Delay in the Detrended Fluctuation Analysis Crossover Point as a Risk Factor for Type 2 Diabetes Mellitus

Abstract

Detrended Fluctuation Analysis (DFA) measures the complexity of a glucose time series obtained by means of a Continuous Glucose Monitoring System (CGMS) and has proven to be a sensitive marker of glucoregulatory dysfunction. Furthermore, some authors have observed a crossover point in the DFA, signalling a change of dynamics, arguably dependent on the beta-insular function. We investigate whether the characteristics of this crossover point have any influence on the risk of developing type 2 diabetes mellitus (T2DM). To this end we recruited 206 patients at increased risk of T2DM (because of obesity, essential hypertension, or a first-degree relative with T2DM). A CGMS time series was obtained, from which the DFA and the crossover point were calculated. Patients were then followed up every 6 months for a mean of 17.5 months, controlling for the appearance of T2DM diagnostic criteria. The time to crossover point was a significant predictor risk of developing T2DM, even after adjusting for other variables. The angle of the crossover was not predictive by itself but became significantly protective when the model also considered the crossover point. In summary, both a delay and a blunting of the crossover point predict the development of T2DM.

Decreased complexity of glucose dynamics preceding the onset of diabetes in mice and rats

“…In an observational study of 37 non-diabetic volunteers age 12–65 years and 49 adults with longstanding type 1 diabetes, multivariate analysis of CGM data revealed low complexity of CGM profiles associated with insulin resistance in both non-diabetic subjects and patients with type 1 diabetes [ 22 ]. In non-diabetic subjects, low complexity could be an earlier marker of glucose regulation failure [ 15 , 16 , 22 ].…”

“…Larger sample entropy values over multiple time scales indicate greater complexity. Detrended fluctuation analysis of 206 patients with essential hypertension, obesity or having a first-degree relative with a diagnosis of diabetes suggested a prognostic value of such analysis for predicting the development of type 2 diabetes [ 15 , 16 ].…”

Application of an Improved Correlation Method in Electrostatic Gait Recognition of Hemiparetic Patients

“…In order to quantitatively analyze the correlation of non-stationary signals, Zebende proposed a DCCA cross-correlation coefficient method based on DFA and DCCA [28]. However, we found that when this method was used to calculate electrostatic gait signals, the results would fluctuate abnormally near the crossover point [29,30]. Further, the traditional DCCA cross-correlation coefficient spectrum of electrostatic gait signals of the same type of subjects has random and incomplete similarity, so it was difficult to distinguish hemiplegic patients from healthy control subjects by the DCCA cross-correlation coefficient.…”