Deriving consensus sepsis clusters via goal-directed subgroup identification in multi-omics study

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 and Leo Anthony Celi
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Zhongheng Zhang: Zhejiang University School of Medicine, Department of Emergency Medicine, Sir Run Run Shaw Hospital
Lin Chen: Zhejiang University School of Medicine, Department of Neurosurgery, Neurological Intensive Care Unit, Affiliated Jinhua Hospital
Hongjie Shen: Zhejiang University School of Medicine, Department of Emergency Medicine, Sir Run Run Shaw Hospital
Jing Wang: Qingdao University, Department of Critical Care Medicine, Yantai Yuhuangding Hospital
Jie Yang: Zhejiang University School of Medicine, Department of Emergency Medicine, Sir Run Run Shaw Hospital
Suibi Yang: Zhejiang University School of Medicine, Department of Emergency Medicine, Sir Run Run Shaw Hospital
Weimin Zhang: Affiliated Dongyang Hospital of Wenzhou Medical University, Intensive Care Unit
Xuandong Jiang: Affiliated Dongyang Hospital of Wenzhou Medical University, Intensive Care Unit
Xiaojun Wu: Fujian Medical University, Department of Emergency Medicine, Mindong Hospital of Ningde
Xianglin Meng: The First Affiliated Hospital of Harbin Medical University, Department of Critical Care Medicine
Fengzhi Zhao: Jinan University, Department of Intensive Care Unit, The First Affiliated Hospital
Wanjie Gu: Jinan University, Department of Intensive Care Unit, The First Affiliated Hospital
Haiyan Yin: Jinan University, Department of Intensive Care Unit, The First Affiliated Hospital
Lihui Wang: Shanghai Jiao Tong University School of Medicine, Department of Critical Care Medicine, Renji Hospital
Yuetian Yu: Shanghai Jiao Tong University School of Medicine, Department of Critical Care Medicine, Renji Hospital
Lingxia Cheng: Zigong Fourth People’s Hospital, Emergency Department
Ping Xu: Zigong Fourth People’s Hospital, Emergency Department
Danting Fei: Affiliated Hospital of Jiaxing University The First Hospital of Jiaxing, Department of Emergency Medicine
Huijie Yu: Affiliated Hospital of Jiaxing University The First Hospital of Jiaxing, Department of Emergency Medicine
Xuning Shen: Affiliated Hospital of Jiaxing University The First Hospital of Jiaxing, Department of Emergency Medicine
Yuhong Jin: Ningbo Medical Center Lihuili Hospital, Department of Critical Care Medicine
Bingyang Liu: Ningbo Medical Center Lihuili Hospital, Department of Critical Care Medicine
Jian Sun: Lishui Central Hospital, Department of Critical Care Medicine
Hongying Ni: Zhejiang University School of Medicine, Department of Critical care medicine, Affiliated Jinhua Hospital
Mihir R. Atreya: Cincinnati Children’s Hospital Medical Center, Division of Critical Care Medicine, MLC2005
Paul W. G. Elbers: Vrije Universiteit, Department of Intensive Care Medicine, Center for Critical Care Computational Intelligence (C4I), Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Science (ACS), Amsterdam Institute for Infection and Immunity (AI&II), Amsterdam Public Health (APH), Amsterdam UMC
Kwok Ming Ho: The Chinese University of Hong Kong, Department of Anaesthesia and Intensive Care
Leo Anthony Celi: Massachusetts Institute of Technology, Laboratory for Computational Physiology

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-65271-4

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