Mechanistic modeling of the digestion of dairy solutions and gels as compared with mini-pig in vivo data

A number of in vitro and in vivo studies have shown that the digestion kinetics of food materials depend on the matrix structure. However, up to now, very few mathematical models have been proposed to explain and/or simulate the phenomena observed. Our present study is based on the recent mini-pig in vivo results of Barbé et al. (to be published) and aims at better understanding and modeling the effect of the matrix structure on the digestion kinetics of dairy solutions and gels. The model is based on a compartmental approach and is adjusted, using a Monte-Carlo Markov-Chain algorithm, to a series of experimental data: dry matter, Cr-EDTA (a non degradable marker) and residual intact protein (caseins and β-lactoglobulin) concentrations at the stomach level as well as to amino-acid concentrations within the blood. The model structure accounts for food ingestion, transit rates, gastric secretions, gastric emptying, protein hydrolysis within the stomach as well as amino-acid absorption in the intestinal tract. The model provides a good fitting of all the experimental data and enables recovering satisfactory estimates of the model variables as well as simulated physiological parameters - such as stomach volume, gastric secretions or emptying rates - that are consistent with the available literature. Besides, results show that the gastric phase is considerably longer for dairy gels than for dairy solutions, and that these different gastric behaviors have a measurable influence on the amino-acid rate of absorption. Since it has been shown that postprandial metabolic responses partly depend on the kinetics of nutrient uptake, our results suggest that the structure of dairy products is an important feature to consider when trying understanding the relations between alimentation and health. It is conclude that mechanistic modeling can be of great help to unravel the complexity of the mechanisms involved during the digestion process.