范围内时间、狭小范围内时间和平均血糖关系受血糖变化的调节:利用真实世界数据确定连接血糖参数的血糖分布模型。

IF 5.7 2区 医学 Q1 ENDOCRINOLOGY & METABOLISM
Diabetes technology & therapeutics Pub Date : 2024-07-01 Epub Date: 2024-02-26 DOI:10.1089/dia.2023.0564
Yongjin Xu, Timothy C Dunn, Richard M Bergenstal, Alan Cheng, Yaghoub Dabiri, Ramzi A Ajjan
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引用次数: 0

摘要

背景 在量程内的时间(TIR)、在紧量程内的时间(TITR)和平均血糖(AG)用于调整糖尿病患者的血糖疗法。然而,TIR/TITR 和 AG 可能会出现脱节,这可能会造成管理上的困难。我们旨在了解影响这些血糖指标之间关系的因素。材料和方法 使用闪存连续血糖监测仪(CGM)收集自我认定的糖尿病患者的真实血糖数据。用葡萄糖变异系数(CV)评估血糖变异性(GV)对 AG 和 TIR/TITR 之间关系的影响,并研究解决这些关系的最佳拟合葡萄糖分布模型。结果 收集了 29,164 名用户(16,367 名 T1D 用户、11,061 名 T2D 用户和 1,736 名其他用户,38,259 个读数/受试者)的葡萄糖读数。比较低 CV 和高 CV 三等分,平均血糖为 150 mg/dL 时的 TIR 分别为 80±5.6% 和 62±6.8% (p
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Time in Range, Time in Tight Range, and Average Glucose Relationships Are Modulated by Glycemic Variability: Identification of a Glucose Distribution Model Connecting Glycemic Parameters Using Real-World Data.

Background: Time in range (TIR), time in tight range (TITR), and average glucose (AG) are used to adjust glycemic therapies in diabetes. However, TIR/TITR and AG can show a disconnect, which may create management difficulties. We aimed to understand the factors influencing the relationships between these glycemic markers. Materials and Methods: Real-world glucose data were collected from self-identified diabetes type 1 and type 2 diabetes (T1D and T2D) individuals using flash continuous glucose monitoring (FCGM). The effects of glycemic variability, assessed as glucose coefficient of variation (CV), on the relationship between AG and TIR/TITR were investigated together with the best-fit glucose distribution model that addresses these relationships. Results: Of 29,164 FCGM users (16,367 T1D, 11,061 T2D, and 1736 others), 38,259 glucose readings/individual were available. Comparing low and high CV tertiles, TIR at AG of 150 mg/dL varied from 80% ± 5.6% to 62% ± 6.8%, respectively (P < 0.001), while TITR at AG of 130 mg/dL varied from 65% ± 7.5% to 49% ± 7.0%, respectively (P < 0.001). In contrast, higher CV was associated with increased TIR and TITR at AG levels outside the upper limit of these ranges. Gamma distribution was superior to six other models at explaining AG and TIR/TITR interactions and demonstrated nonlinear interplay between these metrics. Conclusions: The gamma model accurately predicts interactions between CGM-derived glycemic metrics and reveals that glycemic variability can significantly influence the relationship between AG and TIR with opposing effects according to AG levels. Our findings potentially help with clinical diabetes management, particularly when AG and TIR appear mismatched.

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来源期刊
Diabetes technology & therapeutics
Diabetes technology & therapeutics 医学-内分泌学与代谢
CiteScore
10.60
自引率
14.80%
发文量
145
审稿时长
3-8 weeks
期刊介绍: Diabetes Technology & Therapeutics is the only peer-reviewed journal providing healthcare professionals with information on new devices, drugs, drug delivery systems, and software for managing patients with diabetes. This leading international journal delivers practical information and comprehensive coverage of cutting-edge technologies and therapeutics in the field, and each issue highlights new pharmacological and device developments to optimize patient care.
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