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Association between 25(OH) vitamin D and multiple sclerosis: cohort, shared genetics, and Causality

Nutrition Journal volume 23, Article number: 151 (2024) Cite this article

Abstract

Background

Multiple Sclerosis (MS), an autoimmune disorder causing demyelination and neurological damage, has been linked to 25-hydroxyvitamin D (25OHD) levels, suggesting its role in immune response and MS onset. This study used GWAS datasets to investigate genetic associations between 25OHD and MS.

Methods

We utilized a large-scale prospective cohort to evaluate serum 25OHD levels and MS risk. Linkage Disequilibrium Score Regression (LDSC) assessed genetic correlations between 25OHD levels and MS. Cross-trait genome-wide pleiotropy analysis revealed shared genetic loci. MAGMA analysis identified pleiotropic genes, enriched tissues, and gene sets. Stratified LDSC estimated tissue-specific and cell-specific heritability enrichment, and multi-trait co-localization analysis identified shared immune cell subsets. Bidirectional Mendelian Randomization (MR) assessed the causal association between 25OHD and MS risk.

Results

The observational study found a nonlinear relationship between 25OHD levels and MS risk, with the lowest quartile showing significant risk elevation. Our findings revealed shared genetic structure between 25OHD levels and MS, suggesting a common biological pathway involving immune function and CNS integrity. We found 24 independent loci shared between 25OHD levels and MS risk, enriched in brain tissues and involved in pathways like LDL, HDL, and TG metabolism. Four loci (6p24.3, 6p22.2, 12q14.1, and 19p13.2) had strong co-localization evidence, with mapped genes as potential drug targets. Bidirectional MR analysis supported a causal effect of 25OHD levels on MS risk, suggesting 25OHD supplementation could modulate MS risk.

Conclusion

This study reveals the complex relationship between 25OHD levels and MS, indicating that higher levels are not always advantageous and recommending moderation in supplementation. We identified SMARCA4 as a potential therapeutic target and detailed key pathways influencing this interaction.

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Introduction

Multiple sclerosis (MS) is a multifaceted autoimmune disease characterized by the immune system’s aberrant attack on the central nervous system (CNS), leading to demyelination and neuronal damage [1,2,3]. Epidemiological evidence suggests a geographical disparity in MS prevalence, with higher latitudes and reduced sunlight exposure correlating with increased incidence, resembling patterns observed in type 1 diabetes. (4–5) Observational studies report a statistically significant increase in MS incidence correlating with greater geographical distances from the equator [6]. Conversely, some studies report the absence of such a geographical gradient in MS prevalence. (7–8) This proposition of rising prevalence and incidence is rooted in the sunlight exposure hypothesis. Despite ongoing debates, it’s noteworthy that this gradient is diminishing [9]. Previous studies have confirmed a significant association between MS risk and serum levels of 25OHD. For instance, several Mendelian randomization (MR) analyses have demonstrated that lower vitamin D levels increase MS risk [10,11,12,13]. The protective effect of higher vitamin D levels, as shown in studies such as the Nurses’ Health Study and US Department of Defense study, strongly supports the role of vitamin D in reducing MS susceptibility. (14–15) An analysis of 35 studies revealed that 60% (21 studies) identified a statistically significant relationship between 25OHD levels and MS disease activity as detected by MRI, whereas 40% (14 studies) found no significant effect of 25OHD levels on MS [16].

Additionally, the therapeutic efficacy of vitamin D in MS remains a subject of ongoing investigation, with conflicting results arising from various studies [17]. While some studies suggest positive outcomes, such as improved relapse rates and magnetic resonance imaging (MRI) findings with increased vitamin D dosage, the optimal treatment regimen remains elusive [18]. Divergent dosages, ranging from high daily doses to prolonged supplementation, complicate the assessment of vitamin D’s therapeutic potential in managing MS [19]. Numerous studies incorporating vitamin D supplementation have been conducted, yet they were often inadequately powered, lacking prolonged follow-up, and featuring diverse methodological biases, thereby limiting their ability to yield conclusive results [20]. Experts recommend that for a randomized controlled trial (RCT) on vitamin D, it may be important to ensure participants are vitamin D deficient at the start and minimize sunlight exposure during the study to control for external factors [21]. Given the complexity of these variables, further in-depth and well-controlled studies are essential to fully elucidate the role of vitamin D in MS risk, highlighting the importance of genetic-focused approach to provide deeper insights into this relationship.

Genome-wide association studies (GWAS) have identified approximately 238 genetic risk variants linked to MS, as reported by the International Multiple Sclerosis Genetics Consortium. (22–23) Therefore, leveraging large-scale GWAS datasets allows for a more comprehensive interpretation of the association between 25OHD levels and MS. In this context, our study seeks to unravel the intricate genetic relationship between 25OHD and MS, employing sophisticated methodologies (Fig. 1B). First, we utilized the large-scale prospective cohort to evaluate serum 25OHD levels and MS risk. Then, Leveraging the Linkage Disequilibrium Score Regression (LDSC) method, we aim to evaluate the genetic correlation between 25OHD and MS, shedding light on shared genetic factors contributing to their association. Furthermore, a comprehensive exploration of pleiotropic loci and genes associated with both traits will be undertaken, unraveling potential common pathways contributing to MS susceptibility and vitamin D metabolism. Crucially, our investigation extends beyond correlation, employing a two-sample two-way MR approach to assess causal associations between 25OHD and MS. By utilizing genetic variants as instrumental variables, we aspire to disentangle the complex web of causality, providing insights into the potential modifiability of MS risk through interventions targeting 25OHD status, and to discover potential intervention targets for the development of new drugs.

Fig. 1
figure 1

Flowchart of overall study design. (A) Selection Process for UK Biobank Cohort in the study. (B) Overview of statistical analyses performed in the study. SNP: Single Nucleotide Polymorphism; LDSC: Linkage Disequilibrium Score Regression; HDL: high-definition likelihood; PLACO: Protein–Ligand Affinity by Comparable Co-Evolution; MAGMA: Multi-marker Analysis of GenoMic Annotation; GWAS: Genome-Wide Association Study; eQTL: Expression Quantitative Trait Locus; S-LDS: Stratified Linkage Disequilibrium Score

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Materials and methods

Study design

This study used LDSC to explore the complex genetic relationship between 25OHD and MS, assessing their genetic correlation. We used cross-trait pleiotropy analysis to identify pleiotropic loci and genes and a series of functional analyzes to identify tissues and pathways involved in disease development. Furthermore, we applied a two-sample bidirectional Mendelian Randomization (MR) approach to determine the causal link between 25OHD and MS.

Observational study

The UK Biobank is a pivotal resource in biomedical research, offering an extensive database that includes genetic and health information from over half a million participants in the United Kingdom [24]. Recruitment began in 2006, targeting individuals aged 40–69 years from across 22 assessment centers. Each participant provided written informed consent and contributed to a wealth of data through baseline questionnaires, interviews, and physical measurements. Comprehensive blood biochemistry analyses and genotyping were also performed, paving the way for detailed investigations into the genetic and environmental underpinnings of various diseases.

In our observational study, we utilized the large-scale UK Biobank cohort (Application ID 76875) to evaluate the association between serum 25OHD levels and MS risk. These data items were collected during the initial assessment visit (2006–2010) and the first repeat assessment visit (2012-13). The concentration of 25OHD (Field ID = 30890) was test for a total of 449,822 individuals, with value types continuous and expressed in nmol/L. Diagnostic for MS were categorized using the ICD-10-CM classification (G35). Participants were followed from the baseline assessment until the occurrence of the first diagnosis of MS, death, loss to follow-up, or until the end of the study period on December 31, 2021. Inclusion and exclusion of all samples are shown in Fig. 1A. Subjects with extreme 25OHD values (< 1% or > 99%) were eliminated from all analysis. In our analysis, we excluded participants diagnosed with MS within six months of baseline from all analyses to minimize the possibility of misclassification. A total of 413, 594 subjects with complete measurement of 25OHD levels were ultimately included in the study, of whom 861 developed MS during follow-up.

We used multivariable Cox proportional hazards models to evaluate the association between 25OHD levels and risk of MS, taking into account risk across quartiles of 25OHD levels. Restricted cubic splines (RCS) were used to evaluate the nonlinear relationship between 25OHD and MS. Our models were adjusted for potential confounders including age, sex, Townsend deprivation index (TDI), type 2 diabetes, cancer, osteoporosis, monocyte count, white blood cell leukocyte count, season of blood sampling and physical activity.

GWAS summary statistics

In order to avoid possible false positives caused by sample overlap, we selected 25OHD and MS summary data generated in completely different cohorts. 25OHD data originated from a GWAS that included 401,460 white British participants [25]. Utilizing a linear mixed model GWAS, 25OHD levels were measured in blood samples collected twice. The analysis incorporated standardized log-transformed 25OHD levels and included covariates such as age, sex, season, and 25OHD supplementation. A meta-analysis was conducted on GWAS results from 42,274 European samples, revealing 138 conditionally independent Single Nucleotide Polymorphisms (SNPs, including 63 novel ones). The estimated SNP heritability for 25OHD was determined to be 16.1%.The GWAS summary data for MS were derived from a two-stage investigation involving 76,755 people with MS and 243,649 control individuals [23]. This study identified 287 common variants associated with MS. In each individual cohort, the association tests were based on an additive logistic regression model using PLINK software, and the results were meta-analyzed by using an inverse variance-weighted fixed-effects model.

Genetic correlation

The LDSC method was employed to assess genetic correlations across phenotypes, facilitating the identification of shared genetic underpinnings across different traits [26]. This approach enabled the examination of the common polygenic structure among phenotypes, utilizing LD scores calculated from European ancestry samples derived from the 1000 Genomes Project and the Hapmap3 project as reference panels [27]. Rigorous quality control measures were applied to SNPs, including the exclusion of non-biallelic and SNPs with ambiguous strand information, removal of SNPs lacking rs tags, elimination of duplicate SNPs or those not present in the 1000 Genomes Project, and SNPs within the major histocompatibility complex (chr6: 28.5–33.5 Mb) were omitted due to their intricate LD structure. Moreover, we retained SNPs with a minor allele frequency (MAF) greater than 0.01. To further explore whether the SNP heritability of 25OHD and MS is concentrated in specific tissues, we utilized stratified LD score regression.

Tissue and cell-specific heritability estimation

We further utilized the S-LDSC method to investigate whether the genetic heritability of SNPs related to 25OHD and MS is enriched in specific tissues. Subsequently, LDSC was applied to diverse immune cell data to assess if specific cell types in these tissues exhibit significant genetic enrichment. We obtained data from 53 human tissues (including 13 brain tissues) [28] through GTEx and acquired information on 292 immune cell types (including B cells, gamma delta T cells, alpha beta T cells, innate lymphocytes, myeloid cells, stromal cells, and stem cells) from the ImmGen consortium [29]. After adjusting for baseline models and all gene sets, we assessed the significance of SNP heritability enrichment estimates in each tissue and cell type using p-values derived from the z-scores of regression coefficients.

Pleiotropic analysis under composite null hypothesis

SNP-Level PLACO is an innovative approach employed to investigate polygenic loci associated with complex traits using summary-level genotype-phenotype association statistics [30]. Specifically, we calculated the square of the Z-scores for each variant, excluding SNPs with excessively high Z [2] (> 80). Considering the potential correlation between 25OHD and MS, we computed the correlation matrix of Z-scores. Subsequently, we applied a level-α intersection-union test (IUT) method to evaluate the hypothesis of no pleiotropy. The final P values for the IUT test was determined as the maximum p-value between H0 and H1.

Building on the PLACO findings, we further associated the identified loci with nearby genes to investigate shared biological mechanisms at these polygenic sites. Employing a Generalized Gene-Set Analysis of GWAS Data (MAGMA), we analyzed genes located at or overlapping with polygenic loci identified through PLACO output and single-trait GWAS, aiming to identify candidate pathways linked to polygenicity and tissue enrichment of polygenic genes [31]. For the MAGMA gene analysis, we applied a threshold of 2.68E-06, and for the gene set analysis, the threshold was 2.94E-06. To ascertain the biological functions of polygenic loci, we used Functional Mapping and Annotation (FUMA) with genome-wide association study data [32]. Pathway enrichment analysis was conducted across various pathways using the Molecular Signatures Database (MSigDB) to delineate the functions of mapped genes [33]. eQTL analysis incorporated SNP-gene association data derived from whole blood tissues.

Colocalization analysis

For multi-trait loci annotated with FUMA, we utilized the Bayesian colocalization analysis tool “coloc” [34] to explore potential shared causal variants between pairs of traits at each multi-trait locus. This analysis operates under the assumption of a single causal variant and provides posterior probabilities (PP) for five hypotheses at each multi-trait locus: H0 - Neither of the two traits has a genetic association in this region; H1 - Only trait 1 exhibits a genetic association in this region; H2 - Only trait 2 shows a genetic association in this region; H3 - Both traits are associated but have different causal variants; H4 - Both traits are associated and share a common causal variant. Employing the “coloc.abf” function, we conducted colocalization analysis with significance thresholds set at P1 = P2 = 1 × 10− 4 and P12 = 1 × 10− 5.

Cell-type enrichment of pleiotropic signals

The cell-type MAGMA method is designed to map gene-level genetic associations to specific cell types using single-cell RNA sequencing (scRNA-seq) data [35]. First, gene-level association p-values are computed using MAGMA’s gene-based analysis, which aggregates SNP-level associations for each gene. Then, the cell-type MAGMA method evaluates the overlap between these gene associations and the expression levels across various cell types. In our study, we used scRNA-seq data from large European single-cell datasets (Onek1K) [36] to perform cell-type MAGMA analysis, which allowed us to identify cellular subtypes that may be significantly associated with disease pathways. This method involved calculating specificity scores for each cell type by quantifying the relative expression of disease-associated genes in each cell subtype. By ranking cell types based on their enrichment scores, cell-type MAGMA enables the identification of cell populations that might play a role in the disease mechanism of interest, such as those relevant to MS risk.

Causal inference

In our analysis, we implemented the clumping procedure within the PLINK software to identify independently significant genetic loci serving as instrumental variables for two traits (P < 5 × 10 − 8)33. The r [2] threshold for instrumental variables was set at 0.001, within a physical distance of a 10,000 kb window. Additionally, we computed the r [2] and F-statistic for each instrumental variable to ensure its strength [35] with the F-statistic calculated using the formula:

$$F=(\frac{{n - 1 - k}}{k})(\frac{{{r^2}}}{{1 - {r^2}}})$$

Here, r [2] represents the proportion of variance explained by the instrumental variable, n is the sample size, and k is the number of SNPs. For MR, we primarily employed the Inverse Variance Weighted (IVW) method, requiring instrumental variables (IVs) to satisfy three assumptions: (1) IV should be associated with the exposure; (2) IV should not be associated with confounding factors related to both the exposure and the outcome; (3) The effect of IV on the outcome is entirely mediated through the exposure. We searched the GWAS catalog (https://www.ebi.ac.uk/gwas) to exclude SNPs related to potentially relevant confounding indicators (such as BMI, metabolites, metabolic diseases, etc.); At the same time, during analysis, we used multiple testing correction to exclude instrumental variables related to outcomes (P < 0.05/N), where N is the number of IVs used for 25OHD and all IVs used are shown in Table S13. Various sensitivity analyses were conducted. Initially, the heterogeneity Q test of IVW and MR-Egger was employed to detect potential violations of assumptions through heterogeneity among individual IVs [36]. Subsequently, the MR-Egger intercept was applied to estimate horizontal pleiotropy, ensuring that genetic variation is independently associated with exposure and outcome [37]. Additional analyses using different modeling assumptions and robust MR methods (weighted median and weighted mode) were incorporated to enhance the stability and robustness of the results.

The multivariable MR analysis further minimizes potential horizontal pleiotropy. The MVMR analysis employs the IVW method to assess whether the association between multiple exposures and outcome risk is primarily influenced by other potential confounders. We incorporated the effects of other traits into a linear model using the following formula and retained instrumental variables present across the three datasets. We performed a multivariable MR framework (38–39) to investigate the potential mediating role of immune cells in the pathway from 25OHD to MS. First, we used the IVW method to estimate the causal effect of 25OHD on 280 immune cell phenotypes from European individuals. Next, we conducted an MVMR analysis to assess the causal impact of these immune cell on MS while adjusting for 25OHD. To test the indirect (mediated) effect, we employed a Bootstrap approach to determine if the pathway from 25OHD through immune cells to MS was significant.

Statistical analyses were conducted using R version 3.5.3 software, and for Mendelian randomization analyses, we employed the Mendelian Randomization package [40].

Results

Relationship between the 25OHD and the risk of developing MS

The relationship between serum levels of 25OHD and the risk of developing MS was examined using Cox regression and RCS analyses. Basic information was presented in Table S1. Cox regression analysis demonstrated a significant association between lower 25OHD levels and an increased risk of MS across three models: Model 1, accounting for batch effects, age, sex, and TDI; Model 2, additionally adjusted for potential confounders such as type 2 diabetes, cancer, osteoporosis, monocyte count, and leukocyte count; and Model 3, additionally adjusted for season of blood sampling and regular physical activity (Table S2). Our RCS analysis identified a nonlinear trend between 25OHD levels and MS risk, with significant nonlinearity in Fig. 2A (P = 0.024) and Fig. 2C (P = 0.044), but not significant in Fig. 2B (p = 0.3445), which suggests different patterns of risk association by sex. In conclusion, A statistically significant P value for nonlinearity (P for nonlinear < 0.05) indicates that the risk relationship is not strictly linear across the range of 25OHD levels. During the follow-up period, our data suggest that participants with the lowest quartile (Q1) of 25OHD had the highest event rate of MS development, whereas those in the highest quartile (Q4) demonstrated the lowest event rate (Fig. 2D-F). This gradient effect reveals a distinct inverse relationship between 25OHD levels and the likelihood of developing MS, with deficiency marking a higher risk status. We also performed a sensitivity analysis and found that without excluding extreme values, the association between VD and MS risk was no longer significant in men but remained significant in women (Table S3). In additional sensitivity analysis, we further excluded participants with only a single MS diagnosis to reduce potential bias. While this adjustment slightly weakened the level of significance, the P value remained below 0.05, and the effect direction was consistent with that observed in the primary analysis(Table S4).

Fig. 2
figure 2

(A) Non-linear association between serum 25OHD levels and Multiple Sclerosis risk in the UKB cohort. (B) Hazard ratios for MS by 25OHD levels in male participants of the UKB cohort. (C) Hazard ratios for MS by 25OHD levels in female participants of the UKB cohort. (D) Standardized rates of MS events in bottom quartile, quartiles 2, quartiles 3), and top quartile 25OHD groups in the UKB cohort. (E) Standardized rates of MS events in bottom quartile, quartiles 2, quartiles 3), and top quartile 25OHD groups in male participants of the UKB cohort. (F) Standardized rates of MS events in bottom quartile, quartiles 2, quartiles 3), and top quartile 25OHD groups in female participants of the UKB cohort. Adjustments were made for confounding variables including age, sex, genotyping batch, assessment center, Townsend Deprivation Index (TDI), and the first 10 principal components of ancestry. MS, Multiple Sclerosis; OR, odds ratio; HR, hazard ratio. The non-linearity of the associations was assessed and indicated by the p-value for nonlinearity in each graph

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We conducted an interaction analysis to examine whether there is a significant interaction between serum 25OHD levels and sex in relation to MS risk. The interaction term’s P-value was 0.052, which is just above the conventional threshold for statistical significance (P < 0.05). This suggests a trend towards a potential interaction between sex and 25OHD levels, but it does not reach statistical significance. Subgroup analyses stratified by gender revealed that this inverse association was consistent across male and female participants (Table S2). However, our analysis reveals an L-shaped relationship between 25OHD levels and the risk of MS. The inflection point of the L-shaped curve is identified at 47.9 nmol/L. Below this threshold, reductions in 25OHD are significantly correlated with an increased risk of MS. Conversely, above 47.9 nmol/L, serum 25OHD levels do not exhibit a significant correlation with MS risk, suggesting a threshold effect. Elevating 25OHD to this specific level can mitigate the risk of MS, but exceeding this threshold does not confer additional benefits. This finding underscores the importance of a balanced approach to vitamin D supplementation. The results from the quartile analysis further confirm the protective trend of higher 25OHD levels against the development of MS. Those in the lower quartiles of 25OHD, particularly Q1, demonstrated a significantly increased risk in both Model 1 and Model 2. The increased risk was consistent even after adjusting for a comprehensive set of confounders, underscoring the robustness of the association.

Genetic correlation between 25OHD and MS

Genetic correlation analysis revealed a significant genetic correlation between 25OHD and MS, estimated using the LDSC method without considering the intercept term (rg = -0.096, P = 2.98 × 10− 8). Consistent findings were obtained when considering the intercept term in the LDSC analysis (rg = -0.1379, P = 1.86 × 10− 8), with an intercept of 0.0134 (SE = 0.0058). This intercept value helps mitigate potential interference from overlapping samples between the 25OHD and MS datasets, reinforcing the robustness of the results.

Pleiotropic gene loci identified for 25OHD and MS

Through PLACO analysis, we identified a total of 24 independent pleiotropic loci shared between 25OHD and MS. Figure 3A illustrates the circular Manhattan plot of the identified signals, while detailed information on these pleiotropic loci is provided in Table 1 and S5. Genomic inflation was not observed in the QQ plot (Figure S1 A), and Figure S1 B displays essential information for each genomic risk locus. The impact of pleiotropic SNPs on gene function is depicted in Figure S1 C and Table S5. In our investigation of the genetic overlap between 25OHD levels and Multiple Sclerosis (MS), region-specific association plots revealed significant single nucleotide polymorphisms (SNPs). Risk loci with PP.H4 > 0.7 were symbolized as 6p24.3 (RREB1), 6p22.2 (SCGN), 12q14.1 (CDK4), and 19p13.2 (SMARCA4 and LDLR). The marginal signal distributions of these four strong co-localization evidences in 25OHD and MS, respectively, are shown in Fig. 2B-E, and the polytropic signal distributions are shown in Figures S1 D-G. Figure 3B presents a localized association plot on chromosome 6, where rs56329220 was identified as the most significantly associated SNP with 25OHD levels, and rs115740542 with MS. Figure 3C delineates a similar association pattern on chromosome 12, highlighting rs11533604 as a key SNP for 25OHD and rs701006 for MS, providing further evidence of shared genetic architecture between the two conditions. Figure 3D details another segment of chromosome 6, underscoring rs41302867’s linkage to 25OHD and rs12211604 to MS, emphasizing the recurrent genetic signals in this chromosomal region. Figure 3E illustrates the findings on chromosome 19 with rs73015021 associated with 25OHD and rs12609500 with MS. Subsequently, we performed gene set enrichment analysis using MAGMA based on the results of pleiotropy. The analysis revealed the top 10 significantly (P < 2.94 × 10− 6) enriched gene sets (Fig. 4A, Table S6), with the top three being the metabolic pathway of LDL HDL and TG including diseases, statin inhibition of cholesterol production, and high-density lipoprotein particle remodeling. Additionally, our MAGMA tissue-specific analysis identified significant enrichments (P < 1 × 10− 3) in the top three tissues (Fig. 5A), namely cells EBV-transformed lymphocytes, spleen, and small intestine terminal ileum. It is noteworthy that this section of MAGMA gene set and tissue-specific analysis utilized the complete distribution of SNP for the analysis.

Fig. 3
figure 3

(A) Circular Manhattan plot of pleiotropic signals between 25OHD and MS. (B) Regional Plot for shared genetic loci between 25OHD and MS (6p22.2). (C) Regional Plot for shared genetic loci between 25OHD and MS (6p24.3). (D) Regional Plot for shared genetic loci between 25OHD and MS (12q14.1). (E) Regional Plot for shared genetic loci between 25OHD and MS (19p13.2)

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Table 1 Information on 24 pleiotropic loci identified between 25OHD and MS
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Fig. 4
figure 4

(A) Results of the MAGMA gene-set analysis (top 10); (B) Enrichment of polygenic SNP heritability in ImmGen cells determined by stratified-LDSC analysis; (C) Enrichment of polygenic SNP heritability across various tissues determined by stratified-LDSC analysis. (D) Cell-type Enrichment of Polygenic SNP Heritability in Immune Cells Using Single-cell MAGMA Analysis

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Fig. 5
figure 5

(A) Tissue-specific analysis of genome-wide pleiotropy between 25OHD and MS; (B) Enrichment of pleiotropic genes between 25OHD and MS across different tissues

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Identification of priority pleiotropic genes and functional enrichment

Given the intricate mechanisms through which SNPs influence gene regulation, we employed three distinct approaches to identify pleiotropic genes. All pleiotropic genes are shown in Table S7. Specifically, we first used the physical locations of lead SNPs to map pleiotropy risk sites to 31 nearby genes. The expression values of these nearby genes in various tissues are shown in Figure S2 and Table S8. Multiple genes (such as SCGN, RREB1, MAF, LDLR) were differentially expressed in brain, whole blood, and EBV-transformed lymphoid tissues. Magma gene test identified 70 pleiotropic genes (P < 2.70E-6 = 0.05/18639) (Figure S3 and Table S9). The QQ plot is shown in Figure S4; the expression of these genes in 54 different tissues is shown in (Figure S5 and Table S8), it was further found that genes such as AGAP2, KIF5A, and CTSS were differentially expressed in brain tissue, and genes such as DTX3 and APOE were differentially expressed in whole blood tissue. Finally, eQTL information in whole blood, EBV-transformed lymphocytes, and spleen tissue was used for gene mapping, and a total of 129 pleiotropic genes were identified, and a large number of genes were differentially expressed in brain tissue (Figure S6 and Table S8). The overlap of pleiotropic genes mapped in different ways is shown in Figure S7. HORMAD1, PDE4C, and TRMT61A were mapped by three methods at the same time. The nearby and magma methods simultaneously identified six genes: APOC3, CDK4, CYP24A1, RREB1, UGT2B28, ZFP36L1; Nearby genes and eQTL genes simultaneously identified three genes, SCGN, SMARCA4, and TTC34; Magma and eQTL simultaneously identified 21 shared genes (Table S7).

All identified pleiotropic genes (nearby genes, MAGMA genes, and eQTL genes) were further combined to annotate the gene functions. Tissue-specific enrichment analysis suggested that the mechanism shared by 25OHD and MS may be involved in pancreas, liver, and 11 brain tissues (Table S10, Fig. 5B). Pathway enrichment including KEGG, wiki, GO and other gene set information showed that pleiotropic genes were significantly enriched in pathways such as steroid metabolic process, statin inhibition of cholesterol production, and regulation of cellular catabolic process (Figure S8). Cell-specific enrichment analysis showed that 18 types of cells were significantly enriched. The first three were FAN OVARY CL6 PUTATIVE EARLY ATRETIC FOLLICLE THECAL CELL 2, MANNO MIDBRAIN NEUROTYPES HMGL, ZHONG PFC MAJOR TYPES MICROGLIA (Figure S9), and the latter two Cells are closely related to the central nervous system and brain diseases. Finally, PPI analysis was performed on the identified pleiotropic genes, and a total of 6 protein networks were identified (Figure S10).

Stratified-LDSC and single-cell MAGMA to Pinpoint critical Immune traits

We applied the S-LDSC method to the PLACO results of 25OHD and MS in an attempt to find potential heritability enrichment between two traits. As a result, significant enrichment of B.FrE. BM and B.FrF. BM in the B cell panel and T.DP69+. Th in the T cell panel was observed (Fig. 4B, Table S11). Macrophage steady-state MF.II + 480lo. PC and MF.II-480hi. PC were also found to be significantly enriched. Tissue-specific S-LDSC enrichment also found that pleiotropy was significantly enriched in blood cells, B lymphocytes, lymphocytes and other tissues (Fig. 4C, Table S12). The Single-cell MAGMA results showed different degrees of enrichment in different subpopulations. Specifically, the pleiotropic signals of vitamin D and MS were most highly enriched in CD4_positive, alpha_beta_cytotoxic_T_cells, followed by memory_B_cells and mucosal_invariant_T_cells (Fig. 4D, Table S13).

Causal association between 25OHD and MS

Finally, the two-sample MR method was used to assess the causal relationship between 25OHD and MS, and IVs were presented in (Table S14). The heterogeneity test found heterogeneity (P < 0.05), so random effects IVW was used as main results. IVW results supported that 25OHD could causally reduce the risk of MS, all other methods (i.e., MR-Egger, Weighted mode, Weighted median, DIVW, MR-RAPS and MR-PRESSO) obtained consistent results (Table 2 and S15). The slope term of MR-Egger excluded possible horizontal pleiotropy (P = 0.508 > 0.05). We further utilized scatter plots and funnel plots to assess the stability of the associations. Scatter plots exclude potential outliers and funnel plots exclude the possibility of potential bias (Fig. 6). In addition, the results of reverse MR did not support the causal effect of MS on 25OHD, and the results of multiple sensitivity analysis methods were consistent. After we used multivariate MR methods to further correct for metabolic indicators such as blood biochemical indicators, diabetes, and BMI, the association between 25OHD and MS is still statistically significant (Figure S11 and Table S16).

Table 2 Results of bidirectional MR analysis of 25OHD and MS
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Fig. 6
figure 6

Scatter and funnel plots from the Mendelian Randomization (MR) analysis. (A) Scatter plot for the causal effect of 25OHD on MS; (B) Funnel plot for the causal effect of 25OHD on MS; (C) Scatter plot for the causal effect of MS on 25OHD; (D) Funnel plot for the causal effect of MS on 25OHD

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Table S17 shows that specific immune cells partially mediate the effect of 25OHD on MS, with mediation proportions between approximately 32% and 45%. Specifically, 25OHD was associated with increased levels of N_CD8bpos.panel1, X_CD8bpos_of_CD3pos.panel1, and N_HLADRpos_in_CD8bpos_EMRA.panel1. Conversely, 25OHD was associated with decreased ratio_CD4_CD8.panel1 and X_CD4pos_of_CD3pos.panel1; these immune cells all showed negative indirect effects, consistent with main effects. These findings suggest that 25OHD may affect MS risk by modulating different immune cell populations in different ways.

Discussion

This study, we first utilized the large-scale UK Biobank cohort to evaluate the association between serum 25OHD levels and MS risk. Based on large-scale GWAS summary data, LDSC analysis revealed a significant genetic correlation between 25OHD and MS. Through cross-trait pleiotropy analysis, we found 24 pleiotropic loci, four of which had high co-localization evidence: 6p24.3 (RREB1), 6p22.2 (SCGN), and 12q14.1 (CDK4), and 19p13.2 (SMARCA4 and LDLR). Subsequent investigations found the SNP heritability of 25OHD and MS is enriched in several tissues and immune cells (e.g., spleen, whole blood, lymphocytes). Gene set analysis identified multiple potential pathways that may be involved in the mechanisms of 25OHD deficiency and MS risk. Finally, two-sample MR analysis identified a potential causal effect of 25OHD deficiency on MS risk.

Both observational studies and genetic evidence in our analysis generally support a significant inverse association between 25OHD and MS risk. This association is consistent with studies by Mokry LE, Rhead B, Gianfrancesco MA, etc. (13, 41–42) Then there are also some studies that find vitamin D does not seem to have a therapeutic effect on EDSS scores or ARR in people with MS [43]. Previous inconsistent associations may be due to dosage factors. For example: A meta-study suggests that vitamin D supplementation may have a therapeutic effect in the treatment of multiple sclerosis. However, there is uncertainty about the most appropriate dose, and higher doses may lead to worse outcomes [44]. Interestingly, this appears to be consistent with our finding in observational studies that there is an L-shaped association between 25OHD concentration and MS, that is, very high concentrations of 25OHD do not reduce the risk of MS and may even slightly increase. Expanding the scope of prior research, our bidirectional MR analysis enhances the evidence base with an enlarged cohort [13], substantially increasing the sample sizes to 417,580 for 25OHD and 76,755 for MS cases. However, MR studies only assume linear regression between VD and MS, and the developed nonlinear MR has also been proven to be unreasonable (45–46), so it is still impossible to infer the causal relationship between the two under nonlinear scenarios.

The Cox regression and RCS analyses point to a non-linear association, suggesting an inverse correlation where levels of 25OHD are linked to risk of MS, particularly pronounced in the lowest quartile of 25OHD. The implications of these results are multifaceted. 25OHD metabolism exhibits significant gender differences, most notably among people with MS [47]. The observed gender-based discrepancies in risk suggest potential biological or behavioral differences in 25OHD metabolism or MS pathogenesis. A study demonstrates that genetic variations, particularly those in 25OHD binding proteins and metabolism enzymes, can significantly influence serum 25OHD levels in response to high-dose vitamin D3 supplementation in relapsing-remitting people with MS [48]. In the male and combined gender subgroup analysis, the relationship between 25OHD levels and MS risk demonstrates an L-shaped curve, with a clear threshold at 47.5 nmol/L. Beyond this point, further increases in 25OHD do not provide additional benefits. However, in the female subgroup, once the 25OHD level exceeds a certain threshold, there is a slight increase in MS risk. These observations significantly diverge from traditional dose-response assumptions. Hormonal differences likely play a key role, as estrogen has been shown to enhance vitamin D receptor expression, which could potentiate the immune-regulatory properties of 25OHD in females [49]. The synergy between estrogen and vitamin D may provide insight into why women exhibit a stronger dose-response relationship to vitamin D, with more pronounced effects on immune modulation. This heightened sensitivity suggests that women might achieve optimal 25OHD levels more quickly with supplementation compared to men. However, this could also render women more susceptible to over-supplementation, where excessive 25OHD levels may paradoxically increase the risk of developing MS. Additionally, female sex has been identified as a significant risk factor for developing MS [50]. Our findings indicate that while vitamin D deficiency is strongly associated with an elevated risk of MS, surpassing a certain 25OHD threshold, particularly beyond the critical level of 47.5 nmol/L, may have adverse effects. This observation is reflected in the nonlinear risk patterns in females, which differ from the more straightforward L-shaped curve seen in males. These results emphasize the need for personalized vitamin D supplementation strategies that account for gender differences in vitamin D metabolism, ensuring that the benefits of supplementation are optimized while avoiding the potential risks of excessive vitamin D levels.

Among the five genes closest to the lead SNP in loci with high co-localization evidence, CDK4 and LDLR have been extensively reported to be associated with 25OHD and MS. The role of CDK4 in the pathogenesis of MS is elucidated through its engagement in various cellular and molecular pathways that intersect with the modulation of 25OHD [51,52,53,54], further confirming our research findings. An increasing amount of evidence suggests that LDL is associated with clinical risks and MRI outcomes in MS [55]. There are existing reports on the relationship between SMARCA4 and 25OHD, and SMARCA4 has been identified as potentially playing a role in regulating gender-specific immune responses and lipid metabolism in MS (56–57). Integrating our results, we consider SMARCA4 a potential therapeutic target between 25OHD and MS, deserving of further experimental investigation. RREB1 and SCGN have not been directly linked to 25OHD and MS, but there are indirect connections, which also warrant deeper future studies. We further discuss the functions of pleiotropic genes and possible mechanisms involved in the Supplementary.

Discussion

The findings revealed that the top 10 significantly enriched gene sets were all related to lipid metabolism pathways. The top three included the metabolic pathways of LDL, HDL, and TG, comprising diseases, statin inhibition of cholesterol production, and high-density lipoprotein particle remodeling. Further consolidation of nearby genes with matching positions, MAGMA genes, and eQTL genes led to tissue-specific enrichment analysis, indicating significant enrichment of pleiotropic genes associated with 25OHD and multiple sclerosis in the ‘Steroid metabolic process’ and ‘Statin inhibition of cholesterol production’ pathways. These enrichment results collectively suggest that lipid metabolism abnormalities may serve as risk factors for metabolic syndrome, providing additional insights into our discoveries concerning the pleiotropic loci of 25OHD and multiple sclerosis. The disruption of metabolism and fatty acid levels drives the occurrence and progression of central nervous system diseases, including MS [58]. The study reports the complex dynamics of microglial cell activation and their involvement in lipid metabolism, particularly the increased expression of key genes such as apolipoproteins, TREM2, and LPL [59]. This underscores the potential connection between microglial cell lipid metabolism and the pathogenesis of neurodegenerative diseases, including multiple sclerosis. The dysregulation of myelin health, due to the absence of microglia, is associated with the emergence of a myelinating oligodendrocyte state with altered lipid metabolism. Moreover, this mechanism is regulated through the disruption of the TGFβ1-TGFβR1 axis [60]. These insights open avenues for exploring targeted therapeutic interventions in MS, focusing on lipid metabolism as a significant factor in disease progression.

Cross-tissue eQTL analysis spanning whole blood, EBV-transformed lymphocytes, and spleen tissues revealed 129 multi-effect genes associated with 25OHD levels and MS. Through the integration of position-matched nearby genes, MAGMA genes, and eQTL genes, tissue-specific enrichment analysis indicated that these genes are not only enriched in various brain tissues but also in organs such as the liver, pancreas, and endometrial tissue. This emphasizes the potential involvement of these gene loci in brain-related processes, highlighting their functional impact on lipid metabolism and hormone regulation. Existing research supports our findings, suggesting that alterations in lipid metabolism and hormone secretion may be risk factors for the onset and progression of MS [55, 61]. Hormone receptors are present on immune cells, and sex hormones (such as estrogen, progesterone, testosterone, and prolactin) can influence different aspects of the immune system, potentially affecting the risk, activity, and progression of MS [61]. Progesterone can increase the proliferation and maturation of oligodendrocyte precursor cells into mature oligodendrocytes, thereby attenuating the activation and proliferation of microglia and astrocytes [62,63,64]. A Mendelian randomization study found that high-density lipoprotein cholesterol has a certain impact on fatigue in MS [65]. Another study identified a direct anti-inflammatory effect of the secondary bile acid TUDCA on astrocytes and microglia in vitro, and supplementing TUDCA improved the disease condition in an MS mouse model through its action on GPBAR1, indicating abnormal changes in bile acid metabolism in MS [66]. APOE was found to be differentially expressed in whole blood, aligning with its known role in immune regulation. Notably, APOE is expressed in monocytes and macrophages, where it plays a critical role in modulating lipid metabolism and immune responses. Research has demonstrated that APOE4-expressing cells, such as microglia and macrophages, exhibit altered phagocytic activity and a heightened pro-inflammatory state, which may influence immune regulation. (67–68) Furthermore, studies on iPSC-derived cells have revealed that APOE4 impacts cholesterol trafficking and lipid droplet accumulation, contributing to increased inflammation [69]. These findings suggest that APOE’s role in lipid metabolism could be linked to immune dysregulation in autoimmune diseases like MS. (69–70) These findings collectively underscore the intricate connections between hormone regulation, lipid metabolism, and immune system activities, suggesting that targeting these pathways could offer new therapeutic strategies for managing MS.

In addition to its well-established role in influencing lipid metabolism, 25OHD has emerged as a key player in immune regulation, exerting influence on both innate and adaptive immunity and effectively mitigating autoimmune responses. (71–72) A study on MS reveals heightened levels of 1,25-(OH)2D, increased EBV load, and a distinctive correlation between VDR expression and EBV load in people with MS, highlighting intricate interactions and opposing effects of EBV and 25OHD on immune dysregulation [73]. The imbalance in Th17/Treg ratios has been implicated in the pathogenesis of various autoimmune and inflammatory diseases. (74–75) 1,25(OH)2D3 exhibits the capability to suppress the Th17 phenotype by inhibiting the transcription of key factors, including RORγt, IL-17, IL-23R, and IL-22. Simultaneously, it fosters the Treg subset by inducing the expression of immunoregulatory elements such as IL-10, Foxp3, and CTLA-4 [76]. Furthermore, 1,25(OH)2D3 suppresses the expression of IL-2 and IFN-γ, thereby modulating the differentiation of Th17 cells [77]. The intricate interaction between 25OHD and the immune system is substantiated in murine model experiments, where vitamin D treatment ameliorated symptoms in the experimental autoimmune encephalomyelitis (EAE, an MS model) mouse model, potentially through the direct action of 1,25OH2D3 on CD4 + T cells [78]. Contingent upon the VDR recruitment site, 1,25(OH)2D3 has the potential to modulate the protein expression of specific vitamin D-sensitive genes, influencing a spectrum of cellular processes. This encompasses cellular growth, proliferation, differentiation, apoptosis, oxidative stress, and membrane transport [79]. In the EAE model, both the in vivo administration of vitamin D and the transfer of vitamin D-induced tolerogenic dendritic cells resulted in a notable elevation in the proportion of CD4(+) CD25(+) Foxp3(+) regulatory T cells and enhanced IL-10 production. Simultaneously, there was a reduction in the count of autoreactive T cells. These interventions demonstrated a considerable decrease in the incidence and severity of EAE. (80–81) Multi-trait colocalization analysis pinpointed 38 immune cell phenotypes with substantial genetic overlap. These molecular insights are pivotal for enhancing our genetic comprehension of MS, emphatically emphasizing the integral role of the immune system in the pathogenesis of the disease.

Future research should aim to delineate the mechanisms underlying these associations, explore the potential benefits of Vitamin D supplementation in at-risk populations, and establish the optimal ranges of serum 25OHD for MS prevention. Incorporating these insights, clinicians and policymakers could refine guidelines on Vitamin D intake, potentially incorporating genetic screening to identify individuals who might benefit most from preventive interventions.

Limitation

It is essential to acknowledge the limitations of our study. Despite the robust evidence from MR analysis, the observational nature of genetic associations cannot definitively prove causation and may overlook confounding factors. Our results, mainly derived from a European ancestry cohort, may not extend to all populations, highlighting the need for validation across diverse ethnicities. Additionally, the potential existence of undetected pleiotropic loci or pathogenic variants could further complicate interpretations of our results. Future research must broaden the scope to include a more diverse genetic background and explore the multifactorial nature of MS and its association with 25OHD. Expanding the scope of investigation to diverse populations and employing complementary research methodologies will be crucial for confirming our findings and translating them into effective clinical interventions.While our interaction analysis suggests a trend towards a potential interaction between sex and 25OHD levels. This trend, despite not reaching statistical significance in our sample, may still hold biological or clinical relevance. Given the near-significant result, further studies with larger sample sizes are warranted to explore this potential interaction more definitively. Such studies could provide more robust evidence and help clarify whether sex modifies the effect of 25OHD levels on MS risk.

Conclusions

This study highlights a significant genetic correlation between 25OHD levels and MS, revealing shared genes and pathways that could contribute to disease etiology and progression. Causal inference analysis suggests a significant causal effect of 25OHD on MS, highlighting the potential of vitamin D-targeted interventions in modifying MS risk. Observational study revealed an “L”-shaped association between serum 25OHD levels and the risk of MS, indicating that increasing serum 25OHD to an optimal threshold of 47.5 nmol/L significantly reduces the risk of MS and higher levels do not necessarily provide additional benefits. Our findings provide a foundation for future research into the shared genetic mechanisms and interplay between 25OHD levels and MS.

Data availability

Data is provided within the manuscript or supplementary information files.

Abbreviations

25OHD:

25-hydroxyvitamin D

ASD:

Autism Spectrum Disorder

BMI:

Body Mass Index

CAD:

Coronary Artery Disease

CDK4:

Cyclin-Dependent Kinase 4

CNS:

Central Nervous System

CRP:

C-reactive Protein

DBP:

Diastolic Blood Pressure

eQTL:

Expression Quantitative Trait Loci

EDSS:

Expanded Disability Status Scale

ERK1/2:

Extracellular Signal-Regulated Kinase 1/2

FGF:

Fibroblast Growth Factor

GWAS:

Genome-Wide Association Study

HbA1c:

Hemoglobin A1c

HDL:

High-Density Lipoprotein

HOMA-IR:

Homeostatic Model Assessment of Insulin Resistance

HOMA-β:

Homeostatic Model Assessment of Beta-cell Function

LDL:

Low-Density Lipoprotein

MAGMA:

Multi-marker Analysis of GenoMic Annotation

MAPK:

Mitogen-Activated Protein Kinase

MF:

Macrophage

mTORC1:

Mechanistic Target of Rapamycin Complex 1

MS:

Multiple Sclerosis

MR:

Mendelian Randomization

RREB1:

Ras Responsive Element Binding Protein 1

SCGN:

Secretagogin

SMARCA4:

SWI/SNF Related, Matrix Associated, Actin Dependent Regulator of Chromatin, Subfamily A, Member 4

SNP:

Single Nucleotide Polymorphism

SBP:

Systolic Blood Pressure

TGFβ:

Transforming Growth Factor Beta

TSC2:

Tuberous Sclerosis Complex 2

VDR:

Vitamin D Receptor

WBC:

White Blood Cell Count

AC:

Absolute Cell Counts

BRD4:

Bromodomain Containing 4

CTSS:

Cathepsin S

DTX3:

Deltex E3 Ubiquitin Ligase 3

Esr1:

Estrogen Receptor 1

FAN:

Follicle-Associated Nucleus

GO:

Gene Ontology

HDL-C:

High-Density Lipoprotein Cholesterol

HORMAD1:

HORMA Domain Containing 1

HyPrColoc:

Hypothesis Prioritization for Multi-Trait Colocalization

IVs:

Instrumental Variables

KEGG:

Kyoto Encyclopedia of Genes and Genomes

KIF5A:

Kinesin Family Member 5 A

LD:

Linkage Disequilibrium

MAF:

Minor Allele Frequency

MFI:

Median Fluorescence Intensity

MRI:

Magnetic Resonance Imaging

PPI:

Protein-Protein Interaction

QQ plot:

Quantile-Quantile plot

RC:

Relative Cell Counts

RCS:

Restricted Cubic Splines

TBNK:

T, B, and Natural Killer cells

TTC34:

Tetratricopeptide Repeat Domain 34

ZFP36L1:

Zinc Finger Protein 36 Like 1

ARR:

Annualized Relapse Rate

CTLs:

Cytotoxic T Lymphocytes

EAE:

Experimental Autoimmune Encephalomyelitis

GTEx:

Genotype-Tissue Expression

ImmGen:

Immunological Genome

IL:

Interleukin

PLACO:

Pleiotropic Analysis under Composite Null Hypothesis

Tregs:

Regulatory T Cells

TUDCA:

Tauroursodeoxycholic Acid

VDR:

Vitamin D Receptor

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Acknowledgements

We thank all the studies for making the summary association statistics data publicly available.

Funding

This work was supported by the Open Funding Project of Jiangsu Provincial Key Laboratory of Anesthesiology, Xuzhou Medical University (XZSYSKF2023011) and the Changzhou Sci&Tech Program (No. CJ20242012) to YJ. The Boxi Youth Natural Science Foundation (BXQN2023032) supported X.Y.

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Author notes

  1. Xing-Hao Yu and Hui-Min Lu contributed equally to this work.

Authors and Affiliations

  1. The Wujin Clinical College of Xuzhou Medical University, Changzhou, Jiangsu, 213000, China

    Xing-Hao Yu, Jun Li, Ming-Zhu Su, Xiao-Min Li & Yi Jin

  2. Jiangsu Province Key Laboratory of Anesthesiology, Xuzhou Medical University, Xuzhou, Jiangsu, 221002, China

    Xing-Hao Yu, Jun Li, Xiao-Min Li & Yi Jin

  3. Department of Pharmacy, Wujin Hospital Affiliated with Jiangsu University, Changzhou, Jiangsu, 213000, China

    Jun Li, Xiao-Min Li & Yi Jin

  4. National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, 215123, China

    Xing-Hao Yu

  5. Institute of Blood and Marrow Transplantation, Collaborative Innovation Center of Hematology, Soochow University, Suzhou, Jiangsu, 215123, China

    Xing-Hao Yu

  6. Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, Jiangsu, 215123, China

    Xing-Hao Yu

  7. Department of Outpatient and Emergency, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, 215123, China

    Hui-Min Lu

  8. Department of Good Clinical Practice, Wujin Hospital Affiliated with Jiangsu University, Changzhou, Jiangsu, 213000, China

    Ming-Zhu Su

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Contributions

Xing-Hao Yu and Hui-Min Lu: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data Curation, Writing - Original Draft, Visualization. Jun Li and Ming-Zhu Su: Validation, Formal analysis, Investigation, Writing - Original Draft. Xiaomin Li: Conceptualization, Methodology, Visualization, Supervision, Project administration. Yijin: Conceptualization, Methodology, Writing - Review & Editing, Visualization, Supervision, Project administration, Funding acquisition. All authors reviewed the manuscript.

Corresponding authors

Correspondence to Xiao-Min Li or Yi Jin.

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This study did not involve any experiments with human or animal subjects directly; therefore, ethical approval was not required. The analysis was based on publicly available, anonymized datasets.

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Yu, XH., Lu, HM., Li, J. et al. Association between 25(OH) vitamin D and multiple sclerosis: cohort, shared genetics, and Causality. Nutr J 23, 151 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12937-024-01059-4

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Nutrition Journal

ISSN: 1475-2891

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