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Arterial stiffness nomogram identification by cluster analysis: A new approach of vascular phenotype modeling

Alexandre Vallée1

  • 1Department of Epidemiology-Data-Biostatistics, Delegation of Clinical Research and Innovation (DRCI), Foch hospital, Suresnes, France.

Journal of Clinical Hypertension (Greenwich, Conn.)
|October 5, 2022
PubMed

Insights

K-means clustering identified distinct patient groups based on arterial stiffness index (ASI) levels. This analysis reveals key differences in cholesterol, triglycerides, smoking, CRP, alcohol consumption, and BMI between groups with and without arterial stiffness.

Area of Science:

  • Cardiovascular Medicine
  • Biostatistics
  • Medical Informatics

Background:

  • Arterial stiffness, quantified by the arterial stiffness index (ASI), is a significant risk factor for cardiovascular diseases.
  • Identifying distinct patient phenotypes with elevated ASI is crucial for effective cardiovascular disease prevention.

Purpose of the Study:

  • To investigate the utility of K-means cluster analysis in identifying homogeneous subgroups of individuals based on ASI levels within the UK Biobank cohort.
  • To characterize the phenotypic differences between these identified clusters.

Main Methods:

  • Applied an ASI nomogram calculation to 132,851 UK Biobank participants without pre-existing cardiovascular diseases.
  • Utilized K-means clustering with an optimal 10-cluster solution to group participants based on ASI nomogram values.
  • Analyzed demographic, clinical, and lifestyle factors to differentiate between clusters.

Main Results:

  • One cluster (41.6% of individuals with arterial stiffness) exhibited significantly higher ASI nomogram values (.26) and ASI measurements (11.6 m/s).
  • A contrasting cluster (38.8% of individuals without arterial stiffness) showed negative ASI nomogram values (-.22) and lower ASI measurements (7.1 m/s).
  • Key differentiating factors included elevated total cholesterol (>5.409 mmol/L), triglycerides (>1.286 mmol/L), smoking pack-years (>11.8), CRP (>0.99), daily alcohol consumption (>1.794 units), and BMI (>26.641 kg/m²).

Conclusions:

  • K-means clustering effectively delineates homogeneous patient profiles with and without arterial stiffness.
  • Understanding the specific phenotypic markers that differentiate these clusters can inform targeted cardiovascular preventive strategies.

Keywords:
CRPalcoholarterial stiffnessarterial stiffness nomogramatherosclerosisbody mass indexcardiovascular diseasecluster analysisk-meanslipidstobacco

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