Publication: Performance of cytokine models in predicting SLE activity
Issued Date
2019-12-16
Resource Type
ISSN
14786362
14786354
14786354
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2-s2.0-85077084700
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Mahidol University
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SCOPUS
Bibliographic Citation
Arthritis Research and Therapy. Vol.21, No.1 (2019)
Suggested Citation
Nopparat Ruchakorn, Pintip Ngamjanyaporn, Thanitta Suangtamai, Thanuchporn Kafaksom, Charin Polpanumas, Veerachat Petpisit, Trairak Pisitkun, Prapaporn Pisitkun Performance of cytokine models in predicting SLE activity. Arthritis Research and Therapy. Vol.21, No.1 (2019). doi:10.1186/s13075-019-2029-1 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/50979
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Title
Performance of cytokine models in predicting SLE activity
Abstract
© 2019 The Author(s). Background: Identification of universal biomarkers to predict systemic lupus erythematosus (SLE) flares is challenging due to the heterogeneity of the disease. Several biomarkers have been reported. However, the data of validated biomarkers to use as a predictor for lupus flares show variation. This study aimed to identify the biomarkers that are sensitive and specific to predict lupus flares. Methods: One hundred and twenty-four SLE patients enrolled in this study and were prospectively followed up. The evaluation of disease activity achieved by the SLE disease activity index (SLEDAI-2K) and clinical SLEDAI (modified SLEDAI). Patients with active SLE were categorized into renal or non-renal flares. Serum cytokines were measured by multiplex bead-based flow cytometry. The correlation and logistic regression analysis were performed. Results: Levels of IFN-α, MCP-1, IL-6, IL-8, and IL-18 significantly increased in active SLE and correlated with clinical SLEDAI. Complement C3 showed a weakly negative relationship with IFN-α and IL-18. IL-18 showed the highest positive likelihood ratios for active SLE. Multiple logistic regression analysis showed that IL-6, IL-8, and IL-18 significantly increased odds ratio (OR) for active SLE at baseline while complement C3 and IL-18 increased OR for active SLE at 12 weeks. IL-18 and IL-6 yielded higher sensitivity and specificity than anti-dsDNA and C3 to predict active renal and active non-renal, respectively. Conclusion: The heterogeneity of SLE pathogenesis leads to different signaling mechanisms and mediates through several cytokines. The monitoring of cytokines increases the sensitivity and specificity to determine SLE disease activity. IL-18 predicts the risk of active renal SLE while IL-6 and IL-8 predict the risk of active non-renal. The sensitivity and specificity of these cytokines are higher than the anti-dsDNA or C3. We propose to use the serum level of IL-18, IL-6, and IL-8 to monitor SLE disease activity in clinical practice.