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

<div><p>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 <i>R</i>² 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.</p></div

Classification into ASD and NEU cohorts using FDA on all FOCM/TS metabolites.

<p>The plotted scores were obtained via cross-validation and the probability distribution functions were obtained from fitting.</p

Illustration of folate-dependent one-carbon metabolism and transsulfuration pathways.

<p>Genetic and environmental effects that increase ASD predisposition are shown in red whereas those that decrease ASD liability are shown in blue.</p

FOCM/TS metabolites considered for analysis.

<p>FOCM/TS metabolites considered for analysis.</p

Selecting the Number of Variables for FDA based on C-statistic.

<p>Five variables were found to be sufficient for separating the ASD and NEU groups while an additional two variables (totaling seven variables) were incorporated to retain separation between ASD and SIB cohorts.</p

KPLS regression results.

<p>(a) maximum cross-validated <i>R</i>² for a given number of variables and (b) cross-validated model predictions versus actual data points for the best combination of five variables (GSSG, tGSH/GSSG, Nitrotyrosine, Tyrosine, and fCysteine).</p

FDA analysis and binary classification using the variables DNA methylation, 8-OHG, Glu.-Cys., fCystine/fCysteine, % oxidized glutathione, Chlorotyrosine, and tGSH/GSSG.

<p>(a) individual cross-validated FDA scores and fitted probability distribution functions and (b) the cross-validated confusion matrix for separation of ASD and neurotypical (NEU) groups. TPR = TP/(TP + FN) is the True Positive Rate, FPR = FP/(FP + TN) is the False Positive Rate, PPV = TP/(TP + FP) is the Positive Predictive Value, and NPV = TN/(TN + FN) is the Negative Predictive Value.</p

Classification performance on the SIB cohort (yellow).

<p>There is significantly more overlap of the SIB cohort with the NEU cohort (red) than with the ASD cohort (blue).</p

The Seahorse assay.

<p>Oxygen consumption rate (OCR) is measured before and after the addition of inhibitors to derive several parameters of mitochondrial respiration. Initially, baseline cellular OCR is measured, from which basal respiration can be derived by subtracting non-mitochondrial respiration. Next oligomycin, a complex V inhibitor, is added and the resulting OCR is used to derive ATP-linked respiration (by subtracting the oligomycin rate from baseline cellular OCR) and proton leak respiration (by subtracting non-mitochondrial respiration from the oligomycin rate). Next carbonyl cyanide-p-trifluoromethoxyphenyl-hydrazon (FCCP), a protonophore, is added to collapse the inner membrane gradient, allowing the ETC to function at its maximal rate, and maximal respiratory capacity is derived by subtracting non-mitochondrial respiration from the FCCP rate. Lastly, antimycin A and rotenone, inhibitors of complex III and I, are added to shut down ETC function, revealing the non-mitochondrial respiration. Mitochondrial reserve capacity is calculated by subtracting basal respiration from maximal respiratory capacity.</p

AD LCLs demonstrate differences in mitochondrial function as compared to control LCLs at baseline and after exposure to DMNQ.

<p>(<b>A</b>) ATP-linked respiration and (<b>B</b>) proton leak respiration were overall significantly higher in the AD LCLs, and there was a greater increase in proton leak respiration with DMNQ as compared to control LCLs. (<b>C</b>) Maximal respiratory capacity was significantly elevated in the AD LCLs at 0 µM and 5 µM DMNQ compared to control LCLs, and the AD LCLs exhibited a greater decrease in maximal capacity as DMNQ increased as compared to control LCLs. (<b>D</b>) Reserve capacity was significantly elevated in the AD LCLs at baseline, and it decreased with DMNQ so that it was significantly lower than control LCLs at 10–15 µM DMNQ. *p<0.001; **p<0.0001; ↕ indicates an overall statistical difference between LCL groups.</p