Primary immune thrombocytopenia (ITP) is an autoimmune hemorrhagic disease. Endothelial cell activation/injury has been found in some autoimmune diseases including SLE, systemic sclerosis, and rheumatoid arthritis, but its role in ITP pathogenesis remains unclear. This study attempted to elucidate the correlation between endothelial dysfunction and disease severity of ITP and find related markers to predict response to low-dose decitabine treatment. Compared with healthy volunteers, higher plasma levels of soluble intercellular adhesion molecule-1 (ICAM-1), vascular endothelial growth factor (VEGF), and Angiopoietin-2 were found in adult corticosteroid resistant ITP patients. Notably, ICAM-1 levels were negatively correlated with the platelet count, and positively associated with the bleeding score. Recently, we have reported the efficacy and safety of low-dose decitabine in adult patients with ITP who failed for the first line therapies. Here, we evaluated the correlation of plasma ICAM-1 level with the efficacy of low-dose decitabine therapy for corticosteroid resistant ITP. A total of 29 adult corticosteroid resistant ITP patients who received consecutive treatments of low-dose decitabine were enrolled in this study. Fourteen patients showed response (nine showed complete response and five showed partial response). The levels of ICAM-1 before and after treatment were significantly higher in the non-responsive ITP patients than in the responsive patients. As shown in the multivariable logistic regression model, the odds of developing no-response to low-dose decitabine increased by 36.8% for per 5 ng/ml increase in plasma ICAM-1 level [odds ratio (OR) 1.368, 95% confidence interval (CI): 1.060 to 1.764]. In summary, this was the first study to elucidate the relationship between endothelial dysfunction and corticosteroid resistant ITP and identify the potential predictive value of ICAM-1 level for response to low-dose decitabine.
Keywords: ICAM-1; decitabine; endothelia cell dysfunction; immune thrombocytopenia; predict.
Copyright © 2021 Li, Li, Sun, Sun, Shao, Xu, Hou, Peng, Wang and Hou.