Used hemodialysis membranes (HD-M) are a valuable reservoir of biological information. Proteins bind to HD-M, but whether this process depends on the type of membrane or patient factors or selectively affects specific protein classes has not been adequately elucidated. State-of-the-art proteomics techniques are capable of identifying and quantifying this therapy-specific subproteome to enable the analysis of disease- or membrane-induced pathophysiologies. We demonstrate the feasibility of the deep proteomic characterization of the extracorporeal proteome adsorbed to HD-M. A shotgun proteomics approach using nano-flow liquid chromatography coupled to mass-spectrometry identified 1648 unique proteins eluted by a chaotropic buffer from the HD-M of eight patients. In total, 995 proteins were present in all eluates; a more stringent approach showed that a core proteome of 310 proteins could be identified independently in all samples. Stability of the dialyzer proteome was demonstrated by a >90% re-identification rate on longitudinal samples of a single patient. The core proteome showed an overrepresentation of pathways of hemostasis and the immune system, and showed differences in membrane materials (polysulfone vs. helixone). This study demonstrates that optimized conditions combined with high-performance proteomics enable the in-depth exploration of the subproteome bound to HD-M, yielding a stable core proteome that can be exploited to study patient-specific factors and improve hemodialysis therapy.

Background: Peritoneal dialysis (PD) effluent of kidney failure patients represents an accessible liquid biopsy of the peritoneal cavity, yet the mechanisms determining its molecular composition remain poorly understood. We applied an integrative multi-omics approach to characterize the composition, transport dynamics, and molecular origin of the PD effluentome. Methods: Cell-free effluent, effluent cells, and plasma were collected from stable PD patients during standardized peritoneal equilibration tests in a randomized clinical trial. Targeted metabolomics, proteomics, and transcriptomic profiling were integrated with a reference human plasma proteome to investigate temporal molecular changes, peritoneal transport characteristics, and protein origin. Results: A total of 207 metabolites and 2970 proteins were identified in PD effluent. Metabolites exhibited distinct class-specific transport kinetics, with rapid equilibration of amino acids and biogenic amines, whereas lipids remained markedly underrepresented despite prolonged dwell times, indicating that transport is governed by physicochemical properties beyond molecular size alone. The effluent proteome underwent concordant alteration, with dwell time-dependent enrichment of pathways related to extracellular matrix organization, angiogenesis, coagulation, and tissue repair. Integrative analysis of the effluent proteome, effluent-cell transcriptome, and human plasma proteome resolved distinct plasma-associated, effluent cell-associated, resident peritoneal tissue-associated, and mixed-origin protein populations. Conclusions: This study establishes the first systems-level approach describing the composition, transport dynamics, and molecular origin of the PD effluentome. By transforming PD effluent into a biologically interpretable molecular readout of peritoneal membrane biology, this work provides a reference for the mechanistic interpretation of effluent-derived biomarkers and supports future therapeutic monitoring and precision medicine in PD.