The following is a summary of “Clustering Patients With Gout Based on Comorbidities and Biomarkers: A Cross-Sectional Study,” published in the June 2023 issue of Rheumatology by Alduraibi et al.
This single-center clinical investigation identifies clusters of patients with gout and associated comorbidities with distinct phenotypes and pathophysiology subtypes. Patients with a clinical diagnosis of gout were enlisted from January 2018 to December 2019. Hierarchical cluster analyses were performed using clinical data or biological markers, inflammatory markers, oxidative stress pathways, and metabolites assayed in serum and plasma samples.
Subgroup clusters were compared using ANOVA for continuous data and chi-square tests for categorical data. Hierarchical cluster analysis revealed three distinct clusters. Cluster 1 (C1; n = 24) comprised dyslipidemia, hypertension, and early-onset gout but lacked tophi. Hypertension, dyslipidemia, nephrolithiasis, and obesity constituted Cluster 2 (C2; n = 25). Multiple comorbidities and tophi comprised Cluster 3 (C3; n = 39). The levels of oxidative stress and inflammation-related markers, such as 3-nitrotyrosine, tumor necrosis factor, C-reactive protein, interleukin (IL) 1, IL-6, platelet-derived growth factor (PDGF)–AA, and PDGF-BB, differed significantly between C1, C2, and C3 patient samples.
Similar clusters were identified by reclustering patients based on all markers and the biological markers that substantially differed among the initial clusters. The development and clinical manifestations (clinical phenotypes) of gout may be influenced by oxidative stress and inflammatory marker levels. The measurement of oxidative stress and inflammatory cytokines is a potential adjunctive tool and biomarker for the early detection and management of gout.
Source: jrheum.org/content/50/6/817
