BACKGROUND: High-quality research in cardiovascular prevention, as in other fields, requires inclusion of a broad range of data sets from different sources. Integrating and harmonizing different data sources are essential to increase generalizability, sample size, and representation of understudied populations—strengthening the evidence for the scientific questions being addressed. METHODS: Here, we describe an effort to build an open-access repository and interactive online portal for researchers to access the metadata and code harmonizing data from 4 well-known cohort studies—the REGARDS (Reasons for Geographic and Racial Differences in Stroke) study, FHS (Framingham Heart Study), MESA (Multi-Ethnic Study of Atherosclerosis), and ARIC (Atherosclerosis Risk in Communities) study. We introduce a methodology and a framework used for preprocessing and harmonizing variables from multiple studies. RESULTS: We provide a real-case study and step-by-step guidance to demonstrate the practical utility of our repository and interactive web page. In addition to our successful development of such an open-access repository and interactive web page, this exercise in harmonizing data from multiple cohort studies has revealed several key themes. These themes include the importance of careful preprocessing and harmonization of variables, the value of creating an open-access repository to facilitate collaboration and reproducibility, and the potential for using harmonized data to address important scientific questions and disparities in cardiovascular disease research. CONCLUSIONS: By integrating and harmonizing these large-scale cohort studies, such a repository may improve the statistical power and representation of understudied cohorts, enabling development and validation of risk prediction models, identification and investigation of risk factors, and creating a platform for racial disparities research. REGISTRATION: URL: https://precision.heart.org/duke-ninds .

中文翻译：

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通过元数据存储库促进 Framingham、MESA、ARIC 和 REGARDS 研究中变量的协调

背景：与其他领域一样，心血管预防领域的高质量研究需要包含来自不同来源的广泛数据集。整合和协调不同的数据源对于提高普遍性、样本量和未充分研究人群的代表性至关重要，从而加强正在解决的科学问题的证据。 方法：在这里，我们描述了建立一个开放访问存储库和交互式在线门户的努力，供研究人员访问元数据和代码协调来自 4 个著名队列研究的数据——REGARDS（中风的地理和种族差异的原因）研究、FHS （弗雷明汉心脏研究）、MESA（动脉粥样硬化多种族研究）和 ARIC（社区动脉粥样硬化风险）研究。我们介绍了一种用于预处理和协调多项研究变量的方法和框架。 结果：我们提供真实的案例研究和分步指导来展示我们的存储库和交互式网页的实用性。除了我们成功开发了这样一个开放访问存储库和交互式网页之外，这项协调多个队列研究数据的活动还揭示了几个关键主题。这些主题包括仔细预处理和协调变量的重要性、创建开放获取存储库以促进协作和可重复性的价值，以及使用协调数据解决心血管疾病研究中的重要科学问题和差异的潜力。 结论：通过整合和协调这些大规模队列研究，这样的存储库可以提高未充分研究队列的统计能力和代表性，从而能够开发和验证风险预测模型，识别和调查风险因素，并为种族差异研究创建平台。 登记：网址：https:// precision.heart.org/duke-ninds。