Background: Acute kidney injury (AKI) is a common complication in sepsis. However, the trajectories of sepsis-induced AKI and their transcriptional profiles are not well characterized. Methods: Sepsis patients admitted to centres participating in Chinese Multi-omics Advances In Sepsis (CMAISE) from November 2020 to December 2021 were enrolled, and gene expression in peripheral blood mononuclear cells was measured on Day 1. The renal function trajectory was measured by the renal component of the SOFA score (SOFArenal) on Days 1 and 3. Transcriptional profiles on Day 1 were compared between these renal function trajectories, and a support vector machine (SVM) was developed to distinguish transient from persistent AKI. Results: A total of 172 sepsis patients were enrolled during the study period. The renal function trajectory was classified into four types: non-AKI (SOFArenal = 0 on Days 1 and 3, n = 50), persistent AKI (SOFArenal > 0 on Days 1 and 3, n = 62), transient AKI (SOFArenal > 0 on Day 1 and SOFArenal = 0 on Day 3, n = 50) and worsening AKI (SOFArenal = 0 on Days 1 and SOFArenal > 0 on Day 3, n = 10). The persistent AKI group showed severe organ dysfunction and prolonged requirements for organ support. The worsening AKI group showed the least organ dysfunction on day 1 but had higher serum lactate and prolonged use of vasopressors than the non-AKI and transient AKI groups. There were 2091 upregulated and 1,902 downregulated genes (adjusted p < 0.05) between the persistent and transient AKI groups, with enrichment in the plasma membrane complex, receptor complex, and T-cell receptor complex. A 43-gene SVM model was developed using the genetic algorithm, which showed significantly greater performance predicting persistent AKI than the model based on clinical variables in a holdout subset (AUC: 0.948 [0.912, 0.984] vs. 0.739 [0.648, 0.830]; p < 0.01 for Delong’s test). Conclusions: Our study identified four subtypes of sepsis-induced AKI based on kidney injury trajectories. The landscape of host response aberrations across these subtypes was characterized. An SVM model based on a gene signature was developed to predict renal function trajectories, and showed better performance than the clinical variable-based model. Future studies are warranted to validate the gene model in distinguishing persistent from transient AKI.
|Original language||English (US)|
|State||Published - Dec 2022|
Bibliographical noteFunding Information:
Z.Z. received funding from the Health Science and Technology Plan of Zhejiang Province (2021KY745), the Fundamental Research Funds for the Central Universities (226-2022-00148), National natural science foundation of China (82272180) and the Project of Drug Clinical Evaluate Research of Chinese Pharmaceutical Association NO.CPA-Z06-ZC-2021-004. Y.H. received funding from the Key Research & Development project of Zhejiang Province (2021C03071), K.C. received funding from the Key Research & Development project of Zhejiang Province (2020C03019).
The members of the CMAISE consortium were as follows: Yucai Hong, Lifeng Xing, Zhongheng Zhang (Sir Run Run Shaw Hospital, Zhejiang University School of Medicine); Senjun Jin (Department of Emergency, Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical College); Lin Chen, Kun Chen (Department of Critical Care Medicine, Affiliated Jinhua Hospital, Zhejiang University School of Medicine); Jian Sun (Department of Critical Care Medicine, Lishui Center Hospital); Yi Yang (Department of Emergency Medicine, The Second Hospital of Jiaxing); Xiaohong Jin (Department of Emergency Medicine, The Affiliated Wenling Hospital of Wenzhou Medical University); Min Yang (The 2nd Department of Intensive Care Unit, the Second Affiliated Hospital of Anhui Medical University); Chunmei Gui, Yingpu Yuan (Department of Critical Care Medicine, The First People’s Hospital of Changde City); Guangtao Dong (Department of Emergency Medicine, the First Affiliated Hospital of Harbin Medical University); Weizhong Zeng, Jing Zeng (Department of critical care medicine, Zhuzhou central hospital); Guoxin Hu, Lujun Qiao (Emergency Department, Shengli Oilfield Central Hospital); Jinhua Wang, Yonglin Xi (Department of critical care medicine, the Second Affiliated Hospital of Xi’an Medical University); Nan Wang, Minmin Wang (Department of critical care medicine, The Fourth Affiliated Hospital of Anhui Medical University, Anhui Medical University); Yan Teng, Junxia Hou (Department of critical care medicine, the first affiliated hospital of Xi'an Jiaotong University); Qiaojie Bi (Department of emergency, Qingdao municipal hospital, Qingdao university school of medicine); Gengsheng Zhang (Department of Critical Care Medicine, Second Affiliated Hospital, Zhejiang University School of Medicine); Junru Dai (Longyou People’s hospital, Longyou, Zhejiang). We would like to thank reviewers and Dr. Ketao Jin from Jinhua central hospital for their thoughtful comments and suggestions on our manuscript.
© 2022, The Author(s).
- Acute kidney injury
- Genetic algorithms
- Support vector machine