Causal Associations between fish consumption and risk of cancer: a Mendelian randomization study

Causal Associations between fish consumption and risk of cancer: a 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 fish consumption and risk of cancer: a Mendelian randomization study Liming Zhang, Shubo Wang, Wangkai Cao, Xiuxin Mo, Yang Liu, Shaoqiang Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3938501/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： Previous studies have identified a correlation between the consumption of fish and the risk of cancer. However, the causal relationship between these two factors remains uncertain and necessitates further investigation. Methods： The GWAS data were obtained from the public GWAS database. In two-sample Mendelian Randomization (MR) analysis, various methods including inverse variance weighting (IVW), mr-egger regression, simple mode, weighted median, and weighted model were employed to evaluate the causal association between fish consumption and the risk of cancer. Results： The results of the MR analysis indicated a causal relationship between fish consumption and pancreatic cancer risk (IVW: OR=0.334, 95% CI: 0.137-0.863, P =0.023). However, no significant causal relationship was observed between fish consumption and other types of cancer, including bladder cancer ( P =0.934), colorectal cancer ( P =0.968), gastric cancer ( P =0.788), liver cell carcinoma ( P =0.732), lung cancer ( P =0.596), melanoma skin cancer ( P =0.198), oropharyngeal cancer ( P =0.090), prostate cancer ( P =0.075), and thyroid cancer ( P =0.848). Heterogeneity was identified in the MR analysis between fish consumption and thyroid cancer tool variables ( P =0.039), with no horizontal pleiotropy observed in any tool variables. The sensitivity analysis, employing the leave-one-out method, demonstrated the robustness of the MR analysis results. Conclusion: The MR analysis results revealed a causal relationship between fish consumption and the risk of pancreatic cancer. However, no such causal relationship was observed between fish consumption and other cancers, including bladder cancer, colon cancer, gastric cancer, liver cell carcinoma, lung cancer, melanoma skin cancer, oropharyngeal cancer, prostate cancer, and thyroid cancer. Mendelian randomization Genome-wide association studies Fish consumption Cancer Causality Figures Figure 1 Figure 2 1 Background As is widely recognized, fish is a commonly consumed food by humans. Fish serve as a valuable source of protein, high-quality unsaturated fatty acids, and essential vitamins such as B2 and B12. The extended carbon chains of unsaturated fatty acids contribute to cholesterol reduction[ 1 , 2 ]. Contemporary global data indicate that cancer is the primary cause of mortality, posing a significant threat to human health[ 3 ]. Despite substantial improvements in patient survival rates due to surgery, chemotherapy, targeted treatment, and immunotherapy, the overall survival rate (OS) for the majority of patients with advanced cancer remains notably low[ 4 ]. Consequently, primary prevention efforts hold paramount significance. Case-control research in Japan demonstrates an association between fish consumption and a reduced incidence of prostate cancer[ 5 ]. According to a prospective study, fish consumption is linked to a reduced risk of lung cancer[ 6 ]. Findings from a case-control study indicate that fish consumption is linked to a decreased risk of breast cancer[ 7 ]. Nevertheless, the association between fish consumption and cancer risk remains a subject of controversy[ 8 , 9 ]. This controversy may arise from the inherent limitations of traditional observational epidemiological methods in investigating disease etiology. They are susceptible to potential confounding factors and reverse causality, leading to a potential false causal correlation. The concept of Mendelian Randomization (MR) was introduced by Katan in 1986[ 10 ]. MR employs single nucleotide polymorphisms (SNPs) as instrumental variables (IVs) to estimate the causal effects of exposure and outcome[ 11 ]. Since genetic variation originates from the maternal generation and remains constant, MR not only adheres to the temporality criterion but is also less susceptible to traditional confounding factors, such as environment and behavior[ 12 ]. Consequently, MR holds significant potential for application in the realms of disease prevention, treatment, and the formulation of public health policies. Upon literature review, our research group identified the absence of Mendelian randomized studies investigating the causal relationship between fish consumption and cancer risk. Consequently, our study relies on the open GWAS database and employs two-sample Mendelian randomized analysis to investigate the relationship between fish consumption and cancer. 2 Materials and methods 2.1 Research and design In this study, fish consumption served as the exposure factor, and SNPs significantly associated with fish consumption were chosen as instrumental variables (IVs). The outcome variable comprised various types of cancer, including Bladder cancer, Colonic cancer, Gastric cancer, Liver cell carcinoma, Lung cancer, Melanoma skin cancer, Oropharyngeal cancer, Pancreas cancer, Prostate cancer, and Thyroid cancer. The instrumental variables (IVs) in the MR study must satisfy three core hypotheses[ 13 ]: (1) IVs are highly correlated with exposure factors. (2) IVs influence the outcome solely through exposure factors, without a direct association with the outcome. (3) IVs are unrelated to confounding factors (Fig. 1 ). 2.2 Data source The exposure and outcome data for this study were obtained from the GWAS catalog[ 14 ]( https://www.ebi.ac.uk/gwas/ ) and the UK biological sample bank IEU OpenGWAS[ 15 ]( https://gwas.mrcieu.ac.uk/ ). The above figures are all from Europe. Details are shown in Supplementary Table 1. Our MR studies are conducted using the published GWAS database, which has obtained ethical approval and informed consent, so no additional ethical and informed consent is required for this article. 2.3 Selection of instrumental variables In this study, GWAS data extraction is performed using RStudio software. The selection of SNPs as instrumental variables adheres to the following criteria: (1) A high correlation between SNP and exposure factor with a significance threshold of P < 5×10 − 8 ; (2) To mitigate the impact of linkage disequilibrium (LD), the R software establishes parameter conditions R 2 = 0.001 and kb = 10000, selecting mutually independent SNPs as instrumental variables[ 16 , 17 ]; (3) F statistics assess the strength of instrumental variables, where F > 10 indicates the absence of weak instrumental variable bias. Additionally, SNPs associated with confounding factors are excluded by referencing the PhenoScanner database ( http://www.phenoscanner.medschl.cam.ac.uk/ ). The identified SNPs serve as instrumental variables in this study. 2.4 Mendelian randomization analyses The TwoSampleMR package was employed for MR analysis to assess the causal relationship between fish consumption and cancer. The relationship was evaluated using odds ratio (OR) and a 95% confidence interval (95% CI) in diverse regression models, encompassing inverse variance weighting (IVW), MR-Egger regression, simple mode, weighted median, and weighted model. IVW utilizes the reciprocal of the variance of the tool variables for fitting when all tool variables satisfy the three conditions, ensuring an unbiased estimation. MR-Egger regression incorporates the reciprocal of the variance of the outcome for weighted calculation, including an intercept term in the regression[ 18 ]. It considers multiple effects, and the straight line is not mandated to pass through the origin. The weighted median method involves ranking the estimated values of each SNP ratio from small to large and utilizing the median for a weighted average. This method requires more than 50% of SNPs in the model as valid tool variables to obtain unbiased estimates of causality effects. However, the estimation accuracy and efficiency of the weighted median method are lower than that of the IVW method[ 19 ]. The primary results in this study are based on IVW analysis. Heterogeneity was assessed using Cochran's Q test, with P < 0.05 indicating its presence[ 20 ]. In the presence of heterogeneity, the random-effect IVW method was employed for MR analysis. Pleiotropy was identified through the MR-Egger intercept test, with P < 0.05 indicating the presence of pleiotropy. In the absence of horizontal gene pleiotropy in genetic tool variables, IVW can furnish causal estimates with reduced bias. Sensitivity analysis using the "leave one method" was employed to assess stability and examine whether the removal of a single variation affected the relationship between exposure and outcome. Scatter plots were employed to visualize the impact of each genetic tool variable on exposure and outcome. MR analysis was conducted using the "TwoSampleMR" and "ggplot2" packages in R software (version 4.3.1). 3 Results 3.1 Selection of tool variable Following the screening criteria for tool variables in this study, fish consumption serves as the exposure factor, and depression acts as the outcome variable. After screening, 22 SNPs were chosen as tool variables. The calculated F statistics, ranging from 29 to 75, all exceed 10, suggesting a low likelihood of weak tool bias. No SNP related to confounding factors was identified in the PhenoScanner database (Supplementary Table 2). 3.2 Causal relationship between fish consumption and cancer MR analysis revealed that fish consumption was a protective factor against pancreatic cancer (OR = 0.334, 95% CI: 0.137–0.863, P = 0.023). However, no significant causal relationship was observed between fish consumption and other types of cancer (Bladder cancer: P = 0.934, Colorectal cancer: P = 0.968, Gastric cancer: P = 0.788, Liver cell carcinoma: P = 0.732, Lung cancer: P = 0.596, Melanoma skin cancer: P = 0.198, Oropharyngeal cancer: P = 0.090, Prostate cancer: P = 0.075, Thyroid cancer: P = 0.848; Fig. 2 ). The forest plot and scatter plot further illustrate the causal relationship between fish consumption and cancer (Supplementary Figs. 1 and 2). 3.3 Sensitivity Analysis of the MR Results We employed IVW and MR-Egger regression to assess heterogeneity among the tool variables. The results indicated heterogeneity among the tool variables for thyroid cancer ( P = 0.039, Supplementary Tables 3). Consequently, we applied the IVW random effect model for MR analysis, and the results closely aligned with those of MR ( P = 0.004), affirming the validity of the causal effect (Fig. 2 ). Analyses for other types of cancer revealed no significant heterogeneity among the tool variables (Bladder cancer: P = 0.705, Colorectal cancer: P = 0.292, Gastric cancer: P = 0.833, Liver cell carcinoma: P = 0.305, Lung cancer: P = 0.062, Melanoma skin cancer: P = 0.109, Oropharyngeal cancer: P = 0.467, Pancreas cancer: P = 0.733, Prostate cancer: P = 0.175). Additionally, we included funnel plots for each association pair to enhance heterogeneity identification (Supplementary Fig. 3). The MR-Egger analysis revealed no horizontal pleiotropy in any of the tool variables (Bladder cancer: P = 0.076, Colorectal cancer: P = 0.472, Gastric cancer: P = 0.670, Liver cell carcinoma: P = 0.420, Lung cancer: P = 0.167, Melanoma skin cancer: P = 0.955, Oropharyngeal cancer: P = 0.989, Pancreas cancer: P = 0.693, Prostate cancer: P = 0.498, Thyroid cancer: P = 0.323; Supplementary Tables 4). This indicates that tool variables do not significantly affect the outcome in ways other than exposure. We performed sensitivity analysis using the leave-one-out method, removing SNPs one by one. The causal effect of the remaining SNP was compared with the results of the MR analysis using all SNPs to determine if the causal correlation was influenced by a single tool variable. The leave-one-out analysis demonstrated that no single SNP substantially influenced the overall MR estimation (Supplementary Fig. 4). The results of the sensitivity analysis confirmed the robustness of the MR analysis. 4 Discussion Fish constitutes a vital component of the human diet, often encouraged for its abundance in high-quality proteins, essential micronutrients, and fatty acids[ 21 ]. Notably, fish serves as a primary source of long-chain omega-3 polyunsaturated fatty acids, encompassing eicosapentaenoic acid and 22-carbohexaenoic acid, known for their anti-inflammatory and immunomodulatory effects[ 22 ]. Research indicates that omega-3 polyunsaturated fatty acids derived from fish can impede cancer progression. However, it's essential to acknowledge that fish may harbor environmental pollutants capable of disrupting endocrine function and potentially triggering cancer[ 23 ]. Consequently, the impact of edible fish on cancer incidence remains uncertain. Over the past decade, researchers have conducted numerous observational studies to investigate the correlation between fish consumption and cancer risk. However, the results are contradictory. A case-control study conducted in Galicia, northwestern Spain, suggests a potential increase in the risk of lung cancer associated with fish consumption[ 24 ]. In contrast, a prospective study involving 478,021 participants across 10 European countries indicated that consuming fish was not linked to the risk of lung cancer[ 25 ]. Similarly, findings in prostate cancer studies vary; some report an association between fish consumption and a reduced risk of prostate cancer, while others find no significant association[ 26 – 29 ]. In this study, we employed the Mendelian randomization (MR) method to elucidate the causal relationship between fish consumption and cancer risk. Our findings substantiate the presence of a causal relationship between fish consumption and pancreatic cancer risk. Conversely, we observed limited evidence supporting an association between fish consumption and other cancers, including bladder cancer, colorectal cancer, gastric cancer, liver cell carcinoma, lung cancer, melanoma skin cancer, oropharyngeal cancer, prostate cancer, and thyroid cancer. Furthermore, sensitivity analysis indicates the robustness of the overall results. Pancreatic cancer stands as a prominent cause of cancer-related mortality globally, imposing a substantial public health burden on both men and women. Given its grim prognosis and elevated mortality rates, identifying preventive factors for pancreatic cancer is of paramount public health importance. Established risk factors encompass chronic pancreatitis, smoking, and diabetes[ 30 ]. Dietary factors may also contribute to pancreatic cancer development, though consensus remains elusive. Notably, a sizable prospective cohort study within the Japanese population reveals an inverse correlation between fish consumption and pancreatic cancer risk[ 31 ]. Our study concurs, concluding that fish consumption serves as a protective factor against pancreatic cancer, suggesting increased fish intake as an effective preventive measure. In vitro and in vivo investigations underscore the pivotal role of inflammation in pancreatic cancer initiation and progression[ 32 ]. An epidemiological meta-analysis indicates an association between high-dose aspirin and other anti-inflammatory agents and a diminished risk of pancreatic cancer[ 33 ]. Evidence suggests that marine-derived polyunsaturated fatty acids, including eicosapentaenoic acid and 22-carbon hexaenoic acid, exhibit inhibitory effects on inflammation[ 34 ]. Thus, the consumption of fish-rich foods, rich in these fatty acids, may confer protection against pancreatic cancer due to their anti-inflammatory properties[ 35 ]. Furthermore, animal models demonstrate that dietary fish oil supplementation hampers pancreatic cancer development. Particularly noteworthy, Mendelian randomization investigations can furnish crucial evidence on causality, pivotal for public health guidance. Distinguished by a substantial sample size and enhanced statistical power compared to previous observational studies, our research underscores the causal link between fish consumption and pancreatic cancer risk. Despite supporting evidence, the role of fish consumption in pancreatic cancer prevention warrants further investigation. In this study, Mendelian randomization (MR) was employed to infer the causal relationship between exposure factors and the research outcome, utilizing genetic variation strongly correlated with exposure factors as a tool variable. As a recent advancement in epidemiological research, MR offers significant advantages. Primarily, the formation of genetic variation remains unaffected by lifestyle or social environment, safeguarding the outcome from confounding factors. Secondly, MR relies on a public database, thereby saving considerable research costs and time. Most importantly, the genetic variation, originating from the maternal generation and remaining unchanged, not only adheres to the rationality of the time sequence but is also less susceptible to traditional confounding factors such as environment and behavior. However, several limitations warrant consideration in this study. Firstly, the GWAS data predominantly originate from European populations, potentially limiting the generalizability of the results to other population groups. A more extensive GWAS with a larger sample size is essential to identify additional genetic variation for MR research, as the efficacy of tool variables hinges on the sample size of GWAS. Lastly, we exclusively explored the causal relationship between fish consumption and cancer without further subdividing the fish species. Furthermore, this study is purely statistical and does not delve into the biological mechanisms underlying the association between fish consumption and cancer risk. Nevertheless, our results serve as a foundation for exploring more in-depth mechanisms. To the best of our knowledge, this study marks the inaugural application of the MR method to investigate the causal relationship between fish consumption and cancer risk. Collectively, our findings substantiate a potential link between fish consumption and cancer risk. Further research is imperative to unravel the underlying mechanism. Declarations Conflicts of interest The authors declare that they have no conflict of interest. Funding This work was supported by the National Natural Science Foundation of China (81802290) and Shandong Provincial Natural Science Foundation (No. ZR2018BH020). Author Contribution LZ, SW designed and performed MR analysis. LZ, SW, WC and XM analyzed the data and organized pictures. LZ, SW and YL wrote and revised the paper. All authors read and approved the final version of the manuscript. Data availability statement All the data generated and analyzed during this study are included in the manuscript and the additional materials. References Shimakata T. [Polyunsaturated fatty acid]. Nihon Rinsho. 2001;59(Suppl 2):45–9. Chiesa G, Busnelli M, Manzini S, Parolini C. 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Br J Nutr. 2009;101(4):533–40. Turunen AW, Suominen AL, Kiviranta H, Verkasalo PK, Pukkala E. Cancer incidence in a cohort with high fish consumption. Cancer Causes Control. 2014;25(12):1595–602. Dosil-Díaz O, Ruano-Ravina A, Gestal-Otero JJ, Barros-Dios JM. Meat and fish consumption and risk of lung cancer: A case-control study in Galicia, Spain. Cancer Lett. 2007;252(1):115–22. Linseisen J, Rohrmann S, Bueno-de-Mesquita B, Büchner FL, Boshuizen HC, Agudo A, Gram IT, Dahm CC, Overvad K, Egeberg R, et al. Consumption of meat and fish and risk of lung cancer: results from the European Prospective Investigation into Cancer and Nutrition. Cancer Causes Control. 2011;22(6):909–18. Terry P, Lichtenstein P, Feychting M, Ahlbom A, Wolk A. Fatty fish consumption and risk of prostate cancer. Lancet. 2001;357(9270):1764–6. Watling CZ, Schmidt JA, Dunneram Y, Tong TYN, Kelly RK, Knuppel A, Travis RC, Key TJ, Perez-Cornago A. Risk of cancer in regular and low meat-eaters, fish-eaters, and vegetarians: a prospective analysis of UK Biobank participants. BMC Med. 2022;20(1):73. Allen NE, Key TJ, Appleby PN, Travis RC, Roddam AW, Tjønneland A, Johnsen NF, Overvad K, Linseisen J, Rohrmann S, et al. Animal foods, protein, calcium and prostate cancer risk: the European Prospective Investigation into Cancer and Nutrition. Br J Cancer. 2008;98(9):1574–81. Augustsson K, Michaud DS, Rimm EB, Leitzmann MF, Stampfer MJ, Willett WC, Giovannucci E. A prospective study of intake of fish and marine fatty acids and prostate cancer. Cancer Epidemiol Biomarkers Prev. 2003;12(1):64–7. Vincent A, Herman J, Schulick R, Hruban RH, Goggins M. Pancreatic cancer. Lancet. 2011;378(9791):607–20. Hidaka A, Shimazu T, Sawada N, Yamaji T, Iwasaki M, Sasazuki S, Inoue M, Tsugane S. Fish, n-3 PUFA consumption, and pancreatic cancer risk in Japanese: a large, population-based, prospective cohort study. Am J Clin Nutr. 2015;102(6):1490–7. Garcea G, Dennison AR, Steward WP, Berry DP. Role of inflammation in pancreatic carcinogenesis and the implications for future therapy. Pancreatology. 2005;5(6):514–29. Cui XJ, He Q, Zhang JM, Fan HJ, Wen ZF, Qin YR. High-dose aspirin consumption contributes to decreased risk for pancreatic cancer in a systematic review and meta-analysis. Pancreas. 2014;43(1):135–40. Calder PC. Omega-3 polyunsaturated fatty acids and inflammatory processes: nutrition or pharmacology? Br J Clin Pharmacol. 2013;75(3):645–62. Wall R, Ross RP, Fitzgerald GF, Stanton C. Fatty acids from fish: the anti-inflammatory potential of long-chain omega-3 fatty acids. Nutr Rev. 2010;68(5):280–9. Additional Declarations No competing interests reported. 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2","display":"","copyAsset":false,"role":"figure","size":161412,"visible":true,"origin":"","legend":"\u003cp\u003eMendelian randomization analysis showing the causal associations between fish consumption and risk of cancer.\u003c/p\u003e","description":"","filename":"OnlineFig2.png","url":"https://assets-eu.researchsquare.com/files/rs-3938501/v1/f63f4be608598d7288f19f47.png"},{"id":55365134,"identity":"e05876a6-9212-4d5f-b5b8-454e9bad48c8","added_by":"auto","created_at":"2024-04-26 09:36:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":822949,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3938501/v1/c3ffd9ae-bb56-4727-88cb-e7df93164615.pdf"},{"id":52137196,"identity":"b279d007-59c5-4c3d-959e-9fe5485aff40","added_by":"auto","created_at":"2024-03-07 10:18:34","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1205624,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-3938501/v1/fbe6c8f6f2868f2303b581fb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal Associations between fish consumption and risk of cancer: a Mendelian randomization study","fulltext":[{"header":"1 Background","content":"\u003cp\u003eAs is widely recognized, fish is a commonly consumed food by humans. Fish serve as a valuable source of protein, high-quality unsaturated fatty acids, and essential vitamins such as B2 and B12. The extended carbon chains of unsaturated fatty acids contribute to cholesterol reduction[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Contemporary global data indicate that cancer is the primary cause of mortality, posing a significant threat to human health[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Despite substantial improvements in patient survival rates due to surgery, chemotherapy, targeted treatment, and immunotherapy, the overall survival rate (OS) for the majority of patients with advanced cancer remains notably low[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Consequently, primary prevention efforts hold paramount significance. Case-control research in Japan demonstrates an association between fish consumption and a reduced incidence of prostate cancer[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. According to a prospective study, fish consumption is linked to a reduced risk of lung cancer[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Findings from a case-control study indicate that fish consumption is linked to a decreased risk of breast cancer[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Nevertheless, the association between fish consumption and cancer risk remains a subject of controversy[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This controversy may arise from the inherent limitations of traditional observational epidemiological methods in investigating disease etiology. They are susceptible to potential confounding factors and reverse causality, leading to a potential false causal correlation.\u003c/p\u003e \u003cp\u003eThe concept of Mendelian Randomization (MR) was introduced by Katan in 1986[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. MR employs single nucleotide polymorphisms (SNPs) as instrumental variables (IVs) to estimate the causal effects of exposure and outcome[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Since genetic variation originates from the maternal generation and remains constant, MR not only adheres to the temporality criterion but is also less susceptible to traditional confounding factors, such as environment and behavior[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Consequently, MR holds significant potential for application in the realms of disease prevention, treatment, and the formulation of public health policies.\u003c/p\u003e \u003cp\u003eUpon literature review, our research group identified the absence of Mendelian randomized studies investigating the causal relationship between fish consumption and cancer risk. Consequently, our study relies on the open GWAS database and employs two-sample Mendelian randomized analysis to investigate the relationship between fish consumption and cancer.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Research and design\u003c/h2\u003e \u003cp\u003eIn this study, fish consumption served as the exposure factor, and SNPs significantly associated with fish consumption were chosen as instrumental variables (IVs). The outcome variable comprised various types of cancer, including Bladder cancer, Colonic cancer, Gastric cancer, Liver cell carcinoma, Lung cancer, Melanoma skin cancer, Oropharyngeal cancer, Pancreas cancer, Prostate cancer, and Thyroid cancer. The instrumental variables (IVs) in the MR study must satisfy three core hypotheses[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]: (1) IVs are highly correlated with exposure factors. (2) IVs influence the outcome solely through exposure factors, without a direct association with the outcome. (3) IVs are unrelated to confounding factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data source\u003c/h2\u003e \u003cp\u003eThe exposure and outcome data for this study were obtained from the GWAS catalog[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e](\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) and the UK biological sample bank IEU OpenGWAS[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e](\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 above figures are all from Europe. Details are shown in Supplementary Table\u0026nbsp;1. Our MR studies are conducted using the published GWAS database, which has obtained ethical approval and informed consent, so no additional ethical and informed consent is required for this article.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Selection of instrumental variables\u003c/h2\u003e \u003cp\u003eIn this study, GWAS data extraction is performed using RStudio software. The selection of SNPs as instrumental variables adheres to the following criteria: (1) A high correlation between SNP and exposure factor with a significance threshold of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e; (2) To mitigate the impact of linkage disequilibrium (LD), the R software establishes parameter conditions R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.001 and kb\u0026thinsp;=\u0026thinsp;10000, selecting mutually independent SNPs as instrumental variables[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]; (3) F statistics assess the strength of instrumental variables, where F\u0026thinsp;\u0026gt;\u0026thinsp;10 indicates the absence of weak instrumental variable bias. Additionally, SNPs associated with confounding factors are excluded by referencing the PhenoScanner database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.phenoscanner.medschl.cam.ac.uk/\u003c/span\u003e\u003cspan address=\"http://www.phenoscanner.medschl.cam.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The identified SNPs serve as instrumental variables in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Mendelian randomization analyses\u003c/h2\u003e \u003cp\u003eThe TwoSampleMR package was employed for MR analysis to assess the causal relationship between fish consumption and cancer. The relationship was evaluated using odds ratio (OR) and a 95% confidence interval (95% CI) in diverse regression models, encompassing inverse variance weighting (IVW), MR-Egger regression, simple mode, weighted median, and weighted model. IVW utilizes the reciprocal of the variance of the tool variables for fitting when all tool variables satisfy the three conditions, ensuring an unbiased estimation. MR-Egger regression incorporates the reciprocal of the variance of the outcome for weighted calculation, including an intercept term in the regression[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. It considers multiple effects, and the straight line is not mandated to pass through the origin. The weighted median method involves ranking the estimated values of each SNP ratio from small to large and utilizing the median for a weighted average. This method requires more than 50% of SNPs in the model as valid tool variables to obtain unbiased estimates of causality effects. However, the estimation accuracy and efficiency of the weighted median method are lower than that of the IVW method[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The primary results in this study are based on IVW analysis. Heterogeneity was assessed using Cochran's Q test, with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating its presence[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In the presence of heterogeneity, the random-effect IVW method was employed for MR analysis. Pleiotropy was identified through the MR-Egger intercept test, with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating the presence of pleiotropy. In the absence of horizontal gene pleiotropy in genetic tool variables, IVW can furnish causal estimates with reduced bias. Sensitivity analysis using the \"leave one method\" was employed to assess stability and examine whether the removal of a single variation affected the relationship between exposure and outcome. Scatter plots were employed to visualize the impact of each genetic tool variable on exposure and outcome. MR analysis was conducted using the \"TwoSampleMR\" and \"ggplot2\" packages in R software (version 4.3.1).\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Selection of tool variable\u003c/h2\u003e \u003cp\u003eFollowing the screening criteria for tool variables in this study, fish consumption serves as the exposure factor, and depression acts as the outcome variable. After screening, 22 SNPs were chosen as tool variables. The calculated F statistics, ranging from 29 to 75, all exceed 10, suggesting a low likelihood of weak tool bias. No SNP related to confounding factors was identified in the PhenoScanner database (Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Causal relationship between fish consumption and cancer\u003c/h2\u003e \u003cp\u003eMR analysis revealed that fish consumption was a protective factor against pancreatic cancer (OR\u0026thinsp;=\u0026thinsp;0.334, 95% CI: 0.137\u0026ndash;0.863, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023). However, no significant causal relationship was observed between fish consumption and other types of cancer (Bladder cancer: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.934, Colorectal cancer: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.968, Gastric cancer: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.788, Liver cell carcinoma: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.732, Lung cancer: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.596, Melanoma skin cancer: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.198, Oropharyngeal cancer: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.090, Prostate cancer: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.075, Thyroid cancer: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.848; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The forest plot and scatter plot further illustrate the causal relationship between fish consumption and cancer (Supplementary Figs.\u0026nbsp;1 and 2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Sensitivity Analysis of the MR Results\u003c/h2\u003e \u003cp\u003eWe employed IVW and MR-Egger regression to assess heterogeneity among the tool variables. The results indicated heterogeneity among the tool variables for thyroid cancer (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039, Supplementary Tables\u0026nbsp;3). Consequently, we applied the IVW random effect model for MR analysis, and the results closely aligned with those of MR (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), affirming the validity of the causal effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Analyses for other types of cancer revealed no significant heterogeneity among the tool variables (Bladder cancer: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.705, Colorectal cancer: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.292, Gastric cancer: P\u0026thinsp;=\u0026thinsp;0.833, Liver cell carcinoma: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.305, Lung cancer: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.062, Melanoma skin cancer: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.109, Oropharyngeal cancer: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.467, Pancreas cancer: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.733, Prostate cancer: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.175). Additionally, we included funnel plots for each association pair to enhance heterogeneity identification (Supplementary Fig.\u0026nbsp;3). The MR-Egger analysis revealed no horizontal pleiotropy in any of the tool variables (Bladder cancer: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.076, Colorectal cancer: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.472, Gastric cancer: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.670, Liver cell carcinoma: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.420, Lung cancer: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.167, Melanoma skin cancer: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.955, Oropharyngeal cancer: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.989, Pancreas cancer: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.693, Prostate cancer: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.498, Thyroid cancer: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.323; Supplementary Tables\u0026nbsp;4). This indicates that tool variables do not significantly affect the outcome in ways other than exposure. We performed sensitivity analysis using the leave-one-out method, removing SNPs one by one. The causal effect of the remaining SNP was compared with the results of the MR analysis using all SNPs to determine if the causal correlation was influenced by a single tool variable. The leave-one-out analysis demonstrated that no single SNP substantially influenced the overall MR estimation (Supplementary Fig.\u0026nbsp;4). The results of the sensitivity analysis confirmed the robustness of the MR analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eFish constitutes a vital component of the human diet, often encouraged for its abundance in high-quality proteins, essential micronutrients, and fatty acids[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Notably, fish serves as a primary source of long-chain omega-3 polyunsaturated fatty acids, encompassing eicosapentaenoic acid and 22-carbohexaenoic acid, known for their anti-inflammatory and immunomodulatory effects[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Research indicates that omega-3 polyunsaturated fatty acids derived from fish can impede cancer progression. However, it's essential to acknowledge that fish may harbor environmental pollutants capable of disrupting endocrine function and potentially triggering cancer[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Consequently, the impact of edible fish on cancer incidence remains uncertain.\u003c/p\u003e \u003cp\u003eOver the past decade, researchers have conducted numerous observational studies to investigate the correlation between fish consumption and cancer risk. However, the results are contradictory. A case-control study conducted in Galicia, northwestern Spain, suggests a potential increase in the risk of lung cancer associated with fish consumption[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In contrast, a prospective study involving 478,021 participants across 10 European countries indicated that consuming fish was not linked to the risk of lung cancer[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Similarly, findings in prostate cancer studies vary; some report an association between fish consumption and a reduced risk of prostate cancer, while others find no significant association[\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we employed the Mendelian randomization (MR) method to elucidate the causal relationship between fish consumption and cancer risk. Our findings substantiate the presence of a causal relationship between fish consumption and pancreatic cancer risk. Conversely, we observed limited evidence supporting an association between fish consumption and other cancers, including bladder cancer, colorectal cancer, gastric cancer, liver cell carcinoma, lung cancer, melanoma skin cancer, oropharyngeal cancer, prostate cancer, and thyroid cancer. Furthermore, sensitivity analysis indicates the robustness of the overall results.\u003c/p\u003e \u003cp\u003ePancreatic cancer stands as a prominent cause of cancer-related mortality globally, imposing a substantial public health burden on both men and women. Given its grim prognosis and elevated mortality rates, identifying preventive factors for pancreatic cancer is of paramount public health importance. Established risk factors encompass chronic pancreatitis, smoking, and diabetes[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Dietary factors may also contribute to pancreatic cancer development, though consensus remains elusive. Notably, a sizable prospective cohort study within the Japanese population reveals an inverse correlation between fish consumption and pancreatic cancer risk[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Our study concurs, concluding that fish consumption serves as a protective factor against pancreatic cancer, suggesting increased fish intake as an effective preventive measure. In vitro and in vivo investigations underscore the pivotal role of inflammation in pancreatic cancer initiation and progression[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. An epidemiological meta-analysis indicates an association between high-dose aspirin and other anti-inflammatory agents and a diminished risk of pancreatic cancer[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Evidence suggests that marine-derived polyunsaturated fatty acids, including eicosapentaenoic acid and 22-carbon hexaenoic acid, exhibit inhibitory effects on inflammation[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Thus, the consumption of fish-rich foods, rich in these fatty acids, may confer protection against pancreatic cancer due to their anti-inflammatory properties[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Furthermore, animal models demonstrate that dietary fish oil supplementation hampers pancreatic cancer development. Particularly noteworthy, Mendelian randomization investigations can furnish crucial evidence on causality, pivotal for public health guidance. Distinguished by a substantial sample size and enhanced statistical power compared to previous observational studies, our research underscores the causal link between fish consumption and pancreatic cancer risk. Despite supporting evidence, the role of fish consumption in pancreatic cancer prevention warrants further investigation.\u003c/p\u003e \u003cp\u003eIn this study, Mendelian randomization (MR) was employed to infer the causal relationship between exposure factors and the research outcome, utilizing genetic variation strongly correlated with exposure factors as a tool variable. As a recent advancement in epidemiological research, MR offers significant advantages. Primarily, the formation of genetic variation remains unaffected by lifestyle or social environment, safeguarding the outcome from confounding factors. Secondly, MR relies on a public database, thereby saving considerable research costs and time. Most importantly, the genetic variation, originating from the maternal generation and remaining unchanged, not only adheres to the rationality of the time sequence but is also less susceptible to traditional confounding factors such as environment and behavior. However, several limitations warrant consideration in this study. Firstly, the GWAS data predominantly originate from European populations, potentially limiting the generalizability of the results to other population groups. A more extensive GWAS with a larger sample size is essential to identify additional genetic variation for MR research, as the efficacy of tool variables hinges on the sample size of GWAS. Lastly, we exclusively explored the causal relationship between fish consumption and cancer without further subdividing the fish species. Furthermore, this study is purely statistical and does not delve into the biological mechanisms underlying the association between fish consumption and cancer risk. Nevertheless, our results serve as a foundation for exploring more in-depth mechanisms.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, this study marks the inaugural application of the MR method to investigate the causal relationship between fish consumption and cancer risk. Collectively, our findings substantiate a potential link between fish consumption and cancer risk. Further research is imperative to unravel the underlying mechanism.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of interest\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Natural Science Foundation of China (81802290) and Shandong Provincial Natural Science Foundation (No. ZR2018BH020).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLZ, SW designed and performed MR analysis. LZ, SW, WC and XM analyzed the data and organized pictures. LZ, SW and YL wrote and revised the paper. All authors read and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eData availability statement\u003c/h2\u003e \u003cp\u003eAll the data generated and analyzed during this study are included in the manuscript and the additional materials.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eShimakata T. [Polyunsaturated fatty acid]. Nihon Rinsho. 2001;59(Suppl 2):45\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChiesa G, Busnelli M, Manzini S, Parolini C. Nutraceuticals and Bioactive Components from Fish for Dyslipidemia and Cardiovascular Risk Reduction. Mar Drugs 2016, 14(6).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. 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Diet and lung cancer risk from a 14-year population-based prospective study in Japan: with special reference to fish consumption. Nutr Cancer. 2003;45(2):160\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim J, Lim SY, Shin A, Sung MK, Ro J, Kang HS, Lee KS, Kim SW, Lee ES. Fatty fish and fish omega-3 fatty acid intakes decrease the breast cancer risk: a case-control study. BMC Cancer. 2009;9:216.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEngeset D, Alsaker E, Lund E, Welch A, Khaw KT, Clavel-Chapelon F, Thi\u0026eacute;baut A, Chaj\u0026egrave;s V, Key TJ, Allen NE, et al. Fish consumption and breast cancer risk. The European Prospective Investigation into Cancer and Nutrition (EPIC). Int J Cancer. 2006;119(1):175\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTerry P, Rohan TE, Wolk A, Maehle-Schmidt M, Magnusson C. Fish consumption and breast cancer risk. 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Omega-3 polyunsaturated fatty acids and inflammatory processes: nutrition or pharmacology? Br J Clin Pharmacol. 2013;75(3):645\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWall R, Ross RP, Fitzgerald GF, Stanton C. Fatty acids from fish: the anti-inflammatory potential of long-chain omega-3 fatty acids. Nutr Rev. 2010;68(5):280\u0026ndash;9.\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":"Mendelian randomization, Genome-wide association studies, Fish consumption, Cancer, Causality","lastPublishedDoi":"10.21203/rs.3.rs-3938501/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3938501/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground：\u003c/strong\u003ePrevious studies have identified a correlation between the consumption of fish and the risk of cancer. However, the causal relationship between these two factors remains uncertain and necessitates further investigation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods：\u003c/strong\u003eThe GWAS data were obtained from the public GWAS database. In two-sample Mendelian Randomization (MR) analysis, various methods including inverse variance weighting (IVW), mr-egger regression, simple mode, weighted median, and weighted model were employed to evaluate the causal association between fish consumption and the risk of cancer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults：\u003c/strong\u003eThe results of the MR analysis indicated a causal relationship between fish consumption and pancreatic cancer risk (IVW: OR=0.334, 95% CI: 0.137-0.863, \u003cem\u003eP\u003c/em\u003e=0.023). However, no significant causal relationship was observed between fish consumption and other types of cancer, including bladder cancer (\u003cem\u003eP\u003c/em\u003e=0.934), colorectal cancer (\u003cem\u003eP\u003c/em\u003e=0.968), gastric cancer (\u003cem\u003eP\u003c/em\u003e=0.788), liver cell carcinoma (\u003cem\u003eP\u003c/em\u003e=0.732), lung cancer (\u003cem\u003eP\u003c/em\u003e=0.596), melanoma skin cancer (\u003cem\u003eP\u003c/em\u003e=0.198), oropharyngeal cancer (\u003cem\u003eP\u003c/em\u003e=0.090), prostate cancer (\u003cem\u003eP\u003c/em\u003e=0.075), and thyroid cancer (\u003cem\u003eP\u003c/em\u003e=0.848). Heterogeneity was identified in the MR analysis between fish consumption and thyroid cancer tool variables (\u003cem\u003eP\u003c/em\u003e=0.039), with no horizontal pleiotropy observed in any tool variables. The sensitivity analysis, employing the leave-one-out method, demonstrated the robustness of the MR analysis results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe MR analysis results revealed a causal relationship between fish consumption and the risk of pancreatic cancer. However, no such causal relationship was observed between fish consumption and other cancers, including bladder cancer, colon cancer, gastric cancer, liver cell carcinoma, lung cancer, melanoma skin cancer, oropharyngeal cancer, prostate cancer, and thyroid cancer.\u003c/p\u003e","manuscriptTitle":"Causal Associations between fish consumption and risk of cancer: a Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-07 10:18:29","doi":"10.21203/rs.3.rs-3938501/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":"43682ba1-390d-4c5d-bff1-0cfe08a55e6e","owner":[],"postedDate":"March 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-26T09:11:10+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-07 10:18:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3938501","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3938501","identity":"rs-3938501","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}