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Multiomic signals associated with maternal epidemiological factors contributing to preterm birth in low- and

Camilo A Espinosa1,2,3, Waqasuddin Khan4, Rasheda Khanam5

  • 1Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.

Science Advances
|May 24, 2023
PubMed

Insights

This study identifies biological markers linked to preterm birth (PTB) and maternal factors like age and BMI. Understanding these signatures can improve prediction and management of PTB, a leading cause of infant mortality.

Area of Science:

  • Reproductive biology
  • Genomics
  • Proteomics
  • Metabolomics
  • Lipidomics

Background:

  • Preterm birth (PTB) is a major global health challenge, leading to significant infant mortality.
  • The complex etiologies of PTB necessitate advanced research methods for comprehensive understanding.
  • Previous studies identified epidemiological links between PTB and maternal characteristics, but biological underpinnings remain incompletely understood.

Purpose of the Study:

  • To investigate the biological signatures associated with epidemiological factors of preterm birth (PTB).
  • To explore the multiomic (proteomic, metabolomic, lipidomic) basis of maternal characteristics influencing PTB.
  • To develop predictive models for PTB and related clinical covariates using biological data.

Main Methods:

  • Collected maternal covariates from 13,841 pregnant women across five international sites.
  • Generated proteomic, metabolomic, and lipidomic data from plasma samples of 231 participants.
  • Employed multivariate modeling and machine learning for robust prediction of PTB, time-to-delivery, maternal age, gravidity, and BMI.

Main Results:

  • Machine learning models accurately predicted PTB (AUROC = 0.70) and time-to-delivery (r = 0.65).
  • Biological correlates for time-to-delivery included fetal-associated proteins (ALPP, AFP, PGF) and immune proteins (PD-L1, CCL28, LIFR).
  • Maternal age, gravidity, and BMI showed distinct correlations with specific proteins like COL9A1, CXCL13, and FABP4, respectively.

Conclusions:

  • This study provides an integrated multiomic view of epidemiological factors associated with PTB.
  • Identified specific biological signatures linked to maternal age, gravidity, and BMI.
  • The findings offer novel insights into the biological mechanisms underlying PTB and associated maternal characteristics, potentially aiding clinical management.

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