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: 8610316
• 负责人: Olaf Dammann
• 金额: $ 48.52万
• 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/8610316
• 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（由申请人提供）：早产儿视网膜病变（ROP）是一种潜在的可预防的视网膜疾病，主要发生在早产新生儿中。最近的证据表明，暴露于全身炎症可能有助于ROP的病因和发病机制。在本申请中，我们提出（目标#1）在来自极低胎龄新生儿的多中心、NIH资助的队列研究（ELGAN研究，U 01 NS 40069）的现有血液样本中测量1，206个ELGAN中的18种蛋白质生物标志物，并将此信息添加到我们已经在相同的1，206个ELGAN中的924个中测量的25种生物标志物的现有数据库中。我们还将在其余282名新生儿中测量这些先前的25个标记物。其结果将是目前全球最大的生物标志物数据库，其中包括43种生物标志物（出生后第1、7、14、21和28天），可用于1，206名胎龄<28周的新生儿。新的生物标志物测量将由使用Meso Scale Discovery（MSD）多重平台测量最初25种生物标志物的相同ELGAN合作研究者完成。根据已发表的证据，特别为ROP相关性研究选择了额外的生物标志物，这些证据表明氧气的可用性、炎症的存在、血管生成和红细胞生成的概念以及预先存在的母体妊娠状况如先兆子痫都似乎在ROP病因学和发病机制中起作用。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零假设：（1）生物标志物不能预测严重ROP的发生;（2）生物标志物不能预测ROP的进展。这两种假设将由原始ELGAN数据分析团队的成员在综合多变量回归模型中进行检验，该模型允许适当控制混杂因素和分层分析，以发现效应调节剂。事前功效分析表明，我们的样本量足够大，可以检测到具有足够统计功效的明显相对风险。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。