Classification and adaptive behavior prediction of children with autism spectrum disorder based upon multivariate data analysis of markers of oxidative stress and DNA methylation

Abstract

The number of diagnosed cases of Autism Spectrum Disorders (ASD) has increased dramatically over the last four decades; however, there is still considerable debate regarding the underlying pathophysiology of ASD. This lack of biological knowledge restricts diagnoses to be made based on behavioral observations and psychometric tools. However, physiological measurements should support these behavioral diagnoses in the future in order to enable earlier and more accurate diagnoses. Stepping towards this goal of incorporating biochemical data into ASD diagnosis, this paper analyzes measurements of metabolite concentrations of the folate-dependent one-carbon metabolism and transulfuration pathways taken from blood samples of 83 participants with ASD and 76 age-matched neurotypical peers. Fisher Discriminant Analysis enables multivariate classification of the participants as on the spectrum or neurotypical which results in 96.1% of all neurotypical participants being correctly identified as such while still correctly identifying 97.6% of the ASD cohort. Furthermore, kernel partial least squares is used to predict adaptive behavior, as measured by the Vineland Adaptive Behavior Composite score, where measurement of five metabolites of the pathways was sufficient to predict the Vineland score with an R2 of 0.45 after cross-validation. This level of accuracy for classification as well as severity prediction far exceeds any other approach in this field and is a strong indicator that the metabolites under consideration are strongly correlated with an ASD diagnosis but also that the statistical analysis used here offers tremendous potential for extracting important information from complex biochemical data sets.

Multivariate techniques enable a biochemical classification of children with autism spectrum disorder versus typically‐developing peers: A comparison and validation study

“…The FDA‐sub model had the best validation performance with a false positive rate of 19/154 at an expected false negative rate of 5%. As expected, these misclassification rates are higher than the false negative rate of 3.6% at a false positive rate of 2.6% found previously due in part to the absence of “% DNA methylation” and “8‐OHG” in the validation data, two of the most important variables for separating ASD and TD cohorts . CART and LR of a subset of metabolites produced results that were slightly worse than PCA with false positive rates of 38/154 and 32/154, respectively; both of these results are at an expected false negative rate of 5%.…”

“…The folate‐dependent one‐carbon metabolism (FOCM) and transsulfuration (TS) pathways comprise a promising source for a multivariate biomarker for ASD. These pathways incorporate both genetic and environmental factors linked to ASD liability . The authors have previously developed a multivariate biomarker, based upon Fisher Discriminant Analysis (FDA) for ASD that achieved a positive predictive value and negative predictive value of 97.6 and 96.1%, respectively .…”

“…These pathways incorporate both genetic and environmental factors linked to ASD liability . The authors have previously developed a multivariate biomarker, based upon Fisher Discriminant Analysis (FDA) for ASD that achieved a positive predictive value and negative predictive value of 97.6 and 96.1%, respectively . This investigation will be extended in this article in two different directions: (a) By comparing univariate analysis with four different multivariate methods on FOCM/TS data for ASD biomarker development to ensure that the identified results are not restricted to FDA and (b) to test and validate multivariate FOCM/TS biomarkers on data collected from a new cohort of ASD participants.…”

Folate Metabolism Abnormalities in Autism: Potential Biomarkers

“…The folate pathways are connected to several other important pathways that are abnormal in ASD (Figure 2). Biomarkers in these pathways may also be useful in understanding ASD, detecting ASD early, diagnosing ASD, predicting treatment response and identifying ASD subtypes [62]. However, further research is needed.…”

Cellular stress and apoptosis contribute to the pathogenesis of autism spectrum disorder

“…Indeed, metabolic markers of oxidative stress were previously detected in plasma from autistic subjects(Frye and James, 2014; Howsmon et al , 2017; James et al , 2004; Melnyk et al , 2012; Rose et al , 2012b). It has been demonstrated that micronutrient supplementation targeting redox metabolism exerts beneficial effects on some children with autism, implicating the presence of oxidative stress in autistic brains(Blossom et al , 2012; Frye et al , 2013; Hamlin et al , 2013; Hendren et al , 2016).…”