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Updated: May 7, 2026

Glycemic Impact on Knee Osteoarthritis Symptoms on Physical, Radiographic, and Inflammatory Markers among Individuals Aged 50 and Over with Diabetes
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Revisiting comorbidities in gout: a cluster analysis.

Pascal Richette1, Pierre Clerson2, Laure Périssin3

  • 1Université Paris 7, UFR médicale, Assistance Publique-Hôpitaux de Paris, Hôpital Lariboisière, Fédération de Rhumatologie, Paris, Cedex 10, France.

Annals of the Rheumatic Diseases
|October 11, 2013
PubMed
Summary

Cluster analysis identified five distinct patient groups based on comorbidities in gout. These phenotypes may indicate different underlying disease processes, aiding personalized gout management.

Keywords:
Cardiovascular DiseaseEpidemiologyGout

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Area of Science:

  • Rheumatology
  • Internal Medicine
  • Epidemiology

Background:

  • Gout is frequently associated with numerous comorbidities, complicating patient management.
  • The relationship between gout and its comorbidities is complex and not fully understood.
  • Identifying distinct patient subgroups based on comorbidities can improve understanding of gout's pathophysiology.

Purpose of the Study:

  • To identify distinct clinical phenotypes of gout based on comorbid conditions using cluster analysis.
  • To explore the heterogeneity within a large cohort of patients diagnosed with gout.

Main Methods:

  • A cross-sectional multicentre study involving 2763 patients with gout.
  • Cluster analysis was performed on variables and observations to identify homogeneous patient subgroups.
  • Key comorbidities analyzed included hypertension, obesity, diabetes, dyslipidaemia, heart failure, coronary heart disease, renal failure, liver disorders, and cancer.

Main Results:

  • Five distinct clusters of gout patients were identified based on their comorbidity profiles.
  • Cluster C1 (12%) had isolated gout with few comorbidities.
  • Cluster C2 (17%) featured obesity and hypertension; C3 (24%) had high rates of type 2 diabetes; C4 (28%) presented with dyslipidaemia; C5 (18%) showed high prevalence of cardiovascular disease and renal failure.

Conclusions:

  • Cluster analysis successfully differentiated gout patients into five distinct phenotypes.
  • These identified phenotypes may represent different pathophysiological pathways in gout.
  • This phenotyping approach could inform more targeted treatment strategies for gout patients.