Department of Dairy Science, University of Wisconsin, Madison 53706.
Department of Animal Science, University of Lavras, Lavras, Minas Gerais, 37200-000, Brazil.
J Dairy Sci. 2017 Nov;100(11):8977-8994. doi: 10.3168/jds.2017-12908. Epub 2017 Aug 31.
The objectives of this study were to investigate the relationship between dry matter intake (DMI) and urinary purine derivative (PD) excretion, to develop equations to predict DMI and to determine the endogenous excretion of PD for beef and dairy cattle using a meta-analytical approach. To develop the models, 62 published studies for both dairy (45 studies) and beef cattle (17 studies) were compiled. Twenty models were tested using DMI (kg/d) and digestible DMI (dDMI, kg/d) as response variables and PD:creatinine (linear term: PD:C, and quadratic term: PD:C), allantoin:creatinine (linear term: ALLA:C, and quadratic term: ALLA:C), metabolic body weight (BW, kg), milk yield (MY, kg/d), and their combination as explanatory variables for dairy and beef (except for MY) cattle. The models developed to predict DMI for dairy cattle were validated using an independent data set from 2 research trials carried out at the University of Wisconsin (trial 1: n = 45; trial 2: n = 50). A second set of models was developed to estimate the endogenous PD excretion. In all evaluated models, the effect of PD (either as PD:C or ALLA:C) was significant, supporting our hypothesis that PD are in fact correlated with DMI. Despite the BW-independent relationship between PD and DMI, the inclusion of BW in the models with PD:C and ALLA:C as predictors slightly decreased the values of root mean square error (RMSE) and Akaike information criterion for the models of DMI. Our models suggest that both DMI and dDMI can be equally well predicted by PD-related variables; however, predicting DMI seems more useful from a practical and experimental standpoint. The inclusion of MY into the dairy models substantially decreased RMSE and Akaike information criterion values, and further increased the precision of the equations. The model including PD:C, BW, and MY presented greater concordance correlation coefficient (0.93 and 0.63 for trials 1 and 2, respectively) and lower RMSE of prediction (1.90 and 3.35 kg/d for trials 1 and 2, respectively) when tested in the validation data set, emerging as a potentially useful estimator of nutrient intake in dairy cows. Endogenous PD excretion was estimated by the intercept of the linear regression between DMI (g/kg of BW) and PD excretion (mmol/kg of BW) for beef (0.404 mmol/kg of BW) and dairy cattle (0.651 mmol/kg of BW). Based on the very close agreement between our results for beef cattle and the literature, the linear regression appears to be an adequate method to estimate endogenous PD excretion.
本研究的目的是探讨干物质采食量(DMI)与尿嘌呤衍生物(PD)排泄之间的关系,通过荟萃分析建立预测 DMI 的方程,并确定肉牛和奶牛的 PD 内源性排泄量。为了建立模型,我们汇集了 62 项关于奶牛(45 项研究)和肉牛(17 项研究)的已发表研究。使用 DMI(kg/d)和可消化 DMI(dDMI,kg/d)作为响应变量,PD:肌酐(线性项:PD:C,二次项:PD:C)、尿囊素:肌酐(线性项:ALLA:C,二次项:ALLA:C)、代谢体重(BW,kg)、产奶量(MY,kg/d)及其组合作为解释变量,对 20 个模型进行了测试,这些模型用于奶牛和肉牛(除 MY 外)。使用在威斯康星大学进行的 2 项研究试验的独立数据集(试验 1:n = 45;试验 2:n = 50)验证了为奶牛开发的预测 DMI 的模型。第二组模型用于估计 PD 的内源性排泄量。在所有评估的模型中,PD(无论是 PD:C 还是 ALLA:C)的影响都是显著的,这支持了我们的假设,即 PD 实际上与 DMI 相关。尽管 PD 与 DMI 之间存在与 BW 无关的关系,但将 BW 纳入 PD:C 和 ALLA:C 作为预测因子的模型中,略微降低了 DMI 模型的均方根误差(RMSE)和赤池信息量准则的值。我们的模型表明,PD 相关变量可以同样好地预测 DMI 和 dDMI;然而,从实际和实验的角度来看,预测 DMI 似乎更有用。在奶牛模型中加入 MY,大大降低了 RMSE 和赤池信息量准则的值,并进一步提高了方程的精度。在验证数据集测试中,包括 PD:C、BW 和 MY 的模型具有更高的一致性相关系数(试验 1 和 2 分别为 0.93 和 0.63)和更低的预测 RMSE(试验 1 和 2 分别为 1.90 和 3.35 kg/d),这是一种有潜力的奶牛营养素摄入量的估算器。通过 DMI(g/kg BW)与 PD 排泄(mmol/kg BW)之间的线性回归的截距估计肉牛(0.404 mmol/kg BW)和奶牛(0.651 mmol/kg BW)的内源性 PD 排泄量。基于我们对肉牛的结果与文献非常吻合,线性回归似乎是估计内源性 PD 排泄量的一种合适方法。