There have been promising results regarding the capability of statistical and machine learning techniques to offer insight into unique metabolomic patterns observed in ASD. This work re-examines a comparative study contrasting metabolomic and nutrient measurements of children with ASD (n=55) against their typically developing (TD) peers (n=44) through a multivariate statistical lens. Hypothesis testing, receiver characteristic curve assessment and correlation analysis were consistent with prior work, and served to underscore prominent areas where metabolomic and nutritional profiles between the groups diverged. Improved univariate analysis revealed 46 nutritional/metabolic differences being significantly different between ASD and TD groups, with individual area under the receiver operator curve (AUROC) scores of 0.6-0.9. Many of the significant measurements had correlations with many others, forming two integrated networks of inter-related metabolic differences in ASD. The TD group had 189 significant correlation pairs between metabolites, vs only 106 for the ASD group, calling attention to underlying differences in metabolic processes. Furthermore, multivariate techniques identified potential biomarker panels with up to six metabolites that were able to attain a predictive accuracy of up to 98% for discriminating between ASD and TD, following cross-validation. Assessing all optimized multivariate models demonstrated concordance with prior physiological pathways identified in the literature, with some of the most important metabolites for discriminating ASD and TD being sulfate, transulfuration pathway, uridine (methylation biomarker) and beta-amino isobutyrate (regulator of carbohydrate and lipid metabolism).
Journal of Personalized Medicine 12, No. 6, 923 (2022)