Multi-Omics for Maternal Health after Preeclampsia

总结 美国是所有工业化国家中孕产妇死亡率最高的国家，这一趋势一直在稳步发展。 增长了二十年。近三分之二的孕产妇死亡是可以预防的，包括心血管疾病（CVD） 成为主要原因。先兆子痫（PE）和其他妊娠期高血压疾病（HDP）是主要的 产妇和胎儿发病率和死亡率的来源。值得注意的是， 发生在分娩后的一年。尽管所有种族和族裔群体的孕产妇发病率都在上升， 黑人、西班牙裔和美洲原住民妇女受到不成比例的影响。我们和其他人 研究表明，PE/HDP和产后CVD之间有很强的相关性，但尚不清楚这些是否 联系源于潜在的遗传，环境和生理状态，怀孕前或怀孕后， PE/HDP的直接作用。PE/HDP的异质性和复杂性要求采用一种方法， 研究了一系列临床表型，并整合了表型，环境暴露（EE）和多组学 数据使用计算建模和机器学习来构建不同的多组分签名， PE/HDP亚型，并解开它们与孕产妇健康结果的关系，最终使我们能够 制定精确的方法，以优化产后孕产妇健康。 PE（MOM-Health）疾病后孕产妇健康多组学研究中心的中心目标是使用 生物液体和胎盘组织的多组学分析与综合表型和EE测量相关， 一个多样化的人群，以揭示从PE/HDP到可干预的产后孕产妇健康的机制 结果。我们将招募680名参与者（180名高风险和500名低风险）在怀孕的第二个三个月， 通过连续收集表型和EE数据以及母体生物样品， PE/HDP组200例，对照组480例。分娩时，将采集胎盘组织和脐带血样本， 共680名参与者。所有200例病例和100例对照的子集将随访一年 产后，收集一系列表型（包括功能CV检测）和EE测量结果， 母体生物样本。我们期待与将生成多组学数据的OPC密切合作 从收集的生物样品，以及DACC，对多组学的综合分析和解释， 表型和EE数据。我们的研究中心由在招聘和保留方面具有丰富经验的研究人员领导 通过新颖的社区参与资源以及NIH联盟的经验， 使用组学数据进行疾病分型和不同生物样本的生物库。此外，我们将利用 我们组正在进行NIH资助的研究，其中胎盘单细胞/单核和空间转录组学 在当前研究中，正在进行循环靶点的优先级排序。该项目有可能为 整合纵向多组分和多组学数据的方法，并有助于改善 了解PE/HDP和PE/HDP女性的风险分层。

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.