Table_4_CGMS and Glycemic Variability, Relevance in Clinical Research to Evaluate Interventions in T2D, a Literature Review.xlsx

Glycemic variability (GV) appears today as an integral component of glucose homeostasis for the management of type 2 diabetes (T2D). This review aims at investigating the use and relevance of GV parameters in interventional and observational studies for glucose control management in T2D. It will first focus on the relationships between GV parameters measured by continuous glucose monitoring system (CGMS) and glycemic control and T2D-associated complications markers. The second part will be dedicated to the analysis of GV parameters from CGMS as outcomes in interventional studies (pharmacological, nutritional, physical activity) aimed at improving glycemic control in patients with T2D. From 243 articles first identified, 63 articles were included (27 for the first part and 38 for the second part). For both analyses, the majority of the identified studies were pharmacological. Lifestyle studies (including nutritional and physical activity-based studies, N-AP) were poorly represented. Concerning the relationships of GV parameters with those for glycemic control and T2D related-complications, the standard deviation (SD), the coefficient of variation (CV), the mean blood glucose (MBG), and the mean amplitude of the glycemic excursions (MAGEs) were the most studied, showing strong relationships, in particular with HbA1c. Regarding the use and relevance of GV as an outcome in interventional studies, in pharmacological ones, SD, MAGE, MBG, and time in range (TIR) were the GV parameters used as main criteria in most studies, showing significant improvement after intervention, in parallel or not with glycemic control parameters’ (HbA1c, FBG, and PPBG) improvement. In N-AP studies, the same results were observed for SD, MAGE, and TIR. Despite the small number of N-AP studies addressing both GV and glycemic control parameters compared to pharmacological ones, N-AP studies have shown promising results on GV parameters and would require more in-depth work. Evaluating CGMS-GV parameters as outcomes in interventional studies may provide a more integrative dimension of glucose control than the standard postprandial follow-up. GV appears to be a key component of T2D dysglycemia, and some parameters such as MAGE, SD, or TIR could be used routinely in addition to classical markers of glycemic control such as HbA1c, fasting, or postprandial glycemia.

血糖变异性（GV）如今已成为管理2型糖尿病（T2D）血糖稳态的关键组成部分。本综述旨在探讨GV参数在干预性和观察性研究中用于T2D血糖控制管理中的应用与相关性。首先，将聚焦于由连续血糖监测系统（CGMS）测量的GV参数与血糖控制和T2D相关并发症指标之间的关系。其次，将致力于分析CGMS中GV参数作为干预性研究（药物、营养、体力活动）中改善T2D患者血糖控制效果的指标。从最初确定的243篇文章中，共纳入63篇文章（第一部分27篇，第二部分38篇）。对于两项分析，大多数被识别的研究均为药物性研究。生活方式研究（包括基于营养和体力活动的N-AP研究）代表性不足。关于GV参数与血糖控制和T2D相关并发症之间的关系，标准差（SD）、变异系数（CV）、平均血糖（MBG）和血糖波动幅度（MAGEs）是最常研究的内容，显示出与HbA1c的强相关性。在干预性研究中，GV作为结果的运用，在药物研究中，SD、MAGE、MBG和血糖范围时间（TIR）是大多数研究中用作主要标准的GV参数，显示干预后显著改善，且与血糖控制参数（HbA1c、空腹血糖和餐后血糖）的改善平行或非平行。在N-AP研究中，观察到类似的结果。尽管与药物研究相比，N-AP研究中关注GV和血糖控制参数的研究数量较少，但N-AP研究在GV参数方面显示出有前景的结果，并需要更深入的工作。将CGMS-GV参数作为干预性研究中的结果可能比标准的餐后随访提供更全面的血糖控制维度。GV似乎是T2D糖代谢紊乱的关键组成部分，一些参数如MAGE、SD或TIR可以作为常规指标，除此外，还应包括经典的血糖控制标志物，如HbA1c、空腹血糖或餐后血糖。