Center for Diabetes Technology, Department of Psychiatry and Neurobehavioral Sciences, University of Virginia, Charlottesville, Virginia, USA.
Science Consulting in Diabetes GmbH, Neuss, Germany.
Diabetes Technol Ther. 2020 Jul;22(7):501-508. doi: 10.1089/dia.2020.0236.
To bridge the gap between laboratory-measured hemoglobin A1c (HbA1c) and continuous glucose monitoring (CGM)-derived time in target range (TIR), introducing TIR-driven estimated A1c (eA1c). Data from Protocol 1 (training data set) and Protocol 3 (testing data set) of the International Diabetes Closed-Loop Trial were used. Training data included 3 months of CGM recordings from 125 individuals with type 1 diabetes, and HbA1c at 3 months; testing data included 9 months of CGM recordings from 168 individuals, and HbA1c at 3, 6, and 9 months. Hemoglobin glycation was modeled by a first-order differential equation driven by TIR. Three model parameters were estimated in the training data set and fixed thereafter. A fourth parameter was estimated in the testing data set, to individualize the model by calibration with month 3 HbA1c. The accuracy of eA1c was assessed on months 6 and 9 HbA1c. eA1c was tracked for each individual in the testing data set for 6 months after calibration. Mean absolute differences between HbA1c and eA1c 3- and 6-month postcalibration were 0.25% and 0.24%; Pearson's correlation coefficients were 0.93 and 0.93; percentages of eA1c within 10% from reference HbA1c were 97.6% and 96.3%, respectively. HbA1c and TIR are reflections of the same underlying process of glycemic fluctuation. Using a model individualized with one HbA1c measurement, TIR provides an accurate approximation of HbA1c for at least 6 months, reflecting blood glucose fluctuations and nonglycemic biological factors. Thus, eA1c is an intermediate metric that mathematically adjusts a CGM-based assessment of glycemic control to individual glycation rates.
为了弥合实验室测量的血红蛋白 A1c(HbA1c)与连续血糖监测(CGM)衍生的目标范围内时间(TIR)之间的差距,引入 TIR 驱动的估计 A1c(eA1c)。数据来自国际糖尿病闭环试验的方案 1(训练数据集)和方案 3(测试数据集)。训练数据包括 125 名 1 型糖尿病患者的 3 个月 CGM 记录和 3 个月的 HbA1c;测试数据包括 168 名患者的 9 个月 CGM 记录和 3、6 和 9 个月的 HbA1c。血红蛋白糖化作用由 TIR 驱动的一阶微分方程建模。在训练数据集中估计了三个模型参数,此后固定不变。在测试数据集中估计了第四个参数,通过与 3 个月 HbA1c 的校准来对模型进行个体化。在 6 个月和 9 个月的 HbA1c 上评估了 eA1c 的准确性。在校准后,对测试数据集中的每个个体进行了 6 个月的 eA1c 跟踪。校准后 3 个月和 6 个月的 HbA1c 和 eA1c 的平均绝对差异分别为 0.25%和 0.24%;Pearson 相关系数分别为 0.93 和 0.93;eA1c 与参考 HbA1c 的差值在 10%以内的百分比分别为 97.6%和 96.3%。HbA1c 和 TIR 反映了血糖波动的同一基本过程。使用一个 HbA1c 测量值进行个体化的模型,TIR 至少可以在 6 个月内准确估算 HbA1c,反映血糖波动和非血糖生物因素。因此,eA1c 是一种中间指标,它从数学上调整了基于 CGM 的血糖控制评估,以适应个体糖化率。