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Hypertension Phenotypes and Mortality Risk in the United States of America: A Data-Driven Cluster Analysis

Rodrigo M Carrillo-Larco1,2, Jithin Sam Varghese1,2, Arshed Quyyumi3

  • 1Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, USA, emory.edu.

International Journal of Hypertension
|January 1, 2026
PubMed

Insights

This study identified four distinct hypertension phenotypes in adults. Glucose-related hypertension showed the highest mortality risk, highlighting the need for personalized hypertension management.

Area of Science:

  • Cardiovascular Medicine
  • Clinical Epidemiology
  • Biostatistics

Background:

  • Hypertension is a major global cause of mortality, with current treatments lacking personalization.
  • Existing blood pressure classifications do not capture the full biological heterogeneity of hypertension.
  • Variations in treatment outcomes suggest underlying differences in hypertension subtypes.

Purpose of the Study:

  • To identify data-driven clinical phenotypes of primary hypertension.
  • To examine the associations between these identified phenotypes and mortality risk.

Main Methods:

  • Pooled analysis of 10 cross-sectional surveys (NHANES 1999-2020) including 4084 adults (≥30 years) with newly diagnosed or undiagnosed hypertension.
  • K-means clustering of eight variables (age, BMI, blood pressure, cholesterol, HbA1c, eGFR) to define phenotypes.
  • Mortality risk (all-cause and cardiovascular) assessed using data linked to the National Death Index.

Main Results:

  • Four distinct phenotypes were identified: Early-onset hypertension (EOH), Late-onset hypertension (LOH), Glucose-related hypertension (GRH), and Lipid-related hypertension (LRH).
  • GRH (4.5% of cohort) exhibited the highest all-cause and cardiovascular mortality risk compared to EOH.
  • LOH and LRH did not show significantly different mortality risks compared to EOH.

Conclusions:

  • Data-driven cluster analysis successfully identified four hypertension phenotypes with differential mortality risks.
  • These findings underscore the biological heterogeneity of primary hypertension and its impact on patient outcomes.
  • The identified phenotypes may inform more personalized approaches to hypertension diagnosis and management.
Abstract

Keywords:
artificial intelligencecardiovascular diseasesmachine learningprecision medicineunsupervised learning

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