Developing serum proteomics based prediction models of disease progression in ADPKD

Hande Ö. Aydogan Balaban, Sita Arjune, Franziska Grundmann, Jan-Wilm Lackmann, Thomas Rauen, Philipp Antczak () and Roman-Ulrich Müller ()
Additional contact information
Hande Ö. Aydogan Balaban: Faculty of Medicine and University Hospital Cologne
Sita Arjune: Faculty of Medicine and University Hospital Cologne
Franziska Grundmann: Faculty of Medicine and University Hospital Cologne
Jan-Wilm Lackmann: University of Cologne and University Hospital Cologne
Thomas Rauen: RWTH Aachen University Hospital
Philipp Antczak: Faculty of Medicine and University Hospital Cologne
Roman-Ulrich Müller: Faculty of Medicine and University Hospital Cologne

Nature Communications, 2025, vol. 16, issue 1, 1-11

Abstract: Abstract Autosomal Dominant Polycystic Kidney Disease is the most common genetic cause of kidney failure. Outcome prediction is essential to guide therapeutic decisions. However, currently available models are of limited accuracy. We aimed to examine the potential of serum proteomics for improved risk stratification. Here we show that 29 proteins are significantly associated with yearly kidney function decline. Functional enrichment on these 29 proteins reveals GO:BP terms related to immune response, lipoproteins and metabolic processes. A comparison to an Immunoglobulin A nephropathy cohort provides information regarding the eGFR-dependency and disease specificity of these proteins. The final outcome prediction model (adjusted R² 0.31) contains six proteins, namely Endothelial Plasminogen Activator Inhibitor (SERPINF1), Glutathione Peroxidase 3 (GPX3), Afamin (AFM), FERM Domain Containing Kindlin-3 (FERMT3), Complement Factor H Related 1 (CFHR1), and Retinoic Acid Receptor Responder 2 (RARRES2), the predictive value of which is independent from the clinical and imaging parameters currently used in clinical care. The validation of these models in different cohorts indicates the accuracy of the models. It will now be important to move towards targeted validation in a prospective study.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-61887-8 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-61887-8

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-61887-8

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().