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. <\/p>\n
\"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.\" <\/p>\n
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. <\/p>\n
\"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.\" <\/p>\n
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. <\/p>\n
\"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.\" <\/p>\n
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Follow this link: 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. Continue reading
\nAutism Blood Test Incorporates Big Data Techniques - Genetic Engineering & Biotechnology News<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"