Machine learning analysis of pregnancy data enables early identification of a subpopulation of newborns with ASD.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
25 03 2021
25 03 2021
Historique:
received:
10
08
2020
accepted:
10
03
2021
entrez:
26
3
2021
pubmed:
27
3
2021
medline:
21
10
2021
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 33767300
doi: 10.1038/s41598-021-86320-0
pii: 10.1038/s41598-021-86320-0
pmc: PMC7994821
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
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