Machine Learning Identifies Blood Biomarkers Of Autism

Specific group of biomarkers identified in the blood – nine serum proteins using machine learning tools strongly predicts ASD.

Specific group of biomarkers identified in the blood – nine serum proteins using machine learning tools strongly predicts ASD. This may lead to an earlier diagnosis of children with autism spectrum disorder (ASD) that in turn, would help develop more effective therapies, as per research at UT Southwestern, published in PLOS ONE.

Autism spectrum disorder (ASD) is a broad spectrum of disorder due to neuro-developmental delay. It is primarily characterized by social, communication and behavioral challenges.

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‘Specific group of biomarkers identified in the blood – nine serum proteins using machine learning tools strongly predicts ASD. This may lead to an earlier diagnosis of children with autism spectrum disorder (ASD) that in turn, would help develop more effective therapies. Thus earlier identification of children with autism can invite a better understanding of disease management to improve their quality of life.’

Around 1 in 59 children are diagnosed with autism in the United States, with an average age of 4 years. Earlier diagnosis before the age of 4, trailed by prompt therapeutic support and intervention, could even have a significant effect on their symptoms like inflexible behaviors and the lack of communication or social skills.

Several blood-based biomarkers like neurotransmitters, cytokines, and markers of mitochondrial dysfunction, oxidative stress, and impaired methylation have been investigated. But the use of machine learning allows incorporating demographic and clinical data into the analysis (given the prevalence of ASD) that may help examine disease status and symptom severity.

Blood Biomarkers of Autism

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The team analyzed serum samples of more than 1,100 proteins using the SomaLogic protein analysis platform from 76 boys with ASD and 78 from typically developing boys of ages 18 months to 8 years at The Johnson Center for Child Health & Development, a multidisciplinary treatment center in Austin, Texas. The quality of the biomarker panel was assessed using machine learning methods.

It was seen that all nine proteins in the biomarker panel correlated with symptom severity and were significantly different in boys with ASD when compared with typically developing boys.

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"The more significantly affected the child is, the higher or lower than normal the blood biomarker is. Ideally, there will be a day when a child is identified using blood biomarkers as being at risk for developing ASD and therapies can be started immediately. That would help the child develop skills to optimize their communication and learning", says Dwight German, Ph.D., professor of psychiatry at UT Southwestern and senior author of the study.

The study thereby states that earlier identification of children with autism can invite a better understanding of supportive care and therapies to improve their quality of life.

Source-Medindia