Combining longitudinal cohort studies to examine cardiovascular risk factor trajectories across the adult lifespan and their association with disease

项目概要/摘要 临床心血管疾病（CVD）的发展是一个发生在整个生命周期中的过程， 随着临床事件发生率的增加，在生命早期和生命后期。我们对影响的理解 心血管危险因素的研究来自于研究测量的危险因素水平之间的关联， 在一个单一的时间点，往往在中年，与事件的疾病在短期至中期。然而，在这方面， 年轻人的风险因素水平与以后生活中CVD的发展显著相关， 我们最近的研究表明，不仅特定年龄的水平，而且累积暴露和 心血管健康的长期轨迹与后续CVD的风险显著相关。因此，我们认为， 为了了解心血管危险因素如何发展和影响， 个人在以后的生活中发生CVD事件的风险。然而，没有一项研究收集了详细的表型 从青年到老年的广泛代表性的美国人口样本的数据。 为响应NOT-HL-19 - 712：创新的健康数据评估和分析，我们建议 velop是一个统计框架，用于结合纵向风险因素和来自多个 队列研究，以创建一个"合成队列"，使长期心血管健康的研究开始于 成年早期这项提案的调查小组汇集了20个社区CVD的数据， 通过终身风险汇集项目（LRPP）的队列，该项目现在有超过1100万人年的随访- 心血管疾病危险因素的重复测量数据，药物使用的详细信息（包括血液 降压和降胆固醇治疗），近100%的生命状态随访，以及详细的CVD事件调整， dication. LRPP中很少有队列涵盖整个成人寿命;因此，我们建议查看风险因素， 每个队列研究中未包括的年龄的结局作为缺失数据，并使用多重插补来填充这些数据。 未观察到的测量以便于分析。本项目的总体目标是识别和测量 成年人一生中最容易发生的CVD危险因素轨迹的特征 干预测量这些特征有助于更精确地确定干预的关键时期 定义已知风险因素的阈值，阐明生活方式行为的作用，解释健康差异 在人群中，并在年轻时推广心血管疾病预防策略。我们的具体目标是：1）发展 并验证一个统计框架，用于估算未观察到的CVD风险因素和整个生命周期的事件 使用来自LRPP的数据; 2）通过识别临床相关特征来告知治疗策略， 与CVD结局相关的最终风险因素轨迹; 3）利用目标1和2的工作， 促进研究界传播和使用综合LRPP数据。

PROJECT SUMMARY/ABSTRACT The development of clinical cardiovascular disease (CVD) is a process that occurs across the lifespan, beginning early in life and spanning late into life as clinical event rates increase. Much of our understanding of the impact of cardiovascular risk factors comes from studies examining the association between risk factor levels measured at a single point in time, often in middle age, with incident disease over the short- to intermediate-term. However, risk factor levels in young adulthood are significantly associated with the development of CVD later in life and our recent work has demonstrated that not only the levels at specific ages, but also cumulative exposures and long-term trajectories in cardiovascular health are significantly related to the risk for subsequent CVD. Therefore, a life course approach is critical in order to understand how cardiovascular risk factors develop and impact an individual's risk for CVD events later in life. Yet there is no single study that has collected detailed phenotypic data spanning young adulthood through old age on a broadly representative sample of the U.S. population. In response to NOT-HL-19-712: Innovative Data Evaluation and Analysis to Health, we propose to de- velop a statistical framework for combining longitudinal risk factors and clinical outcomes data from multiple cohort studies to create a “synthetic cohort” enabling the study of long-term cardiovascular health starting in early adulthood. The investigative team of this proposal has pooled the data from 20 community-based CVD cohorts through the Lifetime Risk Pooling Project (LRPP), which now has >11 million person-years of follow- up data on repeated measures of CVD risk factors, detailed information about medication use (including blood pressure- and cholesterol-lowering therapy), nearly 100% follow-up for vital status, and detailed CVD event adju- dication. Few cohorts in the LRPP cover the entire adult lifespan; therefore, we propose to view risk factors and outcomes at ages not included in each cohort study as missing data, and to use multiple imputation to fill in these unobserved measurements to facilitate analysis. The overall goal of this project is to identify and measure the characteristics of CVD risk factor trajectories across the adult lifespan that are most amenable to intervention. Measuring these characteristics can help identify critical periods for intervention, more precisely define thresholds for known risk factors, elucidate the role of lifestyle behaviors, explain differences in health among populations, and promote CVD prevention strategies at younger ages. Our specific aims are: 1) Develop and validate a statistical framework for imputing unobserved CVD risk factors and events across the lifespan using data from the LRPP; 2) Inform treatment strategies by identifying clinically relevant features of longitudi- nal risk factor trajectories that are associated with CVD outcomes; 3) Leverage the work from Aims 1 and 2 to facilitate the dissemination and use of the synthetic LRPP data by the research community.