Lean I J, DeGaris P J, McNeil D M, Block E
Bovine Research Australasia, Camden 2570, NSW, Australia.
J Dairy Sci. 2006 Feb;89(2):669-84. doi: 10.3168/jds.S0022-0302(06)72130-0.
Data from 137 published trials involving 2,545 calvings were analyzed using random effects normal logistic regression models to identify risk factors for clinical hypocalcemia in dairy cows. The aim of the study was to examine which form, if any, of the dietary cation anion difference (DCAD) equation provided the best estimate of milk fever risk and to clarify roles of calcium, magnesium, and phosphorus concentrations of prepartum diets in the pathogenesis of milk fever. Two statistically equivalent and biologically plausible models were developed that predict incidence of milk fever. These models were validated using data from 37 trials excluded from the original data used to generate the models; missing variables were replaced with mean values from the analyzed data. The preferred models differed slightly; Model 1 included prepartum DCAD, and Model 2 included prepartum dietary concentrations of potassium and sulfur alone, but not sodium and chloride. Other factors, included in both models were prepartum dietary concentrations of calcium, magnesium, phosphorus; days exposed to the prepartum diet; and breed. Jersey cows were at 2.25 times higher risk of milk fever than Holstein cows in Model 1. The results support the DCAD theory of greater risk of milk fever with higher prepartum dietary DCAD (odds ratio = 1.015). The only DCAD equation supported in statistical analyses was (Na(+) + K(+)) - (Cl(-) + S(2-)). This finding highlights the difference between developing equations to predict DCAD and those to predict milk fever. The results support a hypothesis of a quadratic role for Ca in the pathogenesis of milk fever (model 1, odds ratio = 0.131; Model 2, odds ratio = 0.115). Milk fever risk was highest with a prepartum dietary concentration of 1.35% calcium. Increasing prepartum dietary magnesium concentrations had the largest effect on decreasing incidence of milk fever in both Model 1 (odds ratio = 0.006) and Model 2 (odds ratio = 0.001). Increasing dietary phosphorus concentrations prepartum increased the risk of milk fever (Model 1, odds ratio = 6.376; Model 2, odds ratio = 9.872). The models presented provide the basis for the formulation of diets to reduce the risk of milk fever and strongly support the need to evaluate macro mineral nutrition apart from DCAD of the diet.
使用随机效应正态逻辑回归模型分析了137项已发表试验中的数据,这些试验涉及2545次产犊,以确定奶牛临床低钙血症的风险因素。本研究的目的是检验哪种形式(如果有的话)的日粮阴阳离子差(DCAD)方程能最好地估计产乳热风险,并阐明产前日粮中钙、镁和磷浓度在产乳热发病机制中的作用。开发了两个在统计学上等效且生物学上合理的模型来预测产乳热的发生率。使用从用于生成模型的原始数据中排除的37项试验的数据对这些模型进行了验证;缺失变量用分析数据的平均值代替。首选模型略有不同;模型1包括产前DCAD,模型2仅包括产前日粮中的钾和硫浓度,而不包括钠和氯。两个模型中都包括的其他因素有产前日粮中的钙、镁、磷浓度;产前日粮摄入天数;以及品种。在模型1中,娟姗牛患产乳热的风险比荷斯坦牛高2.25倍。结果支持了产前日粮DCAD越高产乳热风险越大的DCAD理论(优势比 = 1.015)。统计分析中唯一支持的DCAD方程是(Na(+) + K(+)) - (Cl(-) + S(2-))。这一发现突出了预测DCAD的方程与预测产乳热的方程之间的差异。结果支持了钙在产乳热发病机制中起二次作用的假设(模型1,优势比 = 0.131;模型2,优势比 = 0.115)。产前日粮钙浓度为1.35%时产乳热风险最高。在模型1(优势比 = 0.006)和模型2(优势比 = 0.001)中,增加产前日粮镁浓度对降低产乳热发生率的影响最大。产前增加日粮磷浓度会增加产乳热风险(模型1,优势比 = 6.376;模型2,优势比 = 9.872)。所提出的模型为制定降低产乳热风险的日粮提供了依据,并有力地支持了除日粮DCAD外评估常量矿物质营养的必要性。