Proteomic markers enhance mortality prediction in heart failure

Area of Science:

• Cardiology
• Genomics
• Proteomics

Background:

• Established clinical models for heart failure (HF) do not fully represent the underlying molecular mechanisms of disease progression.
• There is a need for improved prognostic tools that incorporate molecular data for better risk stratification in HF patients.

Purpose of the Study:

• To determine if molecular risk stratification offers additional prognostic value beyond current clinical predictors in HF.
• To evaluate the predictive performance of genetic, epigenetic, and proteomic scores for mortality in HF.

Main Methods:

• Analysis of 2432 patients from the Global Congestive Heart Failure (G-CHF) registry with comprehensive molecular profiling (genotyping, DNA methylation, proteomics).
• Assessment of three molecular scores: polygenic risk score (PRS), methylation risk score (MRS), and a 23-protein score (ProteomicDeath23).
• Comparison of molecular scores against the Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score and N-terminal pro-B-type natriuretic peptide (NT-proBNP) for mortality prediction, with validation in the UK Biobank.

Main Results:

• The proteomic score (ProteomicDeath23) was the strongest independent predictor of all-cause mortality (HR 2.23), surpassing NT-proBNP, MRS, PRS, and the MAGGIC score.
• A combined model of ProteomicDeath23, MAGGIC score, and NT-proBNP demonstrated the highest mortality prediction accuracy (C-index 0.77).
• The proteomic score identified increased mortality risk in patients with initially low clinical risk scores, validated in an independent cohort.

Conclusions:

• A proteomic-based risk score is a powerful molecular predictor of mortality in heart failure.
• Integrating proteomic data with established clinical risk factors significantly enhances the prediction of mortality in HF patients.