Key Points

Established risk factor models demonstrate excellent discrimination for CKD outcomes but do not adequately reflect disease activity or mechanisms.We have developed models solely from novel biomarkers that reflect key disease pathways driving CKD progression with equivalent discrimination.Although biomarkers offer limited incremental gains in risk prediction, they may provide critical insights into disease mechanisms and treatment response.

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

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 α1 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

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.