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

The DREAM Preterm Birth Prediction Challenge Consortium

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

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).

Original languageEnglish (US)
Article number100323
JournalCell Reports Medicine
Volume2
Issue number6
DOIs
StatePublished - Jun 15 2021
Externally publishedYes

Keywords

  • aptamers
  • collaborative competition
  • human transcriptome arrays
  • machine learning
  • plasma proteomics
  • predictive modeling
  • preterm labor and delivery
  • spontaneous preterm birth
  • whole blood transcriptomics

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

Fingerprint

Dive into the research topics of 'Crowdsourcing assessment of maternal blood multi-omics for predicting gestational age and preterm birth'. Together they form a unique fingerprint.

Cite this