Cluster-defined gout subtypes exhibit divergent chronic kidney disease trajectories: a machine learning approach in a Chinese prospective cohort

OBJECTIVE: To systematically evaluate the contribution of clinical heterogeneity to chronic kidney disease (CKD) progression in gout patients using data-driven phenotyping, and to assess whether incorporating genetic risk improves prediction of renal outcomes. METHODS: In this prospective cohort study, 1497 Chinese gout patients were enrolled and followed for CKD progression. K-means clustering was applied to four core clinical variables: serum urate (SU), fractional excretion of uric acid (FEUA) (i.e. high- and low-excretion), gout duration and kidney stone burden. The primary outcome was the incidence of CKD stage ≥3, assessed using Cox proportional hazards models. Genetic risk was evaluated using an unweighted genetic risk score derived from 20 single nucleotide polymorphisms associated with gout and hyperuricaemia. RESULTS: Over a follow-up of 4166 person-years, 153 participants (10.22%) developed CKD stage ≥3. Five clinical clusters were identified: Cluster 1 (high-excretion, low-urate), Cluster 2 (low-excretion, low-urate), Cluster 3 (long-duration), Cluster 4 (low-excretion, high-urate) and Cluster 5 (nephrolithiasis). Cluster 4 and Cluster 5 were significantly associated with increased risk of CKD progression, with adjusted hazard ratios of 2.19 (95% CI 1.20-4.01) and 3.52 (95% CI 1.91-6.48), respectively. Genetic predisposition, via risk score, further amplified renal risk in Cluster 4. Achieving target SU levels and reductions in kidney stone burden were independently associated with a lower risk of CKD progression. CONCLUSIONS: This study proposes a novel K-means clustering-based classification of gout, identifying subgroups with distinct CKD trajectories. Integration of clinical phenotyping and genetic profiling may enhance individualized risk stratification and guide targeted prevention strategies in gout-associated CKD.