The relationship between insulin and glucagon concentrations in non-diabetic humans

Abnormal postprandial suppression of glucagon in Type 2 diabetes (T2DM) has been attributed to impaired insulin secretion. Prior work suggests that insulin and glucagon show an inverse coordinated relationship. However, dysregulation of α-cell function in prediabetes occurs early and independently of changes in β-cells, which suggests insulin having a less significant role on glucagon control. We therefore, sought to examine whether hepatic vein hormone concentrations provide evidence to further support the modulation of glucagon secretion by insulin. As part of a series of experiments to measure the effect of diabetes-associated genetic variation in TCF7L2 on islet cell function, hepatic vein insulin and glucagon concentrations were measured at 2-minute intervals during fasting and a hyperglycemic clamp. The experiment was performed on 29 nondiabetic subjects (age = 46 ± 2 years, BMI 28 ± 1 Kg/m2 ) and enabled post-hoc analysis, using Cross-Correlation and Cross-Approximate Entropy (Cross-ApEn) to evaluate the interaction of insulin and glucose. Mean insulin concentrations rose from fasting (33 ± 4 vs. 146 ± 12 pmol/L, p < 0.01) while glucagon was suppressed (96 ± 8 vs. 62 ± 5 ng/L, p < 0.01) during the clamp. Cross-ApEn was used to measure pattern reproducibility in the two hormones using glucagon as control mechanism (0.78 ± 0.03 vs. 0.76 ± 0.03, fasting vs. hyperglycemia) and using insulin as a control mechanism (0.78 ± 0.02 vs. 0.76 ± 0.03, fasting vs. hyperglycemia). Values did not differ between the two scenarios. Cross-correlation analysis demonstrated a small in-phase coordination between insulin and glucagon concentrations during fasting, which inverted during hyperglycemia. This data suggests that the interaction between the two hormones is not driven by either. On a minute-to-minute basis, direct control and secretion of glucagon is not mediated (or restrained) by insulin.

Monitoring the Evolution of Asynchrony between Mean Arterial Pressure and Mean Cerebral Blood Flow via Cross-Entropy Methods

Cerebrovascular control is carried out by multiple nonlinear mechanisms imposing a certain degree of coupling between mean arterial pressure (MAP) and mean cerebral blood flow (MCBF). We explored the ability of two nonlinear tools in the information domain, namely cross-approximate entropy (CApEn) and cross-sample entropy (CSampEn), to assess the degree of asynchrony between the spontaneous fluctuations of MAP and MCBF. CApEn and CSampEn were computed as a function of the translation time. The analysis was carried out in 23 subjects undergoing recordings at rest in supine position (REST) and during active standing (STAND), before and after surgical aortic valve replacement (SAVR). We found that at REST the degree of asynchrony raised, and the rate of increase in asynchrony with the translation time decreased after SAVR. These results are likely the consequence of the limited variability of MAP observed after surgery at REST, more than the consequence of a modified cerebrovascular control, given that the observed differences disappeared during STAND. CApEn and CSampEn can be utilized fruitfully in the context of the evaluation of cerebrovascular control via the noninvasive acquisition of the spontaneous MAP and MCBF variability.

Interrelationships Between Pituitary Hormones as Assessed From 24-hour Serum Concentrations in Healthy Older Subjects

CONTEXT

Hormones of the hypothalamic-pituitary-target gland axes are mostly investigated separately, whereas the interplay between hormones might be as important as each separate hormonal axis.

OBJECTIVE

Our aim is to determine the interrelationships between GH, TSH, ACTH, and cortisol in healthy older individuals.

DESIGN

We made use of 24-hour hormone serum concentrations assessed with intervals of 10 minutes from 38 healthy older individuals with a mean age (SD) of 65.1 (5.1) years from the Leiden Longevity Study. Cross-correlation analyses were performed to assess the relative strength between 2 24-hour hormone serum concentration series for all possible time shifts. Cross-approximate entropy was used to assess pattern synchronicity between 2 24-hour hormone serum concentration series.

RESULTS

Within an interlinked hormonal axis, ACTH and cortisol were positively correlated with a mean (95% confidence interval) correlation coefficient of 0.78 (0.74-0.81) with cortisol following ACTH concentrations with a delay of 10 minutes. Between different hormonal axes, we observed a negative correlation coefficient between cortisol and TSH of -0.30 (-0.36 to -0.25) with TSH following cortisol concentrations with a delay of 170 minutes. Furthermore, a positive mean (95% confidence interval) correlation coefficient of 0.29 (0.22-0.37) was found between TSH and GH concentrations without any delay. Moreover, cross-approximate entropy analyses showed that GH and cortisol exhibit synchronous serum concentration patterns.

CONCLUSIONS

This study demonstrates that interrelations between hormones from interlinked as well as different hypothalamic-pituitary-target gland axes are observed in healthy older individuals. More research is needed to determine the biological meaning and clinical consequences of these observations.

(Multiscale) Cross-Entropy Methods: A Review

Cross-entropy was introduced in 1996 to quantify the degree of asynchronism between two time series. In 2009, a multiscale cross-entropy measure was proposed to analyze the dynamical characteristics of the coupling behavior between two sequences on multiple scales. Since their introductions, many improvements and other methods have been developed. In this review we offer a state-of-the-art on cross-entropy measures and their multiscale approaches.

Combination of R-R Interval and Crest Time in Assessing Complexity Using Multiscale Cross-Approximate Entropy in Normal and Diabetic Subjects

The present study aimed at testing the hypothesis that application of multiscale cross-approximate entropy (MCAE) analysis in the study of nonlinear coupling behavior of two synchronized time series of different natures [i.e., R-R interval (RRI) and crest time (CT, the time interval from foot to peakof a pulse wave)] could yield information on complexity related to diabetes-associated vascular changes. Signals of a single waveform parameter (i.e., CT) from photoplethysmography and RRI from electrocardiogram were simultaneously acquired within a period of one thousand cardiac cycles for the computation of different multiscale entropy indices from healthy young adults (n = 22) (Group 1), upper-middle aged non-diabetic subjects (n = 34) (Group 2) and diabetic patients (n = 34) (Group 3). The demographic (i.e., age), anthropometric (i.e., body height, body weight, waist circumference, body-mass index), hemodynamic (i.e., systolic and diastolic blood pressures), and serum biochemical (i.e., high- and low-density lipoprotein cholesterol, total cholesterol, and triglyceride) parameters were compared with different multiscale entropy indices including small- and large-scale multiscale entropy indices for CT and RRI [MEI_SS(CT), MEI_LS(CT), MEI_SS(RRI), MEI_LS(RRI), respectively] as well as small- and large-scale multiscale cross-approximate entropy indices [MCEI_SS, MCEI_LS, respectively]. The results demonstrated that both MEI_LS(RRI) and MCEI_LS significantly differentiated between Group 2 and Group 3 (all p < 0.017). Multivariate linear regression analysis showed significant associations of MEI_LS(RRI) and MCEI_LS(RRI,CT) with age and glycated hemoglobin level (all p < 0.017). The findings highlight the successful application of a novel multiscale cross-approximate entropy index in non-invasively identifying diabetes-associated subtle changes in vascular functional integrity, which is of clinical importance in preventive medicine.