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BackgroundIt is now well established that patients treated with rituximab (RTX) for auto-immune disease (AID) have a diminished antibody response to COVID-19 vaccines after two doses. Optimizing antibody response is a key objective in this population. To achieve this goal, a 3rd booster dose may be considered.ObjectivesFocusing on the population of AID patients treated with RTX without any antibody response after two doses we sought to explore how these patients could respond to a 3rd dose and identify factors of response.MethodsWe performed a French retrospective bicentric observational trial which is a follow up of previously published work (1). We included consecutive patients treated with RTX that were non-responders regarding their anti-Spike antibody (anti-S) status at least one month after the second dose of COVID-19 vaccination. Patients were included between March and October 2021. All patients then received a third dose according to local guidelines and had an anti-S measurement at least one month after the third dose. Some patients without response to a third dose had a fourth dose. Anti-S were measured in serum with various kits, but all results were in BAU/mL with upper quantification limit at 243. Patients with anti-S above 49 BAU/mL, which has been demonstrated to be the threshold associated with detectable neutralizing response were considered as responders (1).Results60 patients treated with RTX without response to 2 doses (Anti-S Ab < 49 BAU/mL) were included in the study. 9/60 (15%) patients responded to the 3rd vaccine dose with anti-S > 49BAU/ml.Responders and non-responders had similar demographic characteristics (Table 1). There was a positive correlation between anti-S Ab levels after dose 3 and time between 2nd and 3rd doses (r=0,41 p=0,001) (Figure 1). Nevertheless, the median time between 2nd and 3rd doses was numerically but not significantly higher in responders than in non-responders (129 vs 80 days, p=0.30). There was no correlation between anti-S levels after the third dose and the time between the last RTX infusion and the third vaccine dose. There was a trend towards more patients with methotrexate co-medication in the non-responder group 31/51 (61%) vs 3/9 (33%) in the responder group (p=0,15). Seven non-responder patients after the 3rd dose received a 4th dose and 4 (57%) responded.Table 1.Characteristics of patients without response after the second dose of vaccine treated with Rituximab for auto-immune diseases according to their seroconversion status after the third dose.Non responders (n=51)Responders (n=9)pAge, median (min-max)61 (17-85)58 (36-78)0,33Female sex n (%)42 (82)6 (67)0,36Underlying disease n (%) Rheumatoid Arthritis36 (69)5 (56)0,46 Other auto-immune disease16 (31)4 (44)Rituximab treatmentReceived a RTX infusion between dose 2 and 3 n (%)14(27)3(33)0,70Time between the last RTX infusion and the 3rd dose of vaccine in days median (min-max)162 (1-687)187 (79-959)0,16Previous number of RTX injections, median (min-max)7 (0-34)6 (1-22)0,58Vaccination schemeInterval between 2nd and 3rd vaccine dose in days, median (min-max)80 (23-287)129 (24-220)0,30Less than 3 months between 2nd and 3rd vaccine dose n (%)27(53)2(22)0,14Co-medicationsPatients on steroids n (%)20(40)5 (37,5)0,99Prednisone equivalent dose per day in mg median (min-max)5(2-25)5(5-10)0,48Figure 1.Correlation between anti- spike Ab levels after the third dose and time in days between doses 2 and 3.ConclusionA third vaccine dose of COVID-19 vaccine leads to only 15% of response in previously non-responding RTX treated AID patients. A longer time between 2nd and 3rd doses might positively influences response to a 3rd COVID-19 vaccine. Finally exploratory findings show that a fourth dose could be useful to obtain response non-responders.References[1]Bitoun S, Henry J, Desjardins D, et al. Rituximab impairs B-cell response but not T-cell response to COVID-19 vaccine in auto-immune diseases. Arthritis Rheumatol Hoboken NJ Published Online First: 28 December 2021.Disclosure of InterestsNone declared

BackgroundNo immunomodulatory drug has ever demonstrated its efficacy in primary Sjögren’s Syndrome (pSS). Drug repurposing, or drug repositioning, refers to the use in another disease of an existing drug, originally approved or evaluated in a different disease.ObjectivesThe objective of our study was to repurpose existing therapeutic drugs in pSS using a transcriptomic approach.MethodsWe generated pSS transcriptomic signatures from peripheral blood samples of patients with pSS compared to healthy controls in two cohorts (ASSESS and a Norwegian cohort) and data mined available pSS transcriptomic signatures in public databases. We compared each disease signature to transcriptomic signatures, obtained from the biological action of 2837 drugs, 2160 knock-in and 3799 knock-down genes, available in the Connectivity Map database. A median similarity score with regard to disease signatures was computed for each candidate drug/gene. Drugs and genes were selected if p│80│. If this score is sufficiently high and statistically significant (>80, pFigure 1.Methods of drug-repurposing transcriptomic analysis (adapted from Toro-Dominguez et al, Arthritis Res Ther 2017;19:54)Results1091 peripheral blood transcriptomes were analyzed from 6 independent studies (906 patients with pSS and 185 healthy controls). Our analysis identified 11 transcriptomic drug signatures significantly associated with pSS signature. We identified 72 transcriptomic knock-in (11) or knock-down (61) gene signatures significantly associated with that of pSS, including 21 with a negative similarity score (Table 1).Table 1.Knock-down and knock-in genes significantly associated with the pSS transcriptomic signaturesType ofexperimentSimilarity scoreGenesNumber of genesKnock-in+IFNG, DUSP28, IFNB1, LYN, BCL2L2, TNFRSF1A, CD40, BCL10, NLK, ZNF39810-SLC52A2111Knock-down+SLC25A14, GOLIM4, DTYMK, DCXR, RRM2, IMPA1, CLTB, F12, CAB39, ID1, ISOC1, UBAP1, HIGD2A, UFD1L, SOD2, BTG1, PRKCI, HIST2H2BE, NISCH, TEAD4, MTX2, TYK2, GTF2B, NDUFS7, NNT, ACADSB, GSTP1, HOMER2, SORBS3, PCK2, PHB2, PDXK, TES, TM9SF2, TBX2, HOXA6, KIF2C, MED1, NR2F6, CD14, BECN141-TM9SF3, E2F3, PRMT3, KD, PKN2, SUCLA2, CD44, GRN, SP3, ATP5S, MYCBP2, TRAF7, POLA2, ADRB2, PSMG1, PPP2R3C, PMAIP1, ETFA, ANKRD37, SPECC1L2061Type I and II interferons were highly ranked (similarity score >99), and their overexpression mimicked the disease signature. CD40 appeared also as a very relevant target (similarity score = 98.8). Three drugs had a significant negative similarity score: ampicillin (-88.69, p=0.0019), amylocaine (-88.28, p=0.0026), and droxinostat (-85.42, p=0.0027). Droxinostat is a HDAC inhibitor. HDAC activity has been shown to be an essential element of the coactivation system for IFN-induced gene regulation and the IFN-induced innate immune response.ConclusionThis first drug repositioning transcriptomic approach in Sjögren’s syndrome confirms the interest of targeting interferons and identifies histone deacetylases as potential therapeutic targets.AcknowledgementsInvestigators of the ASSESS cohort: Emmanuelle Dernis, Valerie Devauchelle-Pensec, Philippe Dieude, Jean-Jacques Dubost, Anne-Laure Fauchais, Vincent Goeb, Eric Hachulla, Pierre Yves Hatron, Claire Larroche, Véronique Le Guern, Jacques Morel, Aleth Perdriger, Carinne Salliot, Stephanie Rist, Alain Saraux, Jean Sibilia, Olivier Vittecoq, Gaétane Nocturne, Philippe Ravaud, Raphaèle SerorCentre de Ressources Biologiques de l’Hôpital Bichat: Sarah TubianaJohan G. Brun for contributing to the Norwegian cohort.Funding SourcesThis work was supported by the Innovative Medicines Initiative 2 Joint Undertaking (IMI 2 JU) (NECESSITY grant 806975). The Joint Undertaking received support from the European Union’s Horizon 2020 Research and Innovation Program and from the European Federation of Pharmaceutical Industries and Associations. This work was also supported by R01 AR065953 Beth the NIH, United States. The contents are the sole responsibility of the authors and do not necessarily the official views of the NIH.JEG received an unrestricted grant from Bristol-Myer-Squibbs to do the transcriptomic analysis of the ASSESS and Norwegian cohorts. JEG received a grant from Geneviève Garnier (Association Française du Syndrome de Gougerot-Sjögren et des syndromes secs).The ASSESS cohort is promoted by the French Society of Rheumatology and received two research grants from the French Society of Rheumatology.The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Disclosure of InterestsRenaud FELTEN: None declared, Tao Ye: None declared, Cédric Schleiss: None declared, Benno Schwikowski: None declared, Jean Sibilia: None declared, Fanny Monneaux: None declared, Hélène Dumortier: None declared, Roland Jonsson: None declared, Christopher Lessard: None declared, Wan Fai Ng: None declared, Tsutomu Takeuchi: None declared, Xavier Mariette: None declared, Jacques-Eric Gottenberg Grant/research support from: JEG received an unrestricted grant from Bristol-Myer-Squibbs to do the transcriptomic analysis of the ASSESS and Norwegian cohorts.

BackgroundThirteen drugs with different mechanisms of action may be considered to treat patients with methotrexate (MTX) inadequate response in rheumatoid arthritis (RA). TNF inhibitors (TNFi) are frequently the first choice in this situation. Unfortunately, 30% to 40% of RA patients do not respond to TNFi, resulting in a delay for beginning the appropriate targeted DMARD (tDMARD). Predicting the patient response to TNFi before prescribing the treatment is therefore a major goal and could help physicians to prescribe a tDMARD suited to the patient.ObjectivesWe aimed to build machine learning models based on simple clinical and biological data to predict patient response to TNFi.MethodsWe used data from the ESPOIR early arthritis cohort (1) to train the models, and the ABIRISK cohort (2) to validate the results. We included patients that fulfilled the EULAR/ACR 2010 criteria and that were treated with a TNFi. The models take as inputs patient’s characteristics at treatment initiation and predicts the therapeutic response, defined as the EULAR response 12 months (+/- 6 months) after treatment initiation. We compared the performances of four models (Linear Regression, Random Forest, XGBoost, and Catboost) on the training set by cross-validated them using the Area Under the ROC Curve (AUCROC). The best model was then evaluated on the validation dataset (ABIRISK cohort). We conducted the methodology both on all TNFi together and on etanercept and monoclonal anti-TNF antibodies separated. We analyzed how clinical and biological variables impacted response to provide explainability of the prediction.ResultsWe included 164 patients from the ESPOIR cohort and 118 patients from the ABIRISK cohort. Better results were obtained when etanercept and monoclonal anti-TNF antibodies were analyzed separately.These models predict a probability for a patient to respond to TNFi. This probability is compared to a decision threshold to obtain the binary outcome. Two decision thresholds were tested. The first prioritizes a high confidence when identifying responders (Strategy 1) while the second prioritizes a high confidence when identifying non-responders (Strategy 2). The model’s results are presented in Table 1.Table 1.Sensitivity, Specificity, PPV and NPV are computed on the ABIRISK validation cohort for each strategyDrugAUC(ESPOIR)AUC(ABIRA)STRATEGY 1 (high confidence in response)STRATEGY 2 (high confidence in non-response)SensitivitYSpecificityPPVNPVSensitivitySpecificityPPVNPVOverall TNFi0.720.6518%91%76%42%90%30%67%67%(0.68-0.73)(0.54 - 0.75)(10%-27%)(82%-98%)(54%-95%)(32%-51%)(83%-96%)(18%-44%)(58%-76%(45%-86%Etanercept0.740.7060%73%78%53%95%15%64%67%(0.68-0.75)(0.57- 0.82)(44%-74%)(55%-89%)(63%-92%)(36%-69%)(88%-100%)(4%-30%)(52%-76%)(20%-100%)Monoclonal anti-TNF antibodies0.740.7137%95%92%50%90%40%69%73%(0.69-0.77)(0.55-0.86)(20%-55%)(83%-100%)(73%-100%)(35%-66%)(78%-100%)(19%-62%)(54%-84%)(44%-100%)Using SHAP, we were able to analyze how each variable impacted the predictions. In particular, a DAS28 around 5 had the highest positive impact on response. Higher and lower values of DAS28 had either less impact or even negative impact on the patient response to TNFi treatment (Figure 1). This allows to identify non-linear relations between variables and patient response.ConclusionThe machine learning models developed in this study can predict RA patients’ response to TNFi using exclusively data available in clinical routine. These models also allow to analyze how these variables are used to predict response. Along with similar models for other tDMARDs, such algorithms could lead to a personalized therapeutic strategy.References[1]Combe B, Benessiano J, Berenbaum F, Cantagrel A, Daurès J-P, Dougados M, et al. The ESPOIR cohort: A ten-year follow-up of early arthritis in France.[2]Anon. ABIRISK Anti-Biopharmaceutical Immunization: Prediction and Analysis of Clinical Relevance to Minimize the RISK. 2019.AcknowledgementsFor the first 5 years of the ESPOIR cohort, an unrestricted grant from Merck Sharp and Dohme (MSD) was allocated. Two additional grants from INSERM were obtained to support part of the biological database. The French Society of Rheumatology, Pfizer, AbbVie, Lilly, and more recently Fresenius and Biogen also supported the ESPOIR cohort study. We also wish to thank Nathalie Rincheval (Montpellier) who did expert monitoring and data management and all the investigators who recruited and followed the patients (F.Berenbaum, Paris-Saint Antoine, MC.Boissier, Paris-Bobigny, A.Cantagrel, Toulouse, B.Combe, Montpellier, M.Dougados, Paris-Cochin, P.Fardellone et P.Boumier Amiens, B.Fautrel, Paris-La Pitié, RM. Flipo, Lille, Ph. Goupille, Tours, F. Liote, Paris-Lariboisière, O.Vittecoq, Rouen, X.Mariette, Paris Bicetre, P.Dieude, Paris Bichat, A.Saraux, Brest, T.Schaeverbeke, Bordeaux, J.Sibilia, Strasbourg) as well as S.Martin (Paris Bichat) who did all the central dosages of CRP, IgA and IgM rheumatoid.SB was supported by FHU CARE.Disclosure of InterestsNone declared.

BackgroundThe goal of treatment for RA is achieving low disease activity and/or remission1,2; however, disease course and management can be complicated by additional factors that may be influenced by serostatus. Anti-citrullinated protein antibodies (ACPAs) and RF contribute to a more severe RA disease pattern3 and may be useful in predicting response to treatment.4 ACTION (AbataCepT In rOutiNe clinical practice; NCT02109666) and ASCORE (Abatacept SubCutaneOus in Routine Clinical PracticE; NCT02090556) were 2-year, international, observational, prospective, multicenter studies of IV and SC abatacept, respectively, for the treatment of RA in routine clinical practice.4,5 Previous analyses have shown that ACPA/RF double-positive serostatus was associated with better treatment outcomes compared with ACPA/RF double-negative serostatus.4–6ObjectivesTo assess the independent effect of ACPA or RF single seropositivity among patients with RA on achieving remission after treatment with abatacept for 2 years, and to compare outcomes among patients with single versus double serostatus.MethodsThis post hoc analysis included patients from ACTION and ASCORE who initiated IV (body weight–adjusted dosing) or SC abatacept (125 mg once weekly), respectively. Patients were stratified by baseline ACPA/RF status: ACPA+/RF− (ACPA+ only), ACPA/RF double positive (+/+), ACPA−/RF+ (RF+ only), and ACPA/RF double negative (−/−). DAS28 (CRP) and CDAI remission rates (defined as < 2.6 and 0–2.8, respectively) at 2 years for patients who were ACPA+ or RF+ only at baseline were assessed and compared with those who were +/+ and −/−. Patients with missing baseline ACPA/RF status were excluded. Last observation carried forward efficacy analyses were used to impute missing values.ResultsThis analysis included 1679 patients from ACTION (ACPA+ only, n = 98; +/+, n = 1028; RF+ only, n = 161; and −/−, n = 392) and 1748 patients from ASCORE (ACPA+ only, n = 184; +/+, n = 1079; RF+ only, n = 142; and −/−, n = 343). Across studies and serogroups, baseline demographics and disease characteristics were similar (data not shown). In both ACTION and ASCORE, a higher proportion of patients who were only ACPA+ achieved DAS28 (CRP) and CDAI remission at 2 years compared with patients who were only RF+ (Figure 1). Additionally, a similar proportion of patients who were only ACPA+ achieved DAS28 (CRP) and CDAI remission at 2 years compared with patients who were +/+. In contrast, a lower proportion of patients who were only RF+ achieved DAS28 (CRP) and CDAI remission at 2 years compared with patients who were +/+.ConclusionIn this post hoc analysis of real-world data from ACTION and ASCORE, ACPA positivity was associated with an increased likelihood of achieving DAS28 (CRP) and CDAI remission at 2 years. Patients who were ACPA+ only were as likely to achieve remission as +/+ patients, suggesting that RF serostatus had less influence than ACPA serostatus on remission status at 2 years. In line with this, patients who were RF+ only were less likely to achieve remission at 2 years. This is the first large, real-world study to show that ACPA positivity plays a more important role than RF positivity in achieving remission whilst on abatacept. These results highlight the importance of assessing baseline ACPA status when considering treatment options for patients with RA.References[1]Smolen JS, et al. Ann Rheum Dis 2020;79:685–99.[2]Fraenkel L, et al. Arthritis Care Res (Hoboken) 2021;73:924–39.[3]Katchamart, W, et al. Rheumatol Int 2015;35:1693–9.[4]Alten R, et al. Ann Rheum Dis 2021;80(suppl 1):OP0180.[5]Alten R, et al. Clin Rheumatol 2019;38:1413–24.[6]Alten R, et al. RMD Open 2017;3:e000345.AcknowledgementsThis study was sponsored by Bristol Myers Squibb. Medical writing and editorial assistance was provided by Rachel Rankin, PhD, of Caudex, and was funded by Bristol Myers Squibb. Study management provided by Syneos (CRO).Disclosure of InterestsRieke Alten Speakers bureau: AbbVie, Bristol Myers Squibb, Celltrion, Galapagos, Gilead, Janssen, Lilly, Novartis, Pfizer, Roche, Paid instructor for: AbbVie, Bristol Myers Squibb, Celltrion, Galapagos, Gilead, Janssen, Lilly, Novartis, Pfizer, Roche, Consultant of: AbbVie, Bristol Myers Squibb, Celltrion, Galapagos, Gilead, Janssen, Lilly, Novartis, Pfizer, Roche, Grant/research support from: AbbVie, Bristol Myers Squibb, Celltrion, Galapagos, Gilead, Janssen, Lilly, Novartis, Pfizer, Roche, Christiane Rauch Shareholder of: Bristol Myers Squibb, Employee of: Bristol Myers Squibb, Melanie Chartier Shareholder of: Bristol Myers Squibb, Employee of: Bristol Myers Squibb, M.T. Nurmohamed Speakers bureau: AbbVie, Bristol Myers Squibb, Eli Lilly, Janssen, Pfizer, Consultant of: AbbVie, Bristol Myers Squibb, Eli Lilly, Janssen, Pfizer, Grant/research support from: AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Galapagos, Janssen, MSD, Pfizer, Sean Connolly Shareholder of: Bristol Myers Squibb, Employee of: Bristol Myers Squibb, Maya H Buch Speakers bureau: AbbVie, Consultant of: AbbVie, Galapagos, Gilead, Pfizer, Grant/research support from: Gilead, Pfizer, UCB, Peter Peichl Speakers bureau: Janssen, GlaxoSmithKline, Xavier Mariette Consultant of: Bristol Myers Squibb, Galapagos, GlaxoSmithKline, Janssen, Novartis, Pfizer, Sanofi, UCB, Yusuf Patel: None declared, Sara Marsal Speakers bureau: Bristol Myers Squibb, Lilly, MSD, Novartis - Sandoz, Pfizer, Roche, Consultant of: AbbVie, Galapagos, Pfizer, Sanofi; IMIDomics (executive role), Grant/research support from: AbbVie, Bristol Myers Squibb, Galapagos, Janssen, Lilly, MSD, Novartis - Sandoz, Pfizer, Roche, Sanofi, UCB, Roberto Caporali Speakers bureau: AbbVie, Amgen, Bristol Myers Squibb, Celltrion, Fresenius-Kabi, Galapagos, Janssen, Lilly, MSD, Novartis, Pfizer, Roche, Sandoz, UCB, Consultant of: AbbVie, Amgen, Bristol Myers Squibb, Celltrion, Galapagos, Janssen, Lilly, MSD, Novartis, Pfizer, Sandoz, UCB, Hedley Griffiths Consultant of: Amgen, Raimón Sanmartí Speakers bureau: AbbVie, Bristol Myers Squibb, Lilly, MSD, Pfizer, Roche, Sanofi, Grant/research support from: AbbVie, Bristol Myers Squibb, MSD, Pfizer, Roche, Bettina Bannert: None declared, Yedid Elbez Consultant of: Bristol Myers Squibb, Employee of: Signifience, Karissa Lozenski Shareholder of: Bristol Myers Squibb, Employee of: Bristol Myers Squibb

BackgroundTo date, no study has clinically focused on primary Sjögren’s Syndrome (pSS) patients presenting without or with low level of dryness features.ObjectivesTo describe pSS patients presenting no dryness or with low level of dryness and to compare them with pSS patients with dryness features to determine whether a specific clinical presentation can be identified in these patients.MethodsAll patients diagnosed with pSS according to AECG or ACR/EULAR criteria in our tertiary reference center were included. All patients were recruited between 1999 and 2020. Patient were considered without subjective dryness if the VAS for dry eyes or dry mouth was ≤ 30/100 and/or without objective dryness if having both normal Schirmer and salivary flow rate.ResultsOverall, 509 patients were included in the subjective dryness comparison group and 434 in the objective one (Table 1). Compared to patients with subjective dryness (n=456), patients without subjective dryness (n=53) were significantly younger (p=0,0025), were diagnosed earlier (p=0,0056), were more frequently anti-SSA positive (p=0,008), had lower levels of fatigue (p=0,0005). But no difference was observed regarding the level of disease activity or frequency of organ involvement. By contrast, patients reporting subjective dryness had more frequently chronic cough (p=0,022), with no more frequent objective lung involvement. Thus, the higher prevalence of chronic cough might be explained by dryness of the upper airways. Table 1.Comparison of pSS patients with or without drynessNo Subjective drynessSubjective dryness n=456P valuen=53Age, median (IQR)49 (39-62)58 (47-67)0,0025**Diagnostic delay, mean, years4,326,270,0056**ESSDAI, mean (SD)3,55 (3,38)3,22 (3,28)0,52MSGB# Chisolm score of 3 or 4, n (%)36 (68)362 (79)0,08Anti Ro/SSA, n (%)44 (83)292 (64)0,008**Chronic cough, n (%)5 (9)110 (24)0,022*VAS pain, median (IQR)40 (4-70)53 (23-77)0,06VAS fatigue, median (IQR)47 (13-71)65 (44-82)0,0005***No Objective drynessObjective dryness n=326n=108Age, median (IQR)52 (41-60)59 (47-67)0,0026**Diagnostic delay, mean, years5,96,10,75ESSDAI, mean (SD)3,462,960,056MSGB# Chisolm score of 3 or 4, n (%)81 (75)255 (78)0,49Anti Ro/SSA, n (%)84 (78)204 (62)0,0042**Arthralgia, n (%)81 (75)210 (64)0,04*VAS pain, median (IQR)59,5 (32,5-80)50 (20-73)0,03*VAS fatigue, median (IQR)66 (44-83)61 (39-80)0,27VAS eye dryness, median (IQR)49 (17-71)57 (25-80)0,019*VAS mouth dryness, median (IQR)58 (34-75)70 (48-88)0,001**MSGB: minor salivary gland biopsyThe patients without objective dryness (n=108) were also younger (p=0,0026), and had more frequently anti-SSA positive (p=0,0042). They also had a higher disease activity (mean ESSDAI: 3,46 versus 2,96, p=0,056). In this group of patients, the higher frequency of anti-SSA might be explained by the need of other criteria to fulfil diagnostic criteria, in the absence of abnormal Schirmer and salivary flow. Patients with objective dryness had more frequently arthralgia (p=0,04), a higher level of pain VAS (p=0,03) and subjective dryness VAS (p=0,019 for ocular and p=0,01 for oral) than patients without objective dryness.Among the 108 patients with no objective dryness, only 17 had also had no subjective dryness (VAS P value corresponds to the comparison of the two strata with Student’s t or Mann Whitney-U testConclusionAmong the patients with pSS, those without subjective or objective dryness features had a younger profile, shorter diagnosis delay which may result from a more acute onset, were more frequently anti-SSA positive. Those having no objective dryness also had more systemic disease, arthralgias and pain than patients with dryness features. Subjective dryness was associated with higher level of fatigue.References[1]Characters from table content including title and footnotes:Disclosure of InterestsNone declared

BackgroundThere is a lack of data on Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) vaccination safety in children and young people (CYP) with rheumatic and musculoskeletal diseases (RMDs). Current vaccination guidance is based on data from adults with RMDs or CYP without RMDs.ObjectivesTo describe the characteristics and outcomes of adolescents with inflammatory RMDs and adults with juvenile idiopathic arthritis (JIA) vaccinated against SARS-CoV-2.MethodsWe described patient characteristics, flares, and adverse events in adolescent cases under 18 with inflammatory RMDs and adult cases aged 18 or above with JIA submitted to the European Alliance of Associations for Rheumatology (EULAR) COVAX registry.ResultsThirty-six adolescent cases were reported from 4 countries, the most frequent diagnosis was JIA (42%). Over half (56%) reported early reactogenic-like adverse events (AEs) experienced within 7 days of vaccination. One mild polyarthralgia flare and one serious AE (malaise) were reported. No CYP reported SARS-CoV-2 infection post-vaccination.In addition to the adolescent cases, eleven countries reported 74 adult JIA cases. Among these, 62% reported early reactogenic-like AEs and two flares were reported (mild polyarthralgia and moderate uveitis). No serious AEs of special interest were reported among adults with JIA. Three 20-30 year old females were diagnosed with SARS-CoV-2 post-vaccination; all fully recovered.ConclusionIn this observational registry dataset, SARS-CoV-2 vaccines appeared safe in adolescents with RMDs and adults with JIA, with a low frequency of disease flares, serious AEs, and SARS-CoV-2 re-infection seen in both populations.Table 1.Characteristics of adolescents with RMDs and adults with JIA reported to the EULAR COVAX registryAdolescents with RMDs (N=36)Adults with JIA (N=74)SexFemale21 (58)54 (73)Male15 (42)20 (27)Age (median [IQR])15 [14.5, 17]26 [23, 31]Primary RMD diagnosisNon-systemic JIA10 (28)63 (85)Systemic JIA5 (14)11 (15)Systemic lupus erythematosus5 (14)Spondyloarthritis/psoriatic arthritis5 (14)Vasculitis/other RMD #11 (30)RMD disease activityRemission23 (64)33 (45)Minimal8 (22)21 (28)Moderate2 (6)12 (16)Severe1 (3)1 (1)Not applicable/missing2 (6)7 (10)RMD medicationNone9 (25)3 (4)b-DMARD9 (25)50 (68)cs-DMARD21 (58)25 (34)ts-DMARD5 (14)2 (3)Systemic glucocorticoids5 (14)1 (1)Colchicine7 (10)Other immunosuppressant *COVAX typePfizer/BioNTech33 (92)50 (68)Moderna2 (6)10 (14)AstraZeneca/Oxford1 (3)10 (14)Janssen1 (1)CoronaVac2 (3)UNK1 (1)COVAX doses111 (31)8 (11)22 (24)61 (82)31 (3)5 (7)RMD flareYes1 (3)2 (3)AEYes20 (56)46 (62)Early AEInjection site pain8 (22)16 (22)Redness6 (17)2 (3)Muscle pain1 (3)9 (12)Joint pain4 (11)3 (4)Headache9 (25)10 (14)Fever1 (3)26 (35)Chills2 (6)5 (7)Fatigue1 (3)13 (18)VomitingAE of special interestNon-serious1 (3)1 (1)Serious – important medical event1 (3)All data are N(%) of the column unless stated otherwise.# Other RMD includes Sjogren’s syndrome, systemic sclerosis, undifferentiated connective tissue disease, non-monogenic auto-inflammatory syndrome, chronic recurrent multifocal osteomyelitis, and other inflammatory arthritis* Other immunosuppressant includes ciclosporin, mycophenolate mofetil/mycophenolic acid.RMD, rheumatic and musculoskeletal disease; JIA, juvenile idiopathic arthritis; EULAR, European Alliance of Associations for Rheumatology; ANCA-associated vasculitis, anti-neutrophil cytoplasmic antibody-associated vasculitis; cs-, conventional synthetic; b-, biological; ts-, targeted synthetic; DMARD, disease-modifying anti-rheumatic drug; COVAX, Coronavirus vaccine; AE, adverse event.AcknowledgementsWe wish to thank all healthcare providers who entered data into the registry.Disclosure of InterestsSaskia Lawson-Tovey: None declared, Anja Strangfeld Speakers bureau: AbbVie, MSD, Roche, BMS, Pfizer, Elsa Mateus: None declared, Laure Gossec Consultant of: AbbVie, Amgen, BMS, Galapagos, Gilead, GSK, Janssen, Lilly, Novartis, Pfizer, Samsung Bioepis, Sanofi-Aventis, UCB, Grant/research support from: Amgen, Galapagos, Lilly, Pfizer, Sandoz, Loreto Carmona: None declared, Pedro Machado Speakers bureau: AbbVie, BMS, Celgene, Eli Lilly, Galapagos, Janssen, MSD, Novartis, Orphazyme, Pfizer, Roche, UCB, Consultant of: AbbVie, BMS, Celgene, Eli Lilly, Galapagos, Janssen, MSD, Novartis, Orphazyme, Pfizer, Roche, UCB, BERND RAFFEINER: None declared, Inita Bulina Speakers bureau: AbbVie, Pfizer, Janssen, Boehringer Ingelheim, Daniel Clemente Speakers bureau: Novartis, GSK, Julija Zepa Speakers bureau: AbbVie, Novartis, Janssen/Johnson & Johnson, Ana Maria Rodrigues Speakers bureau: Amgen, AbbVie, Grant/research support from: Amgen, Pfizer, AstraZeneca, Xavier Mariette Consultant of: BMS, Galapagos, Gilead, Janssen, Novartis, Pfizer, Sanofi-Aventis, UCB, Grant/research support from: Ose, Kimme Hyrich Speakers bureau: AbbVie, Grant/research support from: Pfizer, BMS, UCB

BackgroundRheumatoid arthritis (RA) is an inflammatory rheumatic disease of multifactorial aetiology, which preferentially affects women. To date, active personal smoking has been the most reproducibly reported risk factor for anti-citrullinated protein antibodies (ACPA) positive RA. Fish consumption has been thought to reduce the risk of rheumatoid arthritis (RA), but reported data were conflicting.ObjectivesTo assess the association between fish consumption (overall, lean fish, and oily fish) and the risk of RA.MethodsThe E3N study (Etude Epidémiologique auprès des femmes de la Mutuelle générale de l’Education Nationale) is a French prospective cohort investigating environmental factors associated with chronic diseases. It follows 98,995 healthy French women since 1990 covered by a national health insurance primarily involving teachers. RA cases have been previously identified with specific questionnaires and medication reimbursement database. Dietary data were collected via a validated food-frequency questionnaire in 1993. Cox proportional hazards models were used to calculate HRs and 95% CIs for incident RA depending on the tertiles of consumption of overall, lean and oily fish. Models were adjusted for age and for the main potential confounders. Stratified analyses were conducted depending on the active smoking status (never or ever-smoker).ResultsAmong 62,629 women, 480 incident cases of RA were identified. In the overall population, we did not find a linear association between overall fish consumption and RA risk (p for trend 0.65), but a moderate consumption of fish (16.7−31.1 g/day, roughly corresponding to 1 to 2 servings a week) was associated with a decreased risk of RA (HR 0.74; 95% CI 0.59—0.94 for tertile 2 compared with tertile 1), especially among ever smoking women (HR 0.61; 95% CI 0.44—0.85). Although not statistically significant, a trend towards an inverse association was only found with oily fish consumption (HR 0.81; 95% CI 0.65—1.02 for tertile 2), but not with lean fish.ConclusionIn our large population-based cohort study of French women, there was a U-shaped relationship between overall fish consumption and the risk of RA, with a reduced RA risk only in moderate consumers. The inverse association was restricted to ever-smokers. When separately considering oily and lean fish, the second tertile of oily fish consumption was inversely associated with the risk of RA only in never-smokers, while there was no association with lean fish.Table 1.Hazard ratios (95% confidence intervals) for the risk of rheumatoid arthritis (RA) by tertiles of fish consumption (N = 62,629)Fish consumptionNon-cases N (%)RA N (%)Model 1 HR (95% CI)Model 2 HR (95% CI)All populationN=62,149N=480 Tertile 1 (0−16.7 g/day)20,509 (33.00)172 (35.83)ReferenceReference Tertile 2 (16.7−31.1 g/day)19,628 (31.58)121 (25.21)0.74 [0.58; 0.93]0.74 [0.59; 0.94] Tertile 3 (31.1−261 g/day)22,012 (35.42)187 (38.96)0.99 [0.80; 1.22]0.99 [0.80; 1.22] Ptrend0.630.65Never smokersN=33,314N=244 Tertile 1 (0−16.7 g/day)11,322 (33.99)79 (32.38)ReferenceReference Tertile 2 (16.7−31.1 g/day)10,556 (31.69)66 (27.05)0.89 [0.64; 1.24]0.90 [0.65; 1.25] Tertile 3 (31.1−261 g/day)11,436 (34.33)99 (40.57)1.19 [0.88; 1.60]1.21 [0.90; 1.64] Ptrend0.150.12Ever smokersN=28,835N=236 Tertile 1 (0−16.7 g/day)9,187 (31.86)93 (39.41)ReferenceReference Tertile 2 (16.7−31.1 g/day)9,072 (31.46)55 (23.31)0.60 [0.43; 0.84]0.61 [0.44;0.85] Tertile 3 (31.1−261 g/day)10,576 (36.68)88 (37.29)0.81 [0.60; 1.09]0.81 [0.60; 1.08] Ptrend0.390.36M1: Adjusted for total daily intake and ageM2: M1+ body mass index (kg/m2), smoking status (current smoker, non-smoker, former smoker, except for stratified analyses), passive smoking in childhood (no, yes), gastrointestinal transit (normal, diarrhoea, constipation, alternating diarrhoea/constipation), educational level (< High School, up to 2-level university, 3-4 level university), and physical activity (in quartiles)Disclosure of InterestsNone declared

BackgroundPatients with autoimmune/inflammatory rheumatic diseases (AIRD) were suspected to be an at-risk population of severe COVID-19. However, whether this higher risk is linked to the disease or to its treatment is difficult to determine.ObjectivesTo identify, among AIRD patients, factors associated with occurrence of moderate-to-severe COVID19 infection and to evaluate if having an AIRD was associated with an increased risk of severe form of COVID19 infection (defined by hospitalization in ICU or death), compared to general population.MethodsData source: The “Entrepôt des Données de Santé (EDS)” collect data from electronic health records of all patients hospitalized or followed in the AP-HP (39 hospitals in Paris area, France). The French RMD COVID19 cohort is a national multi-center cohort that included patients with confirmed AIRD and diagnosed with COVID-19. All AIRD patients diagnosed with COVID-19 before September 2020 from both cohorts were included.-We Identified factors associated with severe COVID-19 was made in a combined analysis of the 2 cohorts.-Then, we compared COVID-19 infection severity in the EDS-COVID database in AIRD patients and controls, by a propensity score (PS)-matched case-control (1:4) studyResultsAmong 1213 patients (334 in EDS and 879 in RMD cohort), 195 (16.1%) experienced a severe COVID19. In multivariate analysis, greater age, history of interstitial lung disease, arterial hypertension, obesity, sarcoidosis, vasculitis, auto-inflammatory disease and treatment with corticosteroids or rituximab were associated with severe COVID-19 (Table 1).Table 1.AIRD patient’s characteristics associated with severity of COVID-19Patients with mild or moderate infectionPatients with severe infectionOR ajustés 95%CIp-value(N = 1018)(N = 195)Patients characteristics Age55.9 (16.7%)70.3 (14.3%)1.05 [1.03;1.07] Gender: Female695 (68.3%)105 (54.1%)0.59 [0.38;0.94]0.025 Interstitial pneumonia38 (3.7%)20 (10.3%)2.94 [1.34;6.34]0.008 Obesity143 (17.8%)38 (27.7%)2.09 [1.26;3.43]0.004 Hypertension268 (26.3%)114 (58.5%)1.81 [1.13;2.89]0.013Underlying Disease: Chronic inflammatory arthritis618 (60.8%)72 (36.9%)Ref. Auto-inflammatory disease29 (2.9%)5 (2.6%)3.91 [1.2;11.32]0.025 Other29 (2.9%)4 (2.1%)0.35 [0.06;1.41]0.15 Connectivitis190 (18.7%)34 (17.4%)1.13 [0.62;2.01]0.69 Sarcoidosis40 (3.9%)24 (12.3%)5.19 [2.15;12.3] Vasculitis111 (10.9%)56 (28.7%)1.8 [1.02;3.16]0.044Treatments Corticosteroid318 (31.2%)117 (60.0%)2.47 [1.58;3.87] Leflunomide44 (4.3%)2 (1.0%)0.13 [0;0.97]0.045 Rituximab37 (3.7%)22 (11.5%)4.05 [1.96;8.27]Not significant in multivariate analysisCOPD, Asthma, Coronary heart diseases, stroke, diabetes, smoking, cancer, non-steroidal anti-inflammatory drugs, colchicine, hydroxychloroquine, methotrexate, salazopyrine, mycophenolate mofetil, azathioprine, intravenous immunoglobulins, anti-TNFα, anti-IL1, -IL6, -IL17, Abatacept, JAK inhibitorAmong 35741 COVID-19 patients in EDS, 316 with AIRD were compared to 1264 PS-matched controls. Severe form occurred in 118 (37,3%) AIRD cases and 384 (30.4%) controls (Adjusted OR (aOR) for severe form= 1.43 [1.1;1.9], p=0,01). In analysis restricted to rheumatoid arthritis (RA) and spondylarthritis (SpA), no increased risk of severe form (aOR=1.11 [0.68;1.81]) form or death (aOR=1.00 [0.55;1.81]) was observed.ConclusionIn this multicenter study we confirmed that AIRD patients treated with rituximab or corticosteroids were at increased risk of severe COVID-19, as were those with vasculitis, auto-inflammatory disease, and sarcoidosis. Also, when compared to controls from the same cohort of hospitalized patients, AIRD patients had, overall, an increased risk of severe COVID-19, increased risk not observed in an analysis restricted to patients with RA or SpA.AcknowledgementsFAI2R /SFR/SNFMI/SOFREMIP/CRI/IMIDIATE consortium and contributorsPatricia MartelAll clinicians/physicians implicated in COVID-19 patient care in APHP hospital and generated EDS patient dataDisclosure of InterestsNone declared

BackgroundMethotrexate (MTX) is the first line of treatment for rheumatoid arthritis (RA) patients. Unfortunately, 30% to 40% of RA patients do not respond to MTX, resulting in uncontrolled joint pain and potential joints destruction. At the same time, many efficient second-line treatments exist and can be given to the inadequate responder patients. Predicting patient response to MTX before prescribing the treatment is therefore a major goal and could enable physicians to directly prescribe second-line treatments if inadequate response to MTX is predicted.ObjectivesWe aimed to build machine learning models based on simple clinical and biological data to predict patient response to MTX.MethodsWe used data from the ESPOIR early arthritis (1) and Leiden cohorts (2) to train the models, and the tREACH cohort to validate the results. We included patients that fulfilled the EULAR/ACR 2010 criteria and that were treated with MTX in monotherapy as their first treatment for RA. The models take as inputs patient’s characteristics at treatment initiation and predict the therapeutic response, defined as the EULAR response 3 to 12 months after treatment initiation. We evaluated four missing data imputation methods (median, mean, MICE, KNN); we used the backward feature selection algorithm to select the most relevant variables; and compared the performances of four models (Linear Regression, Random Forest, XGBoost, and Catboost) on the training set by cross-validated them using the Area Under the ROC Curve (AUCROC). The best model was then evaluated on the validation dataset.ResultsWe included 435 patients from the ESPOIR cohort, 243 patients from the Leiden cohort and 143 patients from the t-REACH cohort. Results of the model are displayed in Table 1. The variables automatically selected to perform prediction were Sex, DAS28, White blood cells, AST, ALT and lymphocytes. Our model performs well on unseen data, this result comes from the fact that we included two different cohorts in our training set which reduces the overfitting of our model and helps him generalize.Table 1.Sensitivity, Specificity, PPV and NPV are computed on the T-REACH validation cohort for each strategyAUC(ESPOIR, LEIDEN)AUC(T-REACH)STRATEGY 1 (High confidence in responders)STRATEGY 2 (high confidence in non-responDeRS)SensitivitYSpecificityPPVNPVSensitivitySpecificityPPVNPV0.720.7320%98%95%40%91%33%71%68%(0.70-0.73)(0.64-0.81)(12%-28%)(83%-100%)(82%-100%)(32%-49%)(85%-97%)(20%-47%)(63%-79%)(48%-86%)Our model predicts a probability for a patient to respond to MTX. This probability is compared to a decision threshold to obtain the final binary outcome. Two decision thresholds were tested. The first prioritizes a high confidence when identifying responders (Strategy 1) while the second prioritizes a high confidence when identifying non-responders (Strategy 2). This second strategy would enable physicians to identify highly probable inadequate responders to methotrexate and propose them directly a targeted DMARD such as TNF inhibitors, while still treating more than 70% of patients with MTX as first-line treatment.ConclusionThe machine learning models developed in this study can predict RA patients’ response to methotrexate with a good accuracy exclusively using data available in clinical routine. It paves the way for personalized therapeutic strategies in rheumatoid arthritis.References[1]Combe B, Benessiano J, Berenbaum F, Cantagrel A, Daurès J-P, Dougados M, et al. The ESPOIR cohort: A ten-year follow-up of early arthritis in France. Joint Bone Spine 2007;74:440–445.[2]van Aken, J., van Bilsen, J. H., Allaart, C. F., Huizinga, T. W., & Breedveld, F. C. The Leiden Early Arthritis Clinic. Clinical and experimental rheumatology, 21(5 Suppl 31), S100–S105.Disclosure of InterestsNone declared.

BackgroundA treat-to-target approach for RA management is recommended.1,2 However, up to half of patients discontinue DMARD treatment within 18 months.2 Predictive biomarkers, such as anti-citrullinated protein antibodies (ACPAs) and RF, may be useful to stratify patients to the most appropriate treatment. ACTION (AbataCepT In rOutiNe clinical practice; NCT02109666) and ASCORE (Abatacept SubCutaneOus in Routine Clinical PracticE; NCT02090556) were 2-year, international, observational, prospective, multicenter studies of IV and SC abatacept, respectively, for the treatment of RA in routine clinical practice.3,4 Higher retention has been previously reported in patients with double ACPA/RF seropositive RA compared with double ACPA/RF seronegative RA.3,4ObjectivesTo assess the independent effect of ACPA or RF single seropositivity on abatacept retention in patients with RA receiving abatacept in a post hoc analysis of ACTION and ASCORE.MethodsThis post hoc analysis included patients aged ≥ 18 years, with active moderate-to-severe RA (ACR/EULAR 2010 criteria) who initiated IV (body weight–adjusted dosing) or SC (125 mg once weekly) abatacept.3,4 Patients were stratified by baseline ACPA/RF status: ACPA+/RF− (ACPA+ only), ACPA/RF double positive (+/+), ACPA−/RF+ (RF+ only), and ACPA/RF double negative (−/−). Abatacept retention rate at 2 years was estimated by Kaplan–Meier (KM) analysis.ResultsPatients with ACPA/RF serostatus data from the ACTION and ASCORE studies (N = 1679 and N = 1748, respectively) were evaluated. Baseline demographic and disease characteristics were similar across studies and serostatus groups (Table 1). In patients with ACPA+ only RA, abatacept retention rates were similar to the +/+ group and greater than the RF+ only and −/− groups (Figure 1). In ASCORE (Figure 1A), retention rates were significantly higher in ACPA+ only and +/+ groups when compared with the −/− group. In contrast, retention rates for patients with RF+ only RA were not significantly different vs −/− patients. Results were similar in ACTION, although the higher retention in the ACPA+ group did not reach statistical significance (Figure 1B).Table 1.Baseline demographics and disease characteristics by ACPA/RF status for the ASCORE and ACTION studiesASCORE+/+RF+ onlyACPA+ only−/−(n = 1079)(n = 142)(n = 184)(n = 343)Age, years57.1 (12.8)58.2 (11.8)57.4 (13.5)57.8 (13.9)DAS28 (CRP)4.7 (1.2)4.6 (1.1)4.4 (1.0)4.8 (1.2)CDAI26.6 (12.5)25.8 (12.0)23.6 (10.9)28.2 (13.2)SDAI28.1 (13.0)27.2 (12.4)24.4 (10.8)29.7 (13.9)ACTION+/+RF+ onlyACPA+ only−/−(n = 1028)(n = 161)(n = 98)(n = 392)Age, years58.2 (12.0)58.4 (13.4)58.5 (14.0)57.0 (13.3)DAS28 (CRP)4.9 (1.1)5.0 (1.1)4.9 (1.0)5.0 (1.1)CDAI28.7 (12.2)29.2 (12.4)28.7 (11.5)30.1 (12.9)SDAI30.4 (13.1)31.2 (13.4)29.8 (11.5)31.7 (13.4)Data are mean (SD). Patients with missing data for baseline ACPA/RF status are excluded.ConclusionIn this post hoc analysis of the real-world ACTION and ASCORE studies, ACPA positivity was associated with an increased likelihood of retention over 2 years. Patients with ACPA+ only RA were equally as likely to be retained on abatacept as patients with ACPA/RF double positivity. In contrast, patients with RF+ only RA were less likely to be retained on abatacept over 2 years. These findings suggest that ACPA positivity played a more important role than RF positivity in abatacept retention. The higher retention seen in patients with ACPA+ only vs RF+ only disease demonstrates the key role of ACPA in RA and supports the importance of precision medicine in treating patients.References[1]Fraenkel L, et al. Arthritis Care Res (Hoboken) 2021;73:924–39.[2]Smolen JS, et al. Ann Rheum Dis 2020;79:685–99.[3]Alten R, et al. Clin Rheumatol 2019;38:1413–24.[4]Alten R, et al. Ann Rheum Dis 2021;80(suppl 1):OP0180.AcknowledgementsThis study was sponsored by Bristol Myers Squibb. Medical writing and editorial assistance was provided by Fiona Boswell, PhD, of Caudex, and was funded by Bristol Myers Squibb. Study management provided by Syneos (CRO).Disclosure of InterestsRieke Alten Speakers bureau: AbbVie, Bristol Myers Squibb, Celltrion, Galapagos, Gilead, Janssen, Lilly, Novartis, Pfizer, Roche, Paid instructor for: AbbVie, Bristol Myers Squibb, Celltrion, Galapagos, Gilead, Janssen, Lilly, Novartis, Pfizer, Roche, Consultant of: AbbVie, Bristol Myers Squibb, Celltrion, Galapagos, Gilead, Janssen, Lilly, Novartis, Pfizer, Roche, Grant/research support from: AbbVie, Bristol Myers Squibb, Celltrion, Galapagos, Gilead, Janssen, Lilly, Novartis, Pfizer, Roche, Christiane Rauch Shareholder of: Bristol Myers Squibb, Employee of: Bristol Myers Squibb, Melanie Chartier Shareholder of: Bristol Myers Squibb, Employee of: Bristol Myers Squibb, M.T. Nurmohamed Speakers bureau: AbbVie, Bristol Myers Squibb, Eli Lilly, Janssen, Pfizer, Consultant of: AbbVie, Bristol Myers Squibb, Eli Lilly, Janssen, Pfizer, Grant/research support from: AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Galapagos, Janssen, MSD, Pfizer, Sean Connolly Shareholder of: Bristol Myers Squibb, Employee of: Bristol Myers Squibb, Maya H Buch Speakers bureau: AbbVie, Consultant of: AbbVie, Galapagos, Gilead, Pfizer, Grant/research support from: Gilead, Pfizer, UCB, Peter Peichl Speakers bureau: GlaxoSmithKline, Janssen, Xavier Mariette Consultant of: Bristol Myers Squibb, Galapagos, GlaxoSmithKline, Janssen, Novartis, Pfizer, Sanofi, UCB, Yusuf Patel: None declared, Sara Marsal Speakers bureau: Bristol Myers Squibb, Lilly, MSD, Novartis - Sandoz, Pfizer, Roche, Consultant of: AbbVie, Galapagos, Pfizer, Sanofi; IMIDomics (executive role), Grant/research support from: AbbVie, Bristol Myers Squibb, Galapagos, Janssen, Lilly, MSD, Novartis - Sandoz, Pfizer, Roche, Sanofi, UCB, Roberto Caporali Speakers bureau: AbbVie, Amgen, Bristol Myers Squibb, Celltrion, Fresenius-Kabi, Galapagos, Janssen, Lilly, MSD, Novartis, Pfizer, Roche, Sandoz, UCB, Consultant of: AbbVie, Amgen, Bristol Myers Squibb, Celltrion, Galapagos, Janssen, Lilly, MSD, Novartis, Pfizer, Sandoz, UCB, Hedley Griffiths Consultant of: Amgen, Raimón Sanmartí Speakers bureau: AbbVie, Bristol Myers Squibb, Lilly, MSD, Pfizer, Roche, Sanofi, Grant/research support from: AbbVie, Bristol Myers Squibb, MSD, Pfizer, Roche, Bettina Bannert Speakers bureau: Novartis Pharma Schweiz AG, Yedid Elbez Consultant of: Bristol Myers Squibb, Employee of: Signifience, Karissa Lozenski Shareholder of: Bristol Myers Squibb, Employee of: Bristol Myers Squibb.

BackgroundJanus kinase inhibitors (JAKi) are effective targeted synthetic DMARDs licensed in rheumatoid arthritis (RA) and in other immune-mediated diseases. Among the adverse events described, the increased risk of herpes zoster is specific of this therapeutic class but variable depending on the drugs, especially lower with filgotinib (FILGO)1. Recently the ORAL Surveillance study highlighted an increased risk of lymphoma and lung cancer with tofacitinib (TOFA) in RA patients older than 50 years with cardio-vascular risk factors2. Because of their antitumor and antiviral role, especially against herpes viruses, effect of JAKi on NK cells may play a causal role in these adverse events. Our working hypothesis is that the different JAKi could have a differential impact on the activation and function of NK cells leading to the variability of the tolerance profiles observed between the molecules.ObjectivesTo evaluate the phenotypic and functional impact of JAKi on NK cells.MethodsWe first performed ex vivo phenotypic analyses of NK cells in RA patients treated with TOFA or baricitinib (BARI) in comparison with patients treated with methotrexate (MTX). To go deeper, we phenotyped sorted NK cells from healthy donors after 6 days of culture with 4 JAKi (TOFA, BARI, upadacitinib (UPA) and FILGO) or control (DMSO). We used 3 different doses carefully chosen to mimic in vivo exposure [therapeutic concentration (therap) defined as mean concentration in patients at the licensed dose, infra therapeutic (infra, mean -2DS) and supra therapeutic concentration (supra, mean +2Ds)]. Thirdly, we stimulated sorted NK cells from healthy donors exposed to TOFA or BARI at therap and supra concentrations with anti-NKp30 crosslinking to assess NK cells function: intra-cellular IFNγ/TNF production and degranulation (CD107a). Finally, we challenged sorted NK cells from healthy donors exposed in vitro to TOFA or BARI at therap and supra doses with 2 different tumor cells line to assess degranulation and cytotoxicity: a lung cancer line A549 and a lymphoma cell line SU-DHL-4.ResultsTwenty-eight RA patients were included in the ex vivo assay (12 MTX, 6 TOFA, and 10 BARI). Patients under TOFA and not under BARI had a significant reduced CD69 (an activation marker) expression on NK cells (pFigure 1.Impact of in vitro exposure to JAKi at therap and supra dose on NK cells regarding phenotype (A) and function after crosslinking (B)ConclusionJAKi have a phenotypic and functional dose-effect impact on NK cells activation, both in ex vivo and in vitro experiments. TOFA has more impact than other JAKi on NK cells phenotype and function and has the property to impair the control of proliferation of lung cancer and lymphoma cell lines by NK cells. The question remains open if this mechanism could explain the increased risk of lung cancer and lymphoma observed with TOFA in the ORAL surveillance trial.References[1]Winthrop, K. L. et al. Ann Rheum Dis. 2022 Feb;81(2):184-192.[2]Ytterberg S. et al. N Engl j Med, 2022, 11.Disclosure of InterestsNone declared

BackgroundGiant cell arteritis (GCA) and polymyalgia rheumatica (PMR) are two associated inflammatory diseases with incompletely known common pathophysiological mechanism. Cardiovascular (CV) risk factors have been thought to play a role in the underlying chronic vascular inflammation involved in GCA and PMR pathophysiology. However, available data on CV risk factors are conflicting.ObjectivesTo investigate the association between CV risk factors and the risk of GCA and/or PMR in a large prospective cohort of French women.MethodsThe E3N cohort study (Etude Epidémiologique auprès des femmes de la Mutuelle générale de l’Education Nationale) is a French prospective cohort including 98,995 French women born between 1925 and 1950, recruited in 1990. They completed biennially mailed questionnaires to update their health-related information. A validation study based on the use of a specific GCA/PMR questionnaire and medication reimbursement database was conducted prior to this study. The history of CV risk factors (hypertension, type 2 diabetes, dyslipidemia, active smoking status and family history of cardiovascular disease) was self-reported at each questionnaire. Hazard ratios (HRs) and their 95% confidence intervals (95%CIs) for incident GCA and/or PMR were estimated by Cox proportional hazards regression models with age as the time scale. Multi-adjusted models included all CV risk factors and educational level. Further analyses were conducted to separately analyse the risk of incident GCA and of PMR.ResultsThe overall population included 79,804 women, during a total follow-up of 1,899,742 person-years. Among them, 399 incident GCA and/or PMR cases were identified (incidence 31/100,000 person-years): 282 PMR alone, 112 GCA cases, and 5 patients who could not be classified. Incident GCA/PMR cases were diagnosed after a mean of 17.9 (± 5.2) years after recruitment. Mean age at diagnosis was 69 (± 7.2) years. Type 2 diabetes was inversely associated with risk of incident GCA/PMR in age-adjusted and multivariable models (HR 0.41; 95%CI 0.2–0.9 in multivariate model). Other CV risk factors such as hypertension, smoking status, dyslipidaemia, familial history of cardiovascular events were not associated with incident GCA/PMR (Table 1). The inverse association remained statistically significant when studying separately the risk of incident PMR (HR 0.3; 95%CI 0.09–0.91) but no longer with incident GCA (HR 0.5; 95%CI 0.1–2.0), probably due to a reduced statistical power.Table 1.Hazard ratios for the risk incident GCA and/or PMR according to cardiovascular risk factorsNON-CASESN=79,405GCA/PMRCASESN=399M1M2Number (%)Number (%)HR [95%CI]HR [95%CI]Smoking status Never-smoker42,2745 (53.2)233 (58.4)ReferenceReference Past smoker30,824 (38.8)134 (33.6)0.91 [0.74;1.13]0.90 [0.73; 1.12] Current smoker6,307(7.9)32 (8.0)1.05 [0.72; 1.52]1.04 [0.72; 1.51]High-blood pressure No hypertension38,813 (48.9)210 (52.6)ReferenceReference Hypertension40,592 (51.1)189 (47.4)0.84 [0.89; 1.03]0.85 [0.69; 1.04]Type 2 diabetes No diabetes75,704 (95.3)386 (96.7)ReferenceReference Diabetes3,701 (4.7)13 (3.3)0.41 [0.18; 0.91]0.41 [0.18; 0.92]BMI 52,054 (65.6)269 (67.4)ReferenceReference ≥ 25 kg/m227,351 (34.4)130 (32.6)1.01 [0.82; 1.25]1.06 [0.85; 1.31]Dyslipidemia No43,944 (55.3)237 (59.4)ReferenceReference Yes33,169 (41.8)147 (36.8)1.17 [0.94; 1.44]1.18 [0.95; 1.45]Familial history of cardiovascular disease No64,878 (81.7)336 (84.2)ReferenceNA Yes3,121 (3.9)20 (5.0)1.30 [0.85; 2.09]NAM1: Adjusted for age and educational level educational level ( 3 years of university),M2: M1+ smoking status, body mass index(kg/m2), hypertension, diabetes, dyslipidemiaConclusionType 2 diabetes was associated with a decreased risk of subsequent GCA/PMR development. Further work should be carried out to determine potential mechanisms, and especially to analyze the respective role of diabetes itself and its treatments.AcknowledgementsThe authors are indebted to all participants for their continued participation. The authors would like to thank Pascale Gerbouin-Rerolle, Mariam Alyaniakian, Sofiane Harizi and Roselyn Rima Gomes for their help on data management. The present work was performed using data from the Inserm E3N cohort and support from the MGEN, Gustave Roussy, and the Ligue contre le Cancer for setting up and maintaining the cohort. The cohort was supported by a state grant ANR-10-COHO-0006 from the Agence Nationale de la Recherche within the Investissement d’Avenir program. The present work was conducted thanks to a research grant from the Agence Régionale de Santé – Île de France.ledgements to declare.Disclosure of InterestsNone declared

BackgroundSolitary bone plasmacytoma (SPB) is a rare malignancy whose main risk is progression to multiple myeloma (MM).ObjectivesTo describe the characteristics and outcome of patients treated for SBP and to identify factors associated with progression to MM and death.MethodsThis retrospective study was conducted between 1992 and 2020. Patients were included if they met the International Myeloma Working Group (IMWG) criteria for the diagnosis of SBP (1). Treatment response was assessed according to the IMWG criteria (2) for patients with detectable monoclonal protein and according to radiological response only for non-secretory SBP. Hazard ratios (HR) and their 95% confidence intervals (CI) were estimated by Cox proportional hazards models, adjusted for potential confounders. When available, 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) were reviewed by two experienced nuclear physicians.ResultsA total of 77 patients diagnosed with SBP were included. The median age was 59 years [range: 27-89]. Median follow-up duration was 87.1 months [range: 1.6-306.8]. At the end of the follow-up, 45 (58.4%) patients had developed a MM and 13 (16.9%) deaths occurred, including 10 patients with progression to MM. Five-year myeloma-free survival and overall survival were 47.9% and 86.8% respectively. All patients received radiotherapy with a median dose of 45 Grays [range: 30-55]. Chemotherapy was prescribed in 32 (41.6%) patients, concomitant to radiotherapy in 8, because of high risk of local extension, or adjuvant in 24 because of persistent disease after radiotherapy. Chemotherapy included mainly immunomodulatory drugs (28/32 (87.5%)) and/or proteasome inhibitors (11/32 (34.4%)) based combinations. Adjuvant chemotherapy after radiotherapy was associated with a reduced risk of MM compared to patients treated by radiotherapy alone, in a multivariate model adjusted for potential confounding factors, including response after radiotherapy alone (adjuvant chemotherapy versus no chemotherapy, HR 0.30, 95%CI [0.14-0.64]). Response to the first line of treatment (radiotherapy +/- chemotherapy) was the main factor associated with progression to MM (complete response versus partial response or progressive disease, HR 0.25, 95%CI [0.11-0.59]) and death (HR 0.22, 95%CI [0.05-0.99]). A non-significant trend between bone marrow plasmacytosis ≥ 5% at diagnosis and higher risk of MM was observed (≥ 5% versus 18F-FDG PET/CT images were available for review in only 36/77 patients. A significant decrease of 18F-FDG uptake after treatment was observed whereas the lesion size was unchanged. No association was observed with the risk of MM but the limited available images may have resulted in underpowered analyses.ConclusionIn this large study of patients with SBP, we observed that even though mostly prescribed for insufficient response to radiotherapy alone, treatment with adjuvant chemotherapy was associated with a decreased risk of evolution to MM, suggesting that this treatment might be systematically prescribed in addition to radiotherapy alone. These observations need to be confirmed and justify conduction of a prospective trial evaluating adjuvant chemotherapy in patients with SBP.References[1]Rajkumar SV, Dimopoulos MA, Palumbo A, Blade J, Merlini G, Mateos M-V, et al. International Myeloma Working Group updated criteria for the diagnosis of multiple myeloma. Lancet Oncol. nov 2014;15(12):e538-548.[2]Kumar S, Paiva B, Anderson KC, Durie B, Landgren O, Moreau P, et al. International Myeloma Working Group consensus criteria for response and minimal residual disease assessment in multiple myeloma. Lancet Oncol. août 2016;17(8):e328‑46.AcknowledgementsThe authors are indebted to all participants for their continued participation.Disclosure of InterestsNone declared

BackgroundThe low prevalence of systemic autoimmune diseases and the diversity of their clinical manifestations make complex to conduct randomised clinical trials to assess the potential efficacy of targeted treatments.ObjectivesTo assess the tolerance and efficacy of targeted therapies prescribed off-label in refractory autoimmune diseases.MethodsThe TATA registry (TArgeted Therapy in Autoimmune Diseases) is a prospective, observational, national and independent cohort follow-up. The inclusion criteria in the registry are as follows: age > 18 years; rare systemic autoimmune disease (systemic lupus erythematosus, Sjögren’s syndrome, systemic scleroderma, inflammatory myopathy, vasculitis) or other refractory rheumatism treated with off-label drugs started after 1st January 2019.ResultsHundred (100) patients (79 females) were enrolled. The median age was 52.5 years [49;56], the median disease duration before enrolment was 5 years [3;7]. The targeted therapies at enrolment were as follows: JAK/STAT inhibitors (44%), anti-IL6R (22%), anti-IL12/23, anti-IL23 and anti-IL17 (9%), anti-BAFF (5%), abatacept (5%), other targeted treatments (9%), and combination of targeted treatments (6%). 73% of patients were receiving corticosteroid therapy at enrolment (median dose 10 mg/day). The current median follow-up time is 9 months [8;10].Safety: 11 serious infections (incidence rate of 14.8 /100 patient-years) and 1 cancer (1.3 cancers/100 patient-years) were observed. Two patients died from severe COVID-19 (2.7 deaths/100 patient-years).Efficacy: The targeted treatment was considered effective by the clinician in 56% of patients and allowed in responders a median reduction of oral corticosteroids of 15 [9-21] mg/day.ConclusionThese initial results of the TATA registry confirm the diversity of targeted treatments prescribed off-label in refractory autoimmune diseases and their corticosteroid-sparing effect when effective. Tolerance was acceptable in these refractory patients with a long history of treatment with immunosuppressive drugs.References[1]B. Terrier et al., Safety and efficacy of rituximab in systemic lupus erythematosus: results from 136 patients from the French AutoImmunity and Rituximab registry. Arthritis Rheum 62, 2458-2466 (2010).[2]J. E. Gottenberg et al., Efficacy of rituximab in systemic manifestations of primary Sjogren’s syndrome: results in 78 patients of the AutoImmune and Rituximab registry. Ann Rheum Dis 72, 1026-1031 (2013).[3]J. E. Gottenberg et al., Risk factors for severe infections in patients with rheumatoid arthritis treated with rituximab in the autoimmunity and rituximab registry. Arthritis Rheum 62, 2625-2632 (2010).[4]F. R. S. S. S. C. I. consortium, contributors, Severity of COVID-19 and survival in patients with rheumatic and inflammatory diseases: data from the French RMD COVID-19 cohort of 694 patients. Ann Rheum Dis, (2020).[5]R. Felten et al., B-cell targeted therapy is associated with severe COVID-19 among patients with inflammatory arthritides: a 1-year multicentre study in 1116 successive patients receiving intravenous biologics. Ann Rheum Dis 81, 143-145 (2022).[6]D. J. Wallace et al., Baricitinib for systemic lupus erythematosus: a double-blind, randomised, placebo-controlled, phase 2 trial. Lancet 392, 222-231 (2018).[7]J. J. Paik et al., Study of Tofacitinib in Refractory Dermatomyositis: An Open-Label Pilot Study of Ten Patients. Arthritis Rheumatol 73, 858-865 (2021).[8]S. Cole et al., Integrative analysis reveals CD38 as a therapeutic target for plasma cell-rich pre-disease and established rheumatoid arthritis and systemic lupus erythematosus. Arthritis Res Ther 20, 85 (2018).[9]S. J. Bowman et al., Safety and efficacy of subcutaneous ianalumab (VAY736) in patients with primary Sjogren’s syndrome: a randomised, double-blind, placebo-controlled, phase 2b dose-finding trial. Lancet 399, 161-171 (2022).AcknowledgementsFrench networks (FAI2R, CRI, IMIDIATE, SFR, SNFMI) focused on rare systemic autoimmune diseases contributed this work by the contribution of network-affiliated physicians.Disclosure of InterestsJacques-Eric Gottenberg Consultant of: Abbvie, BMS, Gilead, Galapagos, Novartis, Lilly Roche Chugai, Sanofi, Janssen, Pfizer, Grant/research support from: BMS.Lilly and Pfizer for this register (with no access to data)., Aurore Chaudier: None declared, Yves Allenbach: None declared, Arsene Mekinian: None declared, Zahir Amoura: None declared, Patrice cacoub: None declared, Divi Cornec: None declared, Eric Hachulla: None declared, Pierre Quartier: None declared, isabelle melki: None declared, Christophe Richez: None declared, Raphaèle Seror: None declared, Benjamin Terrier: None declared, Valerie Devauchelle-Pensec: None declared, Julien Henry: None declared, MARC GATFOSSE: None declared, LAURENCE BOUILLET: None declared, Emeline GAIGNEUX: None declared, Vincent ANDRE: None declared, Gildas BAULIER: None declared, Aurélie SAUNIER: None declared, Marie Desmurs: None declared, Antoine POULET: None declared, Mathieu ETE: None declared, Marie-Elise Truchetet: None declared, Martin Michaud: None declared, Claire Larroche: None declared, AZEDDINE DELLAL: None declared, Amelie LEURS: None declared, Sebastien Ottaviani: None declared, Hubert NIELLY: None declared, Guillaume VIAL: None declared, Roland JAUSSAUD: None declared, Benedicte ROUVIERE: None declared, Pierre-Yves JEANDEL: None declared, Aurelien GUFFROY: None declared, Anne-Sophie Korganow: None declared, Mathieu JOUVRAY: None declared, alain meyer: None declared, Emmanuel Chatelus: None declared, Christelle Sordet: None declared, Renaud FELTEN: None declared, Jean Sibilia: None declared, Samira AHMED YAHIA: None declared, Jean François Kleinmann: None declared, Xavier Mariette Consultant of: BMS, Galapagos, GSK, Janssen, Novartis, Pfizer, Sanofi, UCB

BackgroundSjogren’s disease (SjD) is an autoimmune disease characterized by reduced function of exocrine glands (i.e., salivary and lacrimal glands). Epithelial cell damage resulting from lymphocytic infiltration has been implicated in SjD etiology [1]. How genetic and epigenetic changes influence epithelial-immune cell interactions in SjD pathogenesis remain understudied.ObjectivesEvaluate the role of SjD risk loci in salivary gland tissue to gain insights into the potential genes involved in salivary gland dysfunction.MethodsSNPs from 16 regions with SNP-SjD associations (PResultsIn total, 5884 genome-wide significant SNPs from 16 SjD risk loci were identified as potential minor salivary gland eQTLs using two discovery thresholds: p(FDR)0.05 and pIRF5 and TNPO3 on chromosome 7 at 128Mb; however, this locus had 12 additional genes that were eQTLs in minor salivary gland. In contrast, other loci had no reported eQTLs for the index gene, but several reported eQTLs for other genes, such TYK2 on chromosome 19 at 10Mb that showed no change in TYK2 expression but eQTLs for 8 distant genes, including ICAM1. Pathway enrichment analysis revealed an enrichment in Butyrophilin (BTN) family interactions (R-HSA-8851) (PAdj=1.564x10-5), including the BTN2A1, BTN2A2, BTN3A1, BTN3A2 and BTN3A3 gene cluster in the MHC region. In further support, TWAS of the minor salivary gland and the SjD GWAS summary statistics (after Bonferroni correction) showed association between SjD and BTN3A2 (p=1.24x10-42), as well as many other loci in the MHC region. In addition, several long non-coding (lnc) RNAs on chromosome 17 were significant, peaking at RP11-259G18.1 (p=4.43x10-10).ConclusionThis study shows that SjD-associated risk alleles influence disease by altering gene expression in immune cells and minor salivary glands. Further, our analysis suggests that altered gene expression in the minor salivary gland expands beyond effects on the index gene to several genes on each locus. Interestingly, we observed minor salivary gland eQTLs for several BTN family genes, which act as cell-surface binding partners to regulate cell-cell interactions, including interactions between epithelial cells and activated T cells [3]. Future work will assess chromatin-chromatin-interactions within the 10 SjD risk loci in salivary gland cells and tissues to map local chromatin regulatory networks that regulate gene expression. Additional transcriptional studies of SjD minor salivary gland tissues will provide further insights into how altered gene expression in the salivary gland influences SjD pathology.References[1]Verstappen. Nat Rev Rheumatol 2021;17(6):333-348.[2]Khatri, et al. Annals of Rheumatic Diseases 2020;79:30-31.[3]Arnett HA, Viney JL. Nature Reviews Immunology 2014;14:559-569.Disclosure of InterestsFarhang Aghakhanian: None declared, Mandi M Wiley: None declared, Bhuwan Khatri: None declared, Kandice L Tessneer: None declared, Astrid Rasmussen: None declared, Simon J. Bowman Consultant of: Abbvie, Galapagos, and Novartis in 2020-2021., Lida Radfar: None declared, Roald Omdal: None declared, Marie Wahren-Herlenius: None declared, Blake M Warner: None declared, Torsten Witte: None declared, Roland Jonsson: None declared, Maureen Rischmueller: None declared, Patrick M Gaffney: None declared, Judith A. James: None declared, Lars Ronnblom: None declared, R Hal Scofield: None declared, Xavier Mariette: None declared, Marta Alarcon-Riquelme: None declared, Wan Fai Ng: None declared, Kathy Sivils Employee of: Current employee of Janssen, Gunnel Nordmark: None declared, Umesh Deshmukh: None declared, A Darise Farris: None declared, Christopher Lessard: None declared

BackgroundDespite a high morbi-mortality rate, there are no definite strategy for subclinical interstitial lung disease (ILD) screening in patients with rheumatoid arthritis (RA).ObjectivesOur objectives were: 1. to identify risk factors for subclinical RA-ILD in a prospective discovery cohort (ESPOIR) 2.to develop a risk score for subclinical RA-ILD and 3. to validate the risk score in an independent replication cohort (TRANSLATE 2).MethodsPatients without pulmonary symptoms from 2 prospective RA cohorts who underwent chest HRCT scans were included. All patients were genotyped for MUC5B rs35705950. A risk score based on independent risk factors for subclinical RA-ILD was developed using multiple logistic regression in the discovery cohort. The risk score was tested for validation in the replication cohort.ResultsDiscovery and replication cohorts included 163 and 89 patients, respectively. Subclinical ILD was detected in 19.0% and 16.9% of the patients, respectively. In the discovery cohort, independent risk factors for subclinical RA-ILD were the MUC5B rs35705950 T risk allele (odds ratio [OR]=3.74; 95% confidence interval [CI] [1.37–10.39], male sex (OR=3.93; 95%CI [1.40–11.39]), older age at RA onset (for each year, OR=1.10; 95%CI [1.04–1.16]) and increased mean DAS28-ESR (for each unit, OR=2.03; 95%CI [1.24–3.42]). We developed a risk score for subclinical RA-ILD with AUC=0.82; 95%CI [0.70–0.94] (sensitivity (Se)=71.0%) and specificity (Sp)=79.6%). The risk score was validated in the replication cohort with AUC=0.78; 95%CI [0.65–0.92] (Se=86.7%, Sp=62.2%).ConclusionOur risk score could help identifying patients at high-risk for subclinical RA-ILD before the onset of pulmonary symptoms.Disclosure of InterestsPierre-Antoine Juge Speakers bureau: AstraZeneca, Boehringer Ingelheim, Bristol Myers Squibb, Consultant of: Bristol Myers Squibb, Benjamin Granger: None declared, Marie-Pierre Debray: None declared, Esther Ebstein: None declared, Fabienne Louis-Sidney: None declared, Joanna KEDRA: None declared, Tracy Doyle: None declared, Raphael Borie: None declared, Arnaud Constantin Consultant of: Abbvie, Amgen, Biogen, BMS, Boehringer Ingelheim, Fresenius Kabi, Galapagos, Janssen, Lilly, Medac, MSD, Mylan, Novartis, Pfizer, Procter & Gamble, Roche, Sanofi, UCB, Viatris, Bernard Combe Consultant of: AbbVie, BMS, Eli-Lilly, Gilead, Janssen, Merck, Novartis, Pfizer, Roche-Chugai, Sanofi, UCB, René-Marc Flipo Consultant of: Abbvie, Janssen, MSD and Pfizer. He reports research grants from Abbvie, Janssen, Novartis and Pfizer, Xavier Mariette Consultant of: BMS, Gilead, Janssen, Pfizer, Samsung, UCB, Olivier VITTECOQ: None declared, Alain Saraux: None declared, Guillermo CARVAJAL ALEGRIA: None declared, Jean Sibilia Consultant of: AbbVie, Lilly, MSD, Amgen, Pfizer, BMS, Janssen, Roche, Sandoz, Sanofi-Genzyme, SOBI, UCB, Novartis, Grant/research support from: AbbVie, Lilly, MSD, Amgen, Pfizer, BMS, Janssen, Roche, Sandoz, Sanofi-Genzyme, SOBI, UCB, Novartis, Francis Berenbaum: None declared, Caroline Kannengiesser: None declared, Catherine Boileau: None declared, Jeffrey Sparks Consultant of: AbbVie, Boehringer Ingelheim, Bristol Myers Squibb, Gilead, Inova Diagnostics, Janssen, Optum, and Pfizer, Grant/research support from: National Institute of Arthritis and Musculoskeletal and Skin Diseases (grant numbers R01 AR077607, P30 AR070253, and P30 AR072577), The R. Bruce and Joan M. Mickey Research Scholar Fund, Bristol Myers Squibb,Bruno Crestani Speakers bureau: Boehringer Ingelheim, AstraZeneca, Roche, Sanofi, Grant/research support from: MedImmune, Roche, Boehringer Ingelheim, Bruno Fautrel Consultant of: AbbVie, Amgen, Biogen, BMS, Celgene, Fresenius Kabi, Janssen, Lilly, Medac, MSD, NORDIC Pharma, Novartis, Pfizer, Roche, SOBI, UCB, Grant/research support from: AbbVie, Lilly, MSD, Pfizer, Philippe Dieudé Speakers bureau: Roche – Chugai, Bristol Myers Squibb, Consultant of: Pfizer, Roche – Chugai, Bristol Myers Squibb, Abbvie, MSD, Grant/research support from: Novartis

BackgroundSjögren’s Disease (SjD) and Systemic Lupus Erythematosus (SLE) are autoimmune diseases with several shared characteristics and similar genome-wide significant associations with the DDX6-CXCR5 locus. DDX6 suppresses interferon-stimulated gene expression and CXCR5 regulates T cell functions implicated in autoimmunity.ObjectivesTo identify and characterize functional SNPs in the DDX6-CXCR5 interval.MethodsImmunoChip data from European populations (3785 SLE cases; 1916 SjD cases; 6893 controls) were imputed and SNP-trait associations tested. Bayesian statistics defined a credible SNP set that was refined using bioinformatic analyses (RegulomeDB, Haploreg, ENCODE, promoter capture Hi-C, eQTLs, etc.). Electrophoretic mobility shift assays (EMSAs) and luciferase expression assays were used to test allele-specific SNP function in EBV-transformed B (EBV B) cells, Daudi B cells, Jurkat T cells, THP1 monocytes, and A253 salivary gland cell lines. Chromatin conformation capture with quantitative PCR (3C-qPCR) was used to assess long-range chromatin interactions.ResultsFine mapping of the SjD and SLE associations found similar SNP associations. Bioinformatic analyses identified 5 common SNPs with strong evidence of functionality in immune cell types: rs57494551 in an intron of DDX6, and rs4938572, rs4936443, rs7117261, and rs4938573 in the promoter/enhancer region of DDX6 and CXCR5. EMSAs and luciferase experiments showed cell type-specific differences in protein binding and promoter or enhancer activity, respectively, at each SNP. Risk allele of rs57494551 increased enhancer activity in B cells and A253 cells (pDDX6 in T cells, and the risk allele significantly increased protein binding, promoter and enhancer activity in T cells (pDDX6 and CXCR5, rs57494551 and/or rs4938572 are reported eQTLs for several other genes of interest in the local chromatin regulatory network: IL10RA in T cells, TRAPPC4 in salivary gland and activated macrophages, and long non-coding (lnc)RNA AP002954.1 in T cells and whole blood. 3C-qPCR in EBV B and A253 cells showed that the two regulatory regions carrying rs4938572 or rs57494551 interacted with a region upstream of DDX6 that includes AP002954.1. Hi-C data showed looping between AP002954.1 and the regulatory region carrying rs4938572 and rs57494551 in T cells.ConclusionSjD and SLE share similar genomic architecture across the DDX6-CXCR5 risk interval with several common SNPs showing immune and salivary gland cell type-specific allelic effects on protein binding and/or enhancer/promoter activity. Extensive bioinformatic analyses suggest that the SNPs likely work within the local chromatin regulatory network to regulate cell type-specific expression of several genes on the interval. Ongoing studies will use 3C-qPCR to assess allele-specific chromatin interactions between the SNPs and these genes in different cells types, and CRISPR to determine how the risk alleles alters expression.Disclosure of InterestsMandi M Wiley: None declared, Bhuwan Khatri: None declared, Kandice L Tessneer: None declared, Michelle L Joachims: None declared, Anna M Stolarczyk: None declared, Anna Nagel: None declared, Astrid Rasmussen: None declared, Simon J. Bowman Consultant of: Abbvie, Galapagos, and Novartis in 2020-2021, Lida Radfar: None declared, Roald Omdal: None declared, Marie Wahren-Herlenius: None declared, Blake M Warner: None declared, Torsten Witte: None declared, Roland Jonsson: None declared, Maureen Rischmueller: None declared, Patrick M Gaffney: None declared, Judith A. James: None declared, Lars Ronnblom: None declared, R Hal Scofield: None declared, Xavier Mariette: None declared, Wan Fai Ng: None declared, Kathy Sivils Employee of: current employee of Janssen., Gunnel Nordmark: None declared, Betty Tsao: None declared, Christopher Lessard: None declared

BackgroundIt has been shown that smoking could be negatively associated with the risk of developing primary Sjögren’s Syndrome (pSS), and that amongst smoking pSS patients, seropositivity for anti SSA antibodies and frequency of focal sialadenitis were lower. However, data are scarce concerning the impact of smoking on disease activity, glandular features, patient reported outcomes (PROs) and disease progression.ObjectivesTo describe the impact on smoking on pSS disease characteristics and outcome.MethodsASSESS (Assessment of Systemic Signs and Evolution in Sjögren’s Syndrome) is a French prospective multicenter, longitudinal cohort involving 395 patients with pSS enrolled in 15 French centers between 2006 and 2009 and followed since then. In these patients, we compared baseline characteristics and outcome (change in activity and PROs) during the first 5 years of follow-up, according to their smoking status (ever/never). For change during follow-up, any change of ≥ 3 points of ESSDAI or ≥ 1 point of ESSPRI or individual VAS, ≥ 25% of unstimulated salivary flow (UWSF) were considered.ResultsData on smoking at inclusion were available for 394 patients, 106 (26.9%) of them were ever-smokers. Compared to never smokers, ever-smokers were significantly younger (54.0 ± 11.3 vs 59.2 ± 12.2, p < 0.001), and more frequently men (14 (13.2%) vs 12 (4.2%), p = 0.0013). The mean ESSDAI did not differ according to smoking status, (5.0 ± 4.9 vs 5.5 ± 5.8, p = 0.436).There were no significant differences regarding the use of corticosteroids (49 (46.2%) vs 159 (55.2%), p = 0.113), immunosuppressive drugs (21 (19.8%) vs 77 (26.7%), p = 0.158), or hydroxychloroquine (49 (46.2%) vs 165 (57.3%), p = 0.05), even though numerically less smokers were taking these treatments. Smokers tended to have a lower dryness VAS and were less likely to use treatment for dry mouth (64 (60.4%) vs 210 (73%), p = 0.016).No difference was observed in PROs (ESSPRI 5.2 ± 2.2 vs 5.5 ± 2.1, p = 0.259), and in UWSF (0.5 ± 0.75 vs 0.4 ± 1.0, p = 0.672) between smokers and non-smokers.After five years, no difference in change of disease activity (ESSDAI change ≥ 3 points: 12/77 (15.6%) vs 30/207 (14.5%), p = 0.818), or in PROs (ESSPRI change ≥ 1: 30/70 (42.9%) vs 91/198 (46%) p = 0.654) was observed according smoking status, as well as no change in dryness (p = 0.968), pain (p = 0.280), fatigue (p = 0.605) or in UWSF (p = 0.132).Table 1.Baseline characteristics and change in patient-reported outcomes at 5 yearsBaselineSmokers n = 106Non smokers n = 288p-valueAge (years)54.0 ± 11.359.2 ± 12.20.00008Sex, females92 (86.0)276 (95,8)0.0013Disease duration (years) median [IQR]3.9 [1.1; 8.7]5.5 [2.6; 9.7]0.257ESSPRI5.2 ± 2.2 (n = 97)5.5 ± 2.1 (n = 267)0.259 Dryness4.9 ± 2.5 (n = 98)5.5 ± 2.2 (n = 270)0.053 Fatigue5.9 ± 2.8 (n =101)6.0 ± 2.6 (n = 275)0.924 Pain4.6 ± 2.8 (n = 101)4.9 ± 2.9 (n = 274)0.359Unstimuled salivary flow (mL/min)0.5 ± 0.75 (n = 89)0.4 ± 1.0 (n = 241)0.672Treatment for dry mouth64 (60.4)210 (73.0)0.016ESSDAI5.0 ± 4.9 (n = 104)5.5 ± 5.8 (n = 281)0.436Corticosteroids49 (46.2)159 (55.2)0.113Hydroxychloroguine49 (46.2)165 (57.3)0.050Immunosuppresive drugs21 (19.8)77 (26.7)0.158Lymphocytes count (/mm3)1661.1 ± 919.31492.1 ± 1072.0 (n = 280)0.128After five yearsChange in ESSDAI ≥ 312 (15.6) (n = 77)30 (14.5) (n = 207)0.818Change in ESSPRI ≥ 130 (42.9) (n = 70)91 (46.0) (n = 198)0.654Change in salivary flow ≥ 25%29 (44.6) (n = 65)55 (34.0) (n = 162)0.132Data are given as mean ± SD or n (%) unless otherwise specified. SD, standard deviation; IQR, interquartile range; ESSPRI, EULAR Sjögren’s Syndrome Patient Reported Index; ESSDAI, EULAR Sjögren’s Syndrome Disease Activity Index.Continuous variables were compared using student t test and qualitative variable using chi-squareConclusionSmokers pSS patients are more likely to be men and younger than non-smokers. Ever smokers tended to have less dryness, and take less frequently treatment for dryness, corticosteroids, hydroxychloroquine or immunosuppressants. After five years, no difference was observed, according to smoking status, in terms of change in disease activity or PROs.Disclosure of InterestsNone declared

BackgroundToday, there are still no DMARDs licensed for primary Sjögren Syndrome (pSS) patients. Among the explanations, are the limitations of current outcome measures used as primary endpoints: e.g; high placebo response rate, evaluation of either symptoms or systemic activity, and important features not being assessed. The NECESSITY consortium (https://www.necessity-h2020.eu/), including pSS experts from academia, pharmaceutical industry and patient groups formed to develop a new composite responder index, the Sjögren’s Tool for Assessing Response (STAR) that solve the issues of current outcome measures in pSS and is intended for use in clinical trials as an efficacy endpoint.ObjectivesTo develop a composite responder index in primary Sjögren’s syndrome (pSS): the STAR.MethodsTo develop the STAR, the NECESSITY consortium used data-driven methods, based on 9 randomized controlled trials (RCTs), and consensus techniques, involving 78 experts and 20 patients. Based on reanalysis of rituximab trials (TRACTISS and TEARS) and literature review, the Delphi panel identified a core set of domains to include in the STAR, with their respective outcome measures. STAR options combining these domains were designed and proposed to the panel to select and improve them. For each STAR option, sensitivity to change was estimated by the C-index (derived from Effect size) in all 9 RCTs. Delphi rounds were run for selecting STAR among these options. The Delphi panel also voted to classify trials as positive, negative or “in between” in regards to primary but also key secondary endpoints. For the options remaining before the final vote, meta-analyses of the RCTs were performed separately for positive and “in between” trials together, and for negative trials.ResultsThe Delphi panel identified 5 core domains (systemic activity, patient symptoms, lachrymal gland function, salivary gland function and biological parameters), and 227 STAR options, combining these domains, were selected to be tested for sensitivity to change. After two Delphi rounds, meta-analyses of the 20 remaining options were performed. The candidate STAR was selected by a final vote based on metrological properties and clinical relevance. In positive/in between trials, candidate STAR detected a difference between arms (OR 3.29, 95%-CI [2.07;5.22], whereas it did not in negative trials (OR 1.53, 95%-CI [0.81;2.91]).ConclusionThe candidate STAR is a composite responder index, including in a single tool all main disease features, and is designed for use as a primary endpoint in pSS RCTs. Its rigorous and consensual development process ensures its face and content validity. The candidate STAR showed good sensitivity and specificity to change. The candidate STAR will be prospectively validated in a dedicated three arms RCT of the NECESSITY consortium that will evaluate combination of synthetic DMARDs (hydroxychloroquine + lefunomide or hydroxychloroquine + mycophenolate vs placebo). We encourage the use of STAR in any ongoing and future trials.Table 1.Candidate STARDomainPointDefinition of responseSystemic activity3Decrease of clinESSDAI ≥ 3Patient reported outcome3Decrease of ESSPRI ≥ 1 point or ≥ 15%Lachrymal gland function1Schirmer:If abnormal score at baseline: increase ≥ 5 mm from baselineIf normal score at baseline: no change to abnormalOrOcular Staining Score:If abnormal score at baseline: decrease ≥ 2 points from baselineIf normal score at baseline: no change to abnormalSalivary gland function1Unstimulated Whole Salivary Flow:If score > 0 at baseline: increase ≥ 25% from baselineIf score is 0 at baseline: any increase from baselineorUltrasound:Decrease ≥ 25% in total Hocevar score from baselineBiological1Serum IgG levels: decrease ≥ 10%orRheumatoid Factor levels: decrease ≥ 25%Candidate STAR responder≥ 5 pointsESSDAI: EULAR Sjögren syndrome disease activity index; ESSPRI: EULAR Sjögren syndrome patient reported index; IgG: Immunoglobulin G;AcknowledgementsNECESSITY WP5 STAR development participants: Suzanne Arends (University Medical Center Groningen, Department of Rheumatology and Clinical Immunology, Groningen 9700 RB, Netherlands), Francesca Barone (Centre for Translational Inflammation Research, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK), Albin Björk (Division of Rheumatology, Department of Medicine, Karolinska Institutet, Stockholm, Sweden), Coralie Bouillot (Association Française du Gougerot Sjögren et des Syndromes Secs, France), Guillermo Carvajal Alegria (University of Brest, Inserm, CHU de Brest, LBAI, UMR1227, Brest, France; Service de Rhumatologie, Centre de Référence Maladies Autoimmunes Rares CERAINO, CHU Cavale Blanche, Brest, France), Wen-Hung Chen (GlaxoSmithKline, Research Triangle Park, North Carolina, USA), Kenneth Clark (GlaxoSmithKline Medicines Research Centre, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, United Kingdom), Konstantina Delli (Department of Oral and Maxillofacial Surgery, University Medical Center Groningen (UMCG), University of Groningen, The Netherlands), Salvatore de Vita (Rheumatology Clinic, University Hospital of Udine, Italy), Liseth de Wolff (University Medical Center Groningen, Department of Rheumatology and Clinical Immunology, Groningen 9700 RB, Netherlands), Jennifer Evans (Novartis Pharmaceuticals corporation USA), Stéphanie Galtier (Institut de Recherches Internationales Servier (IRIS), Suresnes Cedex, France), Saviana Gandolfo (Rheumatology Clinic, Department of Medical area, University of Udine, ASUFC, 33100 Udine, Italy), Mickael Guedj (Institut de Recherches Internationales Servier (IRIS), Suresnes Cedex, France), Dewi Guellec (CHU de Brest, Service de Rhumatologie, Inserm, CIC 1412, Brest, France), Safae Hamkour (Center of Translational Immunology, Department of Immunology, University Medical Center Utrecht, Utrecht 3584 GA, Netherlands), Dominik Hartl (Novartis Institutes for BioMedical Research, Basel, Switzerland), Malin Jonsson (Section for Oral and Maxillofacial Radiology, Department of Clinical Dentistry, Faculty of Medicine and Dentistry, University of Bergen, Norway), Roland Jonsson (Broegelmann Research Laboratory, Department of Clinical Science, University of Bergen, Department of Rheumatology, Haukeland University Hospital, Bergen, Norway), Frans Kroese (University Medical Center Groningen, Department of Rheumatology and Clinical Immunology, Groningen 9700 RB, Netherlands), Aike Albert Kruize (University Medical Center Utrecht, Department Rheumatology and Clinical Immunology, Utrecht, Netherlands), Laurence Laigle (Institut de Recherches Internationales Servier (IRIS), Suresnes Cedex, France), Véronique Le Guern (AP-HP, Hôpital Cochin, Centre de référence maladies auto-immunes et systémiques rares, service de médecine interne, Paris, France), Wen-Lin Luo (Department of Biometrics and Statistical Science, Novartis Pharmaceuticals, East Hanover, New Jersey), Esther Mossel (University Medical Center Groningen, Department of Rheumatology and Clinical Immunology, Groningen 9700 RB, Netherlands), Wan-Fai Ng (Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne NE2 4HH, UK), Gaëtane Nocturne (Department of Rheumatology, Université Paris-Saclay, INSERM U1184: Centre for Immunology of Viral Infections and Autoimmune Diseases, Assistance Publique-Hôpitaux de Paris, Hôpital Bicêtre, Le Kremlin Bicêtre, Paris, France), Marleen Nys (Global Biometric Sciences, Bristol Myers Squibb, Braine L’Alleud, Belgium), Roald Omdal (Clinical Immunology Unit, Department of Internal Medicine, Stavanger University Hospital, PO Box 8100, 4068, Stavanger, Norway), Jacques-Olivier Pers (LBAI, UMR1227, University of Brest, Inserm, Brest, France and CHU de Brest, Brest, France), Maggy Pincemin (Association Française du Gougerot Sjögren et des Syndromes Secs, France), Manel Ramos-Casals (Department of Autoimmune Diseases, Hospital Clinic de Barcelona Institut Clinic de Medicinai Dermatologia, Barcelona, Catalunya, Spain), Philippe Ravaud (Centre d’Epidémiologie Clinique, Hôpital Hôtel-Dieu, Assistance Publique-Hôpitaux de Paris, Paris, France), Neelanjana Ray (Global Drug Development - Immunology, Bristol Myers Squibb Company, Princeton, New Jersey, USA), Alain Saraux (HU de Brest, Service de Rhumatologie, Univ Brest, Inserm, UMR1227, Lymphocytes B et Autoimmunité, Univ Brest, Inserm, LabEx IGO, Brest, France), Athanasios Tzioufas (Rheumatology Clinic, Department of Medical area, University of Udine, ASUFC, 33100 Udine, Italy), Gwenny Verstappen (University Medical Center Groningen, Department of Rheumatology and Clinical Immunology, Groningen 9700 RB, Netherlands), Arjan Vissink, Marie Wahren-Herlenius (Division of Rheumatology, Department of Medicine, Karolinska Institutet, Stockholm, Sweden). We thank the following experts: Esen Karamursel Akpek, Alan Baer, Chiara Baldini, Elena Bartoloni, Marí-Alfonso Begona, Johan Brun, Vatinee Bunya, Laurent Chiche, Troy Daniels, Paul Emery, Robert Fox, Roberto Giacomelli, John Gonzales, John Greenspan, Robert Moots, Susumu Nishiyama, Elizabeth Price, Christophe Richez, Caroline Shiboski, Roser Solans Laque, Muthiah Srinivasan, Peter Olsson, Tsutomu Takeuchi, Frederick Vivino, Paraskevi Voulgari, Daniel Wallace, Ava Wu, Wen Zhang. We thank the anonymous patients from the NECESSITY Patient Advisory Group and the Sjögren Foundation for their valuable contribution to the Delphi process. We thank EW StClair and AN Baer who generated the baminercept data and made them publicly available.Disclosure of InterestsRaphaèle Seror Consultant of: GlaxoSmithKline, Boehringer, Janssen and Novartis, Grant/research support from: GlaxoSmithKline and Amgen, Gabriel Baron: None declared, Marine Camus: None declared, Divi Cornec Consultant of: GlaxoSmithKline, Bristol Myers Squibb, Janssen, Amgen, Pfizer and Roche, Elodie Perrodeau: None declared, Simon J. Bowman Consultant of: Abbvie, Astra Zeneca, Galapagos and Novartis Pharmaceuticals, Michele Bombardieri Consultant of: UCB, Amgen/Medimmune, Janssen, and GlaxoSmithKline, Grant/research support from: Amgen/Medimmune, Janssen, and GlaxoSmithKline, Hendrika Bootsma: None declared, Jacques-Eric Gottenberg Consultant of: AbbVie, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, Pfizer, Roche, Sanofi, Novartis, MSD, CSL-Behring and Genzyme, Grant/research support from: Bristol Myers Squibb, Benjamin Fisher Speakers bureau: Bristol Myers Squibb and Novartis, Consultant of: Novartis, Bristol Myers Squibb, Janssen and Servier, Grant/research support from: Servier, Galapagos and Janssen, Wolfgang Hueber Shareholder of: Novartis Pharma, Employee of: Novartis Pharma, Joel van Roon: None declared, Valerie Devauchelle-Pensec: None declared, Peter Gergely Shareholder of: Novartis Pharma, Employee of: Novartis Pharma, Xavier Mariette Consultant of: Bristol Myers Squibb, Galapagos, GlaxoSmithKline, Janssen, Novartis, Pfizer and UCB, Grant/research support from: Ose Pharmaceuticals, Raphaël Porcher: None declared

BackgroundGiant cell arteritis (GCA) and polymyalgia rheumatica (PMR) are two associated inflammatory diseases that probably share common pathophysiological mechanisms. Data on environmental risk factors are lacking. Population based cohort studies are the most adequate and less biased sources for identifying such factors. But case validation of disease diagnoses is the first necessary step for running such studies, even though it is not easy to perform.ObjectivesTo assess the accuracy of self-reported GCA/PMR diagnoses and to develop algorithms to ascertain GCA/PMR in a large French population-based cohort, using combined data of a dedicated questionnaire and medication reimbursement database.MethodsThe E3N cohort study (Etude Epidémiologique auprès des femmes de la Mutuelle générale de l’Education Nationale) includes 98,995 healthy French women born between 1925 and 1950, recruited in 1990 and was designed to investigate lifestyle and environmental factors associated with chronic conditions. Participants completed biennially mailed questionnaires to update their health-related information, lifestyle characteristics, and newly diagnosed diseases. Women who self-reported a diagnosis of GCA and/or PMR were sent a specific validation questionnaire designed to ascertain the diagnosis including clinical, biological, and therapeutic data, along with ACR 1990 classification criteria for GCA and ACR/EULAR 2012 classification criteria for PMR. We then devised algorithms based on self-reported answers and a medication reimbursement database, and evaluated their accuracy, comparing them with diagnoses obtained from medical chart review.ResultsAmong the 98,995 participants, 1,392 women self-reported GCA/PMR. The specific questionnaire was sent to 1,143 (82.1%) of the eligible women (249 women could not be contacted because of death or withdrawn consent) and response was obtained for 830 women (59.6%). Among them, 202 women provided sufficient medical data to ascertain a diagnosis and study accuracy of developed algorithms. 56 women were classified as ACG and 121 as PMR. Self-reported diagnoses alone had an accuracy of 87.6% with medical chart review. If women additionally self-reported a diagnosis confirmation by a physician and the use of glucocorticoids for ≥ 3 months, the accuracy was improved to 89.9%. For patients who did not respond to validation questionnaire, adding the use of glucocorticoids for ≥ 3 months in the reimbursement database also improved the diagnosis accuracy to 92.8%. These two designed algorithms also had the benefit of reducing the number of false positive cases by 10 and 16 respectively. Finally, 589 GCA and/or PMR cases were confirmed by our two devised algorithms: 401 cases with algorithm using the specific GCA/PMR questionnaire and 188 with medication reimbursement database. The mean age at diagnosis was 70.3 (± 8.0) years [73.4 (± 6.2) years for cases detected using the specific GCA/PMR questionnaire and 68.9 (± 8.3) years for cases detected with medication reimbursement database]. Demographic and clinical data were similar between our population of validated cases by medical chart review and the cases detected by our algorithms in the cohort.ConclusionThe accuracy of self-reported diagnosis of GCA/PMR was high in the E3N-cohort. Using additional data such as medication reimbursement and/or other self-reported data from a specific questionnaire, particularly the prolonged use of glucocorticoids led to a better accuracy with a very small number of false positive cases and seemed to be sufficient to correctly ascertained GCA and/or PMR diagnoses. With the validation of nearly 600 GCA and/or PMR cases in our cohort, we will be able to conduct epidemiological studies to identify risk factors of these diseases.AcknowledgementsThe authors are indebted to all participants for their continued participation. The authors would like to thank Pascale Gerbouin-Rerolle, Mariam Alyaniakian, Sofiane Harizi and Roselyn Rima Gomes for their help on data management. The present work was performed using data from the Inserm E3N cohort and support from the MGEN, Gustave Roussy, and the Ligue contre le Cancer for setting up and maintaining the cohort. The cohort was supported by a state grant ANR-10-COHO-0006 from the Agence Nationale de la Recherche within the Investissement d’Avenir program. The present work was conducted thanks to a research grant from the Agence Régionale de Santé – Île de France.Disclosure of InterestsNone declared

BackgroundThe role of nutrition in the pathogenesis of RA has been suggested in many studies but remains controversial.ObjectivesTo assess the relationship between consumption of the most frequently drinked specific beverages (coffee, tea, alcohol, and soft drinks) and the risk of RA.MethodsThe E3N Study (Étude Épidémiologique auprès des femmes de la Mutuelle Générale de l’Éducation Nationale) is a French prospective cohort including 98,995 women since 1990. RA cases have been previously identified with specific questionnaires and medication reimbursement database (1). Food and beverage consumption was assessed using a validated food-frequency questionnaire. Hazard ratios (HR) and their 95% confidence intervals (CI) for incident RA were estimated by Cox proportional hazards models, adjusted for age and the main potential confounders, including smoking. Tests for linear trends were performed. As smoking is a major risk factor for RA, stratified analyses were conducted according to the smoking status (ever or never-smoker) even in the absence of statistically significant interaction between the smoking status and beverage consumptions. Sensitivity analyses were performed in the subgroup of seropositive cases (positive RF and/or ACPA).ResultsDuring a total follow-up of 21 years (mean 20.78 (SD 2.26) years), 62,630 women contributed 1,300,874 person-years, and 481 developed RA (incidence 37/100 000 person-years). Incident RA cases were diagnosed a mean 11.7 (SD 5.9) years after baseline. Of the 179 (37.2%) RA cases with known antibody status, 153 (31.8%) were seropositive. Consumptions of total alcohol, tea and sugar-sweetened soft drinks were not associated with RA risk. Coffee consumption was associated with RA risk with a dose-effect relationship (≥4 cups/day versus ≤ 1cup/day, HR 1.24, 95% CI [0.94; 1.64], ptrend = 0.04), only in never-smokers. According to the type of coffee consumed, only high caffeinated coffee consumption was associated with an increased risk of RA among never-smokers (≥ 3.5 cups/day versus ≤ 1 cup/day, HR=1.62 95% CI [1.12;2.34], ptrend = 0.006, pinteraction = 0.07). Decaffeinated coffee consumption was not associated with RA risk. Risk of RA was higher with artificially-sweetened soft-drinks consumption (consumers versus non-consumers, HR 1.66 [1.12; 2.45]), in never-smokers. No association was observed with other soft drinks. Moderate liquor (spirits or aperitifs) intake was associated with a reduced risk of RA among ever-smokers (consumption of 1 - 3 glasses/week versus non-consumers HR 0.63, 95% CI [0.43; 0.91]) and moderate wine consumption was associated with a decreased risk of seropositive RA (consumption of 4 -10 glasses/week versus ≤ 1glass/week HR 0.57, 95% CI [0.35 to 0.94], whereas no association was observed with other alcohols.ConclusionConsumptions of tea, total alcohol, and sugar-sweetened soft drinks were not associated with RA risk, whereas consumptions of coffee (especially caffeinated coffee), and artificially-sweetened soft drinks were associated with higher RA risk, among never-smokers. By contrast, moderate liquor consumption was associated with a decreased risk of RA in ever-smokers, and moderate wine consumption was associated with a decreased risk of seropositive RA. If further confirmed, these results could lead to novel mechanistic hypotheses and to simple prevention measures.References[1]Nguyen Y, Salliot C, Gusto G, Descamps E, Mariette X, Boutron-Ruault M-C, et al. Improving accuracy of self-reported diagnoses of rheumatoid arthritis in the French prospective E3N-EPIC cohort: a validation study. BMJ Open. d caffeinated coffee),AcknowledgementsThe authors would like to thank Pascale Gerbouin-Rerolle, Mariam Alyaniakian, Sofiane Harizi and Roselyn Rima Gomes for their help on data management. The present work was performed using data from the Inserm E3N cohort and support from the MGEN, Gustave Roussy, and the Ligue contre le Cancer for setting up and maintaining the cohort. The cohort was supported by a state grant ANR-10-COHO-0006 from the Agence Nationale de la Recherche within the Investissement d’Avenir program.The present work was conducted thanks to a research grant from the Société Française de Rhumatologie.Disclosure of InterestsNone declared

Background Rheumatic immune-related adverse events (irAEs) are increasingly recognized musculoskeletal manifestations in cancer patients receiving immune checkpoint targeted immunotherapy. Since they represent a spectrum of new clinical entities and a robust evidence base is lacking, a task force was convened to harmonize expert consensus regarding their identification and management due to the lack of dedicated clinical trials. Our aim was to develop EULAR recommendations for the diagnosis and the management of rheumatic irAEs due to cancer immunotherapy, based on literature and expert opinion. Methods Recommendations were developed according to the 2014 EULAR Standard Operating Procedures. The task force consisted of 19 clinical experts from Europe and North America (14 rheumatologists, 2 internists and 3 oncologists), 1 clinical epidemiologist, 1 allied health professional and 2 patient representatives. During the first meeting, the group defined the focus of the task force, the target population, and formulated research questions. A systematic literature research was performed by one fellow (MK) with the help of a librarian. Based on available evidence and using a consensus procedure, recommendations were developed during a second meeting. The level of agreement was determined by an anonymous voting process. Results 4 overarching principles and 10 recommendations were developed. The overarching principles define the role of rheumatologists and highlight the shared decision-making process between patients, oncologists and rheumatologists. One recommendation addresses the referral process, two address the diagnosis, and five address the therapeutic strategy of cancer patients experiencing rheumatic, musculoskeletal, and systemic signs or symptoms while receiving immunotherapy. An additional recommendation was included to address pre-existing rheumatic conditions and the last focuses on the diagnostic approach before immunotherapy. Conclusions These recommendations provide the basis of a EULAR consensus on the diagnosis and the management of rheumatic irAEs, worthwhile for rheumatologists, internists and oncologists. Legal entity responsible for the study EULAR, European League Against Rheumatism. Funding EULAR, European League Against Rheumatism. Disclosure All authors have declared no conflicts of interest.

To define parameters predictive of lymphoma development in patients with primary Sjögren's syndrome (SS).A multicenter case-control survey was performed to identify predictors of lymphoma. Cases were patients who developed lymphoma after diagnosis of primary SS and were mainly recruited through the Club Rhumatismes et Inflammation network. For each case, 2 controls (matched for disease duration and age) were randomly selected among patients with primary SS and without lymphoma. Cases and controls were compared using univariate analysis and then using multivariate analysis to identify independent predictors of lymphoma.One hundred one patients with primary SS and lymphoma were included. Eighty-seven patients were women (86.1%), and the mean ± SD age at lymphoma diagnosis was 57.4 ± 12.6 years. The most frequent histologic type was B cell non-Hodgkin's lymphoma (NHL) in 99 of 101 patients, with marginal-zone lymphoma in 76 of the 99 patients (76.8%) including 58 (58.6%) with lymphoma of the mucosa-associated lymphoid tissue type. Lymphomas were most frequently located in the salivary glands (43 patients). A specific treatment was initiated at diagnosis in 87 patients with B cell NHL, and 61 patients (61.6%) achieved complete sustained remission after the first line of treatment. In the multivariate analysis, salivary gland enlargement, the presence of rheumatoid factor (RF), low C4, cryoglobulinemia, lymphopenia, and disease activity according to the European League Against Rheumatism Sjögren's Syndrome Disease Activity Index (excluding the lymphoma domain) were found to be predictors of lymphoma. No previous treatment for primary SS was associated with any effect on lymphoma occurrence.In addition to previously known factors predictive of lymphoma occurrence, the independent roles of RF and disease activity were demonstrated in this case-control study of primary SS-associated lymphoma. Our findings highlight the roles of chronic antigenic stimulation and disease activity in the development of this severe complication.

International audience; Blood concentrations of hydroxychloroquine (HCQ) vary widely among patients with systemic lupus erythematosus (SLE). A pharmacokinetic/pharmacodynamic relationship has been found in different situations, and a very low blood concentration of HCQ is a simple marker of nonadherence to treatment. Therefore, interest in blood HCQ concentration measurement has increased, but little is known about factors that influence blood HCQ concentration variability. This study was undertaken to analyze determinants of blood HCQ concentrations. We conducted a retrospective analysis of patient data, including data from the Plaquenil Lupus Systemic (PLUS) study, to determine the association of epidemiologic, clinical, and biologic factors with blood HCQ concentrations. Data for nonadherent patients (blood HCQ concentration

Journal de Gynecologie Obstetrique et Biologie de la Reproduction - Vol. 35 - N° 2 - p. 169-175

Resume L'atteinte osseuse au cours des lymphomes est un evenement rare, en dehors des leucemies/lymphomes associes a human T-cell lymphoma virus (HTLV)-I. Il s’agit de lesions osteolytiques preferentiellement localisees sur la metaphyse des os longs ou le squelette axial. La survenue d'une localisation osseuse reflete l'evolutivite du lymphome, dont le pronostic reste lie a son type histologique et a son extension. La survenue de lesions osseuses au cours de certains syndromes lymphoproliferatifs, leucemie lymphoide chronique ou maladie de Waldenstrom, ou de syndromes myeloproliferatifs, peut traduire une transformation en lymphome plus agressif, dont le pronostic reste alors tres reserve. L'existence de rares localisations osseuses specifiques ne doit bien sur pas faire meconnaitre les complications infectieuses des hemopathies malignes, osteite ou osteomyelite, chez des sujets immunodeprimes. Les manifestations articulaires des hemopathies malignes sont le plus souvent liees a un processus infectieux (arthrite infectieuse), ou metabolique (goutte). Les arthrites specifiques sont l’apanage des leucemies aigues. Des manifestations articulaires secondaires a une vascularite principalement leucocytoclasique sont surtout frequentes dans les leucemies a tricholeucocytes. Mais ces manifestations ont ete rapportees dans toutes les hemopathies malignes, qu’elles peuvent parfois preceder. La connaissance de ces manifestations osseuses et articulaires est importante dans le diagnostic des douleurs osseuses et des arthrites, et il faudra evoquer la possibilite d’une hemopathie en l’absence de reponse aux traitements usuels, en presence de signes systemiques tels qu’une fievre ou un syndrome inflammatoire persistant, ou de lesions radiologiques atypiques.

La lesion osea en el transcurso evolutivo de los linfomas es inusual, salvo en el caso de las leucemias/linfomas relacionadas con el virus del linfoma T humano (VLTH)-I. Se trata de lesiones osteoliticas predominantes en la metafisis de los huesos largos o en el esqueleto axial. El hallazgo de una localizacion osea refleja la evolutividad del linfoma, cuyo pronostico depende del tipo histologico y de la extension. El desarrollo de lesiones oseas en el transcurso de algunos sindromes linfoproliferativos, leucemia linfoide cronica o enfermedad de Waldenstrom, o de sindromes mieloproliferativos, puede indicar la transformacion en un linfoma mas agresivo, cuyo pronostico permanece entonces muy reservado. Si bien existen algunas localizaciones oseas especificas, en pacientes inmunodeprimidos no se deben pasar por alto las complicaciones infecciosas de las hemopatias malignas (osteitis u osteomielitis). Las manifestaciones articulares de las hemopatias malignas suelen ser producto de un proceso infeccioso (artritis infecciosa) o metabolico (gota). Las artritis especificas son propias de las leucemias agudas. Las manifestaciones articulares secundarias a una vasculitis, en especial leucocitoclastica, son sobre todo frecuentes en las tricoleucemias. Sin embargo, esas manifestaciones se observan en todas las hemopatias malignas y a veces las preceden. El conocimiento de las manifestaciones oseas y articulares es importante para el diagnostico de los dolores oseos y de las artritis; ante la falta de respuesta a los tratamientos habituales, y en presencia de manifestaciones sistemicas (fiebre, sindrome inflamatorio persistente) o de imagenes radiograficas atipicas, hay que pensar en la posibilidad de una hemopatia.

Resume Propos. – Parmi l’ensemble des symptomes du syndrome de Sjogren (SS), la xerostomie est ressentie souvent comme l’un des plus invalidants. Ce symptome permanent au quotidien est responsable d’une alteration importante de la qualite de vie. Actualites et point forts. – A ce jour, aucun traitement de fond n’a fait la preuve de son efficacite dans des etudes controlees pour diminuer la severite de la xerostomie ou des autres signes cliniques du SS. L’hydroxychloroquine (Plaquenil®) est souvent utilise mais la seule etude randomisee contre placebo a mis en evidence une diminution de l’hypergammaglobulinemie polyclonale mais n’a pas demontre d’effets cliniques du Plaquenil®. Les traitements sont donc symptomatiques. Les seuls medicaments ayant demontre une efficacite sur la secheresse sont les medicaments agonistes des recepteurs muscariniques (a action cholinergique). En effet, il existe dans le SS une inhibition de la secretion des glandes saines restantes qui peut etre levee par l’adjonction de doses supraphysiologiques du mediateur neurologique agissant sur les recepteurs muscariniques des glandes exocrines. Le chlorhydrate de pilocarpine (Salagen®) a la dose d’une gelule a 5 mg, 4 a 6 fois par jour est le seul medicament de cette classe disponible en Europe. Il vient d’obtenir en juillet 2003 une autorisation de mise sur le marche pour le SS en France. La cevimeline n’est toujours pas disponible en Europe. Perspectives et projets. – L’utilisation des immunosuppresseurs est possible dans le SS. Malheureusement, les resultats prometteurs d’une etude ouverte d’un inhibiteur du TNFα, l’infliximab (Remicade®) n’ont pas ete confirme par une etude controlee randomisee evaluant l’infliximab versus placebo et par une etude ouverte utilisant l’etanercept. De nouvelles etudes randomisees sont indispensables dans cette maladie soit avec des substances anciennes comme l’hydroxychloroquine dont l’effet clinique n’a jamais ete prouve, soit avec de nouvelles biotherapies comme le rituximab.