Background

Autism spectrum disorder (ASD) is now diagnosed in 1 of 68 children, with recent reports citing as many as 1 in 45 children in the United States. The average age of diagnosis is greater than 4 years (CDC, 2014). Diagnosis is complicated and requires a referral to a behavioral pediatrician, child psychiatrist or developmental psychologist. Parents and pediatricians are challenged to identify autism earlier because children develop at different rates and there is a tendency to wait hoping the child will “catch up”. Further, the wait time from referral to specialist examination can be as long as a year or more. As a result, years can be lost during which intensive behavioral intervention can be used to help alleviate the symptoms of autism and promote better long term outcome.

Refining Current Treatments

Currently, many ASD patients are treated with a modified diet, dietary supplements, and vitamins, with little evidence that there is an underlying condition to treat. In some cases, these interventions improve behavioral and cognitive performance, but in many cases, parents and families can be disappointed by the results. A metabolomics profile would provide firm evidence that an altered diet or some form of supplementation is appropriate. This will undoubtedly lead to more effective outcomes for these targeted interventions.

Relevance of study

There is a need for a reliable biomarker-based test for diagnosing autism. Earlier diagnosis of children with ASD will improve outcomes, including higher cognitive and social function and improved communication. This could help alleviate the financial and emotional burden on families and society. Children who receive early intensive therapy show significant improvement such that many do not require special education. In addition, a metabolomic diagnostic approach will allow better understanding of differences in the metabolism of patients with autism from typically developing children and from children with other developmental delays. This test, based on the individual’s biochemical profile, offers the promise to select treatments matched to the metabolic subtype of the patient including modified diet, dietary supplements, and existing and new therapeutics developed from newly identified drug targets. Furthermore, increased understanding regarding likely responders to a particular therapy will increase efficacy rates in clinical trials enabling new therapies and companion diagnostic tests.

Objectives

The purpose of this study is to identify one or more metabolite signatures in blood plasma and/or urine that differentiate children with autism from other children. This could lead to the development of an algorithm that maximizes sensitivity and specificity of the biomarker profile, and to evaluate the algorithm as a diagnostic tool.

The secondary objective of the research is to define metabolites capable of classifying subtypes of autism that may increase understanding of the metabolic basis of the condition, as well as help with creating personalized therapy for children.