New biochemical method accurately diagnoses autism in children

19 March, 2017, 15:28 | Author: Eloise Marshall
  • Breakthrough? The new method designed by New York's Rensselaer Polytechnic Institute is the first to analyze metabolic biomarkers that come before behavioral changes

Autism Speaks states that "autism spectrum disorder" consists of varying conditions involving behavioural problems, issues with social skills, speech and nonverbal communication, and repetitive behavior. The fundamental causes of ASD still remain a mystery, but it is understood that early diagnosis can lead to more effective treatment and management as a child develops.

According to the findings of a recent research, an experimental blood test can help in detecting early signs of autism in children. But researchers have struggled to translate these into new diagnostic tools.

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.

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. However, most of the current diagnosis practices and psychometric tools rely purely on the detection of behavioral signs.

A blood test for autism has been developed at Rensselaer Polytechnic Institute. While prior research often focused on single metabolites or biomarkers, this new process measures 24 separate metabolites from a blood sample and, through a complex algorithm, can allegedly determine whether an individual is on the autism spectrum.

According to RPI News, the researchers were investigating patterns of various metabolites and found out that there are significant differences between the metabolites of children with ASD and to those who are neurotypical. The children were between 3 and 10 years old.

These are produced by chemical reactions known as FOCM (folate-dependent one-carbon metabolism) and TS (transulfuration).

Both of these substances have previously been shown to become altered in people with an increased risk of ASD.

Hahn's study, however, is based on multivariate statistical models that enabled his team to classify children with autism based on their neurological status.

"The models developed herein have much stronger predictability than any existing approaches from the scientific literature". By using the algorithm, the prediction about the data from the omitted individual correctly identified the 97.6 percent of children with autism, and 96.1 percent of those neurotypical participants.

"These differences allow us to categorize whether an individual is on the Autism spectrum", said Juergen Hahn, lead author, systems biologist, professor, and head of the Rensselaer Department of Biomedical Engineering.


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