  Using longitudinal study data, we dynamically model how aging affects
homeostasis in both mice and humans. We operationalize homeostasis as a
multivariate mean-reverting stochastic process. We hypothesize that biomarkers
have stable equilibrium values, but that deviations from equilibrium of each
biomarker affects other biomarkers through an interaction network - this
precludes univariate analysis. We therefore looked for age-related changes to
homeostasis using dynamic network stability analysis, which transforms observed
biomarker data into independent "natural" variables and determines their
associated recovery rates. Most natural variables remained near equilibrium and
were essentially constant in time. A small number of natural variables were
unable to equilibrate due to a gradual drift with age in their homeostatic
equilibrium, i.e. allostasis. This drift caused them to accumulate over the
lifespan course and makes them natural aging variables. Their rate of
accumulation was correlated with risk of adverse outcomes: death or dementia
onset. We call this tendency for aging organisms to drift towards an
equilibrium position of ever-worsening health "mallostasis". We demonstrate
that the effects of mallostasis on observed biomarkers are spread out through
the interaction network. This could provide a redundancy mechanism to preserve
functioning until multi-system dysfunction emerges at advanced ages.
