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

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Tarca, Adi L 
Pataki, Bálint Ármin 
Romero, Roberto 
Sirota, Marina 
Guan, Yuanfang 


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).



aptamers, collaborative competition, human transcriptome arrays, machine learning, plasma proteomics, predictive modeling, preterm labor and delivery, spontaneous preterm birth, whole blood transcriptomics, Adult, Asymptomatic Diseases, Biomarkers, Blood Proteins, Cell-Free Nucleic Acids, Crowdsourcing, Female, Gestational Age, Humans, Infant, Newborn, Longitudinal Studies, Pre-Eclampsia, Pregnancy, Premature Birth, Proteomics, ROC Curve, Transcriptome

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Cell Rep Med

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Elsevier BV