A blood-based proteomics model could significantly enhance the prediction of disease progression in autosomal dominant polycystic kidney disease (ADPKD), according to a recent study. The work addresses the limitations of current risk stratification tools, which typically rely on MRI imaging or genetic tests that may not be available in routine care.
Researchers at the University of Cologne and the Center for Molecular Medicine Cologne profiled serum samples from over 800 individuals across three major ADPKD cohorts. Using a semi-automated workflow combining robotic sample preparation with data-independent acquisition (DIA) mass spectrometry, the team performed high-throughput, standardized quantification of circulating proteins to identify biomarkers predictive of kidney function decline.
The final prediction model included six proteins – SERPINF1, GPX3, AFM, FERMT3, CFHR1, and RARRES2 – that collectively accounted for nearly 30% of the variance in estimated glomerular filtration rate (eGFR) decline, independently of conventional clinical or imaging markers. Functional enrichment analysis showed that these biomarkers are primarily associated with immune function, lipid metabolism, and oxidative stress pathways. Notably, GPX3 and AFM are linked to antioxidant defense, while RARRES2 is implicated in inflammation and adipogenesis – processes already tied to ADPKD pathophysiology.
To benchmark the model's performance, the researchers compared it against the Mayo Imaging Classification (MIC), a leading clinical standard. The proteome-based models outperformed MIC in both internal and external validation cohorts, including the Dutch DIPAK cohort, even when accounting for variability in sample types (serum vs. EDTA plasma).
“Our study shows that blood proteins can offer powerful clues about how fast a patient's kidney function is likely to decline,” said lead investigator Roman-Ulrich Müller, in the team’s press release. “By identifying specific proteins linked to disease progression, we’ve taken a meaningful step towards more accurate and earlier prediction, beyond what current clinical tools can provide.”
The authors note that serum proteomics not only supports improved risk prediction but also reveals underlying disease mechanisms, with proteins like SERPINF1 and CFHR1 previously linked to vascular and immune dysregulation in kidney disorders. They are now aiming to translate the six-protein panel into clinically accessible formats – such as ELISA or targeted mass spectrometry – and investigate how treatments like tolvaptan affect proteomic signatures over time.
Newsletters
Receive the latest analytical science news, personalities, education, and career development – weekly to your inbox.
