AssociatioN of Genetic Polymorphisms With ATrial fibrOsis and Thrombogenic Substrate in Patients With Non-vaLvular atrIAl Fibrillation

Atrial fibrillation (AF) is the most frequently encountered cardiac arrhythmia. Emerging data suggests that common genetic variants are associated with the development of AF. The main feature of the structural remodelling in AF is atrial fibrosis and is considered the substrate for AF perpetuation. Genome-wide association studies suggest that AF-susceptibility variants may modulate atrial fibrosis. However, the association between atrial fibrosis and genetic polymorphisms in humans has not yet been specifically investigated. In this study, we plan to investigate the relationship between genetic polymorphisms, atrial fibrosis and other components of thrombogenic substrate in patients with non-valvular AF. Primary objectives of this study are to assess associations between (i) polymorphic genetic variants and atrial fibrosis (detected by magnetic resonance imaging), (ii) polymorphic genetic variants and components of thrombogenic substrate (inflammation, endothelial function, prothrombotic state, atrial functions). Patients are planned to be recruited from four major cardiology departments: Memorial Ankara Hospital, Ministry of Health Subspecialty Training Hospital of Turkey, Ufuk University and Gazi University hospitals. Key variables that will be recorded include the clinical, contrast-enhanced MRI, biomarkers, echocardiographic and assessment of endothelial function. All statistical analyses will be conducted using Stata version 11.0 (StataCorp, College Station, TX). Univariate and multivariate regression models will be used to determine the odds ratio of each variable to assess the association of the clinical and laboratory parameters, and genotype profiles with the presence of LA fibrosis. Furthermore, data mining methods like support vector machines and/or random forests are planned to be used for detecting the impact of each potential predictor on the risk of LA fibrosis. Additionally, to determine the effect of different alleles of the identified risk SNPs, 2-way and 3-way multi dimensionality reduction (MDR) analysis will be performed. Bioinformatics investigations to determine SNP-SNP, SNP-gene and SNP-Phenotype interactions will be performed by combined p-value and biological network analysis.