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开发CGMap：2型糖尿病患者连续血糖监测数据特征分析

Developing CGMap: Characterizing Continuous Glucose Monitoring Data in Patients with Type 2 Diabetes.

作者信息

Bai Shuzhen, Lin Chu, Cai Xiaoling, Hu Suiyuan, Wu Jing, Chen Ling, Yang Wenjia, Ji Linong

机构信息

Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing 100044, China.

出版信息

Biomedicines. 2025 Apr 29;13(5):1080. doi: 10.3390/biomedicines13051080.

DOI:10.3390/biomedicines13051080
PMID:40426908
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12109104/
Abstract

: This study will characterize continuous glucose monitoring (CGM) data in patients with type 2 diabetes in China, and assess the relationship between CGM-derived indicators and diabetes-related clinical parameters. : The data for this study were collected from a randomized trial in China (ChiCTR2000039424) from February 2020 to July 2022 in which patients wore a CGM device for 14 days. Glycemia risk index (GRI), coefficient of variation (CV), standard deviation (SD), mean amplitude of glycemic excursions (MAGE), time in range (TIR), time above range (TAR), time below range (TBR), and estimate glycated hemoglobin (eA1c) were analyzed. Ordinary least square linear regression and the Spearman method were used to test the relationship between CGM-derived indicators and diabetes-related clinical parameters. : In all, 528 patients with type 2 diabetes from a randomized controlled trial were analyzed. It was shown that CV, SD, and MAGE increased with age and diabetes duration, but decreased with an increase in body mass index. Higher fasting plasma glucose, higher baseline HbA1c, and higher insulin resistance levels were associated with higher GRI, SD, MAGE, TAR, and eA1c, and they were associated with lower TIR. In addition, higher HOMA-2β was associated with higher TIR and TBR, and with lower TAR and eA1c. Hemoglobin had positive correlations to SD, TAR, and eA1c. : It was found that glucose variability increased with age and the duration of diabetes. However, glucose variability decreased with increased BMI. Meanwhile, greater glycemic variability was associated with worse islet function, higher baseline glucose level, and higher hemoglobin.

摘要

本研究将对中国2型糖尿病患者的持续葡萄糖监测（CGM）数据进行特征分析，并评估CGM衍生指标与糖尿病相关临床参数之间的关系。 本研究的数据收集自中国一项随机试验（ChiCTR2000039424），时间为2020年2月至2022年7月，患者佩戴CGM设备14天。分析了血糖风险指数（GRI）、变异系数（CV）、标准差（SD）、血糖波动平均幅度（MAGE）、血糖在目标范围内时间（TIR）、高于目标范围时间（TAR）、低于目标范围时间（TBR）以及估算糖化血红蛋白（eA1c）。采用普通最小二乘线性回归和Spearman方法检验CGM衍生指标与糖尿病相关临床参数之间的关系。 总共分析了来自一项随机对照试验的528例2型糖尿病患者。结果显示，CV、SD和MAGE随年龄和糖尿病病程增加而升高，但随体重指数增加而降低。较高的空腹血糖、较高的基线糖化血红蛋白和较高的胰岛素抵抗水平与较高的GRI、SD、MAGE、TAR和eA1c相关，且与较低的TIR相关。此外，较高的HOMA-2β与较高的TIR和TBR相关，与较低的TAR和eA1c相关。血红蛋白与SD、TAR和eA1c呈正相关。 研究发现，血糖变异性随年龄和糖尿病病程增加而升高。然而，血糖变异性随BMI增加而降低。同时，更大的血糖变异性与更差的胰岛功能、更高的基线血糖水平和更高的血红蛋白相关。

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相似文献

[1]
Developing CGMap: Characterizing Continuous Glucose Monitoring Data in Patients with Type 2 Diabetes.

Biomedicines. 2025-4-29

[2]
Study of Glycemic Variability in Well-controlled Type 2 Diabetic Patients Using Continuous Glucose Monitoring System.

J Assoc Physicians India. 2024-1

[3]
Relationship between key continuous glucose monitoring-derived metrics and specific cognitive domains in patients with type 2 diabetes mellitus.

BMC Neurol. 2023-5-20

[4]
Glycaemia in low-premixed insulin analogue type 2 diabetes patients in a real-world setting: are the CGM targets met?

Eur J Med Res. 2023-3-7

[5]
Associations Between HbA1c and Glucose Time in Range Using Continuous Glucose Monitoring in Type 1 Diabetes: Cross-Sectional Population-Based Study.

Diabetes Ther. 2024-6

[6]
Factors associated with glycemic variability in children with type 1 diabetes mellitus based on flash glucose monitoring system.

Zhong Nan Da Xue Xue Bao Yi Xue Ban. 2022-4-28

[7]
Comparing the effectiveness of continuous subcutaneous insulin infusion with multiple daily insulin injection for patients with type 1 diabetes mellitus evaluated by retrospective continuous glucose monitoring: A real-world data analysis.

Front Public Health. 2022

[8]
Hybrid Closed-Loop Versus Manual Insulin Delivery in Adults With Type 1 Diabetes: A Post Hoc Analysis Using the Glycemia Risk Index.

J Diabetes Sci Technol. 2024-7

[9]
Effects of Continuous Glucose Monitoring on Metrics of Glycemic Control in Diabetes: A Systematic Review With Meta-analysis of Randomized Controlled Trials.

Diabetes Care. 2020-5

[10]
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World J Diabetes. 2025-3-15

本文引用的文献

[1]
Comparison of glucose fluctuation between metformin combined with acarbose or sitagliptin in Chinese patients with type 2 diabetes: A multicenter, randomized, active-controlled, open-label, parallel design clinical trial.

Chin Med J (Engl). 2025-5-5

[2]
Real-time continuous glucose monitoring vs. self-monitoring of blood glucose: cost-utility in South Korean type 2 diabetes patients on intensive insulin.

J Med Econ. 2024

[3]
CGMap: Characterizing continuous glucose monitor data in thousands of non-diabetic individuals.

Cell Metab. 2023-5-2

[4]
Continuous glucose monitoring.

BMJ. 2023-3-3

[5]
7. Diabetes Technology: Standards of Care in Diabetes-2023.

Diabetes Care. 2023-1-1

[6]
The continuous spectrum of glycaemic variability changes with pancreatic islet function: A multicentre cross-sectional study in China.

Diabetes Metab Res Rev. 2022-11

[7]
A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings.

J Diabetes Sci Technol. 2023-9

[8]
Association of Real-time Continuous Glucose Monitoring With Glycemic Control and Acute Metabolic Events Among Patients With Insulin-Treated Diabetes.

JAMA. 2021-6-8

[9]
Effect of Continuous Glucose Monitoring on Glycemic Control in Patients With Type 2 Diabetes Treated With Basal Insulin: A Randomized Clinical Trial.

JAMA. 2021-6-8

[10]
Log-linear relationship between endogenous insulin secretion and glycemic variability in patients with type 2 diabetes on continuous glucose monitoring.

Sci Rep. 2021-4-27

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