Role of biomarkers in predicting disease severity in acute dengue and SARs-CoV-2-Infected patients

The early stages of both dengue infection and COVID-19 can present similarly with acute febrile illness or influenza-like symptoms, and individuals with initially mild disease may progress to more severe symptoms. We performed biomarker analysis to determine if host immune responses can predict the disease severity of both diseases. Differential immune response profiles in patient populations were compared during acute dengue or COVID-19 using a panel of 22 soluble biomarkers. Patient plasma biomarkers were measured by ELISA or the Meso Scale Discovery platform, and statistical analysis was performed using SAS software. Receiver operating characteristic (ROC) curves were created to identify the optimal cut-off values for differentially upregulated biomarkers in severe cases. Multiple logistic regression models were developed to predict disease severity using a combination of selected biomarkers, with or without demographic data, and were analyzed using GraphPad Prism software. Almost all of the biomarkers were higher in dengue compared to COVID-19 patients. Comparing severe to mild dengue illness, biomarkers related to monocyte activation (IL-1β, IL-12p70, soluble CD14) and Th2 cytokines (IL-4 and IL-13) were significantly elevated. Additionally, 1,3 β-D-glucan, a biomarker related to gut barrier disruption and microbial translocation, was elevated in patients with severe dengue and emerged as a key severity biomarker. In COVID-19 patients, the chemokine CXCL10 (IP-10) was the best predictive biomarker for severity. Moreover, biomarkers related to gut mucosal barrier disruption (lipopolysaccharide-binding protein, soluble CD14, and 1,3 β-D-glucan) and neutrophil extracellular trap (NET) markers were elevated in moderate to severe COVID-19. The multiple logistic regression models predicting severity for both diseases yielded ROC curves with an excellent area under the curve (AUC) greater than 0.95 and demonstrated sensitivity, specificity, positive predictive value, and negative predictive value greater than 90%. Our analyses indicate that gut barrier disruption and subsequent microbial translocation are common phenomena in severe cases of dengue and COVID-19. Multiple logistic regression models using a combination of specific biomarkers have the potential to improve severity prediction. The early stages of both dengue infection and COVID-19 can present similarly with acute febrile illness or influenza-like symptoms, and individuals with initially mild disease may progress to more severe symptoms. Therefore, we analyzed host immune responses to determine whether specific biomarkers could predict disease severity in both infections. Using a panel of 22 soluble biomarkers measured by ELISA or the Meso Scale Discovery platform, we compared differential immune response profiles in acute dengue and COVID-19, generated receiver operating characteristic (ROC) curves to identify optimal severity-associated cut-off values, and constructed multiple logistic regression models—with and without demographic data—to evaluate predictive performance. We found that almost all of the biomarkers were higher in dengue compared to COVID-19 patients. Comparing dengue with warning signs to mild dengue illness, biomarkers related to monocyte activation (IL-1β, IL-12p70, soluble CD14) and Th2 cytokines (IL-4 and IL-13) were significantly elevated. Moreover, 1,3 β-D-glucan, a biomarker related to gut barrier disruption and microbial translocation, was elevated in dengue patients with warning signs and emerged as a key severity biomarker. In COVID-19, the chemokine CXCL10 (IP-10) was the strongest predictor of severity, while biomarkers linked to gut mucosal barrier damage (lipopolysaccharide-binding protein, soluble CD14, and 1,3 β-D-glucan) and neutrophil extracellular traps were elevated in moderate to severe cases. The multiple logistic regression models predicting severity for both diseases yielded ROC curves with an excellent area under the curve (AUC) greater than 0.95 and demonstrated sensitivity, specificity, positive predictive value, and negative predictive value greater than 90%. Overall, our analyses indicate that gut barrier disruption and subsequent microbial translocation are common phenomena in severe cases of dengue and COVID-19. Multiple logistic regression models using a combination of specific biomarkers have the potential to improve severity prediction.