Genetic causality between multiple sclerosis and atopic dermatitis: A univariable and multivariable Mendelian randomization study

Genetic causality between multiple sclerosis and atopic dermatitis: A univariable and multivariable Mendelian randomization study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Genetic causality between multiple sclerosis and atopic dermatitis: A univariable and multivariable Mendelian randomization study Yuping Xie, Hongjin Liu, Yingkun Qiu, Yingping Cao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4992688/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective We sought to estimate the genetic causal association between multiple sclerosis (MS) and atopic dermatitis (AD) and identify potential mediating factors. Methods We used univariable Mendelian randomization (UVMR) with inverse variance weighting (IVW) as the primary study method to estimate the causal effect of MS on AD, supplemented by weighted median and MR Egger validation analyses. Furthermore, we conducted a reverse MR analysis. Sensitivity analyses were performed using Cochran's Q test, MR-Egger intercept test, leave-one-out, and funnel plot analysis to evaluate the robustness of the MR findings. Additionally, multivariable MR (MVMR) was employed to estimate the direct causal effect of MS on the risk of AD. Results UVMR analysis demonstrated a genetic predisposition associated with the risk of MS and AD with an odds ratio of 1.10 (95% Confidence Interval: 1.05 to 1.15, P = 1.87 × 10^ −5 ). Consistent results were observed after adjusting for potential confounders, including Body Mass Index (BMI), telomere length, vitamin deficiencies, and smoking-related factors in MVMR analyses. However, following adjustment for C-reactive protein, serum levels of 25-hydroxyvitamin D, and smoking status as confounders, MS was no longer identified as a risk factor for AD. Conclusions The findings indicate that while there may be a genetic link between MS and AD, the causal pathway is complex and influenced by multiple biological and environmental factors. Further research is needed to elucidate these interactions and their implications for disease prevention and treatment strategies. Biological sciences/Genetics Biological sciences/Immunology multiple sclerosis atopic dermatitis univariable Mendelian randomization multivariable MR single nucleotide polymorphisms Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Multiple sclerosis (MS) is a chronic progressive inflammatory demyelinating disease affecting the white and gray matter of the central nervous system (CNS), characterized by a range of complex clinical manifestations, primarily including visual and sensory abnormalities, motor deficits, and cognitive impairment [ 1 , 2 ] .It is known that the genetic and environmental factors contribute to MS pathogenesis, but the complex interactions are not yet fully understood [ 3 ] .The disease predominantly affects young and middle-aged adults, with a higher incidence in women compared to men (female-to-male ratio of approximately 3:1) [ 4 ] .There are over 2.8 million patients worldwide diagnosed with MS, who experience a lower life expectancy compared to the general population [ 5 ] . Furthermore, it stands as the most significant cause of disability in young individuals affected by central nervous system disorders. Atopic dermatitis (AD) is one of the most common chronic inflammatory allergic skins worldwide [ 6 ] . Clinical symptoms of AD have a wide spectrum, mainly include severe pruritus, erythema, eczematous lesions, flaking, edema, and thickening of the skin [ 7 ] . A recent systematic review estimated a 12-month prevalence of AD up to 22.6% among children and 17.1% among adults, with 9.6% of new cases of AD occurring in children [ 8 ] . AD leads to substantial social and financial costs and accounts for the largest global burden of disability owing to skin diseases. It has previously been observed that individuals with AD may manifest increased immune susceptibility to developing MS throughout the disease progression [ 9 ] . a potential association between MS and AD has been observed to some extent, yet the results remain uncertain [ 10 ] . Nonetheless, conclusive evidence establishing a direct correlation between these conditions remains elusive, necessitating additional research endeavors. Delving into the causality between MS and AD holds promise in enhancing a better comprehension of the pathogenesis underlying these maladies. However, the true association between MD and AD may be influenced by potential confounding factors, such as obesity, smoking, and vitamin D deficiency, as well as reverse causality bias [ 11 ] . These complexities pose challenges for conducting clinical trials. Mendelian randomization (MR) analysis serves as an analytical model employing genetic variation as an instrumental variable, extensively utilized in contemporary research to assess causal associations between exposures and outcomes [ 12 ] . MR analysis employs single nucleotide polymorphisms (SNPs) as genetic proxies to estimate the effects of relevant exposures on outcomes [ 13 ] . Conceptually, MR parallels a natural randomized clinical trial (RCT). A notable advantage of MR over conventional RCT designs lies in the random assignment of SNPs as risk factor instruments, thereby circumventing potential confounding factors or reverse causality. Importantly, the formation of genetic variation remains unaffected by social environments, lifestyle habits, and other traits, theoretically mitigating the influence of confounding factors [ 14 ] . Methods Data sources for exposure and outcome Publicly accessible data were utilized in this study, including genome-wide association studies (GWAS) data for each of the genetic traits listed in Table 1 , thus ethical clearance was deemed unnecessary for the present investigation. The study involved human participants, each of whom provided written informed consent following ethical review and approval in accordance with local regulations and institutional guidelines. The MS data repository was sourced from the FinnGen consortium ( https://storage.googleapis.com/finngen-public-data-r10/summary_stats/finngen_R10_G6_MS.gz ), comprising 2,409 cases, 408,561 controls. AD data could be found from the IEU OPEN GWAS PROJECT ( https://gwas.mrcieu.ac.uk/ ), encompassing 6,224 cases, 475,075 controls. To mitigate potential bias in bidirectional MR analyses arising from population stratification, summary statistics for both MS and AD datasets were exclusively sourced from individuals of European ancestry. Table 1 Details of GWAS genetic traits used for bidirectional MR analysis GWAS ID Traits Author and year Consortium Sample size Ncase Ncontrol Population ebi-a-GCST90018784 AD Sakaue,S et al.,2021 [15] NA 481299 6224 475075 European finn-b-G6_MS MS-disease/ multiple sclerosis NA FinnGen 410970 2409 408561 European GWAS, Genome wide association study; AD, atopic dermatitis; MS, multiple sclerosis; NA, not available MR assumptions and Instrumental Variables (IVs) selection The two-sample MR analysis should satisfy the following three fundamental assumptions: (1) the IVs employed are firmly linked with the exposure. (2) the chosen IV have no association with any potential confounders which are relevant to exposure and outcome. (3) and the IVs can exert their influence on outcome solely through the exposure and not through other pathways [ 16 ] . What’s more, the MR analysis would be unfolded in three steps: firstly, MS was performed as the exposure whereas AD was investigated as the outcome; subsequently, MS and AD reversed the direction to determine the causality between them. Additionally, multivariable MR (MVMR) was performed to assess potential genetic causal associations between MS and AD. An overview of the three assumptions and research design are depicted in Fig. 1 . MR analysis relies on three fundamental assumptions: 1. The IVs should have a significant association with the exposure. 2. The IVs should not have any potential confounders related to exposure and outcome. 3. The IVs should exclusively influence on outcome through their impact on the exposure. IVs: instrumental variables, MS: multiple sclerosis, AD: atopic dermatitis, MR: mendelian randomization, BMI: body mass index. Initially, The SNPs robustly associated with exposure were identified at the genome-wide significance level ( P 0.001 and a kilobase pair (kb) distance of 1000 were excluded to ensure the elimination of any linkage disequilibrium (LD) associations. In cases where LD effect among SNPs were observed, the SNP with the lowest P value was retained. Furthermore, to evaluate the susceptibility of retained SNPs to weak instrument bias, the F statistic was used and calculated, those SNPs with an F statistic < 10 were deemed weak instruments and consequently excluded from further analysis. Subsequent to the harmonization of exposure and outcome datasets, wherein palindromic and weak instrumental variants were removed, the remaining SNPs were utilized for MR analysis. Additionally, the PhenoScanner V2, which is an expanded tool facilitating exploration of human genotype-phenotype associations [ 17 ] , was utilized to examine whether the selected SNPs exhibited associations with confounders ( P < 1 × 10 − 5 ) in the MS and AD relationship. Upon detection of confounders, adjustments wound be made accordingly in further analyses. More details regarding the IVs selection can be found in the previous study [ 18 ] . Statistical analysis In this study, utilizing the "TwoSampleMR" package in R version 4.3.2 (R Foundation for Statistical Computing, Vienna, Austria), we performed a bidirectional MR analysis to investigate the causal relationship between MS and AD. The analytical methods employed encompassed inverse variance-weighted (IVW), MR-Egger (MRE) and weighted median (WMed). IVW provides as the primary approach for assessing causal associations when no horizontal pleiotropy is present or horizontal pleiotropy is balanced from the IV. Additionally, supplementary analyses were performed using MRE and WMed [ 19 ] . Significant disparities in the results of the two-sample MR analysis ( P < 0.05) suggest the presence of a potential causal link between MS and AD. Moreover, to assess the robustness of the two-sample MR study, Cochran's Q test and the "leave-one-out" method were employed to quantify instrumental variable heterogeneity. In cases of observed heterogeneity ( P 0.05), the IVW fixed-effects model (IVW-FE) was applied. These methodologies collectively contributed to a comprehensive assessment of causal estimates, thereby augmenting the reliability and interpretability of the study findings. Additionally, MVMR extends univariable MR(UVMR) by utilizing genetic variants associated with multiple exposures. This approach allows the joint detection of causal effects from multiple risk factors. In this study, we undertook MVMR involving body mass index (BMI), smoking-related factors, C-reactive protein (CRP) levels, serum 25-hydroxyvitamin D (25(OH)D) levels, vitamin deficiencies and telomere length as previous MR studies have shown [ 11 , 20 , 21 ] , to examine the potential causal relationship between MS and AD. Similar to the UVMR analyses, the IVW method was used as a primary deterministic approach, supplemented by MR-Egger and weighted median. Results After excluding IVs in linkage disequilibrium for the IV selection, this study ultimately identified five independent SNPs (with an R 2 < 0.01) that were significantly associated ( P < 5 × 10 − 8 ) for subsequent analysis (Table 2 ). In this study, the F-statistic for each IV related to the instrument exposure was greater than 10, ranging from 30.92 to 260.58, suggesting that there is a low likelihood of weak IV bias in this study, indicating a strong correlation between IVs and exposure. Additional calculations were conducted to determine the odds ratios (OR) across UVMR analyses, examining the relationship between MS and AD (Fig. 2 ). Table 2 The detailed characteristics of the selected genetic variants SNP EA OA EAF Multiple sclerosis Atopic dermatitis F statistic β SE P -value β SE P -value rs141298848 T C 0.02965 0.8046 0.1447 2.67E-08 -0.0745 0.0624 0.2327 30.91854252 rs149765808 A G 0.01355 1.2764 0.2115 1.60E-09 0.1857 0.1085 0.0869901 36.42074848 rs55782725 A G 0.03668 0.9198 0.1265 3.62E-13 0.0463 0.044 0.2923 52.8690831 rs9264277 C T 0.73 -0.4545 0.0518 1.67E-18 -0.0426 0.0213 0.04559 76.98467394 rs9271069 G A 0.8612 -1.1429 0.0708 1.54E-58 -0.1251 0.0217 8.35E-09 260.5829337 SNP, Single-nucleotide polymorphism; EA, effect allele; OA, other allele; EAF, effect allele frequency, β, the per-allele effect on each trait; SE, standard error The forward MR Analysis The forward MR analysis, utilizing IVW analysis as the primary method, indicated a significant causal association between MS and AD (Fig. 3 A and B). The IVW analysis suggested that genetically predicted MS is positively associated with an increased risk of AD, with an estimated OR of 1.10 (95% CI: 1.05 to 1.15, P = 1.87 × 10 − 5 ). Weighted median and MR Egger yielded similar results, with ORs of 1.110 and 1.126. (Fig. 2 A). The reverse MR Analysis The reverse MR analysis, estimating the causal effect of atopic dermatitis on multiple sclerosis, demonstrated no significant association (Fig. 4 A, B), with an OR of 1.09 (95% CI: 0.84 to 1.42, P = 0.53) (Fig. 2 B), thereby suggesting a predominant direction of causality from MS to AD rather than the reverse. Sensitivity Analyses Sensitivity analyses were conducted to assess the robustness of the MR findings. Cochran's Q test revealed no significant heterogeneity among the SNPs used in the analysis ( P = 0.11) (Supplementary Table S1 ). The MR-Egger intercept test did not suggest the presence of directional horizontal pleiotropy ( P = 0.72) (Supplementary Table S2). The leave-one-out analysis demonstrated consistent results across all SNPs, with no single SNP driving the observed association (Fig. 3 C, 4 C). Additionally, funnel plot analysis also showed no evidence of publication bias or asymmetry in the distribution of effect estimates (Fig. 3 D, 4 D). The MVMR Analysis The MVMR analysis was employed to estimate the direct causal effect of MS on the risk of AD, adjusting for potential confounding factors. The results indicated that after adjusting for genetically predicted BMI, telomere length, vitamin deficiencies, and smoking-related factors, the association between MS and the risk of AD was not altered (Table 3 ). However, upon adjustment for CRP levels, serum levels of 25(OH)D, and smoking status as confounders, MS was no longer identified as a risk factor for AD. Table 3 Estimates of MS on AD by potential mediators. GWAS ID Potential mediators Cases/ Controls SNPs Estimate SE 95%CI P -value ukb-b-19953 Body mass index 461,460/NA 410 3.90% 0.013 0.014, 0.064 0.002 ebi-a-GCST90029070 C-reactive protein levels 575,531/NA 246 6.30% 0.05 -0.034, 0.16 0.201 ieu-b-4879 telomere length 472,174/NA 130 7.10% 0.017 0.038, 0.103 < 0.001 ebi-a-GCST90000618 Serum 25-Hydroxyvitamin D levels 496,946/NA 112 8.20% 0.103 -0.12, 0.238 0.429 finn-b-VIT_DEF Vitamin deficiency 632/209,607 4 7.70% 0.038 0.003, 0.152 0.043 ieu-b-24 Age Of Smoking Initiation 341,427/NA 10 9.60% 0.018 0.06, 0.132 < 0.001 ieu-b-4877 smoking initiation 311,629/321,173 88 8.30% 0.018 0.048, 0.117 < 0.001 ebi-a-GCST90029014 Smoking status 468,170/NA 121 3.70% 0.18 -0.315, 0.389 0.837 ukb-b-223 Current tobacco smoking 462,434/NA 38 8.50% 0.02 0.045, 0.124 < 0.001 ukb-b-960 Smoking/smokers in household 425,516/NA 3 10.00% 0.018 0.066, 0.135 < 0.001 ukb-b-2134 Past tobacco smoking 424,960/NA 92 6.70% 0.019 0.029, 0.105 0.001 ukb-b-5779 Alcohol intake frequency 462,346/NA 93 7.00% 0.017 0.037, 0.102 < 0.001 GWAS, Genome wide association study; AD, Atopic dermatitis; MS, multiple sclerosis; NA, not available; SNPs, Single-nucleotide polymorphisms; SE , standard error; CI, confidence interval Our research methods and reporting align with the STROBE-MR guidelines, as presented in Supplementary Table S3 . Discussion This study employs UVMR and MVMR analyses to reveal a positive genetic causal relationship between MS and the risk of AD. Upon conducting a MVMR analysis that incorporated a range of potential confounders—such as BMI, CRP levels, telomere length, serum 25(OH)D levels, vitamin deficiencies, the age of smoking initiation, smoking status, current tobacco smoking, presence of smokers in household, past tobacco smoking, and the frequency of alcohol intake—it was determined that the causal link is modulated by smoking status, serum 25(OH)D levels, and CRP levels as independent factors. These findings offer new perspectives for understanding the potential connections between MS and AD, potentially guiding future prevention and treatment strategies for AD. MS is an autoimmune disease with a high recurrence and fatality rate [ 3 ] , while patients with AD often have one or more autoimmune diseases [ 22 ] . The association between AD and MS has been controversial. Some observational studies suggest a higher prevalence of MS in patients with AD [ 9 , 23 ] . A large Swedish case-control study identified an increased prevalence of MS exclusively in female patients with AD, highlighting sex differences in comorbidities [ 10 ] . Conversely, another large study found no association, potentially due to variations in study design, sample selection, or population characteristics [ 24 ] . The majority of existing studies are cross-sectional, retrospective, or case-control in nature, which limits their ability to establish causality. To address this limitation, we employed MR analysis to investigate the genetic causal relationship between MS and AD. Additionally, we conducted MVMR analysis to mitigate the effects of pleiotropy at the GWAS database level. After adjusting for BMI, telomere length, vitamin deficiency, and smoking-related factors, MS remained positively associated with AD. However, adjustments for CRP, serum 25(OH)D levels, and smoking status did not reveal a significant effect of MS on the risk of AD, suggesting a non-negligible interaction between these factors in the risk of MS on AD occurrence. Studies have demonstrated that the incidence of autoimmune comorbidities is significantly higher in patients with a history of smoking compared to non-smoking patients MS [ 24 ] . Previous studies have indicated that CRP [ 25 , 26 ] , serum 25(OH)D) levels [ 27 , 28 ] , and smoking status [ 29 , 30 ] are all influential factors in the association between MS and AD. Our study supports the notion that these factors may play a role in this causal relationship, and the precise effects of these variables require further investigation in subsequent studies. The pathogenesis of AD and MS is complex, involving genetic, immunologic, and environmental factors, with both conditions characterized by chronic recurrent exacerbations. A meta-analysis of genome-wide association studies has revealed substantial genetic overlap between susceptibility loci for AD and various autoimmune diseases [ 31 ] . Additionally, commonalities exist in the immune regulation implicated in both MS and AD. T helper cell (Th) 1 and Th17 cells, along with the inflammatory factors they secrete, play crucial roles in the progression of MS. These cells migrate to CNS, triggering an immune response. Th1 cells and interferon-γ (IFN-γ) they secrete cause demyelination of CNS neurons and neuronal degeneration [ 32 ] . Interleukin (IL)-17A secreted by Th17 cells disrupts the blood-brain barrier, allowing more Th17 cells to migrate into the brain parenchyma, producing additional IL-17A and resulting in severe neurological deficits [ 33 , 34 ] . Th17 cells are increased in the peripheral blood, cerebrospinal fluid, and brain lesions of MS patients, with their counts increasing further during relapses [ 35 ] . Similarly, AD is associated with Th1 and Th17 cells. Th1 cytokines, such as IFN-γ, are expressed in the skin lesions of AD patients [ 36 ] . In the chronic phase of AD, Th1 cells predominate, producing IFN-γ and tumor necrosis factor-α(TNF-α), leading to chronic inflammatory lesions in eczema [ 37 , 38 ] . Recent studies have found that Th17 cytokines, such as IL-17 and IL-22, are expressed in the skin lesions of patients with AD [ 39 – 41 ] . Elevated levels of IL-17 promote the differentiation of B-cells into IgE-producing plasma cells [ 42 ] , leading to eosinophil- mediated and neutrophil-mediated inflammation [ 43 ] . The expression of Th17 cells and IL-17 is increased in the peripheral blood of AD patients and correlates with disease severity. Immunohistochemistry has shown that Th17 cells also infiltrate the papillary dermis of AD lesion skin [ 44 ] . Therefore, the overexpression of key factors such as IFN-γ and IL-17, along with the proliferation of T-cell subsets Th1 and Th17, play significant roles in both AD and MS. These findings provide a biological basis for the potential association between AD and MS. The MR analysis employed in this study leverages genetic variants as IVs to simulate random allocation in a controlled trial setting, thereby assessing the causal relationship between MS and AD. This approach circumvents the biases introduced by confounding factors and reverse causality inherent in observational studies. The use of European population data minimizes bias due to population heterogeneity, and the multivariate nature of our analysis further validates our findings. However, this study has limitations. The analysis is based on GWAS data from European populations, which may limit the generalizability of the results. Future research should validate these findings in diverse racial and geographical populations. Additionally, differences in age and gender distribution between MS and AD may affect the interpretation of the causal relationship, suggesting a need for age and gender subgroup analyses in future studies. Lastly, while MR analysis is a robust tool, its findings should be corroborated by large-sample, multicenter, randomized controlled trials. Conclusion In summary, this study offers novel insights into the genetic causality linking MS and AD through MR analysis. Our findings underscore the necessity for further investigation into the underlying biological connections between these two conditions, suggesting directions for future research. This includes cross-population studies, subgroup analyses, and randomized controlled trials. Such research endeavors will enhance our comprehension of the pathogenesis of MS and AD, potentially leading to the development of more effective prevention and treatment strategies for patients. Declarations Supplementary materials Supplementary Table S1 : Results of heterogeneity in the bidirectional MR analysis; Supplementary Table S2 Table: Results of pleiotropy in the bidirectional MR analysis; Supplementary Table S3 Table: STROBE-MR Checklist of Recommended Items to Address in Reports of Mendelian Randomization Studies Conflict of interest disclosure The authors declare that they have no conflict of interest. Funding statement This work was supported by the Startup Fund for scientific research, Fujian Medical University (2020QH1075). Author Contribution Yuping Xie conceived the study. Yuping Xie and Kun Ying Qiu collected and analyzed the data, Yuping Xie, Hongjin Liu, Yingkun Qiu and Yingping Cao authored the manuscript. All authors contributed to revising the article and approved the submitted version. Acknowledgement We thank the consortiums for sharing these data. 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The Asian atopic dermatitis phenotype combines features of atopic dermatitis and psoriasis with increased TH17 polarization. J. Allergy Clin. Immunol. 136 (5), 1254–1264. 10.1016/j.jaci.2015.08.015 (2015). Esaki, H. et al. Early-onset pediatric atopic dermatitis is T(H)2 but also T(H)17 polarized in skin. J. Allergy Clin. Immunol. 138 (6), 1639–1651. 10.1016/j.jaci.2016.07.013 (2016). Sugaya, M. The Role of Th17-Related Cytokines in Atopic Dermatitis. Int. J. Mol. Sci. 21 (4). 10.3390/ijms21041314 (2020). Milovanovic, M., Drozdenko, G., Weise, C., Babina, M. & Worm, M. Interleukin-17A promotes IgE production in human B cells. J. Invest. Dermatol. 130 (11), 2621–2628. 10.1038/jid.2010.175 (2010). Gur Cetinkaya, P. & Sahiner, U. M. Childhood atopic dermatitis: current developments, treatment approaches, and future expectations. Turk. J. Med. Sci. 49 (4), 963–984. 10.3906/sag-1810-105 (2019). Eyerich, K. et al. IL-17 in atopic eczema: linking allergen-specific adaptive and microbial-triggered innate immune response. J. Allergy Clin. Immunol. 123 (1), 59–66e4. 10.1016/j.jaci.2008.10.031 (2009). Additional Declarations No competing interests reported. Supplementary Files S1S3Table.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4992688","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":359385901,"identity":"2d0bfbd9-2708-4863-b851-b82128dfe517","order_by":0,"name":"Yuping Xie","email":"","orcid":"","institution":"Department of Laboratory Medicine, Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yuping","middleName":"","lastName":"Xie","suffix":""},{"id":359385902,"identity":"175b9dc0-a9fd-4ebe-ae68-60b959f4d7c9","order_by":1,"name":"Hongjin Liu","email":"","orcid":"","institution":"Department of Cardiovascular Surgery, Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hongjin","middleName":"","lastName":"Liu","suffix":""},{"id":359385903,"identity":"898e77e6-2740-4aad-9c5c-831dc396e812","order_by":2,"name":"Yingkun Qiu","email":"","orcid":"","institution":"Department of Laboratory Medicine, Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yingkun","middleName":"","lastName":"Qiu","suffix":""},{"id":359385904,"identity":"c3b30d87-f0bb-4e39-b1fe-4691a4ad8752","order_by":3,"name":"Yingping Cao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYLCCBCBmY2BgfMBQIEGaFmYDBgNitUABmwSDARHK+NsPH5N4uKM2j0+6/VrFDwOLxA23Gxg/fMzBrUXiTFqaROKZ48VsMmfKbvYYSCRuuHOAWXLmNtxaDBhyzCQS244ltknkpN3gAWm5kcDGzItPC/8bhJbCP0RpkQDbUgPUkn6MmShbJG48S7ZIbDtQzCaRwywtYyBhPPNGYjNev/D3Jx+8+bOtLk9+RvrDj28q6mT7biQf/PARjxYgYAFG3+EEBgYecKQ4NjAwNuBVDwTMHxgY6oBa2B+AePaElI+CUTAKRsHIAwAmUlPii04S0QAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Laboratory Medicine, Fujian Medical University Union Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yingping","middleName":"","lastName":"Cao","suffix":""}],"badges":[],"createdAt":"2024-08-28 16:56:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4992688/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4992688/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":65427695,"identity":"8a2a698f-8f14-4559-af5b-40d1203e8456","added_by":"auto","created_at":"2024-09-27 09:34:03","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":339018,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of three assumptions and MR analysis design.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4992688/v1/6c7bdd0af5f7b280aef05e27.jpg"},{"id":65427699,"identity":"6d3f1796-2d4f-4278-bfb7-5402fd50de59","added_by":"auto","created_at":"2024-09-27 09:34:03","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":243086,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plots of UVMR analyses. \u003c/strong\u003e(A) Multiple sclerosis on atopic dermatitis. (B) Atopic dermatitis on multiple sclerosis\u003c/p\u003e","description":"","filename":"Fig2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4992688/v1/f73fd47cbc946d85da740759.jpg"},{"id":65428566,"identity":"0fb2a641-7dfa-4d3c-b96c-e0fc659d80ad","added_by":"auto","created_at":"2024-09-27 09:42:03","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":477704,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMultiple sclerosis on atopic dermatitis.\u003c/strong\u003e(A) Comparison of results using different MR methods. (B) Forest plot of single-SNP MR. (C) Leave-one-out sensitivity analysis. (D) Funnel plot.\u003c/p\u003e","description":"","filename":"Fig3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4992688/v1/82e257825dc62a0c55e6592f.jpg"},{"id":65427696,"identity":"f7adfab6-4af0-4ff6-b1d1-24cec59b6d10","added_by":"auto","created_at":"2024-09-27 09:34:03","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":587862,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAtopic dermatitis on multiple sclerosis. \u003c/strong\u003e(A) Comparison of results using different MR methods. (B) Forest plot of single-SNP MR. (C) Leave-one-out sensitivity analysis. (D) Funnel plot.\u003c/p\u003e","description":"","filename":"Fig4.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4992688/v1/55c4a594c1985b06c3872cb5.jpg"},{"id":77739156,"identity":"c9cea7c8-9667-4ec2-bd4b-c0866fe73b76","added_by":"auto","created_at":"2025-03-05 04:47:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2595904,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4992688/v1/a6a5f6eb-9e40-4906-a218-84cbeea13c44.pdf"},{"id":65427698,"identity":"ffd65f3e-0035-4e54-b742-249a1952dbc0","added_by":"auto","created_at":"2024-09-27 09:34:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":165881,"visible":true,"origin":"","legend":"","description":"","filename":"S1S3Table.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4992688/v1/03fa14c4033dcc66bbef7928.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genetic causality between multiple sclerosis and atopic dermatitis: A univariable and multivariable Mendelian randomization study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMultiple sclerosis (MS) is a chronic progressive inflammatory demyelinating disease affecting the white and gray matter of the central nervous system (CNS), characterized by a range of complex clinical manifestations, primarily including visual and sensory abnormalities, motor deficits, and cognitive impairment \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e.It is known that the genetic and environmental factors contribute to MS pathogenesis, but the complex interactions are not yet fully understood \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.The disease predominantly affects young and middle-aged adults, with a higher incidence in women compared to men (female-to-male ratio of approximately 3:1) \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.There are over 2.8\u0026nbsp;million patients worldwide diagnosed with MS, who experience a lower life expectancy compared to the general population \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Furthermore, it stands as the most significant cause of disability in young individuals affected by central nervous system disorders.\u003c/p\u003e \u003cp\u003eAtopic dermatitis (AD) is one of the most common chronic inflammatory allergic skins worldwide \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Clinical symptoms of AD have a wide spectrum, mainly include severe pruritus, erythema, eczematous lesions, flaking, edema, and thickening of the skin \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. A recent systematic review estimated a 12-month prevalence of AD up to 22.6% among children and 17.1% among adults, with 9.6% of new cases of AD occurring in children \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. AD leads to substantial social and financial costs and accounts for the largest global burden of disability owing to skin diseases. It has previously been observed that individuals with AD may manifest increased immune susceptibility to developing MS throughout the disease progression \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. a potential association between MS and AD has been observed to some extent, yet the results remain uncertain \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Nonetheless, conclusive evidence establishing a direct correlation between these conditions remains elusive, necessitating additional research endeavors. Delving into the causality between MS and AD holds promise in enhancing a better comprehension of the pathogenesis underlying these maladies.\u003c/p\u003e \u003cp\u003eHowever, the true association between MD and AD may be influenced by potential confounding factors, such as obesity, smoking, and vitamin D deficiency, as well as reverse causality bias \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. These complexities pose challenges for conducting clinical trials. Mendelian randomization (MR) analysis serves as an analytical model employing genetic variation as an instrumental variable, extensively utilized in contemporary research to assess causal associations between exposures and outcomes \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. MR analysis employs single nucleotide polymorphisms (SNPs) as genetic proxies to estimate the effects of relevant exposures on outcomes \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Conceptually, MR parallels a natural randomized clinical trial (RCT). A notable advantage of MR over conventional RCT designs lies in the random assignment of SNPs as risk factor instruments, thereby circumventing potential confounding factors or reverse causality. Importantly, the formation of genetic variation remains unaffected by social environments, lifestyle habits, and other traits, theoretically mitigating the influence of confounding factors \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData sources for exposure and outcome\u003c/h2\u003e \u003cp\u003ePublicly accessible data were utilized in this study, including genome-wide association studies (GWAS) data for each of the genetic traits listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, thus ethical clearance was deemed unnecessary for the present investigation. The study involved human participants, each of whom provided written informed consent following ethical review and approval in accordance with local regulations and institutional guidelines. The MS data repository was sourced from the FinnGen consortium (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://storage.googleapis.com/finngen-public-data-r10/summary_stats/finngen_R10_G6_MS.gz\u003c/span\u003e\u003cspan address=\"https://storage.googleapis.com/finngen-public-data-r10/summary_stats/finngen_R10_G6_MS.gz\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), comprising 2,409 cases, 408,561 controls. AD data could be found from the IEU OPEN GWAS PROJECT (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), encompassing 6,224 cases, 475,075 controls. To mitigate potential bias in bidirectional MR analyses arising from population stratification, summary statistics for both MS and AD datasets were exclusively sourced from individuals of European ancestry.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetails of GWAS genetic traits used for bidirectional MR analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGWAS ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAuthor and year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConsortium\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNcase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNcontrol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eebi-a-GCST90018784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSakaue,S et al.,2021 \u003csup\u003e[15]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e481299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e475075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efinn-b-G6_MS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMS-disease/\u003c/p\u003e \u003cp\u003emultiple sclerosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFinnGen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e410970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e408561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eGWAS, Genome wide association study; AD, atopic dermatitis; MS, multiple sclerosis; NA, not available\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMR assumptions and Instrumental Variables (IVs) selection\u003c/h2\u003e \u003cp\u003eThe two-sample MR analysis should satisfy the following three fundamental assumptions: (1) the IVs employed are firmly linked with the exposure. (2) the chosen IV have no association with any potential confounders which are relevant to exposure and outcome. (3) and the IVs can exert their influence on outcome solely through the exposure and not through other pathways \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. What\u0026rsquo;s more, the MR analysis would be unfolded in three steps: firstly, MS was performed as the exposure whereas AD was investigated as the outcome; subsequently, MS and AD reversed the direction to determine the causality between them. Additionally, multivariable MR (MVMR) was performed to assess potential genetic causal associations between MS and AD. An overview of the three assumptions and research design are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMR analysis relies on three fundamental assumptions: 1. The IVs should have a significant association with the exposure. 2. The IVs should not have any potential confounders related to exposure and outcome. 3. The IVs should exclusively influence on outcome through their impact on the exposure. IVs: instrumental variables, MS: multiple sclerosis, AD: atopic dermatitis, MR: mendelian randomization, BMI: body mass index.\u003c/p\u003e \u003cp\u003eInitially, The SNPs robustly associated with exposure were identified at the genome-wide significance level (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e). Subsequently, A stringent threshold R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.001 and a kilobase pair (kb) distance of 1000 were excluded to ensure the elimination of any linkage disequilibrium (LD) associations. In cases where LD effect among SNPs were observed, the SNP with the lowest P value was retained. Furthermore, to evaluate the susceptibility of retained SNPs to weak instrument bias, the F statistic was used and calculated, those SNPs with an \u003cem\u003eF\u003c/em\u003e statistic\u0026thinsp;\u0026lt;\u0026thinsp;10 were deemed weak instruments and consequently excluded from further analysis. Subsequent to the harmonization of exposure and outcome datasets, wherein palindromic and weak instrumental variants were removed, the remaining SNPs were utilized for MR analysis. Additionally, the PhenoScanner V2, which is an expanded tool facilitating exploration of human genotype-phenotype associations \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, was utilized to examine whether the selected SNPs exhibited associations with confounders (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e) in the MS and AD relationship. Upon detection of confounders, adjustments wound be made accordingly in further analyses. More details regarding the IVs selection can be found in the previous study \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eIn this study, utilizing the \"TwoSampleMR\" package in R version 4.3.2 (R Foundation for Statistical Computing, Vienna, Austria), we performed a bidirectional MR analysis to investigate the causal relationship between MS and AD. The analytical methods employed encompassed inverse variance-weighted (IVW), MR-Egger (MRE) and weighted median (WMed). IVW provides as the primary approach for assessing causal associations when no horizontal pleiotropy is present or horizontal pleiotropy is balanced from the IV. Additionally, supplementary analyses were performed using MRE and WMed \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Significant disparities in the results of the two-sample MR analysis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) suggest the presence of a potential causal link between MS and AD. Moreover, to assess the robustness of the two-sample MR study, Cochran's Q test and the \"leave-one-out\" method were employed to quantify instrumental variable heterogeneity. In cases of observed heterogeneity (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), the IVW random-effects model (IVW-RE) was utilized; conversely (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), the IVW fixed-effects model (IVW-FE) was applied. These methodologies collectively contributed to a comprehensive assessment of causal estimates, thereby augmenting the reliability and interpretability of the study findings.\u003c/p\u003e \u003cp\u003eAdditionally, MVMR extends univariable MR(UVMR) by utilizing genetic variants associated with multiple exposures. This approach allows the joint detection of causal effects from multiple risk factors. In this study, we undertook MVMR involving body mass index (BMI), smoking-related factors, C-reactive protein (CRP) levels, serum 25-hydroxyvitamin D (25(OH)D) levels, vitamin deficiencies and telomere length as previous MR studies have shown \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e, to examine the potential causal relationship between MS and AD. Similar to the UVMR analyses, the IVW method was used as a primary deterministic approach, supplemented by MR-Egger and weighted median.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eAfter excluding IVs in linkage disequilibrium for the IV selection, this study ultimately identified five independent SNPs (with an R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) that were significantly associated (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) for subsequent analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In this study, the F-statistic for each IV related to the instrument exposure was greater than 10, ranging from 30.92 to 260.58, suggesting that there is a low likelihood of weak IV bias in this study, indicating a strong correlation between IVs and exposure. Additional calculations were conducted to determine the odds ratios (OR) across UVMR analyses, examining the relationship between MS and AD (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe detailed characteristics of the selected genetic variants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEAF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMultiple sclerosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003eAtopic dermatitis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e statistic\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers141298848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.67E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.0745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.2327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e30.91854252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers149765808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.2764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.60E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.1857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.1085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0869901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e36.42074848\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers55782725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.62E-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.2923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e52.8690831\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers9264277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.4545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.67E-18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.0426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.04559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e76.98467394\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers9271069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.1429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.54E-58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.1251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8.35E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e260.5829337\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSNP, Single-nucleotide polymorphism; EA, effect allele; OA, other allele; EAF, effect allele frequency, β, the per-allele effect on each trait; SE, standard error\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eThe forward MR Analysis\u003c/h2\u003e \u003cp\u003eThe forward MR analysis, utilizing IVW analysis as the primary method, indicated a significant causal association between MS and AD (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and B). The IVW analysis suggested that genetically predicted MS is positively associated with an increased risk of AD, with an estimated OR of 1.10 (95% CI: 1.05 to 1.15, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.87 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e). Weighted median and MR Egger yielded similar results, with ORs of 1.110 and 1.126. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eThe reverse MR Analysis\u003c/h2\u003e \u003cp\u003eThe reverse MR analysis, estimating the causal effect of atopic dermatitis on multiple sclerosis, demonstrated no significant association (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B), with an OR of 1.09 (95% CI: 0.84 to 1.42, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.53) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), thereby suggesting a predominant direction of causality from MS to AD rather than the reverse.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity Analyses\u003c/h2\u003e \u003cp\u003eSensitivity analyses were conducted to assess the robustness of the MR findings. Cochran's Q test revealed no significant heterogeneity among the SNPs used in the analysis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.11) (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The MR-Egger intercept test did not suggest the presence of directional horizontal pleiotropy (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.72) (Supplementary Table S2). The leave-one-out analysis demonstrated consistent results across all SNPs, with no single SNP driving the observed association (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Additionally, funnel plot analysis also showed no evidence of publication bias or asymmetry in the distribution of effect estimates (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eThe MVMR Analysis\u003c/h2\u003e \u003cp\u003eThe MVMR analysis was employed to estimate the direct causal effect of MS on the risk of AD, adjusting for potential confounding factors. The results indicated that after adjusting for genetically predicted BMI, telomere length, vitamin deficiencies, and smoking-related factors, the association between MS and the risk of AD was not altered (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, upon adjustment for CRP levels, serum levels of 25(OH)D, and smoking status as confounders, MS was no longer identified as a risk factor for AD.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimates of MS on AD by potential mediators.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGWAS ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePotential mediators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCases/\u003c/p\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSNPs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eukb-b-19953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e461,460/NA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.014, 0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eebi-a-GCST90029070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC-reactive protein levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e575,531/NA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.034, 0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eieu-b-4879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etelomere length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e472,174/NA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.038, 0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eebi-a-GCST90000618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSerum 25-Hydroxyvitamin D levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e496,946/NA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.12, 0.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efinn-b-VIT_DEF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitamin deficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e632/209,607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003, 0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.043\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eieu-b-24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge Of Smoking Initiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e341,427/NA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.06, 0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eieu-b-4877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esmoking initiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e311,629/321,173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.048, 0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eebi-a-GCST90029014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmoking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e468,170/NA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.315, 0.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eukb-b-223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent tobacco smoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e462,434/NA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.045, 0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eukb-b-960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmoking/smokers in household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e425,516/NA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.066, 0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eukb-b-2134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePast tobacco smoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e424,960/NA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.029, 0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eukb-b-5779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlcohol intake frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e462,346/NA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.037, 0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eGWAS, Genome wide association study; AD, Atopic dermatitis; MS, multiple sclerosis; NA, not available; SNPs, Single-nucleotide polymorphisms; \u003cem\u003eSE\u003c/em\u003e, standard error; CI, confidence interval\u003c/p\u003e \u003cp\u003eOur research methods and reporting align with the STROBE-MR guidelines, as presented in Supplementary Table S3 .\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study employs UVMR and MVMR analyses to reveal a positive genetic causal relationship between MS and the risk of AD. Upon conducting a MVMR analysis that incorporated a range of potential confounders\u0026mdash;such as BMI, CRP levels, telomere length, serum 25(OH)D levels, vitamin deficiencies, the age of smoking initiation, smoking status, current tobacco smoking, presence of smokers in household, past tobacco smoking, and the frequency of alcohol intake\u0026mdash;it was determined that the causal link is modulated by smoking status, serum 25(OH)D levels, and CRP levels as independent factors. These findings offer new perspectives for understanding the potential connections between MS and AD, potentially guiding future prevention and treatment strategies for AD.\u003c/p\u003e \u003cp\u003eMS is an autoimmune disease with a high recurrence and fatality rate \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e, while patients with AD often have one or more autoimmune diseases \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. The association between AD and MS has been controversial. Some observational studies suggest a higher prevalence of MS in patients with AD \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. A large Swedish case-control study identified an increased prevalence of MS exclusively in female patients with AD, highlighting sex differences in comorbidities \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Conversely, another large study found no association, potentially due to variations in study design, sample selection, or population characteristics \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. The majority of existing studies are cross-sectional, retrospective, or case-control in nature, which limits their ability to establish causality. To address this limitation, we employed MR analysis to investigate the genetic causal relationship between MS and AD. Additionally, we conducted MVMR analysis to mitigate the effects of pleiotropy at the GWAS database level. After adjusting for BMI, telomere length, vitamin deficiency, and smoking-related factors, MS remained positively associated with AD. However, adjustments for CRP, serum 25(OH)D levels, and smoking status did not reveal a significant effect of MS on the risk of AD, suggesting a non-negligible interaction between these factors in the risk of MS on AD occurrence. Studies have demonstrated that the incidence of autoimmune comorbidities is significantly higher in patients with a history of smoking compared to non-smoking patients MS \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Previous studies have indicated that CRP \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e, serum 25(OH)D) levels \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e, and smoking status \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e are all influential factors in the association between MS and AD. Our study supports the notion that these factors may play a role in this causal relationship, and the precise effects of these variables require further investigation in subsequent studies.\u003c/p\u003e \u003cp\u003eThe pathogenesis of AD and MS is complex, involving genetic, immunologic, and environmental factors, with both conditions characterized by chronic recurrent exacerbations. A meta-analysis of genome-wide association studies has revealed substantial genetic overlap between susceptibility loci for AD and various autoimmune diseases \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Additionally, commonalities exist in the immune regulation implicated in both MS and AD. T helper cell (Th) 1 and Th17 cells, along with the inflammatory factors they secrete, play crucial roles in the progression of MS. These cells migrate to CNS, triggering an immune response. Th1 cells and interferon-γ (IFN-γ) they secrete cause demyelination of CNS neurons and neuronal degeneration \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Interleukin (IL)-17A secreted by Th17 cells disrupts the blood-brain barrier, allowing more Th17 cells to migrate into the brain parenchyma, producing additional IL-17A and resulting in severe neurological deficits \u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Th17 cells are increased in the peripheral blood, cerebrospinal fluid, and brain lesions of MS patients, with their counts increasing further during relapses \u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Similarly, AD is associated with Th1 and Th17 cells. Th1 cytokines, such as IFN-γ, are expressed in the skin lesions of AD patients \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. In the chronic phase of AD, Th1 cells predominate, producing IFN-γ and tumor necrosis factor-α(TNF-α), leading to chronic inflammatory lesions in eczema \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. Recent studies have found that Th17 cytokines, such as IL-17 and IL-22, are expressed in the skin lesions of patients with AD \u003csup\u003e[\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. Elevated levels of IL-17 promote the differentiation of B-cells into IgE-producing plasma cells \u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e, leading to eosinophil- mediated and neutrophil-mediated inflammation \u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. The expression of Th17 cells and IL-17 is increased in the peripheral blood of AD patients and correlates with disease severity. Immunohistochemistry has shown that Th17 cells also infiltrate the papillary dermis of AD lesion skin \u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. Therefore, the overexpression of key factors such as IFN-γ and IL-17, along with the proliferation of T-cell subsets Th1 and Th17, play significant roles in both AD and MS. These findings provide a biological basis for the potential association between AD and MS.\u003c/p\u003e \u003cp\u003eThe MR analysis employed in this study leverages genetic variants as IVs to simulate random allocation in a controlled trial setting, thereby assessing the causal relationship between MS and AD. This approach circumvents the biases introduced by confounding factors and reverse causality inherent in observational studies. The use of European population data minimizes bias due to population heterogeneity, and the multivariate nature of our analysis further validates our findings.\u003c/p\u003e \u003cp\u003eHowever, this study has limitations. The analysis is based on GWAS data from European populations, which may limit the generalizability of the results. Future research should validate these findings in diverse racial and geographical populations. Additionally, differences in age and gender distribution between MS and AD may affect the interpretation of the causal relationship, suggesting a need for age and gender subgroup analyses in future studies. Lastly, while MR analysis is a robust tool, its findings should be corroborated by large-sample, multicenter, randomized controlled trials.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, this study offers novel insights into the genetic causality linking MS and AD through MR analysis. Our findings underscore the necessity for further investigation into the underlying biological connections between these two conditions, suggesting directions for future research. This includes cross-population studies, subgroup analyses, and randomized controlled trials. Such research endeavors will enhance our comprehension of the pathogenesis of MS and AD, potentially leading to the development of more effective prevention and treatment strategies for patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eSupplementary materials\u003c/h2\u003e \u003cp\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e: Results of heterogeneity in the bidirectional MR analysis; Supplementary Table S2 Table: Results of pleiotropy in the bidirectional MR analysis; Supplementary Table S3 Table: STROBE-MR Checklist of Recommended Items to Address in Reports of Mendelian Randomization Studies\u003c/p\u003e \u003ch2\u003eConflict of interest disclosure\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\u003ch2\u003eFunding statement\u003c/h2\u003e \u003cp\u003eThis work was supported by the Startup Fund for scientific research, Fujian Medical University (2020QH1075).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYuping Xie conceived the study. Yuping Xie and Kun Ying Qiu collected and analyzed the data, Yuping Xie, Hongjin Liu, Yingkun Qiu and Yingping Cao authored the manuscript. All authors contributed to revising the article and approved the submitted version.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank the consortiums for sharing these data.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript or supplementary information files\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDi Pauli, F. \u0026amp; Berger, T. Myelin Oligodendrocyte Glycoprotein Antibody-Associated Disorders: Toward a New Spectrum of Inflammatory Demyelinating CNS Disorders? \u003cem\u003eFront. 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Immunol.\u003c/em\u003e \u003cb\u003e123\u003c/b\u003e (1), 59\u0026ndash;66e4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jaci.2008.10.031\u003c/span\u003e\u003cspan address=\"10.1016/j.jaci.2008.10.031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2009).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"multiple sclerosis, atopic dermatitis, univariable Mendelian randomization, multivariable MR, single nucleotide polymorphisms","lastPublishedDoi":"10.21203/rs.3.rs-4992688/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4992688/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eWe sought to estimate the genetic causal association between multiple sclerosis (MS) and atopic dermatitis (AD) and identify potential mediating factors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe used univariable Mendelian randomization (UVMR) with inverse variance weighting (IVW) as the primary study method to estimate the causal effect of MS on AD, supplemented by weighted median and MR Egger validation analyses. Furthermore, we conducted a reverse MR analysis. Sensitivity analyses were performed using Cochran's Q test, MR-Egger intercept test, leave-one-out, and funnel plot analysis to evaluate the robustness of the MR findings. Additionally, multivariable MR (MVMR) was employed to estimate the direct causal effect of MS on the risk of AD.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eUVMR analysis demonstrated a genetic predisposition associated with the risk of MS and AD with an odds ratio of 1.10 (95% Confidence Interval: 1.05 to 1.15, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.87 \u0026times; 10^\u003csup\u003e\u0026minus;5\u003c/sup\u003e). Consistent results were observed after adjusting for potential confounders, including Body Mass Index (BMI), telomere length, vitamin deficiencies, and smoking-related factors in MVMR analyses. However, following adjustment for C-reactive protein, serum levels of 25-hydroxyvitamin D, and smoking status as confounders, MS was no longer identified as a risk factor for AD.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe findings indicate that while there may be a genetic link between MS and AD, the causal pathway is complex and influenced by multiple biological and environmental factors. Further research is needed to elucidate these interactions and their implications for disease prevention and treatment strategies.\u003c/p\u003e","manuscriptTitle":"Genetic causality between multiple sclerosis and atopic dermatitis: A univariable and multivariable Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-27 09:33:58","doi":"10.21203/rs.3.rs-4992688/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"147732c2-4e0c-43a1-be9f-b9eb551c8cb2","owner":[],"postedDate":"September 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":38241807,"name":"Biological sciences/Genetics"},{"id":38241808,"name":"Biological sciences/Immunology"}],"tags":[],"updatedAt":"2025-03-05T04:39:01+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-27 09:33:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4992688","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4992688","identity":"rs-4992688","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}