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Background Predicting progression to clinical arthritis in individuals at-risk of developing rheumatoid arthritis is a prerequisite to developing stratification groups for prevention strategies. Selecting accurate predictive criteria is the critical step to define the population at-risk. While positivity for anti-citrullinated protein antibodies (ACPA) remains the main recruitment biomarker, positivity for other autoantibodies (AutoAbs) identified before the onset of symptoms, may provide additional predictive accuracy for stratification.Objective To perform a multiple AutoAbs analysis for both the prediction and the time of progression to inflammatory arthritis (IA).Methods 392 individuals were recruited based on a new musculoskeletal complaint and positivity for ACPA or rheumatoid factor (RF). ELISAs were performed for ACPA, RF, anti-nuclear Ab, anti-carbamylated protein (anti-CarP) and anti-collagen AutoAbs. Logistic and COX regression were used for analysis.Results Progression to IA was observed in 125/392 (32%) of cases, of which 78 progressed within 12 months. The AutoAbs ACPA, RF, anti-CarP were individually associated with progression (p77%; area under the curve (AUC) >0.789), compared with prediction using only demographic/clinical data (72.9%, AUC=0.760). Multiple AutoAbs testing provided added value, with +6.4% accuracy for number of positive AutoAbs (AUC=0.852); +5.4% accuracy for AutoAbs levels (ACPA/anti-CarP, AUC=0.832); and +6.2% accuracy for risk-groups based on high/low levels (ACPA/RF/anti-CarP, AUC=0.837). Time to imminent progression was best predicted using ACPA/anti-CarP levels (AUC=0.779), while the number of positive AutoAbs was/status/risk were as good (AUC=0.778).Conclusion We confirm added value of multiple AutoAbs testing for identifying progressors to clinical disease, allowing more specific stratification for intervention studies.

Serum anti-citrullinated protein antibodies (ACPAs), present in 70% of people with rheumatoid arthritis (RA), can be detected ≤10years before the onset of clinical disease. RA and periodontitis are epidemiologically associated and we have reported a high incidence of periodontitis in people who are ACPA+ and at risk of RA. Periodontal bacteria may contribute by multiple routes to the generation of RA-autoantibodies. This study aims to characterise the subgingival microbiomes from periodontitis and health in individuals with/without RA and at risk of RA. Forty-five ACPA+ no RA (RA-at-risk; RAR), 31 healthy controls (HC) and 30 ACPA+ RA patients (RA) underwent a periodontal examination. DNA from subgingival plaque from healthy and deep pocket sites were paired-end sequenced using the Illumina HiSeq3000 and data analysed using MG-RAST + DESeq. Metagenomes in RA samples had high proportions of Actinobacteria; RAR microbiomes contained higher proportions of Bacteroidetes than HC. The relative abundance of P. gingivalis was high in periodontitis and RAR; Aggregatibacter actinomycetemcomitans was detected with similar frequency in each group. Other bacteria implicated in periodontitis and/or autoantibody generation (Filifactor alocis, Prevotella spp, Leptotrichia spp.) were detected. Analyses are on-going to elucidate the diversity and functional potential of the subgingival microbiome associated with RA.