Association of phenotypic aging, lifestyle, and genetic risk with incidence of atrial fibrillation: A large prospective cohort study in the UK Biobank

OBJECTIVES: Our study aimed to investigate the association of phenotypic aging, lifestyle, and genetic risk with the risk of incident atrial fibrillation (AF). DESIGN: A large prospective cohort study. SETTING AND PARTICIPANTS: This study included 327,122 participants from the UK Biobank. METHODS: PhenoAge acceleration (PhenoAgeAccel) was calculated by regressing phenotypic age (PhenoAge) on chronological age. Two key stratification tools were derived from previous research: the Healthy Lifestyle Score (HLS) based on smoking, body mass index (BMI), physical activity, and diet, to assess participants' lifestyles; and the polygenic risk score (PRS) based on 104 AF-associated SNPs and their effect sizes identified in a GWAS to evaluate genetic risk. Cox proportional hazards models were employed to assess both independent and combined effects of PhenoAgeAccel, HLS, and PRS with AF risk. RESULTS: At a median follow-up of 10.84 (10.08-11.56) years, 15,997 cases of AF were identified. Each standard deviation (SD) increase in PhenoAgeAccel was associated with a 30% higher AF risk (HR 1.30, 95% CI 1.28-1.31). Participants biologically older (PhenoAgeAccel>0) had a significantly higher risk of AF (HR 1.47, 95% CI 1.42-1.51) compared to those biologically younger (PhenoAgeAccel≤0), whereas ideal HLS was significantly associated with a lower risk of AF (HR 0.52, 95% CI 0.49-0.56 vs. poor HLS), and high genetic risk was significantly associated with a higher risk of AF (HR 2.30, 95% CI 2.21-2.39 vs. low genetic risk). Joint effects and multiplicative/additive interactions were noted between PhenoAgeAccel and HLS (or genetic risk). When combined PhenoAgeAccel and genetic risk, participants biologically older and in high genetic risk had the highest AF risk (HR 3.52, 95% CI 3.31-3.74). When combined PhenoAgeAccel and HLS, participants who were biologically older and had a poor lifestyle had the highest AF risk (HR 2.42, 95% CI 2.23-2.62). Further analysis categorized PhenoAgeAccel into quartiles based on its population distribution, and the associations remained consistent. CONCLUSIONS: Increased PhenoAgeAccel is significantly associated with increased risk of AF. When combined with a poor lifestyle or high genetic risk, the risk is further increased. These findings highlight the importance of integrating phenotypic aging, genetic risk, and lifestyle factors into AF prevention strategies.