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Thyroid Disorders and Treatments · Journal article
Lara D. Veeken · August 12, 2026
Early or partial results. Treat as a signal, not a conclusion.
This retrospective cluster analysis of 94,759 gout patients identified three distinct clinical phenotypes—isolated/genetic, metabolic (diabetes-dominant), and hypertensive—with differing comorbidity profiles and serum urate control outcomes. The study is observational and descriptive, mapping heterogeneity in gout presentation and current management patterns rather than testing an intervention or demonstrating causation.
Retrospective cohort study with K-prototypes cluster analysis. Patients with incident gout from SIDIAP database (Catalonia, Spain), 2012–2023; incident cases included; setting: population-based primary care.. Intervention: None; observational characterisation of gout phenotypes and management patterns. n = 94,759. Catalonia, Spain (SIDIAP database).
Three clusters identified: Cluster 1 (37.0%) younger (mean age 54) with fewer comorbidities and highest SU control; Cluster 2 (22.8%) older (mean age 74) with 100% type 2 diabetes prevalence and 51.3% obesity; Cluster 3 (40.2%) older with hypertension and dyslipidaemia but no diabetes Overall sustained serum urate control (SU ≤6 mg/dL for ≥80% of follow-up) achieved in only 12% of all patients Cluster 2 had higher ULT prescription rates but Cluster 1 showed better adherence and SU control
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This phenotyping provides a framework for recognising distinct gout presentations and their comorbidity profiles, suggesting that comorbidity-driven and tailored management strategies may be warranted. However, the study does not test interventions or demonstrate superiority of any specific approach, so it informs stratification and hypothesis generation rather than immediate clinical action.
Population-based cluster analysis identifying three gout phenotypes, but no intervention comparison, no control group, and outcomes are observational descriptors of care patterns rather than clinical trial endpoints.
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Quoted from the source exactly as published.
This phenotyping provides a framework for recognising distinct gout presentations and their comorbidity profiles, suggesting that comorbidity-driven and tailored management strategies may be warranted. However, the study does not test interventions or demonstrate superiority of any specific approach, so it informs stratification and hypothesis generation rather than immediate clinical action.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
Abstract 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 (2012–2023). 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, MPR). 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.
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