Microbial lipopolysaccharides (LPS) have been implicated in the pathogenesis of rheumatoid arthritis (RA), possibly driving a systemic inflammatory response that may trigger the development and/or exacerbation of the disease. To explore the existence of this mechanism in African RA patients, we have measured systemic levels of LPS and its surrogate, LPS-binding protein (LBP), as well as those of intestinal fatty acid-binding protein (I-FABP), pulmonary surfactant protein D (SP-D), and cotinine in serum to identify possible origins of LPS, as well as associations of these biomarkers with rheumatoid factor (RF) and anticitrullinated peptide (aCCP) autoantibodies and the DAS 28-3 clinical disease severity score. A cohort of 40 disease-modifying antirheumatic drug-naïve, black South African RA patients rated by compound disease scores and 20 healthy subjects and 10 patients with chronic obstructive pulmonary disease (COPD) as controls were included in this study. Levels of the various biomarkers and autoantibodies were measured using a combination of ELISA and immunofluorimetric and immunoturbidometric procedures. LPS levels were lowest in the RA group compared to the healthy controls (p = 0.026) and COPD patients (p = 0.017), while LBP levels were also significantly lower in RA compared to the healthy individuals (p = 0.036). Levels of I-FABP and SP-D were comparable between all three groups. Categorisation of RA patients according to tobacco usage revealed the following significant positive correlations: LBP with C-reactive protein (p = 0.0137); a trend (p = 0.073) towards an association of LBP with the DAS 28-3 disease severity score; RF-IgG antibodies with both LPS and LBP (p = 0.033 and p = 0.041, respectively); aCCP-IgG antibodies with LPS (p = 0.044); and aCCP-IgG with RF-IgM autoantibodies (p = 0.0016). The findings of this study, several of them novel, imply that tobacco products, as opposed to microbial translocation, represent a potential source of LPS in this study cohort of RA patients, again underscoring the risks posed by tobacco usage for the development and severity of RA.

Background: Health care workers (HCWs) are at increased risk of coronavirus disease 2019 (COVID-19). Appropriate risk assessments and testing are essential to reduce transmission and avoid workforce depletion. Objective: To investigate the risk of COVID-19 infection among HCWs who fulfil the person under investigation case definition or had exposure to a confirmed COVID-19 contact. Methods: We conducted a retrospective review of HCWs who were exposed to and/or tested for COVID-19 during the first wave of the pandemic. Data collected included demographics, exposure type, risk level, and COVID-19 test result. Frequency distribution tables, bivariate analyses, univariate and multivariate analyses were conducted. Results: Among the 1111 HCWs reviewed, 643 were tested with 35.6% positive results. PUI's accounted for 62.4% of positive cases. Symptomatic HCWs with no known contact were at a greater risk of infection than those with a patient exposure (p═0.001). The risk of testing positive was higher after a patient exposure (p═0.000) compared to a co-worker contact. Patient-facing designations had a higher risk of SARS-CoV-2 infection than non-clinical designations (p═0.013). The exception to this was security personnel who were 28 times more likely to test positive than any other designation (p═0.000). Conclusion: There is a higher positivity rate among HCWs than the general population. The presence of symptoms warrants testing. Nosocomial transmission was derived from patients more than co-worker contacts. Precautions in the workplace need to be reinforced to protect the health and safety of HCWs during this pandemic. These findings should assist with preparedness for future pandemics.

Background:Calprotectins(CLP) S100 A8/A9 are small calcium binding proteins[1] belonging to the group of damage-associated molecular patterns (DAMPs) or alarmins. They play a key role in the inflammatory response in RA. [2, 3]The measurement of CLP S100 A8/A9 in serum may be a useful strategy to optimize management of patients with RA.[4]Objectives:To evaluate serum calprotectin S100 protein (A8 and A8/A9) levels in a South African RA Cohort in relation to disease severity at presentation in comparison with traditional RA-associated autoantibodies.Methods:This was an observational, single-centre study, involving patients attending the Rheumatology Clinic of the Department of Internal Medicine, Chris Hani Baragwanath Academic Hospital (CHBAH) and University of the Witwatersrand, South Africa. The cohort consisted of 128 ethnic black RA DMARD-naïve patients. The study was approved by the local ethics committee and patients gave informed consent to participate.Results:The baseline demographics and clinical data of the cohort are summarized in table 1. Calprotectin S100 A8 demonstrated a statistically significant association with disease severity[(both SDAI (p=0.005) and DAS 28 (p=0.016)] by linear regression analysis. Calprotectin S100A8/A9 also showed significant associations with SDAI (p=0.010) and DAS28 (p=0.022) figure 1.Table 1Clinical and demographic data of patients with RASexFreqPercentCum Male2317.9717.97Female10582.03100.00DAS 28Inactive2519.5319.53DAS 28Moderate5744.5364.06DAS 28Very Active4635.94100.00SDAIRemission97.037.03SDAILow DA1612.5019.53SDAIMod DA4535.1654.69SDAIHigh DA5845.31100.00CCP Negative107.817.81CCP Positive11892.19100.00RF Negative86.256.25RF Positive12093.75100.00MCV Negative75.475.47 MCV Positive12194.53100.00Conclusions:Unlike those of traditional autoantibodies, serum levels of calprotectin correlate strongly with disease severity of RA patients. These findings suggest that calprotectin S100 is a promising biomarker for assessment and monitoring of disease activity in RA. References1. Cesaro A, Anceriz N, Plante A, Page N, Tardif MR, Tessier PA. An inflammation loop orchestrated by S100A9 and calprotectin is critical for development of arthritis. PLoS ONE2012;7(9):e45478.2. Geven EJ, Van Den Bosch MH, Di Ceglie I, Ascone G, Abdollahi-Roodsaz S, Sloetjes AW, et al. S100A8/A9, a potent serum and molecular imaging biomarker for synovial inflammation and joint destruction in seronegative experimental arthritis. Arthritis Res Ther2016;18(1):247.3. Nielsen UB, Bruhn LV, Ellingsen T, Stengaard-Pedersen K, Hornung N. Calprotectin in patients with chronic rheumatoid arthritis correlates with disease activity and responsiveness to methotrexate. Scand J Clin Lab Invest2017. doi:10.1080/00365513.2017.1413591:1-64. Obry A, Lequerre T, Hardouin J, Boyer O, Fardellone P, Philippe P, et al. Identification of S100A9 as biomarker of responsiveness to the methotrexate/etanercept combination in rheumatoid arthritis using a proteomic approach. PLoS ONE2014;9(12):e115800.Disclosure of Interest:None declared

BackgroundPreviously a study in the QUEST-RA database, based on rheumatoid arthritis (RA) patients included between January 2005 and June 2006, has shown that the ratio between biological disease modifying antirheumatic drug (bDMARD) use and glucocorticoid (GC) use strongly differs between countries. Considering the difference in affordability between bDMARDs and GCs, it may be hypothesized, that when less bMDARDs are used, glucocorticoid use increases.ObjectivesTo investigate globally, in more recent data, the ratio between bDMARD and GC use, and to assess whether this relates to a country’s socioeconomic status (SES).MethodsData on bDMARD and GC use between 1-1-2007 and 28-12-2020 were extracted from the METEOR registry: an international database capturing daily clinical practice data from patients with a clinical diagnosis of RA. The ratio between the proportion of patients who had ever used a bDMARD and the proportion of patients who had ever used a GC, with no concomitant bDMARD use, during a total follow up duration of two years (bDMARD/GC ratio) was calculated per country. Univariable linear regression was used to analyze the bDMARD/GC ratios according to publicly available country-level indicators of SES (Worldbank, OECD).ResultsData from 10,856 patients covering eight countries showed varying proportions of bDMARD use. The number of patients included ranged from 64 (Spain) to 8484 (India). Baseline characteristics varied per country (Table 1) The percentage of patients who used a bDMARD (with or without a GC) during two years follow up ranged from 1% (South Africa, India) to 26% (United States, state of Massachusetts) and for those who used a GC at some time during two years follow up ranged from 19% (Great Britain) to 94% (South Africa). Higher country-level wealth measured in GDP per capita, health expenditure and household net adjusted disposable income were related to a higher bDMARD/GC ratio. For every 10,000 IntI$ increase in GDP per capita, household net adjusted disposable income and health expenditure per capita, the bDMARD/GC ratio (range 0 to 1) was observed to increase by a value of β 0.1 (Figure 1A, 95% CI 0.05;0.1, p<0.05), β 0.2 (Figure 1B, 95% CI 0.08;0.3, p<0.05) and β 0.6 (Figure 1C, 95% CI 0.4;0.8, p<0.05), respectively.ConclusionIn this analysis based on a worldwide cohort capturing 8 countries, we show that the bDMARD/GC ratio differs across countries. These differences are significantly related to general country-level indicators of level of wealth, where greater wealth went with a higher proportion of patients using bDMARDs and/or a smaller proportion of patients using GCs.Table 1.Baseline characteristics and proportions of bDMARD and GC use per countryUSNLPortugalSouth AfricaSpainUKIndiaMexicoN Included patients215369334754644668484170Female, %7468738286648584Rheumatoid factor +, %4449619383668248ACPA +, %3946517581625128BMI, median (IQR)29 (25;33)25 (22;28)27 (23;29)28 (24;33)25 (22;7.2)27 (24;32)25 (22;29)28 (24;31)Current or ever smoker,%472724243655214Age at diagnosis, mean (SD)53 (15)57 (16)55 (15)50 (13)52 (15)58 (15)46 (12)48 (14)Symptom duration at baseline (years), mean (SD)2.0 (3.8)1.3 (3.5)1.7(3.1)3.6 (4.6)0.7 (0.8)1.1 (2.3)5.5 (6.1)1.4 (3.3)DAS28, mean (Sd)5.1(1.4)4.5 (1.5)5.1(1.5)5.5 (1.4)4.8 (1.5)5.1 (1.3)6.3 (1.3)5.1(1.6)GC use only,% Within 1 year4930719375196077 2 years4729709470196477bDMARD and GC use, % Within 1 year123315001 2 years144616002bDMARD use only, % Within 1 year96205111 2 years1911409211REFERENCES:NIL.Acknowledgements:NIL.Disclosure of InterestsIsabell Nevins: None declared, Cornelia Allaart: None declared, David Vega-Morales: None declared, Lai-Ling Winchow: None declared, Arvind Chopra: None declared, Ana Maria Rodrigues: None declared, Thomas Huizinga: None declared, Maarten Boers: None declared, Sytske Anne Bergstra Grant/research support from: Received APIRE grant from Pfizer.