OP0098 PREDICTING TREATMENT OUTCOME IN PATIENTS WITH SPONDYLOARTHRITIS USING MICROBIOTA ANALYSIS

Abstract

BackgroundAutoimmune-related rheumatic diseases such as rheumatoid arthritis (RA) and spondyloarthritis (SpA) are caused by an interplay of various factors including genetics, environmental factors and lifestyle. Among them, the intestinal microbiome has been suggested to influence disease initiation and progression. While the pathogenic connection between the microbiome and autoimmunity remains ambiguous, there is evidence that, for instance, microbiota-derived antigens cross-react with autoantibodies triggering an immune response and disease development1. However, other hypothesis such as the leaky gut model have been explored2. In addition to this potential involvement in disease initiation, microbial signatures have been identified to predict treatment outcome, i.e., as shown for the first-line medication Methotrexate (MTX) in RA patients3.ObjectivesOur study aimed to longitudinally compare gut microbiota composition between treatment-naïve patients with different forms of rheumatic disorders from disease onset to remission/relapse to identify i) disease-specific signatures and ii) their impact on therapeutic responses.MethodsPatients with new onset of rheumatic disorders as well as their household members were recruited in the Rheuma-VOR cohort. The patients presented without treatment with steroids or DMARDs. The diagnosis was made by experienced rheumatologists. Native and stabilized fecal samples were collected, and 16S rRNA amplicon sequencing was performed to determine microbiota composition. Patients with non-inflammatory rheumatic disorders served as controls, whereas treatment was initiated in patients with inflammatory rheumatic diseases. Clinical data of patients was recorded including monitoring of medication and treatment response.ResultsA total of 422 fecal samples were analyzed from patients diagnosed with different forms of immune-mediated rheumatic diseases, non-inflammatory rheumatic diseases as well as from household controls (Table 1). Using 16S rRNA amplicon sequencing, we did not detect any clustering of patient groups during disease onset based on the microbiota composition (Figure 1A). However, PsA patients responding to MTX (MTX-R) (=patients in DAPSA remission 3-6 months after MTX initiation or patients showing partial response) and patients not in remission (MTX-NR) differed in microbial diversity (Figure 1B) and specific bacteria were enriched in MTX responders.Table 1.Overview of RheumaVOR samples analyzed in this studyDiagnosistotal number of samplesMTX treatmentMTX responseremissionno remissionresponse data not available yetHousehold controls188Non-inflammatory rheumatic diseases (NRD)110Rheumatoid arthritis (RA)332811611Psoriatic arthritis (PsA)473413129Axial spondyloarthritis (axSpA)35Reactive arthritis (ReA)9In total422Figure 1.Microbiome analysis of RheumaVOR fecal samples. A: Analysis of beta diversity (PCoA) using Bray-Curtis distances. B: Alpha diversity of PsA patients receiving MTX as first-line medication. P values represent an unpaired nonparametric Mann–Whitney test, *p < 0.05.ConclusionWhile our study did not reveal global differences in the bacterial composition of the gut microbiota between patients diagnosed with RA and SpA as well as control groups, we identified changes in microbial diversity and the abundance of specific bacteria between MTX responders and non-responders in PsA patients. Further investigations are needed to clarify the complex interactions between the microbiome, disease onset in rheumatic disorders and DMARD treatment response to ameliorate disease progression and improve therapeutic outcome.