(190h) Multivariate Data Analysis of Markers of Oxidative Stress and DNA Methylation in Children with Autism Spectrum Disorder

MULTIVARIATE DATA ANALYSIS OF MARKERS OF OXIDATIVE STRESS AND DNA METHYLATION IN CHILDREN WITH AUTISM SPECTRUM DISORDER

Daniel Howsmon1, Uwe Kruger1, Stepan Melnyk2, Jill James2, and Juergen Hahn1 1Rensselaer Polytechnic Institute; 2Arkansas Childrenâ??s Hospital

Autism Spectrum Disorder (ASD) encompasses a large group of early-onset neurological diseases characterized by difficulties with social communication/interaction and expression of restricted repetitive behaviors and interests [1]. In addition to these defining behavioral symptoms, individuals with ASD frequently have one or more co-occurring conditions, including intellectual disability, ADHD, speech and language delays, psychiatric diagnoses, epilepsy, sleep disorders, and gastrointestinal problems [2]â??[5]. ASD affects approximately 2% of the population and affects males disproportionately [6], [7]. It is often associated with an impaired quality of life [8] and the lifetime cost of supporting an individual with ASD amounts to $1.4 â?? 2.4MM, depending on co-existing intellectual disorders [9].

Abnormalities in folate-dependent one-carbon metabolism (FOCM) and transsulfuration (TS) pathways may contribute to ASD liability. Adenylosuccinate lyase and methylenetetrahydrofolate reductase (MTHFR) polymorphisms have been shown to increase ASD liability. Limited evidence for the involvement of reduced folate carrier (RFC1) [10], [11], transcobalamin II (TCII) [10], serine hydroxymethyltransferase I (SHMT1) [12], 5- methyltetrahydrofolate-homocysteine methyltransferase reductase (MTRR) [10], [12], and catechol-O- methyltransferase (COMT) [10], [13] polymorphisms in ASD liability also suggests that the FOCM/TS pathways are involved in ASD etiology. Environmentally-derived evidence associates valproate [14], [15] and traffic-related air pollution [16], [17] exposure with increased ASD risk and prenatal folate supplementation with protective effects against ASD [18], [19].

Multivariable statistical techniques enable researchers to investigate simultaneously occurring changes in metabolites of an entire pathway rather than just single metabolites. Metabolite data from the IMAGE study at Arkansas Childrenâ??s Hospital Research Institute [20] was used to investigate differences in FOCM/TS metabolites between children with ASD and age-matched neurotypical controls (NEU). Dimensionality reduction via Fisher discriminant analysis (FDA) prior to classification allows multivariate relationships to influence the separation of the two groups. Additionally, Vineland scores, evaluating adaptive behavior, were regressed on the metabolite data for the ASD cases via kernel partial least squares (KPLS) to investigate the importance of metabolite combinations on ASD severity.

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