Joint inference of physiological network and survival analysis identifies factors associated with aging rate.

We describe methodology for joint reconstruction of physiological-survival networks from observational data capable of identifying key survival-associated variables, inferring a minimal physiological network structure, and bridging this network to the Gompertzian survival layer. Using synthetic network structures, we show that the method is capable of identifying aging variables in cohorts as small as 5,000 participants. Applying the methodology to the observational human cohort, we find that interleukin-6, vascular calcification, and red-blood distribution width are strong predictors of baseline fitness. More important, we find that red blood cell counts, kidney function, and phosphate level are directly linked to the Gompertzian aging rate. Our model therefore enables discovery of processes directly linked to the aging rate of our species. We further show that this epidemiological framework can be applied as a causal inference engine to simulate the effects of interventions on physiology and longevity.