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A Novel UHPLC-MS Method Targeting Urinary Metabolomic Markers for Autism Spectrum Disorder.

ABSTRACT: Autism spectrum disorder is a heterogeneous neurodevelopmental disease. Currently, no biomarker of this disease is known. Diagnosis is performed through observation, standardized behavioral scales, and interviews with parents. In practice, diagnosis is often delayed to the average age of four years or even more which adversely affects a child's perspective. A laboratory method allowing to detect the disorder at earlier stages is of a great need, as this could help the patients to start with treatment at a younger age, even prior to the clinical diagnosis. Recent evidence indicates that metabolomic markers should be considered as diagnostic markers, also serving for further differentiation and characterization of different subgroups of the autism spectrum. In this study, we developed an ultra-high performance liquid chromatography-tandem triple quadrupole mass spectrometry method for the simultaneous determination of six metabolites in human urine. These metabolites, namely methylguanidine, N-acetyl arginine, inosine, indole-3-acetic acid, indoxyl sulfate and xanthurenic acid were selected as potential biomarkers according to prior metabolomic studies. The analysis was carried out by means of reversed-phase liquid chromatography with gradient elution. Separation of the metabolites was performed on a Phenomenex Luna® Omega Polar C18 (100 × 1.0 mm, 1.6 µm) column at a flow rate of 0.15 mL/min with acetonitrile/water 0.1% formic acid aqueous as the mobile phase. The analysis was performed on a group of children with autism spectrum disorder and age-matched controls. In school children, we have detected disturbances in the levels of oxidative stress markers connected to arginine and purine metabolism, namely methylguanidine and N-acetylargine. Also, products of gut bacteria metabolism, namely indoxyl sulfate and indole-3-acetic acid, were found to be elevated in the patients' group. We can conclude that this newly developed method is fast, sensitive, reliable, and well suited for the quantification of proposed markers.

SUBMITTER: Olesova D

PROVIDER: S-EPMC7693535 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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A Novel UHPLC-MS Method Targeting Urinary Metabolomic Markers for Autism Spectrum Disorder.

Olesova Dominika D Galba Jaroslav J Piestansky Juraj J Celusakova Hana H Repiska Gabriela G Babinska Katarina K Ostatnikova Daniela D Katina Stanislav S Kovac Andrej A

Metabolites 20201102 11

Autism spectrum disorder is a heterogeneous neurodevelopmental disease. Currently, no biomarker of this disease is known. Diagnosis is performed through observation, standardized behavioral scales, and interviews with parents. In practice, diagnosis is often delayed to the average age of four years or even more which adversely affects a child's perspective. A laboratory method allowing to detect the disorder at earlier stages is of a great need, as this could help the patients to start with trea ...[more]

PMID: 33147863

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Project description: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.

Project description:BACKGROUND:Although temperament has been recognized as an important contributor to childhood psychopathology, its role in emergent autism spectrum phenotypes is not well understood. This study examined whether toddlers with autism spectrum disorder (ASD) display temperamental vulnerabilities compared to toddlers with other developmental challenges, whether these characteristics are distinct from core autism symptoms, if they are stable over time, and if they contribute to social outcomes in preschool. METHODS:Parents of 165 toddlers with ASD, 58 nonverbal ability- and chronological age- (CA) matched developmentally delayed (DD) toddlers, and 92 CA-matched typically developing (TD) toddlers completed the Toddler Behavior Assessment Questionnaire-Supplemental (TBAQ-S) at 26 months (SD = 6; Time 1). TBAQ-S data were also available for a subset of toddlers with ASD (n = 126) at 43 months (SD = 9; Time 2). RESULTS:Compared to the DD and TD groups, toddlers with ASD exhibited vulnerabilities within the Effortful Control domain as well as the Surgency domain. They also displayed greater Negative Emotionality compared to TD peers. In the ASD group, temperamental characteristics were not concurrently related to autism severity or developmental level and individual differences were highly stable over time. Changes in Perceptual Sensitivity, Inhibitory Control, and Low-Intensity Pleasure from age 2 to 3.5 uniquely predicted autism symptom severity and adaptive social skill level at Time 2. CONCLUSIONS:Temperamental vulnerabilities in toddlers with ASD are stable over time and involve attentional and behavioral control as well as affective reactivity. They contribute uniquely to social outcomes in preschool and are likely to signal risk for developing later maladaptive attentional, affective, and behavioral symptoms. Considering biologically based dimensions may shed light on noncore facets of the early ASD phenotype that are potentially relevant to the emergence of comorbid conditions later in childhood.

Dataset Information

A Novel UHPLC-MS Method Targeting Urinary Metabolomic Markers for Autism Spectrum Disorder.

Publications

A Novel UHPLC-MS Method Targeting Urinary Metabolomic Markers for Autism Spectrum Disorder.

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