Prevalence, severity, and predictors of fatigue in subjects with primary Sjögren's syndrome.

OBJECTIVE: To investigate the relationship of fatigue severity to other clinical features in primary Sjögren's syndrome (SS) and to identify factors contributing to the physical and mental aspects of fatigue. METHODS: We identified 94 subjects who met the American-European Consensus Group criteria for the classification of primary SS. Fatigue was assessed with a visual analog scale, the Fatigue Severity Scale (FSS), and the Profile of Fatigue (ProF). Associations with fatigue were compared using multivariate regression. RESULTS: Abnormal fatigue, defined as an FSS score >or=4, was present in 67% of the subjects. Pain, helplessness, and depression were the strongest predictors of fatigue according to the FSS and the somatic fatigue domain of the ProF (ProF-S), both with and without adjustment for physiologic and serologic characteristics. Depression was associated with higher levels of fatigue; however, the majority of subjects with abnormal fatigue were not depressed. Anti-Ro/SSA-positive subjects were no more likely to report fatigue than seronegative subjects. The regression models explained 62% of the variance in FSS and 78% of the variance in ProF-S scores. Mental fatigue was correlated with depression and helplessness, but the model predicted only 54% of the variance in mental fatigue scores. CONCLUSION: Psychosocial variables are determinants of fatigue, but only partially account for it. Although fatigue is associated with depression, depression is not the primary cause of fatigue in primary SS. Investigation of the pathophysiologic correlates of physical and mental aspects of fatigue is needed to guide the development of more effective interventions.

The use of microarrays to study autoimmunity.

As with the development of any novel and potentially powerful technology, the prospect of revealing new information that may dramatically change our understanding of biological processes can generate much excitement. Such is true for the emerging genomic approaches that make possible high-density assays using microarray platforms. Indeed, it is difficult, if not impossible, to imagine any area of biology that could not be affected by the wide range of potential applications of microarray technology. Numerous examples, such as those from the field of oncology, provide striking evidence of the power of microarrays to bring about extraordinary advances in molecularly defining important disease phenotypes that were otherwise unrecognized using conventional approaches such as histology. However, only a few studies in autoimmunity are available to date. Very recent work in alopecia areata, multiple sclerosis, systemic lupus erythematosus, and Sjögren's syndrome illustrates the potential for gaining new insights into the pathophysiology of these complex autoimmune disorders on a global, molecular scale. These new insights are likely to significantly improve our understanding of disease processes, diagnosis, identification of new therapeutic targets, and identification of patients most likely to benefit from specific and tailored therapies.