Compartmental modeling of postprandial dietary nitrogen distribution in humans

Abstract

A linear 11-compartment model was developed to describe and simulate the postprandial distribution of dietary nitrogen. The values of its 15 constant diffusion coefficients were estimated from the experimental measurement of (15)N nitrogen kinetics in the intestine, blood, and urine after the oral administration of (15)N-labeled milk protein in humans. Model structure development, parameter estimation, and sensibility analysis were achieved using SAAM II and SIMUSOLV softwares. The model was validated at each stage of its development by testing successively its a priori and a posteriori identifiability. The model predicted that, 8 h after a meal, the dietary nitrogen retained in the body comprised 28% free amino acids and 72% protein, approximately 30% being recovered in the splanchnic bed vs. 70% in the peripheral area. Twelve hours after the meal, these values had decreased to 18 and 23% for the free amino acid fraction and splanchnic nitrogen, respectively. Such a model constitutes a useful, explanatory tool to describe the processes involved in the metabolic utilization of dietary proteins.

Contribution of plasma proteins to splanchnic and total anabolic utilization of dietary nitrogen in humans

“…We previously developed a compartmental model that specifically follows ingested N in the postprandial nonsteady state and simulates its incorporation into total splanchnic and peripheral proteins after the ingestion of a single protein meal in humans (26). In the present work, our objectives were to modify this model structure so as to distinguish between splanchnic exported and constitutive proteins by use of experimental data obtained in different pools, including plasma proteins, after the ingestion of mixed soy protein meals in healthy humans.…”

The challenge of diabetes in Asia

“…This NPPU method has been applied to a variety of different protein foods (Bos et al, 1999(Bos et al, , 2005(Bos et al, , 2007Gaudichon et al, 1995;Gausserès et al, 1997;Lacroix et al, 2006Lacroix et al, , 2008Mariotti et al, 1999Mariotti et al, , 2000Mariotti et al, , 2002). True ileal nitrogen digestibility measured for different protein sources were: milk protein, 95% Gaudichon et al, 2002;Mahé et al, 1994b); soya and pea protein, 91 % Gaudichon et al, 2002;Gausserès et al, 1997); wheat protein, 85-90% (Bos et al, 2005;Juillet et al, 2008); rapeseed protein, 84% (Bos et al, 2007).…”

“…The model calculations are fairly complex but allow one to predict the kinetics of dietary nitrogen in the body (Juillet et al, 2006). Both the amino acid profi le of the protein and the kinetics of amino acid delivery to the blood can affect the postprandial splanchnic and peripheral uptake of amino acid in humans (Deglaire et al, 2009b;Fouillet et al, 2000Fouillet et al, , 2001Fouillet et al, , 2002bFouillet et al, , 2003Fouillet et al, , 2009Juillet et al, 2008). Increasing protein intake increases splanchnic catabolic use while splanchnic catabolic use and peripheral anabolic use are inversely affected (Fouillet et al, 2009).…”

Nutrition et santé : lipides et protéines d’origine végétale - De nouvelles données pour juger de la qualité des protéines végétales chez l’homme - Implications et perspectives

“…Ainsi, dans la phase postprandiale, la composition en acides aminés pourrait influencer le métabolisme des différents tissus et définir le degré d'incorporation des acides aminés dans les différentes protéines, sans pour autant nécessairement affecter l'utilisation globale au niveau de l'ensemble de l'organisme [57]. L'analyse compartimentale à l'aide de modèles globaux du métabolisme de l'azote ou des acides aminés peut permettre de proposer une vue intégrée de la valorisation spécifique des protéines dans les différentes zones de l'organisme [61,62]. Des progrès pourront également être accomplis en étudiant directement sur certains tissus, comme le muscle, le transport et l'utilisation anabolique des acides aminés alimentaires, en situation dynamique, ce qui appelle également à un traitement des données par analyse compartimentale [63].…”