The immune landscape of sepsis and using immune clusters for identifying sepsis endotypes

• Published in: Frontiers in immunology 2024, Vol.15, p.1287415-1287415
• Main Authors: Tang, Guoxing, Luo, Ying, Song, Huijuan, Liu, Wei, Huang, Yi, Wang, Xiaochen, Zou, Siyu, Sun, Ziyong, Hou, Hongyan, Wang, Feng
• Format: Article
• Language: English
• Subjects: Adaptive Immunity; Adult; Aged; Biomarkers; Cluster Analysis; Cytokines - immunology; Cytokines - metabolism; endotypes; Female; Humans; immune indicators; Immunity, Innate; Male; MDSCs; Middle Aged; prediction model; sepsis; Sepsis - diagnosis; Sepsis - immunology; Sepsis - mortality
• ISSN: 1664-3224; 1664-3224
• DOI: 10.3389/fimmu.2024.1287415

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.