Maternal autoantibody–related autism spectrum disorder (MAR ASD) is marked by specific maternal antibodies that react to certain proteins in the fetal brain. Examining the plasma of expecting mothers, researchers found mothers with reactivity to one of the nine MAR ASD patterns were eight times as likely to have a child diagnosed with autism.
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Using machine learning, researchers at the UC Davis MIND Institute have identified several patterns of maternal autoantibodies highly associated with the diagnosis and severity of autism.
Their study, published Jan. 22 in
Molecular Psychiatry, specifically focused on maternal autoantibody-related autism spectrum disorder (MAR ASD), a condition accounting for around 20% of all autism cases. The implications from this study are tremendous, said Judy Van de Water, a professor of rheumatology, allergy and clinical immunology at UC Davis and the lead author of the study. It s the first time that machine learning has been used to identify with 100% accuracy MAR ASD-specific patterns as potential biomarkers of ASD risk.