Zhang Jingyun, Cao Xingqi, Chen Chen, He Liu, Ren Ziyang, Xiao Junhua, Han Liyuan, Wu Xifeng, Liu Zuyun
Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China.
National Institute of Environmental and Health, Chinese Center for Disease Control and Prevention, Beijing, China.
Front Med (Lausanne). 2022 Apr 22;9:831260. doi: 10.3389/fmed.2022.831260. eCollection 2022.
Aging, as a multi-dimensional process, can be measured at different hierarchical levels including biological, phenotypic, and functional levels. The aims of this study were to: (1) compare the predictive utility of mortality by three aging measures at three hierarchical levels; (2) develop a composite aging measure that integrated aging measures at different hierarchical levels; and (3) evaluate the response of these aging measures to modifiable life style factors.
Data from National Health and Nutrition Examination Survey 1999-2002 were used. Three aging measures included telomere length (TL, biological level), Phenotypic Age (PA, phenotypic level), and frailty index (FI, functional level). Mortality information was collected until December 2015. Cox proportional hazards regression and multiple linear regression models were performed.
A total of 3,249 participants (20-84 years) were included. Both accelerations (accounting for chronological age) of PA and FI were significantly associated with mortality, with HRs of 1.67 [95% confidence interval (CI) = 1.41-1.98] and 1.59 (95% CI = 1.35-1.87), respectively, while that of TL showed non-significant associations. We thus developed a new composite aging measure (named PC1) integrating the accelerations of PA and FI, and demonstrated its better predictive utility relative to each single aging measure. PC1, as well as the accelerations of PA and FI, were responsive to several life style factors including smoking status, body mass index, alcohol consumption, and leisure-time physical activity.
This study demonstrates that both phenotypic (i.e., PA) and functional (i.e., FI) aging measures can capture mortality risk and respond to modifiable life style factors, despite their inherent differences. Furthermore, the PC1 that integrated phenotypic and functional aging measures outperforms in predicting mortality risk in comparison with each single aging measure, and strongly responds to modifiable life style factors. The findings suggest the complementary of aging measures at different hierarchical levels and highlight the potential of life style-targeted interventions as geroprotective programs.
衰老作为一个多维度的过程,可以在不同层次水平上进行衡量,包括生物学、表型和功能水平。本研究的目的是:(1)比较三个层次水平上三种衰老测量方法对死亡率的预测效用;(2)开发一种综合不同层次水平衰老测量方法的复合衰老测量指标;(3)评估这些衰老测量方法对可改变生活方式因素的反应。
使用了1999 - 2002年国家健康与营养检查调查的数据。三种衰老测量方法包括端粒长度(TL,生物学水平)、表型年龄(PA,表型水平)和衰弱指数(FI,功能水平)。收集死亡率信息直至2015年12月。进行了Cox比例风险回归和多元线性回归模型分析。
共纳入3249名参与者(20 - 84岁)。PA和FI的加速变化(考虑到实际年龄)均与死亡率显著相关,风险比(HR)分别为1.67 [95%置信区间(CI)= 1.41 - 1.98]和1.59(95% CI = 1.35 - 1.87),而TL的加速变化显示无显著关联。因此,我们开发了一种新的复合衰老测量指标(命名为PC1),它整合了PA和FI的加速变化,并证明其相对于每个单一衰老测量指标具有更好的预测效用。PC1以及PA和FI的加速变化对包括吸烟状况、体重指数、饮酒量和休闲时间身体活动在内的几种生活方式因素有反应。
本研究表明,尽管表型(即PA)和功能(即FI)衰老测量方法存在固有差异,但它们都能捕捉死亡风险并对可改变的生活方式因素做出反应。此外,整合了表型和功能衰老测量方法的PC1在预测死亡风险方面比每个单一衰老测量指标表现更优,并且对可改变的生活方式因素有强烈反应。这些发现表明不同层次水平衰老测量方法具有互补性,并突出了以生活方式为目标的干预措施作为老年保护计划的潜力。