Lu Jianwen, Chen Danrui, Lin Beisi, Liu Zhigu, Yang Yanling, He Ling, Yan Jinhua, Yang Daizhi, Xu Wen
Department of Metabolism and Endocrinology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, China.
Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China.
Diabetol Metab Syndr. 2025 May 24;17(1):169. doi: 10.1186/s13098-025-01713-9.
This study aims to predict risk factors for hypoglycemia in patients with type 2 diabetes mellitus (T2DM) using continuous glucose monitoring (CGM) and with time in range (TIR) > 70%.
Data from 111 patients with T2DM who underwent CGM with TIR > 70% were analyzed. A hypoglycemia episode was defined as CGM-detected glucose < 3.9mmol/L sustained for at least 5 min. Logistic regression analysis was performed to examine the relationship between hypoglycemia and mean blood glucose (MBG), glycemic variability (GV) metrics [including mean amplitude of glucose excursion (MAGE), largest amplitude of glycemic excursion (LAGE), mean of daily difference (MODD), coefficient of variation (CV), standard deviation (SD)], and low blood glucose index (LBGI). A nomogram model was constructed, and its diagnostic performance was assessed. Data were bootstrapped 1000 times for internal validation, and a calibration curve was drawn to evaluate the model's predictive ability. Decision curve analysis was performed to assess its clinical usefulness.
Among the 111 included patients, 53 experienced hypoglycemic event during wearing CGM (47.75%). GV metrics were higher in hypoglycemia group, while MBG was lower. The multivariable logistic regression analysis showed that the MBG, GV metrics, LBGI were independently associated with hypoglycemia. The receiver operating characteristics (ROC) analysis indicated that the area under the curve (AUC) for the MBG-SD-LBGI model was 0.93 (95% CI = 0.88-0.97). The calibration curve showed good consistency between the predicted and observed probabilities. Decision curve analysis demonstrated strong clinical applicability.
This study demonstrates a significant correlation between CGM metrics and hypoglycemia in patients with T2DM who achieved TIR > 70%. These findings suggest that CGM metrics can predict the risk of hypoglycemia in T2DM patients with a TIR > 70%, and the nomogram developed from these metrics holds strong potential for clinical application.
本研究旨在使用持续葡萄糖监测（CGM）且血糖达标时间（TIR）>70%的情况下，预测2型糖尿病（T2DM）患者低血糖的危险因素。
分析了111例接受CGM且TIR>70%的T2DM患者的数据。低血糖发作定义为CGM检测到的血糖<3.9mmol/L持续至少5分钟。进行逻辑回归分析以检验低血糖与平均血糖（MBG）、血糖变异性（GV）指标[包括血糖波动平均幅度（MAGE）、最大血糖波动幅度（LAGE）、每日差异均值（MODD）、变异系数（CV）、标准差（SD）]以及低血糖指数（LBGI）之间的关系。构建了列线图模型，并评估其诊断性能。数据进行1000次自抽样用于内部验证，并绘制校准曲线以评估模型的预测能力。进行决策曲线分析以评估其临床实用性。
在纳入的111例患者中，53例在佩戴CGM期间发生低血糖事件（47.75%）。低血糖组的GV指标较高，而MBG较低。多变量逻辑回归分析表明，MBG、GV指标、LBGI与低血糖独立相关。受试者工作特征（ROC）分析表明，MBG-SD-LBGI模型的曲线下面积（AUC）为0.93（95%CI=0.88-0.97）。校准曲线显示预测概率与观察概率之间具有良好的一致性。决策曲线分析表明具有很强的临床适用性。
本研究表明，在TIR>70%的T2DM患者中，CGM指标与低血糖之间存在显著相关性。这些发现表明，CGM指标可以预测TIR>70%的T2DM患者发生低血糖的风险，并且由这些指标开发的列线图具有很强的临床应用潜力。
