“Our contribution is using big data techniques that are able to look at a suite of metabolites that have been correlated with ASD and make statistically a much stronger case.” “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,” Hahn said. Hahn said the more sophisticated techniques he applied revealed patterns that would not have been apparent with earlier efforts. Researchers have looked at individual metabolites produced by the methionine cycle and the transulfuration pathways and found possible links with ASD, but the correlation has been inconclusive. Related Article: Scientists Use Saliva Test to Diagnose Autism “This is the first physiological diagnostic and it’s highly accurate and specific.” “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,” said Hahn, a member of the Rensselaer Center for Biotechnology and Interdisciplinary Studies (CBIS). His method correctly identified 96.1 percent of all neurotypical participants and 97.6 percent of the ASD cohort. Hahn cross-validated the results, swapping a different individual out of the group and repeating the process for all 149 participants. The algorithm then makes a prediction about the data from the omitted individual. Deliberately omitting data from one of the individuals in the group, Hahn subjects the dataset to advanced analysis techniques, and uses the results to generate a predictive algorithm. In the article, Hahn describes an application of Fisher Discriminant Analysis-a big data analysis technique-to data from a group of 149 people, about half on the autism spectrum. Hahn’s research, titled “ Classification and Adaptive Behavior Prediction of Children with Autism Spectrum Disorder based upon Multivariate Data Analysis of Markers of Oxidative Stress and DNA Methylation,” appears today in PLOS Computational Biology, an open access journal published by the Public Library of Science. Most children are not diagnosed with ASD until after age 4 years. Research shows that early intervention can improve development, but diagnosis currently depends on clinical observation of behavior, an obstacle to early diagnosis and treatment. People with ASD “may communicate, interact, behave, and learn in ways that are different from most other people.” According to the CDC, the total economic costs per year for children with ASD in the United States are estimated between $11.5 billion and $60.9 billion. The physiological basis for ASD is not known, and genetic and environmental factors are both believed to play a role. The methionine cycle is linked to several cellular functions, including DNA methylation and epigenetics, and the transulfuration pathway results in the production of the antioxidant glutathione, decreasing oxidative stress.Īutism Spectrum Disorder is estimated to affect approximately 1.5 percent of individuals and is characterized as “a developmental disability caused by differences in the brain,” according to the Centers for Disease Control and Prevention. You may unsubscribe at any time.īig data techniques applied to biomedical data 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 transulfuration pathway. “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.”īy subscribing, you agree to receive email related to Lab Manager content and products. These differences allow us to categorize whether an individual is on the Autism spectrum,” said Juergen Hahn, lead author, systems biologist, professor, and head of the Rensselaer Department of Biomedical Engineering. “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. The algorithm, developed by researchers at Rensselaer Polytechnic Institute, is the first physiological test for autism and opens the door to earlier diagnosis and potential future development of therapeutics. Photo courtesy of Rensselaer Polytechnic InstituteĪn algorithm based on levels of metabolites found in a blood sample can accurately predict whether a child is on the autism spectrum of disorder (ASD), based upon a recent study.
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