Multi-Omics for Maternal Health after Preeclampsia

先兆子痫后孕产妇健康的多组学

• 批准号: 10744684
• 负责人: Marni Jacobs
• 金额: $ 83.37万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-12 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10744684
• 关键词: 37 weeks gestation; Academic Medical Centers; Adherence; Affect; Animals; Black American; Black Populations; Blood Pressure; Blood specimen; Cardiovascular Diseases; Cell Nucleus; Cells; Cessation of life; Chronic; Clinical; Collaborations; Collection; Communities; Computer Models; Consent; Coupled; Data; Data Analyses; Developed Countries; Disease; Enrollment; Environmental Exposure; Ethnic Population; Funding; Genetic; Genomics; Gestational Age; Goals; Grant; Health; Heterogeneity; Hispanic Americans; Hispanic Populations; Home; Hospitalization; Hypertension; Infrastructure; Link; Machine Learning; Maternal Health; Maternal Mortality; Measurement; Measures; Methods; Molecular; Monitor; Morbidity - disease rate; Multiomic Data; Native Americans; Outcome; Participant; Patients; Phenotype; Physiological; Physiology; Placenta; Plasma; Population; Population Heterogeneity; Postpartum Period; Pre-Eclampsia; Pregnancy; Pregnancy Complications; Pregnancy Trimesters; Preventive therapy; Production; Proteomics; Recontacts; Research Personnel; Resources; Risk; Sample Size; Second Pregnancy Trimester; Serum; Site; Source; Study Subject; Testing; Tissues; Umbilical Cord Blood; United States National Institutes of Health; Urine; Visit; Whole Blood; Woman; biobank; clinical phenotype; community engagement; data sharing; disorder subtype; epigenomics; experience; fetal; fitness; genome sequencing; high risk; improved; in vitro Model; knowledge of results; lipidomics; maternal morbidity; metabolomics; method development; mortality; multiple omics; novel; personalized approach; pregnancy disorder; pregnancy hypertension; pregnancy related death; preventable death; protocol development; racial population; recruit; research study; risk stratification; social health determinants; stem; transcriptome sequencing; transcriptomics; trend; whole genome

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.

概括 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 持续二十年不断增加。近三分之二的孕产妇死亡是可以预防的，其中包括心血管疾病 (CVD) 是主要原因。先兆子痫 (PE) 和其他妊娠期高血压疾病 (HDP) 是主要疾病 孕产妇和胎儿发病率和死亡率的来源。值得注意的是，一半与妊娠相关的孕产妇死亡 发生在分娩后一年。尽管所有种族和族裔群体的孕产妇发病率都在增加， 黑人、西班牙裔和美洲原住民女性受到的影响尤为严重。我们和其他人有 研究表明，PE/HDP 与产后 CVD 之间存在很强的相关性，但目前尚不清楚这些是否 这些联系源于怀孕前或怀孕期间的潜在遗传、环境和生理状态。 PE/HDP 的直接影响。 PE/HDP 的异质性和复杂性需要一种有意识的方法 研究一系列临床表型，并整合表型、环境暴露 (EE) 和多组学 使用计算建模和机器学习的数据来构建不同的多组件签名 PE/HDP 亚型并阐明它们与孕产妇健康结果的关系，最终使我们能够 制定精确的方法来优化产后孕产妇健康。 PE 后孕产妇健康多组学 (MOM-Health) 疾病研究网站的中心目标是使用 与综合表型和 EE 测量相关的生物体液和胎盘组织的多组学分析 不同人群揭示从 PE/HDP 到可干预产后孕产妇健康的机制 结果。我们将招募 680 名怀孕第二个月的参与者（180 名高风险和 500 名低风险） 通过一系列表型和 EE 数据以及母体生物样本的收集来跟踪他们整个怀孕过程， 产生 200 个 PE/HDP 病例和 480 个对照病例。分娩时，将采集胎盘组织和脐带血样本 从所有 680 名参与者中收集。所有 200 个病例和 100 个对照的子集将被跟踪一年 产后，收集系列表型（包括功能性 CV 测试）和 EE 测量值 母体生物样本。我们预计与将生成多组学数据的 OPC 密切合作 从收集的生物样本以及DACC中，对多组学进行综合分析和解释， 表型和 EE 数据。我们的网站由在招聘和保留方面拥有丰富经验的调查人员领导 通过新颖的社区参与资源以及 NIH 联盟的经验来覆盖不同人群 使用组学数据进行疾病亚型分析和不同生物样本的生物库。此外，我们将利用 我们小组正在进行 NIH 资助的工作，其中胎盘单细胞/单核和空间转录组学 正在执行当前研究中循环目标的优先顺序。该项目有潜力提供信息 整合纵向多成分和多组学数据并有助于改进机制的方法 了解 PE/HDP 以及患有 PE/HDP 的女性的风险分层。