Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Front Immunol. 2024 Apr 19;15:1287415. doi: 10.3389/fimmu.2024.1287415. eCollection 2024.
The dysregulated immune response to sepsis still remains unclear. Stratification of sepsis patients into endotypes based on immune indicators is important for the future development of personalized therapies. We aimed to evaluate the immune landscape of sepsis and the use of immune clusters for identifying sepsis endotypes.
The indicators involved in innate, cellular, and humoral immune cells, inhibitory immune cells, and cytokines were simultaneously assessed in 90 sepsis patients and 40 healthy controls. Unsupervised k-means cluster analysis of immune indicator data were used to identify patient clusters, and a random forest approach was used to build a prediction model for classifying sepsis endotypes.
We depicted that the impairment of innate and adaptive immunity accompanying increased inflammation was the most prominent feature in patients with sepsis. However, using immune indicators for distinguishing sepsis from bacteremia was difficult, most likely due to the considerable heterogeneity in sepsis patients. Cluster analysis of sepsis patients identified three immune clusters with different survival rates. Cluster 1 (36.7%) could be distinguished from the other clusters as being an "effector-type" cluster, whereas cluster 2 (34.4%) was a "potential-type" cluster, and cluster 3 (28.9%) was a "dysregulation-type" cluster, which showed the lowest survival rate. In addition, we established a prediction model based on immune indicator data, which accurately classified sepsis patients into three immune endotypes.
We depicted the immune landscape of patients with sepsis and identified three distinct immune endotypes with different survival rates. Cluster membership could be predicted with a model based on immune data.
脓毒症患者的免疫反应失调仍然不清楚。基于免疫指标将脓毒症患者分层为内型对于未来个性化治疗的发展很重要。我们旨在评估脓毒症的免疫图谱，并使用免疫聚类来识别脓毒症内型。
我们同时评估了 90 例脓毒症患者和 40 例健康对照者固有免疫、细胞免疫和体液免疫细胞、抑制性免疫细胞和细胞因子等相关指标。使用免疫指标数据的无监督 k-均值聚类分析来识别患者聚类，并用随机森林方法构建用于分类脓毒症内型的预测模型。
我们描述了脓毒症患者伴随炎症增加而固有和适应性免疫受损是最突出的特征。然而，使用免疫指标来区分脓毒症和菌血症是困难的，这很可能是由于脓毒症患者的异质性很大。脓毒症患者的聚类分析确定了具有不同生存率的三个免疫聚类。聚类 1（36.7%）可以与其他聚类区分开来，被认为是“效应型”聚类，而聚类 2（34.4%）是“潜在型”聚类，聚类 3（28.9%）是“失调型”聚类，其生存率最低。此外，我们基于免疫指标数据建立了一个预测模型，该模型可以准确地将脓毒症患者分为三种免疫内型。
我们描述了脓毒症患者的免疫图谱，并确定了三种具有不同生存率的不同免疫内型。可以通过基于免疫数据的模型来预测聚类成员。
