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Phenotypically driven subgroups of ASD display distinct metabolomic profiles

代谢组 代谢组学 表型 生物 鞘脂 自闭症谱系障碍 脂质体 脂类学 生物信息学 自闭症 遗传学 计算生物学 基因 心理学 发展心理学
作者
Nicole Prince,Su H. Chu,Yulu Chen,Kevin Mendez,Ellen Hanson,LeeAnne Green‐Snyder,Elizabeth Brooks,Susan Korrick,Jessica Lasky‐Su,Rachel S. Kelly
出处
期刊:Brain Behavior and Immunity [Elsevier BV]
卷期号:111: 21-29 被引量:2
标识
DOI:10.1016/j.bbi.2023.03.026
摘要

Autism Spectrum Disorder (ASD) is a heterogeneous condition that includes a broad range of characteristics and associated comorbidities; however, the biology underlying the variability in phenotypes is not well understood. As ASD impacts approximately 1 in 100 children globally, there is an urgent need to better understand the biological mechanisms that contribute to features of ASD. In this study, we leveraged rich phenotypic and diagnostic information related to ASD in 2001 individuals aged 4 to 17 years from the Simons Simplex Collection to derive phenotypically driven subgroups and investigate their respective metabolomes. We performed hierarchical clustering on 40 phenotypes spanning four ASD clinical domains, resulting in three subgroups with distinct phenotype patterns. Using global plasma metabolomic profiling generated by ultrahigh-performance liquid chromatography mass spectrometry, we characterized the metabolome of individuals in each subgroup to interrogate underlying biology related to the subgroups. Subgroup 1 included children with the least maladaptive behavioral traits (N = 862); global decreases in lipid metabolites and concomitant increases in amino acid and nucleotide pathways were observed for children in this subgroup. Subgroup 2 included children with the highest degree of challenges across all phenotype domains (N = 631), and their metabolome profiles demonstrated aberrant metabolism of membrane lipids and increases in lipid oxidation products. Subgroup 3 included children with maladaptive behaviors and co-occurring conditions that showed the highest IQ scores (N = 508); these individuals had increases in sphingolipid metabolites and fatty acid byproducts. Overall, these findings indicated distinct metabolic patterns within ASD subgroups, which may reflect the biological mechanisms giving rise to specific patterns of ASD characteristics. Our results may have important clinical applications relevant to personalized medicine approaches towards managing ASD symptoms.
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