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Abstract: TH-PO0332

Urinary Complement Profiling as a Tool for Disease Activity in Glomerular Disorders

Session Information

Category: Glomerular Diseases

  • 1401 Glomerular Diseases: Mechanisms, including Podocyte Biology

Authors

  • Tzali, Eleni, Nephrology Clinic, University Hospital of Heraklion, Heraklion, Greece
  • Kaforou, Sofia, Proteomics Unit, Institute of Molecular Biology and Biotechnology (IMBB- FORTH), Heraklion, Greece
  • Kountourakis, Nikos, Proteomics Unit, Institute of Molecular Biology and Biotechnology (IMBB- FORTH), Heraklion, Greece
  • Antonakis, Andreas, Nephrology Clinic, University Hospital of Heraklion, Heraklion, Greece
  • Lygerou, Dimitra, Nephrology Clinic, University Hospital of Heraklion, Heraklion, Greece
  • Drosataki, Eleni, Nephrology Clinic, University Hospital of Heraklion, Heraklion, Greece
  • Dermitzaki, Eleftheria-Kleio, Nephrology Clinic, University Hospital of Heraklion, Heraklion, Greece
  • Pleros, Christos, Nephrology Clinic, University Hospital of Heraklion, Heraklion, Greece
  • Androvitsanea, Ariadni, Nephrology Clinic, University Hospital of Heraklion, Heraklion, Greece
  • Gakiopoulou, Harikleia, 1st Department of Pathology, Medical School, National and Kapodistrian Unversity of Athens, Athens, Greece
  • Gouridis, Giorgos, Proteomics Unit, Institute of Molecular Biology and Biotechnology (IMBB- FORTH), Heraklion, Greece
  • Stylianou, Konstantinos, Nephrology Clinic, University Hospital of Heraklion, Heraklion, Greece
  • Petrakis, Ioannis, Nephrology Clinic, University Hospital of Heraklion, Heraklion, Greece
Background

Complement overactivation is increasingly recognized in glomerular disease, but urinary proteomic studies focused on complement remain limited. This study evaluates urinary complement proteomics as a biomarker of kidney disease activity.

Methods

From May to August 2025, a longitudinal cohort of 27 patients with chronic kidney disease was recruited, yielding 1,416 patient-visits. Twenty patients had active disease (1,029visits), seven had inactive disease (387visits), and five healthy individuals served as controls. Diagnoses included IgA nephropathy, ANCA-associated vasculitis, thrombotic microangiopathy, interstitial nephritis, cryoglobulinemic membranoproliferative glomerulonephritis, and primary membranous nephropathy. Disease activity was assessed by treating nephrologists at sampling. Morning urine samples were analyzed using label-free nLC–MS/MS with a targeted focus on complement proteins. Pathway-level scores and protein-level intensity matrices were evaluated using logistic regression models trained on (i) proteomics, (ii) eGFR and (iii) combined proteomics and eGFR. Performance was assessed using cross-validated ROC AUC and sensitivity at fixed specificity thresholds, with robustness evaluated via bootstrap sampling, Bayesian analyses, non-inferiority testing, and pathway ablation.

Results

Models incorporating urinary complement proteomics consistently outperformed those based on eGFR alone in distinguishing Active from Inactive disease or healthy controls. Adding proteomics to eGFR increased ROC AUC from 0.741 to 0.809. Sensitivity improved from 0.37 to 0.57 (~80% specificity) and from 0.23 to 0.47 (90% specificity), supported by robustness tests. Pathway ablation identified the alternative pathway as the dominant contributor to that gain. Protein-level analyses highlighted CFI, CFB, and C3 fragments as strong standalone markers, while multivariate ablation demonstrated that regulatory components, particularly CFH and CFHR, were central drivers of discrimination, rendering several activation markers redundant.

Conclusion

Urinary complement proteomics provides independent and clinically meaningful information for kidney disease activity detection beyond eGFR.
The signal is driven mainly by dysregulation of the alternative pathway and its regulatory components, supporting urinary proteomics as a minimally invasive tool for activity monitoring. Further validation is necessary in larger cohorts.