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Identification of Hypertension Subgroups through Topological Analysis of Symptom-Based Patient Similarity

Yi-Fei Wang1, Jing-Jing Wang2, Wei Peng1

  • 1Clinical Research Base, the Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, 250014, China.

Chinese Journal of Integrative Medicine
|June 1, 2021
PubMed

Insights

This study identified 7 distinct hypertension patient subgroups using symptom data and network analysis. These subgroups reveal unique clinical and molecular characteristics, aiding in personalized hypertension management.

Area of Science:

  • Cardiology
  • Medical Informatics
  • Genomics

Background:

  • Hypertension is a complex condition with diverse clinical presentations.
  • Understanding hypertension subtypes is crucial for effective treatment and management.
  • Comorbidities significantly impact hypertension outcomes.

Purpose of the Study:

  • To classify clinical hypertension populations into distinct subtypes based on shared symptoms.
  • To investigate the intricate relationships between hypertension and its associated comorbidities.
  • To explore the phenotypic and molecular characteristics of identified hypertension subgroups.

Main Methods:

  • Utilized a large dataset of 33,458 hypertension inpatient electronic medical records.
  • Constructed a hypertension disease comorbidity network (HDCN) and a hypertension patient similarity network (HPSN).
  • Applied community detection algorithms to identify 7 main hypertension patient subgroups from HPSN.

Main Results:

  • Identified 7 distinct hypertension patient subgroups with unique clinical phenotypes.
  • Subgroup symptoms and diseases correlated with specific hypertension-related organ damage.
  • Phenotypic features (symptoms, diseases, CM syndromes) aligned with molecular features (pathways) within subgroups.

Conclusions:

  • Disease classification via community detection of patient networks highlights the importance of hypertension subgroups.
  • Shared symptom phenotypes provide a robust basis for classifying hypertension patients.
  • This approach offers a comprehensive method for understanding hypertension heterogeneity.
Abstract

Keywords:
hypertensionnetwork medicinepatient subgroupprecision medicinesymptom phenotype

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