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Researchers at the Rensselaer Polytechnic Institute, who have come up with a blood test to help diagnose autism spectrum disorders, have now successfully applied their distinct Big Data-based approach to evaluating treatments. possible.
The results, recently published in Frontiers in Cellular Neuroscience, have the potential to accelerate the development of successful medical interventions. One of the challenges in badessing the effectiveness of a treatment for autism is how to measure improvement. Currently, the diagnosis and badessment of the success of an intervention rely largely on the observations of professionals and wardens.
"Having some sort of measurement that measures something happening in the body is really important," said Juergen Hahn, Systems Biologist, Professor and Head of Rensselaer's Department of Biomedical Engineering.
Hahn and his team use machine learning algorithms to badyze complex datasets. This is how he had previously discovered patterns with certain metabolites in the blood of autistic children that could be used successfully to predict the diagnosis. You can watch Hahn discuss it here.
In this most recent badysis, the team used a similar set of measures from three different clinical trials of potential metabolic interventions. The researchers were able to compare the data before and after the treatment and look for correlations between these results and any observed changes in adaptive behavior.
"What we have done here has shown that if you are actively trying to alter the concentrations of these measured metabolites, you will also see changes in behavior," Hahn said.
Hahn said this approach was unique in that she badyzed several medical markers at the same time, revealing non-visible correlations in the data if each measure was studied individually.
"This can speed up the development process because you now have an additional tool that tells you if a treatment has worked," he said.
Hahn expects this type of approach to become an important part of clinical trials for autism in the future. "Having medical tests that measure quantities directly related to physiology is important and we hope they will be incorporated into future trials," he said.
Hahn, a member of the Rensselaer Center for Biotechnology and Interdisciplinary Studies, worked on this study with Troy Vargason, a graduate student at Rensselaer, with Emily Roth, an undergraduate student, and Uwe Kruger, a professor in the Department of Biomedical Engineering.
In addition to developing and successfully testing the first physiological test for autism and this recent work, Hahn has also been working with colleagues to apply her method to determining the relative likelihood of a pregnant woman d & # 39; Have a child with autism spectrum disorder.
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