Table_1_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）现已成为2型糖尿病（T2D）血糖稳态管理的一个核心组成部分。本综述旨在探讨GV参数在干预性研究和观察性研究中，对T2D血糖控制管理的应用及其相关性。首先，本文将聚焦于通过连续葡萄糖监测系统（CGMS）测定的GV参数与血糖控制及T2D相关并发症标志物之间的关系。其次，本文将致力于分析CGMS中GV参数作为干预性研究（包括药物治疗、营养和体力活动）的结局，以改善T2D患者的血糖控制。在最初的243篇文章中，共有63篇文章被纳入（第一部分27篇，第二部分38篇）。对于这两部分分析，大多数被识别的研究都是基于药物治疗的。生活方式研究（包括基于营养和体力活动的N-AP研究）在代表性方面相对不足。关于GV参数与血糖控制及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）以及血糖波动幅度（MAGEs）是最常研究的参数，它们显示出与HbA1c的强烈相关性。在将GV作为干预性研究的结局的应用及其相关性方面，在药物治疗研究中，SD、MAGE、MBG和时间在范围内（TIR）是大多数研究中用作主要标准的GV参数，在干预后显示出显著的改善，与血糖控制参数（HbA1c、FBG和PPBG）的改善相平行或不同。在N-AP研究中，同样观察到SD、MAGE和TIR的结果。尽管与药物治疗研究相比，N-AP研究中涉及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，并需要更深入的研究。将CGMS-GV参数作为干预性研究的结局，可能比标准餐后随访提供更全面的血糖控制维度。GV似乎是T2D血糖异常的关键组成部分，某些参数如MAGE、SD或TIR可以与传统的血糖控制标志物（如HbA1c、空腹血糖或餐后血糖）一同常规使用。