Multiomics approaches in Kawasaki disease: insights into pathogenesis and emerging directions for diagnosis and

Jong Gyun Ahn1,2,3, Insoo Kang3

  • 1Department of Pediatrics, Severance Children's Hospital, Yonsei University College of Medicine, Seoul, Korea.

PubMed

Insights

Kawasaki disease (KD) research uses multiomics to uncover inflammation and vascular pathways. Integration of these findings offers a framework for future biomarker discovery and therapeutic strategies in pediatric heart disease.

Area of Science:

  • Pediatric Rheumatology
  • Cardiovascular Research
  • Genomics and Systems Biology

Background:

  • Kawasaki disease (KD) is a critical pediatric vasculitis causing acquired heart disease.
  • The exact cause and mechanisms linking inflammation to coronary artery damage in KD remain unclear.
  • Advanced high-throughput technologies offer new insights into KD's complex pathophysiology.

Purpose of the Study:

  • To review current multiomics applications in Kawasaki disease research.
  • To identify and prioritize consistently supported inflammatory and vascular pathways in KD.
  • To propose a roadmap for clinical translation of multiomics findings.

Main Methods:

  • System-level profiling of KD using genomics, transcriptomics, proteomics, metabolomics, epigenomics, and immunomics.
  • Integration of multiomics data to identify convergent mechanistic axes.
  • Literature review of current multiomics applications and findings in KD.

Main Results:

  • Multiomics data reveal reproducible inflammatory and vascular pathways in KD.
  • Key pathways include IL-1/IL-6-neutrophil programs and Fcγ-receptor signaling.
  • Evidence supports roles for T-cell activation and endothelial remodeling in coronary outcomes.

Conclusions:

  • Multiomics integration provides a robust framework for KD biomarker discovery and therapeutic development.
  • Further validation, standardized sampling, and scalable assays are needed for clinical implementation.
  • Findings pave the way for improved risk stratification, disease monitoring, and outcome prediction in KD.

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