Use of consensus clustering to identify distinct subtypes of chronic kidney disease and associated mortality risk.

Background: Chronic kidney disease (CKD) is a complex condition with diverse etiology and outcomes. Utilizing a data-driven clustering approach holds promise in identifying distinct CKD subgroups associated with specific risk profiles for death.

Methods: Unsupervised consensus clustering was utilized to classify chronic kidney disease (CKD) into subtypes based on 45 baseline characteristics in a cohort of 6,526 participants from the US National Health and Nutrition Examination Survey (NHANES) spanning the years 1999-2000 to 2017-2018.We examined the associations between CKD subgroups and clinical endpoints related to mortality, including all-cause mortality, cardiovascular disease mortality, cancer mortality, and mortality due to other causes.

Results: A total of 6,526 individuals with CKD were classified into four clusters at baseline. Cluster 1 (n = 508) comprised patients with relatively favorable levels of cardiac and kidney function markers, lower prevalence of cancer and higher prevalence of obesity, lower medication usage, and younger age. Cluster 4 (n = 2,029) comprised patients with the worst cardiac and kidney function markers. The characteristics of cluster 2 (n = 1,439) and 3 (n = 2,550) fell in between these two clusters. From cluster 1 to cluster 4, we observed a gradual increase in the hazard ratios of all-cause mortality, cardiovascular disease mortality, and mortality due to other causes. Additionally, further sensitivity analysis revealed patient heterogeneity among predefined subgroups with similar baseline kidney function and mortality risks.

Conclusions: Consensus clustering integrated baseline clinical and laboratory measures, revealing distinct CKD subgroups with markedly different risks of death, suggesting that further examination of patient subgroups could advance precision medicine.