Novel Urinary Peptidomic Classifier Predicts Incident Heart Failure

Abstract

Background

Detection of preclinical cardiac dysfunction and prognosis of left ventricular heart failure ( HF) would allow targeted intervention, and appears to be the most promising approach in its management. Novel biomarker panels may support this approach and provide new insights into the pathophysiology.

Methods and Results

A retrospective comparison of urinary proteomic profiles generated by mass spectrometric analysis from 49 HF patients, 36 patients who progressed to HF within 2.6±1.6 years, and 192 sex‐ and age‐matched controls who did not progress to HF enabled identification of 96 potentially HF‐specific peptide biomarkers. Based on these 96 peptides, the classifier called Heart Failure Predictor ( HFP) was established by support vector machine modeling. The incremental prognostic value of HFP was subsequently evaluated in urine samples from 175 individuals with asymptomatic diastolic dysfunction from an independent population cohort. Within 4.8 years, 17 of these individuals progressed to overt HF. The area under receiver‐operating characteristic curve was 0.70 (95% CI, 0.56–0.82); P=0.0047 for HFP and 0.57 (0.42–0.72; P=0.62) for N‐terminal pro b‐type natriuretic peptide. Hazard ratios were 1.63 ( CI, 1.04–2.55; P=0.032) per 1‐ SD increment in HFP and 0.70 ( CI, 0.35–1.41; P=0.32) for a doubling of the logarithmically transformed N‐terminal pro b‐type natriuretic peptide.

Conclusions

HFP is a novel biomarker derived from the urinary proteome and might serve as a sensitive tool to improve risk stratification, patient management, and understanding of the pathophysiology of HF.