Homeostatic dysregulation proceeds in parallel in multiple physiological systems.

Qing Li,Patrick Bergeron,Emmanuel Milot,Shengrui Wang,Luigi Ferrucci,Alan A Cohen,Linda P Fried

doi:10.1111/acel.12402

Abstract

An increasing number of aging researchers believes that multi-system physiological dysregulation may be a key biological mechanism of aging, but evidence of this has been sparse. Here, we used biomarker data on nearly 33, 000 individuals from four large datasets to test for the presence of multi-system dysregulation. We grouped 37 biomarkers into six a priori groupings representing physiological systems (lipids, immune, oxygen transport, liver function, vitamins, and electrolytes), then calculated dysregulation scores for each system in each individual using statistical distance. Correlations among dysregulation levels across systems were generally weak but significant. Comparison of these results to dysregulation in arbitrary 'systems' generated by random grouping of biomarkers showed that a priori knowledge effectively distinguished the true systems in which dysregulation proceeds most independently. In other words, correlations among dysregulation levels were higher using arbitrary systems, indicating that only a priori systems identified distinct dysregulation processes. Additionally, dysregulation of most systems increased with age and significantly predicted multiple health outcomes including mortality, frailty, diabetes, heart disease, and number of chronic diseases. The six systems differed in how well their dysregulation scores predicted health outcomes and age. These findings present the first unequivocal demonstration of integrated multi-system physiological dysregulation during aging, demonstrating that physiological dysregulation proceeds neither as a single global process nor as a completely independent process in different systems, but rather as a set of system-specific processes likely linked through weak feedback effects. These processes--probably many more than the six measured here--are implicated in aging.

Highlights

Research on aging biomarkers has traditionally focused on individual biomarkers; this has been changing as single-mechanism explanations of aging such as oxidative stress, telomeres, and inflammation increasingly give way to multi-factorial explanations, in which many mechanisms interact (Weinert & Timiras, 2003; Ferrucci, 2005; Fried et al, 2009; Cohen et al, 2013)
Li et al Recently, we demonstrated a novel, rigorous way to measure dysregulation based on the statistical distance of a biomarker profile (Cohen et al, 2013, 2014; Milot et al, 2014b)
For each of the six physiological systems, we calculated a dysregulation score based on the Mahalanobis distance to measure dysregulation for each participant at each visit

Summary

Introduction

Research on aging biomarkers has traditionally focused on individual biomarkers; this has been changing as single-mechanism explanations of aging such as oxidative stress, telomeres, and inflammation increasingly give way to multi-factorial explanations, in which many mechanisms interact (Weinert & Timiras, 2003; Ferrucci, 2005; Fried et al, 2009; Cohen et al, 2013). We define physiological dysregulation as the breakdown with age in the capacity of the complex regulatory networks to maintain organismal homeostasis due to changes in the state of these networks; we exclude from this definition adaptive changes with age and transient (i.e., reversible) responses to environmental challenges (Yashin et al, 2012). This framework of homeostatic dysregulation supports the hypothesis that aging does not result from the downstream effects of a single factor, pathway, or process. Rather it suggests the following testable predictions: (i) multiple aging mechanisms should operate simultaneously; there could be several or many pathways, either independent or correlated (Kirkwood, 2005); (ii) markers of system state should be poorer predictors of aging-related outcomes than measures of system dynamics (Varadhan et al, 2008; Yashin et al, 2010a); and (iii) risk of aging-related outcomes (e.g., diseases) should often change as a function of deviations of parameters (e.g., biomarkers) from their normal ranges, rather than as a linear function of the parameters (Seplaki et al, 2005; Arbeev et al, 2011; Yashin et al, 2012; Cohen et al, 2013)

Results

Discussion

Conclusion