Large-scale proteomic profiling identifies distinct inflammatory phenotypes in Acute Respiratory Distress Syndrome (ARDS): A multi-center, prospective cohort study
European Respiratory Journal, 2025
Lin M., Xu F., Deng Y., Wei Y., Shi F., Xie Y., Xie C., Chen C., Song J., Shen Y., Lin Y., Ding H., Zhou Y., Lu S., Chen Y., Lan L., Zhao W., Zhu J., Kuang Z., Pang W., Que S., Fang X., Ji R., Dong C., Zhang J., Liu Q., Zhang Z., Gao C., Chen L., Song Y., Zhan L., Huang L., Wu X., Wang R., Song Z.
| Disease area | Application area | Sample type | Products |
|---|---|---|---|
Respiratory Diseases | Patient Stratificaton | Serum |  Olink Explore 3072/384 |
Abstract
Background
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.
Methods
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.
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, shock incidence, and fewest ventilator-free days, followed by C3, while C2 patients had the best outcomes (p<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 (P interaction<0.05). Finally, a 12-biomarker classifier can accurately distinguish phenotypes.
Conclusions
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.