RT Journal Article SR Electronic T1 Fatigue in primary Sjögren's syndrome is associated with lower levels of proinflammatory cytokines JF RMD Open JO RMD Open FD EULAR SP e000282 DO 10.1136/rmdopen-2016-000282 VO 2 IS 2 A1 Nadia Howard Tripp A1 Jessica Tarn A1 Andini Natasari A1 Colin Gillespie A1 Sheryl Mitchell A1 Katie L Hackett A1 Simon J Bowman A1 Elizabeth Price A1 Colin T Pease A1 Paul Emery A1 Peter Lanyon A1 John Hunter A1 Monica Gupta A1 Michele Bombardieri A1 Nurhan Sutcliffe A1 Costantino Pitzalis A1 John McLaren A1 Annie Cooper A1 Marian Regan A1 Ian Giles A1 David A Isenberg A1 Vadivelu Saravanan A1 David Coady A1 Bhaskar Dasgupta A1 Neil McHugh A1 Steven Young-Min A1 Robert Moots A1 Nagui Gendi A1 Mohammed Akil A1 Bridget Griffiths A1 Dennis W Lendrem A1 Wan-Fai Ng YR 2016 UL http://rmdopen.bmj.com/content/2/2/e000282.abstract AB Objectives This article reports relationships between serum cytokine levels and patient-reported levels of fatigue, in the chronic immunological condition primary Sjögren's syndrome (pSS).Methods Blood levels of 24 cytokines were measured in 159 patients with pSS from the United Kingdom Primary Sjögren's Syndrome Registry and 28 healthy non-fatigued controls. Differences between cytokines in cases and controls were evaluated using Wilcoxon test. Patient-reported scores for fatigue were evaluated, classified according to severity and compared with cytokine levels using analysis of variance. Logistic regression was used to determine the most important predictors of fatigue levels.Results 14 cytokines were significantly higher in patients with pSS (n=159) compared to non-fatigued healthy controls (n=28). While serum levels were elevated in patients with pSS compared to healthy controls, unexpectedly, the levels of 4 proinflammatory cytokines—interferon-γ-induced protein-10 (IP-10) (p=0.019), tumour necrosis factor-α (p=0.046), lymphotoxin-α (p=0.034) and interferon-γ (IFN-γ) (p=0.022)—were inversely related to patient-reported levels of fatigue. A regression model predicting fatigue levels in pSS based on cytokine levels, disease-specific and clinical parameters, as well as anxiety, pain and depression, revealed IP-10, IFN-γ (both inversely), pain and depression (both positively) as the most important predictors of fatigue. This model correctly predicts fatigue levels with reasonable (67%) accuracy.Conclusions Cytokines, pain and depression appear to be the most powerful predictors of fatigue in pSS. Our data challenge the notion that proinflammatory cytokines directly mediate fatigue in chronic immunological conditions. Instead, we hypothesise that mechanisms regulating inflammatory responses may be important.