Novel Urinary Peptidomic Classifier Predicts Incident Heart Failure.

Area of Science:

• Biomarker Discovery
• Proteomics
• Cardiovascular Disease Research

Background:

• Early detection of preclinical cardiac dysfunction is crucial for managing left ventricular heart failure (HF).
• Novel biomarker panels can aid in early intervention and provide insights into HF pathophysiology.

Purpose of the Study:

• To identify novel peptide biomarkers in urine for the detection of preclinical cardiac dysfunction.
• To develop and validate a predictive classifier for heart failure (HF) progression.

Main Methods:

• Retrospective analysis of urinary proteomic profiles using mass spectrometry.
• Development of a Heart Failure Predictor (HFP) classifier based on 96 identified peptide biomarkers using support vector machine modeling.
• Independent validation of HFP's prognostic value in a cohort with asymptomatic diastolic dysfunction.

Main Results:

• Identification of 96 potentially HF-specific peptide biomarkers from urinary proteomic profiles.
• The HFP classifier demonstrated significant prognostic value (AUC=0.70, P=0.0047) in predicting HF progression, outperforming N-terminal pro b-type natriuretic peptide.
• HFP showed a significant hazard ratio (1.63, P=0.032) for HF progression per 1-SD increment.

Conclusions:

• The Heart Failure Predictor (HFP) is a novel urinary biomarker derived from the proteome.
• HFP may serve as a sensitive tool for improving risk stratification and patient management in heart failure.
• This biomarker aids in understanding the pathophysiology of heart failure.