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Objective To explore the potential of a panel of ECM remodelling markers as endotyping tools for axial spondyloarthritis (axSpA) by separating patients into subtypes and investigate how they differ among each other in disease activity scores and response to treatment with adalimumab. Methods In three axSpA studies, a panel of 14 blood-based ECM biomarkers related to formation of collagen (PRO-C2, PRO-C3, PRO-C6), degradation of collagen by metalloproteinases (C1M, C2M, T2CM, C3M, C4M, C6M, C10C), matrix metalloproteinase (MMP)-degraded prolargin (PROM), MMP-degraded and citrullinated vimentin (VICM), basement membrane turnover (PRO-C4) and neutrophil activity (CPa9-HNE) were assessed to enable patient clustering (endotyping). MASH (n=41) was a cross-sectional study, while Adalimumab in Axial Spondyloarthritis study (ASIM,n=45) and Danish Multicenter Study of Adalimumab in Spondyloarthritis (DANISH, n=49) were randomised, double-blind placebo-controlled trials of adalimumab versus placebo every other week for 6 or 12 weeks, respectively, followed by active treatment. Biomarker data were log-transformed, standardised by mean centering and scaled by the SD prior to principal component analysis and K-means clustering. Results Based on all three studies, we identified two orthogonal dimensions reflecting: (1) inflammation and neutrophil activity (driven by C1M and CPa9-HNE) and (2) collagen turnover (driven by PRO-C2). Three endotypes were identified: high inflammation endotype (Endotype1), low inflammation endotype (Endotype 2) and high collagen turnover endotype (Endotype3). Endotype1 showed higher disease activity (Ankylosing Spondylitis Disease Activity Score (ASDAS)) at baseline compared with Endotype2 and Endotype3 and higher percentage of patients responding to adalimumab based on ASDAS clinical improvement at week 24. Endotype3 showed higher percentage of patients with 50% improvement in Bath Ankylosing Spondylitis Disease Activity Index respons, Objective To explore the potential of a panel of ECM remodelling markers as endotyping tools for axial spondyloarthritis (axSpA) by separating patients into subtypes and investigate how they differ among each other in disease activity scores and response to treatment with adalimumab. Methods In three axSpA studies, a panel of 14 blood-based ECM biomarkers related to formation of collagen (PRO-C2, PRO-C3, PRO-C6), degradation of collagen by metalloproteinases (C1M, C2M, T2CM, C3M, C4M, C6M, C10C), matrix metalloproteinase (MMP)-degraded prolargin (PROM), MMP-degraded and citrullinated vimentin (VICM), basement membrane turnover (PRO-C4) and neutrophil activity (CPa9-HNE) were assessed to enable patient clustering (endotyping). MASH (n=41) was a cross-sectional study, while Adalimumab in Axial Spondyloarthritis study (ASIM,n=45) and Danish Multicenter Study of Adalimumab in Spondyloarthritis (DANISH, n=49) were randomised, double-blind placebo-controlled trials of adalimumab versus placebo every other week for 6 or 12 weeks, respectively, followed by active treatment. Biomarker data were log-transformed, standardised by mean centering and scaled by the SD prior to principal component analysis and K-means clustering. Results Based on all three studies, we identified two orthogonal dimensions reflecting: (1) inflammation and neutrophil activity (driven by C1M and CPa9-HNE) and (2) collagen turnover (driven by PRO-C2). Three endotypes were identified: high inflammation endotype (Endotype1), low inflammation endotype (Endotype 2) and high collagen turnover endotype (Endotype3). Endotype1 showed higher disease activity (Ankylosing Spondylitis Disease Activity Score (ASDAS)) at baseline compared with Endotype2 and Endotype3 and higher percentage of patients responding to adalimumab based on ASDAS clinical improvement at week 24. Endotype3 showed higher percentage of patients with 50% improvement in Bath Ankylosing Spondylitis Disease Activity Index response at week 24 co

[Abstract] Objectives. To efficiently assess the disease-modifying potential of new osteoarthritis treatments, clinical trials need progression-enriched patient populations. To assess whether the application of machine learning results in patient selection enrichment, we developed a machine learning recruitment strategy targeting progressive patients and validated it in the IMI-APPROACH knee osteoarthritis prospective study. Design. We designed a two-stage recruitment process supported by machine learning models trained to rank candidates by the likelihood of progression. First stage models used data from pre-existing cohorts to select patients for a screening visit. The second stage model used screening data to inform the final inclusion. The effectiveness of this process was evaluated using the actual 24-month progression. Results. From 3500 candidate patients, 433 with knee osteoarthritis were screened, 297 were enrolled, and 247 completed the 2-year follow-up visit. We observed progression related to pain (P, 30%), structure (S, 13%), and combined pain and structure (P ​+ ​S, 5%), and a proportion of non-progressors (N, 52%) ∼15% lower vs an unenriched population. Our model predicted these outcomes with AUC of 0.86 [95% CI, 0.81–0.90] for pain-related progression and AUC of 0.61 [95% CI, 0.52–0.70] for structure-related progression. Progressors were ranked higher than non-progressors for P ​+ ​S (median rank 65 vs 143, AUC = 0.75), P (median rank 77 vs 143, AUC = 0.71), and S patients (median rank 107 vs 143, AUC = 0.57). Conclusions. The machine learning-supported recruitment resulted in enriched selection of progressive patients. Further research is needed to improve structural progression prediction and assess this strategy in an interventional trial.

Protein citrullination and degradation by matrix metalloproteinases (MMP) plays a central role in the pathology of rheumatoid arthritis (RA). Autoantibodies are known to target citrullinated vimentin. The aim of this study was to investigate the relationship between the blood levels of MMP-degraded and citrullinated vimentin (VICM), as compared with the levels of MMP-degraded and non-citrullinated vimentin (VIM), and the standard anti-CCP biomarker in RA patients undergoing treatment. Thus, VIM, VICM and anti-CCP were quantified by ELISA in serum samples from baseline and week 8 of patients (n = 257) with RA, treated with either tocilizumab (8 mg/kg), methotrexate (7.5–15 mg/kg) or a placebo and compared with a reference cohort (n = 64). The three biomarkers were elevated in RA serum compared with the reference cohort: medians were 1.7 vs. 0.8 ng/mL (p < 0.05) for VIM; 7.5 vs. 0.7 ng/mL (p < 0.0001) for VICM; 57 vs. 4 RU/mL (p < 0.001) for anti-CCP. VICM was decreased in response to tocilizumab (2.9-fold, p < 0.0001) and to methotrexate (1.5-fold, p < 0.05) compared with the placebo, while anti-CCP was not. Serum VIM was also modulated by both drugs, although to a lesser degree. A high baseline level of VICM was predictive of a low disease activity response at week 8. In conclusion, VICM can differentiate between RA and healthy donors in a similar manner to anti-CCP; furthermore, VICM is also a pharmacodynamic marker.

[Abstract] Objectives: Osteoarthritis (OA) patient stratification is an important challenge to design tailored treatments and drive drug development. Biochemical markers reflecting joint tissue turnover were measured in the IMI-APPROACH cohort at baseline and analysed using a machine learning approach in order to study OA-dominant phenotypes driven by the endotype-related clusters and discover the driving features and their disease-context meaning. Method: Data quality assessment was performed to design appropriate data preprocessing techniques. The k-means clustering algorithm was used to find dominant subgroups of patients based on the biochemical markers data. Classification models were trained to predict cluster membership, and Explainable AI techniques were used to interpret these to reveal the driving factors behind each cluster and identify phenotypes. Statistical analysis was performed to compare differences between clusters with respect to other markers in the IMI-APPROACH cohort and the longitudinal disease progression. Results: Three dominant endotypes were found, associated with three phenotypes: C1) low tissue turnover (low repair and articular cartilage/subchondral bone turnover), C2) structural damage (high bone formation/resorption, cartilage degradation) and C3) systemic inflammation (joint tissue degradation, inflammation, cartilage degradation). The method achieved consistent results in the FNIH/OAI cohort. C1 had the highest proportion of non-progressors. C2 was mostly linked to longitudinal structural progression, and C3 was linked to sustained or progressive pain. Conclusions: This work supports the existence of differential phenotypes in OA. The biomarker approach could potentially drive stratification for OA clinical trials and contribute to precision medicine strategies for OA progression in the future.

[Abstract] Objective: To investigate the association of the lipidomic profile with osteoarthritis (OA) severity, considering the outcomes radiographic knee and hand OA, pain and function. Design: We used baseline data from the Applied Public-Private Research enabling OsteoArthritis Clinical Headway (APPROACH) cohort, comprising persons with knee OA fulfilling the clinical American College of Rheumatology classification criteria. Radiographic knee and hand OA severity was quantified with Kellgren-Lawrence sum scores. Knee and hand pain and function were assessed with validated questionnaires. We quantified fasted plasma higher order lipids and oxylipins with liquid chromatography with tandem mass spectrometry (LC-MS/MS)-based platforms. Using penalised linear regression, we assessed the variance in OA severity explained by lipidomics, with adjustment for clinical covariates (age, sex, body mass index (BMI) and lipid lowering medication), measurement batch and clinical centre. Results: In 216 participants (mean age 66 years, mean BMI 27.3 kg/m2, 75% women) we quantified 603 higher order lipids (triacylglycerols, diacylglycerols, cholesteryl esters, ceramides, free fatty acids, sphingomyelins, phospholipids) and 28 oxylipins. Lipidomics explained 3% and 2% of the variance in radiographic knee and hand OA severity, respectively. Lipids were not associated with knee pain or function. Lipidomics accounted for 12% and 6% of variance in hand pain and function, respectively. The investigated OA severity outcomes were associated with the lipidomic fraction of bound and free arachidonic acid, bound palmitoleic acid, oleic acid, linoleic acid and docosapentaenoic acid. Conclusions: Within the APPROACH cohort lipidomics explained a minor portion of the variation in OA severity, which was most evident for the outcome hand pain. Our results suggest that eicosanoids may be involved in OA severity.

Introduction: Rheumatoid arthritis (RA) is a chronic autoimmune disease that affects approximately 1% of the adult population. RA is multi-factorial, and as such our understanding of the molecular pathways involved in the disease is currently limited. An increasing number of studies have suggested that several molecular phenotypes (i.e. endotypes) of RA exist, and that different endotypes respond differently to various treatments. Biochemical markers may be an attractive means for achieving precision medicine, as they are objective and easily obtainable. Areas covered: We searched recent publications on biochemical markers in RA as either diagnostic or prognostic markers, or as markers of disease activity. Here, we provide a narrative overview of different classes of markers, such as autoantibodies, citrulline products, markers of tissue turnover and cytokines, that have been tested in clinical cohorts or trials including RA patients. Expert opinion: Although many biochemical markers have been identified and tested, few are currently being used in clinical practice. As more treatment options are becoming available, the need for precision medicine tools that can aid physicians and patients in choosing the right treatment is growing.

Objective: Associations between rheumatoid arthritis (RA) and effect of treatment at the tissue levels are poorly understood. We investigated the scope of released extracellular matrix (ECM) metabolites as a consequence of tissue remodelling in patients treated with methotrexate (MTX) and tocilizumab (TCZ) compared to placebo. Methods: Tissue metabolites from 387 RA patients treated with either TCZ (8 mg/kg) or MTX monotherapy (7.5–20 mg/kg) were measured at baseline and 8 weeks sera by validated ELISA assays. The levels of collagen biomarkers (C1M, C2M, C3M and C4M) together with C-reactive protein (CRP) and CRP metabolite (CRPM) were investigated. Baseline levels of biomarkers have been compared with 72 age- and gender-matched healthy controls. Comparison between treatment and response groups were done by ANCOVA, Spearman’s correlation and logistic regression adjusted for age, gender, BMI and disease duration. Results: C1M and C3M were significantly (P < 0.05) inhibited by TCZ and C3M by MTX (P < 0.01) compared to placebo. C1M and C3M inhibition with TCZ was respectively 23% and 16% greater than that of MTX (P < 0.01 and P < 0.0001). C4M was inhibited by TCZ and MTX, but the effect of TCZ was 22% greater than MTX (P < 0.0001). TCZ and MTX had minimal effect on C2M levels. MTX had no effect on CRP and CRPM, whereas TCZ reduced their levels to 69% and 27% from baseline. Investigated biomarkers revealed a significant (P < 0.05) difference in biomarker profiles of MTX ACR50 treatment responders and non-responders. Change to week 8 in levels of C3M, C4M, CRP and CRPM in MTX patients correlated significantly (rho = 0.41 to 0.18, P < 0.0001 to 0.039) with change in disease activity (DAS28) at weeks 8, 16 and 24, whereas only CRP in TCZ patients (rho = 0.32 to 0.21, P < 0.0001 to 0.01). Conclusion: Patients receiving TCZ treatment for 8 weeks had higher suppression of tissue remodelling and inflammatory biomarkers over patients treated with MTX.

[Abstract] Objectives: Osteoarthritis (OA) patients with a neuropathic pain (NP) component may represent a specific phenotype. This study compares joint damage, pain and functional disability between knee OA patients with a likely NP component, and those without a likely NP component. Methods: Baseline data from the Innovative Medicines Initiative Applied Public-Private Research enabling OsteoArthritis Clinical Headway knee OA cohort study were used. Patients with a painDETECT score ≥19 (with likely NP component, n=24) were matched on a 1:2 ratio to patients with a painDETECT score ≤12 (without likely NP component), and similar knee and general pain (Knee Injury and Osteoarthritis Outcome Score pain and Short Form 36 pain). Pain, physical function and radiographic joint damage of multiple joints were determined and compared between OA patients with and without a likely NP component. Results: OA patients with painDETECT scores ≥19 had statistically significant less radiographic joint damage (p≤0.04 for Knee Images Digital Analysis parameters and Kellgren and Lawrence grade), but an impaired physical function (p<0.003 for all tests) compared with patients with a painDETECT score ≤12. In addition, more severe pain was found in joints other than the index knee (p≤0.001 for hips and hands), while joint damage throughout the body was not different. Conclusions: OA patients with a likely NP component, as determined with the painDETECT questionnaire, may represent a specific OA phenotype, where local and overall joint damage is not the main cause of pain and disability. Patients with this NP component will likely not benefit from general pain medication and/or disease-modifying OA drug (DMOAD) therapy. Reserved inclusion of these patients in DMOAD trials is advised in the quest for successful OA treatments.