Researchers have identified a group of blood biomarkers that could help lead to an earlier diagnosis of children with autism spectrum disorder (ASD).
The UT Southwestern team has used machine learning tools to analyse hundreds of proteins that has led to the identification of nine serum proteins that predict the disorder. The researchers hope this will help develop more effective therapies for ASD sooner.
The study has been published in the journal PLOS ONE.
Machine learning
Early diagnosis of ASD is vital to make a difference to the lives of young children living with ASD who are typically not diagnosed until the age of four, says Dwight German, PhD, professor of psychiatry at UT Southwestern and senior author of the study.
To date, blood-based biomarkers such as neurotransmitters, cytokines, and markers of mitochondrial dysfunction, oxidative stress, and impaired methylation, have been investigated. The researchers wanted to improve analysis using machine learning by incorporating demographic and clinical data to examine disease status and symptom severity more powerfully.
The findings from the analysis of the samples showed that all nine proteins in the biomarker panel were significantly different in boys with ASD compared with typically developing boys and that each of the nine serum proteins correlated with symptom severity.
More than 1,100 proteins were examined using the SomaLogic protein analysis platform and a panel of nine proteins was identified as optimal for predicting ASD using three computational methods. The researchers then evaluated the biomarker panel for quality using machine learning methods, however, they say that future studies are needed to fully validate the present findings.
German said: “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 optimise their communication and learning.”
