Risk prediction of atrial fibrillation: clinical, biological and genetic markers

描述(由研究人员提供)：此应用程序涉及广泛的挑战领域(04)：临床研究，以及特定的挑战主题，04-HL-104：对现有数据进行二次分析，以回答重要的临床和预防医学研究问题。心房颤动(房颤)是临床上最常见的持续性心律失常，在美国有200多万人受到影响，随着人口老龄化，房颤的患病率不断上升。此外，房颤与较高的中风、心力衰竭和死亡率有关。及早识别普通人群中风险较高的个人将能够进行有针对性的预防，从而降低未来的卫生保健成本。弗雷明翰心脏研究(FHS)最近的一份出版物开发了一个风险评分，其中包含可以在初级保健中评估的因素。然而，这个分数对普通美国人的普适性是未知的，因为它比FHS样本更具多样性。此外，这一评分没有考虑在临床实践中广泛使用的生物标记物或来自基因研究的新信息。在这项拨款中，我们建议汇集美国和欧洲五项前瞻性研究的数据，以在更多样化的人群中验证先前的房颤Framingham评分，并开发考虑房颤生物标记物和遗传因素的新风险评分。我们将分析来自以下队列的数据：年龄、基因/环境易感性雷克雅未克研究、社区动脉粥样硬化风险研究、心血管健康研究、FHS和鹿特丹研究。总体而言，这项综合分析将包括3万多名个人和3000多例房颤事件的样本。这些队列之间的合作已经作为正在进行的基因组流行病学心脏和衰老研究(CHARE)联盟的一部分而存在。具体地说，我们将解决以下目标：1.使用广泛可用的临床因素在不同的社区验证、重新校准和潜在地修改FHS房颤临床风险预测模型；2.测试生物标记物，特别是利钠肽或C-反应蛋白是否在加入标准临床因素后提高房颤风险预测(辨别、校准、重新分类)；3.检验来自Charge AF的顶级SNPs的基因分值是否改善了对房颤的风险预测、校准、重新分类)；4.开发利用生存数据和交叉队列荟萃分析进行重新分类的新方法。这一应用的主要优势包括高素质、多学科、国际化的研究团队，利用现有的关于风险因素和房颤的数据，包括生物标记物和基因变异，以及纳入一大批非裔美国人。风险预测工具和统计方法将在网上发布，并在世界各地向患者、临床医生和研究人员提供。 公共卫生相关性：房颤是一种非常普遍的不规则心律，增加了中风和死亡的风险，但如何预测和预防个体的房颤尚不清楚。来自五个社区研究的研究人员组成了一个合作，以创建一个模型来 预测房颤的发展，并确定生物或遗传标记物是否有助于进一步分类标准临床因素以外的房颤风险。研究人员将开发新的方法和预测工具，为临床决策提供信息，并针对旨在预防房颤的干预措施。

DESCRIPTION (provided by investigator): This application addresses broad Challenge Area (04): Clinical Research, and specific Challenge Topic, 04-HL-104: Perform secondary analysis of existing data to answer important clinical and preventive medicine research questions. Atrial fibrillation (AF) is the most common sustained arrhythmia in clinical practice, affecting more than 2 million individuals in the US, with growing prevalence as the population ages. Moreover, AF is associated with a higher incidence of stroke, heart failure, and mortality. Early identification of individuals who are at higher risk in the general population would allow targeted prevention, decreasing future health-care costs. A recent publication from the Framingham Heart Study (FHS) developed a risk score with factors that could be assessed in primary care. However, the generalizability of this score to the general US population, more diverse than the FHS sample, is unknown. Also, this score does not consider biomarkers broadly used in the clinical practice or new information derived from genetic studies. In this grant, we propose to pool data from five prospective studies in the US and Europe to validate in a more diverse population, the previous Framingham score for AF, and to develop new risk scores that take into account biomarkers of AF and genetic factors. We will analyze data from the following cohorts: Age, Gene/Environment Susceptibility Reykjavik Study, Atherosclerosis Risk in Communities Studies, Cardiovascular Health Study, FHS and Rotterdam Study. Overall, this pooled analysis will include a sample of more than 30,000 individuals and more than 3,000 incident cases of AF. Collaboration between these cohorts already exists as part of the ongoing Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium. Specifically, we will address the following aims: 1. To validate, recalibrate and potentially modify the FHS AF clinical risk prediction model in diverse communities using widely available clinical factors; 2. To test whether biomarkers, specifically natriuretic peptides or C-reactive protein enhance risk prediction (discrimination, calibration, reclassification) of AF when added to standard clinical factors; 3. To examine whether a genotype score of top SNPs from CHARGE AF improves risk prediction discrimination, calibration, reclassification) of AF over and above standard clinical factors and 4. To develop novel methods for reclassification with survival data and cross-cohort meta-analysis. Main advantages of this application include the highly qualified, multidisciplinary, international research team, the use of existing data on risk factors and AF, including biomarkers and genetic variants, and the inclusion of a large group of African-Americans. Risk prediction instruments and statistical methods will be web- posted and available to patients, clinicians and investigators through-out the world. PUBLIC HEALTH RELEVANCE: Atrial fibrillation, a highly prevalent, irregular heart rhythm, increases the risk of stroke and death, yet how to predict and prevent atrial fibrillation in the individual is not well understood. Investigators from five community studies have formed a collaboration to create a model to predict the development of atrial fibrillation and to determine whether biological or genetic markers help to classify further the risk of atrial fibrillation beyond standard clinical factors. The investigators will develop new methods and predictive tools that will inform clinical decisions and target interventions aimed at the prevention of atrial fibrillation.