The causal relationship between alcohol consumption, smoking, coffee, tea intake and cutaneous melanoma: a two-sample Mendelian randomization study

The causal relationship between alcohol consumption, smoking, coffee, tea intake and cutaneous melanoma: a two-sample Mendelian randomization study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The causal relationship between alcohol consumption, smoking, coffee, tea intake and cutaneous melanoma: a two-sample Mendelian randomization study Yuming Sun, Xi Yan, Qian Zhou, Lifang Zhang, Furong Zeng, Shaorong Lei, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3350096/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Alcohol consumption and smoking have been associated with high risk, but coffee intake with a lower risk of cutaneous melanoma in observational studies. However, it is unclear whether these lifestyles are causally associated cutaneous melanoma. Objectives This study aimed to investigate causal relationship of alcohol consumption, smoking, coffee and tea intake with cutaneous melanoma using the two-sample Mendelian randomization design. Methods We obtained the exposure data (alcohol consumption, alcoholic drinks per week, alcohol dependence, smoking initiation, cigarettes per day, smoking cessation, coffee intake and tea intake) and outcome data (cutaneous melanoma) from the IEU Open GWAS and GWAS catalog project. The SNPs independently associated with lifestyles at genome-wide significance levels ( P < 5×10 − 6 ). Linkage disequilibrium score regression was used to compute the genetic correlation (r 2 10000kb). We then performed two-sample Mendelian randomization (MR) to validate whether these lifestyles are causally associated with cutaneous melanoma. Results We found that the alcohol consumption (OR = 0.715, 95% CI: 0.322–1.587), alcoholic drinks per week (OR = 0.878, 95% CI: 0.591–1.305) and alcohol dependence (OR = 1.012, 95% CI: 0.957–1.071) was not causally associated with cutaneous melanoma. The result showed no significant evidence to support an increased risk of cutaneous melanoma on smoking initiation (OR = 0.927, 95% CI: 0.753–1.142), cigarettes per day (OR = 0.970, 95% CI: 0.802–1.174) and smoking cessation (OR = 1.862, 95% CI: 0.685–5.059). Likewise, no significant associations were observed between genetically predicted coffee intake (OR = 0.978, 95% CI: 0.586–1.633) and tea intake (OR = 0.696, 95% CI: 0.462–1.048) with cutaneous melanoma. Conclusions According to our MR analysis, we found no evidence to support a causal association between alcohol consumption, smoking, coffee intake and tea intake with cutaneous melanoma. Biological sciences/Cancer Health sciences/Risk factors cutaneous melanoma alcohol consumption smoking coffee intake tea intake SNPs Figures Figure 1 Figure 2 Introduction Cutaneous melanoma is a malignancy with high mortality, arising from skin melanocytes 1 . Its incidence has been steadily rising worldwide, with over 324,000 new cases and more than 57,000 new deaths in 2020 2 . There is an urgent need to better comprehend whether lifestyle factors affect the risk of cutaneous melanoma, for which to adjust the lifestyle to prevent cutaneous melanoma. Smoking and alcohol consumption have been associated with an increasing risk of cutaneous melanoma in the systematic review and meta-analysis pooled from observational studies 3 , 4 . However, the recent population case–control and prospective cohort studies found that smoking and coffee consumption appears to protect from the development of cutaneous melanoma 5 – 7 . Laboratory studies indicate that tea can protect against melanoma, but epidemiological results are inconsistent 8 – 10 . Nevertheless, due to the potential for unmeasured confounding in observational studies, such as natural sunlight and indoor tanning. The causal associations between modifiable lifestyle factors and cutaneous melanoma have not been definitively established. Unlike observational studies, randomized controlled trials of smoking, alcohol, coffee and tea consumption may potentially help establish a causal relationship with cutaneous melanoma. Unfortunately, owing to the influence of study population size, duration of the research and confounding effects between different lifestyles still has great limitations. Mendelian randomization (MR) is a statistical analysis method that uses genetic variation as an instrumental variables (IVs) to infer the causal relationship between the exposure factors (eg, smoking and alcohol consumption) and population health outcomes 11 , 12 . The genetic variants are randomly allocated at conception and the effector alleles in Mendelian randomization are randomly allocated, which can overcome the limitation of residual confounding and reverse causation bias 12 , 13 . MR has been used to investigate the cause-effect association between lifestyle factors and a variety of diseases 14 – 17 . In this study, we aimed to utilize two-sample MR to investigate the causal association between the risk of developing cutaneous melanoma and four modifiable lifestyle factors: alcohol consumption, smoking, coffee and tea intake. Methods Study design The three assumptions of MR to investigate the causal associations between lifestyle factors and cutaneous melanoma are shown in Fig. 1 . Firstly, the genetic variants selected as IVs are robustly associated with the alcohol consumption, smoking, coffee and tea intake; Secondly, the IVs should be not associated with any confounders; Thirdly, the selected IVs should affect the risk of the cutaneous melanoma merely through alcohol consumption, smoking, coffee and tea intake, not via alternative pathways 13 . We performed MR analysis to investigate the causal effect of four lifestyles on cutaneous melanoma and then replicated these relationships in the UK Biobank (UKB) data. Our study was based on the publicly available genome-wide association summary-level data obtained from IEU open GWAS ( https://gwas.mrcieu.ac.uk/ ) and GWAS catalog ( https://www.ebi.ac.uk/gwas/ ) databases. Data sources In this study, the exposure factors’ genetic variables for alcohol consumption (N = 112,117) 18 , alcohol dependence (N = 456,348) 19 ,coffee intake (N = 428,860) and tea intake (N = 447,485) from genome-wide association study (GWAS) released by UK Biobank. The alcoholic drinks per week (N = 335,394), smoking initiation (N = 607,291), cigarettes per day (N = 337,334) and smoking cessation (N = 547,219) obtained from the IEU open GWAS database released by the GSCAN consortium (GWAS and Sequencing Consortium of Alcohol and Nicotine use) 20 . We obtained outcome genetic instruments for cutaneous melanoma from a large genome-wide association study (2824 cutaneous melanoma cases and 453,524 controls) 19 . Moreover, we utilized the summary statistics for cutaneous melanoma from IEU open GWAS database released by UKB including 361,194 samples for replication to enhance the accuracy of conclusions. All the participants are European ancestry. The detailed information on exposure and outcome data are shown in Table 1 . Table 1 Characteristics of data in this study Trait Year GWAS ID Sample size (cases) Population Data source Number of SNPs Alcohol consumption 2017 ieu-a-1283 112,117 European Open GWAS/ UKB 12,935,395 Alcoholic drinks per week 2019 ieu-b-73 335,394 European Open GWAS / GSCAN 11,887,865 Alcohol dependence 2021 GCST90043727 456,348 European GWAS catalog 11,831,932 Smoking initiation 2019 ieu-b-4877 607,291 European Open GWAS/ GSCAN 11,802,365 Cigarettes per day 2019 ieu-b-25 337,334 European Open GWAS/ GSCAN 11,913,712 Smoking cessation 2019 GCST007460 547,219 European GWAS catalog 12,197,133 Coffee intake 2018 ukb-b-5237 428,860 European Open GWAS/UKB 9,851,867 Tea intake 2018 ukb-b-6066 447,485 European Open GWAS/UKB 9,851,867 Cutaneous melanoma 2021 GCST90041829 456,348 European GWAS catalog 11,831,932 Cutaneous melanoma 2018 ukb-d-C43 361,194 European Open GWAS/UKB 10,235,188 Instrumental variable selection The single-nucleotide polymorphisms (SNPs) were selected as IVs to validate the causal relationship between four lifestyles and cutaneous melanoma. Firstly, the SNPs independently associated with four lifestyles at genome-wide significance levels ( P < 5×10 − 8 ). If fewer SNPs obtained, then a higher cut-off ( P < 5×10 − 6 ) was applied. Secondly, to identify independent SNPs, we restricted low linkage disequilibrium (LD, r 2 10000kb). Thirdly, the F-statistics were calculated to ensure instrumental strength associated with exposure traits. The SNPs with an F less than 10 were excluded. MR analysis We applied the inverse-variance weighted (IVW) method as the main model in the MR analysis 21 . In view of assumption 3, the IVW method can reveal precise causal estimates, but may be affected by pleiotropic or invalid instrumentation bias. So, we used MR Egger, weighted median, weighted modeand simple mode methods for sensitivity analysis 22 – 24 . The MR Egger regression was based on the InSIDE (INstrument Strength Independent of Direct Effect) hypothesis, which can detect and adjust the potential pleiotropy 14 . When the P-value of intercept < 0.05, that indicates exists pleiotropy 22 . The weighted median method can check for invalid instrument bias and provides steady causal estimates when more than 50% of IVs are valid 23 . Compared to other methods, the weighted mode methods exhibit smaller bias and lower type I error rates, but smaller power to detect a causal effect 21 . In addition, the MR PRESSO method was used to detect the potentially pleiotropic SNPs 25 . Moreover, Cochrane Q value was performed to assess the heterogeneity in IVW and MR Egger estimators. Finally, we adopted the “leave-one out” method to validate whether each SNP can independently influence the summary estimate by calculating the meta-effect after removing each SNP. Results are presented in odds ratios (OR) and corresponding 95% confidence intervals (CI). All the analyses were 2-sided and performed using the TwoSampleMR 25 and MR-PRESSO 26 packages in R software (version 4.3.0). Results SNP selection Instrumental variables were screened according to inclusion criteria (genome-wide statistical significance threshold, P 10), and after excluding confound phenotypic-related SNPs, the SNPs included in our study are shown in Tables S2-S9 (see supplementary materials). There are 16, 120, 9, 219, 70, 24, 104 and 130 SNPs as IVs for alcohol consumption, alcoholic drinks per week, alcohol dependence, smoking initiation, cigarettes per day, smoking cessation, coffee intake and tea intake, respectively. Causal effects of four lifestyles on the development cutaneous melanoma Figure 2 shows the results of Mendelian analysis. In the IVW model, we found that the alcohol consumption (OR = 0.715, 95% CI: 0.322–1.587), alcoholic drinks per week (OR = 0.878, 95% CI: 0.591–1.305) and alcohol dependence (OR = 1.012, 95% CI: 0.957–1.071) was not causally associated with cutaneous melanoma. Next, we explored the relationship between smoking and cutaneous melanoma. However, the result showed no significant evidence to support an increased risk of cutaneous melanoma on smoking initiation (OR = 0.927, 95% CI: 0.753–1.142), cigarettes per day (OR = 0.970, 95% CI: 0.802–1.174) and smoking cessation (OR = 1.862, 95% CI: 0.685–5.059). Likewise, no significant associations were observed between genetically predicted coffee intake (OR = 0.978, 95% CI: 0.586–1.633) and tea intake (OR = 0.696, 95% CI: 0.462–1.048) with cutaneous melanoma. Meanwhile, all the associations between above lifestyles and cutaneous melanoma were directionally similar in the UKB consortium (Fig. 2 ). Sensitivity analyses The causal estimates for magnitude and direction of MR-Egger, weighted median, weighted mode and simple mode methods are generally consistent with IVW ( Table S1 ). In addition, the results of the Cochrane Q statistics showed the heterogeneity only across in smoking initiation ( P < 0.05) ( Table S1 0 ). Furthermore, the MR-PRESSO analyses showed no evidence of horizontal pleiotropy in all SNPs. Moreover, the leaving out each SNP revealed that the risk estimates of all the lifestyles on cutaneous melanoma still consistent after removing each single SNP. The funnel plots, scatter plot and funnel plots of the causal effect of lifestyles on cutaneous melanoma are shown in Figure S1 -S64 . Discussion To our best knowledge, this is the first MR investigation to validate the causal associations of four lifestyles with cutaneous melanoma based on the genetic instrumental variables. Our MR analysis using two set of outcome data (cutaneous melanoma) included over than 800,000 European ancestry participants. Our principal findings indicated that alcohol consumption, alcoholic drinks per week, alcohol dependence, smoking initiation, cigarettes per day and smoking cessation do not affect the risk of cutaneous melanoma. In addition, we found no genetic evidence to suggest that coffee intake and tea intake can decrease the risk of cutaneous melanoma. In contrast to our findings, meta-analyses of prospective and observational studies concluded that alcohol consumption is positively associated with the risk of cutaneous melanoma 4 , 27 , 28 . A large pooled analysis of 40 clinical centers across the USA cohort study comprising 59,575 white postmenopausal women reported a relative high risk (HR: 1.64; 95% CI: 1.09–2.49) with cutaneous melanoma compared non-drinkers 29 . The most comprehensive meta-analysis including 20 studies and 10,555 cases found that alcohol consumption was associated with risk of melanoma, the RR was 1.29 (95% CI: 1.14–1.45) for those in the highest vs. lowest classifications of current drinkers 30 . The results of another meta-analysis showed that the risk of cutaneous melanoma for alcohol consumption (RR: 1.27; 95% CI: 1.20–1.35), but there was no significant difference after adjusting for sun exposure (RR: 1.15; 95% CI: 0.94–1.41). Although the above-mentioned pooled analysis showed that alcohol consumption was a risk factor for cutaneous melanoma, the studies did not rule out the residual confounding and bias, which also affected the accuracy of the results. Interestingly, different studies have reported diverse associations between smoking and the risk of cutaneous melanoma 3 , 5 , 7 , 31 , 32 . A case-control study found no statistical significance between smoking and melanoma risk after matching population characteristics and adjusting for UV exposure and sunburns frequency 31 . Similarly, another prospective cohort study reported no association between smoking and cutaneous melanoma (OR:1.01; 95% CI: 0.64–1.61), after adjusting for potential confounding factors 32 . These results are consistent with our MR conclusion. In addition, a previous meta-analyses pooled 20 studies suggested that smoking was moderate inversely associated with cutaneous melanoma (RR:070; 95% CI: 063–078) 7 . However, there is a significant publication bias, and the quality of studies which included in this meta-analysis were low. A recent population based, case-control study including 7124 cases found an inverse association between smoking and cutaneous melanoma consistent with the meta-analysis 5 , 7 . The inverse association between smoking and cutaneous melanoma may be explained or contributed by the smoking-related competing risks bias 33 . Furthermore, a meta-analysis involving 832,956 participants suggest that coffee intake can reduce the risk of cutaneous melanoma 34 . This conclusion was verified in another large cohort study in US 8 . Our MR result provides suggestive evidence that no causal relationship between coffee and tea intake with cutaneous melanoma, which is consistent with the findings of a large cohort study 10 . Traditional observational studies exploring the association between lifestyle factors and cutaneous melanoma, which strongly associated with alcohol consumption, smoking and coffee intake are susceptible to confounding and reverse causation 5 , 8 , 27 . When the instrumental variables met inclusion criteria, MR Offered the possibility of using genetic proxies which unrelated to alcohol consumption, smoking, coffee and tea intake, and rule out other confounding factors to overcome confounding and reverse causation. Additionally, we removed potentially pleiotropic SNPs, and validated these causal relationships in 2 independent data sets. Moreover, we applied multiple alcohol consumption and smoking phenotypes, which capture drinking and smoking status at various stages, the consistent results of associations between these lifestyle factors with cutaneous melanoma ensured the robustness of our findings. However, our study also has several limitations. Firstly, we limited our study population to individuals of European ancestry, and although this decreased population bias, it may restrict the generalizability of our findings to other populations. Therefore, replication of our findings with MR in non-European populations is needed. Secondly, the major limitation of MR is pleiotropy-induced bias, which means that genetic instruments influence risk of cutaneous melanoma not via the four lifestyle factors but through other pathways. However, there was no significant difference in our finding, and the results were consistent through sensitivity analysis, which indicate that the estimated effects were approximately unbiased. Thirdly, to perform sensitivity analysis and horizontal pleiotropy detection, more SNPs needed to be involved as genetic instrumental variables. Therefore, the SNPs included in our analysis were different from the traditional GWAS significance threshold ( P < 5×10 − 8 ). For this, we restricted low linkage disequilibrium (LD, r 2 10000kb), and set F statistic > 10 to reduce the bias. Conclusions In summary, our study provides genetic evidence indicate that the alcohol consumption, smoking, coffee and tea intake were not causally associated with cutaneous melanoma. Declarations Acknowledgements We appreciate the IEU open GWAS and GWAS catalog databases for their data download, and appreciate the work of the UK Biobank collaborators, GSCAN consortium and everyone who contributed to the research. Authors’ contributions GT D and SR L conceived of the study design. YM S, X Y, and Q Z performed the statistical analyses. YM S, X Y and LF Z wrote the manuscript. X Y and FR Z performed the data visualization. GT D, SR L and FR Z supervised the study. All authors provided critical revisions of the draft and approved the submitted draft. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. GT D is the guarantor. All authors read and approved the final manuscript. Funding This work was supported by grants from the National Natural Science Foundation of China (nos. 8197081674 to SR L, 82102803 and 82272849 to GT D). Availability of data and materials All data were available in the IEU open GWAS (https://gwas.mrcieu.ac.uk/) and GWAS catalog (https://www.ebi.ac.uk/gwas/) databases. Competing interests There were no any competing interests in our study. References Long, G. V., Swetter, S. M., Menzies, A. M., Gershenwald, J. E. & Scolyer, R. A. Cutaneous melanoma. The Lancet 402, 485–502, doi: 10.1016/s0140-6736(23)00821-8 (2023). Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians 71, 209–249, doi: 10.3322/caac.21660 (2021). Huang, J. et al. Global Incidence, Mortality, Risk Factors and Trends of Melanoma: A Systematic Analysis of Registries. Am J Clin Dermatol, doi: 10.1007/s40257-023-00795-3 (2023). Rota, M. et al. Alcohol drinking and cutaneous melanoma risk: a systematic review and dose-risk meta-analysis. British Journal of Dermatology 170, 1021–1028, doi: 10.1111/bjd.12856 (2014). Gibson, J. A. G. et al. The association of smoking and socioeconomic status on cutaneous melanoma: a population-based, data‐linkage, case–control study. British Journal of Dermatology 182, 1136–1147, doi: 10.1111/bjd.18526 (2019). Loftfield, E. et al. Coffee Drinking and Cutaneous Melanoma Risk in the NIH-AARP Diet and Health Study. JNCI: Journal of the National Cancer Institute 107, doi: 10.1093/jnci/dju421 (2015). Li, Z., Wang, Z., Yu, Y., Zhang, H. & Chen, L. Smoking is inversely related to cutaneous malignant melanoma: results of a meta-analysis. British Journal of Dermatology 173, 1540–1543, doi: 10.1111/bjd.13998 (2015). Caini, S. et al. Coffee, tea and melanoma risk: findings from the European Prospective Investigation into Cancer and Nutrition. International Journal of Cancer 140, 2246–2255, doi: 10.1002/ijc.30659 (2017). Halder, B., Bhattacharya, U., Mukhopadhyay, S. & Giri, A. K. Molecular mechanism of black tea polyphenols induced apoptosis in human skin cancer cells: involvement of Bax translocation and mitochondria mediated death cascade. Carcinogenesis 29, 129–138, doi: 10.1093/carcin/bgm233 (2007). Wu, H., Reeves, K. W., Qian, J. & Sturgeon, S. R. Coffee, tea, and melanoma risk among postmenopausal women. European Journal of Cancer Prevention 24, 347–352, doi: 10.1097/cej.0000000000000093 (2015). Davies, N. M., Holmes, M. V. & Davey Smith, G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. Bmj, doi: 10.1136/bmj.k601 (2018). Yuan, S. & Larsson, S. C. Coffee and Caffeine Consumption and Risk of Kidney Stones: A Mendelian Randomization Study. American Journal of Kidney Diseases 79, 9–14.e11, doi: 10.1053/j.ajkd.2021.04.018 (2022). Little, M. Mendelian Randomization: Methods for using Genetic Variants in Causal Estimation. Journal of the Royal Statistical Society Series a-Statistics in Society 181, 549–550, doi: 10.1111/rssa.12343 (2018). Wei, J. et al. Alcohol consumption and smoking in relation to psoriasis: a Mendelian randomization study. British Journal of Dermatology 187, 684–691, doi: 10.1111/bjd.21718 (2022). Kim, E.-j. et al. Coffee Consumption and Incident Tachyarrhythmias. JAMA Internal Medicine 181, doi: 10.1001/jamainternmed.2021.3616 (2021). Larsson, S. C. et al. Genetic predisposition to smoking in relation to 14 cardiovascular diseases. European Heart Journal 41, 3304–3310, doi: 10.1093/eurheartj/ehaa193 (2020). Sowers, J. R. & Frohlich, E. D. Insulin and insulin resistance. Medical Clinics of North America 88, 63–82, doi: 10.1016/s0025-7125(03)00128-7 (2004). Clarke, T. K. et al. Genome-wide association study of alcohol consumption and genetic overlap with other health-related traits in UK Biobank (N = 112 117). Molecular Psychiatry 22, 1376–1384, doi: 10.1038/mp.2017.153 (2017). Jiang, L., Zheng, Z., Fang, H. & Yang, J. A generalized linear mixed model association tool for biobank-scale data. Nat Genet 53, 1616–1621, doi: 10.1038/s41588-021-00954-4 (2021). Liu, M. et al. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat Genet 51, 237–244, doi: 10.1038/s41588-018-0307-5 (2019). Burgess, S., Butterworth, A. & Thompson, S. G. Mendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data. Genetic Epidemiology 37, 658–665, doi: 10.1002/gepi.21758 (2013). Burgess, S. & Thompson, S. G. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol 32, 377–389, doi: 10.1007/s10654-017-0255-x (2017). Bowden, J., Davey Smith, G., Haycock, P. C. & Burgess, S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol 40, 304–314, doi: 10.1002/gepi.21965 (2016). Hartwig, F. P., Davey Smith, G. & Bowden, J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol 46, 1985–1998, doi: 10.1093/ije/dyx102 (2017). Verbanck, M., Chen, C.-Y., Neale, B. & Do, R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nature Genetics 50, 693–698, doi: 10.1038/s41588-018-0099-7 (2018). Hemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife 7, doi: 10.7554/eLife.34408 (2018). Miura, K. et al. Alcohol consumption and risk of melanoma among women: pooled analysis of eight case–control studies. Archives of Dermatological Research 307, 819–828, doi: 10.1007/s00403-015-1591-x (2015). Rivera, A., Nan, H., Li, T., Qureshi, A. & Cho, E. Alcohol Intake and Risk of Incident Melanoma: A Pooled Analysis of Three Prospective Studies in the United States. Cancer Epidemiology, Biomarkers & Prevention 25, 1550–1558, doi: 10.1158/1055-9965.Epi-16-0303 (2016). Kubo, J. T. et al. Alcohol consumption and risk of melanoma and non-melanoma skin cancer in the Women’s Health Initiative. Cancer Causes & Control 25, 1–10, doi: 10.1007/s10552-013-0280-3 (2013). Gandini, S. et al. Alcohol, alcoholic beverages, and melanoma risk: a systematic literature review and dose–response meta-analysis. European Journal of Nutrition 57, 2323–2332, doi: 10.1007/s00394-018-1613-5 (2018). Kessides, M. C. et al. Cigarette smoking and malignant melanoma: A case-control study. Journal of the American Academy of Dermatology 64, 84–90, doi: 10.1016/j.jaad.2010.01.041 (2011). Dusingize, J. C. et al. Smoking and Cutaneous Melanoma: Findings from the QSkin Sun and Health Cohort Study. Cancer Epidemiology, Biomarkers & Prevention 27, 874–881, doi: 10.1158/1055-9965.Epi-17-1056 (2018). Thompson, C. A., Zhang, Z.-F. & Arah, O. A. Competing risk bias to explain the inverse relationship between smoking and malignant melanoma. European Journal of Epidemiology 28, 557–567, doi: 10.1007/s10654-013-9812-0 (2013). Wang, J., Li, X. & Zhang, D. Coffee consumption and the risk of cutaneous melanoma: a meta-analysis. European Journal of Nutrition 55, 1317–1329, doi: 10.1007/s00394-015-1139-z (2015). Additional Declarations No competing interests reported. Supplementary Files SupFigures.docx SupTables.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3350096","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":239041490,"identity":"09cf6814-b2ed-47e5-8b88-60116484d47f","order_by":0,"name":"Yuming Sun","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuming","middleName":"","lastName":"Sun","suffix":""},{"id":239041491,"identity":"bfe70d60-4e28-428e-96a8-916a21982df8","order_by":1,"name":"Xi Yan","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xi","middleName":"","lastName":"Yan","suffix":""},{"id":239041492,"identity":"92eb58af-5372-427e-9295-1effea926e25","order_by":2,"name":"Qian Zhou","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Zhou","suffix":""},{"id":239041493,"identity":"024279f0-c48d-4f21-abe1-5584d5fcc215","order_by":3,"name":"Lifang Zhang","email":"","orcid":"","institution":"Changsha Mylike Cosmetic Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lifang","middleName":"","lastName":"Zhang","suffix":""},{"id":239041494,"identity":"ef4b3d63-d8b3-4ef1-87b3-84e31562f94c","order_by":4,"name":"Furong Zeng","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Furong","middleName":"","lastName":"Zeng","suffix":""},{"id":239041495,"identity":"b9e82182-2601-420b-9669-be3909540f9a","order_by":5,"name":"Shaorong Lei","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shaorong","middleName":"","lastName":"Lei","suffix":""},{"id":239041496,"identity":"fe6de70b-0b42-46d3-9c62-7f8aca195edd","order_by":6,"name":"Guangtong Deng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIiWNgGAWjYDCCAyi8CiBmZ2BgbCBKCxuIOMPAwMNMkhbGNiK08B3vPfzya5tdnnx887GHX+fZJO5nZj74cAaDnZwuDn2SZ86lWcu2JRcbHmNLN5bdlpbYw8yWbLiBIdnY7AB2LQY3csyMJduYEze28ZhJS247DNTCYyb5gOFA4jZcWu6/AWmph2qZQ4yWGzzGDz+2HU6czwZU+bEBqmUDHi2SZ3LMmBnOHU/cwJaWJs1wLM245zDQLzMMcPuF7/gZ448/yqoT5zcfPib5o8ZGtr29+eDDngo7OVxagIBNmgfkQqACZh6Eg3EqBwHmjz+ApHwDMAZ/4FU4CkbBKBgFIxUAAC3PX/z9Qw9RAAAAAElFTkSuQmCC","orcid":"","institution":"Central South University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Guangtong","middleName":"","lastName":"Deng","suffix":""}],"badges":[],"createdAt":"2023-09-13 01:29:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3350096/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3350096/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":44596306,"identity":"5f805bdd-7dbb-41ab-b1d9-1247ea8bd86b","added_by":"auto","created_at":"2023-10-13 20:15:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":82517,"visible":true,"origin":"","legend":"\u003cp\u003eThe overall design of the Mendelian randomization study. Abbreviation: SNP, single-nucleotide polymorphism.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3350096/v1/598e0e331c82880bfe0dd12e.png"},{"id":44596308,"identity":"18073f68-cc55-4d16-9efc-1b4f1e0715ce","added_by":"auto","created_at":"2023-10-13 20:15:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":345068,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of genetically predicted alcohol consumption, smoking, coffee and tea intake with risk of cutaneous melanoma. Abbreviation: IVW, inverse-variance weighted.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3350096/v1/a4494cd83c908b2767996022.png"},{"id":60066840,"identity":"e7d629ae-0c02-4cc2-a327-722aeabeeb5a","added_by":"auto","created_at":"2024-07-11 10:00:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":858725,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3350096/v1/dd70130f-0bdb-4a24-9d40-2e3edca1b0f8.pdf"},{"id":44596310,"identity":"38f4f0d3-b7a7-49ad-a817-4961ba7ccfa3","added_by":"auto","created_at":"2023-10-13 20:15:04","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10025159,"visible":true,"origin":"","legend":"","description":"","filename":"SupFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-3350096/v1/b3f799c05c306dcae6bc1994.docx"},{"id":44596307,"identity":"cffa404c-ca93-467f-b394-6ed03420aa54","added_by":"auto","created_at":"2023-10-13 20:15:03","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":38786,"visible":true,"origin":"","legend":"","description":"","filename":"SupTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3350096/v1/466dad2ed7644ce9fa772c7b.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The causal relationship between alcohol consumption, smoking, coffee, tea intake and cutaneous melanoma: a two-sample Mendelian randomization study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCutaneous melanoma is a malignancy with high mortality, arising from skin melanocytes \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Its incidence has been steadily rising worldwide, with over 324,000 new cases and more than 57,000 new deaths in 2020 \u003csup\u003e2\u003c/sup\u003e. There is an urgent need to better comprehend whether lifestyle factors affect the risk of cutaneous melanoma, for which to adjust the lifestyle to prevent cutaneous melanoma.\u003c/p\u003e \u003cp\u003eSmoking and alcohol consumption have been associated with an increasing risk of cutaneous melanoma in the systematic review and meta-analysis pooled from observational studies \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. However, the recent population case\u0026ndash;control and prospective cohort studies found that smoking and coffee consumption appears to protect from the development of cutaneous melanoma \u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Laboratory studies indicate that tea can protect against melanoma, but epidemiological results are inconsistent \u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Nevertheless, due to the potential for unmeasured confounding in observational studies, such as natural sunlight and indoor tanning. The causal associations between modifiable lifestyle factors and cutaneous melanoma have not been definitively established. Unlike observational studies, randomized controlled trials of smoking, alcohol, coffee and tea consumption may potentially help establish a causal relationship with cutaneous melanoma. Unfortunately, owing to the influence of study population size, duration of the research and confounding effects between different lifestyles still has great limitations.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) is a statistical analysis method that uses genetic variation as an instrumental variables (IVs) to infer the causal relationship between the exposure factors (eg, smoking and alcohol consumption) and population health outcomes \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. The genetic variants are randomly allocated at conception and the effector alleles in Mendelian randomization are randomly allocated, which can overcome the limitation of residual confounding and reverse causation bias \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. MR has been used to investigate the cause-effect association between lifestyle factors and a variety of diseases \u003csup\u003e\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In this study, we aimed to utilize two-sample MR to investigate the causal association between the risk of developing cutaneous melanoma and four modifiable lifestyle factors: alcohol consumption, smoking, coffee and tea intake.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cb\u003eStudy design\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe three assumptions of MR to investigate the causal associations between lifestyle factors and cutaneous melanoma are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Firstly, the genetic variants selected as IVs are robustly associated with the alcohol consumption, smoking, coffee and tea intake; Secondly, the IVs should be not associated with any confounders; Thirdly, the selected IVs should affect the risk of the cutaneous melanoma merely through alcohol consumption, smoking, coffee and tea intake, not via alternative pathways \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. We performed MR analysis to investigate the causal effect of four lifestyles on cutaneous melanoma and then replicated these relationships in the UK Biobank (UKB) data. Our study was based on the publicly available genome-wide association summary-level data obtained from IEU open GWAS (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and GWAS catalog (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/gwas/\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/gwas/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) databases.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eData sources\u003c/h3\u003e\n\u003cp\u003eIn this study, the exposure factors\u0026rsquo; genetic variables for alcohol consumption (N\u0026thinsp;=\u0026thinsp;112,117) \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, alcohol dependence (N\u0026thinsp;=\u0026thinsp;456,348) \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e,coffee intake (N\u0026thinsp;=\u0026thinsp;428,860) and tea intake (N\u0026thinsp;=\u0026thinsp;447,485) from genome-wide association study (GWAS) released by UK Biobank. The alcoholic drinks per week (N\u0026thinsp;=\u0026thinsp;335,394), smoking initiation (N\u0026thinsp;=\u0026thinsp;607,291), cigarettes per day (N\u0026thinsp;=\u0026thinsp;337,334) and smoking cessation (N\u0026thinsp;=\u0026thinsp;547,219) obtained from the IEU open GWAS database released by the GSCAN consortium (GWAS and Sequencing Consortium of Alcohol and Nicotine use) \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. We obtained outcome genetic instruments for cutaneous melanoma from a large genome-wide association study (2824 cutaneous melanoma cases and 453,524 controls) \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Moreover, we utilized the summary statistics for cutaneous melanoma from IEU open GWAS database released by UKB including 361,194 samples for replication to enhance the accuracy of conclusions. All the participants are European ancestry. The detailed information on exposure and outcome data are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of data in this study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrait\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGWAS ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003cp\u003e(cases)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eData source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNumber of\u0026nbsp;SNPs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eieu-a-1283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e112,117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOpen GWAS/ UKB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12,935,395\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcoholic drinks per week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eieu-b-73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e335,394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOpen GWAS / GSCAN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11,887,865\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol dependence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGCST90043727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e456,348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGWAS catalog\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11,831,932\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking initiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eieu-b-4877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e607,291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOpen GWAS/ GSCAN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11,802,365\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCigarettes per day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eieu-b-25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e337,334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOpen GWAS/ GSCAN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11,913,712\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking cessation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGCST007460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e547,219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGWAS catalog\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12,197,133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoffee intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eukb-b-5237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e428,860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOpen GWAS/UKB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9,851,867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTea intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eukb-b-6066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e447,485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOpen GWAS/UKB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9,851,867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCutaneous melanoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGCST90041829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e456,348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGWAS catalog\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11,831,932\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCutaneous melanoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eukb-d-C43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e361,194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOpen GWAS/UKB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10,235,188\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eInstrumental variable selection\u003c/h3\u003e\n\u003cp\u003eThe single-nucleotide polymorphisms (SNPs) were selected as IVs to validate the causal relationship between four lifestyles and cutaneous melanoma. Firstly, the SNPs independently associated with four lifestyles at genome-wide significance levels (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e). If fewer SNPs obtained, then a higher cut-off (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e) was applied. Secondly, to identify independent SNPs, we restricted low linkage disequilibrium (LD, r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, clump distance\u0026thinsp;\u0026gt;\u0026thinsp;10000kb). Thirdly, the F-statistics were calculated to ensure instrumental strength associated with exposure traits. The SNPs with an F less than 10 were excluded.\u003c/p\u003e\n\u003ch3\u003eMR analysis\u003c/h3\u003e\n\u003cp\u003eWe applied the inverse-variance weighted (IVW) method as the main model in the MR analysis \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. In view of assumption 3, the IVW method can reveal precise causal estimates, but may be affected by pleiotropic or invalid instrumentation bias. So, we used MR Egger, weighted median, weighted modeand simple mode methods for sensitivity analysis \u003csup\u003e\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The MR Egger regression was based on the InSIDE (INstrument Strength Independent of Direct Effect) hypothesis, which can detect and adjust the potential pleiotropy\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. When the P-value of intercept\u0026thinsp;\u0026lt;\u0026thinsp;0.05, that indicates exists pleiotropy \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The weighted median method can check for invalid instrument bias and provides steady causal estimates when more than 50% of IVs are valid \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Compared to other methods, the weighted mode methods exhibit smaller bias and lower type I error rates, but smaller power to detect a causal effect \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. In addition, the MR PRESSO method was used to detect the potentially pleiotropic SNPs \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Moreover, Cochrane Q value was performed to assess the heterogeneity in IVW and MR Egger estimators. Finally, we adopted the \u0026ldquo;leave-one out\u0026rdquo; method to validate whether each SNP can independently influence the summary estimate by calculating the meta-effect after removing each SNP. Results are presented in odds ratios (OR) and corresponding 95% confidence intervals (CI). All the analyses were 2-sided and performed using the TwoSampleMR \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e and MR-PRESSO \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e packages in R software (version 4.3.0).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eSNP selection\u003c/b\u003e \u003c/p\u003e \u003cp\u003eInstrumental variables were screened according to inclusion criteria (genome-wide statistical significance threshold, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e, \u003cem\u003eF\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;10), and after excluding confound phenotypic-related SNPs, the SNPs included in our study are shown in Tables S2-S9 (see supplementary materials). There are 16, 120, 9, 219, 70, 24, 104 and 130 SNPs as IVs for alcohol consumption, alcoholic drinks per week, alcohol dependence, smoking initiation, cigarettes per day, smoking cessation, coffee intake and tea intake, respectively.\u003c/p\u003e\n\u003ch3\u003eCausal effects of four lifestyles on the development cutaneous melanoma\u003c/h3\u003e\n\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the results of Mendelian analysis. In the IVW model, we found that the alcohol consumption (OR\u0026thinsp;=\u0026thinsp;0.715, 95% CI: 0.322\u0026ndash;1.587), alcoholic drinks per week (OR\u0026thinsp;=\u0026thinsp;0.878, 95% CI: 0.591\u0026ndash;1.305) and alcohol dependence (OR\u0026thinsp;=\u0026thinsp;1.012, 95% CI: 0.957\u0026ndash;1.071) was not causally associated with cutaneous melanoma. Next, we explored the relationship between smoking and cutaneous melanoma. However, the result showed no significant evidence to support an increased risk of cutaneous melanoma on smoking initiation (OR\u0026thinsp;=\u0026thinsp;0.927, 95% CI: 0.753\u0026ndash;1.142), cigarettes per day (OR\u0026thinsp;=\u0026thinsp;0.970, 95% CI: 0.802\u0026ndash;1.174) and smoking cessation (OR\u0026thinsp;=\u0026thinsp;1.862, 95% CI: 0.685\u0026ndash;5.059). Likewise, no significant associations were observed between genetically predicted coffee intake (OR\u0026thinsp;=\u0026thinsp;0.978, 95% CI: 0.586\u0026ndash;1.633) and tea intake (OR\u0026thinsp;=\u0026thinsp;0.696, 95% CI: 0.462\u0026ndash;1.048) with cutaneous melanoma. Meanwhile, all the associations between above lifestyles and cutaneous melanoma were directionally similar in the UKB consortium (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eSensitivity analyses\u003c/h3\u003e\n\u003cp\u003eThe causal estimates for magnitude and direction of MR-Egger, weighted median, weighted mode and simple mode methods are generally consistent with IVW (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). In addition, the results of the Cochrane Q statistics showed the heterogeneity only across in smoking initiation (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e0\u003c/b\u003e). Furthermore, the MR-PRESSO analyses showed no evidence of horizontal pleiotropy in all SNPs. Moreover, the leaving out each SNP revealed that the risk estimates of all the lifestyles on cutaneous melanoma still consistent after removing each single SNP. The funnel plots, scatter plot and funnel plots of the causal effect of lifestyles on cutaneous melanoma are shown in \u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-S64\u003c/b\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo our best knowledge, this is the first MR investigation to validate the causal associations of four lifestyles with cutaneous melanoma based on the genetic instrumental variables. Our MR analysis using two set of outcome data (cutaneous melanoma) included over than 800,000 European ancestry participants. Our principal findings indicated that alcohol consumption, alcoholic drinks per week, alcohol dependence, smoking initiation, cigarettes per day and smoking cessation do not affect the risk of cutaneous melanoma. In addition, we found no genetic evidence to suggest that coffee intake and tea intake can decrease the risk of cutaneous melanoma.\u003c/p\u003e \u003cp\u003eIn contrast to our findings, meta-analyses of prospective and observational studies concluded that alcohol consumption is positively associated with the risk of cutaneous melanoma \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. A large pooled analysis of 40 clinical centers across the USA cohort study comprising 59,575 white postmenopausal women reported a relative high risk (HR: 1.64; 95% CI: 1.09\u0026ndash;2.49) with cutaneous melanoma compared non-drinkers \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. The most comprehensive meta-analysis including 20 studies and 10,555 cases found that alcohol consumption was associated with risk of melanoma, the RR was 1.29 (95% CI: 1.14\u0026ndash;1.45) for those in the highest vs. lowest classifications of current drinkers \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. The results of another meta-analysis showed that the risk of cutaneous melanoma for alcohol consumption (RR: 1.27; 95% CI: 1.20\u0026ndash;1.35), but there was no significant difference after adjusting for sun exposure (RR: 1.15; 95% CI: 0.94\u0026ndash;1.41). Although the above-mentioned pooled analysis showed that alcohol consumption was a risk factor for cutaneous melanoma, the studies did not rule out the residual confounding and bias, which also affected the accuracy of the results. Interestingly, different studies have reported diverse associations between smoking and the risk of cutaneous melanoma \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. A case-control study found no statistical significance between smoking and melanoma risk after matching population characteristics and adjusting for UV exposure and sunburns frequency \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Similarly, another prospective cohort study reported no association between smoking and cutaneous melanoma (OR:1.01; 95% CI: 0.64\u0026ndash;1.61), after adjusting for potential confounding factors \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. These results are consistent with our MR conclusion. In addition, a previous meta-analyses pooled 20 studies suggested that smoking was moderate inversely associated with cutaneous melanoma (RR:070; 95% CI: 063\u0026ndash;078)\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. However, there is a significant publication bias, and the quality of studies which included in this meta-analysis were low. A recent population based, case-control study including 7124 cases found an inverse association between smoking and cutaneous melanoma consistent with the meta-analysis \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. The inverse association between smoking and cutaneous melanoma may be explained or contributed by the smoking-related competing risks bias \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Furthermore, a meta-analysis involving 832,956 participants suggest that coffee intake can reduce the risk of cutaneous melanoma \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. This conclusion was verified in another large cohort study in US \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Our MR result provides suggestive evidence that no causal relationship between coffee and tea intake with cutaneous melanoma, which is consistent with the findings of a large cohort study \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTraditional observational studies exploring the association between lifestyle factors and cutaneous melanoma, which strongly associated with alcohol consumption, smoking and coffee intake are susceptible to confounding and reverse causation \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. When the instrumental variables met inclusion criteria, MR Offered the possibility of using genetic proxies which unrelated to alcohol consumption, smoking, coffee and tea intake, and rule out other confounding factors to overcome confounding and reverse causation. Additionally, we removed potentially pleiotropic SNPs, and validated these causal relationships in 2 independent data sets. Moreover, we applied multiple alcohol consumption and smoking phenotypes, which capture drinking and smoking status at various stages, the consistent results of associations between these lifestyle factors with cutaneous melanoma ensured the robustness of our findings.\u003c/p\u003e \u003cp\u003eHowever, our study also has several limitations. Firstly, we limited our study population to individuals of European ancestry, and although this decreased population bias, it may restrict the generalizability of our findings to other populations. Therefore, replication of our findings with MR in non-European populations is needed. Secondly, the major limitation of MR is pleiotropy-induced bias, which means that genetic instruments influence risk of cutaneous melanoma not via the four lifestyle factors but through other pathways. However, there was no significant difference in our finding, and the results were consistent through sensitivity analysis, which indicate that the estimated effects were approximately unbiased. Thirdly, to perform sensitivity analysis and horizontal pleiotropy detection, more SNPs needed to be involved as genetic instrumental variables. Therefore, the SNPs included in our analysis were different from the traditional GWAS significance threshold (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e). For this, we restricted low linkage disequilibrium (LD, r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, clump distance\u0026thinsp;\u0026gt;\u0026thinsp;10000kb), and set F statistic\u0026thinsp;\u0026gt;\u0026thinsp;10 to reduce the bias.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, our study provides genetic evidence indicate that the alcohol consumption, smoking, coffee and tea intake were not causally associated with cutaneous melanoma.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe appreciate the IEU open GWAS and GWAS catalog databases for their data download, and appreciate the work of the UK Biobank collaborators, GSCAN consortium and everyone who contributed to the research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGT D and SR L conceived of the study design. YM S, X Y, and Q Z performed the statistical analyses. YM S, X Y and LF Z wrote the manuscript. X Y and FR Z performed the data visualization. GT D, SR L and FR Z supervised the study. All authors provided critical revisions of the draft and approved the submitted draft. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. GT D is the guarantor. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the National Natural Science Foundation of China (nos. 8197081674 to SR L, 82102803 and 82272849 to GT D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data were available in the IEU open GWAS (https://gwas.mrcieu.ac.uk/) and GWAS catalog (https://www.ebi.ac.uk/gwas/) databases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere were no any competing interests in our study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eLong, G. V., Swetter, S. M., Menzies, A. M., Gershenwald, J. E. \u0026amp; Scolyer, R. A. Cutaneous melanoma. The Lancet 402, 485\u0026ndash;502, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s0140-6736(23)00821-8\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eSung, H. \u003cem\u003eet al.\u003c/em\u003e Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians 71, 209\u0026ndash;249, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3322/caac.21660\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eHuang, J. \u003cem\u003eet al.\u003c/em\u003e Global Incidence, Mortality, Risk Factors and Trends of Melanoma: A Systematic Analysis of Registries. Am J Clin Dermatol, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s40257-023-00795-3\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eRota, M. \u003cem\u003eet al.\u003c/em\u003e Alcohol drinking and cutaneous melanoma risk: a systematic review and dose-risk meta-analysis. British Journal of Dermatology 170, 1021\u0026ndash;1028, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/bjd.12856\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eGibson, J. A. G. \u003cem\u003eet al.\u003c/em\u003e The association of smoking and socioeconomic status on cutaneous melanoma: a population-based, data‐linkage, case\u0026ndash;control study. British Journal of Dermatology 182, 1136\u0026ndash;1147, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/bjd.18526\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLoftfield, E. \u003cem\u003eet al.\u003c/em\u003e Coffee Drinking and Cutaneous Melanoma Risk in the NIH-AARP Diet and Health Study. JNCI: Journal of the National Cancer Institute 107, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/jnci/dju421\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLi, Z., Wang, Z., Yu, Y., Zhang, H. \u0026amp; Chen, L. Smoking is inversely related to cutaneous malignant melanoma: results of a meta-analysis. British Journal of Dermatology 173, 1540\u0026ndash;1543, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/bjd.13998\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eCaini, S. \u003cem\u003eet al.\u003c/em\u003e Coffee, tea and melanoma risk: findings from the European Prospective Investigation into Cancer and Nutrition. International Journal of Cancer 140, 2246\u0026ndash;2255, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/ijc.30659\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eHalder, B., Bhattacharya, U., Mukhopadhyay, S. \u0026amp; Giri, A. K. Molecular mechanism of black tea polyphenols induced apoptosis in human skin cancer cells: involvement of Bax translocation and mitochondria mediated death cascade. Carcinogenesis 29, 129\u0026ndash;138, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/carcin/bgm233\u003c/span\u003e\u003c/span\u003e (2007).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eWu, H., Reeves, K. W., Qian, J. \u0026amp; Sturgeon, S. R. Coffee, tea, and melanoma risk among postmenopausal women. European Journal of Cancer Prevention 24, 347\u0026ndash;352, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/cej.0000000000000093\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eDavies, N. M., Holmes, M. V. \u0026amp; Davey Smith, G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. Bmj, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmj.k601\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eYuan, S. \u0026amp; Larsson, S. C. Coffee and Caffeine Consumption and Risk of Kidney Stones: A Mendelian Randomization Study. American Journal of Kidney Diseases 79, 9\u0026ndash;14.e11, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1053/j.ajkd.2021.04.018\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLittle, M. Mendelian Randomization: Methods for using Genetic Variants in Causal Estimation. Journal of the Royal Statistical Society Series a-Statistics in Society 181, 549\u0026ndash;550, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/rssa.12343\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eWei, J. \u003cem\u003eet al.\u003c/em\u003e Alcohol consumption and smoking in relation to psoriasis: a Mendelian randomization study. British Journal of Dermatology 187, 684\u0026ndash;691, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/bjd.21718\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eKim, E.-j. \u003cem\u003eet al.\u003c/em\u003e Coffee Consumption and Incident Tachyarrhythmias. JAMA Internal Medicine 181, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamainternmed.2021.3616\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLarsson, S. C. \u003cem\u003eet al.\u003c/em\u003e Genetic predisposition to smoking in relation to 14 cardiovascular diseases. European Heart Journal 41, 3304\u0026ndash;3310, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/eurheartj/ehaa193\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eSowers, J. R. \u0026amp; Frohlich, E. D. Insulin and insulin resistance. Medical Clinics of North America 88, 63\u0026ndash;82, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s0025-7125(03)00128-7\u003c/span\u003e\u003c/span\u003e (2004).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eClarke, T. K. \u003cem\u003eet al.\u003c/em\u003e Genome-wide association study of alcohol consumption and genetic overlap with other health-related traits in UK Biobank (N = 112 117). Molecular Psychiatry 22, 1376\u0026ndash;1384, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/mp.2017.153\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eJiang, L., Zheng, Z., Fang, H. \u0026amp; Yang, J. A generalized linear mixed model association tool for biobank-scale data. Nat Genet 53, 1616\u0026ndash;1621, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41588-021-00954-4\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLiu, M. \u003cem\u003eet al.\u003c/em\u003e Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat Genet 51, 237\u0026ndash;244, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41588-018-0307-5\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eBurgess, S., Butterworth, A. \u0026amp; Thompson, S. G. Mendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data. Genetic Epidemiology 37, 658\u0026ndash;665, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/gepi.21758\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eBurgess, S. \u0026amp; Thompson, S. G. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol 32, 377\u0026ndash;389, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10654-017-0255-x\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eBowden, J., Davey Smith, G., Haycock, P. C. \u0026amp; Burgess, S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol 40, 304\u0026ndash;314, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/gepi.21965\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eHartwig, F. P., Davey Smith, G. \u0026amp; Bowden, J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol 46, 1985\u0026ndash;1998, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ije/dyx102\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eVerbanck, M., Chen, C.-Y., Neale, B. \u0026amp; Do, R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nature Genetics 50, 693\u0026ndash;698, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41588-018-0099-7\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eHemani, G. \u003cem\u003eet al.\u003c/em\u003e The MR-Base platform supports systematic causal inference across the human phenome. Elife 7, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7554/eLife.34408\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMiura, K. \u003cem\u003eet al.\u003c/em\u003e Alcohol consumption and risk of melanoma among women: pooled analysis of eight case\u0026ndash;control studies. Archives of Dermatological Research 307, 819\u0026ndash;828, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00403-015-1591-x\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eRivera, A., Nan, H., Li, T., Qureshi, A. \u0026amp; Cho, E. Alcohol Intake and Risk of Incident Melanoma: A Pooled Analysis of Three Prospective Studies in the United States. Cancer Epidemiology, Biomarkers \u0026amp; Prevention 25, 1550\u0026ndash;1558, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/1055-9965.Epi-16-0303\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eKubo, J. T. \u003cem\u003eet al.\u003c/em\u003e Alcohol consumption and risk of melanoma and non-melanoma skin cancer in the Women\u0026rsquo;s Health Initiative. Cancer Causes \u0026amp; Control 25, 1\u0026ndash;10, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10552-013-0280-3\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eGandini, S. \u003cem\u003eet al.\u003c/em\u003e Alcohol, alcoholic beverages, and melanoma risk: a systematic literature review and dose\u0026ndash;response meta-analysis. European Journal of Nutrition 57, 2323\u0026ndash;2332, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00394-018-1613-5\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eKessides, M. C. \u003cem\u003eet al.\u003c/em\u003e Cigarette smoking and malignant melanoma: A case-control study. Journal of the American Academy of Dermatology 64, 84\u0026ndash;90, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jaad.2010.01.041\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eDusingize, J. C. \u003cem\u003eet al.\u003c/em\u003e Smoking and Cutaneous Melanoma: Findings from the QSkin Sun and Health Cohort Study. Cancer Epidemiology, Biomarkers \u0026amp; Prevention 27, 874\u0026ndash;881, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/1055-9965.Epi-17-1056\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eThompson, C. A., Zhang, Z.-F. \u0026amp; Arah, O. A. Competing risk bias to explain the inverse relationship between smoking and malignant melanoma. European Journal of Epidemiology 28, 557\u0026ndash;567, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10654-013-9812-0\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eWang, J., Li, X. \u0026amp; Zhang, D. Coffee consumption and the risk of cutaneous melanoma: a meta-analysis. European Journal of Nutrition 55, 1317\u0026ndash;1329, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00394-015-1139-z\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"cutaneous melanoma, alcohol consumption, smoking, coffee intake, tea intake, SNPs","lastPublishedDoi":"10.21203/rs.3.rs-3350096/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3350096/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAlcohol consumption and smoking have been associated with high risk, but coffee intake with a lower risk of cutaneous melanoma in observational studies. However, it is unclear whether these lifestyles are causally associated cutaneous melanoma.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eThis study aimed to investigate causal relationship of alcohol consumption, smoking, coffee and tea intake with cutaneous melanoma using the two-sample Mendelian randomization design.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe obtained the exposure data (alcohol consumption, alcoholic drinks per week, alcohol dependence, smoking initiation, cigarettes per day, smoking cessation, coffee intake and tea intake) and outcome data (cutaneous melanoma) from the IEU Open GWAS and GWAS catalog project. The SNPs independently associated with lifestyles at genome-wide significance levels (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e). Linkage disequilibrium score regression was used to compute the genetic correlation (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, clump distance\u0026thinsp;\u0026gt;\u0026thinsp;10000kb). We then performed two-sample Mendelian randomization (MR) to validate whether these lifestyles are causally associated with cutaneous melanoma.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe found that the alcohol consumption (OR\u0026thinsp;=\u0026thinsp;0.715, 95% CI: 0.322\u0026ndash;1.587), alcoholic drinks per week (OR\u0026thinsp;=\u0026thinsp;0.878, 95% CI: 0.591\u0026ndash;1.305) and alcohol dependence (OR\u0026thinsp;=\u0026thinsp;1.012, 95% CI: 0.957\u0026ndash;1.071) was not causally associated with cutaneous melanoma. The result showed no significant evidence to support an increased risk of cutaneous melanoma on smoking initiation (OR\u0026thinsp;=\u0026thinsp;0.927, 95% CI: 0.753\u0026ndash;1.142), cigarettes per day (OR\u0026thinsp;=\u0026thinsp;0.970, 95% CI: 0.802\u0026ndash;1.174) and smoking cessation (OR\u0026thinsp;=\u0026thinsp;1.862, 95% CI: 0.685\u0026ndash;5.059). Likewise, no significant associations were observed between genetically predicted coffee intake (OR\u0026thinsp;=\u0026thinsp;0.978, 95% CI: 0.586\u0026ndash;1.633) and tea intake (OR\u0026thinsp;=\u0026thinsp;0.696, 95% CI: 0.462\u0026ndash;1.048) with cutaneous melanoma.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAccording to our MR analysis, we found no evidence to support a causal association between alcohol consumption, smoking, coffee intake and tea intake with cutaneous melanoma.\u003c/p\u003e","manuscriptTitle":"The causal relationship between alcohol consumption, smoking, coffee, tea intake and cutaneous melanoma: a two-sample Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-10-13 20:14:58","doi":"10.21203/rs.3.rs-3350096/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"03006a94-72e3-45c3-b19b-67941d67fde1","owner":[],"postedDate":"October 13th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":25277726,"name":"Biological sciences/Cancer"},{"id":25277727,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2024-07-11T09:52:11+00:00","versionOfRecord":[],"versionCreatedAt":"2023-10-13 20:14:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3350096","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3350096","identity":"rs-3350096","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}