Reflections and a mendelian randomization analysis of patients with vitiligo and pancreatic cancer

Reflections and a mendelian randomization analysis of patients with vitiligo and pancreatic cancer | 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 Research Article Reflections and a mendelian randomization analysis of patients with vitiligo and pancreatic cancer Xusheng Zhang, Shicai Liang, Xuebo Wang, Bendong Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6197588/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background Several special case of vitiligo patient with advanced pancreatic cancer was observed in our clinical practice, which prompted us to think about whether there is an association between the two diseases, and to further investigate whether there is a causal relationship between the two diseases, a two-sample Mendelian randomization analysis was performed. Methods In this study, two-sample Mendelian randomization (MR) analyses were performed using inverse variance weighted (IVW), weighted median, MR-Egger regression, Simple mode and Weighted mode. We used the publicly available Genome-wide association study (GWAS) summary statistics set on vitiligo of European origin (n = 333064; Neale Lab) as the exposed GWAS; Samples of pancreatic cancer from the East Asian Biobank (total = 196187; cases = 442, controls = 195745; Neale Lab) were used as outcome. Results We screened 19 single nucleotide polymorphisms (SNPs) with genome-wide significance from GWASs on vitiligo as instrumental variables (P < 5.00E-06; linkage disequilibrium r 2 < 0.01). Analysis of the results using various methods such as IVW, MR-Egger regression, Weighted median, Simple mode and Weighted mode did not support the existence of a causal relationship between vitiligo and pancreatic cancer (P > 0.05). Cochran's Q test and funnel plot showed no evidence of heterogeneity and asymmetry. And the intercept of MR-Egger analysis result = 0.017, P = 0.666 further suggests that there is no directional multiplicity of results. Conclusion The results of the MR analysis do not support a causal relationship between vitiligo and an increased risk of pancreatic cancer. pancreatic cancer vitiligo Mendelian randomization analysis inverse variance weighting MR-Egger Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Pancreatic cancer (PC) is one of the most malignant tumors in the gastrointestinal tract and one of the worst prognostic tumors, with a 5-year survival rate of about7-10% and a yearly increase in morbidity and mortality in China[ 1 ]. Although the development of imaging provides good diagnostic tools for the diagnosis of PC, the insidious onset of pancreatic cancer leads to the fact that the best time for treatment is often missed once the diagnosis is made. Although tumor markers such as CA199 have been widely used in clinical practice, their contribution to early screening for PC is not outstanding. The current status remains the lack of highly sensitive, and convenient early screening methods. Moreover, PC itself is highly heterogeneous, highly aggressive, highly metastatic, and highly drug-resistant, which exacerbates the difficulty of PC treatment[ 2 – 4 ]. Up to now, the etiology and mechanisms of PC have not been fully elucidated, but with continuous in-depth studies environmental, genetic, and epigenetic factors are associated with the occurrence and development of the disease[ 5 ]. The genetic factors directly or indirectly interact with environmental and epigenetic factors to cause PC, and numerous studies at the molecular level have demonstrated that PC occurs with a large number of mutations in driver genes including KRAS, CDKN2A, TP53 and SMAD and some potential mutated genes [ 6 – 8 ]. With the development of high-throughput sequencing and other technologies, there has been a breakthrough in immunotherapy and targeted therapy for PC, but up to now it still has not changed the status that PC has a very poor prognosis, based on this point it is still significant to continue to discover biological targets for the diagnosis and treatment of PC. Vitiligo is one of the autoimmune skin diseases characterized by white patches of skin that have lost functional melanocytes (the skin's pigment-producing cells). Vitiligo is a common skin disease with a worldwide morbidity of 0.1-2% and no gender bias, and its pathogenesis combines a combination of genetic susceptibility, environmental triggers producing inflammatory mediators, and autoimmune responses [ 9 , 10 ]. Studies at the immune level have found that the immune response in vitiligo patients is often over-activated and that the adaptive immune system is involved in this immune process in vitiligo through cytotoxic CD8 + T cells and melanocyte-specific antibodies, while the innate immune system has also been found to play a role in this process. But what causes its activation has not been clearly elucidated so far[ 11 , 12 ]. Mendelian randomization (MR) is a technique that uses genetic variation as instrumental variables (IVs) to assess whether observed associations between risk factors and outcomes are consistent with causal effects. Whether there is an association between vitiligo and pancreas does not seem to have caught the attention of our previous researchers. However, the frequent finding of patients diagnosed with vitiligo followed by a diagnosis of pancreatic cancer in our clinical practice has raised our concern. The question of whether having vitiligo may promote pancreatic carcinogenesis through certain immune mechanisms needs to be further explored. And considering the high level of evidence of Mendelian randomization and the convenience of the study, the present study firstly explored the association between the two based on MR, in order to be able to explain the issue and provide guidance to the clinic (Fig. 1 ). Materials and Methods Data sources and selection of genetic variants We searched the Open GWAS database (https://gwas.MRcieu.ac.uk/), which contains 767,100,42,351 genetic associations from 246,366 GWAS pooled datasets available for query and download. We used the publicly available GWAS pooled dataset on vitiligo in individuals of European ancestry (n = 333064; Neale Lab) as the exposure. Since the corresponding IVs were too few screened when the P-value threshold was 5.00E-08, therefore, in this study, we relaxed the P threshold to 5.00E-06 and used two-sample MR studies of genetic variants associated with vitiligo as IVs for causal association inference ( Figure 2 ). Finally, we obtained summary statistics (beta coefficients and standard errors) for 19 single nucleotide polymorphisms (SNPs) associated with vitiligo as IVs of vitiligo GWASs. We used the publicly available GWAS summary statistics set: pancreatic cancers in individuals from the East Asian Biobank (total = 196187; cases = 442, controls = 195745; Neale Lab) as the outcome. Statistical analysis of Mendelian randomization Mendelian randomization analyses require that genetic variants be strongly associated with exposure and free of potential confounders. Also, it must be required that IVs can only cause pancreatic cancer through infection with vitiligo. Therefore, we first assessed the independent associations of SNPs with vitiligo as well as exclusive associations. Secondly, we analyzed the association between each SNP and the risk of developing pancreatic cancer. Third, combining these factors, we used MR analysis to assess whether there was a direct causal relationship between vitiligo and pancreatic cancer. We used two samples for MR analysis, which is capable of more objectively estimating the causal effect of an exposure factor (vitiligo) on an outcome (pancreatic cancer). In this study, the inverse variance weighted (IVW) approach uses a meta-analysis method combined with the Wald ratio to evaluate the causal effects obtained from different SNPs and provides a consistently assessed result of the causal effect of exposure when each genetic variant meets the assumptions of IVs. Remarkably, whereas the inclusion of multiple variants in the MR analysis increases its ability to assess, it has the potential to include pleiotropic genetic variants which are invalid IVs. To explore and adjust for pleiotropy (that the association between genetic variance and multiple variables in this study), we used weighted median and MR-Egger regression methods. The MR-Egger regression analyses are robust to inefficient IVs, which account for the presence of unbalanced pleiotropy by introducing a parameter for this bias in the estimation of pooled data from the causal effects of multiple individual variables. MR-Egger performs a weighted linear regression on the results of the gene exposure coefficients. The slope of this regression line represents an estimate of the causal effect, and the intercept can be interpreted as an estimate of the average level of pleiotropic effect measured across genetic variants. The weighted median's calculation provides a more consistent estimate of causal effects, even if as much as 50% of the participants in the analysis have information from invalid genetic variants[13]. The weighted median algorithm has the advantage of sustaining higher confidence in the estimation compared to MR-Egger analysis. In addition, we used both Simple mode and Weighted mode to synergistically assess the causal associations between exposure factors and outcome factors to increase the objectivity of the results. The result of the test was considered statistically significant at P< 0.05. All MR analysis was further conducted out using the package "TwoSampleMR" of the R program (version 4.2.2). Heterogeneity and sensitivity tests The heterogeneity test is a method to quantitatively assess the magnitude of heterogeneity, which is to test for heterogeneity through statistical methods. We assessed the heterogeneity among SNPs using Cochran's Q statistic to determine the feasibility of the analysis and the reliability of the results. We also used the "leave-one-out" method for the sensitivity analysis, which aims to assess whether causality is driven by a single SNP[14, 15]. Results Instrumental variables for Mendelian randomization We obtained 19 independent SNPs as IVs from the GWASs of vitiligo after fine screening. All of them were strongly associated with vitiligo in a genome-wide sense. The P value was 5.00E- 06, which corresponds to an F statistic>10 for each single variable. A threshold of F≤10 was recognized to define "weak IVs". Thus, weak variable deviations were negligible ( Table 1 ). Table 1. List of filtered SNPs SNP Effect Exp. Other Exp. Effect Out. Other Out. beta. Exp. beta. Out. eaf. Exp. eaf. Out. rs10818477 G T G T -0.00086 0.07999 0.98175 0.28156 rs10990184 A T A T -0.00060 0.02735 0.97434 0.68570 rs11232059 A G A G -0.00030 -0.13959 0.86642 0.20974 rs11629402 G A G A -0.00025 0.07583 0.82534 0.06278 rs117472051 C T C T -0.00090 -0.11838 0.98791 0.03257 rs12323804 T C T C -0.00173 0.01775 0.99706 0.05287 rs145895046 G A G A -0.00123 -0.02057 0.99311 0.00568 rs146829879 C T C T -0.00115 0.03822 0.99137 0.09930 rs1806655 C T C T -0.00031 0.03294 0.90012 0.08360 rs2075873 A G A G -0.00055 -0.09344 0.95919 0.30710 rs463001 G A G A 0.00033 0.08076 0.09179 0.88940 rs56320330 A T A T -0.00036 0.22945 0.91474 0.94368 rs60208962 A C A C -0.00255 -0.11995 0.99842 0.09054 rs72809107 A G A G -0.00046 0.06603 0.95434 0.29846 rs7624433 T C T C -0.00079 -0.31040 0.98430 0.04490 rs7652012 T C T C -0.00043 -0.06520 0.94840 0.35139 rs78850272 T A T A -0.00149 0.05371 0.99582 0.82114 rs79504270 G C G C -0.00229 0.02126 0.99828 0.92450 rs8049689 G A G A -0.00180 0.33042 0.99644 0.00248 The results of Mendelian randomization We used 5 popular algorithms to assess whether there is a causal relationship between vitiligo and pancreatic cancer.The results of the analysis of all 5 algorithms are as follows: The IVW method analysis showed no evidence of causality between vitiligo and pancreatic cancer (beta = 3.188, se = 24.571, P = 0.897). The intercepts represent the effect of the average multiplicity of the entire genetic variance (the average direct effect of the variant on the outcome). A nonzero intercept (MR-Egger test) suggests the presence of directional pleiotropy. Whereas in the present study MR-Egger regression analyses showed that directional pleiotropy was unlikely to introduce a large bias in the results (intercept = 0.017; P = 0.666, Supplementary Material Table S1). The results of MR-Egger analysis similarly showed that there did not have a causal relationship that seemed to exist between vitiligo and pancreatic cancer (beta = -11.216, se = 40.943, P = 0.787). The Weighted median method also yielded no significant evidence of a causal relationship between vitiligo and pancreatic cancer (beta = -9.807, se = 32.894, P = 0.766). The Simple mode method also failed to demonstrate a causal relationship between vitiligo and pancreatic cancer (beta = -28.254, se = 48.823, P = 0.570). The Weighted mode method also showed no evidence of a causal relationship between vitiligo and pancreatic cancer (beta = -8.333, se = 32.700, P = 0.802) . Taken together, and combining the results calculated by the five different algorithms, there does seem to be no causal association between vitiligo and pancreatic cancer. Firstly there is an opposite causal trend between the results calculated by IVW and those calculated by the remaining 4 algorithms ( Table 2 ). Secondly the direction of vitiligo as an exposure factor on pancreatic cancer was not consistent between the results of different algorithms, and the results calculated by all algorithms were not statistically meaningful (P>0.05), further suggesting that there is no causal relationship between vitiligo and pancreatic cancer ( Figure 3. A, B ). Table 2. Results of causality between vitiligo and pancreatic cancer Exposure method nSNP B SE P vitiligo Inverse variance weighted 19 3.188 24.571 0.897 vitiligo MR Egger 19 -11.216 40.943 0.787 vitiligo Weighted median 19 -9.808 32.894 0.766 vitiligo Simple mode 19 -28.254 48.823 0.570 vitiligo Weighted mode 19 -8.334 32.700 0.802 Heterogeneity and sensitivity test Heterogeneity (Heterogeneity) refers to the fact that some things differ in some characteristics (i.e., consistency of causal estimates across all SNPs). The Cochran's Q test for the IVW/MR-Egger method showed no evidence of heterogeneity between estimates based on individual variation (P>0.05, Table 3 ). Lower heterogeneity indicated increased reliability of the MR estimates. The sensitivity of the results was investigated using the "leave-one-out" algorithm. Similar to the meta-analysis, we removed individual SNPs one by one and calculated the effect of the remaining SNPs by the IVW method, through which we investigated the impact of individual SNPs on the causal inference, and did not find any abnormal SNPs ( Figure 3. A. ). And in the funnel plot, we further find that the distribution is essentially symmetric on both sides, indicating that there is no multiplicity of effects, which consequently suggests that the MR analysis method produces almost negligible bias ( Figure 3. B. ). Table 3. The results of heterogeneity test Outcome Exposure Method Q Q_df Q_pval PC vitiligo MR Egger 15.538 17 0.557 PC vitiligo IVW 15.731 18 0.611 Discussion We found a patient suffering from advanced pancreatic cancer in the process of clinical diagnosis and treatment. And physical examination revealed that the patient had large white patches on his head and hands, and a history review revealed that the patient had been diagnosed with vitiligo 40 years ago and had not been treated regularly during the period of illness. The patient was recently admitted to the clinic with progressively worsening jaundice, and was finally diagnosed with advanced pancreatic cancer after evaluation of relevant imaging examinations and testing of the levels of laboratory tumor markers. Due to the existence of a temporal sequence in the diagnosis of the two diseases, the long term role of vitiligo as an autoimmune related disease gave us reason to suspect that it has the potential to promote the development of pancreatic cancer. Therefore whether vitiligo is a risk factor for pancreatic cancer and contributes to the pathogenesis of pancreatic cancer has generated a great deal of interest and desire for investigation. A search of the literature revealed that not many studies have been conducted to explore whether vitiligo is associated with the development of cancer, but it seems that some scholars have paid attention to this problem. For example, in a retrospective cohort study, the investigators included 13,824 patients with vitiligo and propensity matched 55,296 reference subjects without vitiligo, and examined whether there was a difference in total cancer incidence between the vitiligo and reference groups over a 16-year period. However, contrary to our initial suspicion, their results showed that patients with vitiligo had a significantly lower risk of developing basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), as well as internal malignancies, compared with reference subjects without vitiligo[ 16 ]. And in another similar study, it was also mentioned that patients with vitiligo had a significantly lower risk of colon, rectal, ovarian and lung cancers[ 17 ]. In the only study involving vitiligo and pancreatic cancer, researchers explored the association between vitiligo and the development of 18 types of cancer, and the findings suggest that vitiligo may be a protective factor for a number of cancers, including lung, breast, ovarian, and melanoma, but no causal relationship was concluded between vitiligo and pancreatic cancer[ 18 ]. Although vitiligo, which is characterized by loss of melanocytes leading to white patches, does not seem to be closely related to melanoma, however, there are some reports mentioning that it can still be secondary to melanoma in situ[ 19 , 20 ]. In addition, some reports have mentioned that vitiligo often appears as a symptom after targeted therapy or chemotherapy for other cancers, and its pathological mechanism has not been clarified as of this moment. And In these cases, it is not clear whether the disease itself is responsible for the susceptibility to vitiligo, or whether the drug treatment has contributed to the development of vitiligo[ 21 , 22 ]. Because there are so few studies exploring the effect of vitiligo on pancreatic cancer, no firm conclusions have been reached on this issue. And we are lucky enough to meet patients with coexisting vitiligo and pancreatic cancer in our clinical work. This has led us to be extremely skeptical about whether vitiligo promotes pancreatic cancer, and we are eager to explore this question. Whether the prolonged effects of vitiligo, as a type of autoimmune-related disease, are associated with the development of pancreatic cancer has not been clarified clearly up to now. Based on this background, we conducted a two-sample Mendelian randomization analysis with vitiligo as an exposure factor and pancreatic cancer as an outcome in an attempt to investigate whether there is a causal relationship between the two diseases. In the study, we used five different assessment methods (inverse-variance weighted method, weighted median method, MR-Egger regression, Simple mode and Weighted mode) to perform MR analysis. Our results showed that the results analyzed using IVW were in the opposite causal trend to those analyzed by the weighted median method, MR-Egger regression, Simple mode, and Weighted mode, and that the results analyzed by the five analytical methods had a P > 0.05. Therefore, our results greatly confirm that there may be no causal relationship between long-term exposure of vitiligo and the development of pancreatic cancer. And our clinical observation that the patient was diagnosed with pancreatic cancer after 40 years of vitiligo was simply a co-existing condition that did not have a causal association and was not generalizable. The presence of genetic variants associated with multiple phenotypes, a phenomenon defined as "pleiotropy", can lead to confounding of the results of MR calculations, which can further lead to highly biased causal estimates. Because the inclusion of multiple variants in MR analyses often enlarges statistical power, this can lead to the inclusion of some invalid polyvalent genetic variants, which in fact do not play a relevant role. Therefore, in order to test the validity of the conclusions of the MR analysis while eliminating pleiotropy, we introduced Weighted median in this analysis, which ensures the validity of the results even if half of the SNPs are invalid tools. We also tested for unbalanced pleiotropy using MR-Egger regression and estimated the effect on the results when it was present. Our results were not uniform across all five methods and were not statistically significant. Our analysis more fully elucidates that vitiligo as an exposure factor does not appear to be causally associated with the development of pancreatic cancer. It is important to reflect on the limitations of this study. First, genetic variants have only a modest effect on a specific exposure (vitiligo), as they may only explain part of the variation in proportions in a given exposure. The results of our analysis are more affected by this. Second, the study on vitiligo and pancreatic cancer was based on participants of European and East Asian ancestry in the same laboratory. Since causality may depend on ethnicity and selection bias, further MR studies in other populations are needed. In conclusion, the present study is more objectively illustrate, through MR analysis, that vitiligo as an exposure factor is not clearly causally associated with the development of pancreatic cancer. The question is answered for researchers who are confused about the situation. Declarations Ethics approval and consent to participate Not applicable to this study. Consent for publication Not applicable to this research. Data Availability The data used in this research were obtained from Open GWAS database (https://gwas.MRcieu.ac.uk/), which contains 767,100,42,351 genetic associations from 246,366 GWAS pooled datasets available for query and download. all of which are available in publicly available databases. This study complies with its data use and publication rules. Conflicts of Interest We declare that there is no conflict of interest regarding the publication of this paper. Acknowledgement Not applicable to our study. Funding Statement No funding support for present study. Author Details Xusheng Zhang 1† : Ningxia Medical University, Yinchuan 750004, China, Email: [email protected] . Shicai Liang 1† : Ningxia Medical University, Yinchuan 750004, China, Email: [email protected] . Xuebo Wang 1 :Ningxia Medical University, Yinchuan 750004, China, Email: [email protected] . Bendong Chen *1,2 : General Hospital of Ningxia Medical University, Yinchuan 750004, China, Email: [email protected] . Authors’ contributions ZX and LS contributed equally. ZX, LS and CB participated in the conception and design of the study. ZX and WX organized the database and statistical analysis. CB, LS and LK divided the work and participated in the picture drawing. ZX wrote the first draft of the manuscript. CB and LS participated in the revision of the manuscript. All authors read and agreed to the final manuscript and authorship arrangement. References Duan Y, Du Y, Gu Z, Zheng X, Wang C: Prognostic Value, Immune Signature, and Molecular Mechanisms of the PHLDA Family in Pancreatic Adenocarcinoma . INT J MOL SCI 2022, 23 (18). King G, Ittershagen S, He L, Shen Y, Li F, Villacorta R: Treatment Patterns in US Patients Receiving First-Line and Second-Line Therapy for Metastatic Pancreatic Ductal Adenocarcinoma in the Real World . ADV THER 2022, 39 (12):5433-5452. Pancreatic Neuroendocrine Tumors (Islet Cell Tumors) Treatment (PDQ(R)): Patient Version . 2002. Pilgrim C, Maciejewska A, Ayres N, Ellis S, Goodwin M, Zalcberg JR, Haydon A: Synoptic CT scan reporting of pancreatic adenocarcinoma to align with international consensus guidelines on surgical resectability: a Victorian pilot . ANZ J SURG 2022, 92 (10):2565-2570. Storz P, Crawford HC: Carcinogenesis of Pancreatic Ductal Adenocarcinoma . GASTROENTEROLOGY 2020, 158 (8):2072-2081. Biankin AV, Waddell N, Kassahn KS, Gingras MC, Muthuswamy LB, Johns AL, Miller DK, Wilson PJ, Patch AM, Wu J et al : Pancreatic cancer genomes reveal aberrations in axon guidance pathway genes . NATURE 2012, 491 (7424):399-405. Witkiewicz AK, McMillan EA, Balaji U, Baek G, Lin WC, Mansour J, Mollaee M, Wagner KU, Koduru P, Yopp A et al : Whole-exome sequencing of pancreatic cancer defines genetic diversity and therapeutic targets . NAT COMMUN 2015, 6 :6744. Ilic M, Ilic I: Epidemiology of pancreatic cancer . WORLD J GASTROENTERO 2016, 22 (44):9694-9705. Bergqvist C, Ezzedine K: Vitiligo: A Review . DERMATOLOGY 2020, 236 (6):571-592. Bergqvist C, Ezzedine K: Vitiligo: A focus on pathogenesis and its therapeutic implications . J DERMATOL 2021, 48 (3):252-270. Boniface K, Taieb A, Seneschal J: New insights into immune mechanisms of vitiligo . GIORN ITAL DERMAT V 2016, 151 (1):44-54. Post NF, Ginski G, Peters R, Van Uden N, Bekkenk MW, Wolkerstorfer A, Netea MG, Luiten RM: Trained immunity in the pathogenesis of vitiligo . PIGM CELL MELANOMA R 2023, 36 (5):348-354. Bowden J, Davey SG, Burgess S: Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression . INT J EPIDEMIOL 2015, 44 (2):512-525. Bowden J, Del GMF, Minelli C, Davey SG, Sheehan NA, Thompson JR: Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic . INT J EPIDEMIOL 2016, 45 (6):1961-1974. Higgins JP, Thompson SG: Quantifying heterogeneity in a meta-analysis . STAT MED 2002, 21 (11):1539-1558. Weng YC, Ho HJ, Chang YL, Chang YT, Wu CY, Chen YJ: Reduced risk of skin cancer and internal malignancies in vitiligo patients: a retrospective population-based cohort study in Taiwan . SCI REP-UK 2021, 11 (1):20195. Bae JM, Chung KY, Yun SJ, Kim H, Park BC, Kim JS, Seo SH, Ahn HH, Lee DY, Kim YC et al : Markedly Reduced Risk of Internal Malignancies in Patients With Vitiligo: A Nationwide Population-Based Cohort Study . J CLIN ONCOL 2019, 37 (11):903-911. Wen Y, Wu X, Peng H, Li C, Jiang Y, Liang H, Zhong R, Liu J, He J, Liang W: Cancer risks in patients with vitiligo: a Mendelian randomization study . J CANCER RES CLIN 2020, 146 (8):1933-1940. Frydkjaer AG, Olivarius FF, Moller MP: [Not Available] . Ugeskr Laeger 2023, 185 (2). Lugo-Somolinos A, Sanchez JL, Garcia ME: Vitiligo-like primary melanoma . AM J DERMATOPATH 2008, 30 (5):451-454. Nemovi K, Jamali A, Matinpour K, Dasanu CA: Widespread vitiligo and poliosis following ipilimumab-nivolumab combination therapy . J ONCOL PHARM PRACT 2023, 29 (5):1278-1282. Chan OB, Su JC, Yazdabadi A, Chan A: Drug induced vitiligo-like depigmentation from a CDK 4/6 inhibitor . ASIA-PAC J CLIN ONCO 2022, 18 (2):e154-e156. Additional Declarations No competing interests reported. Supplementary Files TableS1.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 21 Mar, 2025 Editor assigned by journal 11 Mar, 2025 Submission checks completed at journal 11 Mar, 2025 First submitted to journal 10 Mar, 2025 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-6197588","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":427195903,"identity":"4c35d190-b94d-4ca7-97e0-6630f7d662d1","order_by":0,"name":"Xusheng Zhang","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xusheng","middleName":"","lastName":"Zhang","suffix":""},{"id":427195904,"identity":"df316837-bc22-49b3-ad69-027291567146","order_by":1,"name":"Shicai Liang","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shicai","middleName":"","lastName":"Liang","suffix":""},{"id":427195905,"identity":"042e573c-4a6a-4630-8458-c6c8ac2ef8f8","order_by":2,"name":"Xuebo Wang","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xuebo","middleName":"","lastName":"Wang","suffix":""},{"id":427195906,"identity":"1c68544a-bfe4-4505-839c-a2a0f63109cc","order_by":3,"name":"Bendong Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYDACCQYGZiDF2ADED6BiBkRrYYYpJV4LmwRRWvhnNx97XFBxR7Zfuv1adWHbtsQG9uZtEgw1d3BbcudYuvGMM8+MZ845U3Z7xpnbiQ08x8okGI49w6nFQCLHTJq37XDihhs5abd5KoBagCISjA2H8WjJ/ybN+w+ipZjHAKhF/g0hLTls0rwNIC3px5ghtvDg1yJxI81Mesaxw8YzZ+QwS/OcuW3cxpNWbJFwDLcW/hnJz6QLag7L9kukP/zM23Zbtp/98MYbH2pwa0ECPJDoYAMRCcRoYGBgf0CculEwCkbBKBhxAAAFDVc3prPbwQAAAABJRU5ErkJggg==","orcid":"","institution":"General hospital of Ningxia Medicine University","correspondingAuthor":true,"prefix":"","firstName":"Bendong","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-03-10 17:42:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6197588/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6197588/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78662737,"identity":"989e5c6f-434a-47a9-b3e7-6fb469f5b635","added_by":"auto","created_at":"2025-03-17 10:31:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":143063,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic diagram of the MR analysis for this study.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6197588/v1/76477a3c4ae434e21e05d9fe.png"},{"id":78663683,"identity":"e281332e-4d7d-4428-810b-969432b33fa0","added_by":"auto","created_at":"2025-03-17 10:39:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":74622,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eManhattan plot of screened IVs.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe red line is at the P threshold of 5.00E-06, and above the red line are the preliminary screened IVs, which need to be further screened . Chr 1-22 are the chromosomes corresponding to SNPs.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6197588/v1/e54004a49a1fe10396364767.png"},{"id":78663684,"identity":"c02e66e2-3a91-4cda-a900-966dcec0b074","added_by":"auto","created_at":"2025-03-17 10:39:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":87303,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResults of MR.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003eForest plot of causal effects of SNPs associated with vitiligo on pancreatic cancer. The red lines show the MR results of MR-Egger test and IVW method, respectively. \u003cstrong\u003eB.\u003c/strong\u003e Scatterplot of the genetic association between vitiligo and pancreatic cancer. The slope of each line represents the causal association derived from different algorithms.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6197588/v1/6f95a375fa39f11fc816138f.png"},{"id":78662735,"identity":"32a38e11-57d8-4acc-9a9e-bc84fe610594","added_by":"auto","created_at":"2025-03-17 10:31:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":93820,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 3. Heterogeneity and sensitivity test.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e Forest plots for \"leave-one-out\" analysis. \u003cstrong\u003eB. \u003c/strong\u003eUsing funnel plots to assess heterogeneity. The blue line represents the inverse variance weighted estimate and the dark blue line represents the MR-Egger estimate.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6197588/v1/e2160a716e5e47173565f753.png"},{"id":78664753,"identity":"d2fd42a4-cdf5-4608-b53e-0a2127d9d536","added_by":"auto","created_at":"2025-03-17 10:55:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1972306,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6197588/v1/afec77c2-3c75-4813-b05d-c70cec4ea310.pdf"},{"id":78662733,"identity":"55918b0c-6d1f-4e45-a9e2-92cc251e7f28","added_by":"auto","created_at":"2025-03-17 10:31:11","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11453,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6197588/v1/834a15bb0eadc1aeffadf42e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eReflections and a mendelian randomization analysis of patients with vitiligo and pancreatic cancer\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePancreatic cancer (PC) is one of the most malignant tumors in the gastrointestinal tract and one of the worst prognostic tumors, with a 5-year survival rate of about7-10% and a yearly increase in morbidity and mortality in China[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Although the development of imaging provides good diagnostic tools for the diagnosis of PC, the insidious onset of pancreatic cancer leads to the fact that the best time for treatment is often missed once the diagnosis is made. Although tumor markers such as CA199 have been widely used in clinical practice, their contribution to early screening for PC is not outstanding. The current status remains the lack of highly sensitive, and convenient early screening methods. Moreover, PC itself is highly heterogeneous, highly aggressive, highly metastatic, and highly drug-resistant, which exacerbates the difficulty of PC treatment[\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Up to now, the etiology and mechanisms of PC have not been fully elucidated, but with continuous in-depth studies environmental, genetic, and epigenetic factors are associated with the occurrence and development of the disease[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The genetic factors directly or indirectly interact with environmental and epigenetic factors to cause PC, and numerous studies at the molecular level have demonstrated that PC occurs with a large number of mutations in driver genes including KRAS, CDKN2A, TP53 and SMAD and some potential mutated genes [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. With the development of high-throughput sequencing and other technologies, there has been a breakthrough in immunotherapy and targeted therapy for PC, but up to now it still has not changed the status that PC has a very poor prognosis, based on this point it is still significant to continue to discover biological targets for the diagnosis and treatment of PC.\u003c/p\u003e \u003cp\u003eVitiligo is one of the autoimmune skin diseases characterized by white patches of skin that have lost functional melanocytes (the skin's pigment-producing cells). Vitiligo is a common skin disease with a worldwide morbidity of 0.1-2% and no gender bias, and its pathogenesis combines a combination of genetic susceptibility, environmental triggers producing inflammatory mediators, and autoimmune responses [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Studies at the immune level have found that the immune response in vitiligo patients is often over-activated and that the adaptive immune system is involved in this immune process in vitiligo through cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells and melanocyte-specific antibodies, while the innate immune system has also been found to play a role in this process. But what causes its activation has not been clearly elucidated so far[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) is a technique that uses genetic variation as instrumental variables (IVs) to assess whether observed associations between risk factors and outcomes are consistent with causal effects.\u003c/p\u003e \u003cp\u003eWhether there is an association between vitiligo and pancreas does not seem to have caught the attention of our previous researchers. However, the frequent finding of patients diagnosed with vitiligo followed by a diagnosis of pancreatic cancer in our clinical practice has raised our concern. The question of whether having vitiligo may promote pancreatic carcinogenesis through certain immune mechanisms needs to be further explored. And considering the high level of evidence of Mendelian randomization and the convenience of the study, the present study firstly explored the association between the two based on MR, in order to be able to explain the issue and provide guidance to the clinic (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eData sources and selection of genetic variants\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eWe searched the Open GWAS database (https://gwas.MRcieu.ac.uk/), which contains 767,100,42,351 genetic associations from 246,366 GWAS pooled datasets available for query and download. We used the publicly available GWAS pooled dataset on vitiligo in individuals of European ancestry (n = 333064; Neale Lab) as the exposure. Since the corresponding IVs were too few screened when the P-value threshold was 5.00E-08, therefore, in this study, we relaxed the P threshold to 5.00E-06 and used two-sample MR studies of genetic variants associated with vitiligo as IVs for causal association inference (\u003cstrong\u003eFigure 2\u003c/strong\u003e). Finally, we obtained summary statistics (beta coefficients and standard errors) for 19 single nucleotide polymorphisms (SNPs) associated with vitiligo as IVs of vitiligo GWASs. We used the publicly available GWAS summary statistics set: pancreatic cancers in individuals from the East Asian Biobank (total = 196187; cases = 442, controls = 195745; Neale Lab) as the outcome.\u003c/p\u003e\n\u003col start=\"2\"\u003e\n \u003cli\u003e\u003cstrong\u003eStatistical analysis of Mendelian randomization\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eMendelian randomization analyses require that genetic variants be strongly associated with exposure and free of potential confounders. Also, it must be required that IVs can only cause pancreatic cancer through infection with vitiligo. Therefore, we first assessed the independent associations of SNPs with vitiligo as well as exclusive associations. Secondly, we analyzed the association between each SNP and the risk of developing pancreatic cancer. Third, combining these factors, we used MR analysis to assess whether there was a direct causal relationship between vitiligo and pancreatic cancer. We used two samples for MR analysis, which is capable of more objectively estimating the causal effect of an exposure factor (vitiligo) on an outcome (pancreatic cancer).\u003c/p\u003e\n\u003cp\u003eIn this study, the inverse variance weighted (IVW) approach uses a meta-analysis method combined with the Wald ratio to evaluate the causal effects obtained from different SNPs and provides a consistently assessed result of the causal effect of exposure when each genetic variant meets the assumptions of IVs. Remarkably, whereas the inclusion of multiple variants in the MR analysis increases its ability to assess, it has the potential to include pleiotropic genetic variants which are invalid IVs. To explore and adjust for pleiotropy (that the association between genetic variance and multiple variables in this study), we used weighted median and MR-Egger regression methods. The MR-Egger regression analyses are robust to inefficient IVs, which account for the presence of unbalanced pleiotropy by introducing a parameter for this bias in the estimation of pooled data from the causal effects of multiple individual variables. MR-Egger performs a weighted linear regression on the results of the gene exposure coefficients. The slope of this regression line represents an estimate of the causal effect, and the intercept can be interpreted as an estimate of the average level of pleiotropic effect measured across genetic variants. The weighted median\u0026apos;s calculation provides a more consistent estimate of causal effects, even if as much as 50% of the participants in the analysis have information from invalid genetic variants[13]. The weighted median algorithm has the advantage of sustaining higher confidence in the estimation compared to MR-Egger analysis. In addition, we used both Simple mode and Weighted mode to synergistically assess the causal associations between exposure factors and outcome factors to increase the objectivity of the results. The result of the test was considered statistically significant at P\u0026lt; 0.05. All MR analysis was further conducted out using the package \u0026quot;TwoSampleMR\u0026quot; of the R program (version 4.2.2).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003col start=\"3\"\u003e\n \u003cli\u003e\u003cstrong\u003eHeterogeneity and sensitivity tests\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe heterogeneity test is a method to quantitatively assess the magnitude of heterogeneity, which is to test for heterogeneity through statistical methods. We assessed the heterogeneity among SNPs using Cochran\u0026apos;s Q statistic to determine the feasibility of the analysis and the reliability of the results. We also used the \u0026quot;leave-one-out\u0026quot; method for the sensitivity analysis, which aims to assess whether causality is driven by a single SNP[14, 15].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eInstrumental variables for Mendelian randomization\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eWe obtained 19 independent SNPs as IVs from the GWASs of vitiligo after fine screening. All of them were strongly associated with vitiligo in a genome-wide sense. The P value was 5.00E- 06, which corresponds to an F statistic\u0026gt;10 for each single variable. A threshold of F\u0026le;10 was recognized to define \u0026quot;weak IVs\u0026quot;. Thus, weak variable deviations were negligible (\u003cstrong\u003eTable 1\u003c/strong\u003e).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eList of filtered SNPs \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"557\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEffect\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eExp.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOther\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eExp.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEffect\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eOut.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOther\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eOut.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ebeta.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eExp.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ebeta.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eOut.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eeaf.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eExp.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eeaf.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eOut.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003ers10818477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.00086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.07999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.98175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.28156\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003ers10990184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.00060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.02735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.97434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.68570\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003ers11232059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.00030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.13959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.86642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.20974\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003ers11629402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.00025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.07583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.82534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.06278\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003ers117472051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.00090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.11838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.98791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.03257\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003ers12323804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.00173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.01775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.99706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.05287\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003ers145895046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.00123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.02057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.99311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.00568\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003ers146829879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.00115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.03822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.99137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.09930\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003ers1806655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.00031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.03294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.90012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.08360\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003ers2075873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.00055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.09344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.95919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.30710\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003ers463001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.00033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.08076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.09179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.88940\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003ers56320330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.00036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.22945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.91474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.94368\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003ers60208962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.00255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.11995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.99842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.09054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003ers72809107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.00046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.06603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.95434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.29846\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003ers7624433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.00079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.31040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.98430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.04490\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003ers7652012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.00043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.06520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.94840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.35139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003ers78850272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.00149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.05371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.99582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.82114\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003ers79504270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.00229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.02126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.99828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.92450\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003ers8049689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.00180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.33042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.99644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.00248\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003col start=\"2\"\u003e\n \u003cli\u003e\u003cstrong\u003eThe results of Mendelian randomization\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eWe used 5 popular algorithms to assess whether there is a causal relationship between vitiligo and pancreatic cancer.The results of the analysis of all 5 algorithms are as follows:\u003c/p\u003e\n\u003cp\u003eThe IVW method analysis showed no evidence of causality between vitiligo and pancreatic cancer (beta = 3.188, se = 24.571, P = 0.897). The intercepts represent the effect of the average multiplicity of the entire genetic variance (the average direct effect of the variant on the outcome). A nonzero intercept (MR-Egger test) suggests the presence of directional pleiotropy. Whereas in the present study MR-Egger regression analyses showed that directional pleiotropy was unlikely to introduce a large bias in the results (intercept = 0.017; P = 0.666, Supplementary Material Table S1). The results of MR-Egger analysis similarly showed that there did not have a causal relationship that seemed to exist between vitiligo and pancreatic cancer (beta = -11.216, se = 40.943, P = 0.787). The Weighted median method also yielded no significant evidence of a causal relationship between vitiligo and pancreatic cancer (beta = -9.807, se = 32.894, P = 0.766). The Simple mode method also failed to demonstrate a causal relationship between vitiligo and pancreatic cancer (beta = -28.254, se = 48.823, P = 0.570). The Weighted mode method also showed no evidence of a causal relationship between vitiligo and pancreatic cancer (beta = -8.333, se = 32.700, P = 0.802) .\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTaken together, and combining the results calculated by the five different algorithms, there does seem to be no causal association between vitiligo and pancreatic cancer. Firstly there is an opposite causal trend between the results calculated by IVW and those calculated by the remaining 4 algorithms (\u003cstrong\u003eTable 2\u003c/strong\u003e). Secondly the direction of vitiligo as an exposure factor on pancreatic cancer was not consistent between the results of different algorithms, and the results calculated by all algorithms were not statistically meaningful (P\u0026gt;0.05), further suggesting that there is no causal relationship between vitiligo and pancreatic cancer (\u003cstrong\u003eFigure 3. A, B\u003c/strong\u003e).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Results of causality between vitiligo and pancreatic cancer\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"549\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.9363%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExposure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.0378%;\"\u003e\n \u003cp\u003e\u003cstrong\u003emethod\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7692%;\"\u003e\n \u003cp\u003e\u003cstrong\u003enSNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0548%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8801%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4993%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.5072%;\"\u003e\n \u003cp\u003evitiligo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.4262%;\"\u003e\n \u003cp\u003eInverse variance weighted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7692%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0548%;\"\u003e\n \u003cp\u003e3.188\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8801%;\"\u003e\n \u003cp\u003e24.571\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4993%;\"\u003e\n \u003cp\u003e0.897\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.5072%;\"\u003e\n \u003cp\u003evitiligo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.4262%;\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7692%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0548%;\"\u003e\n \u003cp\u003e-11.216\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8801%;\"\u003e\n \u003cp\u003e40.943\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4993%;\"\u003e\n \u003cp\u003e0.787\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.5072%;\"\u003e\n \u003cp\u003evitiligo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.4262%;\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7692%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0548%;\"\u003e\n \u003cp\u003e-9.808\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8801%;\"\u003e\n \u003cp\u003e32.894\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4993%;\"\u003e\n \u003cp\u003e0.766\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.5072%;\"\u003e\n \u003cp\u003evitiligo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.4262%;\"\u003e\n \u003cp\u003eSimple mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7692%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0548%;\"\u003e\n \u003cp\u003e-28.254\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8801%;\"\u003e\n \u003cp\u003e48.823\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4993%;\"\u003e\n \u003cp\u003e0.570\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.5072%;\"\u003e\n \u003cp\u003evitiligo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.4262%;\"\u003e\n \u003cp\u003eWeighted mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7692%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0548%;\"\u003e\n \u003cp\u003e-8.334\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8801%;\"\u003e\n \u003cp\u003e32.700\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4993%;\"\u003e\n \u003cp\u003e0.802\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003col start=\"3\"\u003e\n \u003cli\u003e\u003cstrong\u003eHeterogeneity and sensitivity test\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eHeterogeneity (Heterogeneity) refers to the fact that some things differ in some characteristics (i.e., consistency of causal estimates across all SNPs). The Cochran\u0026apos;s Q test for the IVW/MR-Egger method showed no evidence of heterogeneity between estimates based on individual variation (P\u0026gt;0.05, \u003cstrong\u003eTable 3\u003c/strong\u003e). Lower heterogeneity indicated increased reliability of the MR estimates.\u003c/p\u003e\n\u003cp\u003eThe sensitivity of the results was investigated using the \u0026quot;leave-one-out\u0026quot; algorithm. Similar to the meta-analysis, we removed individual SNPs one by one and calculated the effect of the remaining SNPs by the IVW method, through which we investigated the impact of individual SNPs on the causal inference, and did not find any abnormal SNPs (\u003cstrong\u003eFigure 3. A.\u003c/strong\u003e). And in the funnel plot, we further find that the distribution is essentially symmetric on both sides, indicating that there is no multiplicity of effects, which consequently suggests that the MR analysis method produces almost negligible bias (\u003cstrong\u003eFigure 3. B.\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003cstrong\u003eTable 3.\u003c/strong\u003e The results of heterogeneity test\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"529\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExposure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMethod\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ_df\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ_pval\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003ePC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003evitiligo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e15.538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.557\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003ePC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003evitiligo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e15.731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.611\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe found a patient suffering from advanced pancreatic cancer in the process of clinical diagnosis and treatment. And physical examination revealed that the patient had large white patches on his head and hands, and a history review revealed that the patient had been diagnosed with vitiligo 40 years ago and had not been treated regularly during the period of illness. The patient was recently admitted to the clinic with progressively worsening jaundice, and was finally diagnosed with advanced pancreatic cancer after evaluation of relevant imaging examinations and testing of the levels of laboratory tumor markers.\u003c/p\u003e \u003cp\u003eDue to the existence of a temporal sequence in the diagnosis of the two diseases, the long term role of vitiligo as an autoimmune related disease gave us reason to suspect that it has the potential to promote the development of pancreatic cancer. Therefore whether vitiligo is a risk factor for pancreatic cancer and contributes to the pathogenesis of pancreatic cancer has generated a great deal of interest and desire for investigation. A search of the literature revealed that not many studies have been conducted to explore whether vitiligo is associated with the development of cancer, but it seems that some scholars have paid attention to this problem. For example, in a retrospective cohort study, the investigators included 13,824 patients with vitiligo and propensity matched 55,296 reference subjects without vitiligo, and examined whether there was a difference in total cancer incidence between the vitiligo and reference groups over a 16-year period. However, contrary to our initial suspicion, their results showed that patients with vitiligo had a significantly lower risk of developing basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), as well as internal malignancies, compared with reference subjects without vitiligo[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. And in another similar study, it was also mentioned that patients with vitiligo had a significantly lower risk of colon, rectal, ovarian and lung cancers[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In the only study involving vitiligo and pancreatic cancer, researchers explored the association between vitiligo and the development of 18 types of cancer, and the findings suggest that vitiligo may be a protective factor for a number of cancers, including lung, breast, ovarian, and melanoma, but no causal relationship was concluded between vitiligo and pancreatic cancer[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Although vitiligo, which is characterized by loss of melanocytes leading to white patches, does not seem to be closely related to melanoma, however, there are some reports mentioning that it can still be secondary to melanoma in situ[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In addition, some reports have mentioned that vitiligo often appears as a symptom after targeted therapy or chemotherapy for other cancers, and its pathological mechanism has not been clarified as of this moment. And In these cases, it is not clear whether the disease itself is responsible for the susceptibility to vitiligo, or whether the drug treatment has contributed to the development of vitiligo[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBecause there are so few studies exploring the effect of vitiligo on pancreatic cancer, no firm conclusions have been reached on this issue. And we are lucky enough to meet patients with coexisting vitiligo and pancreatic cancer in our clinical work. This has led us to be extremely skeptical about whether vitiligo promotes pancreatic cancer, and we are eager to explore this question. Whether the prolonged effects of vitiligo, as a type of autoimmune-related disease, are associated with the development of pancreatic cancer has not been clarified clearly up to now. Based on this background, we conducted a two-sample Mendelian randomization analysis with vitiligo as an exposure factor and pancreatic cancer as an outcome in an attempt to investigate whether there is a causal relationship between the two diseases.\u003c/p\u003e \u003cp\u003eIn the study, we used five different assessment methods (inverse-variance weighted method, weighted median method, MR-Egger regression, Simple mode and Weighted mode) to perform MR analysis. Our results showed that the results analyzed using IVW were in the opposite causal trend to those analyzed by the weighted median method, MR-Egger regression, Simple mode, and Weighted mode, and that the results analyzed by the five analytical methods had a P\u0026thinsp;\u0026gt;\u0026thinsp;0.05. Therefore, our results greatly confirm that there may be no causal relationship between long-term exposure of vitiligo and the development of pancreatic cancer. And our clinical observation that the patient was diagnosed with pancreatic cancer after 40 years of vitiligo was simply a co-existing condition that did not have a causal association and was not generalizable.\u003c/p\u003e \u003cp\u003eThe presence of genetic variants associated with multiple phenotypes, a phenomenon defined as \"pleiotropy\", can lead to confounding of the results of MR calculations, which can further lead to highly biased causal estimates. Because the inclusion of multiple variants in MR analyses often enlarges statistical power, this can lead to the inclusion of some invalid polyvalent genetic variants, which in fact do not play a relevant role. Therefore, in order to test the validity of the conclusions of the MR analysis while eliminating pleiotropy, we introduced Weighted median in this analysis, which ensures the validity of the results even if half of the SNPs are invalid tools. We also tested for unbalanced pleiotropy using MR-Egger regression and estimated the effect on the results when it was present. Our results were not uniform across all five methods and were not statistically significant. Our analysis more fully elucidates that vitiligo as an exposure factor does not appear to be causally associated with the development of pancreatic cancer.\u003c/p\u003e \u003cp\u003eIt is important to reflect on the limitations of this study. First, genetic variants have only a modest effect on a specific exposure (vitiligo), as they may only explain part of the variation in proportions in a given exposure. The results of our analysis are more affected by this. Second, the study on vitiligo and pancreatic cancer was based on participants of European and East Asian ancestry in the same laboratory. Since causality may depend on ethnicity and selection bias, further MR studies in other populations are needed.\u003c/p\u003e \u003cp\u003eIn conclusion, the present study is more objectively illustrate, through MR analysis, that vitiligo as an exposure factor is not clearly causally associated with the development of pancreatic cancer. The question is answered for researchers who are confused about the situation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable to this study.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable to this research.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this research were obtained from Open GWAS database (https://gwas.MRcieu.ac.uk/), which contains 767,100,42,351 genetic associations from 246,366 GWAS pooled datasets available for query and download. all of which are available in publicly available databases. This study complies with its data use and publication rules.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare that there is no conflict of interest regarding the publication of this paper.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable to our study.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding support for present study.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXusheng Zhang\u003csup\u003e1\u0026dagger;\u003c/sup\u003e: Ningxia Medical University, Yinchuan 750004, China,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEmail: [email protected].\u003c/p\u003e\n\u003cp\u003eShicai Liang\u003csup\u003e1\u0026dagger;\u003c/sup\u003e: Ningxia Medical University, Yinchuan 750004, China,\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Email: [email protected].\u003c/p\u003e\n\u003cp\u003eXuebo Wang\u003csup\u003e1\u003c/sup\u003e:Ningxia Medical University, Yinchuan 750004, China,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEmail: [email protected].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBendong Chen\u003csup\u003e*1,2\u003c/sup\u003e: General Hospital of Ningxia Medical University, Yinchuan 750004, China,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEmail: [email protected].\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZX and LS contributed equally. ZX, LS and CB participated in the conception and design of the study. ZX and WX organized the database and statistical analysis. CB, LS and LK divided the work and participated in the picture drawing. ZX wrote the first draft of the manuscript. CB and LS participated in the revision of the manuscript. All authors read and agreed to the final manuscript and authorship arrangement.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDuan Y, Du Y, Gu Z, Zheng X, Wang C: \u003cstrong\u003ePrognostic Value, Immune Signature, and Molecular Mechanisms of the PHLDA Family in Pancreatic Adenocarcinoma\u003c/strong\u003e. \u003cem\u003eINT J MOL SCI\u003c/em\u003e 2022, \u003cstrong\u003e23\u003c/strong\u003e(18).\u003c/li\u003e\n\u003cli\u003eKing G, Ittershagen S, He L, Shen Y, Li F, Villacorta R: \u003cstrong\u003eTreatment Patterns in US Patients Receiving First-Line and Second-Line Therapy for Metastatic Pancreatic Ductal Adenocarcinoma in the Real World\u003c/strong\u003e. \u003cem\u003eADV THER\u003c/em\u003e 2022, \u003cstrong\u003e39\u003c/strong\u003e(12):5433-5452.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003ePancreatic Neuroendocrine Tumors (Islet Cell Tumors) Treatment (PDQ(R)): Patient Version\u003c/strong\u003e. 2002.\u003c/li\u003e\n\u003cli\u003ePilgrim C, Maciejewska A, Ayres N, Ellis S, Goodwin M, Zalcberg JR, Haydon A: \u003cstrong\u003eSynoptic CT scan reporting of pancreatic adenocarcinoma to align with international consensus guidelines on surgical resectability: a Victorian pilot\u003c/strong\u003e. \u003cem\u003eANZ J SURG\u003c/em\u003e 2022, \u003cstrong\u003e92\u003c/strong\u003e(10):2565-2570.\u003c/li\u003e\n\u003cli\u003eStorz P, Crawford HC: \u003cstrong\u003eCarcinogenesis of Pancreatic Ductal Adenocarcinoma\u003c/strong\u003e. \u003cem\u003eGASTROENTEROLOGY\u003c/em\u003e 2020, \u003cstrong\u003e158\u003c/strong\u003e(8):2072-2081.\u003c/li\u003e\n\u003cli\u003eBiankin AV, Waddell N, Kassahn KS, Gingras MC, Muthuswamy LB, Johns AL, Miller DK, Wilson PJ, Patch AM, Wu J\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003ePancreatic cancer genomes reveal aberrations in axon guidance pathway genes\u003c/strong\u003e. \u003cem\u003eNATURE\u003c/em\u003e 2012, \u003cstrong\u003e491\u003c/strong\u003e(7424):399-405.\u003c/li\u003e\n\u003cli\u003eWitkiewicz AK, McMillan EA, Balaji U, Baek G, Lin WC, Mansour J, Mollaee M, Wagner KU, Koduru P, Yopp A\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eWhole-exome sequencing of pancreatic cancer defines genetic diversity and therapeutic targets\u003c/strong\u003e. \u003cem\u003eNAT COMMUN\u003c/em\u003e 2015, \u003cstrong\u003e6\u003c/strong\u003e:6744.\u003c/li\u003e\n\u003cli\u003eIlic M, Ilic I: \u003cstrong\u003eEpidemiology of pancreatic cancer\u003c/strong\u003e. \u003cem\u003eWORLD J GASTROENTERO\u003c/em\u003e 2016, \u003cstrong\u003e22\u003c/strong\u003e(44):9694-9705.\u003c/li\u003e\n\u003cli\u003eBergqvist C, Ezzedine K: \u003cstrong\u003eVitiligo: A Review\u003c/strong\u003e. \u003cem\u003eDERMATOLOGY\u003c/em\u003e 2020, \u003cstrong\u003e236\u003c/strong\u003e(6):571-592.\u003c/li\u003e\n\u003cli\u003eBergqvist C, Ezzedine K: \u003cstrong\u003eVitiligo: A focus on pathogenesis and its therapeutic implications\u003c/strong\u003e. \u003cem\u003eJ DERMATOL\u003c/em\u003e 2021, \u003cstrong\u003e48\u003c/strong\u003e(3):252-270.\u003c/li\u003e\n\u003cli\u003eBoniface K, Taieb A, Seneschal J: \u003cstrong\u003eNew insights into immune mechanisms of vitiligo\u003c/strong\u003e. \u003cem\u003eGIORN ITAL DERMAT V\u003c/em\u003e 2016, \u003cstrong\u003e151\u003c/strong\u003e(1):44-54.\u003c/li\u003e\n\u003cli\u003ePost NF, Ginski G, Peters R, Van Uden N, Bekkenk MW, Wolkerstorfer A, Netea MG, Luiten RM: \u003cstrong\u003eTrained immunity in the pathogenesis of vitiligo\u003c/strong\u003e. \u003cem\u003ePIGM CELL MELANOMA R\u003c/em\u003e 2023, \u003cstrong\u003e36\u003c/strong\u003e(5):348-354.\u003c/li\u003e\n\u003cli\u003eBowden J, Davey SG, Burgess S: \u003cstrong\u003eMendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression\u003c/strong\u003e. \u003cem\u003eINT J EPIDEMIOL\u003c/em\u003e 2015, \u003cstrong\u003e44\u003c/strong\u003e(2):512-525.\u003c/li\u003e\n\u003cli\u003eBowden J, Del GMF, Minelli C, Davey SG, Sheehan NA, Thompson JR: \u003cstrong\u003eAssessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic\u003c/strong\u003e. \u003cem\u003eINT J EPIDEMIOL\u003c/em\u003e 2016, \u003cstrong\u003e45\u003c/strong\u003e(6):1961-1974.\u003c/li\u003e\n\u003cli\u003eHiggins JP, Thompson SG: \u003cstrong\u003eQuantifying heterogeneity in a meta-analysis\u003c/strong\u003e. \u003cem\u003eSTAT MED\u003c/em\u003e 2002, \u003cstrong\u003e21\u003c/strong\u003e(11):1539-1558.\u003c/li\u003e\n\u003cli\u003eWeng YC, Ho HJ, Chang YL, Chang YT, Wu CY, Chen YJ: \u003cstrong\u003eReduced risk of skin cancer and internal malignancies in vitiligo patients: a retrospective population-based cohort study in Taiwan\u003c/strong\u003e. \u003cem\u003eSCI REP-UK\u003c/em\u003e 2021, \u003cstrong\u003e11\u003c/strong\u003e(1):20195.\u003c/li\u003e\n\u003cli\u003eBae JM, Chung KY, Yun SJ, Kim H, Park BC, Kim JS, Seo SH, Ahn HH, Lee DY, Kim YC\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eMarkedly Reduced Risk of Internal Malignancies in Patients With Vitiligo: A Nationwide Population-Based Cohort Study\u003c/strong\u003e. \u003cem\u003eJ CLIN ONCOL\u003c/em\u003e 2019, \u003cstrong\u003e37\u003c/strong\u003e(11):903-911.\u003c/li\u003e\n\u003cli\u003eWen Y, Wu X, Peng H, Li C, Jiang Y, Liang H, Zhong R, Liu J, He J, Liang W: \u003cstrong\u003eCancer risks in patients with vitiligo: a Mendelian randomization study\u003c/strong\u003e. \u003cem\u003eJ CANCER RES CLIN\u003c/em\u003e 2020, \u003cstrong\u003e146\u003c/strong\u003e(8):1933-1940.\u003c/li\u003e\n\u003cli\u003eFrydkjaer AG, Olivarius FF, Moller MP: \u003cstrong\u003e[Not Available]\u003c/strong\u003e. \u003cem\u003eUgeskr Laeger\u003c/em\u003e 2023, \u003cstrong\u003e185\u003c/strong\u003e(2).\u003c/li\u003e\n\u003cli\u003eLugo-Somolinos A, Sanchez JL, Garcia ME: \u003cstrong\u003eVitiligo-like primary melanoma\u003c/strong\u003e. \u003cem\u003eAM J DERMATOPATH\u003c/em\u003e 2008, \u003cstrong\u003e30\u003c/strong\u003e(5):451-454.\u003c/li\u003e\n\u003cli\u003eNemovi K, Jamali A, Matinpour K, Dasanu CA: \u003cstrong\u003eWidespread vitiligo and poliosis following ipilimumab-nivolumab combination therapy\u003c/strong\u003e. \u003cem\u003eJ ONCOL PHARM PRACT\u003c/em\u003e 2023, \u003cstrong\u003e29\u003c/strong\u003e(5):1278-1282.\u003c/li\u003e\n\u003cli\u003eChan OB, Su JC, Yazdabadi A, Chan A: \u003cstrong\u003eDrug induced vitiligo-like depigmentation from a CDK 4/6 inhibitor\u003c/strong\u003e. \u003cem\u003eASIA-PAC J CLIN ONCO\u003c/em\u003e 2022, \u003cstrong\u003e18\u003c/strong\u003e(2):e154-e156.\u003cstrong\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"archives-of-dermatological-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Archives of Dermatological Research](https://www.springer.com/journal/403)","snPcode":"403","submissionUrl":"https://submission.nature.com/new-submission/403/3","title":"Archives of Dermatological Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"pancreatic cancer, vitiligo, Mendelian randomization analysis, inverse variance weighting, MR-Egger","lastPublishedDoi":"10.21203/rs.3.rs-6197588/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6197588/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003eBackground\u003c/b\u003e Several special case of vitiligo patient with advanced pancreatic cancer was observed in our clinical practice, which prompted us to think about whether there is an association between the two diseases, and to further investigate whether there is a causal relationship between the two diseases, a two-sample Mendelian randomization analysis was performed.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethods\u003c/b\u003e In this study, two-sample Mendelian randomization (MR) analyses were performed using inverse variance weighted (IVW), weighted median, MR-Egger regression, Simple mode and Weighted mode. We used the publicly available Genome-wide association study (GWAS) summary statistics set on vitiligo of European origin (n\u0026thinsp;=\u0026thinsp;333064; Neale Lab) as the exposed GWAS; Samples of pancreatic cancer from the East Asian Biobank (total\u0026thinsp;=\u0026thinsp;196187; cases\u0026thinsp;=\u0026thinsp;442, controls\u0026thinsp;=\u0026thinsp;195745; Neale Lab) were used as outcome.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e We screened 19 single nucleotide polymorphisms (SNPs) with genome-wide significance from GWASs on vitiligo as instrumental variables (P\u0026thinsp;\u0026lt;\u0026thinsp;5.00E-06; linkage disequilibrium r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Analysis of the results using various methods such as IVW, MR-Egger regression, Weighted median, Simple mode and Weighted mode did not support the existence of a causal relationship between vitiligo and pancreatic cancer (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Cochran's Q test and funnel plot showed no evidence of heterogeneity and asymmetry. And the intercept of MR-Egger analysis result\u0026thinsp;=\u0026thinsp;0.017, P\u0026thinsp;=\u0026thinsp;0.666 further suggests that there is no directional multiplicity of results.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusion\u003c/b\u003e The results of the MR analysis do not support a causal relationship between vitiligo and an increased risk of pancreatic cancer.\u003c/p\u003e","manuscriptTitle":"Reflections and a mendelian randomization analysis of patients with vitiligo and pancreatic cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-17 10:31:06","doi":"10.21203/rs.3.rs-6197588/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-21T20:23:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-11T11:34:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-11T11:31:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Archives of Dermatological Research","date":"2025-03-10T17:10:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"archives-of-dermatological-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Archives of Dermatological Research](https://www.springer.com/journal/403)","snPcode":"403","submissionUrl":"https://submission.nature.com/new-submission/403/3","title":"Archives of Dermatological Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f11ada67-529a-4ace-880b-ac3f6ae8b2c9","owner":[],"postedDate":"March 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-05-08T00:38:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-17 10:31:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6197588","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6197588","identity":"rs-6197588","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}