|Support type:||Scholarships in Brazil - Post-Doctorate|
|Effective date (Start):||May 01, 2016|
|Effective date (End):||December 31, 2017|
|Field of knowledge:||Agronomical Sciences - Animal Husbandry - Animal Nutrition and Feeding|
|Principal Investigator:||Aulus Cavalieri Carciofi|
|Grantee:||Bruna Agy Loureiro|
|Home Institution:||Faculdade de Ciências Agrárias e Veterinárias (FCAV). Universidade Estadual Paulista (UNESP). Campus de Jaboticabal. Jaboticabal , SP, Brazil|
Cats are carnivores with a high requirement for nitrogen and amino acids. Starch digestion and metabolism of these animals has been studied, however important aspects are not completely understood, especially the effects of protein to starch ratios on feline energy and protein metabolism. This is important to promote long-term health benefits and as a potential tool to prevent obesity development. The study aims to evaluate the consumption of diets with different proportions of protein and carbohydrates on energy and protein metabolism. The experiment will follow two simultaneous 4x4 Latin square designs, with 4 diets, 4 periods of 7 weeks, and 4 cats, totaling 8 replicates per diet. Extruded diets with different proportions of protein (CP) and starch (CHO) will be used: 20% of CP and 48.5% of CHO; 34% of CP and 33.5% of CHO; 48% of CP and 18.5% of CHO and 62% of CP and 3.5% of CHO. Cats will be fed the experimental diets for 7 weeks, and the following parameters will be evaluated: body composition by deuterium oxide (lean body mass and fat mass); nutrient and energy digestibility of the diets; protein metabolism through nitrogen balance, 24h urinary excretion of urea, muscle catabolism (measuring urinary 3-methyl-histidine), retinol binding protein and lean body mass; daily energy expenditure through indirect calorimetry in respirometry chambers, evaluating the maintenance energy requirement, basal metabolic rate, heat increment, and net energy requirement, respiratory quotient to measure protein, carbohydrate and fat oxidation; and study of metabolites profile. Data will be evaluated by polynomial contrasts, repeated measures and principal component analysis (P<0.05).