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Revisiting comorbidities in gout: a cluster analysis

Objectives The reciprocal links between comorbidities and gout are complex. We used cluster analysis to attempt to identify different phenotypes on the basis of comorbidities in a large cohort of patients with gout. Methods This was a cross-sectional multicentre study of 2763 gout patients conducted... Full description

Journal Title: Annals of the rheumatic diseases 2015-01, Vol.74 (1), p.142-147
Main Author: Richette, Pascal
Other Authors: Clerson, Pierre , Périssin, Laure , Flipo, René-Marc , Bardin, Thomas
Format: Electronic Article Electronic Article
Language: English
Subjects:
Publisher: England: British Medical Association
ID: ISSN: 0003-4967
Link: https://www.ncbi.nlm.nih.gov/pubmed/24107981
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recordid: cdi_proquest_miscellaneous_1808711253
title: Revisiting comorbidities in gout: a cluster analysis
format: Article
creator:
  • Richette, Pascal
  • Clerson, Pierre
  • Périssin, Laure
  • Flipo, René-Marc
  • Bardin, Thomas
subjects:
  • Aged
  • Aged, 80 and over
  • Alcohol
  • Analysis
  • Angina pectoris
  • Bias
  • Cardiovascular disease
  • Cluster Analysis
  • Cohort Studies
  • Comorbidity
  • Complications and side effects
  • Coronary Disease - epidemiology
  • Diabetes
  • Diabetes Mellitus - epidemiology
  • Diuretics
  • Drugs
  • Dyslipidemias - epidemiology
  • Enzymes
  • Family physicians
  • Fasting
  • Female
  • France - epidemiology
  • Gout
  • Gout - epidemiology
  • Heart failure
  • Heart Failure - epidemiology
  • Humans
  • Hypertension
  • Hypertension - epidemiology
  • Insulin resistance
  • Lipids
  • Liver
  • Liver Diseases - epidemiology
  • Male
  • Metabolism
  • Middle Aged
  • Mortality
  • Neoplasms - epidemiology
  • Obesity - epidemiology
  • Phenotype
  • Physicians
  • Prevalence
  • Renal Insufficiency - epidemiology
  • Risk factors
  • Variables
  • Women
ispartof: Annals of the rheumatic diseases, 2015-01, Vol.74 (1), p.142-147
description: Objectives The reciprocal links between comorbidities and gout are complex. We used cluster analysis to attempt to identify different phenotypes on the basis of comorbidities in a large cohort of patients with gout. Methods This was a cross-sectional multicentre study of 2763 gout patients conducted from November 2010 to May 2011. Cluster analysis was conducted separately for variables and for observations in patients, measuring proximity between variables and identifying homogeneous subgroups of patients. Variables used in both analyses were hypertension, obesity, diabetes, dyslipidaemia, heart failure, coronary heart disease, renal failure, liver disorders and cancer. Results Comorbidities were common in this large cohort of patients with gout. Abdominal obesity, hypertension, metabolic syndrome and dyslipidaemia increased with gout duration, even after adjustment for age and sex. Five clusters (C1–C5) were found. Cluster C1 (n=332, 12%) consisted of patients with isolated gout and few comorbidities. In C2 (n=483, 17%), all patients were obese, with a high prevalence of hypertension. C3 (n=664, 24%) had the greatest proportion of patients with type 2 diabetes (75%). In C4 (n=782, 28%), almost all patients presented with dyslipidaemia (98%). Finally, C5 (n=502, 18%) consisted of almost all patients with a history of cardiovascular disease and renal failure, with a high rate of patients receiving diuretics. Conclusions Cluster analysis of comorbidities in gout allowed us to identify five different clinical phenotypes, which may reflect different pathophysiological processes in gout.
language: eng
source:
identifier: ISSN: 0003-4967
fulltext: no_fulltext
issn:
  • 0003-4967
  • 1468-2060
url: Link


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titleRevisiting comorbidities in gout: a cluster analysis
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descriptionObjectives The reciprocal links between comorbidities and gout are complex. We used cluster analysis to attempt to identify different phenotypes on the basis of comorbidities in a large cohort of patients with gout. Methods This was a cross-sectional multicentre study of 2763 gout patients conducted from November 2010 to May 2011. Cluster analysis was conducted separately for variables and for observations in patients, measuring proximity between variables and identifying homogeneous subgroups of patients. Variables used in both analyses were hypertension, obesity, diabetes, dyslipidaemia, heart failure, coronary heart disease, renal failure, liver disorders and cancer. Results Comorbidities were common in this large cohort of patients with gout. Abdominal obesity, hypertension, metabolic syndrome and dyslipidaemia increased with gout duration, even after adjustment for age and sex. Five clusters (C1–C5) were found. Cluster C1 (n=332, 12%) consisted of patients with isolated gout and few comorbidities. In C2 (n=483, 17%), all patients were obese, with a high prevalence of hypertension. C3 (n=664, 24%) had the greatest proportion of patients with type 2 diabetes (75%). In C4 (n=782, 28%), almost all patients presented with dyslipidaemia (98%). Finally, C5 (n=502, 18%) consisted of almost all patients with a history of cardiovascular disease and renal failure, with a high rate of patients receiving diuretics. Conclusions Cluster analysis of comorbidities in gout allowed us to identify five different clinical phenotypes, which may reflect different pathophysiological processes in gout.
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subjectAged ; Aged, 80 and over ; Alcohol ; Analysis ; Angina pectoris ; Bias ; Cardiovascular disease ; Cluster Analysis ; Cohort Studies ; Comorbidity ; Complications and side effects ; Coronary Disease - epidemiology ; Diabetes ; Diabetes Mellitus - epidemiology ; Diuretics ; Drugs ; Dyslipidemias - epidemiology ; Enzymes ; Family physicians ; Fasting ; Female ; France - epidemiology ; Gout ; Gout - epidemiology ; Heart failure ; Heart Failure - epidemiology ; Humans ; Hypertension ; Hypertension - epidemiology ; Insulin resistance ; Lipids ; Liver ; Liver Diseases - epidemiology ; Male ; Metabolism ; Middle Aged ; Mortality ; Neoplasms - epidemiology ; Obesity - epidemiology ; Phenotype ; Physicians ; Prevalence ; Renal Insufficiency - epidemiology ; Risk factors ; Variables ; Women
ispartofAnnals of the rheumatic diseases, 2015-01, Vol.74 (1), p.142-147
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descriptionObjectives The reciprocal links between comorbidities and gout are complex. We used cluster analysis to attempt to identify different phenotypes on the basis of comorbidities in a large cohort of patients with gout. Methods This was a cross-sectional multicentre study of 2763 gout patients conducted from November 2010 to May 2011. Cluster analysis was conducted separately for variables and for observations in patients, measuring proximity between variables and identifying homogeneous subgroups of patients. Variables used in both analyses were hypertension, obesity, diabetes, dyslipidaemia, heart failure, coronary heart disease, renal failure, liver disorders and cancer. Results Comorbidities were common in this large cohort of patients with gout. Abdominal obesity, hypertension, metabolic syndrome and dyslipidaemia increased with gout duration, even after adjustment for age and sex. Five clusters (C1–C5) were found. Cluster C1 (n=332, 12%) consisted of patients with isolated gout and few comorbidities. In C2 (n=483, 17%), all patients were obese, with a high prevalence of hypertension. C3 (n=664, 24%) had the greatest proportion of patients with type 2 diabetes (75%). In C4 (n=782, 28%), almost all patients presented with dyslipidaemia (98%). Finally, C5 (n=502, 18%) consisted of almost all patients with a history of cardiovascular disease and renal failure, with a high rate of patients receiving diuretics. Conclusions Cluster analysis of comorbidities in gout allowed us to identify five different clinical phenotypes, which may reflect different pathophysiological processes in gout.
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abstractObjectives The reciprocal links between comorbidities and gout are complex. We used cluster analysis to attempt to identify different phenotypes on the basis of comorbidities in a large cohort of patients with gout. Methods This was a cross-sectional multicentre study of 2763 gout patients conducted from November 2010 to May 2011. Cluster analysis was conducted separately for variables and for observations in patients, measuring proximity between variables and identifying homogeneous subgroups of patients. Variables used in both analyses were hypertension, obesity, diabetes, dyslipidaemia, heart failure, coronary heart disease, renal failure, liver disorders and cancer. Results Comorbidities were common in this large cohort of patients with gout. Abdominal obesity, hypertension, metabolic syndrome and dyslipidaemia increased with gout duration, even after adjustment for age and sex. Five clusters (C1–C5) were found. Cluster C1 (n=332, 12%) consisted of patients with isolated gout and few comorbidities. In C2 (n=483, 17%), all patients were obese, with a high prevalence of hypertension. C3 (n=664, 24%) had the greatest proportion of patients with type 2 diabetes (75%). In C4 (n=782, 28%), almost all patients presented with dyslipidaemia (98%). Finally, C5 (n=502, 18%) consisted of almost all patients with a history of cardiovascular disease and renal failure, with a high rate of patients receiving diuretics. Conclusions Cluster analysis of comorbidities in gout allowed us to identify five different clinical phenotypes, which may reflect different pathophysiological processes in gout.
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