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Clinical Phenotypes in Hypertension: A Data-Driven Approach to Risk Stratification

Elisa Rauseo1,2,3, Ahmed M Salih1,3,4,5, Jackie Cooper1

  • 1William Harvey Research Institute, NIHR Barts Biomedical Research Centre (E.R., A.M.S., J.C., M.A., S.C., H.N., P.B.M., N.A., S.E.P.), Queen Mary University London, Charterhouse Square, United Kingdom.

Hypertension (Dallas, Tex. : 1979)
|December 11, 2025
PubMed

Insights

Unsupervised machine learning identified three distinct hypertension phenotypes. One male-predominant cluster showed high atherosclerosis and greatest cardiovascular risk, while another resembled metabolic syndrome with moderate risks.

Area of Science:

  • Cardiology
  • Medical Informatics
  • Genetics

Background:

  • Hypertension is a leading cause of cardiovascular disease, but its diverse nature complicates risk assessment.
  • Unsupervised machine learning offers a way to identify distinct patient subgroups for better risk stratification and targeted prevention.
  • This study aimed to use a data-driven approach to define hypertension phenotypes and their links to cardiovascular outcomes.

Purpose of the Study:

  • To identify distinct clinical phenotypes of hypertension using unsupervised machine learning.
  • To investigate the association of these phenotypes with cardiovascular imaging characteristics and adverse clinical outcomes.
  • To evaluate the mediating role of cardiac imaging features in the relationship between hypertension phenotypes and outcomes.

Main Methods:

  • Analysis of 14,840 UK Biobank participants with diagnosed hypertension and cardiovascular magnetic resonance imaging data.
  • Application of k-means clustering to 77 clinical variables to identify distinct hypertension clusters.
  • Examination of associations between clusters and outcomes (heart failure, atrial fibrillation, atherosclerotic events, mortality) adjusted for risk factors, with mediation analyses for imaging features.

Main Results:

  • Three distinct hypertension clusters were identified.
  • Cluster 2, predominantly male with high atherosclerosis, exhibited the highest risk for all adverse cardiovascular events, linked to severe cardiac remodeling and dysfunction.
  • Cluster 3, resembling metabolic syndrome, showed moderate risks for atrial fibrillation and mortality, with risk partially mediated by left ventricular hypertrophy and left atrial dysfunction.

Conclusions:

  • Clustering analysis successfully identified distinct hypertension phenotypes with unique risk profiles.
  • These findings suggest that a data-driven approach can improve hypertension risk stratification.
  • Tailored treatment strategies may be developed based on these identified phenotypes.
Abstract

Keywords:
atherosclerosisatrial fibrillationheart failuremagnetic resonance imagingphenotype

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