Publication:
Performance of cytokine models in predicting SLE activity

dc.contributor.authorNopparat Ruchakornen_US
dc.contributor.authorPintip Ngamjanyapornen_US
dc.contributor.authorThanitta Suangtamaien_US
dc.contributor.authorThanuchporn Kafaksomen_US
dc.contributor.authorCharin Polpanumasen_US
dc.contributor.authorVeerachat Petpisiten_US
dc.contributor.authorTrairak Pisitkunen_US
dc.contributor.authorPrapaporn Pisitkunen_US
dc.contributor.otherChulalongkorn Universityen_US
dc.contributor.otherFaculty of Medicine, Ramathibodi Hospital, Mahidol Universityen_US
dc.contributor.otherSrinakharinwirot Universityen_US
dc.contributor.otherBridgeAsia Healthcare Venturesen_US
dc.date.accessioned2020-01-27T08:51:25Z
dc.date.available2020-01-27T08:51:25Z
dc.date.issued2019-12-16en_US
dc.description.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.en_US
dc.identifier.citationArthritis Research and Therapy. Vol.21, No.1 (2019)en_US
dc.identifier.doi10.1186/s13075-019-2029-1en_US
dc.identifier.issn14786362en_US
dc.identifier.issn14786354en_US
dc.identifier.other2-s2.0-85077084700en_US
dc.identifier.urihttps://repository.li.mahidol.ac.th/handle/20.500.14594/50979
dc.rightsMahidol Universityen_US
dc.rights.holderSCOPUSen_US
dc.source.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85077084700&origin=inwarden_US
dc.subjectImmunology and Microbiologyen_US
dc.subjectMedicineen_US
dc.titlePerformance of cytokine models in predicting SLE activityen_US
dc.typeArticleen_US
dspace.entity.typePublication
mu.datasource.scopushttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85077084700&origin=inwarden_US

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