Subphenogroups of acute heart failure with preserved ejection fraction: comprehensive proteomics and pathway analysis
Clinical Snapshot
PICO Framework
| P — Population | Hospitalised patients with acute decompensated heart failure with preserved ejection fraction (HFpEF) from the PURSUIT-HFpEF study |
| I — Intervention | Machine learning-based clustering using comprehensive plasma proteomics (SomaScan assay measuring >7000 proteins) |
| C — Comparator | Four distinct phenogroups identified through clustering analysis |
| O — Outcomes | Proteomic profiles, pathway differences, and biological processes distinguishing HFpEF phenogroups |
Bottom Line
This post-hoc analysis identified four distinct HFpEF phenogroups using machine learning clustering of comprehensive plasma proteomics data from 198 hospitalised patients. The study revealed specific protein signatures and biological pathways distinguishing each phenogroup, suggesting involvement of inflammatory activation, tissue injury, and immune modulation in HFpEF pathophysiology. While the comprehensive proteomics approach and mechanistic insights are valuable, the findings are limited by the small subset analysis, potential selection bias, and lack of clinical validation. The identified phenogroups require external validation and demonstration of clinical utility before implementation in routine practice. This research represents an important step toward personalised HFpEF management but needs further validation studies to establish clinical relevance and therapeutic implications.
Key Findings
P Value: Not reported in abstract
Effect Size: Distinct protein expression profiles with ribonuclease 4, tax1-binding protein 1, regenerating islet-derived protein 3-gamma and alpha-1-antichymotrypsin as most significant markers
Primary Outcome: Four distinct HFpEF phenogroups identified: 'rhythm trouble', 'ventricular-arterial uncoupling', 'low output and systemic congestion', and 'systemic failure'
Nnt Or Sensitivity: Pathway analysis revealed differences in immune response, autonomic activation, cellular homeostasis and tissue repair mechanisms
Confidence Interval: Not reported in abstract
Clinical Application
Limited by requirement for comprehensive proteomics testing and machine learning analysis infrastructure Potential relevance for Australian HFpEF management but requires validation in local population. May inform future PBS-funded biomarker testing if validated clinically Hospitalised patients with acute decompensated HFpEF, particularly those requiring phenotype-guided therapy selection
Abstract
BACKGROUND: Heterogeneity of heart failure with preserved ejection fraction (HFpEF) results in significant challenges for treatment development. Identifying and characterising distinct HFpEF phenogroups may aid in tailoring therapeutic strategies for these patients. The objective of this study was to assess proteomic patterns of HFpEF phenogroups identified through a machine-learning-based clustering model, with the aim of uncovering specific biological pathways associated with each phenogroup. METHODS: This study represents a post-hoc analysis of the ongoing Prospective mUlticenteR obServational stUdy of patIenTs with Heart Failure with preserved Ejection Fraction (PURSUIT-HFpEF) study, which is a multicentre prospective observational study of hospitalised patients with acute decompensated HFpEF. Of the overall cohort (N=1238), this study analysed 198 patients with HFpEF with available proteomics data. These patients were classified into four phenogroups using the machine-learning-based clustering model. The SomaScan assay V.4.1 was used to measure levels of >7000 plasma proteins, and subsequent pathway analysis was conducted to determine the biological differences among the phenogroups. RESULTS: We identified four distinct phenogroups: Phenogroup 1 ('rhythm trouble'), Phenogroup 2 ('ventricular-arterial uncoupling'), Phenogroup 3 ('low output and systemic congestion') and Phenogroup 4 ('systemic failure'). The proteomics revealed distinct protein expression profiles among the phenogroups, with ribonuclease 4, tax1-binding protein 1, regenerating islet-derived protein 3-gamma and alpha-1-antichymotrypsin being the most significant markers to specific identified phenogroups. Pathway analysis suggested differences in immune response, autonomic activation, cellular homeostasis and tissue repair mechanisms across the phenogroups. CONCLUSIONS: Using a comprehensive plasma proteomics approach, our study identified distinct proteomic profiles of HFpEF phenogroups, which in turn suggest specific underlying biological processes. These profiles suggest the involvement of inflammatory activation, tissue injury and regenerative responses, immune modulation and systemic stress signalling as key components of HFpEF pathophysiology. TRIAL REGISTRATION NUMBER: UMIN-CTR ID: UMIN000021831.
References
- 1.Sotomi, Y., Matsuoka, Y., Ebert, C., Wang, Z., Ohtani, T., Kammerhoff, K., Liu, L., Schafer, P., Chang, C. P., Gordon, D., Hikoso, S., Chirinos, J. A., Zhao, L., & Sakata, Y. (2026). Subphenogroups of acute heart failure with preserved ejection fraction: comprehensive proteomics and pathway analysis. Heart. https://doi.org/10.1136/heartjnl-2025-326091
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