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 po...