Decreasing complexity of glucose time series derived from continuous glucose monitoring is correlated with deteriorating glucose regulation.
10.1007/s11684-022-0955-9
- Author:
Cheng LI
1
;
Xiaojing MA
1
;
Jingyi LU
1
;
Rui TAO
2
;
Xia YU
2
;
Yifei MO
1
;
Wei LU
1
;
Yuqian BAO
1
;
Jian ZHOU
3
;
Weiping JIA
4
Author Information
1. Department of Endocrinology and Metabolism, Shanghai Jiao Tong University School of Medicine Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, 200233, China.
2. College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China.
3. Department of Endocrinology and Metabolism, Shanghai Jiao Tong University School of Medicine Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, 200233, China. zhoujian@sjtu.edu.cn.
4. Department of Endocrinology and Metabolism, Shanghai Jiao Tong University School of Medicine Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, 200233, China. wpjia@sjtu.edu.cn.
- Publication Type:Journal Article
- Keywords:
complexity of glucose time series;
continuous glucose monitoring;
impaired glucose regulation;
insulin secretion and sensitivity;
refined composite multi-scale entropy
- MeSH:
Humans;
Glucose;
Blood Glucose;
Insulin Resistance/physiology*;
Diabetes Mellitus, Type 2/diagnosis*;
Blood Glucose Self-Monitoring;
Time Factors;
Insulin
- From:
Frontiers of Medicine
2023;17(1):68-74
- CountryChina
- Language:English
-
Abstract:
Most information used to evaluate diabetic statuses is collected at a special time-point, such as taking fasting plasma glucose test and providing a limited view of individual's health and disease risk. As a new parameter for continuously evaluating personal clinical statuses, the newly developed technique "continuous glucose monitoring" (CGM) can characterize glucose dynamics. By calculating the complexity of glucose time series index (CGI) with refined composite multi-scale entropy analysis of the CGM data, the study showed for the first time that the complexity of glucose time series in subjects decreased gradually from normal glucose tolerance to impaired glucose regulation and then to type 2 diabetes (P for trend < 0.01). Furthermore, CGI was significantly associated with various parameters such as insulin sensitivity/secretion (all P < 0.01), and multiple linear stepwise regression showed that the disposition index, which reflects β-cell function after adjusting for insulin sensitivity, was the only independent factor correlated with CGI (P < 0.01). Our findings indicate that the CGI derived from the CGM data may serve as a novel marker to evaluate glucose homeostasis.
