
==== Front
Medicine (Baltimore)
Medicine (Baltimore)
MD
Medicine
0025-7974
1536-5964
Lippincott Williams & Wilkins Hagerstown, MD

MD-D-23-09305
00024
10.1097/MD.0000000000039554
3
7000
Research Article
Observational Study
Causal relationship between multiple sclerosis and spinal stenosis: Two-sample Mendelian randomization
https://orcid.org/0000-0003-1400-8022
Deng Guang-hua MD a*
a Ya’an Hospital of Traditional Chinese Medicine.
* Correspondence: Guang-hua Deng, Ya’an Hospital of Traditional Chinese Medicine (e-mail: dgh_478.163.com).
06 9 2024
06 9 2024
103 36 e3955422 10 2023
05 5 2024
13 8 2024
Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

To investigate the causal relationship between multiple sclerosis and spinal stenosis using Mendelian randomization (MR). Genetic loci independently associated with multiple sclerosis and spinal stenosis in people of European origin were selected as instrumental variables using pooled data from genome wide association studies (GWAS). Three MR analyses, MR-Egger, Weighted median and inverse variance weighting (IVW), were used to investigate the causal relationship between multiple sclerosis and spinal stenosis. Heterogeneity and multiplicity tests were performed, and sensitivity analyses were performed using the “leave-one-out” method to explore the robustness of the results. The IVW results showed an OR (95% CI) of 1.05 (1.01–1.08), P = .016, indicating a causal relationship between MS and spinal stenosis. And no heterogeneity and multiplicity were found by the test, and sensitivity analysis also showed robust results. In this study, genetic data were analyzed and explored using 2-sample MR analysis, and the results showed a causal relationship between multiple sclerosis and the occurrence of spinal stenosis.

Mendelian randomization
multiple sclerosis
spinal stenosis
OPEN-ACCESSTRUE
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pmc 1. Introduction

Spinal stenosis is a neurological disorder that manifests itself as a narrowing of the space within the spinal canal, resulting in compression of the nerve roots or spinal cord.[1,2] This disease is common in the elderly, especially those over 50 years of age.[3] Spinal stenosis can affect a patient’s quality of life, and in severe cases it can even lead to limited mobility. The causes of spinal stenosis are varied and include degenerative changes, bone spurs, and ligamentous hyperplasia. These factors lead to narrowing of the space within the spinal canal, which compresses the nerve roots or spinal cord.[4,5] Symptoms mainly include low back pain, radiating pain in the lower limbs, sensory abnormalities, and loss of muscle strength.[6,7] Severe spinal stenosis may also cause dysfunction in urination and defecation.[8] Multiple sclerosis is a chronic, autoimmune disease that primarily affects the central nervous system.[9,10] Patients may experience a variety of neurological symptoms, such as muscle weakness, sensory abnormalities, balance problems, coordination disorders, and vision problems.[11–13] These symptoms may affect patients’ daily activities and quality of life. Some studies have suggested that patients with multiple sclerosis may be more prone to spinal stenosis, but relevant studies are lacking.[14] Therefore, the causal relationship between multiple sclerosis and spinal stenosis still needs further investigation.

The association between multiple sclerosis and spinal stenosis may be influenced to some extent by confounding factors and reverse causality inherent in traditional observational studies.[15] In contrast, Mendelian randomization (MR), a genetic epidemiological method, is a useful tool for assessing the causal role of multiple sclerosis and spinal stenosis.[16] By using genetic variants such as single nucleotide polymorphisms (SNPs) as instrumental variants that can modify disease risk factors or exposures, MR studies can enhance causal inference of exposure-outcome associations.[17] According to Mendel’s laws of inheritance, genetic variants are not susceptible to confounding factors because they are randomly assigned during gamete formation.[18] In addition, confounders and reverse causality can be minimized as genotypes cannot change as the disease progresses.[19]

To this end, we conducted a 2-sample MR study to examine the causal relationship between multiple sclerosis and spinal stenosis. We aimed to provide significant evidence for the causal role of MS in causing spinal stenosis.

2. Data and methods

2.1. Data sources

The genome wide association studies (GWAS) data for multiple sclerosis and spinal stenosis were obtained via the IEU OpenGWAS project (mr cieu.ac.uk) website. The website was accessed on 2023-08-06.The population source for all final data was European, male and female. Including multiple sclerosis (ebi-a-GCST005531) containing 132,089 SNPs with a sample size of 38,582, and spinal stenosis (finn-b-M13_SPINSTENOSIS) containing 16,380,277 SNPs with 9169 in the experimental group and 164,682 in the control group. This study was a re-analysis of previously collected and published public data and therefore did not require additional ethical approval.

2.2. Conditioning of SNP as an instrumental variable

Instrumental variables were highly correlated with exposure, with F > 10 as a strong correlation criterion.[20] Instrumental variables are not directly correlated with the outcome and only affect the outcome through exposure, i.e. there is no genetic pleiotropy. In this study, the nonexistence of genetic pleiotropy was indicated by the non-zero intercept term of the MR-Egger regression model (P < .05).[21] Instrumental variables were not associated with untested confounding.[22] The human genotype-phenotype association database Phenoscanner V2 was searched for phenotypes associated with the instrumental variables at the genome-wide significance level to determine whether these SNPs were associated with potential risk factors.[23]

2.3. SNP screening

Significant SNPs were screened from the GWAS pooled data for multiple sclerosis (with P < 5 × 10−8 as the screening condition)[24]; the chain imbalance coefficient r2 was set to 0.001 and the width of the chain imbalance region to 10,000 kb to ensure that individual SNPs were independent of each other.[25] The multiple sclerosis-associated SNPs screened above were extracted from the GWAS pooled data of spinal stenosis, while SNPs directly associated with outcome indicators were excluded (P < 5 × 10−8). The F value of each SNP was calculated, and SNPs with weak instrumental variables (F value < 10) were excluded.[26] And the human genotype-phenotype association database was queried to screen for potentially relevant risk factor SNPs and exclude them.[27]

2.4. Causality validation methods

The causal relationship between exposure (multiple sclerosis) and outcome (spinal stenosis) was mainly verified using inverse variance weighted (IVW) as, supplemented by 3 MR analysis methods, MR-Egger and weighted median, with SNPs as instrumental variables.

2.5. Sensitivity analysis

Sensitivity analyses were performed using several methods. First, the Cochran Q test was used to assess the heterogeneity between the individual SNP estimates, and a statistically significant Cochran Q test demonstrated significant heterogeneity in the analyses. Second, Mendelian randomization pleiotropy residual sum and outlier (MR PRESSO) was used to validate the results in the IVW model, to correct for the effect of outliers, and if outliers existed, they were removed and the analysis was repeated. Third, the horizontal multiplicity of SNPs was tested using the MR Egger intercept test (MR Egger intercept test), and if the intercept term in the MR Egger intercept test analysis was statistically significant, it indicated that the MR analysis had significant horizontal multiplicity. Fourth, “leave-one-out” sensitivity analyses were performed by removing a single SNP at a time to assess whether the variant drove the association between the exposure and outcome variables. Fifth, funnel plots and forest plots were constructed to visualize the results of the sensitivity analyses. P < .05 suggests that there is a potential causal relationship in the MR analyses, which is statistically significant. All statistical analyses were performed using the “TwoSampleMR” package in R software version 4.3.0.

3. Results

3.1. Instrumental variables

Forty-six SNPs that were strongly associated with multiple sclerosis (P < 5 × 10−8) without chain imbalance (r2 < 0.001, kb = 10,000) were screened in the present study. Forty-six SNPs were left by taking the intersection with SNPs in the pooled data from the GWAS for spinal stenosis, and also by eliminating SNPs that were directly associated with the outcome metrics. In our study, the F values of each SNP were all >10, indicating no weak instrumental variables (see Table 1 for details). We searched the human genotype-phenotype association database and found no potentially relevant risk factor SNPs.

Table 1 Information on the final screening of multiple sclerosis SNPs from GWAS data (n = 46).

ID	SNP	Effect_Allele	Other_Allele	β	SE	P	F	
1	rs1014486	C	T	0.10075	1.16E-09	0.0165552	37	
2	rs1021156	C	T	−0.115113	5.60E-10	0.0185627	38	
3	rs10420809	T	C	0.121377	7.96E-09	0.0210384	33	
4	rs1077667	T	C	−0.15187	3.54E-13	0.020884	52	
5	rs10892299	T	C	−0.115074	4.03E-08	0.0209625	30	
6	rs11052877	G	A	0.0998453	5.37E-09	0.0171106	34	
7	rs11154801	A	C	0.102557	2.35E-09	0.0171742	35	
8	rs11172342	T	G	−0.121377	9.46E-12	0.0178116	46	
9	rs11554159	A	G	−0.141448	2.58E-13	0.0193376	53	
10	rs11865086	A	C	−0.0935417	1.77E-08	0.0166057	31	
11	rs12087340	T	C	0.19721	5.13E-12	0.0285732	47	
12	rs12927355	T	C	−0.193099	8.19E-27	0.0180128	114	
13	rs1323292	A	G	0.160404	3.68E-13	0.0220734	52	
14	rs13426106	A	G	0.154436	1.67E-13	0.0209466	54	
15	rs17066096	G	A	0.131028	5.91E-12	0.0190398	47	
16	rs1800693	C	T	0.134531	6.92E-16	0.0166665	65	
17	rs1813375	T	G	0.143234	5.75E-18	0.016583	74	
18	rs2104286	C	T	−0.18983	7.61E-23	0.0192926	96	
19	rs212407	A	G	0.138917	2.56E-15	0.0175607	62	
20	rs2364482	G	T	0.124869	1.40E-09	0.0206208	36	
21	rs3115627	G	A	−0.313342	1.77E-67	0.0180536	301	
22	rs3130283	C	A	−0.82549	1.00E-200	0.0216086	1459	
23	rs34383631	T	C	0.105261	5.69E-10	0.0169808	38	
24	rs3748817	C	T	−0.129312	1.33E-12	0.0182356	50	
25	rs41286801	T	C	0.181488	7.92E-16	0.0225299	64	
26	rs4410871	C	T	0.112385	1.98E-09	0.0187326	35	
27	rs4780355	C	T	−0.100705	3.47E-08	0.0182572	30	
28	rs4796791	C	T	−0.0962189	1.81E-08	0.0170927	31	
29	rs4944958	G	A	−0.107059	8.69E-09	0.0186044	33	
30	rs4947337	C	T	−0.414153	1.34E-21	0.0433821	91	
31	rs4976646	C	T	0.123102	1.04E-12	0.0172772	50	
32	rs57271503	A	G	−0.172213	4.11E-15	0.0219337	61	
33	rs60600003	G	T	0.149282	2.53E-08	0.0267953	31	
34	rs6677309	C	A	−0.292628	1.45E-28	0.0263937	122	
35	rs67297943	C	T	−0.11149	4.83E-08	0.020429	29	
36	rs6881706	T	G	−0.110596	4.87E-09	0.0189003	34	
37	rs706015	G	T	0.129272	1.29E-09	0.0213017	36	
38	rs71624119	A	G	−0.116871	2.70E-09	0.0196459	35	
39	rs74796499	A	C	−0.270759	8.47E-11	0.0417066	42	
40	rs7783	G	A	0.0989399	9.56E-09	0.0172419	32	
41	rs7923837	A	G	−0.100705	4.58E-09	0.01718	34	
42	rs8070345	C	T	−0.134531	5.43E-16	0.0166057	65	
43	rs842639	A	G	0.107919	1.70E-09	0.0179144	36	
44	rs9277641	A	G	−0.219899	1.19E-24	0.0214547	105	
45	rs9282641	A	G	−0.193949	1.74E-10	0.0303867	40	
46	rs9967792	C	T	0.102587	1.80E-09	0.0170554	36	
GWAS = genome-wide association study, SE, SNP = single nucleotide polymorphism.

3.2. Causal relationship between multiple sclerosis and spinal stenosis

By MR analysis, the results of IVW showed a positive correlation between multiple sclerosis and spinal stenosis, and the differences were all statistically significant, i.e., there was a causal relationship between multiple sclerosis and spinal stenosis. IVW:OR = 1.05, 95% CI = 1.01–1.08, P = .016; weighted median:OR = 1.00, 95% CI = 0.95–1.05, P = .928; MR Egger:OR = 1.02, 95% CI = 0.96–1.09, P = .509 (see Table 2 for details). We can see from both the scatter plot (Fig. 1) and the forest plot (Fig. 2) that having MS increases the risk of developing spinal stenosis.

Table 2 MR regression results of the 3 methods.

Method	β	SE	OR (95% CI)	P	
IVW	0.044	0.018	1.05 (1.01–1.08)	.016	
WME	0.002	0.025	1.00 (0.95–1.05)	.928	
MR-Egger	0.022	0.034	1.02 (0.96–1.09)	.509	
CI = confidence interval, IVW = inverse variance weighting, MR =Mendelian randomization, OR = odds ratio, SE, WME = weighted median.

Figure 1. Scatter plot of multiple sclerosis and spinal stenosis. MR = Mendelian randomization, SNP = single nucleotide polymorphism.

Figure 2. Forest plot of multiple sclerosis and spinal stenosis. MR = Mendelian randomization.

3.3. Sensitivity analysis

Heterogeneity was tested using the IVW method (Cochran Q test, P = .143), and the results suggested that there was no heterogeneity. A funnel plot was drawn to show the heterogeneity results, as shown in Figure 3. MR-PRESSO was used to screen for SNPs that could lead to heterogeneity, and the results did not reveal any SNPs that would lead to heterogeneity in the results. The result of Global test by MR-PRESSO suggested that there was no pleiotropy (P = .448). The “leave-one-out” method uses the IVW method by default, and as can be seen in Figure 4, no single SNP will have a large impact on the overall results after eliminating any SNP, indicating that the results are robust.

Figure 3. Funnel plot of multiple sclerosis and spinal stenosis. MR = Mendelian randomization, SE.

Figure 4. Analysis of multiple sclerosis and spinal stenosis by the leave-one-out method. MR = Mendelian randomization.

4. Discussion

It is known that multiple sclerosis may be an observational risk factor for spinal stenosis, but the causality of this association is unclear. Our MR study aimed to reveal the causal relationship between multiple sclerosis and spinal stenosis. The results showed a causal relationship between multiple sclerosis and the occurrence of spinal stenosis, as demonstrated by the 2-sample MR results, with an OR (95% CI) of 1.05 (1.01–1.08), P = .016.

Young et al[14] found that the probability of suffering from spinal stenosis in the population of patients with multiple sclerosis was much higher than in the general population.

The present study confirms the causal relationship between multiple sclerosis and with the occurrence of spinal stenosis from a genetic point of view. The results of this study are consistent with Young’s conclusion that multiple sclerosis is a risk factor for the development of spinal stenosis and that having multiple sclerosis increases the incidence of spinal stenosis.

The present study used a Mendelian randomization study rather than a retrospective study, which is susceptible to confounding factors and reverse causation, and therefore the causal inferences obtained are considered to be of limited value. In contrast, Mendelian randomization (MR) analysis is a new epidemiological approach that uses genetic variation as an instrumental variable of exposure to enhance causal inference. This approach reduces the effects caused by confounding factors.[28]

At the same time this study has some limitations. Firstly, as all the data are from people of European origin, the results are not representative of a truly random population sample, nor are they applicable to other so races. Secondly, although various sensitivity analyses have been performed in this study to test the hypotheses of the MR study, it is difficult to completely rule out horizontal pleiotropy of instrumental variables. Finally, the current sample size of GWAS data is still not large enough, and more in-depth studies using more GWAS data are needed in the future.

5. Conclusion

In conclusion, this study used 2-sample MR analysis to analyze and explore the genetic data, and the results showed a causal relationship between multiple sclerosis and the occurrence of spinal stenosis.

Author contributions

Conceptualization: Gaung-hua Deng.

Data curation: Gaung-hua Deng.

Formal analysis: Gaung-hua Deng.

Funding acquisition: Gaung-hua Deng.

Investigation: Gaung-hua Deng.

Methodology: Gaung-hua Deng

Project administration: Gaung-hua Deng.

Resources: Gaung-hua Deng.

Software: Gaung-hua Deng.

Supervision: Gaung-hua Deng.

Validation: Gaung-hua Deng.

Visualization: Gaung-hua Deng.

Writing – original draft: Gaung-hua Deng.

Writing – review & editing: Gaung-hua Deng.

Abbreviations:

GWAS genome-wide association study

IVW inverse variance weighting

MR Mendelian randomization

SNP single nucleotide polymorphism.

The authors have no funding and conflicts of interest to disclose.

The datasets generated during and/or analyzed during the current study are publicly available.

How to cite this article: Deng G-h. Causal relationship between multiple sclerosis and spinal stenosis: Two-sample Mendelian randomization. Medicine 2024;103:36(e39554).
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