ArticleHypertension (Dallas, Tex. : 1979)2026
Clinical Phenotypes in Hypertension: A Data-Driven Approach to Risk Stratification.
Article in Hypertension (Dallas, Tex. : 1979), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Cardiovascular Phenotypes Across Major Cancer Types: Distribution and Association with All-Cause Mortality in a Retrospective Cohort Study.Diagnostics (Basel, Switzerland) · 2026Article
- Applications of artificial intelligence and computational approaches to imaging for hypertension identification, phenotyping, and outcome prediction: a systematic review.European heart journal. Digital health · 2026Review
- Machine-learning-derived phenotypes of hypertensive patients using multidimensional clinical and echocardiographic data including strain imaging.European heart journal. Digital health · 2026Article
- Phenotyping cardiogenic shock: an insight from the gulf cardiogenic shock registry.Frontiers in artificial intelligence · 2026Article
Corrections and comments
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Authors and funding
12 authors.
Funding
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Abstract
backgroundHypertension is a major contributor to cardiovascular morbidity and mortality. Its heterogeneity complicates risk stratification. Unsupervised machine learning can uncover risk profiles and refine preventative strategies. This study applied a data-driven approach to identify clinical phenotypes of hypertension, examine their associations with cardiovascular imaging characteristics and adverse outcomes, and assess the mediating role of cardiac imaging features in these associations.
methodsFourteen thousand eight hundred forty UK Biobank participants with diagnosed hypertension and cardiovascular magnetic resonance imaging were analyzed. K-means clustering was applied to 77 clinical variables. Associations with incident heart failure, atrial fibrillation, atherosclerotic events, all-cause mortality, and major adverse cardiovascular events were examined and adjusted for cardiovascular risk factors. Mediation analyses assessed the role of cardiovascular imaging features in the association between clusters and outcomes.
resultsThree clusters emerged. Cluster 1, predominantly female with the most favorable metabolic profile, had the lowest risk. Cluster 2, predominantly male with the highest atherosclerosis burden, carried the greatest risk for all adverse events, independent of cardiovascular risk factors. They showed severe cardiac remodeling, impaired cardiac mechanisms, and global left atrial dysfunction. Cluster 3 had a profile resembling metabolic syndrome, with moderate risk for atrial fibrillation and all-cause death (hazard ratio, 1.65 and 1.58;
conclusionsClustering analysis identified distinct hypertension phenotypes with specific risk profiles, suggesting potential for improved stratification and more tailored treatment approaches.
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