  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.
