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.
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.
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