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Claudio Cobelli

Publications and source records attributed to Claudio Cobelli.

56 records · Page 4Linked to original sources

Insulin release in impaired glucose tolerance: oral minimal model predicts normal sensitivity to glucose but defective response times.

The availability of quantitative indexes describing beta-cell function in normal life conditions is important for the characterization of impaired mechanisms of insulin secretion in pathophysiological states. Recently, an oral C-peptide minimal model has been proposed and applied to subjects with normal glucose tolerance (NGT) during graded up-and-down glucose infusion protocols (40-min periods at 4, 8, 16, 8, 4, and 0 mg.kg(-1).min(-1)) and oral glucose tolerance tests. These tests are characterized by slow glucose and C-peptide dynamics, which reproduce prandial conditions. In view of the importance of beta-cell dysfunction in the pathogenesis of type 2 diabetes, our aim was to test and use the oral minimal model in subjects with impaired glucose tolerance (IGT) to identify deranged mechanisms of beta-cell function. Plasma C-peptide and glucose data from graded up-and-down glucose infusions were analyzed in nine NGT and four IGT subjects using the classic deconvolution approach and the oral minimal model, and indexes of beta-cell function were derived. An index of insulin sensitivity was also obtained for each subject from minimal model analysis of glucose and insulin levels achieved during the test. Both deconvolution and minimal model analyses revealed that individuals with IGT have a relative defect in the ability to secrete enough insulin to adequately compensate for insulin resistance. Additionally, minimal model analysis suggests that insulin secretory defect in IGT arises from delays in the timing of the beta-cell response to glucose.

Adult↗

Generalization of map estimation in SAAM II: validation against ADAPT II in a glucose model case study.

Bayesian 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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