CXCL12 Gene Polymorphisms and Serum Levels: Associations with Multiple Sclerosis Prevalence and Clinical Parameters in Lithuania
by
,
Paulius Valiukevicius
1
,
Kriste Kaikaryte
2,
Greta Gedvilaite-Vaicechauskiene
2,
Renata Balnyte
3 and
Rasa Liutkeviciene
4,* 1
Faculty of Medicine, Medical Academy, Lithuanian University of Health Sciences, A. Mickeviciaus 9, 44307 Kaunas, Lithuania
2
Laboratory of Ophthalmology, Neuroscience Institute, Medical Academy, Lithuanian University of Health Sciences, Eiveniu 2, 50161 Kaunas, Lithuania
3
Department of Neurology, Medical Academy, Lithuanian University of Health Sciences, Eiveniu 2, 50161 Kaunas, Lithuania
4
Department of Ophthalmology, Lithuanian University of Health Sciences, Eiveniu 2, 50161 Kaunas, Lithuania
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2024, 25(17), 9554; https://doi.org/10.3390/ijms25179554
Submission received: 19 July 2024
/
Revised: 28 August 2024
/
Accepted: 1 September 2024
/
Published: 3 September 2024
(This article belongs to the Special Issue Molecular Insights into Multiple Sclerosis)
Abstract
:Our study aimed to investigate the associations between CXCL12 rs1029153, rs1801157, and rs2297630 single-nucleotide polymorphisms (SNPs), CXCL12 protein levels, MS prevalence, and clinical parameters. This study included 250 individuals diagnosed with MS and 250 sex- and age-matched healthy control individuals from Lithuania. The SNPs were genotyped with real-time PCR-based assays. The CXCL12 protein concentration was evaluated in serum using the ELISA method. Of the studied CXCL12 SNPs, we found that the rs1801157 CT genotype in the males was associated with 2.3 times reduced MS odds when compared with the CC genotype according to the overdominant and codominant models (p = 0.011 and p = 0.012, respectively). There was a tendency, which did not reach adjusted statistical significance, for a lower CXCL12 protein concentration in the healthy individuals with the rs1801157 CT genotype (p = 0.028). Sensory symptoms were rarer in the women with the rs1801157 TT genotype (p = 0.004); however, this genotype was also associated with a shorter MS disease duration (p = 0.007). CXCL12 rs1801157 was associated with reduced odds of MS occurrence in the male individuals. In women, rs1801157 was associated with a sensory symptom prevalence.
1. Introduction
Multiple sclerosis (MS) is a chronic inflammatory and demyelinating disease of the central nervous system (CNS), which affects approximately 3 million people worldwide [1]. MS is typically diagnosed between the ages of 20 and 40 [2]. MS imposes a considerable socioeconomic burden since it primarily affects younger individuals, requires lifelong disease-modifying therapy (DMT), and results in significant disability [3].
MS results from a complex interplay between genetic and environmental factors. Currently, the most studied demographic risk factors are smoking, vitamin D deficiency, Epstein–Barr virus infection (EBV), and obesity [4]. Women are 2–3 times more likely to develop MS than men, which suggests that hormonal differences also play a significant role [5]. Finally, several HLA genes, such as HLA-DRB115:01 and HLA-DQA101:01, along with non-HLA genes, including NFKB1, IL2Ra, and TYK2, have been associated with an increased risk of MS. In fact, there are more than 200 identified genetic loci associated with MS [6]. A recent study demonstrated that by combining an MS genetic risk score with demographic factors, such as age and sex, it is possible to significantly enhance the prediction of MS among patients presenting with optic neuritis (ON), which is a critical distinction that could guide timely and targeted treatment strategies [7].
The pathogenesis of MS is complex and results in CNS inflammation due to immune cell infiltration, cytokine release, and microglia activation [8]. Ultimately, this leads to axonal demyelination, damage, and neurodegeneration, which causes a characteristic wide array of neurological symptoms [9,10]. Soluble signaling proteins, such as cytokines and chemokines, play a significant role in MS pathogenesis. Many non-HLA single-nucleotide polymorphisms (SNPs) associated with MS are located in genes that encode chemokine and cytokine proteins or their receptors, such as IL7R and TNFRSF1A, which have been associated with the attack severity and frequency [11]. These SNPs likely influence the production of bioactive molecules or the downstream signaling of immune cell receptors, which result in an aberrant immune response [12].
C-X-C motif chemokine 12 (CXCL12), also known as stromal cell-derived factor 1 (SDF-1), is thought to be important in the pathogenesis of MS. It is a pleiotropic chemokine: it guides neural progenitor cells during embryonic neurogenesis, participates in angiogenesis, and attracts leukocytes during inflammation [13]. In MS patient CNS tissue, CL12 expression is redistributed toward the vessel lumen within active lesions, which promotes autoreactive lymphocyte extravasation. Additionally, tissue CXCL12 levels are correlated with increased leukocyte infiltration and the severity of histological disease [14]. It could also be associated with plasma cell persistence since plasma cells retain the CXCR4 receptor during differentiation. Also, CXCL12 is associated with the astrocyte production of B-cell survival factor [15] and cortical lesions, which confirms the link between B-cell-associated CNS inflammation and adjacent cortical pathology [16]. Such observations could have clinical prognostic value, as cortical pathology has been recognized as a crucial substrate for the progression and irreversible clinical and cognitive disability in MS [17]. Furthermore, the breakdown of CXCL12 by metalloproteinases results in neurotoxic byproducts that contribute to neurodegeneration near MS lesions [15]. Finally, high cerebrospinal fluid (CSF) CXCL12 levels are detected in patients with increased grey matter lesion number and volume, which shows that CXCL12 could be used as a marker of MS progression [18].
Our study evaluated three CXCL12 SNPs: rs1029153, rs1801157, and rs2297630. Only rs1801157, which is in the 3′ untranslated region (UTR) of the gene, was studied in MS patients. rs1029153 and rs2297630 (3′ UTR and intron variants, respectively) have been implicated as possibly significant in other diseases, such as type 2 diabetes, primary pediatric immune thrombocytopenia, and response to hepatitis C treatment [19,20,21], which indicates that these SNPs may have an impact on CXCL12 expression. Given the significant involvement of CXCL12 in the inflammatory cascade associated with MS, we chose to explore these polymorphisms as potential factors that influence genetic susceptibility and various aspects of the disease.
In this study, we compared the prevalence of specific CXCL12 gene SNPs (rs1029153, rs1801157, and rs2297630) between MS patients and healthy controls in Lithuania. We also assessed CXCL12 protein levels in the sera of these groups. Furthermore, we explored the relationships between CXCL12 SNPs and various clinical parameters of MS, including the age at diagnosis, disease duration, phenotypic variation, affected functional systems, and progression rate.
2. Results
2.1. CXCL12 Single-Nucleotide Polymorphism Was Associated with MS Risk
This study included 250 individuals with diagnosed MS and 250 control individuals. The control group was selected to match the age and gender distributions of the MS group. There were no statistically significant differences between the MS and control group gender and age distributions (data are presented in Table 1).
The rs1029153, rs1801157, and rs2297630 genotypes within the MS and control groups were distributed according to the Hardy–Weinberg equilibrium (HWE), with no statistically significant deviations observed. Furthermore, the analysis revealed no statistically significant differences in the distributions of CXCL12 SNP genotypes between the MS and control groups (Table 2).
After conducting a binary logistic regression analysis of the CXCL12 genetic models (rs1029153, rs1801157, and rs2297630) in the study groups, no significant associations were found between the examined SNPs and the occurrence of MS (Table 3).
We conducted additional analyses after stratifying the groups by age (≤40 or >40) and gender to determine whether age-specific or gender-specific variations influenced the association between the CXCL12 SNP genotypes and the MS incidence.
Age-stratification into two groups (<40 years and ≥40 years) did not result in statistically significant differences between the MS and control group CXCL12 SNP genotypes or allele distributions. The rs1801157 SNP exhibited a trend toward a higher frequency of C alleles in the MS subjects under 40 years old compared with the controls (p = 0.022, Table S1). However, this result did not achieve statistical significance after an adjustment for multiple comparisons (adjusted p > 0.0167). The binary logistic regression did not reach the required statistical significance for all studied CXCL12 SNPs across the age-stratified groups (p > 0.0167) (Supplementary Material, Table S2). The gender-stratified analysis of the CXCL12 SNP distribution also did not reveal any statistically significant differences between the control and MS groups (p > 0.0167, Supplementary Material, Table S3). There was, however, a tendency in the male group for a higher rs1801157 CC genotype frequency in the MS subjects when compared with the controls (p = 0.037). Similarly, the rs1801157 C allele frequency was also higher in the male MS subjects when compared with the controls (p = 0.034).
Gender-stratified binary logistic regression demonstrated statistically significant associations for the rs1801157 SNP in males (Table 4). According to the codominant model, the CT genotype reduced the odds of MS by 2.3 times when compared with the CC genotype, with an odds ratio (OR) of 0.43 (95% CI: 0.22–0.83) and p = 0.012. In the overdominant model, the CT genotype also reduced the odds of MS by 2.3 times when compared with the combined TT and CC genotypes, with an odds ratio (OR) of 0.43 (95% CI: 0.23–0.83) and p = 0.011. Both the codominant and overdominant models fit the data similarly, with AIC values of 229.004 and 229.016, respectively.
2.2. CXCL12 Serum Levels in MS Patients and Healthy Subjects
We assessed the serum CXCL12 protein levels in 40 individuals from each group, which were matched for age and gender. For two control group individuals, the CXCL12 protein levels were above the detection range; therefore, these values were excluded from the analysis. The control and MS groups were uniform in their age and gender distributions (Supplementary Material, Table S4).
The median (IQR) CXCL12 serum protein concentrations in the control and MS groups were 36.6 (6.8) and 35.4 (10.3), respectively, with a not statistically significant tendency for a higher CXCL12 concentration in the control group (p = 0.166). There were no statistically significant differences in the CXCL12 protein concentration when comparing across genders or age groups in both the control and MS subjects (Supplementary Material, Table S5).
To assess whether there was an association between the CXCL12 (rs1029153, rs1801157, and rs2297630) SNPs and the concentration of the protein encoded by this gene in the serum, an analysis was performed that compared the protein concentrations in the control and MS groups between different SNP genotypes. We did not find any statistically significant differences (Table 5). We also assessed whether there were differences across genotypes in the control or MS groups. There was a tendency for higher CXCL12 protein concentrations among the control group subjects with the rs1801157 C/T genotype when compared with the C/C genotype; however, it did not reach adjusted statistical significance (p = 0.028).
2.3. Patients with Multiple Sclerosis Clinical Characteristics and Their Associations with CXCL12 SNP Genotypes
The median (IQR) duration of MS in the MS group was 5 (7) years, with 31 (15) years of age at diagnosis. The EDSS was 3.5 (2.0) and MSSS 5.9 (2.1). The disease phenotypes within the MS group were as follows: the most prevalent was RRMS (90%), followed by PPMS (4.8%), CIS (4%), and SPMS (1.2%). Regarding the presence of symptoms, 87.2% of the group exhibited pyramidal symptoms, 63.6% had sensory symptoms, 58.4% were affected in the brain stem, 54.0% showed cerebellar symptoms, 35.6% had pelvic organ involvement, and 55.6% experienced vision symptoms. There was a tendency for more frequent sensory symptoms among the female MS patients as compared with the males (p = 0.05) (Table 6).
The CXCL12 (rs1029153, rs1801157, and rs2297630) genotype distribution was not statistically significantly different in the CIS, RRMS, SPMS, and PPMS patient phenotype subgroups (Table 7).
We analyzed whether there were associations between the prevalence of the affected functional systems and the CXCL12 (rs1029153, rs1801157, and rs2297630) genotypes. None of the analyzed distributions reached the corrected significance level (p > 0.0167); however, there was a tendency for a higher prevalence of sensory system symptoms in the rs1801157 CC genotype patients compared with the CT genotype (p = 0.030, Table 8).
Since symptom prevalence was not homogeneously distributed between different genders, we analyzed the prevalence of affected functional systems after stratifying the subjects by gender. We found that the sensory symptoms were rarer in the women with the rs1801157 TT genotype when compared with the other genotypes (p = 0.004, Table 9). Additionally, in the men, there was a tendency, which almost reached statistical significance, of lower pyramidal symptom prevalence in the individuals with the rs1801157 TT genotype when compared with the other genotypes (p = 0.02).
Finally, we analyzed the associations between the CXCL12 (rs1029153, rs1801157, and rs2297630) genotypes and clinical parameters (age at MS diagnosis, MS duration, EDSS, and MSSS). Our analysis showed that there was a statistically significant association between a shorter MS disease duration and rs1801157 T/T genotype when compared with the other genotypes (p = 0.007), with the results shown in Table 10.
3. Discussion
In this study, we investigated the CXCL12 SNPs (rs1029153, rs1801157, rs2297630) genotypes and CXCL12 serum levels in the MS patients and control subjects. Our aim was to evaluate the associations between CXCL12 SNPs, CXCL12 protein levels, MS prevalence, and clinical parameters. Our findings add to the expanding body of information about the genetic and molecular variables implicated in MS’s development and clinical course.
Our study showed that rs1801157 was associated with genetic risk only in male subjects. In the men, the CT genotype, when compared with the more common CC genotype, was associated with a 2.3 reduced odds of MS development, which possibly indicates a protective effect. A similar study by Azin et al. also reported an association between rs1801157 and MS. The researchers reported that the rs1801157 CT genotype was statistically significantly less prevalent in MS patients than in control individuals [22]. As of yet, CXCL12 SNPs rs1029153 and rs2297630 have not been studied in MS patients.
As previously mentioned, CXCL12 CSF levels and CXCL12 CNS histological changes positively correlate with disease severity, which indicates that CXCL12 could be used as a biomarker for MS [14,18], but there are limited data on CXCL12 serum levels. Azin et al. reported that serum CXCL12 levels were higher in MS patients than in control subjects [22]. We did not find any statistically significant differences in the CXCL12 serum levels between healthy subjects and MS patients. Edwards et al. studied CXCL12 levels in both CSF and serum, where the researchers found that while CXCL12 levels were significantly elevated in the CSF of MS patients, there were no significant differences in the serum [23]. Emamnejad et al. studied CXCL12 serum levels and peripheral blood mononuclear cell CXCL12 gene expression in MS patients and healthy controls. They showed that CXCL12 expression was increased in clinically active patients and decreased in patients who received IFNβ therapy when compared with healthy controls. Additionally, there were no significant differences in the CXCL12 serum levels between the studied groups [24]. In contrast, Azin et al. showed higher serum CXCL12 levels in MS patients compared with the control subjects. Also, the researchers did not find any associations between rs1801157 and the CXCL12 concentration [22]. Therefore, serum CXCL12 levels may not be as robust a biomarker for MS as its CSF levels or CNS histological changes.
We also studied the association between the CXCL12 serum levels and the CXCL12 gene SNPs. Our data do not show any statistically significant associations between the rs1029153, rs1801157, and rs2297630 SNP genotypes and the CXCL12 concentration, with only a non-statistically significant tendency for a higher concentration in the rs1801157 CT genotype control group individuals when compared with the CC genotype subjects. As previously described, the rs1801157 CT genotype in the males was associated with a reduced risk of MS. Consequently, this genotype demonstrated two significant associations: a decreased risk of MS in the men and an elevated serum CXCL12 concentration in the control individuals. Studies showed that CXCL12 is important in adult neural progenitor cell chemotaxis after a CNS injury; constitutive expression also modulates neurotransmission and rescues neurons from apoptosis [25]. Animal studies also showed that blocking CXCL12 signaling significantly impairs remyelination in an MS-like model [26]. However, if CXCL12 expression is induced by inflammation, as is the case during MS, the chemokine may have a direct neurotoxic effect and attract infiltrating leukocytes, as described in the introduction. Therefore, CXCL12 may have a context-dependent effect: neuroprotective at rest, and detrimental if upregulated during inflammation. We hypothesize that the rs1801157 CT genotype could potentially increase the constitutive CXCL12 gene expression, which results in an increased resting state serum concentration, and mitigates the early CNS damage, as well as promotes a more resilient neural environment. The mechanistic impact of the CXCL12 rs1801157 SNP on gene expression—whether through alterations in the transcription factor binding, mRNA splicing, or other mechanisms—has not been described in the literature. However, it is known that some SNPs associated with multiple sclerosis (MS) can influence gene expression. Handel et al. studied the gene expression in a lymphoblastoid cell line derived from healthy volunteers. These volunteers were also genotyped for a substantial number of single-nucleotide polymorphisms (SNPs). The study revealed that alterations in gene expression were linked to 13 out of the 38 SNPs that were investigated [27]. Putscher et al. found that the SNPs rs3851808 (EFCAB13), rs1131123 (HLA-C), rs10783847 (TSFM), and rs2014886 (TSFM) may contribute to a differential splicing pattern, such as exon skipping in the TSFM gene SNP [28]. In silico studies showed that other MS-susceptibility-associated SNPs can impact miRNA binding and influence the overall expression [29]. As with most MS-associated SNPs [30], the CXCL12 rs1801157 SNP is located in the untranslated region of the gene and could potentially affect regulatory element binding or mRNA splicing. However, further experimental studies are necessary to test this hypothesis.
There is a growing body of evidence that shows various associations between MS clinical parameters and patient genotypes. We did not find differences in the rs1029153, rs1801157, and rs2297630 genotype distributions in different MS phenotypes, although the sample size for progressive disease phenotypes was small. As for affected functional systems, we found that sensory symptoms were rarer in women with the rs1801157 TT genotype when compared with the other genotypes. Furthermore, our data show lower pyramidal symptom prevalence among the male subjects with the rs1801157 TT genotype when compared with the other genotypes, although the results narrowly missed statistical significance, possibly due to the rarity of this genotype in our sample. Both functional system results could have been due to shorter disease duration in individuals with the rs1801157 TT genotype. To our knowledge, there were no studies that have reported associations between CXCL12 gene SNPs, protein serum concentration, and clinical parameters.
Our study had some limitations that should be considered when interpreting the findings. First, the sample size for the progressive disease phenotypes was small, which limited the statistical power. Second, due to multiple comparisons, some findings did not reach the adjusted statistical significance level, which indicates a need for a larger sample size. Additionally, we studied a single population in Lithuania, which could have limited the generalizability of our findings. Furthermore, the cross-sectional design of our study did not capture variations in the CXCL12 serum levels over the course of the disease and could only show associations but not prove causality. Clinical studies would be required to evaluate the significance of our findings. Finally, we did not account for some significant confounders, such as smoking, vitamin D levels, exercise, and treatment regimes. Most patients had a history of treatment with disease-modifying drugs, which further increased the heterogeneity of our sample. Further studies could account for these important confounders.
In conclusion, our study revealed some CXCL12 SNP associations with MS risk, CXCL12 protein concentration, and clinical parameters. The rs1801157 CT genotype was associated with a reduced odds of MS in the men. In healthy individuals, this genotype was also associated with a higher CXCL12 serum concentration; however, the results did not reach adjusted statistical significance. Sensory systems were rarer in women with the rs1801157 TT genotype. These findings contribute to advancing our understanding of the genetic factors involved in MS pathogenesis. Further research is warranted to explore these associations and their importance in aiding predictions of the disease course and possibly the development of personalized therapeutic approaches.
4. Materials and Methods
4.1. Ethical Statement and Study Participants
This study was conducted in accordance with the Declaration of Helsinki, and all participants (n = 500) gave written informed consent. The study protocol was approved by the Kaunas Regional Biomedical Research Ethics Committee (BE-2-/102). This study included two groups:
- MS group (n = 250): MS patients that received treatment at the Hospital of Lithuanian University of Health Sciences Kaunas Clinics from 1 January 2020 to 31 December 2023. The participants included individuals 18 years and older diagnosed with MS according to the revised 2017 McDonald criteria [16]. Disability was assessed using the Kurtzke Expanded Disability Status Scale (EDSS) before the venous blood draw, and was used for the MSSS calculation [31]. For the retrospective analysis, the following data were collected: age at the time of MS diagnosis; MS disease course and duration; presence of symptoms related to the pyramidal, sensory, brainstem, cerebellar, pelvic systems, and vision; and disability according the EDSS score.
- Control group (n = 250): Matched by sex and age and comprised individuals without medical history of autoimmune, oncological, or neurological diseases and in self-reported good health. Subjects with recent blood product transfusions, growth factor treatments, organ transplants, or diagnosed with oncological, systemic tissue, infectious, or other autoimmune diseases were excluded.
4.2. DNA Extraction and CXCL12 Genotyping
The DNA extraction and analysis of CXCL12 rs1029153, rs1801157, and rs2297630 SNPs were conducted in the Ophthalmology Laboratory of the Neuroscience Institute of the Lithuanian University of Health Sciences. Peripheral venous blood was collected in vacuum tubes that contained ethylenediaminetetraacetic acid (EDTA) as an anticoagulant. The samples were subsequently stored at −70 °C until DNA extraction. DNA was isolated using the salting-out method. Briefly, the thawed blood samples were transferred to 15 mL centrifuge tubes and treated with lysis buffer I (8.3 g NH4Cl, 1 g KHCO3, 290.20 mg EDTA in 1 L of distilled water, pH = 7.4), followed by centrifugation at 1300× g for 15 min at 4 °C. The supernatant was discarded, and the lysis step was repeated three times. After the final wash, the leukocyte pellet was resuspended in 6 mL of lysis buffer II (1.58 g Tris HCl, 23.37 g NaCl, 584.40 mg EDTA in 1 L of distilled water, pH = 8.2), to which 150 μL of 10% sodium dodecyl sulphate (SDS) and 10 μL of proteinase K were added. The mixture was incubated at 56 °C for 10 min. After the incubation, 1 mL of 6 M NaCl was added, and the tubes were vigorously shaken. Following the addition of chloroform, the tubes were thoroughly mixed and centrifuged at 1500× g for 20 min at 16 °C to form a biphasic solution with a white precipitate of denatured proteins at the interface. The aqueous phase, which contained the DNA, was carefully transferred into new tubes, and an equal volume of cold 96% ethanol was added to precipitate the DNA. The DNA strands that formed were collected using a pipette tip and transferred into 1.5 mL tubes that contained 70% ethanol. These were centrifuged at 21,600× g for 3 min to pellet the DNA. The ethanol was carefully removed, and the residual ethanol was evaporated by placing the tubes in a 37 °C block. Finally, the DNA was resuspended in 100 μL of elution buffer and left in the refrigerator at 4 °C overnight to ensure complete dissolution. The DNA was stored at −20 °C until further analysis. The concentration and purity of the DNA were determined using an Agilent Technologies Cary 60 UV–Vis spectrophotometer (Agilent Technologies, Santa Clara, CA, USA).
The genotyping of CXCL12 SNPs (rs1029153 (assay ID C___7550610_10), rs1801157 (assay ID C___3223115_10), rs2297630 (assay ID C___3223122_1_)) was performed using the StepOnePlus real-time PCR system (Applied Biosystems, Foster City, CA, USA). For each assay, 1.5 μL of DNA, 5 μL of TaqMan Universal Master Mix II (no UNG), 0.25 μL of primers and probes, and 3.25 μL of nuclease-free water were added to each well of a 96-well plate. The plate was sealed with an optical film, centrifuged briefly, and loaded into the thermocycler. Real-time PCR was conducted under the following cycling conditions: denaturation at 95 °C for 15 s, followed by hybridization and elongation at 60 °C for 60 s for a total of 45 cycles. Nuclease-free water was used as a negative control.
4.3. CXCL12 Protein Concentration Measurement
The CXCL12 serum concentrations for 40 control group and 40 MS group subjects were quantified using a Human CXCL12 ELISA Kit (Abbexa LTD, Cambridge, UK) with a detection range of 3.12–200.00 pg/mL. The samples were assayed following the manufacturer’s protocol. After the preparation and incubation of standards and test samples, the optical density was measured at 450 nm using a microplate reader. The concentrations were determined from a standard curve established with prepared CXCL12 solutions. For 2 control group individuals, the detected CXCL12 concentration fell outside the detection range; therefore, these data were excluded from the analysis.
4.4. Statistical Analysis
The MSSS values of the MS patients were calculated using the ms.sev package (Helga Westerlind et al., version 1.0.4) of R Studio (R version 4.3.3, R foundation for Statistical Computing, Austria) [32]. The MSSS is calculated in this package from the Global MSSS database, which includes 9892 patients from 11 countries [33]. The MSSS for each patient was calculated with the global_msss function based on the patient’s EDSS and disease duration.
Statistical data analysis was performed using the statistical program package “Statistical Package for the Social Sciences, version 29.0 for Windows” (SPSS for Windows, IBM Corp., Armonk, NY, USA). The distribution of the CXCL12 serum concentration values was assessed for normality using the Shapiro–Wilk test for sample groups with fewer than 50 individuals, and the Kolmogorov–Smirnov test for other quantitative variables. The data for these variables did not conform to a normal distribution. Therefore, descriptive statistics are reported using the median and interquartile range (IQR), specifically between the 25th and 75th percentiles. For comparisons between two groups of quantitative variables, the Mann–Whitney test was utilized. In cases that involved three or more groups, the Kruskal–Wallis test was employed.
The genotype distributions of polymorphisms within the study groups were analyzed for conformity to the Hardy–Weinberg equilibrium model. Descriptive statistics for the qualitative criteria are expressed as percentages. The relationship between the frequencies of two characteristics was assessed using the chi-squared (χ2) criterion. If the subgroup sizes fell below five individuals, Fisher’s exact test was employed instead of the χ2 criterion. For the comparisons of paired data, the Z test with a Bonferroni correction was utilized. The odds ratio (OR) for the occurrence of MS was estimated through binary logistic regression while accounting for inheritance patterns and genotype combinations and reported with a 95% confidence interval (CI). The model accuracy was evaluated using the Akaike information criterion (AIC), where a lower value indicates a more suitable model. Statistical significance was established at a p-value of less than 0.05 and, where necessary, our significance threshold was adjusted for multiple comparisons to be less than 0.0167 (0.05/3) since three CXCL12 SNPs were evaluated.
Supplementary Materials
The following supporting information can be downloaded from https://www.mdpi.com/article/10.3390/ijms25179554/s1.
Author Contributions
Conceptualization, P.V., R.B. and R.L.; methodology, K.K. and G.G.-V.; investigation, P.V. and K.K.; resources, R.B.; data curation, K.K. and R.B.; writing—original draft preparation, P.V., K.K., G.G.-V., R.B. and R.L.; writing—review and editing, P.V., K.K., G.G.-V., R.B. and R.L.; visualization, P.V.; supervision, R.L. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
This study was conducted in accordance with the Declaration of Helsinki, and all participants (n = 500) gave written informed consent. The Kaunas Regional Biomedical Research Ethics Committee (BE-2-/102) approved the study protocol.
Informed Consent Statement
Informed consent was obtained from all subjects involved in this study.
Data Availability Statement
All data relevant to the study are included in this article.
Conflicts of Interest
The authors declare no conflicts of interest.
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Table 1.
Characteristics of study participants.
| Characteristic | Group | p-Value | |
|---|---|---|---|
| Control (n = 250) | MS (n = 250) | ||
| Men n (%) | 85 (34.0) | 85 (34.0) | 1.000 |
| Women n (%) | 165 (66.0) | 165 (66.0) | |
| Men median age (IQR) | 44 (27) | 40 (14) | 0.094 |
| Women median age (IQR) | 37 (24) | 38 (16) | 0.120 |
IQR—interquartile range, MS—multiple sclerosis.
Table 2.
CXCL12 (rs1029153, rs1801157, and rs2297630) genotype distributions in multiple sclerosis and control groups.
Table 2.
CXCL12 (rs1029153, rs1801157, and rs2297630) genotype distributions in multiple sclerosis and control groups.
| CXCL12 SNP | Genotype/Allele | Control Group n (%) | Control Group HWE p-Value | MS Group n (%) | MS Group HWE p-Value | Difference across Groups p-Value |
|---|---|---|---|---|---|---|
| rs1029153 | AA | 124 (49.6) | 0.903 | 132 (52.8) | 0.186 | 0.591 |
| AG | 102 (40.8) | 91 (36.4) | ||||
| GG | 24 (9.6) | 27 (10.8) | ||||
| A allele | 350 (70.0) | 355 (71.0) | 0.622 | |||
| G allele | 150 (30.0) | 145 (29.0) | ||||
| rs1801157 | CC | 152 (60.8) | 0.824 | 166 (66.4) | 0.348 | 0.244 |
| CT | 88 (35.2) | 71 (28.4) | ||||
| TT | 10 (4.0) | 13 (5.2) | ||||
| C allele | 392 (78.4) | 403 (80.6) | 0.260 | |||
| T allele | 108 (21.6) | 97 (19.4) | ||||
| rs2297630 | GG | 129 (51.6) | 0.659 | 139 (55.6) | 0.508 | 0.659 |
| AG | 97 (38.8) | 90 (36.0) | ||||
| AA | 24 (9.6) | 21 (8.4) | ||||
| G allele | 355 (71.0) | 368 (73.6) | 0.362 | |||
| A allele | 145 (29.0) | 132 (26.4) |
IQR—interquartile range, MS—multiple sclerosis, H-W—Hardy–Weinberg distribution, SNP—single-nucleotide polymorphism. Bonferroni-corrected significance level p < 0.0167.
Table 3.
CXCL12 (rs1029153, rs1801157, and rs2297630) genetic model binary logistic regression.
| Model | Genotype/Allele | Odds Ratio (95% CI) | p-Value | AIC |
|---|---|---|---|---|
| CXCL12 (rs1029153) | ||||
| Codominant | AG vs. AA | 0.84 (0.58–1.22) | 0.355 | 692.093 |
| GG vs. AA | 1.06 (0.58–1.93) | 0.857 | ||
| Dominant | AG+GG vs. AA | 0.88 (0.62–1.25) | 0.474 | 692.635 |
| Recessive | GG vs. AA+AG | 1.14 (0.64–2.04) | 0.658 | 692.951 |
| Overdominant | AG vs. GG+AA | 0.83 (0.58–1.19) | 0.312 | 692.126 |
| Additive | G | 0.96 (0.74–1.24) | 0.738 | 693.035 |
| CXCL12 (rs1801157) | ||||
| Codominant | CT vs. CC | 0.74 (0.5–1.08) | 0.121 | 690.317 |
| TT vs. CC | 1.19 (0.51–2.79) | 0.689 | ||
| Dominant | CT+TT vs. CC | 0.79 (0.55–1.13) | 0.194 | 691.453 |
| Recessive | TT vs. CC+CT | 1.32 (0.57–3.06) | 0.523 | 692.736 |
| Overdominant | CT vs. TT+CC | 0.73 (0.5–1.07) | 0.103 | 690.478 |
| Additive | T | 0.88 (0.65–1.19) | 0.395 | 692.422 |
| CXCL12 (rs2297630) | ||||
| Codominant | AG vs. GG | 0.86 (0.59–1.25) | 0.433 | 692.312 |
| AA vs. GG | 0.81 (0.43–1.53) | 0.519 | ||
| Dominant | AG+AA vs. GG | 0.85 (0.6–1.21) | 0.370 | 692.343 |
| Recessive | AA vs. GG+AG | 0.86 (0.47–1.6) | 0.639 | 692.927 |
| Overdominant | AG vs. AA+GG | 0.89 (0.62–1.28) | 0.518 | 692.729 |
| Additive | A | 0.89 (0.68–1.16) | 0.374 | 692.355 |
CI—confidence interval, AIC—Akaike information criterion. Bonferroni-corrected significance level p < 0.0167.
Table 4.
Gender-stratified binary logistic regression analysis of CXCL12 (rs1029153, rs1801157, and rs2297630) genetic models.
Table 4.
Gender-stratified binary logistic regression analysis of CXCL12 (rs1029153, rs1801157, and rs2297630) genetic models.
| Women | ||||
|---|---|---|---|---|
| Model | Genotype/Allele | Odds Ratio (95% CI) | p-Value | AIC |
| CXCL12 (rs1029153) | ||||
| Codominant | AG vs. AA | 0.66 (0.41–1.04) | 0.075 | 452.270 |
| GG vs. AA | 1.37 (0.65–2.86) | 0.409 | ||
| Dominant | AG+GG vs. AA | 0.77 (0.5–1.18) | 0.226 | 456.006 |
| Recessive | GG vs. AA+AG | 1.66 (0.82–3.37) | 0.161 | 455.467 |
| Overdominant | AG vs. GG+AA | 0.62 (0.4–0.97) | 0.034 | 452.960 |
| Additive | G | 0.96 (0.7–1.33) | 0.806 | 457.417 |
| CXCL12 (rs1801157) | ||||
| Codominant | CT vs. CC | 0.99 (0.62–1.59) | 0.964 | 457.176 |
| TT vs. CC | 1.35 (0.45–4.01) | 0.594 | ||
| Dominant | CT+TT vs. CC | 1.03 (0.65–1.62) | 0.908 | 457.464 |
| Recessive | TT vs. CC+CT | 1.35 (0.46–3.98) | 0.586 | 457.178 |
| Overdominant | CT vs. TT+CC | 0.97 (0.61–1.56) | 0.904 | 457.463 |
| Additive | T | 0 (0–0) | 0.000 | 0.000 |
| CXCL12 (rs2297630) | ||||
| Codominant | AG vs. GG | 0.71 (0.45–1.12) | 0.139 | 454.959 |
| AA vs. GG | 1.07 (0.49–2.3) | 0.871 | ||
| Dominant | AG+AA vs. GG | 0.77 (0.5–1.18) | 0.226 | 456.009 |
| Recessive | AA vs. GG+AG | 1.24 (0.59–2.6) | 0.572 | 457.156 |
| Overdominant | AG vs. AA+GG | 0.7 (0.45–1.09) | 0.115 | 454.986 |
| Additive | A | 0.89 (0.64–1.24) | 0.503 | 457.027 |
| Men | ||||
| Model | Genotype/Allele | Odds Ratio (95% CI) | p-Value | AIC |
| CXCL12 (rs1029153) | ||||
| Codominant | AG vs. AA | 1.42 (0.73–2.74) | 0.301 | 232.733 |
| GG vs. AA | 0.53 (0.17–1.67) | 0.278 | ||
| Dominant | AG+GG vs. AA | 1.16 (0.63–2.13) | 0.641 | 235.452 |
| Recessive | GG vs. AA+AG | 0.47 (0.15–1.44) | 0.184 | 233.810 |
| Overdominant | AG vs. GG+AA | 1.54 (0.81–2.92) | 0.193 | 233.961 |
| Additive | G | 0.95 (0.6–1.5) | 0.814 | 235.615 |
| CXCL12 (rs1801157) | ||||
| Codominant | CT vs. CC | 0.43 (0.22–0.83) | 0.012 | 229.004 |
| TT vs. CC | 0.93 (0.24–3.66) | 0.913 | ||
| Dominant | CT+TT vs. CC | 0.48 (0.26–0.89) | 0.020 | 230.147 |
| Recessive | TT vs. CC+CT | 1.27 (0.33–4.89) | 0.732 | 235.552 |
| Overdominant | CT vs. TT+CC | 0.43 (0.23–0.83) | 0.011 | 229.016 |
| Additive | T | 0.62 (0.37–1.05) | 0.074 | 232.385 |
| CXCL12 (rs2297630) | ||||
| Codominant | AG vs. GG | 1.31 (0.68–2.51) | 0.424 | 232.142 |
| AA vs. GG | 0.41 (0.12–1.39) | 0.152 | ||
| Dominant | AG+AA vs. GG | 1.05 (0.57–1.93) | 0.876 | 235.646 |
| Recessive | AA vs. GG+AG | 0.37 (0.11–1.23) | 0.105 | 232.783 |
| Overdominant | AG vs. AA+GG | 1.45 (0.76–2.75) | 0.256 | 234.374 |
| Additive | A | 0.87 (0.54–1.39) | 0.552 | 235.315 |
CI—confidence interval, AIC—Akaike information criterion. Bonferroni-corrected significance level p < 0.0167. Statistically significant results are shown in bold.
Table 5.
CXCL12 serum protein concentration distribution across multiple sclerosis and control groups based on CXCL12 (rs1029153, rs1801157, and rs2297630) genotypes.
Table 5.
CXCL12 serum protein concentration distribution across multiple sclerosis and control groups based on CXCL12 (rs1029153, rs1801157, and rs2297630) genotypes.
| CXCL12 SNP | Genotype | Number of Individuals with Genotype n (%) | CXCL12 Concentration (pg/mL) Median (IQR) | CXCL12 Concentration Comparison across Groups (p-Value) | ||
|---|---|---|---|---|---|---|
| Control (n = 38) | MS (n = 40) | Control | MS | |||
| rs1029153 | AA | 22 (57.9) | 20 (50.0) | 37.72 (6.84) | 36.575 (10.54) | 0.314 |
| AG | 10 (26.3) | 12 (30.0) | 37.55 (8.9) | 33.75 (7.41) | 0.254 | |
| GG | 6 (15.8) | 8 (20.0) | 35.815 (0.92) | 34.59 (17.94) | 0.985 | |
| Genotype distribution or CXCL12 concentration differences (p-value) | 0.774 | 0.560 | 0.738 | |||
| rs1801157 | CC | 22 (57.9) | 31 (77.5) | 36.14 (4.79) | 35.39 (10.19) | 0.607 |
| CT | 13 (34.2) | 9 (22.5) | 39.72 (5.45) | 36.74 (17.09) | 0.164 | |
| TT | 3 (7.9) | 0 (0.0) | 30.35 (15.4) | - * | - | |
| Genotype distribution or CXCL12 concentration differences (p-value) | 0.074 | 0.028 | 0.910 | |||
| rs2297630 | GG | 22 (57.9) | 23 (57.5) | 37.08 (5.93) | 36.41 (10.26) | 0.401 |
| GA | 9 (23.7) | 12 (30.0) | 39.1 (7.94) | 35.075 (6.71) | 0.310 | |
| AA | 7 (18.4) | 5 (12.5) | 35.92 (4.81) | 30 (18.41) | 0.639 | |
| Genotype distribution or CXCL12 concentration differences (p-value) | 0.693 | 0.678 | 0.976 | |||
SNP—single-nucleotide polymorphism, MS—multiple sclerosis, IQR—interquartile range. Bonferroni-corrected significance level p < 0.0167. *—no individuals with this genotype.
Table 6.
Multiple sclerosis group disease phenotypes and affected functional systems.
| Variable | Gender | p-Value | |
|---|---|---|---|
| Women n (%) | Men n (%) | ||
| CIS | 5 (3.0) | 5 (5.9) | 0.194 |
| RR | 153 (92.7) | 72 (84.7) | |
| SP | 2 (1.2) | 1 (1.2) | |
| PP | 5 (3.0) | 7 (8.2) | |
| Pyramidal system symptoms n (%) | 141 (85.5) | 77 (90.6) | 0.250 |
| Sensory system symptoms n (%) | 112 (67.9) | 47 (55.3) | 0.050 |
| Brainstem system symptoms n (%) | 96 (58.2) | 50 (58.8) | 0.922 |
| Cerebellar system symptoms n (%) | 88 (53.3) | 47 (55.3) | 0.786 |
| Pelvic system symptoms n (%) | 59 (35.8) | 30 (35.3) | 0.942 |
| Visual system symptoms n (%) | 89 (53.9) | 50 (58.8) | 0.462 |
CIS—clinically isolated syndrome, RR—relapsing–remitting, SP—secondary progressive, PP—primary progressive.
Table 7.
Analysis of multiple sclerosis phenotype and CXCL12 (rs1029153, rs1801157, and rs2297630) genotype distribution.
Table 7.
Analysis of multiple sclerosis phenotype and CXCL12 (rs1029153, rs1801157, and rs2297630) genotype distribution.
| CXCL12 SNP | Genotype | Phenotype n (%) | p-Value | |||
|---|---|---|---|---|---|---|
| CIS | RRMS | SPMS | PPMS | |||
| rs1029153 | AA | 5 (50.0) | 121 (53.8) | 0 (0.0) | 6 (50.0) | 0.225 |
| AG | 4 (40.0) | 81 (36.0) | 1 (33.3) | 5 (41.7) | ||
| GG | 1 (10.0) | 23 (10.2) | 2 (66.7) | 1 (8.3) | ||
| rs1801157 | CC | 8 (80.0) | 147 (65.3) | 3 (100.0) | 8 (66.7) | 0.461 |
| CT | 2 (20.0) | 67 (29.8) | 0 (0.0) | 2 (16.7) | ||
| TT | 0 (0.0) | 11 (4.9) | 0 (0.0) | 2 (16.7) | ||
| rs2297630 | GG | 5 (50.0) | 127 (56.4) | 0 (0.0) | 7 (58.3) | 0.097 |
| AG | 5 (50.0) | 79 (35.1) | 1 (33.3) | 5 (41.7) | ||
| AA | 0 (0.0) | 19 (8.4) | 3 (66.7) | 0 (0.0) | ||
SNP—single-nucleotide polymorphism, CIS—clinically isolated syndrome, RR—relapsing–remitting, SP—secondary progressive, PP—primary progressive. Bonferroni-corrected significance level p < 0.0167.
Table 8.
Analysis of affected functional systems and CXCL12 (rs1029153, rs1801157, and rs2297630) genotype distribution.
Table 8.
Analysis of affected functional systems and CXCL12 (rs1029153, rs1801157, and rs2297630) genotype distribution.
| CXCL12 SNP | Genotype | Patients Affected by Symptoms in Different Systems n (%) | |||||
|---|---|---|---|---|---|---|---|
| Pyramidal | Sensory | Brainstem | Cerebellar | Pelvic | Visual | ||
| rs1029153 | AA | 110 (83.3) | 83 (62.9) | 75 (56.8) | 74 (56.1) | 46 (34.8) | 73 (55.3) |
| AG | 84 (92.3) | 58 (63.7) | 56 (61.5) | 48 (52.7) | 34 (37.4) | 51 (56.0) | |
| GG | 24 (88.9) | 18 (66.7) | 15 (55.6) | 13 (48.1) | 9 (33.3) | 15 (55.6) | |
| p-value | 0.138 | 0.932 | 0.743 | 0.721 | 0.897 | 0.994 | |
| rs1801157 | CC | 145 (87.3) | 114 (68.7) | 99 (59.6) | 91 (54.8) | 60 (63.1) | 93 (56.0) |
| CT | 64 (90.1) | 40 (56.3) | 40 (56.3) | 36 (50.7) | 27 (38.0) | 39 (54.9) | |
| TT | 9 (69.2) | 5 (38.5) | 7 (53.8) | 8 (61.5) | 2 (15.4) | 7 (53.8) | |
| p-value | 0.116 | 0.030 | 0.844 | 0.722 | 0.283 | 0.98 | |
| rs2297630 | GG | 117 (84.2) | 88 (63.3) | 81 (58.3) | 79 (56.8) | 51 (36.7) | 79 (56.8) |
| AG | 82 (91.1) | 58 (64.4) | 55 (61.1) | 46 (51.1) | 32 (35.6) | 48 (53.3) | |
| AA | 19 (90.5) | 13 (61.9) | 10 (47.6) | 10 (47.6) | 6 (28.6) | 12 (57.1) | |
| p-value | 0.276 | 0.971 | 0.528 | 0.578 | 0.769 | 0.864 | |
SNP—single-nucleotide polymorphism. Bonferroni-corrected significance level p < 0.0167.
Table 9.
Gender-stratified analysis of affected functional systems and CXCL12 (rs1029153, rs1801157, and rs2297630) genotype distribution.
Table 9.
Gender-stratified analysis of affected functional systems and CXCL12 (rs1029153, rs1801157, and rs2297630) genotype distribution.
| Women | |||||||
|---|---|---|---|---|---|---|---|
| CXCL12 SNP | Genotype | Patients Affected by Symptoms in Different Systems n (%) | |||||
| Pyramidal | Sensory | Brainstem | Cerebellar | Pelvic | Visual | ||
| rs1029153 | AA | 69 (82.1) | 59 (70.2) | 43 (51.2) | 46 (54.8) | 31 (36.9) | 45 (53.6) |
| AG | 53 (89.8) | 39 (66.1) | 42 (71.2) | 33 (55.9) | 23 (39.0) | 33 (55.9) | |
| GG | 19 (86.4) | 14 (63.6) | 11 (50.0) | 9 (40.9) | 5 (22.7) | 11 (50.9) | |
| p-value | 0.435 | 0.786 | 0.041 | 0.451 | 0.379 | 0.889 | |
| rs1801157 | CC | 93 (86.1) | 81 (75.0) | 66 (61.1) | 60 (55.6) | 42 (38.9) | 59 (54.6) |
| CT | 42 (85.7) | 29 (59.2) | 26 (53.1) | 23 (46.9) | 17 (34.7) | 27 (55.1) | |
| TT | 6 (75.0) | 2 (25.0) | 4 (50.0) | 5 (62.5) | 0 (0.0) | 3 (37.5) | |
| p-value | 0.69 | 0.004 | 0.569 | 0.525 | 0.085 | 0.632 | |
| rs2297630 | GG | 75 (83.3) | 63 (70.0) | 48 (53.3) | 50 (55.6) | 35 (38.9) | 50 (55.6) |
| AG | 51 (87.9) | 39 (67.2) | 41 (70.7) | 31 (53.4) | 21 (36.2) | 30 (51.7) | |
| AA | 15 (88.2) | 10 (58.8) | 7 (41.2) | 7 (41.2) | 3 (17.6) | 9 (52.9) | |
| p-value | 0.698 | 0.658 | 0.037 | 0.552 | 0.245 | 0.898 | |
| Men | |||||||
| CXCL12 SNP | Genotype | Patients Affected by Symptoms in Different Systems n (%) | |||||
| Pyramidal | Sensory | Brainstem | Cerebellar | Pelvic | Visual | ||
| rs1029153 | AA | 41 (85.4) | 24 (50.0) | 32 (66.7) | 28 (58.3) | 15 (31.3) | 28 (58.3) |
| AG | 31 (96.9) | 19 (59.4) | 14 (43.8) | 15 (46.9) | 11 (34.4) | 18 (56.3) | |
| GG | 5 (100.0) | 4 (80.0) | 4 (80.0) | 4 (80.0) | 4 (80.0) | 4 (80.0) | |
| p-value | 0.173 | 0.369 | 0.076 | 0.312 | 0.094 | 0.601 | |
| rs1801157 | CC | 52 (89.7) | 33 (56.9) | 33 (56.9) | 31 (53.4) | 18 (31.0) | 34 (58.6) |
| CT | 22 (100.0) | 11 (50.0) | 14 (63.6) | 13 (59.1) | 10 (45.5) | 12 (54.5) | |
| TT | 3 (60.0) | 3 (60.0) | 3 (60.0) | 3 (60.0) | 2 (40.0) | 4 (80.0) | |
| p-value | 0.02 | 0.838 | 0.86 | 0.881 | 0.471 | 0.579 | |
| rs2297630 | GG | 42 (85.7) | 25 (51.0) | 33 (67.3) | 29 (59.2) | 16 (32.7) | 29 (59.2) |
| AG | 31 (96.9) | 19 (59.4) | 14 (43.8) | 15 (46.9) | 11 (34.4) | 18 (56.3) | |
| AA | 4 (100.0) | 3 (75.0) | 3 (75.0) | 3 (75.0) | 3 (75.0) | 3 (75.0) | |
| p-value | 0.196 | 0.547 | 0.086 | 0.397 | 0.232 | 0.77 | |
SNP—single-nucleotide polymorphism. Bonferroni-corrected significance level p < 0.0167. Statistically significant results are shown in bold.
Table 10.
Analysis of disease characteristics and CXCL12 (rs1029153, rs1801157, and rs2297630) genotype distribution.
Table 10.
Analysis of disease characteristics and CXCL12 (rs1029153, rs1801157, and rs2297630) genotype distribution.
| CXCL12 SNP | Genotype | Age of MS Diagnosis Median (IQR) | MS Duration Years Median (IQR) | EDSS Median (IQR) | MSSS Median (IQR) |
|---|---|---|---|---|---|
| rs1029153 | AA | 29 (15) | 5 (8) | 3 (2) | 5.93 (2.3) |
| AG | 32 (17) | 8 (7) | 3.5 (2) | 5.57 (2.1) | |
| GG | 33 (13) | 5 (7) | 3.5 (2) | 6.46 (1.9) | |
| p-value | 0.085 | 0.078 | 0.660 | 0.335 | |
| rs1801157 | CC | 32 (16) | 6 (7) | 3.5 (2.5) | 5.995 (2.2) |
| CT | 30 (13) | 7 (9) | 3.5 (2) | 5.46 (2.4) | |
| TT | 39 (15.5) | 3 (2) | 2.5 (0.3) | 6.37 (1.7) | |
| p-value | 0.262 | 0.007 | 0.261 | 0.677 | |
| rs2297630 | GG | 29 (15) | 5 (8) | 3 (2) | 5.93 (2.3) |
| AG | 32 (16.3) | 7.5 (7) | 3.5 (2.1) | 5.515 (1.9) | |
| AA | 35 (8) | 5 (7) | 3.5 (2) | 6.46 (1.9) | |
| p-value | 0.103 | 0.335 | 0.851 | 0.299 | |
SNP—single-nucleotide polymorphism, MS—multiple sclerosis, EDSS—Expanded Disability Status Scale, MSSS—Multiple Sclerosis Severity Score, IQR—interquartile range. Bonferroni-corrected significance level p < 0.0167. Statistically significant results are shown in bold.
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Valiukevicius, P.; Kaikaryte, K.; Gedvilaite-Vaicechauskiene, G.; Balnyte, R.; Liutkeviciene, R. CXCL12 Gene Polymorphisms and Serum Levels: Associations with Multiple Sclerosis Prevalence and Clinical Parameters in Lithuania. Int. J. Mol. Sci. 2024, 25, 9554. https://doi.org/10.3390/ijms25179554
AMA Style
Valiukevicius P, Kaikaryte K, Gedvilaite-Vaicechauskiene G, Balnyte R, Liutkeviciene R. CXCL12 Gene Polymorphisms and Serum Levels: Associations with Multiple Sclerosis Prevalence and Clinical Parameters in Lithuania. International Journal of Molecular Sciences. 2024; 25(17):9554. https://doi.org/10.3390/ijms25179554
Chicago/Turabian StyleValiukevicius, Paulius, Kriste Kaikaryte, Greta Gedvilaite-Vaicechauskiene, Renata Balnyte, and Rasa Liutkeviciene. 2024. "CXCL12 Gene Polymorphisms and Serum Levels: Associations with Multiple Sclerosis Prevalence and Clinical Parameters in Lithuania" International Journal of Molecular Sciences 25, no. 17: 9554. https://doi.org/10.3390/ijms25179554
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