Observational analysis identifies 21 novel biomarkers for CKD progression, suggesting implications for personalized medicine.
We read with interest the National Unified Renal Translational Research Enterprise-CKD study by Onoja et al., published in JASN, evaluating 21 novel biomarkers (related to kidney injury and/or fibrosis, inflammation, and cardiovascular disease) for CKD progression (kidney failure and all-cause mortality).1 While the identification of key biomarkers such as UCOL1A1, sTNFR1, and hs-cTnT provides valuable mechanistic insights into fibrosis, inflammation, and cardiovascular pathways, several questions remain regarding clinical translation. The predominantly White cohort (87%) limits generalizability to higher-risk populations, and the lack of significant improvement over traditional models (e.g., Kidney Disease Improving Global Outcomes CKD classification system and kidney failure risk equation) raises questions about incremental value—Did analyses include net reclassification improvement or integrated discrimination improvement? In addition, while biomarkers showed good discrimination, their utility for guiding targeted therapies remains unexplored, and multiple testing across 21 biomarkers warrants discussion of false discovery rate adjustments. Future studies should validate these findings in diverse populations, examine dynamic biomarker changes with newer CKD therapies (e.g., sodium-glucose cotransporter 2 inhibitors and glucagon-like peptide-1 agonists), and assess cost-effectiveness of biomarker-guided care. This important work lays the groundwork for further research into personalized risk prediction and treatment strategies in CKD.
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Liang et al. (2025) studied this question.
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