What Can Biosignal Entropy Tell Us About Health and Disease? Applications in Some Clinical Fields.

Many physiological systems are paradigmatic examples of complex networks, displaying behaviors best studied by means of tools derived from nonlinear dynamics and fractal geometry. Furthermore, while conventional wisdom considers health as an 'orderly' situation (and diseases are often called 'disorders'), truth is that health is characterized by a remarkable (pseudo)-randomness, and the loss of this pseudo-randomness (i.e., the 'decomplex-ification' of the system's output) is one of the earliest signs of the system's dysfunction. The potential clinical uses of this information are evident. However, the instruments used to assess complexity are still under debate, and these tools are just beginning to find their place at the bedside. We present a brief overview of the potential uses of complexity analysis in several areas of clinical medicine. We comment on the metrics most frequently used, and we review specifically their application on certain neurologic diseases, aging, diabetes, febrile diseases and the critically ill patient.

Glucose series complexity at the threshold of diabetes.

BACKGROUND: One of the earliest signs of dysfunction in a complex system is the simplification of its output. A well-accepted method to measure this phenomenon is detrended fluctuation analysis (DFA). Herein, we evaluated the usefulness of DFA at the threshold of type 2 diabetes mellitus (T2DM). METHODS: We report on the clinical and glucometric characteristics of a sample of 103 patients at increased risk of developing T2DM. All patients had HbA1c levels 5%-6.4% and met at least one of the following criteria: body mass index (BMI) > 30 kg/m2, essential hypertension or a first-degree relative with T2DM. For each patient, a 24-h glucose time series was obtained, and the clinical and glucometric variables were compared. RESULTS: There was a significant correlation between the number of National Cholesterol Education Program--Adult Treatment Panel (ATP III) metabolic syndrome (MS)-defining criteria and DFA (ρ = 0.231, P = 0.019), and DFA differed significantly between patients meeting or not the ATP III definition of MS (1.443 vs. 1.399, respectively; P = 0.018). The DFA was not correlated with HbA1c. Depending on how it was calculated, the area under the log(Fn)∼log(n) curve correlated with HbA1c levels or the number of MS criteria. Conventional variability metrics (mean amplitude of glycemic excursions) did not differ between patients complying or not with the definition of MS. CONCLUSIONS: Complexity analysis is capable of detecting differences in variables related to the risk of developing T2DM and could be a useful tool to study the initial phases of glucoregulatory dysfunction leading to T2DM.

Glucose series complexity in hypertensive patients.

Nonlinear methods have been applied to the analysis of biological signals. Complexity analysis of glucose time series may be a useful tool for the study of the initial phases of glucoregulatory dysfunction. This observational, cross-sectional study was performed in patients with essential hypertension. Glucose complexity was measured with detrended fluctuation analysis (DFA), and glucose variability was measured by the mean amplitudes of glycemic excursion (MAGE). We included 91 patients with a mean age of 59 ± 10 years. We found significant correlations for the number of metabolic syndrome (MS)-defining criteria with DFA (r = 0.233, P = .026) and MAGE (r = 0.396, P < .0001). DFA differed significantly between patients who complied with MS and those who did not (1.44 vs. 1.39, P = .018). The MAGE (f = 5.3, P = .006), diastolic blood pressures (f = 4.1, P = .018), and homeostasis model assessment indices (f = 4.2, P = .018) differed between the DFA tertiles. Multivariate analysis revealed that the only independent determinants of the DFA values were MAGE (ß coefficient = 0.002, 95% confidence interval: 0.001-0.004, P = .001) and abdominal circumference (ß coefficient = 0.002, 95% confidence interval: 0.000015-0.004, P = .048). In our population, DFA was associated with MS and a number of MS criteria. Complexity analysis seemed to be capable of detecting differences in variables that are arguably related to the risk of the development of type 2 diabetes.