Skip to main content
Advertisement
  • Loading metrics

Serum biomarkers and anti-flavivirus antibodies at presentation as indicators of severe dengue

  • Cynthia Bernal ,

    Contributed equally to this work with: Cynthia Bernal, Sara Ping

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

    Affiliation Universidad Nacional de Asunción, Instituto de Investigaciones en Ciencias de la Salud, San Lorenzo, Paraguay

  • Sara Ping ,

    Contributed equally to this work with: Cynthia Bernal, Sara Ping

    Roles Formal analysis, Methodology, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

    Affiliation Emory University School of Medicine, Division of Infectious Diseases, Atlanta, Georgia, United States of America

  • Alejandra Rojas,

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

    Affiliation Universidad Nacional de Asunción, Instituto de Investigaciones en Ciencias de la Salud, San Lorenzo, Paraguay

  • Oliver Caballero,

    Roles Data curation, Investigation, Methodology, Software, Writing – original draft, Writing – review & editing

    Affiliation Universidad Nacional de Asunción, Instituto de Investigaciones en Ciencias de la Salud, San Lorenzo, Paraguay

  • Victoria Stittleburg,

    Roles Data curation, Formal analysis, Investigation, Methodology, Software, Writing – review & editing

    Affiliation Emory University School of Medicine, Division of Infectious Diseases, Atlanta, Georgia, United States of America

  • Yvalena de Guillén,

    Roles Conceptualization, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing

    Affiliation Universidad Nacional de Asunción, Instituto de Investigaciones en Ciencias de la Salud, San Lorenzo, Paraguay

  • Patricia Langjahr,

    Roles Conceptualization, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing

    Affiliation Universidad Nacional de Asunción, Facultad de Ciencias Químicas, San Lorenzo, Paraguay

  • Benjamin A. Pinsky,

    Roles Conceptualization, Formal analysis, Funding acquisition, Supervision, Visualization, Writing – original draft, Writing – review & editing

    Affiliations Department of Pathology, Stanford University School of Medicine, California, United States of America, Department of Medicine, Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, California, United States of America

  • Marta Von-Horoch,

    Roles Investigation, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

    Affiliation Hospital Central—Instituto de Previsión Social, Departamento de Epidemiología, Asunción, Paraguay

  • Patricia Luraschi,

    Roles Investigation, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

    Affiliation Hospital Central—Instituto de Previsión Social, Departamento de Epidemiología, Asunción, Paraguay

  • Sandra Cabral,

    Roles Investigation, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

    Affiliation Hospital Central—Instituto de Previsión Social, Departamento de Epidemiología, Asunción, Paraguay

  • María Cecilia Sánchez,

    Roles Investigation, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

    Affiliation Hospital Central—Instituto de Previsión Social, Departamento de Laboratorio de Análisis Clínicos, Asunción, Paraguay

  • Aurelia Torres,

    Roles Investigation, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

    Affiliation Hospital Central—Instituto de Previsión Social, Departamento de Laboratorio de Análisis Clínicos, Asunción, Paraguay

  • Fátima Cardozo ,

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

    fati.cardozo@hotmail.com (FC); jjwaggo@emory.edu (JJW)

    ‡ FC and JJW also contributed equally to this work.

    Affiliations Universidad Nacional de Asunción, Instituto de Investigaciones en Ciencias de la Salud, San Lorenzo, Paraguay, Hospital Central—Instituto de Previsión Social, Departamento de Laboratorio de Análisis Clínicos, Asunción, Paraguay

  • Jesse J. Waggoner

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

    fati.cardozo@hotmail.com (FC); jjwaggo@emory.edu (JJW)

    ‡ FC and JJW also contributed equally to this work.

    Affiliations Emory University School of Medicine, Division of Infectious Diseases, Atlanta, Georgia, United States of America, Rollins School of Public Health, Department of Global Health, Atlanta, Georgia, United States of America

Abstract

Background

Dengue is the most common vector-borne viral disease worldwide. Most cases are mild, but some evolve into severe dengue (SD), with high lethality. Therefore, it is important to identify biomarkers of severe disease to improve outcomes and judiciously utilize resources.

Methods/Principal findings

One hundred forty-five confirmed dengue cases (median age, 42; range <1–91 years), enrolled from February 2018 to March 2020, were selected from an ongoing study of suspected arboviral infections in metropolitan Asunción, Paraguay. Cases included dengue virus types 1, 2, and 4, and severity was categorized according to the 2009 World Health Organization guidelines. Testing for anti-dengue virus IgM and IgG and serum biomarkers (lipopolysaccharide binding protein and chymase) was performed on acute-phase sera in plate-based ELISAs; in addition, a multiplex ELISA platform was used to measure anti-dengue virus and anti-Zika virus IgM and IgG. Complete blood counts and chemistries were performed at the discretion of the care team. Age, gender, and pre-existing comorbidities were associated with SD vs. dengue with/without warning signs in logistic regression with odds ratios (ORs) of 1.07 (per year; 95% confidence interval, 1.03, 1.11), 0.20 (female; 0.05,0.77), and 2.09 (presence; 1.26, 3.48) respectively. In binary logistic regression, for every unit increase in anti-DENV IgG in the multiplex platform, odds of SD increased by 2.54 (1.19–5.42). Platelet count, lymphocyte percent, and elevated chymase were associated with SD in a combined logistic regression model with ORs of 0.99 (1,000/μL; 0.98,0.999), 0.92 (%; 0.86,0.98), and 1.17 (mg/mL; 1.03,1.33) respectively.

Conclusions

Multiple, readily available factors were associated with SD in this population. These findings will aid in the early detection of potentially severe dengue cases and inform the development of new prognostics for use in acute-phase and serial samples from dengue cases.

Author summary

Dengue fever is an acute disease caused by dengue virus and transmitted to humans through the bite of infected Aedes mosquitoes. Dengue is the most common vector-borne viral disease worldwide affecting an estimated 50–100 million people and with 10,000 dengue-related deaths each year. Currently, there is no specific treatment, and safe and effective vaccines have not been fully implemented. Most dengue cases present with nonspecific mild symptoms, but some will evolve into severe dengue, which can be fatal. Early detection and subsequent timely treatment have been shown to decrease mortality among severe cases. Therefore, it is very important to identify biomarkers for the early identification of cases at risk for progression to severe disease. In this study we analyze demographic factors, clinical laboratory data, lipopolysaccharide binding protein and chymase to evaluate associations with disease severity. This study was carried out in Paraguay, which is a hyperendemic country for dengue where the disease has been understudied. A number of factors were found to be associated with severe disease in this population, including patient age, male gender, presence of comorbid illnesses, low platelet count, low lymphocyte percentage, and elevated chymase level.

Introduction

Dengue is a common acute febrile illness in tropical and subtropical regions of the world and accounts for upwards of 10% of such illnesses in areas of endemicity [14]. Over the past 30 years, dengue incidence and associated deaths have increased both in those residing in and travelers returning from areas of endemicity [5, 6]. An estimated 50–100 million dengue cases and 10,000 dengue-related deaths annually occur worldwide from infection with one of the four types of dengue virus (DENV, genus Flaviviridae) [1, 35, 7]. Dengue severity ranges dramatically from a mild subclinical illness to dengue fever and clinically severe dengue with plasma leakage, hemorrhage, and/or end-organ dysfunction [1, 3, 4, 8, 9]. Timely diagnosis and the initiation of appropriate supportive care improves clinical outcomes and can lower mortality in clinically severe dengue from 20% to <1% [1, 3, 4, 10, 11]. Although clinically severe cases represent a minority of dengue cases overall, fatal and hospitalized non-fatal cases account for over half of the $8.9 billion USD annual economic burden of dengue [9, 12]. Therefore, early identification of cases at increased risk for developing clinically severe dengue could both improve clinical outcomes and alleviate the economic burden caused by dengue on resource constrained medical systems [13].

Clinically severe dengue results from a complex interplay of virus [1417], host [1825], and epidemiologic factors [1, 9, 18]. The manifestations of severe dengue also differ based on patient age, with children more commonly developing plasma leakage compared to hemorrhage in adults [9, 26, 27]. Studies have identified associations between the detection and/or concentration of various molecules or gene transcripts and severe dengue [2833]. One group of biomarkers that has been studied are proteins released during mast cell degranulation: vascular endothelial growth factor (VEGF) and the proteases tryptase and chymase [3444]. In studies of patients from South and Southeast Asia, chymase was associated with and predictive of the development of clinically severe dengue [34, 35, 37, 38, 41]. Chymase release from mast cells occurs in the presence of DENV and may be increased by pre-existing anti-DENV IgG antibodies [36, 37]. According to a single study in mice, chymase release may be blocked by antibodies against viral non-structural protein 1 (NS1) [45]. Lipopolysaccharide (LPS) and lipopolysaccharide binding protein (LBP) are another set of molecules that have higher levels in dengue cases compared to healthy controls and in clinically severe cases compared to dengue fever, which could indicate their usefulness as a predictor of severity [4648]. Elevated levels of circulating LPS and LBP result from derangements in gut permeability, potentially leading to bacterial translocation, bacteremia, and worsened outcomes. Finally, numerous clinical laboratory findings have been associated with clinically severe dengue, such as thrombocytopenia, lymphopenia, and evidence of liver or kidney injury [16, 20, 27, 4951]. These may either define cases as clinically severe with end-organ dysfunction or predict the development of severe dengue through detection of changes over the course of illness [3].

The objective of the current study was to evaluate biomarkers of dengue severity among participants enrolled in an ongoing study of acute arboviral illness in the metropolitan area of Asunción, Paraguay. Paraguay is hyperendemic for dengue, with sustained viral circulation since 1999 and large disease outbreaks occurring every 2–5 years. In 2018, predominant circulation of DENV-1 was recorded [52], and in 2019–2020, this shifted to DENV-4, resulting in the largest outbreak in the country’s history [53]. Previous studies from Paraguay have found an increased risk of clinically severe dengue with DENV-2 and secondary infections [5456]. However, dengue, and in particular biomarkers of severe disease, remains understudied in the country [57, 58]. Previously, our group evaluated anti-DENV and anti-ZIKV NS1 IgG levels among dengue cases in 2018 using a multiplex serological assay, the pGOLD assay [59]. Anti-DENV IgG levels in the pGOLD assay correlated with focus reduction neutralization test (FRNT50) titers, and an association was observed between hospitalization and detection of both anti-DENV and anti-ZIKV IgG. However, hospitalization is an inexact measure of clinical dengue severity. Therefore, in the current study, we sought to evaluate this earlier finding and levels of chymase and LBP as indicators of dengue severity among participants categorized according to the 2009 World Health Organization guidelines [3].

Methods

Ethics statement

The study protocol was reviewed and approved by the Scientific and Ethics Committee of the Instituto de Investigaciones en Ciencias de la Salud, Universidad Nacional de Asunción (IICS-UNA, IRB00011984), and the Emory University Institutional Review Board (IRB00000569). Written informed consent was obtained from all the participants or their health care decision maker.

Clinical samples

Individuals included in the current study were enrolled in an ongoing parent study of suspected arboviral infections in the Asunción metropolitan area between February 2018 and March 2020. Participants of both genders and all ages were enrolled as outpatients at IICS-UNA in all study years and in the emergency care/inpatient facilities of Hospital Villa Elisa, 2018, and Hospital Central of the Instituto de Previsión Social, 2019–2020. Inclusion criteria for the parent study were an acute illness including two or more of the following symptoms: fever (measured or subjective), red eyes, rash, joint pain involving more than one joint, and/or diffuse muscle pain. Patients with fever and no other localizing signs or symptoms were also included. Day 1 was defined as the first day of symptoms.

One hundred forty-five participants with acute dengue and up to 7 days of symptoms were selected for the current cross-sectional analysis from a total study population of 1566 cases of suspected arboviral illness. Cases were classified according to the 2009 WHO criteria as dengue without warning signs (DWS-), dengue with warning signs (DWS+) and severe dengue (SD) [3]. Cases were classified during the initial visit, and the final classification used for this study was upgraded if the case evolved over time to a more severe category following presentation. For categorization as DWS+, it was necessary to have at least one warning sign. For categorization as SD, an individual had to develop at least one criterion for SD during the clinical course. To maximize study power, all SD cases in the parent study were included in this analysis. A mixture of DWS- and DWS+ cases was then selected to achieve a representative distribution of participants based on age, days of symptoms, comorbidities, and gender from across the study period and to maintain an even distribution of these two categories. The number of included cases was limited by sample volume and availability of demographic and clinical data.

Laboratory testing

Acute-phase serum or plasma samples were collected during the initial visit for study enrollment and transported to the IICS-UNA laboratory. Samples were tested for DENV NS1 antigen using the Standard Q Dengue Duo rapid immunochromatographic test (SD Biosensor, Suwon, South Korea) according to manufacturer recommendations. Qualitative antibody data acquired using this method was not evaluated in this study, see antibody section below. Primary samples were then aliquoted and stored at −80°C until later use or shipment on dry ice to Emory University for additional testing. For molecular testing, total nucleic acids were extracted from 200μL of sample on an EMAG instrument and eluted into 50μL of buffer. Samples were tested for Zika virus, chikungunya virus and DENV by real-time RT-PCR (rRT-PCR) using a validated and published multiplex assay (the ZCD assay) [60], and DENV serotype and viral load were determined with a published DENV multiplex assay [61, 62]. Both rRT-PCRs were performed as previously described [6062].

Serologic testing was performed on acute-phase samples using two different methods. First, anti-DENV IgG and IgM were analyzed using commercial ELISA kits [Dengue ELISA IgG (G1018) and Dengue ELISA IgM Capture (M1018), Vircell Microbiologists, Granada, Spain] according to manufacturer recommendations (interpretation: IgM or IgG index >11 positive, 9–11 indeterminate, <9 negative). Second, a 5μL aliquot of serum from 139 participants with sufficient sample was tested in the pGOLD assay (Nirmidas Biotech, Inc, Palo Alto, CA), which is a multiplex serological assay for IgM and IgG against DENV (DENV-2 whole virus antigen) and ZIKV (NS1 antigen). The pGOLD assay was performed as previously described [59, 63]. In each well of the pGOLD slide, antigens are spotted in triplicate, and average signals are used during analysis. For IgG, the negative control signal was subtracted from the sample signal, and the difference was divided by the average signal of four IgG control spots included in each well. For IgM, a similar calculation was performed using the signal from a known anti-DENV IgM positive control sample included on each run. A positive threshold ratio of 0.1 was established for each isotype, which was ≥ 3 standard deviations above the mean of the negative control.

Chymase and LBP levels were determined using commercial ELISA kits (G-Biosciences, St. Louis, MO, USA), following the manufacturer’s instructions. Complete blood counts and chemistries were performed at the clinical site at the discretion of the care team, and results were included if the sample was obtained within ±1 day of enrollment.

Case definitions

Dengue cases were defined as individuals who met inclusion criteria for the parent study and had 1) detectable DENV RNA in the ZCD and/or DENV multiplex rRT-PCR or 2) detection of DENV NS1 by rapid test. For a single participant with DWS-, dengue was defined based on clinical presentation and a strong epidemiologic during a large was of DENV-4 cases.

Statistical analysis

Basic statistical analyses were performed using Excel software (Microsoft, Redmond, WA). Comparisons between group means and medians were made by the ANOVA, Welch’s test, both pooled and non-pooled two sample t-tests, Mann-Whitney U test, and Kruskal Wallis tests. Comparisons of proportions were made using chi-squared tests or Fisher exact tests (if the expected number in each cell was <5). Graphs were prepared with GraphPad Prism version 9 (GraphPad, San Diego, CA). Crude associations, statistical analysis and modeling were performed using SAS version 9.4. To calculate odds ratios for SD, domain models were developed using demographic (age, gender, comorbidities) and laboratory variables (basic clinical laboratory results, DENV viral load, chymase and LBP). Models were evaluated using binomial logistic regression (DWS-/DWS+ vs. SD), and goodness of fit was evaluated using area under the receiver operating curve (AUROC). Significance was set at two-sided p-value ≤0.05 for all analyses. Comorbidities were defined as present or absent for all statistical analysis.

Results

Demographic and clinical information

Of 145 participants in this study, 55 were categorized as DWS-, 67 as DWS+, and 23 as SD. Demographic data and DENV diagnostic test results are shown in Table 1 (binary categories) and S1 Table (three categories). Participants were enrolled primarily at Hospital Central of the Instituto de Previsión Social (n = 124), followed by Hospital Villa Elisa (15) and IICS-UNA (6). Results for DWS- and DWS+ were not significantly different for most analyses performed in this study. As such, results are reported for analyses using the binary outcome of DWS-/DWS+ vs. SD, except where indicated. Data and analyses for the three individual categories are provided in the Supplemental Material.

thumbnail
Table 1. Demographic data and DENV diagnostic test results for participants stratified by dengue severity.

https://doi.org/10.1371/journal.pntd.0010750.t001

SD cases were significantly older than non-SD cases and were significantly more likely to be male and have at least one comorbidity (Table 1). The presence of specific comorbidities also differed by population (Table 1). In logistic regression of these variables in relation to disease severity, age, gender, and comorbidities remained in the model and were predictors of severity with a strong goodness of fit (AUROC = 0.94; Table 2). In logistic regression, comorbidity was defined as a discrete variable. In addition, SD cases presented for care later in the course of illness than non-severe cases (Table 1), and more SD cases were included 2020, consistent with the large DENV-4 outbreak that occurred in Paraguay that year [64].

thumbnail
Table 2. Binomial logistic regression of participant demographics and disease severity.

https://doi.org/10.1371/journal.pntd.0010750.t002

DENV testing

One hundred forty-four of 145 dengue cases (99.3%) tested positive by rRT-PCR, NS1 rapid test, or both; and only one case was included based epidemiologic criteria alone. Over 90% of cases tested positive for DENV by rRT-PCR, and this did not differ between severity categories (Tables 1 and S1). The proportion of DWS-/DWS+ cases with detectable NS1 (77/122, 63.1%) was significantly lower than SD cases (21/23, 91.3%; p = 0.010). DENV-4 was the predominant type, present in 78.5% of the typed samples overall (102/130). Infections by Zika and chikungunya viruses were not detected, nor were coinfections by two DENV serotypes.

Acute-phase samples were tested with two serologic tests: the pGOLD assay for anti-DENV and anti-ZIKV IgM and IgG, and a commercial ELISA for anti-DENV IgM and IgG (Tables 3 and S2). The proportion of individuals with detectable anti-DENV IgM was significantly higher with the pGOLD assay (p<0.001, S3 Table). Although a smaller proportion of SD cases had detectable anti-DENV IgM compared to DWS-/DWS+ cases by either method, this difference only reached significance for the pGOLD assay. Most participants had detectable anti-DENV IgG by either method: 120/139 (86.3%) in the pGOLD, 128/145 (88.3%) by commercial ELISA. The proportion of individuals with detectable anti-DENV IgG did not differ significantly by severity category (Tables 3 and S2) or test method (p = 0.07, S3 Table).

thumbnail
Table 3. Serologic test results stratified by disease severity.

https://doi.org/10.1371/journal.pntd.0010750.t003

The pGOLD assay yields a quantitative result that correlates with DENV neutralizing titers (S1 Fig) [59]. In crude binary logistic regression, for every unit increase in anti-DENV IgG, the odds of SD increased by a factor of 2.54 (95% CI, 1.19–5.42). No interaction was observed between anti-DENV IgG and day post-symptom onset, which was not included in the final logistic regression. No association was found between quantitative anti-DENV IgM results and disease severity in crude binary logistic regression.

Clinical laboratory data

Mean values for most routine laboratory tests, LBP, and chymase differed significantly between DWS-/DWS+ and SD cases (Fig 1, S4 Table). Laboratory values were similar between DWS- and DWS+ cases except for platelet count, which demonstrated a stepwise decrease from DWS- to DWS+ to SD, and serum glutamic oxaloacetic transaminase (SGOT) and LBP, which increased across severity categories (S2 Fig, S5 Table). DENV viral load did not differ by severity category.

thumbnail
Fig 1.

A-I) Clinical laboratory test result distributions by disease severity. J-L) Potential markers of disease severity measured in the current study: J) lipopolysaccharide binding protein (LBP), K) chymase, and L) DENV viral load by disease category. Bars on all graphs represent mean and standard deviation. Labels on the graphs indicate the following: ns, not significant, p>0.05; *, p≤0.05; **, p≤0.01; ***, p≤0.001; ****, p≤0.0001.

https://doi.org/10.1371/journal.pntd.0010750.g001

Routine laboratory tests were obtained at the discretion of the clinical care team, and as a result, many participants were missing data, particularly for analytes in the metabolic panel (S4 and S5 Tables). Due to this fact, crude associations with SD were calculated for all variables by binomial regression (Table 4), and variables evaluated in the laboratory domain multivariable logistic regression were limited to lymphocyte percent, platelet count, hematocrit, LBP, and chymase. These analytes displayed crude associations with SD, had sufficient data points to maintain model strength, and were not collinear with each other (e.g., hemoglobin and hematocrit, neutrophil and lymphocyte percent). After evaluating these five variables, those with non-significant regression coefficients were removed (LBP and hematocrit). In the final binary logistic regression model, lymphocyte percent, platelet count, and chymase were found to be associated with SD with a very good model fit (AUROC statistic, 0.95; Table 5).

thumbnail
Table 4. Crude associations between laboratory results and dengue severity from binomial logistic regression.

https://doi.org/10.1371/journal.pntd.0010750.t004

thumbnail
Table 5. Binomial logistic regression model of laboratory results and disease severity.

https://doi.org/10.1371/journal.pntd.0010750.t005

Chymase and SD

Mean chymase level was significantly higher among individuals with comorbidities (10.75, st. dev. 22.01) compared to those without (2.41, 9.99; p = 0.014). Notably, the single DWS- case with an elevated chymase level (Figs 1K and S1K) occurred in an individual with systemic lupus erythematosus. To evaluate for a potential interaction between chymase and comorbidities on the development of SD, logistic regression was performed including these two variables with comorbidities defined as a discrete variable counting the number of comorbidities each patient has. Interaction product terms were nonsignificant in binomial and multinomial models. Together, comorbidities had an OR of 3.17 (1.71, 5.89) for binomial logistic regression (controlling for chymase); chymase had an OR of 1.11 (1.05, 1.17) (controlling for comorbidities). This model had a strong goodness of fit (C statistic = 0.95).

Anti-NS1 antibodies may modulate chymase release by mast cells in acute dengue. As anti-DENV antibodies detected in the pGOLD assay target whole viral antigen, interactions between chymase and antibodies directed against the NS1 protein of ZIKV were investigated for their association with SD. These antibodies are detected in the pGOLD assay and predominantly represent cross-reactive anti-DENV antibodies in this population [59]. There was no association between chymase level and the quantitative anti-ZIKV NS1 IgM or IgG by linear regression, and no interaction was observed between anti-ZIKV NS1 IgG and chymase in binomial linear regression for SD. However, anti-ZIKV NS1 IgM showed effect modification of chymase in binomial linear regression such that as IgM increased, the chymase OR increased as well. With no detectable anti-ZIKV NS1 IgM, the chymase OR was 1.10 (1.04, 1.17), whereas at the mean level of anti-ZIKV NS1 IgM (0.02 in this population), the chymase OR was 1.21 (1.09, 1.34; AUROC = 0.93).

Discussion

In a predominantly adult population of dengue cases in Paraguay, due to DENV-1, -2, and -4, multiple factors were associated with clinically severe dengue, including patient (age, gender, comorbidities), serologic (elevated anti-DENV IgG), and laboratory variables (low platelet count, relative lymphopenia, and elevated chymase).

Factors identified in the current study are generally consistent with the published dengue literature [4]. Although clinically severe dengue often occurs among children [1, 3, 27], age among adults has been identified as a risk factor for poor outcomes, and in 2019, individuals 15–49 years of age accounted for more deaths and disability adjust life years lost than children [5, 20, 26, 49]. Adults are more likely to develop severe bleeding, and this may be more difficult to manage than plasma leakage that develops in children, for which judicious fluid replacement is often effective [3, 9, 11, 20, 26, 27, 49]. Comorbid illness, including poorly-controlled diabetes mellitus (hemoglobin A1c >7%) and renal disease, have been associated with SD [19, 21], and hypertension has also been identified in certain studies [21]. Notably, in our population, multiple comorbidities were associated with SD, and these demonstrated a cumulative effect when evaluated as a discrete variable. A gender difference among clinically severe dengue has varied across studies [1, 9, 16, 20, 21]. In our population, 66.4% of DWS-/DWS+ cases were female in comparison to only 26.1% of SD cases, and this difference remained significant after controlling for age and comorbidities. Although dengue is often associated with leukopenia [3, 27, 6567], SD cases in the current study had a mild leukocytosis with reduced lymphocyte percentage (and a resulting neutrophil predominance). Thrombocytopenia is a common finding in SD cases and was one of the few factors that demonstrated a stepwise change across disease severity categories (DWS-, DWS+, and SD) [1, 3, 20, 21, 27, 41, 65].

Chymase and LBP were evaluated as two markers of clinically severe dengue based on data from their use in South and Southeast Asia [4, 34, 35, 37, 38, 41, 4547]. Both demonstrated a crude association with SD compared to DWS-/DWS+. Although LBP did not remain in the final laboratory domain model, it demonstrated a stepwise increase across the categories of severity, which may have limited power in this study to identify a significant difference in a binomial model. Chymase, along with tryptase and other mast cell degranulation factors, has been associated with clinically severe dengue in several publications [3538, 68], and the current study confirmed this finding among dengue cases in Paraguay. As clinically severe dengue appears to be more common in Southeast Asia relative to the Americas [2, 69], it is important to study potential differences in pathophysiology between these regions and confirm markers of severity between populations. As markers of dengue severity, mast cell degranulation factors have demonstrated more consistent results that other potential markers such as chemokines and cytokines [3436, 40, 70], tryptase may play a direct pathophysiologic role in endothelial permeability [68], and mast cells can be stabilized by available FDA-approved medications [35]. Chymase release from mast cells may be modulated by specific anti-DENV antibodies. In mice, pre-treatment with anti-DENV IgG increased chymase release in an FCγRIII-dependent manner [36], and anti-NS1 IgG blocked mast cell degranulation [45]. In the current study, we observed an interaction between chymase level and antibodies against the NS1 protein of ZIKV, a closely related flavivirus. Further evaluation of this interaction using an array of DENV NS1 proteins may delineate a mechanism of protection for anti-NS1 antibodies, which demonstrate epitope-specific protection or enhancement [71, 72].

Higher levels of anti-DENV IgG in the pGOLD multiplex serologic assay were also associated with SD in our study population. This is consistent with findings in secondary dengue cases, though this is difficult to determine with certainty in acute-phase samples [3, 73], and SD can occur in primary dengue, particularly among adults experiencing a first infection [74]. Quantitative anti-DENV IgG levels in the pGOLD assay correlate with DENV FRNT50, and we previously observed that higher levels are associated with hospitalization in dengue cases [59]. This finding was confirmed in the current study when applying more consistent criteria for clinically severe dengue [3]. However, simultaneous detection of anti-ZIKV NS1 IgG did not increase the risk for SD in contrast to our earlier findings [59]. Anti-DENV IgM detection in the pGOLD proved more sensitive than a commercial ELISA and demonstrated little cross-reactivity on the ZIKV NS1 antigen. Notably, interpretation of these results required the use of a control sample that previously tested positive for anti-DENV IgM, and inclusion of a calibrator with this assay would improve generalizability.

DENV serum viral load was not associated with SD in this cross-sectional study. Viral load decreases rapidly over the first week post-symptom onset, and viral kinetics differ between primary and secondary dengue [15, 7583]. It is therefore difficult to capture peak viremia in most clinical settings. With only a single data point for each patient in our study, the lack of association between viral load and SD is not unexpected, but this highlights a potential limitation of using viral load as a predictor of severity in clinical practice.

Difficulties in studying predictors of clinically severe dengue stem from the low proportion of severe cases among all DENV infections, lack of rapid and accurate diagnostics, and variability in the definition of study endpoints [3, 8, 9, 13]. The current study relied principally on DENV rRT-PCR for diagnosis, with a subset of participants detected by NS1. As part of the parent study design, participants typically presented with fever, which may bias this group toward more severe cases [52, 84]. Nonetheless, seven factors were associated with clinically severe dengue: five of these are commonly available at the acute visit (age, gender, comorbidities, platelet count, and lymphocyte percentage) and chymase and anti-DENV IgG can be measured by ELISA. Study designs that enroll participants based on rapid antigen test results limit the sample size necessary to include enough severe cases, but this may bias the study population given the clinical performance of current rapid tests [3, 28, 52, 85, 86]. An improved antigen diagnostic in combination with a prognostic test may then increase DENV detection, identify individuals at high risk for SD, and facilitate future trials for clinically severe dengue.

This study had several limitations. First, a single acute-phase sample was available for each participant. Samples were obtained at different timepoints in relation to the development of severe disease among the participants, such that the study was not designed to prospectively evaluate each marker as a predictor of clinically severe dengue. Second, although all available SD cases were included, the sample size was small, particularly for the detection of differences among factors with relatively narrow value ranges, such as quantitative pGOLD values. Third, routine labs were collected at the discretion of the care team, and as a result, not all participants had laboratory values within the correct time frame. This limited the variables included in the laboratory domain multivariable analysis.

Although dengue is endemic in Paraguay, scarce studies evaluate severity markers in this population. Therefore, our findings may aid in the early detection of potentially severe dengue cases and serve as a basis for the development of new combined diagnostic-prognostics for use in acute-phase and serial samples from dengue cases.

Supporting information

S1 Fig. Distributions of DENV pGOLD serology results by disease severity.

https://doi.org/10.1371/journal.pntd.0010750.s001

(PDF)

S2 Fig. Distribution of selected laboratory test results by disease severity.

https://doi.org/10.1371/journal.pntd.0010750.s002

(PDF)

S1 Table. Characteristics of participants by severity category.

https://doi.org/10.1371/journal.pntd.0010750.s003

(PDF)

S2 Table. Serologic test results stratified by disease severity.

https://doi.org/10.1371/journal.pntd.0010750.s004

(PDF)

S3 Table. Comparison of pGOLD and Vircell ELISA detection of anti-DENV IgM and IgG in acute-phase samples.

https://doi.org/10.1371/journal.pntd.0010750.s005

(PDF)

S4 Table. Laboratory values by disease severity.

https://doi.org/10.1371/journal.pntd.0010750.s006

(PDF)

S5 Table. Laboratory values by disease severity categories.

https://doi.org/10.1371/journal.pntd.0010750.s007

(PDF)

Acknowledgments

We thank the members of the study team based at the Instituto de Investigaciones en Ciencias de la Salud, Universidad Nacional de Asunción; Hospital Villa Elisa; and Hospital Central del Instituto de Provisón Social, all located in Paraguay, for their dedication to this study and their excellent work. We are grateful to the study participants and their families.

References

  1. 1. Guzman MG, Harris E. Dengue. Lancet. 2015;385(9966):453–65. pmid:25230594.
  2. 2. L’Azou M, Moureau A, Sarti E, Nealon J, Zambrano B, Wartel TA, et al. Symptomatic Dengue in Children in 10 Asian and Latin American Countries. The New England journal of medicine. 2016;374(12):1155–66. Epub 2016/03/24. pmid:27007959.
  3. 3. World Health Organization. Dengue: guidelines for diagnosis, treatment, prevention and control. France: WHO Press; 2009.
  4. 4. Wilder-Smith A, Ooi EE, Horstick O, Wills B. Dengue. Lancet. 2019;393(10169):350–63. Epub 2019/01/31. pmid:30696575.
  5. 5. Yang X, Quam MBM, Zhang T, Sang S. Global burden for dengue and the evolving pattern in the past 30 years. J Travel Med. 2021;28(8). Epub 2021/09/13. pmid:34510205.
  6. 6. Osman S, Preet R. Dengue, chikungunya and Zika in GeoSentinel surveillance of international travellers: a literature review from 1995 to 2020. J Travel Med. 2020;27(8). Epub 2020/12/02. pmid:33258476.
  7. 7. Stanaway JD, Shepard DS, Undurraga EA, Halasa YA, Coffeng LE, Brady OJ, et al. The global burden of dengue: an analysis from the Global Burden of Disease Study 2013. The Lancet Infectious diseases. 2016;16(6):712–23. pmid:26874619; PubMed Central PMCID: PMC5012511.
  8. 8. Rosenberger KD, Alexander N, Martinez E, Lum LCS, Dempfle CE, Junghanss T, et al. Severe dengue categories as research endpoints-Results from a prospective observational study in hospitalised dengue patients. PLoS neglected tropical diseases. 2020;14(3):e0008076. Epub 2020/03/05. pmid:32130212; PubMed Central PMCID: PMC7055818 following competing interests: Dr. Wills reports personal fees from Takeda Vaccines Inc, outside the submitted work, for her work as a member of the Data Monitoring Committee for their dengue vaccine trials. None of the other authors had anything to disclose.
  9. 9. Sharp TM, Anderson KB, Katzelnick LC, Clapham H, Johansson MA, Morrison AC, et al. Knowledge gaps in the epidemiology of severe dengue impede vaccine evaluation. The Lancet Infectious diseases. 2022;22(2):e42–e51. Epub 2021/07/16. pmid:34265259.
  10. 10. Moraes GH, de Fatima Duarte E, Duarte EC. Determinants of mortality from severe dengue in Brazil: a population-based case-control study. The American journal of tropical medicine and hygiene. 2013;88(4):670–6. pmid:23400577; PubMed Central PMCID: PMC3617850.
  11. 11. Rocha C, Silva S, Gordon A, Hammond SN, Elizondo D, Balmaseda A, et al. Improvement in hospital indicators after changes in dengue case management in Nicaragua. The American journal of tropical medicine and hygiene. 2009;81(2):287–92. pmid:19635885.
  12. 12. Shepard DS, Undurraga EA, Halasa YA, Stanaway JD. The global economic burden of dengue: a systematic analysis. The Lancet Infectious diseases. 2016. pmid:27091092.
  13. 13. Robinson M, Einav S. Towards Predicting Progression to Severe Dengue. Trends Microbiol. 2020;28(6):478–86. Epub 2020/01/27. pmid:31982232.
  14. 14. Rico-Hesse R. Dengue virus evolution and virulence models. Clinical Infectious Diseases. 2007;44:1462. pmid:17479944
  15. 15. Tricou V, Minh NN, Farrar J, Tran HT, Simmons CP. Kinetics of viremia and NS1 antigenemia are shaped by immune status and virus serotype in adults with dengue. PLoS neglected tropical diseases. 2011;5(9):e1309. pmid:21909448
  16. 16. Guilarde AO, Turchi MD, Siqueira JB Jr., Feres VC, Rocha B, Levi JE, et al. Dengue and dengue hemorrhagic fever among adults: clinical outcomes related to viremia, serotypes, and antibody response. The Journal of infectious diseases. 2008;197(6):817–24. Epub 2008/02/14. pmid:18269315.
  17. 17. Libraty DH, Young PR, Pickering D, Endy TP, Kalayanarooj S, Green S, et al. High circulating levels of the dengue virus nonstructural protein NS1 early in dengue illness correlate with the development of dengue hemorrhagic fever. The Journal of infectious diseases. 2002;186(8):1165–8. pmid:12355369.
  18. 18. Guzman MG, Alvarez M, Halstead SB. Secondary infection as a risk factor for dengue hemorrhagic fever/dengue shock syndrome: an historical perspective and role of antibody-dependent enhancement of infection. Archives of virology. 2013;158(7):1445–59. Epub 2013/03/09. pmid:23471635.
  19. 19. Lee IK, Hsieh CJ, Lee CT, Liu JW. Diabetic patients suffering dengue are at risk for development of dengue shock syndrome/severe dengue: Emphasizing the impacts of co-existing comorbidity(ies) and glycemic control on dengue severity. Journal of microbiology, immunology, and infection = Wei mian yu gan ran za zhi. 2020;53(1):69–78. Epub 2018/08/28. pmid:30146413.
  20. 20. Pinto RC, Castro DB, Albuquerque BC, Sampaio Vde S, Passos RA, Costa CF, et al. Mortality Predictors in Patients with Severe Dengue in the State of Amazonas, Brazil. PloS one. 2016;11(8):e0161884. Epub 2016/08/27. pmid:27564084; PubMed Central PMCID: PMC5001629.
  21. 21. Tsheten T, Clements ACA, Gray DJ, Adhikary RK, Furuya-Kanamori L, Wangdi K. Clinical predictors of severe dengue: a systematic review and meta-analysis. Infect Dis Poverty. 2021;10(1):123. Epub 2021/10/11. pmid:34627388; PubMed Central PMCID: PMC8501593.
  22. 22. Katzelnick LC, Gresh L, Halloran ME, Mercado JC, Kuan G, Gordon A, et al. Antibody-dependent enhancement of severe dengue disease in humans. Science. 2017;358(6365):929–32. Epub 2017/11/04. pmid:29097492; PubMed Central PMCID: PMC5858873.
  23. 23. Katzelnick LC, Montoya M, Gresh L, Balmaseda A, Harris E. Neutralizing antibody titers against dengue virus correlate with protection from symptomatic infection in a longitudinal cohort. Proceedings of the National Academy of Sciences of the United States of America. 2016;113(3):728–33. pmid:26729879.
  24. 24. Salje H, Cummings DAT, Rodriguez-Barraquer I, Katzelnick LC, Lessler J, Klungthong C, et al. Reconstruction of antibody dynamics and infection histories to evaluate dengue risk. Nature. 2018;557(7707):719–23. Epub 2018/05/26. pmid:29795354; PubMed Central PMCID: PMC6064976.
  25. 25. Waggoner JJ, Katzelnick LC, Burger-Calderon R, Gallini J, Moore RH, Kuan G, et al. Antibody-Dependent Enhancement of Severe Disease Is Mediated by Serum Viral Load in Pediatric Dengue Virus Infections. The Journal of infectious diseases. 2020;221(11):1846–54. Epub 2020/04/03. pmid:32236481; PubMed Central PMCID: PMC7213574.
  26. 26. Guzman MG, Kouri G. Dengue and dengue hemorrhagic fever in the Americas: lessons and challenges. Journal of clinical virology: the official publication of the Pan American Society for Clinical Virology. 2003;27(1):1–13. Epub 2003/05/03. pmid:12727523.
  27. 27. Hammond SN, Balmaseda A, Perez L, Tellez Y, Saborio SI, Mercado JC, et al. Differences in dengue severity in infants, children, and adults in a 3-year hospital-based study in Nicaragua. The American journal of tropical medicine and hygiene. 2005;73(6):1063–70. Epub 2005/12/16. pmid:16354813.
  28. 28. Robinson M, Sweeney TE, Barouch-Bentov R, Sahoo MK, Kalesinskas L, Vallania F, et al. A 20-Gene Set Predictive of Progression to Severe Dengue. Cell Rep. 2019;26(5):1104–11 e4. Epub 2019/01/31. pmid:30699342; PubMed Central PMCID: PMC6352713.
  29. 29. Assuncao-Miranda I, Amaral FA, Bozza FA, Fagundes CT, Sousa LP, Souza DG, et al. Contribution of macrophage migration inhibitory factor to the pathogenesis of dengue virus infection. FASEB J. 2010;24(1):218–28. Epub 2009/09/25. pmid:19776337.
  30. 30. Jeewandara C, Gomes L, Udari S, Paranavitane SA, Shyamali NL, Ogg GS, et al. Secretory phospholipase A2 in the pathogenesis of acute dengue infection. Immun Inflamm Dis. 2017;5(1):7–15. Epub 2017/03/03. pmid:28250920; PubMed Central PMCID: PMC5322161.
  31. 31. Jeewandara C, Gomes L, Wickramasinghe N, Gutowska-Owsiak D, Waithe D, Paranavitane SA, et al. Platelet activating factor contributes to vascular leak in acute dengue infection. PLoS neglected tropical diseases. 2015;9(2):e0003459. Epub 2015/02/04. pmid:25646838; PubMed Central PMCID: PMC4315531.
  32. 32. Chen HR, Chao CH, Liu CC, Ho TS, Tsai HP, Perng GC, et al. Macrophage migration inhibitory factor is critical for dengue NS1-induced endothelial glycocalyx degradation and hyperpermeability. PLoS pathogens. 2018;14(4):e1007033. Epub 2018/04/28. pmid:29702687; PubMed Central PMCID: PMC6044858.
  33. 33. Chen HR, Chuang YC, Lin YS, Liu HS, Liu CC, Perng GC, et al. Dengue Virus Nonstructural Protein 1 Induces Vascular Leakage through Macrophage Migration Inhibitory Factor and Autophagy. PLoS neglected tropical diseases. 2016;10(7):e0004828. Epub 2016/07/15. pmid:27409803; PubMed Central PMCID: PMC4943727.
  34. 34. Furuta T, Murao LA, Lan NT, Huy NT, Huong VT, Thuy TT, et al. Association of mast cell-derived VEGF and proteases in Dengue shock syndrome. PLoS neglected tropical diseases. 2012;6(2):e1505. Epub 2012/03/01. pmid:22363824; PubMed Central PMCID: PMC3283553.
  35. 35. St John AL, Rathore AP, Raghavan B, Ng ML, Abraham SN. Contributions of mast cells and vasoactive products, leukotrienes and chymase, to dengue virus-induced vascular leakage. Elife. 2013;2:e00481. Epub 2013/05/03. pmid:23638300; PubMed Central PMCID: PMC3639510.
  36. 36. Syenina A, Jagaraj CJ, Aman SA, Sridharan A, St John AL. Dengue vascular leakage is augmented by mast cell degranulation mediated by immunoglobulin Fcgamma receptors. Elife. 2015;4. Epub 2015/03/19. pmid:25783751; PubMed Central PMCID: PMC4362203.
  37. 37. Tissera H, Rathore APS, Leong WY, Pike BL, Warkentien TE, Farouk FS, et al. Chymase Level Is a Predictive Biomarker of Dengue Hemorrhagic Fever in Pediatric and Adult Patients. The Journal of infectious diseases. 2017;216(9):1112–21. Epub 2017/10/03. pmid:28968807; PubMed Central PMCID: PMC5853622.
  38. 38. Rathore APS, Senanayake M, Athapathu AS, Gunasena S, Karunaratna I, Leong WY, et al. Serum chymase levels correlate with severe dengue warning signs and clinical fluid accumulation in hospitalized pediatric patients. Scientific reports. 2020;10(1):11856. Epub 2020/07/18. pmid:32678248; PubMed Central PMCID: PMC7367272.
  39. 39. King CA, Wegman AD, Endy TP. Mobilization and Activation of the Innate Immune Response to Dengue Virus. Front Cell Infect Microbiol. 2020;10:574417. Epub 2020/11/24. pmid:33224897; PubMed Central PMCID: PMC7670994.
  40. 40. Sherif NA, Zayan AH, Elkady AH, Ghozy S, Ahmed AR, Omran ES, et al. Mast cell mediators in relation to dengue severity: A systematic review and meta-analysis. Rev Med Virol. 2020;30(1):e2084. Epub 2019/11/12. pmid:31709696.
  41. 41. Sahu AK, Aggarwal P, Ekka M, Nayer J, Bhoi S, Kumar A, et al. Assessing the serum chymase level as an early predictor of dengue severity. Journal of medical virology. 2021;93(6):3330–7. Epub 2020/08/29. pmid:32857465.
  42. 42. Seet RC, Chow AW, Quek AM, Chan YH, Lim EC. Relationship between circulating vascular endothelial growth factor and its soluble receptors in adults with dengue virus infection: a case-control study. International journal of infectious diseases: IJID: official publication of the International Society for Infectious Diseases. 2009;13(5):e248–53. Epub 2009/03/06. pmid:19261502.
  43. 43. Srikiatkhachorn A, Ajariyakhajorn C, Endy TP, Kalayanarooj S, Libraty DH, Green S, et al. Virus-induced decline in soluble vascular endothelial growth receptor 2 is associated with plasma leakage in dengue hemorrhagic Fever. Journal of virology. 2007;81(4):1592–600. Epub 2006/12/08. pmid:17151115; PubMed Central PMCID: PMC1797579.
  44. 44. Patra G, Saha B, Mukhopadhyay S. Study of serum VEGF levels in patients with severe dengue infection admitted in a tertiary care hospital in Kolkata. Journal of medical virology. 2019;91(10):1873–6. Epub 2019/06/28. pmid:31243775.
  45. 45. Chu YT, Wan SW, Chang YC, Lee CK, Wu-Hsieh BA, Anderson R, et al. Antibodies against nonstructural protein 1 protect mice from dengue virus-induced mast cell activation. Lab Invest. 2017. Epub 2017/02/28. pmid:28240747.
  46. 46. van de Weg CA, Koraka P, van Gorp EC, Mairuhu AT, Supriatna M, Soemantri A, et al. Lipopolysaccharide levels are elevated in dengue virus infected patients and correlate with disease severity. Journal of clinical virology: the official publication of the Pan American Society for Clinical Virology. 2012;53(1):38–42. Epub 2011/10/22. pmid:22014848.
  47. 47. van de Weg CA, Pannuti CS, de Araujo ES, van den Ham HJ, Andeweg AC, Boas LS, et al. Microbial translocation is associated with extensive immune activation in dengue virus infected patients with severe disease. PLoS neglected tropical diseases. 2013;7(5):e2236. Epub 2013/05/30. pmid:23717702; PubMed Central PMCID: PMC3662706.
  48. 48. Kamaladasa A, Gomes L, Jeewandara C, Shyamali NL, Ogg GS, Malavige GN. Lipopolysaccharide acts synergistically with the dengue virus to induce monocyte production of platelet activating factor and other inflammatory mediators. Antiviral research. 2016;133:183–90. Epub 2016/08/01. pmid:27476044.
  49. 49. Lee IK, Liu JW, Yang KD. Clinical and laboratory characteristics and risk factors for fatality in elderly patients with dengue hemorrhagic fever. The American journal of tropical medicine and hygiene. 2008;79(2):149–53. Epub 2008/08/12. pmid:18689614.
  50. 50. Jayaratne SD, Atukorale V, Gomes L, Chang T, Wijesinghe T, Fernando S, et al. Evaluation of the WHO revised criteria for classification of clinical disease severity in acute adult dengue infection. BMC research notes. 2012;5:645. Epub 2012/11/22. pmid:23167997; PubMed Central PMCID: PMC3534490.
  51. 51. Suresh SC, Hanumanthaiah R, Ramakrishna C, Sandeep R, Narasimhasetty PS, Ramakrishna V, et al. Serum Ferritin As a Prognostic Indicator in Adult Dengue Patients. The American journal of tropical medicine and hygiene. 2020;104(3):1072–8. Epub 2020/12/30. pmid:33372649; PubMed Central PMCID: PMC7941811.
  52. 52. Rojas A, Cardozo F, Cantero C, Stittleburg V, Lopez S, Bernal C, et al. Characterization of dengue cases among patients with an acute illness, Central Department, Paraguay. PeerJ. 2019;7:e7852. Epub 2019/10/17. pmid:31616598; PubMed Central PMCID: PMC6790102.
  53. 53. Pan American Health Organization. Dengue 2021 [01 July 2022]. Available from: https://www3.paho.org/data/index.php/es/temas/indicadores-dengue.html.
  54. 54. Lovera D, Martinez-Cuellar C, Galeano F, Amarilla S, Vazquez C, Arbo A. Clinical manifestations of primary and secondary dengue in Paraguay and its relation to virus serotype. Journal of infection in developing countries. 2019;13(12):1127–34. Epub 2020/02/24. pmid:32088700.
  55. 55. Vázquez C, Villalba S, Gamarra ML, Oviedo E, Oviedo A, Ortega MJ, et al. Características Virológicas y Serológicas de Pacientes con Dengue Grave y Fallecidos por Dengue durante la Epidemia del Año 2011 en Paraguay. Rev Inst Med Trop. 2012;7:8–18.
  56. 56. Martínez de Cuellar C, Lovera D, Arbo A. Factores de riesgo asociados a mortalidad por Dengue en menores de 15 años. Paraguay, periodo 2010–2013. Pediatr (Asunción). 2017;44(2):136–42.
  57. 57. Rios González CM. La investigación científica en Dengue, Zika y Chikungunya: evaluación bibliométrica de las contribuciones de la literatura paraguaya. Rev Cient Cienc Med. 2016;19:65.
  58. 58. Talarico LB, Byrne AB, Amarilla S, Lovera D, Vazquez C, Chamorro G, et al. Characterization of type I interferon responses in dengue and severe dengue in children in Paraguay. Journal of clinical virology: the official publication of the Pan American Society for Clinical Virology. 2017;97:10–7. Epub 2017/10/28. pmid:29078078.
  59. 59. Rojas A, Natrajan MS, Weber J, Cardozo F, Cantero C, Ananta JS, et al. Comparison of Anti-Dengue and Anti-Zika IgG on a Plasmonic Gold Platform with Neutralization Testing. The American journal of tropical medicine and hygiene. 2021. Epub 2021/03/31. pmid:33782214; PubMed Central PMCID: PMC8103464.
  60. 60. Waggoner JJ, Gresh L, Mohamed-Hadley A, Ballesteros G, Davila MJ, Tellez Y, et al. Single-Reaction Multiplex Reverse Transcription PCR for Detection of Zika, Chikungunya, and Dengue Viruses. Emerging infectious diseases. 2016;22(7):1295–7. pmid:27184629; PubMed Central PMCID: PMC4918162.
  61. 61. Waggoner JJ, Abeynayake J, Sahoo MK, Gresh L, Tellez Y, Gonzalez K, et al. Comparison of the FDA-approved CDC DENV-1-4 real-time reverse transcription-PCR with a laboratory-developed assay for dengue virus detection and serotyping. Journal of clinical microbiology. 2013;51(10):3418–20. pmid:23903549; PubMed Central PMCID: PMC3811623.
  62. 62. Waggoner JJ, Abeynayake J, Sahoo MK, Gresh L, Tellez Y, Gonzalez K, et al. Single-reaction, multiplex, real-time rt-PCR for the detection, quantitation, and serotyping of dengue viruses. PLoS neglected tropical diseases. 2013;7(4):e2116. pmid:23638191; PubMed Central PMCID: PMC3630127.
  63. 63. Zhang B, Pinsky BA, Ananta JS, Zhao S, Arulkumar S, Wan H, et al. Diagnosis of Zika virus infection on a nanotechnology platform. Nature medicine. 2017;23(5):548–50. Epub 2017/03/07. pmid:28263312.
  64. 64. Dirección General de Vigilancia de la Salud, Ministerio de Salud Pública y Bienestar Social. Boletín Epidemiológico: Semana Epidemiológica 52. 2021;(52).
  65. 65. Lee VJ, Chow A, Zheng X, Carrasco LR, Cook AR, Lye DC, et al. Simple clinical and laboratory predictors of Chikungunya versus dengue infections in adults. PLoS neglected tropical diseases. 2012;6(9):e1786. pmid:23029573; PubMed Central PMCID: PMC3459852.
  66. 66. Chadwick D, Arch B, Wilder-Smith A, Paton N. Distinguishing dengue fever from other infections on the basis of simple clinical and laboratory features: application of logistic regression analysis. Journal of clinical virology: the official publication of the Pan American Society for Clinical Virology. 2006;35(2):147–53. Epub 2005/08/02. pmid:16055371.
  67. 67. Potts JA, Rothman AL. Clinical and laboratory features that distinguish dengue from other febrile illnesses in endemic populations. Tropical medicine & international health: TM & IH. 2008;13(11):1328–40. pmid:18803612; PubMed Central PMCID: PMC2756447.
  68. 68. Rathore AP, Mantri CK, Aman SA, Syenina A, Ooi J, Jagaraj CJ, et al. Dengue virus-elicited tryptase induces endothelial permeability and shock. J Clin Invest. 2019;129(10):4180–93. Epub 2019/07/03. pmid:31265436; PubMed Central PMCID: PMC6763290.
  69. 69. Halstead SB. Dengue in the Americas and Southeast Asia: do they differ? Revista panamericana de salud publica = Pan American journal of public health. 2006;20(6):407–15. pmid:17341332.
  70. 70. Lee YH, Leong WY, Wilder-Smith A. Markers of dengue severity: a systematic review of cytokines and chemokines. The Journal of general virology. 2016;97(12):3103–19. Epub 2016/12/03. pmid:27902364.
  71. 71. Glasner DR, Puerta-Guardo H, Beatty PR, Harris E. The Good, the Bad, and the Shocking: The Multiple Roles of Dengue Virus Nonstructural Protein 1 in Protection and Pathogenesis. Annu Rev Virol. 2018;5(1):227–53. Epub 2018/07/26. pmid:30044715; PubMed Central PMCID: PMC6311996.
  72. 72. Jayathilaka D, Gomes L, Jeewandara C, Jayarathna GSB, Herath D, Perera PA, et al. Role of NS1 antibodies in the pathogenesis of acute secondary dengue infection. Nat Commun. 2018;9(1):5242. Epub 2018/12/12. pmid:30531923; PubMed Central PMCID: PMC6286345.
  73. 73. Nguyen THT, Clapham HE, Phung KL, Nguyen TK, TT DI, Nguyen THQ, et al. Methods to discriminate primary from secondary dengue during acute symptomatic infection. BMC infectious diseases. 2018;18(1):375. Epub 2018/08/09. pmid:30086716; PubMed Central PMCID: PMC6081805.
  74. 74. Huits R, Schwartz E. Fatal outcomes of imported dengue fever in adult travelers from non-endemic areas are associated with primary infections. J Travel Med. 2021;28(5). Epub 2021/02/17. pmid:33590860.
  75. 75. Duyen HT, Ngoc TV, Ha do T, Hang VT, Kieu NT, Young PR, et al. Kinetics of plasma viremia and soluble nonstructural protein 1 concentrations in dengue: differential effects according to serotype and immune status. The Journal of infectious diseases. 2011;203(9):1292–300. pmid:21335562; PubMed Central PMCID: PMC3069728.
  76. 76. Libraty DH, Endy TP, Houng HS, Green S, Kalayanarooj S, Suntayakorn S, et al. Differing influences of virus burden and immune activation on disease severity in secondary dengue-3 virus infections. The Journal of infectious diseases. 2002;185(9):1213–21. Epub 2002/05/10. pmid:12001037.
  77. 77. Nunes PC, Sampaio SA, da Costa NR, de Mendonca MC, Lima Mda R, Araujo SE, et al. Dengue severity associated with age and a new lineage of dengue virus-type 2 during an outbreak in Rio De Janeiro, Brazil. Journal of medical virology. 2016;88(7):1130–6. Epub 2016/04/12. pmid:27061403.
  78. 78. Vaughn DW, Green S, Kalayanarooj S, Innis BL, Nimmannitya S, Suntayakorn S, et al. Dengue viremia titer, antibody response pattern, and virus serotype correlate with disease severity. The Journal of infectious diseases. 2000;181:2. pmid:10608744
  79. 79. Wang WK, Chao DY, Kao CL, Wu HC, Liu YC, Li CM, et al. High levels of plasma dengue viral load during defervescence in patients with dengue hemorrhagic fever: implications for pathogenesis. Virology. 2003;305(2):330–8. Epub 2003/02/08. pmid:12573578.
  80. 80. Wang WK, Chen HL, Yang CF, Hsieh SC, Juan CC, Chang SM, et al. Slower rates of clearance of viral load and virus-containing immune complexes in patients with dengue hemorrhagic fever. Clinical infectious diseases: an official publication of the Infectious Diseases Society of America. 2006;43(8):1023–30. Epub 2006/09/20. pmid:16983615.
  81. 81. Nguyen MT, Ho TN, Nguyen VV, Nguyen TH, Ha MT, Ta VT, et al. An Evidence-Based Algorithm for Early Prognosis of Severe Dengue in the Outpatient Setting. Clinical infectious diseases: an official publication of the Infectious Diseases Society of America. 2017;64(5):656–63. Epub 2016/12/31. pmid:28034883; PubMed Central PMCID: PMC5850639.
  82. 82. Nunes PCG, Nogueira RMR, Heringer M, Chouin-Carneiro T, Damasceno Dos Santos Rodrigues C, de Filippis AMB, et al. NS1 Antigenemia and Viraemia Load: Potential Markers of Progression to Dengue Fatal Outcome? Viruses. 2018;10(6). Epub 2018/06/16. pmid:29903980; PubMed Central PMCID: PMC6024368.
  83. 83. Thomas L, Verlaeten O, Cabie A, Kaidomar S, Moravie V, Martial J, et al. Influence of the dengue serotype, previous dengue infection, and plasma viral load on clinical presentation and outcome during a dengue-2 and dengue-4 co-epidemic. The American journal of tropical medicine and hygiene. 2008;78(6):990–8. pmid:18541782.
  84. 84. Tukasan C, Furlan NB, Estofolete CF, Nogueira ML, da Silva NS. Evaluation of the importance of fever with respect to dengue prognosis according to the 2009 WHO classification: a retrospective study. BMC infectious diseases. 2017;17(1):6. Epub 2017/01/06. pmid:28052760; PubMed Central PMCID: PMC5209937.
  85. 85. Liu YE, Saul S, Rao AM, Robinson ML, Agudelo Rojas OL, Sanz AM, et al. An 8-gene machine learning model improves clinical prediction of severe dengue progression. Genome Med. 2022;14(1):33. Epub 2022/03/30. pmid:35346346; PubMed Central PMCID: PMC8959795.
  86. 86. Guzman MG, Jaenisch T, Gaczkowski R, Hang VTT, Sekaran SD, Kroeger A, et al. Multi-country evaluation of the sensitivity and specificity of two commercially-available NS1 ELISA assays for dengue diagnosis. PLoS neglected tropical diseases. 2010;4(8):e811. pmid:20824173