During Autism Awareness Month, Juergen Hahn, a professor at Rensselaer Polytechnic Institute, is available to speak about his research developing an algorithm that can diagnose autism based on measurements of 24 metabolites taken form a blood sample.
The algorithm is the first physiological test for autism and opens the door to earlier diagnosis and potential future development of therapeutics.
“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, 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.”
Big 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. 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.
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,” appeared in a 2017 edition of PLOS Computational Biology, an open access journal published by the Public Library of Science. 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. 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. The algorithm then makes a prediction about the data from the omitted individual. Hahn cross-validated the results, swapping a different individual out of the group and repeating the process for all 149 participants. His method correctly identified 96.1 percent of all neurotypical participants and 97.6 percent of the ASD cohort.
“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). “This is the first physiological diagnostic and it’s highly accurate and specific.”
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. Hahn said the more sophisticated techniques he applied revealed patterns that would not have been apparent with earlier efforts. “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. “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.”
To arrange an interview with Prof. Hahn, please email email@example.com or call 518-276-2146.