Plasma metabolomics of autism spectrum disorder and influence of shared components in proband families. Issue 1 (7th October 2021)

• Record Type: Journal Article
• Authors: Chung, Ming Kei; Smith, Matthew Ryan; Lin, Yufei; Walker, Douglas I; Jones, Dean; Patel, Chirag J; Kong, Sek Won
• Is Part Of: Exposome. Volume 1:Issue 1(2021)
• Publication Date: 2021-10-07
• Subjects: ASD -- untargeted metabolomics -- correlation globes -- biomarker -- shared environment -- ASD diagnosis; Genotype-environment interaction -- Periodicals; Environmental health -- Periodicals; Environmentally induced diseases -- Periodicals; 576.8
• DOI: 10.1093/exposome/osab004
• Languages: English
• ISSNs: 2635-2265

Abstract

Abstract: Prevalence of autism spectrum disorder (ASD) has been increasing in the United States in the past decades. The exact mechanisms remain enigmatic, and diagnosis of the disease still relies primarily on assessment of behavior. We first used a case–control design (75 idiopathic cases and 29 controls, enrolled at Boston Children's Hospital from 2007-2012) to identify plasma biomarkers of ASD through a metabolome-wide association study approach. Then we leveraged a family-based design (31 families) to investigate the influence of shared genetic and environmental components on the autism-associated features. Using untargeted high-resolution mass spectrometry metabolomics platforms, we detected 19 184 features. Of these, 191 were associated with ASD (false discovery rate < 0.05). We putatively annotated 30 features that had an odds ratio (OR) between <0.01 and 5.84. An identified endogenous metabolite, O-phosphotyrosine, was associated with an extremely low autism odds (OR 0.17; 95% confidence interval 0.06-0.39). We also found that glutathione metabolism was associated with ASD ( P = 0.048). Correlations of the significant features between proband and parents were low (median = 0.09). Of the 30 annotated features, the median correlations within families (proband–parents) were −0.15 and 0.24 for the endogenous and exogenous metabolites, respectively. We hypothesize that, without feature identification, family-based correlation analysis of autism-associated features can be an alternative way to assist the prioritization of potentially diagnostic features. A panel of ASD diagnostic metabolic markers with high specificity could be derived upon further studies.