Diverse combination of factors associated with the development of diabetic kidney disease among data-driven diabetes subtypes: analysis of the J-DREAMS registry
Authors' conclusion
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.
Study context
- Population
- Individuals with T1D or T2D in the Japanese J-DREAMS registry
- Participants
- 12,093 individuals
- Exposure
- Five data-driven diabetes subtypes
- Outcome
- Development of diabetic kidney disease (DKD)/end-stage kidney disease (ESKD)
Methodological detailsdata-driven cluster analysis (k-means), Kaplan-Meier analysis, Cox proportional hazards model
- Data-driven cluster analysis (k-means)
- Kaplan-Meier analysis
- Cox proportional hazards model
Main finding
As reported in the abstract
CKD stages 3b, 4 and 5 events by subtype
- Five subtypes identified, clinical characteristics comparable to previously reported five subtypes
- Chronic kidney disease (CKD) stage 3b, 4 and 5 events highest in severe insulin-resistant diabetes subtype (Kaplan-Meier)
- Severe insulin-resistant diabetes subtype had significant HRs after correction for multiple confounders
Observational data: associations, not effects.
Further findings
Factors associated with CKD stage 3b and proteinuria events
- Each subtype had a diverse combination of associated factors
Original publication
Diverse combination of factors associated with the development of diabetic kidney disease among data-driven diabetes subtypes: analysis of the J-DREAMS registry. Diabetologia. Published 17 November 2025. doi:10.
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