Comprehensive risk factor analysis of sick sinus syndrome: a genetic, sociodemographic, clinical and laboratory investigation using the UK Biobank data

Abstract

Background Sinoatrial node dysfunction and atrial fibrillation (AF) often coexist, and each potentially initiates or perpetuates the other. Consequently, genetic risk factors for AF may be associated with sick sinus syndrome (SSS). This study evaluated clinical, sociodemographic, laboratory and genetic factors in predicting the SSS incidence by using a polygenic risk score (PRS) for AF, given the absence of a dedicated PRS for SSS.Methods A total of 502 421 individuals aged 38-73 years across 22 centres from 2006 to 2010 were enrolled in the UK Biobank. After those with incomplete data, prior AF or valvular disease were excluded, 405 869 participants (median age 58.0 (IQR, 50.0-64.0) years; 213 684 (45.2%) male) were analysed. AF PRS categorised the participants into low (first quintile), intermediate (middle three quintiles) and high (fifth quintile) risks. SSS incidence was recorded, and associations were evaluated using Cox regression with sociodemographic, clinical, laboratory and genetic variables.Results During the median follow-up of 11.9 years, 769 SSS events occurred. Their incidence increased with age (HR 1.52, 95% CI 1.43 to 1.62 per 5 years), male sex (HR 1.39, 95% CI 1.03 to 1.88), prior myocardial infarction (HR 1.54, 95% CI 1.08 to 2.21) and hypertension (HR 1.21, 95% CI 1.02 to 1.42). The high-PRS and intermediate-PRS groups were related to 82% (HR 1.82, 95% CI 1.45 to 2.29) and 32% (HR 1.32, 95% CI 1.07 to 1.62) higher SSS risks, respectively. Cystatin C (HR 1.71, 95% CI 1.33 to 2.22) was also associated with the increased risk. The model's C-index was 0.752 with sociodemographic and clinical variables, but it increased to 0.760 with genetic and biomarker data.Conclusion In addition to sociodemographic and clinical risk factors, AF PRS and cystatin C were linked to the SSS incidence. Although their inclusion modestly improved prediction, these findings highlighted the potential of integrating genetic and biomarker information for comprehensive SSS risk assessments.