1,721,346 research outputs found
The hot IVGTT two-compartment minimal model : indexes of glucose effectiveness and insulin sensitivity
Full text linkA two-compartment minimal model (2CMM) has been proposed [A. Caumo and C. Cobelli. Am. J. Physiol. 264 (Endocrinol. Metab. 27): E829-E841, 1993] to describe intravenous glucose tolerance test (IVGTT) labeled (hereafter hot) glucose kinetics. This model, at variance with the one-compartment minimal model (1CMM), allows the estimation of a plausible profile of glucose production. The aim of this study is to show that the 2CMM also allows the assessment of insulin sensitivity (SI2*), glucose effectiveness (SG2*), and plasma clearance rate (PCR). The 2CMM was identified on stable-isotope IVGTTs performed in normal subjects (n = 14). Results were (means +/- SE) SG2* = 0.85 +/- 0.14 ml.kg-1.min-1, PCR = 2.02 +/- 0.14 ml.kg-1.min-1, and SI2* = 13.83 +/- 2.54 x 10(-2) ml.kg-1.min-1.microU-1.ml. The 1CMM was also identified; glucose effectiveness and insulin sensitivity indexes were SG*V = 1.36 +/- 0.08 ml.kg-1.min-1 and SI*V = 12.98 +/- 2.21 x 10(-2) ml.kg-1.min-1.microU-1.ml, respectively, where V is the 1CMM glucose distribution volume. SG*V was lower than PCR and higher than SG2* and did not correlate with either [r = 0.45 (NS) and r = 0.50 (NS), respectively], whereas SI*V was not different from and was correlated with SI2* (r = 0.95; P < 0.001). SG* compares well (r = 0.78; P < 0.001) with PCR normalized by the 2CMM total glucose distribution volume. In conclusion, the 2CMM is a powerful tool to assess glucose metabolism in vivo
Tracer Experiment Design for Metabolic Fluxes Estimation in Steady and Nonsteady State
No full textThe purpose of this chapter is to illustrate how tracer experiments can be designed to quantify metabolic fluxes. We will review the principles of tracer experimental design by describing the metabolic system under study by a compartmental model with a single pool accessible for measurement (usually plasma) that exchanges with a network of inaccessible pools. We will introduce the notion of tracee (i.e., the endogenous substance under study) versus tracer (i.e., a radioactive or a stable isotope that is administered exogenously and has, at least ideally, the same metabolic fate as the tracee). We will explain how tracer administration allows one to generate dynamic data that help in quantifying the metabolic fluxes of the tracee. Finally, we will illustrate the main approaches to the design of the format of tracer administration under steady- and nonsteady-state conditions
Glucose production during an IVGTT by deconvolution : validation with the tracer-to-tracee clamp technique
Full text linkRecently, a new method, based on a two-compartment minimal model and deconvolution [A. Caumo and C. Cobelli. Am. J. Physiol 264 (Endocrinol. Metab. 37): E829-E841, 1993; P. Vicini, G. Sparacino, A. Caumo, and C. Cobelli. Comput. Meth. Prog. Biomed. 52: 147-156, 1997], has been proposed to estimate endogenous glucose production (EGP) from labeled intravenous glucose tolerance test (IVGTT) data. Our aim here is to compare this EGP profile with that independently obtained with the reference method, based on the tracer-to-tracee ratio (TTR) clamp. An insulin-modified (0.03 U/kg body wt infused over 5 min) [6,6-2H2]glucose-labeled IVGTT (0.33 g/kg of glucose) was performed in 10 normal subjects. A second tracer ([U-13C]glucose) was also infused during the test in a variable fashion to clamp endogenous glucose TTR. The TTR clamp was quite successful. As a result, the EGP profile, reconstructed from [U-13C]glucose data with the models of Steele and Radziuk, were almost superimposable. The deconvolution-obtained EGP profile, calculated from [6,6-2H2]glucose data, showed remarkable agreement with that obtained from the TTR clamp. Some differences between the two profiles were noted in the estimated basal EGP and in the initial modalities of EGP inhibition. A high interindividual variability was also observed with both methods in the resumption of EGP to baseline; variability was high in both the timing and the extent of resumption. In conclusion, the use of the two-compartment minimal model of the IVGTT and deconvolution allows the estimation of a profile of EGP that is in very good agreement with that independently obtained with a TTR clamp
Estimation of endogenous glucose production after a glucose perturbation by nonparametric stochastic deconvolution.
Full text linkThe knowledge of the time course of endogenous glucose production (EGP) after a glucose perturbation is crucially important for understanding the glucose regulation system in both healthy and disease (e.g. diabetes) states. EGP is not directly accessible, and thus an indirect measurement approach is required. The estimation of EGP during an intravenous glucose tolerance test (IVGTT) can be posed as an input estimation problem solvable as a Fredholm integral equation of the first kind (A. Caumo and C. Cobelli, Am. J. Physiol., 264 (1993) E829-E841). The time-varying model of the kernel of the glucose system was identified from a concomitant tracer experiment, and EGP was reconstructed by employing the Phillips-Tikhonov regularization (deconvolution) algorithm. However, the proposed deconvolution approach left some issues open, e.g. how to choose the amount of regularization and how to deal with nonuniform/infrequent sampling. Here, a solution to these problems is provided by resorting to a new deconvolution algorithm. Thanks to the stochastic embedding into which the new deconvolution method is stated, the amount of regularization is determined in a statistically sound manner. In addition, in face of infrequent sampling, a time continuous profile of EGP is obtained. The method is shown to work reliably for reconstructing EGP in different IVGTT experimental protocols, both in normal and disease states
Models to assess masses, fluxes, and regulatory interactions of an endocrine control system: the glucose-insulin prototype
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Generalization of map estimation in SAAM II : validation against ADAPT II in a glucose model case study
No full textBayesian approaches to model identification [e.g., maximum a posteriori (MAP) estimation] are receiving increasing attention in metabolism since important quantitative knowledge has become available in the last decades, e.g., from tracer experiments. By suitably exploiting this knowledge, more complex physiological models than those solely based on experimental data (Fisherian approach) become resolvable. While ADAPT II is the reference software for MAP estimation in pharmacokinetic/pharmacodynamic/metabolic system analysis, another popular, user-friendly and state-of-the-art software is SAAM II. However, SAAM II does not handle a priori information on correlation among parameters, thus allowing a limited version of MAP estimation to be performed. The aim here is twofold. First, we show that this limitation of SAAM II can be easily overcome by resorting to a probability theory result. Second, we test SAAM II vs ADAPT II implementation of MAP estimation in a real case study: the Bayesian identification of a recently proposed two-compartment minimal model of glucose kinetics during an intravenous glucose tolerance test. SAAM II MAP estimates of glucose effectiveness (SG) and insulin sensitivity (S(I)) obtained in a group of 22 healthy humans are in excellent agreement with those of ADAPT II: S(G) = 2.84 +/- 0.27 vs. 2.84 +/- 0.27 (mlmin(-1) kg(-1), mean +/- SD) and S(I) = 11.46 +/- 1.69 vs. 11.47 +/- 1.69 [10(-2) ml kg(-1) min(-1)/ (microU ml(-1))]. The SAAM II vs. ADAPT II estimates are virtually identical (P > 0.44 and 0.68 for S(G) and S(I), respectively) and also closely correlated (p = 0.9998 and 0.9999)
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