ArticleEuropean journal of heart failure2021
Machine learning based on biomarker profiles identifies distinct subgroups of heart failure with preserved ejection fraction.
Article in European journal of heart failure, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 82 papers, 2 of them syntheses that pooled it.
What it found
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The trial behind it
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Who cites it
82 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Phenotype characterization of heart failure with preserved ejection fraction in medical device and surgical trials.ESC heart failure · 2025Pooled it
- Discovering Distinct Phenotypical Clusters in Heart Failure Across the Ejection Fraction Spectrum: a Systematic Review.Current heart failure reports · 2023Pooled it
- Echocardiographic phenotypes of diabetic myocardial disorder: evolution over 15 months follow-up in the ARISE-HF trial.Cardiovascular diabetology · 2025Trial
- Neuromodulation of Inflammation to Treat Heart Failure With Preserved Ejection Fraction: A Pilot Randomized Clinical Trial.Journal of the American Heart Association · 2022Trial
- Proteomics Profiling Reveals Molecular Subtypes of Transthyretin Cardiac Amyloidosis.Circulation. Heart failure · 2026Article
- Review
- Distinct Exercise Response Patterns in Patients With Heart Failure With Preserved Ejection Fraction.Journal of the American Heart Association · 2026Article
- CONFIDENT-HFpEF: a machine learning-based risk stratification for mortality and hospitalization using multimodal real-world data.ESC heart failure · 2026Observational
- Absence of GDF15 Aggravates Pressure Overload-Induced Cardiac Remodelling in Mice Hallmarked by Perivascular Fibrosis and Signs of Endothelial-to-Mesenchymal Transition.International journal of molecular sciences · 2026Article
- Machine Learning Applications for Risk Stratification in Heart Failure with Preserved Ejection Fraction: A New Era in Cardiology.Diagnostics (Basel, Switzerland) · 2026Review
- Precision cardiovascular medicine with big data and AI.NPJ digital medicine · 2026Review
- Artificial intelligence in heart failure.The Egyptian heart journal : (EHJ) : official bulletin of the Egyptian Society of Cardiology · 2026Review
- Growth Differentiation Factor-15 as an Emerging Biomarker in Cardiology: Diagnostic and Prognostic Implications.Journal of personalized medicine · 2026Review
- Development and external validation of a machine learning model for cardiac valve calcification early screening in dialysis patients: a multicenter study.Renal failure · 2025Article
- Machine learning approach to identify phenotypes in patients with ischaemic heart failure with reduced ejection fraction.European journal of heart failure · 2025Article
- Clustering in dilated cardiomyopathy at initial evaluation: An effective tool for clinical stratification.European journal of heart failure · 2025Article
- Machine-Learning-Driven Phenotyping in Heart Failure with Preserved Ejection Fraction: Current Approaches and Future Directions.Medicina (Kaunas, Lithuania) · 2025Review
- Machine learning-based phenotyping and assessment of treatment responses in heart failure with preserved ejection fraction.EClinicalMedicine · 2025Article
- The Right Approach: Power of Biomarkers in the Assessment and Management of Right Ventricular Dysfunction.International journal of molecular sciences · 2025Review
- Molecular Phenogroups in Heart Failure: Large-Scale Proteomics in a Population-Based Cohort.Circulation. Genomic and precision medicine · 2025Article
22 more citing papers are in PubMed but not listed here.
Corrections and comments
- Commented on by
- Commented on by
- Erratum issued
Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsThe 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
resultsWe 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, largest body size, least symptoms and lowest N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels; and cluster 4 with highest prevalence of ischaemic aetiology, smoking and chronic lung disease, most symptoms, as well as highest NT-proBNP and troponin levels. Over a median follow-up of 21 months, the occurrence of death or heart failure hospitalization was highest in clusters 1 and 4 (62.1% and 62.8%, respectively) and lowest in cluster 3 (25.6%). Pathway over-representation analyses revealed that the biomarker profile of patients in cluster 1 was associated with activation of inflammatory pathways while the biomarker profile of patients in cluster 4 was specifically associated with pathways implicated in cell proliferation regulation and cell survival.
conclusionUnsupervised cluster analysis based on biomarker profiles identified mutually exclusive subgroups of patients with HFpEF with distinct biomarker profiles, clinical characteristics and outcomes, suggesting different underlying pathophysiological pathways.
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