Introduction
Type 1 diabetes mellitus (T1DM) is generally believed to be a chronic autoimmune disease characterised by the destruction of insulin-producing β-cells that results from a complex interaction between genetic susceptibility, immunological factors and environmental agents [Reference Op1]. It has been reported that the present global number of individuals with diabetes was estimated at 415 million, but has reached as much as 642 million by 2040. The estimated incidence rates of T1DM increased annually by 1.4% during 2002–2012 in America [Reference Mayer-Davis2], 1.01% during 2010–2015 in China respectively [Reference Weng3, Reference Luk4]. The rapid increase in incidence, especially in children under the age of 5 years [Reference Desai5], cannot be fully attributed to genetic factors. Reports have linked viral infections [Reference Dotta6], obesity [Reference Grabia7], socioeconomic status [Reference Grudziąż-Sękowska8], vitamin D deficiency [Reference Manousaki9, Reference Federico10], diet, immunisation, seasonal variation [Reference Mikulecký11] to an increased risk of T1DM. However more recent evidence regarding a putative role for enterovirus (EV) infection in the development of clinical T1DM comes from case–control studies that have shown a significant temporal association after enterovirus epidemics, and the detection of EV RNA or EV capsid protein in pancreatic biopsies of patients with current onset T1DM [Reference Dotta6]. On the other hand, evidence from diabetic animal models and cell studies suggests that EVs are likely to destroy the pancreas via immunological cross-reaction (molecular mimicry) because of the sequence homology between the coxsackievirus P2 protein and glutamic acid decarboxylase 65 (GAD65) or is directed to destroy insulin-producing islet cells via T lymphocytes (bystander damage) [Reference Op1]. T1DM may also contribute to the children's psychological and mental problems [Reference Munkácsi12], such as depression and anxiety, since a strong correlation between diabetes and the status of mind or quality of life in children has been reported. Besides, diabetes could increase the economic burden to families and societies in low- and middle-income countries because of lifelong treatment and management of illness. Furthermore, identification of these risk factors could lead to a better understanding of T1DM and contribute to developing strategies to prevent T1DM so as to reduce the economic burden of diabetes and improve the quality of life.
In 2004, a systematic review of coxsackie B virus serology did not indicate a relation with T1DM [Reference Green13], but there was another study showing that EV infection, confirmed only by reverse transcription-polymerase chain reaction (RT-PCR), did show a clinically significant association with T1DM in 2011 [Reference Yeung W14]. Moreover, these case–control or cohort studies did not increase the statistical power and provided precise estimates because of the relatively small sample size for each individual study. However, the correlation between EV and clinical T1DM remains unclear due to the source of EV samples, different methods to confirm EV infection and study type and so on. Based on these facts, we conducted a systematic review and meta-analysis to clarify the relationship between EV infection and the risk of clinical T1DM.
Methods
This study was performed in accordance with the Meta-analysis Of Observational Studies in Epidemiology (MOOSE) [Reference Stroup D15] and was registered on PROSPERO (registration number: CRD42021236044).
Search strategy
Two reviewers (SY and XL D) independently performed a systematic search for observational studies of enterovirus (EV) infection and clinical T1DM according to Medical Subject Headings (MeSH) or Emtree combined with free-text terms on PubMed and Embase, from inception to April, 2021. The search terms used were ‘type 1 diabetes’, ‘enterovirus infection’, ‘echovirus infection’ and ‘coxsackievirus infection’. The search strategy is reported in detail in the Supplementary Materials. The search was confined to original articles including humans in any language and was conducted by manually searching the reference lists of the eligible studies and by direct contact with authors.
Study selection criteria
Studies were eligible for inclusion in line with the below criteria: (1) a case–control or cohort study design; (2) assessment of the relation between EV infection and clinical T1DM; (3) established or newly diagnosed T1DM without HIV or hepatitis viruses; (4) evidence of EV infection via measuring virus RNA or specific antibodies in blood, stool or tissue of patients or laboratory investigations or other biopsies; (5) available data on the odds ratio (OR) with 95% confidence interval (CI) or numbers of events/total in both case and control; (6) non-human studies were excluded. Disagreements between the two reviewers were resolved via discussion with a third arbitrary investigator (LW C), when necessary.
Data extraction
The information of each study were extracted using a standardised form as follows: first author's name, date of publication, design of the study, location, age distribution, number of cases and controls (matching criteria), methods to confirm EV infection, virus species or serotypes, assessment of diabetes and islet autoantibodies. One investigator (ZZ) extracted above the data checked by another investigator (YL Z).
Quality assessment
Two investigators (SY and BY Z) independently evaluated the included study quality independently using the Newcastle-Ottawa scale (NOS) for case–control studies or cohort studies, as recommended by the Cochrane Collaboration, and different opinions were resolved through consensus. This tool assessed three areas-selection, comparability, exposure or outcome-total score of 9 stars, with 5 stars or more deemed as good methods. In the comparability category, we highlighted the evaluation of controls matched for age and sampling time, as these two factors are most likely to affect the incidence of EV infection. The possibility of publication bias was evaluated by visual inspection of the funnel plot.
Statistical analysis
Review Manager software (version 5.3) was used to calculate pooled ORs with 95% CI and P value for EVs infection in patients with diabetes vs. no diabetes from the published data in studies using the Mantel–Haenszel method. We analysed the association between EV infection and clinical T1DM using both fixed and random-effects models. However, only combined ORs from the random-effects models are presented because of a high level of heterogeneity in the study design. A forest plot summarised the results of all eligible studies. Statistical heterogeneity was explored using Cochran's Q and I 2 statistics, indicating the proportion of variance in outcomes between studies. Statistical significance was defined as P < 0.05 was considered statistically significant heterogeneity, while I 2 less than 25%, 50% and 75% were regarded as low, moderate and high heterogeneity respectively. Subgroup and sensitivity analyses were conducted for age distribution, the initial time of clinical T1DM, methods to confirm EV infection, source of EV sample and control subjects, virus species or serotypes, study type and summary ORs were calculated. We also performed sensitivity analyses by individual study, study size, study location and NOS score.
Results
Study selection
Our search returned all 706 publications after the removal of 272 duplicate articles. We identified and included 53 potential original articles by screening titles and abstracts. Furthermore, 25 relevant studies were evaluated by reviewing the full text and finally included in the systematic review and meta-analysis. Four studies were excluded owing to the same case–control subjects for different research aims. Figure 1 shows the search flowchart for eligible studies.
Flow diagram of the literature selection process.
Characteristics of included studies
Demographic characteristics of the included 25 studies are presented in Table 1. All studies were case–control designs, and of these samples of three nested case–control studies were collected from diabetic cohort studies. The majority of included study subjects were from Europe and a few participants were from non-European countries. Twenty-five studies included 2150 patients with T1DM and 2704 controls, aged range between 0 and 70 years, but who were mostly children, adolescents and young adults. EV infection was confirmed by real RT-PCR and in situ hybridisation (ISH) to detect EV-RNA in 15 studies, specific IgM antibodies using neutralisation test (NT), ELISA, immunofluorescence assay (IFA) and radioimmunoassay (RIA) to identify antibodies against EV or coxsackievirus for seven studies, and immunohistochemistry (IHC) to investigate enteroviral capsid protein vp1 for three studies. Of the 25 studies, five studies simultaneously used two methods of EV detection. Most articles did not report data on EV species or serotypes, but only five studies provided data on IgM antibodies against coxsackievirus B serotypes and of which one only examined echovirus and coxsackievirus A.
Summary of an individual study investigating type 1 diabetes and enterovirus infection
T1DM, type 1 diabetes mellitus; NT, neutralisation test; RIA, radioimmunoassay; IFA, immunofluorescence assay; IHC, immunohistochemistry; RT-PCR, reverse transcription-polymerase chain reaction; CB, group B coxsackievirus; EV, enterovirus.
Meta-analysis results
The results suggested that EV infection was significantly related to clinical T1DM mellitus as compared with no T1DM, but with evidence of high heterogeneity between the 25 studies (P < 0.001, I 2 = 80%) (Fig. 2). ORs ranged from 0.14 to 426.19, with a combined OR of 5.75 (95% CI 3.61~9.16)(Fig. 2).
Forrest plot of the association between clinical T1DM and EV infection.
Subgroup and sensitivity analyses
We also performed subgroup analyses with respect to methods to confirm EV infection, source of EV sample, virus species or serotypes, initial time of clinical T1DM, age distribution, source of control subjects and study type (Table 2). The combined ORs for NT, RIA, ISH, IHC and RT-PCR were 1.58 (95% CI 0.76–3.30), 3.02 (95% CI 0.17–56.64), 5.21 (95% CI 2.31–11.79), 7.29 (95% CI 1.42–37.58), 7.48 (95% CI 4.20–13.32) respectively. There was no heterogeneity (I 2 = 0%, P = 0.35)across the two studies that measured EV-RNA in the intestinal mucosa by ISH, but the other subgroups by NT, RIA, IHC and RT-PCR showed significant heterogeneity (Table 2).
Summary odds ratios and heterogeneity for an association of EV infection and clinical T1DM in subgroup and sensitivity analyses
T1DM, type 1 diabetes mellitus; NT, neutralisation test; RIA, radioimmunoassay; IFA, immunofluorescence assay; IHC, immunohistochemistry; RT-PCR, reverse transcription-polymerase chain reaction; CB, group B coxsackievirus; CC, case–control study; NCC, nested case–control study.
When we analysed the source of EV sample, the summary ORs for serum, intestinal mucosa, plasma, peripheral blood mononuclear cells (PBMCs) and pancreatic tissue were 3.90 (95% CI 3.24–4.70), 4.36 (95% CI 2.24–8.49), 4.49 (95% CI 3.12–6.46), 11.42 (95% CI 4.27–30.58), 27.60 (95% CI 8.48–89.78) respectively, with significantly statistically heterogeneity (I 2 = 72.30%, P = 0.006) across the 5 groups, while there was no heterogeneity in PBMCs group (I 2 = 0%, P = 0.52) and pancreatic tissue group (I 2 = 0%, P = 0.67), moderate heterogeneous in intestinal mucosa group (I 2 = 50%, P = 0.14), high heterogeneous in serum group (I 2 = 83%, P < 0.001) and plasma group (I 2 = 77%, P = 0.002)respectively (Table 2).
For only six studies that examined specific IgM antibodies to coxsackievirus B (CB) serotypes, the pooled ORs for CB1, CB2, CB3, CB5, CB6 were 1.76 (95% CI 1.18–2.63), 0.90 (95% CI 0.50–1.64), 0.95 (95% CI 0.68–1.33), 0.91 (95% CI 0.37–2.21), 0.88 (95% CI 0.50–1.53) respectively, while the pooled OR for CB4 was comparatively higher (2.03 (95% CI 0.87~4.75)) for the other CB1, CB2, CB3, CB5, CB6 serotypes and with moderate heterogeneity (I 2 = 60%, P = 0.04) vs. no or mild heterogeneity respectively (I 2 = 0%, P = 0.67; I 2 = 25%, P = 0.26; I 2 = 0%, P = 0.51; I 2 = 43%, P = 0.15; I 2 = 0%, P = 0.87) (Table 2).
The combined ORs for newly diagnosed clinical T1DM and previously diagnosed clinical T1DM were 4.76 (95% CI 2.84–7.98), 4.91 (95% CI 2.49–9.67), with high heterogeneity (I 2 = 82%, P < 0.001) vs. mild heterogeneity (I 2 = 32%, P = 0.22) respectively (Table 2).
For the summary, OR of age 0~9 years group (33.82 (95% CI 1.87–612.91)) was significantly higher in the 0~20 years group (4.89 (95% CI 2.51–9.51)) and age 0~71 years group (7.53 (95% CI 3.61–15.72)), probably because of the high rates of EV infection in children [Reference Chehadeh18, Reference Elfaitouri21, Reference D'Alessio D34]. It is generally believed to children with the immature immune system in whom they have a greater risk of infection in comparison with adults who have a mature immune system. The rates background refers to this. Although there was no appropriate data on HLA genotypes in all cases and controls to perform subgroup analysis, we investigated the relationship between EV infection and clinical T1DM affected by HLA typing based on different sources of control subjects, The relative controls are selected from the siblings, while the normal subjects are collected from the unrelated individuals, so that we are able to elucidate the effect of a genetic factor on the result. The combined OR of relative controls (3.19 (95% CI 1.65–6.14)) was significantly higher for the normal (Table 2) subjects (56.41 (95% CI 3.54–899.40)), indirectly demonstrating that EV infection could increase the risk of clinical T1DM in genetically susceptible individuals (Table 2).
When we analysed the results from the only three nested case–control studies, the summary OR (2.16 (95% CI 0.95–4.92))was lower than that of the 22 case–control studies (7.49 (95% CI 4.20–13.36)), probably because of the variance in study design (Table 2). In summary, subgroup analyses indicate that none of the subsets significantly affected the stability of overall results, in addition to group B coxsackievirus that obviously decreased the combined OR of 5.75 (95% CI 3.61–9.16) for a relation between EV infection and clinical T1DM to that of 1.14 (95% CI 0.92–1.41) for an association between CB infection and clinical T1DM.
We also carried out a sensitivity analysis by study size, study location and NOS score to examine the robustness of the correlation. The summary ORs for more than 100 participants and less than 100 participants were 5.88 (95% CI 3.46–9.99), 6.00 (95% CI 2.21–16.26) respectively, with no heterogeneity between the groups. The pooled ORs for the European area and non-European area were 5.72 (95% CI 2.94–11.94), 6.02 (95% CI 3.02–12.01) respectively, with no heterogeneity between both groups. Sensitivity analysis by study quality was classified into three groups (8~9 score group, 7 score group, 5~6 score group) because all studies scored more than 5 on the NOS (Table 2). Although most studies did not report diagnostic criteria in detail for clinical T1DM, and clinical presentation and laboratory investigations were poorly described, insulin therapy was started in patients with T1DM after diagnosis. Finally, sensitivity analysis by individual studies did not significantly affect the summary effect estimates.
Quality assessment
Newcastle Ottawa scores ranged from 5 to 9 stars, with all studies scoring 5 stars or more, suggesting good methodological quality overall, with no studies reporting a non-response rate (Table 1). The funnel plot showed reasonable symmetry, with no evidence of publication bias (Fig. 3). However there was great variability across the studies, with significant statistical heterogeneity; therefore, the meta-analysis results should be interpreted with caution when extended to the general population.
Funnel plot of the association between clinical T1DM and EV infection.
Discussion
To our knowledge, this meta-analysis and systematic review are the first to report the relationship between EV infection and the risk of clinical T1DM by systematically reviewing molecular and serological observational studies. This study, which included 25 articles, suggest that EV infection had more than about six times the risk of clinical T1DM, approximate 34 times the risk in children when compared with the control. Those who have clinical TIDM with positivity for islet autoantibodies are not at greater risk of EV infection than those with negative results for islet autoantibodies; therefore, so enterovirus infection might be a risk factor for clinical type 1 diabetes. Our results suggest that the pathogenesis of clinical T1DM triggered by EV may not be one mechanism. Subgroup analysis demonstrated that age group, methods of EV detection, source of EV sample, study design and genetic factors may have a tremendous influence on the results.
To date, a great many epidemiological and observational studies have investigated the relationship between EV infection and the risk of T1DM [Reference Dotta6, Reference Federico10, Reference Mikulecký11, Reference Nairn17, Reference Moya-Suri20, Reference Sarmiento22–Reference Boussaid29, Reference Karaoglan31–Reference Alberti A33, Reference Frisk35–Reference Mercalli37]. However, their findings have been inconsistent because the prevalence and incidence of T1DM differ greatly for geographic areas, methods of EV detection, age and source of EV samples and genetically predisposed individuals. Since J. E. Banatvala et al. first reported evidence of the association between Coxsackie B1–5 viruses and children under the age of 5 years with clinical T1DM in Austria, England and Australia [Reference Li38], subsequently most studies have been conducted in Europe. Overall, it is recognised by most investigators that EV infection could accelerate the progression of T1DM or transiently emerge autoantibodies associated with T1DM in genetically susceptible populations, however, we perform an analysis of the islet autoantibody-positive individuals due to lack of sufficient data that provided unreliable results. We included children and adults with clinical T1DM, decreasing the high rates background of bacterial and viral infections in children. Global studies were included to decrease the risk of geographical bias associated with infection rates. Most studies, however, were from European countries [Reference Dotta6, Reference Federico10, Reference Frisk16–Reference Oikarinen26, Reference Oikarinen28, Reference El-Senousy W30–Reference Alberti A33] where the incidence of diabetes is higher than that in Asian and African countries. Given the heterogeneity of study populations' heterogeneity, complex pathogenesis and multiple environmental agents, we used random-effects models due to high heterogeneity across individual studies, providing more conservative and reliable effect estimates. However, our results should be carefully interpreted due to significant statistical differences among all studies, when particular, as extended to the external population.
Although the initial factors of the anti-islet cell autoimmune response are not understood, a few possible mechanisms for the relationship between EV infection and risk of T1DM have been inferred. First, patients with clinical T1DM are more likely to be infected with a variety of pathogens, such as bacteria, viruses and fungi, compared to individuals without T1DM. Viruses can promote diabetes either by directly infecting and destroying islet beta cells or by triggering an autoimmune attack on islet cells [Reference Op1]. In addition, seasonal variation plays an important role in the pathogenesis of T1DM [Reference Mikulecký11]. Furthermore, diet and perinatal factors are more likely to increase the risk of developing T1DM [Reference Allen D39]. Secondly, there was one possible fact that molecular mimicry due to homology between Glutamic acid decarboxylase 65 (GAD65) and a causative agent such as Coxsackie B4 virus. A search has investigated Several autoantigens (IAA, ICA, IA-2A and ZnT8) within pancreatic beta cells play significant roles in the initiation or progression of autoimmune pancreatic injury. Nonetheless, children or adults with another autoimmunity, most commonly autoimmune thyroiditis and coeliac disease, are at increased risk for catching T1DM; however, there was an extreme lack of available data in the eligible studies, so that we could not perform the related analysis. In the long term, clinical T1DM is autoimmunity that gives rise to a complex interaction between genetically susceptible populations and environmental factors [Reference Op1].
In the future, larger multicentre international prospective or birth cohort studies could investigate the relationships between clinical T1DM and age distribution, genetic factors, enterovirus various and EV serotypes. Moreover, clinical trials are needed to develop useful and feasible strategies, as vaccines against EV species or serotypes, to lessen the prevalence and incidence of T1DM.
Limitations
We performed a set of standard and comprehensive literature searches, and made no language restrictions to limit our ability to evaluate the association between EVs infection and the risk of T1DM. However, there were several limitations in our meta-analysis. First, the included studies were confined to case–control studies with inherent factors, such as different data collection, various detection methods of viral infections (RT-PCR, specific antiviral neutralising antibodies and hybridisation, and samples from different sites. Second, it was true that we performed subgroup and sensitivity analyses to reduce potential confounding factors, but all eligible studies also had a high level of heterogeneity. Third, other environmental agents might alter the risk of T1DM, such as maternal virus infection [Reference Tangjittipokin40], cow's milk and vitamin D [41], moreover, it is impossible to improve all of these potential confounders in retrospective studies. Fourth, the results of this meta-analysis cannot prove that EV infection has a cause-and-effect role in the development of clinical T1DM. Despite these limitations, this meta-analysis has increased the statistical power by pooling the findings of a single case–control study with overall good methodological quality, to some extent, which was sufficient evidence to draw this conclusion.
In summary, we demonstrated that EV infection may be a dependent risk factor for clinical T1DM. Further studies are needed to explore the potential pathways and focus on whether the virus vaccine could decrease the risk of clinical T1DM or not.
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/S0950268821002442
Acknowledgement
All of the authors thank http://www.dxy.cn and WeChat Official Account (wechatID Xing Hua Yi Xue) for providing methodological knowledge about meta-analysis and systematic review for junior research fellow.
Author contributions
SY and BY Z were involved in searching databases and screening articles and drafted the first manuscript. BY Z and XL D were engaged in data extraction data and reviewing the quality of included studies with the aid of YL Z. Statistical analysis was carried by SY and Z Z. All authors reviewed this article and contributed the revisions. LW C is the guarantor for this article. SY and BY Z contributed to the work equally and should be regarded as co-first authors.
Financial support
This study was funded by the Ministry of Science and Technology of the People Republic of China (2016YFCI305301).
Conflict of interest
All of the authors have no conflict of interest.
Data availability statement
The data that support the findings of this study are available as Supplementary Materials.
Open access
