Crowdsourcing assessment of maternal blood multi-omics for predicting gestational age and preterm birth

Identification of pregnancies at risk of preterm birth (PTB), the leading cause of newborn deaths, remains challenging given the syndromic nature of the disease. We report a longitudinal multi-omics study coupled with a DREAM challenge to develop predictive models of PTB. The findings indicate that whole-blood gene expression predicts ultrasound-based gestational ages in normal and complicated pregnancies (r = 0.83) and, using data collected before 37 weeks of gestation, also predicts the delivery date in both normal pregnancies (r = 0.86) and those with spontaneous preterm birth (r = 0.75). Based on samples collected before 33 weeks in asymptomatic women, our analysis suggests that expression changes preceding preterm prelabor rupture of the membranes are consistent across time points and cohorts and involve leukocyte-mediated immunity. Models built from plasma proteomic data predict spontaneous preterm delivery with intact membranes with higher accuracy and earlier in pregnancy than transcriptomic models (AUROC = 0.76 versus AUROC = 0.6 at 27–33 weeks of gestation). [Display omitted] Blood gene expression predicts gestational age in normal and complicated pregnanciesRNA changes preceding preterm prelabor rupture of the membranes are shared between cohortsPlasma proteomic profiles from asymptomatic women predict spontaneous preterm birth Harnessing the wisdom of crowds in a DREAM Challenge, Tarca et al. developed methods to predict gestational age and preterm birth from longitudinal multi-omics data. The authors show that blood RNAs predict ultrasound-based gestational age, and they identify molecular changes preceding a diagnosis of spontaneous preterm birth in asymptomatic women.