Long-range Correlated Glucose Fluctuations in Diabetes

H. Ogata; K. Tokuyama; S. Nagasaka; A. Ando; I. Kusaka; N. Sato; A. Goto; S. Ishibashi; K. Kiyono; Z. R. Struzik; Y. Yamamoto

doi:10.1055/s-0038-1625411

Methods of Information in Medicine, Table of Contents

Methods Inf Med 2007; 46(02): 222-226
DOI: 10.1055/s-0038-1625411

Original Article

Schattauer GmbH

Long-range Correlated Glucose Fluctuations in Diabetes

Authors

H. Ogata

¹Graduate School of Comprehensive Human Sciences, University of Tsukuba, Japan
K. Tokuyama

¹Graduate School of Comprehensive Human Sciences, University of Tsukuba, Japan
S. Nagasaka

²Division of Endocrinology and Metabolism, Department of Medicine, Jichi Medical School, Japan
A. Ando

²Division of Endocrinology and Metabolism, Department of Medicine, Jichi Medical School, Japan
I. Kusaka

²Division of Endocrinology and Metabolism, Department of Medicine, Jichi Medical School, Japan
N. Sato

²Division of Endocrinology and Metabolism, Department of Medicine, Jichi Medical School, Japan
A. Goto

²Division of Endocrinology and Metabolism, Department of Medicine, Jichi Medical School, Japan
S. Ishibashi

²Division of Endocrinology and Metabolism, Department of Medicine, Jichi Medical School, Japan
K. Kiyono

³Educational Physiology Laboratory, Graduate School of Education, The University of Tokyo, Japan
Z. R. Struzik

³Educational Physiology Laboratory, Graduate School of Education, The University of Tokyo, Japan
Y. Yamamoto

³Educational Physiology Laboratory, Graduate School of Education, The University of Tokyo, Japan

Abstract

Summary

Objectives : Our objective is to investigate diabetes- related alteration of glucose control in diurnal fluctuations in normal daily life by detrended fluctuation analysis (DFA).

Methods : The fluctuations of glucose of 12 non-diabetic subjects and 15 diabetic patients were measured using a continuous glucose monitoring system (CGMS) over a period of one day. The glucose data was calculated by the DFA method, which is capable of revealing the presence of long-range correlations in time series with inherent non-stationarity.

Results : Compared with the non-diabetic subjects, the mean glucose level and the standard deviation are significantly higher in the diabetic group.

The DFA exponent α is calculated, and glucose time series are searched for the presence of negatively (0.5 < α <1.5) or positively (1.5 < α) correlated fluctuations. A crossover phenomenon, i.e. a change in the level of correlations, is observed in the non-diabetic subjects at about two hours; the net effects of glucose flux/reflux causing temporal changes in glucose concentration are negatively correlated in a “long-range" (> two hours) regime. However, for diabetic patients, the DFA exponent α = 1.65 ± 0.30, and in the same regime positively correlated fluctuations are observed, suggesting that the net effects of the flux and reflux persist for many hours.

Conclusions : Such long-range positive correlation in glucose homeostasis may reflect pathogenic mechanisms of diabetes, i.e., the lack of the tight control in blood glucose regulation. Using modern time series analysis methods such as DFA, continuous evaluation of glucose dynamics could promote better diagnoses and prognoses of diabetes and a better understanding of the fundamental mechanism of glucose dysregulation in diabetes.

Keywords

Diabetes mellitus - continuous glucose monitoring system - detrended fluctuation analysis - positive/negative correlation

Full Text

References

References
1. Cauter EV, Polonsky KS, Scheen AJ. Roles of circadian rhythmicity and sleep in human glucose regulation. Endocrine Rev 1997; 18: 716-738.
2. Picardi A, Pozzilli P. Dynamic tests in the clinical management of diabetes. J Endocrinol Invest 2003; 26: 99-106.
3. Leibiger IB, Leibiger B, Berggren PO. Insulin feedback action on pancreatic B-cell function. FEBSLett 2002; 532: 1-6.
4. Tony KT, Gutierrez-Juarez LR, Pocai A, Rossetti L. Regulation of blood glucose by hypothalamic pyruvate metabolism. Science 2005; 3: 943-947
5. Peng CK, Havlin S, Stanley HE, Goldberger AL. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos 1995; 5: 82-87.
6. Rebrin K, Steil GM. Can interstitial glucose assessment replace blood glucose measurements?. Diabetes Technology & Therapeutics 2000; 2: 461-472.
7. Koschinsky T, Heinemann L. Sensors for glucose monitoring: technical and clinical aspects. Diabetes Metab Res Rev 2001; 17: 113-123.
8. Pizziol A, Tikhonoff V, Paleari CD, Russo E, Mazza A, Ginocchio G, Onesto C, Pavan L, Casiglia E, Pessina AC. Effects of caffeine on glucose tolerance: A placebo-controlled study. Eur J Clin Nutr 1998; 52: 846-849.
9. Johnston KL, Clifford MN, Morgan LM. Coffee acutely modifies gastrointestinal hormone secretion and glucose tolerance in humans: glycemic effects of chlorogenic acid and caffeine. Am J Clin Nutr 2003; 78: 728-733.
10. Akaike H. Information theory and an extension of the maximum likelihood principle. Budapest: Akademiai Kiado; 1973: 267-281.
11. Taqqu MS, Teverovsky V, Willinger W. Estimators for long-range dependence: an empirical study. Fractals 1995; 3: 785-798.
12. Gough DA, Kreutz-Delgado K, Bremer TM. Frequency characterization of blood glucose dynamics. Ann Biomed Eng 2003; 31: 91-97.
13. The ExpertCommittee on the Diagnosis and Classification of Diabetes Mellitus.. Report of the expert committee on the diagnosis and classification of diabetes mellitus. Diabetes Care 1997; 20: 1183-1197.
14. World Health Organization diagnosis and classification of diabetes mellitus and its complications.. In a report of a WHO Consultation. Part 1, Diagnosis and Classification of Diabetes Mellitus. World Health Organization, Geneva; 1999.
15. Merl V, Peters A, Oltmanns KM, Kern W, Hubold C, Hallschmid M, Born J, Fehm HL, Schultes B. Preserved circadian rhythm of serum insulin concentration at low plasma glucose during fasting in lean and overweight humans. Metabolism 2004; 53: 1449-1453.
16. Hu K, Ivanov PC, Hilton MF, Chen Z, Ayers RT, Stanley HE, Shea SA. Endogenous circadian rhythm in an index of cardiac vulnerability independent of changes in behavior. PNAS 2004; 101: 18223-18227.
17. Rebrin K, Steil GM, Van Antwerp WP, Mastrototaro JJ. Subcutaneous glucose predicts plasma glucose independent of insulin: implications for continuous monitoring. Am J Physiol 1999; 277: E561-E571.
18. Bantle JP, Thomas W. Glucose measurement in patients with diabetes mellitus with dermal interstitial fluid. Lab Clin Med 1997; 130: 436-441.
19. Peng CK, Mietus J, Hausdorff JM, Havlin S, Stanley HE, Goldberger AL. Long-range anticorrelations and non-Gaussian behavior of the heartbeat. Phys Rev Lett 1993; 70: 1343-1346.
20. Yamamoto Y, Hughson RL. On the fractal nature of heart rate variability in humans: effects of data length and ß-adrenergic blockade. Am J Physiol 1994; 266: R40-R49.
21. Yamamoto Y, Nakamura Y, Sato H, Yamamoto M, Kato K, Hughson RL. On the fractal nature of heart rate variability in humans: effects of vagal blockade. Am J Physiol 1995; 269: R830-R837.
22. Amaral LAN, Ivanov PCh, Aoyagi N, Hidaka I, Tomono S, Goldberger AL, Stanley HE, Yamamoto Y. Behavioral-independent features of complex heartbeat dynamics. Phys Rev Lett 2001; 86: 6026-6029.