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Background  Currently, several assays can diagnose acute dengue infection. However, none of these assays can predict the severity of the disease. Biomarkers that predicts the likelihood that a dengue patient will develop a severe form of the disease could permit more efficient patient triage and allows better supportive care for the individual in need, especially during dengue outbreaks.    Methods  We measured 20 plasma markers i.e. IFN-γ, IL-10, granzyme-B, CX3CL1, IP-10, RANTES, CXCL8, CXCL6, VCAM, ICAM, VEGF, HGF, sCD25, IL-18, LBP, sCD14, sCD163, MIF, MCP-1 and MIP-1β in 141 dengue patients in over 230 specimens and correlate the levels of these plasma markers with the development of dengue without warning signs (DWS−), dengue with warning signs (DWS+) and severe dengue (SD).    Results  Our results show that the elevation of plasma levels of IL-18 at both febrile and defervescence phase was significantly associated with DWS+ and SD; whilst increase of sCD14 and LBP at febrile phase were associated with severity of dengue disease. By using receiver operating characteristic (ROC) analysis, the IL-18, LBP and sCD14 were significantly predicted the development of more severe form of dengue disease (DWS+/SD) (AUC = 0.768,  P  < 0.0001; AUC = 0.819,  P  < 0.0001 and AUC = 0.647,  P  = 0.014 respectively). Furthermore, we also found that the levels of VEGF were directly correlated and sCD14 was inversely correlated with platelet count, suggesting that the endothelial activation and microbial translocation may played a role in pathogenesis of dengue disease.    Conclusions  Given that the elevation IL-18, LBP and sCD14 among patients with severe form of dengue disease, our findings suggest a pathogenic role for an aberrant inflammasome and monocyte activation in the development of severe form of dengue disease.    Electronic supplementary material  The online version of this article (doi:10.1186/s12967-017-1226-4) contains supplementary material, which is available to authorized users.

Aberrant monocyte responses predict and characterize dengue virus infection in individuals with severe disease