Regulatory Bias Constrains Epigenetic Aging Trajectories
Aging reflects both stochastic fluctuation and biological regulation. We present a Markov chain framework for epigenetic aging that extends noise-driven models by adding a state-dependent bias term representing regulatory constraint. Using DNA methylation data from mice, rats, and bats, we show that empirical epigenetic aging is characterized by a progressive restriction of the accessible state space. We demonstrate that a stochastic model incorporating state-dependent regulatory bias successfully reproduces this constraint, whereas standard noise-driven models fail to capture it. Subsequently, the results indicate that a regression-based drift model can be used to predict future trajectories and achieve lower mean, covariance, and state-increment dependence errors than the biased model. The pattern of state loss is consistent with discrete bifurcation events, suggesting resilience declines stepwise rather than continuously. This implies that the solution space for intervention narrows irreversibly at each transition, making intervention timing critical.