Genetic and Epigenetic Biomarkers in the 1-Carbon Metabolism Cycle as Potential Health Predictors in Systemic Lupus Erythematosus: An Integrative Perspective on Clinical Status and Quality of Life

Abstract

SLE is a chronic autoimmune disease that affects various organs and systems of the body, presenting diverse symptoms such as fatigue, joint pain, skin rashes, and damage to vital organs. The treatment of SLE aims to control symptoms and protect the affected organs, making it crucial to identify markers that can predict the clinical status of patients. The classification of SLE poses significant challenges due to its highly heterogeneous nature and symptomatology. The wide range of clinical manifestations and the diversity of affected organs and systems make the identification and classification of SLE a complex task. Therefore, it is necessary to employ methods for detecting biomarkers to trace an assertive clinical profile according to the patient's SLE status.It is known that the 1-carbon metabolism plays a significant role in the homeostasis of the immune system, and alterations in this process may be associated with disease progression. Thus, the study aims to investigate a possible association between DNA methylation levels and single nucleotide polymorphisms (SNPs) in genes related to 1-carbon metabolism (MTHFR and MTR) with the clinical manifestations of the disease and its impact on quality of life. Additionally, the study seeks to evaluate whether such genetic variants can be used as biomarkers to predict the clinical status of SLE patients.The methodology involves the collection of sociodemographic, clinical, and disease data through medical records and interviews with patients. Genetic (genotyping) and epigenetic (pyrosequencing) analyses, as well as measurements of metabolic, inflammatory, hematological, and immunological serum levels from fasting blood samples, will be conducted. Additionally, anthropometric and body composition assessments, along with an evaluation of quality of life (using the LupusQol questionnaire), will be performed. Data will be analyzed using statistical tests such as chi-square and regression models to assess associations and effects.