Talifu Zuliyaer, Ren Ziyang, Chen Chen, Guo Shuai, Wu Yu, Li Yuling, Su Binbin, Zheng Xiaoying
School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.
Aging Cell. 2025 Jul 13:e70142. doi: 10.1111/acel.70142.
As the global population ages, multimorbidity has become a critical public health issue. We analyzed 332,012 adults from the UK Biobank (2006-2022) to investigate the association between biological age-measured by the Klemera-Doubal method (KDM-BA) and phenotypic age (PhenoAge)-and a new comorbidity model encompassing physical, psychological, and cognitive disorders, with overall mortality outcomes over a median follow-up of 13.6 years. Logistic regression models examined the association between baseline health status and accelerated aging, while Cox proportional hazards models assessed mortality risk and disorder development. Cross-sectional analysis showed that accelerated aging was linked to higher comorbidity prevalence. Longitudinal follow-up revealed that individuals in the highest quartile (Q4) of aging speed (residual difference between estimated biological age and chronological age) had a 16%-17% higher risk of developing a single disorder, a 41%-44% higher risk of multimorbidity, and a 54% higher overall mortality risk compared with the lowest quartile (Q1). Among those with baseline single disorder, dual comorbidity, and triple morbidity, Q4 mortality risk increased by 89%-116%, 118%-166%, and 119%-156%, respectively. Multistate Markov models confirmed that accelerated aging (residual > 0) increased the risk of transitioning to disorder, comorbidity, and death by 12%-37%. Individuals aged 45 with triple comorbidity lost an average of 5.3 years in life expectancy (LE), further reduced by 5.8 to 7.0 years due to accelerated aging. This study highlights that KDM-BA and PhenoAge robustly predict multimorbidity trajectories, mortality, and shortened LE, supporting their integration into risk stratification frameworks to optimize interventions for high-risk populations.
随着全球人口老龄化，多种疾病并存已成为一个关键的公共卫生问题。我们分析了英国生物银行（2006 - 2022年）的332,012名成年人，以研究通过克莱梅拉 - 杜巴尔方法（KDM - BA）测量的生物学年龄和表型年龄（PhenoAge）与一种涵盖身体、心理和认知障碍的新合并症模型之间的关联，以及在13.6年的中位随访期内的总体死亡率结果。逻辑回归模型检验了基线健康状况与加速衰老之间的关联，而Cox比例风险模型评估了死亡风险和疾病发展情况。横断面分析表明，加速衰老与更高的合并症患病率相关。纵向随访显示，衰老速度最高四分位数（Q4）（估计生物学年龄与实际年龄之间的残余差异）的个体患单一疾病的风险比最低四分位数（Q1）高16% - 17%，患多种疾病的风险高41% - 44%，总体死亡风险高54%。在那些基线患有单一疾病、双重合并症和三重疾病的人群中，Q4的死亡风险分别增加了89% - 116%、118% - 166%和119% - 156%。多状态马尔可夫模型证实，加速衰老（残余>0）使转变为疾病、合并症和死亡的风险增加了12% - 37%。患有三重合并症的45岁个体平均预期寿命（LE）损失5.3年，由于加速衰老，预期寿命进一步减少5.8至7.0年。这项研究强调，KDM - BA和PhenoAge能够有力地预测多种疾病并存的轨迹、死亡率和缩短的预期寿命，支持将它们纳入风险分层框架，以优化对高危人群的干预措施。
