Investigation of the glucose dynamics with an approach of refined composite multi-scale entropy analysis_Journal of Biomedical Engineering

Authors：

LAI Yunyun ¹ , XIN Yi ¹ , GU Weijun ² , ZHANG Nan ³ , LI Peiyao ³ ,  ZHANG Zhengbo ³

1. School of Life Science, Beijing Institute of Technology, Beijing 100081, P.R.China;
2. Department of Endocrinology, Chinese PLA General Hospital, Beijing 100853, P.R.China;
3. Department of Biomedical Engineering, Chinese PLA General Hospital, Beijing 100853, P.R.China;

Corresponding author：

ZHANG Zhengbo, Email: zhengbozhang@126.com

Keywords：

72-hour dynamic glucose; glycemic fluctuation; complexity; refined composite multi-scale entropy analysis; mean absolute glycemic excursion

DOI：

10.7507/1001-5515.201606015

Video：

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The study on complexity of glucose fluctuation not only helps us understand the regulation of the glucose homeostasis system but also brings us a new insight of the research methodology on glucose regulation. In the experiments, we analyzed the complexity of the temporal structure of the 72 hours continuous glucose time series from a group of 93 subjects with type Ⅱ diabetes mellitus using the multi-scale entropy method. We adapted the most recently improved refined composite multi-scale entropy (RCMSE) algorithm which could overcome the shortcomings on the 72 hours short time series analysis. We then quantified and compared the complexity of continuous glucose time series between groups with type Ⅱ diabetes mellitus with different mean absolute glycemic excursion (MAGE) and glycated hemoglobin (HbA1c). The results implied that the complexity of glucose time series decreased on lower MAGE group compared to high MAGE group, and the entropy on scale 1 to 6 which corresponded to 5 to 30 min had significant differences between these two groups; the complexity of glucose time series decreased with the increasing HbA1c level but the entropy had no statistical difference among groups at different scales. Therefore, RCMSE provided us with a new prospect to analyze the glucose time series and it was proved that less complexity of glucose dynamics could indicate the impaired gluco-regulation function from the MAGE point of view or HbA1c for patients, and the glucose complexity had the potential to become a new biomarker to reflect the fluctuation of the glucose time series.

Citation： LAI Yunyun, XIN Yi, GU Weijun, ZHANG Nan, LI Peiyao, ZHANG Zhengbo. Investigation of the glucose dynamics with an approach of refined composite multi-scale entropy analysis. Journal of Biomedical Engineering, 2017, 34(1): 123-128. doi: 10.7507/1001-5515.201606015 Copy

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3.	盛冲霄(综述), 黎红华(审校). 氧化应激与糖尿病血糖波动. 医学综述, 2015(5): 854-857.
4.	Su Jianbin, Wang Xueqin, Chen Jinfeng, et al. Glycemic variability in insulin treated type 2 diabetes with well-controlled hemoglobin A1c and its response to further treatment with acarbose. Chinese medical journal, 2011, 124(1): 144-147.
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14.	Costa M, Goldberger A L, Peng C K. Multiscale entropy analysis. Int J Numer Methods Eng, 2004, 59(9): 1147-1166.
15.	Chen Jinlong, Chen Pinfan, Wang H M. 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.
16.	Goldberger A L, Amaral L A, Hausdorff J M, et al. Fractal dynamics in physiology: alterations with disease and aging. Proc Natl Acad Sci U S A, 2002, 99(Suppl 1): 2466-2472.
17.	Goldstein B, Fiser D H, Kelly M M, et al. Decomplexification in critical illness and injury: relationship between heart rate variability, severity of illness, and outcome. Crit Care Med, 1998, 26(2): 352-357.
18.	van Hooijdonk R T, abu-Hanna Ameen, SCHULTZ M J. Glycemic variability is complex--is glucose complexity variable?. Crit Care, 2012, 16(6): 178.
19.	Wu S D, Wu C W, Lin S G, et al. Analysis of complex time series using refined composite multiscale entropy. Phys Lett A, 2014, 378(20): 1369-1374.
20.	Costa M, Peng C K, Goldberger A L, et al. Multiscale entropy analysis of human gait dynamics. Physica A: Statistical Mechanics and its Applications, 2003, 330(1/2): 53-60.
21.	周健, 贾伟平, 喻明, 等. 应用动态血糖监测系统评估不同糖耐量个体血糖稳定性的特征. 上海医学, 2008, 31(1): 10-13.
22.	贾伟平. 中国动态血糖监测临床应用指南(2012年版). 慢性病学杂志, 2013, 4(5): 321-330.
23.	Li Qing, Li Li, Li Yong. Enhanced RBC aggregation in type 2 diabetes patients. J Clin Lab Anal, 2015, 29(5): 387-389.
24.	Kohnert K D, Heinke P, Vogt L, et al. Utility of different glycemic control metrics for optimizing management of diabetes. World J Diabetes, 2015, 6(1): 17-29.
25.	Rodbard D. New and improved methods to characterize glycemic variability using continuous glucose monitoring. Diabetes Technol Ther, 2009, 11(9): 551-565.
26.	Wang Jing, Shang Pengjian, Xia Jianan, et al. EMD based refined composite multiscale entropy analysis of complex signals. Physica A: Statistical Mechanics and its Applications, 2015, 421(1): 583-593.
27.	Peng C K, Costa M, Goldberger A L. Adaptive data analysis of complex fluctuation in physiological time series. Adv Adapt Data Anal, 2011, 1(01): 61-70.
28.	Costa M, Goldberger A L, Peng C K. Multiscale entropy analysis of biological signals. Phys Rev E Stat Nonlin Soft Matter Phys, 2005, 71(2 Pt 1): 021906.

1. 刘艳辉(综述), 石泽亚, 刘小明. 重症患者的血糖管理研究进展. 齐齐哈尔医学院学报, 2015(6): 870-872.
2. Cobelli C, Man C D, Sparacino G, et al. Diabetes: models, signals, and control. IEEE Rev Biomed Eng, 2009, 2(1): 54-96.
3. 盛冲霄(综述), 黎红华(审校). 氧化应激与糖尿病血糖波动. 医学综述, 2015(5): 854-857.
4. Su Jianbin, Wang Xueqin, Chen Jinfeng, et al. Glycemic variability in insulin treated type 2 diabetes with well-controlled hemoglobin A1c and its response to further treatment with acarbose. Chinese medical journal, 2011, 124(1): 144-147.
5. Monnier L, Mas E, Ginet C, et al. Activation of oxidative stress by acute glucose fluctuations compared with sustained chronic hyperglycemia in patients with type 2 diabetes. JAMA, 2006, 295(14): 1681-1687.
6. Suh S, Kim J H. Glycemic variability: how do we measure it and why is it important?. Diabetes Metab J, 2015, 39(4): 273-282.
7. Hill N R, Oliver N S, Choudhary P, et al. Normal reference range for mean tissue glucose and glycemic variability derived from continuous glucose monitoring for subjects without diabetes in different ethnic groups. Diabetes Technol Ther, 2011, 13(9): 921-928.
8. Fritzsche G, Kohnert K D, Heinke P, et al. The use of a computer program to calculate the mean amplitude of glycemic excursions. Diabetes Technol Ther, 2011, 13(3): 319-325.
9. Costa M D, Henriques T, Munshi M N, et al. Dynamical glucometry: use of multiscale entropy analysis in diabetes. Chaos, 2014, 24(3): 033139.
10. Association A D. American diabetes association. diagnosis and classification of diabetes mellitus. diabetes care 29(suppl 1), S43-S48. Diabetes, 2009, 33(Suppl 1): S62-S69.
11. Cui Xingran, Abduljalil A, Manor B D, et al. Multi-scale glycemic variability: a Link to gray matter atrophy and cognitive decline in type 2 diabetes. PLoS One, 2014, 9(1): e86284.
12. Weissman A, Binah O. The fractal Nature of blood glucose fluctuations. J Diabetes Complications, 2014, 28(5): 646-651.
13. Churruca J, Vigil L, Luna E, et al. The route to diabetes: Loss of complexity in the glycemic profile from health through the metabolic syndrome to type 2 diabetes. Diabetes Metab Syndr Obes, 2008, 1: 3-11.
14. Costa M, Goldberger A L, Peng C K. Multiscale entropy analysis. Int J Numer Methods Eng, 2004, 59(9): 1147-1166.
15. Chen Jinlong, Chen Pinfan, Wang H M. 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.
16. Goldberger A L, Amaral L A, Hausdorff J M, et al. Fractal dynamics in physiology: alterations with disease and aging. Proc Natl Acad Sci U S A, 2002, 99(Suppl 1): 2466-2472.
17. Goldstein B, Fiser D H, Kelly M M, et al. Decomplexification in critical illness and injury: relationship between heart rate variability, severity of illness, and outcome. Crit Care Med, 1998, 26(2): 352-357.
18. van Hooijdonk R T, abu-Hanna Ameen, SCHULTZ M J. Glycemic variability is complex--is glucose complexity variable?. Crit Care, 2012, 16(6): 178.
19. Wu S D, Wu C W, Lin S G, et al. Analysis of complex time series using refined composite multiscale entropy. Phys Lett A, 2014, 378(20): 1369-1374.
20. Costa M, Peng C K, Goldberger A L, et al. Multiscale entropy analysis of human gait dynamics. Physica A: Statistical Mechanics and its Applications, 2003, 330(1/2): 53-60.
21. 周健, 贾伟平, 喻明, 等. 应用动态血糖监测系统评估不同糖耐量个体血糖稳定性的特征. 上海医学, 2008, 31(1): 10-13.
22. 贾伟平. 中国动态血糖监测临床应用指南(2012年版). 慢性病学杂志, 2013, 4(5): 321-330.
23. Li Qing, Li Li, Li Yong. Enhanced RBC aggregation in type 2 diabetes patients. J Clin Lab Anal, 2015, 29(5): 387-389.
24. Kohnert K D, Heinke P, Vogt L, et al. Utility of different glycemic control metrics for optimizing management of diabetes. World J Diabetes, 2015, 6(1): 17-29.
25. Rodbard D. New and improved methods to characterize glycemic variability using continuous glucose monitoring. Diabetes Technol Ther, 2009, 11(9): 551-565.
26. Wang Jing, Shang Pengjian, Xia Jianan, et al. EMD based refined composite multiscale entropy analysis of complex signals. Physica A: Statistical Mechanics and its Applications, 2015, 421(1): 583-593.
27. Peng C K, Costa M, Goldberger A L. Adaptive data analysis of complex fluctuation in physiological time series. Adv Adapt Data Anal, 2011, 1(01): 61-70.
28. Costa M, Goldberger A L, Peng C K. Multiscale entropy analysis of biological signals. Phys Rev E Stat Nonlin Soft Matter Phys, 2005, 71(2 Pt 1): 021906.

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