自我血糖监测中血糖波动指标与平均血糖波动幅度的相关性研究  

Correlation between glycemic variability indices calculated by self-monitoring of blood glucose and mean amplitude of glycemicexcursion in patients with type 2 diabetes mellitus

作　　者：刘芷谷 姚斌[1] 林倍思[1] 杨黛稚[1] 吴金萍[1] 曾咏梅[1] 杨旭斌[1] 龚丽 许雯[1] Liu Zhigu;Yao Bin;Lin Beisi;Yang Daizhi;Wu Jinping;Zeng Yongmei;Yang Xubin;Gong Li;Xu Wen(Department of Endocrinology and Metabolic Disease,the Third Affiliated Hospital of Sun Yat-sen University,Guangdong Provincial Key Laboratory of Diabetology,Guangzhou 510630,China;Department of Diabetes,Shenzhen Bao′an Traditional Chinese Medicine Hospital,Guangzhou University of Chinese Medicine,Shenzhen 518101,China)

机构地区：[1]中山大学附属第三医院内分泌与代谢病学科,广东省糖尿病防治重点实验室,广州510630

出　　处：《中华糖尿病杂志》2021年第5期476-481,共6页CHINESE JOURNAL OF DIABETES MELLITUS

基　　金：国家重点研发计划(2016YFC1304801);广东省科技创新战略专项资金(2018A030313915)

摘　　要：目的:探讨由自我血糖监测(SMBG)点血糖计算不同的血糖波动指标与持续葡萄糖监测(CGM)系统获得的平均血糖波动幅度(MAGE)的相关性。方法:本研究为回顾性研究。纳入2018年1月至2019年10月在中山大学附属第三医院内分泌与代谢病学科住院同时接受48~72 h CGM及7点(三餐前、三餐后2 h以及睡前)SMBG的105例2型糖尿病患者作为研究对象,根据7点SMBG数据计算以下血糖波动指标:血糖水平的标准差(SDBG)、最大血糖波动幅度(LAGE)、餐后血糖波动幅度(PPGE)、血糖变异系数(CV)和平均血糖波动幅度(MAGE′,由SMBG计算所得)。分别采用Spearman相关分析、简单线性回归和多元逐步回归分析法分析由SMBG计算的血糖波动各指标与由CGM获得的MAGE的关系,并绘制受试者工作特征(ROC)曲线评估前者对后者的预测能力。结果:105例2型糖尿病患者中,7点SMBG计算的SDBG为(2.02±0.77)mmol/L、PPGE为(2.75±1.13)mmol/L、LAGE为(5.62±2.13)mmol/L、CV为(25.92±0.77)%及MAGE′为4.11(2.84,5.92)mmol/L,均与CGM获得的MAGE[4.00(2.65,5.00)mmol/L]显著相关(r值分别为0.614、0.499、0.588、0.533和0.473,均P<0.01)。以MAGE为因变量,SDBG、PPGE、LAGE、CV和MAGE′为自变量进行多元逐步回归分析,仅SDBG进入方程(P<0.01)。ROC曲线下面积分别为:SDBG为0.795[95%可信区间(CI)0.708~0.882]、LAGE为0.782(95%CI 0.692~0.872)及CV为0.769(95%CI 0.677~0.846),均大于PPGE[0.718(95%CI 0.620~0.816)]和MAGE′[0.704(95%CI 0.607~0.789)]的ROC曲线下面积。结论:由7点SMBG数据计算的血糖波动指标(SDBG、LAGE、PPGE、CV和MAGE′)与CGM获得的MAGE相关性好,其中,SDBG准确性更高。Objective To explore the correlation between the different glycemic variability(GV)indices calculated by self-monitoring of blood glucose(SMBG)and mean amplitude of glycemic excursion(MAGE)obtained from the continuous glucose monitor(CGM)system in patients with type 2 diabetes mellitus(T2DM).Methods This was a retrospective study.We analyzed the data of T2DM patients who received 48-72 h CGM and 7-point SMBG(pre-and post-breakfast,lunch,dinner and prior to bedtime)simultaneously in Department of Endocrinology and Metabolic Disease,the Third Affiliated Hospital of Sun Yat-sen University from January 2018 to October 2019.The GV indices calculated from the 7-point SMBG data included the standard deviation(SDBG)of the 7-point glucose profiles,the largest amplitude of glycemic excursions(LAGE)and the postprandial glucose excursion(PPGE),coefficient of variation of blood glucose(CV)and mean amplitude of glucose excursion(MAGE′,calculated by SMBG profile).Spearman′s correlation analysis,simple linear regression and multiple stepwise regression analysis were used to analyze the relationship between the different GV indices calculated from 7-point SMBG and MAGE obtained by CGM,and the receiver operator characteristic(ROC)curve was drawn to evaluate the ability of the former to predict the latter.Results Among 105 patients with T2DM,the SDBG,PPGE,LAGE and CV calculated by 7-point glucose profiles of SMBG were(2.02±0.77)mmol/L,(2.75±1.13)mmol/L,(5.62±2.13)mmol/L and(25.92±0.77)%,respectively,while the level of MAGE′and median MAGE were 4.11(2.84,5.92)mmol/L and 4.00(2.65,5.00)mmol/L,respectively.SDBG,PPGE,LAGE,CV and MAGE′were significantly correlative with MAGE(r=0.614,0.499,0.588,0.533 and 0.473,respectively,all P<0.01).Multiple stepwise regression analysis was performed with MAGE as dependent variable and SDBG,PPGE,LAGE,CV and MAGE′as independent variables,and only SDBG entered the equation(P<0.01).The areas under ROC curve for SDBG[0.795,95%confidence interval(CI)was 0.708-0.882],LAGE(0.782,95%CI was 0.692

关 键 词：糖尿病 2型 血糖波动 自我血糖监测 持续葡萄糖监测 

分 类 号：R587.1[医药卫生—内分泌]