Accuracy of Predictive Equations for Metabolizable Energy Compared to Energy Content of Foods for Dogs and Cats Estimated by In Vivo Methods in Brazil - Pedro Henrique Marchi; Andressa Rodrigues Amaral; Leonardo de Andrade Príncipe; Larissa Wünsche Risolia; Mariana Fragoso Rentas; Ana Beatriz Fasolai; Rafael Vessecchi Amorim Zafalon; Gabriela Luiza Fagundes Finardi; Juliana Toloi Jeremias; Raquel Silveira Pedreira; Júlio Cesar de Carvalho Balieiro; Thiago Henrique Annibale Vendramini (Animals)
In small animal nutrition, the caloric content of a diet is expressed as metabolizable energy (ME). The gold standard method for determining this variable is through feeding trials with the target species. However, the high cost and intricacy of this assay lead to the use of indirect estimation methods. The aim of this study was to analyze the main equations employed for estimating ME and compare them with the results of in vivo tests. In total, 451 pet food products in Brazil were evaluated. The ME values were determined via the bromatological values available on labels. The data were analyzed using SAS, and Student’s t test was used with a significance of 5%. For all predictive equations, there was a non-conformity between their results and those obtained by in vivo methods. Thus, the prediction equations are only accurate when the exact values of food composition are used, which is hardly applicable since veterinarians and animal owners only have access to the information provided on the labels. Nonetheless, the Atwater system equation proved to be the most reliable for estimating ME, showing the smallest disparity among the evaluated methods. The overall differences between in vivo results and ME estimation with the Atwater system amounted to 3.59% for dry cat foods and −1.94% for dry dog foods. Moreover, although it was also the most accurate for wet foods, the differences were substantially greater (11.99% for cat foods and 8.25% for dog foods). These findings highlight the need for further research to refine ME estimation, which could contribute to improved pet food formulation and help reduce cases of malnutrition in dogs and cats.