https://orcid.org/0000-0003-0901-7661
Figures
Abstract
Background
Dengue fever (DF) and West Nile fever (WNF) have become endemic worldwide in the last two decades. Studies suggest that individuals with diabetes mellitus (DM) are at a higher risk of developing severe complications from these diseases. Identifying the factors associated with a severe clinical presentation is crucial, as prompt treatment is essential to prevent complications and fatalities. This article aims to summarize and assess the published evidence regarding the link between DM and the risk of severe clinical manifestations in cases of DF and WNF.
Methodology/Principal findings
A systematic search was conducted using the PubMed and Web of Science databases. 27 studies (19 on DF, 8 on WNF) involving 342,873 laboratory-confirmed patients were included in the analysis. The analysis showed that a diagnosis of DM was associated with an increased risk for severe clinical presentations of both DF (OR 3.39; 95% CI: 2.46, 4.68) and WNF (OR 2.89; 95% CI: 1.89, 4.41). DM also significantly increased the risk of death from both diseases (DF: OR 1.95; 95% CI: 1.09, 3.52; WNF: OR 1.74; 95% CI: 1.40, 2.17).
Conclusions/Significance
This study provides strong evidence supporting the association between DM and an increased risk of severe clinical manifestations in cases of DF and WNF. Diabetic individuals in DF or WNF endemic areas should be closely monitored when presenting with febrile symptoms due to their higher susceptibility to severe disease. Early detection and appropriate management strategies are crucial in reducing the morbidity and mortality rates associated with DF and WNF in diabetic patients. Tailored care and targeted public health interventions are needed to address this at-risk population. Further research is required to understand the underlying mechanisms and develop effective preventive and therapeutic approaches.
Author summary
In our study, we investigated the association between diabetes mellitus (DM) and the risk of severe clinical manifestations in cases of dengue fever (DF) and West Nile fever (WNF). By analyzing 27 studies involving over 342,000 laboratory-confirmed patients, we found compelling evidence supporting a link between DM and an increased risk of severe complications in both DF and WNF. Moreover, DM was found to significantly raise the risk of mortality from these diseases.
Our findings emphasize the importance of early detection and appropriate management strategies for diabetic individuals residing in endemic areas. Healthcare providers should be vigilant in monitoring diabetic patients with febrile symptoms, as they are more susceptible to developing severe disease. Tailored care and targeted interventions are crucial to minimize the morbidity and mortality rates associated with DF and WNF in diabetic individuals.
These findings have significant implications for public health, highlighting the need for awareness campaigns and preventive measures aimed at diabetic individuals. Further research is needed to understand the underlying mechanisms and develop effective strategies for prevention and treatment. By addressing these issues, we can reduce the impact of DF and WNF on individuals with DM.
Citation: Lu H-Z, Xie Y-Z, Gao C, Wang Y, Liu T-T, Wu X-Z, et al. (2024) Diabetes mellitus as a risk factor for severe dengue fever and West Nile fever: A meta-analysis. PLoS Negl Trop Dis 18(5): e0012217. https://doi.org/10.1371/journal.pntd.0012217
Editor: Christopher M. Barker, University of California, Davis, UNITED STATES
Received: December 11, 2023; Accepted: May 14, 2024; Published: May 31, 2024
Copyright: © 2024 Lu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript and its Supporting Information files.
Funding: This research was supported by the National Natural Science Foundation of China (82102432 to SQD, 81971971 to YW) and the Anhui Provincial Natural Science Foundation Project (2108085QH347 to SQD). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Flaviviridae are a family of small enveloped viruses comprising four genera: Flavivirus, Pegivirus, Pestivirus, and Hepativirus, which are host-specific and pathogenic, mostly infecting mammals and birds. Clinical signs of flaviviral infection range from asymptomatic to severe or fatal hemorrhagic fever or neurologic disease [1]. Many flaviviruses are transmitted through the bite of infected arthropod vectors, primarily the Aedes genus and Culex genus. Human-to-human transmission from infected blood and tissues is also possible [2]. Flaviviruses have caused serious public health problems over the past decades, with epidemics of dengue virus (DENV), Japanese encephalitis virus (JEV), West Nile virus (WNV), Zika virus (ZIKV), and yellow fever virus (YFV) occurring worldwide [3]. A study of DENV prevalence estimated that 3.9 billion people worldwide are at risk of DENV infection. Over the past 20 years, the number of dengue cases reported to the World Health Organization (WHO) has increased more than sevenfold, from 505,430 in 2000 to more than 2.4 million in 2010 and 5.2 million in 2019 [4,5]. WNV first appeared in the northeastern United States in 1999 and is now distributed throughout most of the United States and southern Canada [6].
Although most people infected with DENV and WNV present asymptomatically or with undifferentiated febrile illnesses, a small number of infected individuals develop acute fever that may progress to severe clinical manifestations such as hemorrhage, vascular leakage, and encephalitis [7]. Because dengue fever (DF) and West Nile fever (WNF) are characterized by dynamic clinical changes over time, it is of great practical importance to identify predictive factors that measure the evolution of the disease into severe illness in the early clinical stages [8,9]. Available evidence suggests that age, sex, genetic background, and comorbidities may adversely affect the clinical presentation of the infection [9–11]. However, current knowledge of the risk factors for both diseases is insufficient to predict whether a patient will develop more severe clinical symptoms or even die. Early indicators of dengue progression to a severe stage (including abdominal pain or pressure, bleeding from mucous membranes, liver enlargement of more than 2 centimeters, and erythrocyte pressurization accompanied by a rapid drop in platelet count) are described in the 2009 WHO dengue guidelines [12]. In areas where the disease has been prevalent for a long time, the monitoring cost of these indicators is high, and unnecessary medical resources may be wasted. In addition, some of the warning signs may appear after the disease has progressed, lacking sensitivity and clinical value [13]. For WNF, there is also a lack of a clinical presentation that has been shown to be specific enough to predict severe illness, and it is now generally accepted that preexisting chronic conditions such as obesity, asthma, diabetes mellitus (DM), and hypertension are risk predictors of severe illness in WNF [14,15]. Movement disorders (muscle spasms, Parkinson’s syndrome) in patients and multifocal chorioretinitis have also been reported to be predictive of the development of WNF [16,17].
When infection occurs, elucidating the factors influencing disease severity is critical to identifying populations at high risk for severe illness, and effective intervention programmes and individualized clinical surveillance practices should specifically target these populations. DM is a multifaceted disease involving chronic metabolic disorders and immune dysfunction that leads to a wide range of clinical complications [18,19]. Additionally, DM is one of the most common and efficient predictors of potential clinical deterioration of flaviviruses [20,21]. The purpose of this study was to systematically review the available literature on the course of DF and WNF diseases associated with DM, to further determine whether DM promotes flavivirus infections (DENV and WNV) and to assess the magnitude of its role in serious versus nonserious clinical outcomes of disease infections.
Methods
Literature search
For this systematic review and meta-analysis, we followed the protocol described in the PRISMA statement [22–24]. We searched two databases (PubMed, Web of Science) to access all relevant published articles as of August 1, 2023 describing the association between DM and DENV and WNV. We used the following keywords: (("diabetes") OR ("mellitus") OR ("glycuresis") OR ("alloxan diabetes") OR ("alloxandiabetes") OR ("maturity-onset diabetes")) AND (("mosquito") OR ("mosquito-borne disease") OR ("MBD") OR ("dengue") OR ("breakbone fever") OR ("DENV") OR ("classical dengues") OR ("West Nile")). Only publications in English were included in this study. Conference abstracts were not included due to the lack of detailed descriptions of the study methods, and thus, the subsequent quality assessment could not be performed.
Inclusion and exclusion criteria
Two investigators (HZ Lu and YZ Xie) reviewed the titles and abstracts independently to identify the potentially eligible studies, for which the full texts were retrieved, and further assessment by reviewing the full text was conducted to identify the eligible studies. All discrepancies were resolved by discussion with the third investigator (C Gao).
The inclusion criteria of the studies in this meta-analysis were as follows: (1) DENV and WNV were clearly defined in the text; (2) both experimental and control groups were patients; and (3) reliable case identification methods were available.
The exclusion criteria were as follows: (1) no available full text or no extracted data; (2) fewer than 3 cases in each study or animal/cell study; (3) data from before 2000; and [4] when there were multiple publications on the same population or based on overlapping data, the latest or the largest study was included.
Data extraction
The following data were independently extracted from the studies by two investigators (Y Wang and TT Liu): (1) information about the publication (article title, first author, year of publication, year of data, region, study design); (2) data acquisition methods and case identification methods; (3) number of participants in the experiment; and (4) criteria for case definition.
Case definitions
Dengue.
Combining the 1997 and 2009 WHO guidelines for the classification of critical illnesses, we classified dengue progression into two groups [5,12]:
(1) Based on routine clinical data collected by the WHO’s 1997 guidelines, these guidelines classify symptomatic dengue virus infections into three clinical categories: undifferentiated fever, DF, and denguehemorrhagic fever (DHF). DHF was further categorized into four severity levels, of which levels III and IV were defined as dengue shock syndrome (DSS). We refer to DHF/DSS as “severe clinical presentation of dengue”.
(2) Applying the WHO 2009 classification criteria for dengue, a severe dengue case is defined as a suspected dengue patient with one or more of the following diseases: (i) severe plasma leakage that leads to shock (dengue shock) and/or fluid accumulation with respiratory distress; (ii) severe bleeding; and (iii) severe organ impairment.
West Nile fever
We refer to those who developed West Nile Neuroinvasive Disease (WNND) as having a “severe clinical presentation of WNV”, such as cases of WN encephalitis (WNE), WN meningitis (WNM), poliomyelitis or acute flaccid paralysis, WNV-associated retinopathy (WNVR), chorioretinitis or fatal cases [15,25–31].
Quality assessment
The qualities of the included studies were assessed by the Newcastle Ottawa Quality Scale (NOS) [32]. This scale evaluates the quality of the study by 8 questions from three aspects, namely, adequate case definition; representativeness of the cases; selection of controls; definition of controls; comparability of cases and controls on the basis of the design or analysis; ascertainment of exposure; same method of ascertainment for cases and controls; and nonresponse rate. For each trial, the results of the assessment were given. The quality assessment was performed by two investigators independently (LHZ and GC), and discrepancies were resolved by discussion with the third investigator (DSQ).
Statistical analysis
Meta-analyses were performed using Stata 16.0. We used odds ratios as the “primary model” and used random-effects or fixed-effects meta-analysis across all studies. The results were visualized in forest plots. Subgroup analyses were used to address heterogeneity and variability in the dependent variable and age of patients in the control and experimental groups. Heterogeneity between studies was assessed by using the I2 test, the chi-squared test and forest plots. Heterogeneity was considered statistically significant when the P value < 0.05 or I2 values > 50% [33,34]. A random-effects model was used when heterogeneity was considered; otherwise, a fixed-effects model was used. In addition, sensitivity analyses were used to assess the robustness.
Result
General characteristics of the included studies
In total, 1700 studies were retrieved from the database in the initial search, of which 278 were considered potentially eligible after reviewing the titles and abstracts. After reading the full text of the articles, 31 articles were eligible, of which 4 studies used the same dataset as the others, and 27 articles were included in this meta-analysis [15,20,25–31,35–52]. The processes of study screening are shown in Fig 1, and the general characteristics of the included studies and the corresponding NOS scores are shown in Tables 1 and 2. Of these 27 studies, 19 were studies on dengue, 1 study was only able to extract data on deaths [49], and the remaining 8 were studies on WNF. In addition, most of these studies were case–control studies based on hospital administrative records. Both dengue and WNF were diagnosed by ELISA or RT–PCR, whereas DM was mostly derived from case records or self-reported by patients, and nine of these studies did not report the source of the DM diagnosis. Seven studies on dengue defined severe illness according to the WHO 1997 criteria, 7 studies defined it according to the WHO 2009 criteria, and the remaining studies combined both criteria. Subsequently, we divided the severe cases of both diseases into DHF (8 studies) and DSS (2 studies) according to the WHO 1997 criteria, and the remaining 8 studies used the WHO 2009 criteria and therefore were not included in the subgroup analysis. WNF was classified as WNM (1 study), WNE (2 studies), and WNVR (1 study). Finally, we tried to extract mortality data to analyze the effect of DM on mortality. We extracted 10 studies from 27 studies, 6 studies on dengue and 4 studies on WNF(S1 Table).
Risk of bias assessment
All 31 studies were evaluated by the NOS tool. Five data points scored 9, nine data points scored 8, four data points scored 7, and eight data points scored 6. The findings of the funnel plot were confirmed by Egger’s test, indicating no significant publication bias in the analysis except for the results of two studies on dengue (P>0.05) (Table 3).
Effects of DM on dengue and WNF
Eighteen and 8 studies reported the effect of DM on dengue and West Nile fever severity, respectively. Heterogeneity tests showed a high degree of heterogeneity in the effect of DM on the two diseases, with I2(P) values of 76% (P < 0.001) and 66% (P = 0.003), respectively. Therefore, a random effects model was used to estimate the combined effect of DM on both diseases. The results showed that DM significantly increased the risk of severe dengue and WNF, with ORs of 3.39 (95% CI: 2.46, 4.68) and 2.89 (95% CI: 1.89, 4.41), respectively (Table 3 and Figs 2 and 3). The funnel plot showed publication bias (P<0.05) on the effect of DM on dengue fever, while there was no publication bias (P<0.05) on WNF (Table 3 and S1, S2, S3 and S4 Figs). Sensitivity analysis showed a significant change in the study results when two articles, Mallhi (2015) and Mirza (2016), were excluded, and the heterogeneity was reduced from 76% to 47%.
The size of the black square corresponding to each study is proportional to the sample size, and the center of each square represents the OR. Horizontal line shows the corresponding 95% CI of the OR. Pooled OR is represented by hollow diamond.
The size of the black square corresponding to each study is proportional to the sample size, and the center of each square represents the OR. Horizontal line shows the corresponding 95% CI of the OR. Pooled OR is represented by hollow diamond.
We then stratified dengue (DHF, DSS) and WNF (WNM, WNE, WNVR) by disease progression or clinical symptoms to estimate the effect of DM on both. The combined effect of DM on DHF (n = 8) (OR = 2.73, 95% CI: 1.68, 4.44) and DSS (n = 2) (OR = 7.29, 95% CI: 3.09, 17.20) was statistically significant in both classifications of dengue (Table 3 and Fig 4). Among the 3 classifications of WNF, the combined effect of DM on WNE (n = 2) (OR = 3.29, 95% CI: 1.15, 9.40) and WNVR (n = 1) (OR = 11.00, 95% CI: 1.13, 106.84) was statistically significant, whereas WNM (n = 1) (OR = 1.15, 95% CI: 0.38, 3.49) did not show statistical significance (Table 3 and Fig 5). We then extracted mortality data for subgroup analyses to analyze the impact of DM on mortality from both diseases. The results showed a statistically significant combined effect of DM on dengue deaths (n = 6) (OR = 1.95, 95% CI: 1.09, 3.52) and West Nile fever deaths (n = 4) (OR = 1.74, 95% CI: 1.40, 2.17) (Table 3 and Fig 6). With the exception of all studies (P = 0.006) and DHF (P = 0.032), the funnel plot did not show publication bias in several stratified studies (P>0.05) (S5, S6, S7, S8, S9 and S10 Figs).
Forest plot of denguehemorrhagic fever (A). Forest plot of dengue shock syndrome (B). The size of the black square corresponding to each study is proportional to the sample size, and the center of each square represents the OR. Horizontal line shows the corresponding 95% CI of the OR. Pooled OR is represented by hollow diamond.
Forest plot of West Nile meningitis (A). Forest plot of West Nile encephawlitis (B). Forest plot of West Nile virus-associated retinopathy (C). The size of the black square corresponding to each study is proportional to the sample size, and the center of each square represents the OR. Horizontal line shows the corresponding 95% CI of the OR. Pooled OR is represented by hollow diamond.
Forest plot of diabetes mellitus on death of dengu (A). Forest plot of diabetes mellitus on death of West Nile fever (B). The size of the black square corresponding to each study is proportional to the sample size, and the center of each square represents the OR. Horizontal line shows the corresponding 95% CI of the OR. Pooled OR is represented by hollow diamond.
Discussion
In this meta-analysis, we found that having DM was a demographic risk factor for the progression of DF and WNF to severe disease. Additionally, the results of the subgroup analyses showed that differences in the dependent variable (deterioration or death) did not lead to changes in the conclusions.
Our study also suffers from a number of shortcomings that cannot be addressed at this time. Most of the studies selected were retrospective, had different clinical and laboratory diagnostic criteria and control groups and were heterogeneous in terms of exposure and outcome. Some studies only used ELISA for diagnosis, which may result in serological cross-reactivity [57]. Most patients with DM have other comorbidities (e.g., hypertension, coronary artery disease), and we were unable to assess the impact of DM on the conditions of DF and WNF in isolation. At the same time, the judgment and inclusion criteria of DM in these articles are not clear, and only three studies showed a specific typology of DM (type 2) [39,43,44]. The diagnosis of DM mostly comes from case records, and some patients report themselves. It is difficult to distinguish whether DM is diagnosed before, after or during the onset of infection. Based on the literature included, we found that the majority of WNF studies originated in the United States, which may be related to the repeated outbreaks of WNF in North America over the past decade, which have resulted in the continued spread of WNF in communities [58–60]. This strong geographic trend may lead to impeded extrapolation of results. Most of the studies in dengue have adopted the new case classification of the WHO in 2009 to improve case management in the clinical setting. However, a proportion of reports still used the 1997 version, which was developed by the WHO based on a model of DF in Thai children and lacked clarity in the description of the measurement of outcome endpoints [61].
DM has been listed as a significant predictor of flavivirus-caused disease in the past, and this is supported by the findings of our meta-analysis. The underlying pathophysiologic mechanisms regarding the role of DM in the progression of DF and WNF are not completely clear at this time. Lee et al. found that patients with difficult glycemic control (HbA1c >7%) had a higher risk of dengue exacerbation than diabetic patients with better glycemic control (both without additional comorbidities) [44]. Hyperglycemia in diabetic patients is thought to lead to immune response dysfunction, with suppression of cytokine production, defective phagocytosis, and immune cell dysfunction, in addition to the risk of natural barrier impairment due to neuropathy [62,63]. This provides more opportunities for viruses to invade. In addition, platelet activity is increased to varying degrees in both type 1 and type 2 diabetic patients [64]. Platelets can interact with neutrophils to promote their activation and release of platelet factor (CXCL4), which has been shown to significantly inhibit the interferon pathway and enhance DENV replication in cells both in vitro and in vivo [65]. In addition, there is some biological evidence that can prove that patients with DM who are infected with DENV and WNV are more likely to aggravate infection. A study has shown that blood sugar is conducive to DENV replication, and it promotes virus transmission in mosquitoes through AKT and TOR signaling [66]. Furthermore, the study reveals that mosquito cells incubated in a high glucose medium exhibit upregulated levels of DENV proteins NS1, NS5, E, and prM, as well as AKT signaling (AKT phosphorylation) and TOR signaling (S6K phosphorylation) [66]. Some articles have found that monocytes infected with DENV in type 2 DM increase the production of interleukin-4 (IL-4), interleukin-10 (IL-10) and granulocyte-macrophage colony stimulating factor (GM-CSF) [67]. According to records, T helper (Th) cells play an important role in the immune pathogenesis of DHF [68]. According to the type of cytokine produced during activation, Th cells are divided into Th1 and Th2 cells. Activated Th1 cells produce IFN-γ (interferon), IL-2 and IL-12, while Th2 cells produce IL-4, IL-5, IL-10 and IL-13 [69]. Among the various mechanisms of the pathogenesis of DF, it has been reported that in secondarily infected hosts, a high DENV load is indirectly associated with DHF [70], and the overwhelming activation of Th2 cytokines has been documented in the development of DHF in patients with primary and secondary infections with DF [68,71]. Specifically, in the Th2 cytokine spectrum, IL-4 is the most effective cytokine for inducing Th2 cell differentiation, and IL-10 is responsible for the anti-inflammatory response in host immune activity [72]. In addition, it has been reported that compared with mild DF patients, the serum GM-CSF of severe DF patients is significantly increased [73]. Our stratified analysis similarly confirms this observation, with the OR value for DSS (7.29) being significantly higher than that for DHF (2.73).
Mukesh Kumar and others performed experiments on WNV infection in diabetic mouse models. The diabetic mouse model showed a high susceptibility to WNV disease, showing higher tissue tropism and mortality than wild-type mice. This is related to WNV infection and increased inflammation in diabetic mice and severely impaired and delayed specific immune response, which is characterized by delayed induction of IFN-α (interferon), and the concentration of WNV-specific IgM and IgG antibodies decreased in viremia [74]. Later, they discovered that the presence of DM significantly changed the recruitment of white blood cells in the brain, resulting in failure to clear the WNV infection in the brains of diabetic mice [75]. These findings are consistent with our study, emphasizing the importance of researching the role of DM in DF and WNF infections, and highlighting that DM could worsen the symptoms and severity of these diseases. Thus, our study further supports the need to focus on DM in the management of DF and WNF infections, as well as the importance of individualized intervention measures for patients with DM.
In addition to dengue and WNF, several studies have shown the effect of DM on Zika virus disease, Japanese encephalitis, and yellow fever. Azar et al. demonstrated increased susceptibility of Aedes aegypti that fed on “diabetic” bloodmeals to ZIKV by in vitro and in vivo modeling of type II DM and suggested that the prevalence of type II DM in the population may have a significant impact on ZIKV transmission [76]. Ahlers et al. showed that mammalian insulin can trigger AKT and ERK signaling in mosquitoes, leading to the transcription of JAK/STAT-associated antiviral genes [77]. DM was one of the most common comorbidities in the study patients (9.94%), and patients with comorbid JEV had higher medical costs than patients without DM [78]. Studies have shown that JEV comorbid with DM significantly increased the risk of death by 2.47 times (P<0.05) [74]. Our results indicate that diabetes has a statistically significant combined impact on dengue fever mortality (OR = 1.95) and West Nile fever mortality (OR = 1.74). Patients with YFV and DM had a higher case fatality rate (CFR) of 80% compared with 65% in patients without DM [79]. In addition, DM attenuates the YFV vaccine effect by reducing 2’,5’-oligoadenylate synthase levels. Basal 2’,5’-oligoadenylate activity increased several-fold in response to YFV vaccination. In DM subjects, this increase was significantly lower (P = 0.025) [80]. Based on these reports, DM can be shown to increase the risk of adverse outcomes of mosquito-borne flavivirus infection [14,76,80].
Overall, studying DM for DF and WNF infections is important to reduce the burden of disease by guiding approaches to improve patient prognosis or differential case management. We provide evidence that the prevalence of DM is higher in severe cases of dengue and WNF infection than in nonsevere cases. This means that DM may exacerbate the symptoms of DF and WNF infections. A further study with more focus on DM, DENV and WNV is therefore suggested. Examples include longitudinal studies of DM, DF and WNF, i.e., the effect of blood glucose concentration on the clinical symptoms of the disease. In addition, standardized prospective cohort studies in areas with high rates of infection will help to better understand the etiological role of DM in serious disease outcomes and to evaluate the causal relationship between them. This study can also provide some warnings for doctors who have DENV or WNV patients. For example, when a DENV or WNV patient with DM appears, the doctor should promptly decide whether it needs close observation, adequate treatment or hospitalization, and when a patient with DF has severe clinical symptoms, the doctor should promptly ask about past medical history, especially the history of DM. Patients with DM living in areas with high rates of DENV and WNV infection should be given a higher level of attention after diagnosis.
Supporting information
S4 Fig. Trim and fill analysis of West Nile fever.
https://doi.org/10.1371/journal.pntd.0012217.s004
(TIF)
S5 Fig. Egger’s test of dengue hemorrhagic fever.
https://doi.org/10.1371/journal.pntd.0012217.s005
(TIF)
S6 Fig. Trim and fill analysis of dengue hemorrhagic fever.
https://doi.org/10.1371/journal.pntd.0012217.s006
(TIF)
S8 Fig. Trim and fill analysis of death of dengue.
https://doi.org/10.1371/journal.pntd.0012217.s008
(TIF)
S9 Fig. Egger’s test of death of West Nile fever.
https://doi.org/10.1371/journal.pntd.0012217.s009
(TIF)
S10 Fig. Trim and fill analysis of death of West Nile fever.
https://doi.org/10.1371/journal.pntd.0012217.s010
(TIF)
S1 Table. Data collection of the studies included in the meta-analysis.
https://doi.org/10.1371/journal.pntd.0012217.s011
(DOCX)
References
- 1. Postler TS, Beer M, Blitvich BJ, Bukh J, de Lamballerie X, Drexler JF, et al. Renaming of the genus Flavivirus to Orthoflavivirus and extension of binomial species names within the family Flaviviridae. Arch Virol. 2023;168(9):224.
- 2. Nanaware N, Banerjee A, Mullick Bagchi S, Bagchi P, Mukherjee A. Dengue Virus Infection: A Tale of Viral Exploitations and Host Responses. Viruses. 2021;13(10). pmid:34696397
- 3. Mackenzie JS, Gubler DJ, Petersen LR. Emerging flaviviruses: the spread and resurgence of Japanese encephalitis, West Nile and dengue viruses. Nat med. 2004;10(Suppl 12):S98–S109. pmid:15577938
- 4. Roy SK, Bhattacharjee S. Dengue virus: epidemiology, biology, and disease aetiology. Can J Microbiol. 2021;67(10):687–702. pmid:34171205
- 5. Dengue and severe dengue. March 17, 2023[cited August 1, 2023]. Available from: http://www.who.int/csr/resources/publications/dengue/Denguepublication/en/
- 6. Fagre AC, Lyons S, Staples JE, Lindsey N. West Nile Virus and Other Nationally Notifiable Arboviral Diseases—United States, 2021. MMWR Morb Mortal Wkly Rep. 2023;72(34):901–6. pmid:37616182
- 7. Messina JP, Brady OJ, Scott TW, Zou C, Pigott DM, Duda KA, et al. Global spread of dengue virus types: mapping the 70 year history. Trends microbiol. 2014;22(3):138–46. pmid:24468533
- 8. 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. Arch Virol. 2013;158:1445–59. pmid:23471635
- 9. Zak IT, Altinok D, Merline JR, Chander S, Kish KK. West Nile virus infection. Am J Roentgenol. 2005;184(3):957–61. pmid:15728624
- 10. Kouri GP, Guzmán MG, Bravo JR, Triana C. Dengue haemorrhagic fever/dengue shock syndrome: lessons from the Cuban epidemic, 1981. Bulletin of the World Health Organization. 1989;67(4):375. pmid:2805215
- 11. Khor CC, Chau TNB, Pang J, Davila S, Long HT, Ong RT, et al. Genome-wide association study identifies susceptibility loci for dengue shock syndrome at MICB and PLCE1. Nat Rev Genet. 2011;43(11):1139–41. pmid:22001756
- 12.
WHO. Dengue guidelines for diagnosis t, prevention and control: new edition. Geneva: World Health Organization, 2009. [cited August 1, 2023]. Available from: https://www.who.int/publications/i/item/9789241547871
- 13. Kalayanarooj S. Dengue classification: current WHO vs. the newly suggested classification for better clinical application. J Med Assoc Thai. 2011;94(Suppl 3):S74–84. pmid:22043757
- 14. Badawi A, Velummailum R, Ryoo SG, Senthinathan A, Yaghoubi S, Vasileva D, et al. Prevalence of chronic comorbidities in dengue fever and West Nile virus: A systematic review and meta-analysis. PloS one. 2018;13(7):e0200200. pmid:29990356
- 15. Snyder RE, Cooksey GS, Kramer V, Jain S, Vugia DJ. West Nile virus–associated hospitalizations, California, 2004–2017. Clin Infect Dis. 2021;73(3):441–7. pmid:32525967
- 16. Sejvar JJ, Haddad MB, Tierney BC, Campbell GL, Marfin AA, Van Gerpen JA, et al. Neurologic manifestations and outcome of West Nile virus infection. Jama. 2003;290(4):511–5. pmid:12876094
- 17.
Abroug F, Ouanes-Besbes L, Letaief M, Romdhane FB, Khairallah M, Triki H, et al., editors. A cluster study of predictors of severe West Nile virus infection. Mayo Clinic Proceedings; 2006: Elsevier.
- 18. Turner R, Holman R, Stratton I, Cull C, Frighi V, Manley S. UK Prospective Diabetes Study Group: Tight blood pressure control and risk of macrovascular and microvascular complications in type 2 diabetes. BMJ. 1998;317(7160):703–13.
- 19. Stratton IM, Adler AI, Neil HAW, Matthews DR, Manley SE, Cull CA, et al. Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. Bmj. 2000;321(7258):405–12. pmid:10938048
- 20. Pang J, Salim A, Lee VJ, Hibberd ML, Chia KS, Leo YS, et al. Diabetes with hypertension as risk factors for adult dengue hemorrhagic fever in a predominantly dengue serotype 2 epidemic: a case control study. P Plos Neglect Trop D. 2012;6(5):e1641.
- 21. Nash D, Mostashari F, Fine A, Miller J, O’Leary D, Murray K, et al. The outbreak of West Nile virus infection in the New York City area in 1999. N Engl J Med. 2001;344(24):1807–14. pmid:11407341
- 22. Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JP, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. Ann Intern Med. 2009;151(4):W-65–W-94.
- 23. Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic reviews. 2015;4(1):1–9. pmid:25554246
- 24. Huy NT, Van Giang T, Thuy DHD, Kikuchi M, Hien TT, Zamora J, et al. Factors associated with dengue shock syndrome: a systematic review and meta-analysis. Plos Neglect Trop D. 2013;7(9):e2412. pmid:24086778
- 25. Vrioni G, Mavrouli M, Kapsimali V, Stavropoulou A, Detsis M, Danis K, et al. Laboratory and clinical characteristics of human West Nile virus infections during 2011 outbreak in southern Greece. Vector-Borne Zoonot. 2014;14(1):52–8. pmid:24359426
- 26. Racsa L, Gander R, Chung W, Southern P, Le J, Beal S, et al. Clinical features of West Nile virus epidemic in Dallas, Texas, 2012. Diagn Micr Infec Dis. 2014;78(2):132–6. pmid:24316017
- 27. Lindsey NP, Staples JE, Lehman JA, Fischer M. Medical risk factors for severe West Nile virus disease, United States, 2008–2010. The Am J Trop Med Hyg. 2012;87(1):179. pmid:22764311
- 28. Khairallah M, Yahia SB, Letaief M, Attia S, Kahloun R, Jelliti B, et al. A prospective evaluation of factors associated with chorioretinitis in patients with West Nile virus infection. Ocul Immunol Inflamm. 2007;15(6):435–9. pmid:18085487
- 29. Jean CM, Honarmand S, Louie JK, Glaser CA. Risk factors for West Nile virus neuroinvasive disease, California, 2005. Emerg Infect Dis. 2007;13(12):1918. pmid:18258047
- 30. Murray K, Baraniuk S, Resnick M, Arafat R, Kilborn C, Cain K, et al. Risk factors for encephalitis and death from West Nile virus infection. Epidemiology & Infection. 2006;134(6):1325–32. pmid:16672108
- 31. Bode AV, Sejvar JJ, Pape WJ, Campbell GL, Marfin AA. West Nile virus disease: a descriptive study of 228 patients hospitalized in a 4-county region of Colorado in 2003. Clin Infect Dis. 2006;42(9):1234–40. pmid:16586381
- 32. Wells GA, Shea B, O’Connell D, Peterson J, Welch V, Losos M, et al. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. 2000.
- 33. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. Bmj. 2003;327(7414):557–60. pmid:12958120
- 34. Zintzaras E, Lau J. Synthesis of genetic association studies for pertinent gene–disease associations requires appropriate methodological and statistical approaches. J Clin Epidemiol. 2008;61(7):634–45. pmid:18538260
- 35. Abualamah WA, Banni HS, Almasmoum HA, Allohibi YA, Samarin HM, Bafail MA. Determining Risk Factors for Dengue Fever Severity in Jeddah City, a Case-Control Study (2017). Pol J Microbiol. 2020;69(3):331–7. pmid:33574862
- 36. Agrawal VK, Prusty BSK, Reddy CS, Mohan Reddy GK, Agrawal RK, Sekher Srinivasarao Bandaru VC. Clinical profile and predictors of Severe Dengue disease: A study from South India. Caspian J Intern Med. 2018;9(4):334–40. pmid:30510647
- 37. Baig Mirza AM, Fida M, Murtaza G, Niazi R, Hanif A, Irfan K, et al. Association of metabolic factors with dengue viral infection on admission triage which predict its clinical course during Lahore dengue epidemic. J Pak Med Assoc. 2016;66(9):1102–6. pmid:27654728
- 38. Boillat-Blanco N, Klaassen B, Mbarack Z, Samaka J, Mlaganile T, Masimba J, et al. Dengue fever in Dar es Salaam, Tanzania: clinical features and outcome in populations of black and non-black racial category. BMC Infect Dis. 2018;18(1):644. pmid:30541456
- 39. Chen C-Y, Lee M-Y, Lin K-D, Hsu W-H, Lee Y-J, Hsiao P-J, et al. Diabetes mellitus increases severity of thrombocytopenia in dengue-infected patients. International journal of molecular sciences. 2015;16(2):3820–30. pmid:25674854
- 40. Figueiredo MA, Rodrigues LC, Barreto ML, Lima JW, Costa MC, Morato V, et al. Allergies and diabetes as risk factors for dengue hemorrhagic fever: results of a case control study. PLoS Negl Trop Dis. 2010;4(6):e699. pmid:20532230
- 41. Jisamerin J, Mohamedkalifa A, Gaur A, Geetha J, Sakthivadivel V. Dengue: a neglected disease of concern. Cureus. 2021;13(10). pmid:34692260
- 42. Karunakaran A, Ilyas WM, Sheen SF, Jose NK, Nujum ZT. Risk factors of mortality among dengue patients admitted to a tertiary care setting in Kerala, India. J Infect Public Health. 2014;7(2):114–20. pmid:24290074
- 43. Kuo HJ, Lee IK, Liu JW. Analyses of clinical and laboratory characteristics of dengue adults at their hospital presentations based on the World Health Organization clinical-phase framework: Emphasizing risk of severe dengue in the elderly. J Microbiol Immunol Infect. 2018;51(6):740–8. pmid:28734676
- 44. 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. J Microbiol Immunol Infect. 2020;53(1):69–78. pmid:30146413
- 45. Lee MS, Hwang KP, Chen TC, Lu PL, Chen TP. Clinical characteristics of dengue and dengue hemorrhagic fever in a medical center of southern Taiwan during the 2002 epidemic. J Microbiol Immunol Infect. 2006;39(2):121–9. pmid:16604244
- 46. Mallhi TH, Khan AH, Adnan AS, Sarriff A, Khan YH, Jummaat F. Clinico-laboratory spectrum of dengue viral infection and risk factors associated with dengue hemorrhagic fever: a retrospective study. BMC Infect Dis. 2015;15:399. pmid:26423145
- 47. Md Sani SS, Han WH, Bujang MA, Ding HJ, Ng KL, Amir Shariffuddin MA. Evaluation of creatine kinase and liver enzymes in identification of severe dengue. BMC Infect Dis. 2017;17(1):505. pmid:28732476
- 48. Ng WY, Atan R, Mohd Yunos N, Bin Md Kamal AH, Roslan MH, Quah KY, et al. A double whammy: The association between comorbidities and severe dengue among adult patients-A matched case-control study. PLoS One. 2022;17(9):e0273071. pmid:36126060
- 49. Pang J, Thein TL, Leo YS, Lye DC. Early clinical and laboratory risk factors of intensive care unit requirement during 2004–2008 dengue epidemics in Singapore: a matched case-control study. BMC Infect Dis. 2014;14:649. pmid:25475217
- 50. Wang WH, Lin CY, Chang K, Urbina AN, Assavalapsakul W, Thitithanyanont A, et al. A clinical and epidemiological survey of the largest dengue outbreak in Southern Taiwan in 2015. Int J Infect Dis. 2019;88:88–99. pmid:31521850
- 51. Wei HY, Shu PY, Hung MN. Characteristics and Risk Factors for Fatality in Patients with Dengue Hemorrhagic Fever, Taiwan, 2014. Am J Trop Med Hyg. 2016;95(2):322–7. pmid:27273649
- 52. Werneck GL, Macias AE, Mascarenas C, Coudeville L, Morley D, Recamier V, et al. Comorbidities increase in-hospital mortality in dengue patients in Brazil. Mem Inst Oswaldo Cruz. 2018;113(8):e180082. pmid:30043823
- 53. Thein TL, Leo YS, Fisher DA, Low JG, Oh HM, Gan VC, et al. Risk factors for fatality among confirmed adult dengue inpatients in Singapore: a matched case-control study. PLoS One. 2013;8(11):e81060. pmid:24278377
- 54. Wong JG, Thein TL, Leo YS, Pang J, Lye DC. Identifying Adult Dengue Patients at Low Risk for Clinically Significant Bleeding. PLoS One. 2016;11(2):e0148579. pmid:26849556
- 55. Murray KO, Koers E, Baraniuk S, Herrington E, Carter H, Sierra M, et al. Risk factors for encephalitis from West Nile Virus: a matched case-control study using hospitalized controls. Zoonoses Public Health. 2009;56(6–7):370–5. pmid:19175570
- 56. Weatherhead JE, Miller VE, Garcia MN, Hasbun R, Salazar L, Dimachkie MM, et al. Long-term neurological outcomes in West Nile virus-infected patients: an observational study. Am J Trop Med Hyg. 2015;92(5):1006–12. pmid:25802426
- 57. Mansfield KL, Horton DL, Johnson N, Li L, Barrett ADT, Smith DJ, et al. Flavivirus-induced antibody cross-reactivity. J Gen Virol. 2011;92(Pt 12):2821–9. pmid:21900425
- 58. Petersen LR, Marfin AA. Shifting epidemiology of Flaviviridae. J Travel Med. 2005;12(suppl_1):s3–s11. pmid:16225801
- 59. Krow-Lucal E, Lindsey NP, Lehman J, Fischer M, Staples JE. West Nile virus and other nationally notifiable arboviral diseases—United States, 2015. Mmwr-Morbid Mortal W. 2017;66(2):51.
- 60. Chancey C, Grinev A, Volkova E, Rios M. The global ecology and epidemiology of West Nile virus. Biomed Res Int. 2015;2015. pmid:25866777
- 61. World Health Organization. Dengue haemorrhagic fever Diagnosis t, prevention and control. 2nd edition. 1997. Report No.: ISBN 92 4 154500 3. [cited August 1 2023]. Available from: http://www.who.int/csr/resources/publications/dengue/Denguepublication/en/.
- 62. Berbudi A, Rahmadika N, Tjahjadi AI, Ruslami R. Type 2 diabetes and its impact on the immune system. Curr Diabetes Rev. 2020;16(5):442–9. pmid:31657690
- 63. Roep BO, Thomaidou S, van Tienhoven R, Zaldumbide A. Type 1 diabetes mellitus as a disease of the β-cell (do not blame the immune system?). Nat Rev Endocrinol. 2021;17(3):150–61.
- 64. Kaur R, Kaur M, Singh J. Endothelial dysfunction and platelet hyperactivity in type 2 diabetes mellitus: molecular insights and therapeutic strategies. Cardiovascular diabetology. 2018;17(1):1–17.
- 65. Singh A, Bisht P, Bhattacharya S, Guchhait P. Role of platelet cytokines in dengue virus infection. Front Cell Infect Mi. 2020;10:561366. pmid:33102253
- 66. Weng SC, Tsao PN, Shiao SH. Blood glucose promotes dengue virus infection in the mosquito Aedes aegypti. Parasit Vectors. 2021;14(1):376.
- 67. Lee I-K, Hsieh C-J, Chen R-F, Yang Z-S, Wang L, Chen C-M, et al. Increased production of interleukin-4, interleukin-10, and granulocyte-macrophage colony-stimulating factor by type 2 diabetes’ mononuclear cells infected with dengue virus, but not increased intracellular viral multiplication. Biomed Res Int. 2013;2013.
- 68. Chaturvedi U, Agarwal R, Elbishbishi E, Mustafa A. Cytokine cascade in dengue hemorrhagic fever: implications for pathogenesis. FEMS Immunology & Medical Microbiology. 2000;28(3):183–8. pmid:10865168
- 69. Mosmann TR, Sad S. The expanding universe of T-cell subsets: Th1, Th2 and more. Immunology today. 1996;17(3):138–46. pmid:8820272
- 70. Wang W-K, Chao D-Y, Kao C-L, Wu H-C, Liu Y-C, Li C-M, 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. pmid:12573578
- 71. Chaturvedi U, Elbishbishi E, Agarwal R, Raghupathy R, Nagar R, Tandon R, et al. Sequential production of cytokines by dengue virus-infected human peripheral blood leukocyte cultures. Eur J Clin Microbiol. 1999;59(3):335–40. pmid:10502266
- 72. Kurane I. Dengue hemorrhagic fever with special emphasis on immunopathogenesis. Eur J Clin Microbiol. 2007;30(5–6):329–40. pmid:17645944
- 73. Bozza FA, Cruz OG, Zagne SM, Azeredo EL, Nogueira RM, Assis EF, et al. Multiplex cytokine profile from dengue patients: MIP-1beta and IFN-gamma as predictive factors for severity. BMC Infect D. 2008;8(1):1–11. pmid:18578883
- 74. Kumar M, Roe K, Nerurkar PV, Namekar M, Orillo B, Verma S, et al. Impaired virus clearance, compromised immune response and increased mortality in type 2 diabetic mice infected with West Nile virus. 2012. pmid:22953001
- 75. Kumar M, Roe K, Nerurkar PV, Orillo B, Thompson KS, Verma S, et al. Reduced immune cell infiltration and increased pro-inflammatory mediators in the brain of Type 2 diabetic mouse model infected with West Nile virus. J Neuroinflamm. 2014;11:1–17. pmid:24750819
- 76. Azar SR, Campos RK, Yun R, Strange T, Rossi SL, Hanley KA, et al. Aedes aegypti Shows Increased Susceptibility to Zika Virus via Both In Vitro and In Vivo Models of Type II Diabetes. Viruses. 2022;14(4):665.
- 77. Ahlers LR, Trammell CE, Carrell GF, Mackinnon S, Torrevillas BK, Chow CY, et al. Insulin potentiates JAK/STAT signaling to broadly inhibit flavivirus replication in insect vectors. Cell reports. 2019;29(7):1946–60. e5. pmid:31722209
- 78. Chen H-Y, Yang C-Y, Hsieh C-Y, Yeh C-Y, Chen C-C, Chen Y-C, et al. Long-term neurological and healthcare burden of adults with Japanese encephalitis: A nationwide study 2000–2015. PLoS neglect Trop D. 2021;15(9):e0009703.
- 79. Ho Y-L, Joelsons D, Leite GF, Malbouisson LM, Song AT, Perondi B, et al. Severe yellow fever in Brazil: clinical characteristics and management. J travel Med. 2019;26(5):taz040. pmid:31150098
- 80. Bonnevie-Nielsen V, Larsen M, Frifelt J, Michelsen B, Lernmark Å. Association of IDDM and attenuated response of 2′, 5′-oligoadenylate synthetase to yellow fever vaccine. Diabetes. 1989;38(12):1636–42. pmid:2573556