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March 17, 2017

Autism Blood Test Incorporates Big Data Techniques

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    The autism awareness ribbon symbolizes the diverse experiences of people and families living with ASD. Here, it also represents the various genetic and environmental effects on the pathophysiology of ASD. The white pieces represent currently unknown effects. However, the red piece illustrating folate-dependent one-carbon metabolism and transsulfuration contributes important information to our knowledge of ASD. [Daniel P. Howsmon/CCBY]

    For a disease as broad and often nebulous as autism spectral disorder (ASD), having a simple, yet definitive method to assess whether a child is on the disease spectrum is imperative for providing the appropriate level of care. Now, a group of investigators led by scientists at the Rensselaer Polytechnic Institute has just published their findings about a novel method of identifying a child as being on the autism spectrum based on concentrations of specific substances found in a blood sample. These substances are produced by metabolic processes known as the folate-dependent one-carbon (FOCM) metabolism and transsulfuration (TS) pathways, both of which are altered in children with autism.   

    The results from this new study were published recently in PLOS Computational Biology in an article entitled “Classification and Adaptive Behavior Prediction of Children with Autism Spectrum Disorder Based upon Multivariate Data Analysis of Markers of Oxidative Stress and DNA Methylation.”

    ASD affects about 1.5% of all children, but its exact cause remains unknown, and diagnosis requires a multidisciplinary team of doctors. Previous research has revealed certain differences in metabolic processes between children on the autism spectrum and neurotypical children. However, researchers have struggled to translate these differences into new diagnostic tools.

    "Instead of looking at individual metabolites, we investigated patterns of several metabolites and found significant differences between metabolites of children with ASD and those that are neurotypical,” explained senior study investigator Juergen Hahn, Ph.D., professor, and head of the Rensselaer Department of Biomedical Engineering. “These differences allow us to categorize whether an individual is on the autism spectrum. By measuring 24 metabolites from a blood sample, this algorithm can tell whether or not an individual is on the autism spectrum, and even to some degree where on the spectrum they land."

    Utilizing big data techniques and applying them to biomedical data, the researchers found different patterns in metabolites relevant to two connected cellular pathways (a series of interactions between molecules that control cell function) that have been hypothesized to be linked to ASD—the methionine cycle and the transsulfuration pathway. The methionine cycle is linked to several cellular functions, including DNA methylation and epigenetics, and the transsulfuration pathway results in the production of the antioxidant glutathione, decreasing oxidative stress.

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    An algorithm based on levels of metabolites found in a blood sample has accurately predicted whether a child is on the autism spectrum. [Rensselaer Polytechnic Institute]

    The research team used blood sample data, collected at Arkansas Children's Hospital, from 83 children with autism and 76 neurotypical children, all between 3 and 10 years old. With the help of advanced modeling and statistical analysis tools, the metabolic data allowed the researchers to correctly classify 97.6% of the children with autism and 96.1% of the neurotypical children.

    "Because we did everything possible to make the model independent of the data, I am very optimistic we will be able to replicate our results with a different cohort," Dr. Hahn remarked. "This is the first physiological diagnostic, and it's highly accurate and specific."

    Previously, researchers had looked at individual metabolites produced by the methionine cycle and the transsulfuration pathways, finding possible links with ASD, but the correlation has been inconclusive. Dr. Hahn said the more sophisticated techniques he applied revealed patterns that would not have been apparent with earlier efforts.

    "A lot of studies have looked at one biomarker, one metabolite, one gene, and have found some differences, but most of the time those differences weren't statistically significant, or the results could not be reliably replicated," Dr. Hahn stated. "Our contribution is using big data techniques that can look at a suite of metabolites that have been correlated with ASD and statistically make a much stronger case."

    The research team was excited by their findings and are looking ahead to replicate the results with a new cohort. In the long run, the researchers hope the model and diagnostic tool will aid in developing improved treatment options.

    "If these pathways are different, what happens if I can manipulate the pathway so that it works similarly to the neurotypical ones?" Dr. Hahn proposed. "What do I need to prod? Which molecules do I need to add or take away? Having a model that describes these pathways makes it a lot easier to adjust them."

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