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Assessment of Glucose Control Metrics by Discriminant Ratio.判别比评估血糖控制指标。
Diabetes Technol Ther. 2020 Oct;22(10):719-726. doi: 10.1089/dia.2019.0415.
2
Randomized Controlled Trial of Mobile Closed-Loop Control.移动闭环控制的随机对照试验
Diabetes Care. 2020 Mar;43(3):607-615. doi: 10.2337/dc19-1310. Epub 2020 Jan 14.
3
A Kinetic Model for Glucose Levels and Hemoglobin A1c Provides a Novel Tool for Individualized Diabetes Management.血糖和糖化血红蛋白的动力学模型为个体化糖尿病管理提供了新工具。
J Diabetes Sci Technol. 2021 Mar;15(2):294-302. doi: 10.1177/1932296819897613. Epub 2020 Jan 8.
4
Glucose Time In Range, Time Above Range, and Time Below Range Depend on Mean or Median Glucose or HbA1c, Glucose Coefficient of Variation, and Shape of the Glucose Distribution.血糖时间在范围内、血糖时间超过范围和血糖时间低于范围取决于平均血糖或 HbA1c、血糖变异系数和血糖分布形状。
Diabetes Technol Ther. 2020 Jul;22(7):492-500. doi: 10.1089/dia.2019.0440. Epub 2020 Feb 18.
5
Six-Month Randomized, Multicenter Trial of Closed-Loop Control in Type 1 Diabetes.1 型糖尿病闭环控制的 6 个月随机、多中心试验。
N Engl J Med. 2019 Oct 31;381(18):1707-1717. doi: 10.1056/NEJMoa1907863. Epub 2019 Oct 16.
6
Time in Range Is Associated with Carotid Intima-Media Thickness in Type 2 Diabetes.达标时间与 2 型糖尿病患者颈动脉内中膜厚度相关。
Diabetes Technol Ther. 2020 Feb;22(2):72-78. doi: 10.1089/dia.2019.0251. Epub 2019 Oct 11.
7
Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range.临床连续血糖监测数据解读目标:时间范围国际共识推荐意见。
Diabetes Care. 2019 Aug;42(8):1593-1603. doi: 10.2337/dci19-0028. Epub 2019 Jun 8.
8
Glucose Management Indicator (GMI): Insights and Validation Using Guardian 3 and Navigator 2 Sensor Data.血糖管理指标(GMI):使用Guardian 3和Navigator 2传感器数据的见解与验证
Diabetes Care. 2019 Apr;42(4):e60-e61. doi: 10.2337/dc18-2479. Epub 2019 Feb 6.
9
The Relationships Between Time in Range, Hyperglycemia Metrics, and HbA1c.血糖达标时间、高血糖指标与糖化血红蛋白之间的关系。
J Diabetes Sci Technol. 2019 Jul;13(4):614-626. doi: 10.1177/1932296818822496. Epub 2019 Jan 13.
10
Validation of Time in Range as an Outcome Measure for Diabetes Clinical Trials.验证时间在范围内作为糖尿病临床试验的结果测量。
Diabetes Care. 2019 Mar;42(3):400-405. doi: 10.2337/dc18-1444. Epub 2018 Oct 23.

从 1 型糖尿病患者的连续血糖监测数据估算糖化血红蛋白:我们是否只需要达标时间?

Estimation of Hemoglobin A1c from Continuous Glucose Monitoring Data in Individuals with Type 1 Diabetes: Is Time In Range All We Need?

机构信息

Center for Diabetes Technology, Department of Psychiatry and Neurobehavioral Sciences, University of Virginia, Charlottesville, Virginia, USA.

Science Consulting in Diabetes GmbH, Neuss, Germany.

出版信息

Diabetes Technol Ther. 2020 Jul;22(7):501-508. doi: 10.1089/dia.2020.0236.

DOI:10.1089/dia.2020.0236
PMID:32459124
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7336887/
Abstract

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.

摘要

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。