ArticleJournal of clinical hypertension (Greenwich, Conn.)2020
Unsupervised hierarchical clustering identifies a metabolically challenged subgroup of hypertensive individuals.
Article in Journal of clinical hypertension (Greenwich, Conn.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Unsupervised Clustering for POAG Phenotyping.Investigative ophthalmology & visual science · 2026Article
- Clinical Phenotypes in Hypertension: A Data-Driven Approach to Risk Stratification.Hypertension (Dallas, Tex. : 1979) · 2026Article
- Machine-learning-derived phenotypes of hypertensive patients using multidimensional clinical and echocardiographic data including strain imaging.European heart journal. Digital health · 2026Article
- Blood Pressure and Hypertension Among Adults Aged 80 and Above: Findings From the Population-Based German Health Survey Gesundheit 65.International journal of hypertension · 2026Article
- Identification of disease subtypes associated with prognosis in patients with intermediate-high-risk pulmonary embolism based on hierarchical cluster analysis.Journal of thoracic disease · 2025Article
- Machine Learning and Statistical Shape Modelling Methodologies to Assess Vascular Morphology before and after Aortic Valve Replacement.Journal of clinical medicine · 2024Article
- Social and Metabolic Determinants of Prevalent Hypertension in Men and Women: A Cluster Analysis from a Population-Based Study.International journal of environmental research and public health · 2023Article
- Identification of Novel Metabolic Subtypes Using Multi-Trait Limited Mixed Regression in the Chinese Population.Biomedicines · 2022Article
- Multi-Trait Genetic Analysis Reveals Clinically Interpretable Hypertension Subtypes.Circulation. Genomic and precision medicine · 2022Article
- Unsupervised hierarchical clustering identifies a metabolically challenged subgroup of hypertensive individuals.Journal of clinical hypertension (Greenwich, Conn.) · 2020Article
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6 authors.
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Abstract
The current classification of hypertension does not reflect the heterogeneity in characteristics or cardiovascular outcomes of hypertensive individuals. Our objective was to identify distinct phenotypes of hypertensive individuals with potentially different cardiovascular risk profiles using data-driven cluster analysis. We performed clustering, a procedure that identifies groups with similar characteristics, in 3726 individuals (mean age 59.4 years, 49% women) with grade 2 hypertension (blood pressure ≥160/100 mmHg or antihypertensive medication) selected from FINRISK 1997, 2002, and 2007 cohorts. We computed clusters based on eight factors associated with hypertension: mean arterial pressure, pulse pressure, non-high-density lipoprotein cholesterol, blood glucose, BMI, C-reactive protein, estimated glomerular filtration rate, and alcohol. After that, we used Cox regression models adjusted for age and sex to assess the relative risk of cardiovascular disease (CVD) outcomes between the clusters and a reference group of 11 020 individuals. We observed two comparable clusters in both men and women. The Metabolically Challenged (MC) cluster was characterized by high blood glucose (Z-score 4.4 ± 1.1 vs 0.2 ± 0.8, men; 3.5 ± 1.1 vs 0.0 ± 0.6, women) and elevated BMI (30.4 ± 4.1 vs 28.9 ± 4.3, men; 32.7 ± 4.9 vs 29.3 ± 5.5, women). Over a 10-year follow-up (1034 CVD events), MC had 1.6-fold (95% CI 1.1-2.4) CVD risk compared to non-MC and 2.5-fold (95% CI 1.7-3.7) CVD risk compared to the reference group (P ≤ .009 for both). Using unsupervised hierarchical clustering, we found two phenotypically distinct hypertension subgroups with different risks of CVD complications. This substratification could be used to design studies that explore the differential effects of antihypertensive therapies among subgroups of hypertensive individuals.
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