School of Public Health, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China.
Department of Occupational and Environmental Health, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China.
BMC Med. 2023 Jan 16;21(1):20. doi: 10.1186/s12916-022-02674-w.
Visit-to-visit body weight variability (BWV), pulse rate variability (PRV), and blood pressure variability (BPV) have been respectively linked to multiple health outcomes. The associations of the combination of long-term variability in physiological measures with mortality and epigenetic age acceleration (EAA) remain largely unknown.
We constructed a composite score of physiological variability (0-3) of large variability in BWV, PRV, and BPV (the top tertiles) in 2006/2008-2014/2016 in the Health and Retirement Study (HRS) and 2011-2015 in the China Health and Retirement Longitudinal Study (CHARLS). All-cause mortality was documented through 2018. EAA was calculated using thirteen DNA methylation-based epigenetic clocks among 1047 participants in a substudy of the HRS. We assessed the relation of the composite score to the risk of mortality among 6566 participants in the HRS and 6906 participants in the CHARLS by Cox proportional models and then investigated its association with EAA using linear regression models.
A higher score of variability was associated with higher mortality risk in both cohorts (pooled hazard ratio [HR] per one-point increment, 1.27; 95% confidence interval [CI], 1.18, 1.39; P-heterogeneity = 0.344), after adjustment for multiple confounders and baseline physiological measures. Specifically, each SD increment in BWV, PRV, and BPV was related to 21% (95% CI: 15%, 28%), 6% (0%, 13%), and 12% (4%, 19%) higher hazard of mortality, respectively. The composite score was significantly related to EAA in second-generation clocks trained on health outcomes (e.g., standardized coefficient = 0.126 in the Levine clock, 95% CI: 0.055, 0.196) but not in most first-generation clocks trained on chronological age.
Larger variability in physiological measures was associated with a higher risk of mortality and faster EAA.
体重波动、脉搏率波动和血压波动分别与多种健康结果相关。长期生理指标波动与死亡率和表观遗传年龄加速(EAA)的综合关联在很大程度上仍不清楚。
我们构建了一个生理变异性综合评分(0-3),用于衡量 2006/2008 年至 2014/2016 年健康与退休研究(HRS)和 2011 年至 2015 年中国健康与退休纵向研究(CHARLS)中体重波动、脉搏率波动和血压波动(前三分位数)的大变异。通过 2018 年的记录来确定全因死亡率。使用 HRS 子研究中 1047 名参与者的 13 个基于 DNA 甲基化的表观遗传时钟计算 EAA。我们使用 Cox 比例风险模型评估 HRS 中 6566 名参与者和 CHARLS 中 6906 名参与者的综合评分与死亡率风险的关系,然后使用线性回归模型研究其与 EAA 的相关性。
在两个队列中,变异性评分较高与死亡率风险较高相关(综合风险比[HR]每增加一个点,1.27;95%置信区间[CI],1.18,1.39;P 异质性=0.344),调整了多个混杂因素和基线生理指标后。具体而言,BWV、PRV 和 BPV 的每一个标准差增加与死亡率的风险分别增加 21%(95%CI:15%,28%)、6%(0%,13%)和 12%(4%,19%)相关。复合评分与基于健康结果的第二代时钟(如 Levine 时钟的标准化系数=0.126,95%CI:0.055,0.196)训练的 EAA 显著相关,但与基于年龄的第一代时钟训练的 EAA 相关性不大。
生理指标的较大波动与死亡率风险增加和 EAA 加速有关。