Defining Immunity to Placental Malaria using a Multi-assay Predictive Model

使用多检测预测模型定义对胎盘疟疾的免疫力

• 批准号: 8701767
• 负责人: John J Chen
• 金额: $ 22.75万
• 依托单位: UNIVERSITY OF HAWAII AT MANOA
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-03-01 至 2016-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8701767
• 关键词: Age; Anemia; Antibodies; Antibody-mediated protection; Antigens; Area; Area Under Curve; Avidity; Binding; Binding Sites; Biological Assay; Birth Weight; Caring; Cell Adhesion Molecules; Characteristics; Chondroitin Sulfate A; Classification; Complex; Consensus; Data; Developing Countries; Diagnostic; Diagnostic Procedure; Ensure; Erythrocytes; Family; Fetal Growth; Flow Cytometry; Fluorescence; Future; Goals; Government Officials; Gravidity; Health Personnel; Health Policy; Healthcare; Hematocrit procedure; Human; Immune; Immunity; Infant; Infection; Inflammation; Inflammatory Response; Insecticides; Intervention; Laboratories; Laboratory Animals; Length; Ligand Binding; Ligands; Logistic Models; Low Birth Weight Infant; Malaria; Measures; Mediating; Methods; Modeling; National Institute of Allergy and Infectious Disease; Newborn Infant; Parasitemia; Pathology; Pathway interactions; Pharmaceutical Preparations; Physicians; Placenta; Plasmodium falciparum; Play; Predictive Value; Pregnancy; Pregnancy Outcome; Pregnant Women; Premature Birth; Prenatal care; Prevalence; Prevention; Preventive; Receiver Operating Characteristics; Relative (related person); Research Personnel; Risk; Sampling; Scientist; Serological; Specificity; Spontaneous abortion; Statistical Models; Surface; Testing; Time; Trees; United States National Institutes of Health; Vaccination; Vaccines; Variant; Woman; analytical tool; base; cost effective; design; efficacy testing; fetal; forest; improved; indexing; member; predictive modeling; pregnant; premature; prevent; public health relevance; response; safety testing; transmission process; trophoblast; user-friendly; vaccine candidate; vaccine efficacy

DESCRIPTION (provided by applicant): Malaria, caused by Plasmodium falciparum, is especially severe in pregnant women because infected erythrocytes (IE) express VAR2CSA, a ligand that binds to chondroitin sulfate A (CSA) on trophoblasts, causing IE to accumulate in the placenta. As a result, inflammation and pathology occur, increasing the risk of spontaneous abortions, premature deliveries, and low birth weight babies. Fortunately, antibodies (Ab) against VAR2CSA significantly improve pregnancy outcomes. VAR2CSA-based vaccines are being designed with the goal of inducing high levels of protective Ab, and safety testing of one candidate vaccine will begin in humans this year. However, there is no method for determining if a woman is immune to placental malaria (PM). The availability of a cost-effective diagnostic approach that identifies a woman's level of immunity will allow 1) doctors to provide better prenatal care, 2) vaccine developers to assess the level of immunity women have before and after vaccination, and 3) government officials to make intelligent health policies for pregnant women, especially with the changing malaria landscape due to implementation of intervention strategies. Therefore, our goal is to use a combination of serological and functional assays to characterize Ab that mediate clearance of IE from the placenta and then use the data to develop statistical models that predict whether a woman has sufficient immunity to 1) prevent placental pathology and 2) prevent PM. Archival samples from pregnant Cameroonian women with different levels of immunity to PM will be screened in assays that measure different characteristics of Ab to VAR2CSA, including specificity, avidity, and function. This part of the study is straight-forward as the assays are already optimized in our laboratory. In addition, we seek to develop two new functional assays that measure the ability of Ab to (a) block the interaction of VAR2CSA with CSA and (b) prevent activation and dysregulation of placental trophoblasts exposed to IE, a mechanism that contributes to placental pathology. Results from the serological and functional assays will help identify characteristics of protective Ab. Then, th data will be used to build multi-assay statistical prediction models. Two statistical approaches will be used. First, multivariable logistic models will be built based on a combination of Ab characteristics and other key variables related to immunity (e.g., age, gravidity, hematocrit, length of gestation). This approach will result in two "user friendly" simple risk indexes (formulas) based on the least number of assays and variables that provide the optimal prediction for prevention of pathology and infection. Second, a recursive partitioning approach will be taken to develop classification and regression trees (CART) and random forests (RF) using the data. The resulting binary classification trees will be easy to interpret and allow more complex immunological pathways to be incorporated. Once the models are developed, data from archival samples collected monthly throughout pregnancy will be used to determine when during pregnancy the models have the best predictive value in high and low malaria transmission settings.

描述（由申请人提供）：由恶性疟原虫引起的疟疾在孕妇中尤其严重，因为感染的红细胞（IE）表达VAR2CSA，VAR2CSA是一种与滋养层上的硫酸软骨素A（CSA）结合的配体，导致IE在胎盘中积累。结果，炎症和病理学发生，增加了自然流产、早产和低出生体重婴儿的风险。幸运的是，针对VAR2CSA的抗体（Ab）显著改善妊娠结局。基于VAR2CSA的疫苗正在设计中，目的是诱导高水平的保护性抗体，一种候选疫苗的安全性测试将于今年开始在人体中进行。然而，没有方法确定妇女是否对胎盘疟疾（PM）具有免疫力。一种具有成本效益的诊断方法，可以确定妇女的免疫水平，这将使1）医生能够提供更好的产前护理，2）疫苗开发人员能够评估妇女在接种疫苗前后的免疫水平，3）政府官员能够为孕妇制定明智的卫生政策，特别是由于实施干预战略而导致疟疾状况发生变化。因此，我们的目标是使用血清学和功能测定的组合来表征介导IE从胎盘中清除的Ab，然后使用数据开发统计模型来预测女性是否具有足够的免疫力来1）预防胎盘病理学和2）预防PM。将在测定针对VAR2CSA的抗体的不同特征（包括特异性、亲合力和功能）的测定中筛选来自对PM具有不同免疫水平的爱沙尼亚孕妇的存档样本。该研究的这一部分是直接的，因为我们的实验室已经优化了测定。此外，我们寻求开发两种新的功能测定法，其测量Ab（a）阻断VAR2CSA与CSA的相互作用和（B）防止暴露于IE的胎盘滋养层细胞的活化和失调的能力，所述胎盘滋养层细胞是促成胎盘病理学的机制。从血清学和功能测定的结果将有助于确定保护性抗体的特性。然后，将这些数据用于建立多测定统计预测模型。将使用两种统计方法。首先，将基于Ab特征和与免疫相关的其他关键变量（例如，年龄、妊娠、红细胞压积、妊娠时间）。这种方法将产生两个“用户友好”的简单风险指数（公式），基于最少数量的检测和变量，为预防病理和感染提供最佳预测。其次，将采用递归分区方法，利用数据开发分类和回归树（CART）和随机森林（RF）。由此产生的二元分类树将易于解释，并允许纳入更复杂的免疫途径。一旦开发出这些模型，将使用整个怀孕期间每月收集的档案样本数据来确定怀孕期间模型在高和低疟疾传播环境中何时具有最佳预测价值。