FRI0395 Five phenotype profiles are revealed by cluster analysis in patients with gout. Results from a cohort of 2762 patients. (23rd January 2014)

• Record Type: Journal Article
• Authors: Richette, P.; Flipo, R.-M.; Errieau, G.; Perrissin, L.; Clerson, P.
• Is Part Of: Annals of the rheumatic diseases. Volume 71(2012)Supplement 3
• Page Start: 448
• Page End: 448
• Publication Date: 2014-01-23
• Subjects: Rheumatism -- Periodicals; 616.723005
• DOI: 10.1136/annrheumdis-2012-eular.2852
• Languages: English
• ISSNs: 0003-4967

Abstract

Abstract : Background: Diet and genetic predisposition seem to be the main causal factors of primary gout. Gout is associated with hypertension, diabetes mellitus, metabolic syndrome, and renal and cardiovascular diseases that may combine differently among patients. In addition, elevation of uricemia is a well-known side-effect of diuretics. Objectives: To examine whether demographics, comorbidities and use of diuretics would combine into different phenotype profiles in gouty patients. Methods: CACTUS was a non-interventional cross-sectional multicentre study conducted in France from November 2010 to May 2011. It included 2762 gouty patients whose characteristics were analyzed with multiple correspondence analysis. The associations between the studied variables were graphically assessed. Cluster analysis was performed to identify subgroups of patients with similar characteristics using cluster method based on the complete linkage model. Variables used in cluster analysis were age, hypertension, obesity (BMI >30 kg/m 2 ), diabetes (use of oral ant diabetic agents or fasting glycaemia >1.26 g/L), dyslipidaemia (grouping hypercholesterolemia and hypertriglyceridemia), heart failure, ischemic cardiac disease, renal failure, liver disorders (including liver insufficiency and increase in liver enzymes), cancer and use of diuretics. The number of clusters was chosen according to the values of the pseudo t 2 statistic. Results: Five clusters (C1 to C5) have been individualised. Patients from C1 (N=801) were 52 years old with a familial history of hyperuricemia (26%); 27% were obese; none had cardiac, renal, nor hepatic failure. C2 (N=178) clustered the oldest patients (mean age 73 years) with hypertension (97%), heart failure (100%) and renal failure (33%); 76% were treated with diuretics. Obesity, diabetes and dyslipidemia were frequent (53%, 42% and 75% respectively). In C3, (N=1425) patients were 69 years old; 41% were obese; 73% were hypertensive and 76% presented with hypercholesterolemia; 31% were diuretics users. Patients from C4 (N=290) were 60 years old with metabolic disorders: obesity (49%) hypertension (72%), hypercholesterolemia (82%), hypertriglyceridemia (63%), liver enzymes disorders (71%); alcohol, beer and soda consumption was more frequent than for other clusters. 21% presented with ischaemic heart disease. All patients from C5 (N=68, mean age 72 years) but none from other clusters were suffering from a cancer. Conclusions: Cluster analysis of risk factors for gout allowed us to clearly identify 5 different groups of clinical phenotypes, which could reflect different mechanisms of hyperuricemia. These data might help to optimize the management of risks factors for hyperuricemia in patients with gout. Disclosure of Interest: None Declared