Abstract
Background: The prevalence rate of hypertension and breast cancer increases with advancing age. Renin-angiotensin system inhibitors (RASIs), β-blockers (BBs), calcium channel blockers (CCBs), and diuretics are widely used to treat patients with hypertension. Although, the association between the use of antihypertensive medication and breast cancer has been highly debated, recent evidence supporting this association remains controversial.
Objective: To evaluate the association between the use of antihypertensive medication and the risk of breast cancer and its prognosis.
Methods: This study was conducted using data from the PubMed, Embase, and Cochrane Library databases retrieved for the period from January 2000 to April 2021. Articles and their references were checked and summary effects were calculated using random- and fixed-effects models. Heterogeneity test and sensitivity analysis were also performed.
Results: This meta-analysis included 57 articles, which were all related to breast cancer risk or prognosis. Assessment of breast cancer risk using the pooled data showed that the use of BBs or CCBs or diuretics was associated with increased cancer risk [BB: relative risk (RR) = 1.20, 95% confidence interval (CI) = 1.09–1.32; CCBs: RR = 1.06, 95% CI 1.03–1.08; diuretics: RR = 1.06, 95% CI 1.01–1.11]. Long-term use of diuretic increased the risk of breast cancer (RR = 1.10, 95% CI 1.01–1.20), whereas long-term RASIs treatment reduced the risk (RR = 0.78, 95% CI 0.68–0.91). In addition, we found that diuretic users may be related to elevated breast cancer-specific mortality [hazard ratio (HR) = 1.18, 95% CI 1.04–1.33], whereas using other antihypertensive medications was not associated with this prognosis in patients with breast cancer.
Conclusion: Using CCBs, BBs, and diuretics increased the risk of breast cancer. In addition, diuretics may elevate the risk of breast cancer-specific mortality. The long-term use of RASIs was associated with a significantly lower breast cancer risk, compared with non-users. Thus, this analysis provides evidence to support the benefits of the routine use of RASIs in patients with hypertension, which has important public health implications.
Introduction
In 2017, hypertension was identified as a major risk factor for cardiovascular disease globally, accounting for 10.4 (9.39–11.5) million deaths and 218 (198–237) million disability-adjusted life-years (Stanaway et al., 2018). Fortunately, antihypertensive therapy significantly reduces the risk of cardiovascular disease and death in various populations. A meta-analysis showed that reducing the systolic blood pressure by 10 mm Hg would reduce the risk of major cardiovascular disease, coronary heart disease, stroke, and heart failure by 20, 17, 27, and 28%, respectively, whereas all-cause mortality was reduced by 13% (Ettehad et al., 2016). As an effective measure to control blood pressure, antihypertensive medications are commonly prescribed worldwide, and many patients take these drugs for a long period.
Several studies have found that breast cancer is associated with a variety of antihypertensive medications, including angiotensin-converting enzyme inhibitors/angiotensin receptor blockers (ACEIs/ARBs), β-blockers (BBs), calcium channel blockers (CCBs), and diuretics. However, these results remain controversial. Raebel et al. (2017) concluded that long-term use of CCBs was not related to breast cancer risk and long-term use of ACEIs might protect against breast cancer. The Chang et al. (2016) conducted a nested case-control study of 794,533 women and did not find any relationship between antihypertensive medication use and breast cancer risk. In contrast, other studies found that long-term treatment with CCBs was associated with a higher risk of breast cancer; however, no association was observed with the use of diuretics, BB, and renin-angiotensin system inhibitors (RASIs) (Li et al., 2013; Gomez-Acebo et al., 2016). A population-based case-control study demonstrated that treatment with ACEIs for more than 5 years increased breast cancer risk by 14% [odds ratio (OR) = 1.14, 95% confidence interval (CI) = 1.06–1.22] (Hallas et al., 2012).
A recent systematic review of observational studies concluded that BB reduced the risk of breast cancer recurrence (Caparica et al., 2021), whereas ACEIs and CCBs were not associated with breast cancer development (Zhao et al., 2017a; Naghibzadeh et al., 2020). In addition, the result of a meta-analysis suggested an association between CCBs use and breast cancer risk (Wright et al., 2017). However, uncertainty still exists regarding the effect of long-term use of antihypertensive medications and a potential association between different populations, breast cancer sub-types, or both.
This question remains controversial and, therefore, in this updated meta-analysis, we aimed to assess the association between the use of various classes of antihypertensive medication and breast cancer risk, prognosis [breast cancer-specific mortality, recurrence, overall survival (OS), and disease specific survival (DSS)].
Materials and Methods
Data Sources and Search Strategy
Supplementary Table S1 shows the strategy we used to conduct a comprehensive literature search of the PubMed, Embase, and Cochrane Library electronic databases without any restriction regarding geographical parameters and publication type or language. We reviewed research articles published over a span of nearly 20 years rather than those published in the 1990s, because we considered that the technique and treatment strategies that affect patient prognosis and survival have improved with time. The search was based on the framework of adult populations exposed to antihypertensive medication compared with non-users and the articles were evaluated for diagnosis or progress of breast cancer. Based on a combination of MeSH terms, keywords, and substance names, we conducted the following string search: “antihypertensive medication,” “calcium channel blockers,” “beta blockers,” “angiotensin-converting enzyme inhibitors,” “angiotensin receptor blockers,” “renin-angiotensin system inhibitors,” or “diuretics” combined with “breast cancer” or “breast carcinoma.” In addition, the reference lists of other reviews or meta-analyses were manually searched to identify additional related articles. This meta-analysis is not registered in the PROSPERO database, but the process is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Page et al., 2021). The PRISMA 2020 checklist was shown in Supplementary Table S2.
Selection Criteria
Studies fulfilling the following inclusion criteria were included: 1) the exposed group included individuals who had received antihypertensive medication; 2) the study had a comparative design: antihypertensive medication users vs. non-users; 3) the outcome was breast cancer risk, breast cancer-specific mortality, recurrence, or survival (DSS or OS); 4) the study reported the relative risk (RR), OR, or hazard ratio (HR) with corresponding 95% CIs or sufficient data to calculate these parameters. When the results of multiple studies were from the same database, the study including the largest number of participants was included. The following types of studies were excluded from this meta-analysis: meta-analyses, systematic or narrative reviews, case studies, experimental laboratory articles, conference abstracts, commentaries, randomized controlled trials, repeated publications, or if the reference group was administered another class of antihypertensive medication.
Data Extraction and Quality Assessment
Two of the authors (Yuxiu Xie and Men Wang) extracted data from the included studies and any disagreements between them were resolved by discussions with between the two other relevant co-authors. We extracted the following information from the selected articles: first author and year of publication, source of data, country, study design, duration of follow-up, study outcome, sample size of participants, and covariates used for the adjustment of confounders. The risk estimate adjusted for the largest number of confounding factors was extracted.
The nine-star Newcastle–Ottawa Scale (NOS) was used to assess the methodological quality of cohort studies and case-control studies (Stang, 2010; Ma et al., 2020). A score of ≥7 was considered to indicate high quality.
Statistical Analysis
We conducted this meta-analysis to assess the association between the use of antihypertensive medication and breast cancer risk and prognosis. The summary risk estimates are presented as forest plots. The I2 test was used to assess potential heterogeneity between individual studies, and an I2 > 50% indicated significant heterogeneity (Higgins et al., 2003). A fixed-effects model was used when there was no significant heterogeneity among the studies; whereas, the random-effects models was used when there was. To explore the source of heterogeneity and evaluate the potential impact of variables, we conducted subgroup analyses based on study design, country, and duration of antihypertensive medication use in years (e.g., <5, 5–10, ≥10 years). Articles with results that showed separate risk estimates based on different classes of CCBs or a different pathological pattern of breast cancer without a summary result were treated as different studies according to the result. Funnel plots were generated and examined visually, and gauged Egger’s tests were performed to assess publication bias (Egger et al., 1997). Sensitivity analyses were conducted excluding one study at a time to test the robustness of this association.
Results
Characteristics and Quality of Studies
A total of 842 potentially eligible studies were retrieved from the selected databases during the initial search. After removing duplicates and further screening the titles and abstracts, 26 studies on breast cancer risk (Meier et al., 2000; Li et al., 2003; Gonzalez-Perez et al., 2004; Fryzek et al., 2006; Largent et al., 2006; Davis and Mirick, 2007; Van Der Knaap et al., 2008; Coogan et al., 2009; Largent et al., 2010; Huang et al., 2011; Hallas et al., 2012; Lee et al., 2012; Mackenzie et al., 2012; Biggar et al., 2013; Li et al., 2013; Saltzman et al., 2013; Devore et al., 2015; Numbere et al., 2015; Azoulay et al., 2016; Chang et al., 2016; Gomez-Acebo et al., 2016; Wilson et al., 2016; Brasky et al., 2017; Raebel et al., 2017; Busby et al., 2018b; Zheng et al., 2021), and 30 studies on breast cancer prognosis (Powe et al., 2010; Barron et al., 2011; Ganz et al., 2011; Melhem-Bertrandt et al., 2011; Shah et al., 2011; Şendur et al., 2012; Holmes et al., 2013a; Holmes et al., 2013b; Botteri et al., 2013; Cardwell et al., 2013; Chae et al., 2013; Sorensen et al., 2013; Boudreau et al., 2014; Cardwell et al., 2014; Sakellakis et al., 2014; Babacan et al., 2015; Chen et al., 2015; Springate et al., 2015; Cardwell et al., 2016; Choy et al., 2016; Chen et al., 2017; Spera et al., 2017; Busby et al., 2018a; Musselman et al., 2018; Cui et al., 2019; Takada et al., 2019; Modi et al., 2020; Santala et al., 2020; Shih et al., 2020; Wei et al., 2020) and one study on both risk and prognosis (Sorensen et al., 2000) finally met the inclusion criteria of this meta-analysis. The study selection process is depicted in a flow chart (Figure 1) and the characteristics of the included studies are summarized in Table 1. The sample sizes of the studies included for breast cancer risk ranged from 654 to 2,300,000 with a total of 3,726,281 participants, and that for breast cancer prognosis varied from 218 to 73,170 with a total of 270,745 participants. Although the adjusted covariates of individual studies differed, most risk estimates were adjusted for age, body mass index (BMI), alcohol intake, and hormone replacement therapy use. The quality scores in this analysis ranged from 6 to 8 stars (Supplementary Table S3).
TABLE 1
| Author, Year | Country | Class of medication vs. reference group | Study period | Source of data | Participants | Design type | Outcomes | Adjustment/match for covariates | NOS |
|---|---|---|---|---|---|---|---|---|---|
| Azoulay et al. (2016) | United Kingdom | CCB vs. non | 1995–2010 | The United Kingdom General Practice Research Database | 27,315 | Cohort study | Risk | Age, year of cohort entry, excessive alcohol use, smoking status, body mass index, previous cancer, oophorectomy, use of antidiabetic drugs, aspirin, other nSaiDs, statins, hormone replacement therapy, oral contraceptives, use of angiotensin receptor blockers, angiotensin-converting enzyme inhibitors, beta-blockers, diuretics, and other antihypertensive drugs | 7 |
| Biggar et al. (2013) | Denmark | Diuretics vs. non | 1995–2010 | The Danish Register of Medicinal Products Statistics | 2300000 | Cohort study | Risk | Age and calendar-year | 6 |
| Brasky et al. (2017) | United States | CCB vs. non | 1993–2010 | Women’s Health Initiative | 28,561 | Case-control | Risk | Age, WHI-CT intervention assignment, education, race, BMI, physical activity, smoking, alcohol, and breast cancer screening | 7 |
| Busby et al. (2018a) | United Kingdom | CCB (verapamil) vs. non | 1995–2010 | The United Kingdom Clinical Practice Research Datalink | 90,294 | Case-control | Risk | Comorbidities (AIDS, cerebrovascular disease, chronic pulmonary disease, congestive heart disease, dementia, diabetes, diabetes with complications, ductal carcinoma in situ, hemiplegia, mild liver disease, moderate liver disease, myocardial infarction, peptic ulcer disease, peripheral vascular disease, renal disease, and rheumatological disease), confounder medications (aspirin, digoxin, hormone replacement therapy, metformin, oral contraceptive, and statin), deprivation, smoking status, alcohol consumption, and obesity, age, GP practice, and year of diagnosis | 7 |
| Chang et al. (2016) | China | BB, CCB, RASI vs. non | 2001–2011 | Taiwan National Health Insurance claims database | 46,985 | nested Case-control | Risk | socioeconomic status, diabetes mellitus, ischemic heart disease, myocardial infarction, heart failure, cerebrovascular disease, chronic kidney disease, chronic liver disease, chronic lung disease, depression, Charlson’s index, diuretics, human insulin, statin, fibrates, aspirin, hormone replacement therapy, number of lipid measurements, number of mammography, number of outpatient visits, number of hospitalizations, and length of hospital admission more than 7 days matched on age (within 5 years) and follow-up duration | 8 |
| Coogan et al. (2009) | United States | Diuretics vs. non | 1976–2007 | Boston, New York, Philadelphia and Baltimore | 11,493 | Case-control | Risk | Race (white, non-white), years of education, menopausal status, parity, body mass index, use of female hormones, use of oral contraceptives and alcohol use | 8 |
| Davis and Mirick (2007) | United States | BB, CCB vs. non | 2000–2001 | The Seattle metropolitan area | 1247 | Case-control | Risk | Parity, age at first pregnancy, mother/sister breast cancer, early double oophorectomy, oral contraceptive use, ever upper gastro-intestinal series, and ever smoker (all subjects); mother/sister breast cancer younger than age 45 and alcohol intake; hormone replacement therapy. 5-year age | 6 |
| Devore et al. (2015) | United States | BB, CCB, ACEI vs. non | 1988–2012 | The Nurses’ Health Study | 148668 | Cohort study | Risk | Age and body-mass index, height, oral contraceptive use, menopausal status, age at menopause, postmenopausal hormone use, parity and age at first birth, age at menarche, family history of breast cancer, history of benign breast disease, alcohol intake, physical activity level, smoking | 8 |
| Fryzek et al. (2006) | Denmark | BB, CCB, ACEI, ARB, Diuretics vs. non | 1990–2002 | The Central Population Register, The Danish Cancer Registry, The Pharmaco-Epidemiologic Prescription Database of North Jutland | 20,088 | Cohort study | Risk | Age, calendar period, HRT use, NSAID use, parity, and age at first birth | 7 |
| Gomez-Acebo et al. (2016) | Spain | BB, CCB, ACEI, ARB, Diuretics vs. non | 2008–2013 | The Multi Case-Control-Spain Study | 3631 | Case-control | Risk | Age, area of resident, education, body mass index, active smoking, alcohol intake, family history of breast cancer, age of menarche, age first full-term births, number of full-term births, menopausal status, hormonal therapy | 7 |
| Gonzalez-Perez et al. (2004) | United Kingdom | BB, CCB, ACEI, Diuretics vs. non | 1995–2001 | The United Kingdom General Practice Research Database | 23,708 | Nested case-control | Risk | Age, calendar year, BMI, alcohol intake, smoking status, HRT use, prior breast lump and/or biopsy, hypertension and all the variables in the table using logistic regression | 8 |
| Hallas et al. (2012) | Denmark | ACEI, ARB vs. non | 2000–2005 | The Danish Cancer Registry, the Danish National Registry of Patients, the Prescription Database of the Danish Medicines Agency and the Danish Person Registry | 97,573 | Case-control | Risk | A prior discharge diagnosis of chronic obstructive pulmonary disease as a crude marker of heavy smoking, a prior discharge diagnosis of inflammatory bowel disease, a modified Charlson Index that contains 19 categories of co-morbidity, match age and gender | 8 |
| Huang et al. (2011) | China | ARB vs. non | 1998–2007 | The Taiwan National Health Insurance database | 109344 | Cohort study | Risk | Age, gender, co-morbidities, and medications for hypertension control | 7 |
| Largent et al. (2010) | United States | CCB, ACEI, Diuretics vs. non | 1995–2006 | The California Teachers Study cohort | 118700 | Cohort study | Risk | Race, family history of breast cancer, age at first full-term pregnancy and number of full-term pregnancies combined variable, hormone therapy and menopausal status combined variable, lifetime physical activity, diabetes, body mass index, smoking history, alcohol use, hysterectomy, breastfeeding, and quartiles of percent calories from fat | 7 |
| Largent et al. (2006) | United States | Diuretics vs. non | 1994–1999 | The Cancer Surveillance Program of Orange County | 654 | Case-control | Risk | Age, body mass index (BMI), diabetes, smoking, alcohol use, menopausal status, family history of breast or ovarian cancer, age at first full-term pregnancy and education | 6 |
| Lee et al. (2012) | China | ACEI vs. non | 2002–2008 | The Taiwan National health Insurance Research Database | 67,388 | Case-control | Risk | Urbanization, income, diabetes mellitus, metformin usage, statin usage, estrogen usage, and progesterone usage | 7 |
| Li et al. (2003) | United States | BB, CCB, RASI, Diuretics vs. non | 1997–1999 | The westeren Washington State | 1982 | Case-control | Risk | Age | 7 |
| Li et al. (2013) | United States | BB, CCB, ACEI, ARB, Diuretics vs. non | 2000–2008 | The three-county Seattle-Puget Sound metropolitan area | 2763 | Case-control | Risk | Age, reference year, county, race/ethnicity, and recency of alcohol use | 8 |
| Mackenzie et al. (2012) | United Kingdom | Spironolactone vs. non | 1987–2010 | The United Kingdom General Practice Research Database | 85,452 | Cohort study | Risk | Age, calendar year of entry to study, Townsend score, use of combined oral contraceptive pill or hormone replacement therapy, history of benign breast disease, alcohol intake, body mass index, family history of breast cancer, use of drugs that may protect against breast cancer (aspirin, metformin), use of drugs causing gynecomastia (digoxin, finasteride, cimetidine, nifedipine), and history of hypertension, heart failure, or diabetes mellitus, socioeconomic score | 7 |
| Meier et al. (2000) | United Kingdom | BB, CCB, ACEI vs. non | 1992–1997 | The United Kingdom General Practice Research Database | 17,861 | Case-control | Risk | Smoking and body mass index, match age (same year of birth), physician practice, calendar date, and number of years of medical history | 7 |
| Numbere et al. (2015) | United Kingdom | BB, CCB vs. non | 1987–2012 | The Clinical Practice Research Datalink | 320251 | Case-control | Risk | Age, sex, smoking, alcohol use, and a number of potentially confounding comorbidities and coprescriptions | 7 |
| Raebel et al. (2017) | United States | CCB, ACEI vs. non | 1997–2012 | The Kaiser Permanente health-care system | 111882 | Cohort study | Risk | Age, body mass index, hysterectomy, diabetes, alcohol abuse, estrogen replacement, use of statins, mammography, and use of angiotensin receptor blockers, β-blockers, and/or diuretics, Kaiser Permanente site, race/ethnicity, education, and year of cohort entry, match Length of follow-up | 6 |
| Saltzman et al. (2013) | United States | BB, CCB, ACEI, Diuretics vs. non | 1989–2001 | Cardiovascular health study | 3389 | Cohort study | Risk | Age, alcohol use, income, age at menopause, waist-hip ratio | 7 |
| Van Der Knaap et al. (2008) | Netherlands | RASI vs. non | 1991–2004 | The Rotterdam Study | 7821 | Cohort study | Risk | Age, BMI, total pack-years, age at menarche and menopause, use of hormone-replacement therapy, number of children, diabetes mellitus, NSAID use, physical activity, hypertension, and myocardial infarction | 7 |
| Wilson et al. (2016) | United States | CCB vs. non | 2003–2013 | The Sister Study | 17,782 | Cohort study | Risk | Race/ethnicity, categorized body mass index, parity, age at menarche, menopause status, statin use, smoking status, hormone therapy use, and reported hours of physical activity per week | 8 |
| Zheng, G et al. (2021) | Sweden | BB (propranolol, metoprolol, atenolol and bisoprolol) vs. non | 2005–2014 | The Swedish Prescribed Drug Registry, The Swedish Cancer Registry | 38,282 | Cohort study | Risk | For propranolol was use of lipid-modifying agents, for metoprolol were family history of breast cancer, education level, income and use of cardiac therapy, antihypertensive, diuretics, calcium channel blockers, hormone replacement therapy and aspirin, for atenolol were family history of breast cancer, education level and use of antihypertensive, and for bisoprolol were use of cardiac therapy, diuretics, lipid-modifying agents and hormone replacement therapy, and personal history of chronic pulmonary disease | 7 |
| Babacan et al. (2015) | Turkey | RASI vs. non | 2005–2012 | Hacettepe Cancer Institute | 218 | Cohort study | OS, DFS | NA | 7 |
| Barron et al. (2011) | Ireland | BB (Propranolol, Atenolol) vs. non | 2001–2006 | The National Cancer Registry Ireland | 5801 | Case-control | BCSM | age, stage, grade, and comorbidity | 8 |
| Botteri et al. (2013) | Italy | BB vs. non | 1997–2008 | The European Institute of Oncology in Milan | 800 | Cohort study | BCSM, recurrence | Age, tumor stage, and treatment, peritumoral vascular invasion and use of other antihypertensive drugs, anti-thrombotics, and statins | 7 |
| Boudreau et al. (2014) | United States | ACEI, BB, CCB, Diuretics vs. non | 1990–2008 | The western Washington Cancer Surveillance System | 4216 | Cohort study | recurrence | All other medication classes of interest, age; diagnosis year; AJCC stage; hormone receptor status; primary treatment for initial breast cancer; endocrine therapy for the incident breast cancer); BMI; smoking; menopausal statu; Charlson co-morbidity score; diabetes; prescription non-steroidal anti-inflammatory medication use, Cox-2 inhibitors, and aspiri; and receipt of screening mammogram in the 12 months prior to events | 8 |
| Busby et al. (2018b) | United Kingdom | CCB vs. non | 1998–2012 | United Kingdom the Clinical Practice Research Datalink, and to death records from the Office for National Statistics | 23,669 | Cohort study | BCSM | Age at diagnosis, year of diagnosis, deprivation quintile, comorbidities, prior use of HRT or oral contraceptives, and treatment within six months of diagnosis | 7 |
| Cardwell et al. (2013) | United Kingdom | BB vs. non | 1998–2007 | United Kingdom clinical practice research datalink cohort | 7132 | nested Case-control | BCSM | Surgery within 6 months of diagnosis, chemotherapy within 6 months, radiotherapy within 6 months, tamoxifen, aromatase inhibitors, NSAID use, ACEI use, ARB use, statin use, hormone replacement therapy, comorbidities (pre-diagnosis, including myocardial infarction, cerebrovascular disease, congestive heart disease, chronic pulmonary disease, peripheral vascular disease, peptic ulcer disease and diabetes), and smoking, stage, restricted to individuals with an available stage record. match year and age at diagnosis | 8 |
| Cardwell et al. (2014) | United Kingdom | ARB, ACEI vs. non | 1998–2006 | United Kingdom clinical practice research datalink cohort | 8541 | Nested case-control | BCSM | Surgery, chemotherapy, radiotherapy, low dose aspirin, statins, comorbidities (myocardial infarction, cerebrovascular disease, congestive heart disease, chronic pulmonary disease, peripheral vascular disease, renal disease, peptic ulcer disease and diabetes), and smoking, tamoxife, aromatase inhibitors, hormone replacement therapy | 8 |
| Cardwell et al. (2016) | European | BB vs. non | 1998–2012 | The European Cancer Pharmacoepidemiology Network | 55,252 | Cohort study | BCSM | Age, year, stage and confounders | 7 |
| Chae et al. (2013) | United States | ACEI, ARB, BB vs. non | 1995–2007 | The MD Anderson Cancer Center | 1449 | Case-control | DSS, OS | Age, stage of disease, tumor grade, tumor subtype, LVI, and BMI | 7 |
| Chen et al. (2017) | United States | ACEI, ARB, BB, CCB, Diuretics vs. non | 2007–2011 | The linked Surveillance, Epidemiology and End-Results-Medicare database | 14,766 | Cohort study | BCSM, recurrence | Age at diagnosis, year of diagnosis, cancer stage, estrogen receptor status, receipt of complete first course treatment, receipt of chemotherapy, use of adjuvant hormone therapy, baseline diabetes, baseline hypertension at breast cancer diagnosis and use of other classes of antihypertensive medications | 7 |
| Chen et al. (2015) | United States | ACEI, BB, CCB, Diuretics vs. non | 1990–2005 | The Cancer Surveillance System | 1010 | Nested case-control | recurrence | adjuvant hormone therapy, radiation therapy and chemotherapy, match age, year of diagnosis, county, race/ethnicity, and cancer stage | 7 |
| Choy et al. (2016) | United States | BB vs. non | 2000–2010 | The City of Hope Cancer Center | 1029 | Cohort study | Recurrence | NA | 6 |
| Cui et al. (2019) | China | ACEI, ARB, BB, CCB, Diuretics vs. non | 1996–2000 | The Shanghai Women’s Health Study | 2891 | prospective study | OS, DSS | Age at diagnosis, sex, education, annual family income, body mass index, alcohol consumption, cigarette smoking status, Charlson Comorbidity Index score, year of cancer diagnosis, cancer stage, and cancer treatment. In model 2, we added a 6-month lag period into defining exposure in order to address potential reverse causation | 7 |
| Ganz et al. (2011) | United States | ACEI, BB vs. non | 1997–2010 | The LACE Study cohort | 1779 | Cohort study | BCSM, recurrence,OS | Age at diagnosis, race, stage of disease, pre-diagnosis BMI, adjuvant treatment, hormone receptor status, tamoxifen use, and self-reported hypertension and diabetes | 7 |
| Holmes et al. (2013a) | United States | ACEI, BB vs. non | 1988–2008 | The Nurses’ Health Study | 4661 | Cohort study | BCSM | Calendar year, disease stage, smoking status, body mass index, age at first birth and parity, oral contraceptive use, menopausal status and use of hormone replacement, radiation treatment, systemic treatment with chemotherapy and/or hormonal therapy | 8 |
| Holmes et al. (2013b) | Canada | ACEI, ARB, BB, CCB, Diuretics vs. non | 2004–2010 | Provincial Cancer Registry data | 4019 | Retrospective cohort study | BCSM | Age, stage at diagnosis, history of previous cancer, and urban/rural residence | 7 |
| Melhem-Bertrandt et al. (2011) | United States | ACEI, ARB, BB vs. non | 1995–2007 | The Breast Cancer Management System Database | 1413 | Cohort study | OS,RFS | Age, race, stage, grade, receptor status, lymphovascular invasion, body mass index, diabetes, hypertension, and angiotensin-converting enzyme inhibitor use | 7 |
| Modiet al. (2020) | Australia | BB vs. non | – | Clinical trials EMILIA, TH3RESA, MARIANNE, and CLEOPATRA | 2777 | Retrospective study | OS | Age, Race, BMI, Albumin count, ECOG PS, ER/PR Status, Presence of Visceral Disease and Brain Metastasis, Arrhythmia, Coronary Artery Disease, Heart Failure, Hypertension, Cerebrovascular disease, Diabetes and Other Cardiovascular diseases, Prior treatment to anthracyclines, taxanes and trastuzumab | 8 |
| Musselman et al. (2018) | Canada | BB vs. non | 2002–2010 | The Institute for Clinical and Evaluative Sciences | 4876 | Cohort study | BCSM | Statin use and socioeconomic status | 7 |
| Powe et al. (2010) | United Kingdom | BB vs. non | 1987–1994 | The Nottingham City Hospital | 466 | Cohort study | DSS, recurrence | Adjuvant therapy and age | 6 |
| Sakellakis et al. (2014) | Greece | BB vs. non | 1983–2013 | Department of Medicine, University Hospital Patras Medical School | 610 | Retrospective study | DFS | Age, tumor stage, hormone receptor status and HER2 status | 6 |
| Şendur et al. (2012) | Turkey | ARB vs. non | 2004–2011 | Department of Medical Oncology, Ankara Numune Education and Research Hospital | 486 | Cohort study | OS, DFS | NA | 7 |
| Shah et al. (2011) | United Kingdom | BB vs. other AHT | 1997–2007 | The Doctors Independent Network Database | 3462 | Cohort study | OS | Age, gender, year of diagnosis, smoking status, number of medications received in the year before diagnosis, area deprivation and national region | 7 |
| Shih et al. (2020) | China | CCB vs. non | 2007–2015 | The National Health Insurance Research Database | 4840 | Case-control | recurrence | Demographic characteristics, comorbidities, and tumor-node-metastasis stage, age, monthly income, geographic region, urbanization level, hypertension, hyperlipidemia, diabetes, chronic kidney disease, tumor-node-metastasis (TNM) classification of malignant tumors, and index date | 7 |
| Santala et al. (2020) | Finland | ACEI, ARB, BB, CCB, Diuretics vs. non | 1995–2013 | Finnish Cancer Registry | 73,170 | Cohort study | BCSM | Age at diagnosis, tumor extent, charlson-comorbidity index, primary treatment of breast cancer, obesity, participation in national screening program and use of hormone-receptor antagonists after breast cancer diagnosis | 7 |
| Sorensen et al. (2013) | Denmark | ACEI, ARB, BB vs. non | 1996–2003 | The Danish Breast Cancer Cooperative Group registry | 18,733 | Cohort study | recurrence | Age at diagnosis, menopausal status at diagnosis, UICC stage, histologic grade, ER status and receipt of adjuvant endocrine therapy, receipt of adjuvant chemotherapy, type of primary surgery received, Charlson comorbidity index, prediagnosis combination HRT, and coprescriptions of any BB, ACEI, ARB, aspirin, and simvastatin | 8 |
| Spera et al. (2017) | Canada | BB vs. non | NA | The ROSE/TRIO-012 study, BCIRG-005 data | 1144 | Retrospective study | PFS, OS | Age, Eastern Cooperative Oncology Group performance status and menopausal status | 7 |
| Springate et al. (2015) | United Kingdom | BB vs. other AHT | 1997–2006 | The Clinical Practice Research Datalink (CPRD) and Doctors’ Independent Network (DIN) | 14,964 | Cohort study | BCSM | Patient age, gender, year of diagnosis, smoking status, number of medications received in year prior to diagnosis, Regional Health Authority, and practice postcode Index of Multiple Deprivation, cancer site prevalence, year of diagnosis and area deprivation | 7 |
| Sorensen et al. (2000) | Denmark | CCB vs. non | 1989–1995 | The Danish Cancer Registry, and the Danish Death Registry | 23,167 | Cohort study | Risk, BCSM | NA | 6 |
| Takada et al. (2019) | Japan | CCB, RASI vs. non | 2007–2018 | The Osaka City University Hospital | 338 | Retrospective study | DFS, OS | NA | 7 |
| Wei et al. (2020) | United States | Diuretics vs. non | 2005–2017 | Humana Insurance database | 1492 | Retrospective study | recurrence | Age, spironolactone, alopecia, acne vulgaris, hirsutism, hypertension, congestive heart failure, primary aldosteronism, nephrotic syndrome, ascites, alcohol abuse, smoking, illicit drug abuse, and insurance plan | 7 |
Characteristics of the articles included in the meta-analysis.
Abbreviations: NOS, Newcastle-Ottawa Scale; ACEI, angiotensin-converting enzyme inhibitors; ARB, angiotensin II receptor blockers; CCB, calcium-channel blockers; BB, beta-blockers; RASI, renin-angiotensin system inhibitors; BCSM, breast cancer-specific mortality; OS, overall survival; DSS, disease-specific survival; DFS, disease-free survival; NA, Not Available.
Association of Antihypertensive Medication Use with Breast Cancer Risk
Pooled data from 18 studies related to breast cancer risk showed that the use of BBs was associated with increased cancer risk (RR = 1.20, 95% CI 1.09–1.32; Figure 2A and Table 2). A subgroup analysis showed a significant association with case-control (RR = 1.09, 95% CI 1.05–1.12), nested case-control (RR = 1.11, 95% CI 1.03–1.20) studies, and cohort (RR = 1.43, 95% CI 1.07–1.91) studies. In addition, a significant association was observed in Europe (RR = 1.41, 95% CI 1.16–1.70) and Asia (RR = 1.12, 95% CI 1.02–1.23), but not in North America (RR = 1.02, 95% CI 0.94–1.11). However, subgroup analysis of the duration of administration in years showed that the use of BBs even for ≥10 years was not associated with breast cancer risk.
FIGURE 2
TABLE 2
| Comparison | BB vs. non | CCB vs. non | Diuretics vs. non | RASI vs. non | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Category | N | RR (95%CI) | I2 (%) | P | N | RR (95%CI) | I2 (%) | P | N | RR (95%CI) | I2 (%) | P | N | RR (95%CI) | I2 (%) | P |
| Risk | 19 | 1.20 (1.09–1.32) | 32.4 | 0.116 | 20 | 1.06 (1.03–1.08) | 35.0 | 0.062 | 16 | 1.06 (1.01–1.11) | 55.8 | 0.003 | 24 | 0.98 (0.93–1.04) | 50.3 | 0.003 |
| Study design | ||||||||||||||||
| Case-control study | 7 | 1.09 (1.05–1.12) | 0.0 | 0.455 | 9 | 1.09 (1.05–1.13) | 7.7 | 0.371 | 6 | 1.04 (0.92–1.18) | 39.3 | 0.144 | 12 | 1.03 (0.97–1.09) | 40.0 | 0.074 |
| Cohort study | 9 | 1.43 (1.07–1.91) | 0.0 | 0.457 | 9 | 1.01 (0.97–1.05) | 0.0 | 0.648 | 9 | 1.06 (1.00–1.13) | 66.7 | 0.002 | 10 | 0.89 (0.80–1.01) | 48.6 | 0.041 |
| Nest case-control study | 2 | 1.11 (1.03–1.20) | 0.0 | 0.837 | 2 | 1.18 (1.03–1.36) | 80.8 | 0.022 | 1 | 1.10 (0.95–1.27) | – | – | 2 | 0.99 (0.89–1.10) | 0.0 | 0.440 |
| Geographic area | ||||||||||||||||
| North America | 8 | 1.02 (0.94–1.11) | 17.5 | 0.292 | 11 | 1.08 (1.02–1.14) | 0.0 | 0.661 | 10 | 1.03 (0.97–1.10) | 35.8 | 0.122 | 11 | 0.96 (0.88–1.05) | 24.9 | 0.206 |
| Europe | 9 | 1.41 (1.16–1.70) | 0.0 | 0.434 | 8 | 1.04 (1.01–1.07) | 45.3 | 0.077 | 6 | 1.10 (1.04–1.17) | 47.5 | 0.090 | 9 | 1.07 (1.00–1.13) | 5.8 | 0.387 |
| Asia | 1 | 1.12 (1.02–1.23) | – | – | 1 | 1.39 (1.14–1.69) | – | – | 4 | 0.93 (0.84–1.04) | 79.1 | 0.002 | ||||
| Duration of use | ||||||||||||||||
| <5 years | 9 | 1.05 (0.99–1.11) | 0.0 | 0.49 | 11 | 1.04 (0.93–1.16) | 50.7 | 0.027 | 9 | 1.04 (0.98–1.11) | 0.0 | 0.901 | 14 | 0.94 (0.90–0.99) | 0.0 | 0.736 |
| 5–10 years | 10 | 0.98 (0.89–1.07) | 0.0 | 0.908 | 12 | 1.09 (0.99–1.19) | 0.0 | 0.854 | 10 | 1.03 (0.90–1.19) | 58.1 | 0.011 | 18 | 0.98 (0.86–1.11) | 67.0 | 0.000 |
| ≥10 years | 7 | 1.08 (0.91–1.29) | 59.0 | 0.023 | 8 | 1.05 (0.72–1.53) | 62.9 | 0.009 | 7 | 1.10 (1.01–1.20) | 0.0 | 0.652 | 10 | 0.78 (0.68–0.91) | 0.0 | 0.563 |
The results of the association between antihypertensive medication use and breast cancer risk.
Abbreviations: BB, beta-blockers; CCB, calcium-channel blockers; RASI, renin-angiotensin system inhibitors; RR, relative risk; CI, confidence intervals; N, number of study.
Twenty studies reported the relationship between CCBs and breast cancer risk (Figure 2B; Table 2). The pooled RR of breast cancer risk for CCBs use vs. non-use was 1.06 (95% CI 1.03–1.08), indicating a significant positive association. Case-control studies (RR = 1.09, 95% CI 1.05–1.13), nested case-control studies (RR = 1.18, 95% CI 1.03–1.36), and every area (North America: RR = 1.08, 95% CI 1.02–1.14; Europe: RR = 1.04, 95% CI 1.01–1.07; Asia: RR = 1.39, 95% CI 1.14–1.69) showed a positive association. There was no duration-response association between long-term use of CCB and breast cancer risk.
As shown in Figure 2C, exposure to diuretics (16 studies) showed a significant association where the pooled RR was 1.06 (95% CI 1.01–1.11). A significant relationship was noted in Europe (RR = 1.10, 95% CI 1.04–1.17) but not in North America (RR = 1.03, 95% CI 0.97–1.10). Notably, a significant association was observed with long-term use of diuretics (≥10 years; RR = 1.10, 95% CI 1.01–1.20, Table 2).
A total of 24 studies reported a connection between RASIs exposure of any duration and breast cancer risk. RASIs did not increase the risk of breast cancer with a pooled RR (0.98, 95% CI 0.93–1.04), as shown in Figure 2D. However, it is worth noting that RASIs played a protective role in breast cancer when studies were restricted to those where the duration of RASI use was <5 years (RR = 0.94, 95% CI 0.90–0.99) and ≥10 years (RR = 0.78, 95% CI 0.68–0.91). Fifteen and seven studies were related to ACEI and ARB use, respectively, and the results suggested that use of ACEI or ARB was not related to breast cancer risk (ACEI: RR = 0.99, 95% CI 0.93–1.04; ARB: RR = 0.98, 95% CI 0.81–1.19, Table 2).
Association of Antihypertensive Medication Use with Breast Cancer Prognosis Breast Cancer-Specific Mortality
Twenty-nine independent studies examined the association between antihypertensive medication use and breast cancer-specific mortality, with a pooled HR of 1.04 (95% CI 0.98–1.10). When HRs for the individual antihypertensive medication (BBs, CCBs, and RASIs) were calculated in subgroup analyses, no significant results were observed (Figure 3A). However, a significant association was observed in diuretic users (HR = 1.18, 95% CI 1.04–1.33).
FIGURE 3
OS
Twenty-one studies revealed no significant link between antihypertensive medication use and breast cancer OS (HR = 1.05, 95% CI 0.96–1.14, Figure 3B). Furthermore, a subgroup analysis of the classes of antihypertensive medication showed no significant association was observed.
Recurrence
In total, 27 studies reported breast cancer recurrence with the use of antihypertensive medication. However, as shown in Figure 3C, no effect was observed for any type of antihypertensive medication (HR = 1.00, 95% CI 0.92–1.09).
DSS
The results of the meta-analyses on DSS are summarized in forest plots for all 10 studies; no association was found between antihypertensive medication use and breast cancer DSS (HR = 0.82, 95% CI 0.63–1.08, Figure 3D).
Risk of Publication Bias
The results of the Begg’s funnel plot and Egger’s regression test, showed no evidence of potential publication bias in articles reporting the use of BBs (P = 0.168), CCBs (P = 0.151), diuretics (P = 0.348), or RASIs (P = 0.397) in relation to the risk of breast cancer risk (Supplementary Figure S1). In addition, a potential publication bias was observed in studies on the use of antihypertensive medication with recurrence (P = 0.001), but not in other studies on prognosis (mortality: P = 0.717; OS: P = 0.096; DSS: P = 0.125) (Supplementary Figure S2).
Sensitivity Analysis
A sensitivity analysis was performed as shown in Supplementary Figures S3 and S4. There was no significant alteration in the pooled RRs or HRs after sequentially deleting a single study from the overall pooled analyses each time. In the sensitive analysis of diuretics users with risk of breast cancer, the pooled RR was not statistically significant after the deletion of an article (RR = 1.03, 95% CI 0.98–1.08) (Biggar et al., 2013).
Discussion
Our meta-analysis showed that the use of BBs, CCBs, or diuretics was associated with a moderate increase in the risk of breast cancer. Diuretic therapy for ≥10 years could increase breast cancer risk by 10%. Long-term use of RASIs exerted a protective effect against breast cancer, especially when used for ≥10 years. In addition, this meta-analysis indicated that diuretics are related to elevated breast cancer-specific mortality, but antihypertensive medication use did not affect recurrence, OS, or DSS.
Our conclusion that BBs were associated with increased breast cancer risk was consistent with this highly influential and large studies (Numbere et al., 2015; Chang et al., 2016; Zheng et al., 2021). The prospective study data minimizes the recall bias and in the study in question, some important confounders such as alcohol status, smoking status, BMI, medication and comorbidities, were adjusted. Similarly, the positive association between diuretics and breast cancer risk mainly depended on the study that evaluated cancer risk in 2.3 million Danish women who were followed-up for 28.8 million person-years (Biggar et al., 2013). The study reported that breast cancer risk increased in the first-year of diuretic use, but the association was attributed to reverse causality, such as where symptoms of cancer, including abdominal swelling or incidental findings such as hypertension, led to diuretic exposure. To exclude care practices that affect the risk from true etiologic impact, the study further examined risk only in women who were exposed to drugs for more than one year prior to diagnosis, and found a slightly increased risk of breast cancer with increasing duration of diuretic exposure. Consistent with its results, we observed a duration-response association between the duration of diuretic therapy ≥10 years and breast cancer risk. Studies found that thiazide diuretics, but not other diuretics, were linked to an increasing risk of breast cancer, and the authors considered the increase in insulin resistance due to thiazide diuretics as a possible explanation (Sarafidis et al., 2007; Ni et al., 2017). A recent study showed that the use of furosemide or other diuretics before breast cancer diagnosis increased the risk of breast cancer-related death (Santala et al., 2020). Our results showed that the breast cancer mortality increased with the use of diuretics; however, we failed to analyze the effect of diuretic subtypes on breast cancer prognosis because there were few relevant studies. Thus, we could not arrive at the conclusion that different subclass of diuretics had varying associations with breast cancer. Future studies are warranted to determine the potential difference among various diuretics.
Our meta-analysis showed a positive association between CCBs use and the risk of breast cancer, which was consistent with the results of previous studies (Wright et al., 2017; Busby et al., 2018a). The elevated levels of cytosolic calcium could affect the process of apoptosis through different signaling pathways such as caspase activation, induction of endonuclease activity, or the miRNA-524-5p-BRI3-extracellular signal-regulated kinase (Erk) pathway (Iwasawa et al., 2011; Guo et al., 2014; Zhao et al., 2017b). CCBs blocks calcium entry, inhibiting this vital process, which could destroy the body’s natural defense mechanism against cancer growth and promote cell survival by initiating autophagy (Sun et al., 2018). However, there is no conclusive evidence to accurately explain this observed link and the benefits of the long-term use of CCBs are controversial.
The association between RASIs and breast cancer remains controversial with some studies reporting that ARB/ACEI was not related to breast cancer risk (Teo, 2011; Datzmann et al., 2019), whereas another concluded that users of ACEIs had an increased risk (Hallas et al., 2012). However, our result demonstrated a significant (22%) reduction in breast cancer risk with ≥10 years, which is consistent with previously published observational studies (Huang et al., 2011; Li et al., 2013; Wang et al., 2013; Ni et al., 2017). In addition, in vivo and in vitro studies on RASIs have demonstrated a protective anti-inflammatory, anti-proliferative, and pro-apoptotic effect on breast cancer (Herr et al., 2008; George et al., 2010). Although the effect of RASIs on breast cancer is still contradictory, RASIs should be selected more often for patients undergoing antihypertensive therapy considering its availability, feasibility, and established safety with reduced breast cancer risk.
It is important to assess the possible source of heterogeneity. First, the significance of our results might have been affected by an indication bias, such as high blood pressure (Coogan, 2013), as both hypertension and cancer are associated with obesity, alcohol, smoking, cardiometabolic abnormalities, and other comorbidities. One study reported a relationship between hypertension and cancer (Han et al., 2017). In addition, in this meta-analysis, not all included studies adjusted for the confounding factor of hypertension which would cause a certain degree of heterogeneity. Second, in observational studies of the general population, it is important to determine a dose or duration-response relationship to properly interpret the potential link between drug exposure and cancer development. However, because of the differences in drug exposure time and dose between the included studies, the results will eventually be heterogeneous. Third, most of studies were from North America or Europe but a few studies are from Asia, which produced regional heterogeneity and limited the extent to which the results can be generalized. In the subgroup analysis according to geographical area, we found reduced between-study heterogeneity and observed different results, which indicated that the association varied among different areas. Finally, because the included studies were retrieved from some population registration database, the analysis results could reduce recall bias and misclassification bias to some extent. However, population-based registries also have some shortcomings, such as the limitation of potential confounders by stored data, and the fact that a prescription was collected does not necessarily indicate that the prescribed drugs were taken.
Our meta-analysis provides convincing and clear evidence of the relationship between antihypertensive drugs and the risk and prognosis of breast cancer. One strength of our study is that we conducted a stratified analysis according to the duration of antihypertensive drug use and found that using diuretics for ≥10 years could increase the risk of breast cancer, but using RASIs for ≥10 years would reduce the risk of breast cancer. This suggests that RASIs would be the best choice for patients with long-term antihypertensive drugs. Another strength of the study is that we focused on breast cancer risk and prognosis to obtain a more homogeneous group of studies. We studied the possible source of heterogeneity between studies, including study design, geographic area, and duration of antihypertensive medication use. It is noteworthy that there were also some limitations to this study. First, biases such as confounders, possible detection bias, and selection bias is inevitable in observational studies. Secondly, subgroup analyses based on age, sex, pathology of breast cancer, dose of antihypertensive medication, subclass of BB (selective BBs and non-selective BBs), CCBs (dihydropyridines and non-dihydropyridines), and diuretics (thiazide, loop, and potassium sparing diuretics) were not conducted because of the lack of relevant studies. Thirdly, our current summary evidence did not support a clear relation between long-term use of CCBs or BBs and breast cancer risk. Finally, significant heterogeneity was observed among studies focused on antihypertensive medication use and breast cancer risk and this persisted even when the data were stratified into subgroups.
The results of this meta-analysis demonstrated that BBs, CCBs, and diuretics were significantly associated with breast cancer risk. Unexpectedly, a beneficial effect of long-term use of RASI was observed against breast cancer risk. Additionally, according to the analysis of prognosis, no association was observed with any class of antihypertensive medication. Considering the limitations in this meta-analysis, further research is needed to fully clarify the association between antihypertensive medication use and breast cancer risk.
FIGURE 1
Statements
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Author contributions
All authors read, critically reviewed and approved the final manuscript. YX and MW conducted the database searches, screened titles, abstracts and full-texts for eligibility, PX, YD, YZ, and SY performed study quality assessments. JC and ZD planed and designed the research; DZ, NL, and NW provided methodological support/advice; PX and YD tested the feasibility of the study; YX, MW, and DZ extract data; YX performed the statistical analysis; YX and MW wrote the manuscript.
Acknowledgments
We thank all members of our study team for their wonderful cooperation and the original authors of the included studies for their results.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2021.609901/full#supplementary-material
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Summary
Keywords
antihypertensive drugs, breast cancer, meta-analysis, prognosis, risk
Citation
Xie Y, Wang M, Xu P, Deng Y, Zheng Y, Yang S, Wu Y, Zhai Z, Zhang D, Li N, Wang N, Cheng J and Dai Z (2021) Association Between Antihypertensive Medication Use and Breast Cancer: A Systematic Review and Meta-Analysis. Front. Pharmacol. 12:609901. doi: 10.3389/fphar.2021.609901
Received
26 October 2020
Accepted
29 April 2021
Published
13 May 2021
Volume
12 - 2021
Edited by
Xian-Tao Zeng, Wuhan University, China
Reviewed by
Jinhong Zhu, Harbin Medical University Cancer Hospital, China
Jiang-Bo Liu, The First Affiliated Hospital of Henan University of Science and Technology, China
Updates
Copyright
© 2021 Xie, Wang, Xu, Deng, Zheng, Yang, Wu, Zhai, Zhang, Li, Wang, Cheng and Dai.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Zhijun Dai, dzj0911@126.com; Jing Cheng, chenjin1118@hotmail.com
†These authors have contributed equally to this work
This article was submitted to Pharmacology of Anti-Cancer Drugs, a section of the journal Frontiers in Pharmacology
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