ArticleNature communications2025
Developing serum proteomics based prediction models of disease progression in ADPKD.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Evolution of Clinical Trial Design in ADPKD.Kidney international reports · 2026Review
- Feasibility of Integrating Urinary Proteomics and Machine Learning for Diagnosing Diabetic Nephropathy.Journal of proteome research · 2026Article
- From albuminuria to multi-omics signatures: emerging biomarkers and drug targets for early-stage chronic kidney disease.Frontiers in pharmacology · 2026Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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