ArticleJournal of cardiovascular translational research2025
Unsupervised Learning Based on Proteomic Signatures Identifies Distinct Subgroups of Heart Failure With Mildly Reduced Ejection Fraction.
Article in Journal of cardiovascular translational research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Angiography-Derived Coronary Flow Reserve for Integrated Assessment of Coronary Physiology.Journal of cardiovascular translational research · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Heart failure with mildly-reduced ejection fraction (HFmrEF) lacks therapeutic strategies due to heterogeneity and dynamic transitions between HFrEF/HFpEF. Proteins constitute predominant drug targets and primary mediators of signaling pathways in HF. We measured 92 plasma proteins (Olink CardiovascularIII) in 230 HF patients from BIOMS-HF registry. Fifteen, eighteen, and fifteen baseline proteins discriminated MACEs were determined in HFmrEF, HFpEF, and HFrEF, respectively. Pathway enrichment revealed shared signaling in HFmrEF/HFpEF (apoptosis, etc.), HFmrEF/HFrEF (vascular regulation, etc.), and HFmrEF/HFrEF/HFpEF (inflammatory/hormonal signaling). Four patient phenotypes were identified according to proteomic signatures using unsupervised learning: Cluster1 (younger, smokers, lowest MACEs [29.5%]); Cluster2 (elderly, higher comorbidity, diastolic dysfunction); Cluster3 (systolic dysfunction, elevated heart rates, responsive to HFrEF therapies); Cluster4 (high inflammation, cardiometabolic disturbances, highest MACEs [74.4%]). Cross-referenced with druggable genome database, TNF-R1 was revealed as an appealing druggable target for cluster2/4, while OPN and MMP-2 for cluster3/4. Unsupervised learning based on proteomics identified four HFmrEF phenotypes, each providing druggable targets according to distinct pathophysiological pathways.
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Registered trials
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