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

 描述(申请人提供)：心血管疾病的发展开始于生命的早期；然而，关于从成年早期开始的心血管危险因素水平的长期模式以及在生活的后期发生心血管疾病的数据很少。考虑到人们对生命周期内的心血管健康状况的兴趣，汇集来自多个队列的现有数据以创建一个合成队列，从而能够研究生命周期内的心血管健康状况，可能会提供关键的见解。这种方法直观上很吸引人，但在方法上很复杂。我们建议使用终身风险池项目(包括21项研究，n=150,000)的数据来创建一个合成队列，该队列可以深入了解心血管健康的长期模式与心血管疾病的发病率之间的关系。我们将利用复杂的统计方法，包括多水平多重归因来利用现有数据来创建一个合成队列，使用这个合成队列，我们将能够确定从18岁开始的心血管风险因素的长期轨迹，包括血压、体重、血脂和血糖及其对心血管疾病发病率的时间变化影响。这项研究将提供一种有效的方法，可以将多个队列汇集在一起，创建一个能够检查心血管风险因素水平的长期轨迹及其与心血管疾病事件的时间变化关联的合成队列。虽然缺失的纵向数据的发展在方法上是复杂的，但一旦建立了更传统的分析方法，就可以用来检查广泛的问题。

 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.