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

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.

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Publication Details

Journal
Nature Communications
Published
2025-11-24
DOI
https://doi.org/10.1038/s41467-025-65271-4
Citations
3
Primary Topic
Sepsis Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
4.06

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article

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

Xuandong Jiang, Fengzhi Zhao, Jie Yang, D. W. T. Fei et al.
3 citations
Nature Communications
Sepsis Diagnosis and Treatment
4.06
article

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

Xuandong Jiang, Fengzhi Zhao, Jie Yang, D. W. T. Fei, Huijie Yu, Leo Anthony Celi, Kwok Ming Ho, Hongjie Shen, Xiaojun Wu, Yuetian Yu, Weimin Zhang, Paul Elbers, Mihir R. Atreya, Xianglin Meng, Haiyan Yin, Yuhong Jin, Wan‐Jie Gu, Zhongheng Zhang, Jing Wang, Lihui Wang, Lin Chen, Lingxia Cheng, Jian Sun, Bingyang Liu, Ping Xu, Xuning Shen, Hongying Ni, Suibi Yang
article en
3 citations

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.

Nature CommunicationsVol. 16(1)
Cincinnati Children's Hospital Medical Center (US), Fujian Medical University (CN), Beth Israel Deaconess Medical Center (US), Harvard University (US), Harbin Medical University (CN), Chinese University of Hong Kong (HK), Shanghai Jiao Tong University (CN), Shaoxing University (CN), Wenzhou Medical University (CN), Renji Hospital (CN), Sir Run Run Shaw Hospital (CN), Yuhuangding Hospital (CN), Cancer Research And Biostatistics (US), Lishui Central Hospital (CN), Ningbo Medical Center Lihuili Hospital (CN), Ningde Normal University (CN), Jinhua Academy of Agricultural Sciences (CN), First Hospital of Jiaxing (CN), Amsterdam University Medical Centers (NL), Zigong First People's Hospital (CN), First Affiliated Hospital of Harbin Medical University (CN), Dongyang People's Hospital (CN), Jinhua Central Hospital (CN), First Affiliated Hospital of Jinan University (CN), Massachusetts Institute of Technology (US), Zhejiang University (CN), Vrije Universiteit Amsterdam (NL)
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 Percentile: Top 5%
Sepsis Diagnosis and Treatment
4.06
DOIOpen Access PDFOpenAlex
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