{"id":13231,"date":"2021-05-14T11:57:18","date_gmt":"2021-05-14T16:57:18","guid":{"rendered":"https:\/\/last-drum.flywheelsites.com\/?p=13231"},"modified":"2024-04-28T17:47:18","modified_gmt":"2024-04-28T22:47:18","slug":"maternal-biomarkers-identify-odds-of-child-having-mar-asd","status":"publish","type":"post","link":"https:\/\/autism.org\/maternal-biomarkers-identify-odds-of-child-having-mar-asd\/","title":{"rendered":"Maternal biomarkers identify odds of child having MAR-ASD"},"content":{"rendered":"

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Researchers at UC Davis report that they can accurately predict a mother\u2019s risk of having a child with one particular subtype of autism spectrum disorder (ASD).<\/p>\n

Alexandra Ramirez-Celis and colleagues focused on maternal autoantibody-related ASD (MAR-ASD), which accounts for approximately 20% of all cases of ASD. In this subtype of autism, a pregnant woman\u2019s autoantibodies (which are antibodies directed against the body\u2019s own proteins) react with her baby\u2019s brain, altering its development.<\/p>\n<\/div>

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Ramirez-Celis and colleagues analyzed plasma samples from 450 mothers of children with ASD and 342 mothers of neurotypical children to detect reactivity to eight different proteins that are abundant in the fetal brain. Using a machine learning algorithm, they identified three top autoantibody patterns associated with a diagnosis of MAR ASD: CRMP1+GDA, CRMP1+CRMP2 and NSE+STIP1.<\/p>\n

\u201cFor example,\u201d senior author Judy Van de Water says, \u201cif the mother has autoantibodies to CRIMP1 and GDA (the most common pattern), her odds of having a child with autism [are] 31 times greater than the general population, based on this current dataset. That\u2019s huge. There\u2019s very little out there that is going to give you that type of risk assessment.\u201d In addition, the researchers found that reactivity to CRMP1 in any of the top patterns significantly increases the likelihood of a child having severe autism.<\/p>\n

The study\u2019s authors conclude, \u201cThis is the first report that uses machine learning to identify a set of biomarkers that demonstrate an association with MAR-ASD with 100% accuracy.\u201d They say their new findings could potentially lead to pre-conception testing for high-risk women\u2014for instance, those who have previously had a child on the autism spectrum, those who are older than 35 years of age, or those with maternal risk factors associated with ASD (such as metabolic syndrome).<\/p>\n

\u2014<\/p>\n

\u201cRisk assessment analysis for maternal autoantibody-related autism (MAR-ASD): a subtype of autism,\u201d Alexandra Ramirez-Celis, Martin Becker, Miriam Nu\u00f1o, Joseph Schauer, Nima Aghaeepour, and Judy Van de Water, Molecular Psychiatry<\/em>, January 22, 2021 (free online). Address: Judy Van de Water, Department of Internal Medicine, Division of Rheumatology, Allergy, and Clinical Immunology, One Shields Avenue, University of California, Davis, CA 95616, javandewater@ ucdavis.edu.<\/p>\n

\u2014and\u2014<\/p>\n

\u201cBiomarkers in mother\u2019s plasma predict a type of autism in offspring with 100% accuracy,\u201d news release, UC Davis, January 25, 2021<\/p>\n

This article originally appeared in Autism Research Review International, <\/em><\/a>Vol. 35, No. 1, 2021<\/em><\/p>\n<\/div>

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