A novel biomarker method could accurately assess if a child is on the autism spectrum, according to a new study.
Autism spectrum disorder is thought to affect around 1.5 percent of children, but diagnosis continues to prove difficult and currently relies on a multidisciplinary team of doctors. While past research has revealed distinctive metabolic processes in children on the autism spectrum, these have not previously been exploited in diagnosis.
Researchers Juergen Hahn, Daniel Howsmon and colleagues have now announced their successful development of an accurate diagnostic method for children based on blood sampling. The method detects substances in the blood produced by two metabolic processes known as the folate-dependent one-carbon (FOCM) metabolism and the transulfuration (TS) pathways, both of which are altered in children with autism.
The scientists compared blood sample data from children with autism and neurotypical children, all between 3 and 10 years old. Advanced modeling and statistical analysis tools allowed the researchers to correctly classify 97.6 percent of the children with autism and 96.1 percent of the neurotypical children based solely on their blood biomarkers.
“The method presented in this work is the only one of its kind that can classify an individual as being on the autism spectrum or as being neurotypical,” says study author Juergen Hahn. “We are not aware of any other method, using any type of biomarker, that can do this, much less with the degree of accuracy that we see in our work.”
While further research is needed to confirm the findings and to examine any impact of medications on the blood concentrations of the biomarkers, this study provides hope that in the future there might be a simple, accurate method to diagnose autism in children.
Research Article: Howsmon DP, Kruger U, Melnyk S, James SJ, Hahn J (2017) Classification and adaptive behavior prediction of children with autism spectrum disorder based upon multivariate data analysis of markers of oxidative stress and DNA methylation. PLoS Comput Biol 13(3): e1005385. doi:10.1371/journal.pcbi.1005385
Image Credit: Daniel P. Howsmon