Ultradian oscillations of plasma glucose, insulin, and C-peptide in man during continuous enteral nutrition

Circadian clock communication during homeostasis and ageing

Maintaining homeostasis is essential for continued health, and the progressive decay of homeostatic processes is a hallmark of ageing. Daily environmental rhythms threaten homeostasis, and circadian clocks have evolved to execute physiological processes in a manner that anticipates, and thus mitigates, their effects on the organism. Clocks are active in almost all cell types; their rhythmicity and functional output are determined by a combination of tissue-intrinsic and systemic inputs. Numerous inputs for a specific tissue are produced by the activity of circadian clocks of other tissues or cell types, generating a form of crosstalk known as clock communication. In mammals, the central clock in the hypothalamus integrates signals from external light-dark cycles to align peripheral clocks elsewhere in the body. This regulation is complemented by a tissue-specific milieu of external, systemic and niche inputs that modulate and cooperate with the cellular circadian clock machinery of a tissue to tailor its functional output. These mechanisms of clock communication decay during ageing, and growing evidence suggests that this decline might drive ageing-related morbidities. Dietary, behavioural and pharmacological interventions may offer the possibility to overcome these changes and in turn improve healthspan.

Influence of circadian phase and extended wakefulness on glucose levels during forced desynchrony

OBJECTIVES

Circadian misalignment and sleep deprivation often occur in tandem, and both negatively impact glucose homeostasis and metabolic health. The present study employed a forced desynchrony protocol to examine the influence of extended wakefulness and circadian misalignment on hourly glucose levels.

METHODS

Nine healthy adults (4F/5M; 26 ± 4years) completed a 31-day in-laboratory protocol. After three 24 hour baseline days with 8 hours scheduled sleep opportunities, participants were scheduled to 14 consecutive 42.85 hour sleep-wake cycles, with 28.57 hours extended wakefulness and 14.28 hours sleep opportunities each cycle. Blood was sampled hourly across the forced desynchrony and over 600 plasma samples per participant were analyzed for glucose levels.

RESULTS

Both hours into the 42.85 hours forced desynchrony day and circadian phase modulated glucose levels (p < .0001). Glucose peaked after each meal during scheduled wakefulness and decreased during scheduled sleep/fasting. Glucose levels were, on average, lowest during the biological daytime and rose throughout the biological night, peaking in the biological morning. When analyzed separately for scheduled sleep vs. wakefulness, the peak timing of the circadian rhythm in glucose was later during sleep (p < .05). Glucose area under the curve levels increased rapidly from the beginning of the forced desynchrony protocol and were highest on the second forced desynchrony day (p < .01), returning towards forced desynchrony day 1 levels thereafter.

CONCLUSIONS

These findings have important implications for understanding factors contributing to altered glucose metabolism during sleep loss and circadian misalignment, and for potential physiological adaptation of metabolism in healthy adults, who are increasingly exposed to such conditions in our society.

Night fasting as an alternative to improve nutritional support and glycaemic control in hospitalised patients with exclusive enteral nutrition

BACKGROUND

Enteral nutrition (EN) assists in the nutritional status of hospitalised patients unable to feed orally. The aim of this study was to determine which method-continuous EN or discontinuous EN, a diet in which the infusion is discontinued for 4h during the night,-is more effective in meeting nutrient recommendations and improving glycaemic control and biochemical parameters related to protein anabolism.

METHODS

Patients were divided into two groups: discontinuous (EN administered in mL/h, 18h/day, 4-h night fasting) and continuous (EN administered in mL/h, 22h/day). All patients with EN receive the diet over a 22-h daily period, in which the diet is suspended for two hours/day for daily hospital routines such as bathing, and physiotherapy, and followed for seven days. Evaluated data: prescribed and administered volume, calories, protein, and fibre; capillary blood glucose; erythrogram; serum albumin.

RESULTS

52 patients were followed-up, with 23 (44.2%) in the discontinuous group and 29 (55.8%) in the continuous group. Compared with the continuous group, the discontinuous group received volumes closer to those prescribed, equal or higher calories, and more protein. The capillary glucose values were within the reference range in the discontinuous group, while the continuous group presented elevated values. Both groups presented hypoalbuminaemia, haemoglobin, and haematocrit below the reference values; however, in the discontinuous group, the serum albumin values improved during hospitalisation relative to the continuous.

CONCLUSIONS

The method involving discontinuation of EN for 4h was more effective in meeting nutrient recommendations compared with the continuous method. Additionally, in the discontinuous group, we observed a better control of glycaemia when compared to that of the continuous group.

Co-expression of diurnal and ultradian rhythms in the plasma metabolome of common voles (Microtus arvalis)

Metabolic rhythms include rapid, ultradian (hourly) dynamics, but it remains unclear what their relationship to circadian metabolic rhythms is, and what role meal timing plays in coordinating these ultradian rhythms in metabolism. Here, we characterized widespread ultradian rhythms under ad libitum feeding conditions in the plasma metabolome of the vole, the gold standard animal model for behavioral ultradian rhythms, naturally expressing ~2-h foraging rhythms throughout the day and night. These ultradian metabolite rhythms co-expressed with diurnal 24-h rhythms in the same metabolites and did not align with food intake patterns. Specifically, under light-dark entrained conditions we showed twice daily entrainment of phase and period of ultradian behavioral rhythms associated with phase adjustment of the ultradian cycle around the light-dark and dark-light transitions. These ultradian activity patterns also drove an ultradian feeding pattern. We used a unique approach to map this behavioral activity/feeding status to high temporal resolution (every 90 min) measures of plasma metabolite profiles across the 24-h light-dark cycle. A total of 148 known metabolites were detected in vole plasma. Supervised, discriminant analysis did not group metabolite concentration by feeding status, instead, unsupervised clustering of metabolite time courses revealed clusters of metabolites that exhibited significant ultradian rhythms with periods different from the feeding cycle. Two clusters with dissimilar ultradian dynamics, one lipid-enriched (period = 3.4 h) and one amino acid-enriched (period = 4.1 h), both showed co-expression with diurnal cycles. A third cluster solely comprised of glycerophospholipids (specifically ether-linked phosphatidylcholines) expressed an 11.9 h ultradian rhythm without co-expressed diurnal rhythmicity. Our findings show coordinated co-expression of diurnal metabolic rhythms with rapid dynamics in feeding and metabolism. These findings reveal that ultradian rhythms are integral to biological timing of metabolic regulation, and will be important in interpreting the impact of circadian desynchrony and meal timing on metabolic rhythms.

Low and high carbohydrate isocaloric diets on performance, fat oxidation, glucose and cardiometabolic health in middle age males

High carbohydrate, low fat (HCLF) diets have been the predominant nutrition strategy for athletic performance, but recent evidence following multi-week habituation has challenged the superiority of HCLF over low carbohydrate, high fat (LCHF) diets, along with growing interest in the potential health and disease implications of dietary choice. Highly trained competitive middle-aged athletes underwent two 31-day isocaloric diets (HCLF or LCHF) in a randomized, counterbalanced, and crossover design while controlling calories and training load. Performance, body composition, substrate oxidation, cardiometabolic, and 31-day minute-by-minute glucose (CGM) biomarkers were assessed. We demonstrated: (i) equivalent high-intensity performance (@∼85%VO2max), fasting insulin, hsCRP, and HbA1c without significant body composition changes across groups; (ii) record high peak fat oxidation rates (LCHF:1.58 ± 0.33g/min @ 86.40 ± 6.24%VO2max; 30% subjects > 1.85 g/min); (iii) higher total, LDL, and HDL cholesterol on LCHF; (iv) reduced glucose mean/median and variability on LCHF. We also found that the 31-day mean glucose on HCLF predicted 31-day glucose reductions on LCHF, and the 31-day glucose reduction on LCHF predicted LCHF peak fat oxidation rates. Interestingly, 30% of athletes had 31-day mean, median and fasting glucose > 100 mg/dL on HCLF (range: 111.68-115.19 mg/dL; consistent with pre-diabetes), also had the largest glycemic and fat oxidation response to carbohydrate restriction. These results: (i) challenge whether higher carbohydrate intake is superior for athletic performance, even during shorter-duration, higher-intensity exercise; (ii) demonstrate that lower carbohydrate intake may be a therapeutic strategy to independently improve glycemic control, particularly in those at risk for diabetes; (iii) demonstrate a unique relationship between continuous glycemic parameters and systemic metabolism.

Multi-Timescale Rhythmicity of Blood Glucose and Insulin Delivery Reveals Key Advantages of Hybrid Closed Loop Therapy

BACKGROUND

Blood glucose and insulin exhibit coordinated daily and hourly rhythms in people without diabetes (nonT1D). Although the presence and stability of these rhythms are associated with euglycemia, it is unknown if they (1) are preserved in individuals with type 1 diabetes (T1D) and (2) vary by therapy type. In particular, Hybrid Closed Loop (HCL) systems improve glycemia in T1D compared to Sensor Augmented Pump (SAP) therapies, but the extent to which either recapitulates coupled glucose and insulin rhythmicity is not well described. In HCL systems, more rapid modulation of glucose via automated insulin delivery may result in greater rhythmic coordination and euglycemia. Such precision may not be possible in SAP systems. We hypothesized that HCL users would exhibit fewer hyperglycemic event, superior rhythmicity, and coordination relative to SAP users.

METHODS

Wavelet and coherence analyses were used to compare glucose and insulin delivery rate (IDR) within-day and daily rhythms, and their coordination, in 3 datasets: HCL (n = 150), SAP (n = 89), and nonT1D glucose (n = 16).

RESULTS

Glycemia, correlation between normalized glucose and IDR, daily coherence of glucose and IDR, and amplitude of glucose oscillations differed significantly between SAP and HCL users. Daily glucose rhythms differed significantly between SAP, but not HCL, users and nonT1D individuals.

CONCLUSIONS

SAP use is associated with greater hyperglycemia, higher amplitude glucose fluctuations, and a less stably coordinated rhythmic phenotype compared to HCL use. Improvements in glucose and IDR rhythmicity may contribute to the overall effectiveness of HCL systems.

Assessing Different Temporal Scales of Calcium Dynamics in Networks of Beta Cell Populations

Beta cells within the pancreatic islets of Langerhans respond to stimulation with coherent oscillations of membrane potential and intracellular calcium concentration that presumably drive the pulsatile exocytosis of insulin. Their rhythmic activity is multimodal, resulting from networked feedback interactions of various oscillatory subsystems, such as the glycolytic, mitochondrial, and electrical/calcium components. How these oscillatory modules interact and affect the collective cellular activity, which is a prerequisite for proper hormone release, is incompletely understood. In the present work, we combined advanced confocal Ca2+ imaging in fresh mouse pancreas tissue slices with time series analysis and network science approaches to unveil the glucose-dependent characteristics of different oscillatory components on both the intra- and inter-cellular level. Our results reveal an interrelationship between the metabolically driven low-frequency component and the electrically driven high-frequency component, with the latter exhibiting the highest bursting rates around the peaks of the slow component and the lowest around the nadirs. Moreover, the activity, as well as the average synchronicity of the fast component, considerably increased with increasing stimulatory glucose concentration, whereas the stimulation level did not affect any of these parameters in the slow component domain. Remarkably, in both dynamical components, the average correlation decreased similarly with intercellular distance, which implies that intercellular communication affects the synchronicity of both types of oscillations. To explore the intra-islet synchronization patterns in more detail, we constructed functional connectivity maps. The subsequent comparison of network characteristics of different oscillatory components showed more locally clustered and segregated networks of fast oscillatory activity, while the slow oscillations were more global, resulting in several long-range connections and a more cohesive structure. Besides the structural differences, we found a relatively weak relationship between the fast and slow network layer, which suggests that different synchronization mechanisms shape the collective cellular activity in islets, a finding which has to be kept in mind in future studies employing different oscillations for constructing networks.

Oscillations of C-peptide in the euglycemic clamp and their effect on the pharmacodynamic assessment of insulin preparations

C-peptide should be continuously suppressed. However, increased postdosing C-peptide is not an uncommon phenomenon in euglycemic clamp studies involving healthy participants. This study aimed to determine the extent to which the postdosing C-peptide increases from the baseline that could affect the accuracy of glucodynamics in euglycemic clamp studies involving healthy subjects. First, 10 healthy males underwent a 10-h euglycemic clamp without exogenous insulin administration to obtain a reference interval (RI) for the ratio of C-peptide after 0 min (CP_t ) to baseline C-peptide (CP₀ ). Then, the data from a pharmacokinetic and pharmacodynamic study of insulin aspart (IAsp) were analyzed, and 70 eligible clamps were grouped by CP_t /CP₀ : group A ([CP_t /CP₀ ]_max   > upper limit of RI), group B (1<[CP_t /CP₀ ]_max  ≤ upper limit of RI), and group C ([CP_t /CP₀ ]_max  ≤ 1). The differences in basal and clamped blood glucose, CP_t /CP₀ , and the pharmacokinetics and pharmacodynamics of IAsp were compared, and the relationship between elevated CP_t and the accuracy of pharmacodynamics was analyzed. The RI of CP_t /CP₀ was 22.7%-152.1%; 1.5 × baseline might be a ceiling for the increase in CP_t under stable conditions. The maximum glucose infusion rate (GIR) in group A tended to be higher than that in group B or C (P_{for trend}  = 0.033). The AUC_GIR,0-10h in groups A, B, and C was 1983 ± 650,1682 ± 454, and 1479 ± 440 mg/kg (P = 0.047), respectively, under comparable IAsp exposure. No intergroup difference was detected in clamped glucose, IAsp dose, or body mass index. In conclusion, postdosing C-peptide over 1.5× baseline indicates insufficient inhibition of endogenous insulin secretion, which could compromise the pharmacodynamics of insulin preparations.

Consistency of compact and extended models of glucose-insulin homeostasis: The role of variable pancreatic reserve

Published compact and extended models of the glucose-insulin physiologic control system are compared, in order to understand why a specific functional form of the compact model proved to be necessary for a satisfactory representation of acute perturbation experiments such as the Intra Venous Glucose Tolerance Test (IVGTT). A spectrum of IVGTT’s of virtual subjects ranging from normal to IFG to IGT to frank T2DM were simulated using an extended model incorporating the population-of-controllers paradigm originally hypothesized by Grodsky, and proven to be able to capture a wide array of experimental results from heterogeneous perturbation procedures. The simulated IVGTT’s were then fitted with the Single-Delay Model (SDM), a compact model with only six free parameters, previously shown to be very effective in delivering precise estimates of insulin sensitivity and secretion during an IVGTT. Comparison of the generating, extended-model parameter values with the obtained compact model estimates shows that the functional form of the nonlinear insulin-secretion term, empirically found to be necessary for the compact model to satisfactorily fit clinical observations, captures the pancreatic reserve level of the simulated virtual patients. This result supports the validity of the compact model as a meaningful analysis tool for the clinical assessment of insulin sensitivity.

Evidence for a Coupled Oscillator Model of Endocrine Ultradian Rhythms

Whereas long-period temporal structures in endocrine dynamics have been well studied, endocrine rhythms on the scale of hours are relatively unexplored. The study of these ultradian rhythms (URs) has remained nascent, in part, because a theoretical framework unifying ultradian patterns across systems has not been established. The present overview proposes a conceptual coupled oscillator network model of URs in which oscillating hormonal outputs, or nodes, are connected by edges representing the strength of node-node coupling. We propose that variable-strength coupling exists both within and across classic hormonal axes. Because coupled oscillators synchronize, such a model implies that changes across hormonal systems could be inferred by surveying accessible nodes in the network. This implication would at once simplify the study of URs and open new avenues of exploration into conditions affecting coupling. In support of this proposed framework, we review mammalian evidence for (1) URs of the gut-brain axis and the hypothalamo-pituitary-thyroid, -adrenal, and -gonadal axes, (2) UR coupling within and across these axes; and (3) the relation of these URs to body temperature. URs across these systems exhibit behavior broadly consistent with a coupled oscillator network, maintaining both consistent URs and coupling within and across axes. This model may aid the exploration of mammalian physiology at high temporal resolution and improve the understanding of endocrine system dynamics within individuals.

COEXISTENCE OF THREE OSCILLATORY MODES OF INSULIN SECRETION: MATHEMATICAL MODELING AND RELEVANCE TO GLUCOSE REGULATION

Insulin secretion in pancreatic [Formula: see text]-cells exhibits three oscillatory modes with distinct period ranges, called fast, slow, and ultradian modes. To unveil the mechanism underlying such oscillatory behaviors and their roles in blood glucose regulation, we propose a combined model for the glucose–insulin regulation system, incorporating both the cell-level insulin secretion mechanism and inter-organ interactions in the blood glucose regulation. Special emphasis is placed on the identification of the mechanism of the slow oscillation and its role associated with the whole-body glucose regulation. Via extensive numerical simulations, we obtain macroscopic behaviors of the three types of insulin/glucose oscillations in the whole-body as well as microscopic behaviors of the membrane potential and the calcium concentration in the [Formula: see text]-cell. Finally, optimal regulatory strategies for the blood glucose level are discussed on the basis of the quantitative information obtained from the mathematical modeling and numerical simulations.

A Unifying Organ Model of Pancreatic Insulin Secretion

The secretion of insulin by the pancreas has been the object of much attention over the past several decades. Insulin is known to be secreted by pancreatic β-cells in response to hyperglycemia: its blood concentrations however exhibit both high-frequency (period approx. 10 minutes) and low-frequency oscillations (period approx. 1.5 hours). Furthermore, characteristic insulin secretory response to challenge maneuvers have been described, such as frequency entrainment upon sinusoidal glycemic stimulation; substantial insulin peaks following minimal glucose administration; progressively strengthened insulin secretion response after repeated administration of the same amount of glucose; insulin and glucose characteristic curves after Intra-Venous administration of glucose boli in healthy and pre-diabetic subjects as well as in Type 2 Diabetes Mellitus. Previous modeling of β-cell physiology has been mainly directed to the intracellular chain of events giving rise to single-cell or cell-cluster hormone release oscillations, but the large size, long period and complex morphology of the diverse responses to whole-body glucose stimuli has not yet been coherently explained. Starting with the seminal work of Grodsky it was hypothesized that the population of pancreatic β-cells, possibly functionally aggregated in islets of Langerhans, could be viewed as a set of independent, similar, but not identical controllers (firing units) with distributed functional parameters. The present work shows how a single model based on a population of independent islet controllers can reproduce very closely a diverse array of actually observed experimental results, with the same set of working parameters. The model's success in reproducing a diverse array of experiments implies that, in order to understand the macroscopic behaviour of the endocrine pancreas in regulating glycemia, there is no need to hypothesize intrapancreatic pacemakers, influences between different islets of Langerhans, glycolitic-induced oscillations or β-cell sensitivity to the rate of change of glycemia.

Determination of personalized diabetes treatment plans using a two-delay model

Diabetes cases worldwide have risen steadily over the past few decades, lending urgency to the search for more efficient, effective, and personalized ways to treat the disease. Current treatment strategies, however, may fail to maintain oscillations in blood glucose concentration that naturally occur multiple times per day, an important element of normal human physiology. Building upon recent successes in mathematical modeling of the human glucose-insulin system, we show that both food intake and insulin therapy likely demand increasingly precise control over insulin sensitivity if oscillations at a healthy average glucose concentration are to be maintained. We then model and describe personalized treatment options for patients with diabetes that maintain these oscillations. We predict that for a person with type II diabetes, both blood glucose levels can be controlled and healthy oscillations maintained when the patient gets an hour of daily exercise and is placed on a combination of Metformin and sulfonylurea drugs. We note that insulin therapy and an additional hour of exercise will reduce the patient׳s need for sulfonylureas. Results of a modeling analysis suggest that, with constant nutrition and controlled exercise, the blood glucose levels of a person with type I diabetes can be properly controlled with insulin infusion between 0.45 and 0.7μU/mlmin. Lastly, we note that all suggested strategies rely on existing clinical techniques and established treatment measures, and so could potentially be of immediate use in the design of an artificial pancreas.

Mathematical model for glucose regulation in the whole-body system

The human body needs continuous and stable glucose supply for maintaining its biological functions. Stable glucose supply comes from the homeostatic regulation of the blood glucose level, which is controlled by various glucose consuming or producing organs. Therefore, it is important to understand the whole-body glucose regulation mechanism. In this article, we describe various mathematical models proposed for glucose regulation in the human body, and discuss the difficulty and limitation in reproducing real processes of glucose regulation.

A composite computational model of liver glucose homeostasis. II. Exploring system behaviour

Using a composite model of the glucose homeostasis system, consisting of seven interconnected submodels, we enumerate the possible behaviours of the model in response to variation of liver insulin sensitivity and dietary glucose variability. The model can reproduce published experimental manipulations of the glucose homeostasis system and clearly illustrates several important properties of glucose homeostasis-boundedness in model parameters of the region of efficient homeostasis, existence of an insulin sensitivity that allows effective homeostatic control and the importance of transient and oscillatory behaviour in characterizing homeostatic failure. Bifurcation analysis shows that the appearance of a stable limit cycle can be identified.

Sleep and metabolic function

Evidence for the role of sleep on metabolic and endocrine function has been reported more than four decades ago. In the past 30 years, the prevalence of obesity and diabetes has greatly increased in industrialized countries, and self-imposed sleep curtailment, now very common, is starting to be recognized as a contributing factor, alongside with increased caloric intake and decreased physical activity. Furthermore, obstructive sleep apnea, a chronic condition characterized by recurrent upper airway obstruction leading to intermittent hypoxemia and sleep fragmentation, has also become highly prevalent as a consequence of the epidemic of obesity and has been shown to contribute, in a vicious circle, to the metabolic disturbances observed in obese patients. In this article, we summarize the current data supporting the role of sleep in the regulation of glucose homeostasis and the hormones involved in the regulation of appetite. We also review the results of the epidemiologic and laboratory studies that investigated the impact of sleep duration and quality on the risk of developing diabetes and obesity, as well as the mechanisms underlying this increased risk. Finally, we discuss how obstructive sleep apnea affects glucose metabolism and the beneficial impact of its treatment, the continuous positive airway pressure. In conclusion, the data available in the literature highlight the importance of getting enough good sleep for metabolic health.

A composite computational model of liver glucose homeostasis. I. Building the composite model

A computational model of the glucagon/insulin-driven liver glucohomeostasis function, focusing on the buffering of glucose into glycogen, has been developed. The model exemplifies an 'engineering' approach to modelling in systems biology, and was produced by linking together seven component models of separate aspects of the physiology. The component models use a variety of modelling paradigms and degrees of simplification. Model parameters were determined by an iterative hybrid of fitting to high-scale physiological data, and determination from small-scale in vitro experiments or molecular biological techniques. The component models were not originally designed for inclusion within such a composite model, but were integrated, with modification, using our published modelling software and computational frameworks. This approach facilitates the development of large and complex composite models, although, inevitably, some compromises must be made when composing the individual models. Composite models of this form have not previously been demonstrated.

"Smart" continuous glucose monitoring sensors: on-line signal processing issues

The availability of continuous glucose monitoring (CGM) sensors allows development of new strategies for the treatment of diabetes. In particular, from an on-line perspective, CGM sensors can become “smart” by providing them with algorithms able to generate alerts when glucose concentration is predicted to exceed the normal range thresholds. To do so, at least four important aspects have to be considered and dealt with on-line. First, the CGM data must be accurately calibrated. Then, CGM data need to be filtered in order to enhance their signal-to-noise ratio (SNR). Thirdly, predictions of future glucose concentration should be generated with suitable modeling methodologies. Finally, generation of alerts should be done by minimizing the risk of detecting false and missing true events. For these four challenges, several techniques, with various degrees of sophistication, have been proposed in the literature and are critically reviewed in this paper.

Modeling of oscillatory bursting activity of pancreatic beta-cells under regulated glucose stimulation

A mathematical model to describe the oscillatory bursting activity of pancreatic beta-cells is combined with a model of glucose regulation system in this work to study the bursting pattern under regulated extracellular glucose stimulation. The bursting electrical activity in beta-cells is crucial for the release of insulin, which acts to regulate the blood glucose level. Different types of bursting pattern have been observed experimentally in glucose-stimulated islets both in vivo and in vitro, and the variations in these patterns have been linked to changes in glucose level. The combined model in this study enables us to have a deeper understanding on the regime change of bursting pattern when glucose level changes due to hormonal regulation, especially in the postprandial state. This is especially important as the oscillatory components of electrical activity play significant physiological roles in insulin secretion and some components have been found to be lost in type 2 diabetic patients.

A molecular mathematical model of glucose mobilization and uptake

A new molecular mathematical model is developed by considering the kinetics of GLUT2, GLUT3, and GLUT4, the process of glucose mobilization by glycogen phosphorylase and glycogen synthase in liver, and the dynamics of the insulin signaling pathway. The new model can qualitatively reproduce the experimental glucose and insulin data. It also enables us to use the Bendixson criterion about the existence of periodic orbits of a two-dimensional dynamical system to mathematically predict that the oscillations of glucose and insulin are not caused by liver, instead they would be caused by the mechanism of insulin secretion from pancreatic beta cells. Furthermore it enables us to conduct a parametric sensitivity analysis. The analysis shows that both glucose and insulin are most sensitive to the rate constant for conversion of PI(3,4,5)P(3) to PI(4,5)P(2), the multiplicative factor modulating the rate constant for conversion of PI(3,4,5)P(3) to PI(4,5)P(2), the multiplicative factor that modulates insulin receptor dephosphorylation rate, and the maximum velocity of GLUT4. Moreover, the sensitivity analysis predicts that an increase of the apparent velocity of GLUT4, a combination of elevated mobilization rate of GLUT4 to the plasma membrane and an extended duration of GLUT4 on the plasma membrane, will result in a decrease in the needs of plasma insulin. On the other hand, an increase of the GLUT4 internalization rate results in an elevated demand of insulin to stimulate the mobilization of GLUT4 from the intracellular store to the plasma membrane.

Development of a neural network for prediction of glucose concentration in type 1 diabetes patients

BACKGROUND

A major difficulty in the management of diabetes is the optimization of insulin therapies to avoid occurrences of hypoglycemia and hyperglycemia. Many factors impact glucose fluctuations in diabetes patients, such as insulin dosage, nutritional intake, daily activities and lifestyle (e.g., sleep-wake cycles and exercise), and emotional states (e.g., stress). The overall effect of these factors has not been fully quantified to determine the impact on subsequent glycemic trends. Recent advances in diabetes technology such as continuous glucose monitoring (CGM) provides significant sources of data, such that quantification may be possible. Depending on the CGM technology utilized, the sampling frequency ranges from 1-5 min. In this study, an intensive electronic diary documenting the factors previously described was created. This diary was utilized by 18 patients with insulin-dependent diabetes mellitus in conjunction with CGM. Utilizing this dataset, various neural network models were constructed to predict glucose in these diabetes patients while varying the predictive window from 50-180 min. The predictive capability of each neural network within the fully trained dataset was analyzed as well as the predictive capabilities of the neural networks on unseen data.

METHODS

Neural network models were created using NeuroSolutions software with variable predictive windows of 50, 75, 100, 120, 150, and 180 min. Neural network models were trained using patient datasets ranging from 11-17 patients and evaluated on patient data not included in the neural network formulation. Performance analysis was completed for the neural network models using MATLAB. Performance measures include the calculation of the mean absolute difference percent overall and at hypoglycemic and hyperglycemic extremes, and the percentage of hypoglycemic and hyperglycemic occurrences were predicted.

RESULTS

Overall, the neural network models perform adequately at predicting at normal (>70 and <180 mg/dl) and hyperglycemic ranges (> or =180 mg/dl); however, glucose concentrations in areas of hypoglycemia were commonly overestimated. One potential reason for the "high" predictions in areas of hypoglycemia is due to the minimal occurrences of hypoglycemic events within the training data. The entire 18-patient dataset (consisting of 18,400 glucose values) had a relatively low incidence of hypoglycemia (1460 CGM values < or =70 mg/dl), which corresponds to approximately 7.9% of the dataset. On the contrary, hyperglycemia comprised approximately 35.7% of the dataset (6560 CGM values >or =180 mg/dl), and euglycemic values allotted for 56.4% of the dataset (10,380 CGM values >70 and <180 mg/dl). Results further indicate that an increase in predictive window leads to a decrease in predictive accuracy of the neural network model. It is hypothesized that the underestimation of hyperglycemic extremes is due to the extension of the predictive window and the associated inability of the neural network to determine oscillations and trends in glycemia as well as the occurrence of other relevant input events such as lifestyle, emotional states, insulin dosages, and meals, which may occur within the predicted time window and may impact or change neural network weights.

CONCLUSIONS

In this investigation, the feasibility of utilizing neural network models for the prediction of glucose using predictive windows ranging from 50-180 min is demonstrated. The predictive windows were chosen arbitrarily to cover a wide range; however, longer predictive windows were implemented to gain a predictive view of 120-180 min, which is very important for diabetes patients, specifically after meals and insulin dosages. Neural networks, such as those generated in this investigation, could be utilized in a semiclosed-loop device for guiding therapy in diabetes patients. Use of such a device may lead to better glycemic control and subsequent avoidance of complications.

Insulin resistance: a proinflammatory state mediated by lipid-induced signaling dysfunction and involved in atherosclerotic plaque instability

The dysregulation of the insulin-glucose axis represents the crucial event in insulin resistance syndrome. Insulin resistance increases atherogenesis and atherosclerotic plaque instability by inducing proinflammatory activities on vascular and immune cells. This condition characterizes several diseases, such as type 2 diabetes, impaired glucose tolerance (IGT), impaired fasting glucose (IFG), obesity, hypertension, dyslipidemia, and other endocrinopathies, but also cancer. Recent studies suggest that the pathophysiology of insulin resistance is closely related to interferences with insulin-mediated intracellular signaling on skeletal muscle cells, hepatocytes, and adipocytes. Strong evidence supports the role of free fatty acids (FFAs) in promoting insulin resistance. The FFA-induced activation of protein kinase C (PKC) delta, inhibitor kappaB kinase (IKK), or c-Jun N-terminal kinase (JNK) modulates insulin-triggered intracellular pathway (classically known as PI3-K-dependent). Therefore, reduction of FFA levels represents a selective target for modulating insulin resistance.

Blood glucose dynamics

Measurement of blood glucose concentration is central to the diagnosis and treatment of diabetes. Although there are large numbers of historic glucose measurements in individuals with diabetes, until recently there have been very few data sets that were recorded continuously or sampled frequently enough to reveal intrinsic blood glucose dynamics, or the change in blood glucose with time. There have even fewer such recordings from individuals not having diabetes to serve as a therapeutic target. As a result, blood glucose dynamics have generally not been used in the diagnosis or treatment of the disease. Although present blood glucose monitoring is based largely on discrete measurements, future monitoring will likely focus on analysis of blood glucose excursions. New measurements are now being obtained, and there is a need for new methods of analysis to extract the maximal information from the data. Several approaches are demonstrated here for characterization of blood glucose dynamics, and a patient profiling system is proposed. An example of new insights is the observation that there are four time scales of blood glucose variations in individuals without diabetes, and these time scales are modified or lost in diabetes.

The influence of sleep and sleep loss upon food intake and metabolism

The present review investigates the role of sleep and its alteration in triggering metabolic disorders. The reduction of the amount of time sleeping has become an endemic condition in modern society and the current literature has found important associations between sleep loss and alterations in nutritional and metabolic aspects. Studies suggest that individuals who sleep less have a higher probability of becoming obese. It can be related to the increase of ghrelin and decrease of leptin levels, generating an increase of appetite and hunger. Sleep loss has been closely associated with problems in glucose metabolism and a higher risk for the development of insulin resistance and diabetes, and this disturbance may reflect decreased efficacy of the negative-feedback regulation of the hypothalamic–pituitary–adrenal axis. The period of sleep is also associated with an increase of blood lipid concentrations, which can be intensified under conditions of reduced sleep time, leading to disorders in fat metabolism. Based on a review of the literature, we conclude that sleep loss represents an important risk factor for weight gain, insulin resistance, type 2 diabetes and dyslipidaemia. Therefore, an adequate sleep pattern is fundamental for the nutritional balance of the body and should be encouraged by professionals in the area.

Chronobiology in the endocrine system

Biological signaling occurs in a complex web with participation and interaction of the central nervous system, the autonomous nervous system, the endocrine glands, peripheral endocrine tissues including the intestinal tract and adipose tissue, and the immune system. All of these show an intricate time structure with rhythms and pulsatile variations in multiple frequencies. Circadian (about 24-hour) and circannual (about 1-year) rhythms are kept in step with the cyclic environmental surrounding by the timing and length of the daily light span. Rhythmicity of many endocrine variables is essential for their efficacy and, even in some instances, for the qualitative nature of their effects. Indeed, the continuous administration of certain hormones and their synthetic analogues may show substantially different effects than expected. In the design of drug-delivery systems and treatment schedules involving directly or indirectly the endocrine system, consideration of the human time organization is essential. A large amount of information on the endocrine time structure has accumulated, some of which is discussed in this review.

Daytime variations in glucose tolerance in people with impaired glucose tolerance

To determine whether glucose tolerance varies throughout the day in people with impaired glucose tolerance (IGT). We studied 15 healthy IGT, and 18 matched normal glucose tolerant (NGT) individuals. Blood samples were taken every 30-120 min during a 24h period in which all individuals had three mixed meals and nocturnal sleep. We measured glucose, free fatty acids, specific insulin, intact proinsulin, cortisol and growth hormone. Variable responses were considered as concentrations and areas under the curves. Comparison between the groups was by Student's t-test, Mann-Whitney, and analysis of variance. Higher total glucose response, inappropriate normal total insulin response, and unproportionally increased proinsulin total response were observed in the IGT group. Lower glucose tolerance occurred in IGT after dinner, as in the NGT, and after breakfast associated with increased insulin response after breakfast, and similar proinsulin response after all three meals. IGT had higher glucose response than NGT after breakfast and lunch, similar insulin responses, and increased proinsulin-insulin ratio after all three meals. Data from this study demonstrate that IGT individuals present lower glucose tolerance in the evening, as those with NGT, and in the morning, as reported in patients with type 2 diabetes.

Sleep loss: a novel risk factor for insulin resistance and Type 2 diabetes

Chronic sleep loss as a consequence of voluntary bedtime restriction is an endemic condition in modern society. Although sleep exerts marked modulatory effects on glucose metabolism, and molecular mechanisms for the interaction between sleeping and feeding have been documented, the potential impact of recurrent sleep curtailment on the risk for diabetes and obesity has only recently been investigated. In laboratory studies of healthy young adults submitted to recurrent partial sleep restriction, marked alterations in glucose metabolism including decreased glucose tolerance and insulin sensitivity have been demonstrated. The neuroendocrine regulation of appetite was also affected as the levels of the anorexigenic hormone leptin were decreased, whereas the levels of the orexigenic factor ghrelin were increased. Importantly, these neuroendocrine abnormalities were correlated with increased hunger and appetite, which may lead to overeating and weight gain. Consistent with these laboratory findings, a growing body of epidemiological evidence supports an association between short sleep duration and the risk for obesity and diabetes. Chronic sleep loss may also be the consequence of pathological conditions such as sleep-disordered breathing. In this increasingly prevalent syndrome, a feedforward cascade of negative events generated by sleep loss, sleep fragmentation, and hypoxia are likely to exacerbate the severity of metabolic disturbances. In conclusion, chronic sleep loss, behavioral or sleep disorder related, may represent a novel risk factor for weight gain, insulin resistance, and Type 2 diabetes.

Mechanism-Based Modeling of Complex Biomedical Systems

Mechanism-based modeling is an approach in which the physiological, pathological and pharmacological processes of relevance to a given problem are represented as directly as possible. This approach allows us (i) to test whether assumed hypotheses are consistent with observed behaviour, (ii) to examine the sensitivity of a system to parameter variation, (iii) to learn about processes not directly amenable to experimentation, and (iv) to predict system behavior under conditions not previously experienced. The paper illustrates different aspects of the application of mechanism-based modeling through three different examples of relevance to the treatment of diabetes and hypertension: subcutaneous absorption of insulin, pulsatile insulin secretion in normal young persons, and synchronization of the pressure and flow regulation in neighbouring nephrons. The underlying ideas are that each regulatory mechanism represents the target for intervention and that the development of new and more effective drugs must be based on a deeper understanding of the biological processes.

Calcium and glycolysis mediate multiple bursting modes in pancreatic islets

Pancreatic islets of Langerhans produce bursts of electrical activity when exposed to stimulatory glucose levels. These bursts often have a regular repeating pattern, with a period of 10-60 s. In some cases, however, the bursts are episodic, clustered into bursts of bursts, which we call compound bursting. Consistent with this are recordings of free Ca2+ concentration, oxygen consumption, mitochondrial membrane potential, and intraislet glucose levels that exhibit very slow oscillations, with faster oscillations superimposed. We describe a new mathematical model of the pancreatic beta-cell that can account for these multimodal patterns. The model includes the feedback of cytosolic Ca2+ onto ion channels that can account for bursting, and a metabolic subsystem that is capable of producing slow oscillations driven by oscillations in glycolysis. This slow rhythm is responsible for the slow mode of compound bursting in the model. We also show that it is possible for glycolytic oscillations alone to drive a very slow form of bursting, which we call "glycolytic bursting." Finally, the model predicts that there is bistability between stationary and oscillatory glycolysis for a range of parameter values. We provide experimental support for this model prediction. Overall, the model can account for a diversity of islet behaviors described in the literature over the past 20 years.

L-arginine: an ultradian-regulated substrate coupled with insulin oscillations in healthy volunteers

OBJECTIVE

Coupled oscillations of 50-110 min in insulin and glucose have been found previously in healthy men under continuous enteral nutrition. Because L-arginine induces insulin release as glucose does, we tested the hypothesis that L-arginine can also display such an ultradian rhythm.

RESEARCH DESIGN AND METHODS

Seven healthy male subjects participated in one experimental night during which blood was sampled every 10 min from 2300 to 0700. Plasma glucose, C-peptide, and L-arginine levels were measured simultaneously. The insulin secretion rate (ISR) was calculated from plasma C-peptide levels by a deconvolution procedure.

RESULTS

Plasma glucose followed the recognizable profiles, with oscillations closely linked to similar changes in the ISR. Pulse analysis of L-arginine profiles revealed significant oscillations linked to glucose and ISR oscillations, with the highest cross-correlation coefficients at time lag 0 ranging from 0.380 to 0.680 for glucose and L-arginine and from 0.444 to 0.726 for ISR and L-arginine (P < 0.01). The mean period of L-arginine oscillations was 77.2 +/- 6.2 min, and their mean amplitude was 19.9 +/- 1.7%, similar to that of glucose (17.0 +/- 1.9%), when expressed as the percentage of mean overnight levels.

CONCLUSIONS

This newly discovered ultradian rhythm of L-arginine and its coupling with glucose and ISR oscillations sheds new light on the regulation of L-arginine, the substrate of numerous metabolic pathways, including nitric oxide synthesis. These oscillations may be of significance in conditions of hyperinsulinemia or abnormal glucose tolerance.

Pathophysiology of hyperinsulinemia following pancreas transplantation: altered pulsatile versus Basal insulin secretion and the role of specific transplant anatomy in dogs

OBJECTIVE

To evaluate the effect of the anatomical alterations of the pancreas required for transplantation on pulsatile insulin secretion.

SUMMARY BACKGROUND DATA

Pancreas transplantation involves anatomical changes that have unknown consequences on glucose homeostasis. Pancreas transplant patients are free of exogenous insulin requirements, yet appear to have endogenous hyperinsulinemia. The effect of surgical alterations on posttransplant insulin release is not completely known, specifically with regards to possible alterations in patterns of pulsatile release.

METHODS

Pulsatile and invariant basal insulin secretion was studied in normal dogs (n = 4) and three canine models of the anatomical alterations of pancreas transplantation: 70% partial pancreatectomy (PPX, n = 4), partial pancreatectomy with splenocaval venous diversion (SC, n = 4), and partial pancreatectomy with remnant autotransplantation (PAT, n = 4). Plasma insulin kinetics were determined for each dog, and then blood sampled at 1-minute intervals in a fasted and IV glucose-stimulated state twice to delineate the time structure of insulin secretion by multiple parameter deconvolution analysis utilizing dog-specific insulin half-lives.

RESULTS

Fasting plasma glucose concentrations in each group were similar, but all surgical groups were hyperglycemic with IV glucose challenge. Secretory pulse amplitude was decreased with decreased beta cell mass (PPX), partially normalized with systemic insulin release (SC), and further normalized with denervation (PAT). Interpulse interval and pulse duration were increased in all surgical groups when stimulated. Denervation of PAT resulted in a threefold increase in fasting basal invariant insulin secretion. Stimulated basal insulin secretion is inconsequential.

CONCLUSIONS

Hyperinsulinemia and apparent insulin insensitivity after pancreas transplantation may be due to increased less potent basal secretion in the fasting state and less frequent, less discrete pulsatile insulin secretion in the simulated state.

The evolution of beta-cell dysfunction and insulin resistance in type 2 diabetes

Insulin resistance and beta-cell dysfunction have important roles in the pathogenesis and evolution of type 2 diabetes. The development of precise methods to measure these factors has helped us to define the relationship between them and evidence is reviewed that changes in insulin sensitivity are compensated by inverse changes in beta-cell responsiveness such that the product of insulin sensitivity and insulin secretion (the disposition index) remains constant. While the disposition index promises to be a useful tool to predict individuals at high risk of developing type 2 diabetes, other factors that contribute to beta-cell dysfunction and mark disease onset and progression include impairments in proinsulin processing and insulin secretion, decreased beta-cell mass and islet amyloid deposition. Emerging data indicate that anti-diabetic agents, such as the thiazolidinediones that simultaneously target insulin resistance and beta-cell dysfunction, may have a beneficial impact on disease onset and progression. Several landmark clinical studies are underway to investigate if their initial promise is supported by data from large-scale trials.

Ultradian oscillations of insulin secretion in humans

Ultradian rhythmicity appears to be characteristic of several endocrine systems. As described for other hormones, insulin release is a multioscillatory process with rapid pulses of about 10 min and slower ultradian oscillations (50--120 min). The mechanisms underlying the ultradian circhoral oscillations of insulin secretion rate (ISR), which arise in part from a rhythmic amplification of the rapid pulses, are not fully understood. In humans, included in the same period range is the alternation of rapid eye movement (REM) and non-REM (NREM) sleep cycles and the associated opposite oscillations in sympathovagal balance. During sleep, the glucose and ISR oscillations were amplified by about 150%, but the REM-NREM sleep cycles did not entrain the glucose and ISR ultradian oscillations. Also, the latter were not related to either the ultradian oscillations in sympathoagal balance, as inferred from spectral analysis of cardiac R-R intervals, or the plasma fluctuations of glucagon-like peptide-1 (GLP-1), an incretin hormone known to potentiate glucose-stimulated insulin. Other rhythmic physiological processes are currently being examined in relation to ultradian insulin release.

Pulsatile insulin secretion: detection, regulation, and role in diabetes

Insulin concentrations oscillate at a periodicity of 5-15 min per oscillation. These oscillations are due to coordinate insulin secretory bursts, from millions of islets. The generation of common secretory bursts requires strong within-islet and within-pancreas coordination to synchronize the secretory activity from the beta-cell population. The overall contribution of this pulsatile mechanism dominates and accounts for the majority of insulin release. This review discusses the methods involved in the detection and quantification of periodicities and individual secretory bursts. The mechanism by which overall insulin secretion is regulated through changes in the pulsatile component is discussed for nerves, metabolites, hormones, and drugs. The impaired pulsatile secretion of insulin in type 2 diabetes has resulted in much focus on the impact of the insulin delivery pattern on insulin action, and improved action from oscillatory insulin exposure is demonstrated on liver, muscle, and adipose tissues. Therefore, not only is the dominant regulation of insulin through changes in secretory burst mass and amplitude, but the changes may affect insulin action. Finally, the role of impaired pulsatile release in early type 2 diabetes suggests a predictive value of studies on insulin pulsatility in the development of this disease.

Effects of fasting on physiologically pulsatile insulin release in healthy humans

Insulin is released as secretory bursts superimposed on basal release. The overall contribution of secretory bursts was recently quantified as at least 75%, and the main regulation of insulin secretion is through perturbation of the amount of insulin released and the frequency of these secretory bursts. The mode of delivery of insulin into the circulation seems important for insulin action, and therefore physiological conditions that alter the pattern of insulin release may affect insulin action through this mechanism. To assess the mechanisms by which fasting changes the amount of insulin released and the frequency, amplitude, and overall contribution of pulsatile insulin secretion, we used a validated deconvolution model to examine pulsatile insulin secretion during 10 and 58 h of fasting in seven healthy subjects. The subjects were studied for 75 min before (0-75 min) and 75 min during (115-190 min) a glucose infusion (2.5 mg.kg(-1).min(-1)). We found that the pulsatile insulin release pattern was preserved and that, at fasting, overall insulin release is adjusted to needs by a reduced amount of insulin released (10.1 +/- 1.7 vs. 16.0 +/- 3.2 pmol/l/pulse, P < 0.05) but similar frequency (6.3 +/- 0.4 vs. 6.1 +/- 0.4 min/pulse) of the insulin secretory bursts. In both states, glucose infusion caused an increase (P < 0.05) in amount (100-200%) and frequency (approximately 20%). The impact of increased glucose concentration on pulse frequency seems distinct for in vivo versus in vitro pulsatile insulin secretion and may indicate the presence of a glucose-sensitive pacemaker, which initiates the coordinated secretory bursts. Increased insulin/C-peptide ratio at long-term fasting (6.0 vs. 9.1%, P < 0.01) indicates that the changes in insulin release patterns may be accompanied by changes in hepatic insulin extraction.

Insulin signalling through ultradian oscillations

Periodic oscillations appear to be a characteristic of insulin secretion at various different levels. Very rapid pulsations are seen in the isolated beta-cell and islet, while rapid (10- to 15-min) pulsations are seen both in the intact organism and in the isolated pancreas. Ultradian oscillations, particularly evident in situations of sustained exogenous glucose loading, appear to be a characteristic of intact organisms and have been hypothesized to be intrinsic to the normal glucose-insulin feedback system. Many of the features seen in experimental situations and in abnormalities of the system can be predicted by computer modelling of this system, supporting this hypothesis. A further theoretical feature of this hypothesis, borne out by experiment, is the ability to entrain insulin pulsatility by oscillations in an exogenous glucose infusion. Identification of defective ultradian oscillations and entrainment can identify subtle abnormalities of insulin sensitivity and pancreatic function, and restoration of normal function can be demonstrated after pharmaceutical intervention.