医学
心力衰竭
射血分数
蛋白质组学
内科学
射血分数保留的心力衰竭
心脏病学
前瞻性队列研究
观察研究
生物标志物
析因分析
生物信息学
基因
生物化学
化学
生物
作者
Yohei Sotomi,Yuki Matsuoka,Christina Ebert,Zhaoqing Wang,Tomohito Ohtani,Karl Kammerhoff,Laura Liu,Peter Schäfer,Ching‐Pin Chang,David Gordon,Shungo Hikoso,Julio A. Chirinos,Lei Zhao,Yasushi Sakata
出处
期刊:Heart
[BMJ]
日期:2025-08-01
卷期号:: heartjnl-326091
标识
DOI:10.1136/heartjnl-2025-326091
摘要
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.
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