Predicting autism risk in pregnant mothers

DN Bureau

According to the study, approximately 90 percent accuracy whether a pregnant mother has a 1.7 percent or a tenfold increased risk of having a child diagnosed with Autism Spectrum Disorder.

Representational image
Representational image

Washington D.C: Researchers at Rensselaer Polytechnic Institute- led by Juergen Hahn, professor and head of biomedical engineering- are continuing to make remarkable progress with their research focused on Autism Spectrum Disorder (ASD).

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A recent paper authored by Hahn and Jill James from the University of Arkansas for Medical Sciences discusses the work on predicting with approximately 90 percent accuracy whether a pregnant mother has a 1.7 percent or a tenfold increased risk of having a child diagnosed with ASD.

Currently, there is no test for pregnant mothers that can predict the probability of having a child that will be diagnosed with ASD. Recent estimates indicate that if a mother has previously had a child with ASD, the risk of having a second child with ASD is approximately 18.7 percent, whereas the risk of ASD in the general population is approximately 1.7 percent.

In this study, metabolites of the folate-dependent transmethylation and transsulfuration biochemical pathways of pregnant mothers were measured to determine whether or not the risk of having a child with autism could be predicted by her metabolic profile.

Pregnant mothers who have had a child with autism before were separated into two groups based on the diagnosis of their child whether the child had autism or not. Then these mothers were compared to a group of control mothers who have not had a child with autism before.

The researchers concluded that while it is not possible to determine during a pregnancy if a child will be diagnosed with ASD by age 3, they did find that differences in the plasma metabolites are indicative of the relative risk for having a child with ASD.

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This new research follows an earlier study published in 2017, which developed an algorithm based on levels of metabolites found in a blood sample that can accurately predict whether a child is on the autism spectrum.

The full findings are present in the journal- Research in Autism Spectrum Disorders. (ANI)

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