Lin Tingting, Fan Ximin, Zeng Liangtang, Li Qiang, Wang Feilong, Lu Hao
Department of Pulmonary and Critical Care Medicine, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200092, China; Medical College, Tongji University, Shanghai, 200092, China.
Department of Cardiology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200092, China.
J Nutr Health Aging. 2025 May 6;29(7):100562. doi: 10.1016/j.jnha.2025.100562.
Our study aimed to investigate the association of phenotypic aging, lifestyle, and genetic risk with the risk of incident atrial fibrillation (AF).
A large prospective cohort study.
This study included 327,122 participants from the UK Biobank.
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.
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.
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.
我们的研究旨在调查表型衰老、生活方式和遗传风险与房颤（AF）发病风险之间的关联。
一项大型前瞻性队列研究。
本研究纳入了来自英国生物银行的327,122名参与者。
通过将表型年龄（PhenoAge）对实际年龄进行回归分析来计算表型年龄加速（PhenoAgeAccel）。从先前的研究中得出了两个关键的分层工具：基于吸烟、体重指数（BMI）、身体活动和饮食的健康生活方式评分（HLS），以评估参与者的生活方式；以及基于104个与房颤相关的单核苷酸多态性（SNP）及其在全基因组关联研究（GWAS）中确定的效应大小的多基因风险评分（PRS），以评估遗传风险。采用Cox比例风险模型来评估PhenoAgeAccel、HLS和PRS对房颤风险的独立和联合效应。
在中位随访10.84（10.08 - 11.56）年期间，共识别出15,997例房颤病例。PhenoAgeAccel每增加一个标准差（SD），房颤风险就会增加30%（风险比[HR] 1.30，95%置信区间[CI] 1.28 - 1.31）。与生物学年龄较小（PhenoAgeAccel≤0）的参与者相比，生物学年龄较大（PhenoAgeAccel>0）的参与者患房颤的风险显著更高（HR 1.47，95% CI 1.42 - 1.51），而理想的HLS与较低的房颤风险显著相关（HR 0.52，95% CI 0.49 - 0.56，与不良HLS相比），高遗传风险与较高的房颤风险显著相关（HR 2.30，95% CI 2.21 - 2.39，与低遗传风险相比）。在PhenoAgeAccel与HLS（或遗传风险）之间观察到了联合效应以及相乘/相加相互作用。当将PhenoAgeAccel和遗传风险相结合时，生物学年龄较大且遗传风险高的参与者房颤风险最高（HR 3.52，95% CI 3.31 - 3.74）。当将PhenoAgeAccel和HLS相结合时，生物学年龄较大且生活方式不良的参与者房颤风险最高（HR 2.42，95% CI 2.23 - 2.62）。进一步分析根据PhenoAgeAccel在人群中的分布将其分为四分位数，且这些关联仍然一致。
PhenoAgeAccel升高与房颤风险增加显著相关。当与不良生活方式或高遗传风险相结合时，风险会进一步增加。这些发现凸显了将表型衰老、遗传风险和生活方式因素纳入房颤预防策略的重要性。
