Introduction: Chondroitin sulfate (CS) is a symptomatic slow-acting drug for osteoarthritis (OA) widely used in the clinic. The aim of this work is to find proteins whose secretion from cartilage cells under proinflammatory stimuli (IL-1β) is regulated by CS, employing a novel quantitative proteomic approach. Methods: Human articular chondrocytes released from three normal cartilages were grown in SILAC medium. When complete incorporation of the heavy isotope was achieved, chondrocytes were stimulated with IL-1β 5 ng/ml with or without CS pretreatment (200 µg/ml). Forty-eight hours later, chondrocyte secretomes were analyzed by nano-scale liquid chromatography-mass spectrometry. Real-time PCR, western blot and immunohistochemistry analyses were employed to confirm some of the results. Results: We could identify 75 different proteins in the secretome of human articular chondrocytes. Eighteen of these were modulated by CS with statistical significance (six increased and 12 decreased). In normal chondrocytes stimulated with IL-1β, CS reduces inflammation directly by decreasing the presence of several complement components (CFAB, C1S, CO3, and C1R) and also indirectly by increasing proteins such as TNFα-induced protein (TSG6). TSG6 overexpression correlates with a decrease in pro-matrix metalloproteinase activation (observed in MMP1 and MMP3 levels). Finally, we observed a strong CS-dependent increase of an angiogenesis inhibitor, thrombospondin-1. Conclusion: We have generated a quantitative profile of chondrocyte extracellular protein changes driven by CS in the presence of IL-1β. We have also provided novel evidences of its anti-angiogenic, anti-inflammatory, and anti-catabolic properties. Demonstration of the anti-angiogenic action of CS might provide a novel therapeutic approach for OA targeting.

Objectives: The aim of this study was to identify robust predictive markers which may help personalize tapering protocols, minimizing flare risk while optimizing long-term disease management in RA patients. Methods: The OPTIBIO trial (EudraCT 2012–004482-40) was a phase IV, randomized, open-label, non-inferiority study conducted in five hospitals in Spain. RA patients in sustained remission on stable bDMARD therapy were randomized 1:1 to standard care or dose reduction. The primary outcome was to compare the proportion of joint flare between baseline and 12 months by a non-inferiority analysis analysed by the intention-to-treat principle and to identify predictors for flare and sustained remission. Results: A total of 195 patients were randomized: 99 to the control group and 96 to the optimization group. Thirty-nine flares occurred (optimization: 22.7%, control: 17.2%), with a risk difference of −5.5% (95% CI: −16.8% to 5.7%; P = 0.33). Two predictive models were developed: one for flares (AUC: 0.84) including 3v-DAS28-CRP, VAS pain, erosions, systolic blood pressure and haemoglobin, and another for sustained remission (AUC: 0.77) including 3v-DAS28-CRP, age and rheumatoid factor. Adding molecular biomarkers improved AUCs to 0.91 and 0.88, respectively. No significant differences in adverse events were observed. Conclusion: bDMARD dose optimization was not non-inferior to standard therapy on the flare rate but demonstrated similar safety. Predictive models for remission and flares were developed, which may help select patients to ensure safe implementation of this strategy, highlighting the need for personalized treatment. Trial registration: www.clinicaltrialsregister.eu; EudraCT 2012–004482-40

Objective: The objective of this study was to discover autoantibodies to non-modified proteins associated with the presence/absence of ACPAs in RA. Methods: The autoantibody repertoire of 80 ACPA-negative and 80 ACPA-positive RA subjects from the Swedish population-based Epidemiological Investigation of RA (EIRA) cohort was screened using a suspension bead array built on protein fragments earlier described as autoimmunity targets. Four autoantibodies positive in the initial screening were validated in another set of EIRA samples containing 317 ACPA-positive, 302 ACPA-negative and 372 age- and sex-matched controls. The relationship between the four autoantibodies and lung abnormalities on high-resolution CT (HRCT) was examined in 93 early-RA patients from the LURA cohort. Association between the autoantibodies, smoking and MHC class II alleles was assessed by logistic regression analysis. Results: Anti-ANOS1 and anti-MURC IgG levels were associated with ACPA-positive status [odds ratio (OR) = 3.02; 95% CI 1.87–4.89; and OR = 1.86; 95% CI 1.16–2.97, respectively] and increased in ACPA-positive patients compared with controls. Anti-ANOS1 IgG was associated with smoking habit (OR = 2.11; 95% CI 1.22–3.69) and anti-MURC IgG with the presence of the MHC class II ‘shared-epitope’ genes (OR = 1.95; 95% CI 1.11–3.46). Anti-TSPYL4 IgG was associated with being ACPA negative (OR = 0.41; 95% CI 0.19–0.89). Anti-TSPYL4 IgG and anti-MAP2K6 IgG levels were increased in the ACPA-negative patients compared with controls. Presence of anti-MAP2K6 IgG and anti-TSPYL4 IgG correlated negatively with HRCT-defined lung abnormalities. Conclusion: These four autoantibodies may be useful in diagnostics and in predicting clinical phenotypes of RA.

Background: The improvement of the existing diagnostic methods to detect pre-radiographic knee OA (KOA) may facilitate the development of preventive strategies. It has been postulated that combining biochemical with clinical markers, may increase the prognostic power to detect who is at high risk for developing KOA. Objectives: To validate and qualify the ability of 6 proteins with biomarker potential to generate a prognostic model of knee OA prediction through the combination of validated OA biomarkers and clinical markers. Methods: In the validation phase (Figure 1), 749 sera at the baseline visit belonging to participants from the Osteoarthritis Initiative (OAI) Cohort were randomly selected to blindly quantify 6 biomarkers using in-house custom sandwich microarrays built using the xMAP technology. Among these, only 540 participants have a Kellgren and Lawrence (KL) grade = 0-1 at the beginning of the OAI study in at least one knee. After a follow-up period of 96 months, 209 participants developed KOA in at least one knee (KL ≥ 2) and were classified as incident group, whereas 331 did not developed the disease (KL = 0-1) and were classified as not-incident group. Statistical differences between the outcome groups were assessed by non-parametric Mann-Whitney U tests. In the qualification phase (n=540), univariate regression analyses were carried out to investigate whether the individual biomarkers were associated with the risk of KOA development. A clinical prognostic model was defined by stepwise regression analysis using clinical non-radiographic variables significantly associated with the OA incidence. The utility of the potential biomarkers, alone or in combination, was evaluated by comparing the Area Under the Curve (AUC) of the clinical prognostic model with the biomarkers plus clinical prognostic models. In addition, sen Study desing Results: The incident group showed significant higher serum concentrations at the baseline visit (p < 0.05) for all the potential biomarkers analyzed in this study. Moreover, 5 of them were also significantly associated with the future appearance of radiographic KOA, yielding Odds Ratios (OR) ≥ 10 per 10 µg/ml increase. Among all the possible combinations, the inclusion of 2 biomarkers to the clinical prognostic model showed a significant improvement of the predictive capacity (AUCs = 0.78 vs 0.82, p= 0,044) with 65% (95% Confidence Interval (95%CI): 60-70%) specificity and 88% (95%CI: 81-91%) sensitivity. Variables included in the regression model and all metrics comparing the biomarkers plus clinical prognostic model with the clinical prognostic model are shown in Figure 2A. The ROC curves of the biomarkers-only model, clinical prognostic model and biomarkers plus clinical prognostic model are represented in Figure 2B. (A) Multivariate regression analysis (B) ROC curves of the models Conclusion: We have generated a prognostic model for the prediction of KOA by combining biomarkers and clinical variables, which showed a putative utility in the clinical setting by improving the predictive capacity of a clinical prognostic model to identify patients at a higher risk to develop radiographic KOA. Disclosure of Interests: Maria Camacho Encina: None declared, Vanesa Balboa-Barreiro: None declared, Ignacio Rego-Perez: None declared, Rocío Paz González: None declared, Valentina Calamia: None declared, Lucía Lourido: None declared, Cristina Ruiz-Romero: None declared, Francisco J. Blanco Grant/research support from: Sanofi-Aventis, Lilly, Bristol MS, Amgen, Pfizer, Abbvie, TRB Chemedica International, Glaxo SmithKline, Archigen Biotech Limited, Novartis, Nichi-iko pharmaceutical Co, Genentech, Jannsen Research & Development, UCB Biopharma, Centrexion Theurapeutics, Celgene, Roche, Regeneron Pharmaceuticals Inc, Biohope, Corbus Pharmaceutical, Tedec Meiji Pharma, Kiniksa Pharmaceuticals, Ltd, Gilead Sciences Inc, Consultant of: Lilly, Bristol MS, Pfizer

Objectives Optimization of biological DMARDs (bDMARDs) in patients with rheumatoid arthritis (RA) may be feasible in those who have maintained remission for at least six months. However, this approach carries the risk of disease flare, underscoring the need for reliable predictors to guide clinical decisions. Methods The OPTIBIO trial (EudraCT 2012-004482-40) was a phase IV, randomized, open-label, non-inferiority study conducted in five hospitals in Spain. RA patients in sustained remission on stable bDMARD therapy were randomized 1:1 to standard care or dose reduction. The primary outcome was to compare to proportion of joint flare at 12 months by a non-inferiority analysis analyzed by the intention-to-treat principle and identify predictors for flare and sustained remission. Results 195 patients were randomized: 99 to the control group and 96 to the optimization group. Thirty-nine flares occurred (optimization: 22.7%, control: 17.2%), with a risk difference of -5.5% (95% CI: -16.8% to 5.7%; P = 0.33). Two predictive models were developed: one for flares (AUC: 0.84) including 3v-DAS28-CRP, VAS pain, erosions, systolic blood pressure, and hemoglobin, and another for sustained remission (AUC: 0.77) including 3v-DAS28-CRP, age, and rheumatoid factor. Adding molecular biomarkers improved AUCs to 0.91 and 0.88, respectively. No significant differences in adverse events were observed. Conclusion bDMARD dose optimization was non-inferior to standard therapy on the flare rate but demonstrated similar safety. Predictive models for remission and flares were developed, which may help select patients to ensure safe implementation of this strategy, highlighting the need for personalized treatment. Key messages bDMARD dose optimization was non-inferior to standard therapy on the flare Clinical models classified patients in terms of disease severity Incorporation of molecular markers to clinical models improved their prediction power