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OBJECTIVES: ANA-associated RMDs (ANA-RMDs-SLE, pSS, scleroderma, inflammatory myositis, mixed connective tissue disease (MCTD) and undifferentiated connective tissue disease) are a disease spectrum with overlapping clinical and immunological features. Musculoskeletal inflammation is common and impactful across ANA-RMDs. We evaluated musculoskeletal inflammation (ANA-arthritis) prevalence in a multi-disease ANA-RMD study, assessed its clinical impact across ANA-RMD diagnoses, proposed new basket groupings of patients and evaluated immunological profiles in legacy and new basket contexts. METHODS: An observational study enrolled ANA-RMD patients. Demographic variables, comorbidities, therapies, disease activity instruments (BILAG, SLEDAI, ESSDAI, physician-VAS), patient-reported outcomes (SF36, FACIT-Fatigue, EQ5D, ICECAP-A, WPAI, patient-VAS) and biomarker profile (6 gene expression scores, flow cytometry, autoantibody profile) were analysed. Reclustering utilized Gaussian Mixture Modelling (GMM). Clinical and immune features of new and legacy clusters were compared. RESULTS: Inflammatory MSK symptoms were prevalent across ANA-RMDs, in 213/294 patients. In ANA-arthritis patients, most variables did not differ between diagnoses, excluding EQ5D-5L index and mobility domains (lower in MCTD/pSS, both p< 0.05). Fibromyalgia and osteoarthritis prevalence were similar across diagnoses. Therapy use differed significantly, biologic use being greatest in SLE (p< 0.05).GMM yielded two multi-disease clusters; High-MSK disease activity (n = 89) and Low-MSK disease activity (n = 124). High-MSK disease activity contained all patients with active joint swelling and had significantly higher prednisolone usage, PGA and Sm/RNP/SmRNP/Chromatin positivity, Tetherin-MFI and Interferon Score-A activity; with numerically lower fibromyalgia and osteoarthritis prevalence. CONCLUSION: We define ANA-Arthritis, a more clinically and immunologically homogeneous population than existing RMDs for trials, and a more prevalent population for therapies in the clinic.
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I was one of more than 40 nursing students who travelled to Berlin in June for a five-day trip focusing on the health and care of refugees and asylum seekers - an area I previously knew little about.
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The purpose of this study was to assess the reproducibility of an on-demand motorised treadmill to measure 2-mile (3.2 km) race performance and to examine the physiological variables that best predict this free-running performance in active men. Twelve men (mean (SD): age, 28 (9) years; stature, 1.79 (0.05) m; body mass, 72 (9) kg) completed the study in which maximum oxygen uptake (VO2 max), running economy, and running speedin the abstract section. They appear in the rest of the paper.), running economy, and running speed at VO2 max (vVO2 max), lactate threshold (vLT), and 4 mmol.L-1 fixed blood lactate concentration (v4) were measured. Subsequently, the maximal lactate steady state (MLSS) was identified using a series of 30-min treadmill runs. Finally, each participant completed a 2-mile running performance trial on 2 separate occasions, using an on-demand treadmill that adjusts belt speed according to the participant's position on the moving belt. The average 2-mile run speed was 15.7 (SD, 1.9) km.h-1, with small individual differences between repeat-performance trials (intraclass correlation coefficient = 0.99, 95% CI 0.953 to 0.996; standard error of measurement as coefficient of variation = 1.5%, 95% CI 1.0% to 2.5%). Bivariate regression analyses identified VO2 max, vVO2 max, VO2 (mL.kg-1.min-1) at MLSS, vLT, v4, and velocity at MLSS (vMLSS) as the strongest individual predictor variables (r2 = 0.69 to 0.87; standard error of the estimate = 1.08 to 0.72 km.h-1) for 2-mile running performance. The vLT and vMLSS explained 85% and 87% of the variance in running performance, respectively, suggesting that there is considerable shared variance between these parameters. In conclusion, the on-demand treadmill system provided a reliable measure of distance running performance. Both vLT and vMLSS were strong predictors of 2-mile running performance, with vMLSS explaining marginally more of the variance.
