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

作者：Zhongheng Zhang, Lin Chen, Hongjie Shen, Jing Wang, Jie Yang, Suibi Yang, Weimin Zhang, Xuandong Jiang, Xiaojun Wu, Xianglin Meng, Fengzhi Zhao, Wan‐Jie Gu, Haiyan Yin, Lihui Wang, Yuetian Yu, Lingxia Cheng, Ping Xu, D. W. T. Fei, Huijie Yu, Xuning Shen, Yuhong Jin, Bingyang Liu, Jian Sun, Hongying Ni, Mihir R. Atreya, Paul Elbers, Kwok M. Ho, Leo Anthony Celi · 发表于：Nature Communications · 年份：2025 · DOI：10.1038/s41467-025-65271-4 · 被引用次数：8 · 研究领域：Sepsis Diagnosis and Treatment、Bioinformatics and Genomic Networks、Single-cell and spatial transcriptomics

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 managemen...