Biomarkers of Kidney Failure and All-Cause Mortality in CKD

Background CKD carries a variable risk for multiple adverse outcomes, highlighting the need for a personalized approach. This study evaluated several novel biomarkers linked to key disease mechanisms to predict the risk of kidney failure (first event of eGFR <15 ml/min per 1.73 m(2) or KRT), all-cause mortality, and a composite of both. Methods We included 2884 adults with nondialysis CKD from 16 nephrology centers across the United Kingdom. Twenty-one biomarkers associated with kidney damage, fibrosis, inflammation, and cardiovascular disease were analyzed in urine, plasma, or serum. Cox proportional hazards models were used to assess biomarker associations and develop risk prediction models. Results Participants had mean age 63 (15) years; 58% were male and 87% White. Median eGFR was 35 (25-47) ml/min per 1.73 m2, and the median urinary albumin-to-creatinine ratio was 197 (32-895) mg/g. During median 48 (33-55) months of follow-up, 680 kidney failure events and 414 all-cause mortality events occurred. For kidney failure, a model combining three biomarkers (soluble TNF receptor 1, soluble cluster of differentiation 40, and urinary collagen type 1 alpha 1 chain) showed good discrimination (C-index, 0.86; 95% confidence interval [CI], 0.83 to 0.89) but was outperformed by a model using established risk factors (age, sex, ethnicity, eGFR, and urinary albumin-to-creatinine ratio; C-index, 0.90; 95% CI, 0.88 to 0.92). For all-cause mortality, 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 (C-index, 0.80; 95% CI, 0.75 to 0.84) to an established risk factor model (C-index, 0.80; 95% CI, 0.76 to 0.84). For the composite outcome, the biomarker model discrimination (C-index, 0.78; 95% CI, 0.76 to 0.81) was numerically higher than for established risk factors (C-index, 0.77; 95% CI, 0.74 to 0.80), and the addition of biomarkers to the established risk factors led to a small but statistically significant improvement in discrimination (C-index, 0.80; 95% CI, 0.77 to 0.82; P value <0.01). Conclusions Risk prediction models incorporating novel biomarkers showed comparable discrimination to established risk factors of kidney failure and all-cause mortality.Clinical Trial registry name and registration number:ClinicalTrials.gov, NCT04084145.