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Research Article

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

    Andrea Stoccoro‡

    Department of Translational Research & of New Surgical & Medical Technologies, University of Pisa, Medical School, Via Roma 55, Pisa, 56126, Italy

    ‡Authors contributed equally

    Search for more papers by this author

    ,
    Roberta Gallo‡

    Department of Translational Research & of New Surgical & Medical Technologies, University of Pisa, Medical School, Via Roma 55, Pisa, 56126, Italy

    ‡Authors contributed equally

    Search for more papers by this author

    ,
    Sara Calderoni

    IRCCS Stella Maris Foundation, Calambrone, Pisa, 56128, Italy

    Department of Clinical & Experimental Medicine, University of Pisa, Via Roma 55, Pisa, 56126, Italy

    ,
    Romina Cagiano

    IRCCS Stella Maris Foundation, Calambrone, Pisa, 56128, Italy

    ,
    Filippo Muratori

    IRCCS Stella Maris Foundation, Calambrone, Pisa, 56128, Italy

    Department of Clinical & Experimental Medicine, University of Pisa, Via Roma 55, Pisa, 56126, Italy

    ,
    Lucia Migliore

    Department of Translational Research & of New Surgical & Medical Technologies, University of Pisa, Medical School, Via Roma 55, Pisa, 56126, Italy

    ,
    Enzo Grossi

    Villa Santa Maria Foundation, Tavernerio, Como, 22038, Italy

    &
    Fabio Coppedè

    *Author for correspondence: Tel.: +39 050 221 8544;

    E-mail Address: fabio.coppede@med.unipi.it

    Department of Translational Research & of New Surgical & Medical Technologies, University of Pisa, Medical School, Via Roma 55, Pisa, 56126, Italy

    Published Online:https://doi.org/10.2217/epi-2022-0179

    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.

    Papers of special note have been highlighted as: • of interest; •• of considerable interest

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