Primary sclerosing cholangitis (PSC) is traditionally described as a chronic, progressive cholestatic liver disease. However, this characterization does not fully account for the marked variability, episodic deterioration, and nonlinear clinical trajectories observed in practice. This paper proposes an alternative interpretation: PSC is better understood as a dynamic system operating near instability thresholds, where disease progression occurs through transitions between states rather than continuous linear change. Building on dynamic systems theory, PSC is conceptualized as a network involving immune regulation, cholangiocyte function, microbiome interactions, and fibrotic processes. Within such systems, progression may reflect qualitative shifts in system behaviour rather than gradual accumulation of damage. The paper introduces the concept of state transitions in PSC and discusses how clinical phenomena—such as sudden deterioration, fluctuating disease activity, and discordance between biomarkers and outcomes—can be interpreted within this framework. This perspective aligns with emerging models of disease dynamics in which early warning signals, including increased variability and reduced system resilience, precede critical transitions. Positioning PSC within this framework may have implications for clinical monitoring, risk assessment, and therapeutic strategy, shifting focus from static staging toward dynamic assessment of system stability. Series and conceptual context This work is part of the Dynamic Foundations of Disease series. It extends the conceptual framework established in previous papers by demonstrating that system transitions are not limited to externally triggered conditions, but also occur in chronic progressive diseases such as primary sclerosing cholangitis. Together with earlier works, this paper contributes to the development of a unified dynamic systems framework for disease, in which progression is understood as transitions between states of stability and instability rather than linear accumulation. This work is aligned with the Universal Resonance Model (URM), which defines disease dynamics through instability signals, feedback structures, and transition thresholds in biological systems.
Anita Domargård (Sun,) studied this question.