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A causal effects of neutrophil extracellular traps and its biomarkers on acute respiratory distress syndrome: a two-sample Mendelian randomization study.

Scientific reports2025-04-09PubMed
Total: 73.0Innovation: 8Impact: 7Rigor: 7Citation: 7

Summary

Two-sample Mendelian randomization indicates a causal association between genetically predicted IL-13 levels and increased ARDS risk (OR 1.52). Other NETs-related biomarkers showed no causal effect, and reverse causality from ARDS to NETs traits was not supported. Sensitivity analyses found no substantial pleiotropy, heterogeneity, or outliers.

Key Findings

  • Genetically predicted IL-13 increases ARDS risk (OR 1.52, 95% CI 1.03–2.23; P=0.047).
  • No causal effects of other NETs-related biomarkers on ARDS (all P>0.05).
  • No evidence for reverse causation from ARDS to NETs traits (all P>0.05).
  • Sensitivity analyses (MR-Egger, MR-PRESSO, Cochran’s Q, leave-one-out) showed no pleiotropy, heterogeneity, or dominant instruments.

Clinical Implications

IL-13 may be a viable target for therapeutic modulation and a candidate biomarker for ARDS susceptibility. Translation will require mechanistic validation and trials testing IL-13–directed interventions and clinical assays.

Why It Matters

This is among the first genetic causal analyses linking IL-13 to ARDS, elevating IL-13 from an associative to a putative causal mediator. It prioritizes IL-13 as a therapeutic target and risk stratification biomarker.

Limitations

  • Abstract does not specify GWAS sources, ancestry, or sample sizes, limiting generalizability assessment.
  • Borderline statistical significance (P=0.047) and reliance on genetic proxies warrant cautious interpretation and replication.

Future Directions

Validate IL-13 causality with mechanistic studies, multi-ancestry MR, and prospective cohorts; evaluate IL-13–targeted therapies and clinical assays in interventional trials.

Study Information

Study Type
Case-control
Research Domain
Pathophysiology
Evidence Level
III - Observational genetic instrumental variable analysis using two-sample MR from GWAS summary data.
Study Design
OTHER