Urinary metabolic phenotyping differentiates children with autism from their unaffected siblings and age-matched controls

  • Yap I
  • Angley M
  • Veselkov K
 et al. 
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Abstract

Autism is an early onset developmental disorder with a severe life-long impact on behavior and social functioning that has associated metabolic abnormalities. The urinary metabolic phenotypes of individuals (age range=3?9 years old) diagnosed with autism using the DSM-IV-TR criteria (n = 39; male = 35; female = 4), together with their nonautistic siblings (n = 28; male = 14; female = 14) and age-matched healthy volunteers (n = 34, male = 17; female = 17) have been characterized for the first time using 1H NMR spectroscopy and pattern recognition methods. Novel findings associated with alterations in nicotinic acid metabolism within autistic individuals showing increased urinary excretion of N-methyl-2-pyridone-5-carboxamide, N-methyl nicotinic acid, and N-methyl nicotinamide indicate a perturbation in the tryptophan?nicotinic acid metabolic pathway. Multivariate statistical analysis indicated urinary patterns of the free amino acids, glutamate and taurine were significantly different between groups with the autistic children showing higher levels of urinary taurine and a lower level of urinary glutamate, indicating perturbation in sulfur and amino acid metabolism in these children. Additionally, metabolic phenotype (metabotype) differences were observed between autistic and control children, which were associated with perturbations in the relative patterns of urinary mammalian-microbial cometabolites including dimethylamine, hippurate, and phenyacetylglutamine. These biochemical changes are consistent with some of the known abnormalities of gut microbiota found in autistic individuals and the associated gastrointestinal dysfunction and may be of value in monitoring the success of therapeutic interventions.

Author-supplied keywords

  • NMR
  • autism
  • gut microbiota
  • metabolic phenotype
  • metabolic profiling
  • metabonomics
  • microbial co-metabolites
  • pattern recognition

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