Causal associations between micronutrients and malignant neoplasms: a two-sample mendelian Randomization study

Causal associations between micronutrients and malignant neoplasms: 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 Research Article Causal associations between micronutrients and malignant neoplasms: a two-sample mendelian Randomization study Xiaowei Zhu, Pei Liu, Changsheng Sui, Aijia Yang, Zeping Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6966622/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 Malignant neoplasms remain a major global public health issue and are among the most serious diseases affecting human health. Micronutrients play a critical role in physiological processes, yet existing literature presents inconsistent findings regarding their relationship with malignancies. Methods A two-sample Mendelian randomization (MR) design was employed to investigate the potential causal associations between 15 micronutrients and the risk of malignant neoplasms. Summary statistics were sourced from large-scale genome-wide association studies (GWAS). MR analyses were conducted using inverse variance weighted (IVW), MR-Egger, weighted median, weighted mode, and simple mode methods. Selenium and folate were further analyzed using multivariable MR to assess independent effects. Result MR analysis revealed a significant inverse association between selenium levels and malignant neoplasms (OR = 0.96, 95% CI: 0.94–0.98, p = 0.002). Selenium remained independently associated with reduced cancer risk in multivariable MR analysis (OR = 0.967, 95% CI: 0.945–0.989, p = 0.003). Conversely, folate did not show an independent causal effect (p = 0.157). Conclusions These findings suggest selenium may have a protective role against malignant neoplasms. Further research is warranted to explore underlying mechanisms and validate these associations. Micronutrients Malignancies Selenium Folate Mendelian randomization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 68 Figure 72 1. Introduction At present, Malignant neoplasms are still a major public health problems worldwide, and they are among the most serious diseases that endanger human health. Many studies have explored the disease effects of micronutrients, including their effects on malignancies, and some studies have reported antitumor effects of micronutrient supplementation in patients with malignancies[ 1 ]. Many studies have shown that micronutrients have health benefits, but there is no clear cause-effect relationship. Many studies on the relationships between various micronutrients and various cancers have shown that only a few micronutrients are a potentially link to cancer risk[ 2 – 7 ]. Mendelian randomization is a powerful tool in epidemiology, primarily using genetic variation as a tool to assess the causal relationship between exposure factors and a particular disease. Confounding factors are important interfering factors for causality inference in epidemiological studies, and the genetic variation in the Mendelian randomization research method follows the principle of random allocation of alleles to offspring, similar to randomized controlled experiments, which can effectively avoid confounding factors obtained in observational studies. Therefore, this study further explored the potential causal relationships between 15 trace elements and Malignant neoplasmss through MR analysis. This study provides a relevant theoretical basis for understanding the relationship between Malignant neoplasms and micronutrients. 2. Materials and methods 2.1 Study Design We used micronutrients as exposure factors and Malignant neoplasmss as outcome variables. We used single nucleotide polymorphisms (SNPs), which are highly correlated with exposure factors and outcome variables, as instrumental variables to ensure their independence. The MR package was used for analysis in R software to investigate the causal relationship between micronutrients and Malignant neoplasms[ 8 ]. Subsequently, to control for confounders affecting Malignant neoplasms, screened SNPS-related factors were included in multivariate Mendelian randomisation analyses to exclude any other effects of these factors on Malignant neoplasms risk. In addition, we verified the reliability of the causal association results by using heterogeneity tests and pleiotropy and sensitivity analysis tests. To explore the potential causal relationship between the micronutrients and Malignant neoplasms. MR Methods must meet three basic criteria[ 9 ]: (i) The genetic variation selected as an instrumental variable (IV) should be closely related to 15 micronutrients; (ii) Genetic instruments should be independent of malignant tumor outcomes and independent of potential confounding factors. (iii) Genetic variations should be specifically associated with Malignant neoplasms through micronutrients rather than through other pathways. See Fig. 1 for details. 2.2 Data on the genetic epidemiology of micronutrients GWAs for trace elements were searched in the Genome-Wide Association Studies (GWAS) catalog. It contains copper, calcium, carotene, folate, iron, magnesium, potassium, selenium, vitamin A, vitamin B12, vitamin B6, vitamin C, vitamin D, vitamin E, zinc and 15 other micronutrients. The GWAS data for copper, selenium, and zinc were derived from a study by Evans[ 10 ], which included 2,603 individuals and 2,543,646 SNPs. The remaining 12 trace elements and nutrients were obtained from a comprehensive GWAS meta-analysis conducted by Ben Elsworth[ 11 ], which encompassed a cohort of 64,979 individuals of European ancestry and included 9,851,867 SNPs, These data are stored in the IEU OpenGWAS database( https://gwas.mrcieu.ac.uk ). The ID numbers of the relevant GWAS datasets are provided in supplementary Table 1. 2.3 Data on the genetic epidemiology of malignant neoplasms The relevant data on Malignant neoplasms were obtained from the FinnGen consortium database, specifically from a phenotype dataset named C3_CANCER_EXALLC. This dataset includes 87,531 patients with Malignant neoplasms and 314,193 controls, with the control group excluding any individuals with a history of cancer. All data were derived from individuals of European ancestry. 2.4 Statistical analysis To ensure the reliability and validity of the research results, we adopted a series of statistical methods. Analysis methods such as inverse Variance Weighting (IVW) method, weighted median method, and MR-Egger method were adopted for analysis. The pleipotency of the selected iv was evaluated using MR-Egger regression and MR-PRESSO global test. The heterogeneity among the selected IVs was also evaluated using the Cochrane Q value. Furthermore, we also conducted a "leave-one-out" analysis to examine whether individual SNPS disproportionately affected the overall estimation. All statistical analyses were conducted using the "TwoSampleMR (version 0.6.6)" package in version R 4.4.1. 2.5 Genetic variable tool selection In the context of MR analysis, the genetic variable tool is used to investigate causal relationships between exposure factors and outcome variables. These genetic variable tools usually contain genetic variants, with SNPS being the most commonly tool[ 12 ]. Taking into account linkage imbalances, we compiled genome-wide significant (P < 5×10 − 6 ) SNPs from the micronutrient dataset. We set the parameter (r2) to 0.01 and the genetic distance between genes to 10,000 kb. In the end, we obtained a set of independent SNPs most closely related to the outcome variable as the final instrumental variable. 2.6 Mendelian randomization analysis Inverse variance weighting (IVW), MR Egger[ 13 ] regression and weighted median estimation (WME) were used for MR analysis. We established a statistical strength threshold of F > 10[ 14 ], determined by the formula F = (R2/1-R2) (n-K-1/K), where N represents the sample size, K represents the number of instrumental variables, and R2 represents the proportion of exposure variance explained by the instrumental variables. 2.7 Heterogeneity test and Sensitivity analysis To assess heterogeneity among individual genetic instrumental variables, we used a heterogeneity test. If p 0.05 indicates no heterogeneity.We performed a retention sensitivity test to assess how each SNP individually affected the results. Specifically, we iteratively exclude each SNP and recalculate the MR results with the remaining SNPs. If the exclusion of a specific SNP results in a material difference from the overall result, it indicates that the MR result is sensitive to the SNP result. 3. Results 3.1 Genetic variable tool selection After excluding SNPs associated with linkage disequilibria and ensuring an F-number greater than 10, we included 188 micronutrient-related SNPs as instrumental variables. See supplementary Table 2 for details. Including β values, standard errors, effect alleles, and other alleles. All SNPs have an F-statistic greater than 10, indicating the absence of weak instrumental variables. 3.2 The causal relationship between 15 trace elements and nutrients and Malignant neoplasms. In our Mendelian randomization (MR) analysis, we evaluated the potential causal relationship between 15 trace elements and nutrients and Malignant neoplasms. We estimated the effects of each micronutrient using various methods, including inverse variance weighting (IVW), MR-Egger, weighted median, simple mode, and weighted mode, with IVW being the primary approach. The corresponding p-values and 95% confidence intervals were calculated. The results indicated that, except for selenium and folate, all other trace elements and nutrients had p-values greater than 0.05, suggesting no statistically significant association. Detailed analysis results are presented in Fig. 2 and Fig. 3 . We primarily used the inverse variance weighting (IVW) method to identify two micronutrients associated with Malignant neoplasms. The results were as follows: selenium:OR (95% CI) = 0.959569(0.935027–0.984755), p = 0.001795; folate:OR (95% CI) = 1.1354(1.008621–1.278114), p = 0.035545. The IVW method revealed that the p values of the two micronutrients were less than 0.05, and the difference was statistically significant. The above data indicate that the OR value of selenium is less than 1, indicating that it may be a protective factor for malignant neoplasms. In addition, the IVW results revealed that the OR value of folate was greater than 1, indicating that folate may be a risk factor for Malignant neoplasms. We also carried out MR Egger regression and WME analysis, and the results are shown in supplementary Table 3 . Scatter plots show that consistent causal effects are observed in the IVW, MR Egger, and WME methods, as shown in Fig. 4 and Fig. 5 . A funnel plot was used for visual analysis. as shown in Fig. 6 and Fig. 7 . 3.3 Sensitivity analysis According to the Cochran Q test[ 15 ], the p values of both micronutrients exceeded 0.05, indicating no heterogeneity among the SNPS. The intercept term in the MR Egger regression produced a p value greater than 0.05, indicating a lack of statistically significant difference, The details are shown in supplementary Table 3. Therefore, there is no horizontal pleiotropy between SNPS. Additional information is provided in supplementary Table 2 . Using a leave-one sensitivity analysis, we examined the effect of each SNP on the results. The results of the "leave-one-out" sensitivity analysis revealed that the MR analysis did not change significantly when each SNP was excluded individually, and no single-nucleotide polymorphism (SNP) had a substantial effect on causation (Fig. 8 and Fig. 9 ). 3.4 Multivariate MR analysis In the multivariate analysis[ 16 ], we extracted the two micronutrients associated with Malignant neoplasms for further analysis. The results showed that selenium was negatively correlated with the occurrence of Malignant neoplasms, and the effect was significant (P = 0.003, OR = 0.967,95%CI: 0.945–0.989), indicating that selenium had an independent causal relationship with the occurrence of Malignant neoplasms, independent of folic acid. In addition, we found that the effect of folic acid on the risk of Malignant neoplasms was not independent (P = 0.157, OR = 1.104, 95%CI 0.962–1.267). Then, we tested the results of multivariate analysis for pleiotropy and heterogeneity, with p values greater than 0.05. The results showed no pleiotropy or heterogeneity. See Supplementary Table 4 and Fig. 10 for details. 4. Discussion Micronutrients, including vitamins and trace elements, play an important role in maintaining the body's physiological function and immune homeostasis. Numerous studies have confirmed that multiple members of the vitamin family can be effective in preventing and alleviating certain diseases, such as rickets, dry eye, pellagra and beriberi. In addition to vitamins, the role of trace elements in regulating body metabolism, oxidative stress and immune response should not be ignored. In recent years, there has been increasing evidence that micronutrients play an important role in the occurrence, development and prevention of cancer[ 17 ]. In this study, we used Mendelian randomization (MR) analysis based on GWAS data and the FinnGen database to investigate the potential causal effect of micronutrients on malignancies. Random effect inverse variance weighting (IVW), Egger regression and weighted median method were used to reduce the bias in causal inference. The results suggest that selenium may act as a protective factor and be associated with a reduced risk of Malignant neoplasms. In addition, preliminary analyses suggested that folic acid may increase the risk of Malignant neoplasms, but this association did not remain independent in multivariate analyses, suggesting that folic acid may influence tumorigenesis through other indirect mechanisms rather than direct causation Selenium enters the body through a variety of sources, including food, supplements, and mineral drinking water, in both inorganic and organic forms, and absorption of selenium and its compounds occurs in the duodenum and small intestine[ 18 ]. The role of selenium in tumor prevention and treatment has been demonstrated in several experimental and clinical studies[ 2 ]. Selenium is an important and indispensable trace element, which exerts its biological function mainly through selenoproteins such as glutathione peroxidase, thioredoxin reductase and selenoprotein p. These selenoproteins not only maintain REDOX homeostasis, but also inhibit the proliferation and metastasis of cancer cells by regulating cellular DNA signaling pathways and immune responses. In addition, the role of selenium in cancer is dose-dependent, known as a "biphasic effect." At low doses, selenium reduces oxidative damage mainly through antioxidant mechanisms and promotes the survival of normal cells. High doses of selenium can induce the production of reactive oxygen species (ROS), trigger apoptosis and necrosis, and inhibit the growth of cancer cells. The relationship between selenium and cancer has been thoroughly discussed in epidemiological studies. A study by Cai X[ 19 ] showed that selenium plays an important role in prostate cancer. The researchers included 69 relevant literatures, and the results showed that serum selenium level was negatively correlated with the risk of prostate cancer, which was similar to the results of this study. However, a recent study showed the opposite results, showing that there is no clear evidence that selenium has a preventive effect in prostate cancer, and no causal relationship between selenium and overall prostate cancer was found[ 20 ], which is consistent with the results of the SELRCT study[ 21 ]. This inconsistency of findings may be related to differences in selenium nutritional status of the population, individual genetic differences, dietary factors, and study methodologies. In addition, other studies have reported that selenium is not associated with a variety of cancers, such as colorectal cancer[ 22 ], bladder cancer[ 23 ], breast cancer[ 24 ], cervical cancer, endometrial cancer and ovarian cancer[ 25 ]. Therefore, in future studies, the optimal dose range of selenium and individualized nutritional intervention strategies should be further explored to clarify the mechanism of action of selenium in different cancer types. In contrast, the relationship between folic acid and the risk of Malignant neoplasms are more complex. Folic acid is an essential, water-soluble B vitamin that is widely found in a variety of food sources, such as dark green leafy vegetables and beans. Dietary folic acid exists in a reductive state and requires oxidation and hydrolysis to be absorbed. On the other hand, the presence of folic acid as oxidized pteroyl monoglutamate can promote its bioavailability[ 26 ]. Folic acid is a key factor in the process of DNA synthesis and methylation, and insufficient intake can lead to DNA damage and gene mutations, thereby increasing cancer risk[ 27 ]. However, excessive intake of folic acid may promote the proliferation of cancer cells, especially in precancerous lesions or early stages of tumors, and folic acid may accelerate the division and growth of cancer cells by providing an adequate supply of nucleotides[ 28 ]. Therefore, folic acid intake levels need to be balanced and different individuals may have the best dose, rather than simply "more is better". In addition, folic acid metabolism is regulated by multiple genes, and folic acid absorption and utilization may be different among individuals with different genotypes, which may partly explain the inconsistent effects of folic acid on Malignant neoplasms in different studies. Although preliminary analysis in our study showed that folic acid is a risk factor for Malignant neoplasms, it may be related to folic acid receptors FOLR1 and FOLR2, which can bind folic acid on cells and transfer into cells. Studies have shown that FOLR1 and FOLR2 are overexpressed in various cancers such as breast cancer, pancreatic cancer, brain cancer and leukemia. It is often associated with accelerated cancer progression and poor patient prognosis[ 29 ]. However, subsequent multivariate analysis found that it was not an independent risk factor. Therefore, future studies should focus on individualized nutritional interventions combined with genetic background, dietary habits and metabolic characteristics to more accurately assess the role of folic acid in the development of Malignant neoplasms. Our current MR study has several limitations: (1) The data were obtained only from a European population, limiting the generalizability of the findings to other populations. (2) The study was statistical in nature and did not delve into the underlying mechanisms involved. (3) Micronutrients may be affected by interactions with other factors and depend on their bioavailability in the body, which was not adequately considered in our study. 5. Conclusion Our results show that selenium is a protective factor in the risk of Malignant neoplasms. This discovery provides new insights and valuable implications for the prevention and treatment of Malignant neoplasms. However, our findings are primarily based on database analysis. Further clinical studies are needed to elucidate the effects of micronutrients on malignancies and their specific protective mechanisms. Declarations Clinical trial number: not applicable Author contributions All the authors contributed to the conception and design of the research. Zhu wrote the draft, Liu and Sui were responsible for data analysis, Yang, Zhang and Qiao were in charge of data organization, and Yang was responsible for the verification of the manuscript. All the authors read and approved the final manuscript. Funding No funds for conducting this research were received. Availability of data and materials All the data of the results of this study can be found in the paper and its supplementary information. Data sharing declaration All the data generated during this research process are included in this published article. In the process of analysis, data are derived from GWAS database (https://gwas.micieu.ac.uk) and biological sample library FinnGen R10 version (https://www.finngen.fi/en/access_results). Competing interests The author declares that there is no conflict of interest. Ethics, Consent to Participate, and Consent to Publish declarations: not applicable. References Kim JY, Song M, Kim MS, et al. 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Supplementary Files supplementaryTable1.docx SupplementTable2.docx SupplementTable3.docx SupplementTable4.docx exposure.csv Fnumber.csv SNP.csv Folate.heterogeneity.csv Folate.MRPRESSOGlobal.csv Folate.MRPRESSOOutlier.csv Folate.MRresult.csv Folate.pleiotropy.csv Folate.SNP.csv Selenium.heterogeneity.csv Selenium.MRPRESSOGlobal.csv Selenium.MRPRESSOOutlier.csv Selenium.MRresult.csv Selenium.pleiotropy.csv Selenium.SNP.csv heterogeneity.csv MRresult.csv outcome.csv pleiotropy.csv 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. 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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-6966622","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":500634100,"identity":"e8d807b3-f5be-4acc-a444-d4d4e2a7e4f3","order_by":0,"name":"Xiaowei Zhu","email":"","orcid":"","institution":"Gansu University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiaowei","middleName":"","lastName":"Zhu","suffix":""},{"id":500634101,"identity":"40fa36da-a8bc-404c-bf79-1a51d962bd9e","order_by":1,"name":"Pei Liu","email":"","orcid":"","institution":"Gansu University of Chinese 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5","display":"","copyAsset":false,"role":"figure","size":38251,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScatter plot of MR results for folate and malignant neoplasms\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6966622/v1/e4bd2490ec0e0c3805acc936.png"},{"id":89517721,"identity":"df62760a-afa6-471e-8519-5969ff8bb08a","added_by":"auto","created_at":"2025-08-20 20:26:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":11584,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunnel plot of MR results of micronutrient selenium in malignant neoplasms\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"OnlineFigure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6966622/v1/7f8aa9dfc87ac7a6ac184c0f.png"},{"id":89517077,"identity":"38227350-29a1-479d-b21f-ccab2423d5fa","added_by":"auto","created_at":"2025-08-20 20:18:50","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":11618,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunnel plot of MR results of micronutrient folate against malignant neoplasms\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"OnlineFigure7.png","url":"https://assets-eu.researchsquare.com/files/rs-6966622/v1/c76a9a5bf484a1db6832eb2b.png"},{"id":89517098,"identity":"625d65ee-1796-4680-92f2-07ca765aa8cd","added_by":"auto","created_at":"2025-08-20 20:18:51","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":12661,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResults of the sensitivity analysis of the selenium content via the \"leave-one-out\"\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"OnlineFigure8.png","url":"https://assets-eu.researchsquare.com/files/rs-6966622/v1/9a378ab8c387b17bfe9d2482.png"},{"id":89517112,"identity":"e3942e65-6416-4d2b-b4f6-7a80c029af80","added_by":"auto","created_at":"2025-08-20 20:18:52","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":17429,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResults of sensitivity analysis of Folate by \"leave-one-out\"\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"OnlineFigure9.png","url":"https://assets-eu.researchsquare.com/files/rs-6966622/v1/c92a9ef1035a906038cd9bb3.png"},{"id":89517080,"identity":"6d76181b-107d-4c2d-8652-4470affb7b83","added_by":"auto","created_at":"2025-08-20 20:18:50","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":13513,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot results of multivariate MR analysis of two micronutrients\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"OnlineFigure10.png","url":"https://assets-eu.researchsquare.com/files/rs-6966622/v1/d4f8ff702d9e62f78b5022f1.png"},{"id":89517163,"identity":"873145db-95b2-48d4-8d62-f0f5751f00f3","added_by":"auto","created_at":"2025-08-20 20:18:54","extension":"png","order_by":68,"title":"Figure 68","display":"","copyAsset":false,"role":"figure","size":18325,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of Mendelian randomization (MR) design\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6966622/v1/a3f2f4512d3a8790dc68e9db.png"},{"id":89517139,"identity":"c54e1119-552a-4f42-9ac0-ad192a759c31","added_by":"auto","created_at":"2025-08-20 20:18:53","extension":"png","order_by":72,"title":"Figure 72","display":"","copyAsset":false,"role":"figure","size":117956,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMR analysis of 15 micronutrients and Malignant neoplasms\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6966622/v1/b00d66f2cf3a92c61c228750.png"},{"id":93568254,"identity":"723025f0-0f41-463a-9f58-6fc8ce7ce867","added_by":"auto","created_at":"2025-10-15 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20:18:54","extension":"csv","order_by":19,"title":"","display":"","copyAsset":false,"role":"supplement","size":3450,"visible":true,"origin":"","legend":"","description":"","filename":"heterogeneity.csv","url":"https://assets-eu.researchsquare.com/files/rs-6966622/v1/05f4a63e43edf40d56dce661.csv"},{"id":89517096,"identity":"bd66346e-c7af-4b32-94e7-51bd2042b166","added_by":"auto","created_at":"2025-08-20 20:18:51","extension":"csv","order_by":20,"title":"","display":"","copyAsset":false,"role":"supplement","size":16666,"visible":true,"origin":"","legend":"","description":"","filename":"MRresult.csv","url":"https://assets-eu.researchsquare.com/files/rs-6966622/v1/c8b811056e00c3a37adc7ab2.csv"},{"id":89517716,"identity":"cc9bb238-4fea-4625-aa79-22025af964cd","added_by":"auto","created_at":"2025-08-20 20:26:53","extension":"csv","order_by":21,"title":"","display":"","copyAsset":false,"role":"supplement","size":20759,"visible":true,"origin":"","legend":"","description":"","filename":"outcome.csv","url":"https://assets-eu.researchsquare.com/files/rs-6966622/v1/2037456aaa264ac9512185d7.csv"},{"id":89517722,"identity":"2d4edfd5-1d65-4796-8354-23a97b1d36eb","added_by":"auto","created_at":"2025-08-20 20:26:54","extension":"csv","order_by":22,"title":"","display":"","copyAsset":false,"role":"supplement","size":1778,"visible":true,"origin":"","legend":"","description":"","filename":"pleiotropy.csv","url":"https://assets-eu.researchsquare.com/files/rs-6966622/v1/d9d4b66f327f49c65d032fb2.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal associations between micronutrients and malignant neoplasms: a two-sample mendelian Randomization study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAt present, Malignant neoplasms are still a major public health problems worldwide, and they are among the most serious diseases that endanger human health. Many studies have explored the disease effects of micronutrients, including their effects on malignancies, and some studies have reported antitumor effects of micronutrient supplementation in patients with malignancies[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Many studies have shown that micronutrients have health benefits, but there is no clear cause-effect relationship. Many studies on the relationships between various micronutrients and various cancers have shown that only a few micronutrients are a potentially link to cancer risk[\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Mendelian randomization is a powerful tool in epidemiology, primarily using genetic variation as a tool to assess the causal relationship between exposure factors and a particular disease. Confounding factors are important interfering factors for causality inference in epidemiological studies, and the genetic variation in the Mendelian randomization research method follows the principle of random allocation of alleles to offspring, similar to randomized controlled experiments, which can effectively avoid confounding factors obtained in observational studies. Therefore, this study further explored the potential causal relationships between 15 trace elements and Malignant neoplasmss through MR analysis. This study provides a relevant theoretical basis for understanding the relationship between Malignant neoplasms and micronutrients.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Design\u003c/h2\u003e\u003cp\u003eWe used micronutrients as exposure factors and Malignant neoplasmss as outcome variables. We used single nucleotide polymorphisms (SNPs), which are highly correlated with exposure factors and outcome variables, as instrumental variables to ensure their independence. The MR package was used for analysis in R software to investigate the causal relationship between micronutrients and Malignant neoplasms[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Subsequently, to control for confounders affecting Malignant neoplasms, screened SNPS-related factors were included in multivariate Mendelian randomisation analyses to exclude any other effects of these factors on Malignant neoplasms risk. In addition, we verified the reliability of the causal association results by using heterogeneity tests and pleiotropy and sensitivity analysis tests. To explore the potential causal relationship between the micronutrients and Malignant neoplasms. MR Methods must meet three basic criteria[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]: (i) The genetic variation selected as an instrumental variable (IV) should be closely related to 15 micronutrients; (ii) Genetic instruments should be independent of malignant tumor outcomes and independent of potential confounding factors. (iii) Genetic variations should be specifically associated with Malignant neoplasms through micronutrients rather than through other pathways. See Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for details.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Data on the genetic epidemiology of micronutrients\u003c/h2\u003e\u003cp\u003eGWAs for trace elements were searched in the Genome-Wide Association Studies (GWAS) catalog. It contains copper, calcium, carotene, folate, iron, magnesium, potassium, selenium, vitamin A, vitamin B12, vitamin B6, vitamin C, vitamin D, vitamin E, zinc and 15 other micronutrients. The GWAS data for copper, selenium, and zinc were derived from a study by Evans[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], which included 2,603 individuals and 2,543,646 SNPs. The remaining 12 trace elements and nutrients were obtained from a comprehensive GWAS meta-analysis conducted by Ben Elsworth[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], which encompassed a cohort of 64,979 individuals of European ancestry and included 9,851,867 SNPs, These data are stored in the IEU OpenGWAS database(\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). The ID numbers of the relevant GWAS datasets are provided in \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003esupplementary Table\u0026nbsp;1.\u003c/span\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Data on the genetic epidemiology of malignant neoplasms\u003c/h2\u003e\u003cp\u003eThe relevant data on Malignant neoplasms were obtained from the FinnGen consortium database, specifically from a phenotype dataset named C3_CANCER_EXALLC. This dataset includes 87,531 patients with Malignant neoplasms and 314,193 controls, with the control group excluding any individuals with a history of cancer. All data were derived from individuals of European ancestry.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e\u003cp\u003eTo ensure the reliability and validity of the research results, we adopted a series of statistical methods. Analysis methods such as inverse Variance Weighting (IVW) method, weighted median method, and MR-Egger method were adopted for analysis. The pleipotency of the selected iv was evaluated using MR-Egger regression and MR-PRESSO global test. The heterogeneity among the selected IVs was also evaluated using the Cochrane Q value. Furthermore, we also conducted a \"leave-one-out\" analysis to examine whether individual SNPS disproportionately affected the overall estimation. All statistical analyses were conducted using the \"TwoSampleMR (version 0.6.6)\" package in version R 4.4.1.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Genetic variable tool selection\u003c/h2\u003e\u003cp\u003eIn the context of MR analysis, the genetic variable tool is used to investigate causal relationships between exposure factors and outcome variables. These genetic variable tools usually contain genetic variants, with SNPS being the most commonly tool[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Taking into account linkage imbalances, we compiled genome-wide significant (P\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e) SNPs from the micronutrient dataset. We set the parameter (r2) to 0.01 and the genetic distance between genes to 10,000 kb. In the end, we obtained a set of independent SNPs most closely related to the outcome variable as the final instrumental variable.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Mendelian randomization analysis\u003c/h2\u003e\u003cp\u003eInverse variance weighting (IVW), MR Egger[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] regression and weighted median estimation (WME) were used for MR analysis. We established a statistical strength threshold of F\u0026thinsp;\u0026gt;\u0026thinsp;10[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], determined by the formula F = (R2/1-R2) (n-K-1/K), where N represents the sample size, K represents the number of instrumental variables, and R2 represents the proportion of exposure variance explained by the instrumental variables.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Heterogeneity test and Sensitivity analysis\u003c/h2\u003e\u003cp\u003eTo assess heterogeneity among individual genetic instrumental variables, we used a heterogeneity test. If p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, heterogeneity was indicated. In contrast, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicates no heterogeneity.We performed a retention sensitivity test to assess how each SNP individually affected the results. Specifically, we iteratively exclude each SNP and recalculate the MR results with the remaining SNPs. If the exclusion of a specific SNP results in a material difference from the overall result, it indicates that the MR result is sensitive to the SNP result.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Genetic variable tool selection\u003c/h2\u003e\u003cp\u003eAfter excluding SNPs associated with linkage disequilibria and ensuring an F-number greater than 10, we included 188 micronutrient-related SNPs as instrumental variables. See \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003esupplementary Table\u0026nbsp;2\u003c/span\u003e for details. Including β values, standard errors, effect alleles, and other alleles. All SNPs have an F-statistic greater than 10, indicating the absence of weak instrumental variables.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.2 The causal relationship between 15 trace elements and nutrients and Malignant neoplasms.\u003c/h2\u003e\u003cp\u003eIn our Mendelian randomization (MR) analysis, we evaluated the potential causal relationship between 15 trace elements and nutrients and Malignant neoplasms. We estimated the effects of each micronutrient using various methods, including inverse variance weighting (IVW), MR-Egger, weighted median, simple mode, and weighted mode, with IVW being the primary approach. The corresponding p-values and 95% confidence intervals were calculated. The results indicated that, except for selenium and folate, all other trace elements and nutrients had p-values greater than 0.05, suggesting no statistically significant association. Detailed analysis results are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe primarily used the inverse variance weighting (IVW) method to identify two micronutrients associated with Malignant neoplasms. The results were as follows: selenium:OR (95% CI)\u0026thinsp;=\u0026thinsp;0.959569(0.935027\u0026ndash;0.984755), p\u0026thinsp;=\u0026thinsp;0.001795; folate:OR (95% CI)\u0026thinsp;=\u0026thinsp;1.1354(1.008621\u0026ndash;1.278114), p\u0026thinsp;=\u0026thinsp;0.035545. The IVW method revealed that the p values of the two micronutrients were less than 0.05, and the difference was statistically significant. The above data indicate that the OR value of selenium is less than 1, indicating that it may be a protective factor for malignant neoplasms. In addition, the IVW results revealed that the OR value of folate was greater than 1, indicating that folate may be a risk factor for Malignant neoplasms. We also carried out MR Egger regression and WME analysis, and the results are shown in \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003esupplementary Table\u0026nbsp;3\u003c/span\u003e. Scatter plots show that consistent causal effects are observed in the IVW, MR Egger, and WME methods, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eA funnel plot was used for visual analysis. as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Sensitivity analysis\u003c/h2\u003e\u003cp\u003eAccording to the Cochran Q test[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], the p values of both micronutrients exceeded 0.05, indicating no heterogeneity among the SNPS. The intercept term in the MR Egger regression produced a p value greater than 0.05, indicating a lack of statistically significant difference, The details are shown in supplementary Table\u0026nbsp;3. Therefore, there is no horizontal pleiotropy between SNPS. Additional information is provided in \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003esupplementary Table\u0026nbsp;2\u003c/span\u003e. Using a leave-one sensitivity analysis, we examined the effect of each SNP on the results. The results of the \"leave-one-out\" sensitivity analysis revealed that the MR analysis did not change significantly when each SNP was excluded individually, and no single-nucleotide polymorphism (SNP) had a substantial effect on causation (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Multivariate MR analysis\u003c/h2\u003e\u003cp\u003eIn the multivariate analysis[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], we extracted the two micronutrients associated with Malignant neoplasms for further analysis. The results showed that selenium was negatively correlated with the occurrence of Malignant neoplasms, and the effect was significant (P\u0026thinsp;=\u0026thinsp;0.003, OR\u0026thinsp;=\u0026thinsp;0.967,95%CI: 0.945\u0026ndash;0.989), indicating that selenium had an independent causal relationship with the occurrence of Malignant neoplasms, independent of folic acid. In addition, we found that the effect of folic acid on the risk of Malignant neoplasms was not independent (P\u0026thinsp;=\u0026thinsp;0.157, OR\u0026thinsp;=\u0026thinsp;1.104, 95%CI 0.962\u0026ndash;1.267). Then, we tested the results of multivariate analysis for pleiotropy and heterogeneity, with p values greater than 0.05. The results showed no pleiotropy or heterogeneity. See Supplementary Table\u0026nbsp;4 and Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e for details.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eMicronutrients, including vitamins and trace elements, play an important role in maintaining the body's physiological function and immune homeostasis. Numerous studies have confirmed that multiple members of the vitamin family can be effective in preventing and alleviating certain diseases, such as rickets, dry eye, pellagra and beriberi. In addition to vitamins, the role of trace elements in regulating body metabolism, oxidative stress and immune response should not be ignored. In recent years, there has been increasing evidence that micronutrients play an important role in the occurrence, development and prevention of cancer[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn this study, we used Mendelian randomization (MR) analysis based on GWAS data and the FinnGen database to investigate the potential causal effect of micronutrients on malignancies. Random effect inverse variance weighting (IVW), Egger regression and weighted median method were used to reduce the bias in causal inference. The results suggest that selenium may act as a protective factor and be associated with a reduced risk of Malignant neoplasms. In addition, preliminary analyses suggested that folic acid may increase the risk of Malignant neoplasms, but this association did not remain independent in multivariate analyses, suggesting that folic acid may influence tumorigenesis through other indirect mechanisms rather than direct causation\u003c/p\u003e\u003cp\u003eSelenium enters the body through a variety of sources, including food, supplements, and mineral drinking water, in both inorganic and organic forms, and absorption of selenium and its compounds occurs in the duodenum and small intestine[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The role of selenium in tumor prevention and treatment has been demonstrated in several experimental and clinical studies[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Selenium is an important and indispensable trace element, which exerts its biological function mainly through selenoproteins such as glutathione peroxidase, thioredoxin reductase and selenoprotein p. These selenoproteins not only maintain REDOX homeostasis, but also inhibit the proliferation and metastasis of cancer cells by regulating cellular DNA signaling pathways and immune responses. In addition, the role of selenium in cancer is dose-dependent, known as a \"biphasic effect.\" At low doses, selenium reduces oxidative damage mainly through antioxidant mechanisms and promotes the survival of normal cells. High doses of selenium can induce the production of reactive oxygen species (ROS), trigger apoptosis and necrosis, and inhibit the growth of cancer cells. The relationship between selenium and cancer has been thoroughly discussed in epidemiological studies. A study by Cai X[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] showed that selenium plays an important role in prostate cancer. The researchers included 69 relevant literatures, and the results showed that serum selenium level was negatively correlated with the risk of prostate cancer, which was similar to the results of this study. However, a recent study showed the opposite results, showing that there is no clear evidence that selenium has a preventive effect in prostate cancer, and no causal relationship between selenium and overall prostate cancer was found[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], which is consistent with the results of the SELRCT study[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This inconsistency of findings may be related to differences in selenium nutritional status of the population, individual genetic differences, dietary factors, and study methodologies. In addition, other studies have reported that selenium is not associated with a variety of cancers, such as colorectal cancer[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], bladder cancer[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], breast cancer[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], cervical cancer, endometrial cancer and ovarian cancer[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Therefore, in future studies, the optimal dose range of selenium and individualized nutritional intervention strategies should be further explored to clarify the mechanism of action of selenium in different cancer types.\u003c/p\u003e\u003cp\u003eIn contrast, the relationship between folic acid and the risk of Malignant neoplasms are more complex. Folic acid is an essential, water-soluble B vitamin that is widely found in a variety of food sources, such as dark green leafy vegetables and beans. Dietary folic acid exists in a reductive state and requires oxidation and hydrolysis to be absorbed. On the other hand, the presence of folic acid as oxidized pteroyl monoglutamate can promote its bioavailability[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Folic acid is a key factor in the process of DNA synthesis and methylation, and insufficient intake can lead to DNA damage and gene mutations, thereby increasing cancer risk[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. However, excessive intake of folic acid may promote the proliferation of cancer cells, especially in precancerous lesions or early stages of tumors, and folic acid may accelerate the division and growth of cancer cells by providing an adequate supply of nucleotides[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Therefore, folic acid intake levels need to be balanced and different individuals may have the best dose, rather than simply \"more is better\". In addition, folic acid metabolism is regulated by multiple genes, and folic acid absorption and utilization may be different among individuals with different genotypes, which may partly explain the inconsistent effects of folic acid on Malignant neoplasms in different studies. Although preliminary analysis in our study showed that folic acid is a risk factor for Malignant neoplasms, it may be related to folic acid receptors FOLR1 and FOLR2, which can bind folic acid on cells and transfer into cells. Studies have shown that FOLR1 and FOLR2 are overexpressed in various cancers such as breast cancer, pancreatic cancer, brain cancer and leukemia. It is often associated with accelerated cancer progression and poor patient prognosis[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, subsequent multivariate analysis found that it was not an independent risk factor. Therefore, future studies should focus on individualized nutritional interventions combined with genetic background, dietary habits and metabolic characteristics to more accurately assess the role of folic acid in the development of Malignant neoplasms.\u003c/p\u003e\u003cp\u003eOur current MR study has several limitations: (1) The data were obtained only from a European population, limiting the generalizability of the findings to other populations. (2) The study was statistical in nature and did not delve into the underlying mechanisms involved. (3) Micronutrients may be affected by interactions with other factors and depend on their bioavailability in the body, which was not adequately considered in our study.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOur results show that selenium is a protective factor in the risk of Malignant neoplasms. This discovery provides new insights and valuable implications for the prevention and treatment of Malignant neoplasms. However, our findings are primarily based on database analysis. Further clinical studies are needed to elucidate the effects of micronutrients on malignancies and their specific protective mechanisms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003enot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors contributed to the conception and design of the research. Zhu wrote the draft, Liu and Sui were responsible for data analysis, Yang, Zhang and Qiao were in charge of data organization, and Yang was responsible for the verification of the manuscript. All the authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funds for conducting this research were received.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data of the results of this study can be found in the paper and its supplementary information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData sharing declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data generated during this research process are included in this published article. In the process of analysis, data are derived from GWAS database (https://gwas.micieu.ac.uk) and biological sample library FinnGen R10 version (https://www.finngen.fi/en/access_results).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares that there is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics, Consent to Participate, and Consent to Publish declarations:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003enot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKim JY, Song M, Kim MS, et al. An atlas of associations between 14 micronutrients and 22 cancer outcomes: Mendelian randomization analyses. BMC Med. 2023;21(1):316.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVeronese N, Demurtas J, Pesolillo G, et al. 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Selenium and Vitamin E for Prevention of Non-Muscle-Invasive Bladder Cancer Recurrence and Progression: A Randomized Clinical Trial. JAMA Netw Open. 2023;6(10):e2337494.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu X, Pan D, Wang N, et al. Relationship Between Selenium in Human Tissues and Breast Cancer: a Meta-analysis Based on Case-Control Studies. Biol Trace Elem Res. 2021;199(12):4439\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGolara A, Kozłowski M, Guzik P, et al. The Role of Selenium and Manganese in the Formation, Diagnosis and Treatment of Cervical, Endometrial and Ovarian Cancer. Int J Mol Sci. 2023;24(13):10887.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePieroth R, Paver S, Day S, et al. Folate and Its Impact on Cancer Risk. Curr Nutr Rep. 2018;7(3):70\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAglago EK, Qu C, Harlid S, et al. Folate intake and colorectal cancer risk according to genetic subtypes defined by targeted tumor sequencing. Am J Clin Nutr. 2024;120(3):664\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSullivan MR, Darnell AM, Reilly MF, et al. Methionine synthase is essential for cancer cell proliferation in physiological folate environments. Nat Metab. 2021;3(11):1500\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNawaz FZ, Kipreos ET. Emerging roles for folate receptor FOLR1 in signaling and cancer. Trends Endocrinol Metab. 2022;33(3):159\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Micronutrients, Malignancies, Selenium, Folate, Mendelian randomization","lastPublishedDoi":"10.21203/rs.3.rs-6966622/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6966622/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eMalignant neoplasms remain a major global public health issue and are among the most serious diseases affecting human health. Micronutrients play a critical role in physiological processes, yet existing literature presents inconsistent findings regarding their relationship with malignancies.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA two-sample Mendelian randomization (MR) design was employed to investigate the potential causal associations between 15 micronutrients and the risk of malignant neoplasms. Summary statistics were sourced from large-scale genome-wide association studies (GWAS). MR analyses were conducted using inverse variance weighted (IVW), MR-Egger, weighted median, weighted mode, and simple mode methods. Selenium and folate were further analyzed using multivariable MR to assess independent effects.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e\u003cp\u003eMR analysis revealed a significant inverse association between selenium levels and malignant neoplasms (OR\u0026thinsp;=\u0026thinsp;0.96, 95% CI: 0.94\u0026ndash;0.98, p\u0026thinsp;=\u0026thinsp;0.002). Selenium remained independently associated with reduced cancer risk in multivariable MR analysis (OR\u0026thinsp;=\u0026thinsp;0.967, 95% CI: 0.945\u0026ndash;0.989, p\u0026thinsp;=\u0026thinsp;0.003). Conversely, folate did not show an independent causal effect (p\u0026thinsp;=\u0026thinsp;0.157).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThese findings suggest selenium may have a protective role against malignant neoplasms. Further research is warranted to explore underlying mechanisms and validate these associations.\u003c/p\u003e","manuscriptTitle":"Causal associations between micronutrients and malignant neoplasms: a two-sample mendelian Randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-20 20:18:44","doi":"10.21203/rs.3.rs-6966622/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":"f01788b9-4adc-4a8e-b851-2071c0d59694","owner":[],"postedDate":"August 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-15T08:40:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-20 20:18:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6966622","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6966622","identity":"rs-6966622","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}