Article | Open | Published:

A meta-analysis of interaction between Epstein-Barr virus and HLA-DRB1*1501 on risk of multiple sclerosis

Scientific Reports volume 5, Article number: 18083 (2015) | Download Citation

Abstract

Infection with Epstein-Barr virus (EBV) and HLA-DRB1*1501-positivity is a risk factor for multiple sclerosis (MS), but whether an interaction between these two factors causes MS is unclear. We therefore conducted a meta-analysis on the effect of the interaction between HLA-DRB1*1501 and EBV infection on MS. Searches of PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), and the Wanfan databases through February 2015 yielded 5 studies that met the criteria for inclusion in the meta-analysis. EBV infection and HLA-DRB1*1501-positivity were dichotomized. The additive (S) and multiplicative interaction indexes (OR) between EBV infection and HLA-DRB1*1501 and their 95% confidence intervals (95%CI) were calculated for each study and then combined in a meta-analysis. EBV infection was significantly associated with MS (OR = 2.60; 95%CI, 1.48–4.59). HLA-DRB1*1501 was associated with a significantly increased risk of MS (OR, 3.06; 95%CI, 2.30–4.08). An interaction effect between EBV infection and HLA-DRB1*1501 on MS was observed on the additive scale (S, 1.43; 95%CI, 1.05–1.95, P = 0.023), but no interaction effect was observed on the multiplicative scale (OR, 0.86, 95%CI, 0.59–1.26). This meta-analysis provides strong evidence that EBV alone, HLA-DRB1*1501 alone or their interaction is associated with an elevated risks of MS.

Introduction

Multiple sclerosis (MS) is a common chronic inflammatory demyelinating disease of the central nervous system (CNS) that causes severe progressive disability, particularly in young people, and affects more than 2.5 million people worldwide1. MS is a chronic immune-mediated disorder with a complex etiology. The pathogenesis of the disease is not well understood. Both environmental and genetic factors have been implicated in disease causation2. In a large genome-wide association study (GWAS), 465,434 autosomal single nucleotide polymorphisms (SNPs) were analyzed in 9772 cases and 17,376 controls of European descent; the strongest association was between HLA-DRB1*1501 and MS (OR = 3.1, P = 1 × 10−320)3. Furthermore, the HLA-DRB1*1501 allele is considered to be a definite, strong risk factor for MS4,5. Carriers of HLA-DRB1*1501 have an up to four-fold increased risk of MS6.

Serological data from prospective and retrospective studies suggest that past EBV infection is a prerequisite for MS development7,8,9. A cohort study of US military personnel has demonstrated that anti-EBNA-1 titers are associated with a 3-fold increased risk of MS10. In addition, a meta-analysis has revealed a significant OR for sero-positivity to anti-EBNA IgG in MS cases (4.5[95% confidence interval (CI) 3.3 to 6.6, p < 0.00001])11.

The presentation of viral or bacterial peptide antigens to CD4+ T cell receptors may induce cross-activation with self-antigens12. HLA-DRB1 may result in self-antigen cross-reactivity with EBV antigens in addition to serve as a co-receptor for EBV entry into B cells13,14,15. De Jager et al16 have suggested that HLA-DRB1*15 influences the CD4+ Th-mediated immune response to EBV infections13,17,18. In Individuals with HLA-DRB1*1501,EBV-infected B cells may present CNS self-antigens to CD4+ T cell receptors, thereby inducing an autoimmune response19.

Although the independent effects of EBV infection and HLA-DRB1*1501 on the risk of MS have been established6,16, the possible interaction between these factors is not well characterized. Moreover, data from individual studies on the interaction between HLA-DRB1*1501 and EBV infection are not entirely consistent. Some studies have report no interaction16,20,21. Whereas other studies have observed an interaction between EBV infection and HLA-DRB1*1501 in the risk of MS22,23. Thus, whether the interaction of two factors increases the risk of MS remains controversial.

To better illuminate the independent and combined effects of EBV infection and HLA-DRB1*1501 in the etiology of MS, we conducted a meta-analysis to evaluate the interaction between these two factors in MS risk.

Results

Result of the literature search

A total of 659 studies were identified from a primary literature search after 131 duplicates were excluded. A total of 648 of the 659 records were excluded:19 case-reports and commentaries, 92 studies irrelevant to the topic,19 non-human studies, 120 reviews and meta-analyses, 380 studies including unpublished data; 9 conference reports; and 9 reports for which full text was unavailable. The remaining 11 studies were scanned, and 6 additional studies were excluded for the following reasons: one study was not an indicator of EBV, and five lacked key data. Finally, 5 studies were considered eligible for this meta-analysis (Fig. 1).

Figure 1: Flowchart of the meta-analysis of the interaction between HLA-DRB1*1501 and EBV infection on the risk of MS.
Figure 1

The general characteristics of all 5 studies are presented in Table 1. HLA-DRB1*1501 was genotyped in three studies, and in van der Mei et al., rs3135005 was genotyped as a proxy for the HLA-DRB1*1501 allele20. One study only distinguished HLA-DRB1*15 as HLA-DRB1*1501 without specific alleles. IgG antibodies to EBNA-1 were measured in all 5 studies as an indicator of EBV infection. The studies included a total of 2533 participants, including 1069 MS patients. One study was from India, one was from Canada, one was from Australia, and the remaining studies were from Sweden.

Table 1: Summary characteristics of the studies selected.

Meta-analysis of the interaction between EBV infection and HLA-DRB1*1501

There was significant interaction between HLA-DRB1*1501 positivity and EBV infection based on the additive scale (S = 1.43, 95%CI = 1.05–1.95, P = 0.023; AP = 0.29, 95%CI, 0.12–0.47, P = 0.001; RERI = 1.44, 95%CI, 0.30–2.58, P = 0.013) (Table 2). HLA-DRB1*1501-positive individuals infected with the EB virus had a higher risk (OR = 6.11, 95%CI: 3.84–9.74) of MS.

Table 2: The interaction of risk estimates between HLA-DRB1*1501 and EBV based on the additive scale.

Multiplicative interaction analysis revealed no interaction between EBV infection and HLA-DRB1*1501 (OR = 0.86, 95%CI = 0.59–1.26, P = 0.449) (Supplementary Table S2 online). However, EBV infection alone was significantly associated with MS (OR, 2.60; 95%CI, 1.48–4.59) (Fig. 2a), and HLA-DRB1*1501 significantly increased the risk of MS (OR, 3.06; 95%CI, 2.30–4.08) (Fig. 2b).

Figure 2: Logistic regression analyses of EBV infection and HLA-DRB1*1501 on risk of MS.
Figure 2

The boxes and lines indicate the odds ratios (ORs) and their 95% confidence intervals (CIs) on a log scale for each study. The size of the box indicates the relative weight of each estimate.

Publication bias and heterogeneity

There was no publication bias for the interaction in either the additive model (Supplementary Figure S1 online) or multiplicative model (Supplementary Figure S2 online). The Egger test also did not detect any evidence of publication bias on the additive scale (AP: SE = 0.817, P = 0.581; RERI: SE = 0.590, P = 0.685; S: SE = 0.92, P = 0.807) or multiplicative scale (SE = 1.36, P = 0.526).

The heterogeneity test detected low heterogeneity across the additive scale (AP: I2 = 0.000, P = 0.833; RERI: I2 = 0.000, P = 0.824; S: I2 = 0.000, P = 0.723) and multiplicative scale (I2 = 31.758, P = 0.210). Both interaction scales were estimated by a fixed model.

In all 5 studies, the median number of items fulfilled on the STREGA and STROBE checklists was 17 (range 13 to 18). Furthermore, as shown in Supplementary Table S3 online, the criteria for evaluating the quality of this meta-analysis were clearly described for all included studies.

Discussion

The interaction between HLA-DRB1*1501and EBV in MS remains unclear because of the conflicting results in existing studies. To address this issue, we conducted a meta-analysis of published studies. We identified a significant additive interaction between EBV infection and HLA-DRB1*1501 in the risk of MS; however, we did not observe an interaction based on the multiplicative scale.

For G+/E+ and G+/E exposures, there were more frequencies in the case group than control group (39.5% vs 17.6%, P = 0.000, in G+/E+; 12.4% vs 10.9%, P = 0.014, in G+/E). 12.9% of cases and 33.9% controls were in G/E with statistical difference (Z = −10.64, P = 0.000). Finally, there was no statistical difference (Z = −0.76, P = 0.445) for the frequency of G/E+ in between cases (30.4%) and control (33.4%). These results suggested that both HLA-DRB1*1501 and EBV-positive contribute the occurrence of MS in the population. Especially, when the individuals carry the susceptible HLA-DRB1*1501gene, once are infected by EB virus, they have more risk to suffer from MS.

In our meta-analysis, the pooled S was 1.43 (95%CI, 1.05–1.95, P = 0.023); RERI was 1.44 (95%CI = 0.30–2.58, P = 0.013) and the AP was 0.29 (95%CI = 0.12–0.47, P = 0.001). All three indexes indicated that there was indeed a biological interaction between HLA-DRB1*1501 and EBV24. S also indicated that there was a significant synergistic interaction based on the additive scale.

HLA-DRB1*1501 was associated with a 3-fold elevation in MS risk, and EBV infection was associated with a 2.6-fold elevation in MS risk. Furthermore, our data indicate that the combined effects of HLA-DRB1*1501 positivity and Epstein-Barr virus infection result in an up to six-fold increased risk of MS. These findings showed us the importance of the interaction effects between HLA-DRB1*1501 and EBV infection on the occurrence of MS. How the interaction between these two factors contributes to the increased risk of MS remains unclear. One possible mechanism includes HLA class II molecules, which are involved in the processing and presentation of foreign antigens in the immune defense process; this process mainly occurs on the surface of antigen-presenting cells. Therefore, HLA-DRB1*1501 may interfere with this process and prevent the presentation of EBV antigens to CD4 +  Th cell receptor, thereby inhibiting immune defense recognition9,25, and leading to EBV accumulation in B cells26. EBV-infected B cells distributed in the CNS can present CNS antigens, the molecular mimicry of EBV13,27,28, to CD4 +  T-cell receptors under the influence of the virus and may activate cellular and humoral immune responses26,29. Dysfunctional immune regulation induces the excitation of autoimmune responses. Furthermore, EBV-infected B cells can provide costimulatory survival signals to T cells and protect activated CD4 + T-cell from elimination by immunoregulation, thereby leading to the development of MS19. In another aspect, in the development of the thymus, particular HLA molecules cannot very well present self-antigen to developing T cells to undergo effective negative selection process, which lead to those auto-reactive T cells persisting even after the individual mature and launching an immune attack against the self-antigen under certain conditions30,31. Therefore, when EBV infection makes B cells presenting self-antigens13,27,28, auto-reactive T cells can recognize these self-antigens, which further accelerate the progression of MS30. So, when individuals carrying special HLA genotype are infected by EBV, those two factors influence and promote each other, which accelerate the progression of MS.

Our meta-analysis was comprehensive because we assessed the interaction based on both additive and multiplicative scales. We chose this approach because both scales are informative, and arguments can be made in favor of each of the two scales32. Distinguishing gene-environment interactions that may reflect biologic processes such as molecular mimicry will contribute to further dissection of the disease mechanisms that culminate in MS onset33 and provide new insights for the treatment and prevention of MS. Our meta-analysis suggested that the risk of MS would be increased greater when HLA-DRB1*1501 and EBV occur together. Moreover, we evaluated the primary effects of HLA-DRB1*1501 and EBV in MS by performing an extensive analysis of the expectations of the gene-environment interaction.

This meta-analysis has important clinical implications. Due to hereditary nature of the HLA-DRB1*1501 genotype, effective intervention and prevention measures can be implemented by monitoring EBV infection status. An increasingly large body of evidence has indicated that EBV infection plays an essential role in the development of MS, raising the possibility that MS maybe prevented and potentially cured by controlling EBV infection34. Reducing the maximum exposure to EBV may significantly reduce the risk of MS. Consequently, we suggest that EBV infection should be prevented in healthy individuals (particularly HLA-DRB1*1501 carriers). Vaccination of healthy EBV-seronegative young adults with recombinant gp350 is effective in preventing the development of infectious mononucleosis induced by EBV infection, although it does not prevent asymptomatic EBV infection35. Healthy HLA-DRB1*1501-positive individuals should receive regular screening to measure serum titers of anti-EBNA antibodies36. Furthermore, the development and application of a vaccine against EBV may reduce the risk of MS. Our findings may have significance for the prevention of the occurrence or recurrence of MS. Moreover, the immune response to EBV may be a therapeutic option in MS34. In MS patients infected with EBV, MS may be cured by controlling the immune response to EBV infection. Some evidence supports a beneficial effect of vitamin D3 on reducing antibody titers against EBV in MS patients37. We currently prescribe rituximab to boost immunity to EBV and antiviral drugs to treat EBV infection34.

Some limitations of our study merit further discussion. Exposures other than the study exposures exhibited disequilibrium, particularly smoking. For example, in one study an interaction was observed on the multiplicative scale between EBNA1 IgG and smoking21. It is difficult to exclude a potential confounding effect of smoking because smoking is associated with many populations. Therefore, the so-called population imbalance stratification of other exposures may interfere with the results of this study. The case-control study in the meta-analysis may also introduce selection bias. Another limitation of our meta-analysis is that the method used to calculate the interaction on the additive scale might only apply to two factors at two levels. When the variable factors were multiply variable, 95% confidence intervals of S, AP and RERI were not calculated by the Excel calculation spreadsheet used in this study. The 5 independent studies were from different countries with the different genotyping methods for HLA-DRB1*1501. Although these methods were different, they are in consensus at genotyping HLA-DRB1*1501. Hence, the results would not be influenced by the differences in the genotyping methods for HLA-DRB1*1501. Therefore, results are reliable.

Most of the included studies were Caucasian samples and only one study was India population, which might influenced the results. We failed to identify a significant interaction between HLA-DRB1*1501 and Epstein-Barr virus on the risk of MS in the population from India. This result may have two explanations. First, compared to the other included studies, the Indian study had a restricted sample size, which may have limited the power to evaluate the interaction. Therefore, large-scale studies are needed to validate the interaction between HLA-DRB1*1501 and Epstein-Barr virus in MS in Indian populations. Second, ethnic differences may underlie this result. As the prevalence of 3/100,000 in MS was low in India, while the prevalence varies between 60 and 200 per 100,000 in people of north American and northern European origin2. It is possible that the interaction between HLA-DRB1*1501 and Epstein Barr virus in MS is relevant to only Caucasian populations and not other ethnic groups. Further studies are needed to test the interaction between HLA-DRB1*1501 and Epstein Barr virus in MS among other ethnic groups by recruiting patients with non-Caucasian backgrounds38.

In conclusion, our meta-analysis identified an interaction between HLA-DRB1*1501 and EBV infection for the risk of MS on an additive scale; however, we did not observe a significant interaction between these factors on a multiplicative scale. Further study is needed to assess the direct evidence and understand the potential mechanism underlying this finding.

Methods

Study identification

A search of the Pubmed , Web of Science, CNKI and the Wanfang databases was conducted through February 2015 using the search terms HLA, multiple sclerosis, Epstein Barr virus, and interaction.

The retrieved studies fulfilled the following criteria for inclusion in the meta-analysis: (a) multiple sclerosis, (b) EBV infection (EBNA-1 IgG as the index of EBV infection), (c) genotyped HLA-DRBA*1501 status (any method) and (d) the interaction of EBV infection and HLA-DRB1*1501 in MS. Studies were excluded from our analysis if there was an absence of detailed numbers in one of the following four groups: HLA-DRB1*1501-negative subjects without EBV infection; HLA-DRB1*1501-positive subjects with EBV infection; HLA-DRB1*1501-negative subjects with EBV infection; and HLA-DRB1*1501-positive subjects without EBV infection.

Data extraction

Data were independently extracted by two investigators (Di Xiao and Xingguang Ye) who were blinded to each other, using a data recording developed for this purpose. When detailed data were lacking, we attempted to contact the corresponding author to obtain the original data. Studies were excluded if the authors did not provide additional data. Any disagreement between the two data extractors was resolved by consensus. After extraction, the data were also reviewed and compared by Chunxia Jing.

Quality assessment

All included studies were assessed based on the STREGA (Strengthening the Reporting of Genetic Association Studies) and STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklists39,40.

The assessment involved six domains, including title and abstract, introduction, methods, results, discussion and other information. Each item was classified with “ + ” or “−”, which represented fulfillment of the checklist criteria or a lack of fulfilment of the criteria, respectively.

Statistical Analysis

The serum level of IgG antibodies against the Epstein-Barr virus (EBV) nuclear antigen 1 (EBNA-1) is a strong risk factor for MS16,41,42. Because antibodies to the EBNA-1 antigen have emerged as the most consistent predictor of MS in multiple serological studies9,10,43, the anti-EBNA-1 titer was used as the index of the immune response to EBV infection in our meta-analysis.

In all the included studies, the EBNA-1 antibody titer was used as an index of EBV infection rather than EBNA-2(another individual component of the EBNA family), the EBV viral capsid antigen (VCA) or the anti-early antigen complex (diffuse [EA-D]).

Our stratification differed from some original studies2,6,16. For example, two studies6,16 used three types of EBNA-1 antibody titers (low/medium/high), which were converted into a dichotomous variable in our meta-analysis. The high IgG level used in this meta-analysis was combined with the medium and high IgG titers in the original studies. One study2 used an inter-quartile stratification, and we used the value above the 50th percentile of the inter-quartile range as the high IgG level. Low EBNA-1 antibody titers were defined as non-EBV infection, and high EBNA-1 antibody titers were defined as EBV infection.

HLA-DRB1*1501 was also considered a dichotomous variable (positive/negative), consistent with all of the original studies.

The interaction effects were determined by using two models: logistic regression to assess the interaction on the multiplicative scale, and S (the synergy index), RERI (the excess risk due to interaction) and AP (the attributable proportion due to interaction) to assess the interaction on the additive scale.

In additive model, we categorized the study subjects into four groups according to HLA-DRB1*1501 and EBV infection status: HLA-DRB1*1501-negative and EBV-negative (RR00), HLA-DRB1*1501-positive and EBV-negative (RR10), HLA-DRB1*1501-negative and EBV-positive (RR01), and HLA-DRB1*1501-positive and EBV-positive (RR11). We defined subjects who were unexposed to both risk factors as the reference category (i.e., RR00 = 1). These relative risk estimates can be obtained from a logistic regression model. The corresponding covariance matrix and regression coefficients are also needed to calculate the confidence intervals24. To obtain adequate estimates, the model was established with indicator variables for each of the four different combinations of exposure. For convenience, we structured a new variable C and defined it as three indicator variables: dum01, dum10 and dum11 (see Supplementary Table S1 online). An Excel spreadsheet (www.epinet.se) was used to calculate additive interaction: S, RERI and AP. AP refers to the attributable proportion of disease that is due to interaction among individuals with both exposures. S is the excess risk from both exposures when there is an additive interaction, relative to the risk from both exposures without interaction. RERI >0, AP > 0, or S > 1 indicates biological interaction24,44. Furthermore, S > 1 for synergetic effects and S < 1 for antagonistic effects24,45,46.

In the multiplicative model, we fit a multiple logistic regression model with the response variable MS (case/control) and independent variables HLA-DRB1*1501(G), EBV infection (E), and their product G × E. The odds ratio of G × E was the index of the multiplicative model interaction between HLA-DRB1*1501 and EBV infection in the risk of MS. A multiplicative interaction existed when the 95% confidence intervals of OR did not contain 1. Otherwise, the result was reversed.

Sensitivity analysis was performed by removing one study at a time to assess whether the meta-estimates were strongly influenced by any individual study. We used the forest plot and Egger’s regression intercept to assess publication bias47,48.The following cutoffs were used to evaluate heterogeneity: I2 = 0–25%, no heterogeneity; I2 = 25–50%, moderate heterogeneity; I2 = 50–75%, large heterogeneity; and I2 = 75–100%, extreme heterogeneity49. Pooled mean differences were estimated by using a fixed-effects model when there was no heterogeneity or moderate heterogeneity (I2 < 50%) and a random-effects model when there was moderate, large, or extreme heterogeneity (I2 ≥ 50%)50. All statistical analyses were conducted using SPSS version 16.0(SPSS Inc, Chicago, USA), Microsoft Excel2007 (Microsoft, Redmond, WA, USA), and Comprehensive Meta Analysis V2 (Biostat Inc, USA).

Additional Information

How to cite this article: Xiao, D. et al. A meta-analysis of interaction between Epstein-Barr virus and HLA-DRB1*1501 on risk of multiple sclerosis. Sci. Rep. 5, 18083; doi: 10.1038/srep18083 (2015).

References

  1. 1.

    The essential role of Epstein-Barr virus in the pathogenesis of multiple sclerosis. Neuroscientist 17, 351–67 (2011).

  2. 2.

    , , , & Association of Epstein-Barr virus infection with multiple sclerosis in India. J Neurol Sci 325, 86–9 (2013).

  3. 3.

    Consortium, I.M.S.G. Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature 476, 214–9 (2011).

  4. 4.

    Human leukocyte antigen studies in multiple sclerosis. Ann Neurol 36 Suppl, S15–7 (1994).

  5. 5.

    , & HLA-DR15 haplotype and multiple sclerosis: a HuGE review. Am J Epidemiol 165, 1097–109 (2007).

  6. 6.

    , , & EBNA-1 reactivity and HLA DRB1*1501 as statistically independent risk factors for multiple sclerosis: a case-control study. Mult Scler. 14, 1120–2 (2008).

  7. 7.

    et al. Epstein-Barr virus antibodies and risk of multiple sclerosis: a prospective study. JAMA 286, 3083–8 (2001).

  8. 8.

    et al. Multiple sclerosis and Epstein-Barr virus. JAMA 289, 1533–6 (2003).

  9. 9.

    et al. An altered immune response to Epstein-Barr virus in multiple sclerosis: a prospective study. Neurology 62, 2277–82 (2004).

  10. 10.

    et al. Temporal relationship between elevation of epstein-barr virus antibody titers and initial onset of neurological symptoms in multiple sclerosis. JAMA 293, 2496–500 (2005).

  11. 11.

    , , & Systematic review and meta-analysis of the sero-epidemiological association between Epstein Barr virus and multiple sclerosis. PLoS One 8, e61110 (2013).

  12. 12.

    & The HLA system. First of two parts. N Engl J Med 343, 702–9 (2000).

  13. 13.

    et al. A functional and structural basis for TCR cross-reactivity in multiple sclerosis. Nat Immunol 3, 940–3 (2002).

  14. 14.

    et al. Heterogeneity at the HLA-DRB1 locus and risk for multiple sclerosis. Human Molecular Genetics 15, 2813–2824(12) (2006).

  15. 15.

    et al. Epstein-Barr virus uses HLA class II as a cofactor for infection of B lymphocytes. Journal of Virology 71, 71–4657 (1997).

  16. 16.

    et al. Integrating risk factors: HLA-DRB1*1501 and Epstein-Barr virus in multiple sclerosis. Neurology 70, 1113–8 (2008).

  17. 17.

    Epstein-barr virus: environmental trigger of multiple sclerosis? Journal of Virology 81, 6777–6784. (2007).

  18. 18.

    , , , & Identification of a naturally processed HLA-DR-restricted T-helper epitope in Epstein-Barr virus nuclear antigen type 1. J Immunother 26, 212–21 (2003).

  19. 19.

    Genetically determined failure of activation-induced apoptosis of autoreactive T cells as a cause of multiple sclerosis. Lancet 351, 978–81 (1998).

  20. 20.

    et al. Human leukocyte antigen-DR15, low infant sibling exposure and multiple sclerosis: gene-environment interaction. Ann Neurol 67, 261–5 (2010).

  21. 21.

    et al. Combined effects of smoking, anti-EBNA antibodies, and HLA-DRB1*1501 on multiple sclerosis risk. Neurology 74, 1365–71 (2010).

  22. 22.

    P, S.m., M, N.m., K, R. & E, L. Antibodies to specific EBNA-1 domains and HLA DRB1*1501 interact as risk factors for multiple sclerosis. Journal of Neuroimmunology 215, 102–107 (2009).

  23. 23.

    et al. Antibody response to common viruses and human leukocyte antigen-DRB1 in pediatric multiple sclerosis. Mult Scler 19, 891–5 (2013).

  24. 24.

    , , , & Calculating measures of biological interaction. Eur J Epidemiol 20, 575–9 (2005).

  25. 25.

    , , , & A minimal glycine-alanine repeat prevents the interaction of ubiquitinated I kappaB alpha with the proteasome: a new mechanism for selective inhibition of proteolysis. Nat Med 4, 939–44 (1998).

  26. 26.

    Infection of autoreactive B lymphocytes with EBV, causing chronic autoimmune diseases. Trends Immunol 24, 584–8 (2003).

  27. 27.

    , , , & Antibodies against Epstein-Barr nuclear antigen (EBNA) in multiple sclerosis CSF, and two pentapeptide sequence identities between EBNA and myelin basic protein. Neurology 42, 1798–804 (1992).

  28. 28.

    & Molecular mimicry in T cell-mediated autoimmunity: viral peptides activate human T cell clones specific for myelin basic protein. Cell 80, 695–705 (1995).

  29. 29.

    et al. EBNA1-specific T cells from patients with multiple sclerosis cross react with myelin antigens and co-produce IFN-gamma and IL-2. J Exp Med 205, 1763–73 (2008).

  30. 30.

    , & HLA class II molecules influence susceptibility versus protection in inflammatory diseases by determining the cytokine profile. J Immunol 190, 513–8 (2013).

  31. 31.

    , & The why and how of thymocyte negative selection. Curr Opin Immunol 18, 175–83 (2006).

  32. 32.

    TJ., V. & MJ., K. A tutorial on interaction. Epidemiol. Methods 3, 33–72 (2014).

  33. 33.

    et al. Common viruses associated with lower pediatric multiple sclerosis risk. Neurology 76, 1989–95 (2011).

  34. 34.

    Preventing and curing multiple sclerosis by controlling Epstein-Barr virus infection. Autoimmun Rev 8, 563–8 (2009).

  35. 35.

    et al. Recombinant gp350 vaccine for infectious mononucleosis: a phase 2, randomized, double-blind, placebo-controlled trial to evaluate the safety, immunogenicity, and efficacy of an Epstein-Barr virus vaccine in healthy young adults. J Infect Dis 196, 1749–53 (2007).

  36. 36.

    Anti-Epstein-Barr virus antibodies as serological markers of multiple sclerosis: a prospective study among United States military personnel. Multiple Sclerosis Journal 17, 1185–1193 (2011).

  37. 37.

    , , , & The beneficial effects of vitamin D3 on reducing antibody titers against Epstein-Barr virus in multiple sclerosis patients. Cell Immunol 294, 9–12 (2015).

  38. 38.

    , , , & Interaction between stress and the BDNF Val66Met polymorphism in depression: a systematic review and meta-analysis. BMC Med 12, 7 (2014).

  39. 39.

    et al. STrengthening the REporting of Genetic Association Studies (STREGA)—an extension of the STROBE statement. Genet Epidemiol 33, 581–98 (2009).

  40. 40.

    et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet 370, 1453–7 (2007).

  41. 41.

    & Environmental risk factors for multiple sclerosis. Part I: the role of infection. Ann Neurol 61, 288–99 (2007).

  42. 42.

    et al. Lack of replication of interaction between EBNA1 IgG and smoking in risk for multiple sclerosis. Neurology 79, 1363–8 (2012).

  43. 43.

    et al. Epstein-Barr virus and multiple sclerosis: evidence of association from a prospective study with long-term follow-up. Arch Neurol 63, 839–44 (2006).

  44. 44.

    et al. Low LDL cholesterol, albuminuria, and statins for the risk of cancer in type 2 diabetes: the Hong Kong diabetes registry. Diabetes Care 32, 1826–32 (2009).

  45. 45.

    The estimation of synergy or antagonism. American Journal of Epidemiology 103, 506–511 (1976).

  46. 46.

    A conditional synergy index to assess biological interaction. Eur J Epidemiol 24, 485–94 (2009).

  47. 47.

    et al. The association of PTPN22 rs2476601 polymorphism and CTLA-4 rs231775 polymorphism with LADA risks: a systematic review and meta-analysis. Acta Diabetol 51, 691–703 (2014).

  48. 48.

    , , & Bias in meta-analysis detected by a simple, graphical test. BMJ 315, 629–34 (1997).

  49. 49.

    , , & Measuring inconsistency in meta-analyses. BMJ 327, 557–60 (2003).

  50. 50.

    et al. Effects of salt substitutes on blood pressure: a meta-analysis of randomized controlled trials. Am J Clin Nutr 100, 1448–54 (2014).

Download references

Acknowledgements

This work was supported in part by National Natural Science Foundation of China (Grant numbers: 30901249; 81101267;81541070; 91543132), the Guangdong Natural Science Foundation (Grant numbers: 10151063201000036; S2011010002526), Guangdong Province Medical Research Foundation (Grant number: A2014374;A2015310) and Project from Jinan university (Grant number: 21612426,21615427).We would like to thank Ammarin Thakkinstian from the section for Clinical Epidemiology and Biostatistics, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Thailand; John R. Thompson from the department of Health Sciences, University of Leicester, UK.; Sasivimol Rattanasiri from the section for Clinical Epidemiology and Biostatistics, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Thailand; for providing the statistical computing algorithm support. We thank the participants, Jiankun Zang for involving with the study of pathogenesis of MS.

Author information

Affiliations

  1. Department of Epidemiology, School of Medicine, Jinan University, Guangzhou, China

    • Di Xiao
    • , Xingguang Ye
    • , Na Zhang
    • , Meiling Ou
    • , Congcong Guo
    • , Baohuan Zhang
    • , Yang Liu
    • , Man Wang
    •  & Chunxia Jing
  2. Department of Parasitology, School of Medicine, Jinan University, No.601, Huangpu Avenue West, Guangzhou, Guangdong, China

    • Guang Yang
  3. Key Laboratory of environmental exposure and health in Guangzhou, Jinan University, Guangzhou, Guangdong, China

    • Guang Yang
    •  & Chunxia Jing

Authors

  1. Search for Di Xiao in:

  2. Search for Xingguang Ye in:

  3. Search for Na Zhang in:

  4. Search for Meiling Ou in:

  5. Search for Congcong Guo in:

  6. Search for Baohuan Zhang in:

  7. Search for Yang Liu in:

  8. Search for Man Wang in:

  9. Search for Guang Yang in:

  10. Search for Chunxia Jing in:

Contributions

D.X. and C.X.J. designed the study. D.X., X.G.Y., M.L.O. and C.C.G. collected data. B.H.Z., Y.L., M.W. and G.Y. contributed the statistical analyses and discussion. D.X. drafted the manuscript. All authors read and approved the final manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Guang Yang or Chunxia Jing.

Supplementary information

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/srep18083

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.