A quarterly publication of the Autism Research Institute

The Autism Research Review International is quarterly publication of the Autism Research Institute

Spring, 2017 | Number 2, Volume 31

Metabolites in blood can predict autism diagnosis

An algorithm based on levels of metabolites in the blood can predict whether a child has autism spectrum disorder (ASD) with a remarkable degree of accuracy, a new study reports. 

“Instead of looking at individual metabolites,” senior author Juergen Hahn says, “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. 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.” 

Hahn and colleagues analyzed data from 149 children, focusing on metabolites relevant to two cellular pathways linked to ASD: the methionine cycle and the transulfuration pathway. About half of the children had ASD, while the other half were neurotypical. 

The researchers deliberately omitted data for one individual at a time, subjected the remaining data to advanced analysis techniques, and used the results to generate an algorithm to predict the data from the omitted individual. Repeating this process for all 149 children, the researchers correctly identified 96.1% of neurotypical children and 97.6 percent of children with ASD. 

The researchers say, “This level of accuracy for classification as well as severity prediction far exceeds any other approach in this field and is a strong indicator that the metabolites under consideration are strongly correlated with an ASD diagnosis.” Hahn concludes, “This is the first physiological diagnostic and it’s highly accurate and specific.”


Citations

“Classification and adaptive behavior prediction of children with autism spectrum disorder based upon multivariate data analysis of markers of oxidative stress and DNA methylation,” Daniel P. Howsmon, Uwe Kruger, Stepan Melnyk, S. Jill James, and Juergen Hahn, PLOS Computational Biology, March 16, 2017 (free online). The address is not listed. 

—and— 

“A blood test for autism,” news release, Rensselaer Polytechnic Institute, March 16, 2017.