Comput Methods Programs Biomed - Estimating postprandial glucose fluxes using hierarchical Bayes modelling.

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Resumo

A new stochastic computational method was developed to estimate the endogenous glucose production, the meal-related glucose appearance rate (R(a meal)), and the glucose disposal (R(d)) during the meal tolerance test. A prior probability distribution was adopted which assumes smooth glucose fluxes with individualized smoothness level within the context of a Bayes hierarchical model. The new method was contrasted with the maximum likelihood method using data collected in 18 subjects with type 2 diabetes who ingested a mixed meal containing [U-??C]glucose. Primed [6,6-?H2]glucose was infused in a manner that mimicked the expected endogenous glucose production. The mean endogenous glucose production, R(a meal), and R(d) calculated by the new method and maximum likelihood method were nearly identical. However, the maximum likelihood gave constant, nonphysiological postprandial endogenous glucose production in two subjects whilst the new method gave plausible estimates of endogenous glucose production in all subjects. Additionally, the two methods were compared using a simulated triple-tracer experiment in 12 virtual subjects. The accuracy of the estimates of the endogenous glucose production and R(a meal) profiles was similar [root mean square error (RMSE) 1.0?0.3 vs. 1.4?0.7 ?mol/kg/min for EGP and 2.6?1.0 vs. 2.9?0.9 ?mol/kg/min for R(a meal); new method vs. maximum likelihood method; P=NS, paired t-test]. The accuracy of R(d) estimates was significantly increased by the new method (RMSE 5.3?1.9 vs. 4.2?1.3; new method vs. ML method; P<0.01, paired t-test). We conclude that the new method increases plausibility of the endogenous glucose production and improves accuracy of glucose disposal compared to the maximum likelihood method.

Resumo Limpo

new stochast comput method develop estim endogen glucos product mealrel glucos appear rate ra meal glucos dispos rd meal toler test prior probabl distribut adopt assum smooth glucos flux individu smooth level within context bay hierarch model new method contrast maximum likelihood method use data collect subject type diabet ingest mix meal contain ucglucos prime hglucos infus manner mimick expect endogen glucos product mean endogen glucos product ra meal rd calcul new method maximum likelihood method near ident howev maximum likelihood gave constant nonphysiolog postprandi endogen glucos product two subject whilst new method gave plausibl estim endogen glucos product subject addit two method compar use simul tripletrac experi virtual subject accuraci estim endogen glucos product ra meal profil similar root mean squar error rmse vs molkgmin egp vs molkgmin ra meal new method vs maximum likelihood method pns pair ttest accuraci rd estim signific increas new method rmse vs new method vs ml method p pair ttest conclud new method increas plausibl endogen glucos product improv accuraci glucos dispos compar maximum likelihood method

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