A multicenter prospective observational multi-omics study protocol to identify biomarkers for severe dengue: COMBAT...

Area of Science:

• Infectious Diseases
• Systems Biology
• Genomics and Proteomics

Background:

• Dengue virus (DENV) infection is a major global health burden, with severe disease risk increasing upon repeated exposure.
• Current dengue classification systems face challenges in early prediction of severe disease due to overlapping clinical and laboratory features.
• There is a critical need for predictive biomarkers to enable early risk stratification and timely intervention for dengue patients.

Purpose of the Study:

• To generate comprehensive systems-level insights into host-virus interactions driving dengue severity.
• To identify prognostic biomarkers predictive of dengue disease progression.
• To inform improved patient triage and early intervention strategies for dengue.

Main Methods:

• A prospective, multicenter, observational longitudinal study (COMBAT) in dengue-endemic regions of Guatemala and India.
• Classification of patients into dengue without warning signs, dengue with warning signs, and severe dengue groups, with age- and sex-matched healthy controls.
• Multi-omics analyses (transcriptomics, proteomics, glycomics, metabolomics) in discovery and validation cohorts using integrated systems biology approaches.

Main Results:

• The study aims to identify host immune and metabolic pathways associated with dengue severity.
• Mechanism-based prognostic biomarker discovery for dengue progression.
• Validation of identified biomarkers in an independent cohort.

Conclusions:

• Findings are expected to provide insights into host-virus interactions driving dengue severity.
• Identification of novel biomarkers for early prediction of severe dengue.
• Potential to inform novel therapeutic targets for dengue management.