Long-term variability in physiological measures in relation to mortality and epigenetic aging: prospective studies in the USA and China

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusions: Larger variability in physiological measures was associated with a higher risk of mortality and faster EAA.

Keywords: Blood pressure variability; Body weight variability; Epigenetic aging; Mortality; Pulse rate variability.

Fig. 1

Study design. BWV, body weight variability; PRV, pulse rate variability; BPV, blood pressure variability; BMI, body mass index

Fig. 2

Multivariable adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) of mortality associated with the composite score of variability in the HRS and CHARLS. HRS, Health and Retirement Study; CHARLS, China Health and Retirement Longitudinal Study; HR, hazard ratio; CI, confidence interval. Models were adjusted for age (continuous), gender (female/male), education (high school degree level, yes/no), marriage status (yes/no), residence (rural/urban) in the CHARLS, race (White/Black/others) in the HRS, drinking status (current/ever/never), smoking status (current/ever/never), physical activity (> 1 time per week/1–3 times per month/never in the HRS and any/never/missing in the CHARLS), household income (quartiles in the HRS and ≤ 9999 yuan/≥ 10,000 yuan/missing in the CHARLS), body mass index, systolic blood pressure, and pulse rate (all continuous) in 2014/2016 (HRS) or in 2015 (CHARLS). Fixed effect model was used to pool the results

Fig. 3

Multivariable adjusted differences and 95% confidence intervals (CIs) in epigenetic age acceleration indicated by second-generation clocks associated with the composite score of variability in the overall population and by gender. EAA, epigenetic age acceleration. β-Coefficients were derived from models adjusted for age (continuous), gender (female or male, unless in gender-stratified analyses), education (high school degree level, yes/no), marriage status (yes/no), race (White/Black/others), drinking status (current/ever/never), smoking status (current/ever/never), physical activity (> 1 time per week/1–3 times per month/never), household income (in quartiles), body mass index, systolic blood pressure, and pulse rate (all continuous) in 2014/2016. P-values were adjusted using the Benjamini-Hochberg methods