Abstract
Background and objective: Rheumatoid arthritis is an autoimmune disease. This study investigated the potential value of combining cartilage oligometric matrix protein, anti-cyclic citrullinated protein, and 14-3-3 eta protein with traditional biomarkers to reduce the diagnostic gap. Methods: This case-control study included 46 male and female patients and 42 age- and gender-matched adults as control group. The biomarkers were measured using ELISA technique. Results: Tests for anti-cyclic citrullinated protein and cartilage oligometric matrix protein are excellent tools to diagnose rheumatoid arthritis because anti-cyclic citrullinated protein and cartilage oligometric matrix protein are associated with the highest ROC area. The validity of the test for 14-3-3 eta protein, which is a good test to predict rheumatoid arthritis, ranks second. The optimum cut-off values for high cartilage oligometric matrix protein, high anti-cyclic citrullinated protein, and high 14-3-3 eta protein were ≥0.242µg/L, ≥0.566ng/L, and ≥0.145ng/L, respectively. 14-3-3η protein, cartilage oligometric matrix protein status as a parallel combination which is considered as a wonderful combination in classification criteria for rheumatoid arthritis. Parallel combination, both criteria two tests are positive, namely “high cartilage oligometric matrix protein (≥0.242) + high 14-3-3η protein (≥0.145)” was associated with a perfect test, that the patients have rheumatoid arthritis (sensitivity 100%, specificity 100%, accuracy 100%, positive predictive value at pre-test probability 50% and 90% = 100%). A positive test using this combination is 100% diagnostic and establishes a possible diagnosis of rheumatoid arthritis with 100%, while a negative test would exclude a possible diagnosis of rheumatoid arthritis with 100% confidence. Conclusion: Results confirmed that high serum level of cartilage oligometric matrix protein, anti-cyclic citrullinated protein, and 14-3-3 eta protein are significantly associated with increased risk for rheumatoid arthritis, demonstrating the potential value of combining these new biomarkers with traditional biomarkers to enhance diagnostic sensitivity and specificity and ultimately reduce the diagnostic gap.
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