New Biomarker Models Offer Mechanistic Insights in Chronic Kidney Disease Management

Novel biomarkers demonstrated equal predictive value in determining all-cause mortality and composite outcomes when compared to established risk factors for chronic kidney disease (CKD), according to an article published this past June in the Journal of the American Society of Nephrology. The investigation into novel biomarkers was an effort to have a better mechanistic insight into CKD progression such that the data can then be used to support more personalized approaches to CKD care, such as personalized medicine and novel drug development.

Led by primary author Anthony Onoja, Ph.D., Faculty of Health and Medical Sciences, School of Health Sciences, University of Surrey, Guildford, United Kingdom, his team collected data from 2,884 participants enrolled in the UK-based National Unified Renal Translational Research Enterprise CKD study. The research evaluated 21 blood and urine biomarkers to assess their predictive value for kidney failure, all-cause mortality and composite outcomes.

The impetus behind this study stems from a growing recognition that standard risk factors, namely, estimated glomerular filtration rate and urinary albumin-to-creatinine ratio, although highly predictive in population-based models like the Kidney Failure Risk Equation, offer limited insight into ongoing disease activity and underlying mechanisms. As CKD is increasingly managed through individualized care pathways, novel biomarkers may facilitate better disease monitoring, therapeutic targeting and risk stratification.

Over a median follow-up of four years, researchers found that risk models composed exclusively of three biomarkers, soluble TNF receptor 1 (sTNFR1), soluble cluster of differentiation 40 (sCD40) and urinary collagen type I alpha 1 chain (UCOL1A1), demonstrated strong discrimination for kidney failure (C-index 0.86). Established risk factors for kidney failure, however, performed better with a C-index of 0.90.

For all-cause mortality, a trio of cardiovascular-related biomarkers, high-sensitivity cardiac troponin T (hs-cTnT), N-terminal pro-brain natriuretic peptide (NT-proBNP) and soluble urokinase plasminogen activator receptor (suPAR), performed comparably to established risk models (C-index 0.80). Composite outcomes incorporating both kidney failure and mortality achieved a C-index of 0.78 using six biomarkers, slightly better than the C-index of 0.77 from established risk factors.

These biomarker models did not considerably surpass traditional predictors; nonetheless, they matched them and, more crucially, facilitated the development of more tailored and mechanistically informed therapy. For instance, UCOL1A1 shows pathways that cause fibrosis and the turnover of the extracellular matrix, which are two important causes of kidney scarring. sTNFR1 and sCD40 indicate inflammatory signaling, which is therapeutically significant as cytokine regulation becomes increasingly important in CKD research.

suPAR, hs-cTnT and NT-proBNP reveal cardiovascular risk and systemic disease burden frequently neglected in renal-centric therapy.

In clinical practice, the value proposition transitions from mere prediction accuracy to the capacity of these biomarkers to guide and assess therapeutic response. For example, suPAR and sTNFR1 may help find people with increased inflammation who could benefit from biologics that target them. Collagen indicators may also aid in stratifying progression risk in fibrotic CKD phenotypes and in monitoring the efficacy of anti-fibrotic therapies.

This study lays the groundwork for biomarker-guided trials and clinical algorithms that extend beyond mere risk estimation to address the underlying causes of risk, as managed care models increasingly incorporate precision tools. It is important to do more tests on different groups of people and in different types of care settings. The work highlights a fundamental transformation: biomarkers serve not merely as statistical instruments but as insights into disease biology.