The initial rheumatoid arthritis (RA) identification is considered fundamental for effective disease management. Rheumatoid arthritis is a systematic autoimmune disease causing joint damage, mainly pain, swelling, stiffness and loss of the joint function. The regulation and classification criteria from 2010 provided by American College of Rheumatology (ACR)/European League Against Rheumatism (EULAR) were developed to improve the stratification of patients with inflammatory arthritis. Based on this evaluation, early diagnosis and further risk stratification of RA patients including those with severe forms requiring medical treatment are not optimal. For instance, the diagnosis of the RA based on the anti-citrullinated protein (ACPA) or immunoglobulin M (IgM) rheumatoid factor (RF) reached sensitivity and specificity values in a range of 65-69% and 85-95, respectively. Therefore, novel and more reliable biomarkers are necessary to contribute to accurate and early diagnosis, particularly improving treatment efficacy.
In a pilot study performed by Mosaiques, identification of the potential biomarkers for diagnosis of RA patients was performed by the analysis of the urine peptidomic profiles from 33 RA patients and 30 healthy individuals. A total of 292 peptides were detected to be significantly different between both groups. Using tandem mass spectrometry, sequence information for 70 peptides was retrieved. A peptide biomarker classifier was established based on 39 sequenced peptides showing no correlation with age or gender. Validation of the biomarker panel was performed in a blinded test set of 16 RA patients and 15 healthy controls, achieving an accuracy of 91% and an area under the curve (AUC) value of 0.93 in the receiver operating characteristic (ROC) analysis (Figure 1A). Following this pilot study, the ability of the urinary peptide signatures to discriminate different inflammatory subgroups was further evaluated (Figure 1B).
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