Circulating Neutrophil Extracellular Traps Signature for Identifying Organ Involvement and Response to Glucocorticoid in Adult-Onset Still’s Disease: A Machine Learning Study

• Published in: Frontiers in immunology Vol. 11; p. 563335
• Main Authors: Jia, Jinchao, Wang, Mengyan, Ma, Yuning, Teng, Jialin, Shi, Hui, Liu, Honglei, Sun, Yue, Su, Yutong, Meng, Jianfen, Chi, Huihui, Chen, Xia, Cheng, Xiaobing, Ye, Junna, Liu, Tingting, Wang, Zhihong, Wan, Liyan, Zhou, Zhuochao, Wang, Fan, Yang, Chengde, Hu, Qiongyi
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 09.11.2020
• Subjects: Adult; adult-onset Still’s disease; Case-Control Studies; Cell-Free Nucleic Acids; circulating neutrophil extracellular traps; Cytokines - blood; Extracellular Traps - metabolism; Female; Glucocorticoids - administration & dosage; Humans; Immunology; machine learning; Male; Middle Aged; organ involvement; Prognosis; response to glucocorticoid; Still's Disease, Adult-Onset - blood; Still's Disease, Adult-Onset - diagnosis; Still's Disease, Adult-Onset - drug therapy; Still's Disease, Adult-Onset - immunology; Support Vector Machine; Treatment Outcome; Young Adult; adult-onset Still’s disease; circulating neutrophil extracellular traps; organ involvement; machine learning; response to glucocorticoid
• ISSN: 1664-3224; 1664-3224
• DOI: 10.3389/fimmu.2020.563335

Adult-onset Still's disease (AOSD) is an autoinflammatory disease with multisystem involvement. Early identification of patients with severe complications and those refractory to glucocorticoid is crucial to improve therapeutic strategy in AOSD. Exaggerated neutrophil activation and enhanced formation of neutrophil extracellular traps (NETs) in patients with AOSD were found to be closely associated with etiopathogenesis. In this study, we aim to investigate, to our knowledge for the first time, the clinical value of circulating NETs by machine learning to distinguish AOSD patients with organ involvement and refractory to glucocorticoid. Plasma samples were used to measure cell-free DNA, NE-DNA, MPO-DNA, and citH3-DNA complexes from training and validation sets. The training set included 40 AOSD patients and 24 healthy controls (HCs), and the validation set included 26 AOSD patients and 16 HCs. Support vector machines (SVM) were used for modeling and validation of circulating NETs signature for the diagnosis of AOSD and identifying patients refractory to low-dose glucocorticoid treatment. The training set was used to build a model, and the validation set was used to test the predictive capacity of the model. A total of four circulating NETs showed similar trends in different individuals and could distinguish patients with AOSD from HCs by SVM (AUC value: 0.88). Circulating NETs in plasma were closely correlated with systemic score, laboratory tests, and cytokines. Moreover, circulating NETs had the potential to distinguish patients with liver and cardiopulmonary system involvement. Furthermore, the AUC value of combined NETs to identify patients who were refractory to low-dose glucocorticoid was 0.917. In conclusion, circulating NETs signature provide added clinical value in monitoring AOSD patients. It may provide evidence to predict who is prone to be refractory to low-dose glucocorticoid and help to make efficient therapeutic strategy.