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

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