Identifying the Relationship Between CGM Time in Range and Basal Insulin Adherence in People With Type 2 Diabetes.

IF 4.1 Q2 ENDOCRINOLOGY & METABOLISM

Journal of Diabetes Science and Technology Pub Date : 2024-11-10 DOI:10.1177/19322968241296828

Jannie Toft Damsgaard Nørlev, Thomas Kronborg, Morten Hasselstrøm Jensen, Peter Vestergaard, Ole Hejlesen, Stine Hangaard

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引用次数: 0

Abstract

Background: The study aimed to determine the relationship between basal insulin adherence and glycemic control evaluated by time in range (TIR) in people with insulin-treated type 2 diabetes (T2D), using data from both continuous glucose monitors (CGM) and connected insulin pens. Furthermore, the study aimed to determine the best basal insulin adherence metric.

Methods: CGM data and basal insulin data were collected from 106 insulin-treated people (aged ≥18 years) with T2D. Three different adherence metrics were employed (dose deviation, dose deviation ≤20%, and a traditional metric) and a three-step methodology was used to measure insulin adherence level. The coefficient of determination (R²), based on a univariate linear regression analysis, was used to determine the relationship between each adherence metric and TIR.

Results: A statistically significant relationship was observed between TIR and adherence quantified as the dose deviation ≤20% metric (R² = 0.67, P = .006). Neither the relationship between the dose deviation metric and TIR (R² = 0.43, P = .08) nor the relationship between the traditional metric and TIR (R² = 0.35, P =.23) was found to be statistically significant.

Conclusions: Our study indicates a relationship between basal insulin adherence and TIR in people with insulin-treated T2D. This seems to underscore the role of basal insulin adherence for optimal glycemic outcomes and utilizing TIR as a clinical marker. Furthermore, the results suggest that the magnitude of deviation from the recommended basal insulin dose impacts glycemic control, indicating dose deviation ≤20% as a more accurate metric for quantifying adherence.

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确定 2 型糖尿病患者 CGM 时间在范围内与基础胰岛素依从性之间的关系。

研究背景该研究旨在利用连续血糖监测仪（CGM）和连接胰岛素笔的数据，确定接受胰岛素治疗的2型糖尿病（T2D）患者基础胰岛素依从性与血糖控制之间的关系，以时间范围（TIR）评估血糖控制情况。此外，该研究还旨在确定最佳的基础胰岛素依从性指标：收集了 106 名接受过胰岛素治疗的 T2D 患者（年龄≥18 岁）的 CGM 数据和基础胰岛素数据。采用三种不同的依从性指标（剂量偏差、剂量偏差≤20%和传统指标）和三步法测量胰岛素依从性水平。在单变量线性回归分析的基础上，使用决定系数（R2）来确定每种依从性指标与TIR之间的关系：结果：TIR 与以剂量偏差 ≤20% 度量量化的依从性之间存在统计学意义上的重大关系（R2 = 0.67，P = .006）。剂量偏差指标与 TIR 之间的关系（R2 = 0.43，P = .08）以及传统指标与 TIR 之间的关系（R2 = 0.35，P =.23）均无统计学意义：我们的研究表明，在接受胰岛素治疗的 T2D 患者中，基础胰岛素依从性与 TIR 之间存在关系。这似乎强调了基础胰岛素依从性在优化血糖结果和利用 TIR 作为临床指标方面的作用。此外，研究结果表明，与推荐胰岛素基础剂量的偏差程度会影响血糖控制，这表明剂量偏差≤20%是量化胰岛素依从性的更准确指标。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Diabetes Science and Technology Medicine-Internal Medicine

CiteScore

7.50

自引率

12.00%

发文量

148

期刊介绍： The Journal of Diabetes Science and Technology (JDST) is a bi-monthly, peer-reviewed scientific journal published by the Diabetes Technology Society. JDST covers scientific and clinical aspects of diabetes technology including glucose monitoring, insulin and metabolic peptide delivery, the artificial pancreas, digital health, precision medicine, social media, cybersecurity, software for modeling, physiologic monitoring, technology for managing obesity, and diagnostic tests of glycation. The journal also covers the development and use of mobile applications and wireless communication, as well as bioengineered tools such as MEMS, new biomaterials, and nanotechnology to develop new sensors. Articles in JDST cover both basic research and clinical applications of technologies being developed to help people with diabetes.