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

Aim and methods: 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). Results: Methylation levels of MECP2, HTR1A and OXTR genes were connected to females, and those of EN2, BCL2 and RELN 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. Conclusion: 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.

Automated summary

This study used artificial neural networks to examine the connections among blood gene methylation levels, sex, maternal risk factors, and symptom severity in children with autism spectrum disorder (ASD). The results showed that the methylation levels of MECP2, HTR1A, and OXTR genes were associated with females, while the methylation levels of EN2, BCL2, and RELN genes were associated with males. High gestational weight gain, lack of folic acid supplements, advanced maternal age, preterm birth, low birthweight, and living in a rural context were the best predictors of a high ADOS-2 score.