Abstract
Diabetes mellitus is considered a disease of great impact and the prospects are that it should increase greatly in the coming years, due to people's lifestyle. Among the two types of diabetes. type 2 (DM2) is the one with the highest prevalence (90%). Some studies have indicated that fluoride (F) may influence insulin resistance / sensitivity, an early feature of DM2 development. Thus, studies that relate this pathology to the administration of F are important, since this ion is present in water supply and other sources, such as dentifrices and food. Thus, the objective of this study is to implement a new animal model (mice) for DM2, induced by diet and streptozotocin, which may be useful to evaluate the effect of chronic administration of F on insulin resistance and on the expression of liver proteins and muscles. For this purpose, 60 male adult mice of the C57BL / 6J lineage will be divided into 3 experimental groups (n = 20) according to the dose of F (0, 10 or 50 ppm) administered through drinking water. The animals will receive a hyperlipid diet for 2 weeks and at the 3rd week they will receive daily injections of streptozotocin (STZ) for 3 to 5 days. From the 4th week treatment will begin with the F, lasting 21 days. Blood glucose tests will be performed periodically to check for glycemic changes in order to detect insulin resistance, and after the administration of STZ and DM2. A group with 10 mice of the C57BL / 6 lineage will be used as control. They will receive for the same experimental period, water without F and normocaloric diet. After the experimental period, the animals will be euthanized, and the blood will be collected for analysis of F (specific ion electrode), glucose (glucose oxidase method) and insulin (ELISA), as well as the liver and gastrocnemius muscle, for quantitative proteomic analysis (Protein Linx Global Service software). We will also select 8 animals from each experimental group to perform an insulin tolerance test (ITT) before euthanasia. Proteomic data will be analyzed by bioinformatics and interaction networks will be produced and evaluated for identification of changes in proteins. After checking the normality and homoscedasticity of the data, they will be submitted to appropriate statistical analysis (p <0.05).
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