Correlation Between Glycemic Variability And Diabetic Kidney Disease In Patients With Type 2 Diabetes Mellitus

Objective :In this study,patients with type 2 diabetes mellitus(T2DM)were studied to obtain glycemic variability indexes and estimated glycated hemoglobin(GMI)through continuous glucose monitoring(CGM)data,to analyze whether glycemic variability indexes and estimated glycated hemoglobin were correlated with DKD,and to explore the influencing factors of DKD.Methods :A total of 287 patients with type 2 diabetes mellitus hospitalized at the Department of Endocrinology,Xi’an Central Hospital from April 2020 to December 2022 were collected for inclusion in this study.The study subjects were grouped according to the Clinical Guidelines for the Prevention and Treatment of Diabetic Kidney Disease in China 2021,and were divided into three groups according to the urinary albumin/creatinine ratio(UACR): UACR <30 mg/g for control group(162 cases),UACR30-299 mg/g called the microproteinuria group(87 cases),and ≥300 mg/g for macroalbuminuria group(38 cases).Data collection: general data: age,gender,duration of diabetes,BMI,SBP,DBP;biochemical indexes: FPG,Hb A1 c,GA,TG,TC,HDL,LDL,e GFR,UA,SCr,BUN,UACR;continuous glucose monitoring indexes: MBG、SDBG、CV、MAGE、GMI、TIR、TAR、TBR and statistical analysis of the above indicators were performed using SPSS25.0.Results:1.Basic information: 287 cases were enrolled,control group(162 cases):mean age 58.6±9.52 years old,males accounted for 61.7%(100 cases),females 38.3%(62 cases);microproteinuria group(87 cases): mean age 61.76± 10.24 years old,males accounted for 65.5%(57 cases),females 34.5%(30 cases);macroalbuminuria group(38cases): mean age 63.53±10.38 years old,males accounted for 78.9%(30 cases),females21.1%(8 cases).2.Comparison of clinical data among control group,microproteinuria group and macroalbuminuria group: control group SDBG 1.44±0.43mmol/L,CV 19.5±5.08%,MAGE 3.66±1.38mmol/L,GMI 6.27±0.59%,microproteinuria group SDBG1.96±0.68mmol/L,CV 23.46±6.75%,MAGE 4.59±1.75 mmol/L,GMI 6.85±0.89%,macroalbuminuria group SDBG 2.41±0.87mmol/L,CV 26.29±7.94%,MAGE5.44±2.73mmol/L,GMI 7.33±1.08%.The differences in age,duration of diabetes,SBP,FPG,Hb A1 c,GA,e GFR,SCr,BUN,CysC,TIR,TAR,MBG,SDBG,CV,MAGE,and GMI levels among the three groups were statistically significant(P < 0.05).3.MBG,SDBG,CV,TIR,TAR,GMI,Hb A1 c,and cystatin C were significantly different between each of the three groups when compared between the two groups(P <0.05).MAGE was significantly different between control and microproteinuria groups,control and macroalbuminuria groups(P < 0.05),and not statistically significant between the microproteinuria and macroalbuminuria groups(P = 0.085).FPG was statistically significantly different between the control and microproteinuria groups and between the control and macroalbuminuria groups(P < 0.05),and not between the microproteinuria and macroalbuminuria groups(P = 0.084).GA was statistically significantly different between the control and microproteinuria groups and between the control and macroalbuminuria groups(P < 0.05),and not statistically significant between the microproteinuria and macroalbuminuria groups(P = 0.129).4.Correlation analysis between UACR and each of the assessed parameters showed that UACR was positively correlated with age,duration of diabetes,SBP,FBG,Hb A1 c,GA,SCr,BUN,and CysC levels in all patients(r = of 0.176,0.207,0.301,0.304,0.481,0.343,0.236,0.247,and 0.309,P<0.05).Positive correlations were found with TAR,MBG,SDBG,CV,MAGE,and GMI levels(r = 0.422,0.423,0.387,0.252,0.239,0.412,P < 0.001).There was a negative correlation with e GFR and TIR(r=-0.208,-0.4,P < 0.001).5.The correlation analysis between GMI and Hb A1 c,GA and TIR showed that GMI was significantly positively correlated with Hb A1 c and GA(r=0.558 and 0.551,P <0.001).GMI was highly negatively correlated with TIR(r=-0.864,P < 0.001).6.Dichotomous Logstic regression analysis showed that BMI(OR = 1.217,P <0.05),SBP(OR = 1.03,P < 0.05),SCr(OR = 1.076,P < 0.05),CysC(OR = 9.719,P <0.05),CV(OR = 1.2,P < 0.05)and GMI(OR = 2.952,P < 0.05)were risk factors for DKD.Conclusion: 1.SDBG,CV and MAGE were positively correlated with DKD,and the risk of proteinuria increased with the increase of SDBG and CV.GMI was positively correlated with UACR,and the risk of proteinuria increased with the increase of GMI.GMI was significantly positively correlated with Hb A1 c and GA,and highly negatively correlated with TIR.And it is a risk factor for DKD.2.TIR was negatively correlated with UACR,and as TIR decreased,the risk of proteinuria increased.3.CysC was positively correlated with UACR,with an increased risk of proteinuria with higher CysC,and was a risk factor for DKD.