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Chiba University, Japan
Title:The Detection of Factors for Heterogeneous Aggravation of Diabetic Kidney Disease (DKD) Considering Individual Diversity.
Diabetic Kidney Disease (DKD) is a broad range of renal impairments associated with diabetes mellitus; approximately 40% of patients with type 2 diabetes complicate DKD1, and DKD accounts for about 50% of end-stage renal disease (ESRD)2. The prognosis for DKD that has progressed to ESRD is poor3,4, 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), and albuminuria, which reflects renal impairment2,5. In classical diabetic nephropathy, moderate albuminuria first appears, and then renal function declines. However, many studies reported that atypical cases, in which renal function declines even in the absence of albuminuria, have increased year by year6–8. The heterogeneous 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, as mentioned above, has been defined by multiple risk factors’ changes at each stages9. 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—the border among aggravation and alleviatioin—especially.
Mr. Yukihiro Imakiire has completed his Master and is studying in doctoral course of Graduate School of Medicine at Chiba University. He specializes in epidemilogy and artificial intelligence with medical data. He is conducting research activities on the topic of predicting the pathophysiology of chronic diseases. His achievements include lectures at academic conferences.