Identification of novel candidate biomarkers for heart failure with preserved ejection fraction by the Olink proteomics platform

Background: The pathophysiology of heart failure with preserved ejection fraction (HFpEF) remains incompletely understood. Objective: This study aimed to identify potential protein biomarkers for the accurate diagnosis and phenotyping of HFpEF and to construct a machine learning-based diagnostic model incorporating these biomarkers and key clinical features. Methods: In a cross-sectional study of 249 cardiac patients, HFpEF-associated plasma proteins were identified using Olink PEA and validated by ELISA. A machine learning nomogram was developed and its diagnostic performance was evaluated. Results: Analysis identified 92 plasma proteins,among which Serine protease 27(PRSS27), P-selectin glycoprotein ligand 1 (PSGL-1), Biregional Cell Adhesion Molecule-related (BOC), NF-κB essential modulator (NEMO), Glyoxalase 1(GLO1))) were specifically expressed in HFpEF group. Enrichment analysis indicated these differential proteins were primarily involved in inflammatory response, immune response, and the Phosphatidylinositol 3-kinase-AKT serine/threonine kinase (PI3K-AKT) signaling pathway. A diagnostic model integrating three proteins with clinical features (LDL-C, ALB) demonstrated excellent performance (AUC: 0.895), showing strong discriminatory power, good calibration, and potential clinical applicability. Conclusion: This study identifies potential protein biomarkers for HFpEF diagnosis, provides new insights into its pathophysiology, and offers a practical diagnostic tool for clinical use.