Deriving consensus sepsis clusters via goal-directed subgroup identification in multi-omics study
Year: 2025
Type: article
Abstract: Sepsis, a syndrome of life-threatening organ dysfunction caused by dysregulated host responses to infection, exhibits profound pathobiological heterogeneity, hindering the development of effective therapies. Current subtyping approaches, often reliant on single-omics data or unsupervised clustering, yield poorly reproducible and therapeutically misaligned classifications. Here, we introduce a goal-directed subgroup identification (GD-SI) framework that optimizes patient stratification for differential treatment responses, integrating longitudinal multi-omics data (transcriptomic, proteomic, metabolomic, phenomic) from 1327 subjects across 43 hospitals. While supervised multi-omics integration frameworks (e.g., DIABLO) effectively capture shared biological signals, our approach anchors subgroup discovery directly to treatment-effect optimization. This strategy achieves substantial cross-omic concordance and, crucially, generalizes to predict differential treatment response across international critical care databases. Patients stratified by GD-SI-derived benefit scores for restrictive versus liberal fluid resuscitation exhibited marked survival differences, with similar advantages observed for ulinastatin immunomodulation. External validations in MIMIC-IV and ZiGongDB confirm prognostic generalizability. This framework reconciles biological heterogeneity with clinical actionability, offering a scalable infrastructure for precision trial design and personalized sepsis management. Our findings underscore the translational potential of omics-driven, goal-directed stratification to overcome decades of therapeutic stagnation in critical care.
Source: Nature Communications
Authors: Zhongheng Zhang, Lin Chen, Hongjie Shen, Jing Wang, Jie Yang, Suibi Yang, Weimin Zhang, Xuandong Jiang, Xiaojun Wu, Xianglin Meng, Fengzhi Zhao, Wanjie Gu, Haiyan Yin, Lihui Wang, Yuetian Yu, Lingxia Cheng, Ping Xu, Danting Fei, Huijie Yu, Xuning Shen, Yuhong Jin, Bingyang Liu, Jian Sun, Hongying Ni, Mihir R. Atreya, Paul W. G. Elbers, Kwok Ming Ho, Leo Anthony Celi
Institutions: Shaoxing University, Sir Run Run Shaw Hospital, Zhejiang University, Qingdao University, Yuhuangding Hospital, Wenzhou Medical University, Fujian Medical University, Harbin Medical University, First Affiliated Hospital of Harbin Medical University, Jinan University, First Affiliated Hospital of Jinan University, Shanghai Jiao Tong University, Renji Hospital, Jiaxing University, First Hospital of Jiaxing, Ningbo Medical Center Lihuili Hospital, Lishui Central Hospital, Cincinnati Children's Hospital Medical Center, Amsterdam Public Health, Amsterdam University Medical Centers, Amsterdam Cardiovascular Sciences, Vrije Universiteit Amsterdam, Chinese University of Hong Kong, Beth Israel Deaconess Medical Center, Harvard University, Massachusetts Institute of Technology
Language: English
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $6,990
Funders: National Natural Science Foundation of China, Zhejiang University, Wu Jieping Medical Foundation, National Key Research and Development Program of China, Zhejiang Traditional Chinese Medicine Administration, Natural Science Foundation of Zhejiang Province, Science and Technology Program of Zhejiang Province
OpenAlex ID: W4416593333