Prebiotics promote health benefits, however, there is no consensus on the minimal intake period required in order to obtain good results. This study evaluated the effect of the time of ingestion of prebiotics on fecal fermentation products and immunological features in dogs. Twenty-four adult dogs were randomly distributed in a block design with six groups and four treatments. Diet and intake period were variation factors. Diets were either a control diet without the addition of prebiotic (CO) or with the inclusion of 1% of a commercial product containing a minimum of 0.38% galactooligosaccharides (GOS), 0.5% (B1) or 1% (B2) of a prebiotic blend. Time variable was set at 30 and 60 days for evaluation of immunity and gut health. Results were analyzed in the Statistical Analysis System software (SAS), version 9.4, considering the repeated measures over time design, and means were compared by the Tukey test and p < 0.05 was significant. Propionic acid was the only variable that had an interaction effect, with reduction of this metabolite in treatment B2 in the period of 60 days. At T60, concentrations of immunoglobulin A, lactic acid, and pH in the feces increased (p < 0.05) in all treatments regardless of prebiotic inclusion or not. GOS increased fecal score and lactic acid concentrations. Therefore, a 60-day intake period of a prebiotic blend was not sufficient to modulate fecal and immune variables and higher concentrations of a single prebiotic would be more relevant for results.

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.