Fatigue in primary Sjögren's syndrome is associated with lower levels of proinflammatory cytokines

Nadia Howard Tripp, Jessica Tarn, Andini Natasari, Colin Gillespie, Sheryl Mitchell, Katie L. Hackett, Simon J. Bowman, Elizabeth Price, Colin T. Pease, Paul Emery, Peter Lanyon, John Hunter, Monica Gupta, Michele Bombardieri, Nurhan Sutcliffe, Costantino Pitzalis, John McLaren, Annie Cooper, Marian Regan, Ian GilesDavid A. Isenberg, Vadivelu Saravanan, David Coady, Bhaskar Dasgupta, Neil McHugh, Steven Young-Min, Robert Moots, Nagui Gendi, Mohammed Akil, Bridget Griffiths, Dennis W. Lendrem, Wan Fai Ng*, UK Primary Sjögren's Syndrome Registry

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

42 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article numbere000282
JournalRMD Open
Volume2
Issue number2
DOIs
Publication statusPublished - 19 Jul 2016

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