  We present a systems-level analysis of end-stage kidney disease (ESKD) with a
dynamical network analysis of 14 commonly measured blood-based biomarkers in
patients undergoing regular haemodialysis. Utilizing a validated pipeline for
declining homeostatic systems, our approach learns a dynamical model together
with an invertible transformation that simplifies the behaviour of observed
biomarkers into natural variables. Within the natural variables, we identified
two distinct dynamical behaviours: (i) stochastic accumulation, the random
accumulation of abnormal values, and (ii) mallostasis, a deterministic drift
towards worse health. These behaviours are identified by persistent
fluctuations indicating weak stability, or a gradual shift in homeostatic set
point, respectively. Both lead to worsening natural variable values, making the
natural variables salient survival predictors with preferred directions of
increasing risk. When this worsening is transformed back into observable
biomarkers, it generates a coherent spectrum of worsening medical signs
characteristic of a medical syndrome. Specifically, we found that small modules
of natural variables corresponded to two existing syndromes commonly afflicting
ESKD patients: protein-energy wasting and sepsis. We also identified new
prospective syndromes. Our findings suggest that natural variables are robust,
systems-level biomarkers, capturing the complex, holistic changes in health
associated with ESKD.
