Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Genetic proxies for calcium channel blockers and cancer: a Mendelian randomization study

Journal of Human Hypertension volume 37, pages 1028–1032 (2023)Cite this article

Subjects

Abstract

Calcium channel blockers (CCBs) are commonly prescribed antihypertensives. However, concerns exist about potential off-target effects on cancer. This Mendelian randomization (MR) study examined the associations of genetic proxies for CCBs with the risk of cancer. We used published genetic proxies in the target genes of CCBs as instruments, and obtained MR estimates by applying them to large studies of 17 site-specific cancers (non-Hodgkin lymphoma, melanoma, leukemia, thyroid, rectal, pancreatic, oral cavity/pharyngeal, kidney, esophagus/stomach, colon, bladder, endometrial, cervical and breast, prostate, lung and ovarian cancer) from the Pan-Cancer study, with replication for breast cancer (133,384 cases, 113,789 controls from the Breast Cancer Association Consortium), prostate cancer (79,148 cases, 61,106 controls from the Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome consortium), lung cancer (11,348 cases, 15,861 controls from the International Lung Cancer Consortium), and ovarian cancer (25,509 cases, 40,941 controls from the Ovarian Cancer Association Consortium). We used inverse variance weighting for the main analysis and the weighted median, MR-Egger and Mendelian Randomization Pleiotropy Residual Sum and Outlier as sensitivity analyses. Genetic proxies for CCBs were not associated with any cancer after Bonferroni-correction (at the threshold of p < 0.003). Associations were robust to different MR methods. In conclusion, our study suggests no association of genetic proxies for CCBs with 17 different cancers. While the findings add some support to the safety profile of CCBs in long-term use, future replication is necessary to provide definitive evidence.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Associations of genetic proxies for CCBs and 17 site-specific cancers.

Similar content being viewed by others

Data availability

The summary GWAS statistics can be downloaded from the websites: BCAC (http://bcac.ccge.medschl.cam.ac.uk/bcacdata/), OCAC (http://ocac.ccge.medschl.cam.ac.uk), PRACTICAL (http://practical.icr.ac.uk/blog/), ILCCO (https://ilcco.iarc.fr) consortia and the Pan-Cancer study (https://github.com/Wittelab/pancancer_pleiotropy). UK Biobank summary statistics (https://docs.google.com/spreadsheets/d/1kvPoupSzsSFBNSztMzl04xMoSC3Kcx3CrjVf4yBmESU/edit?ts=5b5f17db#gid=227859291).

Code availability

Available upon request.

References

  1. Zhou B, Bentham J, Di Cesare M, Bixby H, Danaei G, Cowan MJ, et al. Worldwide trends in blood pressure from 1975 to 2015: a pooled analysis of 1479 population-based measurement studies with 19·1 million participants. Lancet. 2017;389:37–55.

    Article  Google Scholar 

  2. Copland E, Canoy D, Nazarzadeh M, Bidel Z, Ramakrishnan R, Woodward M, et al. Antihypertensive treatment and risk of cancer: an individual participant data meta-analysis. Lancet Oncol. 2021;22:558–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Chen Q, Zhang Q, Zhong F, Guo S, Jin Z, Shi W, et al. Association between calcium channel blockers and breast cancer: a meta-analysis of observational studies. Pharmacoepidemiol Drug Saf. 2014;23:711–8.

    Article  PubMed  Google Scholar 

  4. Gandini S, Palli D, Spadola G, Bendinelli B, Cocorocchio E, Stanganelli I, et al. Anti-hypertensive drugs and skin cancer risk: a review of the literature and meta-analysis. Crit Rev Oncol Hematol. 2018;122:1–9.

    Article  PubMed  Google Scholar 

  5. Cao L, Zhang S, Jia C-M, He W, Wu L-T, Li Y-Q, et al. Antihypertensive drugs use and the risk of prostate cancer: a meta-analysis of 21 observational studies. BMC Urol. 2018;18:17.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Yang H, Yu Y, Hu X, Wang W, Yang X, Liu H, et al. Association between the overall risk of prostate cancer and use of calcium channel blockers: a systematic review and meta-analysis. Clin Ther. 2020;42:1715–27.

    Article  CAS  PubMed  Google Scholar 

  7. Grossman E. Antihypertensive therapy and the risk of malignancies. Eur Heart J. 2001;22:1343–52.

    Article  CAS  PubMed  Google Scholar 

  8. Wu L, Lin W, Liao Q, Wang H, Lin C, Tang L, et al. Calcium channel blocker nifedipine suppresses colorectal cancer progression and immune escape by preventing NFAT2 nuclear translocation. Cell Rep. 2020;33:108327.

    Article  CAS  PubMed  Google Scholar 

  9. Rotshild V, Azoulay L, Zarifeh M, Masarwa R, Hirsh-Raccah B, Perlman A, et al. The risk for lung cancer incidence with calcium channel blockers: a systematic review and meta-analysis of observational studies. Drug Saf. 2018;41:555–64.

    Article  CAS  PubMed  Google Scholar 

  10. Xie Y, Xu P, Wang M, Zheng Y, Tian T, Yang S, et al. Antihypertensive medications are associated with the risk of kidney and bladder cancer: a systematic review and meta-analysis. Aging. 2020;12:1545–62.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Suissa S. Immortal time bias in pharmaco-epidemiology. Am J Epidemiol. 2008;167:492–9.

    Article  PubMed  Google Scholar 

  12. Battistoni A, Tocci G, Coluccia R, Burnier M, Ruilope LM, Volpe M. Antihypertensive drugs and the risk of cancer: a critical review of available evidence and perspective. J Hypertens. 2020;38:1005–15.

    Article  CAS  PubMed  Google Scholar 

  13. Schooling CM, Freeman G, Cowling BJ. Mendelian randomization and estimation of treatment efficacy for chronic diseases. Am J Epidemiol. 2013;177:1128–33.

    Article  CAS  PubMed  Google Scholar 

  14. Smith GD, Ebrahim S. What can Mendelian randomisation tell us about modifiable behavioural and environmental exposures? BMJ. 2005;330:1076–9.

    Article  Google Scholar 

  15. Gill D, Georgakis MK, Koskeridis F, Jiang L, Feng Q, Wei WQ, et al. Use of genetic variants related to antihypertensive drugs to inform on efficacy and side effects. Circulation. 2019;140:270–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Zhao JV, Schooling CM. Using Mendelian randomization study to assess the renal effects of antihypertensive drugs. BMC Med. 2021;19:79.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Walker VM, Kehoe PG, Martin RM, Davies NM. Repurposing antihypertensive drugs for the prevention of Alzheimer’s disease: a Mendelian randomization study. Int J Epidemiol. 2020;49:1132–40.

    Article  PubMed  Google Scholar 

  18. Evangelou E, Warren HR, Mosen-Ansorena D, Mifsud B, Pazoki R, Gao H, et al. Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. Nat Genet. 2018;50:1412–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Fretheim A, Odgaard-Jensen J, Brørs O, Madsen S, Njølstad I, Norheim OF, et al. Comparative effectiveness of antihypertensive medication for primary prevention of cardiovascular disease: systematic review and multiple treatments meta-analysis. BMC Med. 2012;10:33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Rashkin SR, Graff RE, Kachuri L, Thai KK, Alexeeff SE, Blatchins MA, et al. Pan-cancer study detects genetic risk variants and shared genetic basis in two large cohorts. Nat Commun. 2020;11:4423.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12:e1001779.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Kvale MN, Hesselson S, Hoffmann TJ, Cao Y, Chan D, Connell S, et al. Genotyping Informatics and Quality Control for 100,000 Subjects in the Genetic Epidemiology Research on Adult Health and Aging (GERA) Cohort. Genetics. 2015;200:1051.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Zhang H, Ahearn TU, Lecarpentier J, Barnes D, Beesley J, Qi G, et al. Genome-wide association study identifies 32 novel breast cancer susceptibility loci from overall and subtype-specific analyses. Nat Genet. 2020;52:572–81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Schumacher FR, Al Olama AA, Berndt SI, Benlloch S, Ahmed M, Saunders EJ, et al. Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci. Nat Genet. 2018;50:928–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Wang Y, McKay JD, Rafnar T, Wang Z, Timofeeva MN, Broderick P, et al. Rare variants of large effect in BRCA2 and CHEK2 affect risk of lung cancer. Nat Genet. 2014;46:736–41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Phelan CM, Kuchenbaecker KB, Tyrer JP, Kar SP, Lawrenson K, Winham SJ, et al. Identification of 12 new susceptibility loci for different histotypes of epithelial ovarian cancer. Nat Genet. 2017;49:680–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Reicher-Reiss H. Calcium antagonists in heart failure. Eur Heart J. 1988;9:101–4.

    Article  PubMed  Google Scholar 

  28. Shah S, Henry A, Roselli C, Lin H, Sveinbjornsson G, Fatemifar G, et al. Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure. Nat Commun. 2020;11:163.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Bowden J, Davey, Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol. 2016;40:304–14.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44:512–25.

    Article  PubMed  PubMed Central  Google Scholar 

  31. 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;50:693–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Schooling CM, Lopez PM, Yang Z, Zhao JV, Au Yeung SL, Huang JV. Use of multivariable Mendelian randomization to address biases due to competing risk before recruitment. Front Genet. 2021;11:610852.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Schooling CM, Fei K, Terry MB. Reassessing the causal role of early-life adiposity in breast cancer: could the apparent inverse associations be a manifestation of survival bias? Int J Epidemiol. 2023:dyad027. https://doi.org/10.1093/ije/dyad027. Online ahead of print.

  34. Coleman CI, Baker WL, Kluger J, White CM. Antihypertensive medication and their impact on cancer incidence: a mixed treatment comparison meta-analysis of randomized controlled trials. J Hypertens. 2008;26:622–9.

    Article  CAS  PubMed  Google Scholar 

  35. Schooling CM. Selection bias in population-representative studies? A commentary on Deaton and Cartwright. Soc Sci Med. 2018;210:70.

    Article  PubMed  Google Scholar 

  36. Gill D, Georgakis MK, Walker VM, Schmidt AF, Gkatzionis A, Freitag DF, et al. Mendelian randomization for studying the effects of perturbing drug targets. Wellcome Open Res. 2021;6:16.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Koene RJ, Prizment AE, Blaes A, Konety SH. Shared risk factors in cardiovascular disease and cancer. Circulation. 2016;133:1104–14.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Burgess S, Davies NM, Thompson SG. Bias due to participant overlap in two-sample Mendelian randomization. Genet Epidemiol. 2016;40:597–608.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Minelli C, Del Greco MF, Van Der Plaat DA, Bowden J, Sheehan NA, Thompson J. The use of two-sample methods for Mendelian randomization analyses on single large datasets. Int J Epidemiol. 2021;50:1651–9.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Freeman G, Cowling BJ, Schooling CM. Power and sample size calculations for Mendelian randomization studies using one genetic instrument. Int J Epidemiol. 2013;42:1157–63.

    Article  PubMed  Google Scholar 

  41. Burgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Glymour MM, et al. Guidelines for performing Mendelian randomization investigations. Wellcome Open Res. 2020;4:186.

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank consortia and study participants for sharing the data.

Author information

Authors and Affiliations

  1. School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China

    Bohan Fan, C. Mary Schooling & Jie V. Zhao

  2. City University of New York, School of Public Health and Health Policy, New York, NY, USA

    C. Mary Schooling

Authors
  1. Bohan Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C. Mary Schooling
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jie V. Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

JVZ generated the idea and designed the study. BHF conducted the analysis and interpreted the results with the help of JVZ. BHF drafted the paper, JVZ and CMS critically revised the paper, and all authors reviewed and approved the final version.

Corresponding author

Correspondence to Jie V. Zhao.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fan, B., Schooling, C.M. & Zhao, J.V. Genetic proxies for calcium channel blockers and cancer: a Mendelian randomization study. J Hum Hypertens 37, 1028–1032 (2023). https://doi.org/10.1038/s41371-023-00835-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41371-023-00835-9

Search

Advanced search

Quick links