Factors predicting the progression of diabetic kidney disease in type 2 diabetic patients using continuous glucose monitoring

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Background. An increase in the prevalence of type 2 diabetes mellitus (DM) is accompanied by an increase in the number of patients with severe chronic complications. Diabetic kidney disease (DKD) is the leading cause of death in these patients after cardiovascular diseases. The purpose was to predict the progression of DKD in patients with type 2 diabetes mellitus depending on the glucose variability (GV) measured by continuous glucose monitoring. Materials and methods. We examined 53 type 2 DM patients aged 57.0 (51.0; 64.0) years with an average disease duration of 9.0 (6.0; 13.0) years. The laboratory examination included determination of glycated hemoglobin, blood creatinine, albuminuria (AU), glomerular filtration rate (GFR) according to CKD-EPI equation. GV was measured by iPro2 GMS system. The maximum and minimum blood glucose levels and standard deviation (SD) of glycemia were considered. The role of GV in predicting DKD progression has been established using stepwise multiple regression analysis. Results. DKD was detected in 41.51% of patients. In regression analysis, we created a linear multiple regression equation to describe the dependence of AU on the GV, F=10.39 (p<0.001). The variability of AU by 36.7% is due to the minimum level of glycemia and SD of glycemia— multiple correlation coefficient R is 0.6372, the coefficient of determination R2 is 0.4060, adjusted R2 is 0.3670. Partial coefficient of correlation between AU and SD of glycemia, r=0.25 (p=0.027); between AU and the minimum blood glucose level, r=0.31 (p=0.005). Conclusions. According to the results of correlation analysis, a significant effect of GV, as well as the value of minimum blood glucose level on AU was established. It is statistically proved that high fluctuations of glycemia (SD) should be considered as a factor predicting the progression of DKD in type 2 DM patients. Using regression analysis, a mathematical model of DKD progression in type 2 DM patients was developed based on GV parameters.

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