Long-Term Patterns in Cardiovascular Risk Factors and Their Time-Varying Impact on the Incidence of Cardiovascular Disease

 描述（由申请人提供）：心血管疾病的发生始于生命早期;然而，关于成年早期开始的心血管危险因素水平的长期模式以及晚年心血管疾病的发生的数据很少。考虑到对整个生命周期内CV健康的兴趣，汇集来自多个队列的现有数据以创建一个合成队列，从而能够研究整个生命周期内的CV健康，这可能会提供重要的见解。这种方法直观上很吸引人，但方法论上很复杂。我们建议使用来自终身风险汇总项目（包括21项研究，n= 150，000）的数据来创建一个合成队列，该队列可以提供关于心血管健康的长期模式与CVD发病率相关的见解。我们将利用复杂的统计方法，包括多水平多重插补，利用现有数据创建一个合成队列。使用该合成队列，我们将能够确定从18岁开始的CV风险因素（包括血压、体重、血脂和血糖）的长期轨迹及其对CVD发病率的时变影响。本研究将提供一种经验证的方法，通过该方法可以将多个队列合并在一起，以创建一个能够检查CV风险因素水平的长期轨迹及其与CVD事件的时变相关性的合成队列。虽然缺失的纵向数据的开发在方法上是复杂的，但一旦创建，可以使用更传统的分析方法来检查广泛的问题。

 DESCRIPTION (provided by applicant): The development of cardiovascular disease starts early in life; however, data are sparse regarding the long- term patterns of cardiovascular risk factor levels starting in early adulthood and the occurrence of CVD later in life. Given the interest in CV health across the lifespan, pooling existing data from multiple cohorts to create a synthetic cohort enabling the study of CV health across the lifespan may provide critical insights. This approach is intuitively appealing, but methodologically complex. We propose to use data from The Lifetime Risk Pooling Project (including 21 studies, n=150,000) to create a synthetic cohort which can provide insight on how long-term patterns in cardiovascular health are associated with the incidence of CVD. We will utilize sophisticated statistical methods including multilevel multiple imputation to leverage the existing data to create a synthetic cohort Using this synthetic cohort we will be able to determine long-term trajectories, starting at age 18 in CV risk factors including blood pressure, body weight, lipids and glucose and their time-varying impact on the incidence of CVD. This study will provide a validated method by which multiple cohorts can be pooled together to create a synthetic cohort capable of examining the long-term trajectories in CV risk factor levels and their time-varying association with CVD events. While the development of the missing longitudinal data is methodologically complex, once created more traditional analytic methods can be used to examine a broad range of questions.