Integrated Clinical and Proteomic Precision Subgrouping for Severe Dengue Endotype Signature

BackgroundSevere dengue remains difficult to predict because patients with different clinical trajectories may present with overlapping features, and conventional severity classifications may not fully capture underlying biological heterogeneity. In this study, we applied an integrated clinical and proteomic endotyping approach to dissect dengue disease heterogeneity and identify molecular signatures associated with severity. MethodsPlasma proteomic profiles were analyzed together with detailed clinical, biochemical, hematological, coagulation, and immunological parameters from healthy controls and dengue patients classified according to WHO 2009 severity criteria. High-throughput proteomic analysis, unsupervised clustering, pathway enrichment, and machine-learning-based classification were used to identify dengue endotypes and define molecular features associated with predicted severe disease. ResultsIncreasing dengue severity was associated with progressive abnormalities in liver function, coagulation parameters, hematological indices, and inflammatory mediators, including IL-6, IL-15, HGF, and MUC-16. However, proteomic profiling revealed substantial overlap across conventional severity categories, indicating that clinical classification alone does not fully resolve dengue host-response heterogeneity. Integrated clinical-proteomic clustering identified distinct dengue endotypes, including a predicted severe endotype enriched for inflammatory, antiviral, and cytotoxic lymphocyte-associated pathways. This high-risk endotype was characterized by elevated IL-15, IFN-{gamma}, and granzymes, consistent with coordinated activation of cytotoxic lymphocyte-associated antiviral responses. Machine-learning analysis further showed that proteomic features were strong discriminators of this endotype, supporting their potential utility as biomarkers of severe host-response states. ConclusionIntegrated clinical-proteomic endotyping provides molecular resolution beyond conventional severity grading and identifies immune pathways associated with severe dengue. This framework may improve biological understanding of dengue progression and support future risk stratification and biomarker development.