Abstract
Autism spectrum disorders (ASD) affect 1% of children. Although there is no cure, early diagnosis and behavioral intervention can relieve the symptoms. The clinical heterogeneity of ASD has created a need for improved sensitive and specific laboratory diagnostic methods. Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based analysis of the metabolome has shown great potential to uncover biomarkers for complex diseases such as ASD. Here, we used a two-step discovery–validation approach to identify potential novel metabolic biomarkers for ASD. Urine samples from 57 children with ASD and 81 matched children with typical development (TD) were analyzed by LS-MS/MS to assess differences in urinary amino acids and their metabolites (referred to as UAA indicators). A total of 63 UAA indicators were identified, of which 21 were present at significantly different levels in the urine of ASD children compared with TD children. Of these 21, the concentrations of 19 and 10 were higher and lower, respectively, in the urine of ASD children compared with TD children. Using support vector machine modeling and receiver operating characteristic curve analysis, we identified a panel of 7 UAA indicators that discriminated between the samples from ASD and TD children (lysine, 2-aminoisobutyric acid, 5-hydroxytryptamine, proline, aspartate, arginine/ornithine, and 4-hydroxyproline). Among the significantly changed pathways in ASD children were the ornithine/urea cycle (decreased levels of the excitatory amino acid aspartate [p = 2.15 × 10-10] and increased arginine/ornithine [p = 5.21 × 10-9]), tryptophan metabolism (increased levels of inhibitory 5-hydroxytryptamine p = 3.62 × 10-9), the methionine cycle (increased methionine sulfoxide [p = 1.46 × 10-10] and decreased homocysteine [p = 2.73 × 10-7]), and lysine metabolism (reduced lysine [p = 7.8 × 10-9], α-aminoadipic acid [p = 1.16 × 10-9], and 5-aminovaleric acid [p = 1.05 × 10-5]). Collectively, the data presented here identify a possible imbalance between excitatory and inhibitory amino acid metabolism in ASD children. The significantly altered UAA indicators could therefore be potential diagnostic biomarkers for ASD.
Highlights
Autism spectrum disorders affect more than 1% of children worldwide (Developmental Disabilities Monitoring Network Surveillance Year 2010 Principal Investegators and Centers for Disease Control Prevention, 2014)
This dataset was analyzed by partial least squares discriminant analysis (PLS-DA) and we found that the autism spectrum disorder (ASD) and typically developing (TD) children could be clearly distinguished (p < 0.0014; Figure 2A)
Among the 63 urinary amino acid (UAA) indicators identified, 27 were present at significantly different concentrations in the urine samples from ASD children compared with TD children; of these 27, 15 were decreased and 12 were increased in the ASD group compared with the TD group, it is worth noting that most (24 of 27) of the differences in UAA indicators were significantly after adjusting for gender (Supplementary Table S4)
Summary
Autism spectrum disorders affect more than 1% (from 1 in 88 to 1 in 68) of children worldwide (Developmental Disabilities Monitoring Network Surveillance Year 2010 Principal Investegators and Centers for Disease Control Prevention, 2014). There is currently no cure, early diagnosis and behavioral intervention can effectively alleviate the symptoms (Dawson et al, 2010). There is an urgent need for the development of laboratory tests for early diagnosis of ASD. Laboratory diagnosis mainly relies on chromosome microarray analysis (Miller et al, 2010), which has a detection rate of about 10–20% (Carter and Scherer, 2013). Developed diagnostic methods based on whole exome/genome sequencing have increased the detection rate to 20–40% (Jiang et al, 2013; Tammimies et al, 2015), but the cost is high. ASD displays strong clinical heterogeneity (Betancur, 2011), and a large number of cases may not be diagnosed by genetic methods
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