beta-Cell function and insulin sensitivity in adolescents from an OGTT

Quantifying beta cell function in the preclinical stages of type 1 diabetes

Clinically symptomatic type 1 diabetes (stage 3 type 1 diabetes) is preceded by a pre-symptomatic phase, characterised by progressive loss of functional beta cell mass after the onset of islet autoimmunity, with (stage 2) or without (stage 1) measurable changes in glucose profile during an OGTT. Identifying metabolic tests that can longitudinally track changes in beta cell function is of pivotal importance to track disease progression and measure the effect of disease-modifying interventions. In this review we describe the metabolic changes that occur in the early pre-symptomatic stages of type 1 diabetes with respect to both insulin secretion and insulin sensitivity, as well as the measurable outcomes that can be derived from the available tests. We also discuss the use of metabolic modelling to identify insulin secretion and sensitivity, and the measurable changes during dynamic tests such as the OGTT. Finally, we review the role of risk indices and minimally invasive measures such as those derived from the use of continuous glucose monitoring.

β-Cell Function and Insulin Sensitivity in Youth With Early Type 1 Diabetes From a 2-Hour 7-Sample OGTT

CONTEXT

The oral minimal model is a widely accepted noninvasive tool to quantify both β-cell responsiveness and insulin sensitivity (SI) from glucose, C-peptide, and insulin concentrations during a 3-hour 9-point oral glucose tolerance test (OGTT).

OBJECTIVE

Here, we aimed to validate a 2-hour 7-point protocol against the 3-hour OGTT and to test how variation in early sampling frequency impacts estimates of β-cell responsiveness and SI.

METHODS

We conducted a secondary analysis on 15 lean youth with stage 1 type 1 diabetes (T1D; ≥ 2 islet autoantibodies with no dysglycemia) who underwent a 3-hour 9-point OGTT. The oral minimal model was used to quantitate β-cell responsiveness (φtotal) and insulin sensitivity (SI), allowing assessment of β-cell function by the disposition index (DI = φtotal × SI). Seven- and 5-point 2-hour OGTT protocols were tested against the 3-hour 9-point gold standard to determine agreement between estimates of φtotal and its dynamic and static components, SI, and DI across different sampling strategies.

RESULTS

The 2-hour estimates for the disposition index exhibited a strong correlation with 3-hour measures (r = 0.975; P < .001) with similar results for β-cell responsiveness and SI (r = 0.997 and r = 0.982; P < .001, respectively). The agreement of the 3 estimates between the 7-point 2-hour and 9-point 3-hour protocols fell within the 95% CI on the Bland-Altman grid with a median difference of 16.9% (-35.3 to 32.5), 0.2% (-0.6 to 1.3), and 14.9% (-1.4 to 28.3) for DI, φtotal, and SI. Conversely, the 5-point protocol did not provide reliable estimates of φ dynamic and static components.

CONCLUSION

The 2-hour 7-point OGTT is reliable in individuals with stage 1 T1D for assessment of β-cell responsiveness, SI, and DI. Incorporation of these analyses into current 2-hour diabetes staging and monitoring OGTTs offers the potential to more accurately quantify risk of progression in the early stages of T1D.

Minimal and Maximal Models to Quantitate Glucose Metabolism: Tools to Measure, to Simulate and to Run in Silico Clinical Trials

Several models have been proposed to describe the glucose system at whole-body, organ/tissue and cellular level, designed to measure non-accessible parameters (minimal models), to simulate system behavior and run in silico clinical trials (maximal models). Here, we will review the authors' work, by putting it into a concise historical background. We will discuss first the parametric portrait provided by the oral minimal models-building on the classical intravenous glucose tolerance test minimal models-to measure otherwise non-accessible key parameters like insulin sensitivity and beta-cell responsivity from a physiological oral test, the mixed meal or the oral glucose tolerance tests, and what can be gained by adding a tracer to the oral glucose dose. These models were used in various pathophysiological studies, which we will briefly review. A deeper understanding of insulin sensitivity can be gained by measuring insulin action in the skeletal muscle. This requires the use of isotopic tracers: both the classical multiple-tracer dilution and the positron emission tomography techniques are discussed, which quantitate the effect of insulin on the individual steps of glucose metabolism, that is, bidirectional transport plasma-interstitium, and phosphorylation. Finally, we will present a cellular model of insulin secretion that, using a multiscale modeling approach, highlights the relations between minimal model indices and subcellular secretory events. In terms of maximal models, we will move from a parametric to a flux portrait of the system by discussing the triple tracer meal protocol implemented with the tracer-to-tracee clamp technique. This allows to arrive at quasi-model independent measurement of glucose rate of appearance (Ra), endogenous glucose production (EGP), and glucose rate of disappearance (Rd). Both the fast absorbing simple carbs and the slow absorbing complex carbs are discussed. This rich data base has allowed us to build the UVA/Padova Type 1 diabetes and the Padova Type 2 diabetes large scale simulators. In particular, the UVA/Padova Type 1 simulator proved to be a very useful tool to safely and effectively test in silico closed-loop control algorithms for an artificial pancreas (AP). This was the first and unique simulator of the glucose system accepted by the U.S. Food and Drug Administration as a substitute to animal trials for in silico testing AP algorithms. Recent uses of the simulator have looked at glucose sensors for non-adjunctive use and new insulin molecules.

A New Oral Model to Assess Postprandial Lactate Production Rate

OBJECTIVE

Pediatric obesity predisposes children and adolescents to early onset insulin resistance and dysglycemia. In the last 20 years this has led to a rise in the prevalence of prediabetes, diabetes and fatty liver in youngsters, due to the high degree of insulin resistance experienced by these patients and the consequent high availability of glucose. As glucose accesses the liver, it is partly metabolized through glycolysis, whose main product is pyruvate that is then converted into Acetyl CoA and lactate. Therefore, lactate production rate (LPR) represents the best proxy for the assessment of glycolysis. Since to date there are not methods to estimate postprandial LPR, here we proposed a novel oral glucose-lactate model to estimate LPR during an oral glucose tolerance test and tested it in 24 youth with and without obesity.

METHODS

The model is based on the oral glucose minimal model and assumes that LPR is a fraction (fr) of glucose disposal rate, proportional to glucose concentration and controlled by insulin action.

RESULTS

The model well fitted the glucose and lactate data, and provided both precise parameter estimates (e.g. fr=22.5 [12.6-54.1]%, median [IQR]), CV=18 [13-25]%) and LPR time course.

CONCLUSIONS

The proposed model is a valid tool to assess LPR, and thus glycolysis, during OGTT in nondiabetic subjects.

SIGNIFICANCE

The proposed methodology will allow to assess postprandial LPR in simple and cost-effective way.

Early Impairment of Insulin Sensitivity, β-Cell Responsiveness, and Insulin Clearance in Youth with Stage 1 Type 1 Diabetes

CONTEXT

Clinical onset of type 1 diabetes (Stage 3 T1D) is preceded by a presymptomatic phase characterized by multiple islet autoantibodies with normal glucose tolerance (Stage 1 T1D).

OBJECTIVE

The aim was to explore the metabolic phenotypes of β-cell function and insulin sensitivity and clearance in normoglycemic youth with Stage 1 T1D and compare them with healthy nonrelated peers during a 3-hour oral glucose tolerance test (OGTT).

METHODS

Twenty-eight lean youth, 14 with ≥2 islet autoantibodies (cases) and 14 healthy controls underwent a 3-hour 9-point OGTT with measurement of glucose, C-peptide, and insulin. The oral minimal model was used to quantitate β-cell responsiveness (φtotal) and insulin sensitivity (SI), allowing assessment of β-cell function by the disposition index (DI=φtotal×SI). Fasting insulin clearance (CL0) was calculated as the ratio between the fasting insulin secretion rate (ISR) and plasma insulin levels (ISR0/I0), while postload clearance (CL180) was estimated by the ratio of AUC of ISR over the plasma insulin AUC for the 3-hour OGTT (ISRAUC/IAUC). Participants with impaired fasting glucose, impaired glucose tolerance, or any OGTT glucose concentration ≥200 mg/dL were excluded.

RESULTS

Cases (10.5 years [8, 15]) exhibited reduced DI (P < .001) due to a simultaneous reduction in both φtotal (P < 0.001) and SI (P = .008) compared with controls (11.5 years [10.4, 14.9]). CL0 and CL180 were lower in cases than in controls (P = .005 and P = .019).

CONCLUSION

Presymptomatic Stage 1 T1D in youth is associated with reduced insulin sensitivity and lower β-cell responsiveness, and the presence of blunted insulin clearance.

A comparison of meal tolerance test and oral glucose tolerance test for predicting insulin therapy in patients with gestational diabetes

AIMS

To identify factors predicting a need for insulin therapy in gestational diabetes mellitus (GDM) by comparing plasma glucose (PG) levels in a 75-g oral glucose tolerance test (75-g OGTT) with those in a 500-kcal meal tolerance test (MTT) containing 75 g of carbohydrate.

SUBJECTS AND METHODS

The MTT was performed in 61 patients who diagnosed with GDM by a 75-g OGTT (age, 33.2 ± 4.5 years; prepregnancy body mass index, 22.6 ± 4.7 kg/m2; number of gestational weeks, 25.1 ± 6.4 weeks). PG and serum insulin levels were measured before the meal and up to 180 min after the meal. The insulin secretion capacity and resistance index were calculated.

RESULTS

PG levels increased from 86.8 ± 8.8 mg/dL at fasting to 132.7 ± 20.1 mg/dL at 30 min, and 137.8 ± 27.7 mg/dL at 60 min after MTT in the 35 patients with needed insulin therapy; these levels were significantly higher than those in the 26 patients, who only needed diet therapy. The patients with needed insulin therapy had significantly higher fasting PG levels in the 75-g OGTT, PG levels at fasting and 30 min after the MTT, and homeostasis model assessment of insulin resistance (HOMA-IR), and a significantly lower disposition index (DI) and insulin index than patients treated by diet alone. Receiver operating characteristic curve analysis was performed for factors involved in insulin therapy, with the following cutoff values: fasting PG in the 75-g OGTT, 92 mg/dL; PG 30 min after MTT, 129 mg/dL; HOMA-IR, 1.51; DI, 3.9; HbA1c, 5.4%. Multivariate analysis revealed that the 30-min PG level after MTT and HOMA-IR predicted insulin therapy.

CONCLUSION

PG levels at 30 min after MTT may be useful for identifying patients with GDM, who need insulin therapy.

Oral minimal model-based estimates of insulin sensitivity in obese youth depend on oral glucose tolerance test protocol duration

Introduction

The Oral Minimal Model (OMM), a differential-equations based mathematical model of glucose-insulin dynamics, utilizes data from a frequently sampled oral glucose tolerance test (OGTT) to quantify insulin sensitivity ( S I ). OMM-based estimates of S I can detect differences in insulin resistance (IR) across population groups and quantify effects of clinical or behavioral interventions. These estimates of S I have been validated in healthy adults using data from OGTTs with durations from 2 to 7 h. However, data demonstrating how protocol duration affects S I estimates in highly IR populations such as adolescents with obesity are limited.

Methods

A 6-h frequently sampled OGTT was performed in adolescent females with obesity. Two, 3-, and 4- hour implementations of OMM assuming an exponentially-decaying rate of glucose appearance beyond measured glucose concentrations were compared to the 6-h implementation. A 4- hour OMM implementation with truncated data (4h Tr) was also considered.

Results

Data from 68 participants were included (age 15.8 ± 1.2 years, BMI 35.4 ± 5.6 kg/m²). Although S I values were highly correlated for all implementations, they varied with protocol duration (2h: 2.86 ± 3.31, 3h: 2.55 ± 2.62, 4h: 2.81 ± 2.59, 4h tr: 3.13 ± 3.14, 6h: 3.06 ± 2.85 x 10^-4 dl/kg/min per U/ml). S I estimates based on 2 or 3 h of data underestimated S I values, whereas 4-h S I estimates more closely approximated 6-h S I values.

Discussion

These results suggest that OGTT protocol duration should be considered when implementing OMM to estimate S I in adolescents with obesity and other IR populations.

Effect of Slow Wave Sleep Disruption on Metabolic Parameters in Adolescents

STUDY OBJECTIVES

Cross-sectional studies report a correlation between slow wave sleep (SWS) duration and insulin sensitivity (SI) in children and adults. Suppression of SWS causes insulin resistance in adults but effects in children are unknown. This study was designed to determine the effect of SWS fragmentation on SI in children.

METHODS

Fourteen pubertal children (11.3-14.1 y, body mass index 29(th) to 97(th) percentile) were randomized to sleep studies and mixed meal (MM) tolerance tests with and without SWS disruption. Beta-cell responsiveness (Φ) and SI were determined using oral minimal modeling.

RESULTS

During the disruption night, auditory stimuli (68.1 ± 10.7/night; mean ± standard error) decreased SWS by 40.0 ± 8.0%. SWS fragmentation did not affect fasting glucose (non-disrupted 76.9 ± 2.3 versus disrupted 80.6 ± 2.1 mg/dL), insulin (9.2 ± 1.6 versus 10.4 ± 2.0 μIU/mL), or C-peptide (1.9 ± 0.2 versus 1.9 ± 0.1 ng/mL) levels and did not impair SI (12.9 ± 2.3 versus 10.1 ± 1.6 10(-4) dL/kg/min per μIU/mL) or Φ (73.4 ± 7.8 versus 74.4 ± 8.4 10(-9) min(-1)) to a MM challenge. Only the subjects in the most insulin-sensitive tertile demonstrated a consistent decrease in SI after SWS disruption.

CONCLUSION

Pubertal children across a range of body mass indices may be resistant to the adverse metabolic effects of acute SWS disruption. Only those subjects with high SI (i.e., having the greatest "metabolic reserve") demonstrated a consistent decrease in SI. These results suggest that adolescents may have a unique ability to adapt to metabolic stressors, such as acute SWS disruption, to maintain euglycemia. Additional studies are necessary to confirm that this resiliency is maintained in settings of chronic SWS disruption.

Prehepatic secretion and disposal of insulin in obese adolescents as estimated by three-hour, eight-sample oral glucose tolerance tests

The body compensates for early-stage insulin resistance by increasing insulin secretion. A reliable and easy-to-use mathematical assessment of insulin secretion and disposal could be a valuable tool for identifying patients at risk for the development of type 2 diabetes. Because the pathophysiology of insulin resistance is incompletely understood, assessing insulin metabolism with minimal assumptions regarding its metabolic regulation is a major challenge. To assess insulin secretion and indexes of insulin disposal, our marginalized and regularized absorption approach (MRA) was applied to a sparse sampling oral glucose tolerance test (OGTT) protocol measuring the insulin and C-peptide concentrations. Identifiability and potential bias of metabolic parameters were estimated from published data with dense sampling. The MRA was applied to OGTT data from 135 obese adolescents to demonstrate its clinical applicability. Individual prehepatic basal and dynamic insulin secretion and clearance levels were determined with a precision and accuracy greater than 10% of the nominal value. The intersubject variability in these parameters was approximately four times higher than the intrasubject variability, and there was a strong negative correlation between prehepatic secretion and plasma clearance of insulin. MRA-based analysis provides reliable estimates of insulin secretion and clearance, thereby enabling detailed glucose homeostasis characterization based on restricted datasets that are obtainable during routine patient care.

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.

The oral minimal model method

The simultaneous assessment of insulin action, secretion, and hepatic extraction is key to understanding postprandial glucose metabolism in nondiabetic and diabetic humans. We review the oral minimal method (i.e., models that allow the estimation of insulin sensitivity, β-cell responsivity, and hepatic insulin extraction from a mixed-meal or an oral glucose tolerance test). Both of these oral tests are more physiologic and simpler to administer than those based on an intravenous test (e.g., a glucose clamp or an intravenous glucose tolerance test). The focus of this review is on indices provided by physiological-based models and their validation against the glucose clamp technique. We discuss first the oral minimal model method rationale, data, and protocols. Then we present the three minimal models and the indices they provide. The disposition index paradigm, a widely used β-cell function metric, is revisited in the context of individual versus population modeling. Adding a glucose tracer to the oral dose significantly enhances the assessment of insulin action by segregating insulin sensitivity into its glucose disposal and hepatic components. The oral minimal model method, by quantitatively portraying the complex relationships between the major players of glucose metabolism, is able to provide novel insights regarding the regulation of postprandial metabolism.

Effects of a hypercaloric diet on β-cell responsivity in lean healthy men

OBJECTIVE

Insulin resistance and hyperinsulinaemia precede the onset of obesity-induced DM2. The early adaptation of the β-cell during the initial phase of overfeeding and weight gain has only been partly elucidated. We studied the early changes in insulin clearance and β-cell responsivity during a positive and negative energy balance in lean healthy men.

DESIGN

We studied in nine healthy lean men [age, 37 (27-43) years; BMI, 23·6 (20·6-25·6) kg/m(2) ] insulin sensitivity, insulin clearance, insulin secretion and static and dynamic β-cell responsivity at baseline and after the hypercaloric and subsequent hypocaloric diet.

RESULTS

Participants gained 7 [5·1-7·6]% of their initial body weight on the hypercaloric diet. Compared to baseline, insulin sensitivity and insulin clearance decreased, while glucose-stimulated insulin secretion was higher. The GLP-1 response to oral glucose did not change. The dynamic β-cell responsivity index increased but the basal and static responsivity indexes did not change. Total and static disposition indexes (DIs) in the hypercaloric state showed a trend towards a decrease. During the hypocaloric diet, insulin sensitivity, glucose-stimulated insulin secretion and insulin clearance returned to baseline. The responsivity and the DIs were not different in the hypocaloric phase compared to baseline.

CONCLUSION

A positive energy balance resulting in weight gain in lean men induces hyperinsulinaemia, which is explained by a combined effect on insulin clearance and insulin secretion. Increased insulin secretion was related to insulin resistance-induced higher glucose concentrations but also to increased dynamic β-cell responsivity. Glucose sensitivity of the β-cell did not change. These early adaptations are completely reversible during a negative energy balance after loss of the gained weight.

Fitting dynamic models with forcing functions: application to continuous glucose monitoring in insulin therapy

The artificial pancreas is an emerging technology to treat type 1 diabetes (T1D). It has the potential to revolutionize diabetes care and improve quality of life. The system requires extensive testing, however, to ensure that it is both effective and safe. Clinical studies are resource demanding and so a principle aim is to develop an in silico population of subjects with T1D on which to conduct pre-clinical testing. This paper aims to reliably characterize the relationship between blood glucose and glucose measured by subcutaneous sensor as a major step towards this goal. Blood-and sensor-glucose are related through a dynamic model, specified in terms of differential equations. Such models can present special challenges for statistical inference, however. In this paper we make use of the BUGS software, which can accommodate a limited class of dynamic models, and it is in this context that we discuss such challenges. For example, we show how dynamic models involving forcing functions can be accommodated. To account for fluctuations away from the dynamic model that are apparent in the observed data, we assume an autoregressive structure for the residual error model. This leads to some identifiability issues but gives very good predictions of virtual data. Our approach is pragmatic and we propose a method to mitigate the consequences of such identifiability issues.

Diabetes: Models, Signals, and Control

The control of diabetes is an interdisciplinary endeavor, which includes a significant biomedical engineering component, with traditions of success beginning in the early 1960s. It began with modeling of the insulin-glucose system, and progressed to large-scale in silico experiments, and automated closed-loop control (artificial pancreas). Here, we follow these engineering efforts through the last, almost 50 years. We begin with the now classic minimal modeling approach and discuss a number of subsequent models, which have recently resulted in the first in silico simulation model accepted as substitute to animal trials in the quest for optimal diabetes control. We then review metabolic monitoring, with a particular emphasis on the new continuous glucose sensors, on the analyses of their time-series signals, and on the opportunities that they present for automation of diabetes control. Finally, we review control strategies that have been successfully employed in vivo or in silico, presenting a promise for the development of a future artificial pancreas and, in particular, discuss a modular architecture for building closed-loop control systems, including insulin delivery and patient safety supervision layers. We conclude with a brief discussion of the unique interactions between human physiology, behavioral events, engineering modeling and control relevant to diabetes.