Clinimetric indices had high concordance and correlation, especially for rheumatoid arthritis patients in remission or low disease activity, without being interchangeable among them.
The development of autoimmune disorders requires a combination of genetic, immunological, and environmental factors. Infectious agents, such as viruses and bacteria, can trigger autoimmunity through different mechanisms, and for systemic vasculitis in particular, microbial agents have been suggested to be involved in its pathogenesis. Although the exact mechanisms have not been fully elucidated, different theories have been postulated. This review considers the role of infections in the etiology of primary vasculitis, emphasizing their related immunological events.
Objective We investigated the auto-antibody (auto-Ab) profiles in anti-nuclear antibody-positive (ANA+) individuals lacking Systemic Autoimmune Rheumatic Disease (SARD) and early SARD patients, to determine the key differences between these groups and identify factors that are associated with an increased risk of symptomatic progression within the next two years in ANA+ individuals. Methods Using custom antigen (Ag) microarrays, 144 IgM and IgG auto-Abs were surveyed in 84 asymptomatic and 123 symptomatic (48 undifferentiated connective tissue disease (UCTD) and 75 SARD patients) ANA+ individuals. Auto-Ab were compared in ANA+ individuals lacking a SARD diagnosis with ≥ 2 years follow-up (n = 52), including all those who demonstrated progression (n = 14) during this period, with changes over time assessed in a representative subset. Results We show that ANA+ individuals have auto-Ab to many self-Ag that are not being captured by current screening techniques and very high levels of these auto-Abs are predominantly restricted to early SARD patients, with SLE patients displaying reactivity to many more auto-Ags than the other groups. In general, the symptoms that developed in progressors mirrored those seen in SARD patients with similar patterns of auto-Ab. Only anti-Ro52 Abs were found to predict progression (positive predictive value 46%, negative predictive value 89%). Surprisingly, over 2 years follow-up the levels of auto-Ab remained remarkably stable regardless of whether individuals progressed or not. Conclusion Our findings strongly argue that development of assays with an expanded set of auto-Ags and enhanced dynamic range would improve the diagnostic and prognostic ability of auto-Ab testing.
Objective To phenotype SLE based on symptom burden (disease damage, system involvement and patient reported outcomes), with a specific focus on objective and subjective cognitive function. Methods SLE patients aged 18–65 underwent objective cognitive assessment using the ACR Neuropsychological Battery (ACR-NB) and data was collected on demographic and clinical variables, disease burden/activity, health related quality of life (HRQoL), depression, anxiety, fatigue and perceived cognitive deficits. Similarity network fusion (SNF) was used to identify patient subtypes. Differences between the subtypes were evaluated using Kruskal-Wallis and chi-square tests. Results Of the 238 patients, 90% were female, mean age 41 ± 12 and disease duration 14 ± 10 years at the study visit. The SNF analysis defined two subtypes (A and B) with distinct patterns in objective and subjective cognitive function, disease burden/damage, HRQoL, anxiety and depression. Subtype A performed worst on all significantly different tests of objective cognitive function (p < 0.03) compared with subtype B. Subtype A also, had greater levels of subjective cognitive function (p < 0.001), disease burden/damage (p < 0.04), HRQoL (p < 0.001) and psychiatric measures (p < 0.001) compared with subtype B. Conclusion This study demonstrates the complexity of cognitive impairment (CI) in SLE and that individual, multi-factorial phenotypes exist. Those with greater disease burden, from SLE specific factors or other factors associated with chronic conditions, report poorer cognitive functioning and perform worse on objective cognitive measures. By exploring different ways of phenotyping SLE we may better define CI in SLE. Ultimately, this will aid our understanding of personalised CI trajectories and identification of appropriate treatments.
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