Proteomics-based clustering outperforms clinical clustering in identifying people with heart failure with distinct outcomes.

ABSTRACT:

Background

Heart failure (HF) clustering typically relies on clinical characteristics which may not reflect underlying pathophysiology relevant for personalized medicine. We aimed to identify plasma protein profiles of HF patients with reduced ejection fraction (HFrEF).

Methods

Using latent class analysis, we derived clusters based on 1) clinical characteristics, and 2) proteomics (SomaScan) from 379 HFrEF patients (median age 64 years [Q1 56; Q3 72], 73% male). Survival analysis assessed associations with major cardiovascular (CV) events (HF hospitalization, CV death, or advanced therapy), HF hospitalization, CV death, and all-cause mortality. Associations were validated in 511 external patients (median age 72 years [Q1 63; Q3 79], 70% male). We identified differentially ex

SUBMITTER: van Vugt M 

PROVIDER: S-EPMC12669581 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature