Abstract

This analytical paper explores how laboratory biomarkers can be used to predict how patients with rheumatoid arthritis (RA) will respond to anti-TNF (anti–tumor necrosis factor) therapy. Rheumatoid arthritis is a long-term autoimmune disorder marked by chronic inflammation, often associated with elevated TNF-alpha levels. Biologic anti-inflammatory medications—such as infliximab, adalimumab, and etanercept—help reduce inflammation and slow disease progression. However, individual responses to these treatments vary significantly. Therefore, biomarkers like C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), rheumatoid factor (RF), and anti-CCP antibodies play a key role in predicting treatment outcomes. Future studies should aim to further explore, validate, and integrate the most promising biomarkers discussed, as well as identify new potential indicators. Researchers are encouraged to focus on clinical scenarios where predictive models, even with limited accuracy, could meaningfully guide treatment decisions. A comprehensive review of current studies suggests that biomarker-based prediction can support more personalized treatment approaches, help avoid unnecessary drug exposure, and reduce overall healthcare costs.