First Department of Internal Medicine, School of Medicine, University of Occupational and Environmental Health , Kitakyushu-shi, Japan .
Diabetes Technol Ther. 2018 Sep;20(9):603-612. doi: 10.1089/dia.2018.0017. Epub 2018 Aug 2.
The objective of this study was to determine the risk factors of hypoglycemia by evaluating the glycemic profile using continuous glucose monitoring (CGM) in patients with type 2 diabetes mellitus (T2DM).
The participants were 294 patients with T2DM who received inpatient diabetes education. The mean blood glucose (MBG), coefficient of variation (CV), mean postprandial glucose excursion, low blood glucose index (LBGI), and percentage of time with blood glucose (BG) at <70 mg/dL were measured on admission using CGM. We predicted the risk of hypoglycemia utilizing transform to Gaussian model. The primary end point was the relationship between CGM parameters and hypoglycemia.
Multivariate logistic regression analysis showed that disease duration, MBG, CV, LBGI, and Predicted% of BG correlated significantly with hypoglycemia. Receiver operating characteristic curve analysis showed that the optimal cutoff points for MBG and CV in predicting hypoglycemia were 152 mg/dL and 22%, respectively. The proportion of patients with hypoglycemia was 0% for the group with no hypoglycemia risk factors, 4.2% for the group with one risk factor, and 36.6% for the group with two risk factors, showing a linear increase across the groups (P < 0.001). LBGI was the best predictor of hypoglycemia; and Predicted% BG <70 mg/dL was very useful as an index to predict hypoglycemia.
Patients with low MBG levels and large fluctuations in BG were more likely to develop hypoglycemia, suggesting that assessment of these two variables is useful for the prediction of hypoglycemia. To achieve good glycemic control free of hypoglycemia, approaches are needed that do not only lower BG level but also minimize fluctuations in blood and interstitial fluid glucose level.
本研究旨在通过连续血糖监测(CGM)评估血糖谱,确定 2 型糖尿病(T2DM)患者发生低血糖的危险因素。
共纳入 294 例接受住院糖尿病教育的 T2DM 患者,采用 CGM 测定患者入院时的平均血糖(MBG)、变异系数(CV)、餐后血糖波动幅度、低血糖指数(LBGI)、血糖<70mg/dL 的时间比例。利用转化为高斯模型预测低血糖风险。主要终点为 CGM 参数与低血糖的关系。
多变量 logistic 回归分析显示,病程、MBG、CV、LBGI 和预测的 BG%与低血糖显著相关。ROC 曲线分析显示,MBG 和 CV 预测低血糖的最佳截断点分别为 152mg/dL 和 22%。无低血糖危险因素组患者低血糖发生率为 0%,有 1 个危险因素组为 4.2%,有 2 个危险因素组为 36.6%,随着危险因素数量的增加,低血糖发生率呈线性增加(P<0.001)。LBGI 是预测低血糖的最佳指标;预测的 BG<70mg/dL 作为低血糖的预测指标非常有用。
MBG 水平低和血糖波动大的患者更易发生低血糖,提示评估这两个变量有助于预测低血糖。为了实现无低血糖的良好血糖控制,需要采取不仅降低血糖水平而且最小化血糖和间质液水平波动的方法。