血糖变异系数与2型糖尿病周围神经病变的相关性分析

The correlation between the coefficient of variation of blood glucose and peripheral neuropathy in type 2 diabetes

  • 摘要:
    目的 探讨2型糖尿病(T2DM) 住院患者的临床资料及持续葡萄糖监测系统(CGM)收集的血糖数据与糖尿病周围神经病变(DPN)的相关性及风险预测价值。
    方法 收集安徽医科大学第一附属医院的97例T2DM患者的临床资料,并通过CGM记录血糖情况,计算平均血糖、血糖变异系数(CV)、血糖标准差(SD)、24 h平均血糖波动幅度(MAGE)、日间血糖平均绝对差(MODD)及葡萄糖正常目标范围内时间(TIR)。将入组患者分为无DPN (NDPN) 组(n=49)和DPN组(n=48)。分析2组临床资料及CGM相关指标; 采用二元Logistic回归分析探讨CGM相关指标与DPN发生风险的相关性; 采用受试者工作特征(ROC) 曲线分析CV对DPN发生的预测价值。
    结果 DPN组年龄、收缩压、血小板(PLT)与NDPN组比较,差异有统计学意义(P < 0.05)。CGM相关数据分析结果提示, 2组CV、SD、MAGE、MODD比较,差异有统计学意义(P < 0.05)。二元Logistic回归分析结果显示, CV高是发生T2DM周围神经病变的危险因素(P < 0.05)。ROC曲线的曲线下面积为0.714(95%CI: 0.613~0.814, P < 0.001), 截断值为45.80%, 敏感度为66.70%, 特异度为65.30%。
    结论 NDPN组和DPN组年龄、收缩压、PLT、CV、SD、MAGE、MODD有显著差异, CV是DPN发生的影响因素,对于DPN的发生具有较好的预测价值。

     

    Abstract:
    Objective To explore the correlations of clinical data and glucose data collected by continuous glucose monitoring system (CGM) with diabetic peripheral neuropathy (DPN) in hospitalized type 2 diabetic patients(T2DM) and its risk prediction value.
    Methods The clinical data of 97 T2DM patients hospitalized in the Department the First Affiliated Hospital of Anhui Medical University were collected, and their blood glucose was recorded by CGM, and the mean blood glucose, coefficient of variation of blood glucose (CV), standard deviation of blood glucose (SD), mean 24-hour fluctuation of blood glucose (MAGE), mean absolute difference of day-to-day blood glucose (MODD) and time within normal target range for glucose (TIR). The enrolled patients were divided into non-DPN free (NDPN) group (n=49) and DPN group (n=48). Clinical data and CGM-related indicators were analyzed between the two groups, and the correlations between CGM-related indicators and the risk of DPN development were analyzed using binary Logistic regression analysis; the predictive value of CV for the development of DPN was analyzed using the receiver operating characteristic (ROC) curve.
    Results The differences in age, systolic blood pressure and platelets (PLT) were found between the DPN group and the NDPN group (P < 0.05). The results of CGM correlation data analysis suggested statistically significant differences in CV, SD, MAGE and MODD between the two groups (P < 0.05). Binary Logistic regression analysis showed that higher CV was a risk factor for T2DM peripheral neuropathy (P < 0.05). The area under the ROC curve was 0.714 (95%CI, 0.613 to 0.814, P < 0.001), with a cut-off value of 45.80%, the sensitivity was 66.70% and the specificity was 65.30%.
    Conclusion There are significant differences in age, systolic pressure, PLT, CV, SD, MAGE and MODD between the two groups; CV is a influencing factor for the development of DPN, and has a good predictive value for the development of DPN.

     

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