Machine learning analysis of pregnancy data enables early identification of a subpopulation of newborns with ASD

Abstract To identify newborns at risk of developing ASD and to detect ASD biomarkers early after birth, we compared retrospectively ultrasound and biological measurements of babies diagnosed later with ASD or neurotypical (NT) that are collected routinely during pregnancy and birth. We used a superv...

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Autores principales:	Hugues Caly, Hamed Rabiei, Perrine Coste-Mazeau, Sebastien Hantz, Sophie Alain, Jean-Luc Eyraud, Thierry Chianea, Catherine Caly, David Makowski, Nouchine Hadjikhani, Eric Lemonnier, Yehezkel Ben-Ari
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/aa746bd949174332adf32d8f2626063e
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spelling	oai:doaj.org-article:aa746bd949174332adf32d8f2626063e2021-12-02T17:04:06ZMachine learning analysis of pregnancy data enables early identification of a subpopulation of newborns with ASD10.1038/s41598-021-86320-02045-2322https://doaj.org/article/aa746bd949174332adf32d8f2626063e2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86320-0https://doaj.org/toc/2045-2322Abstract To identify newborns at risk of developing ASD and to detect ASD biomarkers early after birth, we compared retrospectively ultrasound and biological measurements of babies diagnosed later with ASD or neurotypical (NT) that are collected routinely during pregnancy and birth. We used a supervised machine learning algorithm with a cross-validation technique to classify NT and ASD babies and performed various statistical tests. With a minimization of the false positive rate, 96% of NT and 41% of ASD babies were identified with a positive predictive value of 77%. We identified the following biomarkers related to ASD: sex, maternal familial history of auto-immune diseases, maternal immunization to CMV, IgG CMV level, timing of fetal rotation on head, femur length in the 3rd trimester, white blood cell count in the 3rd trimester, fetal heart rate during labor, newborn feeding and temperature difference between birth and one day after. Furthermore, statistical models revealed that a subpopulation of 38% of babies at risk of ASD had significantly larger fetal head circumference than age-matched NT ones, suggesting an in utero origin of the reported bigger brains of toddlers with ASD. Our results suggest that pregnancy follow-up measurements might provide an early prognosis of ASD enabling pre-symptomatic behavioral interventions to attenuate efficiently ASD developmental sequels.Hugues CalyHamed RabieiPerrine Coste-MazeauSebastien HantzSophie AlainJean-Luc EyraudThierry ChianeaCatherine CalyDavid MakowskiNouchine HadjikhaniEric LemonnierYehezkel Ben-AriNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hugues Caly Hamed Rabiei Perrine Coste-Mazeau Sebastien Hantz Sophie Alain Jean-Luc Eyraud Thierry Chianea Catherine Caly David Makowski Nouchine Hadjikhani Eric Lemonnier Yehezkel Ben-Ari Machine learning analysis of pregnancy data enables early identification of a subpopulation of newborns with ASD
description	Abstract To identify newborns at risk of developing ASD and to detect ASD biomarkers early after birth, we compared retrospectively ultrasound and biological measurements of babies diagnosed later with ASD or neurotypical (NT) that are collected routinely during pregnancy and birth. We used a supervised machine learning algorithm with a cross-validation technique to classify NT and ASD babies and performed various statistical tests. With a minimization of the false positive rate, 96% of NT and 41% of ASD babies were identified with a positive predictive value of 77%. We identified the following biomarkers related to ASD: sex, maternal familial history of auto-immune diseases, maternal immunization to CMV, IgG CMV level, timing of fetal rotation on head, femur length in the 3rd trimester, white blood cell count in the 3rd trimester, fetal heart rate during labor, newborn feeding and temperature difference between birth and one day after. Furthermore, statistical models revealed that a subpopulation of 38% of babies at risk of ASD had significantly larger fetal head circumference than age-matched NT ones, suggesting an in utero origin of the reported bigger brains of toddlers with ASD. Our results suggest that pregnancy follow-up measurements might provide an early prognosis of ASD enabling pre-symptomatic behavioral interventions to attenuate efficiently ASD developmental sequels.
format	article
author	Hugues Caly Hamed Rabiei Perrine Coste-Mazeau Sebastien Hantz Sophie Alain Jean-Luc Eyraud Thierry Chianea Catherine Caly David Makowski Nouchine Hadjikhani Eric Lemonnier Yehezkel Ben-Ari
author_facet	Hugues Caly Hamed Rabiei Perrine Coste-Mazeau Sebastien Hantz Sophie Alain Jean-Luc Eyraud Thierry Chianea Catherine Caly David Makowski Nouchine Hadjikhani Eric Lemonnier Yehezkel Ben-Ari
author_sort	Hugues Caly
title	Machine learning analysis of pregnancy data enables early identification of a subpopulation of newborns with ASD
title_short	Machine learning analysis of pregnancy data enables early identification of a subpopulation of newborns with ASD
title_full	Machine learning analysis of pregnancy data enables early identification of a subpopulation of newborns with ASD
title_fullStr	Machine learning analysis of pregnancy data enables early identification of a subpopulation of newborns with ASD
title_full_unstemmed	Machine learning analysis of pregnancy data enables early identification of a subpopulation of newborns with ASD
title_sort	machine learning analysis of pregnancy data enables early identification of a subpopulation of newborns with asd
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/aa746bd949174332adf32d8f2626063e
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Machine learning analysis of pregnancy data enables early identification of a subpopulation of newborns with ASD

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