Genetic effect of metformin use on risk of cancers: Evidence from Mendelian randomization analysis

Genetic effect of metformin use on risk of cancers: Evidence from Mendelian randomization analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genetic effect of metformin use on risk of cancers: Evidence from Mendelian randomization analysis Yao Chen, Bingjun Bai, Shuchang Ye, Xing Gao, Kangkang Ying, Hongming Pan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3174656/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Dec, 2023 Read the published version in Diabetology & Metabolic Syndrome → Version 1 posted 7 You are reading this latest preprint version Abstract Background Increasing number of studies reported the positive effect of metformin on the prevention and treatment of cancers. However, the genetic causal effect of metformin utilization on the risk of common cancers was not completely demonstrated. Methods Two-sample Mendelian Randomization (two-sample MR) analysis was conducted to uncover the genetically predicted causal association between metformin use and 26 kinds of cancers. Besides, two-step Mendelian Randomization (two-step MR) assessment was applied to clarify the mediators which mediated the causal effect of metformin on certain cancer. We utilized five robust analytical methods, in which the inverse variance weighting (IVW) method served as the major one. Sensitivity, pleiotropy, and heterogeneity were assessed. The genetic statistics of exposure, outcomes, and mediators were downloaded from publicly available datasets, including the Open Genome-Wide Association Study (GWAS), FinnGen consortium (FinnGen), and UK Biobank (UKB). Results Among 26 kinds of common cancers, HER-positive breast cancer was presented with a significant causal relationship with metformin use (Beta: -4.0982; OR: 0.0166 (95%CI: 0.0008, 0.3376); P-value: 0.0077), which indicated metformin could prevent people from HER-positive breast cancer. Other cancers only showed modest associations with metformin use. Potential mediators were included in two-step MR, among which total testosterone levels (mediating effect: 24.52%) displayed significant mediating roles. Leave-one-out, MR-Egger, and MR-PRESSO analyses produced consistent outcomes. Conclusion Metformin use exhibited a genetically protective effect on HER-positive breast cancer, which was partially mediated by total testosterone levels. Metformin Cancers Genetic association Mendelian Randomization Testosterone Figures Figure 1 Introduction The growing frequency and high mortality of malignant tumors imposed a significant burden on people all over the world [ 1 ]. Clinicians have been searching for effective and secure approaches to prevent cancers for a few decades but in vain. Metformin is the most frequently prescribed drug for type 2 diabetes, whose potential anti-tumorigenic effects have attracted growing attention [ 2 ]. An increasing number of clinical studies attempted to reveal the efficacy of metformin on different types of cancer, but the controversial conclusions left the issue unsolved [ 3 – 5 ]. Biases induced by confounders which were hard to avoid in observational studies might be responsible for this. Scientists were also interested in the biological pathways of metformin in cancer treatment. In addition to improvements in glucose metabolism, metformin's molecular mechanisms against many malignancies have been extensively investigated in recent years, with reduction of leukocyte-endothelium interactions, alteration of oxidative stress, and regulation of AMP-activated protein kinase (AMPK) all being implicated [ 6 , 7 ]. However, researches related to the genetic effect of metformin use on the risk of cancers were not complete yet. Contrary to conventional observational research, Mendelian Randomization analysis (MR) provided a cost- and time-saving approach with high efficiency to investigate the genetically predicted causal relationships [ 8 ]. Robust-associated genetic variants were selected to explore the genetic association between exposures and outcomes. In this work, two-sample MR was applied to figure out the causal relationship between metformin use and 26 prevalent cancers, and two-step MR was supplemented to determine the mediators and their contribution to the genetic causal effect. Ultimately, we reviewed previous clinical studies to further understand the association between metformin use and cancers. Materials and methods 1 Study design The overview of the study design was demonstrated in Fig. 1 . As shown in Fig. 1 -A, The MR analysis requires three basic assumptions to be met: (1) instrumental variables (IVs) are strongly correlated to exposure; (2) IVs are independent of any potential confounders; and (3) IVs only affect the outcome through exposure. Two distinct genetic datasets should be integrated into a single MR study, which is the fundamental prerequisite of two-sample MR. To determine the mediating factors in the genetic causal relationship, two-step MR was performed as illustrated in Fig. 1 -B. In the first step, single-nucleotide polymorphisms (SNPs) to genetically predict metformin use were incorporated to evaluate the causal relationship of metformin use on 22 potential mediators (e.g. BMI, CRP, and testosterone levels) in the univariable MR method. And SNPs robustly related to mediators were used to calculate the causal association of mediators and cancer outcome(s) [ 9 ]. It should be noted that the genetic information utilized in this study is freely accessible to researchers around the world and is therefore not subject to additional ethical review or informed consent. 2 Selection of instrumental variants (IVs) of metformin use Genetic variants of metformin use in European ancestry were obtained from the UK Biobank dataset (8392 cases/ 328767 controls). The following inclusion criteria guided our selection of the IVs: (1) SNPs should have a genome-wide significance level (P 0.001) were excluded. The LD between SNPs was assessed to clump the independence of SNPs; (3) The F-statistics (beta2/se2) > 10. SNPs with F-statistics less than 10 may have inferior statistical power. Supplemental Table 1 summarizes the IVs of metformin use involved in this work. 3 Selection of cancer outcomes The genetic information associated with the following types of malignant tumors was obtained from the FinnGen consortium: colorectal cancer (3022 cases/ 215770 controls), stomach cancer (633 cases/218159 controls), pancreas cancer (605 cases/218187 controls), oral pharynx cancer (126 cases/218666 controls), oesophagus cancer (212 cases/218560 controls), bone and articular cartilage cancer (119 cases/218673 controls), kidney cancer (971 cases/ 217821 controls), melanoma (98 cases/218694 controls), non-melanoma skin cancer (10382 cases/208410 controls), thyroid gland cancer (989 cases/217803 controls), overall breast cancer (8401 cases/115178 controls), HER-negative breast cancer (3092 cases/99267 controls), HER-positive breast cancer (4263 cases/99267 controls), lung cancer (1681 cases, 217111 controls), non-small cell lung cancer (NSCLC) (1627 cases/217165 controls) and small cell lung cancer (SCLC) (179 cases/218613 controls). The genetic information of other cancers was gotten from UKB: colon cancer (2226 cases/358968 controls), rectum cancer (1085 cases/461925 controls), liver cancer (168 cases/372016 controls), small intestine cancer (156 cases/337003 controls), bladder cancer (1554 cases/359640 controls), overall skin cancer (1436 cases/461497 controls). Only the European population was incorporated into this study, and no sample overlap in this MR study. 4 Statistical analyses 4.1 Two-sample Mendelian Randomization Two-sample MR studies were conducted using TwoSampleMR package (version 0.5.6) and R software (version 4.2.1) [ 10 ]. A total of five different approaches were used. The inverse variance weighting (IVW) method, which evaluates the causal influence of genetically predicted exposures on outcomes by weighted regression of SNP-specific Wald ratios, acted as the major approach. To examine the consistency and heterogeneity of our findings, four additional assessment techniques—weighted median, MR Egger, simple model, and weighted model—were performed [ 11 – 13 ]. When the variable in MR has an impact on illness independent of its impact on exposure, this is known as horizontal pleiotropy. To avoid the biases of horizontal pleiotropy, MR-PRESSO method was performed to identify the outliers with MRPRESSO package (version 1.0) [ 14 ]. Pleiotropy was tested by leave-one-out analysis and MR-Egger intercept method [ 15 , 16 ]. Heterogeneity was evaluated by Cochran's Q-statistic, and any MR results with heterogeneity were excluded. 4.2 Median analysis The genetic information of potential mediators was downloaded from publicly accessible GWAS consortia, and relevant GWAS identifiers or available references were listed in Table 2 . Two-step MR analysis was applied to figure out if the potential mediator attributed any mediating effect between exposure and outcome [ 9 ]. Of note, the mediator has to meet the premise of a continuous variable [ 17 ]. In the first step, genetic variants of exposure (metformin use) were obtained to determine the causal effect of exposure on potential mediators. After that, genetic variants of mediators were also acquired to assess the causal role of mediators on outcomes (cancers) in the second step. Beta 1 and beta 2 were calculated in step one and step two respectively (Fig. 1 B). Potential mediator which presented supporting evidence in two-step MR would be included in the median analysis. Multivariable MR (MVMR) analysis was performed on metformin use-TT level-HER(+) breast cancer. The mediation effect was obtained by multiplying beta1 by beta2. Table 2 Genetic causal effect of metformin use on potential mediators. All the results above were derived from the IVW method. *P1_value < 0.05; Lo_95CI: the lower margin of beta 1’s 95% confidence interval; Up_95CI: the upper margin of beta 1’s 95% confidence interval; SHBG:Sex hormone-binding globulin; VATV: Visceral adipose tissue volume; ASATV: Abdominal subcutaneous adipose tissue volume; HDL: high-density lipoprotein; LDL: low-density lipoprotein; WBC: White blood cell. Potential Mediators GWAS identifier / Reference Beta1 Lo_95CI Up_95CI P1_value BMI ebi-a-GCST006802 [ 1 ] -4.00E-01 -1.51E + 00 7.10E-01 4.80E-01 Weight ukb-b-11842 -1.72E-01 -2.02E + 00 1.68E + 00 8.55E-01 Waist circumference ieu-a-67 [ 2 ] 5.12E-01 -1.70E + 00 2.73E + 00 6.50E-01 ASATV ebi-a-GCST90016672 [ 3 ] -2.40E-01 -2.56E + 00 2.08E + 00 8.40E-01 VATV ebi-a-GCST90016671 [ 3 ] 2.41E-01 -1.61E + 00 2.09E + 00 7.99E-01 Whole body fat-free mass ukb-b-13354 -6.68E-02 -1.32E + 00 1.19E + 00 9.17E-01 Body fat percentage ukb-b-8909 -1.82E-01 -1.45E + 00 1.08E + 00 7.78E-01 WBC ebi-a-GCST004610 [ 4 ] -1.21E-01 -1.05E + 00 8.11E-01 7.99E-01 CRP ieu-b-4764 -1.99E-01 -1.35E + 00 9.52E-01 7.35E-01 HDL cholesterol ieu-b-109 [ 5 ] -2.38E + 00 -4.40E + 00 -3.62E-01 2.08E-02* LDL cholesterol ieu-b-5089 -2.37E + 00 -3.21E + 00 -1.54E + 00 2.38E-08* Total cholesterol ieu-a-301 [ 6 ] -6.69E-01 -3.02E + 00 1.68E + 00 5.77E-01 SHBG ebi-a-GCST90012111 [ 7 ] -1.50E + 00 -2.08E + 00 -9.24E-01 3.57E-07* Total testosterone ebi-a-GCST90012114 [ 7 ] -6.04E-01 -9.99E-01 -2.08E-01 2.76E-03* Bioavailable testosterone ebi-a-GCST90012102 [ 7 ] 9.28E-01 3.35E-01 1.52E + 00 2.15E-03* Estradiol levels ebi-a-GCST90012105 [ 7 ] -1.23E-01 -2.32E-01 -1.31E-02 2.82E-02* Fasting insulin ebi-a-GCST90002238 [ 8 ] -3.85E-01 -1.34E + 00 5.68E-01 4.28E-01 Fasting glucose ebi-a-GCST005186 [ 9 ] 2.65E + 00 1.06E + 00 4.23E + 00 1.06E-03* HbA1c ieu-b-104 [ 10 ] -3.96E-01 -9.88E-01 1.97E-01 1.90E-01 Telomere length ieu-b-4879 -6.25E-03 -4.43E-01 4.30E-01 9.78E-01 5 Comparison with clinical studies To further confirm our findings, we reviewed the prevention and treatment effects of metformin on breast cancer in previous clinical studies. Phrase II, Phrase III randomized clinical trials (RCT), prospective studies and retrospective studies that published on Pubmed, Medline and Embase were included. Results 1 Selected genetic instrumental variants (IVs) We meticulously followed the aforementioned criteria when selecting the IVs. As a consequence, 26 independent SNPs were selected out of the total amount of 10,894,596 SNPs, acting as the IVs of metformin use. Detailed information could be found in Supplementary Table 1 . F-statistics, which were also presented in the supplementary document, showed no evidence of weak instrumental bias. 2 Assessment of the genetic causal effect of metformin on cancers 2.1 Two-sample MR results The brief results of two-sample MR analyses of metformin use on 26 prevalent cancers were listed in Table 1 . IVW results presented the genetically predicted protective effect of metformin use on HER-positive breast cancer (Beta: -4.0982; OR: 0.0166 (95%CI: 0.0008, 0.3376); P-value: 0.0077). The scatter plots and funnel plots were illustrated in Supplementary Fig. 1 . The leave-one-out analysis showed no pleiotropy in the MR result ( Supplementary Fig. 2 ). And no significant genetic relationship existed between metformin use and other types of cancers. 2.2 Median analysis results The following 20 probable mediators were investigated to figure out whether MR is shown to be causally related to both the effect of metformin use on them (step one) and the mediators' effects on HER-positive breast cancer (step two): inflammation-related factors (white blood cell counts and C-reactive protein), body shape-related index (BMI, weight, waist circumference, body fat percentage, visceral adipose tissue volume, and abdominal subcutaneous adipose tissue volume), metabolism-related biomarkers (HbA1c, fasting insulin, and fasting glucose) and sex hormone-related biomarkers (SHBG, estradiol, total testosterone levels, and bioavailable testosterone levels). As shown in Table 2 , we determined that metformin treatment had a causal influence on HDL cholesterol, LDL cholesterol, SHBG, total testosterone, bioavailable testosterone, estradiol, and fasting glucose levels. MR analyses were further conducted to evaluate the causal effect of the seven mediators above on HER-positive breast cancer (Table 3 ). Significant causal associations was exhibited in total testosterone levels (Beta: 0.4058, 95%CI: 0.0562 to 0.7556, P-value: 0.0229). Hence, total testosterone (TT) levels was selected for mediation effect calculation ( Supplementary Fig. 3–10 ). Table 3 Genetic causal effect of potential mediators on HER-positive breast cancer. All the results above were derived from the IVW method. *P2_value < 0.05; Lo_95CI: the lower margin of beta 2’s 95% confidence interval; Up_95CI: the upper margin of beta 2’s 95% confidence interval; SHBG:Sex hormone-binding globulin; HDL: high-density lipoprotein; LDL: low-density lipoprotein. Potential Mediators Beta2 Lo_95CI Up_95CI P2_value HDL cholesterol 2.85E-02 -8.63E-02 1.43E-01 6.27E-01 LDL cholesterol 6.97E-02 -5.83E-02 1.98E-01 2.86E-01 SHBG -8.30E-02 -3.50E-01 1.84E-01 5.42E-01 Total testosterone 4.06E-01 5.62E-02 7.56E-01 2.29E-02* Bioavailable testosterone 1.73E-01 3.61E-02 3.81E-01 1.05E-01 Estradiol levels 1.34E + 00 -6.24E-01 3.30E + 00 1.82E-01 Fasting glucose -1.66E-01 -4.72E-01 1.39E-01 2.86E-01 In the MVMR of metformin-TT-HER(+) breast cancer, the direct effect of metformin on HER(+) breast cancer was OR 0.0992 (95%CI: 0.0038 to 2.5986, P-value: 0.1655) after being adjusted by TT levels, and the direct effect of TT on HER(+) breast cancer was OR 1.5964 (95%CI: 1.1334 to 2.2486, P-value: 0.0074) after being adjusted by metformin use. ( Supplementary Table 2 ). The mediation effect of TT levels was 24.52%. 3 Review of previous clinical studies With the help of the three databases mentioned above, we listed the literature reviews of clinical studies concerning metformin use on breast cancer in Table 4 , both therapeutic and preventive effect were reviewed here. Discussion In addition to alleviating persistently high plasma glucose and insulin levels, and serving as the major prescription for type 2 diabetes, metformin also has a promising prospect in the prevention and treatment of malignant tumors. Recent years have witnessed the promising efficacy of metformin in the management of several types of cancer, however, clinical outcomes have been inconsistent [ 5 , 18 , 19 ]. To optimize the anti-tumor effect of metformin, researchers have focused on the underlying mechanisms for decades. As introduced before, various pathways and mediating molecules between the association of metformin and cancers have been revealed, represented by the activation of AMPK-related pathways [ 20 , 21 ], promotion of apoptotic cancer cell death [ 22 , 23 ], and inhibition of mitochondrial metabolism [ 24 , 25 ]. These mechanistically correlated studies have provided insight into the role and regulatory mechanisms of metformin in cancer therapy, contributing substantially to future clinical management and pharmaceutical development. A rising emphasis is being paid to research on the gene level of metformin in cancer treatment. Here, we conducted MR analysis to figure out the genetically predicted association between metformin use and the risk of common cancers. Compared to clinical studies, irrelevant confounders, and environmental exposures will be eliminated with MR analysis, as well as reduce the impact of reverse causality, and strengthen the evidence for causal inference [ 26 ]. Therefore, MR analysis is a relatively reliable and cost-saving method, based on global genome databases. Several MR studies have demonstrated the genetic influence of metformin on a variety of diseases. The association between metformin use and lung cancer risk was demonstrated by Zhou et al. in 2020. Their MR analysis findings showed no genetic causality existed between the two [ 27 ], which was consistent with our results. Modest genetic associations were reported in breast cancer and prostate cancer too [ 28 ]. Of note, the breast cancer outcomes in this MR study were overall, estrogen receptor (ER)-positive and ER-negative subtypes, whose results were consistent with ours too. Apart from cancers, the causal role of metformin on other diseases has been also assessed. Zhang et al. reported the protective causal relationship between metformin targets and osteoarthritis, pointing out AMPK and GDF-15 as promising targets for osteoarthritis treatment [ 29 ]. Additionally, GDF-15 as a therapeutic target of metformin might increase the risk of gallstone disorders [ 30 ]. However, metformin has multiple drug targets which could not be simplified into one or two certain targets, limiting the accuracy of drug target MR analysis for explaining its therapeutic effect. The relationship between sex hormone levels and breast cancer is complicated. According to a review study combining 44 breast cancer research, the risk of breast cancer increased after taking oral contraceptives [ 31 ]. And the breast cancer risk was positively correlated with the duration of oral contraceptive use [ 32 ]. These findings linked oestrogen and progestogen with the prevalence of breast cancer. Furthermore, testosterone also plays a significant role in the development of breast cancer. The association between premenopausal serum sex steroid levels and eventual breast cancer risk was established through a case-control analysis of the European Prospective Investigation into Cancer and Nutrition cohort. Researchers found that elevated blood testosterone concentrations were linked with an increased incidence of breast cancer (OR:1.73, 95% CI:1.16 to2.57; P-value:0.01) [ 33 ]. Similar outcomes were reported by researchers from other countries [ 34 – 36 ]. Two-sample MR studies of the sex steroid hormones and risk of breast cancer were also conducted by scientists from the UK, pointing out that testosterone and bioavailable testosterone could raise both overall and ER-positive breast cancer risk [ 37 , 38 ]. These results were highly consistent with our findings, accounting for a robust association between testosterone levels and breast cancer. Interestingly, the present study also found metformin could reduce the HER-positive breast cancer risk partially through both total testosterone levels. The testosterone reduction effect of metformin has been observed in previous reports [ 39 , 40 ]. Some clinical trials have administrated metformin on non-diabetic breast cancer women, ending with a significant reduction of both insulin and testosterone levels [ 41 , 42 ]. Moreover, metformin lowered estradiol levels primarily by diminishing testosterone, and these hormonal alterations may be relevant to certain clinical settings. Our study has some strengths. To our knowledge, this is the first study figuring out the genetic effect of metformin use on multiple prevalent cancer risks. And the mediators on the genetic pathway were clarified, and their mediating effects were calculated. Moreover, the genetic information incorporated in this study was giant, which increases the credibility of the conclusion. There are also some limitations here. First, to ascertain the consistency of genetic background, this MR analysis only concluded European populations, which could not be extended to other ethnicities. Second, MR analysis of tumors with a small number of instances was less accurate (fewer than 1000). For the validation analysis, more genetic data from large samples need to be added. The association between metformin use and other malignancies cannot be determined at this time; however, this will be clarified in follow-up research. Conclusion The current MR study revealed that metformin use could genetically shield individuals from HER-positive breast cancer, which was mediated by total testosterone levels. Further investigation is required to determine whether metformin-induced changes in total testosterone levels could potentially serve as a predictor or biomarker in HER-positive breast cancer development and progression. Declarations Ethics approval and consent to participate All participating studies involved in the GWAS obtained informed consent from the study populations. As we utilized publicly available datasets to conduct MR, no additional ethics approval was required. An certification of ethics approval waiver was consented to by the ethics committee of Zhejiang University Affiliated Sir Run Run Shaw Hospital. Consent for publication All authors approved the final manuscript and the submission to this journal. Availability of data and material All of the genetic data used in this work was publicly available. The relevant data can be found here: Open GWAS summary dataset (https://gwas.mrcieu.ac.uk/); UK Biobank (https://www.ukbiobank.ac.uk/); FinnGen database (https://www.finngen.fi/). Declaration of competing interest The authors declare no conflict of interest. Funding This work was supported by grants from the National Natural Science Foundation of China (No. 82100542 to B. Xie), Zhejiang Provincial Natural Science Foundation of China (No. LQ21H160027 to B. Xie), the China Postdoctoral Science Foundation (No. 2020M681893 to B. Xie). Author contributions Y. Chen and S. Ye: conceptualization and writing of the manuscript. B. Bai, X. Gao and K. Ying: making and correcting the tables and figures. H. Pan and B. Xie: reviewing, editing, and providing critical discussion. Acknowledgment We are grateful to the public open accessible databases mentioned above. References Sung, H., et al., Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians, 2021. 71 (3): p. 209-249. Mallik, R. and T.A. Chowdhury, Metformin in cancer. Diabetes Res Clin Pract, 2018. 143 : p. 409-419. Lai, S.W., et al., Antidiabetes drugs correlate with decreased risk of lung cancer: a population-based observation in Taiwan. Clin Lung Cancer, 2012. 13 (2): p. 143-8. Bragagnoli, A.C., et al., Metformin plus lrinotecan in patients with refractory colorectal cancer: a phase 2 clinical trial. Br J Cancer, 2021. 124 (6): p. 1072-1078. Barakat, H.E., et al., The impact of metformin use on the outcomes of locally advanced breast cancer patients receiving neoadjuvant chemotherapy: an open-labelled randomized controlled trial. Sci Rep, 2022. 12 (1): p. 7656. Vancura, A., et al., Metformin as an Anticancer Agent. Trends Pharmacol Sci, 2018. 39 (10): p. 867-878. Apostolova, N., et al., Mechanisms of action of metformin in type 2 diabetes: Effects on mitochondria and leukocyte-endothelium interactions. Redox Biol, 2020. 34 : p. 101517. Sekula, P., et al., Mendelian Randomization as an Approach to Assess Causality Using Observational Data. J Am Soc Nephrol, 2016. 27 (11): p. 3253-3265. Relton, C.L. and G. Davey Smith, Two-step epigenetic Mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease. Int J Epidemiol, 2012. 41 (1): p. 161-76. Yavorska, O.O. and S. Burgess, MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int J Epidemiol, 2017. 46 (6): p. 1734-1739. Bowden, J., et al., Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol, 2016. 40 (4): p. 304-14. Bowden, J., G. Davey Smith, and S. Burgess, Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol, 2015. 44 (2): p. 512-25. Jones, H.J., et al., Associations between plasma fatty acid concentrations and schizophrenia: a two-sample Mendelian randomisation study. Lancet Psychiatry, 2021. 8 (12): p. 1062-1070. Verbanck, M., et al., Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet, 2018. 50 (5): p. 693-698. Hemani, G., J. Bowden, and G. Davey Smith, Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum Mol Genet, 2018. 27 (R2): p. R195-r208. Bowden, J., et al., A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization. Stat Med, 2017. 36 (11): p. 1783-1802. Carter, A.R., et al., Mendelian randomisation for mediation analysis: current methods and challenges for implementation. Eur J Epidemiol, 2021. 36 (5): p. 465-478. Yang, B.Y., et al., Metformin plus megestrol acetate compared with megestrol acetate alone as fertility-sparing treatment in patients with atypical endometrial hyperplasia and well-differentiated endometrial cancer: a randomised controlled trial. Bjog, 2020. 127 (7): p. 848-857. Skuli, S.J., et al., Metformin and Cancer, an Ambiguanidous Relationship. Pharmaceuticals (Basel), 2022. 15 (5). Zheng, Z., et al., Metformin activates AMPK/SIRT1/NF-κB pathway and induces mitochondrial dysfunction to drive caspase3/GSDME-mediated cancer cell pyroptosis. Cell Cycle, 2020. 19 (10): p. 1089-1104. Chen, Y.H., et al., Metformin induces apoptosis and inhibits migration by activating the AMPK/p53 axis and suppressing PI3K/AKT signaling in human cervical cancer cells. Mol Med Rep, 2021. 23 (1). Haugrud, A.B., et al., Dichloroacetate enhances apoptotic cell death via oxidative damage and attenuates lactate production in metformin-treated breast cancer cells. Breast Cancer Res Treat, 2014. 147 (3): p. 539-50. Klose, K., et al., Metformin and sodium dichloroacetate effects on proliferation, apoptosis, and metabolic activity tested alone and in combination in a canine prostate and a bladder cancer cell line. PLoS One, 2021. 16 (9): p. e0257403. Vasan, K., M. Werner, and N.S. Chandel, Mitochondrial Metabolism as a Target for Cancer Therapy. Cell Metab, 2020. 32 (3): p. 341-352. Wheaton, W.W., et al., Metformin inhibits mitochondrial complex I of cancer cells to reduce tumorigenesis. Elife, 2014. 3 : p. e02242. Zoccali, C., et al., Mendelian randomization: a new approach to studying epidemiology in ESRD. Am J Kidney Dis, 2006. 47 (2): p. 332-41. Zhou, H., et al., Mendelian randomization study showed no causality between metformin use and lung cancer risk. Int J Epidemiol, 2020. 49 (4): p. 1406-1407. Au Yeung, S.L. and C.M. Schooling, Impact of glycemic traits, type 2 diabetes and metformin use on breast and prostate cancer risk: a Mendelian randomization study. BMJ Open Diabetes Res Care, 2019. 7 (1): p. e000872. Zhang, Y., et al., Evaluating the impact of metformin targets on the risk of osteoarthritis: a mendelian randomization study. Osteoarthritis Cartilage, 2022. 30 (11): p. 1506-1514. Yu, L., et al., GDF-15 as a Therapeutic Target of Diabetic Complications Increases the Risk of Gallstone Disease: Mendelian Randomization and Polygenic Risk Score Analysis. Front Genet, 2022. 13 : p. 814457. Gierisch, J.M., et al., Oral contraceptive use and risk of breast, cervical, colorectal, and endometrial cancers: a systematic review. Cancer Epidemiol Biomarkers Prev, 2013. 22 (11): p. 1931-43. Type and timing of menopausal hormone therapy and breast cancer risk: individual participant meta-analysis of the worldwide epidemiological evidence. Lancet, 2019. 394 (10204): p. 1159-1168. Kaaks, R., et al., Serum sex steroids in premenopausal women and breast cancer risk within the European Prospective Investigation into Cancer and Nutrition (EPIC). J Natl Cancer Inst, 2005. 97 (10): p. 755-65. Micheli, A., et al., Plasma testosterone and prognosis of postmenopausal breast cancer patients. J Clin Oncol, 2007. 25 (19): p. 2685-90. Arthur, R.S., A.J. Dannenberg, and T.E. Rohan, The association of prediagnostic circulating levels of cardiometabolic markers, testosterone and sex hormone-binding globulin with risk of breast cancer among normal weight postmenopausal women in the UK Biobank. Int J Cancer, 2021. 149 (1): p. 42-57. Watts, E.L., et al., 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): p. 573-584. Nounu, A., et al., Sex steroid hormones and risk of breast cancer: a two-sample Mendelian randomization study. Breast Cancer Res, 2022. 24 (1): p. 66. Tang, S.N., V. Zuber, and K.K. Tsilidis, Identifying and ranking causal biochemical biomarkers for breast cancer: a Mendelian randomisation study. BMC Med, 2022. 20 (1): p. 457. Cai, T., et al., Effect of Metformin on Testosterone Levels in Male Patients With Type 2 Diabetes Mellitus Treated With Insulin. Front Endocrinol (Lausanne), 2021. 12 : p. 813067. Andræ, F., et al., Sustained Maternal Hyperandrogenism During PCOS Pregnancy Reduced by Metformin in Non-obese Women Carrying a Male Fetus. J Clin Endocrinol Metab, 2020. 105 (12): p. 3762-70. Campagnoli, C., et al., Effect of different doses of metformin on serum testosterone and insulin in non-diabetic women with breast cancer: a randomized study. Clin Breast Cancer, 2012. 12 (3): p. 175-82. Campagnoli, C., et al., Metformin decreases circulating androgen and estrogen levels in nondiabetic women with breast cancer. Clin Breast Cancer, 2013. 13 (6): p. 433-8. Table 1 and 4 Table 1 and 4 are available in Supplementary Files section. Table References Wood, A.R., et al., Variants in the FTO and CDKAL1 loci have recessive effects on risk of obesity and type 2 diabetes, respectively. Diabetologia, 2016. 59 (6): p. 1214-21. Shungin, D., et al., New genetic loci link adipose and insulin biology to body fat distribution. Nature, 2015. 518 (7538): p. 187-196. Liu, Y., et al., Genetic architecture of 11 organ traits derived from abdominal MRI using deep learning. Elife, 2021. 10 . Astle, W.J., et al., The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease. Cell, 2016. 167 (5): p. 1415-1429.e19. Richardson, T.G., et al., Evaluating the relationship between circulating lipoprotein lipids and apolipoproteins with risk of coronary heart disease: A multivariable Mendelian randomisation analysis. PLoS Med, 2020. 17 (3): p. e1003062. Willer, C.J., et al., Discovery and refinement of loci associated with lipid levels. Nat Genet, 2013. 45 (11): p. 1274-1283. Ruth, K.S., et al., Using human genetics to understand the disease impacts of testosterone in men and women. Nat Med, 2020. 26 (2): p. 252-258. Chen, J., et al., The trans-ancestral genomic architecture of glycemic traits. Nat Genet, 2021. 53 (6): p. 840-860. Manning, A.K., et al., A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Nat Genet, 2012. 44 (6): p. 659-69. Soranzo, N., et al., Common variants at 10 genomic loci influence hemoglobin A ₁(C) levels via glycemic and nonglycemic pathways. Diabetes, 2010. 59 (12): p. 3229-39. Goodwin, P.J., et al., Effect of Metformin vs Placebo on Invasive Disease-Free Survival in Patients With Breast Cancer: The MA.32 Randomized Clinical Trial. Jama, 2022. 327 (20): p. 1963-1973. Barakat, H.E., et al., The impact of metformin use on the outcomes of locally advanced breast cancer patients receiving neoadjuvant chemotherapy: an open-labelled randomized controlled trial. Sci Rep, 2022. 12 (1): p. 7656. Huang, J., et al., Neoadjuvant docetaxel, epirubicin, and cyclophosphamide with or without metformin in breast cancer patients with metabolic abnormality: results from the randomized Phase II NeoMET trial. Breast Cancer Res Treat, 2023. 197 (3): p. 525-533. Pimentel, I., et al., A phase II randomized clinical trial of the effect of metformin versus placebo on progression-free survival in women with metastatic breast cancer receiving standard chemotherapy. Breast, 2019. 48 : p. 17-23. Nanni, O., et al., Metformin plus chemotherapy versus chemotherapy alone in the first-line treatment of HER2-negative metastatic breast cancer. The MYME randomized, phase 2 clinical trial. Breast Cancer Res Treat, 2019. 174 (2): p. 433-442. Yam, C., et al., Efficacy and safety of the combination of metformin, everolimus and exemestane in overweight and obese postmenopausal patients with metastatic, hormone receptor-positive, HER2-negative breast cancer: a phase II study. Invest New Drugs, 2019. 37 (2): p. 345-351. Essa, N.M., et al., Efficacy of Metformin as Adjuvant Therapy in Metastatic Breast Cancer Treatment. J Clin Med, 2022. 11 (19). Sonnenblick, A., et al., Impact of Diabetes, Insulin, and Metformin Use on the Outcome of Patients With Human Epidermal Growth Factor Receptor 2-Positive Primary Breast Cancer: Analysis From the ALTTO Phase III Randomized Trial. J Clin Oncol, 2017. 35 (13): p. 1421-1429. Feng, J.L. and X. Qin, Metformin and cancer-specific survival among breast, colorectal, or endometrial cancer patients: A nationwide data linkage study. Diabetes Res Clin Pract, 2021. 175 : p. 108755. Kim, B.H., M.J. Cho, and J. Kwon, Potential intrinsic subtype dependence on the association between metformin use and survival in surgically resected breast cancer: a Korean national population-based study. Int J Clin Oncol, 2021. 26 (11): p. 2004-2016. Hui, T., et al., Metformin improves the outcomes in Chinese invasive breast cancer patients with type 2 diabetes mellitus. Sci Rep, 2021. 11 (1): p. 10034. Hosio, M., et al., Survival after breast cancer in women with type 2 diabetes using antidiabetic medication and statins: a retrospective cohort study. Acta Oncol, 2020. 59 (9): p. 1110-1117. El-Benhawy, S.A. and H.G. El-Sheredy, Metformin and survival in diabetic patients with breast cancer. J Egypt Public Health Assoc, 2014. 89 (3): p. 148-53. Kim, H.J., et al., Metformin increases survival in hormone receptor-positive, HER2-positive breast cancer patients with diabetes. Breast Cancer Res, 2015. 17 (1): p. 64. Park, Y.M., et al., A prospective study of type 2 diabetes, metformin use, and risk of breast cancer. Ann Oncol, 2021. 32 (3): p. 351-359. Chlebowski, R.T., et al., Diabetes, metformin, and breast cancer in postmenopausal women. J Clin Oncol, 2012. 30 (23): p. 2844-52. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx Table1and4.docx TableReferences.docx Cite Share Download PDF Status: Published Journal Publication published 06 Dec, 2023 Read the published version in Diabetology & Metabolic Syndrome → Version 1 posted Editorial decision: Major revision 25 Sep, 2023 Reviews received at journal 07 Aug, 2023 Reviewers agreed at journal 27 Jul, 2023 Reviewers invited by journal 24 Jul, 2023 Editor assigned by journal 19 Jul, 2023 Submission checks completed at journal 17 Jul, 2023 First submitted to journal 16 Jul, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3174656","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":219163254,"identity":"45661b11-b37f-4350-932b-2b639e1d26f1","order_by":0,"name":"Yao Chen","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Yao","middleName":"","lastName":"Chen","suffix":""},{"id":219163255,"identity":"51b4c66d-26eb-4cfa-ac52-b94372d25d3b","order_by":1,"name":"Bingjun Bai","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Bingjun","middleName":"","lastName":"Bai","suffix":""},{"id":219163256,"identity":"62a2d338-0974-45f5-bcf8-d58356364476","order_by":2,"name":"Shuchang Ye","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Shuchang","middleName":"","lastName":"Ye","suffix":""},{"id":219163257,"identity":"ae7df6c6-d81d-4268-a120-e198e05113db","order_by":3,"name":"Xing Gao","email":"","orcid":"","institution":"Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Xing","middleName":"","lastName":"Gao","suffix":""},{"id":219163258,"identity":"0a422752-1be4-441d-ae8f-1a579d34ab95","order_by":4,"name":"Kangkang Ying","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Kangkang","middleName":"","lastName":"Ying","suffix":""},{"id":219163259,"identity":"f60bab60-61e3-441c-94b1-d144df0d6cf7","order_by":5,"name":"Hongming Pan","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Hongming","middleName":"","lastName":"Pan","suffix":""},{"id":219163260,"identity":"a46dc1f4-2103-4c2b-b1d8-863861dea239","order_by":6,"name":"Binbin Xie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYDACZgb2xx8qgBQQSBCrhc1Y4gxJWhgY2KR52yAs4rQYHGc+Ziw5r47d4ADzwds8DHZ5BLVINrOlP/64jY3Z4ABbsjUPQ3IxQS38zDxmxpLbeIBaeMykeRgOJDYQ0sLGzP9NmneOBFALkEGUFqAtQO83GIBsYSNOC9AvZsYSxxKYJQ+zGVvOMUgmrMXg/OHnjz/U1CXzHW9+eONNhR1hLTCQDIlMA2LVA4EdCWpHwSgYBaNgpAEAXVAwvUQCs0gAAAAASUVORK5CYII=","orcid":"","institution":"Zhejiang University","correspondingAuthor":true,"prefix":"","firstName":"Binbin","middleName":"","lastName":"Xie","suffix":""}],"badges":[],"createdAt":"2023-07-16 08:59:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3174656/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3174656/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13098-023-01218-3","type":"published","date":"2023-12-06T15:01:47+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":40341936,"identity":"4fde169e-3abd-49e4-90de-35516432de8b","added_by":"auto","created_at":"2023-07-20 22:18:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1492502,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of the study design. \u003c/strong\u003e(A) We firstly applied two-sample MR analyses to figure out the genetic effect of metformin use on 26 prevalent cancers with five robust methods. (B) Two-step MR analysis and MVMR were further conducted to figure out the potential mediator who mediate the protective genetic effect of metformin on HER-positive breast cancer.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3174656/v1/40a8351fd1d0172d2c06c125.png"},{"id":47989482,"identity":"17e6b2e4-81cb-4493-badb-952a6033400c","added_by":"auto","created_at":"2023-12-11 15:09:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":840223,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3174656/v1/b0044c86-1454-4d44-bc66-e0264f21f72c.pdf"},{"id":40341935,"identity":"5caed368-4117-4802-a62d-125a5cb82d04","added_by":"auto","created_at":"2023-07-20 22:18:00","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1402244,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-3174656/v1/451bb67cc4781160c549b869.docx"},{"id":40341934,"identity":"3d0ccee1-c0e7-48a0-8446-3a7e5d4d0a0f","added_by":"auto","created_at":"2023-07-20 22:18:00","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":85626,"visible":true,"origin":"","legend":"","description":"","filename":"Table1and4.docx","url":"https://assets-eu.researchsquare.com/files/rs-3174656/v1/35b17233e7cf1165d34ba15c.docx"},{"id":40341933,"identity":"e647c5b1-173a-4965-aaa9-3a59a4001b07","added_by":"auto","created_at":"2023-07-20 22:18:00","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":15312,"visible":true,"origin":"","legend":"","description":"","filename":"TableReferences.docx","url":"https://assets-eu.researchsquare.com/files/rs-3174656/v1/b69828a84e1040d136edafa5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genetic effect of metformin use on risk of cancers: Evidence from Mendelian randomization analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe growing frequency and high mortality of malignant tumors imposed a significant burden on people all over the world [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Clinicians have been searching for effective and secure approaches to prevent cancers for a few decades but in vain. Metformin is the most frequently prescribed drug for type 2 diabetes, whose potential anti-tumorigenic effects have attracted growing attention [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. An increasing number of clinical studies attempted to reveal the efficacy of metformin on different types of cancer, but the controversial conclusions left the issue unsolved [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Biases induced by confounders which were hard to avoid in observational studies might be responsible for this.\u003c/p\u003e \u003cp\u003eScientists were also interested in the biological pathways of metformin in cancer treatment. In addition to improvements in glucose metabolism, metformin's molecular mechanisms against many malignancies have been extensively investigated in recent years, with reduction of leukocyte-endothelium interactions, alteration of oxidative stress, and regulation of AMP-activated protein kinase (AMPK) all being implicated [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, researches related to the genetic effect of metformin use on the risk of cancers were not complete yet.\u003c/p\u003e \u003cp\u003eContrary to conventional observational research, Mendelian Randomization analysis (MR) provided a cost- and time-saving approach with high efficiency to investigate the genetically predicted causal relationships [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Robust-associated genetic variants were selected to explore the genetic association between exposures and outcomes. In this work, two-sample MR was applied to figure out the causal relationship between metformin use and 26 prevalent cancers, and two-step MR was supplemented to determine the mediators and their contribution to the genetic causal effect. Ultimately, we reviewed previous clinical studies to further understand the association between metformin use and cancers.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1 Study design\u003c/h2\u003e \u003cp\u003eThe overview of the study design was demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-A, The MR analysis requires three basic assumptions to be met: (1) instrumental variables (IVs) are strongly correlated to exposure; (2) IVs are independent of any potential confounders; and (3) IVs only affect the outcome through exposure. Two distinct genetic datasets should be integrated into a single MR study, which is the fundamental prerequisite of two-sample MR.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo determine the mediating factors in the genetic causal relationship, two-step MR was performed as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-B. In the first step, single-nucleotide polymorphisms (SNPs) to genetically predict metformin use were incorporated to evaluate the causal relationship of metformin use on 22 potential mediators (e.g. BMI, CRP, and testosterone levels) in the univariable MR method. And SNPs robustly related to mediators were used to calculate the causal association of mediators and cancer outcome(s) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. It should be noted that the genetic information utilized in this study is freely accessible to researchers around the world and is therefore not subject to additional ethical review or informed consent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2 Selection of instrumental variants (IVs) of metformin use\u003c/h2\u003e \u003cp\u003eGenetic variants of metformin use in European ancestry were obtained from the UK Biobank dataset (8392 cases/ 328767 controls). The following inclusion criteria guided our selection of the IVs: (1) SNPs should have a genome-wide significance level (P\u0026thinsp;\u0026lt;\u0026thinsp;5 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e), which strongly indicates genetic association with exposure. (2) Genetic variants with linkage disequilibrium (LD) (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.001) were excluded. The LD between SNPs was assessed to clump the independence of SNPs; (3) The F-statistics (beta2/se2)\u0026thinsp;\u0026gt;\u0026thinsp;10. SNPs with F-statistics less than 10 may have inferior statistical power. \u003cb\u003eSupplemental Table\u0026nbsp;1\u003c/b\u003e summarizes the IVs of metformin use involved in this work.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3 Selection of cancer outcomes\u003c/h2\u003e \u003cp\u003eThe genetic information associated with the following types of malignant tumors was obtained from the FinnGen consortium: colorectal cancer (3022 cases/ 215770 controls), stomach cancer (633 cases/218159 controls), pancreas cancer (605 cases/218187 controls), oral pharynx cancer (126 cases/218666 controls), oesophagus cancer (212 cases/218560 controls), bone and articular cartilage cancer (119 cases/218673 controls), kidney cancer (971 cases/ 217821 controls), melanoma (98 cases/218694 controls), non-melanoma skin cancer (10382 cases/208410 controls), thyroid gland cancer (989 cases/217803 controls), overall breast cancer (8401 cases/115178 controls), HER-negative breast cancer (3092 cases/99267 controls), HER-positive breast cancer (4263 cases/99267 controls), lung cancer (1681 cases, 217111 controls), non-small cell lung cancer (NSCLC) (1627 cases/217165 controls) and small cell lung cancer (SCLC) (179 cases/218613 controls). The genetic information of other cancers was gotten from UKB: colon cancer (2226 cases/358968 controls), rectum cancer (1085 cases/461925 controls), liver cancer (168 cases/372016 controls), small intestine cancer (156 cases/337003 controls), bladder cancer (1554 cases/359640 controls), overall skin cancer (1436 cases/461497 controls). Only the European population was incorporated into this study, and no sample overlap in this MR study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e4 Statistical analyses\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e4.1 Two-sample Mendelian Randomization\u003c/h2\u003e \u003cp\u003eTwo-sample MR studies were conducted using TwoSampleMR package (version 0.5.6) and R software (version 4.2.1) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. A total of five different approaches were used. The inverse variance weighting (IVW) method, which evaluates the causal influence of genetically predicted exposures on outcomes by weighted regression of SNP-specific Wald ratios, acted as the major approach. To examine the consistency and heterogeneity of our findings, four additional assessment techniques\u0026mdash;weighted median, MR Egger, simple model, and weighted model\u0026mdash;were performed [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. When the variable in MR has an impact on illness independent of its impact on exposure, this is known as horizontal pleiotropy. To avoid the biases of horizontal pleiotropy, MR-PRESSO method was performed to identify the outliers with MRPRESSO package (version 1.0) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Pleiotropy was tested by leave-one-out analysis and MR-Egger intercept method [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Heterogeneity was evaluated by Cochran's Q-statistic, and any MR results with heterogeneity were excluded.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Median analysis\u003c/h2\u003e \u003cp\u003eThe genetic information of potential mediators was downloaded from publicly accessible GWAS consortia, and relevant GWAS identifiers or available references were listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Two-step MR analysis was applied to figure out if the potential mediator attributed any mediating effect between exposure and outcome [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Of note, the mediator has to meet the premise of a continuous variable [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In the first step, genetic variants of exposure (metformin use) were obtained to determine the causal effect of exposure on potential mediators. After that, genetic variants of mediators were also acquired to assess the causal role of mediators on outcomes (cancers) in the second step. Beta 1 and beta 2 were calculated in step one and step two respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Potential mediator which presented supporting evidence in two-step MR would be included in the median analysis. Multivariable MR (MVMR) analysis was performed on metformin use-TT level-HER(+) breast cancer. The mediation effect was obtained by multiplying beta1 by beta2.\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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eGenetic causal effect of metformin use on potential mediators.\u003c/b\u003e All the results above were derived from the IVW method. *P1_value\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Lo_95CI: the lower margin of beta 1\u0026rsquo;s 95% confidence interval; Up_95CI: the upper margin of beta 1\u0026rsquo;s 95% confidence interval; SHBG:Sex hormone-binding globulin; VATV: Visceral adipose tissue volume; ASATV: Abdominal subcutaneous adipose tissue volume; HDL: high-density lipoprotein; LDL: low-density lipoprotein; WBC: White blood cell.\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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotential Mediators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGWAS identifier / Reference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeta1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLo_95CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUp_95CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP1_value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eebi-a-GCST006802 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.00E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.51E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.10E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.80E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eukb-b-11842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.72E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.02E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.68E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.55E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eieu-a-67 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.12E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.70E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.73E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.50E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASATV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eebi-a-GCST90016672 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.40E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.56E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.08E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.40E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVATV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eebi-a-GCST90016671 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.41E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.61E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.09E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.99E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhole body fat-free mass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eukb-b-13354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.68E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.32E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.19E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.17E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody fat percentage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eukb-b-8909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.82E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.45E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.08E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.78E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eebi-a-GCST004610 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.21E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.05E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.11E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.99E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eieu-b-4764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.99E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.35E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.52E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.35E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eieu-b-109 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.38E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.40E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.62E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.08E-02*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eieu-b-5089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.37E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.21E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.54E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.38E-08*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eieu-a-301 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.69E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.02E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.68E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.77E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSHBG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eebi-a-GCST90012111 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.50E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.08E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-9.24E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.57E-07*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal testosterone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eebi-a-GCST90012114 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.04E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-9.99E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.08E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.76E-03*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBioavailable testosterone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eebi-a-GCST90012102 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.28E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.35E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.52E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.15E-03*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstradiol levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eebi-a-GCST90012105 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.23E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.32E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.31E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.82E-02*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting insulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eebi-a-GCST90002238 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.85E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.34E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.68E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.28E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting glucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eebi-a-GCST005186 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.65E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.23E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.06E-03*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eieu-b-104 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.96E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-9.88E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.97E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.90E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTelomere length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eieu-b-4879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.25E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.43E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.30E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.78E-01\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 \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e5 Comparison with clinical studies\u003c/h2\u003e \u003cp\u003eTo further confirm our findings, we reviewed the prevention and treatment effects of metformin on breast cancer in previous clinical studies. Phrase II, Phrase III randomized clinical trials (RCT), prospective studies and retrospective studies that published on Pubmed, Medline and Embase were included.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e1 Selected genetic instrumental variants (IVs)\u003c/h2\u003e\n \u003cp\u003eWe meticulously followed the aforementioned criteria when selecting the IVs. As a consequence, 26 independent SNPs were selected out of the total amount of 10,894,596 SNPs, acting as the IVs of metformin use. Detailed information could be found in \u003cstrong\u003eSupplementary Table\u0026nbsp;1\u003c/strong\u003e. F-statistics, which were also presented in the supplementary document, showed no evidence of weak instrumental bias.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e2 Assessment of the genetic causal effect of metformin on cancers\u003c/h2\u003e\n \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n \u003ch2\u003e2.1 Two-sample MR results\u003c/h2\u003e\n \u003cp\u003eThe brief results of two-sample MR analyses of metformin use on 26 prevalent cancers were listed in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. IVW results presented the genetically predicted protective effect of metformin use on HER-positive breast cancer (Beta: -4.0982; OR: 0.0166 (95%CI: 0.0008, 0.3376); P-value: 0.0077). The scatter plots and funnel plots were illustrated in \u003cstrong\u003eSupplementary Fig.\u0026nbsp;1\u003c/strong\u003e. The leave-one-out analysis showed no pleiotropy in the MR result (\u003cstrong\u003eSupplementary Fig.\u0026nbsp;2\u003c/strong\u003e). And no significant genetic relationship existed between metformin use and other types of cancers.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Median analysis results\u003c/h2\u003e\n \u003cp\u003eThe following 20 probable mediators were investigated to figure out whether MR is shown to be causally related to both the effect of metformin use on them (step one) and the mediators\u0026apos; effects on HER-positive breast cancer (step two): inflammation-related factors (white blood cell counts and C-reactive protein), body shape-related index (BMI, weight, waist circumference, body fat percentage, visceral adipose tissue volume, and abdominal subcutaneous adipose tissue volume), metabolism-related biomarkers (HbA1c, fasting insulin, and fasting glucose) and sex hormone-related biomarkers (SHBG, estradiol, total testosterone levels, and bioavailable testosterone levels). As shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, we determined that metformin treatment had a causal influence on HDL cholesterol, LDL cholesterol, SHBG, total testosterone, bioavailable testosterone, estradiol, and fasting glucose levels. MR analyses were further conducted to evaluate the causal effect of the seven mediators above on HER-positive breast cancer (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Significant causal associations was exhibited in total testosterone levels (Beta: 0.4058, 95%CI: 0.0562 to 0.7556, P-value: 0.0229). Hence, total testosterone (TT) levels was selected for mediation effect calculation (\u003cstrong\u003eSupplementary Fig.\u0026nbsp;3\u0026ndash;10\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenetic causal effect of potential mediators on HER-positive breast cancer.\u003c/strong\u003e All the results above were derived from the IVW method. *P2_value\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Lo_95CI: the lower margin of beta 2\u0026rsquo;s 95% confidence interval; Up_95CI: the upper margin of beta 2\u0026rsquo;s 95% confidence interval; SHBG:Sex hormone-binding globulin; HDL: high-density lipoprotein; LDL: low-density lipoprotein.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePotential Mediators\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBeta2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLo_95CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUp_95CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP2_value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDL cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.85E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.63E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.43E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.27E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.97E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.83E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.98E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.86E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSHBG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.30E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.50E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.84E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.42E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal testosterone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.06E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.62E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.56E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.29E-02*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBioavailable testosterone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.73E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.61E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.81E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEstradiol levels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.24E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.30E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.82E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFasting glucose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.66E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.72E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.39E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.86E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eIn the MVMR of metformin-TT-HER(+) breast cancer, the direct effect of metformin on HER(+) breast cancer was OR 0.0992 (95%CI: 0.0038 to 2.5986, P-value: 0.1655) after being adjusted by TT levels, and the direct effect of TT on HER(+) breast cancer was OR 1.5964 (95%CI: 1.1334 to 2.2486, P-value: 0.0074) after being adjusted by metformin use. (\u003cstrong\u003eSupplementary Table\u0026nbsp;2\u003c/strong\u003e). The mediation effect of TT levels was 24.52%.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3 Review of previous clinical studies\u003c/h2\u003e\n \u003cp\u003eWith the help of the three databases mentioned above, we listed the literature reviews of clinical studies concerning metformin use on breast cancer in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, both therapeutic and preventive effect were reviewed here.\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn addition to alleviating persistently high plasma glucose and insulin levels, and serving as the major prescription for type 2 diabetes, metformin also has a promising prospect in the prevention and treatment of malignant tumors. Recent years have witnessed the promising efficacy of metformin in the management of several types of cancer, however, clinical outcomes have been inconsistent [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. To optimize the anti-tumor effect of metformin, researchers have focused on the underlying mechanisms for decades. As introduced before, various pathways and mediating molecules between the association of metformin and cancers have been revealed, represented by the activation of AMPK-related pathways [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], promotion of apoptotic cancer cell death [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and inhibition of mitochondrial metabolism [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. These mechanistically correlated studies have provided insight into the role and regulatory mechanisms of metformin in cancer therapy, contributing substantially to future clinical management and pharmaceutical development.\u003c/p\u003e \u003cp\u003eA rising emphasis is being paid to research on the gene level of metformin in cancer treatment. Here, we conducted MR analysis to figure out the genetically predicted association between metformin use and the risk of common cancers. Compared to clinical studies, irrelevant confounders, and environmental exposures will be eliminated with MR analysis, as well as reduce the impact of reverse causality, and strengthen the evidence for causal inference [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Therefore, MR analysis is a relatively reliable and cost-saving method, based on global genome databases.\u003c/p\u003e \u003cp\u003eSeveral MR studies have demonstrated the genetic influence of metformin on a variety of diseases. The association between metformin use and lung cancer risk was demonstrated by Zhou et al. in 2020. Their MR analysis findings showed no genetic causality existed between the two [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], which was consistent with our results. Modest genetic associations were reported in breast cancer and prostate cancer too [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Of note, the breast cancer outcomes in this MR study were overall, estrogen receptor (ER)-positive and ER-negative subtypes, whose results were consistent with ours too. Apart from cancers, the causal role of metformin on other diseases has been also assessed. Zhang et al. reported the protective causal relationship between metformin targets and osteoarthritis, pointing out AMPK and GDF-15 as promising targets for osteoarthritis treatment [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Additionally, GDF-15 as a therapeutic target of metformin might increase the risk of gallstone disorders [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. However, metformin has multiple drug targets which could not be simplified into one or two certain targets, limiting the accuracy of drug target MR analysis for explaining its therapeutic effect.\u003c/p\u003e \u003cp\u003eThe relationship between sex hormone levels and breast cancer is complicated. According to a review study combining 44 breast cancer research, the risk of breast cancer increased after taking oral contraceptives [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. And the breast cancer risk was positively correlated with the duration of oral contraceptive use [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. These findings linked oestrogen and progestogen with the prevalence of breast cancer. Furthermore, testosterone also plays a significant role in the development of breast cancer. The association between premenopausal serum sex steroid levels and eventual breast cancer risk was established through a case-control analysis of the European Prospective Investigation into Cancer and Nutrition cohort. Researchers found that elevated blood testosterone concentrations were linked with an increased incidence of breast cancer (OR:1.73, 95% CI:1.16 to2.57; P-value:0.01) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Similar outcomes were reported by researchers from other countries [\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Two-sample MR studies of the sex steroid hormones and risk of breast cancer were also conducted by scientists from the UK, pointing out that testosterone and bioavailable testosterone could raise both overall and ER-positive breast cancer risk [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. These results were highly consistent with our findings, accounting for a robust association between testosterone levels and breast cancer.\u003c/p\u003e \u003cp\u003eInterestingly, the present study also found metformin could reduce the HER-positive breast cancer risk partially through both total testosterone levels. The testosterone reduction effect of metformin has been observed in previous reports [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Some clinical trials have administrated metformin on non-diabetic breast cancer women, ending with a significant reduction of both insulin and testosterone levels [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Moreover, metformin lowered estradiol levels primarily by diminishing testosterone, and these hormonal alterations may be relevant to certain clinical settings.\u003c/p\u003e \u003cp\u003eOur study has some strengths. To our knowledge, this is the first study figuring out the genetic effect of metformin use on multiple prevalent cancer risks. And the mediators on the genetic pathway were clarified, and their mediating effects were calculated. Moreover, the genetic information incorporated in this study was giant, which increases the credibility of the conclusion.\u003c/p\u003e \u003cp\u003eThere are also some limitations here. First, to ascertain the consistency of genetic background, this MR analysis only concluded European populations, which could not be extended to other ethnicities. Second, MR analysis of tumors with a small number of instances was less accurate (fewer than 1000). For the validation analysis, more genetic data from large samples need to be added. The association between metformin use and other malignancies cannot be determined at this time; however, this will be clarified in follow-up research.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe current MR study revealed that metformin use could genetically shield individuals from HER-positive breast cancer, which was mediated by total testosterone levels. Further investigation is required to determine whether metformin-induced changes in total testosterone levels could potentially serve as a predictor or biomarker in HER-positive breast cancer development and progression.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participating studies involved in the GWAS obtained informed consent from the study populations. As we utilized publicly available datasets to conduct MR, no additional ethics approval was required. An certification of ethics approval waiver was consented to by the ethics committee of Zhejiang University Affiliated Sir Run Run Shaw Hospital.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors approved the final manuscript and the submission to this journal.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll of the genetic data used in this work was publicly available. The relevant data can be found here: Open GWAS summary dataset (https://gwas.mrcieu.ac.uk/); UK Biobank (https://www.ukbiobank.ac.uk/); FinnGen database (https://www.finngen.fi/).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eDeclaration of competing interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the National Natural Science Foundation of China (No. 82100542 to B. Xie), Zhejiang Provincial Natural Science Foundation of China (No. LQ21H160027 to B. Xie), the China Postdoctoral Science Foundation (No. 2020M681893 to B. Xie).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY. Chen and S. Ye: conceptualization and writing of the manuscript. B. Bai, X. Gao and K. Ying: making and correcting the tables and figures. H. Pan and B. Xie: reviewing, editing, and providing critical discussion.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to the public open accessible databases mentioned above.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung, H., et al., \u003cem\u003eGlobal Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries.\u003c/em\u003e CA: A Cancer Journal for Clinicians, 2021. \u003cstrong\u003e71\u003c/strong\u003e(3): p. 209-249.\u003c/li\u003e\n\u003cli\u003eMallik, R. and T.A. Chowdhury, \u003cem\u003eMetformin in cancer.\u003c/em\u003e Diabetes Res Clin Pract, 2018. \u003cstrong\u003e143\u003c/strong\u003e: p. 409-419.\u003c/li\u003e\n\u003cli\u003eLai, S.W., et al., \u003cem\u003eAntidiabetes drugs correlate with decreased risk of lung cancer: a population-based observation in Taiwan.\u003c/em\u003e Clin Lung Cancer, 2012. \u003cstrong\u003e13\u003c/strong\u003e(2): p. 143-8.\u003c/li\u003e\n\u003cli\u003eBragagnoli, A.C., et al., \u003cem\u003eMetformin plus lrinotecan in patients with refractory colorectal cancer: a phase 2 clinical trial.\u003c/em\u003e Br J Cancer, 2021. \u003cstrong\u003e124\u003c/strong\u003e(6): p. 1072-1078.\u003c/li\u003e\n\u003cli\u003eBarakat, H.E., et al., \u003cem\u003eThe impact of metformin use on the outcomes of locally advanced breast cancer patients receiving neoadjuvant chemotherapy: an open-labelled randomized controlled trial.\u003c/em\u003e Sci Rep, 2022. \u003cstrong\u003e12\u003c/strong\u003e(1): p. 7656.\u003c/li\u003e\n\u003cli\u003eVancura, A., et al., \u003cem\u003eMetformin as an Anticancer Agent.\u003c/em\u003e Trends Pharmacol Sci, 2018. \u003cstrong\u003e39\u003c/strong\u003e(10): p. 867-878.\u003c/li\u003e\n\u003cli\u003eApostolova, N., et al., \u003cem\u003eMechanisms of action of metformin in type 2 diabetes: Effects on mitochondria and leukocyte-endothelium interactions.\u003c/em\u003e Redox Biol, 2020. \u003cstrong\u003e34\u003c/strong\u003e: p. 101517.\u003c/li\u003e\n\u003cli\u003eSekula, P., et al., \u003cem\u003eMendelian Randomization as an Approach to Assess Causality Using Observational Data.\u003c/em\u003e J Am Soc Nephrol, 2016. \u003cstrong\u003e27\u003c/strong\u003e(11): p. 3253-3265.\u003c/li\u003e\n\u003cli\u003eRelton, C.L. and G. Davey Smith, \u003cem\u003eTwo-step epigenetic Mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease.\u003c/em\u003e Int J Epidemiol, 2012. \u003cstrong\u003e41\u003c/strong\u003e(1): p. 161-76.\u003c/li\u003e\n\u003cli\u003eYavorska, O.O. and S. Burgess, \u003cem\u003eMendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data.\u003c/em\u003e Int J Epidemiol, 2017. \u003cstrong\u003e46\u003c/strong\u003e(6): p. 1734-1739.\u003c/li\u003e\n\u003cli\u003eBowden, J., et al., \u003cem\u003eConsistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator.\u003c/em\u003e Genet Epidemiol, 2016. \u003cstrong\u003e40\u003c/strong\u003e(4): p. 304-14.\u003c/li\u003e\n\u003cli\u003eBowden, J., G. Davey Smith, and S. Burgess, \u003cem\u003eMendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression.\u003c/em\u003e Int J Epidemiol, 2015. \u003cstrong\u003e44\u003c/strong\u003e(2): p. 512-25.\u003c/li\u003e\n\u003cli\u003eJones, H.J., et al., \u003cem\u003eAssociations between plasma fatty acid concentrations and schizophrenia: a two-sample Mendelian randomisation study.\u003c/em\u003e Lancet Psychiatry, 2021. \u003cstrong\u003e8\u003c/strong\u003e(12): p. 1062-1070.\u003c/li\u003e\n\u003cli\u003eVerbanck, M., et al., \u003cem\u003eDetection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases.\u003c/em\u003e Nat Genet, 2018. \u003cstrong\u003e50\u003c/strong\u003e(5): p. 693-698.\u003c/li\u003e\n\u003cli\u003eHemani, G., J. Bowden, and G. Davey Smith, \u003cem\u003eEvaluating the potential role of pleiotropy in Mendelian randomization studies.\u003c/em\u003e Hum Mol Genet, 2018. \u003cstrong\u003e27\u003c/strong\u003e(R2): p. R195-r208.\u003c/li\u003e\n\u003cli\u003eBowden, J., et al., \u003cem\u003eA framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization.\u003c/em\u003e Stat Med, 2017. \u003cstrong\u003e36\u003c/strong\u003e(11): p. 1783-1802.\u003c/li\u003e\n\u003cli\u003eCarter, A.R., et al., \u003cem\u003eMendelian randomisation for mediation analysis: current methods and challenges for implementation.\u003c/em\u003e Eur J Epidemiol, 2021. \u003cstrong\u003e36\u003c/strong\u003e(5): p. 465-478.\u003c/li\u003e\n\u003cli\u003eYang, B.Y., et al., \u003cem\u003eMetformin plus megestrol acetate compared with megestrol acetate alone as fertility-sparing treatment in patients with atypical endometrial hyperplasia and well-differentiated endometrial cancer: a randomised controlled trial.\u003c/em\u003e Bjog, 2020. \u003cstrong\u003e127\u003c/strong\u003e(7): p. 848-857.\u003c/li\u003e\n\u003cli\u003eSkuli, S.J., et al., \u003cem\u003eMetformin and Cancer, an Ambiguanidous Relationship.\u003c/em\u003e Pharmaceuticals (Basel), 2022. \u003cstrong\u003e15\u003c/strong\u003e(5).\u003c/li\u003e\n\u003cli\u003eZheng, Z., et al., \u003cem\u003eMetformin activates AMPK/SIRT1/NF-\u0026kappa;B pathway and induces mitochondrial dysfunction to drive caspase3/GSDME-mediated cancer cell pyroptosis.\u003c/em\u003e Cell Cycle, 2020. \u003cstrong\u003e19\u003c/strong\u003e(10): p. 1089-1104.\u003c/li\u003e\n\u003cli\u003eChen, Y.H., et al., \u003cem\u003eMetformin induces apoptosis and inhibits migration by activating the AMPK/p53 axis and suppressing PI3K/AKT signaling in human cervical cancer cells.\u003c/em\u003e Mol Med Rep, 2021. \u003cstrong\u003e23\u003c/strong\u003e(1).\u003c/li\u003e\n\u003cli\u003eHaugrud, A.B., et al., \u003cem\u003eDichloroacetate enhances apoptotic cell death via oxidative damage and attenuates lactate production in metformin-treated breast cancer cells.\u003c/em\u003e Breast Cancer Res Treat, 2014. \u003cstrong\u003e147\u003c/strong\u003e(3): p. 539-50.\u003c/li\u003e\n\u003cli\u003eKlose, K., et al., \u003cem\u003eMetformin and sodium dichloroacetate effects on proliferation, apoptosis, and metabolic activity tested alone and in combination in a canine prostate and a bladder cancer cell line.\u003c/em\u003e PLoS One, 2021. \u003cstrong\u003e16\u003c/strong\u003e(9): p. e0257403.\u003c/li\u003e\n\u003cli\u003eVasan, K., M. Werner, and N.S. Chandel, \u003cem\u003eMitochondrial Metabolism as a Target for Cancer Therapy.\u003c/em\u003e Cell Metab, 2020. \u003cstrong\u003e32\u003c/strong\u003e(3): p. 341-352.\u003c/li\u003e\n\u003cli\u003eWheaton, W.W., et al., \u003cem\u003eMetformin inhibits mitochondrial complex I of cancer cells to reduce tumorigenesis.\u003c/em\u003e Elife, 2014. \u003cstrong\u003e3\u003c/strong\u003e: p. e02242.\u003c/li\u003e\n\u003cli\u003eZoccali, C., et al., \u003cem\u003eMendelian randomization: a new approach to studying epidemiology in ESRD.\u003c/em\u003e Am J Kidney Dis, 2006. \u003cstrong\u003e47\u003c/strong\u003e(2): p. 332-41.\u003c/li\u003e\n\u003cli\u003eZhou, H., et al., \u003cem\u003eMendelian randomization study showed no causality between metformin use and lung cancer risk.\u003c/em\u003e Int J Epidemiol, 2020. \u003cstrong\u003e49\u003c/strong\u003e(4): p. 1406-1407.\u003c/li\u003e\n\u003cli\u003eAu Yeung, S.L. and C.M. Schooling, \u003cem\u003eImpact of glycemic traits, type 2 diabetes and metformin use on breast and prostate cancer risk: a Mendelian randomization study.\u003c/em\u003e BMJ Open Diabetes Res Care, 2019. \u003cstrong\u003e7\u003c/strong\u003e(1): p. e000872.\u003c/li\u003e\n\u003cli\u003eZhang, Y., et al., \u003cem\u003eEvaluating the impact of metformin targets on the risk of osteoarthritis: a mendelian randomization study.\u003c/em\u003e Osteoarthritis Cartilage, 2022. \u003cstrong\u003e30\u003c/strong\u003e(11): p. 1506-1514.\u003c/li\u003e\n\u003cli\u003eYu, L., et al., \u003cem\u003eGDF-15 as a Therapeutic Target of Diabetic Complications Increases the Risk of Gallstone Disease: Mendelian Randomization and Polygenic Risk Score Analysis.\u003c/em\u003e Front Genet, 2022. \u003cstrong\u003e13\u003c/strong\u003e: p. 814457.\u003c/li\u003e\n\u003cli\u003eGierisch, J.M., et al., \u003cem\u003eOral contraceptive use and risk of breast, cervical, colorectal, and endometrial cancers: a systematic review.\u003c/em\u003e Cancer Epidemiol Biomarkers Prev, 2013. \u003cstrong\u003e22\u003c/strong\u003e(11): p. 1931-43.\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eType and timing of menopausal hormone therapy and breast cancer risk: individual participant meta-analysis of the worldwide epidemiological evidence.\u003c/em\u003e Lancet, 2019. \u003cstrong\u003e394\u003c/strong\u003e(10204): p. 1159-1168.\u003c/li\u003e\n\u003cli\u003eKaaks, R., et al., \u003cem\u003eSerum sex steroids in premenopausal women and breast cancer risk within the European Prospective Investigation into Cancer and Nutrition (EPIC).\u003c/em\u003e J Natl Cancer Inst, 2005. \u003cstrong\u003e97\u003c/strong\u003e(10): p. 755-65.\u003c/li\u003e\n\u003cli\u003eMicheli, A., et al., \u003cem\u003ePlasma testosterone and prognosis of postmenopausal breast cancer patients.\u003c/em\u003e J Clin Oncol, 2007. \u003cstrong\u003e25\u003c/strong\u003e(19): p. 2685-90.\u003c/li\u003e\n\u003cli\u003eArthur, R.S., A.J. Dannenberg, and T.E. Rohan, \u003cem\u003eThe association of prediagnostic circulating levels of cardiometabolic markers, testosterone and sex hormone-binding globulin with risk of breast cancer among normal weight postmenopausal women in the UK Biobank.\u003c/em\u003e Int J Cancer, 2021. \u003cstrong\u003e149\u003c/strong\u003e(1): p. 42-57.\u003c/li\u003e\n\u003cli\u003eWatts, E.L., et al., \u003cem\u003eProspective analyses of testosterone and sex hormone-binding globulin with the risk of 19 types of cancer in men and postmenopausal women in UK Biobank.\u003c/em\u003e Int J Cancer, 2021. \u003cstrong\u003e149\u003c/strong\u003e(3): p. 573-584.\u003c/li\u003e\n\u003cli\u003eNounu, A., et al., \u003cem\u003eSex steroid hormones and risk of breast cancer: a two-sample Mendelian randomization study.\u003c/em\u003e Breast Cancer Res, 2022. \u003cstrong\u003e24\u003c/strong\u003e(1): p. 66.\u003c/li\u003e\n\u003cli\u003eTang, S.N., V. Zuber, and K.K. Tsilidis, \u003cem\u003eIdentifying and ranking causal biochemical biomarkers for breast cancer: a Mendelian randomisation study.\u003c/em\u003e BMC Med, 2022. \u003cstrong\u003e20\u003c/strong\u003e(1): p. 457.\u003c/li\u003e\n\u003cli\u003eCai, T., et al., \u003cem\u003eEffect of Metformin on Testosterone Levels in Male Patients With Type 2 Diabetes Mellitus Treated With Insulin.\u003c/em\u003e Front Endocrinol (Lausanne), 2021. \u003cstrong\u003e12\u003c/strong\u003e: p. 813067.\u003c/li\u003e\n\u003cli\u003eAndr\u0026aelig;, F., et al., \u003cem\u003eSustained Maternal Hyperandrogenism During PCOS Pregnancy Reduced by Metformin in Non-obese Women Carrying a Male Fetus.\u003c/em\u003e J Clin Endocrinol Metab, 2020. \u003cstrong\u003e105\u003c/strong\u003e(12): p. 3762-70.\u003c/li\u003e\n\u003cli\u003eCampagnoli, C., et al., \u003cem\u003eEffect of different doses of metformin on serum testosterone and insulin in non-diabetic women with breast cancer: a randomized study.\u003c/em\u003e Clin Breast Cancer, 2012. \u003cstrong\u003e12\u003c/strong\u003e(3): p. 175-82.\u003c/li\u003e\n\u003cli\u003eCampagnoli, C., et al., \u003cem\u003eMetformin decreases circulating androgen and estrogen levels in nondiabetic women with breast cancer.\u003c/em\u003e Clin Breast Cancer, 2013. \u003cstrong\u003e13\u003c/strong\u003e(6): p. 433-8.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1 and 4","content":"\u003cp\u003eTable 1 and 4 are available in Supplementary Files section.\u003c/p\u003e"},{"header":"Table References","content":"\u003col\u003e\n\u003cli\u003eWood, A.R., et al., \u003cem\u003eVariants in the FTO and CDKAL1 loci have recessive effects on risk of obesity and type 2 diabetes, respectively.\u003c/em\u003e Diabetologia, 2016. \u003cstrong\u003e59\u003c/strong\u003e(6): p. 1214-21.\u003c/li\u003e\n\u003cli\u003eShungin, D., et al., \u003cem\u003eNew genetic loci link adipose and insulin biology to body fat distribution.\u003c/em\u003e Nature, 2015. \u003cstrong\u003e518\u003c/strong\u003e(7538): p. 187-196.\u003c/li\u003e\n\u003cli\u003eLiu, Y., et al., \u003cem\u003eGenetic architecture of 11 organ traits derived from abdominal MRI using deep learning.\u003c/em\u003e Elife, 2021. \u003cstrong\u003e10\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eAstle, W.J., et al., \u003cem\u003eThe Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease.\u003c/em\u003e Cell, 2016. \u003cstrong\u003e167\u003c/strong\u003e(5): p. 1415-1429.e19.\u003c/li\u003e\n\u003cli\u003eRichardson, T.G., et al., \u003cem\u003eEvaluating the relationship between circulating lipoprotein lipids and apolipoproteins with risk of coronary heart disease: A multivariable Mendelian randomisation analysis.\u003c/em\u003e PLoS Med, 2020. \u003cstrong\u003e17\u003c/strong\u003e(3): p. e1003062.\u003c/li\u003e\n\u003cli\u003eWiller, C.J., et al., \u003cem\u003eDiscovery and refinement of loci associated with lipid levels.\u003c/em\u003e Nat Genet, 2013. \u003cstrong\u003e45\u003c/strong\u003e(11): p. 1274-1283.\u003c/li\u003e\n\u003cli\u003eRuth, K.S., et al., \u003cem\u003eUsing human genetics to understand the disease impacts of testosterone in men and women.\u003c/em\u003e Nat Med, 2020. \u003cstrong\u003e26\u003c/strong\u003e(2): p. 252-258.\u003c/li\u003e\n\u003cli\u003eChen, J., et al., \u003cem\u003eThe trans-ancestral genomic architecture of glycemic traits.\u003c/em\u003e Nat Genet, 2021. \u003cstrong\u003e53\u003c/strong\u003e(6): p. 840-860.\u003c/li\u003e\n\u003cli\u003eManning, A.K., et al., \u003cem\u003eA genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance.\u003c/em\u003e Nat Genet, 2012. \u003cstrong\u003e44\u003c/strong\u003e(6): p. 659-69.\u003c/li\u003e\n\u003cli\u003eSoranzo, N., et al., \u003cem\u003eCommon variants at 10 genomic loci influence hemoglobin A\u003c/em\u003e\u003cem\u003e₁(C) levels via glycemic and nonglycemic pathways.\u003c/em\u003e Diabetes, 2010. \u003cstrong\u003e59\u003c/strong\u003e(12): p. 3229-39.\u003c/li\u003e\n\u003cli\u003eGoodwin, P.J., et al., \u003cem\u003eEffect of Metformin vs Placebo on Invasive Disease-Free Survival in Patients With Breast Cancer: The MA.32 Randomized Clinical Trial.\u003c/em\u003e Jama, 2022. \u003cstrong\u003e327\u003c/strong\u003e(20): p. 1963-1973.\u003c/li\u003e\n\u003cli\u003eBarakat, H.E., et al., \u003cem\u003eThe impact of metformin use on the outcomes of locally advanced breast cancer patients receiving neoadjuvant chemotherapy: an open-labelled randomized controlled trial.\u003c/em\u003e Sci Rep, 2022. \u003cstrong\u003e12\u003c/strong\u003e(1): p. 7656.\u003c/li\u003e\n\u003cli\u003eHuang, J., et al., \u003cem\u003eNeoadjuvant docetaxel, epirubicin, and cyclophosphamide with or without metformin in breast cancer patients with metabolic abnormality: results from the randomized Phase II NeoMET trial.\u003c/em\u003e Breast Cancer Res Treat, 2023. \u003cstrong\u003e197\u003c/strong\u003e(3): p. 525-533.\u003c/li\u003e\n\u003cli\u003ePimentel, I., et al., \u003cem\u003eA phase II randomized clinical trial of the effect of metformin versus placebo on progression-free survival in women with metastatic breast cancer receiving standard chemotherapy.\u003c/em\u003e Breast, 2019. \u003cstrong\u003e48\u003c/strong\u003e: p. 17-23.\u003c/li\u003e\n\u003cli\u003eNanni, O., et al., \u003cem\u003eMetformin plus chemotherapy versus chemotherapy alone in the first-line treatment of HER2-negative metastatic breast cancer. The MYME randomized, phase 2 clinical trial.\u003c/em\u003e Breast Cancer Res Treat, 2019. \u003cstrong\u003e174\u003c/strong\u003e(2): p. 433-442.\u003c/li\u003e\n\u003cli\u003eYam, C., et al., \u003cem\u003eEfficacy and safety of the combination of metformin, everolimus and exemestane in overweight and obese postmenopausal patients with metastatic, hormone receptor-positive, HER2-negative breast cancer: a phase II study.\u003c/em\u003e Invest New Drugs, 2019. \u003cstrong\u003e37\u003c/strong\u003e(2): p. 345-351.\u003c/li\u003e\n\u003cli\u003eEssa, N.M., et al., \u003cem\u003eEfficacy of Metformin as Adjuvant Therapy in Metastatic Breast Cancer Treatment.\u003c/em\u003e J Clin Med, 2022. \u003cstrong\u003e11\u003c/strong\u003e(19).\u003c/li\u003e\n\u003cli\u003eSonnenblick, A., et al., \u003cem\u003eImpact of Diabetes, Insulin, and Metformin Use on the Outcome of Patients With Human Epidermal Growth Factor Receptor 2-Positive Primary Breast Cancer: Analysis From the ALTTO Phase III Randomized Trial.\u003c/em\u003e J Clin Oncol, 2017. \u003cstrong\u003e35\u003c/strong\u003e(13): p. 1421-1429.\u003c/li\u003e\n\u003cli\u003eFeng, J.L. and X. Qin, \u003cem\u003eMetformin and cancer-specific survival among breast, colorectal, or endometrial cancer patients: A nationwide data linkage study.\u003c/em\u003e Diabetes Res Clin Pract, 2021. \u003cstrong\u003e175\u003c/strong\u003e: p. 108755.\u003c/li\u003e\n\u003cli\u003eKim, B.H., M.J. Cho, and J. Kwon, \u003cem\u003ePotential intrinsic subtype dependence on the association between metformin use and survival in surgically resected breast cancer: a Korean national population-based study.\u003c/em\u003e Int J Clin Oncol, 2021. \u003cstrong\u003e26\u003c/strong\u003e(11): p. 2004-2016.\u003c/li\u003e\n\u003cli\u003eHui, T., et al., \u003cem\u003eMetformin improves the outcomes in Chinese invasive breast cancer patients with type 2 diabetes mellitus.\u003c/em\u003e Sci Rep, 2021. \u003cstrong\u003e11\u003c/strong\u003e(1): p. 10034.\u003c/li\u003e\n\u003cli\u003eHosio, M., et al., \u003cem\u003eSurvival after breast cancer in women with type 2 diabetes using antidiabetic medication and statins: a retrospective cohort study.\u003c/em\u003e Acta Oncol, 2020. \u003cstrong\u003e59\u003c/strong\u003e(9): p. 1110-1117.\u003c/li\u003e\n\u003cli\u003eEl-Benhawy, S.A. and H.G. El-Sheredy, \u003cem\u003eMetformin and survival in diabetic patients with breast cancer.\u003c/em\u003e J Egypt Public Health Assoc, 2014. \u003cstrong\u003e89\u003c/strong\u003e(3): p. 148-53.\u003c/li\u003e\n\u003cli\u003eKim, H.J., et al., \u003cem\u003eMetformin increases survival in hormone receptor-positive, HER2-positive breast cancer patients with diabetes.\u003c/em\u003e Breast Cancer Res, 2015. \u003cstrong\u003e17\u003c/strong\u003e(1): p. 64.\u003c/li\u003e\n\u003cli\u003ePark, Y.M., et al., \u003cem\u003eA prospective study of type 2 diabetes, metformin use, and risk of breast cancer.\u003c/em\u003e Ann Oncol, 2021. \u003cstrong\u003e32\u003c/strong\u003e(3): p. 351-359.\u003c/li\u003e\n\u003cli\u003eChlebowski, R.T., et al., \u003cem\u003eDiabetes, metformin, and breast cancer in postmenopausal women.\u003c/em\u003e J Clin Oncol, 2012. \u003cstrong\u003e30\u003c/strong\u003e(23): p. 2844-52.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"diabetology-and-metabolic-syndrome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dims","sideBox":"Learn more about [Diabetology \u0026 Metabolic Syndrome](http://dmsjournal.biomedcentral.com/)","snPcode":"13098","submissionUrl":"https://submission.nature.com/new-submission/13098/3","title":"Diabetology \u0026 Metabolic Syndrome","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Metformin, Cancers, Genetic association, Mendelian Randomization, Testosterone","lastPublishedDoi":"10.21203/rs.3.rs-3174656/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3174656/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIncreasing number of studies reported the positive effect of metformin on the prevention and treatment of cancers. However, the genetic causal effect of metformin utilization on the risk of common cancers was not completely demonstrated.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTwo-sample Mendelian Randomization (two-sample MR) analysis was conducted to uncover the genetically predicted causal association between metformin use and 26 kinds of cancers. Besides, two-step Mendelian Randomization (two-step MR) assessment was applied to clarify the mediators which mediated the causal effect of metformin on certain cancer. We utilized five robust analytical methods, in which the inverse variance weighting (IVW) method served as the major one. Sensitivity, pleiotropy, and heterogeneity were assessed. The genetic statistics of exposure, outcomes, and mediators were downloaded from publicly available datasets, including the Open Genome-Wide Association Study (GWAS), FinnGen consortium (FinnGen), and UK Biobank (UKB).\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAmong 26 kinds of common cancers, HER-positive breast cancer was presented with a significant causal relationship with metformin use (Beta: -4.0982; OR: 0.0166 (95%CI: 0.0008, 0.3376); P-value: 0.0077), which indicated metformin could prevent people from HER-positive breast cancer. Other cancers only showed modest associations with metformin use. Potential mediators were included in two-step MR, among which total testosterone levels (mediating effect: 24.52%) displayed significant mediating roles. Leave-one-out, MR-Egger, and MR-PRESSO analyses produced consistent outcomes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMetformin use exhibited a genetically protective effect on HER-positive breast cancer, which was partially mediated by total testosterone levels.\u003c/p\u003e","manuscriptTitle":"Genetic effect of metformin use on risk of cancers: Evidence from Mendelian randomization analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-07-20 22:17:55","doi":"10.21203/rs.3.rs-3174656/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-09-25T17:03:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-08-07T16:58:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"70d1df27-a9e6-4800-ad7c-a136e6ab19c1","date":"2023-07-27T13:58:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-07-24T13:36:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-07-19T10:25:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-07-17T10:06:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Diabetology \u0026 Metabolic Syndrome","date":"2023-07-16T08:53:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"diabetology-and-metabolic-syndrome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dims","sideBox":"Learn more about [Diabetology \u0026 Metabolic Syndrome](http://dmsjournal.biomedcentral.com/)","snPcode":"13098","submissionUrl":"https://submission.nature.com/new-submission/13098/3","title":"Diabetology \u0026 Metabolic Syndrome","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4c70b005-68de-4943-adf2-03edb2a27c55","owner":[],"postedDate":"July 20th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-12-11T15:07:20+00:00","versionOfRecord":{"articleIdentity":"rs-3174656","link":"https://doi.org/10.1186/s13098-023-01218-3","journal":{"identity":"diabetology-and-metabolic-syndrome","isVorOnly":false,"title":"Diabetology \u0026 Metabolic Syndrome"},"publishedOn":"2023-12-06 15:01:47","publishedOnDateReadable":"December 6th, 2023"},"versionCreatedAt":"2023-07-20 22:17:55","video":"","vorDoi":"10.1186/s13098-023-01218-3","vorDoiUrl":"https://doi.org/10.1186/s13098-023-01218-3","workflowStages":[]},"version":"v1","identity":"rs-3174656","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3174656","identity":"rs-3174656","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}