» Articles » PMID: 35028569

Plasma Metabolomics of Autism Spectrum Disorder and Influence of Shared Components in Proband Families

Overview
Journal Exposome
Date 2022 Jan 14
PMID 35028569
Authors
Affiliations
Soon will be listed here.
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 ( = 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.

Citing Articles

Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs).

Chung M, House J, Akhtari F, Makris K, Langston M, Islam K Exposome. 2024; 4(1):osae001.

PMID: 38344436 PMC: 10857773. DOI: 10.1093/exposome/osae001.


Metabolomics: Perspectives on Clinical Employment in Autism Spectrum Disorder.

Siracusano M, Arturi L, Riccioni A, Noto A, Mussap M, Mazzone L Int J Mol Sci. 2023; 24(17).

PMID: 37686207 PMC: 10487559. DOI: 10.3390/ijms241713404.


Proteomic-Based Approach Reveals the Involvement of Apolipoprotein A-I in Related Phenotypes of Autism Spectrum Disorder in the BTBR Mouse Model.

Li Q, Shi Y, Li X, Yang Y, Zhang X, Xu L Int J Mol Sci. 2022; 23(23).

PMID: 36499620 PMC: 9737945. DOI: 10.3390/ijms232315290.

References
1.
Rappaport S, Barupal D, Wishart D, Vineis P, Scalbert A . The blood exposome and its role in discovering causes of disease. Environ Health Perspect. 2014; 122(8):769-74. PMC: 4123034. DOI: 10.1289/ehp.1308015. View

2.
Smith A, Natowicz M, Braas D, Ludwig M, Ney D, Donley E . A Metabolomics Approach to Screening for Autism Risk in the Children's Autism Metabolome Project. Autism Res. 2020; 13(8):1270-1285. PMC: 7496373. DOI: 10.1002/aur.2330. View

3.
Kong S, Shimizu-Motohashi Y, Campbell M, Lee I, Collins C, Brewster S . Peripheral blood gene expression signature differentiates children with autism from unaffected siblings. Neurogenetics. 2013; 14(2):143-52. PMC: 3686296. DOI: 10.1007/s10048-013-0363-z. View

4.
Christensen D, Braun K, Baio J, Bilder D, Charles J, Constantino J . Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years - Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2012. MMWR Surveill Summ. 2018; 65(13):1-23. PMC: 6237390. DOI: 10.15585/mmwr.ss6513a1. View

5.
Stamate D, Kim M, Proitsi P, Westwood S, Baird A, Nevado-Holgado A . A metabolite-based machine learning approach to diagnose Alzheimer-type dementia in blood: Results from the European Medical Information Framework for Alzheimer disease biomarker discovery cohort. Alzheimers Dement (N Y). 2020; 5:933-938. PMC: 6928349. DOI: 10.1016/j.trci.2019.11.001. View