Unsupervised hierarchical clustering identifies a metabolically challenged subgroup of hypertensive individuals.

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

SUBMITTER: Vaura FC 

PROVIDER: S-EPMC8029868 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature