Associative gene networks reveal novel candidates important for ADHD and dyslexia comorbidity.

BACKGROUND: Attention deficit hyperactivity disorder (ADHD) is commonly associated with developmental dyslexia (DD), which are both prevalent and complicated pediatric neurodevelopmental disorders that have a significant influence on children's learning and development. Clinically, the comorbidity incidence of DD and ADHD is between 25 and 48%. Children with DD and ADHD may have more severe cognitive deficiencies, a poorer level of schooling, and a higher risk of social and emotional management disorders. Furthermore, patients with this comorbidity are frequently treated for a single condition in clinical settings, and the therapeutic outcome is poor. The development of effective treatment approaches against these diseases is complicated by their comorbidity features. This is often a major problem in diagnosis and treatment. In this study, we developed bioinformatical methodology for the analysis of the comorbidity of these two diseases. As such, the search for candidate genes related to the comorbid conditions of ADHD and DD can help in elucidating the molecular mechanisms underlying the comorbid condition, and can also be useful for genotyping and identifying new drug targets. RESULTS: Using the ANDSystem tool, the reconstruction and analysis of gene networks associated with ADHD and dyslexia was carried out. The gene network of ADHD included 599 genes/proteins and 148,978 interactions, while that of dyslexia included 167 genes/proteins and 27,083 interactions. When the ANDSystem and GeneCards data were combined, a total of 213 genes/proteins for ADHD and dyslexia were found. An approach for ranking genes implicated in the comorbid condition of the two diseases was proposed. The approach is based on ten criteria for ranking genes by their importance, including relevance scores of association between disease and genes, standard methods of gene prioritization, as well as original criteria that take into account the characteristics of an associative gene network and the presence of known polymorphisms in the analyzed genes. Among the top 20 genes with the highest priority DRD2, DRD4, CNTNAP2 and GRIN2B are mentioned in the literature as directly linked with the comorbidity of ADHD and dyslexia. According to the proposed approach, the genes OPRM1, CHRNA4 and SNCA had the highest priority in the development of comorbidity of these two diseases. Additionally, it was revealed that the most relevant genes are involved in biological processes related to signal transduction, positive regulation of transcription from RNA polymerase II promoters, chemical synaptic transmission, response to drugs, ion transmembrane transport, nervous system development, cell adhesion, and neuron migration. CONCLUSIONS: The application of methods of reconstruction and analysis of gene networks is a powerful tool for studying the molecular mechanisms of comorbid conditions. The method put forth to rank genes by their importance for the comorbid condition of ADHD and dyslexia was employed to predict genes that play key roles in the development of the comorbid condition. The results can be utilized to plan experiments for the identification of novel candidate genes and search for novel pharmacological targets.

Analysis of influencing factors of attention deficit hyperactivity disorder in children from 7 to 16 years old and the establishment and verification of Nomogram prediction model / 中华实用儿科临床杂志

Objective:To analyze the influencing factors of attention deficit hyperactivity disorder (ADHD) in children and construct a Nomogram prediction model.Methods:A total of 5 409 children aged 7 to 16 from 5 schools in Xinjiang were investigated by using SNAP-Ⅳ assessment scale and influencing factors questionnaire.Least absolute shrinkage and selection operator (LASSO) regression and multivariate Logistic regression were used to analyze and investigate the influencing factors of ADHD in children, and then Nomogram prediction model was established. Results:(1)The detection rate of ADHD was 7.3%.(2) The LASSO- Logistic regression model showed that the history of febrile convulsions ( OR=5.97, 95% CI: 3.52-9.86), the history of epilepsy disease ( OR=11.86, 95% CI: 7.83-17.89), the history of head trauma disease ( OR=10.0, 95% CI: 7.27-13.71), mother′s delivery method ( OR=2.53, 95% CI: 1.99-3.23), mother′s education level ( OR=2.26, 95% CI: 1.45-3.67), mother′s smoking more than 1 year ( OR=12.65, 95% CI: 8.30-19.34), whether the family environment is quiet ( OR=1.27, 95% CI: 1.00-1.63), and the education method of beating and scolding ( OR=3.05, 95% CI: 2.13-4.31) was an indepen-dent risk factor for children with ADHD; (3)The Nomogram prediction model was built and verified by Bootstrap for 1 000 samples.The C-index was 0.81(95% CI: 0.78-0.83), suggesting that the Nomogram prediction model has good prediction ability, accuracy, and distinction.Decision curve analysis (DCA) of the clinical decision curve suggested that patients with Nomogram model with a predictive probability threshold greater than 0.2 had a higher clinical net benefit. Conclusions:The detection rate of ADHD was 7.3%, which was higher than the national average.The Nomogram prediction model drawn here can provide individualized ADHD risk predictions for children based on the history of hyperthermia, epilepsy, and head trauma, maternal mode of childbirth, maternal education level, maternal education level, maternal smoking for more than 1 year, quiet family environment, and scolding education methods.

Comparative Study of Environmental Risk Factors for Han and Uyghur Children with Dyslexia / 听力学及言语疾病杂志

Objective To investigate differences between Han and Uyghur children in dyslexia prevalence and potential environmental risk factors as well as to provide diagnosis and treatment evidence for dyslexia children . Methods We used cluster sampling to recruit 2 854 students in grades 3~6 from five Uyghur -Chinese bilingual primary schools in Xinjiang province .The children with dyslexia were selected step by step according to the defini‐tion of ICD-10 and DSM -IV .The children with DD and children without DD were selected and compared by 1∶1 of the same class ,ages and genders .Then single factor analysis and logistic regression analysis were used to as‐sess children'environmental risk factors .Results In total ,2 438 effective quostionnaires have been got .The difference between Han (3 .89% ) and Uyghur (7 .05% ) dyslexia prevalence was statistically significant .The factor analysis revealed that educational grades ,family income ,father's and mother's occupations ,and their education levels as well as some home literacy environmental factors were significantly different for the two groups of children with dyslexia (P<0 .05) .Conclusion The prevalence of dyslexia was high in both groups ,and especially for Uyghur children . Some environmental factors may be responsible for the differences noted ,especially for the occupation of mother .