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. 2019 Oct 3;9(1):243.
doi: 10.1038/s41398-019-0578-3.

Metabolomics analysis of children with autism, idiopathic-developmental delays, and Down syndrome

Affiliations

Metabolomics analysis of children with autism, idiopathic-developmental delays, and Down syndrome

Jennie Sotelo Orozco et al. Transl Psychiatry. .

Abstract

Although developmental delays affect learning, language, and behavior, some evidence suggests the presence of disturbances in metabolism are associated with psychiatric disorders. Here, the plasma metabolic phenotype of children with autism spectrum disorder (ASD, n = 167), idiopathic-developmental delay (i-DD, n = 51), and Down syndrome (DS, n = 31), as compared to typically developed (TD, n = 193) controls was investigated in a subset of children from the case-control Childhood Autism Risk from Genetics and the Environment (CHARGE) Study. Metabolome profiles were obtained using nuclear magnetic resonance spectroscopy and analyzed in an untargeted manner. Forty-nine metabolites were identified and quantified in each sample that included amino acids, organic acids, sugars, and other compounds. Multiple linear regression analysis revealed significant associations between 11 plasma metabolites and neurodevelopmental outcome. Despite the varied origins of these developmental disabilities, we observed similar perturbation in one-carbon metabolism pathways among DS and ASD cases. Similarities were also observed in the DS and i-DD cases in the energy-related tricarboxylic acid cycle. Other metabolites and pathways were uniquely associated with DS or ASD. By comparing metabolic signatures between these conditions, the current study expands on extant literature demonstrating metabolic alterations associated with developmental disabilities and provides a better understanding of overlapping vs specific biological perturbations associated with these disorders.

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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1
Flow chart of the study population
Fig. 2
Fig. 2
a One-carbon metabolism: metabolic pathways converging at homocysteine metabolism, glutathione biosynthesis, folate cycle, and choline/betaine metabolism. Colored arrows indicate differences in metabolite concentrations comparing several diagnostic groups to those with typical development. The color coding key is on the right side of the figure. Boxed metabolites were measured in this study. Solid boxes identify analytes significantly different after FDR correction, while dashed boxes identify those with a trend (i.e. significant unadjsuted p value) as compared to TD controls. Gray shading identifies metabolites that were not significantly different as compared to TD controls. Enzymes are represented in black ovals. b Boxplots of choline (μM), N,N-DMG (μM), and betaine (μM). c Boxplot of glycine (μM) and serine (μM). Side-by-side boxplots of metabolite concentrations (μM) display the distribution (range and interquartile range) across the different neurodevelopmental groups as compared to controls. The variance illustrated in the boxplots is similar across groups but within expectation given the sample size of each group for each metabolite. Significant p values (*< 0.05, **< 0.01, ***< 0.001) after controlling for Benjamin–Hochberg false discovery rate (α < 0.05) are presented. Dx diagnosis, MTHFR methylenetetrahydrofolate reductase, N-N-DMG: N-N-dimethylglycine, THF tetrahydrofolate, SAM S-adenosylmethionine, SAH S-adenosylhomocysteine
Fig. 3
Fig. 3
a Overview of the tricarboxylic acid (TCA) cycle. Colored arrows indicate differences in metabolite concentrations comparing several diagnostic groups to those with typical development. The color coding key is indicated on the figure. Boxed metabolites were measured in this study. Solid boxes identify analytes significantly different after FDR correction, while dashed boxes identify those with a trend (i.e. significant unadjsuted p value) as compared to TD controls. Gray shading identifies metabolites that were not significantly different as compared to TD controls. Enzymes are represented in black ovals. b Boxplots of alanine (μM) and lactate (μM). c Boxplots of cis-aconitate (μM), 2-oxoglutarate (μM), and succinate(μM). d Boxplots of carnitine (μM) and O-acetylcarnitine (μM). Boxplots of metabolite concentrations (μM) elevated in DS and ASD cases not already shown in one-carbon metabolism or the tricarboxylic acid (TCA) cycle: e creatinine (μM), f urea (μM), g dimethyl sulfone (μM), h myo-Inositol (μM), i ortnithine (μM). Side-by-side boxplots of metabolites concentrations (μM) display the distribution (range and interquartile range) across the different neurodevelopmental groups as compared to controls. The variance illustrated in the boxplots is similar across groups but within expectation given the sample size of each group for each metabolite. Significant p values (*< 0.05, **< 0.01, ***< 0.001) after controlling for Benjamin–Hochberg false discovery rate (α < 0.05) are presented. TCA tricarboxylic acid cycle, SDH succinate dehydrogenase

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