Urinary Metabonomic Profiling Discriminates Between Children with Autism and Their Healthy Siblings

Abstract

BACKGROUND Autism spectrum disorder (ASD) is a complicated neuropsychiatric disease that displays significant heterogeneity. The diagnosis of ASD is currently primarily dependent upon descriptions of clinical symptoms, and it remains urgent to find biological markers for the detection and diagnosis of autism. The current study applied the urinary metabolic profiling approach to characterize metabolic phenotypes in ASD. MATERIAL AND METHODS Urine was obtained from children with ASD and their matched healthy siblings. Samples were analyzed using 1H NMR-based methods designed to measure a broad range of metabolites. Partial least-square-discriminant analysis (PLS-DA) was used to develop models to identify metabonomic variations that can be used to distinguish between individuals with ASD and their unaffected siblings. RESULTS A significant difference was observed between the metabolomic profiles of children with ASD and that of their healthy siblings. An increase in the levels of tryptophan, hippurate, glycine, and creatine, and a decrease in trigonelline, melatonin, pantothenate, serotonin, and taurine were observed compared to the control group. We conclude that several metabolic pathways are affected by autism, which suggests that a gut-brain link may be important in the pathophysiology of ASD. CONCLUSIONS 1H NMR-based metabonomic analysis of the urine can determine perturbations of specific metabolic pathways related to ASD and help identify a characteristic metabolic fingerprint to better understand the disease and its causes.

Figure 1

Flow diagram of patients through the trial.

Figure 2

NMR spectrum (δ0.5–6.2 and δ6.2–9.5) of urine at 600 MHz, with a typical ¹H spectrum as an external spectrum, showing the assignment of the significant metabolites responsible for distinguishing children with ASD from non-autistic children. The region of δ6.2–9.5 (in the dashed box) was magnified 5 times compared with the corresponding region of δ0.5–6.2 for the purpose of clarity. Ace – acetate; Ach – acetylcholine; Aco – trans-aconitate; Act – acetone; Ad – acetamide; AH – aminohippurate; Ala – alanine; Asc – ascorbate; Bu – butyrate; Cap – caprate; Cho – choline; Ci – citrate; Cn – creatinine; Cr – creatine; DHT – dihydrothymine; DMA – dimethylamine; DMG – N, N-dimethylglycine; EA – ethanolamine; For – formate; Fum – fumarate; GA – guanidoacetate; GABA – Gama-aminobutyrate; Glu – glutamate; Gly – glycine; HIB – 2-hydroxyisobutyrate; Hip – hippurate; HMM – 3-hydroxy-4-methoxymandelate; HP – 3-hydroxypyruvate; IB – isobutyrate; IL – indolelactate; Ino – inosine; KG – α-ketoglutarate; Lac – lactate; Leu – leucine; Lys – lysine; M – malonate; Mal – malate; Met – methionine; MG – methylguanidine; MM – methylmalonate; Mol – methanol; NAA – N-acetylalanine; NMN – N-methylnicotinamide; NP – neopterin; OA – oxaloacetate; PA –picolinate; PAG – phenylacetylglycine; Pan – pantothenate; Py – pyruvate; Set – serotonin; Suc – succinate; Sum – succinimide; Tau – taurine; TMA – trimethylamine; TMAO – trimethylamine N-oxide; Tri – trigonelline; U – unassigned; Val – valine.

Figure 3

(A) Scatter plot of PLS-DA scores of the first principal component obtained from (■) controls and (●) ASD. t[1]p – PLS component 1; t [2]p – PLS component 2. (B) Validation of the corresponding partial least-squares discriminant analysis model by random permutation analysis. (C) The corresponding loading plots represent single NMR spectral region segments. The color map shows the significance of metabolite variations between the 2 clusters.

Figure 4

(A–C) Conventional biochemical measurements of metabolite indices in urine from control and ASD groups.