title: Generalization of map estimation in SAAM II: validation against ADAPT II in a glucose model case study.
abstract: 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).
authors.affiliation: Department of Information Engineering, University of Padova, Italy.
authors.forename: Tiziano
authors.initials: T
authors.lastname: Callegari
authors.forename: Andrea
authors.initials: A
authors.lastname: Caumo
authors.forename: Claudio
authors.initials: C
authors.lastname: Cobelli
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001786:Blood Glucose / Q000032:analysis*; D003198:Computer Simulation; D005947:Glucose / Q000494:pharmacology*; D018149:Glucose Intolerance / Q000097:blood; D005951:Glucose Tolerance Test / Q000592:standards; D006801:Humans; D007328:Insulin / Q000097:blood / Q000378:metabolism*; D007333:Insulin Resistance / Q000502:physiology*; D007700:Kinetics; D016013:Likelihood Functions; D008954:Models, Biological; D008956:Models, Chemical; D015233:Models, Statistical; D011786:Quality Control; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013223:Statistics as Topic
chemical_list: D001786:Blood Glucose; D007328:Insulin; D005947:Glucose