A personalized medicine is a contemporary form of medicine focusing on a combination of genetic, metabolic and clinical information to help predict the risk of diseases thus preventing illness early and treating them in a very personalized manner. Personalized approaches have been shown to act as a way of enhancing diagnosis, treatment and management in rheumatoid arthritis (RA), a chronic autoimmune disease that is seen in 1 out of 100 adults. It is a disease that causes immune-mediated inflammation of the synovial membrane which causes cartilage and bone destruction. The application of traditional treatment varies because of the clinical characteristics of age, sex, smoking, BMI and adherence to medications. The improvement of omics technologies including genomics, transcriptomics, proteomics, metabolomics and epigenetics has improved the knowledge of RA pathogenesis and made the discovery of biomarkers to optimize therapy easier. Pharmacogenomics determines genetic changes that affect the reaction to drugs whereas metabolomics and proteomics identify biochemical and protein changes associated with the disease activity. Multi-omics and clinical information are further combined with artificial intelligence (AI) and machine learning (ML) to forecast responses to treatments and optimize personalized care. TNF- alpha, IL-1, IL-6 and IL-17 are biomarkers that are important in the process of inflammation and are used as a form of therapy. In general, personalized medicine of RA is a predictive, preventive, personalized and participatory model which improves the treatment outcomes, reduces the adverse effects and improves precision medicine in case of autoimmune diseases.
Guttikonda* et al. (2026) studied this question.