In a new study, researchers found several maternal autoantibodies that are highly linked to the diagnosis and severity of autism.
They focused on maternal autoantibody-related autism spectrum disorder (MAR ASD), a condition accounting for around 20% of all autism cases.
They found machine learning can identify with 100% accuracy MAR ASD-specific patterns as potential biomarkers of autism risk.
The research was conducted by a team at the UC Davis MIND Institute.
Autoantibodies are immune proteins that attack a person’s own tissues.
Previously, Van de Water found that a pregnant mother’s autoantibodies can react with her growing fetus’ brain and alter its development.
In the study, the team obtained plasma samples from 450 mothers of children with autism and 342 mothers of typically developing children.
They then used a machine-learning algorithm to determine which autoantibody patterns were specifically linked to a diagnosis of autism.
The researchers created and validated a test to identify autism-specific maternal autoantibody patterns of reactivity in the developing brain.
This simple maternal blood test uses an ELISA (Enzyme-Linked-ImmunoSorbent Assay) platform, which is very quick and accurate.
The machine learning program crunched roughly 10,000 patterns and identified three top patterns associated with this type of autism.
The team says that with these maternal biomarkers, there are possibilities for very early diagnosis of MAR autism and more effective behavioral intervention.
The study opens the door for more research on potential pre-conception testing, particularly useful for high-risk women older than 35 or who have already given birth to a child with autism.
One author of the study is Judy Van de Water, a professor of rheumatology, allergy and clinical immunology.
The study is published in Molecular Psychiatry.
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