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Performance of cytokine models in predicting SLE activity

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...

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Autores principales: Ruchakorn, Nopparat, Ngamjanyaporn, Pintip, Suangtamai, Thanitta, Kafaksom, Thanuchporn, Polpanumas, Charin, Petpisit, Veerachat, Pisitkun, Trairak, Pisitkun, Prapaporn
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
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Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6915901/
https://www.ncbi.nlm.nih.gov/pubmed/31842967
http://dx.doi.org/10.1186/s13075-019-2029-1
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author Ruchakorn, Nopparat
Ngamjanyaporn, Pintip
Suangtamai, Thanitta
Kafaksom, Thanuchporn
Polpanumas, Charin
Petpisit, Veerachat
Pisitkun, Trairak
Pisitkun, Prapaporn
author_facet Ruchakorn, Nopparat
Ngamjanyaporn, Pintip
Suangtamai, Thanitta
Kafaksom, Thanuchporn
Polpanumas, Charin
Petpisit, Veerachat
Pisitkun, Trairak
Pisitkun, Prapaporn
author_sort Ruchakorn, Nopparat
collection PubMed
description 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.
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spelling pubmed-69159012019-12-30 Performance of cytokine models in predicting SLE activity Ruchakorn, Nopparat Ngamjanyaporn, Pintip Suangtamai, Thanitta Kafaksom, Thanuchporn Polpanumas, Charin Petpisit, Veerachat Pisitkun, Trairak Pisitkun, Prapaporn Arthritis Res Ther Research Article 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. BioMed Central 2019-12-16 2019 /pmc/articles/PMC6915901/ /pubmed/31842967 http://dx.doi.org/10.1186/s13075-019-2029-1 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Ruchakorn, Nopparat
Ngamjanyaporn, Pintip
Suangtamai, Thanitta
Kafaksom, Thanuchporn
Polpanumas, Charin
Petpisit, Veerachat
Pisitkun, Trairak
Pisitkun, Prapaporn
Performance of cytokine models in predicting SLE activity
title Performance of cytokine models in predicting SLE activity
title_full Performance of cytokine models in predicting SLE activity
title_fullStr Performance of cytokine models in predicting SLE activity
title_full_unstemmed Performance of cytokine models in predicting SLE activity
title_short Performance of cytokine models in predicting SLE activity
title_sort performance of cytokine models in predicting sle activity
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6915901/
https://www.ncbi.nlm.nih.gov/pubmed/31842967
http://dx.doi.org/10.1186/s13075-019-2029-1
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