1,721,053 research outputs found
The hot IVGTT two compartment minimal model: indices of glucose effectiveness and insulin sensitivity.
No full textA 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
Bayesian two-compartment and classic single-compartment minimal models: comparison on insulin modified IVGTT and effect of experiment reduction
No full textModels describing plasma glucose and insulin concentration of an intravenous glucose tolerance test (IVGTT) allow a noninvasive cost-effective approach to estimate important indexes characterizing the efficiency of glucose-insulin control system, i.e., glucose effectiveness (SG) and insulin sensitivity (SI). To overcome some limitations of the classic single compartment minimal model (1CMM) of glucose kinetics , a two-compartment Bayesian minimal model (2CBMM) has been recently proposed for the standard IVGTT. This study aims to assess 2CBMM ability to describe the insulin-modified IVGTT (IM-IVGTT) which is the protocol of choice since it allows one to study insulinopenic states. Both a full-length IM-IVGTT (240 min) as well as a reduced version (90 min) of it are studied. Results of the maximum a posteriori identification of IM-IVGTT (240 min) in 13 normals agree with those of standard IVGTT, i.e., a 42% decrease (P<0.002) of SG and a 13% increase (P<0.006) of SI with respect to 1CMM. When identified from IM-IVGTT (90 min), 2CBMM not only provides SG and SI estimates 46% lower (P<0.002) and 41% higher (P<0.002) than 1CMM ones respectively, but also seems to overcome some limitations of the 240 min-based identification that probably arise because the minimal model is unable to properly account for the hyperglycemic hormonal response taking place in the second half of IM-IVGTT
The oral glucose minimal model: estimation of insulin sensitivity from a meal test
No full textRecently, a new approach has been proposed to estimate insulin sensitivity (S(I)) from an oral glucose tolerance test or a meal using an "integral equation". Here, we improve on the "integral equation" by resorting to a "differential equation" approach. The classic glucose kinetics minimal model was used with the addition of a parametric model for the rate of appearance into plasma of oral glucose (Ra). Three behavioral models of Ra were proposed: piecewise-linear (P), spline (S) and dynamic (D). All three models performed satisfactorily allowing a precise estimation of S(I) and a plausible reconstruction of Ra. Mean S(I) estimates were virtually identical: S(I)P = 6.81 +/- 0.87 (SE); S(I)S = 6.53 +/- 0.80; and S(I)D = 6.62 +/- 0.79. S(I) strongly correlated with the integral-equation index (I) S(I)I: r = 0.99, p < 0.01 for models D and S, and r 0.97, p < 0.01 for P. Also, SI compared well with insulin sensitivity estimated from intravenous glucose tolerance test in the same subjects (r = 0.75, p < 0.01; r = 0.71, p < 0.01; r = 0.73, p < 0.01, respectively, for P, S, and D models versus s(I)IVGTT). Finally, the novel approach allows estimation of SI from a shorter test (120 min): model P yielded S(I)R = 7.16 +/- 1.0 (R for reduced) which correlated very well with S(I)P and S(I)I (respectively, r = 0.94, p < 0.01; r = 0.95, p < 0.01) and still satisfactorily with S(I)IVGTT (r = 0.77, p < 0.01)
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)
Hepatic glucose production during the labeled IVGTT: estimation by deconvolution with a new minimal model.
No full textA method for the estimation of hepatic glucose production during a labeled intravenous glucose tolerance test (IVGTT) is proposed. Stable-label IVGTT data in normal subjects have been considered. The method is based on deconvolution and uses a new two-compartment minimal model of glucose kinetics to describe the time-varying impulse response of the glucose system. A new model of glucose kinetics was needed because the available single-compartment minimal model, specifically developed to interpret labeled IVGTT data, provided a nonphysiological pattern of hepatic glucose production. The new minimal model has two novel features: glucose kinetics are described by a two-compartment structure, and insulin exerts its action on the irreversible loss of the slowly exchanging glucose pool. The deconvolution scheme used to reconstruct hepatic glucose production is described in detail both in terms of computational aspects and reliability. Confidence limits of the reconstructed hepatic glucose production in each individual are derived by taking into account both the measurement error of the data and the uncertainty associated with the description of the impulse response. Physiological plausibility of the time course of hepatic glucose production provided by this new method is discussed. The ability of the new model to reconstruct hepatic glucose production considerably enriches the kinetic portrait of glucose metabolism that can be obtained from the minimal-model analysis of labeled IVGTT data
The dual tracer time-varying volume method for measuring hepatic glucose release in nonsteady state: theoretical and simulation results.
No full textMeasurement of hepatic glucose release in nonsteady state is difficult and experimental approaches have been developed in order to circumvent Steele's model inadequacy. Recently, a resurgence of interest in the time-varying volume method developed by Issekutz has taken place. Issekutz's approach assumes that the volume of Steele's model is not constant but time-varying and that its time course can be measured by infusing two tracers with different patterns. The time-varying volume is then substituted into Steele's equation and hepatic glucose release is estimated. The aim of this study was to analyze some basic aspects of Issekutz's method and to determine the accuracy of its estimate of hepatic glucose release. A theoretical analysis showed that the time-varying volume measured by Issekutz's approach is not unique but depends on the format of administration of the two tracers. In addition, such a volume allows an accurate estimate of hepatic glucose release if one of the two tracers is infused in such a way that its specific activity is maintained perfectly constant during the experiment. Since it is impossible to achieve a perfect clamp of specific activity, we also evaluated the performance of Issekutz's approach in more realistic experimental conditions which were reproduced by resorting to computer simulation. We simulated a euglycaemic clamp with insulin rising from basal to a plateau of approximately 40 microU/ml and then returning to basal. Nonsteady-state glucose kinetics were described by a previously validated two-compartment model while the time course of hepatic glucose release was derived from the literature. Both noise-free and noisy experimental conditions were simulated. We showed that the degree of accuracy of Issekutz's approach is very good and better than the one associated with the hot-ginf method. On the other hand, the major problem with Issekutz's approach is the sensitivity of the volume estimate to the measurement noise, which may limit its applicability in practice. In conclusion, we elucidated the theoretical grounds of Issekutz's approach and assessed its performance during nonsteady state in a realistic scenario using computer simulation
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Using what is accessible to measure that which is not: Necessity of model of system
No full textUnderstanding the in vivo functioning of endocrine-metabolic systems requires the quantitative knowledge of system parameters like production/utilization of substrales, secretion/degradation of hormones, and substrate-hormone signaling. Unfortunately, these system parameters are not directly accessible and an indirect measurement approach is needed based on a model of the system. We review first the principals of the model of system methodology focusing on compartmental and input-output modeling. Then, the model of system methodology is applied to the measurement of nonaccessible parameters/variables of the glucose system like glucose fluxes, insulin fluxes, and glucose-insulin signaling
Minimal model estimate of glucose effectiveness: role of the minimal model volume and of the second hidden compartment.
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