Scientists have used big data to develop an algorithm for diagnosing autism based on a blood test.Scientists at New York’s Rensselaer Polytechnic Institute have developed an algorithm that can accurately predict whether a child has an autism spectrum disorder (ASD), based on a blood sample.The study, published in the open journal PLOS One, represents the world’s first physiological test for autism, and potentially takes researchers one step closer to earlier autism diagnosis and new therapy developments.For the work, investigators measured 24 different metabolites in a blood sample, and then used big data techniques to find patterns tied to two connected pathways that have been theorized as being linked to ASD.By using big data, it was possible to establish patterns that may not otherwise have been discovered.“Instead of looking at the metabolites one at a time, we were able to look at them altogether,” Professor Juergen Hahn, lead author and head of the Rensselaer Department of Biomedical Engineering, told Digital Trends.
Troy, N.Y. - One year after researchers published their work on a physiological test for autism, a follow-up study confirms its exceptional success in assessing whether a child is on the autism spectrum.A physiological test that supports a clinician's diagnostic process has the potential to lower the age at which children are diagnosed, leading to earlier treatment.Results of the study, which uses an algorithm to predict if a child has autism spectrum disorder (ASD) based on metabolites in a blood sample, published online today, appear in the June edition of Bioengineering & Translational Medicine.It is estimated that approximately 1.7 percent of all children are diagnosed with ASD, characterized as "a developmental disability caused by differences in the brain," according to the Centers for Disease Control and Prevention."Juergen's work in developing a physiological test for autism is an example of how the interdisciplinary life science-engineering interface at Rensselaer brings new perspectives and solutions to improve human health," said Deepak Vashishth, CBIS director."This is a great result from the larger emphasis on Alzheimer's and neurodegenerative diseases at CBIS, where our work joins multiple approaches to develop better diagnostic tools and biomanufacture new therapeutics."
Researchers at Rensselaer Polytechnic Institute who developed a blood test to help diagnose autism spectrum disorder have now successfully applied their distinctive big data-based approach to evaluating possible treatments.Currently, diagnosis and evaluating the success of an intervention rely heavily on observations by professionals and caretakers."Having some kind of a measure that measures something that's happening inside the body is really important," said Juergen Hahn, systems biologist, professor, and head of the Rensselaer Department of Biomedical Engineering.Hahn and his team use machine-learning algorithms to analyze complex data sets.That is how he previously discovered patterns with certain metabolites in the blood of children with autism that can be used to successfully predict diagnosis.In this most recent analysis, the team used a similar set of measurements from three different clinical trials that examined potential metabolic interventions.
Children with autism spectrum disorder (ASD) are often affected by co-occurring conditions, such as epilepsy, immune disorders, gastrointestinal problems, and developmental delays.According to research published today in Autism Research, creating a classification system for ASD based on co-occurring conditions could provide useful insights into the underlying mechanics of ASD and these conditions.The study was produced by a Rensselaer Polytechnic Institute team, led by Juergen Hahn, a professor of biomedical engineering, which analyzed de-identified administrative claims data from the OptumLabs Data Warehouse for thousands of children with and without ASD over five years.These findings, Hahn said, lay the groundwork for creating a sub-classification system within ASD.I'm not saying it's the only way to do it but I think it's an important step in that direction," he said.The analysis also showed that certain conditions like gastrointestinal and immune disorders, and seizure and sleep disorders often co-occurred at similar points in time in children with autism.
(Rensselaer Polytechnic Institute) Developing a physiological test for diagnosing autism spectrum disorder (ASD), one that measures certain components in the blood, has the potential to be a paradigm shift for diagnosing ASD. However, the large heterogeneity of how ASD affects individuals has long been viewed as a key obstacle to the development of such a test. Research conducted at Rensselaer Polytechnic Institute, and published online today in the journal Research in Autism Spectrum Disorders, represents a significant step toward addressing this challenge.
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