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A consensus immune dysregulation framework for sepsis and critical illnesses.

Nature medicine2025-10-01PubMed
Total: 84.5Innovation: 9Impact: 0Rigor: 0Citation: 0

Summary

Across 7,074 samples from 37 cohorts, the authors derived cell type–specific gene signatures quantifying myeloid and lymphoid dysregulation that tracked with severity and mortality and generalized to ARDS, trauma, and burns. Post hoc analyses of RCTs indicated these signatures related to differential mortality with anakinra or corticosteroids, supporting prognostic and therapeutic stratification.

Key Findings

  • Developed cell type–specific gene expression signatures quantifying myeloid and lymphoid dysregulation across 7,074 samples from 37 cohorts.
  • Myeloid and lymphoid dysregulation associated with disease severity and mortality and generalized to ARDS, trauma, and burns.
  • In RCT datasets (SAVE-MORE anakinra; VICTAS and VANISH corticosteroids), dysregulation scores related to differential mortality, suggesting therapeutic relevance.

Clinical Implications

The signatures could enable early risk stratification and guide immunomodulatory therapy selection (e.g., steroids, IL-1 blockade) in ARDS/sepsis once prospectively validated and operationalized.

Why It Matters

It provides a unifying, cell-compartment–based framework for immune dysregulation with cross-cohort validation and therapeutic implications, bridging discovery and precision medicine in ARDS and sepsis.

Limitations

  • Primarily retrospective transcriptomic datasets without prospective interventional validation
  • Post hoc analyses of trials may be hypothesis-generating and subject to confounding

Future Directions

Prospective validation of the signatures, incorporation into clinical workflows, and biomarker-guided interventional trials to test therapy selection based on dysregulation profiles.

Study Information

Study Type
Meta-analysis
Research Domain
Pathophysiology
Evidence Level
II - Large multi-cohort transcriptomic meta-analysis with validation and correlative analyses with RCT data.
Study Design
OTHER