Optimal Versus Realized Trajectories of Physiological Dysregulation in Aging and Their Relation to Sex-Specific Mortality Risk

Abstract

While longitudinal changes in biomarker levels and their impact on health have been characterized for individual markers, little is known about how overall marker profiles may change during aging and affect mortality risk. We implemented the recently developed measure of physiological dysregulation based on the statistical distance of biomarker profiles in the framework of the stochastic process model of aging, using data on blood pressure, heart rate, cholesterol, glucose, hematocrit, body mass index, and mortality in the Framingham original cohort. This allowed us to evaluate how physiological dysregulation is related to different aging-related characteristics such as decline in stress resistance and adaptive capacity (which typically are not observed in the data and thus can be analyzed only indirectly), and, ultimately, to estimate how such dynamic relationships increase mortality risk with age. We found that physiological dysregulation increases with age; that increased dysregulation is associated with increased mortality, and increasingly so with age; and that, in most but not all cases, there is a decreasing ability to return quickly to baseline physiological state with age. We also revealed substantial sex differences in these processes, with women becoming dysregulated more quickly but with men showing a much greater sensitivity to dysregulation in terms of mortality risk.

Circulating miRNAs and miRNA shuttles as biomarkers: Perspective trajectories of healthy and unhealthy aging

“…Therefore c-miR-21-5p could be a promising candidate as biomarker of deviation from the healthy trajectory (as illustrated in Fig. 2), especially at old age, when the effects of adaptation and remodeling strongly interact with the selective effects due to mortality (Arbeev et al, 2016). …”

Classification for Longevity Potential: The Use of Novel Biomarkers

“…Previously, in population-based studies, estimations of physiological dysregulation or biological age have been associated with health outcomes and mortality (2, 3, 5). The difficulty in the comparison of each of these studies is that different study cohorts have been used and that different parameters have been determined to be included in analyses.…”

“…In large population-based studies, several measures of physiological dysregulation (1–3) and biological age scores (4–6) have been developed using cross sectional and longitudinal data. The challenge in biomarker development lays in the prediction of residual lifespan or mortality risk for an individual.…”

Dynamics of biomarkers in relation to aging and mortality

“…Yashin et al (2008) considered a two-dimensional SPM in application to FI and medical costs. Recently we applied DM in the context of SPM using data on several biomarkers with repeated measurements available in Framingham data (Arbeev et al, 2016). This short list indicates that the potential of applications of SPM to cumulative measures is largely underused, given the large and growing number of longitudinal studies collecting repeated measurements of biomarkers along with extensive data on socio-demographic, behavioral, and other covariates, follow-up data on mortality and onset of diseases as well as genetic markers.…”