ANALYZE OF INTESTINAL MICROBIOTA AND ITS METABOLIC IMPACT IN WOMAN WITH OBESITY, EUTROPHY, CONSTITUTIONAL LEANNESS AND ANOREXIA NERVOSA

• Grant number: 17/05305-5
• Support Opportunities: Regular Research Grants
• Duration: March 01, 2018 - May 31, 2021
• Field of knowledge: Health Sciences - Medicine
• Principal Investigator: Marcio Corrêa Mancini
• Grantee: Marcio Corrêa Mancini
• Host Institution: Hospital das Clínicas da Faculdade de Medicina da USP (HCFMUSP). Secretaria da Saúde (São Paulo - Estado). São Paulo , SP, Brazil
• Associated researchers: Maria Edna de Melo ; Paula Waki lopes da Rosa

Abstract

Obesity and anorexia nervosa have multifactorial causes, identifying factors that influence these conditions could lead to the discovery of new therapeutic targets. The intestinal microbiota presents as one of these factors: several studies show differences between the microbiota of obese and eutrophic and between anorexic and eutrophic. However, there are no studies that evaluate the microbiota of constitutional leanness. In addition, recent evidence suggests the importance of the microbiota in the energy balance, acting in the central nervous system by several mechanisms, including the short chain fatty acid signaling pathway (SCFA). SCFA can act on multiple pathways related to body homeostasis, but studies that verify the relationship between SCFA and genes involved in energy balance are scarce. The objective of this study is to evaluate the influence of the intestinal microbiota through SCFA on the expression of genes related to body weight homeostasis in obese, eutrophic, contitutional lean and anorexic women. It is a cross-sectional study of 80 women aged 18-35 years (20 obese, 20 eutrophic, 20 constitutional lean and 20 anorexic). Metabolic, clinical and food consumption parameters will be evaluated. The microbiota and SCFA will be analyzed on sample collected by anal swab. Methylation of 22 genes involved in SCFA signaling will be evaluated, and those with differentiated methylation will be submitted to gene expression analysis by real-time PCR. For statistical analysis the data will be submitted to the normal distribution check, the differences in the continuous variables will be calculated through multivariate analysis models, with p <0.05. (AU)