Kidney Disease Biomarkers Improve Heart Failure Risk Prediction in the General Population

Background: The kidneys play an important role in heart failure (HF), but it is unclear if renal biomarkers improve HF risk prediction beyond established risk factors. We aimed to assess whether adding biomarkers of kidney disease to conventional risk factors improved 10-year risk prediction for incident HF in a contemporary community sample. Methods: We included 450 212 participants in the UK Biobank aged 39 to 70 years without HF who had been assessed in 2006 to 2010 with the urine albumin-to-creatinine ratio and estimated glomerular filtration rate (eGFR) based on serum creatinine and cystatin C. There were 1701 incident cases of HF during up to 10.3 years of follow-up (mean 8.2 +/- 0.7 years). We used the Atherosclerosis Risk in Communities study heart failure risk score excluding natriuretic peptides as the base model to which we added eGFR and urine albumin-to-creatinine ratio. Harrell's C-statistic of ARIC-HF was 0.845 (95% CI, 0.831-0.859). Results: Each combination of added kidney measures (creat-eGFR, cysC-eGFR, and urine albumin-to-creatinine ratio) led to significant improvement in risk discrimination, calibration, and reclassification. The optimal pair of added kidney measures was cysC-eGFR and urine albumin-to-creatinine ratio (Delta C=0.019 [95% CI, 0.015-0.022]). Addition of cysC-eGFR made the largest contribution to reclassification improvement (continuous net reclassification improvement 0.323 [95% CI, 0.278-0.360]). Conclusions: In a large community sample, the addition of kidney disease markers to conventional risk factors improved prediction of 10-year HF risk. Our results support including kidney disease markers in the identification of persons at highest risk of HF and demonstrate a possible role of impaired kidney function in HF development in asymptomatic persons.