Determination of modified nucleosides in the urine of children with autism spectrum disorder.

Metabolic disorders and nutritional deficiencies in ASD children may be identified by the determination of urinary-modified compounds. In this study, levels of selected seven modified compounds: O-methylguanosine, 7-methylguanosine, 1-methyladenosine, 1-methylguanine, 7-methylguanine, 3-methyladenine, and 8-hydroxy-2`-deoxyguanosine in the group of 143 ASD children and 68 neurotypical controls were analyzed. An ancillary aim was to verify if the reported levels differed depending on the pathogenetic scoring of ASD (mild deficit, moderate deficit, severe deficit). Elevated O-methylguanosine and 7-methylguanosine levels and significantly lower levels of 3-methyladenine, 1-methylguanine, 1-methyladenosine, 7-methylguanine, and 8-hydroxy-'2'-deoxyguanosine were observed in ASD children compared to controls. O-methylguanosine levels were elevated in the mild and moderate groups, while the levels of 1-methylguanine, 1-methyladenosine, 7-methylguanine, and 8-hydroxy-'2'-deoxyguanosine in the same groups were lower than in neurotypical controls. The reported evidence shows that modified nucleosides/bases can play a potential role in the pathophysiology of ASD and that each nucleoside/base shows a unique pattern depending on the degree of the deficit.

Impact of attention deficit hyperactivity disorder on metabolic control in adolescents with type1 diabetes.

OBJECTIVE: Diabetes mellitus type 1 (T1D) incidence is increasing in pediatric population. Good metabolic control, measured by glycated hemoglobin (HbA1c), significantly reduces the risk for chronic complications. Comorbid disorders, including attention-deficit hyperactivity disorder (ADHD), may influence glycemic control. To date little is known about the prevalence of ADHD among adolescents with T1D and its influence on diabetes self-management. Therefore, we aimed to identify adolescents with T1D and ADHD and assess the effect of ADHD on metabolic control. METHOD: This cross-sectional case-control study included 101 patients (11-17 years old) with T1D. Development and Well-Being Assessment (DAWBA) questionnaire and subsequent psychiatric clinical examination were used to identify ADHD in a group with T1D. Indicators of metabolic control were collected from available medical documentation for preceding 12 months and compared between the group of patients with T1D and ADHD and the group of T1D patients without ADHD. RESULTS: ADHD was diagnosed in 11.9% adolescents with T1D (12 of 101). We found a statistically significant difference (p = .022) in HbA1c between the two groups - higher in the group with T1D and ADHD (8.4% or 68.3 mmol/mol) than in the group with T1D without ADHD (7.8% or 61.7 mmol/mol). CONCLUSIONS: Almost 12% of adolescents with type 1 diabetes were diagnosed with ADHD and they had poorer glycemic control. Adolescents with T1D and ADHD must be diagnosed early and offered appropriate treatment focused on preventing negative ADHD impact on metabolic control.

Urinary Markers of Oxidative Stress in Children with Autism Spectrum Disorder (ASD).

Background: Autism spectrum disorder (ASD) is a developmental disorder characterized by deficits in social interaction, restricted interest and repetitive behavior. Oxidative stress in response to environmental exposure plays a role in virtually every human disease and represents a significant avenue of research into the etiology of ASD. The aim of this study was to explore the diagnostic utility of four urinary biomarkers of oxidative stress. Methods: One hundred and thirty-nine (139) children and adolescents with ASD (89% male, average age = 10.0 years, age range = 2.1 to 18.1 years) and 47 healthy children and adolescents (49% male, average age 9.2, age range = 2.5 to 20.8 years) were recruited for this study. Their urinary 8-OH-dG, 8-isoprostane, dityrosine and hexanoil-lisine were determined by using the ELISA method. Urinary creatinine was determined with the kinetic Jaffee reaction and was used to normalize all biochemical measurements. Non-parametric tests and support vector machines (SVM) with three different kernel functions (linear, radial, polynomial) were used to explore and optimize the multivariate prediction of an ASD diagnosis based on the collected biochemical measurements. The SVM models were first trained using data from a random subset of children and adolescents from the ASD group (n = 70, 90% male, average age = 9.7 years, age range = 2.1 to 17.8 years) and the control group (n = 24, 45.8% male, average age = 9.4 years, age range = 2.5 to 20.8 years) using bootstrapping, with additional synthetic minority over-sampling (SMOTE), which was utilized because of unbalanced data. The computed SVM models were then validated using the remaining data from children and adolescents from the ASD (n = 69, 88% male, average age = 10.2 years, age range = 4.3 to 18.1 years) and the control group (n = 23, 52.2% male, average age = 8.9 years, age range = 2.6 to 16.7 years). Results: Using a non-parametric test, we found a trend showing that the urinary 8-OH-dG concentration was lower in children with ASD compared to the control group (unadjusted p = 0.085). When all four biochemical measurements were combined using SVMs with a radial kernel function, we could predict an ASD diagnosis with a balanced accuracy of 73.4%, thereby accounting for an estimated 20.8% of variance (p < 0.001). The predictive accuracy expressed as the area under the curve (AUC) was solid (95% CI = 0.691-0.908). Using the validation data, we achieved significantly lower rates of classification accuracy as expressed by the balanced accuracy (60.1%), the AUC (95% CI = 0.502-0.781) and the percentage of explained variance (R2 = 3.8%). Although the radial SVMs showed less predictive power using the validation data, they do, together with ratings of standardized SVM variable importance, provide some indication that urinary levels of 8-OH-dG and 8-isoprostane are predictive of an ASD diagnosis. Conclusions: Our results indicate that the examined urinary biomarkers in combination may differentiate children with ASD from healthy peers to a significant extent. However, the etiological importance of these findings is difficult to assesses, due to the high-dimensional nature of SVMs and a radial kernel function. Nonetheless, our results show that machine learning methods may provide significant insight into ASD and other disorders that could be related to oxidative stress.

Is there a correlation between ADHD symptom expression between parents and children?

BACKGROUND: Attention-deficit hyperactivity disorder (ADHD) is one of the most common mental health disorders in childhood; symptoms persist into adulthood in a majority of patients. It is among the most heritable of psychiatric disorders with a high risk for familial aggregation and has been linked in adulthood with impairment across a variety of domains, including parenting. Parental gender, ADHD status and symptom expression could be related to the severity of ADHD symptoms in the child. METHODS: We used prospective, observational study of clinical group of 30 children with diagnosed ADHD and control group of 37 healthy subjects. Only children with both biological parents available were included. Data on ADHD symptomatology for all subjects was gathered by a set of clinical tools (CBCL1991, TRF1991, WURS, self-report scale modified from DSM IV). Under the assumption that ADHD is a dimensional disorder, raw scores from questionnaires were used as they display the complete range of values. RESULTS: Clinical group showed higher values in all areas of children symptomatology, the same was observed for parental ADHD symptomatology. Significant correlation was found between children and paternal current ADHD symptomatology in the clinical group. This was not confirmed for mothers. CONCLUSION: Our study stresses an importance of screening for ADHD symptoms in parents of clinically referred children with ADHD as the correlation between severity of paternal and child's ADHD symptoms was confirmed. Our results stress the importance of including the father into the clinical assessment.