Minimal model: perspective from 2005

Evaluating Deep Q-Learning Algorithms for Controlling Blood Glucose in In Silico Type 1 Diabetes

Patients with type 1 diabetes must continually decide how much insulin to inject before each meal to maintain blood glucose levels within a healthy range. Recent research has worked on a solution for this burden, showing the potential of reinforcement learning as an emerging approach for the task of controlling blood glucose levels. In this paper, we test and evaluate several deep Q-learning algorithms for automated and personalized blood glucose regulation in an in silico type 1 diabetes patient with the goal of estimating and delivering proper insulin doses. The proposed algorithms are model-free approaches with no prior information about the patient. We used the Hovorka model with meal variation and carbohydrate counting errors to simulate the patient included in this work. Our experiments compare different deep Q-learning extensions showing promising results controlling blood glucose levels, with some of the proposed algorithms outperforming standard baseline treatment.

Minimally-Invasive and Efficient Method to Accurately Fit the Bergman Minimal Model to Diabetes Type 2

Introduction

Diabetes mellitus is a global burden that is expected to grow 25 % by 2030. This will increase the need for prevention, diagnosis and treatment of diabetes. Animal and individualized in silico models will allow understanding and compensation for inter and intra-individual differences in treatment and management strategies for diabetic patients. The method presented here can advance the concept of personalized medicine.

Methods

Twenty experiments were performed with Sprague-Dawley rats with streptozotocin induced experimental diabetes in which the insulin-glucose response curve was recorded over 60-100 min using only an insulin pump and a percutaneous glucose sensor. The information was used to fit the five-parameter Bergman Minimal Model to the experimental results using a genetic algorithm with a root-mean-squared optimization rule.

Results

The Bergman Minimal Model parameters were estimated with high accuracy, low prediction bias, and low average root-mean-squared error of 15.27 mg/dl glucose.

Conclusions

This study demonstrates a simple method to accurately parameterize the Bergman Minimal Model. We used Sprague-Dawley rats since their physiology is close to that of humans. The parameters can be used to objectively characterize the physiological severity of diabetes. In this way, planned treatments can compensate for natural variations of conditions both inter and intra patients. Changes in parameters indicate the patient's diabetic condition using values of glucose effectiveness (S G = p 1 ) and insulin sensitivity (S I = p 3 / p 2 ). Quantifying the diabetic patient's condition is consistent with the trend toward personalized medicine. Parameter values can also be used to explain atypical research results of other studies and increase understanding of diabetes.

Brain Mass (Energy) Resistant to Hyperglycaemic Oversupply: A Systematic Review

Cerebral energy supply is determined by the energy content of the blood. Accordingly, the brain is undersupplied during hypoglycaemia. Whether or not there is an additional cerebral energy demand that depends upon the energy content of the brain is considered differently in two opposing theoretical approaches. The Selfish-Brain theory postulates that the brain actively demands energy from the body when needed, while long-held theories, the gluco-lipostatic theory and its variants, deny such active brain involvement and view the brain as purely passively supplied. Here we put the competing theories to the test. We conducted a systematic review of a condition in which the rival theories make opposite predictions, i.e., experimental T1DM. The Selfish-Brain theory predicts that induction of experimental type 1 diabetes causes minor mass (energy) changes in the brain as opposed to major glucose changes in the blood. This prediction becomes our hypothesis to be tested here. A total of 608 works were screened by title and abstract, and 64 were analysed in full text. According to strict selection criteria defined in our PROSPERO preannouncement and complying with PRISMA guidelines, 18 studies met all inclusion criteria. Thirteen studies provided sufficient data to test our hypothesis. The 13 evaluable studies (15 experiments) showed that the diabetic groups had blood glucose concentrations that differed from controls by +294 ± 96% (mean ± standard deviation) and brain mass (energy) that differed from controls by −4 ± 13%, such that blood changes were an order of magnitude greater than brain changes (T = 11.5, df = 14, p < 0.001). This finding confirms not only our hypothesis but also the prediction of the Selfish-Brain theory, while the predictions of the gluco-lipostatic theory and its variants were violated. The current paper completes a three-part series of systematic reviews, the two previous papers deal with a distal and a proximal bottleneck in the cerebral brain supply, i.e., caloric restriction and cerebral artery occlusion. All three papers demonstrate that accurate predictions are only possible if one regards the brain as an organ that regulates its energy concentrations independently and occupies a primary position in a hierarchically organised energy metabolism.

Systematic Review Registration:https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=156816, PROSPERO, identifier: CRD42020156816.

GLYFE: review and benchmark of personalized glucose predictive models in type 1 diabetes

Due to the sensitive nature of diabetes-related data, preventing them from being easily shared between studies, and the wide discrepancies in their data processing pipeline, progress in the field of glucose prediction is hard to assess. To address this issue, we introduce GLYFE (GLYcemia Forecasting Evaluation), a benchmark of machine learning-based glucose predictive models. We present the accuracy and clinical acceptability of nine different models coming from the literature, from standard autoregressive to more complex neural network-based models. These results are obtained on two different datasets, namely UVA/Padova Type 1 Diabetes Metabolic Simulator (T1DMS) and Ohio Type-1 Diabetes Mellitus (OhioT1DM), featuring artificial and real type 1 diabetic patients respectively. By providing extensive details about the data flow as well as by providing the whole source code of the benchmarking process, we ensure the reproducibility of the results and the usability of the benchmark by the community. Those results serve as a basis of comparison for future studies. In a field where data are hard to obtain, and where the comparison of results from different studies is often irrelevant, GLYFE gives the opportunity of gathering researchers around a standardized common environment.

An algorithm mimicking pancreas pulsatile behavior improves artificial pancreas performance

BACKGROUND

Artificial pancreas design using subcutaneous insulin infusion without pre-meal feed-forward boluses often induces an over-response leading to hypoglycemia due to the increase of blood insulin concentration sustained in time. The objective of this work was to create an algorithm for controlling the function of insulin pumps in closed-loop systems to improve blood glucose management in type 1 diabetic patients by mimicking the pulsatile behaviour of the pancreas.

METHODS

A controller tuned in a pulsatile way promotes damped oscillations of blood insulin concentration injected through an insulin pump. We tested it in a simulated environment, using nine 'in silica' subjects. The control algorithm is founded on feedback linearization where through a change of variables, the nonlinear system turns into an equivalent linear system, suitable for implementing through a PID controller. We compared the results obtained 'in silica' with the volume injected by an insulin pump controlled by this algorithm.

RESULTS

The use of this algorithm resulted in a pulsatile control of postprandial blood glucose concentration, avoiding hypoglycaemic episodes. The results obtained 'in silica' were replicated in a real pump 'in vitro'.

CONCLUSIONS

With this proposed linear system, an appropriate control input can be designed. The controller works with a damped pulsatile pattern making the insulin infusion from the pump and blood insulin concentration pulsatile. This operational would improve the performance of an artificial pancreas.

Dissociation Between Insulin Resistance and Abnormalities in Lipoprotein Particle Concentrations and Sizes in Normal-Weight Chinese Adults

Insulin resistance in obesity coincides with abnormalities in lipid profile and lipoprotein subclass distribution and size even before abnormalities in glucose homeostasis manifest. We aimed to assess this relationship in the absence of obesity. Insulin sensitivity (3-h intravenous glucose tolerance test and minimal modeling) and lipoprotein particle concentrations and sizes (proton nuclear magnetic resonance spectroscopy) were evaluated in 15 insulin-resistant and 15 insulin-sensitive lean Asians of Chinese descent with normal glucose tolerance, matched on age, sex, and body mass index. Despite a ~50% lower insulin sensitivity index (Si) in insulin-resistant than in insulin-sensitive subjects, which was accompanied by significantly greater acute insulin response to glucose (AIRg) and fasting insulin concentration but not different fasting glucose concentration, there were no significant differences between groups in the blood lipid profile (p ≥ 0.44) or the lipoprotein subclass concentrations (p ≥ 0.30) and particle sizes (p ≥ 0.43). We conclude that, contrary to observations in subjects with obesity, insulin resistance is not accompanied by unfavorable changes in the plasma lipid profile and lipoprotein particle concentrations and sizes in lean Asians with normal glucose tolerance. Therefore, insulin resistance at the level of glucose metabolism is mechanistically or temporally dissociated from lipid and lipoprotein metabolism.

Trial Registration:clinicaltrials.gov, NCT03264001.

Driver versus navigator causation in biology: the case of insulin and fasting glucose

BACKGROUND

In biomedicine, inferring causal relation from experimental intervention or perturbation is believed to be a more reliable approach than inferring causation from cross-sectional correlation. However, we point out here that even in interventional inference there are logical traps. In homeostatic systems, causality in a steady state can be qualitatively different from that in a perturbed state. On a broader scale there is a need to differentiate driver causality from navigator causality. A driver is essential for reaching a destination but may not have any role in deciding the destination. A navigator on the other hand has a role in deciding the destination and the path but may not be able to drive the system to the destination. The failure to differentiate between types of causalities is likely to have resulted into many misinterpretations in physiology and biomedicine.

METHODS

We illustrate this by critically re-examining a specific case of the causal role of insulin in glucose homeostasis using five different approaches (1) Systematic review of tissue specific insulin receptor knock-outs, (2) Systematic review of insulin suppression and insulin enhancement experiments, (3) Differentiating steady state and post-meal state glucose levels in streptozotocin treated rats in primary experiments, (4) Mathematical and theoretical considerations and (5) Glucose-insulin relationship in human epidemiological data.

RESULTS

All the approaches converge on the inference that although insulin action hastens the return to a steady state after a glucose load, there is no evidence that insulin action determines the steady state level of glucose. Insulin, unlike the popular belief in medicine, appears to be a driver but not a navigator for steady state glucose level. It is quite likely therefore that the current line of clinical action in the field of type 2 diabetes has limited success largely because it is based on a misinterpretation of glucose-insulin relationship. The insulin-glucose example suggests that we may have to carefully re-examine causal inferences from perturbation experiments and set up revised norms for experimental design for causal inference.

Arbitrary-order sliding mode-based robust control algorithm for the developing artificial pancreas mechanism

In Diabetes Mellitus, the pancreas remains incapable of insulin administration that leads to hyperglycaemia, an escalated glycaemic concentration, which may stimulate many complications. To circumvent this situation, a closed-loop control strategy is much needed for the exogenous insulin infusion in diabetic patients. This closed-loop structure is often termed as an artificial pancreas that is generally established by the employment of different feedback control strategies. In this work, the authors have proposed an arbitrary-order sliding mode control approach for development of the said mechanism. The term, arbitrary, is exercised in the sense of its applicability to any n-order controllable canonical system. The proposed control algorithm affirms the finite-time effective stabilisation of the glucose-insulin regulatory system, at the desired level, with the alleviation of sharp fluctuations. The novelty of this work lies in the sliding manifold that incorporates indirect non-linear terms. In addition, the necessary discontinuous terms are filtered-out once before its employment to the plant, i.e. diabetic patient. The robustness, in the presence of external disturbances, i.e. meal intake is confirmed via rigorous mathematical stability analysis. In addition, the effectiveness of the proposed control strategy is ascertained by comparing the results with the standard literature.

A phenomenological-based semi-physical model of the kidneys and its role in glucose metabolism

The kidneys play an important role in glucose homeostasis in three ways: Via endogenous glucose production from non-carbohydrate precursors (e.g. glutamine, lactate, alanine, glycerol) during both postprandial and post-absorptive states; via glucose filtration and reabsorption by the glomerulus and proximal tubule, respectively; and via glucose utilization and the elimination of its excess in the urine when glucose levels exceed 180mg/dl. The renal release of glucose into the circulation occurs mainly in the renal cortex and results from the glucose phosphorylating capacity of those renal cells, meaning that, cells in the renal cortex can form glucose-6-phosphate. Considering glucose filtration and reabsorption, the kidneys filtrate and reabsorb all circulating glucose, rendering the urine virtually glucose-free in a healthy person. Finally, the kidneys take up glucose from the circulation for energetic self-supply. Besides their role in glucose metabolism, the kidneys are the major site of insulin clearance from the systemic circulation, removing approximately 50% of peripheral insulin. In this regard, insulin clearance by kidneys occurs by degradation in the proximal tubule after being filtered in the glomerulus. All the aforementioned mechanisms affect the glucose concentration levels in the blood, preventing the parametrization of a mathematical model for patients with diabetes mellitus, in the implementation of an artificial pancreas. Aiming for a complete physiological model of the glucose homeostasis, a physiological submodel of the kidneys is presented in a way not described in the literature so far. This submodel is a phenomenological-based semi-physical model with a basic structure rooted in the conservation law and for which the parameters are interpretable. The model's results coincide well with the available clinical data reported for kidney functions associated with glucose and insulin.

Pharmacodynamics of Metformin in Pregnant Women With Gestational Diabetes Mellitus and Nonpregnant Women With Type 2 Diabetes Mellitus

Gestational diabetes mellitus is a condition similar to type 2 diabetes mellitus (T2DM) in that patients are unable to compensate for the degree of insulin resistance, and both conditions are often treated with metformin. The comparative pharmacodynamic response to metformin in these 2 populations has not been studied. This study characterized insulin sensitivity, β-cell responsivity, and disposition index following a mixed-meal tolerance test utilizing a minimal model of glucose, insulin, and C-peptide kinetics before and during treatment with metformin. The study included women with gestational diabetes mellitus (n = 34), T2DM (n = 14), and healthy pregnant women (n = 30). Before treatment, the gestational diabetes mellitus group had significantly higher baseline (45%), dynamic (68%), static (71%), and total β-cell responsivity (71%) than the T2DM group. Metformin significantly increased insulin sensitivity (51%) as well as disposition index (97%) and decreased mixed-meal tolerance test peak glucose concentrations (8%) in women with gestational diabetes mellitus after adjustment for gestational age-dependent effects; however, in women with T2DM metformin only significantly affected peak glucose concentrations (22%) and had no significant effect on any other parameters. Metformin had a greater effect on the change in disposition index (Δ disposition index) in women with gestational diabetes mellitus than in those with T2DM (P = .01). In conclusion, response to metformin in women with gestational diabetes mellitus is significantly different from that in women with T2DM, which is likely related to the differences in disease severity.

A Novel Synthetic Model of the Glucose-Insulin System for Patient-Wise Inference of Physiological Parameters From Small-Size OGTT Data

Existing mathematical models for the glucose-insulin (G-I) dynamics often involve variables that are not susceptible to direct measurement. Standard clinical tests for measuring G-I levels for diagnosing potential diseases are simple and relatively cheap, but seldom give enough information to allow the identification of model parameters within the range in which they have a biological meaning, thus generating a gap between mathematical modeling and any possible physiological explanation or clinical interpretation. In the present work, we present a synthetic mathematical model to represent the G-I dynamics in an Oral Glucose Tolerance Test (OGTT), which involves for the first time for OGTT-related models, Delay Differential Equations. Our model can represent the radically different behaviors observed in a studied cohort of 407 normoglycemic patients (the largest analyzed so far in parameter fitting experiments), all masked under the current threshold-based normality criteria. We also propose a novel approach to solve the parameter fitting inverse problem, involving the clustering of different G-I profiles, a simulation-based exploration of the feasible set, and the construction of an information function which reshapes it, based on the clinical records, experimental uncertainties, and physiological criteria. This method allowed an individual-wise recognition of the parameters of our model using small size OGTT data (5 measurements) directly, without modifying the routine procedures or requiring particular clinical setups. Therefore, our methodology can be easily applied to gain parametric insights to complement the existing tools for the diagnosis of G-I dysregulations. We tested the parameter stability and sensitivity for individual subjects, and an empirical relationship between such indexes and curve shapes was spotted. Since different G-I profiles, under the light of our model, are related to different physiological mechanisms, the present method offers a tool for personally-oriented diagnosis and treatment and to better define new health criteria.

Pharmacodynamics of Glyburide, Metformin, and Glyburide/Metformin Combination Therapy in the Treatment of Gestational Diabetes Mellitus

In gestational diabetes mellitus (GDM), women are unable to compensate for the increased insulin resistance during pregnancy. Data are limited regarding the pharmacodynamic effects of metformin and glyburide during pregnancy. This study characterized insulin sensitivity (SI), β-cell responsivity, and disposition index (DI) in women with GDM utilizing a mixed-meal tolerance test (MMTT) before and during treatment with glyburide monotherapy (GLY, n = 38), metformin monotherapy (MET, n = 34), or GLY and MET combination therapy (COMBO; n = 36). GLY significantly decreased dynamic β-cell responsivity (31%). MET and COMBO significantly increased SI (121% and 83%, respectively). Whereas GLY, MET, and COMBO improved DI, metformin (MET and COMBO) demonstrated a larger increase in DI (P = 0.05) and a larger decrease in MMTT peak glucose concentrations (P = 0.03) than subjects taking only GLY. Maximizing SI with MET followed by increasing β-cell responsivity with GLY or supplementing with insulin might be a more optimal strategy for GDM management than monotherapy.

Modelling the effects of glucagon during glucose tolerance testing

Background

Glucose tolerance testing is a tool used to estimate glucose effectiveness and insulin sensitivity in diabetic patients. The importance of such tests has prompted the development and utilisation of mathematical models that describe glucose kinetics as a function of insulin activity. The hormone glucagon, also plays a fundamental role in systemic plasma glucose regulation and is secreted reciprocally to insulin, stimulating catabolic glucose utilisation. However, regulation of glucagon secretion by α-cells is impaired in type-1 and type-2 diabetes through pancreatic islet dysfunction. Despite this, inclusion of glucagon activity when modelling the glucose kinetics during glucose tolerance testing is often overlooked. This study presents two mathematical models of a glucose tolerance test that incorporate glucose-insulin-glucagon dynamics. The first model describes a non-linear relationship between glucagon and glucose, whereas the second model assumes a linear relationship.

Results

Both models are validated against insulin-modified and glucose infusion intravenous glucose tolerance test (IVGTT) data, as well as insulin infusion data, and are capable of estimating patient glucose effectiveness (s_G) and insulin sensitivity (s_I). Inclusion of glucagon dynamics proves to provide a more detailed representation of the metabolic portrait, enabling estimation of two new diagnostic parameters: glucagon effectiveness (s_E) and glucagon sensitivity (δ).

Conclusions

The models are used to investigate how different degrees of pax‘tient glucagon sensitivity and effectiveness affect the concentration of blood glucose and plasma glucagon during IVGTT and insulin infusion tests, providing a platform from which the role of glucagon dynamics during a glucose tolerance test may be investigated and predicted.

Hypoglycemia Prevention via Personalized Glucose-Insulin Models Identified in Free-Living Conditions

BACKGROUND

The objective of this research is to show the effectiveness of individualized hypoglycemia predictive alerts (IHPAs) based on patient-tailored glucose-insulin models (PTMs) for different subjects. Interpatient variability calls for PTMs that have been identified from data collected in free-living conditions during a one-month trial.

METHODS

A new impulse-response (IR) identification technique has been applied to free-living data in order to identify PTMs that are able to predict the future glucose trends and prevent hypoglycemia events. Impulse response has been applied to seven patients with type 1 diabetes (T1D) of the University of Amsterdam Medical Centre. Individualized hypoglycemia predictive alert has been designed for each patient thanks to the good prediction capabilities of PTMs.

RESULTS

The PTMs performance is evaluated in terms of index of fitting (FIT), coefficient of determination, and Pearson's correlation coefficient with a population FIT of 63.74%. The IHPAs are evaluated on seven patients with T1D with the aim of predicting in advance (between 45 and 10 minutes) the unavoidable hypoglycemia events; these systems show better performance in terms of sensitivity, precision, and accuracy with respect to previously published results.

CONCLUSION

The proposed work shows the successful results obtained applying the IR to an entire set of patients, participants of a one-month trial. Individualized hypoglycemia predictive alerts are evaluated in terms of hypoglycemia prevention: the use of a PTM allows to detect 84.67% of the hypoglycemia events occurred during a one-month trial on average with less than 0.4% of false alarms. The promising prediction capabilities of PTMs can be a key ingredient for new generations of individualized model predictive control for artificial pancreas.

Spatial and temporal studies of metabolic activity: contrasting biochemical kinetics in tissues and pathways during fasted and fed states

The regulation of nutrient homeostasis, i.e., the ability to transition between fasted and fed states, is fundamental in maintaining health. Since food is typically consumed over limited (anabolic) periods, dietary components must be processed and stored to counterbalance the catabolic stress that occurs between meals. Herein, we contrast tissue- and pathway-specific metabolic activity in fasted and fed states. We demonstrate that knowledge of biochemical kinetics that is obtained from opposite ends of the energetic spectrum can allow mechanism-based differentiation of healthy and disease phenotypes. Rat models of type 1 and type 2 diabetes serve as case studies for probing spatial and temporal patterns of metabolic activity via [²H]water labeling. Experimental designs that capture integrative whole body metabolism, including meal-induced substrate partitioning, can support an array of research surrounding metabolic disease; the relative simplicity of the approach that is discussed here should enable routine applications in preclinical models.

Rapid automatic identification of parameters of the Bergman Minimal Model in Sprague-Dawley rats with experimental diabetes for adaptive insulin delivery

Glucose-Insulin regulation models can be used to individualize insulin therapy. However, the experimental techniques currently used to identify the appropriate parameter sets of an individual are expensive, time consuming, and very unpleasant for the patient. Since there is a wide range of intrapersonal parameter variability, the identified parameters in a laboratory setting (at rest) are not optimal for dynamic conditions of daily activities. In this study we propose a methodology to identify three parameters of Bergman's Minimal Model in streptozotocin-induced diabetic rats from the experimental data of the glucose response to exogenous insulin doses, based on a genetic algorithm (GA). The algorithm requires glucose measurements from a continuous subcutaneous sensor once every 5 min and the amount of injected insulin. The model parameters of 20 in vivo experiments (from 19 rats) were identified with high accuracy and the average root-mean squared (RMS) error between predicted and measured glucose concentration was 17.6 mg/dl. Since the algorithm requires a relatively short (60-120 min) observation time it can be used for real-time parameter identification to optimize insulin infusion systems. Model parameter changes due to experimental settings like drug testing or in natural lifestyle changes should be calculable, on-the-fly, using data from only the glucose sensor and the amount of insulin delivered.

Adaptive fuzzy integral sliding mode control of blood glucose level in patients with type 1 diabetes: In silico studies

Currently, artificial pancreas is an alternative treatment instead of insulin therapy for patients with type 1 diabetes mellitus. Closed-loop control of blood glucose level (BGL) is one of the difficult tasks in biomedical engineering field due to nonlinear time-varying dynamics of insulin-glucose relation that is combined with time delays and model uncertainties. In this paper, we propose a novel adaptive fuzzy integral sliding mode control scheme for BGL regulation. System dynamics is identified online using fuzzy logic systems. The presented method is evaluated in silico studies by nine different virtual patients in three different groups for two continuous days. Simulation results demonstrate effective performance of the proposed control scheme of BGL regulation in presence of simultaneous meal and physical exercise disturbances. Comparison of the proposed control method with proportional-integral-derivative (PID) control and model predictive control (MPC) shows a superiority of the adaptive fuzzy integral sliding mode control with regard to two conventional methods of BGL regulation (PID and MPC) and sliding mode control.

Control Limitations in Models of T1DM and the Robustness of Optimal Insulin Delivery

BACKGROUND

In insulin therapy, the blood glucose level is constrained from below by the hypoglycemic threshold, that is, the blood glucose level must remain above this threshold. It has been shown that this constraint fundamentally limits the ability to lower the maxima of the blood glucose level predicted by many mathematical models of glucose metabolism. However, it is desirable to minimize hyperglycemia as well. Hence, a desirable insulin input is one that minimizes the maximum glucose concentration while causing it to remain above the hypoglycemic, or higher, threshold. It has been shown that this input, which we call optimal, is characterized by glucose profiles for which either each maximum of the glucose concentration is followed by a minimum or each minimum is followed by a maximum.

METHODS

We discuss the implication of this inherent control limitation for clinical practice and test, through simulation, the robustness of the optimal input to a number of different model and parameter uncertainties. We further develop guidelines on how to design an optimal insulin input that is robust to such uncertainties.

RESULTS

The optimal input is in general not robust to uncertainties. However, a number of strategies may be used to ensure the blood glucose level remains above the hypoglycemic threshold and the maximum blood glucose level achieved is less than that achieved by standard therapy.

CONCLUSIONS

An understanding of the limitations on the controllability of the blood glucose level is important for future treatment improvements and the development of artificial pancreas systems.

Addition of dairy lipids and probiotic Lactobacillus fermentum in infant formula programs gut microbiota and entero-insular axis in adult minipigs

Clinical and animal studies have demonstrated beneficial effects of early consumption of dairy lipids and a probiotic, Lactobacillus fermentum (Lf), on infant gut physiology. The objective of this study was to investigate their long-term effects on gut microbiota and host entero-insular axis and metabolism. Piglets were suckled with a milk formula containing only plant lipids (PL), a half-half mixture of plant lipids and dairy lipids (DL), or this mixture supplemented with Lf (DL + Lf). They were weaned on a standard diet and challenged with a high-energy diet until postnatal day 140. DL and DL + Lf modulated gut microbiota composition and metabolism, increasing abundance of several Clostridia genera. Moreover, DL + Lf specifically decreased the faecal content of 2-oxoglutarate and lysine compared to PL and 5-aminovalerate compared to PL and DL. It also increased short-chain fatty acid concentrations like propionate compared to DL. Furthermore, DL + Lf had a beneficial effect on the endocrine function, enhancing caecal GLP-1 and GLP-1 meal-stimulated secretion. Correlations highlighted the consistent relationship between microbiota and gut physiology. Together, our results evidence a beneficial programming effect of DL + Lf in infant formula composition on faecal microbiota and entero-insular axis function.

A long-term mechanistic computational model of physiological factors driving the onset of type 2 diabetes in an individual

A computational model of the physiological mechanisms driving an individual's health towards onset of type 2 diabetes (T2D) is described, calibrated and validated using data from the Diabetes Prevention Program (DPP). The objective of this model is to quantify the factors that can be used for prevention of T2D. The model is energy and mass balanced and continuously simulates trajectories of variables including body weight components, fasting plasma glucose, insulin, and glycosylated hemoglobin among others on the time-scale of years. Modeled mechanisms include dynamic representations of intracellular insulin resistance, pancreatic beta-cell insulin production, oxidation of macronutrients, ketogenesis, effects of inflammation and reactive oxygen species, and conversion between stored and activated metabolic species, with body-weight connected to mass and energy balance. The model was calibrated to 331 placebo and 315 lifestyle-intervention DPP subjects, and one year forecasts of all individuals were generated. Predicted population mean errors were less than or of the same magnitude as clinical measurement error; mean forecast errors for weight and HbA1c were ~5%, supporting predictive capabilities of the model. Validation of lifestyle-intervention prediction is demonstrated by synthetically imposing diet and physical activity changes on DPP placebo subjects. Using subject level parameters, comparisons were made between exogenous and endogenous characteristics of subjects who progressed toward T2D (HbA1c > 6.5) over the course of the DPP study to those who did not. The comparison revealed significant differences in diets and pancreatic sensitivity to hyperglycemia but not in propensity to develop insulin resistance. A computational experiment was performed to explore relative contributions of exogenous versus endogenous factors between these groups. Translational uses to applications in public health and personalized healthcare are discussed.

Maternal obesity increases insulin resistance, low-grade inflammation and osteochondrosis lesions in foals and yearlings until 18 months of age

INTRODUCTION

Obesity is a growing concern in horses. The effects of maternal obesity on maternal metabolism and low-grade inflammation during pregnancy, as well as offspring growth, metabolism, low-grade inflammation, testicular maturation and osteochondrotic lesions until 18 months of age were investigated.

MATERIAL AND METHODS

Twenty-four mares were used and separated into two groups at insemination according to body condition score (BCS): Normal (N, n = 10, BCS ≤4) and Obese (O, n = 14, BCS ≥4.25). BCS and plasma glucose, insulin, triglyceride, urea, non-esterified fatty acid, serum amyloid A (SAA), leptin and adiponectin concentrations were monitored throughout gestation. At 300 days of gestation, a Frequently Sampled Intravenous Glucose Tolerance Test (FSIGT) was performed. After parturition, foals' weight and size were monitored until 18 months of age with plasma SAA, leptin, adiponectin, triiodothyronine (T3), thyroxine (T4) and cortisol concentrations measured at regular intervals. At 6, 12 and 18 months of age, FSIGT and osteoarticular examinations were performed. Males were gelded at one year and expression of genes involved in testicular maturation analysed by RT-qPCR.

RESULTS

Throughout the experiment, maternal BCS was higher in O versus N mares. During gestation, plasma urea and adiponectin were decreased and SAA and leptin increased in O versus N mares. O mares were also more insulin resistant than N mares with a higher glucose effectiveness. Postnatally, there was no difference in offspring growth between groups. Nevertheless, plasma SAA concentrations were increased in O versus N foals until 6 months, with O foals being consistently more insulin resistant with a higher glucose effectiveness. At 12 months of age, O foals were significantly more affected by osteochondrosis than N foals. All other parameters were not different between groups.

CONCLUSION

In conclusion, maternal obesity altered metabolism and increased low-grade inflammation in both dams and foals. The risk of developing osteochondrosis at 12 months of age was also higher in foals born to obese dams.

Vascular endothelial growth factor-B: Impact on physiology and pathology

Angiogenesis plays an important role in controlling tissue development and maintaining normal tissue function. Dysregulated angiogenesis is implicated in the pathogenesis of a variety of diseases, particularly diabetes, cancers, and neurodegenerative disorders. As the major regulator of angiogenesis, the vascular endothelial growth factor (VEGF) family is composed of a group of crucial members including VEGF-B. While the physiological roles of VEGF-B remain debatable, increasing evidence suggests that this protein is able to protect certain type of cells from apoptosis under pathological conditions. More importantly, recent studies reveal that VEGF-B is involved in lipid transport and energy metabolism, implicating this protein in obesity, diabetes and related metabolic complications. This article summarizes the current knowledge and understanding of VEGF-B in physiology and pathology, and shed light on the therapeutic potential of this crucial protein.

Oscillatory dynamics of an intravenous glucose tolerance test model with delay interval

Type 2 diabetes mellitus (T2DM) has become prevalent pandemic disease in view of the modern life style. Both diabetic population and health expenses grow rapidly according to American Diabetes Association. Detecting the potential onset of T2DM is an essential focal point in the research of diabetes mellitus. The intravenous glucose tolerance test (IVGTT) is an effective protocol to determine the insulin sensitivity, glucose effectiveness, and pancreatic β-cell functionality, through the analysis and parameter estimation of a proper differential equation model. Delay differential equations have been used to study the complex physiological phenomena including the glucose and insulin regulations. In this paper, we propose a novel approach to model the time delay in IVGTT modeling. This novel approach uses two parameters to simulate not only both discrete time delay and distributed time delay in the past interval, but also the time delay distributed in a past sub-interval. Normally, larger time delay, either a discrete or a distributed delay, will destabilize the system. However, we find that time delay over a sub-interval might not. We present analytically some basic model properties, which are desirable biologically and mathematically. We show that this relatively simple model provides good fit to fluctuating patient data sets and reveals some intriguing dynamics. Moreover, our numerical simulation results indicate that our model may remove the defect in well known Minimal Model, which often overestimates the glucose effectiveness index.

Thymoquinone ameliorates diabetic phenotype in Diet-Induced Obesity mice via activation of SIRT-1-dependent pathways

Thymoquinone, a natural occurring quinone and the main bioactive component of plant Nigella sativa, undergoes intracellular redox cycling and re-oxidizes NADH to NAD⁺. TQ administration (20 mg/kg/bw/day) to the Diet-Induced Obesity (DIO) mice reduced their diabetic phenotype by decreasing fasting blood glucose and fasting insulin levels, and improved glucose tolerance and insulin sensitivity as evaluated by oral glucose and insulin tolerance tests (OGTT and ITT). Furthermore, TQ decreased serum cholesterol levels and liver triglycerides, increased protein expression of phosphorylated Akt, decreased serum levels of inflammatory markers resistin and MCP-1, and decreased NADH/NAD⁺ ratio. These changes were paralleled by an increase in phosphorylated SIRT-1 and AMPKα in liver and phosphorylated SIRT-1 in skeletal muscle. TQ also increased insulin sensitivity in insulin-resistant HepG2 cells via a SIRT-1-dependent mechanism. These findings are consistent with the TQ-dependent re-oxidation of NADH to NAD⁺, which stimulates glucose and fatty acid oxidation and activation of SIRT-1-dependent pathways. Taken together, these results demonstrate that TQ ameliorates the diabetic phenotype in the DIO mouse model of type 2 diabetes.

Evidence That the Sympathetic Nervous System Elicits Rapid, Coordinated, and Reciprocal Adjustments of Insulin Secretion and Insulin Sensitivity During Cold Exposure

Dynamic adjustment of insulin secretion to compensate for changes of insulin sensitivity that result from alteration of nutritional or metabolic status is a fundamental aspect of glucose homeostasis. To investigate the role of the brain in this coupling process, we used cold exposure as an experimental paradigm because the sympathetic nervous system (SNS) helps to coordinate the major shifts of tissue glucose utilization needed to ensure that increased thermogenic needs are met. We found that glucose-induced insulin secretion declined by 50% in rats housed at 5°C for 28 h, and yet, glucose tolerance did not change, owing to a doubling of insulin sensitivity. These potent effects on insulin secretion and sensitivity were fully reversed by returning animals to room temperature (22°C) for 4 h or by intravenous infusion of the α-adrenergic receptor antagonist phentolamine for only 30 min. By comparison, insulin clearance was not affected by cold exposure or phentolamine infusion. These findings offer direct evidence of a key role for the brain, acting via the SNS, in the rapid, highly coordinated, and reciprocal changes of insulin secretion and insulin sensitivity that preserve glucose homeostasis in the setting of cold exposure.

Linear feedback-based control of blood glucose in a modified model for glucose–insulin kinetics: A theoretical study

In this paper, we present a modified minimal model for glucose and insulin kinetics model. The model proposed here is a smooth approximation of the original non-smooth minimal model. The dynamical properties like dissipativity, existence of equilibrium and stability of the system at the equilibrium points are investigated. A linear feedback-based control strategy is studied to control the blood glucose level in the situation where the physical system fails to regulate the blood glucose level automatically. A critical control parameter value [Formula: see text] is determined in terms of the system parameters. Extensive numerical simulation is performed with different parameter sets. Assuming different values for the feedback gain parameter, ranges of physiological parameter [Formula: see text] are determined where the feedback gain is sufficient to stabilize the control system.

Maternal Nutrition during Pregnancy Affects Testicular and Bone Development, Glucose Metabolism and Response to Overnutrition in Weaned Horses Up to Two Years

Introduction

Pregnant mares and post-weaning foals are often fed concentrates rich in soluble carbohydrates, together with forage. Recent studies suggest that the use of concentrates is linked to alterations of metabolism and the development of osteochondrosis in foals. The aim of this study was to determine if broodmare diet during gestation affects metabolism, osteoarticular status and growth of yearlings overfed from 20 to 24 months of age and/or sexual maturity in prepubertal colts.

Material and methods

Twenty-four saddlebred mares were fed forage only (n = 12, group F) or cracked barley and forage (n = 12, group B) from mid-gestation until foaling. Colts were gelded at 12 months of age. Between 20 and 24 months of age, all yearlings were overfed (+140% of requirements) using an automatic concentrate feeder. Offspring were monitored for growth between 6 and 24 months of age, glucose homeostasis was evaluated via modified frequently sampled intra veinous glucose tolerance test (FSIGT) at 19 and 24 months of age and osteoarticular status was investigated using radiographic examinations at 24 months of age. The structure and function of testicles from prepubertal colts were analyzed using stereology and RT-qPCR.

Results

Post-weaning weight growth was not different between groups. Testicular maturation was delayed in F colts compared to B colts at 12 months of age. From 19 months of age, the cannon bone was wider in B vs F yearlings. F yearlings were more insulin resistant at 19 months compared to B yearlings but B yearlings were affected more severely by overnutrition with reduced insulin sensitivity. The osteoarticular status at 24 months of age was not different between groups.

Conclusion

In conclusion, nutritional management of the pregnant broodmare and the growing foal may affect sexual maturity of colts and the metabolism of foals until 24 months of age. These effects may be deleterious for reproductive and sportive performances in older horses.

Urea impairs β cell glycolysis and insulin secretion in chronic kidney disease

Disorders of glucose homeostasis are common in chronic kidney disease (CKD) and are associated with increased mortality, but the mechanisms of impaired insulin secretion in this disease remain unclear. Here, we tested the hypothesis that defective insulin secretion in CKD is caused by a direct effect of urea on pancreatic β cells. In a murine model in which CKD is induced by 5/6 nephrectomy (CKD mice), we observed defects in glucose-stimulated insulin secretion in vivo and in isolated islets. Similarly, insulin secretion was impaired in normal mouse and human islets that were cultured with disease-relevant concentrations of urea and in islets from normal mice treated orally with urea for 3 weeks. In CKD mouse islets as well as urea-exposed normal islets, we observed an increase in oxidative stress and protein O-GlcNAcylation. Protein O-GlcNAcylation was also observed in pancreatic sections from CKD patients. Impairment of insulin secretion in both CKD mouse and urea-exposed islets was associated with reduced glucose utilization and activity of phosphofructokinase 1 (PFK-1), which could be reversed by inhibiting O-GlcNAcylation. Inhibition of O-GlcNAcylation also restored insulin secretion in both mouse models. These results suggest that insulin secretory defects associated with CKD arise from elevated circulating levels of urea that increase islet protein O-GlcNAcylation and impair glycolysis.

Tissue-Specific and Genetic Regulation of Insulin Sensitivity-Associated Transcripts in African Americans

Integrative multiomics analyses of adipose and muscle tissue transcripts, S, and genotypes revealed novel genetic regulatory mechanisms of insulin resistance in African Americans.

Evaluation of Four Diagnostic Tests for Insulin Dysregulation in Adult Light-Breed Horses

BACKGROUND

Several tests have been evaluated in horses for quantifying insulin dysregulation to support a diagnosis of equine metabolic syndrome. Comparing the performance of these tests in the same horses will provide clarification of their accuracy in the diagnosis of equine insulin dysregulation.

OBJECTIVES

The aim of this study was to evaluate the agreement between basal serum insulin concentrations (BIC), the oral sugar test (OST), the combined glucose-insulin test (CGIT), and the frequently sampled insulin-modified intravenous glucose tolerance test (FSIGTT).

ANIMALS

Twelve healthy, light-breed horses.

METHODS

Randomized, prospective study. Each of the above tests was performed on 12 horses.

RESULTS

Minimal model analysis of the FSIGTT was considered the reference standard and classified 7 horses as insulin resistant (IR) and 5 as insulin sensitive (IS). In contrast, BIC and OST assessment using conventional cut-off values classified all horses as IS. Kappa coefficients, measuring agreement among BIC, OST, CGIT, and FSIGTT were poor to fair. Sensitivity of the CGIT (positive phase duration of the glucose curve >45 minutes) was 85.7% and specificity was 40%, whereas CGIT ([insulin]45 >100 μIU/mL) sensitivity and specificity were 28.5% and 100%, respectively. Area under the glucose curve (AUCg0-120 ) was significantly correlated among the OST, CGIT, and FSIGTT, but Bland-Altman method and Lin's concordance coefficient showed a lack of agreement.

CONCLUSIONS

Current criteria for diagnosis of insulin resistance using BIC and the OST are highly specific but lack sensitivity. The CGIT displayed better sensitivity and specificity, but modifications may be necessary to improve agreement with minimal model analysis.

Modeling glucose and free fatty acid kinetics in glucose and meal tolerance test

BACKGROUND

Quantitative evaluation of insulin regulation on plasma glucose and free fatty acid (FFA) in response to external glucose challenge is clinically important to assess the development of insulin resistance (World J Diabetes 1:36-47, 2010). Mathematical minimal models (MMs) based on insulin modified frequently-sampled intravenous glucose tolerance tests (IM-FSIGT) are widely applied to ascertain an insulin sensitivity index (IEEE Rev Biomed Eng 2:54-96, 2009). Furthermore, it is important to investigate insulin regulation on glucose and FFA in postprandial state as a normal physiological condition. A simple way to calculate the appearance rate (Ra) of glucose and FFA would be especially helpful to evaluate glucose and FFA kinetics for clinical applications.

METHODS

A new MM is developed to simulate the insulin modulation of plasma glucose and FFA, combining IM-FSIGT with a mixed meal tolerance test (MT). A novel simple functional form for the appearance rate (Ra) of glucose or FFA in the MT is developed. Model results are compared with two other models for data obtained from 28 non-diabetic women (13 African American, 15 white).

RESULTS

The new functional form for Ra of glucose is an acceptable empirical approximation to the experimental Ra for a subset of individuals. When both glucose and FFA are included in FSIGT and MT, the new model is preferred using the Bayes Information Criterion (BIC).

CONCLUSIONS

Model simulations show that the new MM allows consistent application to both IM-FSIGT and MT data, balancing model complexity and data fitting. While the appearance of glucose in the circulation has an important effect on FFA kinetics in MT, the rate of appearance of FFA can be neglected for the time-period modeled.

Development and assessment of the disposition index based on the oral glucose tolerance test in subjects with different glycaemic status

Insulin secretion and insulin sensitivity indexes are related by hyperbolic functions, allowing the calculation of the disposition index (DI) as the product of the acute insulin response (AIR) and the insulin sensitivity index (Si) from intravenous glucose tolerance test (IVGTT). Our objective was to develop an oral-DI based on the oral glucose tolerance test (OGTT) and to assess its association with glucose tolerance status. This research is structured in three studies. Study 1: OGTT were performed in 833 non-diabetic Chilean women (18-60 years) without family history of diabetes mellitus. Study 2: an independent group of n = 57 non-diabetic (18-46 years) without family history of diabetes mellitus carried out an OGTT and an abbreviated IVGTT. Study 3: a sample of 1674 Chilean adults (18-60 years) with different glycaemic status performed an OGTT. An adequate statistical fit for a rectangular hyperbola was found between the area under the curve of insulin-to-glucose ratio (AUCI/G-R) and the Matsuda ISI-COMP index (study 1). The oral-DI derived as AUCI/G-R × ISI-COMP was previously termed insulin-secretion-sensitivity index-2 (ISSI-2). ISSI-2 significantly correlated with DI from IVGTT (rho = 0.34; p = 0.009) (study 2). ISSI-2 shows important differences across groups of subjects with different glycaemic status (study 3). We have confirmed that ISSI-2 replicates the mathematical properties of DI, showing significant correlations with DI from the abbreviated MM-IVGTT. These results indicate that ISSI-2 constitutes a surrogate measure of insulin secretion relative to insulin sensitivity and emphasizes the pivotal role of impaired insulin secretion in the development of glucose homeostasis dysregulation.

Activation of innate immunity modulates insulin sensitivity, glucose effectiveness and pancreatic β-cell function in both African ancestry and European ancestry healthy humans

OBJECTIVE

Insulin resistance is a risk factor for type 2 diabetes, and is associated with inflammatory cardiometabolic disease. Given differences between African ancestry (AA) and European ancestry (EA) in the epidemiology of type 2 diabetes as well as in response to inflammatory stress, we investigated potential race differences in glucose homeostasis responses during experimental endotoxemia in humans.

METHODS

Healthy volunteers (age 18-45 years, BMI 18-30 kg/m(2), 47% female, African-ancestry (AA, n=42) and European-ancestry (EA, n=106)) were recruited as part of the Genetics of Evoked Responses to Niacin and Endotoxemia (GENE) Study. Subjects underwent an inpatient endotoxin challenge (1 ng/kg LPS) and two frequently-sampled intravenous glucose tolerance tests (FSIGTT). Insulin and glucose values obtained during FSIGTT pre- and 24-hours post-LPS were analyzed using the minimal model.

RESULTS

FSIGTT derived insulin sensitivity index (SI), disposition index (DI) and glucose effectiveness (SG) decreased significantly following LPS (p<0.0001) while the acute insulin response to glucose (AIR(g)) increased (p<0.0001). Although expected race differences were observed in glucose homeostasis parameters at baseline prior to LPS e.g., lower SI (2.5 vs. 4.1 μU/L/min, p<0.0001) but higher AIR(g) (median 848 vs. 290 μU/L/min, p<0.0001) in AA vs. EA, the changes in glucose homeostasis responses to LPS were directionally and proportionally consistent across race e.g., SI median -35% in EA and -29% in AA and AIR(g) median +17% in EA and +26% in AA.

CONCLUSION

Both EA and AA samples modulated glucose and insulin homeostasis similarly during endotoxemia.

IMPLICATIONS

Race differences in response to environmental inflammatory stress are unlikely to be a substantial contributor to the observed difference in diabetes incidence and complications between EA and AA.

Insulin response to oral stimuli and glucose effectiveness increased in neuroglycopenia following gastric bypass

OBJECTIVE

Hyperinsulinemic hypoglycemia with neuroglycopenia is a rare complication following Roux-en-Y gastric bypass (RYGB) surgery for weight management. Insulin secretion and action in response to oral and intravenous stimuli in persons with and without neuroglycopenia following RYGB are evaluated in this study.

METHODS

Cross-sectional cohort studies were performed at a single academic institution to assess insulin secretion and action during oral mixed meal tolerance test and intravenous glucose tolerance test (IVGTT).

RESULTS

Insulin secretion was increased more following oral mixed meal than intravenous glucose in individuals with neuroglycopenia compared to the asymptomatic group. Reduced insulin clearance did not contribute to higher insulinemia. Glucose effectiveness at zero insulin, estimated during the IVGTT, was also higher in those with neuroglycopenia. Insulin sensitivity did not differ between groups.

CONCLUSIONS

Increased beta-cell response to oral stimuli and insulin-independent glucose disposal may both contribute to severe hypoglycemia after RYGB.

A physiology-based model describing heterogeneity in glucose metabolism: the core of the Eindhoven Diabetes Education Simulator (E-DES)

Current diabetes education methods are costly, time-consuming, and do not actively engage the patient. Here, we describe the development and verification of the physiological model for healthy subjects that forms the basis of the Eindhoven Diabetes Education Simulator (E-DES). E-DES shall provide diabetes patients with an individualized virtual practice environment incorporating the main factors that influence glycemic control: food, exercise, and medication. The physiological model consists of 4 compartments for which the inflow and outflow of glucose and insulin are calculated using 6 nonlinear coupled differential equations and 14 parameters. These parameters are estimated on 12 sets of oral glucose tolerance test (OGTT) data (226 healthy subjects) obtained from literature. The resulting parameter set is verified on 8 separate literature OGTT data sets (229 subjects). The model is considered verified if 95% of the glucose data points lie within an acceptance range of ±20% of the corresponding model value. All glucose data points of the verification data sets lie within the predefined acceptance range. Physiological processes represented in the model include insulin resistance and β-cell function. Adjusting the corresponding parameters allows to describe heterogeneity in the data and shows the capabilities of this model for individualization. We have verified the physiological model of the E-DES for healthy subjects. Heterogeneity of the data has successfully been modeled by adjusting the 4 parameters describing insulin resistance and β-cell function. Our model will form the basis of a simulator providing individualized education on glucose control.

A Long-Term Model of the Glucose-Insulin Dynamics of Type 1 Diabetes

A new glucose-insulin model is introduced which fits with the clinical data from in- and outpatients for two days. Its stability property is consistent with the glycemia behavior for type 1 diabetes. This is in contrast to traditional glucose-insulin models. Prior models fit with clinical data for a few hours only or display some nonnatural equilibria. The parameters of this new model are identifiable from standard clinical data as continuous glucose monitoring, insulin injection, and carbohydrate estimate. Moreover, it is shown that the parameters from the model allow the computation of the standard tools used in functional insulin therapy as the basal rate of insulin and the insulin sensitivity factor. This is a major outcome as they are required in therapeutic education of type 1 diabetic patients.

Gut-brain mechanisms controlling glucose homeostasis

Our current understanding of glucose homeostasis is centered on glucose-induced secretion of insulin from pancreatic islets and insulin action on glucose metabolism in peripheral tissues. In addition, however, recent evidence suggests that neurocircuits located within a brain-centered glucoregulatory system work cooperatively with pancreatic islets to promote glucose homeostasis. Among key observations is evidence that, in addition to insulin-dependent mechanisms, the brain has the capacity to potently lower blood glucose levels via mechanisms that are insulin-independent, some of which are activated by signals emanating from the gastrointestinal tract. This review highlights evidence supporting a key role for a “gut-brain-liver axis” in control of glucose homeostasis by the brain-centered glucoregulatory system and the implications of this regulatory system for diabetes pathogenesis and treatment.

Thrombin stimulates insulin secretion via protease-activated receptor-3

The disease mechanisms underlying type 2 diabetes (T2D) remain poorly defined. Here we aimed to explore the pathophysiology of T2D by analyzing gene co-expression networks in human islets. Using partial correlation networks we identified a group of co-expressed genes ('module') including F2RL2 that was associated with glycated hemoglobin. F2Rl2 is a G-protein-coupled receptor (GPCR) that encodes protease-activated receptor-3 (PAR3). PAR3 is cleaved by thrombin, which exposes a 6-amino acid sequence that acts as a 'tethered ligand' to regulate cellular signaling. We have characterized the effect of PAR3 activation on insulin secretion by static insulin secretion measurements, capacitance measurements, studies of diabetic animal models and patient samples. We demonstrate that thrombin stimulates insulin secretion, an effect that was prevented by an antibody that blocks the thrombin cleavage site of PAR3. Treatment with a peptide corresponding to the PAR3 tethered ligand stimulated islet insulin secretion and single β-cell exocytosis by a mechanism that involves activation of phospholipase C and Ca(2+) release from intracellular stores. Moreover, we observed that the expression of tissue factor, which regulates thrombin generation, was increased in human islets from T2D donors and associated with enhanced β-cell exocytosis. Finally, we demonstrate that thrombin generation potential in patients with T2D was associated with increased fasting insulin and insulinogenic index. The findings provide a previously unrecognized link between hypercoagulability and hyperinsulinemia and suggest that reducing thrombin activity or blocking PAR3 cleavage could potentially counteract the exaggerated insulin secretion that drives insulin resistance and β-cell exhaustion in T2D.

Serum pentadecanoic acid (15:0), a short-term marker of dairy food intake, is inversely associated with incident type 2 diabetes and its underlying disorders

BACKGROUND

Growing evidence suggests that dairy consumption is associated with lower type 2 diabetes risk. However, observational studies have reported inconsistent results, and few have examined dairy's association with the underlying disorders of insulin resistance and β-cell dysfunction.

OBJECTIVE

We investigated the association of the dairy fatty acid biomarkers pentadecanoic acid (15:0) and trans-palmitoleic acid (trans 16:1n-7) with type 2 diabetes traits by evaluating 1) prospective associations with incident diabetes after 5 y of follow-up and 2) cross-sectional associations with directly measured insulin resistance and β-cell dysfunction.

DESIGN

The study analyzed 659 adults without diabetes at baseline from the triethnic multicenter Insulin Resistance Atherosclerosis Study (IRAS). Diabetes status was assessed by using oral-glucose-tolerance tests. Frequently sampled intravenous-glucose-tolerance tests measured insulin sensitivity (SI) and β-cell function [disposition index (DI)]. Serum fatty acids were quantified by using gas chromatography. Logistic and linear regression models were adjusted for demographic, lifestyle, and dietary variables.

RESULTS

Serum 15:0 was a significant biomarker for total dairy intake in the IRAS cohort. It was associated with a decreased incident diabetes risk (OR: 0.73, P = 0.02) and was positively associated with log SI (β: 0.84, P = 0.03) and log DI (β: 2.21, P = 0.02) in fully adjusted models. trans 16:1n-7 was a marker of total partially hydrogenated dietary fat intake and was not associated with outcomes in fully adjusted models.

CONCLUSIONS

Serum 15:0, a marker of short-term intake of this fatty acid, was inversely associated with diabetes risk in this multiethnic cohort. This study may contribute to future recommendations regarding the benefits of dairy products on type 2 diabetes risk.

Vegetable consumption is linked to decreased visceral and liver fat and improved insulin resistance in overweight Latino youth

There are limited data on the influence of vegetable consumption on adiposity and metabolic health, specifically nonstarchy vegetables and vegetables that are dark green and deep orange/yellow (also known as nutrient-rich vegetables). Our study examines the relationship between vegetable intake and adiposity, liver fat, and insulin dynamics in overweight Latino youth. This cross-sectional study of 175 overweight (body mass index ≥85th percentile) Latino youth (aged 8 to 18 years), with data collected during 2006-2011, included the following: dietary intake via multiple 24-hour recalls, total body fat via dual-energy x-ray absorptiometry, adipose tissue distribution and liver fat via magnetic resonance imaging, and insulin dynamics via frequently sampled intravenous glucose tolerance test. Linear regression and analysis of covariance were used for analysis, with the following a priori covariates: age, sex, energy intake, and total body fat. Participants who consumed the most nonstarchy vegetables (mean intake=1.7±1.0 servings/day) compared with the least (mean intake=0.1±0.1 servings/day) had 44% less liver fat (10.0%±8.5% vs 5.6%±8.7%; P=0.01). Nutrient-rich vegetable intake was positively correlated with insulin sensitivity (r=0.19; P=0.03). Consumers of nutrient-rich vegetables (mean intake=0.3±0.4 servings/day [n=107]), compared with nonconsumers (n=68), had 31% increased insulin sensitivity (1.6±1.6 vs 2.1±1.3×10(⁻⁴)/min/μU/mL; P=0.03) and 17% less visceral adipose tissue (2.3±0.9 vs 1.9±0.7 L; P=0.01). Consumption of specific vegetable types by overweight Latino youth is associated with positive metabolic outcomes, including reduced visceral and liver fat and risk factors for type 2 diabetes, even when consumed in small quantities. These may be relevant targets for interventions.

High heritability and genetic correlation of intravenous glucose- and tolbutamide-induced insulin secretion among non-diabetic family members of type 2 diabetic patients

AIMS/HYPOTHESIS

The aim of this study was to estimate the heritability of quantitative measures of glucose regulation obtained from a tolbutamide-modified frequently sampled IVGTT (t-FSIGT) and to correlate the heritability of the glucose-stimulated beta cell response to the tolbutamide-induced beta cell response. In addition, single nucleotide polymorphisms (SNPs) having an exclusive effect on either glucose- or tolbutamide-stimulated insulin release were identified.

METHODS

Two hundred and eighty-four non-diabetic family members of patients with type 2 diabetes underwent a t-FSIGT with intravenous injection of glucose at t = 0 min and tolbutamide at t = 20 min. Measurements of plasma glucose, serum insulin and serum C-peptide were taken at 33 time points from fasting to 180 min. Insulin secretion rate, acute insulin response (AIR), disposition index (DI) after glucose and disposition index after tolbutamide (DIT), insulin sensitivity (SI), glucose effectiveness (SG) and beta cell responsiveness to glucose were calculated. A polygenic variance component model was used to estimate heritability, genetic correlations and associations.

RESULTS

We found high heritabilities for acute insulin secretion subsequent to glucose stimulation (AIRglucose h (2) ± SE: 0.88 ± 0.14), but these were slightly lower after tolbutamide (AIRtolbutamide h (2) ± SE: 0.69 ± 0.14). We also estimated the heritabilities for SI (h (2) ± SE: 0.26 ± 0.12), SG (h (2) ± SE: 0.47 ± 0.13), DI (h (2) ± SE: 0.56 ± 0.14), DIT (h (2) ± SE: 0.49 ± 0.14) and beta cell responsiveness to glucose (h (2) ± SE: 0.66 ± 0.12). Additionally, strong genetic correlations were found between measures of beta cell response after glucose and tolbutamide stimulation, with correlation coefficients ranging from 0.77 to 0.88. Furthermore, we identified five SNPs with an exclusive effect on either glucose-stimulated (rs5215, rs1111875, rs11920090) or tolbutamide-stimulated (rs10946398, rs864745) insulin secretion.

CONCLUSIONS/INTERPRETATION

Our data demonstrate that both glucose- and tolbutamide-induced insulin secretions are highly heritable traits, which are largely under the control of the same genes.

Use of a simple liquid meal test to evaluate insulin sensitivity and beta-cell function in children

Insulin sensitivity and β-cell function are useful indices of metabolic disease risk but are difficult to assess in young children because of the invasive nature of commonly used methodology. A meal-based method for assessing insulin sensitivity and β-cell function may at least partially alleviate concerns. The objectives of this study were to: (i) determine the association of insulin sensitivity assessed by liquid meal test with that determined by an insulin-modified frequently sampled intravenous glucose tolerance test (FSIGT); (ii) examine the association of insulin sensitivity derived from each test with measures of body composition, fat distribution and metabolic health (lipids, fasting insulin and glucose, and surrogate indices of insulin sensitivity); and (iii) examine the associations of indices of β-cell function derived from each test with total and regional adiposity. Forty-seven children (7-12 years) underwent both a liquid meal test and an FSIGT. The insulin sensitivity index derived from the meal test (SI-meal) was positively associated with that from the FSIGT (SI-FSIGT; r = 0.63; P < 0.001), and inversely with all measures of insulin secretion derived from the meal test. Both SI-meal and SI-FSIGT were associated with measures of total and regional adiposity. SI-meal, but not SI-FSIGT, was associated with triglycerides and fasting insulin, after adjusting for ethnicity, gender, pubertal stage and fat mass. Basal insulin secretion measured during the meal test was positively associated with all measures of adiposity, independent of insulin sensitivity. In conclusion, a liquid meal offers a valid and sensitive means of assessing insulin sensitivity and β-cell responsivity in young children.

Long-term models of oxidative stress and mitochondrial damage in insulin resistance progression

Insulin resistance, characterized by a reduced cellular response to insulin, is a major factor in type 2 diabetes pathogenesis, with a complex etiology consisting of a combination of environmental and genetic factors. Oxidative stress, which develops through an accumulation of toxic reactive oxygen species generated by mitochondria, is believed to contribute to insulin resistance in certain tissues. We develop mathematical models of feedback between reactive oxygen species production and dysfunction in mitochondria to provide insight into the role of oxidative stress in insulin resistance. Our models indicate that oxidative stress generated by glucose overload accelerates irreversible mitochondrial dysfunction. These models provide a foundation for understanding the long-term progression of insulin resistance and type 2 diabetes.

Population pharmacodynamic modeling of hyperglycemic clamp and meal tolerance tests in patients with type 2 diabetes mellitus

In this study, glucose and insulin concentration-time profiles in subjects with type 2 diabetes mellitus (T2DM) under meal tolerance test (MTT) and hyperglycemic clamp (HGC) conditions were co-modeled simultaneously. Blood glucose and insulin concentrations were obtained from 20 subjects enrolled in a double-blind, placebo-controlled, randomized, two-way crossover study. Patients were treated with palosuran or placebo twice daily for 4 weeks and then switched to the alternative treatment after a 4-week washout period. The MTT and HGC tests were performed 1 h after drug administration on days 28 and 29 of each treatment period. Population data analysis was performed using NONMEM. The HGC model incorporates insulin-dependent glucose clearance and glucose-induced insulin secretion. This model was extended for the MTT, in which glucose absorption was described using a transit compartment with a mean transit time of 62.5 min. The incretin effect (insulin secretion triggered by oral glucose intake) was also included, but palosuran did not influence insulin secretion or sensitivity. Glucose clearance was 0.164 L/min with intersubject and interoccasion variability of 9.57% and 31.8%. Insulin-dependent glucose clearance for the HGC was about 3-fold greater than for the MTT (0.0111 vs. 0.00425 L/min/[mU/L]). The maximal incretin effect was estimated to enhance insulin secretion 2-fold. The lack of palosuran effect coupled with a population-based analysis provided quantitative insights into the variability of glucose and insulin regulation in patients with T2DM following multiple glucose tolerance tests. Application of these models may also prove useful in antihyperglycemic drug development and assessing glucose-insulin homeostasis.