Objective: The UVA/Padova Type 1 Diabetes (T1DM) Simulator has been shown to be representative of a T1DM population observed in a clinical trial, but has not yet been identified on T1DM data. ...Moreover, the current version of the simulator is "single meal" while making it "single-day centric," i.e., by describing intraday variability, would be a step forward to create more realistic in silico scenarios. Here, we propose a Bayesian method for the identification of the model from plasma glucose and insulin concentrations only, by exploiting the prior model parameter distribution. Methods: The database consists of 47 T1DM subjects, who received dinner, breakfast, and lunch (respectively, 80, 50, and 60 CHO grams) in three 23-h occasions (one openand one closed-loop). The model is identified using the Bayesian Maximum a Posteriori technique, where the prior parameter distribution is that of the simulator. Diurnal variability of glucose absorption and insulin sensitivity is allowed. Results: The model well describes glucose traces (coefficient of determination R 2 = 0.962 ± 0.027) and the posterior parameter distribution is similar to that included in the simulator. Absorption parameters at breakfast are significantly different from those at lunch and dinner, reflecting more rapid dynamics of glucose absorption. Insulin sensitivity varies in each individual but without a specific pattern. Conclusion: The incorporation of glucose absorption and insulin sensitivity diurnal variability into the simulator makes it more realistic. Significance: The proposed method, applied to the increasing number of longterm artificial pancreas studies, will allow to describe week/month variability, thus further refining the simulator.
The Oral Minimal Model Method COBELLI, Claudio; MAN, Chiara Dalla; TOFFOLO, Gianna ...
Diabetes (New York, N.Y.),
04/2014, Letnik:
63, Številka:
4
Journal Article
Recenzirano
Odprti dostop
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.
Glucose tolerance is lower at night and higher in the morning. Shift workers, who often eat at night and experience circadian misalignment (i.e. misalignment between the central circadian pacemaker ...and the environmental/behavioural cycles), have an increased risk of type 2 diabetes. To determine the separate and relative impacts of the circadian system, behavioural/environmental cycles, and their interaction (i.e. circadian misalignment) on insulin sensitivity and β‐cell function, the oral minimal model was used to quantitatively assess the major determinants of glucose control in 14 healthy adults using a randomized, cross‐over design with two 8‐day laboratory protocols. Both protocols involved 3 baseline inpatient days with habitual sleep/wake cycles, followed by 4 inpatient days with the same nocturnal bedtime (circadian alignment) or with 12‐hour inverted behavioural/environmental cycles (circadian misalignment). The data showed that circadian phase and circadian misalignment affect glucose tolerance through different mechanisms. While the circadian system reduces glucose tolerance in the biological evening compared to the biological morning mainly by decreasing both dynamic and static β‐cell responsivity, circadian misalignment reduced glucose tolerance mainly by lowering insulin sensitivity, not by affecting β‐cell function.
Objective: Glargine 100 U/mL (Gla-100) and 300 U/mL (Gla-300) are long-acting insulin analogs providing basal insulin supply in multiple daily injection (MDI) therapy of type 1 diabetes (T1D). Both ...insulins require extensive testing to arrive at the optimal dosing regimen, e.g., timing and amount. Here we aim at a simulation tool for evaluating benefits/risks of different dosing schemes and up-titration rules for both Gla-100 and Gla-300 before clinical testing. Methods: A new pharmacokinetic (PK) model of both Gla-100 and Gla-300 was incorporated into the FDA-accepted University of Virginia/Padova T1D simulator: Specifically, a joint parameter distribution, built from PK parameter estimates, was used to generate individual PK parametrizations for each in silico subject. A virtual trial comparing Gla-100 vs. Gla-300 was performed and assessed against a clinical study to validate the glargine simulator. Results: Like in vivo, in silico both insulins performed similarly with respect to glucose control: percent time of glucose between 80-140 mg/dL with Gla-100 vs. Gla-300 (primary endpoint) were 41.5 ± 1.1% vs. 39.0 ± 1.2% (P = 0.11) in silico, 31.0 ± 1.6% vs. 31.8 ± 1.5% (P = 0.73) in vivo. Conclusions: The glargine simulator reproduced the main findings of the clinical trial, proving its validity for testing MDI therapies. Significance: In silico testing of MDI therapies can help designing clinical trials. Due to the more standardized settings in silico (e.g., standardized meals and strict adherence to titration rule), any potential treatment effect is reaching statistical significance in simulation vs. clinical trial.
Objective: To date, the lack of a model of glucagon kinetics precluded the possibility of estimating and studying glucagon secretion in vivo, e.g., using deconvolution, as done for other hormones ...like insulin and C-peptide. Here, we used a nonlinear mixed effects technique to develop a robust population model of glucagon kinetics, able to describe both the typical population kinetics (TPK) and the between-subject variability (BSV), and relate this last to easily measurable subject characteristics. Methods: Thirty-four models of increasing complexity (variably including covariates and correlations among random effects) were identified on glucagon profiles obtained from 53 healthy subjects, who received a constant infusion of somatostatin to suppress endogenous glucagon production, followed by a continuous infusion of glucagon (65 ng/kg/min). Model selection was performed based on its ability to fit the data, provide precise parameter estimates, and parsimony criteria. Results: A two-compartment model was the most parsimonious. The model was able to accurately describe both the TPK and the BSV of model parameters as function of body mass and body surface area. Parameters were precisely estimated, with central volume of distribution V 1 = 5.46 L and peripheral volume of distribution V 2 = 5.51 L. The introduction of covariates resulted in a significant shrinkage of the unexplained BSV and considerably improved the model fit. Conclusion: We developed a robust population model of glucagon kinetics. Significance: This model provides a deeper understanding of glucagon kinetics and is usable to estimate glucagon secretion in vivo by deconvolution of plasma glucagon concentration data.
Goal: Quantitative assessment of hepatic insulin extraction (TIE) after an oral glucose challenge, e.g., a meal, is important to understand the regulation of carbohydrate metabolism. The aim of the ...current study is to develop a model of system for estimating TIE. Methods: Nine different models, of increasing complexity, were tested on data of 204 normal subjects, who underwent a mixed meal tolerance test, with frequent measurement of plasma glucose, insulin, and C-peptide concentrations. All these models included a two-compartment model of C-peptide kinetics, an insulin secretion model, a compartmental model of insulin kinetics (with number of compartments ranging from one to three), and different TIE descriptions, depending on plasma glucose and insulin. Model performances were compared on the basis of data fit, precision of parameter estimates, and parsimony criteria. Results: The three-compartment model of insulin kinetics, coupled with TIE depending on glucose concentration, showed the best fit and a good ability to precisely estimate the parameters. In addition, the model calculates basal and total indices of TIE (HE b and HE tot , respectively), and provides an index of TIE sensitivity to glucose (S G HE ). Conclusion: A new physiologically based TIE model has been developed, which allows an improved quantitative description of glucose regulation. Significance: The use of the new model provides an in-depth description of insulin kinetics, thus enabling a better understanding of a given subject's metabolic state.
The UVA/PADOVA Type 1 Diabetes Simulator Man, Chiara Dalla; Micheletto, Francesco; Lv, Dayu ...
Journal of diabetes science and technology,
01/2014, Letnik:
8, Številka:
1
Journal Article
Recenzirano
Objective:
Recent studies have provided new insights into nonlinearities of insulin action in the hypoglycemic range and into glucagon kinetics as it relates to response to hypoglycemia. Based on ...these data, we developed a new version of the UVA/PADOVA Type 1 Diabetes Simulator, which was submitted to FDA in 2013 (S2013).
Methods:
The model of glucose kinetics in hypoglycemia has been improved, implementing the notion that insulin-dependent utilization increases nonlinearly when glucose decreases below a certain threshold. In addition, glucagon kinetics and secretion and action models have been incorporated into the simulator: glucagon kinetics is a single compartment; glucagon secretion is controlled by plasma insulin, plasma glucose below a certain threshold, and glucose rate of change; and plasma glucagon stimulates with some delay endogenous glucose production. A refined statistical strategy for virtual patient generation has been adopted as well. Finally, new rules for determining insulin to carbs ratio (CR) and correction factor (CF) of the virtual patients have been implemented to better comply with clinical definitions.
Results:
S2013 shows a better performance in describing hypoglycemic events. In addition, the new virtual subjects span well the real type 1 diabetes mellitus population as demonstrated by good agreement between real and simulated distribution of patient-specific parameters, such as CR and CF.
Conclusions:
S2013 provides a more reliable framework for in silico trials, for testing glucose sensors and insulin augmented pump prediction methods, and for closed-loop single/dual hormone controller design, testing, and validation.
Evaluation of the existence of a diurnal pattern of glucose tolerance after mixed meals is important to inform a closed-loop system of treatment for insulin requiring diabetes. We studied 20 healthy ...volunteers with normal fasting glucose (4.8 ± 0.1 mmol/L) and HbA(1c) (5.2 ± 0.0%) to determine such a pattern in nondiabetic individuals. Identical mixed meals were ingested during breakfast, lunch, or dinner at 0700, 1300, and 1900 h in randomized Latin square order on 3 consecutive days. Physical activity was the same on all days. Postprandial glucose turnover was measured using the triple tracer technique. Postprandial glucose excursion was significantly lower (P < 0.01) at breakfast than lunch and dinner. β-Cell responsivity to glucose and disposition index was higher (P < 0.01) at breakfast than lunch and dinner. Hepatic insulin extraction was lower (P < 0.01) at breakfast than dinner. Although meal glucose appearance did not differ between meals, suppression of endogenous glucose production tended to be lower (P < 0.01) and insulin sensitivity tended to be higher (P < 0.01) at breakfast than at lunch or dinner. Our results suggest a diurnal pattern to glucose tolerance in healthy humans, and if present in type 1 diabetes, it will need to be incorporated into artificial pancreas systems.
Patients with psychotic disorders are at high risk for type 2 diabetes mellitus, and there is increasing evidence that patients display glucose metabolism abnormalities before significant ...antipsychotic medication exposure. In the present study, we examined insulin action by quantifying insulin sensitivity in first-episode psychosis (FEP) patients and unaffected siblings, compared to healthy individuals, using a physiological-based model and comprehensive assessment battery. Twenty-two unaffected siblings, 18 FEP patients, and 15 healthy unrelated controls were evaluated using a 2-h oral glucose tolerance test (OGTT), with 7 samples of plasma glucose and serum insulin concentration measurements. Insulin sensitivity was quantified using the oral minimal model method. Lipid, leptin, free fatty acids, and inflammatory marker levels were also measured. Anthropometric, nutrient, and activity assessments were conducted; total body composition and fat distribution were determined using whole-body dual-energy X-ray absorptiometry. Insulin sensitivity significantly differed among groups (F = 6.01 and 0.004), with patients and siblings showing lower insulin sensitivity, compared to controls (P = 0.006 and 0.002, respectively). Body mass index, visceral adipose tissue area (cm
), lipids, leptin, free fatty acids, inflammatory markers, and activity ratings were not significantly different among groups. There was a significant difference in nutrient intake with lower total kilocalories/kilogram body weight in patients, compared to siblings and controls. Overall, the findings suggest that familial abnormal glucose metabolism or a primary insulin signaling pathway abnormality is related to risk for psychosis, independent of disease expression and treatment effects. Future studies should examine underlying biological mechanisms of insulin signaling abnormalities in psychotic disorders.
Abstract
Context
The time-to-glucose-peak following the oral glucose tolerance test (OGTT) is a highly reproducible marker for diabetes risk. In obese youths, we lack evidence for the mechanisms ...underlying the effects of the TCF7L2 rs7903146 variant on glucose peak.
Methods
We analyzed the metabolic phenotype and the genotype for the TCF7L2 rs7903146 in 630 obese youths with normal (NGT) and impaired (IGT) glucose tolerance. Participants underwent a 3-hour, 9-point OGTT to estimate, using the oral minimal model, the disposition index (DI), the static (φstatic) and dynamic (φdynamic) components β-cell responsiveness and insulin sensitivity (SI). In a subgroup (n = 241) longitudinally followed for 2 years, we estimated the effect of time-to-glucose-peak on glucose tolerance change.
Results
Participants were grouped into early (<30 minutes) and late (≥30 minutes) glucose peakers. A delayed glucose peak was featured by a decline in φstatic (P < .001) in the absence of a difference in φdynamic. The prevalence of T-risk allele for TCF7L2 rs7903146 variant significantly increased in the late peak group. A lower DI was correlated with higher glucose concentration at 1 and 2 hours, whereas SI was inversely associated with 1-hour glucose. Glucose peak <30 minutes was protective toward worsening of glucose tolerance overtime (odds ratio 0.35 0.15–0.82; P = .015), with no subjects progressing to NGT or persisting IGT, in contrast to the 40% of progressor in those with late glucose peak.
Conclusion
The prevalence of T-risk allele for the TCF7L2 rs7903146 prevailed in the late time-to-glucose peak group, which in turn is associated with impaired β-cell responsiveness to glucose (φ), thereby predisposing to prediabetes and diabetes in obese youths.
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