Clinical phenotypes in hypertension: a data-driven approach to risk stratification and outcome prediction

BackgroundHypertension (HTN) is a major contributor to cardiovascular (CV) morbidity and mortality. Its heterogeneity complicates risk stratification. Unsupervised machine learning can identify risk profiles and refine preventative strategies. ObjectivesThis study applies clustering analysis to clinical data to identify HTN phenogroups and their link with CV abnormalities and outcomes. Methods14,840 UK Biobank participants with a diagnosis of HTN who underwent cardiovascular magnetic resonance (CMR) were analysed. K-means clustering was applied to 77 clinical variables. Associations with incident HF, atrial fibrillation (AF), atherosclerotic events, all-cause mortality, and major adverse cardiovascular events (MACE) were examined. Mediation analysis assessed the role of CV imaging metrics in risk stratification. ResultsThree clusters emerged. Cluster 1, predominantly female with the most favourable metabolic profile, had the lowest risk. Cluster 2, predominantly male with the highest atherosclerosis burden, carried the greatest risk, particularly for AF and HF (Hazard ratio [HR] 1.80 and 1.85; p <0.005). This group showed the most severe cardiac remodelling, impaired cardiac mechanisms, and global left atrial (LA) dysfunction, approaching the HF myocardial substrate. Cluster 3 had an intermediate risk profile resembling metabolic syndrome, with moderate MACE risk and a higher susceptibility to AF (HR 1.30 and 1.61; p <0.05). While in cluster 2 the risk was largely mediated by LV remodelling, in cluster 3, its role was attenuated and more evenly balanced with LA dysfunction. ConclusionsClustering analysis revealed distinct HTN phenotypes with unique clinical and imaging profiles, enhancing risk stratification and supporting more individualised treatment strategies.