Large-scale proteomic profiling identifies distinct inflammatory phenotypes in Acute Respiratory Distress Syndrome (ARDS): A multi-center, prospective cohort study.
Authors
Affiliations (40)
Affiliations (40)
- Department of Emergency Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
- Department of Critical Care Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China.
- Department of Research and Development, United Imaging Intelligence, Shanghai, China.
- Department of Emergency and Critical Care Medicine, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Department of Emergency Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Department of Emergency Medicine, Minhang Hospital, Fudan University, Shanghai, China.
- Department of Respiratory and Critical Care Medicine, Pudong Hospital, Fudan University, Shanghai, China.
- Emergency Intensive Care Ward, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
- Department of Emergency Medicine, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Department of Critical Care Medicine, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.
- Department of Critical Care Medicine, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
- Department of Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
- Department of Respiratory and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
- Shanghai Key Laboratory of Lung Inflammation and Injury, Shanghai, China.
- Department of Biostatistics, Zhongshan Hospital, Fudan University, Shanghai, China.
- Department of Respiratory and Critical Care Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China.
- Institute of Emergency Rescue and Critical Care, Fudan University, Shanghai, China.
- Contributed equally as first authors.
- Contributed equally as senior authors.
- Department of Emergency Medicine, Zhongshan Hospital, Fudan University, Shanghai, China [email protected] [email protected] [email protected].
- Department of Critical Care Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China [email protected] [email protected] [email protected].
- Department of Research and Development, United Imaging Intelligence, Shanghai, China [email protected] [email protected] [email protected].
- Department of Emergency and Critical Care Medicine, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China [email protected] [email protected] [email protected].
- Department of Emergency Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China [email protected] [email protected] [email protected].
- Department of Emergency Medicine, Minhang Hospital, Fudan University, Shanghai, China [email protected] [email protected] [email protected].
- Department of Respiratory and Critical Care Medicine, Pudong Hospital, Fudan University, Shanghai, China [email protected] [email protected] [email protected].
- Emergency Intensive Care Ward, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China [email protected] [email protected] [email protected].
- Department of Emergency Medicine, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China [email protected] [email protected] [email protected].
- Department of Critical Care Medicine, Renmin Hospital of Wuhan University, Wuhan, Hubei, China [email protected] [email protected] [email protected].
- Department of Critical Care Medicine, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China [email protected] [email protected] [email protected].
- Department of Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China [email protected] [email protected] [email protected].
- Department of Respiratory and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China [email protected] [email protected] [email protected].
- Shanghai Key Laboratory of Lung Inflammation and Injury, Shanghai, China [email protected] [email protected] [email protected].
- Department of Biostatistics, Zhongshan Hospital, Fudan University, Shanghai, China [email protected] [email protected] [email protected].
- Department of Respiratory and Critical Care Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China [email protected] [email protected] [email protected].
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China [email protected] [email protected] [email protected].
- Institute of Emergency Rescue and Critical Care, Fudan University, Shanghai, China [email protected] [email protected] [email protected].
- Contributed equally as first authors [email protected] [email protected] [email protected].
- Contributed equally as senior authors [email protected] [email protected] [email protected].
Abstract
Host responses during ARDS are highly heterogeneous, contributing to inconsistent therapeutic outcomes. Proteome-based phenotyping may identify biologically and clinically distinct phenotypes to guide precision therapy. In this multicenter cohort study, we used latent class analysis (LCA) of targeted serum proteomics to identify ARDS phenotypes. Serum samples were collected within 72 h of diagnosis to capture early-phase profiles. Validation was conducted in external cohorts. Pathway enrichment assessed molecular heterogeneity. Lung CT scans were analyzed using machine learning-based radiomics to explore phenotypic distinctions. Heterogeneous treatment effects (HTEs) for glucocorticoids and ventilation strategies were evaluated using inverse probability of treatment weighting (IPTW) adjusted Cox regression. A multinomial XGBoost model was developed to classify phenotypes. Among 1048 patients, three inflammatory phenotypes (C1, C2, C3) were identified and validated in two independent cohorts. The phenotype C1 with a larger proportion of poorly/non-inflated lung compartments had the highest 90-day mortality, shock incidence, and fewest ventilator-free days, followed by C3, while C2 patients had the best outcomes (<i>p</i><0.001). Phenotype C1 was characterized by intense innate immune activation, cytokine amplification, and metabolic reprogramming. Phenotype C2 demonstrated immune suppression, enhanced tissue repair, and restoration of anti-inflammatory metabolism. Phenotype C3, comprising the oldest patients, reflected an intermediate state with moderate immune activation and partial immune resolution. Glucocorticoids therapy and higher positive end-expiratory pressure (PEEP) ventilation improved 90-day outcomes in C1 but increased mortality in C2 patients (<i>P</i> <sub>interaction</sub><0.05). Finally, a 12-biomarker classifier can accurately distinguish phenotypes. We identified and validated three proteome-based ARDS phenotypes with distinct clinical, radiographic, and molecular profiles. Their differential treatment responses highlight the potential of biomarker-driven strategies for ARDS precision medicine.