17556
A Metabolic Profile of Autism Spectrum Disorder from Autism Phenome Project Patient Plasma
Diagnosis of autism spectrum disorder (ASD) at an early age is important for initiating effective intervention. The current average age of diagnosis in the United States is 4.5 years. Patients can be reliably diagnosed through behavioral testing at 2 years of age only at health care facilities with sufficient autism expertise. Increasing evidence indicates that ASD is a complex disorder that has many causes and a variety of genetic risks. Identification of a metabolic signature of autism from blood samples will offer earlier screening and diagnosis to improve both therapy and outcome.
Objectives:
Stemina has recently completed a study of 4 to 6 year old patients in which a metabolic signature was observed in the patients’ blood. We are continuing to perform analysis on blood from patients enrolled in the Autism Phenome Project (APP) to evaluate the metabolic signature of 2 to 4 year old children with ASD as compared to typically developing (TD) children. This research will develop and validate biomarkers associated with ASD discovered in the first study as well as identify new biomarkers in the younger patient cohort that may allow for earlier diagnosis. Our goal is to determine the relevant biomarkers capable of being translated into a broadly available diagnostic test for ASD.
Methods:
Plasma was obtained from 180 children with ASD and from 93 age-matched TD children enrolled in the APP. All subjects were between approximately 2 and 4 years old. Samples were analyzed using 4 different LC/MS-based methods designed to orthogonally measure a broad range of metabolite molecular classes in plasma. Univariate, multivariate and machine learning methods are being used to identify if a predictive metabolic signature exists that is capable of classifying patient plasma samples as being from ASD or TD children. Results from this study will also be compared to the previous study with 4-6 year old children to assess the effect of age on metabolic biomarkers of ASD.
Results:
In the initial study, statistical models were created using 179 statistically significant mass features that classified the ASD and TD samples with an average accuracy of 81%. Metabolites that were altered in children with ASD are derived from multiple biochemical pathways and included organic acids, amino acids, phospholipids and others. This presentation will provide a summary of metabolic signatures identified in the APP cohort; analyses are currently underway.
Conclusions:
The non-targeted, mass spectrometry-based metabolomic analysis of metabolites in plasma shows promise for discovery of metabolic signatures able to differentiate individuals with ASD from TD individuals. These results form the basis for additional work with the goals of 1) developing a diagnostic test to detect ASD in children to improve patient outcomes, 2) gaining new knowledge of biochemical mechanisms involved in ASD 3) identifying biomolecular targets for new modes of therapy, and 4) identifying biomarkers that can be used for personalized treatment and classification of potential responders versus non-responders through metabolomic analysis of a patient’s biochemistry.