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Frontiers of Medicine

ISSN 2095-0217

ISSN 2095-0225(Online)

CN 11-5983/R

Postal Subscription Code 80-967

2018 Impact Factor: 1.847

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Front. Med.    2023, Vol. 17 Issue (1) : 68-74    https://doi.org/10.1007/s11684-022-0955-9
RESEARCH ARTICLE
Decreasing complexity of glucose time series derived from continuous glucose monitoring is correlated with deteriorating glucose regulation
Cheng Li1, Xiaojing Ma1, Jingyi Lu1, Rui Tao2, Xia Yu2, Yifei Mo1, Wei Lu1, Yuqian Bao1, Jian Zhou1(), Weiping Jia1()
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
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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.
Keywords complexity of glucose time series      continuous glucose monitoring      impaired glucose regulation      insulin secretion and sensitivity      refined composite multi-scale entropy     
Corresponding Author(s): Jian Zhou,Weiping Jia   
Just Accepted Date: 28 October 2022   Online First Date: 22 December 2022    Issue Date: 15 March 2023
 Cite this article:   
Cheng Li,Xiaojing Ma,Jingyi Lu, et al. Decreasing complexity of glucose time series derived from continuous glucose monitoring is correlated with deteriorating glucose regulation[J]. Front. Med., 2023, 17(1): 68-74.
 URL:  
https://academic.hep.com.cn/fmd/EN/10.1007/s11684-022-0955-9
https://academic.hep.com.cn/fmd/EN/Y2023/V17/I1/68
Before matching After matching
Total NGT IGR T2D P value
Male/female 383/373 51/60 52/59 49/62 0.919
Age (year) 51 (38–60) 55 (46–63) 55 (46–61) 54 (46–60) 0.416
BMI (kg/m2) 23.50 (21.40–25.73) 23.44 (22.14–24.51) 23.88 (21.91–25.95) 23.83 (22.04–25.69) 0.116
SBP (mmHg) 120 (110–130) 120 (110–128) 122 (116–140) 130 (120–140) < 0.001
DBP (mmHg) 80 (70–80) 80 (70–80) 80 (70–80) 80 (70–85) 0.054
TC (mmol/L) 4.80 (4.14–5.50) 4.98 (4.47–5.43) 4.90 (4.29–5.72) 5.30 (4.60–5.89) 0.029
TG (mmol/L) 1.30 (0.88–1.73) 1.12 (0.80–1.44) 1.40 (1.03–1.90) 1.71 (1.30–2.49) < 0.001
HDL-c (mmol/L) 1.32 (1.10–1.61) 1.50 (1.26–1.80) 1.29 (1.05–1.53) 1.21 (1.02–1.45) < 0.001
LDL-c (mmol/L) 2.83 (2.29–3.45) 2.96 (2.47–3.47) 2.87 (2.43–3.50) 3.20 (2.55–3.67) 0.043
FPG (mmol/L) 5.50 (4.83–6.73) 4.93 (4.56–5.21) 5.93 (5.50–6.40) 7.60 (6.70–9.00) < 0.001
2 h PG (mmol/L) 7.67 (5.43–12.70) 5.60 (5.10–6.50) 8.37 (7.70–9.33) 14.83 (12.70–18.46) < 0.001
HbA1c (%) NA NA 6.0 (5.6–6.3) 7.1 (6.6–8.1) < 0.001*
CGM profile
MSG (mmol/L) 6.35 (5.77–7.43) 5.87 (5.53–6.25) 6.60 (6.08–7.03) 8.06 (7.24–9.05) < 0.001
SDSG (mmol/L) 1.02 (0.66–1.57) 0.73 (0.57–1.07) 1.11 (0.75–1.47) 1.67 (1.09–2.26) < 0.001
CV (%) 15.45 (11.07–21.35) 12.61 (9.56–17.95) 16.81 (11.89–21.90) 19.09 (14.75–25.07) < 0.001
TIR (%) 99.00 (89.00–100.00) 100.00 (98.00–100.00) 99.00 (94.00–100.00) 84.00 (69.00–98.00) < 0.001
Tab.1  Clinical characteristic and CGM profile of subjects in 111 paired sets of NGT, IGR, and newly diagnosed T2D group
Fig.1  Sample entropy at each time scale in 111 paired sets of NGT, IGR, and newly diagnosed T2D group. ** P for trend < 0.001.
Variables r P
Age −0.106 0.052
BMI 0.057 0.302
FPG −0.240 < 0.001
2 h PG −0.319 < 0.001
MSG −0.279 < 0.001
SDSG −0.464 < 0.001
CV −0.471 < 0.001
TIR 0.321 < 0.001
Tab.2  Spearman correlation coefficient analysis of CGI and glycemic parameters in 111 paired sets of NGT, IGR, and newly diagnosed T2D group (n = 333)
Fig.2  (A) HOMA-β in 61 paired sets of NGT, IGR, and newly diagnosed T2D group. (B) AUCINS120/AUCGLU120 in 61 paired sets of NGT, IGR, and newly diagnosed T2D group. (C) △INS30/△GLU30 in 61 paired sets of NGT, IGR, and newly diagnosed T2D group. (D) HOMA-IR in 61 paired sets of NGT, IGR, and newly diagnosed T2D group. (E) ISI in 61 paired sets of NGT, IGR, and newly diagnosed T2D group. (F) DI and CGI in 61 paired sets of NGT, IGR, and newly diagnosed T2D group. Data are presented as median (interquartile).
Variables r P
FINS 0.053 0.480
30 min INS 0.273 < 0.001
2 h INS 0.013 0.859
HOMA-β 0.245 0.001
△INS30/△GLU30 0.328 < 0.001
AUCINS120/AUCGLU120 0.313 < 0.001
HOMA-IR −0.075 0.310
ISI 0.328 < 0.001
DI 0.387 < 0.001
Tab.3  Spearman correlation coefficient analysis of CGI, and insulin secretion and sensitivity parameters in paired subgroup with complete data on insulin secretion/sensitivity (n = 183)
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