Artificial neural networks reveal sex differences in gene methylation, and connections between maternal risk factors and symptom severity in autism spectrum disorder

• Published in: Epigenomics Vol. 14; no. 19; pp. 1181 - 1195
• Main Authors: Stoccoro, Andrea, Gallo, Roberta, Calderoni, Sara, Cagiano, Romina, Muratori, Filippo, Migliore, Lucia, Grossi, Enzo, Coppedè, Fabio
• Format: Journal Article
• Language: English
• Published: England Future Medicine Ltd 01.10.2022
• Subjects: artificial neural networks; ASD; autism spectrum disorder; Autism Spectrum Disorder - diagnosis; Autism Spectrum Disorder - genetics; Child; DNA methylation; epigenetics; Female; Humans; Infant, Newborn; Male; maternal risk factors; Methylation; Neural Networks, Computer; Premature Birth; Risk Factors; Sex Characteristics; sex difference; artificial neural networks; ASD; sex difference; DNA methylation; maternal risk factors; autism spectrum disorder; epigenetics
• ISSN: 1750-1911; 1750-192X
• DOI: 10.2217/epi-2022-0179

Artificial neural networks were used to unravel connections among blood gene methylation levels, sex, maternal risk factors and symptom severity evaluated using the Autism Diagnostic Observation Schedule 2 (ADOS-2) score in 58 children with autism spectrum disorder (ASD). Methylation levels of , and genes were connected to females, and those of , and genes to males. High gestational weight gain, lack of folic acid supplements, advanced maternal age, preterm birth, low birthweight and living in rural context were the best predictors of a high ADOS-2 score. Artificial neural networks revealed links among ASD maternal risk factors, symptom severity, gene methylation levels and sex differences in methylation that warrant further investigation in ASD.