Journal Article

Maternal mid-gestational and child cord blood immune signatures are strongly associated with offspring risk of ASD

Ian, Lipkin W, Hornig Mady, Mjaaland Siri, Stoltenberg Camilla, Che Xiaoyu, Susser Ezra, Magnus, Per, Surén Pål, Bresnahan Michaeline, Reichborn-Kjennerud Ted

2022

Overview

Epidemiological studies and work in animal models indicate that immune activation may be a risk factor for autism spectrum disorders (ASDs). We measured levels of 60 cytokines and growth factors in 869 maternal mid-gestational (MMG) and 807 child cord blood (CB) plasma samples from 457 ASD (385 boys, 72 girls) and 497 control children (418 boys, 79 girls) from the Norwegian Autism Birth Cohort. We analyzed associations first using sex-stratified unadjusted and adjusted logistic regression models, and then employed machine learning strategies (LASSO + interactions, Random Forests, XGBoost classifiers) with cross-validation and randomly sampled test set evaluation to assess the utility of immune signatures as ASD biomarkers. We found prominent case–control differences in both boys and girls with alterations in a wide range of analytes in MMG and CB plasma including but not limited to IL1RA, TNFα, Serpin E1, VCAM1, VEGFD, EGF, CSF1, and CSF2. MMG findings were most striking, with particularly strong effect sizes in girls. Models did not change appreciably upon adjustment for maternal conditions, medication use, or emotional distress ratings. Findings were corroborated using machine learning approaches, with area under the receiver operating characteristic curve values in the test sets ranging from 0.771 to 0.965. Our results are consistent with gestational immunopathology in ASD, may provide insights into sex-specific differences, and have the potential to lead to biomarkers for early diagnosis.

• Publisher: Nature Publishing Group
• Subject: Animal models / Autism / Biomarkers / Children & youth / Cord blood / Epidemiology / Genetic crosses / Girls / Growth factors / Immune response / Interleukin 1 / Interleukin 1 receptor antagonist / Learning algorithms / Machine learning / Regression analysis / Risk factors / Tumor necrosis factor-α