Origins and History of the Minimal Model of Glucose Regulation

Myths and methodologies: Assessing glycaemic control and associated regulatory mechanisms in human physiology research

Accurate measurements of glycaemic control and the underpinning regulatory mechanisms are vital in human physiology research. Glycaemic control is the maintenance of blood glucose concentrations within optimal levels and is governed by physiological variables including insulin sensitivity, glucose tolerance and β-cell function. These can be measured with a plethora of methods, all with their own benefits and limitations. Deciding on the best method to use is challenging and depends on the specific research question(s). This review therefore discusses the theory and procedure, validity and reliability and any special considerations of a range common methods used to measure glycaemic control, insulin sensitivity, glucose tolerance and β-cell function. Methods reviewed include glycosylated haemoglobin, continuous glucose monitors, the oral glucose tolerance test, mixed meal tolerance test, hyperinsulinaemic euglycaemic clamp, hyperglycaemic clamp, intravenous glucose tolerance test and indices derived from both fasting concentrations and the oral glucose tolerance test. This review aims to help direct understanding, assessment and decisions regarding which method to use based on specific physiology-related research questions.

Glucose Transporters Are Key Components of the Human Glucostat

Mouse models are extensively used in metabolic studies. However, inherent differences between the species, notably their blood glucose levels, hampered data translation into clinical settings. In this study, we confirmed GLUT1 to be the predominantly expressed glucose transporter in both adult and fetal human β-cells. In comparison, GLUT2 is detected in a small yet significant subpopulation of adult β-cells and is expressed to a greater extent in fetal β-cells. Notably, GLUT1/2 expression in INS+ cells from human stem cell-derived islet-like clusters (SC-islets) exhibited a closer resemblance to that observed in fetal islets. Transplantation of primary human islets or SC-islets, but not murine islets, lowered murine blood glucose to the human glycemic range, emphasizing the critical role of β-cells in establishing species-specific glycemia. We further demonstrate the functional requirements of GLUT1 and GLUT2 in glucose uptake and insulin secretion through chemically inhibiting GLUT1 in primary islets and SC-islets and genetically disrupting GLUT2 in SC-islets. Finally, we developed a mathematical model to predict changes in glucose uptake and insulin secretion as a function of GLUT1/2 expression. Collectively, our findings illustrate the crucial roles of GLUTs in human β-cells, and identify them as key components in establishing species-specific glycemic set points.

ARTICLE HIGHLIGHTS

Multilevel functional data analysis modeling of human glucose response to meal intake

Glucose meal response information collected via Continuous Glucose Monitoring (CGM) is relevant to the assessment of individual metabolic status and the support of personalized diet prescriptions. However, the complexity of the data produced by CGM monitors pushes the limits of existing analytic methods. CGM data often exhibits substantial within-person variability and has a natural multilevel structure. This research is motivated by the analysis of CGM data from individuals without diabetes in the AEGIS study. The dataset includes detailed information on meal timing and nutrition for each individual over different days. The primary focus of this study is to examine CGM glucose responses following patients' meals and explore the time-dependent associations with dietary and patient characteristics. Motivated by this problem, we propose a new analytical framework based on multilevel functional models, including a new functional mixed R-square coefficient. The use of these models illustrates 3 key points: (i) The importance of analyzing glucose responses across the entire functional domain when making diet recommendations; (ii) The differential metabolic responses between normoglycemic and prediabetic patients, particularly with regards to lipid intake; (iii) The importance of including random, person-level effects when modelling this scientific problem.

Total Pancreatectomy in a Patient Treated with a Sensor-augmented Pump Showing No Evidence of Hyperglycemia or Ketoacidosis without Any Insulin Administration

Total pancreatectomy results in complete loss of insulin and glucagon. Sensor-augmented pumps (SAPs) allow fine-tuning of the basal insulin rate, which helps avoid both hypo- and hyperglycemic events. We herein report a case of total pancreatectomy treated with a SAP with no evidence of ketoacidosis without any insulin administration during a certain period of time. Furthermore, we observed a sudden drop in blood glucose levels without insulin, which may have been due to glucose effectiveness. Our case is valuable in arguing the concept of glucose effectiveness in the absence of insulin and glucagon.

Glucagon kinetics assessed by mathematical modelling during oral glucose administration in people spanning from normal glucose tolerance to type 2 diabetes

Background/Objectives

Glucagon is important in the maintenance of glucose homeostasis, with also effects on lipids. In this study, we aimed to apply a recently developed model of glucagon kinetics to determine the sensitivity of glucagon variations (especially, glucagon inhibition) to insulin levels ("alpha-cell insulin sensitivity"), during oral glucose administration.

Subjects/Methods

We studied 50 participants (spanning from normal glucose tolerance to type 2 diabetes) undergoing frequently sampled 5-hr oral glucose tolerance test (OGTT). The alpha-cell insulin sensitivity and the glucagon kinetics were assessed by a mathematical model that we developed previously.

Results

The alpha-cell insulin sensitivity parameter (named SGLUCA; "GLUCA": "glucagon") was remarkably variable among participants (CV=221%). SGLUCA was found inversely correlated with the mean glycemic values, as well as with 2-hr glycemia of the OGTT. When stratifying participants into two groups (normal glucose tolerance, NGT, N=28, and impaired glucose regulation/type 2 diabetes, IGR_T2D, N=22), we found that SGLUCA was lower in the latter (1.50 ± 0.50·10-2 vs. 0.26 ± 0.14·10-2 ng·L-1 GLUCA/pmol·L-1 INS, in NGT and IGR_T2D, respectively, p=0.009; "INS": "insulin").

Conclusions

The alpha-cell insulin sensitivity is highly variable among subjects, and it is different in groups at different glucose tolerance. This may be relevant for defining personalized treatment schemes, in terms of dietary prescriptions but also for treatments with glucagon-related agents.

An extended minimal model of OGTT: estimation of α- and β-cell dysfunction, insulin resistance, and the incretin effect

Loss of insulin sensitivity, α- and β-cell dysfunction, and impairment in incretin effect have all been implicated in the pathophysiology of type 2 diabetes (T2D). Parsimonious mathematical models are useful in quantifying parameters related to the pathophysiology of T2D. Here, we extend the minimum model developed to describe the glucose-insulin-glucagon dynamics in the isoglycemic intravenous glucose infusion (IIGI) experiment to the oral glucose tolerance test (OGTT). The extended model describes glucose and hormone dynamics in OGTT including the contribution of the incretin hormones, glucose-dependent insulinotropic polypeptide (GIP), and glucagon-like peptide-1 (GLP-1), to insulin secretion. A new function describing glucose arrival from the gut is introduced. The model is fitted to OGTT data from eight individuals with T2D and eight weight-matched controls (CS) without diabetes to obtain parameters related to insulin sensitivity, β- and α-cell function. The parameters, i.e., measures of insulin sensitivity, a1, suppression of glucagon secretion, k1, magnitude of glucagon secretion, γ2, and incretin-dependent insulin secretion, γ3, were found to be different between CS and T2D with P values < 0.002, <0.017, <0.009, <0.004, respectively. A new rubric for estimating the incretin effect directly from modeling the OGTT is presented. The average incretin effect correlated well with the experimentally determined incretin effect with a Spearman rank test correlation coefficient of 0.67 (P < 0.012). The average incretin effect was found to be different between CS and T2D (P < 0.032). The developed model is shown to be effective in quantifying the factors relevant to T2D pathophysiology.NEW & NOTEWORTHY A new extended model of oral glucose tolerance test (OGTT) has been developed that includes glucagon dynamics and incretin contribution to insulin secretion. The model allows the estimation of parameters related to α- and β-cell dysfunction, insulin sensitivity, and incretin action. A new function describing the influx of glucose from the gut has been introduced. A new rubric for estimating the incretin effect directly from the OGTT experiment has been developed. The effect of glucose dose was also investigated.

DI/cle, a Measure Consisting of Insulin Sensitivity, Secretion, and Clearance, Captures Diabetic States

CONTEXT

Insulin clearance is implicated in regulation of glucose homeostasis independently of insulin sensitivity and insulin secretion.

OBJECTIVE

To understand the relation between blood glucose and insulin sensitivity, secretion, and clearance.

METHODS

We performed a hyperglycemic clamp, a hyperinsulinemic-euglycemic clamp, and an oral glucose tolerance test (OGTT) in 47, 16, and 49 subjects with normal glucose tolerance (NGT), impaired glucose tolerance (IGT), and type 2 diabetes mellitus (T2DM), respectively. Mathematical analyses were retrospectively performed on this dataset.

RESULTS

The disposition index (DI), defined as the product of insulin sensitivity and secretion, showed a weak correlation with blood glucose levels, especially in IGT (r = 0.04; 95% CI, -0.63 to 0.44). However, an equation relating DI, insulin clearance, and blood glucose levels was well conserved regardless of the extent of glucose intolerance. As a measure of the effect of insulin, we developed an index, designated disposition index/clearance, (DI/cle) that is based on this equation and corresponds to DI divided by the square of insulin clearance. DI/cle was not impaired in IGT compared with NGT, possibly as a result of a decrease in insulin clearance in response to a reduction in DI, whereas it was impaired in T2DM relative to IGT. Moreover, DI/cle estimated from a hyperinsulinemic-euglycemic clamp, OGTT, or a fasting blood test were significantly correlated with that estimated from 2 clamp tests (r = 0.52; 95% CI, 0.37 to 0.64, r = 0.43; 95% CI, 0.24 to 0.58, r = 0.54; 95% CI, 0.38 to 0.68, respectively).

CONCLUSION

DI/cle can serve as a new indicator for the trajectory of changes in glucose tolerance.

3D printing combined with biopredictive dissolution and PBPK/PD modeling optimization and personalization of pharmacotherapy: Are we there yet?

Precision medicine requires selecting the appropriate dosage regimen for a patient using the right drug, at the right time. Model-Informed Precision Dosing (MIPD) is a concept suggesting utilization of model-based prediction methods for optimizing the treatment benefit-harm balance, based on individual characteristics of the patient, disease, treatment method, and other factors. Here, we discuss a theoretical workflow comprising several elements, beginning from the physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) models, through 3D printed tablets with the model proposed dose, information range and flow, and the patient themselves. We also describe each of these elements, and the connection between them, highlighting challenges and potential obstacles.

Acute pharmacodynamic responses to exenatide: Drug-induced increases in insulin secretion and glucose effectiveness

AIM

Glucagon-like peptide-1 receptor agonists provide multiple benefits to patients with type 2 diabetes, including improved glycaemic control, weight loss and decreased risk of major adverse cardiovascular events. Because drug responses vary among individuals, we initiated investigations to identify genetic variants associated with the magnitude of drug responses.

METHODS

Exenatide (5 μg, subcutaneously) or saline (0.2 ml, subcutaneously) was administered to 62 healthy volunteers. Frequently sampled intravenous glucose tolerance tests were conducted to assess the impact of exenatide on insulin secretion and insulin action. This pilot study was a crossover design in which participants received exenatide and saline in random order.

RESULTS

Exenatide increased first phase insulin secretion 1.9-fold (p = 1.9 × 10-9 ) and accelerated the rate of glucose disappearance 2.4-fold (p = 2 × 10-10 ). Minimal model analysis showed that exenatide increased glucose effectiveness (Sg ) by 32% (p = .0008) but did not significantly affect insulin sensitivity (Si ). The exenatide-induced increase in insulin secretion made the largest contribution to interindividual variation in exenatide-induced acceleration of glucose disappearance while interindividual variation in the drug effect on Sg contributed to a lesser extent (β = 0.58 or 0.27, respectively).

CONCLUSIONS

This pilot study provides validation for the value of a frequently sampled intravenous glucose tolerance test (including minimal model analysis) to provide primary data for our ongoing pharmacogenomic study of pharmacodynamic effects of semaglutide (NCT05071898). Three endpoints provide quantitative assessments of the effects of glucagon-like peptide-1 receptor agonists on glucose metabolism: first phase insulin secretion, glucose disappearance rates and glucose effectiveness.

Acute pharmacodynamic responses to exenatide: Drug-induced increases in insulin secretion and glucose effectiveness

Background

GLP1R agonists provide multiple benefits to patients with type 2 diabetes - including improved glycemic control, weight loss, and decreased risk of major adverse cardiovascular events. Because drug responses vary among individuals, we initiated investigations to identify genetic variants associated with the magnitude of drug responses.

Methods

Exenatide (5 µg, sc) or saline (0.2 mL, sc) was administered to 62 healthy volunteers. Frequently sampled intravenous glucose tolerance tests were conducted to assess the impact of exenatide on insulin secretion and insulin action. This pilot study was designed as a crossover study in which participants received exenatide and saline in random order.

Results

Exenatide increased first phase insulin secretion 1.9-fold (p=1.9×10 -9 ) and accelerated the rate of glucose disappearance 2.4-fold (p=2×10 -10 ). Minimal model analysis demonstrated that exenatide increased glucose effectiveness (S g ) by 32% (p=0.0008) but did not significantly affect insulin sensitivity (S i ). The exenatide-induced increase in insulin secretion made the largest contribution to inter-individual variation in exenatide-induced acceleration of glucose disappearance while inter-individual variation in the drug effect on S g contributed to a lesser extent (β=0.58 or 0.27, respectively).

Conclusions

This pilot study provides validation for the value of an FSIGT (including minimal model analysis) to provide primary data for our ongoing pharmacogenomic study of pharmacodynamic effects of semaglutide ( NCT05071898 ). Three endpoints provide quantitative assessments of GLP1R agonists' effects on glucose metabolism: first phase insulin secretion, glucose disappearance rates, and glucose effectiveness.

Registration

NCT02462421 (clinicaltrials.gov).

Funding

American Diabetes Association (1-16-ICTS-112); National Institute of Diabetes and Digestive and Kidney Disease (R01DK130238, T32DK098107, P30DK072488).

Distinct Roles for Brain and Pancreas in Basal and Postprandial Glucose Homeostasis

The glucose homeostasis system ensures that the circulating glucose level is maintained within narrow physiological limits both in the fasting (or basal) state and following a nutrient challenge. Although glucose homeostasis is traditionally conceptualized as a single overarching system, evidence reviewed here suggests that basal glycemia and glucose tolerance are governed by distinct control systems. Specifically, whereas glucose tolerance appears to be determined largely by interactions between insulin secretion and insulin sensitivity, basal-state glucose homeostasis is predominated by insulin-independent mechanisms governed largely by the brain. In addition to a new perspective on how glucose homeostasis is achieved, this "dual control system" hypothesis offers a feasible and testable explanation for observations that are otherwise difficult to reconcile and sheds new light on the integration of central and peripheral metabolic control mechanisms. The implications of this model for the pathogenesis and treatment of impaired fasting glucose, impaired glucose tolerance, and type 2 diabetes are also discussed.

Nuts in the Prevention and Management of Type 2 Diabetes

Diabetes is a continuously growing global concern affecting >10% of adults, which may be mitigated by modifiable lifestyle factors. Consumption of nuts and their inclusion in dietary patterns has been associated with a range of beneficial health outcomes. Diabetes guidelines recommend dietary patterns that incorporate nuts; however, specific recommendations related to nuts have been limited. This review considers the epidemiological and clinical evidence to date for the role of nut consumption as a dietary strategy for the prevention and management of type 2 diabetes (T2D) and related complications. Findings suggest nut consumption may have a potential role in the prevention and management of T2D, with mechanistic studies assessing nuts and individual nut-related nutritional constituents supporting this possibility. However, limited definitive evidence is available to date, and future studies are needed to elucidate better the impact of nuts on the prevention and management of T2D.

Four-Compartment Diffusion Model of Cortisol Disposition: Comparison With 3 Alternative Models in Current Clinical Use

Context

Estimated rates of cortisol elimination and appearance vary according to the model used to obtain them. Generalizability of current models of cortisol disposition in healthy humans is limited.

Objective

Development and validation of a realistic, mechanistic model of cortisol disposition that accounts for the major factors influencing plasma cortisol concentrations in vivo (Model 4), and comparison to previously described models of cortisol disposition in current clinical use (Models 1-3).

Methods

The 4 models were independently applied to cortisol concentration data obtained for the hydrocortisone bolus experiment (20 mg) in 2 clinical groups: healthy volunteers (HVs, n = 6) and corticosteroid binding globulin (CBG)-deficient (n = 2). Model 4 used Fick's first law of diffusion to model free cortisol flux between vascular and extravascular compartments. Pharmacokinetic parameter solutions for Models 1-4 were optimized by numerical methods, and model-specific parameter solutions were compared by repeated measures analysis of variance. Models and respective parameter solutions were compared by mathematical and simulation analyses, and an assessment tool was used to compare performance characteristics of the four models evaluated herein.

Results

Cortisol half-lives differed significantly between models (all P < .001) with significant model-group interaction (P = .02). In comparative analysis, Model 4 solutions yielded significantly reduced free cortisol half-life, improved fit to experimental data (both P < .01), and superior model performance.

Conclusion

The proposed 4-compartment diffusion model (Model 4) is consistent with relevant experimental observations and met the greatest number of empiric validation criteria. Cortisol half-life solutions obtained using Model 4 were generalizable between HV and CBG-deficient groups and bolus and continuous modes of hydrocortisone infusion.

In silico modeling of endocrine organ-on-a-chip systems

The organ-on-a-chip (OoC) is an artificially reconstructed microphysiological system that is implemented using tissue mimics integrated into miniaturized perfusion devices. OoCs emulate dynamic and physiologically relevant features of the body, which are not available in standard in vitro methods. Furthermore, OoCs provide highly sophisticated multi-organ connectivity and biomechanical cues based on microfluidic platforms. Consequently, they are often considered ideal in vitro systems for mimicking self-regulating biophysical and biochemical networks in vivo where multiple tissues and organs crosstalk through the blood flow, similar to the human endocrine system. Therefore, OoCs have been extensively applied to simulate complex hormone dynamics and endocrine signaling pathways in a mechanistic and fully controlled manner. Mathematical and computational modeling approaches are critical for quantitatively analyzing an OoC and predicting its complex responses. In this review article, recently developed in silico modeling concepts of endocrine OoC systems are summarized, including the mathematical models of tissue-level transport phenomena, microscale fluid dynamics, distant hormone signaling, and heterogeneous cell-cell communication. From this background, whole chip-level analytic approaches in pharmacokinetics and pharmacodynamics will be described with a focus on the spatial and temporal behaviors of absorption, distribution, metabolism, and excretion in endocrine biochips. Finally, quantitative design frameworks for endocrine OoCs are reviewed with respect to support parameter calibration/scaling and enable predictive in vitro-in vivo extrapolations. In particular, we highlight the analytical and numerical modeling strategies of the nonlinear phenomena in endocrine systems on-chip, which are of particular importance in drug screening and environmental health applications.

A glucose-insulin-glucagon coupled model of the isoglycemic intravenous glucose infusion experiment

Type 2 diabetes (T2D) is a pathophysiology that is characterized by insulin resistance, beta- and alpha-cell dysfunction. Mathematical models of various glucose challenge experiments have been developed to quantify the contribution of insulin and beta-cell dysfunction to the pathophysiology of T2D. There is a need for effective extended models that also capture the impact of alpha-cell dysregulation on T2D. In this paper a delay differential equation-based model is developed to describe the coupled glucose-insulin-glucagon dynamics in the isoglycemic intravenous glucose infusion (IIGI) experiment. As the glucose profile in IIGI is tailored to match that of a corresponding oral glucose tolerance test (OGTT), it provides a perfect method for studying hormone responses that are in the normal physiological domain and without the confounding effect of incretins and other gut mediated factors. The model was fit to IIGI data from individuals with and without T2D. Parameters related to glucagon action, suppression, and secretion as well as measures of insulin sensitivity, and glucose stimulated response were determined simultaneously. Significant impairment in glucose dependent glucagon suppression was observed in patients with T2D (duration of T2D: 8 (6–36) months) relative to weight matched control subjects (CS) without diabetes (k1 (mM)−1: 0.16 ± 0.015 (T2D, n = 7); 0.26 ± 0.047 (CS, n = 7)). Insulin action was significantly lower in patients with T2D (a1 (10 pM min)−1: 0.000084 ± 0.0000075 (T2D); 0.00052 ± 0.00015 (CS)) and the Hill coefficient in the equation for glucose dependent insulin response was found to be significantly different in T2D patients relative to CS (h: 1.4 ± 0.15; 1.9 ± 0.14). Trends in parameters with respect to fasting plasma glucose, HbA1c and 2-h glucose values are also presented. Significantly, a negative linear relationship is observed between the glucagon suppression parameter, k1, and the three markers for diabetes and is thus indicative of the role of glucagon in exacerbating the pathophysiology of diabetes (Spearman Rank Correlation: (n = 12; (−0.79, 0.002), (−0.73,.007), (−0.86,.0003)) respectively).

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.

Insulin sensitivity and kinetics in African American and White people with obesity: Insights from different study protocols

OBJECTIVE

Studies that used an intravenous glucose tolerance test (IVGTT) have suggested that race is an important modulator of insulin sensitivity, β-cell function, and insulin clearance. However, the validity of the IVGTT has been challenged.

METHODS

This study assessed insulin sensitivity and insulin kinetics in non-Hispanic White (NHW, n = 29) and African American (AA, n = 14) people with obesity by using a hyperinsulinemic-euglycemic pancreatic clamp with glucose tracer infusion, an oral glucose tolerance test (OGTT), and an IVGTT.

RESULTS

Hepatic insulin sensitivity was better in AA participants than in NHW participants. Muscle insulin sensitivity, insulin secretion in relation to plasma glucose during the OGTT, and insulin clearance during basal conditions during the hyperinsulinemic-euglycemic pancreatic clamp and during the OGTT were not different between AA participants and NHW participants. The acute insulin response to the large glucose bolus administered during the IVGTT was double in AA participants compared with NHW participants because of increased insulin secretion and reduced insulin clearance.

CONCLUSIONS

AA individuals are not more insulin resistant than NHW individuals, and the β-cell response to glucose ingestion and postprandial insulin clearance are not different between AA individuals and NHW individuals. However, AA individuals have greater insulin secretory capacity and reduced insulin clearance capacity than NHW individuals and might be susceptible to hyperinsulinemia after consuming very large amounts of glucose.

The Physiology of Insulin Clearance

In the 1950's, Dr. I. Arthur Mirsky first recognized the possible importance of insulin degradation changes to the pathogenesis of type 2 diabetes. While this mechanism was ignored for decades, insulin degradation is now being recognized as a possible factor in diabetes risk. After Mirsky, the relative importance of defects in insulin release and insulin resistance were recognized as risk factors. The hyperbolic relationship between secretion and sensitivity was introduced, as was the relationship between them, as expressed as the disposition index (DI). The DI was shown to be affected by environmental and genetic factors, and it was shown to be differentiated among ethnic groups. However, the importance of differences in insulin degradation (clearance) on the disposition index relationship remains to be clarified. Direct measure of insulin clearance revealed it to be highly variable among even normal individuals, and to be affected by fat feeding and other physiologic factors. Insulin clearance is relatively lower in ethnic groups at high risk for diabetes such as African Americans and Hispanic Americans, compared to European Americans. These differences exist even for young children. Two possible mechanisms have been proposed for the importance of insulin clearance for diabetes risk: in one concept, insulin resistance per se leads to reduced clearance and diabetes risk. In a second and new concept, reduced degradation is a primary factor leading to diabetes risk, such that lower clearance (resulting from genetics or environment) leads to systemic hyperinsulinemia, insulin resistance, and beta-cell stress. Recent data by Chang and colleagues appear to support this latter hypothesis in Native Americans. The importance of insulin clearance as a risk factor for metabolic disease is becoming recognized and may be treatable.

Insulin Clearance in Obesity and Type 2 Diabetes

Plasma insulin clearance is an important determinant of plasma insulin concentration. In this review, we provide an overview of the factors that regulate insulin removal from plasma and discuss the interrelationships among plasma insulin clearance, excess adiposity, insulin sensitivity, and type 2 diabetes (T2D). We conclude with the perspective that the commonly observed lower insulin clearance rate in people with obesity, compared with lean people, is not a compensatory response to insulin resistance but occurs because insulin sensitivity and insulin clearance are mechanistically, directly linked. Furthermore, insulin clearance decreases postprandially because of the marked increase in insulin delivery to tissues that clear insulin. The commonly observed high postprandial insulin clearance in people with obesity and T2D likely results from the relatively low insulin secretion rate, not an impaired adaptation of tissues that clear insulin.

The progression of secondary diabetes: A review of modeling studies

Mathematical modeling has provided quantitative information consistent with experimental data, greatly improving our understanding of the progression of type 1 and type 2 diabetes. However, diabetes is a complex metabolic disease and has been found to be involved in crosstalk interactions with diverse endocrine diseases. Mathematical models have also been developed to investigate the quantitative impact of various hormonal disorders on glucose imbalance, advancing the precision treatment for secondary diabetes. Here we review the models established for the study of dysglycemia induced by hormonal disorders, such as excessive glucocorticoids, epinephrine, and growth hormone. To investigate the influence of hyperthyroidism on the glucose regulatory system, we also propose a hyperthyroid-diabetes progression model. Model simulations indicate that timely thyroid treatment can halt the progression of hyperglycemia and prevent beta-cell failure. This highlights the diagnosis of hormonal disorders, together withblood sugar tests, as significant measures for the early diagnosis and treatment of diabetes. The work recapitulates updated biological research on the interactions between the glucose regulatory system and other endocrine axes. Further mathematical modeling of secondary diabetes is desired to promote the quantitative study of the disease and the development of individualized diabetic therapies.

Metabolic Homeostasis: It's All in the Timing

Cross-talk between peripheral tissues is essential to ensure the coordination of nutrient intake with disposition during the feeding period, thereby preventing metabolic disease. This mini-review considers the interactions between the key peripheral tissues that constitute the metabolic clock, each of which is considered in a separate mini-review in this collation of articles published in Endocrinology in 2020 and 2021, by Martchenko et al (Circadian rhythms and the gastrointestinal tract: relationship to metabolism and gut hormones); Alvarez et al (The microbiome as a circadian coordinator of metabolism); Seshadri and Doucette (Circadian regulation of the pancreatic beta cell); McCommis et al (The importance of keeping time in the liver); Oosterman et al (The circadian clock, shift work, and tissue-specific insulin resistance); and Heyde et al (Contributions of white and brown adipose tissues to the circadian regulation of energy metabolism). The use of positive- and negative-feedback signals, both hormonal and metabolic, between these tissues ensures that peripheral metabolic pathways are synchronized with the timing of food intake, thus optimizing nutrient disposition and preventing metabolic disease. Collectively, these articles highlight the critical role played by the circadian clock in maintaining metabolic homeostasis.

Using Absorption Models for Insulin and Carbohydrates and Deep Leaning to Improve Glucose Level Predictions

Diabetes is a chronic disease caused by the inability of the pancreas to produce insulin or problems in the body to use it efficiently. It is one of the fastest growing health challenges affecting more than 400 million people worldwide, according to the World Health Organization. Intensive research is being carried out on artificial intelligence methods to help people with diabetes to optimize the way in which they use insulin, carbohydrate intakes, or physical activity. By predicting upcoming levels of blood glucose concentrations, preventive actions can be taken. Previous research studies using machine learning methods for blood glucose level predictions have mainly focused on the machine learning model used. Little attention has been given to the pre-processing of insulin and carbohydrate signals in order to mimic the human absorption processes. In this manuscript, a recurrent neural network (RNN) based model for predicting upcoming blood glucose levels in people with type 1 diabetes is combined with several carbohydrate and insulin absorption curves in order to optimize the prediction results. The proposed method is applied to data from real patients suffering type 1 diabetes mellitus (T1DM). The achieved results are encouraging, obtaining accuracy levels around 0.510 mmol/L (9.2 mg/dl) in the best scenario.