December 31, 2021
Certain air pollutants can produce free radicals and inflammatory cytokines that can penetrate the central nervous system and affect behavior. Long-term exposure to air pollution has been associated with a higher risk of developing ADHD.
There has, however, been little focus on the short-term effects of exposure. Might there be any correlation between levels of air contaminants and subsequent healthcare visits of adolescents for severe spikes in ADHD symptoms (frequently but not always associated with comorbid conduct disorder, oppositional defiance disorder, or mood disorder), such as extreme hyperactivity, serious rule violations, theft, or aggression to people or animals?
A South Korean (Republic of Korea) research team explored this question through a nationwide cohort study using the database of the National Health Insurance Service, a single-payer system, that covers the entire population.
Using a time-series approach, they compared measured levels of three airborne pollutants - particulate matter with a diameter ≤ 10 μm (PM10), nitrogen oxide (NO2), produced by vehicular traffic, and sulfur dioxide (SO2), produced by manufacturing industries- with healthcare visits with a principal diagnosis of ADHD. They chose these three contaminants because they have been associated with ADHD in long-term studies. What made this approach feasible is that healthcare visits are typically unscheduled in Korea, making it possible to get quick medical attention.
The team divided the country into sixteen regions, looked at boys and girls separately, and also split adolescents into two age groups (10 to 14 years and 15 to 19 years). They estimated region-specific daily concentrations of the three pollutants from 318 government-run monitoring sites, located according to population density and distribution.
The researchers next calculated zero(same day) to five-day lag figures for ADHD-related healthcare visits in each region and ran meta-analyses on the time-series data.
There were 7,200 ADHD-related healthcare visits in the 2013-2015 study period. Major increases in PM10 levels were associated with increased ADHD-related healthcare visits from the day of the spike to three days later, peaking the day after the upturn. Major increases in SO2 levels were associated with increased ADHD-related healthcare visits from one to four days later, peaking the day following the upturn. Major increases in NO2 levels were associated with increased ADHD-related healthcare visits from one to four days later, peaking three days after the spike.
There were no significant differences between male and female adolescents, and between younger and older adolescents.
The strongest increased risk for ADHD-related healthcare visits was for NO2 spikes (up 47 percent), followed by SO2 spikes (up 27 percent), with PM10 spikes coming in last (up 12 percent).
Among the limitations, the authors were unable to evaluate the most hazardous types of particulate emissions, because the smaller-diameter PM2.5 particles (≤2.5 μm) have only been measured partially in South Korea since 2015. On the other hand, they pointed out that this was the first study to investigate associations between short-term air pollution exposure and ADHD-related healthcare visits, and that it included all ADHD-related healthcare visits in South Korea, making the possibility of selection bias negligible. They recommended conducting similar studies on other national populations.
Jiyoon Park, JiHoon Sohn, Sung Joon Cho, Hwa Yeon Seo, Il-Ung Hwang, Yun-Chul Hong, Kyoung-Nam Kim, "Association between short-term air pollution exposure and attention-deficit/hyperactivity disorder-related healthcare visits among adolescents: A nationwide time-series study," Environmental Pollution (2020) 226, https://doi.org/10.1016/j.envpol.2020.115369.
This study investigates how certain genetic disorders, called RASopathies, affect the structure of the brain in children. RASopathies are conditions caused by mutations in a specific signaling pathway in the body. Two common RASopathies are Noonan syndrome (NS) and neurofibromatosis type 1 (NF1), both of which are linked to a higher risk of autism spectrum disorder (ASD) and attention deficit and hyperactivity disorder (ADHD).
The researchers analyzed brain scans of children with RASopathies (91 participants) and compared them to typically developing children (74 participants). They focused on three aspects of brain structure: surface area (SA), cortical thickness (CT), and subcortical volumes.
The results showed that children with RASopathies had both similarities and differences in their brain structure compared to typically developing children. They had increased SA in certain areas of the brain, like the precentral gyrus, but decreased SA in other regions, such as the occipital regions. Additionally, they had thinner CT in the precentral gyrus. However, the effects on subcortical volumes varied between the two RASopathies: children with NS had decreased volumes in certain structures like the striatum and thalamus, while children with NF1 had increased volumes in areas like the hippocampus, amygdala, and thalamus.
Overall, this study highlights how RASopathies can impact the development of the brain in children. The shared effects on SA and CT suggest a common influence of RASopathies on brain development, which could be important for developing targeted treatments in the future.
In summary, understanding how these genetic disorders affect the brain's structure can help researchers and healthcare professionals develop better treatments for affected children.
In a recent study, researchers delved into the complex interplay of cognitive processes and eye movements in children with dyslexia and Attention-Deficit/Hyperactivity Disorder. Their findings shed light on predictive models for reading outcomes in these children compared to typical readers.
The study involved 59 children: 19 typical readers, 21 with ADHD, and 19 with developmental dyslexia (DD), all in the 4th grade and around 9 years old on average. Each group underwent thorough neuropsychological and linguistic assessments to understand their psycholinguistic profiles.
During the study, participants engaged in a silent reading task where the text underwent lexical manipulation. Researchers then analyzed eye movement data alongside cognitive factors like memory, attention, and visual processes.
Using multinomial logistic regression, the researchers evaluated predictive models based on three key measures: a linguistic model focusing on phonological awareness, rapid naming, and reading fluency; a cognitive neuropsychological model incorporating memory, attention, and visual processes; and an additive model combining lexical word properties with eye-tracking data, specifically examining word frequency and length effects.
By integrating eye movement data with cognitive factors, the researchers enhanced their ability to predict the development of dyslexia or ADHD, in comparison to typically developing readers. This approach significantly improved the accuracy of predicting reading outcomes in children with learning disabilities.
These findings have profound implications for understanding and addressing reading challenges in children. By considering both cognitive processes and eye movement patterns, educators and clinicians can develop more effective interventions tailored to the specific needs of children with dyslexia and ADHD.
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:
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.
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