Biological Pathways Associated with Vitamins in Autism Spectrum Disorder

Medication Usage Record-Based Predictive Modeling of Neurodevelopmental Abnormality in Infants under One Year: A Prospective Birth Cohort Study

Early identification of children with neurodevelopmental abnormality is a major challenge, which is crucial for improving symptoms and preventing further decline in children with neurodevelopmental abnormality. This study focuses on developing a predictive model with maternal sociodemographic, behavioral, and medication-usage information during pregnancy to identify infants with abnormal neurodevelopment before the age of one. In addition, an interpretable machine-learning approach was utilized to assess the importance of the variables in the model. In this study, artificial neural network models were developed for the neurodevelopment of five areas of infants during the first year of life and achieved good predictive efficacy in the areas of fine motor and problem solving, with median AUC = 0.670 (IQR: 0.594, 0.764) and median AUC = 0.643 (IQR: 0.550, 0.731), respectively. The final model for neurodevelopmental abnormalities in any energy region of one-year-old children also achieved good prediction performance. The sensitivity is 0.700 (IQR: 0.597, 0.797), the AUC is 0.821 (IQR: 0.716, 0.833), the accuracy is 0.721 (IQR: 0.696, 0.739), and the specificity is 0.742 (IQR: 0.680, 0.748). In addition, interpretable machine-learning methods suggest that maternal exposure to drugs such as acetaminophen, ferrous succinate, and midazolam during pregnancy affects the development of specific areas of the offspring during the first year of life. This study established predictive models of neurodevelopmental abnormality in infants under one year and underscored the prediction value of medication exposure during pregnancy for the neurodevelopmental outcomes of the offspring.

Binding Folate Receptor Alpha Autoantibody Is a Biomarker for Leucovorin Treatment Response in Autism Spectrum Disorder

Autism spectrum disorder (ASD) affects up to 1 in 36 children in the United States. It is a heterogeneous neurodevelopmental disorder with life-long consequences. Patients with ASD and folate pathway abnormalities have demonstrated improved symptoms after treatment with leucovorin (folinic acid), a reduced form of folate. However, biomarkers for treatment response have not been well investigated and clinical trials are lacking. In this retrospective analysis, a cohort of prospectively collected data from 110 consecutive ASD clinic patients [mean (SD) age: 10.5 (6.2) years; 74% male] was examined. These patients all underwent testing for folate receptor alpha autoantibodies (FRAAs) and soluble folate binding proteins (sFBPs) biomarkers and were treated with leucovorin, if appropriate. Analyses examined whether these biomarkers could predict response to leucovorin treatment as well as the severity of ASD characteristics at baseline. The social responsiveness scale (SRS), a measure of core ASD symptoms, and the aberrant behavior checklist (ABC), a measure of disruptive behavior, were collected at each clinic visit. Those positive for sFBPs had more severe ASD symptoms, and higher binding FRAA titers were associated with greater ABC irritability. Treatment with leucovorin improved most SRS subscales with higher binding FRAA titers associated with greater response. Leucovorin treatment also improved ABC irritability. These results confirm and expand on previous studies, underscore the need for biomarkers to guide treatment of folate pathways in ASD, and suggest that leucovorin may be effective for children with ASD.