The Young, the Metabolic and the Hypertensive. A cluster analysis of gout patients and its comorbidities.
Maria Antonia Pou, Carlen Reyes, Daniel Martinez-Laguna, Pau Satorra, Cristian Tebe, Nicola Dalbeth, Cesar Diaz-Torne
Affiliation(s):
Rheumatology Department. Hospital De La Santa Creu I Sant Pau. Barcelona. Grempal Study Group. Ics. Barcelona
Background: Relationship between gout and its comorbidities such as the metabolic syndrome is bidirectional; gout patients are at a higher risk of developing these conditions, and vice versa. The management of gout in these patients is challenging. Identifying distinct patient profiles can help to improve gout management.
Objective: To create clusters according to demographic and clinical characteristics and to analyse its impact in the management of the disease.
Methods: A retrospective cohort study using a primary care database (SIDIAP) with routinely collected medical records and pharmacy dispensations covering over 75% population of Catalonia, Spain (~6 million people). People with an incident gout diagnose (ICD-10 M10) aged ≥15 years with ≥1 year of available data were included and followed from 2012 to 2023. Demographic, disease and comorbidities characteristics, medication adherence and uric acid levels were collected. K-prototypes cluster analysis for mixed-type was performed using base demographic and clinical data. Urate Lowering Treatment (ULT) characteristics as well as gout good control (urate levels <6 mg/dl ≥80% of the time) were gathered. Medication Possession Ratio (MPR) over 80% was considered a good adherence.
Results: 97,239 persons were included. Mean age was 66.3±14.6 years. 79.9% were male. Median follow-up was 5.4 [2.5; 8.2] years. Main comorbidities at the time of gout diagnose were: hypertension (65.3%), dyslipidaemia (50.9%), diabetes mellitus 2 (24.3%), ischemic heart disease (11.1%) and cerebrovascular disease (6.8%).
K-prototypes cluster analysis identified 3 clusters. Cluster characteristics are shown in table 1. Cluster 1 (n=28429) were younger, had a better GFR and less comorbidities. In cluster 2 (n=41180) All patients had dyslipidemia and other elements of the metabolic syndrome. In cluster 3 (n=27630) 97% of the patients had hypertension.
Patients from cluster 1 were less treated but had better outcomes than patients from the other clusters. Outcomes in function of the resulting cluster are shown in table 2.
Conclusion: Three distinct clusters were identified. Cluster 1 were younger patients and less comorbidities. Cluster 2 included patients with metabolic syndrome. Cluster 3 included patients with hypertension without dyslipidemia. There was a different response to the same ULT outlining the need to personalize the management and treatment of gout.
