Kidney Week

Abstract: SA-PO0553

Serum Proteomics-Based Outcome Prediction in ADPKD

Session Information

• Cystic Kidney Diseases: Clinical Research
  November 08, 2025 | Location: Exhibit Hall, Convention Center
  Abstract Time: 10:00 AM - 12:00 PM

Category: Genetic Diseases of the Kidneys

• 1201 Genetic Diseases of the Kidneys: Monogenic Kidney Diseases

Authors

• Mueller, Roman-Ulrich, University of Cologne Center for Molecular Medicine Cologne, Cologne, NRW, Germany

Roman-Ulrich Mueller, MD

• Aydogan Balaban, Hande Özge, University of Cologne Center for Molecular Medicine Cologne, Cologne, NRW, Germany

Hande Özge Aydogan Balaban, MSc

• Arjune, Sita, University of Cologne Center for Molecular Medicine Cologne, Cologne, NRW, Germany

Sita Arjune, PhD

• Grundmann, Franziska, Universitatsklinikum Koln, Cologne, NRW, Germany

Franziska Grundmann, MD, DrMed

• Lackmann, Jan-Wilm, Exzellenzcluster CECAD in der Universitat zu Koln, Cologne, NRW, Germany

Jan-Wilm Lackmann, PhD

• Rauen, Thomas, Universitatsklinikum Aachen, Aachen, NRW, Germany

Thomas Rauen, MD

• Antczak, Philipp, University of Cologne Center for Molecular Medicine Cologne, Cologne, NRW, Germany

Philipp Antczak, PhD

Background

Autosomal dominant polycystic kidney disease (ADPKD) is the most common genetic cause of kidney failure. Predicting key outcomes such as the time to kidney failure is crucial to patient counselling and treatment decisions. However, currently available prediction algorithms require MRI images or genetic exams and have limited predictive accuracy. Thus, new approaches based on simple blood values are an important goal. We built proteome-based models to predict yearly eGFR decline.

Methods

Proteomic analysis was performed on blood samples collected from 3 ADPKD cohorts: Screening Cohort (SC, 214 patients, serum), Internal/Temporal Cohort (ITC, 408 patients, serum), and External Cohort (EC, 173 patients, plasma). eGFR-slope-associated proteins were identified using LIMMA and LASSO. LASSO proteins were used to build proteome-based linear models. ITC and EC were used for model validation.

Results

We identified 29 proteins that are significantly associated with eGFR slope (LIMMA), and 6 proteins that are optimal to build prediction models (LASSO). The Proteome Model (PM, adj. R² 0.31) containing only these 6 proteins was superior to a clinical model (age, sex, eGFR and Mayo Imaging Class (MIC), adj. R² 0.23). Adding the protein data to the clinical model showed independent added value (Combined Model (CM, adj. R² 0.34)). The proteome-based model was confirmed in both validation cohorts, but the use of plasma instead of serum decreased the predictive capacity. Lastly, we calculated future eGFR to compare prediction performance of our models to the previously established MIC Model. Both PM and CM performed better than the MIC Model, which underestimated the future eGFR in SC and ITC. In EC, our models overestimated while the MIC model underestimated future eGFR.

Conclusion

Our study shows the great potential for the identification of biomarkers in ADPKD. Importantly, the independent value of a panel of 6 proteins could be confirmed in two validation cohorts. Prospective validation using absolute quantification will now be crucial towards clinical implementation. Beside biomarker identification, the proteome data provides important added information towards molecular processes contributing to disease progression.

Digital Object Identifier (DOI)

• doi: 10.1681/ASN.2025gt40fhjt