Evaluation Of DNA Methylation Pattern In Healthy, Sarcopenic, Obese And Sarcopenic Obese Older Women: A Cross-Sectional Study

Overview

INTRODUCTION: Sarcopenic obesity (SO), a functional and clinical condition, is characterized by the coexistence of obesity, marked by excess fat mass and sarcopenia, characterized by reduced strength and muscle mass. SO is associated with a greater risk of health-related adverse clinical outcomes than older adults with obesity and sarcopenia alone. Aging is accompanied by numerous changes epigenetic. These aging-associated epigenetic changes include DNA methylation, histone modification, chromatin remodeling, non-coding RNA (ncRNA) regulation, and RNA modification. DNA methylation occurs at cytosines in CpG dinucleotides in the genome and undergoes changes with age in various human tissues. Furthermore, many genes can be hypermethylated or hypomethylated on CpG islands with the aging process. Soon, a broad exploration of candidate genes may provide insights into the pathogenesis of Sarcopenic obesity. Therefore, understanding how aging, specifically sarcopenia, obesity and Sarcopenic obesity, is regulated by epigenetic factors, favors the development of new treatment therapies. Thus, the objective will be to evaluate the epigenetic influence on sarcopenic obesity in older women. METHODS: This cross-sectional study will include 32 older women who will be classified as healthy, with sarcopenia, obesity and sarcopenic obesity living in the city of Ribeirão Preto - SP. The older adults will perform total and regional body scan using iDXA, anthropometric assessment, functional capacity tests, peripheral blood collection for analysis of biochemical markers and epigenetics. For statistical analysis will be used t test, ANOVA, linear regression models and Pearson correlation. Analyzes of the complete methylome will be performed using bioinformatics tools, including specific software. EXPECTED RESULTS: It is expected that there will be differences in the patterns of methylation and gene expression in the diseases analyzed. In addition, it is expected to clarify how epigenetic changes occur throughout this process.

The sample size was calculated a priori, considering DNA methylation as the primary outcome, on an exploratory basis. The analyses were conducted using R software (https://www.r-project.org). For the calculation, an effect size of 0.65, alpha 0.05 and statistical power of 0.80 were used. Thus, a total of 32 participants will be included in the study (n = 8 in each group). The degree of methylation will be determined by measuring the amount of marker incorporated for each probe. The image intensities will be extracted using the Illumina Genome Studio 2011.1, Methylation module 1.9.0 software. The methylation score will be represented as a beta value (β) according to the fluorescence intensity ratio. Beta values can assume any value between 0 (unmethylated) and 1 (completely methylated). Subsequently, the image intensities will be extracted from the raw data (IDATS) using the Champ package for R (Statistical Computing, VIE, Austria). All genome-wide methylation data will be analyzed by t-test (within groups), followed by the Benjamini Hochberg test. Pathway enrichment analysis will be performed using WEBGestalt and Reactome. The normality of data distribution will be verified by the Shapiro Wilk test, and descriptive statistics will consist of mean and standard deviation values. ANOVA will be used to compare groups. Pearson correlations will be used to assess the correlation of DNA methylation patterns with phenotypic variables. Linear regression models will be applied to analyze the prediction of methylation pattern and selected variables. The multinomial logistic regression model will be used in the groups for association with telomere length adjusted for BMI, age, ALST). Statistical significance will be set at 5%, and all analyses will be performed using the Statistical Package for Social Sciences software (SPSS version 17.0 Inc. Chicago, IL).