Diverse combination of factors associated with the development of diabetic kidney disease among data-driven diabetes subtypes: analysis of the J-DREAMS registry

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AIMS/HYPOTHESIS: Available methods for predicting the onset and progression of diabetic kidney disease (DKD) and end-stage kidney disease (ESKD) are not yet ready for clinical application. We used a Japanese diabetes cohort study (J-DREAMS) to examine whether the Ahlqvist et al diabetes clustering is useful for stratifying DKD or ESKD outcomes independent of known risk factors in real-world settings. METHODS: and HOMA2 estimates of beta cell function and insulin resistance in 12,093 individuals with type 1 or type 2 diabetes. The risk of developing DKD/ESKD was analysed using Kaplan-Meier analysis and the Cox proportional hazards model. RESULTS: Diabetes clustering classified individuals in the J-DREAMS cohort into five subtypes, the clinical characteristics of which were comparable to those of the previously reported five subtypes. Kaplan-Meier curve analysis showed that events for chronic kidney disease (CKD) stages 3b, 4 and 5 were highest in the severe insulin-resistant diabetes subtype. The Cox proportional hazards model showed that the severe insulin-resistant diabetes subtype had significant HRs after correction for multiple confounding factors. The Cox proportional hazards model showed that each subtype had a diverse combination of factors associated with CKD stage 3b and proteinuria events. CONCLUSIONS/INTERPRETATION: Data-driven analysis provides diabetes subtyping, which can predict the probability of developing DKD/ESKD; each subtype has diverse combinations of factors predisposing to DKD development and progression. Data-driven diabetes subtyping to predict the likelihood of developing DKD/ESKD and mitigating predisposing factors may help personalise prevention strategies.

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