Developing serum proteomics based prediction models of disease progression in ADPKD

Area of Science:

• Nephrology
• Proteomics
• Biomarker Discovery

Background:

• Autosomal Dominant Polycystic Kidney Disease (ADPKD) is the primary genetic cause of kidney failure.
• Current outcome prediction models for ADPKD lack sufficient accuracy for clinical decision-making.
• Improved risk stratification is crucial for guiding therapeutic interventions in ADPKD patients.

Purpose of the Study:

• To investigate the utility of serum proteomics for enhancing risk stratification in ADPKD.
• To identify novel protein biomarkers associated with kidney function decline in ADPKD.
• To develop and validate a predictive model for ADPKD progression using proteomic data.

Main Methods:

• Serum samples were analyzed using proteomics to identify proteins linked to yearly kidney function decline.
• Functional enrichment analysis was performed on identified proteins.
• A predictive model was constructed using a subset of significant proteins and validated in independent cohorts.
• Comparison with an Immunoglobulin A nephropathy cohort assessed protein specificity and eGFR-dependency.

Main Results:

• Twenty-nine proteins were significantly associated with annual kidney function decline in ADPKD.
• Six proteins, including SERPINF1, GPX3, AFM, FERMT3, CFHR1, and RARRES2, formed a predictive model with an adjusted R² of 0.31.
• The predictive performance of the six-protein model was independent of established clinical and imaging parameters.
• Validation in different cohorts confirmed the accuracy and robustness of the developed models.

Conclusions:

• Serum proteomics offers a promising avenue for improving risk stratification in Autosomal Dominant Polycystic Kidney Disease.
• The identified six-protein signature provides accurate, independent predictions of kidney function decline.
• Further prospective validation is warranted to translate these findings into clinical practice for ADPKD management.