..
The WJS brings attention to several issues that parents and prescribers should think about. It documents that some kids with ADHD are on more than one psychiatric medication, and some are receiving drugs like antipsychotics, which have serious side effects. Is that appropriate? Access to good therapy, careful evaluation, and follow-up care can be lacking, especially for low-income families. Can that be improved? On that level, the article is doing something valuable: it’s shining a spotlight on potential problems.
It is, of course, fine for a journalist to raise questions, but it is not OK for them to pretend that they’ve done a scientific investigation that proves anything. Journalism pretending to be science is both bad science and bad journalism.
Journalism vs. Science: Why Peer Review Matters
Journalists can get big datasets, hire data journalists, and present numbers that look scientific. But consider the differences between Journalism and Science. These types of articles are usually checked by editors and fact-checkers. Their main goals are:
Is this fact basically correct?
Are we being fair?
Are we avoiding legal problems?
But editors are not qualified to evaluate scientific data analysis methods. Scientific reports are evaluated by experts who are not part of the project. They ask tough questions like:
Exactly how did you define ADHD?
How did you handle missing data?
Did you address confounding?
Did you confuse correlation with causation?
If the authors of the study cannot address these and other technical issues, the paper is rejected.
The WSJ article has the veneer of science but lacks its methodology.
Correlation vs. Causation: A Classic Trap
The article’s storyline goes something like this: A kid starts ADHD medication. She has additional problems or side effects caused by the ADHD medications. Because of that, the prescriber adds more drugs. That leads to the patient being put on several drugs. Although it is true that some ADHD youth are on multiple drugs, the WSJ is wrong to conclude that the medications for ADHD cause this to occur. That simply confuses correlation with causation, which only the most naïve scientist would do.
In science, this problem is called confounding. It means other factors (like how severe or complex a child’s condition is) explain the results, not just the thing we’re focused on (medication for ADHD).
The WSJ analyzed a database of prescriptions. They did not survey the prescribers who made the prescriptions of the patients who received them. So they cannot conclude that ADHD medication caused the later prescriptions, or that the later medications were unnecessary or inappropriate.
Other explanations are very likely. It has been well documented that youth with ADHD are at high risk for developing other disorders such as anxiety, depression, and substance use. The kids in the WSJ database might have developed these disorders and needed several medications. A peer-reviewed article in a scientific journal would be expected to adjust for other diagnoses. If that is not possible, as it is in the case of the WSJ’s database, a journal would not allow the author to make strong conclusions about cause-and-effect.
Powerful Stories Don’t Always Mean Typical Stories
The article includes emotional accounts of children who seemed harmed by being put on multiple psychiatric drugs. Strong, emotional stories can make rare events feel common. They also frighten parents and patients, which might lead some to decline appropriate care.
These stories matter. They remind us that each data point is a real person. But these stories are the weakest form of data. They can raise important questions and lead scientists to design definitive studies, but we cannot use them to draw conclusions about the experiences of other patients. These stories serve as a warning about the importance of finding a qualified provider, not as against the use of multiple medications. That decision should be made by the parent or adult patient based on an informed discussion with the prescriber.
Many children and adults with ADHD benefit from multiple medications. The WSJ does not tell those stories, which creates an unbalanced and misleading presentation.
Newspapers frequently publish stories that send the message: “Beware! Doctors are practicing medicine in a way that will harm you and your family.” They then use case studies to prove their point. The title of the article is, itself, emotional clickbait designed to get more readers and advertising revenue. Don’t be confused by such journalistic trickery.
What Should We Conclude?
Here’s a balanced way to read the article. It is true that some patients are prescribed more than one medication for mental health problems. But the article does not tell us whether this prescribing practice is or is not warranted for most patients. I agree that the use of antipsychotic medications needs careful justification and close monitoring. I also agree that patients on multiple medications should be monitored closely to see if some of the medications can be eliminated. Many prescribers do exactly that, but the WSJ did not tell their stories.
It is not appropriate to conclude that ADHD medications typically cause combined pharmacotherapy or to suggest that combined pharmacotherapy is usually bad. The data presented by the WSJ does not adequately address these concerns. It does not prove that medications for ADHD cause dangerous medication cascades.
We have to remember that even when a journalist analyzes data, that is not the same as a peer-reviewed scientific study. Journalism pretending to be science is both bad science and bad journalism.
Oppositional Defiant Disorder (ODD)—a pattern of chronic irritability, anger, arguing, or defiance—is one of the most challenging behavioral conditions families and clinicians face.
A new study involving 2,400 children ages 3–17 offers one of the clearest pictures yet. Using parent-reported data from the Pediatric Behavior Scale, researchers compared how often ODD appears in Autism spectrum disorder (ASD), ADHD-Combined presentation (ADHD-C), ADHD-Inattentive presentation (ADHD-I), and those with both ASD and ADHD.
Results:
Children with ADHD-Combined presentation show both hyperactivity/impulsivity and inattention. They had the highest ODD rates of any single diagnosis: 53% of kids with ADHD-Combined met criteria for ODD.
But when autism was added to ADHD-Combined, the prevalence jumped to 62%. This group also had the highest overall ODD scores, suggesting more severe or more impairing symptoms.
This synergy matters: while autism alone increases ODD risk, the presence of ADHD-Combined is what pushes prevalence into the majority range. Other groups showed lower, but still significant, rates of ODD:
These findings echo what clinicians often see: children with inattentive ADHD, while struggling significantly with attention and learning, tend to show fewer behavioral conflict patterns than those with hyperactive/impulsive symptoms.
It is important to note that ODD is considered to have two main components. Across all diagnostic groups, ODD consistently broke down into these two components: either Irritable/Angry (emotion-based) or Oppositional/Defiant (behavior-based). But the balance between these components differed depending on diagnosis. Notably, Autism + ADHD-Combined showed higher levels of the irritable/angry component than ADHD-Combined alone. The oppositional/defiant component did not differ much between groups. This suggests that autism elevates the emotional side of ODD more than the behavioral side, which is important for clinicians to note before tailoring interventions.
The study notes that autism, ADHD, and ODD often cluster together, with 55–90% comorbidity in some combinations.
As the authors explain, “The high co-occurrence of ADHD-Combined in autism (80% in our study) largely explains the high prevalence of ODD in autism.”
Clinical Implications: Why This Study Matters
The researchers point to a straightforward recommendation: clinicians shouldn’t evaluate these conditions in isolation. A child referred for autism concerns might also be struggling with ADHD. A child referred for ADHD might have undiagnosed ODD. And ignoring one disorder can undermine treatment for the others.
Evidence-based interventions (behavioral therapy, parent training, school supports, and/or medication) can reduce symptoms across all three diagnoses while improving long-term outcomes, including overall quality of life.
Background:
Sleep is more than simple rest. When discussing sleep, we tend to focus on the quantity rather than the quality, how many hours of sleep we get versus the quality or depth of sleep. Duration is an important part of the picture, but understanding the stages of sleep and how certain mental health disorders affect those stages is a crucial part of the discussion.
Sleep is an active mental process where the brain goes through distinct phases of complex electrical rhythms. These phases can be broken down into non-rapid eye movement (NREM) and rapid eye movement (REM). The non-rapid eye movement phase consists of three stages of the four stages of sleep, referred to as N1, N2(light sleep), and N3(deep sleep). N4 is the REM phase, during which time vivid dreaming typically occurs.
Two of the most important measurable brain rhythms occur during non-rapid eye movement (NREM) sleep. These electrical rhythms are referred to as slow waves and sleep spindles. Slow waves reflect deep, restorative sleep, while spindles are brief bursts of brain activity that support memory and learning.
The Study:
A new research review has compiled data on how these sleep oscillations differ across psychiatric conditions. The findings suggest that subtle changes in nightly brain rhythms may hold important clues about a range of disorders, from ADHD to schizophrenia.
The Results:
People with ADHD showed increased slow-spindle activity, meaning those brief bursts of NREM activity were more frequent or stronger than in people without ADHD. Why this happens isn’t fully understood, but it may reflect differences in how the ADHD brain organizes information during sleep. Evidence for slow-wave abnormalities was mixed, suggesting that deep sleep disruption is not a consistent hallmark of ADHD.
Among individuals with autism spectrum disorder (ASD), results were less consistent. However, some studies pointed to lower “spindle chirp” (the subtle shift in spindle frequency over time) and reduced slow-wave amplitude. Lower amplitude suggests that the brain’s deep-sleep signals may be weaker or less synchronized. Researchers are still working to understand how these patterns relate to sensory processing, learning differences, or daytime behavior.
People with depression tended to show reduced slow-wave activity and fewer or weaker sleep spindles, but this pattern appeared most strongly in patients taking antidepressant medications. Since antidepressants can influence sleep architecture, researchers are careful not to overinterpret the changes. Nevertheless, these changes raise interesting questions about how both depression and its treatments shape the sleeping brain.
In post-traumatic stress disorder (PTSD), the trend moved in the opposite direction. Patients showed higher spindle frequency and activity, and these changes were linked to symptom severity which suggests that the brain may be “overactive” during sleep in ways that relate to hyperarousal or intrusive memories. This strengthens the idea that sleep physiology plays a role in how traumatic memories are processed.
The clearest and most reliable findings emerged in psychotic disorders, including schizophrenia. Across multiple studies, individuals showed: Lower spindle density (fewer spindles overall), reduced spindle amplitude and duration, correlations with symptom severity, and cognitive deficits.
Lower slow-wave activity also appeared, especially in the early phases of illness. These results echo earlier research suggesting that sleep spindles, which are generated by thalamocortical circuits, might offer a window into the neural disruptions that underlie psychosis.
The Take-Away:
The review concludes with a key message: While sleep disturbances are clearly present across psychiatric conditions, the field needs larger, better-standardized, and more longitudinal studies. With more consistent methods and longer follow-ups, researchers may be able to determine whether these oscillations can serve as reliable biomarkers or future treatment targets.
For now, the take-home message is that the effects of these mental health disorders on sleep are real and measurable.
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