[Early prognostic of ASD: A challenge]. / Pronostiquer tôt les troubles du spectre autistique : Un défi ?

Autism Spectrum Disorders (ASD) are born in the womb generated by intrauterine genetic or environmental insult. ASD diagnostic is made at the age of 3-5 years in Europe and in the US. Relying on this, we have tested the hypothesis of identifying already at birth babies who might be diagnosed later with ASD, thereby facilitating an early use of psychoeducative techniques to attenuate the severity of the symptoms. Here, we discuss the various approaches that have been used to enable an early diagnosis. We have ourselves used an approach based on a "without a priori" machine learning analysis of all maternity biological and ultrasound data available in French maternities (around 116) in utero and after birth. This program made it possible to identify at birth almost all (96%) of babies who will be later neurotypical and around half of those who will be diagnosed with ASD. Some of the parameters allowing this identification were largely unexpected with no known links with ASD. This approach will enable an early identification of babies at risk, but also might be used to diagnose ASD later on, and perhaps could help to get a better understanding of the heterogeneity of ASD.

Title: Pronostiquer tôt les troubles du spectre autistique : Un défi ? Abstract: Les troubles du spectre de l'autisme (TSA) « naissent ¼ in utero à la suite d'évènements pathologiques génétiques ou environnementaux. Le diagnostic des TSA n'est cependant effectué que vers l'âge de 3-5 ans en Europe et aux États-Unis. Un pronostic précoce permettrait pourtant d'atténuer la sévérité des atteintes cognitives, grâce à des approches psycho-éducatives. Une large panoplie d'approches a été suggérée pour établir un pronostic précoce des TSA, se fondant sur l'imagerie cérébrale, sur des enregistrements EEG, sur des biomarqueurs sanguins ou sur l'analyse des contacts visuels. Nous avons développé une approche fondée sur l'analyse par machine learning des données biologiques et échographiques recueillies en routine, du début de la grossesse au lendemain de la naissance, dans les maternités françaises. Ce programme qui permet d'identifier la presque totalité des bébés neurotypiques et la moitié des bébés qui auront un diagnostic de TSA quelques années plus tard, permet aussi d'identifier les paramètres ayant un impact sur le pronostic. Si quelques-uns d'entre eux étaient attendus, d'autres n'ont aucun lien avec les TSA. L'étude sans a priori des données de maternité devrait ainsi permettre un pronostic des TSA dès la naissance, ainsi que de mieux comprendre la pathogenèse de ces syndromes et de les traiter plus tôt.

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

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.