Jingjing Bai1, Chanyuan Wu1, Danli Zhong1, Dong Xu1, Qian Wang2, Xiaofeng Zeng3. 1. Department of Rheumatology, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education, National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Beijing, China. 2. Department of Rheumatology, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education, National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Beijing, China. wangqian_pumch@126.com. 3. Department of Rheumatology, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education, National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Beijing, China. xiaofeng.zeng@cstar.org.cn.
Abstract
OBJECTIVES: Dermatomyositis (DM) is a chronic inflammatory autoimmune disease with notable heterogeneity. The intent of this study was to explore the difference in cytokine profiles of different subsets in DM based on the disease activity and myositis-specific antibodies, and to identify the clinical phenotypes associated with different cytokine profiles. METHODS: Serum levels of 34 cytokines were prospectively measured in 47 consecutive DM patients and healthy controls. Concentrations of the cytokines were compared between the active and stable groups. Univariate and multivariate logistic regression models were used to identify the cytokines associated with DM disease activity. The cytokine profiles of anti-MDA5 and anti-TIF1γ subsets were compared, and the correlation analysis was performed between the elevated cytokines and clinical parameters in the two subsets. Hierarchical cluster analysis was used to establish clinical-cytokine subgroups in DM. RESULTS: Serum levels of MIP-1α, IP-10, IL-8, IL-1RA, MCP-1, GRO-α, and IL-22 were significantly higher in DM patients compared with healthy controls. IP-10, IL-6, IL-1RA, IFN-α, and MCP-1 were significantly elevated in the DM-active subset than the DM-stable subset. The combination of three cytokines (IP-10, IL-1RA, and MCP-1) had a better performance in differentiating between the active subset and the stable subset than the conventional inflammatory markers. SDF-1α, IP-10, IL-7, IL-17A, RANTES, IFN-γ, TNF-α, MIP-1β, IFN-α, MCP-1, GRO-α, and IL-1α were significantly higher in the anti-MDA5 subset than in the TIF1γ subset. Cluster analysis revealed a hypercytokinemic-vasculitis subgroup in patients with DM. CONCLUSIONS: Multiple cytokine signatures were depicted in different subsets of DM. A vasculitis-associated subgroup was firstly identified in DM with regards of cytokinome and deserves further mechanistic study. Key Points • The multivariate regression model of three cytokines (IP-10, IL-1RA, and MCP-1) could be a promising tool for distinguishing between the active and stable subset in DM. • Cytokine profiles of anti-MDA5-DM and anti-TIF1γ-DM were compared to identify the immunopathological differences between the two subsets. • Cluster analysis revealed a hypercytokinemic-vasculitis subgroup in patients with DM.
OBJECTIVES:Dermatomyositis (DM) is a chronic inflammatory autoimmune disease with notable heterogeneity. The intent of this study was to explore the difference in cytokine profiles of different subsets in DM based on the disease activity and myositis-specific antibodies, and to identify the clinical phenotypes associated with different cytokine profiles. METHODS: Serum levels of 34 cytokines were prospectively measured in 47 consecutive DMpatients and healthy controls. Concentrations of the cytokines were compared between the active and stable groups. Univariate and multivariate logistic regression models were used to identify the cytokines associated with DM disease activity. The cytokine profiles of anti-MDA5 and anti-TIF1γ subsets were compared, and the correlation analysis was performed between the elevated cytokines and clinical parameters in the two subsets. Hierarchical cluster analysis was used to establish clinical-cytokine subgroups in DM. RESULTS: Serum levels of MIP-1α, IP-10, IL-8, IL-1RA, MCP-1, GRO-α, and IL-22 were significantly higher in DMpatients compared with healthy controls. IP-10, IL-6, IL-1RA, IFN-α, and MCP-1 were significantly elevated in the DM-active subset than the DM-stable subset. The combination of three cytokines (IP-10, IL-1RA, and MCP-1) had a better performance in differentiating between the active subset and the stable subset than the conventional inflammatory markers. SDF-1α, IP-10, IL-7, IL-17A, RANTES, IFN-γ, TNF-α, MIP-1β, IFN-α, MCP-1, GRO-α, and IL-1α were significantly higher in the anti-MDA5 subset than in the TIF1γ subset. Cluster analysis revealed a hypercytokinemic-vasculitis subgroup in patients with DM. CONCLUSIONS: Multiple cytokine signatures were depicted in different subsets of DM. A vasculitis-associated subgroup was firstly identified in DM with regards of cytokinome and deserves further mechanistic study. Key Points • The multivariate regression model of three cytokines (IP-10, IL-1RA, and MCP-1) could be a promising tool for distinguishing between the active and stable subset in DM. • Cytokine profiles of anti-MDA5-DM and anti-TIF1γ-DM were compared to identify the immunopathological differences between the two subsets. • Cluster analysis revealed a hypercytokinemic-vasculitis subgroup in patients with DM.
Authors: David A Lynch; J David Godwin; Sharon Safrin; Karen M Starko; Phil Hormel; Kevin K Brown; Ganesh Raghu; Talmadge E King; Williamson Z Bradford; David A Schwartz; W Richard Webb Journal: Am J Respir Crit Care Med Date: 2005-05-13 Impact factor: 21.405
Authors: Xiao Švec; Hana Štorkánová; Maja Špiritović; Kryštof Slabý; Sabína Oreská; Aneta Pekáčová; Barbora Heřmánková; Kristýna Bubová; Petr Česák; Haya Khouri; Gulalai Amjad; Heřman Mann; Martin Komarc; Karel Pavelka; Ladislav Šenolt; Josef Zámečník; Jiří Vencovský; Michal Tomčík Journal: Int J Mol Sci Date: 2022-09-28 Impact factor: 6.208