Abstract 4141918: Kidney and cardiac biomarkers and risk of adverse kidney outcomes in patients with type 2 diabetes and kidney disease, cardiovascular disease, or both

Introduction: Kidney (estimated glomerular filtration rate [eGFR] and urine albumin/creatinine ratio [UACR]) and cardiac biomarkers (high-sensitivity troponin T [hsTnT] and N-terminal pro-B-type natriuretic peptide [NT-proBNP] predict cardiovascular (CV) and kidney outcomes in a wide spectrum of patients. However, the discriminatory ability of these biomarkers in isolation and in combination is less clear. Methods: We examined data from participants in the Aliskiren Trial in Type 2 Diabetes Using Cardiorenal Endpoints (ALTITUDE) with available baseline measurements (n=5,597). Cox models were fitted to explore the individual and collective discriminative ability of eGFR, log-UACR, log-NT-proBNP, and log-hsTnT with a kidney composite outcome (kidney failure [dialysis or transplant], kidney-related death, sustained doubling of serum creatinine). Subsequently, the combined biomarker model was additionally adjusted for clinical covariates (age, sex, race, BMI, insulin use, baseline BUN, history of stroke, amputation, or heart failure). Discriminatory capacity was assessed by differences in C-statistics. Results: Over a median follow-up of 2.7 years, 344/5,597 (6.1%) participants experienced the kidney composite outcome. At baseline, mean eGFR was 52 ±23 mL/min/1.73m 2 ; median baseline UACR, hsTnT, and NT-proBNP were 300 [62, 914]mg/g, 15 [7, 24]pg/mL and 176 [77, 430]pg/mL, respectively. For the kidney composite outcome, the C-statistic was 0.583 for eGFR alone, 0.622 for NT-proBNP, 0.686 for hsTnT, and 0.842 for UACR. The combined 4-biomarker model C-statistic was 0.847, which increased to 0.865 with the addition of clinical covariates (Table 1). Conclusion: Among participants of ALTITUDE, higher UACR was the most important prognostic biomarker of future adverse kidney events, beyond that of a multivariable clinical model. The addition of eGFR, hsTnT, and NT-proBNP to UACR marginally improved model discrimination, with further improvement by adding clinical variables.