The latest advancement in diagnostic technology could help doctors identify the presence of autism biomarkers in patients's bloodstream as early as childhood.
This novel diagnostic tool is the first physiological test that can detect the genetic disorder, its accuracy greatly surpassing currently used screening measures, which only focus on behavioral symptoms.
The blood test uses an algorithm to track levels of metabolites, and is designed to predict the occurrence of Autism spectrum disorder or ASD in children, allowing the possibility of earlier diagnosis.
The Rensselaer Polytechnic Institute in New York, which created the algorithm, studied its efficiency through advanced data analysis and published the results in the journal PLOS Computational Biology.
According to Juergen Hahn, one of the study's lead authors, previous research typically focused on only one biomarker: metabolite or gene. Although successful, past results were not statistically significant enough to be replicated in other diagnostic case.
Hahn's study, however, is based on multivariate statistical models that enabled his team to classify children with autism based on their neurological status.
98 Percent Success Rate
Researchers analyzed biomedical data from 149 blood samples, belonging to 83 autistic children and 76 neurotypical participants (children not affected by ASD) — all aged between 3 and 10.
Instead of focusing on individual metabolites, Hahn's team investigated several metabolite patterns correlated with autism and discovered important differences in metabolite concentrations between the ASD test group and neurotypical cohort.
The comparative analysis revealed disparities in two metabolic processes: the methionine cycle (linked to several cellular functions) and the transulfuration pathway (responsible for producing antioxidants to decrease cell oxidation). Past studies showed both pathways go through a process of alteration in people with high risk of autism.
"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," explained Hahn.
This new diagnostic method was shown to be "highly accurate and specific," and helped researchers identify 97.6 percent of children who had autism and 96.1 percent of those who were neurotypical.
First Of Its Kind
No other diagnosis approach currently available can produce an equally precise classification of autistic patients or predict on which end of the spectrum they are found.
Hahn's team believes their algorithm is "a strong indicator that the metabolites under consideration are strongly correlated with an ASD diagnosis."
"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. 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," study authors said in a statement.