Study links glucose time series complexity to diabetes progression

Frontiers JournalsApr 9 2024

Weiping Jia et al., at Shanghai Jiao Tong University School of Medicine Affiliated Sixth People's Hospital, China, conducted a study to explore the relationship between the complexity of glucose time series, as derived from continuous glucose monitoring (CGM), and the deterioration of glucose regulation. The study introduces the use of refined composite multi-scale entropy (RCMSE) analysis to calculate the complexity of glucose time series index (CGI) and demonstrates that a decrease in this complexity is associated with a decline in glucose homeostasis, ranging from normal glucose tolerance (NGT) to impaired glucose regulation (IGR) and type 2 diabetes (T2D).

Key findings from the study include: (1) The complexity of glucose time series progressively decreases across the glycemic continuum, with the most significant decrease observed in individuals with T2D compared to those with NGT and IGR; (2) The CGI was found to be significantly associated with various parameters related to insulin sensitivity and secretion, indicating a close relationship between glucose dynamics and insulin function. (3) The disposition index (DI), which reflects β-cell function after adjusting for insulin sensitivity, was identified as the only independent factor correlated with CGI, suggesting that it may serve as a novel marker for evaluating glucose homeostasis.

The study's methodology involved a post-hoc analysis of a multi-center CGM study conducted in China from 2007 to 2009, including 756 subjects with complete CGM data. The participants were not on any hypoglycemic treatment before or during the study period. The research was approved by the ethics committees of each participating hospital, and all subjects provided informed consent.

The study's results have implications for the clinical practice of diabetes management, as the complexity of glucose time series, calculated using RCMSE analysis, could potentially serve as a new marker for glucose metabolism status. This could aid in the early detection and management of diabetes, as well as provide insights into the characteristics of blood glucose in individuals with IGR.

Overall, the research contributes to the understanding of glucose dynamics and insulin function in the context of diabetes and its progression, offering potential avenues for future investigation and clinical application.