Axial SpA (axSpA) is a common rheumatic disease characterized by inflammation leading to bone formation and functional impairment. TNF-α and IL-17 represent established targets in axSpA. TNF-α and IL-17 inhibitors have demonstrated efficacy in clinical trials and are currently approved biologic DMARDs for all subsets of the disease. Several lines of evidence implicate a role of an IL-23–IL-17 axis in the disease pathogenesis. In this light, and given the success of IL-17 blockade in axSpA, a similar good response to IL-23 was anticipated. Nevertheless, two clinical trials of anti-IL-23 monoclonal antibodies in axSpA have clearly exhibited negative results. This failure has raised theories for a degree of IL-23 independent pathway. The Janus kinase (JAK) pathway is also a potential therapeutic target, since several cytokines, including those involved in the IL-23–IL-17 axis, signal through the JAK family of tyrosine kinases. Further studies and more extended evaluation of response to cytokine inhibition across different tissues will be required to improve our understanding of SpA pathogenesis and determine its optimal management.

Objective: To investigate the performance of a health app with respect to usability, adherence, and equivalence of data in daily care of patients with axial spondyloarthritis (axSpA).Methods: Consecutive patients with axSpA were asked to export patient-reported outcomes (PRO) electronically with the AxSpA Live App regularly every 2 weeks over a period of 6 months. The first clinical visit was followed by 2 further personal visits after 3 and 6 months. Patients completed paper-based PRO at every visit; they also completed the Mobile App Rating Scale and the System Usability Scale after 3 and 6 months.Results: Of 103 patients with axSpA, 69 agreed to participate (67.0%): age 41.5 (11.3) years, 58.0% male, Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) 4.3 (2.0), and 76.8% treated with biologic disease-modifying antirheumatic drugs. Patients’ adherence to regular app exports was 29.0% and 28.4% after 3 and 6 months, respectively. Significant predictors for good adherence were high disease activity (P = 0.02) and older age (P = 0.04). No systematic differences between digital and paper-based BASDAI scores were found (intraclass correlation coefficients 0.99 [95% CI 0.98-0.99]). Performance of the app was rated as good.Conclusion: Collection of digital PROs by AxSpA Live App may be successfully used in patients with axSpA with high disease activity. Our study showed equivalence of digital data, but adherence to the app after 6 months was poor. Higher disease activity and older age resulted in increased adherence to the app. This suggests that the use of health apps like this should concentrate on more severely affected patients.

Objective: Chronic inflammatory back pain (IBP) is frequently reported in axial SpA (axSpA) but also in the general population. We evaluated a recently proposed two-step referral system for early recognition of axSpA in primary care and compare it with other combinations of symptoms and SpA-related items. Methods: Consecutive chronic back pain patients ≤45 years of age answered a questionnaire and were seen by a primary care physician who decided whether HLA-B27 needed to be determined. They were then referred to a rheumatologist who made the diagnosis. Generally sticking to the two-step system with HLA-B27 as an additional option, combinations with a sensitivity ≥90% and a likelihood ratio >4 were compared. Results: A total of 326 patients were included, 46 of whom were diagnosed with axSpA (14.1%). The sensitivity of the strategy was 87%, the specificity was 56.8% and the positive and negative predictive values were 24.8% and 96.4%, respectively. A ‘good response to NSAIDs’, ‘morning stiffness >30 min’ and ‘elevated C-reactive protein’ performed best, with a sensitivity of 91%, specificity of 67%, positive predictive value of 31% and negative predictive value of 98%. On that basis, only three patients had to be seen by a rheumatologist to diagnose one. Conclusion: The earlier proposed referral system worked well but was outperformed by other combinations with high sensitivity and better specificity, which deserve to be prospectively studied.

Rheumatoid arthritis (RA) is associated with systemic osteoporosis, which leads to severe disability and low quality of life. Current therapies target osteoclasts to reduce bone degradation, but more treatment options would be required to promote bone protection by acting directly on osteoblasts (OB). Recently, the local production of dopamine in inflamed joints of RA has been observed. Thus, in this project, we aimed to determine the implication of the neurotransmitter dopamine in the bone formation process in RA. Dopamine receptors (DR) in the human bone tissue of RA or osteoarthritis (OA) patients were examined by immunohistochemistry. DR in isolated human osteoblasts (OB) was analyzed by flow cytometry, and dopamine content was evaluated by ELISA. Osteoclasts (OC) were differentiated from the PBMCs of healthy controls (HC) and RA patients. Isolated cells were treated with specific dopamine agonists. The effect of dopamine on mineralization was evaluated by Alizarin red staining. Cytokine release in supernatants was measured by ELISA. Osteoclastogenesis was evaluated with TRAP staining. OC markers were analyzed via real-time PCR and bone resorption via staining of resorption pits with toluidine blue. All DR were observed in bone tissue, especially in the bone remodeling area. Isolated OB maintained DR expression, which allowed their study in vitro. Isolated OB expressed tyrosine hydroxylase, the rate-limiting enzyme for dopamine production, and contained dopamine. The activation of D2-like DR significantly increased bone mineralization in RA osteoblasts and increased osteoclastogenesis but did not alter the expression of OC markers nor bone resorption. DR were found in the bone remodeling area of human bone tissue and dopamine can be produced by osteoblasts themselves, thus suggesting a local autocrine/paracrine pathway of dopamine in the bone. D2-like DRs are responsible for bone mineralization in osteoblasts from RA patients without an increase in bone resorption, thus suggesting the D2-like DR pathway as a possible future therapeutic target to counteract bone resorption in arthritis.

Background: Differentiating between degenerative disc disease (DDD), diffuse idiopathic skeletal hyperostosis (DISH), and axial spondyloarthritis (axSpA) represents a diagnostic challenge in patients with low back pain (LBP). We aimed to evaluate the distribution of inflammatory and degenerative imaging features in a real-life cohort of LBP patients referred to a tertiary university rheumatology center. Methods: In a retrospective cross-sectional analysis of patients referred for LBP, demographics, symptom information, and available imaging were collected. SpA-like changes were considered in the spine in the presence of one of the following lesions typically related to SpA: erosions, sclerosis, squaring, and syndesmophytes on conventional radiographs (CR) and bone marrow oedema (BMO), erosions, sclerosis, and fat lesions (FL) on MRI. SIJ CR were graded per New York criteria; on MRIs, SIJs were evaluated by quadrant for BMO, erosions, FL, sclerosis and ankylosis, similar to the approach used by the Berlin SIJ MRI scoring system. The final diagnosis made by the rheumatologist was the gold standard. Data were presented descriptively, by patient and by quadrant, and compared among the three diagnosis groups. Results: Among 136 referred patients, 71 had DDD, 38 DISH, and 27 axSpA; median age 62 years [IQR55-73], 63% males. On CR, SpA-like changes were significantly higher in axSpA in the lumbar (50%, vs. DDD 23%, DISH 22%), in DISH in the thoracic (28%, vs. DDD 8%, axSpA 12%), and in DDD in the cervical spine (67% vs. DISH 0%, axSpA 33%). On MRI, BMO was significantly higher in DISH in the thoracic (37%, vs. DDD 22%, axSpA 5%) and equally distributed in the lumbar spine (35-42%). FL were significantly more frequently identified in DISH and axSpA in the thoracic (56% and 52%) and DDD and axSpA in the lumbar spine (65% and 74%, respectively). Degenerative changes were frequent in the three groups. Sacroiliitis (NY criteria) was identified in 49% (axSpA 76%, DDD 48%, DISH 29%). Conclusion: A significant overlap was found among DDD, DISH, and axSpA for inflammatory and degenerative imaging features. Particularly, SpA-like spine CR features were found in one-fourth of patients with DISH, and MRI BMO was found in one-third of those patients.

Objectives In axial spondyloarthritis (axSpA), early diagnosis is crucial, but diagnostic delay remains long and diagnostic criteria do not exist. We aimed to identify a diagnostic model that distinguishes patients with axSpA from patients without axSpA with chronic back pain based on clinical data in routine care. Methods Clinical data from patients with chronic back pain were used, with information on rheumatological examinations based on clinical indications. The total dataset was randomly divided into training and test datasets at a 7:3 ratio. A machine learning-based model was built to distinguish axSpA from non-axSpA using the random forest algorithm. Overall accuracy, sensitivity, specificity and the area under the receiver operating characteristic curve-area under the curve (ROC-AUC) in the test dataset were calculated. The contribution of each variable to the accuracy of the model was assessed. Results Data from 939 randomly selected patients were available: 659 diagnosed with axSpA and 280 with non-axSpA. In the test dataset, the model reached an accuracy of 0.9234, a sensitivity of 0.9586, a specificity of 0.8438 and a ROC-AUC of 0.9717. Human leucocyte antigen B27 (HLA-B27) contributed most to the accuracy of the model; that is, the accuracy would suffer most from not using HLA-B27, followed by insidious onset of back pain and erosions in the sacroiliac joint. Conclusions We provide a machine learning-based model that reveals high performance in diagnosing patients with chronic back pain with axSpA versus without axSpA based on information from a tertiary rheumatology practice. This model has the potential to improve diagnostic delay in patients with axSpA in daily routine settings.