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Frontiers of Medicine

ISSN 2095-0217

ISSN 2095-0225(Online)

CN 11-5983/R

邮发代号 80-967

2019 Impact Factor: 3.421

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Frontiers of Medicine  2023, Vol. 17 Issue (1): 68-74   https://doi.org/10.1007/s11684-022-0955-9
  本期目录
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.
Key words： complexity of glucose time series    continuous glucose monitoring    impaired glucose regulation    insulin secretion and sensitivity    refined composite multi-scale entropy
收稿日期: 2022-03-01      出版日期: 2023-03-15
Corresponding Author(s): Jian Zhou,Weiping Jia   
 引用本文:   
. [J]. Frontiers of Medicine, 2023, 17(1): 68-74.
Cheng Li, Xiaojing Ma, Jingyi Lu, Rui Tao, Xia Yu, Yifei Mo, Wei Lu, Yuqian Bao, Jian Zhou, Weiping Jia. Decreasing complexity of glucose time series derived from continuous glucose monitoring is correlated with deteriorating glucose regulation. Front. Med., 2023, 17(1): 68-74.
 链接本文:  
https://academic.hep.com.cn/fmd/CN/10.1007/s11684-022-0955-9
https://academic.hep.com.cn/fmd/CN/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  
Fig.1  
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  
Fig.2  
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  
1 W Jia, J Weng, D Zhu, L Ji, J Lu, Z Zhou, D Zou, L Guo, Q Ji, L Chen, L Chen, J Dou, X Guo, H Kuang, L Li, Q Li, X Li, J Liu, X Ran, L Shi, G Song, X Xiao, L Yang, Z; Chinese Diabetes Society Zhao. Standards of medical care for type 2 diabetes in China 2019. Diabetes Metab Res Rev 2019; 35(6): e3158
https://doi.org/10.1002/dmrr.3158 pmid: 30908791
2 J Lu, X Ma, J Zhou, L Zhang, Y Mo, L Ying, W Lu, W Zhu, Y Bao, RA Vigersky, W Jia. Association of time in range, as assessed by continuous glucose monitoring, with diabetic retinopathy in type 2 diabetes. Diabetes Care 2018; 41(11): 2370–2376
https://doi.org/10.2337/dc18-1131 pmid: 30201847
3 J Lu, X Ma, Y Shen, Q Wu, R Wang, L Zhang, Y Mo, W Lu, W Zhu, Y Bao, RA Vigersky, W Jia, J Zhou. Time in range is associated with carotid intima-media thickness in type 2 diabetes. Diabetes Technol Ther 2020; 22(2): 72–78
https://doi.org/10.1089/dia.2019.0251 pmid: 31524497
4 A Ceriello, L Monnier, D Owens. Glycaemic variability in diabetes: clinical and therapeutic implications. Lancet Diabetes Endocrinol 2019; 7(3): 221–230
https://doi.org/10.1016/S2213-8587(18)30136-0 pmid: 30115599
5 T Danne, R Nimri, T Battelino, RM Bergenstal, KL Close, JH DeVries, S Garg, L Heinemann, I Hirsch, SA Amiel, R Beck, E Bosi, B Buckingham, C Cobelli, E Dassau, FJ 3rd Doyle, S Heller, R Hovorka, W Jia, T Jones, O Kordonouri, B Kovatchev, A Kowalski, L Laffel, D Maahs, HR Murphy, K Nørgaard, CG Parkin, E Renard, B Saboo, M Scharf, WV Tamborlane, SA Weinzimer, M Phillip. International consensus on use of continuous glucose monitoring. Diabetes Care 2017; 40(12): 1631–1640
https://doi.org/10.2337/dc17-1600 pmid: 29162583
6 T Battelino, T Danne, RM Bergenstal, SA Amiel, R Beck, T Biester, E Bosi, BA Buckingham, WT Cefalu, KL Close, C Cobelli, E Dassau, JH DeVries, KC Donaghue, K Dovc, FJ 3rd Doyle, S Garg, G Grunberger, S Heller, L Heinemann, IB Hirsch, R Hovorka, W Jia, O Kordonouri, B Kovatchev, A Kowalski, L Laffel, B Levine, A Mayorov, C Mathieu, HR Murphy, R Nimri, K Nørgaard, CG Parkin, E Renard, D Rodbard, B Saboo, D Schatz, K Stoner, T Urakami, SA Weinzimer, M Phillip. Clinical targets for continuous glucose monitoring data interpretation: recommendations from the international consensus on time in range. Diabetes Care 2019; 42(8): 1593–1603
https://doi.org/10.2337/dci19-0028 pmid: 31177185
7 D Rodbard. Glucose variability: a review of clinical applications and research developments. Diabetes Technol Ther 2018; 20(S2): S25–S215, S2–15
https://doi.org/10.1089/dia.2018.0092 pmid: 29916742
8 M Costa, AL Goldberger, CK Peng. Multiscale entropy analysis. Int J Numer Methods Eng 2004; 59(9): 1147–1166
https://doi.org/10.1002/nme.904
9 MD Costa, T Henriques, MN Munshi, AR Segal, AL Goldberger. Dynamical glucometry: use of multiscale entropy analysis in diabetes. Chaos 2014; 24(3): 033139
https://doi.org/10.1063/1.4894537 pmid: 25273219
10 JL Chen, PF Chen, HM Wang. Decreased complexity of glucose dynamics in diabetes: evidence from multiscale entropy analysis of continuous glucose monitoring system data. Am J Physiol Regul Integr Comp Physiol 2014; 307(2): R179–R183
https://doi.org/10.1152/ajpregu.00108.2014 pmid: 24808497
11 AG Tabák, C Herder, W Rathmann, EJ Brunner, M Kivimäki. Prediabetes: a high-risk state for diabetes development. Lancet 2012; 379(9833): 2279–2290
https://doi.org/10.1016/S0140-6736(12)60283-9 pmid: 22683128
12 G Acciaroli, G Sparacino, L Hakaste, A Facchinetti, GM Di Nunzio, A Palombit, T Tuomi, R Gabriel, J Aranda, S Vega, C Cobelli. Diabetes and prediabetes classification using glycemic variability indices from continuous glucose monitoring data. J Diabetes Sci Technol 2018; 12(1): 105–113
https://doi.org/10.1177/1932296817710478 pmid: 28569077
13 N Chakarova, R Dimova, G Grozeva, T Tankova. Assessment of glucose variability in subjects with prediabetes. Diabetes Res Clin Pract 2019; 151: 56–64
https://doi.org/10.1016/j.diabres.2019.03.038 pmid: 30935927
14 C Wang, L Lv, Y Yang, D Chen, G Liu, L Chen, Y Song, L He, X Li, H Tian, W Jia, X Ran. Glucose fluctuations in subjects with normal glucose tolerance, impaired glucose regulation and newly diagnosed type 2 diabetes mellitus. Clin Endocrinol (Oxf) 2012; 76(6): 810–815
https://doi.org/10.1111/j.1365-2265.2011.04205.x pmid: 21854404
15 C Li, X Ma, J Yin, Y Mo, L Zhang, J Lu, W Lu, Y Bao, RA Vigersky, J Zhou, W Jia. The dawn phenomenon across the glycemic continuum: implications for defining dysglycemia. Diabetes Res Clin Pract 2020; 166: 108308
https://doi.org/10.1016/j.diabres.2020.108308 pmid: 32650035
16 SD Wu, CW Wu, SG Lin, KY Lee, CK Peng. Analysis of complex time series using refined composite multiscale entropy. Phys Lett A 2014; 378(20): 1369–1374
https://doi.org/10.1016/j.physleta.2014.03.034
17 J Zhou, H Li, X Ran, W Yang, Q Li, Y Peng, Y Li, X Gao, X Luan, W Wang, W Jia. Reference values for continuous glucose monitoring in Chinese subjects. Diabetes Care 2009; 32(7): 1188–1193
https://doi.org/10.2337/dc09-0076 pmid: 19389816
18 J Zhou, Y Mo, H Li, X Ran, W Yang, Q Li, Y Peng, Y Li, X Gao, X Luan, W Wang, Y Xie, W Jia. Relationship between HbA1c and continuous glucose monitoring in Chinese population: a multicenter study. PLoS One 2013; 8(12): e83827
https://doi.org/10.1371/journal.pone.0083827 pmid: 24376762
19 Diabetes Association American. Standards of medical care in diabetes−2007. Diabetes Care 2007; 30(Suppl 1): S4–S41
https://doi.org/10.2337/dc07-S004 pmid: 17192377
20 DR Matthews, JP Hosker, AS Rudenski, BA Naylor, DF Treacher, RC Turner. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985; 28(7): 412–419
https://doi.org/10.1007/BF00280883 pmid: 3899825
21 J Cederholm, L Wibell. Insulin release and peripheral sensitivity at the oral glucose tolerance test. Diabetes Res Clin Pract 1990; 10(2): 167–175
https://doi.org/10.1016/0168-8227(90)90040-Z pmid: 2261853
22 C Li, H Yang, G Tong, S Shen, W Feng, Y Bi, D Zhu. Correlations between A1c, fasting glucose, 2 h postload glucose, and β-cell function in the Chinese population. Acta Diabetol 2014; 51(4): 601–608
https://doi.org/10.1007/s00592-014-0563-5 pmid: 24595519
23 KD Kohnert, P Heinke, L Vogt, P Augstein, A Thomas, E Salzsieder. Associations of blood glucose dynamics with antihyperglycemic treatment and glycemic variability in type 1 and type 2 diabetes. J Endocrinol Invest 2017; 40(11): 1201–1207
https://doi.org/10.1007/s40618-017-0682-2 pmid: 28484994
24 Y Lai, Z Zhang, P Li, X Liu, Y Liu, Y Xin, W Gu. Investigation of glucose fluctuations by approaches of multi-scale analysis. Med Biol Eng Comput 2018; 56(3): 505–514
https://doi.org/10.1007/s11517-017-1692-0 pmid: 28825208
25 KD Kohnert, P Heinke, L Vogt, P Augstein, E Salzsieder. Applications of variability analysis techniques for continuous glucose monitoring derived time series in diabetic patients. Front Physiol 2018; 9: 1257
https://doi.org/10.3389/fphys.2018.01257 pmid: 30237767
26 XD Zhang, D Pechter, L Yang, X Ping, Z Yao, R Zhang, X Shen, NX Li, J Connick, AR Nawrocki, M Chakravarthy, C Li. Decreased complexity of glucose dynamics preceding the onset of diabetes in mice and rats. PLoS One 2017; 12(9): e0182810
https://doi.org/10.1371/journal.pone.0182810 pmid: 28877180
27 RN Bergman, M Ader, K Huecking, G Van Citters. Accurate assessment of beta-cell function: the hyperbolic correction. Diabetes 2002; 51(Suppl 1): S212–S220
https://doi.org/10.2337/diabetes.51.2007.S212 pmid: 11815482
28 L Li, J Sun, L Ruan, Q Song. Time-series analysis of continuous glucose monitoring data to predict treatment efficacy in patients with T2DM. J Clin Endocrinol Metab 2021; 106(8): 2187–2197
https://doi.org/10.1210/clinem/dgab356 pmid: 34010405
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