Animal Nutrition Group, Wageningen University & Research, PO BOX 338, 6700 AH Wageningen, the Netherlands.
Wageningen Livestock Research, PO BOX 338, 6700 AH Wageningen, the Netherlands.
Animal. 2023 Dec;17 Suppl 5:101025. doi: 10.1016/j.animal.2023.101025. Epub 2023 Nov 2.
Current feed formulation and evaluation practices rely on static values for the nutritional value of feed ingredients and assume additivity. Hereby, the complex interplay among nutrients in the diet and the highly dynamic digestive processes are ignored. Nutrient digestion kinetics and diet × animal interactions should be acknowledged to improve future predictions of the nutritional value of complex diets. Therefore, an in silico nutrient-based mechanistic digestion model for growing pigs was developed: "SNAPIG" (Simulating Nutrient digestion and Absorption kinetics in PIGs). Aiming to predict the rate and extent of nutrient absorption from diets varying in ingredient composition and physicochemical properties, the model represents digestion kinetics of ingested protein, starch, fat, and non-starch polysaccharides, through passage, hydrolysis, absorption, and endogenous secretions of nutrients along the stomach, proximal small intestine, distal small intestine, and caecum + colon. Input variables are nutrient intake and the physicochemical properties (i.e. solubility, and rate and extent of degradability). Data on the rate and extent of starch and protein hydrolysis of different ingredients per digestive segment were derived from in vitro assays. Passage of digesta from the stomach was modelled as a function of feed intake level, dietary nutrient solubility and diet viscosity. Model evaluation included testing against independent data from in vivo studies on nutrient appearance in (portal) blood of growing pigs. When simulating diets varying in physicochemical properties and nutrient source, SNAPIG can explain variation in glucose absorption kinetics (postprandial time of peak, TOP: 20-100 min observed vs 25-98 min predicted), and predict variation in the extent of ileal protein and fat digestion (root mean square prediction errors (RMSPE) = 12 and 16%, disturbance error = 12 and 86%, and concordance correlation coefficient = 0.34 and 0.27). For amino acid absorption, the observed variation in postprandial TOP (61 ± 11 min) was poorly predicted despite accurate mean predictions (58 ± 34 min). Recalibrating protein digestion and amino acid absorption kinetics require data on net-portal nutrient appearance, combined with observations on digestion kinetics, in pigs fed diets varying in ingredient composition. Currently, SNAPIG can be used to forecast the time and extent of nutrient digestion and absorption when simulating diets varying in ingredient and nutrient composition. It enhances our quantitative understanding of nutrient digestion kinetics and identifies knowledge gaps in this field of research. Already useful as research tool, SNAPIG can be coupled with a postabsorptive metabolism model to predict the effects of dietary and feeding-strategies on the pig's growth response.
目前的饲料配方和评估实践依赖于饲料成分营养价值的静态值，并假设其具有加和性。因此，忽略了饮食中营养素之间的复杂相互作用以及高度动态的消化过程。为了提高对复杂日粮营养价值的未来预测，应该承认营养消化动力学和日粮与动物的相互作用。因此，为生长猪开发了一种基于营养素的基于计算机的机制消化模型：“SNAPIG”（Simulating Nutrient digestion and Absorption kinetics in PIGs）。该模型旨在预测在成分组成和物理化学特性变化的饮食中营养素吸收的速度和程度，通过通道、水解、吸收和内源性分泌来代表摄入的蛋白质、淀粉、脂肪和非淀粉多糖的消化动力学，沿胃、近端小肠、远端小肠和盲肠+结肠进行。输入变量是营养素摄入量和物理化学特性（即溶解度以及降解速度和程度）。不同消化段中不同成分的淀粉和蛋白质水解速度和程度的数据来自体外测定。胃中食糜的通过被建模为饲料摄入量水平、饮食中营养素溶解度和饮食粘度的函数。模型评估包括针对生长猪门静脉血液中营养素出现的独立体内研究数据进行测试。当模拟物理化学特性和营养素来源变化的饮食时，SNAPIG 可以解释葡萄糖吸收动力学的变化（餐后峰值时间，TOP：20-100 分钟观察到的 vs 25-98 分钟预测的），并预测回肠蛋白质和脂肪消化程度的变化（均方根预测误差（RMSPE）=12%和 16%，干扰误差=12%和 86%，以及协调相关系数=0.34 和 0.27）。对于氨基酸吸收，尽管平均预测值准确（58±34 分钟），但观察到的餐后 TOP 变化（61±11 分钟）预测效果不佳。要重新校准蛋白质消化和氨基酸吸收动力学，需要有关净门静脉营养素出现的数据，同时结合对消化动力学的观察，以饲养在成分组成变化的饮食中的猪。目前，SNAPIG 可用于模拟成分和营养素组成变化的饮食时，预测营养素消化和吸收的时间和程度。它增强了我们对营养消化动力学的定量理解，并确定了该研究领域的知识空白。作为研究工具，SNAPIG 已经很有用，可以与吸收后代谢模型耦合，以预测日粮和饲养策略对猪生长反应的影响。
