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

## TL;DR

This study analyzed CGM data from type 2 diabetes patients in China to understand how glucose levels vary and how these variations relate to clinical factors like age, BMI, and insulin resistance.

## Contribution

The study introduces CGMap to characterize CGM data and reveals novel associations between glucose variability and clinical parameters in type 2 diabetes patients.

## Key findings

- Glucose variability increased with age and diabetes duration but decreased with higher BMI.
- Higher fasting glucose and insulin resistance were linked to greater glycemic variability and worse glucose control.
- Improved islet function was associated with better glucose stability and lower estimated A1c levels.

## Abstract

Objectives: 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. Methods: 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. Results: 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. Conclusions: 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.