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Title: | Performance of cytokine models in predicting SLE activity |
Authors: | Ruchakorn N. Ngamjanyaporn P. Suangtamai T. Kafaksom T. Polpanumas C. Petpisit V. Pisitkun T. Pisitkun P. |
Keywords: | alpha interferon complement component C3 complement component C4 cytokine double stranded DNA antibody gamma interferon interleukin 10 interleukin 12 interleukin 17 interleukin 18 interleukin 1beta interleukin 23 interleukin 33 interleukin 6 interleukin 8 monocyte chemotactic protein 1 tumor necrosis factor autacoid biological marker cytokine interleukin 18 interleukin 6 interleukin 8 adult Article controlled study correlation analysis disease activity disease exacerbation female flow cytometry follow up human logistic regression analysis lupus erythematosus nephritis major clinical study male multivariate logistic regression analysis odds ratio prediction prospective study protein blood level sensitivity and specificity SLEDAI systemic lupus erythematosus blood middle aged prognosis severity of illness index systemic lupus erythematosus young adult Adult Biomarkers Cytokines Female Humans Inflammation Mediators Interleukin-18 Interleukin-6 Interleukin-8 Lupus Erythematosus, Systemic Male Middle Aged Prognosis Sensitivity and Specificity Severity of Illness Index Young Adult |
Issue Date: | 2019 |
Abstract: | 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. © 2019 The Author(s). |
URI: | https://ir.swu.ac.th/jspui/handle/123456789/12216 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077084700&doi=10.1186%2fs13075-019-2029-1&partnerID=40&md5=276c68f597dc89d48c6bbbacb79a2a31 |
ISSN: | 14786354 |
Appears in Collections: | Scopus 1983-2021 |
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