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Blood pressure, calcium channel blockers, and the risk of prostate cancer: a Mendelian randomization study

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Abstract

Background

Observational studies have found some evidence of an association between elevated blood pressure and prostate cancer risk; however, the results are inconclusive. We tested whether systolic blood pressure (SBP) influences prostate cancer risk and evaluated the effect of calcium channel blockers (CCB) on the disease using Mendelian randomization (MR) approach.

Methods

We used 278 genetic variants associated with SBP and 16 genetic variants in CCB genes as instrumental variables. Effect estimates were obtained from the UK Biobank sample of 142,995 males and from PRACTICAL consortium (79,148 cases and 61,106 controls).

Results

For each 10 mm Hg increase in SBP the estimated effect was OR 0.96 (0.90–1.01) for overall prostate cancer; and OR 0.92 (0.85–0.99) for aggressive prostate cancer. The MR-estimated effect of a 10 mm Hg- SBP lowering through CCB genetic variants was OR 1.22 (1.06–1.42) for all prostate cancers and OR 1.49 (1.18–1.89) for aggressive prostate cancer.

Conclusion

The results of our study did not support a causal relationship between SBP and prostate cancer; however, we found weak evidence of a protective effect of high SBP on aggressive prostate cancer and we found that blocking calcium channel receptors may increase prostate cancer risk.

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Data availability

This work has been conducted using the UK Biobank Resource. The UK Biobank is an open access resource and bona fide researchers can apply to use the UK Biobank dataset by registering and applying at http://ukbiobank.ac.uk/register-apply/. Further information is available from the corresponding author upon request.

References

  1. Sung H, Ferlay J, Siegel RL et al (2021) Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71(3):209–249. https://doi.org/10.3322/caac.21660

    Article  PubMed  Google Scholar 

  2. Marugame T, Katanoda K (2006) International comparisons of cumulative risk of breast and prostate cancer, from cancer incidence in five continents Vol. VIII. Jpn J Clin Oncol 36(6):399–400. https://doi.org/10.1093/jjco/hyl049

    Article  PubMed  Google Scholar 

  3. Bostwick DG, Burke HB, Djakiew D et al (2004) Human prostate cancer risk factors. Cancer 101(S10):2371–2490. https://doi.org/10.1002/cncr.20408

    Article  CAS  PubMed  Google Scholar 

  4. Cooperberg MR, Chan JM (2017) Epidemiology of prostate cancer. World J Urol 35(6):849–849. https://doi.org/10.1007/s00345-017-2038-0

    Article  PubMed  Google Scholar 

  5. Navin S, Ioffe V (2017) The association between hypertension and prostate cancer. Rev Urol 19(2):113–118. https://doi.org/10.3909/riu0758

    Article  PubMed  PubMed Central  Google Scholar 

  6. Stocks T, Hergens MP, Englund A, Ye W, Stattin P (2010) Blood pressure, body size and prostate cancer risk in the Swedish Construction Workers cohort. Int J Cancer 127(7):1660–1668. https://doi.org/10.1002/ijc.25171

    Article  CAS  PubMed  Google Scholar 

  7. Romero FR, Romero AW, de Almeida RMS, Oliveira FC Jr, Tambara Filho R (2012) The significance of biological, environmental, and social risk factors for prostate cancer in a cohort study in Brazil. Int Braz J Urol 38(6):769–778. https://doi.org/10.1590/1677-553820133806769

    Article  PubMed  Google Scholar 

  8. Beebe-Dimmer JL, Dunn RL, Sarma AV, Montie JE, Cooney KA (2007) Features of the metabolic syndrome and prostate cancer in African-American men. Cancer 109(5):875–881. https://doi.org/10.1002/cncr.22461

    Article  PubMed  Google Scholar 

  9. Tulinius H, Sigfússon N, Sigvaldason H, Bjarnadóttir K, Tryggvadottir L (1997) Risk factors for malignant diseases: a cohort study on a population of 22,946 Icelanders. Cancer Epidemiol Prev Biomark 6(11):863–873

    CAS  Google Scholar 

  10. Wallner LP, Morgenstern H, McGree ME et al (2011) The effects of metabolic conditions on prostate cancer incidence over 15 years of follow-up: results from the Olmsted County Study: METABOLIC CONDITIONS AND PROSTATE CANCER INCIDENCE. BJU Int 107(6):929–935. https://doi.org/10.1111/j.1464-410X.2010.09703.x

    Article  PubMed  Google Scholar 

  11. Chan II, Kwok MK, Schooling CM (2021) Blood pressure and risk of cancer: a Mendelian randomization study. BMC Cancer 21(1):1338. https://doi.org/10.1186/s12885-021-09067-x

    Article  PubMed  PubMed Central  Google Scholar 

  12. Liang Z, Xie B, Li J et al (2016) Hypertension and risk of prostate cancer: a systematic review and meta-analysis. Sci Rep 6(1):31358. https://doi.org/10.1038/srep31358

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Cabello M, Ge H, Aracil C et al (2019) Extracellular electrophysiology in the prostate cancer cell model PC-3. Sensors 19(1):139. https://doi.org/10.3390/s19010139

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Marchetti C (2022) Calcium signaling in prostate cancer cells of increasing malignancy. Biomol Concepts 13(1):156–163. https://doi.org/10.1515/bmc-2022-0012

    Article  CAS  PubMed  Google Scholar 

  15. Shapovalov G, Skryma R, Prevarskaya N (2012) Calcium channels and prostate cancer. Recent Patents Anticancer Drug Discov 8(1):18–26. https://doi.org/10.2174/1574892811308010018

    Article  Google Scholar 

  16. Pahor M, Guralnik JM, Ferrucci L et al (1996) Calcium-channel blockade and incidence of cancer in aged populations. Lancet 348(9026):493–497. https://doi.org/10.1016/S0140-6736(96)04277-8

    Article  CAS  PubMed  Google Scholar 

  17. Kemppainen KJ, Tammela TLJ, Auvinen A, Murtola TJ (2011) The association between antihypertensive drug use and incidence of prostate cancer in Finland: a population-based case–control study. Cancer Causes Control 22(10):1445–1452. https://doi.org/10.1007/s10552-011-9819-3

    Article  PubMed  Google Scholar 

  18. Azoulay L, Assimes TL, Yin H, Bartels DB, Schiffrin EL, Suissa S (2012) Long-term use of angiotensin receptor blockers and the risk of cancer. PLoS ONE 7(12):e50893. https://doi.org/10.1371/journal.pone.0050893

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Cao L, Zhang S, Jia C-M et al (2018) Antihypertensive drugs use and the risk of prostate cancer: a meta-analysis of 21 observational studies. BMC Urol 18(1):17. https://doi.org/10.1186/s12894-018-0318-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Yang H, Yu Y, Hu X et al (2020) Association between the overall risk of prostate cancer and use of calcium channel blockers: a systematic review and meta-analysis. Clin Ther 42(9):1715-1727.e2. https://doi.org/10.1016/j.clinthera.2020.06.021

    Article  CAS  PubMed  Google Scholar 

  21. Thakur AA, Wang X, Garcia-Betancourt MM, Forse RA (2018) Calcium channel blockers and the incidence of breast and prostate cancer: a meta-analysis. J Clin Pharm Ther 43(4):519–529. https://doi.org/10.1111/jcpt.12673

    Article  CAS  PubMed  Google Scholar 

  22. Sheehan NA, Didelez V, Burton PR, Tobin MD (2008) Mendelian randomisation and causal inference in observational epidemiology. PLoS Med 5(8):e177. https://doi.org/10.1371/journal.pmed.0050177

    Article  PubMed  PubMed Central  Google Scholar 

  23. Schmidt AF, Finan C, Gordillo-Marañón M et al (2020) Genetic drug target validation using Mendelian randomisation. Nat Commun 11(1):3255. https://doi.org/10.1038/s41467-020-16969-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Gaziano L, Giambartolomei C, Pereira AC et al (2021) Actionable druggable genome-wide Mendelian randomization identifies repurposing opportunities for COVID-19. Nat Med 27(4):668–676. https://doi.org/10.1038/s41591-021-01310-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Skrivankova VW, Richmond RC, Woolf BAR et al (2021) Strengthening the reporting of observational studies in epidemiology using mendelian randomisation (STROBE-MR): explanation and elaboration. BMJ. https://doi.org/10.1136/bmj.n2233

    Article  PubMed  PubMed Central  Google Scholar 

  26. Bycroft C, Freeman C, Petkova D et al (2018) The UK Biobank resource with deep phenotyping and genomic data. Nature 562(7726):203–209. https://doi.org/10.1038/s41586-018-0579-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Warren HR, Evangelou E, Cabrera CP et al (2017) Genome-wide association analysis identifies novel blood pressure loci and offers biological insights into cardiovascular risk. Nat Genet 49(3):403–415. https://doi.org/10.1038/ng.3768

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Schumacher FR, Al Olama AA, Berndt SI et al (2018) Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci. Nat Genet 50(7):928–936. https://doi.org/10.1038/s41588-018-0142-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. The 1000 Genomes Project Consortium (2012) An integrated map of genetic variation from 1,092 human genomes. Nature 491(7422):56–65. https://doi.org/10.1038/nature11632

    Article  CAS  PubMed Central  Google Scholar 

  30. Al Olama AA, Kote-Jarai Z, Berndt SI et al (2014) A meta-analysis of 87,040 individuals identifies 23 new susceptibility loci for prostate cancer. Nat Genet 46(10):1103–1109. https://doi.org/10.1038/ng.3094

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Howie B, Marchini J, Stephens M (2011) Genotype imputation with thousands of genomes. G3 Genes Genomes Genet 1(6):457–470. https://doi.org/10.1534/g3.111.001198

    Article  Google Scholar 

  32. Malik R, Chauhan G, Traylor M et al (2018) Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes. Nat Genet 50(4):524–537. https://doi.org/10.1038/s41588-018-0058-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Schunkert H, König IR, Kathiresan S et al (2011) Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat Genet 43(4):333–338. https://doi.org/10.1038/ng.784

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Gavish B, Ben-Dov IZ, Bursztyn M (2008) Linear relationship between systolic and diastolic blood pressure monitored over 24 h: assessment and correlates. J Hypertens 26(2):199–209. https://doi.org/10.1097/HJH.0b013e3282f25b5a

    Article  CAS  PubMed  Google Scholar 

  35. van Rijn MJE, Schut AF, Aulchenko YS et al (2007) Heritability of blood pressure traits and the genetic contribution to blood pressure variance explained by four blood-pressure-related genes. J Hypertens 25(3):565–570. https://doi.org/10.1097/HJH.0b013e32801449fb

    Article  CAS  PubMed  Google Scholar 

  36. Evangelou E, Warren HR, Mosen-Ansorena D et al (2018) Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. Nat Genet 50(10):1412–1425. https://doi.org/10.1038/s41588-018-0205-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Wishart DS, Feunang YD, Guo AC et al (2018) DrugBank 50: a major update to the DrugBank database for 2018. Nucleic Acids Res 46(D1):D1074–D1082. https://doi.org/10.1093/nar/gkx1037

    Article  CAS  PubMed  Google Scholar 

  38. Stelzer G, Rosen N, Plaschkes I et al (2016) The genecards suite: from gene data mining to disease genome sequence analyses. Curr Protoc Bioinform. https://doi.org/10.1002/cpbi.5

    Article  Google Scholar 

  39. Georgakis MK, Gill D, Webb AJS et al (2020) Genetically determined blood pressure, antihypertensive drug classes, and risk of stroke subtypes. Neurology 95(4):e353–e361. https://doi.org/10.1212/WNL.0000000000009814

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Gkatzionis A, Burgess S, Newcombe PJ (2021) Statistical methods for cis-Mendelian randomization. Genet Epidemiol. https://doi.org/10.48550/ARXIV.2101.04081

    Article  Google Scholar 

  41. EPIC- InterAct Consortium, Burgess S, Scott RA, Timpson NJ, Davey-Smith G, Thompson SG (2015) Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. Eur J Epidemiol 30(7):543–552. https://doi.org/10.1007/s10654-015-0011-z

    Article  PubMed Central  Google Scholar 

  42. Bowden J, Davey Smith G, Haycock PC, Burgess S (2016) Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol 40(4):304–314. https://doi.org/10.1002/gepi.21965

    Article  PubMed  PubMed Central  Google Scholar 

  43. Hartwig FP, Davey Smith G, Bowden J (2017) Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol 46(6):1985–1998. https://doi.org/10.1093/ije/dyx102

    Article  PubMed  PubMed Central  Google Scholar 

  44. Bowden J, Davey Smith G, Burgess S (2015) Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol 44(2):512–525. https://doi.org/10.1093/ije/dyv080

    Article  PubMed  PubMed Central  Google Scholar 

  45. Verbanck M, Chen CY, Neale B, Do R (2018) Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet 50(5):693–698. https://doi.org/10.1038/s41588-018-0099-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Burgess S, Thompson SG (2017) Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol 32(5):377–389. https://doi.org/10.1007/s10654-017-0255-x

    Article  PubMed  PubMed Central  Google Scholar 

  47. Yengo L, Sidorenko J, Kemper KE et al (2018) Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry. Hum Mol Genet 27(20):3641–3649. https://doi.org/10.1093/hmg/ddy271

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Kamat MA, Blackshaw JA, Young R et al (2019) PhenoScanner V2: an expanded tool for searching human genotype–phenotype associations. Bioinformatics 35(22):4851–4853. https://doi.org/10.1093/bioinformatics/btz469

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Freedland SJ, Aronson WJ (2004) Examining the relationship between obesity and prostate cancer. Rev Urol 6(2):73

    PubMed  PubMed Central  Google Scholar 

  50. Leggio M, Lombardi M, Caldarone E et al (2017) The relationship between obesity and hypertension: an updated comprehensive overview on vicious twins. Hypertens Res 40(12):947–963. https://doi.org/10.1038/hr.2017.75

    Article  PubMed  Google Scholar 

  51. Phillips MR, Kaiser P, Thabane L et al (2022) Risk of bias: why measure it, and how? Eye 36(2):346–348. https://doi.org/10.1038/s41433-021-01759-9

    Article  PubMed  Google Scholar 

  52. Sanidas E, Velliou M, Papadopoulos D et al (2020) Antihypertensive drugs and risk of cancer: between scylla and charybdis. Am J Hypertens 33(12):1049–1058. https://doi.org/10.1093/ajh/hpaa098

    Article  CAS  PubMed  Google Scholar 

  53. Phan NN, Wang CY, Chen CF, Sun Z, Lai MD, Lin YC (2017) Voltage-gated calcium channels: novel targets for cancer therapy. Oncol Lett 14(2):2059–2074. https://doi.org/10.3892/ol.2017.6457

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Moosmang S, Schulla V, Welling A et al (2003) Dominant role of smooth muscle L-type calcium channel Cav1.2 for blood pressure regulation. EMBO J 22(22):6027–6034. https://doi.org/10.1093/emboj/cdg583

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Berridge MJ, Lipp P, Bootman MD (2000) The versatility and universality of calcium signalling. Nat Rev Mol Cell Biol 1(1):11–21. https://doi.org/10.1038/35036035

    Article  CAS  PubMed  Google Scholar 

  56. Monteith GR, Davis FM, Roberts-Thomson SJ (2012) Calcium channels and pumps in cancer: changes and consequences. J Biol Chem 287(38):31666–31673. https://doi.org/10.1074/jbc.R112.343061

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Loughlin KR (2014) Calcium channel blockers and prostate cancer. Urol Oncol Semin Orig Investig 32(5):537–538. https://doi.org/10.1016/j.urolonc.2013.08.001

    Article  CAS  Google Scholar 

  58. Lawlor DA, Harbord RM, Sterne JAC, Timpson N, Davey SG (2008) Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med 27(8):1133–1163. https://doi.org/10.1002/sim.3034

    Article  PubMed  Google Scholar 

  59. Altman DG, Royston P (2006) The cost of dichotomising continuous variables. BMJ 332(7549):1080.1. https://doi.org/10.1136/bmj.332.7549.1080

    Article  Google Scholar 

  60. Royston P, Altman DG, Sauerbrei W (2006) Dichotomizing continuous predictors in multiple regression: a bad idea. Stat Med 25(1):127–141. https://doi.org/10.1002/sim.2331

    Article  PubMed  Google Scholar 

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Acknowledgements

The authors thank the UK Biobank investigators and participants.

Funding

NKs salary is funded by a research grant from the World Cancer Research Fund (IIG_FULL_2020_01). SJL is supported by a Cancer Research UK 25 (C18281/A29019) programme grant (the Integrative Cancer Epidemiology Programme).

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Authors and Affiliations

  1. MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK

    Nabila Kazmi & Sarah J. Lewis

  2. Population Health Sciences, Bristol Medical School, Bristol, UK

    Nabila Kazmi & Sarah J. Lewis

  3. Central Research Laboratory, Kazan State Medical University, Butlerov Str., 49, Tatarstan, Kazan, Russia, 420012

    Elena V. Valeeva & Denis Plotnikov

  4. Epidemiology and Evidence-Based Medicine Department, Kazan State Medical University, Kazan, Russia

    Gulshat R. Khasanova

  5. School of Optometry and Vision Sciences, Cardiff University, Cardiff, UK

    Denis Plotnikov

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  1. Nabila Kazmi
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  4. Sarah J. Lewis
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PRACTICAL Consortium

Contributions

All authors contributed to the study conception and design. Material preparation, data collection were performed by NK, EV, GK, SJL and DP. Analyses were performed by NK and DP. The first draft of the manuscript was written by NK and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Denis Plotnikov.

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Kazmi, N., Valeeva, E.V., Khasanova, G.R. et al. Blood pressure, calcium channel blockers, and the risk of prostate cancer: a Mendelian randomization study. Cancer Causes Control 34, 725–734 (2023). https://doi.org/10.1007/s10552-023-01712-z

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