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Updated: Jul 20, 2026
Assessing Respiratory Immune Responses to Haemophilus Influenzae
Published on: June 29, 2021
Large-scale proteomic profiling identifies distinct inflammatory phenotypes in acute respiratory distress syndrome: a
Mengna Lin1,2, Feixiang Xu1,2, Yiyu Deng3,2
1Department of Emergency Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
Background:
Host 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.
Methods:
In 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.
Results:
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 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 (pinteraction<0.05). Finally, a 12-biomarker classifier can accurately distinguish phenotypes.
Conclusions:
We 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.
