EPIGENETIC COMPARISON OF CELL TYPE PROPORTION BETWEEN PRE-MENOPAUSE AND POST-MENOPAUSE WOMEN IN EPIGENOME-WIDE ASSOCIATION STUDIES

• Grant number: 24/17705-1
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• Start date: March 17, 2025
• End date: July 01, 2025
• Field of knowledge: Health Sciences - Physical Education
• Principal Investigator: Carlos Roberto Bueno Júnior
• Grantee: Guilherme da Silva Rodrigues
• Supervisor: Macsue Jacques
• Host Institution: Escola de Educação Física e Esporte de Ribeirão Preto (EEFERP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil
• Institution abroad: Monash University, Australia
• Associated to the scholarship: 23/08249-0 - Genetic and epigenetic predictive scores for muscle strength in women aged 50 to 70 years: association with response variability to combined training, BP.DR

Abstract

Introduction: Population aging, coupled with a decline in immunity known as immunosenescence, results in increased susceptibility to infections, autoimmune diseases, and compromised cellular immunity. This process is influenced by hormonal changes, notably the sharp decline in estrogen levels during menopause, which leads to a decrease in CD4 T lymphocytes and the cytotoxic effect of natural killer (NK) cells. Regular training benefits both the adaptive and innate immune systems, including improvements in NK cell function among the elderly. Aim: The aim of the study is to investigate the effect of hormonal changes (menopause) on the distribution of cell proportion and its implications for health and adaptation to exercise, in addition to comparing if there is a difference between the sexes after the exercise protocol. Methodology: To maintain a standard of probes they will be normalized and filtered by: probes with a detection p-value above 0.01, with a beadcount <3 in at least 5% of samples, NoCG, SNPs and MultiHit. For cell type analysis, we will use the EpiDISH package, which is a package compatible with the R language. EpiDISH is interesting because it uses DNAm data from whole blood, epithelial tissue, and breast tissue for calculations, increasing the number of study samples and the impact of the study.