RPI researcher talks about his groundbreaking research on Autism
This week RPI announced that an 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.
The algorithm is based on levels of metabolites found in a blood sample, and researchers say based on a recent study at RPI, it can accurately predict whether a child is on the Autism spectrum of disorder (ASD).
“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. 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.
“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," said Hahn.
Juergen Hahn joined CBS 6's Heather Kovar to discuss his work.
Hahn says this is the first physiological test for autism, and it’s 98 percent accurate.
Hahn first explains why the current way of diagnosing autism could be improved.
Hahn says research has loosely linked autism to a few processes in the body, but not conclusively. "So there’s no biological test, and because of that we diagnose autism with clinical observation. Doctors observes a child and, based on their behavior, determines if they have autism. The problem with this is that it’s hard to diagnose the behavior of young children, and most children aren’t diagnosed until after age 4, which means parents lose several years before they can begin treatment."
Hahn says researchers have found inconclusive links between many biological markers – essentially chemicals your body produces – and autism. And in particular, there’s been a lot of evidence to suggest that processes related to how our environment can alter gene expression, and how our cells maintain themselves under stressful conditions, have links to autism.
He says most researchers look at individual indicators – levels of one chemical, or one gene – at a time. The difference is that we used big data techniques to look at multiple indicators simultaneously and were able to find a strong correlation. Really, the difference in our research is the approach – using big data algorithms to examine 24 indicators (taken from a blood sample) in a group of 150 people simultaneously.
As for what this means for autism testing and treatment, Hahn says we have to repeat this success with a larger sample size to be certain that we’re on to something.
" If we’re able to replicate our results, this could be a really valuable tool for parents and doctors in diagnosing autism as early as possible. And it could also help researchers to gauge the effects of possible therapeutics," says Hahn.