Department of Rheumatology and Immunology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Department of Rheumatology and Immunology, The First People's Hospital of Yancheng, The Fourth Affiliated Hospital of Nantong University, Yancheng, China.
Front Immunol. 2020 Nov 9;11:563335. doi: 10.3389/fimmu.2020.563335. eCollection 2020.
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.
成人斯蒂尔病（AOSD）是一种多系统受累的自身炎症性疾病。早期识别有严重并发症和对糖皮质激素耐药的患者对于改善 AOSD 的治疗策略至关重要。研究发现，AOSD 患者中性粒细胞过度激活和中性粒细胞胞外诱捕网（NETs）的形成增强与发病机制密切相关。在这项研究中，我们旨在首次通过机器学习研究循环 NETs 的临床价值，以区分有器官受累和对糖皮质激素耐药的 AOSD 患者。使用血浆样本测量来自训练集和验证集的无细胞 DNA、NE-DNA、MPO-DNA 和 citH3-DNA 复合物。训练集包括 40 例 AOSD 患者和 24 例健康对照者（HCs），验证集包括 26 例 AOSD 患者和 16 例 HCs。支持向量机（SVM）用于构建循环 NETs 特征模型，以诊断 AOSD 并识别对低剂量糖皮质激素治疗耐药的患者。训练集用于构建模型，验证集用于测试模型的预测能力。共有 4 种循环 NETs 在不同个体中呈现出相似的趋势，可以通过 SVM 区分 AOSD 患者和 HCs（AUC 值：0.88）。血浆中的循环 NETs 与系统评分、实验室检查和细胞因子密切相关。此外，循环 NETs 具有区分肝和心肺系统受累患者的潜力。此外，联合 NETs 识别对低剂量糖皮质激素耐药的患者的 AUC 值为 0.917。总之，循环 NETs 特征为监测 AOSD 患者提供了附加的临床价值。它可能为预测谁容易对低剂量糖皮质激素耐药提供证据，并有助于制定有效的治疗策略。
