Machine learning based on biomarker profiles identifies distinct subgroups of heart failure with preserved ejection fraction.

ABSTRACT:

Aims

The lack of effective therapies for patients with heart failure with preserved ejection fraction (HFpEF) is often ascribed to the heterogeneity of patients with HFpEF. We aimed to identify distinct pathophysiologic clusters of HFpEF based on circulating biomarkers.

Methods and results

We performed an unsupervised cluster analysis using 363 biomarkers from 429 patients with HFpEF. Relative differences in expression profiles of the biomarkers between clusters were assessed and used for pathway over-representation analyses. We identified four distinct patient subgroups based on their biomarker profiles: cluster 1 with the highest prevalence of diabetes mellitus and renal disease; cluster 2 with oldest age and frequent age-related comorbidities; cluster 3 with youngest age,

SUBMITTER: Woolley RJ 

PROVIDER: S-EPMC8360080 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature