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It is unknown how chronic inflammation impacts the brain. Here, we examined whether higher levels of peripheral inflammation were associated with brain connectivity and structure in 54 rheumatoid arthritis patients using functional and structural MRI. We show that higher levels of inflammation are associated with more positive connections between the inferior parietal lobule (IPL), medial prefrontal cortex, and multiple brain networks, as well as reduced IPL grey matter, and that these patterns of connectivity predicted fatigue, pain and cognitive dysfunction. At a second scan 6 months later, some of the same patterns of connectivity were again associated with higher peripheral inflammation. A graph theoretical analysis of whole-brain functional connectivity revealed a pattern of connections spanning 49 regions, including the IPL and medial frontal cortex, that are associated with peripheral inflammation. These regions may play a critical role in transducing peripheral inflammatory signals to the central changes seen in rheumatoid arthritis. Many diseases, such as rheumatoid arthritis, are characterized by a chronic inflammatory state, but it is not clear whether or how this affects the brain. Here, the authors show that the severity of on-going inflammation predicts altered functional brain connectivity in people with rheumatoid arthritis.

Studies published in 2024 suggest that although the repurposing of established rheumatology drugs seems to deliver incremental benefits for pain management, greater benefits could be gained in the future by targeting newly discovered pain mechanisms.Key advancesIn a randomized double-blinded placebo-controlled study, high doses of methotrexate reduced pain in knee osteoarthritis1Gene expression in synovial fibroblasts was linked to pain in patients with rheumatoid arthritis with limited synovial inflammation2In a mouse model, a neural circuit comprising the rostral anterior cingulate cortex and the pontine nucleus seemed to subserve placebo analgesia3

Studies published in 2024 suggest that although the repurposing of established rheumatology drugs seems to deliver incremental benefits for pain management, greater benefits could be gained in the future by targeting newly discovered pain mechanisms. Key advances In a randomized double-blinded placebo-controlled study, high doses of methotrexate reduced pain in knee osteoarthritis 1 Gene expression in synovial fibroblasts was linked to pain in patients with rheumatoid arthritis with limited synovial inflammation 2 In a mouse model, a neural circuit comprising the rostral anterior cingulate cortex and the pontine nucleus seemed to subserve placebo analgesia 3

Fatigue is a common and burdensome symptom in Rheumatoid Arthritis (RA), yet is poorly understood. Currently, clinicians rely solely on fatigue questionnaires, which are inherently subjective measures. For the effective development of future therapies and stratification, it is of vital importance to identify biomarkers of fatigue. In this study, we identify brain differences between RA patients who improved and did not improve their levels of fatigue based on Chalder Fatigue Scale variation ([DELTA]CFS[greater than or equal to] 2), and we compared the performance of different classifiers to distinguish between these samples at baseline. Fifty-four fatigued RA patients underwent a magnetic resonance (MR) scan at baseline and 6 months later. At 6 months we identified those whose fatigue levels improved and those for whom it did not. More than 900 brain features across three data sets were assessed as potential predictors of fatigue improvement. These data sets included clinical, structural MRI (sMRI) and diffusion tensor imaging (DTI) data. A genetic algorithm was used for feature selection. Three classifiers were employed in the discrimination of improvers and non-improvers of fatigue: a Least Square Linear Discriminant (LSLD), a linear Support Vector Machine (SVM) and a SVM with Radial Basis Function kernel. The highest accuracy (67.9%) was achieved with the sMRI set, followed by the DTI set (63.8%), whereas classification performance using clinical features was at the chance level. The mean curvature of the left superior temporal sulcus was most strongly selected during the feature selection step, followed by the surface are of the right frontal pole and the surface area of the left banks of the superior temporal sulcus. The results presented evidence a superiority of brain metrics over clinical metrics in predicting fatigue changes. Further exploration of these methods may support clinicians to triage patients towards the most appropriate fatigue alleviating therapies.

Disease-monitoring in large vessel vasculitis (LVV) is challenging. Simultaneous 18 F-fluorodeoxyglucose positron emission tomography with magnetic resonance imaging (PET/MRI) provides functional assessment of vascular inflammation alongside high-definition structural imaging with a relatively low burden of radiation exposure. Here, we investigate the ability of PET/MRI to monitor LVV disease activity longitudinally in a prospective cohort of patients with active LVV. We demonstrate that both the PET and MRI components of the scan can distinguish active from inactive disease using established quantification methods. Using logistic-regression modelling of PET/MRI metrics, we devise a novel PET/MRI-specific V asculitis A ctivity using M R P ET ( VAMP ) score which is able to distinguish active from inactive disease with more accuracy than established methods and detects changes in disease activity longitudinally. These findings are evaluated in an independent validation cohort. Finally, PET/MRI improves clinicians’ assessment of LVV disease activity and confidence in disease management, as assessed via clinician survey. In summary, PET/MRI may be useful in tracking disease activity and assessing treatment-response in LVV. Based on our findings, larger, prospective studies assessing PET/MRI in LVV are now warranted. Disease-monitoring in large vessel vasculitis is challenging, often leading to a mismatch between disease activity and treatment intensity. Here, the authors show that PET/MRI scanning can distinguish active from inactive large vessel vasculitis and track disease longitudinally, potentially allowing more stratified treatment for patients.

Background Pain remains a principal complaint for people with psoriatic arthritis (PsA), despite successful mitigation of inflammation. This situation alludes to the co-existence of distinct pain mechanisms. Nociceptive and nociplastic mechanisms are clinically challenging to distinguish. Advances in brain functional magnetic resonance imaging (fMRI) have successfully characterised distinct pain mechanisms across several disorders, in particular implicating the insula. This is the first study to characterise neurobiological markers of pain mechanisms in PsA employing fMRI. Methods PsA participants underwent a 6-minutes resting-state fMRI brain scan, and questionnaire assessments of nociplastic pain (2011 ACR fibromyalgia criteria) and body pain, assessed using the Numeric Rating Scale (NRS, 0-100). Functional connectivity between insula seeds (anterior, mid, posterior), and the whole brain was correlated with the above pain outcomes correcting for age and sex, and false discovery rate (FDR) for multiple comparisons. Results A total of 46 participants were included (age 49 ± 11.2; 52% female; FM score 12.5 ± 5.7; overall pain 34.8 ± 23.5). PsA participants with higher fibromyalgia scores displayed increased connectivity between: (1) right anterior insula to DMN ( P  < 0.05), (2) right mid and left posterior insula to parahippocampal gyri ( P  < 0.01 FDR); and (3) right mid insula to left frontal pole ( P  = 0.001 FDR). Overall pain was correlated with connectivity of left posterior insula to classical nociceptive regions, including thalamus ( P  = 0.01 FDR) and brainstem ( P  = 0.002 FDR). Conclusion For the first time, we demonstrate objectively that nociceptive and nociplastic pain mechanisms co-exist in PsA. PsA pain cannot be assumed to be only nociceptive in origin and screening for nociplastic pain in the future will inform supplementary analgesic approaches.

Background Work disability remains a significant problem in ankylosing spondylitis (AS) and rheumatoid arthritis (RA), despite biological therapy. This study aimed to test the hypothesis that the prevalent symptom of fatigue longitudinally predicts work disability among RA and AS patients commencing etanercept. Methods Two observational studies, comprising RA and AS etanercept commencers, respectively, were analysed. Both provided data on work disability over 1 year and a comprehensive set of putative predictors, including fatigue. A series of repeated measures models were conducted, including baseline variables, visit (6/12 months), and the interaction between visit and each of the explanatory variables. Results A total of 1003 AS and 1747 RA patients were assessed. For AS, fatigue was significantly associated with presenteeism (linear mixed model coefficient 3.75, 95% confidence interval (CI) 2.14 to 5.36) and activity impairment (2.62, 1.26 to 3.98), but not with work productivity loss (1.81, −0.40 to 4.02) or absenteeism (generalised linear mixed model odds ratio (OR) 1.18, 95% CI 0.92 to 1.51). In RA, fatigue was associated with presenteeism (coefficient 3.44, 95% CI 2.17 to 4.70), activity impairment (1.52, 0.79 to 2.26), work productivity loss (4.16, 2.47 to 5.85), and absenteeism (OR 1.23, 95% CI 1.02 to 1.49). The lack of significant interactions between fatigue and visit supported a consistent effect of baseline fatigue over time. Conclusions Among patients beginning etanercept therapy, fatigue has a significant and independent effect on absenteeism, presenteeism, productivity loss, and activity impairment for RA patients and a significant but dimension-selective effect on work disability among AS patients. Trial registration ClinicalTrials.gov, NCT00544557 . Registered on 16 October 2007. ClinicalTrials.gov, NCT00488475 . Registered on 20 June 2006.

Background Intravenous pulse methylprednisolone (MP) is commonly included in the management of severe ANCA associated vasculitis (AAV) despite limited evidence of benefit. We aimed to evaluate outcomes in patients who had, or had not received MP, along with standard therapy for remission induction in severe AAV. Methods We retrospectively studied 114 consecutive patients from five centres in Europe and the United States with a new diagnosis of severe AAV (creatinine > 500 μmol/L or dialysis dependency) and that received standard therapy (plasma exchange, cyclophosphamide and high-dose oral corticosteroids) for remission induction with or without pulse MP between 2000 and 2013. We evaluated survival, renal recovery, relapses, and adverse events over the first 12 months. Results Fifty-two patients received pulse MP in addition to standard therapy compared to 62 patients that did not. There was no difference in survival, renal recovery or relapses. Treatment with MP associated with higher risk of infection during the first 3 months (hazard ratio (HR) 2.7, 95%CI [1.4–5.3], p  = 0.004) and higher incidence of diabetes (HR 6.33 [1.94–20.63], p  = 0.002), after adjustment for confounding factors. Conclusions The results of this study suggest that addition of pulse intravenous MP to standard therapy for remission induction in severe AAV may not confer clinical benefit and may be associated with more episodes of infection and higher incidence of diabetes.

Abstract Objectives Relapses affect 30–50% of patients with ANCA-associated vasculitis (AAV) over 5 years, necessitating long-term treatment. Although there have been studies looking at predictors of relapse in AAV, this research has yet to translate clinically into guidance on tailored therapy. The aim of this systematic review was to identify and meta-analyse existing risk factors from the literature and produce a model to calculate individualised patient risk of relapse. Method A search strategy was developed to include all studies identifying predictors of AAV relapse using multivariate analysis. Individual risk factors were extracted and pooled hazard ratios (HRs) calculated. A model to predict the time to first relapse based on identified risk factors was tested retrospectively using a cohort of patients with AAV. Results The review of 2674 abstracts identified 117 papers for full text review, with 16 eligible for inclusion. Pooled HRs were calculated from significant risk factors, including anti-PR3 ANCA positivity [HR 1.69 (95% CI 1.46, 1.94)], cardiovascular involvement [HR 1.78 (95% CI 1.26, 2.53)], creatinine >200 µmol/l (relative to creatinine ≤100) [HR 0.39 (95% CI 0.22, 0.69)] and creatinine 101–200 µmol/l [HR 0.81 (95% CI 0.77, 0.85)]. Using data from 182 AAV patients to validate the model gave a C-statistic of 0.61. Conclusion Anti-PR3 ANCA positivity, lower serum creatinine and cardiovascular system involvement are all associated with an increased risk of relapse, and a combination of these risk factors can be used to predict the individualised risk of relapse. In order to produce a clinically useful model to stratify risk, we need to identify more risk factors, with a focus on robust biomarkers.

Environmental factors amplified by climate change contribute significantly to the global burden of disease, disproportionately impacting vulnerable populations, such as individuals with rare diseases. Researchers require innovative, dynamic data linkage methods to enable the development of risk prediction models, particularly for diseases like vasculitis with unknown aetiology but potential environmental triggers. In response, we present the Semantic Environmental and Rare Disease Data Integration Framework (SERDIF). SERDIF was evaluated with researchers studying climate-related health hazards of vasculitis disease activity across European countries ( N P1  = 10, N P2  = 17, N P3  = 23). Usability metrics consistently improved, indicating SERDIF’s effectiveness in linking complex environmental and health datasets. Furthermore, SERDIF-enabled epidemiologists to study environmental factors in a pregnancy cohort in Lombardy, showcasing its versatility beyond rare diseases. This framework offers for the first time a user-friendly, FAIR-compliant design for environment-health data linkage with export capabilities enabling data analysis to mitigate health risks posed by climate change.