Evidence map›Paper›PMID 41067873›Full record

ArticleThe European respiratory journal2026

Large-scale proteomic profiling identifies distinct inflammatory phenotypes in acute respiratory distress syndrome: a multicentre, prospective cohort study.

Mengna Lin, Feixiang Xu, Yiyu Deng, Ying Wei, Feng Shi, Yun Xie, Cuiying Xie, Chen Chen, Jianfeng Song, Yao Shen and 26 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in The European respiratory journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Trial
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  4. Article
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  7. Article
  8. Splenic PD-L1Research (Washington, D.C.) · 2026
    Article
  9. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

36 authors.

Mengna LinDepartment of Emergency Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
Feixiang XuDepartment of Emergency Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
Yiyu DengDepartment of Critical Care Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China.
Ying WeiDepartment of Research and Development, United Imaging Intelligence, Shanghai, China.
Feng ShiDepartment of Research and Development, United Imaging Intelligence, Shanghai, China.
Yun XieDepartment of Emergency and Critical Care Medicine, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Cuiying XieDepartment of Emergency Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Chen ChenDepartment of Emergency Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
Jianfeng SongDepartment of Emergency Medicine, Minhang Hospital, Fudan University, Shanghai, China.
Yao ShenDepartment of Respiratory and Critical Care Medicine, Pudong Hospital, Fudan University, Shanghai, China.
Yiyan LinDepartment of Critical Care Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China.
Hailin DingDepartment of Emergency Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
Yannan ZhouDepartment of Emergency Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
Su LuDepartment of Emergency Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
Yumei ChenDepartment of Emergency Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
Lulu LanDepartment of Emergency Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
Wenxin ZhaoEmergency Intensive Care Ward, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Jing ZhuDepartment of Emergency Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
Zhongshu KuangDepartment of Emergency Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
Wei PangDepartment of Emergency Medicine, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Sijin QueDepartment of Emergency Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
Xiaoyu FangDepartment of Critical Care Medicine, Renmin Hospital of Wuhan University, Wuhan, China.
Ran JiDepartment of Critical Care Medicine, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Chenyang DongDepartment of Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Jiancheng ZhangDepartment of Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Qi LiuEmergency Intensive Care Ward, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.ORCID https://orcid.org/0000-0002-9868-3705
Zhaocai ZhangDepartment of Critical Care Medicine, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Chengjin GaoDepartment of Emergency Medicine, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Lei ChenDepartment of Research and Development, United Imaging Intelligence, Shanghai, China.
Yuanlin SongDepartment of Respiratory and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
Liying ZhanDepartment of Critical Care Medicine, Renmin Hospital of Wuhan University, Wuhan, China.
Lihong HuangDepartment of Biostatistics, Zhongshan Hospital, Fudan University, Shanghai, China.
Xueling WuDepartment of Respiratory and Critical Care Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Ruilan WangDepartment of Emergency and Critical Care Medicine, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Zhenju SongDepartment of Emergency Medicine, Zhongshan Hospital, Fudan University, Shanghai, China song.zhenju@zs-hospital.sh.cn.
Sepsis and ARDS Clinical Research Collaborative Network of China

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHost responses during acute respiratory distress syndrome are highly heterogeneous, contributing to inconsistent therapeutic outcomes. Proteome-based phenotyping may identify biologically and clinically distinct phenotypes to guide precision therapy.

methodsIn this multicentre cohort study, we used latent class analysis of targeted serum proteomics to identify acute respiratory distress syndrome 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 computed tomography scans were analysed using machine learning-based radiomics to explore phenotypic distinctions. Heterogeneous treatment effects for glucocorticoids and ventilation strategies were evaluated using inverse probability of treatment weighting adjusted Cox regression. A multinomial XGBoost model was developed to classify phenotypes.

resultsAmong 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 and shock incidence and fewest ventilator-free days, followed by C3, while C2 patients had the best outcomes (p<0.001). Phenotype C1 was characterised 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. Glucocorticoid therapy and higher positive end-expiratory pressure ventilation improved 90-day outcomes in C1 but increased mortality in C2 patients (p

conclusionsWe identified and validated three proteome-based acute respiratory distress syndrome phenotypes with distinct clinical, radiographic and molecular profiles. Their differential treatment responses highlight the potential of biomarker-driven strategies for acute respiratory distress syndrome precision medicine.

Indexed as

InflammationProteomicsRespiratory Distress SyndromeAdultAgedBiomarkersFemaleGlucocorticoidsHumansLungMaleMiddle AgedPhenotypeProspective StudiesProteomeTomography, X-Ray ComputedBiomarkersGlucocorticoidsProteome

Identifiers

PMID41067873
PMCPMC12873462

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.