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Updated: Mar 22, 2026

An Adoptive Transfer Model of Rheumatoid Arthritis in Mice
Published on: June 6, 2025
[Immunological markers of rheumatoid arthritis]
Agnieszka Matuszewska1, Marta Madej2, Piotr Wiland2
1Katedra i Zakład Farmakologii Uniwersytetu Medycznego we Wrocławiu.
Abstract:
Rheumatoid arthritis (RA) is the most common connective tissue disease of autoimmune origin. The disease is characterized by chronic inflammation leading to bone erosions and organ involvement. RA is a progressive disease. It affects the quality of life, leading to disability and death mainly due to premature cardiovascular disease. Early diagnosis and appropriate treatment are essential for prognosis and quality of life improvement. In 2010 the American College of Rheumatology (ACR) and The European League Against Rheumatism (EULAR) established new RA classification criteria. Besides clinical symptoms it includes two immunologic criteria: rheumatoid factor (RF) and anti-citrullinated protein antibodies (anti-CCP antibodies). RF is the first well-known RA immunologic marker. It is observed in 80-85% of patients with RA. Elevated serum level of RF has been associated with increased disease activity, radiographic progression, and the presence of extraarticular manifestations. The sensitivity of RF is 50-90%, and specificity is 50-95%. Anti-CCP antibodies appear to be a more specific marker than RF. They are often present at the very beginning of the disease, or even years before the first symptoms. The prognostic value of anti-CCP antibodies is well established. High serum level of anti-CCP correlates with poor prognosis and early erosions of the joints. The sensitivity of anti-CCP2 is 48-80%, and specificity is 96-98%. New immunologic markers include anti-carbamylated protein antibodies (anti-CarP) and antibodies against heterogeneous nuclear ribonucleoproteins (anti-hnRNP A2/B1, RA33). Scientists aim to identify a highly sensitive and specific biomarker of the disease that not only has diagnostic and prognostic value but also may predict the response to treatment.
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