The Relationship between Modifiable Lifestyle Factors and Breast Diseases: A Mendelian Randomization Study

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Methods In this mendelian randomization study, we leveraged large-scale genetic data from genome-wide association studies (GWAS) to assess the causal effects of modifiable lifestyle factors. Instrumental variable analysis was performed using genetic variants associated with each lifestyle factor as instruments. Sensitivity analyses were conducted to assess the robustness of findings. Results We applied instrumental variable weighted (IVW) analysis to investigate causal link. Specifically, an increased risk of overall BC was observed with longer sleep duration ( OR =1.33, 95% CI : 1.01-1.74, P =0.04) and decreased with more years of schooling ( OR =0.91, 95% CI : 0.83-0.99, P =0.04) and greater fresh fruit intake ( OR =0.64, 95% CI : 0.46-0.90, P =0.01). For ER+ BC, both increased sleep duration ( OR =1.49, 95% CI : 1.12-2.00, P =0.007) and greater fresh fruit consumption ( OR =0.65, 95% CI : 0.44-0.95, P =0.02) showed significant associations. In contrast, the risk of developing ER-BC decreased with increased education ( OR =0.73, 95% CI : 0.64-0.84, P =0.000005) and fresh fruit intake ( OR =0.55, 95% CI : 0.31-0.99, P =0.04) but increased with increased processed meat consumption ( OR =1.78, 95% CI : 1.11-2.84, P =0.016). Benign neoplasm of breast was linked to higher physical activity levels ( OR =3.13, 95% CI : 1.07-9.10, P =0.0368), more years of education ( OR =0.63, 95% CI : 0.46-0.866, P =0.003), and greater processed meat consumption ( OR =3.84, 95% CI : 1.25-11.84, P =0.019). Moreover, inflammatory disorders of breast were correlated with pack years of smoking ( OR =4.18, 95% CI : 1.10-15.70, P =0.034), higher BMI ( OR =1.97, 95% CI : 1.40-2.72, P =0.00004), and fewer years of schooling ( OR =0.47, 95% CI : 0.29-0.77, P =0.003). These findings underscore the complexity of lifestyle influences on different types of breast pathologies and highlight the importance of considering specific disease mechanisms in lifestyle recommendations. Conclusions This MR study provides evidence supporting the significant role of modifiable lifestyle factors in breast diseases. The findings underscore the importance of adopting healthy lifestyle habits for the prevention and management of breast diseases. Modifiable lifestyle Breast cancer Benign neoplasm of breast Inflammatory disorders of breast Mendelian randomization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Breast diseases, encompassing a broad spectrum from benign proliferative lesions to malignant tumors, constitute a substantial public health challenge, exerting a profound impact on morbidity, mortality, and healthcare costs worldwide. Breast pathologies, including breast cancer, benign neoplasm of breast and inflammatory disorders of breast for women globally. Breast cancer, in particular, is the most commonly diagnosed cancer and the leading cause of cancer-related mortality among women globally and includes estrogen receptor-positive (ER + BC) and estrogen receptor-negative subtypes (ER- BC) [ 1 – 3 ] . Within developed nations, breast cancer ranks foremost in both incidence and mortality among malignancies, precipitating millions of fatalities annually [ 4 , 5 ] . Beyond breast cancer, benign neoplasm of breast and inflammatory disorders of breast have emerged as notable entities, drawing increasing scrutiny. With breast diseases being the most prevalent diseases among women worldwide, there is a pressing need to understand the factors driving the development and progression of these diseases. Among the myriad factors implicated in breast diseases, modifiable lifestyle factors have emerged as pivotal determinants. In contemporary times, amid shifting living environments and heightened standards of living, a plethora of investigations have scrutinized the interplay between diverse lifestyle elements and breast pathologies [ 6 – 8 ] . The modifiable lifestyle factors included pack years of smoking, alcoholic drinks per week, physical activity, sleep duration, sleeplessness/insomnia, years of schooling, household income, coffee intake, processed meat intake, fresh fruit intake and body mass index(BMI). Several cohort studies have indicated an association between adopting a healthy lifestyle and breast cancer risk reduction [ 9 – 12 ] . In addition, the findings from these studies have been inconsistent [ 13 , 14 ] . Although previous epidemiologic investigations have explored the correlation between lifestyle factors and breast cancer, fewer studies have been conducted on the correlation between lifestyle factors and breast diseases and the elucidation of causality remains elusive due to the inherent limitations of the observational research paradigm. Mendelian randomization (MR) constitutes a statistical strategy leveraging genetic variants as instrumental variables to discern causal associations between exposures—such as lifestyle determinants—and disease sequelae [ 15 ] . Grounded in Mendel's laws of heredity, wherein genotypes are randomly conferred during fertilization, this modality capitalizes on genetic markers as proxies for exposures. Should a genetic variant exhibit an association with a particular exposure while concurrently influencing disease manifestation, the likelihood of causality supersedes confounding influences. The robustness of this methodology resides in its capacity to mitigate confounding biases and retrograde causality endemic to conventional observational inquiries, thereby furnishing more robust causal deductions [ 16 , 17 ] . In this study, we aimed to leverage the robust methodology of MR to elucidate the causal relationships between pack years of smoking, alcoholic drinks per week, physical activity, sleep duration, sleeplessness/insomnia, years of schooling, household income, coffee intake, processed meat intake, fresh fruit intake, body mass index and the risk of various breast diseases. Through comprehensive analyses of large-scale genetic data and sophisticated statistical techniques, we seek to unravel the intricate interplay between lifestyle factors and breast disease outcomes. Understanding the causal pathways linking lifestyle choices to breast diseases has profound implications for public health interventions, preventive strategies, and personalized healthcare approaches aimed at mitigating the burden of breast diseases on individuals and societies worldwide. Methods MR design This investigation employed two-sample MR analysis to elucidate the intricate causal nexus between modifiable lifestyle factors and breast diseases. MR analysis, a robust methodological framework, operates under three fundamental assumptions: first, the imperative requirement of a robust correlation between instrumental variables and exposure variables; second, the pivotal need for instrumental variables to remain independent of both observed and unobserved confounders; and third, the essential condition that instrumental variables exert their effects solely through the modulation of exposure. The elucidation of these assumptions is pivotal for ensuring the integrity and validity of MR analysis outcomes. Flowchart delineating the bidirectional MR analysis is elegantly depicted in Fig. 1 , which illustrates the methodological intricacies. Selection criteria for the data sources and genetic variants This study leverages extensive GWAS summary datasets in which participants provided informed consent in their original studies. Ethical approval is not required because we rely solely on summary statistics. The data were derived from GWAS of individuals of European ancestry, employing a stringent genome-wide significance threshold of P < 5×10 − 8 to identify SNPs strongly associated with modifiable lifestyle factors and breast diseases. Exposes include pack years of smoking, alcoholic drinks per week [ 18 ] , physical activity [ 19 ] , sleep duration, sleeplessness/insomnia, years of schooling [ 20 ] , household income, coffee intake, processed meat intake, fresh fruit intake and BMI [ 21 ] . Outcomes included overall BC, ER + BC, ER- BC [ 22 ] , benign neoplasm of breast and inflammatory disorders of breast. To ensure SNP independence and mitigate linkage disequilibrium effects, we applied an LD parameter threshold of 0.001 (r 2 ) and a genetic distance of 10000 kb. The robustness of the correlation of the instrumental variables with the exposure factors was assessed using the F statistic. To address bias from weak instrumental variables, only SNPs with F statistics exceeding 10 were included. The detailed data sources are presented in Table 1 . Statistical analysis The analyses were conducted utilizing the "Two Sample MR" package in R software (version 4.3.3) [ 23 ] . The primary analytical approach employed was the instrumental variable weighted (IVW) method, which utilizes a multiplicative random-effects model. To account for potential pleiotropy or invalid instrument bias when MR assumptions were not fully met, a series of sensitivity analyses were conducted to ensure the validity and robustness of our findings. The sensitivity analyses included the MR-Egger, weighted median (WM), simple mode, and weighted mode methods [ 24 , 25 ] . Notably, the weighted median method addresses invalid instrument bias by providing a consistent estimate, even when more than 50% of the information originates from weak or invalid instruments. Moreover, the MR-Egger method offers a less susceptible estimate of the causal effect in the presence of pleiotropy. We calculated the intercept of MR-Egger regression to assess average horizontal pleiotropy. Additionally, a leave-one-SNP-out analysis was conducted to evaluate the impact of potentially pleiotropic SNPs on causal estimates by systematically excluding one SNP at a time. Finally, the strength of the genetic instrument was assessed using F-statistics, with a value exceeding 10 indicating sufficient instrument strength for MR analysis. Table 1 Detailed information on the studies used Phenotype GWAS ID Consortium Sample size Population Number of SNPs Pack years of smoking ukb-b-10831 MRC-IEU 142387 European 9851867 Alcoholic drinks per week ieu-b-73 GWAS and Sequencing Consortium of Alcohol and Nicotine use 335394 European 11887865 Physical activity ebi-a-GCST006097 NA 377234 European 11808007 Sleep duration ukb-b-4424 NA 377234 European 11808007 Sleeplessness/insomnia ukb-b-3957 MRC-IEU 462341 European 9851867 Years of schooling ieu-a-1239 SSGAC 766345 European 10,101,242 household income ukb-b-7408 MRC-IEU 397,751 European 9,851,867 Coffee intake ukb-b-5237 MRC-IEU 428860 European 9851867 Processed meat intake ukb-b-6324 MRC-IEU 461981 European 9851867 Tea intake ukb-b-6066 MRC-IEU 447485 European 9851867 Fresh fruit intake ukb-b-3881 MRC-IEU 446462 European 9851867 Body mass index ieu-b-40 GIANT 681275 European 2336260 Overall breast cancer ieu-a-1126 BCAC 228951 European 10680257 ER + Breast cancer ieu-a-1127 BCAC 175475 European 10680257 ER- Breast cancer ieu-a-1128 BCAC 127442 European 10680257 Benign neoplasm of breast finn-b-CD2_BENIGN_BREAST_EXALLC NA / European 16378990 Inflammatory disorders of breast finn-b-N14_INFLAMMBREAST NA / European 16379554 Results The impact of modifiable lifestyle factors on overall BC incidence Weighted MR (IVW) revealed a significant association between shorter sleep duration and increased overall risk of breast cancer ( OR − IVW =1.33, 95% CI : 1.01–1.74, P = 0.04). This finding was further corroborated by the WM method, which indicated that sleep duration may be a modifiable factor for breast cancer risk ( OR − WM =1.32, 95% CI : 1.03–1.68, P = 0.03). Additionally, the IVW analysis revealed a notable link between longer education duration and decreased risk of breast cancer ( OR − IVW =0.91, 95% CI : 0.83–0.99, P = 0.04), a result supported by the WM analysis ( OR − WM =0.87, 95% CI : 0.78–0.96, P = 0.008), suggesting a protective effect of education level against breast cancer risk. Furthermore, an increase in fruit intake was significantly associated with a decreased risk of breast cancer ( OR − IVW =0.64, 95% CI : 0.46–0.90, P = 0.01), a finding confirmed by the WM analysis ( OR − WM =0.66, 95% CI : 0.46–0.95, P = 0.03), highlighting the potential importance of healthy dietary habits in breast cancer prevention. Decreased BMI was significantly correlated with reduced breast cancer risk ( OR − IVW =0.88, 95% CI : 0.82–0.94, P = 8.82E-05). Both WM ( OR − WM =0.90, 95% CI : 0.83–0.97, P = 0.004) and MR-Egger ( OR − MR−Egger =0.65, 95% CI : 0.55–0.77, P = 0.0000009) analyses validated this result, albeit with notable pleiotropy ( P < 0.05), suggesting that BMI may influence breast cancer risk through multiple biological pathways(Fig. 2 ). The impact of modifiable lifestyle factors on ER + BC Analysis using the IVW method revealed significant associations between sleep duration, fruit intake, and BMI and the risk of ER + BC. IVW analysis of sleep duration demonstrated a significant association with an increased risk of ER + BC for shorter sleep durations ( OR − IVW =1.49, 95% CI : 1.12-2.00, P = 0.007), while greater fruit intake was significantly associated with a reduced risk of ER + BC ( OR − IVW =0.65, 95% CI : 0.44–0.95, P = 0.02), and lower BMI was significantly associated with a decreased risk of ER + BC ( OR − IVW =0.89, 95% CI : 0.83–0.96, P = 0.001). Heterogeneity and horizontal pleiotropy tests were conducted; however, the horizontal pleiotropy test ( P < 0.05) suggested that BMI may influence the risk of ER + BC through multiple biological pathways. Additionally, no significant associations were detected between insomnia ( OR − IVW =0.91, 95% CI : 0.64–1.29, P = 0.59) or household income ( OR − IVW =1.10, 95% CI : 0.87–1.40, P = 0.41) and the risk of ER + BC. Notably, sensitivity analysis using the WM method revealed significant associations between insomnia ( OR − WM =0.64, 95% CI : 0.43–0.95, P = 0.02) and household income ( OR − WM =1.21, 95% CI : 1.05–1.57, P = 0.015) and the risk of ER + BC, suggesting the potential impact of different genetic instrumental variable methods on the results (Fig. 3 ). The impact of modifiable lifestyle factors on ER- BC IVW analysis revealed significant associations between education duration, meat intake, fruit intake, BMI, and the risk of ER-BC. A longer education duration was significantly associated with a reduced risk of ER-BC ( OR − IVW =0.73, 95% CI : 0.64–0.84, P = 0.000005). This result was validated through the WM and MR-Egger regression sensitivity analyses ( OR − WM =0.76, 95% CI : 0.63–0.91, P = 0.003; OR − MR−Egger =0.49, 95% CI : 0.29–0.84, P = 0.01), indicating a substantial protective effect of education level on ER-BC risk. Higher meat intake was associated with an increased risk of ER-BC ( OR − IVW =1.78, 95% CI : 1.11–2.84, P = 0.016). This finding was further confirmed through sensitivity analysis ( OR − WM =1.94, 95% CI : 1.10–3.45, P = 0.02), suggesting that meat consumption might be a potential risk factor for ER-BC. Higher fruit intake was significantly associated with a reduced risk of ER-BC ( OR − IVW =0.55, 95% CI : 0.31–0.99, P = 0.04). Sensitivity analysis further supported this finding ( OR − WM =0.49, 95% CI : 0.27–0.89, P = 0.02). Decreased BMI was significantly associated with a reduced risk of ER-BC ( OR − IVW =0.84, 95% CI : 0.76–0.92, P = 0.0002). Both sensitivity analyses ( OR − WM =0.80, 95% CI : 0.71–0.91, P = 0.0001) and ( OR − MR−Egger =0.53, 95% CI : 0.41–0.67, P = 0.0000006) validated this result. However, the pleiotropy test for BMI ( P < 0.05) suggested the existence of other biological pathways beyond weight management that may influence the risk of ER-BC(Fig. 4 ). The impact of modifiable lifestyle factors on benign neoplasm of breast The results demonstrated a significant inverse association between longer education duration and the risk of benign breast tumors ( OR − IVW =0.63, 95% CI : 0.46–0.87, P = 0.003), which was further supported by WM sensitivity testing ( OR − WM =0.46, 95% CI : 0.22–0.99, P = 0.048). Additionally, physical activity ( OR − IVW =3.13, 95% CI : 1.07–9.12, P = 0.0368) and higher meat intake ( OR − IVW =3.84, 95% CI : 1.25–11.84, P = 0.019) were significantly associated with an increased risk of benign breast tumors, although these findings were not confirmed by sensitivity analysis. Smoking did not show a significant association in the IVW analysis, but sensitivity testing via the WM ( OR − WM =2.57, 95% CI : 1.06–6.25, P = 0.04) and MR-Egger regression ( OR − MR−Egger =9.56, 95% CI : 2.04–44.88, P = 0.02) revealed a significant association between smoking and an increased risk of benign breast tumors. Similarly, the IVW analysis for household income did not reveal significant associations, but MR-Egger regression sensitivity testing ( OR − MR−Egger =0.03, 95% CI : 0.01–0.53, P = 0.02) suggested a potential inverse association between lower household income and the risk of benign breast tumors. These findings suggest that education level and meat intake may be two significant lifestyle factors influencing the risk of benign breast tumors, while smoking may also be a potential risk factor(Fig. 5 ). The impact of modifiable lifestyle factors on inflammatory disorders of breast The results from this MR study indicated a significant association between smoking and an increased risk of mastitis, with an odds ratio ( OR − IVW =4.18, 95% CI : 1.1–15.70, P = 0.034). This suggests that smoking might be a potential risk factor for the development of mastitis. Moreover, a longer education duration was significantly associated with a decreased risk of mastitis ( OR − IVW =0.47, 95% CI : 0.29–0.77, P = 0.003). This relationship was further validated by the WM sensitivity analysis ( OR − WM =0.46, 95% CI : 0.22–0.98, P = 0.04). Similarly, a higher BMI was significantly associated with an increased risk of mastitis ( OR − IVW =1.97, 95% CI : 1.4–2.72, P = 0.00004), with additional support from the WM sensitivity analysis ( OR − WM =1.97, 95% CI : 1.16–3.33, P = 0.01). However, it is noteworthy that the IVW analysis for fruit consumption did not reveal a significant association with mastitis risk, while the MR-Egger regression sensitivity analysis yielded an unusually high odds ratio ( OR − MR−Egger =418.2, 95% CI : 1.22-142985.65, P = 0.047), suggesting possible uncertainty or data variability(Fig. 6 ). Discussion In this dual-sample investigation, we sought to unravel the causal relationships between 12 modifiable lifestyle parameters and breast pathologies. Our analyses revealed significant correlations between these modifiable lifestyle variables—such as years of schooling, pack years of smoking, sleep duration, household income, processed meat intake, fresh fruit intake and body mass index—and the incidence of breast diseases. It is important to note that while our findings offer valuable insights, their interpretation requires caution due to the complex interplay of multifactorial influences. MR analysis revealed that more years of schooling, as predicted by genetic factors, is associated with reduced risks of overall BC,ER + BC, benign neoplasm of breast, and inflammatory disorders of breast. Previous investigations within European populations have not consistently established a link between years of schooling and breast disease risk Some studies suggest a lower risk of breast diseases with increased educational attainment, consistent with the findings presented here [ 26 ] . However, conflicting evidence, indicates that women with higher education levels may delay childbearing, resulting in fewer pregnancies and potentially increasing their susceptibility to breast cancer [ 27 , 28 ] . In addition, individuals with higher educational levels may exhibit better adherence to screening protocols, leading to increased detection rates of breast cancer, although this may not necessarily translate to reduced mortality rates [ 29 , 30 ] . Healthy dietary habits confer protection against cancer risk [ 31 ] .Our research revealed a significant association between diet and breast cancer incidence. Increased intake of fresh fruits is correlated with a reduced risk of breast cancer, primarily attributed to their high content of polyphenolic compounds, endowing them with outstanding antioxidant activity, thus potentially mitigating cancer risk [ 32 – 34 ] . According to a meta-analysis comprising fifteen prospective studies, elevated fruit consumption was mildly associated with a decreased risk of breast cancer [ 35 ] . Furthermore, our investigation revealed a positive correlation between high consumption of meat and increased risk of ER-positive breast cancer. Studies suggest a link between high intake of processed meats and elevated breast cancer risk [ 36 ] . These findings align with our research outcomes, possibly attributable to the cytotoxicity induction, promotion of apoptosis and proliferation in epithelial cells, lipid peroxidation induction, free radical and DNA adduct formation in epithelial cells, and catalysis of N-nitroso compound formation, stemming from heme iron in red and processed meats, thereby fostering carcinogenesis [ 37 ] . Numerous epidemiological studies have underscored sleep duration as a risk factor for breast cancer [ 38 – 40 ] . Consistently, our findings revealed a positive association between shorter sleep duration and increased risk of breast disorders. Plausibly, sleep disturbances may perturb the homeostasis of various circulating hormones, including melatonin, cortisol, growth hormone, prolactin, glucose, and insulin, which are pivotal regulators implicated in diverse pathophysiological processes, notably breast carcinogenesis [ 41 ] . However, in a meta-analysis of 14 prospective studies and the Million Women Study, which included 65,410 breast cancer cases, short duration versus average sleep duration was not related with breast cancer risk [ 42 ] . So the association between sleep duration and breast cancer remains controversial [ 38 , 43 , 44 ] . Our research findings demonstrated a highly significant correlation between BMI and breast disease incidence. Obesity is widely acknowledged to be a risk factor for various cancers, including breast cancer. Studies have indicated that a genetically predisposed higher BMI is associated with lower serum testosterone levels [ 45 ] , which in turn are positively correlated with breast cancer risk. [ 46 , 47 ] . In premenopausal women, elevated BMI may mitigate breast cancer risk by decreasing estradiol levels. [ 48 ] . Moreover, obesity is a state characterized by chronic low-grade systemic inflammation and has been linked to various chronic conditions, notably breast diseases, in females [ 49 ] . Mounting evidence underscores the pivotal contribution of inflammatory adipokines and cytokines in predicting susceptibility to breast diseases among women [ 50 – 52 ] . A consolidated analysis of longitudinal studies revealed that smoking escalates the susceptibility to ER-positive breast cancer. Intriguingly, A comprehensive synthesis of cohort studies elucidates that early initiation of smoking is consistently linked with a slight elevation in risk, particularly evident among women who commenced smoking prior to their first full-term birth [ 53 ] . Conversely, findings from three large-scale cohort investigations indicate an inverse relationship between the duration of post-menopausal smoking and breast cancer risk, contrasting with a tendency towards increased risk in premenopausal smoker. This pattern mirrors observations in endometrial cancer, wherein post-menopausal smoking, but not premenopausal smoking, is associated with diminished risks [ 54 ] . Currently, there exists a paucity of studies pertaining to the association between smoking and breast pathologies, necessitating further investigation to elucidate the nexus between smoking and breast diseases. The limitation of this study is that all participants included were of European descent. Therefore, it is uncertain whether the findings can be extrapolated to other ethnicities. Conclusion In conclusion, our findings underscore the significant role of lifestyle factors in the occurrence of breast diseases. While our results provide valuable insights for the prevention of breast diseases, further research is required to elucidate the biological mechanisms underlying these associations and validate them across diverse populations. Additionally, our study highlights directions for future research, including exploring how various lifestyle factors influence the risk of breast diseases through distinct biological pathways. Declarations Ethics approval and consent to participate. The data used in this paper are publicly available, ethically approved, and the subjects have given their informed consent. Consent for publication. The datasets [exposure/outcome] generated and analysed during the current study are available in the [IEU Open GWAS], [https://gwas.mrcieu.ac.uk/]. The authors have no relevant financial or non-financial interests to disclose. Authors' contributions All authors made a significant contribution to the work reported and agreed to be accountable for all aspects of the work. Z.Q and Y.Z.J designed the experiments. W.L.L, H.M.H, H.Y.P, and Z,J performed the experiments. Y.Z.J, H.M.H and Z.J prepared the initial draft of the manuscript. Z.Q and W.L.L gave critical feedback during the study or during the submission of the manuscript. All authors had given final approval of the version to be submitted and agreed on the journal to be published. Funding This work was supported by Chinese Research Hospital Association [Grant Number Y2022FH-HLFH06-09]. Acknowledgements Not applicable. References Loibl S, Poortmans P, Morrow M, Denkert C, Curigliano G. Breast cancer. Lancet. 2021;397(10286):1750–69. Eroles P, Bosch A, Pérez-Fidalgo JA, Lluch A. Molecular biology in breast cancer: intrinsic subtypes and signaling pathways. 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Sleep duration and breast cancer risk in the Breast Cancer Detection Demonstration Project follow-up cohort. Br J Cancer. 2015;112(3):567–71. Larsson SC, Burgess S. Causal role of high body mass index in multiple chronic diseases: a systematic review and meta-analysis of Mendelian randomization studies. BMC Med. 2021;19(1):320. Ruth KS, Day FR, Tyrrell J, Thompson DJ, Wood AR, Mahajan A, Beaumont RN, Wittemans L, Martin S, Busch AS, et al. Using human genetics to understand the disease impacts of testosterone in men and women. Nat Med. 2020;26(2):252–8. Watts EL, Perez-Cornago A, Knuppel A, Tsilidis KK, Key TJ, Travis RC. Prospective analyses of testosterone and sex hormone-binding globulin with the risk of 19 types of cancer in men and postmenopausal women in UK Biobank. Int J Cancer. 2021;149(3):573–84. García-Estévez L, Cortés J, Pérez S, Calvo I, Gallegos I, Moreno-Bueno G. Obesity and Breast Cancer: A Paradoxical and Controversial Relationship Influenced by Menopausal Status. Front Oncol. 2021;11:705911. Sung H, Siegel RL, Torre LA, Pearson-Stuttard J, Islami F, Fedewa SA, Goding Sauer A, Shuval K, Gapstur SM, Jacobs EJ, et al. Global patterns in excess body weight and the associated cancer burden. CA Cancer J Clin. 2019;69(2):88–112. Hao Y, Xiao J, Fu P, Yan L, Zhao X, Wu X, Zhou M, Zhang X, Xu B, Li X, et al. Increases in BMI contribute to worsening inflammatory biomarkers related to breast cancer risk in women: a longitudinal study. Breast Cancer Res Treat. 2023;202(1):117–27. Iyengar NM, Arthur R, Manson JE, Chlebowski RT, Kroenke CH, Peterson L, Cheng TD, Feliciano EC, Lane D, Luo J, et al. Association of Body Fat and Risk of Breast Cancer in Postmenopausal Women With Normal Body Mass Index: A Secondary Analysis of a Randomized Clinical Trial and Observational Study. JAMA Oncol. 2019;5(2):155–63. Hayati Z, Jafarabadi MA, Pirouzpanah S. Dietary inflammatory index and breast cancer risk: an updated meta-analysis of observational studies. Eur J Clin Nutr. 2022;76(8):1073–87. Gaudet MM, Carter BD, Brinton LA, Falk RT, Gram IT, Luo J, Milne RL, Nyante SJ, Weiderpass E, Beane Freeman LE, et al. Pooled analysis of active cigarette smoking and invasive breast cancer risk in 14 cohort studies. Int J Epidemiol. 2017;46(3):881–93. Baron JA, Nichols HB, Anderson C, Safe S. Cigarette Smoking and Estrogen-Related Cancer. Cancer Epidemiol Biomarkers Prev. 2021;30(8):1462–71. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.docx Additional file 1: eFigure 1-eFigure 10. Additionalfile2.xls Additional file 2: eTable 1-eTable 6. 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. 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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-4421784","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":305969687,"identity":"50a64ea8-9c0b-4f18-9b33-cafbb9d0f765","order_by":0,"name":"Zhuojing Yang","email":"","orcid":"","institution":"The Nursing Department of Shanxi Provincial People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhuojing","middleName":"","lastName":"Yang","suffix":""},{"id":305969688,"identity":"524b2043-c44b-43ec-ad0d-c58e5e2acdf8","order_by":1,"name":"Lili Wang","email":"","orcid":"","institution":"The Nursing Department of Shanxi Provincial People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lili","middleName":"","lastName":"Wang","suffix":""},{"id":305969689,"identity":"70760cf9-819f-4725-8d18-d7b4239fe7bf","order_by":2,"name":"Minghua Han","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Minghua","middleName":"","lastName":"Han","suffix":""},{"id":305969690,"identity":"ce22a908-6b96-4052-9372-7013bc87a6f7","order_by":3,"name":"Yapeng He","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yapeng","middleName":"","lastName":"He","suffix":""},{"id":305969691,"identity":"137a9eed-d6d3-4cd6-8aaf-ce25b2082733","order_by":4,"name":"Jian Zhao","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Zhao","suffix":""},{"id":305969692,"identity":"f1abc2af-bce4-4657-94df-a9c767ab16f4","order_by":5,"name":"Qian Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYDACCRBhAONVSMjxk6jljIWxZANRWmCAsa0icQMhLfKzmx8+eFNgl8fAf/jZh5/zJBg3MDA/fHQDjxbGOceMDecYJBczSKQZz+zdJsFszsBmbJyDRwuzRIKZNI8Bc2KDBIMxA+82CTbLBh42aXxa2CTSvwG11Cc28B//zPh3jgSPwQECWngkckC2HE5sYMgxZuZtkJAgqEVCIqcY6JfjQIflFDPLHJMwkGwm4Bf5GekbH7z5Uw1y2GbGNzV19f3szQ8f49MCcR0Q2x+A8ZgJKYdpGQWjYBSMglGAEwAA3WlBeBk1qK4AAAAASUVORK5CYII=","orcid":"","institution":"The Nursing Department of Shanxi Provincial People’s Hospital","correspondingAuthor":true,"prefix":"","firstName":"Qian","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2024-05-15 01:03:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4421784/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4421784/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57075334,"identity":"694b266c-d7ed-4b43-bada-e39804d18fa5","added_by":"auto","created_at":"2024-05-24 09:12:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":217452,"visible":true,"origin":"","legend":"\u003cp\u003eStudy design overview\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4421784/v1/0ce9ce18d4998aaf455ffb54.png"},{"id":57075333,"identity":"4734f330-7916-45db-8eac-c2cee1f8c1fd","added_by":"auto","created_at":"2024-05-24 09:12:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":340965,"visible":true,"origin":"","legend":"\u003cp\u003eMendelian randomization results for the risk of overall BC associated with modifiable lifestyle factors. (Highlighted in red are statistically significant results, and error bars indicate 95% confidence intervals).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4421784/v1/c9c6ca83e8370650f2ce82a3.png"},{"id":57075332,"identity":"c8e83f07-5d68-4527-981d-9be06161abcf","added_by":"auto","created_at":"2024-05-24 09:12:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":319088,"visible":true,"origin":"","legend":"\u003cp\u003eMendelian randomization results for the risk of ER+ BC associated with modifiable lifestyle factors. (Highlighted in red are statistically significant results, and error bars indicate 95% confidence intervals).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4421784/v1/fe0cd5651309179af8e28ca1.png"},{"id":57076725,"identity":"f86ca575-37ed-4e88-9f33-163645ecf211","added_by":"auto","created_at":"2024-05-24 09:28:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":340833,"visible":true,"origin":"","legend":"\u003cp\u003eMendelian randomization results for the risk of ER- BC associated with modifiable lifestyle factors. (Highlighted in red are statistically significant results, and error bars indicate 95% confidence intervals).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4421784/v1/d275159f08e7c925837b43f5.png"},{"id":57075813,"identity":"bef6f728-80ac-4200-8ed9-18385a760f2a","added_by":"auto","created_at":"2024-05-24 09:20:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":346093,"visible":true,"origin":"","legend":"\u003cp\u003eMendelian randomization results for the risk of breast benign tumors associated with modifiable lifestyle factors. (Highlighted in red are statistically significant results, and error bars indicate 95% confidence intervals).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4421784/v1/10ef523b951450920c5efde5.png"},{"id":57075811,"identity":"be93e49f-e8c4-4c96-8efd-5adc1117f7d2","added_by":"auto","created_at":"2024-05-24 09:20:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":321353,"visible":true,"origin":"","legend":"\u003cp\u003eMendelian randomization results for the risk of breast inflammatory diseases associated with modifiable lifestyle factors. (Highlighted in red are statistically significant results, and error bars indicate 95% confidence intervals).\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4421784/v1/e0da2b33dd03c6e44479406f.png"},{"id":57198574,"identity":"852700c1-b2ad-4ebf-b867-349dcc865d25","added_by":"auto","created_at":"2024-05-27 09:22:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2074863,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4421784/v1/9b908370-6c9c-4c66-b590-8773855739b7.pdf"},{"id":57075339,"identity":"d777d713-9003-477f-ab50-4f5c9e0129f3","added_by":"auto","created_at":"2024-05-24 09:12:20","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5215258,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 1: eFigure 1-eFigure 10.\u003c/p\u003e","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4421784/v1/e6342810bc694564a22f17b0.docx"},{"id":57075337,"identity":"8aef2c87-9aaa-4c9d-94a3-13bc5277c7d6","added_by":"auto","created_at":"2024-05-24 09:12:20","extension":"xls","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":96256,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 2: eTable 1-eTable 6.\u003c/p\u003e","description":"","filename":"Additionalfile2.xls","url":"https://assets-eu.researchsquare.com/files/rs-4421784/v1/0b679426027c065c03666261.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Relationship between Modifiable Lifestyle Factors and Breast Diseases: A Mendelian Randomization Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast diseases, encompassing a broad spectrum from benign proliferative lesions to malignant tumors, constitute a substantial public health challenge, exerting a profound impact on morbidity, mortality, and healthcare costs worldwide. Breast pathologies, including breast cancer, benign neoplasm of breast and inflammatory disorders of breast for women globally. Breast cancer, in particular, is the most commonly diagnosed cancer and the leading cause of cancer-related mortality among women globally and includes estrogen receptor-positive (ER\u0026thinsp;+\u0026thinsp;BC) and estrogen receptor-negative subtypes (ER- BC)\u003csup\u003e[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Within developed nations, breast cancer ranks foremost in both incidence and mortality among malignancies, precipitating millions of fatalities annually\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Beyond breast cancer, benign neoplasm of breast and inflammatory disorders of breast have emerged as notable entities, drawing increasing scrutiny. With breast diseases being the most prevalent diseases among women worldwide, there is a pressing need to understand the factors driving the development and progression of these diseases. Among the myriad factors implicated in breast diseases, modifiable lifestyle factors have emerged as pivotal determinants. In contemporary times, amid shifting living environments and heightened standards of living, a plethora of investigations have scrutinized the interplay between diverse lifestyle elements and breast pathologies\u003csup\u003e[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. The modifiable lifestyle factors included pack years of smoking, alcoholic drinks per week, physical activity, sleep duration, sleeplessness/insomnia, years of schooling, household income, coffee intake, processed meat intake, fresh fruit intake and body mass index(BMI). Several cohort studies have indicated an association between adopting a healthy lifestyle and breast cancer risk reduction\u003csup\u003e[\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. In addition, the findings from these studies have been inconsistent\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Although previous epidemiologic investigations have explored the correlation between lifestyle factors and breast cancer, fewer studies have been conducted on the correlation between lifestyle factors and breast diseases and the elucidation of causality remains elusive due to the inherent limitations of the observational research paradigm.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) constitutes a statistical strategy leveraging genetic variants as instrumental variables to discern causal associations between exposures\u0026mdash;such as lifestyle determinants\u0026mdash;and disease sequelae\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Grounded in Mendel's laws of heredity, wherein genotypes are randomly conferred during fertilization, this modality capitalizes on genetic markers as proxies for exposures. Should a genetic variant exhibit an association with a particular exposure while concurrently influencing disease manifestation, the likelihood of causality supersedes confounding influences. The robustness of this methodology resides in its capacity to mitigate confounding biases and retrograde causality endemic to conventional observational inquiries, thereby furnishing more robust causal deductions\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, we aimed to leverage the robust methodology of MR to elucidate the causal relationships between pack years of smoking, alcoholic drinks per week, physical activity, sleep duration, sleeplessness/insomnia, years of schooling, household income, coffee intake, processed meat intake, fresh fruit intake, body mass index and the risk of various breast diseases. Through comprehensive analyses of large-scale genetic data and sophisticated statistical techniques, we seek to unravel the intricate interplay between lifestyle factors and breast disease outcomes. Understanding the causal pathways linking lifestyle choices to breast diseases has profound implications for public health interventions, preventive strategies, and personalized healthcare approaches aimed at mitigating the burden of breast diseases on individuals and societies worldwide.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMR design\u003c/h2\u003e \u003cp\u003eThis investigation employed two-sample MR analysis to elucidate the intricate causal nexus between modifiable lifestyle factors and breast diseases. MR analysis, a robust methodological framework, operates under three fundamental assumptions: first, the imperative requirement of a robust correlation between instrumental variables and exposure variables; second, the pivotal need for instrumental variables to remain independent of both observed and unobserved confounders; and third, the essential condition that instrumental variables exert their effects solely through the modulation of exposure. The elucidation of these assumptions is pivotal for ensuring the integrity and validity of MR analysis outcomes. Flowchart delineating the bidirectional MR analysis is elegantly depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which illustrates the methodological intricacies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSelection criteria for the data sources and genetic variants\u003c/h2\u003e \u003cp\u003eThis study leverages extensive GWAS summary datasets in which participants provided informed consent in their original studies. Ethical approval is not required because we rely solely on summary statistics. The data were derived from GWAS of individuals of European ancestry, employing a stringent genome-wide significance threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e to identify SNPs strongly associated with modifiable lifestyle factors and breast diseases. Exposes include pack years of smoking, alcoholic drinks per week\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e, physical activity\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, sleep duration, sleeplessness/insomnia, years of schooling\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e, household income, coffee intake, processed meat intake, fresh fruit intake and BMI\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Outcomes included overall BC, ER\u0026thinsp;+\u0026thinsp;BC, ER- BC\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e, benign neoplasm of breast and inflammatory disorders of breast. To ensure SNP independence and mitigate linkage disequilibrium effects, we applied an LD parameter threshold of 0.001 (r\u003csup\u003e2\u003c/sup\u003e) and a genetic distance of 10000 kb. The robustness of the correlation of the instrumental variables with the exposure factors was assessed using the F statistic. To address bias from weak instrumental variables, only SNPs with F statistics exceeding 10 were included. The detailed data sources are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe analyses were conducted utilizing the \"Two Sample MR\" package in R software (version 4.3.3)\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. The primary analytical approach employed was the instrumental variable weighted (IVW) method, which utilizes a multiplicative random-effects model. To account for potential pleiotropy or invalid instrument bias when MR assumptions were not fully met, a series of sensitivity analyses were conducted to ensure the validity and robustness of our findings. The sensitivity analyses included the MR-Egger, weighted median (WM), simple mode, and weighted mode methods\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Notably, the weighted median method addresses invalid instrument bias by providing a consistent estimate, even when more than 50% of the information originates from weak or invalid instruments. Moreover, the MR-Egger method offers a less susceptible estimate of the causal effect in the presence of pleiotropy. We calculated the intercept of MR-Egger regression to assess average horizontal pleiotropy. Additionally, a leave-one-SNP-out analysis was conducted to evaluate the impact of potentially pleiotropic SNPs on causal estimates by systematically excluding one SNP at a time. Finally, the strength of the genetic instrument was assessed using F-statistics, with a value exceeding 10 indicating sufficient instrument strength for MR analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetailed information on the studies used\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGWAS ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConsortium\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNumber of SNPs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePack years of smoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eukb-b-10831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMRC-IEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9851867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcoholic drinks per week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eieu-b-73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGWAS and Sequencing Consortium of Alcohol and Nicotine use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e335394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11887865\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eebi-a-GCST006097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e377234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11808007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eukb-b-4424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e377234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11808007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleeplessness/insomnia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eukb-b-3957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMRC-IEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e462341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9851867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears of schooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eieu-a-1239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSSGAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e766345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10,101,242\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehousehold income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eukb-b-7408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMRC-IEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e397,751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9,851,867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoffee intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eukb-b-5237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMRC-IEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e428860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9851867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcessed meat intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eukb-b-6324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMRC-IEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e461981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9851867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTea intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eukb-b-6066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMRC-IEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e447485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9851867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFresh fruit intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eukb-b-3881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMRC-IEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e446462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9851867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eieu-b-40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGIANT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e681275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2336260\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall breast cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eieu-a-1126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBCAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e228951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10680257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER\u0026thinsp;+\u0026thinsp;Breast cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eieu-a-1127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBCAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e175475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10680257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER- Breast cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eieu-a-1128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBCAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e127442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10680257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenign neoplasm of breast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efinn-b-CD2_BENIGN_BREAST_EXALLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16378990\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflammatory disorders of breast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efinn-b-N14_INFLAMMBREAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16379554\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eThe impact of modifiable lifestyle factors on overall BC incidence\u003c/h2\u003e \u003cp\u003eWeighted MR (IVW) revealed a significant association between shorter sleep duration and increased overall risk of breast cancer (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;IVW\u003c/sub\u003e=1.33, \u003cem\u003e95% CI\u003c/em\u003e: 1.01\u0026ndash;1.74, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04). This finding was further corroborated by the WM method, which indicated that sleep duration may be a modifiable factor for breast cancer risk (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;WM\u003c/sub\u003e=1.32, \u003cem\u003e95% CI\u003c/em\u003e: 1.03\u0026ndash;1.68, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03). Additionally, the IVW analysis revealed a notable link between longer education duration and decreased risk of breast cancer (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;IVW\u003c/sub\u003e=0.91, \u003cem\u003e95% CI\u003c/em\u003e: 0.83\u0026ndash;0.99, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04), a result supported by the WM analysis (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;WM\u003c/sub\u003e=0.87, \u003cem\u003e95% CI\u003c/em\u003e: 0.78\u0026ndash;0.96, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008), suggesting a protective effect of education level against breast cancer risk. Furthermore, an increase in fruit intake was significantly associated with a decreased risk of breast cancer (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;IVW\u003c/sub\u003e=0.64, \u003cem\u003e95% CI\u003c/em\u003e: 0.46\u0026ndash;0.90, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01), a finding confirmed by the WM analysis (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;WM\u003c/sub\u003e=0.66, \u003cem\u003e95% CI\u003c/em\u003e: 0.46\u0026ndash;0.95, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03), highlighting the potential importance of healthy dietary habits in breast cancer prevention. Decreased BMI was significantly correlated with reduced breast cancer risk (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;IVW\u003c/sub\u003e=0.88, \u003cem\u003e95% CI\u003c/em\u003e: 0.82\u0026ndash;0.94, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.82E-05). Both WM (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;WM\u003c/sub\u003e=0.90, \u003cem\u003e95% CI\u003c/em\u003e: 0.83\u0026ndash;0.97, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) and MR-Egger (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;MR\u0026minus;Egger\u003c/sub\u003e=0.65, \u003cem\u003e95% CI\u003c/em\u003e: 0.55\u0026ndash;0.77, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0000009) analyses validated this result, albeit with notable pleiotropy (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that BMI may influence breast cancer risk through multiple biological pathways(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eThe impact of modifiable lifestyle factors on ER\u0026thinsp;+\u0026thinsp;BC\u003c/h2\u003e \u003cp\u003eAnalysis using the IVW method revealed significant associations between sleep duration, fruit intake, and BMI and the risk of ER\u0026thinsp;+\u0026thinsp;BC. IVW analysis of sleep duration demonstrated a significant association with an increased risk of ER\u0026thinsp;+\u0026thinsp;BC for shorter sleep durations (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;IVW\u003c/sub\u003e=1.49, \u003cem\u003e95% CI\u003c/em\u003e: 1.12-2.00, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007), while greater fruit intake was significantly associated with a reduced risk of ER\u0026thinsp;+\u0026thinsp;BC (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;IVW\u003c/sub\u003e=0.65, \u003cem\u003e95% CI\u003c/em\u003e: 0.44\u0026ndash;0.95, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02), and lower BMI was significantly associated with a decreased risk of ER\u0026thinsp;+\u0026thinsp;BC (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;IVW\u003c/sub\u003e=0.89, \u003cem\u003e95% CI\u003c/em\u003e: 0.83\u0026ndash;0.96, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). Heterogeneity and horizontal pleiotropy tests were conducted; however, the horizontal pleiotropy test (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) suggested that BMI may influence the risk of ER\u0026thinsp;+\u0026thinsp;BC through multiple biological pathways. Additionally, no significant associations were detected between insomnia (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;IVW\u003c/sub\u003e =0.91, \u003cem\u003e95% CI\u003c/em\u003e: 0.64\u0026ndash;1.29, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.59) or household income (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;IVW\u003c/sub\u003e =1.10, \u003cem\u003e95% CI\u003c/em\u003e: 0.87\u0026ndash;1.40, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.41) and the risk of ER\u0026thinsp;+\u0026thinsp;BC. Notably, sensitivity analysis using the WM method revealed significant associations between insomnia (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;WM\u003c/sub\u003e=0.64, \u003cem\u003e95% CI\u003c/em\u003e: 0.43\u0026ndash;0.95, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02) and household income (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;WM\u003c/sub\u003e=1.21, \u003cem\u003e95% CI\u003c/em\u003e: 1.05\u0026ndash;1.57, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015) and the risk of ER\u0026thinsp;+\u0026thinsp;BC, suggesting the potential impact of different genetic instrumental variable methods on the results (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\u003e \u003cb\u003eThe impact of modifiable lifestyle factors on ER- BC\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIVW analysis revealed significant associations between education duration, meat intake, fruit intake, BMI, and the risk of ER-BC. A longer education duration was significantly associated with a reduced risk of ER-BC (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;IVW\u003c/sub\u003e=0.73, \u003cem\u003e95% CI\u003c/em\u003e: 0.64\u0026ndash;0.84, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000005). This result was validated through the WM and MR-Egger regression sensitivity analyses (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;WM\u003c/sub\u003e=0.76, \u003cem\u003e95% CI\u003c/em\u003e: 0.63\u0026ndash;0.91, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003; \u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;MR\u0026minus;Egger\u003c/sub\u003e=0.49, \u003cem\u003e95% CI\u003c/em\u003e: 0.29\u0026ndash;0.84, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01), indicating a substantial protective effect of education level on ER-BC risk. Higher meat intake was associated with an increased risk of ER-BC (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;IVW\u003c/sub\u003e=1.78, \u003cem\u003e95% CI\u003c/em\u003e: 1.11\u0026ndash;2.84, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016). This finding was further confirmed through sensitivity analysis (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;WM\u003c/sub\u003e=1.94, \u003cem\u003e95% CI\u003c/em\u003e: 1.10\u0026ndash;3.45, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02), suggesting that meat consumption might be a potential risk factor for ER-BC. Higher fruit intake was significantly associated with a reduced risk of ER-BC (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;IVW\u003c/sub\u003e=0.55, \u003cem\u003e95% CI\u003c/em\u003e: 0.31\u0026ndash;0.99, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04). Sensitivity analysis further supported this finding (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;WM\u003c/sub\u003e=0.49, \u003cem\u003e95% CI\u003c/em\u003e: 0.27\u0026ndash;0.89, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02). Decreased BMI was significantly associated with a reduced risk of ER-BC (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;IVW\u003c/sub\u003e=0.84, \u003cem\u003e95% CI\u003c/em\u003e: 0.76\u0026ndash;0.92, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0002). Both sensitivity analyses (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;WM\u003c/sub\u003e=0.80, \u003cem\u003e95% CI\u003c/em\u003e: 0.71\u0026ndash;0.91, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001) and (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;MR\u0026minus;Egger\u003c/sub\u003e=0.53, \u003cem\u003e95% CI\u003c/em\u003e: 0.41\u0026ndash;0.67, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0000006) validated this result. However, the pleiotropy test for BMI (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) suggested the existence of other biological pathways beyond weight management that may influence the risk of ER-BC(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eThe impact of modifiable lifestyle factors on benign neoplasm of breast\u003c/h2\u003e \u003cp\u003eThe results demonstrated a significant inverse association between longer education duration and the risk of benign breast tumors (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;IVW\u003c/sub\u003e=0.63, \u003cem\u003e95% CI\u003c/em\u003e: 0.46\u0026ndash;0.87, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), which was further supported by WM sensitivity testing (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;WM\u003c/sub\u003e=0.46, \u003cem\u003e95% CI\u003c/em\u003e: 0.22\u0026ndash;0.99, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048). Additionally, physical activity (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;IVW\u003c/sub\u003e =3.13, \u003cem\u003e95% CI\u003c/em\u003e: 1.07\u0026ndash;9.12, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0368) and higher meat intake (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;IVW\u003c/sub\u003e =3.84, \u003cem\u003e95% CI\u003c/em\u003e: 1.25\u0026ndash;11.84, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019) were significantly associated with an increased risk of benign breast tumors, although these findings were not confirmed by sensitivity analysis. Smoking did not show a significant association in the IVW analysis, but sensitivity testing via the WM (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;WM\u003c/sub\u003e=2.57, \u003cem\u003e95% CI\u003c/em\u003e: 1.06\u0026ndash;6.25, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04) and MR-Egger regression (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;MR\u0026minus;Egger\u003c/sub\u003e=9.56, \u003cem\u003e95% CI\u003c/em\u003e: 2.04\u0026ndash;44.88, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02) revealed a significant association between smoking and an increased risk of benign breast tumors. Similarly, the IVW analysis for household income did not reveal significant associations, but MR-Egger regression sensitivity testing (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;MR\u0026minus;Egger\u003c/sub\u003e=0.03, \u003cem\u003e95% CI\u003c/em\u003e: 0.01\u0026ndash;0.53, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02) suggested a potential inverse association between lower household income and the risk of benign breast tumors. These findings suggest that education level and meat intake may be two significant lifestyle factors influencing the risk of benign breast tumors, while smoking may also be a potential risk factor(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eThe impact of modifiable lifestyle factors on inflammatory disorders of breast\u003c/h2\u003e \u003cp\u003eThe results from this MR study indicated a significant association between smoking and an increased risk of mastitis, with an odds ratio (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;IVW\u003c/sub\u003e=4.18, \u003cem\u003e95% CI\u003c/em\u003e: 1.1\u0026ndash;15.70, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034). This suggests that smoking might be a potential risk factor for the development of mastitis. Moreover, a longer education duration was significantly associated with a decreased risk of mastitis (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;IVW\u003c/sub\u003e=0.47, \u003cem\u003e95% CI\u003c/em\u003e: 0.29\u0026ndash;0.77, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003). This relationship was further validated by the WM sensitivity analysis (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;WM\u003c/sub\u003e=0.46, \u003cem\u003e95% CI\u003c/em\u003e: 0.22\u0026ndash;0.98, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04). Similarly, a higher BMI was significantly associated with an increased risk of mastitis (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;IVW\u003c/sub\u003e=1.97, \u003cem\u003e95% CI\u003c/em\u003e: 1.4\u0026ndash;2.72, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00004), with additional support from the WM sensitivity analysis (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;WM\u003c/sub\u003e=1.97, \u003cem\u003e95% CI\u003c/em\u003e: 1.16\u0026ndash;3.33, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01). However, it is noteworthy that the IVW analysis for fruit consumption did not reveal a significant association with mastitis risk, while the MR-Egger regression sensitivity analysis yielded an unusually high odds ratio (\u003cem\u003eOR\u003c/em\u003e\u003csub\u003e\u0026minus;\u0026thinsp;MR\u0026minus;Egger\u003c/sub\u003e=418.2, \u003cem\u003e95% CI\u003c/em\u003e: 1.22-142985.65, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047), suggesting possible uncertainty or data variability(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this dual-sample investigation, we sought to unravel the causal relationships between 12 modifiable lifestyle parameters and breast pathologies. Our analyses revealed significant correlations between these modifiable lifestyle variables\u0026mdash;such as years of schooling, pack years of smoking, sleep duration, household income, processed meat intake, fresh fruit intake and body mass index\u0026mdash;and the incidence of breast diseases. It is important to note that while our findings offer valuable insights, their interpretation requires caution due to the complex interplay of multifactorial influences.\u003c/p\u003e \u003cp\u003eMR analysis revealed that more years of schooling, as predicted by genetic factors, is associated with reduced risks of overall BC,ER\u0026thinsp;+\u0026thinsp;BC, benign neoplasm of breast, and inflammatory disorders of breast. Previous investigations within European populations have not consistently established a link between years of schooling and breast disease risk Some studies suggest a lower risk of breast diseases with increased educational attainment, consistent with the findings presented here\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. However, conflicting evidence, indicates that women with higher education levels may delay childbearing, resulting in fewer pregnancies and potentially increasing their susceptibility to breast cancer\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. In addition, individuals with higher educational levels may exhibit better adherence to screening protocols, leading to increased detection rates of breast cancer, although this may not necessarily translate to reduced mortality rates\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHealthy dietary habits confer protection against cancer risk\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e.Our research revealed a significant association between diet and breast cancer incidence. Increased intake of fresh fruits is correlated with a reduced risk of breast cancer, primarily attributed to their high content of polyphenolic compounds, endowing them with outstanding antioxidant activity, thus potentially mitigating cancer risk\u003csup\u003e[\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. According to a meta-analysis comprising fifteen prospective studies, elevated fruit consumption was mildly associated with a decreased risk of breast cancer\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Furthermore, our investigation revealed a positive correlation between high consumption of meat and increased risk of ER-positive breast cancer. Studies suggest a link between high intake of processed meats and elevated breast cancer risk \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. These findings align with our research outcomes, possibly attributable to the cytotoxicity induction, promotion of apoptosis and proliferation in epithelial cells, lipid peroxidation induction, free radical and DNA adduct formation in epithelial cells, and catalysis of N-nitroso compound formation, stemming from heme iron in red and processed meats, thereby fostering carcinogenesis\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNumerous epidemiological studies have underscored sleep duration as a risk factor for breast cancer\u003csup\u003e[\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. Consistently, our findings revealed a positive association between shorter sleep duration and increased risk of breast disorders. Plausibly, sleep disturbances may perturb the homeostasis of various circulating hormones, including melatonin, cortisol, growth hormone, prolactin, glucose, and insulin, which are pivotal regulators implicated in diverse pathophysiological processes, notably breast carcinogenesis\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. However, in a meta-analysis of 14 prospective studies and the Million Women Study, which included 65,410 breast cancer cases, short duration versus average sleep duration was not related with breast cancer risk\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. So the association between sleep duration and breast cancer remains controversial\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur research findings demonstrated a highly significant correlation between BMI and breast disease incidence. Obesity is widely acknowledged to be a risk factor for various cancers, including breast cancer. Studies have indicated that a genetically predisposed higher BMI is associated with lower serum testosterone levels\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e, which in turn are positively correlated with breast cancer risk.\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e. In premenopausal women, elevated BMI may mitigate breast cancer risk by decreasing estradiol levels.\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e. Moreover, obesity is a state characterized by chronic low-grade systemic inflammation and has been linked to various chronic conditions, notably breast diseases, in females\u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e. Mounting evidence underscores the pivotal contribution of inflammatory adipokines and cytokines in predicting susceptibility to breast diseases among women\u003csup\u003e[\u003cspan additionalcitationids=\"CR51\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA consolidated analysis of longitudinal studies revealed that smoking escalates the susceptibility to ER-positive breast cancer. Intriguingly, A comprehensive synthesis of cohort studies elucidates that early initiation of smoking is consistently linked with a slight elevation in risk, particularly evident among women who commenced smoking prior to their first full-term birth\u003csup\u003e[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/sup\u003e. Conversely, findings from three large-scale cohort investigations indicate an inverse relationship between the duration of post-menopausal smoking and breast cancer risk, contrasting with a tendency towards increased risk in premenopausal smoker. This pattern mirrors observations in endometrial cancer, wherein post-menopausal smoking, but not premenopausal smoking, is associated with diminished risks\u003csup\u003e[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/sup\u003e. Currently, there exists a paucity of studies pertaining to the association between smoking and breast pathologies, necessitating further investigation to elucidate the nexus between smoking and breast diseases.\u003c/p\u003e \u003cp\u003eThe limitation of this study is that all participants included were of European descent. Therefore, it is uncertain whether the findings can be extrapolated to other ethnicities.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our findings underscore the significant role of lifestyle factors in the occurrence of breast diseases. While our results provide valuable insights for the prevention of breast diseases, further research is required to elucidate the biological mechanisms underlying these associations and validate them across diverse populations. Additionally, our study highlights directions for future research, including exploring how various lifestyle factors influence the risk of breast diseases through distinct biological pathways.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this paper are publicly available, ethically approved, and the subjects have given their informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets [exposure/outcome] generated and analysed during the current study are available in the [IEU Open GWAS], [https://gwas.mrcieu.ac.uk/].\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors made a significant contribution to the work reported and agreed to be accountable for all aspects of the work. Z.Q and Y.Z.J designed the experiments. W.L.L, H.M.H, H.Y.P, and Z,J performed the experiments. Y.Z.J, H.M.H and Z.J prepared the initial draft of the manuscript. Z.Q and W.L.L gave critical feedback during the study or during the submission of the manuscript. All authors had given final approval of the version to be submitted and agreed on the journal to be published.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Chinese Research Hospital Association [Grant Number Y2022FH-HLFH06-09].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLoibl S, Poortmans P, Morrow M, Denkert C, Curigliano G. Breast cancer. 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Cancer Epidemiol Biomarkers Prev. 2021;30(8):1462\u0026ndash;71.\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":"Modifiable lifestyle, Breast cancer, Benign neoplasm of breast, Inflammatory disorders of breast, Mendelian randomization","lastPublishedDoi":"10.21203/rs.3.rs-4421784/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4421784/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e This study aimed to employ mendelian randomization to systematically investigate the causal effects of genetic predispositions and modifiable lifestyle factors on breast diseases.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003eIn this mendelian randomization study, we leveraged large-scale genetic data from genome-wide association studies (GWAS) to assess the causal effects of modifiable lifestyle factors. Instrumental variable analysis was performed using genetic variants associated with each lifestyle factor as instruments. Sensitivity analyses were conducted to assess the robustness of findings.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e We applied instrumental variable weighted (IVW) analysis to investigate causal link. Specifically, an increased risk of overall BC was observed with longer sleep duration (\u003cem\u003eOR\u003c/em\u003e=1.33, \u003cem\u003e95% CI\u003c/em\u003e: 1.01-1.74, \u003cem\u003eP\u003c/em\u003e=0.04) and decreased with more years of schooling (\u003cem\u003eOR\u003c/em\u003e=0.91, \u003cem\u003e95% CI\u003c/em\u003e: 0.83-0.99, \u003cem\u003eP\u003c/em\u003e=0.04) and greater fresh fruit intake (\u003cem\u003eOR\u003c/em\u003e=0.64, \u003cem\u003e95% CI\u003c/em\u003e: 0.46-0.90, \u003cem\u003eP\u003c/em\u003e=0.01). For ER+ BC, both increased sleep duration (\u003cem\u003eOR\u003c/em\u003e=1.49, \u003cem\u003e95% CI\u003c/em\u003e: 1.12-2.00, \u003cem\u003eP\u003c/em\u003e=0.007) and greater fresh fruit consumption (\u003cem\u003eOR\u003c/em\u003e=0.65, \u003cem\u003e95% CI\u003c/em\u003e: 0.44-0.95, \u003cem\u003eP\u003c/em\u003e=0.02) showed significant associations. In contrast, the risk of developing ER-BC decreased with increased education (\u003cem\u003eOR\u003c/em\u003e=0.73, \u003cem\u003e95% CI\u003c/em\u003e: 0.64-0.84, \u003cem\u003eP\u003c/em\u003e=0.000005) and fresh fruit intake (\u003cem\u003eOR\u003c/em\u003e=0.55, \u003cem\u003e95% CI\u003c/em\u003e: 0.31-0.99, \u003cem\u003eP\u003c/em\u003e=0.04) but increased with increased processed meat consumption (\u003cem\u003eOR\u003c/em\u003e=1.78, \u003cem\u003e95% CI\u003c/em\u003e: 1.11-2.84, \u003cem\u003eP\u003c/em\u003e=0.016). Benign neoplasm of breast was linked to higher physical activity levels (\u003cem\u003eOR\u003c/em\u003e=3.13, \u003cem\u003e95% CI\u003c/em\u003e: 1.07-9.10, \u003cem\u003eP\u003c/em\u003e=0.0368), more years of education (\u003cem\u003eOR\u003c/em\u003e=0.63, \u003cem\u003e95% CI\u003c/em\u003e: 0.46-0.866, \u003cem\u003eP\u003c/em\u003e=0.003), and greater processed meat consumption (\u003cem\u003eOR\u003c/em\u003e=3.84, \u003cem\u003e95% CI\u003c/em\u003e: 1.25-11.84, \u003cem\u003eP\u003c/em\u003e=0.019). Moreover, inflammatory disorders of breast were correlated with pack years of smoking (\u003cem\u003eOR\u003c/em\u003e=4.18, \u003cem\u003e95% CI\u003c/em\u003e: 1.10-15.70, \u003cem\u003eP\u003c/em\u003e=0.034), higher BMI (\u003cem\u003eOR\u003c/em\u003e=1.97, \u003cem\u003e95% CI\u003c/em\u003e: 1.40-2.72, \u003cem\u003eP\u003c/em\u003e=0.00004), and fewer years of schooling (\u003cem\u003eOR\u003c/em\u003e=0.47, \u003cem\u003e95% CI\u003c/em\u003e: 0.29-0.77, \u003cem\u003eP\u003c/em\u003e=0.003). These findings underscore the complexity of lifestyle influences on different types of breast pathologies and highlight the importance of considering specific disease mechanisms in lifestyle recommendations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e This MR study provides evidence supporting the significant role of modifiable lifestyle factors in breast diseases. The findings underscore the importance of adopting healthy lifestyle habits for the prevention and management of breast diseases.\u003c/p\u003e","manuscriptTitle":"The Relationship between Modifiable Lifestyle Factors and Breast Diseases: A Mendelian Randomization Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-24 09:12:15","doi":"10.21203/rs.3.rs-4421784/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":"8a653d3e-5def-4c6b-9a67-7f80f73357c0","owner":[],"postedDate":"May 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-27T09:14:20+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-24 09:12:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4421784","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4421784","identity":"rs-4421784","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}