Metabolisable energy content in canine and feline foods is best predicted by the NRC2006 equation

Although animal trials are the most accurate approach to determine the metabolisable energy (ME) content of pet food, these are expensive and labour-intensive. Instead, various equations have been proposed to predict ME content, but no single method is universally recommended. Data from canine and feline feeding studies, conducted according to Association of American Feed Control Officials recommendations, over a 6-year period at a single research site, were utilised to determine the performance of different predictive equations. Predictive equations tested included the modified Atwater (MA equation), NRC 2006 equations using both crude fibre (NRC 2006cf) and total dietary fibre (NRC 2006tdf), and new equations reported in the most recent study assessing ME predictive equations (Hall equations; PLoS ONE 8(1): e54405). Where appropriate, equations were tested using both predicted gross energy (GE) and GE measured by bomb calorimetry. Associations between measured and predicted ME were compared with Deming regression, whilst agreement was assessed with Bland-Altman plots. 335 feeding trials were included, comprising 207 canine (182 dry food; 25 wet food) and 128 feline trials (104 dry food, 24 wet food). Predicted ME was positively associated with measured ME whatever the equation used (Pcf and Hall equations were intermediate in performance, whilst the NRC 2006tdf equations performed best especially when using measured rather than predicted GE, with the narrowest 95% limits of agreement, minimal bias and proportional error. In conclusion, when predicting ME content of pet food, veterinarians, nutritionists, pet food manufacturers and regulatory bodies are strongly advised to use the NRC 2006tdf equations and using measured rather than predicted GE.尽管动物试验是测定宠物食品代谢能（metabolisable energy, ME）的最精准方法，但该方法成本高昂且劳动密集。为此，学界已提出多种预测ME含量的方程，但目前尚无通用推荐的单一方法。本研究利用某单一研究站点6年间按照美国饲料管理协会（Association of American Feed Control Officials, AAFCO）建议开展的犬、猫饲喂研究数据，以评估不同预测方程的性能。本次测试的预测方程包括改良阿特沃特方程（modified Atwater, MA）、分别采用粗纤维（NRC 2006cf）和总膳食纤维（NRC 2006tdf）的美国国家研究委员会2006版方程，以及最新发表的ME预测方程评估研究中提出的新方程（Hall方程；PLoS ONE 8(1): e54405）。在适宜条件下，研究分别使用预测总能（gross energy, GE）与弹式量热法测得的GE，对各方程进行了测试。采用戴明回归（Deming regression）比较实测ME与预测ME的相关性，并通过布兰德-奥特曼图（Bland-Altman plots）评估二者的一致性。本研究共纳入335项饲喂试验，其中207项犬类试验（含182项干粮试验、25项湿粮试验）及128项猫类试验（含104项干粮试验、24项湿粮试验）。无论采用何种方程，预测ME与实测ME均呈正相关；其中NRC 2006cf与Hall方程性能处于中等水平，而NRC 2006tdf方程表现最优，尤其当使用实测而非预测的总能时，其95%一致性区间最窄，偏差与比例误差均最小。综上，在预测宠物食品ME含量时，强烈建议兽医、营养学家、宠物食品生产商及监管机构采用NRC 2006tdf方程，且优先使用实测而非预测的总能。