Attention-Deficit/Hyperactivity Disorder and Comorbid Asthma

Inferring skin-brain-skin connections from infodemiology data using dynamic Bayesian networks

The relationship between skin diseases and mental illnesses has been extensively studied using cross-sectional epidemiological data. Typically, such data can only measure association (rather than causation) and include only a subset of the diseases we may be interested in. In this paper, we complement the evidence from such analyses by learning an overarching causal network model over twelve health conditions from the Google Search Trends Symptoms public data set. We learned the causal network model using a dynamic Bayesian network, which can represent both cyclic and acyclic causal relationships, is easy to interpret and accounts for the spatio-temporal trends in the data in a probabilistically rigorous way. The causal network confirms a large number of cyclic relationships between the selected health conditions and the interplay between skin and mental diseases. For acne, we observe a cyclic relationship with anxiety and attention deficit hyperactivity disorder (ADHD) and an indirect relationship with depression through sleep disorders. For dermatitis, we observe directed links to anxiety, depression and sleep disorders and a cyclic relationship with ADHD. We also observe a link between dermatitis and ADHD and a cyclic relationship between acne and ADHD. Furthermore, the network includes several direct connections between sleep disorders and other health conditions, highlighting the impact of the former on the overall health and well-being of the patient. The average R 2 for a condition given the values of all conditions in the previous week is 0.67: in particular, 0.42 for acne, 0.85 for asthma, 0.58 for ADHD, 0.87 for burn, 0.76 for erectile dysfunction, 0.88 for scars, 0.57 for alcohol disorders, 0.57 for anxiety, 0.53 for depression, 0.74 for dermatitis, 0.60 for sleep disorders and 0.66 for obesity. Mapping disease interplay, indirect relationships, and the key role of mediators, such as sleep disorders, will allow healthcare professionals to address disease management holistically and more effectively. Even if we consider all skin and mental diseases jointly, each disease subnetwork is unique, allowing for more targeted interventions.

Association between type 2 inflammatory diseases and neurodevelopmental disorders in low-birth-weight children and adolescents

Background

Evidence of the association of certain neurodevelopmental disorder with specific type 2 inflammatory (T2) disease has been found. However, the association of various neurodevelopmental disorders with T2 diseases as a whole remains unclear in low-birth-weight (LBW) infants.

Objective

To evaluate the association of type 2 inflammatory (T2) diseases with intellectual disability (ID), autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), and learning disability (LD) in LBW children and adolescents.

Methods

The study sample was derived from 2005 to 2018 National Health Interview Survey sample child files. LBW children and adolescents aged 3-17 were included. History of T2 diseases (including asthma and atopic dermatitis) and four neurodevelopmental disorders were reported by adults in families. The relationship between T2 diseases and the risk of four neurodevelopmental disorders was investigated through multiple-weighted logistic regression. Age, sex, race/ethnicity, region, highest education in family and ratio of family income to the poverty threshold were adjusted as covariates for model estimation. Subgroup analyses were conducted by age stratification (3-11 and 12-17 years), sex (male and female), and race (white and non-white).

Results

11,260 LBW children aged 3-17 years [mean age (SE), 9.73 (0.05) years] were included, in which 3,191 children had T2 diseases. History of T2 diseases was associated with an increased risk of neurodevelopmental disorders, with an OR of 1.35 (95% CI, 0.99-1.84) for ID, 1.47 (95% CI, 1.05-2.05) for ASD, 1.81 (95% CI, 1.51-2.16) for ADHD, and 1.74 (95% CI, 1.49-2.04) for LD following the adjustment of all the covariates. The correlations between T2 disorders and each of the four neurodevelopmental disorders were significantly different by sex and race (all P for interaction < 0.001), and no differences were found in age stratification (all P for interaction > 0.05).

Conclusion

In a nationally representative sample of children, we found a significant association of T2 diseases with ASD, ADHD, and LD, even after adjusting for demographic baseline. We also found that the association of T2 disease with neurodevelopmental disorders differed between sex and race. Further investigation is needed to evaluate causal relationships and elucidate their potential mechanisms.