Biological Age Acceleration and the Dynamic Progression of Cardiovascular‐Kidney‐Metabolic Diseases to Multimorbidity, Dementia and Mortality: A Prospective Cohort Study

医学 痴呆 前瞻性队列研究 预期寿命 疾病 比例危险模型 纵向研究 老年学 危险系数 队列研究 生物年龄 队列 糖尿病 心理干预 公共卫生 多发病率 流行病学转变 肾脏疾病 2型糖尿病 流行病学 老化 风险评估 生命历程法 物理疗法 内科学 人口学 静载荷 低风险 危害
作者
Guojing Yuan,Ying Liu,Jike Qi,Hua Lin,Yuchen Jiang,Xinyi Wang,Yu Yan,Ting Wang,Shuo Zhang,Ping Zeng
出处
期刊:Geriatrics & Gerontology International [Wiley]
卷期号:26 (5): e70557-e70557
标识
DOI:10.1111/ggi.70557
摘要

BACKGROUND: The role of biological age acceleration (BioAgeAccel) in the dynamic progression from single cardiovascular-kidney-metabolic disease (CKMD) to multimorbidity, and subsequently to dementia and mortality, remains poorly understood. Understanding these relationships is crucial for early risk prediction and public health interventions targeting aging-related chronic diseases. OBJECTIVES: This study aimed to investigate the longitudinal association between BioAgeAccel and transitions across multiple disease states-from being healthy to first CKMD (FCKMD), then to cardiovascular-kidney-metabolic multimorbidity (CKMM), dementia, and mortality-and to quantify its impact on disease transition times and life expectancy, with the ultimate goal of informing early risk stratification and targeted preventive strategies. METHODS: We conducted a longitudinal analysis of 433 911 participants from the UK Biobank. CKMM was defined as the coexistence of two or more CKMDs, including cardiovascular disease (CVD), stroke, type 2 diabetes (T2D), and chronic kidney disease. Biological aging was quantified using two biomarkers, PhenoAge and Klemera-Doubal Method Biological Age (KDM-BA). Multistate models were applied to estimate hazard ratios (HRs) for transitions between health states, while restricted mean survival time (RMST) was used to estimate transition duration and life expectancy differences. Stratified analyses were performed by age, physical activity, education, and lifestyle factors. RESULTS: Higher BioAgeAccel was significantly associated with elevated risks across nearly all disease transitions. During CKMM progression, the HRs for transition from healthy to FCKMD were 1.24 (95% CI: 1.23-1.25) for PhenoAgeAccel and 1.16 (1.15-1.17) for KDM-BA-Accel. For the subsequent transition from FCKMD to CKMM, the HRs were 1.20 (1.18-1.22) and 1.19 (1.17-1.21), respectively. In dementia-related transitions, PhenoAgeAccel showed the highest risk for progression from CKMM to dementia (HR = 1.13 [1.04-1.22]). BioAgeAccel shortened transition times and reduced life expectancy-for instance, by approximately 1.09 years from healthy to FCKMD and 1.75 years to CKMM under PhenoAgeAccel. Individuals with CKMM experienced a life expectancy reduction of about 1.36 years, and those with dementia lost approximately 0.77 years. The associations were stronger among individuals with CVD or T2D as the initial CKMD and were moderated by age and lifestyle factors. CONCLUSIONS: BioAgeAccel plays a significant promotive role in the onset and progression of CKMDs toward multimorbidity, dementia, and mortality. These findings highlight the potential of biological age as a dynamic indicator for identifying high-risk individuals and guiding early interventions. Incorporating biological age assessments and promoting healthy lifestyle behaviors in middle-aged populations could represent effective strategies to mitigate the growing burden of CKMDs and dementia.
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