Dynamical network stability analysis of multiple biological ages provides a framework for understanding the aging process

TL;DR

This paper introduces a dynamical network stability framework to analyze multiple biological ages, revealing key biomarkers and stability properties that enhance understanding of aging processes and health decline.

Contribution

It develops a novel multidimensional network approach to integrate biological ages and applies dynamical stability analysis to identify new aging biomarkers.

Findings

Physiological age is a central vulnerability node.

The system is stable but has a weakly stable, slow recovery direction.

The slow direction correlates with chronological age and health decline.

Abstract

Widespread interest in non-destructive biomarkers of aging has led to a curse of plenty: a multitude of biological ages that each proffers a 'true' health-adjusted age of an individual. While each measure provides salient information on the aging process, they are each univariate, in contrast to the "hallmark" and "pillar" theories of aging which are explicitly multidimensional, multicausal and multiscale. Fortunately, multiple biological ages can be systematically combined into a multidimensional network representation. The interaction network between these biological ages permits analysis of the multidimensional effects of aging, as well as quantification of causal influences during both natural aging and, potentially, after anti-aging intervention. The behaviour of the system as a whole can then be explored using dynamical network stability analysis which identifies new, efficient biomarkers that quantify long term resilience scores on the timescale between measurements (years). We demonstrate this approach using a set of 8 biological ages from the longitudinal Swedish Adoption/Twin Study of Aging (SATSA). After extracting an interaction network between these biological ages, we observed that physiological age, a proxy for cardiometabolic health, serves as a central node in the network, implicating it as a key vulnerability for slow, age-related decline. We furthermore show that while the system as a whole is stable, there is a weakly stable direction along which recovery is slow - on the timescale of a human lifespan. This slow direction provides an aging biomarker which correlates strongly with chronological age and predicts longitudinal decline in health - suggesting that it estimates an important driver of age-related changes.