# Stratifying Multiple Sclerosis Susceptibility Risk: The Role of HLA-E*01 and Infectious Mononucleosis in a Population Cohort

Andrea Nova, Davide Gentilini, Giovanni Di Caprio, Sonia

Bourguiba-Hachemi, Nicolas Vince, Pierre-antoine Gourraud, Luisa

Bernardinelli, Teresa Fazia

► To cite this version:

Andrea Nova, Davide Gentilini, Giovanni Di Caprio, Sonia Bourguiba-Hachemi, Nicolas Vince, et al. Stratifying Multiple Sclerosis Susceptibility Risk: The Role of HLA-E*01 and Infectious Mononucleosis in a Population Cohort. European Journal of Neurology, 2025, 32 (4), pp.e70131. 10.1111/ene.70131. inserm-05142749

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ORIGINAL ARTICLE

OPEN ACCESS

# Stratifying Multiple Sclerosis Susceptibility Risk: The Role of HLA-E*01 and Infectious Mononucleosis in a Population Cohort

Andrea Nova<fnref n="1" /> ID | Davide Gentilini<fnref n="1" />,² ID | Giovanni Di Caprio<fnref n="1" /> ID | Sonia Bourguiba-Hachemi³ ID | Nicolas Vince³ ID | Pierre-Antoine Gourraud³ ID | Luisa Bernardinelli<fnref n="1" /> ID | Teresa Fazia<fnref n="1" /> ID

**Correspondence:** Andrea Nova (andrea.nova01@universitadipavia.it)

**Received:** 19 February 2025 | **Revised:** 14 March 2025 | **Accepted:** 20 March 2025

**Keywords:** EBV | HLA | MHC | mononucleosis | multiple sclerosis

## ABSTRACT

**Background:** Epstein-Barr Virus (EBV) and its clinical manifestation, infectious mononucleosis (IM), are strongly linked to MS risk. A recent in vitro study suggests that *HLA-E*\*01:03, in contrast to *HLA-E*\*01:01, may protect against MS through more effective immune responses against EBV-infected B cells. However, the role of *HLA-E*\*01 in MS remains unclear.

**Methods:** We investigated if *HLA-E*\*01:01 was significantly associated with higher MS risk in individuals with a history of IM diagnosis, using 487,144 individuals from the UK Biobank's cohort. We estimated the interaction between *HLA-E*\*01:01 and IM using Cox proportional hazard models, adjusting for demographics, smoking, childhood body size, older siblings, and MS-related HLA alleles (e.g., *HLA-DRB1*\*15:01).

**Results:** *HLA-E*\*01:01 allele alone was not significantly associated with IM or MS (*p* > 0.05). However, a significant interaction between *HLA-E*\*01:01 and IM history was observed in relation to MS risk (*p* < 0.001). Specifically, MS risk was significantly higher in both *HLA-E*\*01:01 heterozygotes (HR = 1.74 [95% CI: 1.36, 1.97], *p* < 0.001) and homozygotes (HR = 3.01 [95% CI: 1.81, 3.88], *p* < 0.001) with IM history, compared to *HLA-E*\*01:03 homozygotes. Conversely, these associations were non-significant in individuals without IM history (*p* > 0.05). The estimated proportion of the combined risk attributable to interaction effects was 40% in *HLA-E*\*01:01 heterozygotes and 65% in *HLA-E*\*01:01 homozygotes.

**Conclusions:** *HLA-E*\*01:01 carriers diagnosed with IM are at significantly increased risk of MS, independently from other MS-related HLA alleles. This supports the hypothesis that *HLA-E*\*01:01 may contribute to MS susceptibility due to weaker immune control over EBV infection. Incorporating *HLA-E*\*01:01 into existing MHC-based MS risk models could then enhance personalized risk assessments in individuals with IM history.

## 1 | Introduction

Multiple sclerosis (MS) is a complex autoimmune disease of the central nervous system, influenced by genetic susceptibility, environmental exposures, and immune system interactions [1-3]. Epstein-Barr virus (EBV) infection is a key environmental

trigger [4-7], with infectious mononucleosis (IM), its symptomatic manifestation, being strongly associated with increased MS risk [8, 9]. However, given that EBV infects over 90% of the global adult population, yet only a fraction develops MS, it is likely that host genetics play a key role in determining disease susceptibility. In this context, several studies highlighted how the presence

<footnote n="1">¹Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy | ²Bioinformatics and Statistical Genomic Unit, IRCCS Istituto Auxologico Italiano, Milan, Italy | ³Center for Research in Transplantation and Translational Immunology, UMR 1064, Nantes Université, CHU Nantes, INSERM, Nantes, France</footnote>

<footnote>This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</footnote>

<footnote>© 2025 The Author(s). *European Journal of Neurology* published by John Wiley & Sons Ltd on behalf of European Academy of Neurology.</footnote>
of the MS-deleterious allele *HLA-DRB1*\*15:01 and the absence of
the MS-protective *HLA-A*\*02:01 synergistically amplify MS risk
in EBV-infected individuals with IM history [10–12].

More recently, attention has shifted to *HLA-E* polymorphisms
and their influence on immune responses to EBV [5]. *HLA-E* is
a non-classical HLA class I molecule with various functions in
both innate and adaptive immunology, including natural killer
cell modulation via interaction with the CD94/NKG2 receptors
[13]. Unlike classical HLA class I genes, *HLA-E* polymorphism
is low in frequency and occurs with two variants, *HLA-E*\*01:01
and *HLA-E*\*01:03. In general, studies have shown that the pre-
dominant presence of the *HLA-E*\*01:03 allele can provide higher
molecular expression of *HLA-E* at the cell surface than the *HLA-
E*\*01:01 allele [14]. A recent study by Vietzen et al. [5] found the
*HLA-E*\*01:03/01:03 genotype (homozygous) to be overrepre-
sented in individuals with high Epstein-Barr nuclear antigen 1
(EBNA1) antibody levels who did not develop MS. Furthermore,
an in vitro co-culture experiment showed that the *HLA-E*\*01:03
allele, in contrast to *HLA-E*\*01:01, enhances immune control over
EBV-infected B cells through potent HLA-E-restricted CD8+ T
cell responses, potentially preventing MS-related autoimmune
processes. However, given the complexity of the major histocom-
patibility complex (MHC) region and the emphasis on HLA genes
relevant to transplantation, the role of *HLA-E*\*01 in MS risk re-
mains largely unexplored. Notably, *HLA-E*\*01 has not been found
to be associated with MS risk in genome-wide association studies
(GWASs) [15]. Based on the findings by Vietzen et al. [5], we hy-
pothesized that the association between *HLA-E*\*01 and MS risk
may be modified by the presence of a significant immune response
triggered by EBV, such as the occurrence of IM [16]. This could be
due to the differing abilities of *HLA-E*\*01:01 and *HLA-E*\*01:03 to
control EBV infection effectively. To investigate this hypothesis,
we used data from the UK Biobank, a large population-based co-
hort, to examine whether *HLA-E*\*01 interacts with IM in modu-
lating MS risk. IM diagnoses, obtained from medical records and
self-reports, were used as a marker of a clinically symptomatic
EBV infection. Time-to-event analyses were then performed con-
sidering *HLA-E*\*01 and IM exposure, as well as other MS-related
HLA alleles (e.g., *HLA-DRB1*\*15:01 and *HLA-A*\*02:01) and key
confounders such as smoking and childhood body size.

We aimed to evaluate (i) whether *HLA-E*\*01 is associated with IM risk, (ii) whether *HLA-E*\*01 influences MS risk, and (iii) whether *HLA-E*\*01 and IM interact to increase MS susceptibility independently of the interactions between IM and *HLA-DRB1*\*15:01 and *HLA-A*\*02:01 alleles. In particular, point (iii) seeks to determine whether individuals carrying *HLA-E*\*01:01 and diagnosed with IM represent a subgroup with higher MS susceptibility. Understanding this interaction could offer new insights into the gene–environment mechanisms underlying MS development and help identify individuals at higher risk based on HLA genotypes and IM history.

2 | Methods

2.1 | Institutional Review Board Approval

This is an observational study conducted using a population
cohort identified within the UK Biobank (REC approval 11/

NW/0382) [17]. All participants gave informed consent on
Biobank registration and are free to withdraw from the study at
any time, at which point their data are censored and cannot be
included in further analyses.

2.2 | Data

The UK Biobank offers a comprehensive range of health-related
phenotypes and genetic data collected from 502,354 individu-
als aged over 40years, recruited between 2006 and 2010. The
sample predominantly consists of English individuals of white
ethnicity, who generally have better overall health and higher
income compared to the general population [18].

## 2.3 | Genotypes

In the UK Biobank, genome-wide genotype data were collected on a large proportion of individuals in the cohort (≈97%). Genotypes were imputed using computationally efficient methods combined with the Haplotype Reference Consortium (HRC) and UK10K haplotype resource, and quality control on genetic variants were subsequently performed [19]. HLA-E*01 was identified via the rs1264457 polymorphism, where the G allele (major) denoted the *HLA-E*\*01:01 variant, while the A allele (minor) denoted the *HLA-E*\*01:03 variant [20]. MS-risk HLA alleles *HLA-DRB1*\*15:01, *HLA-DRB1*\*03:01, *HLA-DRB1*\*13:03, *HLA-DRB1*\*08:01, *HLA-DQA1*\*01:01, *HLA-DQB1*\*03:01, *HLA-DQB1*\*03:02, *HLA-B*\*07:02, and MS-protective HLA alleles *HLA-A*\*02:01, *HLA-B*\*38:01, *HLA-B*\*44:02, and *HLA-B*\*55:01 were obtained by the UK Biobank performing imputation using HLA\*IMP:02 [21]. MS risk HLA alleles were coded according to the number of alleles (0, 1, 2), while MS-protective HLA alleles were coded based on presence/absence (0, 1) of the allele. Lastly, we retrieved the first four genetic principal components as calculated by the UK Biobank. We excluded from the analyses the 15,210 individuals missing genotype data, leaving 487,144 individuals with complete genotype data.

2.4 | Infectious Mononucleosis Diagnosis

All diagnoses of IM were classified using the International
Classification of Disease 10th revision (ICD-10) B27 and were
retrieved as the earliest diagnosis date reported among hospi-
tal admissions, primary care, death registries and self-report
sources (see Table S1 for further details). Self-reported IM di-
agnoses were retrieved by the UK Biobank at the time of re-
cruitment asking the participants the following two interview
questions. First, “Has a doctor ever told you that you have had
any other serious medical conditions or disabilities?” (the ‘other’
referring to cancer, which was discussed separately during the
interview), and second, ‘In the touch screen you selected that
you have been told by a doctor that you have other serious ill-
nesses or disabilities, could you now tell me what they are?’. Therefore, IM diagnoses mainly related to cases with symptom-
atology severe enough to require medical attention. Using the
diagnosis date, we were able to consider IM as a time-varying
exposure along the follow up.
## 2.5 | Outcome

MS cases were defined according to the ICD-10 diagnosis code G35. The diagnosis date was retrieved as the earliest date from at least one source among hospital admissions, primary care, death registries, and self-report (see Table S1 for further details). To better characterize the date of MS onset and hence avoid delayed MS diagnoses [22], we anticipated the date of MS diagnosis in case of an earlier diagnosis of: (i) demyelination (ICD-10: G36-G37), (ii) abnormal findings on diagnostic imaging of the central nervous system (ICD-10: R90), or (iii) the presence of other MS onset-related symptoms occurring in the 5 years prior to the MS diagnosis date, such as optic neuritis (ICD-10: H46) (see Data S2 for a complete list). Age at MS diagnosis was determined by subtracting the date of birth from the date of MS diagnosis.

## 2.6 | Confounders

We considered the following confounders, collected at the initial assessment visit, for the association between IM diagnosis and MS: sex, year of birth, ethnicity (white, non-white), place of birth, ever smoked on most or all days, childhood body size, having older siblings, and the first four genetic principal components. Using the age when the individual reported starting to smoke on most or all days, we were able to consider smoking as a time-varying exposure during the follow-up. UK Biobank data fields for each variable used in the analysis were provided in Table S2.

## 2.7 | Statistical Analysis

Descriptive statistics were calculated for all the analyzed variables, stratified by *HLA-E*\*01 genotype. Missing data for the confounding variables were imputed using multiple imputation [23] (see Supporting Information). We planned the following statistical analyses based on a retrospective cohort study design. In the first analysis, we evaluated the association between *HLA-E*\*01 and IM diagnosis using a time-to-event approach. Individuals were followed from birth to the first date among IM diagnosis or date of censoring, i.e., date of death, date of MS diagnosis, date of loss to follow-up, or date of follow-up end (31 December 2022). *HLA-E*\*01 was coded as a continuous variable according to the number of *HLA-E*\*01:01 alleles (0, 1, 2). We implemented a Cox proportional hazards (PHs) model with age as time scale, including *HLA-E*\*01 as the covariate of interest, and adjusted for the other MS-related HLA alleles, sex, year of birth, place of birth, ethnicity, having older siblings, and the first four genetic principal components. In the second analysis, using the same approach, we investigated the association between *HLA-E*\*01 and MS diagnosis. End of follow-up was defined based on the first date among MS diagnosis or date of censoring, i.e., date of death, date of loss to follow-up, or date of follow-up end. In the third analysis, we further investigated the associations between *HLA-E*\*01, IM, and their interaction with MS diagnosis. To this aim, we considered IM diagnosis as a time-varying exposure by creating a variable coded as “No” for all individuals from the start of follow-up and changing to “Yes” from the time of IM diagnosis onward. In this analysis, we further adjusted for childhood body size and ever being a smoker (coded as a time-varying

confounder). In a Cox PH model, we included multiplicative interaction terms between IM and, respectively, *HLA-E*\*01, *HLA-DRB1*\*15, and *HLA-A*\*02. This method allows for adjustment for IM-specific effects on MS due to known interaction with *HLA-DRB1*\*15:01 and *HLA-A*\*02:01 alleles. Using the Cox model's coefficient of the multiplicative interaction, we estimated the association between *HLA-E*\*01:01 allele and MS within subgroups of individuals with or without IM diagnosis, as well as the association between IM and MS within *HLA-E*\*01 genotype (see Supporting Information). Inspection of Schoenfeld residuals was conducted to assess the validity of the PH assumption, adjusting for any significant deviation including a stratification factor or using time-varying coefficients.

We also estimated interactions between *HLA-E*\*01:01 allele and IM diagnosis on the additive scale, calculating the Relative Excess Risk due to Interaction (RERI) and the Attributable Proportion due to interaction (AP) (see Supporting Information). Additive interactions further help to assess if MS tends to occur only if both *HLA-E*\*01:01 allele and IM diagnosis are present but not if just one or the other is present, suggesting a potential biological interaction [24, 25]. We obtained 95% confidence intervals (CIs) for both multiplicative and additive interactions, implementing bootstrap sampling with replacement over 5000 replications and using the bias-corrected accelerated method [26]. We provided two-sided p-values derived from 95% bootstrap CIs using the R package *boot.pval*, considering *p* < 0.05 as statistically significant.

## 2.8 | Sensitivity Analyses

In the first sensitivity analysis, we evaluated if the strength of the interactions between *HLA-E*\*01 and IM diagnosis changed when excluding MS diagnoses over 50 years of age, i.e., late-onset MS (LOMS) cases [27]. Individuals were followed up to the first date among MS diagnosis or date of censoring, i.e., date of death, date of loss to follow-up, or date when the individuals reached 50 years of age. Similarly, in the second sensitivity analysis, we evaluated if the strengths of these interactions changed when excluding individuals born outside the UK (England, Wales, and Scotland).

Analyses were performed using RStudio 2023.06.1, and R code used for the main analysis was included in the Supporting Information.

# 3 | Results

## 3.1 | Descriptive Statistics

We analyzed a sample of 487,144 individuals. During the follow-up period (median: 70 years, interquartile range (IQR): 64; 77), 3855 individuals (0.8%) had a diagnosis of IM and 2491 (0.5%) had a diagnosis of MS. The median age of IM diagnosis was 20 years (IQR: 17; 30), while the median age of MS diagnosis was 44 years (IQR: 36; 53). The discrepancy between this and the known average age at MS onset (≈33 years) [28] derives from the cohort's demographic structure, which included individuals born between 1934 and 1971. As the cohort aged, the absence
of younger individuals born after 1971 limited the inclusion of
newly diagnosed younger cases, while LOMS cases (> 50years),
whose incidence has been rising in recent years [22, 28, 29], dis-
proportionately influenced the mean age at diagnosis at the end
of follow-up (2022).

Demographic characteristics of the overall sample and stratified by *HLA-E*\*01 genotypes are reported in Table 1. 78,165 individuals (16%) carried the *HLA-E*\*01:03/01:03 genotype, 233,129 (48%) carried the HLA-E\*01:01/01:03 genotype, and 175,850 (36%) carried the HLA-E\*01:01/01:01 genotype. Overall, individuals in the sample were more frequently females, of white ethnicity, born in England around 1950, had more frequently older siblings, and were less frequently smokers. These characteristics were nearly identical across HLA-E\*01 genotypes, while some imbalances can be found in other HLA alleles, i.e., a higher homozygotes' proportion of HLA-DRB1\*15:01, HLA-A\*02:01, HLA-DQB1\*03:02, and HLA-B\*07:02 in individuals carrying the HLA-E\*01:03/01:03 genotype, and a higher homozygotes' proportion of HLA-DRB1\*03:01 and HLA-B\*04:02 in individuals carrying the HLA-E\*01:01/01:01 genotype.

**3.2 | Association Between HLA-E*01:01 and IM Diagnosis**

We first assessed the association between *HLA-E*\*01 and the risk of IM diagnosis using a Cox model adjusted for demographic and genetic covariates, finding no statistically significant association (HR = 0.99 [95% CI: 0.94, 1.03], *p* = 0.56).

**3.3 | Association Between HLA-E*01:01 and MS Diagnosis Stratified by IM Diagnosis**

In the second analysis, we assessed the association between *HLA-E*\*01:01 and the risk of MS diagnosis, adjusting for the same covariates. Since the association between *HLA-DRB1*\*15:01 and MS risk violated the PH assumption (*p* < 0.05), we estimated separate HRs for ages ≤ 30 and > 30 (this threshold was chosen based on the Schoenfeld residuals plot shown in Figure S1, where HR decreases as age progresses and stabilizes around the age of 30). The Cox model revealed no statistically significant association between *HLA-E*\*01:01 and MS risk (HR = 1.06 [95% CI: 0.99, 1.13], *p* = 0.055).

We then included in the model the IM diagnosis as a time-
varying covariate along with its interaction terms with HLA-
E*01:01, HLA-DRB1*15:01, and lack of HLA-A*02:01 allele. The
model was further adjusted for childhood body size and smok-
ing. The results highlighted a statistically significant interaction
between HLA-E*01:01 allele and IM diagnosis (β = 0.50, [95% CI:
0.27; 0.66], p-value < 0.001), implying that the associations be-
tween each factor with MS varied depending on the levels of the
other. Specifically, in individuals without IM diagnosis, HLA-
E*01:01 was not significantly associated with MS (p = 0.051).
Conversely, in individuals with IM diagnosis, MS risk signifi-
cantly increased by 74% in heterozygotes (HLA-E*01:01/01:03)
and 301% in homozygotes (HLA-E*01:01/01:01) (p < 0.001)
(see Table 2 for HRs and 95% CIs). In line with previous find-
ings [10–12], the Cox model also highlighted a statistically

significant interaction between *HLA-DRB1*\*15:01 allele and IM diagnosis (but not between the absence of *HLA-A*\*02:01 allele and IM diagnosis). This highlights how *HLA-E*\*01:01 alleles further increase MS risk through their interaction with IM independently from the interaction already taking place between IM and *HLA-DRB1*\*15:01 alleles. Associations between IM diagnosis and MS given different combinations of *HLA-E*\*01:01 and *HLA-DRB1*\*15:01 alleles were fully described in *Supporting Information* and reported in Figure S2 and Table S3.

**3.4 | Additive Interactions Between IM Diagnosis and HLA-E*01:01**

We also calculated interactions on the additive scale between *HLA-E*\*01:01 allele and IM diagnosis through the RERI and AP, with results reported in Table S4 and depicted in Figure S3. Results showed statistically significant additive interactions between *HLA-E*\*01:01 and IM diagnosis in increasing MS risk for any given combination of *HLA-DRB1*\*15:01 and *HLA-A*\*02:01 alleles (*p* < 0.001), with an estimated AP ≈ 40% in *HLA-E*\*01:01 heterozygotes and ≈ 65% in *HLA-E*\*01:01 homozygotes (see *Supporting Information* for further details).

**3.5 | Sensitivity Analyses**

In the first sensitivity analysis, we censored the follow-up up to
50 years of age to exclude 761 LOMS cases. In the second sensi-
tivity analysis, we excluded 46,870 individuals born outside the
UK. The results for both multiplicative and additive interactions
between *HLA-E*\*01:01 and IM diagnosis were very similar to those
in the main analysis. Multiplicative interactions were, respec-
tively, $\beta$= 0.57 [95% CI: 0.28; 0.83] (p < 0.001), and $\beta$= 0.46 [95%
CI: 0.24; 0.65] (p < 0.001), while additive interactions were re-
ported in Table S5.

**4 | Discussion**

Our study investigated the link between *HLA-E*\*01 polymorphisms, a history of IM diagnosis (a clinical manifestation of EBV infection) and MS risk, using data from 487,144 individuals in the UK Biobank. Previous research suggested that the *HLA-E*\*01:03 allele, compared to *HLA-E*\*01:01, enhances immune activation against EBV-infected B cells, potentially protecting against MS [5]. However, GWASs have not identified a significant association between the *HLA-E*\*01 genotype and MS risk. Based on these insights, our study aimed to provide further investigation by using a statistical approach in a real-world data setting. Specifically, we aimed to investigate whether *HLA-E*\*01:01 is linked to an increased risk of MS following the occurrence of an IM diagnosis, potentially due to the previously mentioned weaker immune control against EBV-infected B cells [5].

Our findings indicate that *HLA-E*\*01:01 alone is not associated
with an increased risk of MS or IM. Instead, when IM was di-
agnosed, individuals carrying the *HLA-E*\*01:01 allele had a sig-
nificantly increased MS risk, with a proportion of combined risk
attributable to interaction effects between *HLA-E*\*01:01 and IM
<table><caption>TABLE 1 | Descriptive statistics for analyzed variables for the overall sample and stratified by HLA-E*01 genotype.</caption><thead><tr><th></th><th>HLA-E*01:03/01:03</th><th>HLA-E*01:01/01:03</th><th>HLA-E*01:01/01:01</th><th>Overall</th></tr><tr><th></th><th>(N=78,165)</th><th>(N=233,129)</th><th>(N=175,850)</th><th>(N=487,144)</th></tr></thead><tbody><tr><td><strong>Multiple sclerosis diagnosis</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>380 (0.5%)</td><td>1191 (0.5%)</td><td>920 (0.5%)</td><td>2491 (0.5%)</td></tr><tr><td>No</td><td>77,785 (99.5%)</td><td>231,938 (99.5%)</td><td>174,930 (99.5%)</td><td>484,653 (99.5%)</td></tr><tr><td><strong>Age at multiple sclerosis diagnosis</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Median [IQR]</td><td>43.2 [35.5, 50.6]</td><td>44.9 [36.0, 54.0]</td><td>44.2 [35.9, 53.4]</td><td>44.2 [35.8, 53.3]</td></tr><tr><td><strong>Infectious mononucleosis diagnosis</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>609 (0.8%)</td><td>1901 (0.8%)</td><td>1345 (0.8%)</td><td>3855 (0.8%)</td></tr><tr><td>No</td><td>77,556 (99.2%)</td><td>231,228 (99.2%)</td><td>174,505 (99.2%)</td><td>483,289 (99.2%)</td></tr><tr><td><strong>Age at infectious mononucleosis diagnosis</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Median [IQR]</td><td>20.3 [16.5, 28.7]</td><td>20.4 [16.5, 29.5]</td><td>20.2 [16.5, 29.8]</td><td>20.3 [16.5, 29.5]</td></tr><tr><td><strong>Sex</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Females</td><td>42,696 (54.6%)</td><td>126,348 (54.2%)</td><td>95,083 (54.1%)</td><td>264,127 (54.2%)</td></tr><tr><td>Males</td><td>35,469 (45.4%)</td><td>106,781 (45.8%)</td><td>80,767 (45.9%)</td><td>223,017 (45.8%)</td></tr><tr><td><strong>Year of birth</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Median [IQR]</td><td>1950 [1945, 1958]</td><td>1950 [1945, 1958]</td><td>1950 [1945, 1958]</td><td>1950 [1945, 1958]</td></tr><tr><td><strong>Ethnicity</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Non-White</td><td>3819 (4.9%)</td><td>9563 (4.1%)</td><td>6510 (3.7%)</td><td>19,892 (4.1%)</td></tr><tr><td>White</td><td>72,425 (92.7%)</td><td>219,693 (94.2%)</td><td>166,982 (95.0%)</td><td>459,100 (94.2%)</td></tr><tr><td>Missing</td><td>1921 (2.5%)</td><td>3873 (1.7%)</td><td>2358 (1.3%)</td><td>8152 (1.7%)</td></tr><tr><td><strong>Place of birth</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Africa</td><td>1789 (2.3%)</td><td>4504 (1.9%)</td><td>3197 (1.8%)</td><td>9490 (1.9%)</td></tr><tr><td>America</td><td>1122 (1.4%)</td><td>3248 (1.4%)</td><td>2202 (1.3%)</td><td>6572 (1.3%)</td></tr><tr><td>Asia/Oceania</td><td>2752 (3.5%)</td><td>6208 (2.7%)</td><td>3915 (2.2%)</td><td>12,875 (2.6%)</td></tr><tr><td>England</td><td>60,855 (77.9%)</td><td>181,852 (78.0%)</td><td>136,361 (77.5%)</td><td>379,068 (77.8%)</td></tr><tr><td>Europe</td><td>1813 (2.3%)</td><td>4661 (2.0%)</td><td>2805 (1.6%)</td><td>9279 (1.9%)</td></tr><tr><td>Ireland/North Ireland</td><td>1011 (1.3%)</td><td>3512 (1.5%)</td><td>3249 (1.8%)</td><td>7772 (1.6%)</td></tr><tr><td>Scotland</td><td>5567 (7.1%)</td><td>18,214 (7.8%)</td><td>15,342 (8.7%)</td><td>39,123 (8.0%)</td></tr><tr><td>Wales</td><td>2974 (3.8%)</td><td>10,258 (4.4%)</td><td>8331 (4.7%)</td><td>21,563 (4.4%)</td></tr><tr><td>Missing</td><td>282 (0.4%)</td><td>672 (0.3%)</td><td>448 (0.3%)</td><td>1402 (0.3%)</td></tr><tr><td><strong>Have older siblings</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>No</td><td>21,243 (27.2%)</td><td>63,743 (27.3%)</td><td>47,357 (26.9%)</td><td>132,343 (27.2%)</td></tr><tr><td>Yes</td><td>55,698 (71.3%)</td><td>165,908 (71.2%)</td><td>125,909 (71.6%)</td><td>347,515 (71.3%)</td></tr><tr><td>Missing</td><td>1224 (1.6%)</td><td>3478 (1.5%)</td><td>2584 (1.5%)</td><td>7286 (1.5%)</td></tr><tr><td><strong>Childhood body size at age 10</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Thinner</td><td>25,596 (32.7%)</td><td>76,107 (32.6%)</td><td>57,166 (32.5%)</td><td>158,869 (32.6%)</td></tr><tr><td>Average</td><td>38,975 (49.9%)</td><td>115,786 (49.7%)</td><td>87,611 (49.8%)</td><td>242,372 (49.8%)</td></tr><tr><td>Plumper</td><td>11,827 (15.1%)</td><td>36,342 (15.6%)</td><td>27,456 (15.6%)</td><td>75,625 (15.5%)</td></tr></tbody></table>

(Continues)
<table><caption>TABLE 1 | (Continued)</caption><thead><tr><th></th><th>HLA-E*01:03/01:03</th><th>HLA-E*01:01/01:03</th><th>HLA-E*01:01/01:01</th><th>Overall</th></tr></thead><tbody><tr><td>Missing</td><td>1767 (2.3%)</td><td>4894 (2.1%)</td><td>3617 (2.1%)</td><td>10,278 (2.1%)</td></tr><tr><td><strong>Ever smoked on most or all days</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>No</td><td>42,627 (54.5%)</td><td>126,747 (54.4%)</td><td>95,919 (54.5%)</td><td>265,293 (54.5%)</td></tr><tr><td>Yes</td><td>35,094 (44.9%)</td><td>105,185 (45.1%)</td><td>79,070 (45.0%)</td><td>219,349 (45.0%)</td></tr><tr><td>Missing</td><td>444 (0.6%)</td><td>1197 (0.5%)</td><td>861 (0.5%)</td><td>2502 (0.5%)</td></tr><tr><td><strong>HLA-DRB1*15:01</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Absent</td><td>53,112 (67.9%)</td><td>169,816 (72.8%)</td><td>137,809 (78.4%)</td><td>360,737 (74.1%)</td></tr><tr><td>Heterozygote</td><td>22,497 (28.8%)</td><td>58,342 (25.0%)</td><td>35,718 (20.3%)</td><td>116,557 (23.9%)</td></tr><tr><td>Homozygote</td><td>2556 (3.3%)</td><td>4971 (2.1%)</td><td>2323 (1.3%)</td><td>9850 (2.0%)</td></tr><tr><td><strong>HLA-A*02:01</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Absent</td><td>40,069 (51.3%)</td><td>125,559 (53.9%)</td><td>99,927 (56.8%)</td><td>265,555 (54.5%)</td></tr><tr><td>Heterozygote</td><td>31,517 (40.3%)</td><td>90,694 (38.9%)</td><td>64,824 (36.9%)</td><td>187,035 (38.4%)</td></tr><tr><td>Homozygote</td><td>6579 (8.4%)</td><td>16,876 (7.2%)</td><td>11,099 (6.3%)</td><td>34,554 (7.1%)</td></tr><tr><td><strong>HLA-DRB1*13:03</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Absent</td><td>76,771 (98.2%)</td><td>228,949 (98.2%)</td><td>172,719 (98.2%)</td><td>478,439 (98.2%)</td></tr><tr><td>Heterozygote</td><td>1388 (1.8%)</td><td>4161 (1.8%)</td><td>3106 (1.8%)</td><td>8655 (1.8%)</td></tr><tr><td>Homozygote</td><td>6 (0.0%)</td><td>19 (0.0%)</td><td>25 (0.0%)</td><td>50 (0.0%)</td></tr><tr><td><strong>HLA-DRB1*08:01</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Absent</td><td>75,278 (96.3%)</td><td>224,899 (96.5%)</td><td>170,026 (96.7%)</td><td>470,203 (96.5%)</td></tr><tr><td>Heterozygote</td><td>2859 (3.7%)</td><td>8150 (3.5%)</td><td>5761 (3.3%)</td><td>16,770 (3.4%)</td></tr><tr><td>Homozygote</td><td>28 (0.0%)</td><td>80 (0.0%)</td><td>63 (0.0%)</td><td>171 (0.0%)</td></tr><tr><td><strong>HLA-DQA1*01:01</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Absent</td><td>57,447 (73.5%)</td><td>171,528 (73.6%)</td><td>129,811 (73.8%)</td><td>358,786 (73.7%)</td></tr><tr><td>Heterozygote</td><td>18,991 (24.3%)</td><td>56,916 (24.4%)</td><td>42,488 (24.2%)</td><td>118,395 (24.3%)</td></tr><tr><td>Homozygote</td><td>1727 (2.2%)</td><td>4685 (2.0%)</td><td>3551 (2.0%)</td><td>9963 (2.0%)</td></tr><tr><td><strong>HLA-DQB1*03:01</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Absent</td><td>54,331 (69.5%)</td><td>159,088 (68.2%)</td><td>117,196 (66.6%)</td><td>330,615 (67.9%)</td></tr><tr><td>Heterozygote</td><td>21,593 (27.6%)</td><td>66,974 (28.7%)</td><td>52,484 (29.8%)</td><td>141,051 (29.0%)</td></tr><tr><td>Homozygote</td><td>2241 (2.9%)</td><td>7067 (3.0%)</td><td>6170 (3.5%)</td><td>15,478 (3.2%)</td></tr><tr><td><strong>HLA-DRB1*03:01</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Absent</td><td>67,476 (86.3%)</td><td>174,939 (75.0%)</td><td>114,725 (65.2%)</td><td>357,140 (73.3%)</td></tr><tr><td>Heterozygote</td><td>10,264 (13.1%)</td><td>54,874 (23.5%)</td><td>54,388 (30.9%)</td><td>119,526 (24.5%)</td></tr><tr><td>Homozygote</td><td>425 (0.5%)</td><td>3316 (1.4%)</td><td>6737 (3.8%)</td><td>10,478 (2.2%)</td></tr><tr><td><strong>HLA-DQB1*03:02</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Absent</td><td>58,536 (74.9%)</td><td>185,623 (79.6%)</td><td>149,816 (85.2%)</td><td>393,975 (80.9%)</td></tr><tr><td>Heterozygote</td><td>18,179 (23.3%)</td><td>45,003 (19.3%)</td><td>24,935 (14.2%)</td><td>88,117 (18.1%)</td></tr><tr><td>Homozygote</td><td>1450 (1.9%)</td><td>2503 (1.1%)</td><td>1099 (0.6%)</td><td>5052 (1.0%)</td></tr><tr><td><strong>HLA-B*38:01</strong></td><td></td><td></td><td></td><td></td></tr></tbody></table>

(Continues)
<table><caption>**TABLE 1** | (Continued)</caption><thead><tr><th></th><th>HLA-E*01:03/01:03</th><th>HLA-E*01:01/01:03</th><th>HLA-E*01:01/01:01</th><th>Overall</th></tr></thead><tbody><tr><td>Absent</td><td>75,804 (97.0%)</td><td>228,343 (97.9%)</td><td>174,149 (99.0%)</td><td>478,296 (98.2%)</td></tr><tr><td>Heterozygote</td><td>2294 (2.9%)</td><td>4733 (2.0%)</td><td>1696 (1.0%)</td><td>8723 (1.8%)</td></tr><tr><td>Homozygote</td><td>67 (0.1%)</td><td>53 (0.0%)</td><td>5 (0.0%)</td><td>125 (0.0%)</td></tr><tr><td><strong>HLA-B*04:02</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Absent</td><td>71,915 (92.0%)</td><td>190,524 (81.7%)</td><td>127,969 (72.8%)</td><td>390,408 (80.1%)</td></tr><tr><td>Heterozygote</td><td>6113 (7.8%)</td><td>41,113 (17.6%)</td><td>43,833 (24.9%)</td><td>91,059 (18.7%)</td></tr><tr><td>Homozygote</td><td>137 (0.2%)</td><td>1492 (0.6%)</td><td>4048 (2.3%)</td><td>5677 (1.2%)</td></tr><tr><td><strong>HLA-B*55:01</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Absent</td><td>76,741 (98.2%)</td><td>225,689 (96.8%)</td><td>167,685 (95.4%)</td><td>470,115 (96.5%)</td></tr><tr><td>Heterozygote</td><td>1415 (1.8%)</td><td>7391 (3.2%)</td><td>8042 (4.6%)</td><td>16,848 (3.5%)</td></tr><tr><td>Homozygote</td><td>9 (0.0%)</td><td>49 (0.0%)</td><td>123 (0.1%)</td><td>181 (0.0%)</td></tr><tr><td><strong>HLA-B*07:02</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Absent</td><td>47,958 (61.4%)</td><td>165,341 (70.9%)</td><td>146,003 (83.0%)</td><td>359,302 (73.8%)</td></tr><tr><td>Heterozygote</td><td>26,202 (33.5%)</td><td>63,098 (27.1%)</td><td>28,432 (16.2%)</td><td>117,732 (24.2%)</td></tr><tr><td>Homozygote</td><td>4005 (5.1%)</td><td>4690 (2.0%)</td><td>1415 (0.8%)</td><td>10,110 (2.1%)</td></tr><tr><td><strong>Genetic principal component 1</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Median [IQR]</td><td>-12.0 [-13.3, -10.6]</td><td>-12.2 [-13.3, -10.8]</td><td>-12.2 [-13.4, -10.9]</td><td>-12.2 [-13.3, -10.8]</td></tr><tr><td><strong>Genetic principal component 2</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Median [IQR]</td><td>3.6 [2.4, 4.8]</td><td>3.7 [2.5, 4.8]</td><td>3.7 [2.5, 4.8]</td><td>3.7 [2.4, 4.8]</td></tr><tr><td><strong>Genetic principal component 3</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Median [IQR]</td><td>-1.5 [-2.7, -0.2]</td><td>-1.5 [-2.7, 98.8]</td><td>-1.5 [-2.7, -0.3]</td><td>-1.5 [-2.7, -0.2]</td></tr><tr><td><strong>Genetic principal component 4</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Median [IQR]</td><td>1.0 [-1.2, 3.3]</td><td>1.2 [-0.9, 3.5]</td><td>1.4 [-0.8, 3.8]</td><td>1.2 [-0.9, 3.6]</td></tr></tbody></table>

Note: Median and interquartile range (IQR) was reported for continuous variables, while absolute and relative frequencies were reported for categorical variables.

<table><caption>**TABLE 2** | Associations between HLA-E*01 genotype and Multiple Sclerosis diagnosis in individuals without or with a history of Infectious Mononucleosis diagnosis based on a Cox model including the interaction between HLA-E*01 and Infectious Mononucleosis.</caption><thead><tr><th></th><th>HR [95% CI]</th><th>p</th></tr></thead><tbody><tr><td><strong>Without IM diagnosis</strong></td><td></td><td></td></tr><tr><td>HLA-E*01:03/01:03</td><td>Reference</td><td></td></tr><tr><td>HLA-E*01:01/01:03</td><td>1.05 [0.99, 1.08]</td><td>0.051</td></tr><tr><td>HLA-E*01:01/01:01</td><td>1.11 [0.99, 1.17]</td><td></td></tr><tr><td><strong>With IM diagnosis</strong></td><td></td><td></td></tr><tr><td>HLA-E*01:03/01:03</td><td>Reference</td><td></td></tr><tr><td>HLA-E*01:01/01:03</td><td>1.74 [1.36, 1.97]</td><td>&lt;0.001</td></tr><tr><td>HLA-E*01:01/01:01</td><td>3.01 [1.81, 3.88]</td><td></td></tr></tbody></table>

Abbreviations: 95% CI, 95% Confidence Interval obtained using bootstrap bias-corrected accelerated method; HR, Hazard Ratio; IM, Infectious Mononucleosis.

estimated around 40% in heterozygotes and 65% in homozygotes [30]. The result supports our hypothesis, as it suggests a biologically relevant interaction between IM and *HLA-E*01:01, where the genetic variant predisposes to MS. Importantly, the interaction between IM and *HLA-E*01:01 in increasing MS risk acts independently from the interaction between IM diagnosis and *HLA-DRB1*15:01 (a well-known MS risk allele), as the association between IM and MS was strongest in individuals carrying both *HLA-E*01:01 and *HLA-DRB1*15:01 alleles. In contrast, individuals who lacked both alleles showed no significant increase in MS risk following IM, implying that genetic predisposition may be necessary for IM to act as a risk factor for MS.

Overall, these findings suggest that *HLA-E*01 is associated with increased MS risk in EBV-infected individuals diagnosed with IM, supporting our hypothesis and the findings by Vietzen et al. [5]. While previous studies focused on class II HLA alleles, such as *HLA-DRB1*15:01, this result highlights the need to incorporate the class I *HLA-E*01:01 allele when modeling multiple MHC components of MS risk, especially
in the context of prior EBV infection. Specifically, future re-
search should aim to identify HLA allele combinations that
confer the highest MS susceptibility in the presence of EBV in-
fection, ideally using detailed data on EBNA1 levels, to better
capture EBV seroconversion and IM symptomatology. If con-
firmed, *HLA-E*\*01:01 screening, along with other MS-related
alleles, could improve MS risk assessment and become a key
tool for targeted monitoring and early detection strategies in
EBV-exposed individuals, particularly those with a history of
IM diagnosis, and who could benefit from closer monitoring
for early intervention. Furthermore, the interaction between
HLA-E\*01:01, IM, and MS risk suggests potential preventive
and therapeutic strategies involving HLA-E-mediated im-
mune responses to EBV.

While this study benefits from a large sample size, a long fol-
low-up that covers an individual's lifetime, extensive con-
founder adjustment, and a prospective design that reduces
reverse causality concerns, several limitations must be ac-
knowledged. The main limitation relates to the collection of
IM cases through diagnoses based on healthcare records or
self-reporting a history of severe IM. Consequently, the preva-
lence of diagnosed IM cases in our cohort (0.8%) predominantly
reflects cases requiring hospitalization or medical attention,
which is lower than the estimated prevalence in the general
population (≈10%) that also includes asymptomatic and mild
cases [31]. This limitation implies that our findings are most
applicable to severe, clinically recognized IM cases, and further
research is needed to explore their generalizability to EBV sero-
conversion and milder IM symptomatology. MS diagnoses were
also derived from healthcare records and self-reports, some
dating back decades, which may have introduced misclassifi-
cation. Additionally, some early-onset MS cases may have been
missed due to mortality before study enrollment. However, the
alignment of MS prevalence in the UK Biobank with national
estimates suggests accurate case identification [32, 33]. Lastly,
the study's predominantly white British population limits gen-
eralizability to other ethnic groups.

Author Contributions

**Andrea Nova:** conceptualization, methodology, software, data cura-
tion, formal analysis, visualization, writing – original draft, writing –
review and editing. **Davide Gentilini:** writing – review and editing.
**Giovanni Di Caprio:** conceptualization, writing – review and editing.
**Sonia Bourguiba-Hachemi:** writing – review and editing. **Nicolas
Vince:** writing – review and editing. **Pierre-Antoine Gourraud:** writ-
ing – review and editing. **Luisa Bernardinelli:** writing – review and
editing. **Teresa Fazia:** conceptualization, methodology, formal analy-
sis, writing – review and editing, supervision.

Acknowledgments

This research has been conducted using the UK Biobank Resource
under Application Number 93731. This work uses data provided by pa-
tients and collected by the NHS as part of their care and support. Open
access publishing facilitated by Universita di Pavia, as part of the Wiley
- CRUI-CARE agreement.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

Researchers may obtain access to the data used in this study upon rea-
sonable request to the UK Biobank study team (https://www.ukbio
bank.ac.uk/).

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## Supporting Information

Additional supporting information can be found online in the Supporting Information section.