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Isolated, metabolic and hypertensive gout: a population-based cluster analysis of 94 759 patients
Maria Antonia Pou1,2,3, Daniel Martinez-Laguna3,4, Carlen Reyes3,5
1Department of Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain.
Insights
Gout presents in three distinct patient groups based on comorbidities. Management needs tailoring, as few patients achieve sustained serum urate control despite varied urate-lowering therapy use.
Area of Science:
- Rheumatology
- Clinical Phenotyping
- Epidemiology
Background:
- Gout exhibits significant clinical heterogeneity due to diverse comorbidity profiles.
- Understanding these differences is crucial for effective management.
- Current gout management may not adequately address patient heterogeneity.
Purpose of the Study:
- To identify distinct clinical phenotypes of gout using cluster analysis.
- To evaluate differences in management, specifically urate-lowering therapy (ULT) patterns.
- To assess sustained serum urate (SU) control across identified phenotypes.
Main Methods:
- Population-based retrospective cohort study (94,759 patients, 2012-2023).
- K-prototypes algorithm for cluster identification based on demographics and comorbidities.
- Outcomes: sustained SU control (<6 mg/dL for ≥80% follow-up) and ULT adherence (MPR).
Main Results:
- Three distinct clusters identified: Cluster 1 (younger, fewer comorbidities, best SU control), Cluster 2 (older, type 2 diabetes, high obesity, cardiovascular burden), Cluster 3 (older, hypertension, dyslipidaemia).
- Overall suboptimal management; only 12% achieved sustained SU control.
- Cluster 2 had higher ULT prescription rates; Cluster 1 showed better adherence and SU control.
Conclusions:
- Gout manifests in three clearly differentiated clinical phenotypes.
- Phenotypes may reflect distinct hyperuricaemia aetiologies (genetic, insulin-resistance, renal-vascular).
- Identification of high-risk clusters necessitates comorbidity-driven management and tailored therapies.
Objectives:
People with gout exhibit significant clinical heterogeneity due to diverse comorbidity profiles. This study aimed to identify distinct clinical phenotypes using cluster analysis and to evaluate differences in management, specifically urate-lowering therapy (ULT) patterns and sustained serum urate (SU) control.
Methods:
A population-based retrospective cohort study was conducted using the SIDIAP database (Catalonia, Spain), including 94 759 patients with incident gout (2012-23). A K-prototypes algorithm identified clusters based on demographics and major comorbidities. Outcomes included sustained SU control (defined as SU <6 mg/dl for ≥80% of the follow-up time) and ULT adherence (Medication Possession Ratio).
Results:
Three distinct clusters were identified. Cluster 1 (37.0%) was comprised of younger patients (mean age 54) with fewer comorbidities and the highest SU control. Cluster 2 (22.8%) included older patients (mean age 74) characterized by 100% type 2 diabetes prevalence, high obesity (51.3%), and the greatest cardiovascular burden. Cluster 3 (40.2%) featured older patients with hypertension and dyslipidaemia but no diabetes. Overall, management was suboptimal in all groups; only 12% of patients achieved sustained SU control. While Cluster 2 had higher ULT prescription rates, Cluster 1 showed better adherence and SU control.
Conclusion:
Gout manifests in three clearly differentiated clinical phenotypes, each potentially reflecting a distinct predominant aetiology of hyperuricaemia (isolated/genetic, insulin-resistance-mediated, or renal-vascular). The identification of these high-risk metabolic and hypertensive clusters underscores the need for comorbidity-driven management and tailored therapeutic strategies.
