Causal relationship between prostate cancer and cardiovascular diseases: Univariable and multivariable Mendelian randomization

Causal relationship between prostate cancer and cardiovascular diseases: Univariable and multivariable Mendelian randomization | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Causal relationship between prostate cancer and cardiovascular diseases: Univariable and multivariable Mendelian randomization xiaojing wu, Weiping Zhang, Huijun Chen, Jianfei Weng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3757050/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Prostate cancer (PCa) and cardiovascular disease (CVD) have a high prevalence worldwide, and the presence of both PCa and CVD signals a poor prognosis; the risk relationship between the two diseases is debatable. Methods This study searched for relationship to PCa and four CVDs using a Mendelian randomisation (MR) approach. Bidirectional causality was investigated using univariate MR investigations. The data were then adjusted for the six major PCa and CVD risk variables using a multivariate MR model and examined for mediated effects. Results PCa was a risk factor for the development of heart failure. Atrial fibrillation and stroke has been a protective effect against the incidence of PCa. Following the adjustment of the multivariate MR model, the association between PCa and heart failure persisted. However, the association between atrial fibrillation and PCa was no longer present after adjustment for BMI. The causal relationship between stroke and PCa was no longer significant in multiple multivariate adjustment models. The mediator MR analysis revealed that atrial fibrillation mediated 15.28% of the causal relationship between BMI and PCa. Conclusions Our study suggests that PCa is a risk factor for heart failure and atrial fibrillation is a protective factor for PCa. Biological sciences/Computational biology and bioinformatics Biological sciences/Genetics Health sciences/Cardiology Mendelian randomization cardiovascular disease prostate cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction One of the most prevalent cancers affecting the male reproductive system in the globe is prostate cancer (PCa). According to cancer statistics released in 2023, PCa incidence has been on the rise and currently accounts for roughly 29% of all malignancies in men. Patients' risk of passing away has decreased due to the growing use of screening techniques including digital rectal exams and prostate-specific antigen testing [ 1 ] . The mainstay of PCa treatment is androgen deprivation therapy (ADT), and the possibility of cardiovascular damage from the medication has been the subject of much debate [ 2 ] . In the United States, cancer and cardiovascular disease (CVD) account for a large portion of the illness burden [ 3 ] . In 2015, 17.7 million people worldwide died from CVD, while 8.8 million died from cancer [ 4 , 5 ] . The cancer population with CVD has a higher mortality rate than the general population [ 6 – 8 ] . so we need to consider whether to intervene in the PCa population for the full range of cardiovascular risks or in advance take precautions against. There is a potential overlap in the pathophysiology of PCa and some CVD, although the available information is limited. The field of cardiac oncology is an emerging interdisciplinary domain that combines the disciplines of cardiology and oncology [ 9 ] . Its primary objective is to investigate the correlation between tumors and CVD. This field aims to explore shared pathogenic pathways and risk factors, with a particular emphasis on the potential cardiovascular toxicity induced by novel cancer treatments. The ultimate goal is to enhance the long-term prognosis and quality of life for individuals to reduce the risk of death [ 10 – 12 ] .To comprehensively examine the causal association between PCa and CVD, we using the method of excluding confounding factors, we conducted a study utilizing univariate Mendelian randomization (UVMR) and multivariate Mendelian randomization (MVMR) techniques. The primary objective of this study was to establish genetic evidence of a correlation between PCa and CVD. 2. Methods 2.1. Study design The application of Mendelian randomization (MR) is the utilization of genetic variation as an instrumental variable to infer the causal impact of exposure on outcomes [ 13 ] . This study aimed to evaluate the causal relationship between PCa and four CVDs using a bidirectional MR approach. For MR studies to be considered valid, they must satisfy three assumptions: (1) Genetic variants must be strongly associated with exposure factors. (2) Genetic variants cannot be directly related to the outcome. (3) Genetic variants cannot be associated with potential confounders [ 13 ] . All magnetic resonance analyses in this study were performed in R software (4.3.0) using the TwoSampleMR, MRPRESSO, and MVMR packages. The ethical conduct of this study did not require Institutional Review Board approval and all assessments were based on publicly available pooled data. 2.2 Genome-Wide Association Study (GWAS) data pertaining to PCa and CVD The pooled association statistics for PCa risk were acquired from the PRACTICAL collaboration [ 14 ] , which comprises a substantial sample size of 79,148 cases and 61,106 controls. This consortium constitutes the biggest collection of research containing genetic data pertaining to PCa. For further details regarding the study types employed, namely cohort and case-control studies, as well as the criteria used for subject selection, please refer to the original GWAS. A total of five CVD independent variables (IVs) were chosen from the GWAS, encompassing atrial fibrillation (AF) [ 15 ] , heart failure (HF) [ 16 ] , coronary artery disease (CAD) [ 17 ] and stroke [ 18 ] . To mitigate potential confounding factors linked to race, the dataset included in this study exclusively consisted of individuals of European ancestry. The data pertaining to CVD were obtained from extensive survey studies with large sample sizes. The main characteristics of the GWASs included are listed in Table 1 . Table 1 Detailed information regarding studies and datasets used in the present study. NAME PMID Sample size PCa 29892016 79,148 cases, 61,106 controls AF 30061737 60,620 cases, 970,216 controls HF 31919418 47,309 cases, 930,014 controls CAD 29212778 122,733 cases, 424,528 controls Stroke 29531354 40,585 cases, 406,111 controls 2.3 Selection of IVs The IVs were evaluated for their adherence to the three prerequisites of MR. A p-value threshold of less than 5×10 − 8 was employed to identify SNPs as instrumental factors. SNPs exhibiting chain imbalance (LD, R 2 < 0.001 and within 10,000 kb) were excluded, as this criterion has been extensively utilized in prior research studies [ 19 , 20 ] . Subsequently, SNPs linked with exposure were retrieved from the result, with the exclusion of SNPs that were ambiguous and had intermediate allele effect genes (0.58 > Minor Allele Frequency > 0.42), as well as SNPs that showed an association with the outcome at a significance level of p < 1×10 − 5 . We excluded SNPs with F < 10 to avoid the effect of unwanted genetic variants [ 21 ] . The formula for the F value is as follows F = R 2 ×(N − 2) / (1 − R 2 ). 2.4 UVMR and Sensitivity Analysis The primary analysis in this research was conducted utilizing the IVW technique, which offers a precise evaluation of causal effects [ 22 ] . To enhance the reliability of the findings, we additionally employed the weighted median (WM) and MR-Egger regression methods to validate the causal association between CVD and PCA [ 23 ] . When the IVW test yielded a p-value less than 0.05, the researchers relied on the directionality of the results obtained from the other two MR procedures to verify the robustness of the findings and enhance the strength of the causal evidence [ 24 ] . The study employed odds ratios (ORs) and their corresponding 95% confidence intervals (CIs) as measures to evaluate the relationship between CVD and PCa. Four analytical methods were utilized to conduct sensitivity studies, including Cochran's Q test, the MR-Egger intercept method, the leave-one-out (LOO) method, and MR-PRESSO. The Cochran's Q test was employed to examine the existence of heterogeneity, and in cases where a statistically significant difference was seen, the random effects IVW model was utilized. The MR-Egger intercept approach [ 25 ] and the MR-PRESSO [ 26 ] test were employed to evaluate the presence of a horizontal multinomial. In cases where horizontal pleiotropy was detected, the findings from the MR-Egger regression were given precedence [ 27 ] . LOO method to test whether the MR results were driven by a single SNP was used to analyze whether our results were robust. Statistical significance was determined by using p-values less than 0.05 in all the aforementioned sensitivity analyses. 2.5 MVMR and mediation MR analysis The method of MVMR addresses the issue of horizontal pleiotropy in two-sample MR analyses. This is achieved by incorporating multiple exposures inside the same statistical model and simultaneously calculating their respective causal effects on the risk of the outcome of interest [ 28 ] . To account for many variables, we choose to include six risk factors (BMI, smoking, alcoholic drinks, sedentary, total cholesterol, and LDL cholesterol) that shown robust relationships between PCa and CVD outcomes in observational studies. These risk factors were chosen for multivariate adjustment. The concept of mediation effect pertains to the influence exerted by a mediating element in the relationship between exposure and outcome [ 29 ] . Specifically, this effect is observed when exposure impacts the mediator, which then affects the outcome. Consequently, the overall effect of exposure on the outcome encompasses both the mediation effect and the direct effect. The mediation effect value of a specific component (Fig. 1 : β1 × β2) can be derived through the adjustment for various exposure factors in a MVMR model [ 30 ] . By dividing the mediation effect by the total effect, we can determine the proportion of the mediator's contribution to the association between exposure and outcome. 3. Results 3.1 UVMR and sensitivity analyses The specific IVs included in the MR analysis may be found in Supplementary Table. The F-statistic values for all instrumental variables selected in this study were found to be greater than 10, indicating their robustness and effectiveness in the analysis. The study found a significant association between genetically predicted PCa and increased odds of HF(OR = 1.03, 95%CI = 1.01–1.05, p = 0.0109)(Fig. 2 ). To explore the potential reverse causation between PCa and CVD, we conducted a reverse MR analysis in our study. which may help us to determine whether the observed associations are causally related to each other. In the reverse MR study, genetically expected AF (OR = 0.95, 95% CI = 0.93–0.98, P = 0.0016) as well as stroke (OR = 0.85, 95%CI = 0.75–0.97, P = 0.0158) was associated with lower odds of PCa (Fig. 3 ). In sensitivity analyses, Cochran's Q tests did not provide any significant results (P > 0.05), indicating that there was no heterogeneity. In the outcome of PCa with stroke,and the bidirectional causal relationship between PCa and CAD, we observed the outcomes of the three MR methods showed different directionality, and therefore we consider that this result lacks robustness. The MR-Egger and ME methods show consistent directionality for all other outcomes (The positive results are shown in Fig. 4 ). all of the above results were performed after the MR-presso test was performed to remove outliers, and the results of the MR-presso test showed no horizontal multiplicity of validity after the removal of outliers. The robustness of the results was further validated by the LOO results. Sensitivity analyses are shown in Supplementary Table. 3.2 MVMR The statistical significance of the correlation between stroke and PCa diminished when controlling for all risk factors except Cigarettes smoked and total cholesterol. This implies that the correlation between stroke and PCa exhibits additional layers of multivariate pleiotropy. Consequently, the credibility of the significant causal relationship between the two variables can be called into question. The statistical association between atrial AF and PCa lost its significance after controlling for BMI (P = 0.0962). However, the association persisted when accounting for other known risk variables, indicating that BMI alone may play a causal role in the relationship between AF and PCa. The association between HF and PCa persisted even after controlling for the aforementioned risk factors. This suggests that the genetic correlation between HF and PCa is strong, indicating that PCa is a potential risk factor for the development of HF (Fig. 5 ). 3.3 mediation MR analysis Our study aimed to examine the potential mediating role of BMI and AF and PCa. Utilizing BMI as an independent variable, our analysis revealed a significant correlation between BMI and AF (OR = 1.3905, 95% CI = 1.3235–1.4607, p = 3.30×10–39), as well as PCa (OR = 0.9297, 95% CI = 0.8744–0.9884, p = 0.0197). However, employing inverse MR techniques, we observed that there was no discernible association between BMI and AF (OR = 0.9904, 95% CI = 0.9731–1.0080, p = 0.2826), nor between BMI and PCa (OR = 1.0004, 95% CI = 0.9935–1.0074, p = 0.9009). Hence, it may be inferred that AF plays a role in the correlation between BMI and PCa. The observed mediation effect had a value of -0.0111, accompanied by a mediation percentage of 15.28%. 4. DISCUSSION Our study employs a two-sample MR approach to examine the genetic basis of the potential causal relationship between PCa and CVD. The data we have collected offer genetic evidence supporting the existence of a causal influence between these two conditions. To the best of our understanding, this study represents the inaugural MR investigation of the genetic associations between PCa and various forms of CVD. The study design successfully mitigated the influence of potential confounding variables and the issue of reverse causality. The results of our study indicate that PCa is correlated with a heightened likelihood of heart HF, and this correlation is independent of shared risk factors between the two conditions. Patients diagnosed with PCa in conjunction with HF experience extended hospitalizations and increased healthcare expenses. Additionally, this comorbidity is related to an elevated mortality risk [ 31 , 32 ] . Moreover, in senior individuals, the coexistence of both PCa and HF is indicative of an unfavorable prognosis. The development of heart failure is influenced by various factors, including cancer metabolic by-products, cachexia, and the use of anti-cancer medications [ 33 ] . Patients with PCa often exhibit increased levels of testosterone (T), and the relationship between T and the risk of HF is a subject of debate. Previous research usually suggests that T is inversely linked to the occurrence of HF and that T therapy can have positive prognostic effects on HF [ 34 ] . Nevertheless, a contentious issue has emerged in recent years due to the prolonged duration of clinical monitoring [ 35 ] . Elevated levels of T beyond physiological norms exert additional stress on the cardiovascular system. In 2014, the Food and Drug Administration issued a statement recommending the inclusion of information regarding the potential elevated risk of heart attack and stroke associated with the use of testosterone products [ 36 ] . A study utilizing MR revealed a noteworthy correlation between levels of endogenous T and the risk of HF in males. However, this link did not retain statistical significance when examining the female population [ 37 ] . The existing body of research has primarily examined the cardiac toxicity that follows ADT for PCa. However, our study highlights the importance of considering the potential elevated risk of HF in untreated patients with PCa. This finding underscores the need for early screening and preventive measures. Additionally, our findings demonstrate that AF exhibits a protective effect against PCa and serves as a mediator in the association between BMI and PCa. BMI has been found to play a role in the development and progression of PCa, as well as in increased death rates associated with the disease [ 38 , 39 ] . However, it is worth noting that BMI may also have a potentially beneficial influence in reducing the likelihood of PCa incidence [ 38 , 40 ] . An elevation in BMI is associated with a heightened likelihood of developing AF, a condition that subsequently serves as a safeguard against the onset of PCa. Additionally, individuals with cancer-related AF exhibit higher mortality rates. Consequently, it is worth exploring whether AF, akin to BMI, fosters the progression and mortality of PCa while simultaneously reducing the risk of its occurrence. In our UVMR investigation, it was observed that stroke was a protective factor to PCa. However, upon accounting for the extent of pleiotropy, the causal connection between PCa and stroke was no longer evident. This suggests that shared factors such as BMI, alcoholic drinks, sedentary, and LDL cholesterol play significant roles in the development of PCa among individuals with stroke. Consequently, it is imperative to prioritize education and lifestyle modifications within the stroke population to mitigate the risk of PCa occurrence. The findings from certain observational studies have demonstrated an elevated likelihood of developing future PCa in individuals with stroke, HF and AF [ 41 – 43 ] . However, our results do not align with this observed association. The substantial disparity in outcomes can be attributed mostly to the following causative factors, in our analysis. Initially, it is worth noting that the occurrence of occult cancers coinciding with the diagnosis of CVD and subsequently detected during a comprehensive cardiovascular examination may lead to a misinterpretation of the causal association between the risk of CVD and the presence of cancer. Consequently, the rate of cancer diagnoses tends to rise in the months following the identification of AF [ 44 ] . Furthermore, the identification and control of confounding factors in observational studies pose a greater challenge. This is due to the higher prevalence of CVD and PCa in older individuals, who often have comorbidities or engage in long-term activities with a heightened pathogenic potential [ 45 ] . Additionally, it is not feasible to clinically ascertain the exclusive influence of other medications and risk factors on the development of both conditions [ 46 ] . The simultaneous presence of CVD and PCa frequently indicates an unfavorable prognosis, and the limited availability of ethical guidelines for clinical monitoring has hindered the establishment of comprehensive observational studies involving substantial sample sizes. These elements play a crucial role in the manifestation of biased outcomes. Given that MR design has the inherent benefit of reducing the influence of confounding variables and relies on the genetic correlation of the disease being studied, we assert that this study possesses a certain level of credibility. This study possesses multiple notable strengths. This study represents the inaugural investigation utilizing MR to evaluate the causal correlation between various cardiovascular disorders and the risk of PCa. Notably, we employed a substantial sample size derived from a comprehensive collection of genetic association abstracts obtained from GWAS. This approach significantly enhanced the accuracy of our measurements and bolstered the overall robustness of our analysis. Nevertheless, it is imperative to acknowledge the existing limitations of our study. Initially, the primary data sources utilized in our study largely consisted of persons of European ancestry. This particular demographic composition imposes limitations on the generalizability of our findings to non-European communities. Furthermore, a comprehensive analysis was not conducted to explicitly stratify characteristics such as age. Ultimately, despite our efforts to conduct sensitivity studies to mitigate the potential violation of MR assumptions, we were unable to fully eliminate the influence of residual pleiotropy. In summary, this research offers valuable insights into the genetic correlation between PCa and CVD. However, it is crucial to acknowledge and tackle the limitations identified in our study through future investigations. Declarations Ethics approval and consent to participate Not applicable. Since all of the submitted assessments were based on publically available data, the Institutional Review Board was not required to grant authorization for the ethical conduct of this study. Consent for publication Not applicable. Competing interests The authors declares that there is no conflict of interest regarding the publication of this paper. Funding The authors have no funding of interest to disclose. Authors' contributions Data curation, Huijun Chen; Supervision, Jianfei Weng; Writing – original draft, xiaojing wu; Writing-review & editing, Weiping Zhang. All authors will be informed about each step of manuscript processing including submission, revision, revision reminder, etc. via emails from our system or assigned Assistant Editor. Acknowledgements Not applicable Availability of data and material The data used in this study is based on publicly available data, which can be found at the URL below: GWAS pipeline output using Phesant derived variables from UKB: https://gwas.mrcieu.ac.uk/datasets/; PGC: https://pgc.unc.edu/. All data generated or analysed during this study are included in this published article and its supplementary information files. References Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023 Jan;73(1):17-48. doi: 10.3322/caac.21763. Hu JR, Duncan MS, Morgans AK, et al. Cardiovascular Effects of Androgen Deprivation Therapy in Prostate Cancer: Contemporary Meta-Analyses. Arterioscler Thromb Vasc Biol. 2020 Mar;40(3):e55-e64. doi: 10.1161/ATVBAHA.119.313046. Hoyert DL, Xu J. Deaths: preliminary data for 2011. Natl Vital Stat Rep. 2012 Oct 10;61(6):1-51. WHO. Cardiovascular diseases. http://www.who.int/mediacentre/factsheets/fs317/en/ (18 October 2019). WHO. Cancer. http://www.who.int/mediacentre/factsheets/fs297/en/ (18 October 2019). Sturgeon KM, Deng L, Bluethmann SM, et al. A population-based study of cardiovascular disease mortality risk in US cancer patients. Eur Heart J. 2019 Dec 21;40(48):3889-3897. doi: 10.1093/eurheartj/ehz766. O'Neill C, Donnelly DW, Harbinson M, et al. Survival of cancer patients with pre-existing heart disease. BMC Cancer. 2022 Aug 3;22(1):847. doi: 10.1186/s12885-022-09944-z. Zaorsky NG, Zhang Y, Tchelebi LT, et al. Stroke among cancer patients. Nat Commun. 2019 Nov 15;10(1):5172. doi: 10.1038/s41467-019-13120-6. de Boer RA, Meijers WC, van der Meer P, van Veldhuisen DJ. Cancer and heart disease: associations and relations. Eur J Heart Fail. 2019 Dec;21(12):1515-1525. doi: 10.1002/ejhf.1539. Epub 2019 Jul 18. Coviello JS. Cardiovascular and Cancer Risk: The Role of Cardio-oncology. J Adv Pract Oncol. 2018 Mar;9(2):160-176. Epub 2018 Mar 1. Abe J, Martin JF, Yeh ET. The Future of Onco-Cardiology: We Are Not Just "Side Effect Hunters". Circ Res. 2016 Sep 30;119(8):896-9. doi: 10.1161/CIRCRESAHA.116.309573. Aboumsallem JP, Moslehi J, de Boer RA. Reverse Cardio-Oncology: Cancer Development in Patients With Cardiovascular Disease. J Am Heart Assoc. 2020 Jan 21;9(2):e013754. doi: 10.1161/JAHA.119.013754. Yarmolinsky J, Wade KH, Richmond RC, et al. Causal Inference in Cancer Epidemiology: What Is the Role of Mendelian Randomization? Cancer Epidemiol Biomarkers Prev. 2018 Sep;27(9):995-1010. doi: 10.1158/1055-9965. Schumacher FR, Al Olama AA, Berndt SI, et al. Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci. Nat Genet. 2018 Jul;50(7):928-936. doi: 10.1038/s41588-018-0142-8. Nielsen JB, Thorolfsdottir RB, Fritsche LG, et al. Biobank-driven genomic discovery yields new insight into atrial fibrillation biology. Nat Genet. 2018 Sep;50(9):1234-1239. doi: 10.1038/s41588-018-0171-3. Shah S, Henry A, Roselli C,et al. Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure. Nat Commun. 2020 Jan 9;11(1):163. doi: 10.1038/s41467-019-13690-5. van der Harst P, Verweij N. Identification of 64 Novel Genetic Loci Provides an Expanded View on the Genetic Architecture of Coronary Artery Disease. Circ Res. 2018 Feb 2;122(3):433-443. doi: 10.1161/CIRCRESAHA.117.312086. Malik R, Chauhan G, Traylor M, et al. Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes. Nat Genet. 2018 Apr;50(4):524-537. doi: 10.1038/s41588-018-0058-3. Epub 2018 Mar 12. Erratum in: Nat Genet. 2019 Jul;51(7):1192-1193. Ren S, Xue C, Xu M, Li X. Mendelian Randomization Analysis Reveals Causal Effects of Polyunsaturated Fatty Acids on Subtypes of Diabetic Retinopathy Risk. Nutrients. 2023 Sep 29;15(19):4208. doi: 10.3390/nu15194208. Wu M, Du Y, Zhang C, et al. Mendelian Randomization Study of Lipid Metabolites Reveals Causal Associations with Heel Bone Mineral Density. Nutrients. 2023 Sep 27;15(19):4160. doi: 10.3390/nu15194160. Burgess S, Thompson SG; CRP CHD Genetics Collaboration. Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol. 2011 Jun;40(3):755-64. doi: 10.1093/ije/dyr036. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013 Nov;37(7):658-65. doi: 10.1002/gepi.21758. Hemani G, Bowden J, Davey Smith G. Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum Mol Genet. 2018 Aug 1;27(R2):R195-R208. doi: 10.1093/hmg/ddy163. Sanderson E, Glymour MM, Holmes MV, Kang H, Morrison J, Munafò MR, Palmer T, Schooling CM, Wallace C, Zhao Q, Smith GD. Mendelian randomization. Nat Rev Methods Primers. 2022 Feb 10;2:6. doi: 10.1038/s43586-021-00092-5. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015 Apr;44(2):512-25. doi: 10.1093/ije/dyv080. Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018 May;50(5):693-698. doi: 10.1038/s41588-018-0099-7. Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol. 2017 May;32(5):377-389. doi: 10.1007/s10654-017-0255-x. Burgess S, Thompson SG. Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects. Am J Epidemiol. 2015 Feb 15;181(4):251-60. doi: 10.1093/aje/kwu283. Carter AR, Sanderson E, Hammerton G, Richmond RC, Davey Smith G, Heron J, Taylor AE, Davies NM, Howe LD. Mendelian randomisation for mediation analysis: current methods and challenges for implementation. Eur J Epidemiol. 2021 May;36(5):465-478. doi: 10.1007/s10654-021-00757-1. Zhao SS, Holmes MV, Zheng J, Sanderson E, Carter AR. The impact of education inequality on rheumatoid arthritis risk is mediated by smoking and body mass index: Mendelian randomization study. Rheumatology (Oxford). 2022 May 5;61(5):2167-2175. doi: 10.1093/rheumatology/keab654. Yu A, Thaliffdeen R, Park SK, Park C. Hospital outcomes and costs for prostate cancer patients with comorbid heart failure by age group: An analysis of the US Nationwide Inpatient Sample. J Eval Clin Pract. 2023 Sep;29(6):1016-1024. doi: 10.1111/jep.13869. Mehtälä J, Zong J, Vassilev Z, et al. Overall survival and second primary malignancies in men with metastatic prostate cancer. PLoS One. 2020 Feb 21;15(2):e0227552. doi: 10.1371/journal.pone.0227552. de Boer RA, Hulot JS, Tocchetti CG, et al. Common mechanistic pathways in cancer and heart failure. A scientific roadmap on behalf of the Translational Research Committee of the Heart Failure Association (HFA) of the European Society of Cardiology (ESC). Eur J Heart Fail. 2020 Dec;22(12):2272-2289. doi: 10.1002/ejhf.2029. Toma M, McAlister FA, Coglianese EE, et al. Testosterone supplementation in heart failure: a meta-analysis. Circ Heart Fail. 2012 May 1;5(3):315-21. doi: 10.1161/CIRCHEARTFAILURE.111.965632. Di Lodovico E, Facondo P, Delbarba A, et al. Testosterone, Hypogonadism, and Heart Failure. Circ Heart Fail. 2022 Jul;15(7):e008755. doi: 10.1161/CIRCHEARTFAILURE.121.008755. FDA adding general warning to testosterone products about potential for venous blood clots US. 2014; updated 2018. https://wayback.archive-it.org/7993/20161022180648/http://www.fda.gov/Drugs/DrugSafety/ucm401746.htm . Luo S, Au Yeung SL, Zhao JV, Burgess S, Schooling CM. Association of genetically predicted testosterone with thromboembolism, heart failure, and myocardial infarction: mendelian randomisation study in UK Biobank. BMJ. 2019 Mar 6;364:l476. doi: 10.1136/bmj.l476. Rodriguez C, Freedland SJ, Deka A, et al. Body mass index, weight change, and risk of prostate cancer in the Cancer Prevention Study II Nutrition Cohort. Cancer Epidemiol Biomarkers Prev. 2007 Jan;16(1):63-9. doi: 10.1158/1055-9965.EPI-06-0754. Cao Y, Ma J. Body mass index, prostate cancer-specific mortality, and biochemical recurrence: a systematic review and meta-analysis. Cancer Prev Res (Phila). 2011 Apr;4(4):486-501. doi: 10.1158/1940-6207.CAPR-10-0229. Porter MP, Stanford JL. Obesity and the risk of prostate cancer. Prostate. 2005 Mar 1;62(4):316-21. O'Rourke K. Heart failure linked with an increased risk of cancer. Cancer. 2022 Feb 15;128(4):646. doi: 10.1002/cncr.34094. Ostenfeld EB, Erichsen R, Pedersen L, Farkas DK, Weiss NS, Sørensen HT. Atrial fibrillation as a marker of occult cancer. PLoS One. 2014 Aug 13;9(8):e102861. doi: 10.1371/journal.pone.0102861. Rioux B, Gioia LC, Keezer MR. Risk of Cancer Following an Ischemic Stroke in the Canadian Longitudinal Study on Aging. Can J Neurol Sci. 2022 Mar;49(2):225-230. doi: 10.1017/cjn.2021.55. Ostenfeld EB, Erichsen R, Pedersen L, Farkas DK, Weiss NS, Sørensen HT. Atrial fibrillation as a marker of occult cancer. PLoS One. 2014 Aug 13;9(8):e102861. doi: 10.1371/journal.pone.0102861. Meijers WC, de Boer RA. Common risk factors for heart failure and cancer. Cardiovasc Res. 2019 Apr 15;115(5):844-853. doi: 10.1093/cvr/cvz035. Fu BC, Wang K, Mucci LA, Clinton SK, Giovannucci EL. Aspirin use and prostate tumor angiogenesis. Cancer Causes Control. 2022 Jan;33(1):149-151. doi: 10.1007/s10552-021-01501-6. Additional Declarations No competing interests reported. Supplementary Files supplementarymaterials.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3757050","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":274096083,"identity":"1a46eb9f-5067-4ca8-bf71-926ce66eccc6","order_by":0,"name":"xiaojing wu","email":"","orcid":"","institution":"Fujian University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"xiaojing","middleName":"","lastName":"wu","suffix":""},{"id":274096084,"identity":"bbc3d2fb-5391-4794-86ab-e89467ce5e38","order_by":1,"name":"Weiping Zhang","email":"","orcid":"","institution":"The Second People's Hospital Affiliated to Fujian University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Weiping","middleName":"","lastName":"Zhang","suffix":""},{"id":274096085,"identity":"2966617a-4e95-46cf-8056-ffa4a18c42df","order_by":2,"name":"Huijun Chen","email":"","orcid":"","institution":"The Second People's Hospital Affiliated to Fujian University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Huijun","middleName":"","lastName":"Chen","suffix":""},{"id":274096086,"identity":"f25b7548-ded9-4efa-8042-d0e613e16708","order_by":3,"name":"Jianfei Weng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIie3QsYrCQBCA4QEhWCykkxFBX2FlIeQg4KvMNqm28BFOAlbaG3wJH2GPhaSJd61wxQk2FhZ72KTTdNpothTcrxhYmL+YBfC8F8R1MzBJWBhmWtvaOUnTYX9VyK984ZpAagTXSphu4JKUS3OMqSM/dWUNMBiFPf08iarvNEYK5Cxbbsw0hnG+ppZkpyLer5nMYLsxKwbEf9uSv1PEkVDOQe0NCxySyY6JPRIXDBS4JR+VigCJhogFbz4Z22/hZSXOSBc2+ckO1tbJKBy0JI1ggLcHPt670/m3Tnue53lv6worxUyFRWkadwAAAABJRU5ErkJggg==","orcid":"","institution":"The Second People's Hospital Affiliated to Fujian University of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Jianfei","middleName":"","lastName":"Weng","suffix":""}],"badges":[],"createdAt":"2023-12-15 06:44:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3757050/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3757050/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51543959,"identity":"aba1a56b-76c3-4f83-98ae-99fbbfb031f7","added_by":"auto","created_at":"2024-02-23 12:18:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":19084,"visible":true,"origin":"","legend":"\u003cp\u003eDiagram of mediation effect analysis(a-Total effect; b-Direct effect; β1：The total effect of exposure on mediators; β2: The effect of mediators on outcome adjustment exposure)\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-3757050/v1/d2b847b00f63b45262ee9635.png"},{"id":51543958,"identity":"a3b617ba-44bb-4fb2-892e-005eba2056c0","added_by":"auto","created_at":"2024-02-23 12:18:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":120477,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of MR Estimates of CVD risk from PCa\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-3757050/v1/b8f19b465fbae5f1f274d365.png"},{"id":51543960,"identity":"9bf728b7-4cef-4cfe-a75a-40ba59ecc7b5","added_by":"auto","created_at":"2024-02-23 12:18:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":125255,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of MR Estimates of PCa risk from CVD\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-3757050/v1/c5edd2018e1f113611b15256.png"},{"id":51543963,"identity":"4a1e1c03-4461-46d9-9e4b-10cc817445f3","added_by":"auto","created_at":"2024-02-23 12:18:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":557108,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plots and leave-one-out plots with significant causal effects (A: PCa on risk of HF; B: AF on risk of PCa; C: stroke on risk of PCa.)\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-3757050/v1/fa9cd1d32cbe27ada4beb3ff.png"},{"id":51543962,"identity":"121cefbb-d16a-4df4-84ec-27b2e4cbc4a7","added_by":"auto","created_at":"2024-02-23 12:18:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":174165,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of MVMR analysis of PCa and CVD.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-3757050/v1/2160e6ab1fc6ba644dc14018.png"},{"id":64069552,"identity":"99b2e8ad-c098-4620-864f-fc6fd2e17b02","added_by":"auto","created_at":"2024-09-06 06:35:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1221010,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3757050/v1/10911b2c-2700-43e8-940d-35379ad64313.pdf"},{"id":51544258,"identity":"266cd616-d417-41da-a3f2-772086aabd45","added_by":"auto","created_at":"2024-02-23 12:26:53","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":299064,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterials.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3757050/v1/0a11a197b2cabdb91e2c6a88.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal relationship between prostate cancer and cardiovascular diseases: Univariable and multivariable Mendelian randomization","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOne of the most prevalent cancers affecting the male reproductive system in the globe is prostate cancer (PCa). According to cancer statistics released in 2023, PCa incidence has been on the rise and currently accounts for roughly 29% of all malignancies in men. Patients' risk of passing away has decreased due to the growing use of screening techniques including digital rectal exams and prostate-specific antigen testing\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. The mainstay of PCa treatment is androgen deprivation therapy (ADT), and the possibility of cardiovascular damage from the medication has been the subject of much debate\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. In the United States, cancer and cardiovascular disease (CVD) account for a large portion of the illness burden\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. In 2015, 17.7\u0026nbsp;million people worldwide died from CVD, while 8.8\u0026nbsp;million died from cancer\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. The cancer population with CVD has a higher mortality rate than the general population\u003csup\u003e[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. so we need to consider whether to intervene in the PCa population for the full range of cardiovascular risks or in advance take precautions against. There is a potential overlap in the pathophysiology of PCa and some CVD, although the available information is limited. The field of cardiac oncology is an emerging interdisciplinary domain that combines the disciplines of cardiology and oncology\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Its primary objective is to investigate the correlation between tumors and CVD. This field aims to explore shared pathogenic pathways and risk factors, with a particular emphasis on the potential cardiovascular toxicity induced by novel cancer treatments. The ultimate goal is to enhance the long-term prognosis and quality of life for individuals to reduce the risk of death\u003csup\u003e[\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.To comprehensively examine the causal association between PCa and CVD, we using the method of excluding confounding factors, we conducted a study utilizing univariate Mendelian randomization (UVMR) and multivariate Mendelian randomization (MVMR) techniques. The primary objective of this study was to establish genetic evidence of a correlation between PCa and CVD.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study design\u003c/h2\u003e \u003cp\u003eThe application of Mendelian randomization (MR) is the utilization of genetic variation as an instrumental variable to infer the causal impact of exposure on outcomes\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. This study aimed to evaluate the causal relationship between PCa and four CVDs using a bidirectional MR approach. For MR studies to be considered valid, they must satisfy three assumptions: (1) Genetic variants must be strongly associated with exposure factors. (2) Genetic variants cannot be directly related to the outcome. (3) Genetic variants cannot be associated with potential confounders\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. All magnetic resonance analyses in this study were performed in R software (4.3.0) using the TwoSampleMR, MRPRESSO, and MVMR packages. The ethical conduct of this study did not require Institutional Review Board approval and all assessments were based on publicly available pooled data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Genome-Wide Association Study (GWAS) data pertaining to PCa and CVD\u003c/h2\u003e \u003cp\u003eThe pooled association statistics for PCa risk were acquired from the PRACTICAL collaboration\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, which comprises a substantial sample size of 79,148 cases and 61,106 controls. This consortium constitutes the biggest collection of research containing genetic data pertaining to PCa. For further details regarding the study types employed, namely cohort and case-control studies, as well as the criteria used for subject selection, please refer to the original GWAS. A total of five CVD independent variables (IVs) were chosen from the GWAS, encompassing atrial fibrillation (AF)\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e, heart failure (HF)\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e, coronary artery disease (CAD)\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e and stroke\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. To mitigate potential confounding factors linked to race, the dataset included in this study exclusively consisted of individuals of European ancestry. The data pertaining to CVD were obtained from extensive survey studies with large sample sizes. The main characteristics of the GWASs included are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetailed information regarding studies and datasets used in the present study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAME\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePMID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29892016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79,148 cases, 61,106 controls\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30061737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60,620 cases, 970,216 controls\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31919418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47,309 cases, 930,014 controls\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29212778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122,733 cases, 424,528 controls\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29531354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40,585 cases, 406,111 controls\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=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Selection of IVs\u003c/h2\u003e \u003cp\u003eThe IVs were evaluated for their adherence to the three prerequisites of MR. A p-value threshold of less than 5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e was employed to identify SNPs as instrumental factors. SNPs exhibiting chain imbalance (LD, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and within 10,000 kb) were excluded, as this criterion has been extensively utilized in prior research studies\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Subsequently, SNPs linked with exposure were retrieved from the result, with the exclusion of SNPs that were ambiguous and had intermediate allele effect genes (0.58\u0026thinsp;\u0026gt;\u0026thinsp;Minor Allele Frequency\u0026thinsp;\u0026gt;\u0026thinsp;0.42), as well as SNPs that showed an association with the outcome at a significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e. We excluded SNPs with F\u0026thinsp;\u0026lt;\u0026thinsp;10 to avoid the effect of unwanted genetic variants\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. The formula for the F value is as follows F\u0026thinsp;=\u0026thinsp;R\u003csup\u003e2\u003c/sup\u003e\u0026times;(N\u0026thinsp;\u0026minus;\u0026thinsp;2) / (1\u0026thinsp;\u0026minus;\u0026thinsp;R\u003csup\u003e2\u003c/sup\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 UVMR and Sensitivity Analysis\u003c/h2\u003e \u003cp\u003eThe primary analysis in this research was conducted utilizing the IVW technique, which offers a precise evaluation of causal effects\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. To enhance the reliability of the findings, we additionally employed the weighted median (WM) and MR-Egger regression methods to validate the causal association between CVD and PCA\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. When the IVW test yielded a p-value less than 0.05, the researchers relied on the directionality of the results obtained from the other two MR procedures to verify the robustness of the findings and enhance the strength of the causal evidence\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. The study employed odds ratios (ORs) and their corresponding 95% confidence intervals (CIs) as measures to evaluate the relationship between CVD and PCa. Four analytical methods were utilized to conduct sensitivity studies, including Cochran's Q test, the MR-Egger intercept method, the leave-one-out (LOO) method, and MR-PRESSO. The Cochran's Q test was employed to examine the existence of heterogeneity, and in cases where a statistically significant difference was seen, the random effects IVW model was utilized. The MR-Egger intercept approach\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e and the MR-PRESSO\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e test were employed to evaluate the presence of a horizontal multinomial. In cases where horizontal pleiotropy was detected, the findings from the MR-Egger regression were given precedence\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. LOO method to test whether the MR results were driven by a single SNP was used to analyze whether our results were robust. Statistical significance was determined by using p-values less than 0.05 in all the aforementioned sensitivity analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 MVMR and mediation MR analysis\u003c/h2\u003e \u003cp\u003eThe method of MVMR addresses the issue of horizontal pleiotropy in two-sample MR analyses. This is achieved by incorporating multiple exposures inside the same statistical model and simultaneously calculating their respective causal effects on the risk of the outcome of interest\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. To account for many variables, we choose to include six risk factors (BMI, smoking, alcoholic drinks, sedentary, total cholesterol, and LDL cholesterol) that shown robust relationships between PCa and CVD outcomes in observational studies. These risk factors were chosen for multivariate adjustment. The concept of mediation effect pertains to the influence exerted by a mediating element in the relationship between exposure and outcome\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Specifically, this effect is observed when exposure impacts the mediator, which then affects the outcome. Consequently, the overall effect of exposure on the outcome encompasses both the mediation effect and the direct effect. The mediation effect value of a specific component (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: β1\u0026thinsp;\u0026times;\u0026thinsp;β2) can be derived through the adjustment for various exposure factors in a MVMR model\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. By dividing the mediation effect by the total effect, we can determine the proportion of the mediator's contribution to the association between exposure and outcome.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1 UVMR and sensitivity analyses\u003c/h2\u003e\n\u003cp\u003eThe specific IVs included in the MR analysis may be found in Supplementary Table. The F-statistic values for all instrumental variables selected in this study were found to be greater than 10, indicating their robustness and effectiveness in the analysis. The study found a significant association between genetically predicted PCa and increased odds of HF(OR\u0026thinsp;=\u0026thinsp;1.03, 95%CI\u0026thinsp;=\u0026thinsp;1.01\u0026ndash;1.05, p\u0026thinsp;=\u0026thinsp;0.0109)(Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). To explore the potential reverse causation between PCa and CVD, we conducted a reverse MR analysis in our study. which may help us to determine whether the observed associations are causally related to each other. In the reverse MR study, genetically expected AF (OR\u0026thinsp;=\u0026thinsp;0.95, 95% CI\u0026thinsp;=\u0026thinsp;0.93\u0026ndash;0.98, P\u0026thinsp;=\u0026thinsp;0.0016) as well as stroke (OR\u0026thinsp;=\u0026thinsp;0.85, 95%CI\u0026thinsp;=\u0026thinsp;0.75\u0026ndash;0.97, P\u0026thinsp;=\u0026thinsp;0.0158) was associated with lower odds of PCa (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). In sensitivity analyses, Cochran's Q tests did not provide any significant results (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating that there was no heterogeneity. In the outcome of PCa with stroke,and the bidirectional causal relationship between PCa and CAD, we observed the outcomes of the three MR methods showed different directionality, and therefore we consider that this result lacks robustness. The MR-Egger and ME methods show consistent directionality for all other outcomes (The positive results are shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). all of the above results were performed after the MR-presso test was performed to remove outliers, and the results of the MR-presso test showed no horizontal multiplicity of validity after the removal of outliers. The robustness of the results was further validated by the LOO results. Sensitivity analyses are shown in Supplementary Table.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2 MVMR\u003c/h2\u003e\n\u003cp\u003eThe statistical significance of the correlation between stroke and PCa diminished when controlling for all risk factors except Cigarettes smoked and total cholesterol. This implies that the correlation between stroke and PCa exhibits additional layers of multivariate pleiotropy. Consequently, the credibility of the significant causal relationship between the two variables can be called into question. The statistical association between atrial AF and PCa lost its significance after controlling for BMI (P\u0026thinsp;=\u0026thinsp;0.0962). However, the association persisted when accounting for other known risk variables, indicating that BMI alone may play a causal role in the relationship between AF and PCa. The association between HF and PCa persisted even after controlling for the aforementioned risk factors. This suggests that the genetic correlation between HF and PCa is strong, indicating that PCa is a potential risk factor for the development of HF (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e3.3 mediation MR analysis\u003c/h2\u003e\n\u003cp\u003eOur study aimed to examine the potential mediating role of BMI and AF and PCa. Utilizing BMI as an independent variable, our analysis revealed a significant correlation between BMI and AF (OR\u0026thinsp;=\u0026thinsp;1.3905, 95% CI\u0026thinsp;=\u0026thinsp;1.3235\u0026ndash;1.4607, p\u0026thinsp;=\u0026thinsp;3.30\u0026times;10\u0026ndash;39), as well as PCa (OR\u0026thinsp;=\u0026thinsp;0.9297, 95% CI\u0026thinsp;=\u0026thinsp;0.8744\u0026ndash;0.9884, p\u0026thinsp;=\u0026thinsp;0.0197). However, employing inverse MR techniques, we observed that there was no discernible association between BMI and AF (OR\u0026thinsp;=\u0026thinsp;0.9904, 95% CI\u0026thinsp;=\u0026thinsp;0.9731\u0026ndash;1.0080, p\u0026thinsp;=\u0026thinsp;0.2826), nor between BMI and PCa (OR\u0026thinsp;=\u0026thinsp;1.0004, 95% CI\u0026thinsp;=\u0026thinsp;0.9935\u0026ndash;1.0074, p\u0026thinsp;=\u0026thinsp;0.9009). Hence, it may be inferred that AF plays a role in the correlation between BMI and PCa. The observed mediation effect had a value of -0.0111, accompanied by a mediation percentage of 15.28%.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eOur study employs a two-sample MR approach to examine the genetic basis of the potential causal relationship between PCa and CVD. The data we have collected offer genetic evidence supporting the existence of a causal influence between these two conditions. To the best of our understanding, this study represents the inaugural MR investigation of the genetic associations between PCa and various forms of CVD. The study design successfully mitigated the influence of potential confounding variables and the issue of reverse causality.\u003c/p\u003e \u003cp\u003eThe results of our study indicate that PCa is correlated with a heightened likelihood of heart HF, and this correlation is independent of shared risk factors between the two conditions. Patients diagnosed with PCa in conjunction with HF experience extended hospitalizations and increased healthcare expenses. Additionally, this comorbidity is related to an elevated mortality risk\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Moreover, in senior individuals, the coexistence of both PCa and HF is indicative of an unfavorable prognosis. The development of heart failure is influenced by various factors, including cancer metabolic by-products, cachexia, and the use of anti-cancer medications\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Patients with PCa often exhibit increased levels of testosterone (T), and the relationship between T and the risk of HF is a subject of debate. Previous research usually suggests that T is inversely linked to the occurrence of HF and that T therapy can have positive prognostic effects on HF\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Nevertheless, a contentious issue has emerged in recent years due to the prolonged duration of clinical monitoring\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Elevated levels of T beyond physiological norms exert additional stress on the cardiovascular system. In 2014, the Food and Drug Administration issued a statement recommending the inclusion of information regarding the potential elevated risk of heart attack and stroke associated with the use of testosterone products\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. A study utilizing MR revealed a noteworthy correlation between levels of endogenous T and the risk of HF in males. However, this link did not retain statistical significance when examining the female population\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. The existing body of research has primarily examined the cardiac toxicity that follows ADT for PCa. However, our study highlights the importance of considering the potential elevated risk of HF in untreated patients with PCa. This finding underscores the need for early screening and preventive measures. Additionally, our findings demonstrate that AF exhibits a protective effect against PCa and serves as a mediator in the association between BMI and PCa. BMI has been found to play a role in the development and progression of PCa, as well as in increased death rates associated with the disease\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. However, it is worth noting that BMI may also have a potentially beneficial influence in reducing the likelihood of PCa incidence\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. An elevation in BMI is associated with a heightened likelihood of developing AF, a condition that subsequently serves as a safeguard against the onset of PCa. Additionally, individuals with cancer-related AF exhibit higher mortality rates. Consequently, it is worth exploring whether AF, akin to BMI, fosters the progression and mortality of PCa while simultaneously reducing the risk of its occurrence. In our UVMR investigation, it was observed that stroke was a protective factor to PCa. However, upon accounting for the extent of pleiotropy, the causal connection between PCa and stroke was no longer evident. This suggests that shared factors such as BMI, alcoholic drinks, sedentary, and LDL cholesterol play significant roles in the development of PCa among individuals with stroke. Consequently, it is imperative to prioritize education and lifestyle modifications within the stroke population to mitigate the risk of PCa occurrence.\u003c/p\u003e \u003cp\u003eThe findings from certain observational studies have demonstrated an elevated likelihood of developing future PCa in individuals with stroke, HF and AF\u003csup\u003e[\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. However, our results do not align with this observed association. The substantial disparity in outcomes can be attributed mostly to the following causative factors, in our analysis. Initially, it is worth noting that the occurrence of occult cancers coinciding with the diagnosis of CVD and subsequently detected during a comprehensive cardiovascular examination may lead to a misinterpretation of the causal association between the risk of CVD and the presence of cancer. Consequently, the rate of cancer diagnoses tends to rise in the months following the identification of AF\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. Furthermore, the identification and control of confounding factors in observational studies pose a greater challenge. This is due to the higher prevalence of CVD and PCa in older individuals, who often have comorbidities or engage in long-term activities with a heightened pathogenic potential\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. Additionally, it is not feasible to clinically ascertain the exclusive influence of other medications and risk factors on the development of both conditions\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. The simultaneous presence of CVD and PCa frequently indicates an unfavorable prognosis, and the limited availability of ethical guidelines for clinical monitoring has hindered the establishment of comprehensive observational studies involving substantial sample sizes. These elements play a crucial role in the manifestation of biased outcomes. Given that MR design has the inherent benefit of reducing the influence of confounding variables and relies on the genetic correlation of the disease being studied, we assert that this study possesses a certain level of credibility.\u003c/p\u003e \u003cp\u003eThis study possesses multiple notable strengths. This study represents the inaugural investigation utilizing MR to evaluate the causal correlation between various cardiovascular disorders and the risk of PCa. Notably, we employed a substantial sample size derived from a comprehensive collection of genetic association abstracts obtained from GWAS. This approach significantly enhanced the accuracy of our measurements and bolstered the overall robustness of our analysis. Nevertheless, it is imperative to acknowledge the existing limitations of our study. Initially, the primary data sources utilized in our study largely consisted of persons of European ancestry. This particular demographic composition imposes limitations on the generalizability of our findings to non-European communities. Furthermore, a comprehensive analysis was not conducted to explicitly stratify characteristics such as age. Ultimately, despite our efforts to conduct sensitivity studies to mitigate the potential violation of MR assumptions, we were unable to fully eliminate the influence of residual pleiotropy. In summary, this research offers valuable insights into the genetic correlation between PCa and CVD. However, it is crucial to acknowledge and tackle the limitations identified in our study through future investigations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eSince all of the submitted assessments were based on publically available data, the Institutional Review Board was not required to grant authorization for the ethical conduct of this study.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declares that there is no conflict of interest regarding the publication of this paper.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThe authors have no funding of interest to disclose.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; contributions\u003c/h2\u003e\n\u003cp\u003eData curation, Huijun Chen; Supervision, Jianfei Weng; Writing \u0026ndash; original draft, xiaojing wu; Writing-review \u0026amp; editing, Weiping Zhang.\u003c/p\u003e\n\u003cp\u003eAll authors will be informed about each step of manuscript processing including submission, revision, revision reminder, etc. via emails from our system or assigned Assistant Editor.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eAvailability of data and material\u003c/h2\u003e\n\u003cp\u003eThe data used in this study is based on publicly available data, which can be found at the URL below: GWAS pipeline output using Phesant derived variables from UKB: https://gwas.mrcieu.ac.uk/datasets/; PGC: https://pgc.unc.edu/.\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article and its supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col class=\"decimal_type\"\u003e\n\u003cli\u003eSiegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023 Jan;73(1):17-48. doi: 10.3322/caac.21763. \u003c/li\u003e\n\u003cli\u003eHu JR, Duncan MS, Morgans AK, et al. Cardiovascular Effects of Androgen Deprivation Therapy in Prostate Cancer: Contemporary Meta-Analyses. Arterioscler Thromb Vasc Biol. 2020 Mar;40(3):e55-e64. doi: 10.1161/ATVBAHA.119.313046.\u003c/li\u003e\n\u003cli\u003eHoyert DL, Xu J. Deaths: preliminary data for 2011. Natl Vital Stat Rep. 2012 Oct 10;61(6):1-51.\u003c/li\u003e\n\u003cli\u003eWHO. Cardiovascular diseases. http://www.who.int/mediacentre/factsheets/fs317/en/ (18 October 2019).\u003c/li\u003e\n\u003cli\u003eWHO. Cancer. http://www.who.int/mediacentre/factsheets/fs297/en/ (18 October 2019).\u003c/li\u003e\n\u003cli\u003eSturgeon KM, Deng L, Bluethmann SM, et al. A population-based study of cardiovascular disease mortality risk in US cancer patients. Eur Heart J. 2019 Dec 21;40(48):3889-3897. doi: 10.1093/eurheartj/ehz766. \u003c/li\u003e\n\u003cli\u003eO\u0026apos;Neill C, Donnelly DW, Harbinson M, et al. Survival of cancer patients with pre-existing heart disease. BMC Cancer. 2022 Aug 3;22(1):847. doi: 10.1186/s12885-022-09944-z. \u003c/li\u003e\n\u003cli\u003eZaorsky NG, Zhang Y, Tchelebi LT, et al. Stroke among cancer patients. Nat Commun. 2019 Nov 15;10(1):5172. doi: 10.1038/s41467-019-13120-6. \u003c/li\u003e\n\u003cli\u003ede Boer RA, Meijers WC, van der Meer P, van Veldhuisen DJ. Cancer and heart disease: associations and relations. Eur J Heart Fail. 2019 Dec;21(12):1515-1525. doi: 10.1002/ejhf.1539. Epub 2019 Jul 18. \u003c/li\u003e\n\u003cli\u003eCoviello JS. Cardiovascular and Cancer Risk: The Role of Cardio-oncology. J Adv Pract Oncol. 2018 Mar;9(2):160-176. Epub 2018 Mar 1. \u003c/li\u003e\n\u003cli\u003eAbe J, Martin JF, Yeh ET. The Future of Onco-Cardiology: We Are Not Just \u0026quot;Side Effect Hunters\u0026quot;. Circ Res. 2016 Sep 30;119(8):896-9. doi: 10.1161/CIRCRESAHA.116.309573.\u003c/li\u003e\n\u003cli\u003eAboumsallem JP, Moslehi J, de Boer RA. Reverse Cardio-Oncology: Cancer Development in Patients With Cardiovascular Disease. J Am Heart Assoc. 2020 Jan 21;9(2):e013754. doi: 10.1161/JAHA.119.013754. \u003c/li\u003e\n\u003cli\u003eYarmolinsky J, Wade KH, Richmond RC, et al. Causal Inference in Cancer Epidemiology: What Is the Role of Mendelian Randomization? Cancer Epidemiol Biomarkers Prev. 2018 Sep;27(9):995-1010. doi: 10.1158/1055-9965.\u003c/li\u003e\n\u003cli\u003eSchumacher FR, Al Olama AA, Berndt SI, et al. Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci. Nat Genet. 2018 Jul;50(7):928-936. doi: 10.1038/s41588-018-0142-8. \u003c/li\u003e\n\u003cli\u003eNielsen JB, Thorolfsdottir RB, Fritsche LG, et al. Biobank-driven genomic discovery yields new insight into atrial fibrillation biology. Nat Genet. 2018 Sep;50(9):1234-1239. doi: 10.1038/s41588-018-0171-3. \u003c/li\u003e\n\u003cli\u003eShah S, Henry A, Roselli C,et al. Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure. Nat Commun. 2020 Jan 9;11(1):163. doi: 10.1038/s41467-019-13690-5. \u003c/li\u003e\n\u003cli\u003evan der Harst P, Verweij N. Identification of 64 Novel Genetic Loci Provides an Expanded View on the Genetic Architecture of Coronary Artery Disease. Circ Res. 2018 Feb 2;122(3):433-443. doi: 10.1161/CIRCRESAHA.117.312086. \u003c/li\u003e\n\u003cli\u003eMalik R, Chauhan G, Traylor M, et al. Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes. Nat Genet. 2018 Apr;50(4):524-537. doi: 10.1038/s41588-018-0058-3. Epub 2018 Mar 12. Erratum in: Nat Genet. 2019 Jul;51(7):1192-1193.\u003c/li\u003e\n\u003cli\u003eRen S, Xue C, Xu M, Li X. Mendelian Randomization Analysis Reveals Causal Effects of Polyunsaturated Fatty Acids on Subtypes of Diabetic Retinopathy Risk. Nutrients. 2023 Sep 29;15(19):4208. doi: 10.3390/nu15194208. \u003c/li\u003e\n\u003cli\u003eWu M, Du Y, Zhang C, et al. Mendelian Randomization Study of Lipid Metabolites Reveals Causal Associations with Heel Bone Mineral Density. Nutrients. 2023 Sep 27;15(19):4160. doi: 10.3390/nu15194160. \u003c/li\u003e\n\u003cli\u003eBurgess S, Thompson SG; CRP CHD Genetics Collaboration. Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol. 2011 Jun;40(3):755-64. doi: 10.1093/ije/dyr036. \u003c/li\u003e\n\u003cli\u003eBurgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013 Nov;37(7):658-65. doi: 10.1002/gepi.21758. \u003c/li\u003e\n\u003cli\u003eHemani G, Bowden J, Davey Smith G. Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum Mol Genet. 2018 Aug 1;27(R2):R195-R208. doi: 10.1093/hmg/ddy163. \u003c/li\u003e\n\u003cli\u003eSanderson E, Glymour MM, Holmes MV, Kang H, Morrison J, Munaf\u0026ograve; MR, Palmer T, Schooling CM, Wallace C, Zhao Q, Smith GD. Mendelian randomization. Nat Rev Methods Primers. 2022 Feb 10;2:6. doi: 10.1038/s43586-021-00092-5. \u003c/li\u003e\n\u003cli\u003eBowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015 Apr;44(2):512-25. doi: 10.1093/ije/dyv080.\u003c/li\u003e\n\u003cli\u003eVerbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018 May;50(5):693-698. doi: 10.1038/s41588-018-0099-7. \u003c/li\u003e\n\u003cli\u003eBurgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol. 2017 May;32(5):377-389. doi: 10.1007/s10654-017-0255-x. \u003c/li\u003e\n\u003cli\u003eBurgess S, Thompson SG. Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects. Am J Epidemiol. 2015 Feb 15;181(4):251-60. doi: 10.1093/aje/kwu283.\u003c/li\u003e\n\u003cli\u003eCarter AR, Sanderson E, Hammerton G, Richmond RC, Davey Smith G, Heron J, Taylor AE, Davies NM, Howe LD. Mendelian randomisation for mediation analysis: current methods and challenges for implementation. Eur J Epidemiol. 2021 May;36(5):465-478. doi: 10.1007/s10654-021-00757-1.\u003c/li\u003e\n\u003cli\u003eZhao SS, Holmes MV, Zheng J, Sanderson E, Carter AR. The impact of education inequality on rheumatoid arthritis risk is mediated by smoking and body mass index: Mendelian randomization study. Rheumatology (Oxford). 2022 May 5;61(5):2167-2175. doi: 10.1093/rheumatology/keab654.\u003c/li\u003e\n\u003cli\u003eYu A, Thaliffdeen R, Park SK, Park C. Hospital outcomes and costs for prostate cancer patients with comorbid heart failure by age group: An analysis of the US Nationwide Inpatient Sample. J Eval Clin Pract. 2023 Sep;29(6):1016-1024. doi: 10.1111/jep.13869. \u003c/li\u003e\n\u003cli\u003eMeht\u0026auml;l\u0026auml; J, Zong J, Vassilev Z, et al. Overall survival and second primary malignancies in men with metastatic prostate cancer. PLoS One. 2020 Feb 21;15(2):e0227552. doi: 10.1371/journal.pone.0227552. \u003c/li\u003e\n\u003cli\u003ede Boer RA, Hulot JS, Tocchetti CG, et al. Common mechanistic pathways in cancer and heart failure. A scientific roadmap on behalf of the Translational Research Committee of the Heart Failure Association (HFA) of the European Society of Cardiology (ESC). Eur J Heart Fail. 2020 Dec;22(12):2272-2289. doi: 10.1002/ejhf.2029. \u003c/li\u003e\n\u003cli\u003eToma M, McAlister FA, Coglianese EE, et al. Testosterone supplementation in heart failure: a meta-analysis. Circ Heart Fail. 2012 May 1;5(3):315-21. doi: 10.1161/CIRCHEARTFAILURE.111.965632. \u003c/li\u003e\n\u003cli\u003eDi Lodovico E, Facondo P, Delbarba A, et al. Testosterone, Hypogonadism, and Heart Failure. Circ Heart Fail. 2022 Jul;15(7):e008755. doi: 10.1161/CIRCHEARTFAILURE.121.008755. \u003c/li\u003e\n\u003cli\u003eFDA adding general warning to testosterone products about potential for venous blood clots US. 2014; updated 2018. https://wayback.archive-it.org/7993/20161022180648/http://www.fda.gov/Drugs/DrugSafety/ucm401746.htm .\u003c/li\u003e\n\u003cli\u003eLuo S, Au Yeung SL, Zhao JV, Burgess S, Schooling CM. Association of genetically predicted testosterone with thromboembolism, heart failure, and myocardial infarction: mendelian randomisation study in UK Biobank. BMJ. 2019 Mar 6;364:l476. doi: 10.1136/bmj.l476.\u003c/li\u003e\n\u003cli\u003eRodriguez C, Freedland SJ, Deka A, et al. Body mass index, weight change, and risk of prostate cancer in the Cancer Prevention Study II Nutrition Cohort. Cancer Epidemiol Biomarkers Prev. 2007 Jan;16(1):63-9. doi: 10.1158/1055-9965.EPI-06-0754. \u003c/li\u003e\n\u003cli\u003eCao Y, Ma J. Body mass index, prostate cancer-specific mortality, and biochemical recurrence: a systematic review and meta-analysis. Cancer Prev Res (Phila). 2011 Apr;4(4):486-501. doi: 10.1158/1940-6207.CAPR-10-0229. \u003c/li\u003e\n\u003cli\u003ePorter MP, Stanford JL. Obesity and the risk of prostate cancer. Prostate. 2005 Mar 1;62(4):316-21. \u003c/li\u003e\n\u003cli\u003eO\u0026apos;Rourke K. Heart failure linked with an increased risk of cancer. Cancer. 2022 Feb 15;128(4):646. doi: 10.1002/cncr.34094. \u003c/li\u003e\n\u003cli\u003eOstenfeld EB, Erichsen R, Pedersen L, Farkas DK, Weiss NS, S\u0026oslash;rensen HT. Atrial fibrillation as a marker of occult cancer. PLoS One. 2014 Aug 13;9(8):e102861. doi: 10.1371/journal.pone.0102861. \u003c/li\u003e\n\u003cli\u003eRioux B, Gioia LC, Keezer MR. Risk of Cancer Following an Ischemic Stroke in the Canadian Longitudinal Study on Aging. Can J Neurol Sci. 2022 Mar;49(2):225-230. doi: 10.1017/cjn.2021.55. \u003c/li\u003e\n\u003cli\u003eOstenfeld EB, Erichsen R, Pedersen L, Farkas DK, Weiss NS, S\u0026oslash;rensen HT. Atrial fibrillation as a marker of occult cancer. PLoS One. 2014 Aug 13;9(8):e102861. doi: 10.1371/journal.pone.0102861. \u003c/li\u003e\n\u003cli\u003eMeijers WC, de Boer RA. Common risk factors for heart failure and cancer. Cardiovasc Res. 2019 Apr 15;115(5):844-853. doi: 10.1093/cvr/cvz035. \u003c/li\u003e\n\u003cli\u003eFu BC, Wang K, Mucci LA, Clinton SK, Giovannucci EL. Aspirin use and prostate tumor angiogenesis. Cancer Causes Control. 2022 Jan;33(1):149-151. doi: 10.1007/s10552-021-01501-6. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mendelian randomization, cardiovascular disease, prostate cancer","lastPublishedDoi":"10.21203/rs.3.rs-3757050/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3757050/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eProstate cancer (PCa) and cardiovascular disease (CVD) have a high prevalence worldwide, and the presence of both PCa and CVD signals a poor prognosis; the risk relationship between the two diseases is debatable.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study searched for relationship to PCa and four CVDs using a Mendelian randomisation (MR) approach. Bidirectional causality was investigated using univariate MR investigations. The data were then adjusted for the six major PCa and CVD risk variables using a multivariate MR model and examined for mediated effects.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePCa was a risk factor for the development of heart failure. Atrial fibrillation and stroke has been a protective effect against the incidence of PCa. Following the adjustment of the multivariate MR model, the association between PCa and heart failure persisted. However, the association between atrial fibrillation and PCa was no longer present after adjustment for BMI. The causal relationship between stroke and PCa was no longer significant in multiple multivariate adjustment models. The mediator MR analysis revealed that atrial fibrillation mediated 15.28% of the causal relationship between BMI and PCa.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur study suggests that PCa is a risk factor for heart failure and atrial fibrillation is a protective factor for PCa.\u003c/p\u003e","manuscriptTitle":"Causal relationship between prostate cancer and cardiovascular diseases: Univariable and multivariable Mendelian randomization","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-23 12:18:48","doi":"10.21203/rs.3.rs-3757050/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a401e691-e6c6-4e7e-b9d0-96ceb363be5c","owner":[],"postedDate":"February 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28891864,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":28891865,"name":"Biological sciences/Genetics"},{"id":28891866,"name":"Health sciences/Cardiology"}],"tags":[],"updatedAt":"2024-09-06T06:27:14+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-23 12:18:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3757050","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3757050","identity":"rs-3757050","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}