Clustering patients with gout based on comorbidities and biomarkers: A cross-sectional study

This single-center clinical study identifies clusters of different phenotypes and pathophysiology subtypes of patients with gout and associated comorbidities.Patients clinically diagnosed with gout were enrolled between January 2018 and December 2019. Hierarchical cluster analyses were performed using clinical data or biological markers, inflammatory markers, and oxidative stress pathway metabolites assayed from serum and plasma samples. Subgroup clusters were compared using the analysis of variance (continuous data) and Chi-square tests (categorical data).Hierarchical cluster analysis identified three clusters—cluster 1 (C1; n = 24) comprising dyslipidemia, hypertension, and early onset of gout, without tophi; cluster 2 (C2; n = 25) comprising hypertension, dyslipidemia, nephrolithiasis, and obesity; cluster 3 (C3; n = 39) comprising multiple comorbidities and tophi. Post-hoc comparisons of data obtained from samples of patients in C1–C3 revealed significant differences in the levels of oxidative stress and inflammation-related markers, including 3-nitrotyrosine, tumor necrosis factor-α, C-reactive protein, interleukin (IL)-1β, IL-6, plateletderived growth factor (PDGF)-AA, and PDGF-BB. Re-clustering patients based on the biological markers that significantly differed among the initial clusters, and all markers, identified similar clusters.Oxidative stress and inflammatory marker levels may affect the development and clinical manifestations (clinical phenotypes) of gout. Measuring oxidative stress and inflammatory cytokines is a potential adjunctive tool and biomarker for early identification and management of gout.