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

项目摘要/摘要 临床心血管疾病(CVD)的发展是一个跨越一生的过程，从 在生命早期，随着临床事件发生率的增加，一直持续到生命后期。我们对影响的大部分理解 的心血管风险因素来自研究测量的风险因素水平之间的关联 在一个时间点上，通常是在中年，在短期到中期内出现疾病。然而， 青年时期的危险因素水平与晚年和老年心血管疾病的发展显著相关。fi 我们最近的工作表明，不仅特定fi年龄的水平，而且累积暴露和 心血管健康的长期轨迹与随后发生心血管疾病的风险显著相关。fi。因此， 生命过程方法对于了解心血管危险因素是如何发展和影响 个人在以后的生活中发生心血管事件的风险。然而，没有一项研究收集了详细的表型 在美国人口中具有广泛代表性的样本中，数据涵盖了从年轻到老年的各个阶段。 为了回应NOT-HL-19-712：对健康的创新数据评估和分析，我们建议去- 开发一个统计框架，将纵向风险因素和临床结果数据从 队列研究，以创建一个“合成队列”，使长期心血管健康的研究从 成人期早。这项提案的调查团队汇集了20个社区心血管疾病的数据 通过终身风险共享项目(LRPP)进行的队列，该项目现在有1100万人年的跟踪- 关于心血管疾病危险因素的重复测量的数据，关于用药的详细信息(包括血液 降血压和降胆固醇治疗)，对生命状态进行近100%的随访，以及详细的心血管事件调整- 敬业精神。LRPP中覆盖整个成年寿命的队列很少；因此，我们建议将风险因素和 未包括在每个队列研究中作为缺失数据的年龄的结果，并在这些研究中使用对fi11的多重归因 未观察到的测量，以便于分析。该项目的总体目标是确定和衡量 最易患心血管疾病风险因素在成人寿命中的轨迹特征 干预。测量这些特征有助于更准确地识别干预的关键时期 已知危险因素的DefiNe阈值，阐明生活方式行为的作用，解释健康差异 在人群中传播心血管疾病，并在年轻时推广预防心血管疾病的战略。我们的专业fic目标是：1)开发 并验证统计框架，以便在整个生命周期内计算未观察到的心血管疾病风险因素和事件 使用来自LRPP的数据；2)通过识别纵隔的临床相关特征来告知治疗策略- 与心血管疾病结局相关的NAL风险因素轨迹；3)利用目标1和目标2的工作 促进研究界传播和使用合成的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.