Summary
The U.S. has the highest maternal mortality rate of all industrialized nations, a trend that has been steadily
increasing for two decades. Nearly 2 in 3 maternal deaths are preventable, with cardiovascular disease (CVD)
being the leading cause. Preeclampsia (PE) and other hypertensive disorders of pregnancy (HDP) are major
sources of maternal and fetal morbidity and mortality. Notably, half of pregnancy-associated maternal deaths
occur in the year after delivery. Although maternal morbidity is increasing across all racial and ethnic groups,
Black, Hispanic, and Native American women are disproportionately affected. We, and others, have
demonstrated a strong association between PE/HDP and postpartum CVD, but it remains unclear whether these
links stem from an underlying genetic, environmental, and physiologic state that precedes pregnancy or is a
direct effect of PE/HDP. The heterogeneity and complexity of PE/HDP demands an approach that intentionally
studies a range of clinical phenotypes, and integrates phenotypic, environmental exposure (EE), and multi-omic
data using computational modeling and machine learning to build multi-component signatures of the different
PE/HDP subtypes and unravel their relationships with maternal health outcomes, ultimately allowing us to
develop a precision approach to optimize postpartum maternal health.
The central goal of the Multi-Omics for Maternal Health after PE (MOM-Health) Disease Study Site is to use
multi-omic analyses of biofluids and placental tissue linked with comprehensive phenotypic and EE measures in
a diverse population to uncover mechanisms leading from PE/HDP to intervenable postpartum maternal health
outcomes. We will recruit 680 participants (180 high-risk and 500 low-risk) in the 2nd trimester of pregnancy and
follow them through pregnancy with serial collections of phenotypic and EE data and maternal biosamples,
yielding 200 cases with PE/HDP and 480 controls. At delivery, placental tissue and cord blood samples will be
collected from all 680 participants. All 200 cases and a subset of 100 controls will be followed for one year
postpartum, with collection of serial phenotypic (including functional CV testing) and EE measurements and
maternal biosamples. We anticipate collaborating closely with the OPCs that will be generating multi-omic data
from the collected biosamples, as well as the DACC, on integrated analysis and interpretation of the multi-omic,
phenotypic, and EE data. Our sites are led by investigators with extensive experience in recruitment and retention
of diverse populations through novel community-engagement resources, as well as experience in NIH consortia
using omic data for disease subtyping and biobanking of diverse biosamples. In addition, we will leverage
ongoing NIH-funded efforts in our group in which placental single cell/single nucleus and spatial transcriptomics
is being performed to prioritize circulating targets in the current study. This project has the potential to inform
methods to integrate longitudinal multi-component and multi-omic data and contribute to improved mechanistic
understanding of PE/HDP and risk stratification of women with PE/HDP.