FRI0040 MULTI-VARIATE APPROACH INCLUDING SEROLOGY AND GENETICS FOR AN IMPROVED IDENTIFICATION OF PATIENTS AT RISK OF DEVELOPING RA
Authors: M. Grundhuber, I. Gehring, C. Lamacchia, P. Roux-Lombard, M. Nissen, U. Walker, B. Moeller, D. Kyburz, A. Ciurea, M. Poorafshar, A. Finckh
Journal: Annals of the Rheumatic Diseases (2020)

Background: First-degree relatives of rheumatoid arthritis (RA) patients (FDR-RA) have a 3 - 5-fold increased prevalence of the disease [1]. RA development is triggered by an interaction between genetic and environmental factors. As the field is moving towards prevention in pre-clinical stages of RA, it is key to identify individuals with imminent RA, prior to onset of symptoms, which will presumably rely on both the measurement of autoantibodies and genetic risk markers. Objectives: Assemble a pattern of serologic biomarkers in combination with genetics to improve the identification of individuals at high risk to develop RA. Methods: The cohort included 827 serum samples from 601 individuals, followed within the Swiss multicenter cohort study SCREEN-RA of FDR [2]. FDR-RA were categorized into four groups according to the presence of symptoms and systemic autoimmunity associated with RA; 1: asymptomatic FDR-RA without anti-CCP or symptoms, 416 (69%); 2: FDR-RA with clinically suspect arthralgia (CSA) [3] or with signs of arthritis, without anti-CCP, 72 (12%); 3: FDR-RAs with no signs of arthritis, positive anti-CCP test, 55 (9%); 4: FDR with signs of arthritis or CSA, positive anti-CCP-test, 58 (10%). Serum samples were analyzed for the presence of anti-CCP (IgG, IgA), RF (IgM, IgA) and anti-RA33 (IgM, IgA, IgG) using the EliA TM instrument platform (Phadia AB, Uppsala, Sweden). Genetic measurements were performed using the AmpliSeq TM technology on the Ion GeneStudio TM instruments (Thermo Fisher Scientific, Carlsbad, USA), covered variants were analyzed using an algorithm focusing on the identification of RA patients. Results: The overall prevalence of biomarkers, considering results above cutoff values, was 1% for anti-CCP IgG and IgA, 10% and 2% for RF IgM and RF IgA, respectively, and 6-15% for all three anti-RA33 isotypes. Several individuals had multiple positive serology tests (Fig 1): 3.6% (22) were positive for 2 tests and 1% (6) were positive for 3 or more tests. Among the 28 individuals positive for ≥2 tests, 17 (61%) were symptomatic. Figure 1. Distribution of positive serology within the different groups. No positivity (none), positive for one (1), equal or more than 2 (2-5) of the serologic tests. Nine of 604 FDR-RA subsequently developed classifiable RA and were positive for serologic biomarkers before date of RA diagnosis (Table 1). The RA converters had a mean age of 39 years (24-75 yrs) and an average follow-up time within the study of 3.6 years (1-7 yrs). Table 1. Biomarker status of subsequent RA converters before date of diagnosis. RA converters CCP IgG CCP IgA RF IgM RF IgA RA33 total 1 + + + + + 2 + − + + − 3 − − + − + 4 − − + − − 5 − − + − − 6 − − − − + 7 − − − − + 8 − − − − − 9 − − − − − Using an algorithm to analyze the RA-associated genetic SNPs, we could highlight 48 FDR-RA (8%) with an increased genetic risk to develop RA. 15 out of 48 individuals (31%) at high genetic risk reported CSA, and 12 out of 48 individuals (25%) displayed signs of systemic autoimmunity associated with RA. Conclusion: When looking at FDR it could help to not only include anti-CCP autoantibody testing but also additional biomarkers like RF and anti-RA33. Furthermore, looking at the genetic risk factors could give additional information. The combination with the multi-variate profile could even improve the early diagnosis of these patients. References: [1]Kuo et al. Rheumatology 2017; 56:928933 [2]Finckh et al. Ann Rheum Dis 2011; 70: S3–282 [3]van Steenbergen HW, et al. Ann Rheum Dis 2017; 76:491–496 Disclosure of Interests: Maresa Grundhuber Grant/research support from: Thermo Fisher Scientific, Employee of: Thermo Fisher Scientific, Isabel Gehring Grant/research support from: Thermo Fisher Scientific, Employee of: Thermo Fisher Scientific, Céline Lamacchia Grant/research support from: Thermo Fisher Scientific partially supported this study, Pascale Roux-Lombard: None declared, Michael Nissen Grant/research support from: Abbvie, Consultant of: Novartis, Lilly, Abbvie, Celgene and Pfizer, Speakers bureau: Novartis, Lilly, Abbvie, Celgene and Pfizer, Ulrich Walker Grant/research support from: Ulrich Walker has received an unrestricted research grant from Abbvie, Consultant of: Ulrich Walker has act as a consultant for Abbvie, Actelion, Boehringer Ingelheim, Bristol-Myers Squibb, Celgene, MSD, Novartis, Pfizer, Phadia, Roche, Sandoz, Sanofi, and ThermoFisher, Paid instructor for: Abbvie, Novartis, and Roche, Speakers bureau: Abbvie, Actelion, Bristol-Myers Squibb, Celgene, MSD, Novartis, Pfizer, Phadia, Roche, Sandoz, and ThermoFisher, Burkhard Moeller: None declared, Diego Kyburz Grant/research support from: Abbvie, Roche, Consultant of: Abbvie, BMS, Novartis, Pfizer, Roche, UCB, Gilead, Sanofi, Speakers bureau: Pfizer, BMS, Novartis, Abbvie, Adrian Ciurea Consultant of: Consulting and/or speaking fees from AbbVie, Bristol-Myers Squibb, Celgene, Eli Lilly, Merck Sharp &amp; Dohme, Novartis and Pfizer., Maryam Poorafshar Grant/research support from: Thermo Fisher Scientific, Employee of: Thermo Fisher Scientific, Axel Finckh Grant/research support from: Pfizer: Unrestricted research grant, Eli-Lilly: Unrestricted research grant, Consultant of: Sanofi, AB2BIO, Abbvie, Pfizer, MSD, Speakers bureau: Sanofi, Pfizer, Roche, Thermo Fisher Scientific