Speakers - DEWC2025

Abstract

Diabetic Kidney Disease (DKD) is a broad range of renal impairments associated with diabetes mellitus; approximately 40% of patients with type 2 diabetes complicate DKD. DKD accounts for about 50% of end-stage renal disease (ESRD), imposing a substantial burden on both patients and society due to the need for regular dialysis therapy. The prognosis for DKD that has progressed to ESRD is poor, and kidney transplantation remains the only curative treatment for ESRD. Therefore, early prevention and treatment of DKD are of utmost importance. Diagnosis and monitoring of DKD has relied on estimated glomerular filtration rate (eGFR), a measure of renal function, and albuminuria, which reflects renal impairment. In classical diabetic nephropathy, moderate albuminuria first appears, and if untreated, albuminuria gradually increases. However, it has been demonstrated that some diabetic patients exhibit reduced renal function even in the absence of albuminuria, and these atypical cases are increasing year by year. The heterogeneous clinical course of DKD requires monitoring and treatment based on multiple biomarkers that complement eGFR and albuminuria. While the traditional picture of DKD was based on persistent albuminuria and gradual decline in GFR, the new portrait—the development from normal albuminuria to macroalbuminuria is bidirectional—has been noted, defined by multiple risk factors’ changes at each stages. Therefore, the prevention of DKD exacerbations requires the assessment of continuous transition in multiple related factors. The purpose of this study is to clarify heterogeneous progress of DKD reflecting individual features from risk factors—and their combinations—varying over time. To meet these requirements, we combined time series model (Markov process) and logistic regression model to characterize change of related factors at the bottleneck of change—the point of disease progression—especially.