Eligibility for clinical trials in primary Sjögren's syndrome: lessons from the UK Primary Sjögren's Syndrome Registry.

OBJECTIVE: To identify numbers of participants in the UK Primary Sjögren's Syndrome Registry (UKPSSR) who would fulfil eligibility criteria for previous/current or potential clinical trials in primary SS (pSS) in order to optimize recruitment. METHODS: We did a retrospective analysis of UKPSSR cohort data of 688 participants who had pSS with evaluable data. RESULTS: In relation to previous/current trials, 75.2% fulfilled eligibility for the Belimumab in Subjects with Primary Sjögren's Syndrome study (Belimumab), 41.4% fulfilled eligibility for the Trial of Remicade in primary Sjögren's syndrome study (Infliximab), 35.4% for the Efficacy of Tocilizumab in Primary Sjögren's Syndrome study (Tocilizumab), 31.6% for the Tolerance and Efficacy of Rituximab in Sjögren's Disease study (Rituximab), 26.9% for the Trial of anti-B-cell therapy in pSS study (Rituximab) and 26.6% for the Efficacy and Safety of Abatacept in Patients With Primary Sjögren's Syndrome study (Abatacept). If recent measures of outcome, such as the EULAR Sjögren's Syndrome Patient Reported Index (ESSPRI) score ⩾5 (measure of patient symptoms) and the EULAR Sjögren's Syndrome Disease Activity Index (ESSDAI) score ⩾5 (measure of systemic disease activity) are incorporated into a study design, with requirements for an unstimulated salivary flow >0 and anti-Ro positivity, then the pool of eligible participants is reduced to 14.3%. CONCLUSION: The UKPSSR identified a number of options for trial design, including selection on ESSDAI ⩾5, ESSPRI ⩾5 and serological and other parameters.

Subjective and Objective Measures of Dryness Symptoms in Primary Sjögren's Syndrome: Capturing the Discrepancy.

OBJECTIVE: To develop a novel method for capturing the discrepancy between objective tests and subjective dryness symptoms (a sensitivity scale) and to explore predictors of dryness sensitivity. METHODS: Archive data from the UK Primary Sjögren's Syndrome Registry (n = 688) were used. Patients were classified on a scale from -5 (stoical) to +5 (sensitive) depending on the degree of discrepancy between their objective and subjective symptoms classes. Sensitivity scores were correlated with demographic variables, disease-related factors, and symptoms of pain, fatigue, anxiety, and depression. RESULTS: Patients were on average relatively stoical for both types of dryness symptoms (mean ± SD ocular dryness -0.42 ± 2.2 and -1.24 ± 1.6 oral dryness). Twenty-seven percent of patients were classified as sensitive to ocular dryness and 9% to oral dryness. Hierarchical regression analyses identified the strongest predictor of ocular dryness sensitivity to be self-reported pain and that of oral dryness sensitivity to be self-reported fatigue. CONCLUSION: Ocular and oral dryness sensitivity can be classified on a continuous scale. The 2 symptom types are predicted by different variables. A large number of factors remain to be explored that may impact symptom sensitivity in primary Sjögren's syndrome, and the proposed method could be used to identify relatively sensitive and stoical patients for future studies.

A Transcriptional Signature of Fatigue Derived from Patients with Primary Sjögren's Syndrome.

BACKGROUND: Fatigue is a debilitating condition with a significant impact on patients' quality of life. Fatigue is frequently reported by patients suffering from primary Sjögren's Syndrome (pSS), a chronic autoimmune condition characterised by dryness of the eyes and the mouth. However, although fatigue is common in pSS, it does not manifest in all sufferers, providing an excellent model with which to explore the potential underpinning biological mechanisms. METHODS: Whole blood samples from 133 fully-phenotyped pSS patients stratified for the presence of fatigue, collected by the UK primary Sjögren's Syndrome Registry, were used for whole genome microarray. The resulting data were analysed both on a gene by gene basis and using pre-defined groups of genes. Finally, gene set enrichment analysis (GSEA) was used as a feature selection technique for input into a support vector machine (SVM) classifier. Classification was assessed using area under curve (AUC) of receiver operator characteristic and standard error of Wilcoxon statistic, SE(W). RESULTS: Although no genes were individually found to be associated with fatigue, 19 metabolic pathways were enriched in the high fatigue patient group using GSEA. Analysis revealed that these enrichments arose from the presence of a subset of 55 genes. A radial kernel SVM classifier with this subset of genes as input displayed significantly improved performance over classifiers using all pathway genes as input. The classifiers had AUCs of 0.866 (SE(W) 0.002) and 0.525 (SE(W) 0.006), respectively. CONCLUSIONS: Systematic analysis of gene expression data from pSS patients discordant for fatigue identified 55 genes which are predictive of fatigue level using SVM classification. This list represents the first step in understanding the underlying pathophysiological mechanisms of fatigue in patients with pSS.