Evidence map›Paper›PMID 42147149›Full record

ArticleResearch square2026

Utilizing maternal autoantibody patterns to predict risk of autism and intellectual disability in offspring.

Judy Van de Water, Katelien Blumenthal, Stacey Alexeeff, Lauren Weiss, Robert Yolken, Paul Ashwood, Lisa Croen, Joseph Schauer

Abstract readPreprint
In one paragraph

Article in Research square, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Judy Van de WaterUniversity of California, Davis.ORCID 0000-0003-1193-5875
Katelien BlumenthalUniversity of California, Davis.
Stacey AlexeeffKaiser Permanente Division of Research.
Lauren WeissDepartment of Psychiatry and Institute for Human Genetics, University of California, San Francisco.ORCID 0000-0002-5700-135X
Robert YolkenJohns Hopkins University School of Medicine.
Lisa CroenKaiser Permanente Division of Research.ORCID 0000-0001-7849-9428
Joseph SchauerUniversity of California, Davis.

Funding

UC Davis Center for Children's Environmental Health (CCEH)P01ES011269 · NIEHS · UNIVERSITY OF CALIFORNIA DAVIS · PI VAN DE WATER, JUDY A. · 2001 to 2018
$12.5M
Research Project: Pathologic Significance of Maternal AutoantibodiesP50HD103526 · NICHD · UNIVERSITY OF CALIFORNIA AT DAVIS · PI LEONARD J. ABBEDUTO, Melissa Dawn Bauman · 2020 to 2026
$9.7M
Prenatal and Neonatal Biologic Markers for AutismR01ES016669 · NIEHS · KAISER FOUNDATION RESEARCH INSTITUTE · PI CROEN, LISA A · 2010 to 2014
$3.5M
NICHD NIH HHS P50 HD103526NIEHS NIH HHS P01 ES011269NIEHS NIH HHS R01 ES016669
6 · The paper itself

Abstract

Maternal autoantibody-related autism (MARA) is a subset of autism in which specific patterns of maternal autoantibodies (aABs) in the circulation of pregnant women have been associated with increased autism risk in offspring. While initial MARA studies identified patterns consisting of two maternal aABs that predicted increased risk of autism, other multi-aAB patterns predictive of autism and other neurodevelopmental disorders have not yet been fully assessed. In this study, we aimed to determine if additional patterns of MARA aABs can be used to predict the risk of autism and intellectual disability (ID). We tested maternal plasma samples from the Early Markers for Autism (EMA) study for reactivity to eight proteins with clinical relevance in our initial MARA studies. Least Absolute Shrinkage and Selection Operator (LASSO) statistical modeling was used to identify patterns of three maternal aABs that were predictive of offspring autism and ID risk. We identified novel patterns consisting of three aABs associated with increased risk of autism or ID compared to general population controls (GP). Additionally, we found that specific patterns of three maternal aABs differentially predicted risk of autism with intellectual disability (AU ID) and autism without intellectual disability (AU noID), compared to GP. Overall, novel patterns consisting of three maternal aABs have been identified and can be used to predict child clinical risk of autism, ID, and autism subgroups, AU ID and AU noID.

Identifiers

PMID42147149
PMCPMC13174784

What Socratic holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.