Novel Biomarker Risk Prediction Models Examined in Chronic Kidney Disease
FRIDAY, Aug. 22, 2025 -- For adults with nondialysis chronic kidney disease (CKD), risk prediction models incorporating novel biomarkers show comparable discrimination to established risk factors, according to a study published online June 20 in the Journal of the American Society of Nephrology.
Anthony Onoja, Ph.D., from the University of Surrey in the United Kingdom, and colleagues examined novel biomarkers linked to key disease mechanisms to predict the risk for kidney failure, all-cause mortality, or a composite of both in a study including 2,884 adults with nondialysis CKD from 16 U.K. nephrology centers.
The researchers found that a model combining three biomarkers (soluble tumor necrosis factor receptor 1, soluble cluster of differentiation 40, and urinary collagen type 1 α1 chain) showed good discrimination for kidney failure (C-index, 0.86), but a model using established risk factors outperformed this model (C-index, 0.90). A model using three biomarkers (high-sensitivity cardiac troponin T, N-terminal pro-brain natriuretic peptide, and soluble urokinase plasminogen activator receptor) demonstrated equivalent discrimination to an established risk factor model for all-cause mortality (C-index, 0.80 and 0.80, respectively). The biomarker model discrimination was numerically higher than for established risk factors for the composite outcome (C-index, 0.78 versus 0.77); a small but significant improvement in discrimination was seen with the addition of biomarkers to the established risk factors (C-index, 0.80).
"The key finding here is that we can use these biomarkers to understand the underlying mechanisms of disease progression, potentially paving the way to more personalized treatments and medicines for CKD patients," Onoja said in a statement.
Several authors disclosed ties to relevant organizations.
Disclaimer: Statistical data in medical articles provide general trends and do not pertain to individuals. Individual factors can vary greatly. Always seek personalized medical advice for individual healthcare decisions.
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