Artificial neural networks reveal sex differences in gene methylation, and connections between maternal risk factors and symptom severity in autism spectrum disorder
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...
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| Veröffentlicht in: | Epigenomics 2022-10, Vol.14 (19), p.1181-1195 |
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| container_title | Epigenomics |
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| creator | Stoccoro, Andrea Gallo, Roberta Calderoni, Sara Cagiano, Romina Muratori, Filippo Migliore, Lucia Grossi, Enzo Coppedè, Fabio |
| description | 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. |
| doi_str_mv | 10.2217/epi-2022-0179 |
| format | Article |
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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.</description><identifier>ISSN: 1750-1911</identifier><identifier>EISSN: 1750-192X</identifier><identifier>DOI: 10.2217/epi-2022-0179</identifier><identifier>PMID: 36325841</identifier><language>eng</language><publisher>England: Future Medicine Ltd</publisher><subject>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</subject><ispartof>Epigenomics, 2022-10, Vol.14 (19), p.1181-1195</ispartof><rights>2022 Future Medicine Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c343t-33f2212a30cbb947d677c647d91c2e1b388869e3b62ede94445309c44cdabff23</citedby><cites>FETCH-LOGICAL-c343t-33f2212a30cbb947d677c647d91c2e1b388869e3b62ede94445309c44cdabff23</cites><orcidid>0000-0002-6768-8466 ; 0000-0002-3081-6647</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36325841$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Stoccoro, Andrea</creatorcontrib><creatorcontrib>Gallo, Roberta</creatorcontrib><creatorcontrib>Calderoni, Sara</creatorcontrib><creatorcontrib>Cagiano, Romina</creatorcontrib><creatorcontrib>Muratori, Filippo</creatorcontrib><creatorcontrib>Migliore, Lucia</creatorcontrib><creatorcontrib>Grossi, Enzo</creatorcontrib><creatorcontrib>Coppedè, Fabio</creatorcontrib><title>Artificial neural networks reveal sex differences in gene methylation, and connections between maternal risk factors and symptom severity in autism spectrum disorder</title><title>Epigenomics</title><addtitle>Epigenomics</addtitle><description>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.</description><subject>artificial neural networks</subject><subject>ASD</subject><subject>autism spectrum disorder</subject><subject>Autism Spectrum Disorder - diagnosis</subject><subject>Autism Spectrum Disorder - genetics</subject><subject>Child</subject><subject>DNA methylation</subject><subject>epigenetics</subject><subject>Female</subject><subject>Humans</subject><subject>Infant, Newborn</subject><subject>Male</subject><subject>maternal risk factors</subject><subject>Methylation</subject><subject>Neural Networks, Computer</subject><subject>Premature Birth</subject><subject>Risk Factors</subject><subject>Sex Characteristics</subject><subject>sex difference</subject><issn>1750-1911</issn><issn>1750-192X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1kUFv1DAQhS0Eaqulx16RjxwIxHY2iY9VBQWpEheQuFmOMwbTxA4zTmF_UP8nTrf0hi_Pz3r-RprH2IWo30opunewhErWUla16PQzdia6fV0JLb89f7oLccrOiX7W5SjZ61acsFPVKrnvG3HG7i8xBx9csBOPsOKD5N8Jb4kj3EHxBH_4GLwHhOiAeIj8O0TgM-Qfh8nmkOIbbuPIXYoR3OaJDwUCEPlsM2AsFAx0y711OSE9pOkwLznNBX8HGPJh49o1BypPS8HgOpexlHAEfMleeDsRnD_qjn398P7L1cfq5vP1p6vLm8qpRuVKKV_WIq2q3TDophvbrnNtUS2cBDGovu9bDWpoJYygm6bZq1q7pnGjHbyXasdeH7kLpl8rUDZzIAfTZCOklYzslOhE35dpO1Ydow4TEYI3C4bZ4sGI2mzlmFKO2coxWzkl_-oRvQ4zjE_pf1WUgD4G_JpXBHJhW7c5uvKjdBThP_C_5-eiqA</recordid><startdate>20221001</startdate><enddate>20221001</enddate><creator>Stoccoro, Andrea</creator><creator>Gallo, Roberta</creator><creator>Calderoni, Sara</creator><creator>Cagiano, Romina</creator><creator>Muratori, Filippo</creator><creator>Migliore, Lucia</creator><creator>Grossi, Enzo</creator><creator>Coppedè, Fabio</creator><general>Future Medicine Ltd</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-6768-8466</orcidid><orcidid>https://orcid.org/0000-0002-3081-6647</orcidid></search><sort><creationdate>20221001</creationdate><title>Artificial neural networks reveal sex differences in gene methylation, and connections between maternal risk factors and symptom severity in autism spectrum disorder</title><author>Stoccoro, Andrea ; Gallo, Roberta ; Calderoni, Sara ; Cagiano, Romina ; Muratori, Filippo ; Migliore, Lucia ; Grossi, Enzo ; Coppedè, Fabio</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c343t-33f2212a30cbb947d677c647d91c2e1b388869e3b62ede94445309c44cdabff23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>artificial neural networks</topic><topic>ASD</topic><topic>autism spectrum disorder</topic><topic>Autism Spectrum Disorder - diagnosis</topic><topic>Autism Spectrum Disorder - genetics</topic><topic>Child</topic><topic>DNA methylation</topic><topic>epigenetics</topic><topic>Female</topic><topic>Humans</topic><topic>Infant, Newborn</topic><topic>Male</topic><topic>maternal risk factors</topic><topic>Methylation</topic><topic>Neural Networks, Computer</topic><topic>Premature Birth</topic><topic>Risk Factors</topic><topic>Sex Characteristics</topic><topic>sex difference</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Stoccoro, Andrea</creatorcontrib><creatorcontrib>Gallo, Roberta</creatorcontrib><creatorcontrib>Calderoni, Sara</creatorcontrib><creatorcontrib>Cagiano, Romina</creatorcontrib><creatorcontrib>Muratori, Filippo</creatorcontrib><creatorcontrib>Migliore, Lucia</creatorcontrib><creatorcontrib>Grossi, Enzo</creatorcontrib><creatorcontrib>Coppedè, Fabio</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Epigenomics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Stoccoro, Andrea</au><au>Gallo, Roberta</au><au>Calderoni, Sara</au><au>Cagiano, Romina</au><au>Muratori, Filippo</au><au>Migliore, Lucia</au><au>Grossi, Enzo</au><au>Coppedè, Fabio</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Artificial neural networks reveal sex differences in gene methylation, and connections between maternal risk factors and symptom severity in autism spectrum disorder</atitle><jtitle>Epigenomics</jtitle><addtitle>Epigenomics</addtitle><date>2022-10-01</date><risdate>2022</risdate><volume>14</volume><issue>19</issue><spage>1181</spage><epage>1195</epage><pages>1181-1195</pages><issn>1750-1911</issn><eissn>1750-192X</eissn><abstract>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.</abstract><cop>England</cop><pub>Future Medicine Ltd</pub><pmid>36325841</pmid><doi>10.2217/epi-2022-0179</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0002-6768-8466</orcidid><orcidid>https://orcid.org/0000-0002-3081-6647</orcidid></addata></record> |
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| language | eng |
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| source | MEDLINE; PubMed Central |
| 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 |
| title | Artificial neural networks reveal sex differences in gene methylation, and connections between maternal risk factors and symptom severity in autism spectrum disorder |
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