TY - JOUR
T1 - Fatigue in primary Sjögren's syndrome is associated with lower levels of proinflammatory cytokines
AU - Tripp, Nadia Howard
AU - Tarn, Jessica
AU - Natasari, Andini
AU - Gillespie, Colin
AU - Mitchell, Sheryl
AU - Hackett, Katie L.
AU - Bowman, Simon J.
AU - Price, Elizabeth
AU - Pease, Colin T.
AU - Emery, Paul
AU - Lanyon, Peter
AU - Hunter, John
AU - Gupta, Monica
AU - Bombardieri, Michele
AU - Sutcliffe, Nurhan
AU - Pitzalis, Costantino
AU - McLaren, John
AU - Cooper, Annie
AU - Regan, Marian
AU - Giles, Ian
AU - Isenberg, David A.
AU - Saravanan, Vadivelu
AU - Coady, David
AU - Dasgupta, Bhaskar
AU - McHugh, Neil
AU - Young-Min, Steven
AU - Moots, Robert
AU - Gendi, Nagui
AU - Akil, Mohammed
AU - Griffiths, Bridget
AU - Lendrem, Dennis W.
AU - Ng, Wan Fai
AU - UK Primary Sjögren's Syndrome Registry
PY - 2016/7/19
Y1 - 2016/7/19
N2 - 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.
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.
U2 - 10.1136/rmdopen-2016-000282
DO - 10.1136/rmdopen-2016-000282
M3 - Article
AN - SCOPUS:84988447167
SN - 2056-5933
VL - 2
JO - RMD Open
JF - RMD Open
IS - 2
M1 - e000282
ER -