DOI: 10.5061/dryad.gqnk98srt
title: The multiomic landscape of epidemiological factors contributing to preterm birth in low- and middle-income countries

description: Preterm birth (PTB) is the leading cause of death in children under five,
 yet comprehensive studies are hindered by its multiple complex etiologies.
 Epidemiological associations between PTB and maternal characteristics have
 been previously described. This work employed multiomic profiling and
 multivariate modeling to investigate the biological signatures of these
 characteristics. Maternal covariates were collected during pregnancy from
 13,841 pregnant women across five sites. Plasma samples from 231
 participants were analyzed to generate proteomic, metabolomic, and
 lipidomic datasets. Machine learning models showed robust performance for
 the prediction of PTB (AUROC=0.70), time-to-delivery (r=0.65), maternal
 age (r=0.59), gravidity (r=0.56), and BMI (r=0.81). Time-to-delivery
 biological correlates included fetal-associated proteins (e.g., ALPP, AFP,
 PGF) and immune proteins (e.g., PD-L1, CCL28, LIFR). Maternal age
 negatively correlated collagen COL9A1; gravidity with endothelial NOS and
 inflammatory chemokine CXCL13; and BMI with leptin and structural protein
 FABP4. These results provide an integrated view of epidemiological factors
 associated with PTB and identify biological signatures of clinical
 covariates impacting this disease. 
description: The study population comprised
 pregnant women selected from 5 biorepository-supported cohorts in Matlab,
 Bangladesh; Lusaka, Zambia; Sylhet, Bangladesh; Karachi, Pakistan; and
 Pemba, Tanzania. The study was approved by the Stanford University
 Institutional Review Board, and ethical exemptions were sought and
 obtained independently from the respective country by each birth cohort
 supported by the Alliance for Maternal and Newborn Health Improvement
 (AMANHI) and the Global Alliance to Prevent Prematurity and Stillbirth
 (GAPPS) biorepositories. Written informed participant consent was obtained
 from each participant in the original cohorts and extends to the present
 study. No compensation or incentives were provided for participating in
 this study. We followed the Transparent Reporting of a Multivariable
 Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) reporting
 guideline. This study analyzed plasma collected from May 2014 to August
 2018. Gestational age at the time of sampling was
 determined by ultrasonographic assessment. From all AMANHI and GAPPS cohorts, trained
 phlebotomists or nursing staff collected blood samples for centrifugation
 and aliquoting of serum, plasma, and buffy coat for storage in -80°C and
 future analyses. Collection and processing of all sample types were
 performed according to harmonized operating procedures at all study
 cohorts. Lipidomics and metabolomics features were generated using
 untargeted liquid-chromatography mass spectrometry, while proteomic
 features were generated using a highly multiplexed immunoassay (Olink
 Proteomics Inc.).
description: Scripts are in R and files are .csv and .xlsx. Xlsx files can be
 opened with Google Sheets as an open-source alternative.