Evaluation of the metabolomic profile of obese dogs supplemented with different sources and concentrations of beta-glucans

• Grant number: 25/12431-3
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: August 01, 2025
• End date: July 31, 2026
• Field of knowledge: Agronomical Sciences - Animal Husbandry - Animal Nutrition and Feeding
• Principal Investigator: Thiago Henrique Annibale Vendramini
• Grantee: Bruna Letícia da Silva Faria
• Host Institution: Faculdade de Medicina Veterinária e Zootecnia (FMVZ). Universidade de São Paulo (USP). São Paulo , SP, Brazil

Abstract

Obesity is the most common nutritional disease in dogs, and is responsible for triggering chronic inflammation, which leads to several metabolic changes. Research suggests that the inclusion of certain ingredients in the diet can help manage this condition. Among the dietary compounds with functional potential, beta-glucans stand out, which have been associated with the modulation of metabolic processes in different species. However, studies investigating the metabolic effects of these polysaccharides in obese dogs are still scarce. In this context, the present study aims to evaluate the impact of supplementation with different sources of beta-glucans on the serum metabolomic profile of obese dogs, using nuclear magnetic resonance (NMR) spectroscopy. Three experimental diets will be tested: a control diet (CTL), free of beta-glucans; a diet supplemented with 1000 ppm of beta-1,3-glucans (BGA); and another with 1000 ppm of beta-1,3/1,6-glucans (BGB). Thirty-six adult, neutered dogs with excess body fat (above 30%) will be randomly allocated to the treatments. Blood will be collected before and after 90 days of dietary intervention for subsequent metabolome analysis. Nuclear Magnetic Resonance (NMR) data will be processed and analyzed for identification and quantification of metabolites using Chenomix NMR Suite and MatLab software, with application of Principal Component Analysis (PCA) and PLS-DA to explore patterns of variation. Differences in the concentration of individual metabolites will be evaluated by linear mixed models in SAS software (version 9.4), considering statistical significance when P < 0.05.