Background Etiopathogenesis of preterm birth (PTB) is multifactorial, with a universe of risk factors interplaying between the mother and the environment. It is of utmost importance to identify the most informative factors in order to estimate the degree of PTB risk and trace an individualized profile. The aims of the present study were: 1) to identify all acknowledged risk factors for PTB and to select the most informative ones for defining an accurate model of risk prediction; 2) to verify predictive accuracy of the model and 3) to identify group profiles according to the degree of PTB risk based on the most informative factors. Methods The Maternal Frailty Inventory (MaFra) was created based on a systematic review of the literature including 174 identified intrauterine (IU) and extrauterine (EU) factors. A sample of 111 pregnant women previously categorized in low or high risk for PTB below 37 weeks, according to ACOG guidelines, underwent the MaFra Inventory. First, univariate logistic regression enabled p-value ordering and the Akaike Information Criterion (AIC) selected the model including the most informative MaFra factors. Second, random forest classifier verified the overall predictive accuracy of the model. Third, fuzzy c-means clustering assigned group membership based on the most informative MaFra factors. Results The most informative and parsimonious model selected through AIC included Placenta Previa, Pregnancy Induced Hypertension, Antibiotics, Cervix Length, Physical Exercise, Fetal Growth, Maternal Anxiety, Preeclampsia, Antihypertensives. The random forest classifier including only the most informative IU and EU factors achieved an overall accuracy of 81.08% and an AUC of 0.8122. The cluster analysis identified three groups of typical pregnant women, profiled on the basis of the most informative IU and EU risk factors from a lower to a higher degree of PTB risk, which paralleled time of birth delivery. Conclusions This study establishes a generalized methodology for building-up an evidence-based holistic risk assessment for PTB to be used in clinical practice. Relevant and essential factors were selected and were able to provide an accurate estimation of degree of PTB risk based on the most informative constellation of IU and EU factors.

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A link between maternal anxiety during pregnancy and adverse socio‐emotional outcomes in childhood has been consistently sustained on the very early neurodevelopmental alteration of structural pathways between fetal limbic and cortical brain regions. In this study, we provide follow‐up evidence for a feed‐forward model linking (i) maternal anxiety, (ii) fetal functional neurodevelopment, (iii) neonatal functional network organization with (iv) socio‐emotional neurobehavioral development in early childhood. Namely, we investigate a sample of 16 mother–fetus dyads and show how a maternal state–trait anxiety profile with pregnancy‐specific worries can significantly influence functional synchronization patterns between regions of the fetal limbic system (i.e., hippocampus and amygdala) and the neocortex, as assessed through resting‐state functional magnetic resonance imaging. Generalization of the findings was supported by leave‐one‐out cross‐validation. We further show how this maternal–fetal cross‐talk propagates to functional network topology in the neonate, specifically targeting connector hubs, and further maps onto socio‐emotional profiles, assessed through Bayley‐III socio‐emotional scale in early childhood (i.e., in the 12–24 months range). Based on this evidence, we put forward the hypothesis of a “Maternal‐Fetal‐Neonatal Anxiety Backbone”, through which neurobiological changes driven by maternal anxiety could trigger a divergence in the establishment of a cognitive–emotional development blueprint, in terms of the nascent functional homeostasis between bottom‐up limbic and top‐down higher‐order neuronal circuitry.