Chao Li

Background: Attention Deficit Hyperactivity Disorder (ADHD) is the most prevalent neurodevelopmental disorder worldwide. The diamine oxidase enzyme (DAO) is responsible for the histamine gastrointestinal degradation. Its deficient functioning may implicate an excess of histamine in the body. The excess of histamine (histamine intolerance, HIT) has been related with a growing number of diseases and pseudo-allergic symptomatology. However, data on the relationship between the DAO enzyme, HIT, and ADHD are lacking. The main objective of this pilot is to study the prevalence of the four most relevant SNP variants of the AOC1 gene affecting DAO enzyme functionality in a sample of patients diagnosed with ADHD attending child and adolescent mental health services. Methods: In a cohort of 303 participants, we measured the SNP variants of the AOC1 gene. Results: The prevalence of having at least one minor dysfunctional allele was 78.8%. No relationship between ADHD severity and DAO deficiency was found. However, some AOC1 gene variants associated with DAO deficiency were related to several meaningful medical comorbidities. Furthermore, we found a strong association between DAO activity and the intelligence quotient, particularly in working memory. Conclusions: Some SNP variants of the AOC1 gene associated with DAO deficiency are related to some medical comorbidities and cognitive dysfunction in ADHD children and adolescents. Studies including patients with other diagnoses and healthy controls and bigger samples are warranted to confirm our preliminary results.

Objectives: To enhance the early detection of Attention Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) by leveraging clinical variables collected at child and adolescent mental health services (CAMHS). Methods: This study included children diagnosed with ADHD and/or ASD (n = 857). Three logistic regression models were developed to predict the presence of ADHD, its subtypes, and ASD. The analysis began with univariate logistic regression, followed by a multicollinearity diagnostic. A backward logistic regression selection strategy was then employed to retain variables with p < 0.05. Ethical approval was obtained from the local ethics committee. The models’ internal validity was evaluated based on their calibration and discriminative abilities. Results: The study produced models that are well-calibrated and validated for predicting ADHD (incorporating variables such as physical activity, history of bone fractures, and admissions to pediatric/psychiatric services) and ASD (including disability, gender, special education needs, and Axis V diagnoses, among others). Conclusions: Clinical variables can play a significant role in enhancing the early identification of ADHD and ASD.