Circulating Immune Biomarkers and Retinopathy of Prematurity

循环免疫生物标志物和早产儿视网膜病变

• 批准号: 8373245
• 负责人: Olaf Dammann
• 金额: $ 54.94万
• 依托单位: TUFTS UNIVERSITY BOSTON
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-02-01 至 2017-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8373245
• 关键词: Bacteriology; Biological Markers; Birth; Blindness; Blood; Blood specimen; Brain; Cerebrum; Cohort Studies; Data; Data Analyses; Databases; Disease; Epidemiology; Erythropoiesis; Etiology; Exposure to; Eye; Funding; Gestational Age; Goals; Histologic; Histology; Immune; Infant; Inflammation; Intestines; Knowledge; Measurement; Measures; Modeling; Neonatal; Newborn Infant; Organism; Outcome; Oxidative Stress; Oxygen; Pathogenesis; Pattern; Perinatal; Placenta; Play; Pre-Eclampsia; Pregnancy; Premature Infant; Prevention; Prevention strategy; Preventive Intervention; Proteins; Publishing; Relative Risks; Research; Research Personnel; Retinal Diseases; Retinopathy of Prematurity; Risk; Role; Sample Size; Sampling; Spottings; Techniques; Testing; United States National Institutes of Health; Whole Blood; Work; angiogenesis; base; design; improved; member; novel; postnatal; pregnancy disorder; public health relevance; treatment strategy; white matter damage

DESCRIPTION (provided by applicant): Retinopathy of prematurity (ROP) is a potentially preventable disorder of the retina that occurs predominantly in preterm newborns. Recent evidence suggests that exposure to systemic inflammation might contribute to the etiology and pathogenesis of ROP. In this application, we propose to (objective #1) measure in existing blood samples from a multi-center, NIH-funded cohort study of extremely low gestational age newborns (ELGAN study, U01 NS40069) 18 protein biomarkers in 1,206 ELGANs and add this information to an existing database of 25 biomarkers we have already measured in 924 of the same 1,206 ELGANs. We will also measure these previous 25 markers in the remainder of 282 newborns. The result will be the currently largest biomarker database worldwide of 43 biomarkers (from postnatal days 1, 7, 14, 21, and 28) available for 1,206 newborns with a gestational age <28wks. The new biomarker measurements will be done by the same ELGAN-Co-investigators who measured the initial 25 biomarkers using the Meso Scale Discovery (MSD) multiplex platform. The additional biomarkers were selected specifically for ROP association studies, based on published evidence that the availability of oxygen, the presence of inflammation, the concepts of angiogenesis and erythropoiesis, as well as pre-existing maternal pregnancy conditions such as preeclampsia all appear to play roles in ROP etiology and pathogenesis. We also propose (objective #2) to subsequently perform epidemiologic (risk) analyses using advanced multivariable biostatistical techniques. We will test two specific null-hypotheses: (1) Biomarkers do not predict severe ROP occurrence; and (2) biomarkers do not predict ROP progression. Both hypotheses will be tested by members of the original ELGAN data analysis team in comprehensive multivariable regression models that allow for appropriate control for confounding and for stratified analyses targeted at the discovery of effect modifiers. Pre hoc power analyses suggest that our sample size is large enough to detect appreciable relative risks with sufficient statistical power. The results from our project will have a significnt impact on our current understanding of ROP pathogenesis and will help pave the way towards new prevention and treatment strategies.

描述(申请人提供)：早产儿视网膜病变(ROP)是一种潜在的可预防的视网膜疾病，主要发生在早产儿中。最近的证据表明，暴露于全身炎症可能参与了ROP的病因和发病机制。在这项应用中，我们建议(目标1)从NIH资助的多中心极低胎龄新生儿队列研究(ELGAN研究，U01 NS40069)的现有血液样本中测量1,206个Elgans中的18个蛋白质生物标记物，并将此信息添加到现有的数据库中，该数据库包含25个生物标记物，我们已经在相同的1,206个Elgans中的924个中测量过。我们还将在剩余的282名新生儿中测量这25个标志物。这一结果将成为目前全球最大的生物标记物数据库，包含43个生物标记物(出生后1、7、14、21和28天)，可用于1206名胎龄为28周的新生儿。新的生物标记物测量将由使用中尺度发现(MSD)多路平台测量最初25个生物标记物的相同Elgan-Co-Investigers进行。额外的生物标志物是专门为ROP相关性研究选择的，基于已发表的证据，即氧气的可获得性、炎症的存在、血管生成和红细胞生成的概念，以及先前存在的孕妇怀孕条件，如先兆子痫，似乎都在ROP的病因和发病机制中发挥作用。我们还建议(目标2)随后使用先进的多变量生物统计技术进行流行病学(风险)分析。我们将检验两个特定的无效假设：(1)生物标记物不能预测严重ROP的发生；(2)生物标记物不能预测ROP的进展。这两个假设都将由最初的埃尔根数据分析团队的成员在全面的多变量回归模型中进行测试，该模型允许对混淆进行适当控制，并进行旨在发现影响因素的分层分析。预先的力量分析表明，我们的样本量足够大，可以通过足够的统计能力来检测可察觉的相对风险。我们项目的结果将对我们目前对ROP发病机制的理解产生重大影响，并将有助于为新的预防和治疗策略铺平道路。