Big Data Offers New Insights Into Biological Components of Autism Spectrum Disorder

In recently pubished research, blood sample analysis showed that mothers of children with autism spectrum disorder (ASD) had several significantly different metabolite levels two to five years after they gave birth when compared to mothers of typically developing children.

The research team behind this finding was Juergen Hahn, the head of the Department of Biomedical Engineering at Rensselaer Polytechnic Institute, a pioneer in the use of big data to investigate biological components of ASD. 

Hahn is available to discuss the findings of his recent research, as well as his overall approach to studying autism. 

Previously, Hahn developed a blood test to help diagnose ASD, and he has since successfully applied his big data approach to evaluating potential ASD treatments. “Having some kind of a measure that measures something that’s happening inside the body is really important,” he said. 

For the recent paper, which Hahn co-authored, his team analyzed blood samples from 30 mothers whose young children had been diagnosed with ASD and 29 mothers of typically developing children. They found differences in several metabolite levels between the two groups of mothers. 

While the samples analyzed were taken two to five years after pregnancy, these research findings raise the question of whether or not the differences in metabolites may have been present during pregnancy as well.

The team was able to use a blood sample analysis to predict which group a mother belonged to with a high degree of accuracy, suggesting that the development of a blood test to screen for mothers who are at a higher risk of having a child with ASD may be possible. 

In addition to his specific findings, Hahn is available to discuss the use of big data in improving society’s understanding of the biological mechanisms at work in ASD.