Using these tools, Hahn and team correctly identified 97.6 percent of the children that had autism, and 96.1 percent of those who were neurotypical.
While past research has revealed distinctive metabolic processes in children on the autism spectrum, these have not previously been exploited in diagnosis.
"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".
The researchers collected blood samples from all the 159 children and found out that the blood test succeeded in diagnosing autism cases nearly accurately in most of the children with autism.
Although ASD affects about 1.5 percent of all children, its exact cause remains unknown, and diagnosis requires many doctors specialising in a number of different disciplines. Thus, they developed a biological method which helps them predict if a child might develop autism.
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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.
Clarity in the diagnosis of children affected by Autism Spectrum Disorder (ASD) is a major challenge facing modern medical researchers. Past studies showed both pathways go through a process of adjustment in people with high risk of autism.
The number of ASD diagnoses has drastically increased over the past few decades, and in the US, the estimates show a 30 percent increase in the number of children with ASD compared with previous years. "These differences allow us to categorize whether an individual is on the autism spectrum", 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", explains lead author of the study, Juergen Hahn. "This is the first physiological diagnostic and it's highly accurate and specific". But researchers have struggled to translate these into new diagnostic tools. For Hahn, the next step is to replicate the results with a new cohort working with his clinical collaborators. Which molecules do I need to add or take away?