Approved Research

Cardiovascular metabolic characteristics of regular exercisers and the effect of exercise on Atrial Fibrillation!A cohort study

Lay summary

Atrial fibrillation (AF) is a common arrhythmia with the incidence increasing multiply with age. Physical activity has a variety of beneficial cardiovascular effects, such as lowering blood pressure levels, improving hyperlipidemia, and increasing insulin sensitivity. Studies showed that the incidence of AF was higher in patients who did not participate in physical activity than in those who did. The odds of progression of atrial fibrillation increased in patients who lacked physical activity. Metabolism is generally an overall term for the organized series of chemical reactions that take place in an organism for the purpose of sustaining life, which includes anabolism and catabolism, the major processes are glucose metabolism, lipid metabolism, and amino acid metabolism. Myocardial metabolism is essential for the pathophysiology of atrial fibrillation, metabolic disorders directly affect the formation of atrial fibrillation substrate. Therefore, the development and progression of AF affect human metabolism and mitochondrial function. In our study, for all included continuous variables, we will analyze after excluding outliers, which are defined as values that are three standard deviations (SD) above or below the mean value, to eliminate the impact of extreme values. Furthermore, the Shapiro-Wilk tests will be applied to check the normal distribution, and for all non-normally distributed continuous variables, the Box-Cox transformation method will be used to transform it into a normal distribution via 'car' package in R Studio, and all data are standardized using the z-score method. Continuous variables will be described as means and SDs, whereas categorical variables will be reported as numbers and percentages. Baseline demographic information, physical activity data, laboratory data, imaging data, and atrial fibrillation data will be tested for intergroup differences using chi-square analysis for classified variables or one-way analysis of variance analysis (ANOVA) for continuous variables.