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

doi:10.21203/rs.3.rs-4992688/v1

Download PDF

Article

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

https://doi.org/10.21203/rs.3.rs-4992688/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

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

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].

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 < 5 × 10^− 8). Subsequently, A stringent threshold R² > 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 random-effects model (IVW-RE) was utilized; conversely (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.

After excluding IVs in linkage disequilibrium for the IV selection, this study ultimately identified five independent SNPs (with an R² < 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
SNP	EA	OA	EAF	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. 3A 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. 2A).

The reverse MR Analysis

The reverse MR analysis, estimating the causal effect of atopic dermatitis on multiple sclerosis, demonstrated no significant association (Fig. 4A, B), with an OR of 1.09 (95% CI: 0.84 to 1.42, P = 0.53) (Fig. 2B), 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. 3C, 4C). Additionally, funnel plot analysis also showed no evidence of publication bias or asymmetry in the distribution of effect estimates (Fig. 3D, 4D).

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 .

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.

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.

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.

Data Availability

Data is provided within the manuscript or supplementary information files

Di Pauli, F. & Berger, T. Myelin Oligodendrocyte Glycoprotein Antibody-Associated Disorders: Toward a New Spectrum of Inflammatory Demyelinating CNS Disorders? Front. Immunol. 9, 2753. 10.3389/fimmu.2018.02753 (2018).
Matias-Guiu, J. A. et al. Identification of Cortical and Subcortical Correlates of Cognitive Performance in Multiple Sclerosis Using Voxel-Based Morphometry. Front. Neurol. 9, 920. 10.3389/fneur.2018.00920 (2018).
Ward, M. & Goldman, M. D. Epidemiology and Pathophysiology of Multiple Sclerosis. Continuum (Minneapolis Minn). 28 (4), 988–1005. 10.1212/CON.0000000000001136 (2022).
Hittle, M. et al. Population-Based Estimates for the Prevalence of Multiple Sclerosis in the United States by Race, Ethnicity, Age, Sex, and Geographic Region. JAMA Neurol. 80 (7), 693–701. 10.1001/jamaneurol.2023.1135 (2023).
Walton, C. et al. Rising prevalence of multiple sclerosis worldwide: Insights from the Atlas of MS, third edition. Mult Scler. 26 (14), 1816–1821. 10.1177/1352458520970841 (2020).
Barbarot, S. et al. Epidemiology of atopic dermatitis in adults: Results from an international survey. Allergy. 73 (6), 1284–1293. 10.1111/all.13401 (2018).
Li, H., Zhang, Z., Zhang, H., Guo, Y. & Yao, Z. Update on the Pathogenesis and Therapy of Atopic Dermatitis. Clin. Rev. Allergy Immunol. 61 (3), 324–338. 10.1007/s12016-021-08880-3 (2021).
Bylund, S., Kobyletzki, L. B., Svalstedt, M. & Svensson, A. Prevalence and Incidence of Atopic Dermatitis: A Systematic Review. Acta Derm Venereol. 100 (12), adv00160. 10.2340/00015555-3510 (2020).
Fuxench, Z. C. C., Mitra, N., Hoffstad, O. J., Phillips, E. J. & Margolis, D. J. Association between atopic dermatitis, autoimmune illnesses, Epstein-Barr virus, and cytomegalovirus. Arch. Dermatol. Res. 315 (9), 2689–2692. 10.1007/s00403-023-02648-9 (2023).
Ivert, L. U. et al. Association between atopic dermatitis and autoimmune diseases: a population-based case-control study. Br. J. Dermatol. 185 (2), 335–342. 10.1111/bjd.19624 (2021).
Vandebergh, M., Degryse, N., Dubois, B. & Goris, A. Environmental risk factors in multiple sclerosis: bridging Mendelian randomization and observational studies. J. Neurol. 269 (8), 4565–4574. 10.1007/s00415-022-11072-4 (2022).
Chen, C. et al. Mendelian randomization as a tool to gain insights into the mosaic causes of autoimmune diseases. Autoimmun. Rev. 21 (12), 103210. 10.1016/j.autrev.2022.103210 (2022).
Klarin, D. et al. Genome-wide association study of thoracic aortic aneurysm and dissection in the Million Veteran Program. Nat. Genet. 55 (7), 1106–1115. 10.1038/s41588-023-01420-z (2023).
Chang, L., Zhou, G. & Xia, J. mGWAS-Explorer 2.0: Causal Analysis and Interpretation of Metabolite-Phenotype Associations. Metabolites. 13 (7). 10.3390/metabo13070826 (2023).
Sakaue, S. et al. A cross-population atlas of genetic associations for 220 human phenotypes. Nat. Genet. 53 (10), 1415–1424. 10.1038/s41588-021-00931-x (2021).
Lawlor, D. A., Harbord, R. M., Sterne, J. A., Timpson, N. & Davey Smith, G. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat. Med. 27 (8), 1133–1163. 10.1002/sim.3034 (2008).
Kamat, M. A. et al. PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations. Bioinformatics. 35 (22), 4851–4853. 10.1093/bioinformatics/btz469 (2019).
Emdin, C. A., Khera, A. V., Kathiresan, S. & Mendelian Randomization JAMA ;318(19):1925–1926. doi: 10.1001/jama.2017.17219. (2017).
Pagoni, P., Dimou, N. L., Murphy, N. & Stergiakouli, E. Using Mendelian randomisation to assess causality in observational studies. Evid. Based Ment Health. 22 (2), 67–71. 10.1136/ebmental-2019-300085 (2019).
Lopez-Armas, G. D. C. et al. Leukocyte Telomere Length Predicts Severe Disability in Relapsing-Remitting Multiple Sclerosis and Correlates with Mitochondrial DNA Copy Number. Int. J. Mol. Sci. 24 (2). 10.3390/ijms24020916 (2023).
Vandebergh, M., Becelaere, S., Group, C. I. W., Dubois, B. & Goris, A. Body Mass Index, Interleukin-6 Signaling and Multiple Sclerosis: A Mendelian Randomization Study. Front. Immunol. 13, 834644. 10.3389/fimmu.2022.834644 (2022).
Lu, Z. et al. Atopic dermatitis and risk of autoimmune diseases: a systematic review and meta-analysis. Allergy Asthma Clin. Immunol. 17 (1), 96. 10.1186/s13223-021-00597-4 (2021).
Narla, S. & Silverberg, J. I. Association between atopic dermatitis and autoimmune disorders in US adults and children: A cross-sectional study. J. Am. Acad. Dermatol. 80 (2), 382–389. 10.1016/j.jaad.2018.09.025 (2019).
Andersen, Y. M., Egeberg, A., Gislason, G. H., Skov, L. & Thyssen, J. P. Autoimmune diseases in adults with atopic dermatitis. J Am Acad Dermatol. ;76(2):274 – 80 e1. doi: (2017). 10.1016/j.jaad.2016.08.047
Yalachkov, Y. et al. C-Reactive Protein Levels and Gadolinium-Enhancing Lesions Are Associated With the Degree of Depressive Symptoms in Newly Diagnosed Multiple Sclerosis. Front. Neurol. 12, 719088. 10.3389/fneur.2021.719088 (2021).
Vekaria, A. S. et al. Moderate-to-severe atopic dermatitis patients show increases in serum C-reactive protein levels, correlating with skin disease activity. F1000Res. 6, 1712. 10.12688/f1000research.12422.2 (2017).
Tian, Y. et al. Maternal serum 25-hydroxyvitamin D levels and infant atopic dermatitis: A prospective cohort study. Pediatr. Allergy Immunol. 32 (8), 1637–1645. 10.1111/pai.13582 (2021).
Sirbe, C., Rednic, S., Grama, A. & Pop, T. L. An Update on the Effects of Vitamin D on the Immune System and Autoimmune Diseases. Int. J. Mol. Sci. 23 (17). 10.3390/ijms23179784 (2022).
Newland, P., Flick, L., Salter, A., Dixon, D. & Jensen, M. P. The link between smoking status and co-morbid conditions in individuals with multiple sclerosis (MS). Disabil. Health J. 10 (4), 587–591. 10.1016/j.dhjo.2017.03.005 (2017).
Pilz, A. C. et al. Atopic dermatitis: disease characteristics and comorbidities in smoking and non-smoking patients from the TREATgermany registry. J. Eur. Acad. Dermatol. Venereol. 36 (3), 413–421. 10.1111/jdv.17789 (2022).
Paternoster, L. et al. Multi-ancestry genome-wide association study of 21,000 cases and 95,000 controls identifies new risk loci for atopic dermatitis. Nat. Genet. 47 (12), 1449–1456. 10.1038/ng.3424 (2015).
Prajeeth, C. K. et al. Effectors of Th1 and Th17 cells act on astrocytes and augment their neuroinflammatory properties. J. Neuroinflammation. 14 (1), 204. 10.1186/s12974-017-0978-3 (2017).
Bozic, I., Savic, D. & Lavrnja, I. Astrocyte phenotypes: Emphasis on potential markers in neuroinflammation. Histol. Histopathol. 36 (3), 267–290. 10.14670/HH-18-284 (2021).
Balasa, R. et al. The action of TH17 cells on blood brain barrier in multiple sclerosis and experimental autoimmune encephalomyelitis. Hum. Immunol. 81 (5), 237–243. 10.1016/j.humimm.2020.02.009 (2020).
Moser, T., Akgun, K., Proschmann, U., Sellner, J. & Ziemssen, T. The role of TH17 cells in multiple sclerosis: Therapeutic implications. Autoimmun. Rev. 19 (10), 102647. 10.1016/j.autrev.2020.102647 (2020).
Czarnowicki, T. et al. Evolution of pathologic T-cell subsets in patients with atopic dermatitis from infancy to adulthood. J. Allergy Clin. Immunol. 145 (1), 215–228. 10.1016/j.jaci.2019.09.031 (2020).
Guglielmo, A. et al. The Role of Cytokines in Cutaneous T Cell Lymphoma: A Focus on the State of the Art and Possible Therapeutic Targets. Cells. 13 (7). 10.3390/cells13070584 (2024).
Krupka-Olek, M. et al. Potential Aspects of the Use of Cytokines in Atopic Dermatitis. Biomedicines. 12 (4). 10.3390/biomedicines12040867 (2024).
Noda, S. et al. 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).

No competing interests reported.

S1S3Table.pdf

Download PDF

Reviewers invited by journal
12 Sep, 2024
Editor assigned by journal
12 Sep, 2024
Editor invited by journal
28 Aug, 2024
Submission checks completed at journal
28 Aug, 2024
First submitted to journal
28 Aug, 2024

You are reading this latest preprint version

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

Status:

Version 1

Abstract

Objective

Methods

Results

Conclusions

Figures

Introduction

Methods

Data sources for exposure and outcome

MR assumptions and Instrumental Variables (IVs) selection

Statistical analysis

Results

The forward MR Analysis

The reverse MR Analysis

Sensitivity Analyses

The MVMR Analysis

Discussion

Conclusion

Declarations

Supplementary materials

Conflict of interest disclosure

Funding statement

Author Contribution

Acknowledgement

Data Availability

References

Additional Declarations

Supplementary Files

Status:

Version 1