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Newborn Metabolic Screening for Prediction of Childhood Respiratory Phenotypes

新生儿代谢筛查用于预测儿童呼吸表型

基本信息

批准号：
9090671
负责人：
Tina V Hartert
金额：
$ 23.89万
依托单位：
UNIVERSITY OF IOWA
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-04-01 至 2018-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9090671
关键词：
6 year old Accounting Address Affect Age of Onset Alanine Allergic Allergic rhinitis Animal Model Arginine Asthma Atopic Dermatitis Biochemical Pathway Biological Biological Factors Biological Process Birth Carnitine Cessation of life Child Childhood Childhood Asthma Chronic Chronic Disease Clinical Cohort Studies Complex Data Data Sources Development Diagnosis Diagnostic Disease Disease Pathway Early Intervention Endocrine System Diseases Enrollment Environment Environmental Exposure Environmental Risk Factor Etiology Exploratory/Developmental Grant Exposure to Family Frequencies Functional disorder Goals Heterogeneity Hospitalization Hypersensitivity IgE Inborn Errors of Metabolism Individual Infant Infection Intervention Lead Life Longitudinal Studies Lower Respiratory Tract Infection Lung Lung Inflammation Measurement Measures Medical Metabolic Modeling Morbidity - disease rate Nature Neonatal Screening Newborn Infant Oxidative Stress Perinatal Exposure Pharmaceutical Preparations Phenotype Predisposition Prevention Public Health Research Respiratory Signs and Symptoms Respiratory Tract Infections Respiratory physiology Respiratory syncytial virus Risk Risk Factors Role Sensitivity and Specificity Severities Supplementation Symptoms Tennessee Testing United States Wheezing asthma prevention base cohort cost design early childhood high risk improved mitochondrial dysfunction novel novel strategies oxidation population based predictive modeling prevent programs prospective public health relevance respiratory respiratory health screening stressor tool

项目摘要

DESCRIPTION (provided by applicant): Childhood asthma is a devastating condition that incurs long-term medical and financial burdens for affected children and their families. But identifying young children at high risk to develop asthma has proven difficult. Current predictive models are simplistic and do not recognize the underlying complexity of asthma; however, more complex models tend to lose clinical utility. Yet such models are needed: asthma is one of the most common chronic childhood diseases, affecting seven million children in the United States alone. Recent studies suggest that clustering early childhood respiratory and allergy symptoms (i.e., wheezing, respiratory infections, atopic dermatitis) into distinct phenotypes may improve the ability to predict asthma development in children. However, little is known about the biologic risks underlying these phenotypes. Metabolic and mitochondrial dysfunction, for instance, has been associated with asthma, but critical gaps remain in our understanding of how it leads to development of the disease. Newborn metabolic screening is a public health initiative aimed at screening every child born for endocrine disorders and rare inborn errors of metabolism, including many disorders indicative of metabolic and mitochondrial dysfunction. This screening represents a unique data source that can be analyzed alongside perinatal and environmental exposures to further our understanding of asthma etiology. We hypothesize that individuals with mild metabolic disturbances at birth will be more prone to further metabolic and mitochondrial dysfunction leading to the development of respiratory and allergy phenotypes later in childhood when exposed to triggers in the environment or to other stressors. Using two prospective cohort studies from Tennessee designed to identify risk factors for asthma; we will address this hypothesis through the following specific study goal and aims. Specific Study Goal: Determine if inclusion of newborn metabolic screening data improves prediction of early childhood respiratory and allergy phenotypes. Aim 1: Identify clusters of respiratory phenotypes based on early childhood respiratory and allergy symptoms. Aim 2: Identify clinical, demographic, environmental and newborn metabolic screening metabolites that are predictive of infant respiratory morbidity and asthma and allergy phenotypic groups. Aim 3: Validate predictive models and determine the sensitivity and specificity of our model. Combining clinical and environmental data with data, such as neonatal screening measurements, routinely captured by state programs is a novel approach for creating predictive models that can be incorporated into clinically available tools, account for heterogeneity in asthma phenotypes, and uncover novel disease pathways. If this approach is successful, our predictive models will improve the diagnosis of asthma in childhood and possibly lead to prevention or strategies for early intervention of a significant illness that affects millions of children worldwide.

描述（由申请人提供）：儿童哮喘是一种毁灭性的疾病，会给受影响的儿童及其家庭带来长期的医疗和经济负担。但事实证明，识别患有哮喘高风险的幼儿非常困难。目前的预测模型过于简单化，没有认识到哮喘潜在的复杂性；然而，更复杂的模型往往会失去临床实用性。然而，我们需要这样的模型：哮喘是最常见的慢性儿童疾病之一，仅在美国就有 700 万儿童受到影响。最近的研究表明，将儿童早期呼吸道和过敏症状（即喘息、呼吸道感染、特应性皮炎）聚类为不同的表型可能会提高预测儿童哮喘发展的能力。然而，人们对这些表型背后的生物学风险知之甚少。例如，代谢和线粒体功能障碍与哮喘有关，但我们对其如何导致疾病发展的理解仍存在重大差距。新生儿代谢筛查是一项公共卫生举措，旨在筛查每个出生的儿童是否患有内分泌失调和罕见的先天性代谢缺陷，包括许多表明代谢和线粒体功能障碍的疾病。该筛查代表了一个独特的数据源，可以与围产期和环境暴露一起进行分析，以进一步了解哮喘病因。我们假设，出生时患有轻度代谢紊乱的个体更容易出现进一步的代谢和线粒体功能障碍，导致儿童时期暴露于环境触发因素或其他压力源时出现呼吸和过敏表型。使用田纳西州的两项旨在确定哮喘危险因素的前瞻性队列研究；我们将通过以下具体的研究目标来解决这一假设。具体研究目标：确定纳入新生儿代谢筛查数据是否可以改善对幼儿呼吸和过敏表型的预测。目标 1：根据儿童早期呼吸道和过敏症状识别呼吸道表型簇。目标 2：确定可预测婴儿呼吸道发病率以及哮喘和过敏表型组的临床、人口、环境和新生儿代谢筛查代谢物。目标 3：验证预测模型并确定模型的敏感性和特异性。将临床和环境数据与国家计划例行捕获的新生儿筛查测量等数据相结合，是一种创建预测模型的新方法，该模型可以纳入临床可用的工具，解释哮喘表型的异质性，并揭示新的疾病途径。如果这种方法成功，我们的预测模型将改善儿童哮喘的诊断，并可能导致对影响全球数百万儿童的重大疾病进行预防或早期干预策略。