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 alpha is calculated, and glucose time series are searched for the presence of negatively (0.5 < alpha < 1.5) or positively (1.5 < alpha) 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 alpha = 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.
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 diabeticpatients 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 alpha is calculated, and glucose time series are searched for the presence of negatively (0.5 < alpha < 1.5) or positively (1.5 < alpha) 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 diabeticpatients, the DFA exponent alpha = 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.
Authors: David Cuesta-Frau; Daniel Novák; Vacláv Burda; Antonio Molina-Picó; Borja Vargas; Milos Mraz; Petra Kavalkova; Marek Benes; Martin Haluzik Journal: Entropy (Basel) Date: 2018-11-12 Impact factor: 2.524
Authors: Manuel Varela; Luis Vigil; Carmen Rodriguez; Borja Vargas; Rafael García-Carretero Journal: J Diabetes Res Date: 2016-05-16 Impact factor: 4.011
Authors: Carmen Rodríguez de Castro; Luis Vigil; Borja Vargas; Emilio García Delgado; Rafael García Carretero; Julián Ruiz-Galiana; Manuel Varela Journal: Diabetes Metab Res Rev Date: 2016-06-30 Impact factor: 4.876