Abstract
Using time-dependent Cox regression models, we examined associations of common antihypertensive medications with overall cancer survival (OS) and disease-specific survival (DSS), with comprehensive adjustment for potential confounding factors. Participants were from the Shanghai Women’s Health Study (1996–2000) and Shanghai Men’s Health Study (2002–2006) in Shanghai, China. Included were 2,891 incident breast, colorectal, lung, and stomach cancer cases. Medication use was extracted from electronic medical records. With a median 3.4-year follow-up after diagnosis (interquartile range, 1.0–6.3), we found better outcomes among users of angiotensin II receptor blockers with colorectal cancer (OS: adjusted hazard ratio (HR) = 0.62, 95% confidence interval (CI): 0.44, 0.86; DSS: adjusted HR = 0.61, 95% CI: 0.43, 0.87) and stomach cancer (OS: adjusted HR = 0.62, 95% CI: 0.41, 0.94; DSS: adjusted HR = 0.63, 95% CI: 0.41, 0.98) and among users of β-adrenergic receptor blockers with colorectal cancer (OS: adjusted HR = 0.50, 95% CI: 0.35, 0.72; DSS: adjusted HR = 0.50, 95% CI: 0.34, 0.73). Better survival was also found for calcium channel blockers (DSS: adjusted HR = 0.67, 95% CI: 0.47, 0.97) and diuretics (OS: adjusted HR = 0.66, 95% CI: 0.45, 0.96; DSS: adjusted HR = 0.57, 95% CI: 0.38, 0.85) with stomach cancer. Our findings suggest angiotensin II receptor blockers, β-adrenergic receptor blockers, and calcium channel blockers might be associated with improved survival outcomes of gastrointestinal cancers.
Keywords: antihypertensive medications, cancer, disease-specific survival, immortal time bias, overall survival
Hypertension is one of the most common comorbidities among cancer patients due to their shared risk factors and the cardiotoxic effects of certain anticancer treatments (1–3). Widely used antihypertensive medications include angiotensin I-converting enzyme inhibitors (ACEIs), angiotensin II receptor blockers (ARBs), calcium channel blockers (CCBs), diuretics, and β-adrenergic receptor blockers (β-blockers) (4). Recently, potential impacts of antihypertensive medications on cancer prognostic outcomes have drawn great attention, particularly with respect to medications that target the renin-angiotensin system (RAS), including ARBs and ACEIs, and those that target the sympathetic nervous system, such as β-blockers.
Angiotensin II is the major bioactive product of the RAS, playing an important role in the regulation of arterial blood pressure and cell proliferation (5). Studies have shown that tumor cells with activated RAS respond to angiotensin II stimulation by expression and/or secretion of various molecules, including interleukin-8, granulocyte-macrophage colony-stimulating factor, vascular endothelial growth factor, monocyte chemotactic protein 1, tissue inhibitor of metalloproteinase 1, and hypoxia-inducible factors HIF1α and HIF2α (6). Inflammatory cells in tumor microenvironments have also been shown to express RAS components and respond to angiotensin II stimulation by increasing secretion of interleukin-1α, interleukin-6, interleukin-8, monocyte chemotactic protein 1, and macrophage colony-stimulating factor (6).
The β-adrenergic signaling pathway mediates sympathetic nervous system–induced stress responses and modulates multiple cellular processes (7, 8). Three subtypes of the β-adrenergic receptor (β1, β2, and β3), have been identified at several tumor sites, including the breast, colon, lung, and stomach in humans (9, 10). Recent studies have shown that β-adrenergic signaling regulates multiple cellular processes, including inflammation, angiogenesis, apoptosis/anoikis, cell motility and trafficking, activation of tumor-associated viruses, DNA damage repair, cellular immune response, and epithelial-mesenchymal transition (7, 8).
Although several lines of evidence have shown certain classes of antihypertensive medications possessing potential antitumor properties, suggesting a rationale for use of antihypertensive medications in clinical oncology, large population-based studies examining the association between antihypertensive medication use and survival outcomes are limited, and results have been mixed (11–19). The inconsistencies might be attributable to multiple factors, such as a difference in study populations, study design, sample size, and cancer types/sites, and the influence of potential confounding factors, including concurrent use of other medications (20). Additionally, the potential influence of immortal time bias (referring to a period of cohort follow-up or observation time during which death cannot occur) has been a major concern for many early studies (21).
In this study, we comprehensively examined associations of commonly used classes of antihypertensive medications (ARBs, ACEIs, CCBs, diuretics, and β-blockers) with survival from breast, colorectal, lung, and stomach cancers, using data from 2 large, prospective cohort studies in Shanghai, China: the Shanghai Women’s Health Study (SWHS) and the Shanghai Men’s Health Study (SMHS).
METHODS
Study population
Details of the SWHS and SMHS have been previously described (22, 23). Briefly, between 1996 and 2000 (SWHS) and 2002 and 2006 (SMHS), 75,221 women aged 40–70 years and 61,480 men aged 40–74 years, who were permanent residents of the Shanghai Changning District, were enrolled in the studies and completed in-person interviews, with respective response rates of 92.7% and 74.0%. Cohort members are followed through in-person follow-up surveys administered every 2–4 years, and annual record linkage with the Shanghai Cancer Registry and Vital Statistics Registry data. Cancer diagnoses are verified through home visits and medical chart review. Information on cancer stage (TNM: tumor size, number of lymph nodes, and whether metastasized) and treatment (surgery, chemotherapy, and radiotherapy) was extracted from medical charts. All study participants provided written informed consent, and study protocols were approved by the institutional review boards of all participating institutions.
SWHS and SMHS participants who developed a first incident cancer of the colorectum (International Classification of Diseases, Ninth Revision (ICD-9) codes 153 and 154), lung (ICD-9 code 162), stomach (ICD-9 code 151), or breast (women only; ICD-9 code 174) after study enrollment were eligible for this analysis. A total of 4,439 eligible cancer patients were identified and sent for linkage with the electronic medical records (EMR) database in the Changning District Health Information System in 2015. The Changning Health Information System was established in 2003 and routinely collects clinical data from all hospitals in the Changning District in which participants were recruited.
Assessment of use of antihypertensive medications
Use of antihypertensive medications of interest, including ARBs, ACEIs, β-blockers, CCBs, and diuretics, was based on prescriptions recorded in the EMR database. Information on medication use from 2004 through 2014 was extracted from EMR for 3,433 out of 4,439 SWHS/SMHS participants with colorectal, lung, stomach, or breast cancers, with a successful matching rate of 77.3%. The vast majority of unsuccessful matches were due to participants’ relocation to neighboring districts, in which our study had no access to the EMR. We excluded participants diagnosed with cancer prior to 2004 due to incompleteness of EMR data on medication use prior to this time point (n = 542), resulting in a final sample size of 2,891 cancer cases for the analyses. Information on potentially confounding coprescription use was also extracted from the EMR, including use of statins, aspirin, and common diabetes medications (i.e., metformin, sulfonylureas, and insulin). For aspirin, self-reported use obtained from the baseline survey was applied in the analysis.
Statistical analyses
We compared patient characteristics by use or never-use of common antihypertensive medications using χ2 tests, and 3-year and 5-year survival rates (according to sociodemographic and clinical factors) were calculated using the Kaplan-Meier method. We used Cox proportional hazards models to estimate associations of overall survival (OS) and disease-specific survival (DSS) with use of antihypertensive medications of interest. Because age is one of the most important determinant factors for cancer outcome, we chose age as the time scale in Cox regression analyses. Age at cancer diagnosis was used as the entry time to reflect the left-truncation nature of the data (24). Age at death or censoring/last follow-up was the exit time. Patients who died of causes other than the cancer of interest were censored for the DSS analysis. Age on the last follow-up date was set on December 31, 2014. We built 2 models to evaluate the associations. In model 1, the exposure period started at time of cancer diagnosis for medication use initiated before cancer diagnosis or at time of first prescription for medications initiated after cancer diagnosis. The latter were treated as time-dependent variables in the Cox regression models to avoid immortal time bias (25). Analysis results applying model 1 are presented with no adjustment or full adjustment. Fully adjusted analyses included mutual adjustment for ARBs, ACEIs, β-blockers, CCBs, diuretics, and potential confounding from coprescriptions (diabetes medications, statins, and aspirin); other covariates in the models included age at diagnosis, sex, education, annual family income (Chinese yuan; 1 Chinese yuan = approximately US$0.15), body mass index (calculated as weight (kg)/height (m)2), alcohol consumption, cigarette smoking status, Charlson Comorbidity Index score (26), year of cancer diagnosis, TNM stages of cancer, and cancer treatment (separate terms for surgery, chemotherapy, and radiotherapy). In model 2, we added a 6-month lag period into defining exposure, because antihypertensive medications of interest might not immediately influence OS or DSS, and a lag period prevents potential reverse causation (27–29). Thus, patients who used antihypertensive medications <6 months before study events or censoring were removed from the user group and treated as unexposed subjects in the analysis. Covariates included in model 2 were the same as those included in model 1. Referents for all analyses were nonusers of antihypertensive medications under investigation. Proportional hazards assumptions were evaluated using Schoenfeld residuals. Stratified Cox proportional hazards models were used for some categorical covariates that did not satisfy proportionality assumptions. Additionally, with consideration to competing risks of noncancer causes of death in the DSS analyses, we followed the approach proposed by Fine and Gray (30), which models the cumulative incidence function. The analyses were performed individually for each type of cancer. All analyses were performed using SAS, version 9.1 (SAS Institute, Inc., Cary, North Carolina). All statistical tests were based on 2-sided probability.
RESULTS
Overall, the median follow-up time was 3.4 years (interquartile range (IQR), 1.0–6.3) after cancer diagnosis for all study participants. The median was 6.3 years (IQR, 4.3–8.7) for breast cancer, 4.0 years (IQR, 1.7–6.6) for colorectal cancer, 1.1 years (IQR, 0.4–3.1) for lung cancer, and 2.0 years (IQR, 0.6–5.1) for stomach cancer. Antihypertensive medication users, compared with never-users, were more likely to be older, less educated, or overweight/obese; have TNM stage I/II cancers; and undergo surgery, but they were less likely to receive chemotherapy or radiotherapy (Table 1). As shown in Table 2, 1,529 total deaths and 1,408 (92.1%) cancer deaths were documented. Three-year survival rates by cancer site were 92.7% (breast), 65.9% (colorectal), 26.2% (lung), and 42.3% (stomach); respective 5-year survival rates were 87.6%, 58.4%, 18.4%, and 36.6%. For all cancers, OS rates were positively associated with educational level and surgical treatment and inversely associated with age at cancer diagnosis and TNM stage. Associations between other variables and OS rate varied by cancer site; for example, for colorectal cancer, income, body mass index, hypertension, chemotherapy, and radiotherapy were all associated with OS rate, while for stomach cancer, only chemotherapy was associated.
Table 1.
Patient Characteristics According to Use of Common Antihypertensive Medications, From the Shanghai Women’s Health Study (1996–2000) and Shanghai Men’s Health Study (2002–2006), Shanghai, China
| Characteristic | No. of Never Users (n = 1,267) | % | No. of Users (n = 1,624) | % | P Value |
|---|---|---|---|---|---|
| Age at cancer diagnosis, years | <0.01 | ||||
| 40–55 | 300 | 23.7 | 187 | 11.5 | |
| 55–65 | 377 | 29.8 | 378 | 23.3 | |
| ≥65 | 590 | 46.5 | 1,059 | 65.2 | |
| Sex | 0.63 | ||||
| Female | 718 | 56.7 | 935 | 57.6 | |
| Male | 549 | 43.3 | 689 | 42.4 | |
| Education | <0.01 | ||||
| Up to elementary school | 254 | 20.1 | 462 | 28.5 | |
| Middle school | 465 | 36.7 | 535 | 32.9 | |
| High school | 349 | 27.5 | 390 | 24.0 | |
| College or beyond | 199 | 15.7 | 237 | 14.6 | |
| Annual family income, Chinese yuana | 0.21 | ||||
| <10,000 | 177 | 14.0 | 229 | 14.1 | |
| 10,000–19,999 | 552 | 43.6 | 766 | 47.2 | |
| 20,000–29,999 | 373 | 29.4 | 433 | 26.7 | |
| ≥30,000 | 165 | 13.0 | 196 | 12.0 | |
| Body mass indexb | <0.01 | ||||
| <25 | 885 | 69.8 | 873 | 53.8 | |
| 25–29 | 332 | 26.2 | 646 | 39.8 | |
| ≥30 | 50 | 4.0 | 105 | 6.5 | |
| Alcohol consumption | 0.53 | ||||
| Never | 1,043 | 82.3 | 1,322 | 81.4 | |
| Ever | 224 | 17.7 | 302 | 18.6 | |
| Smoking status | 0.54 | ||||
| Never | 824 | 65.0 | 1,074 | 66.1 | |
| Ever | 443 | 35.0 | 550 | 33.9 | |
| TNM stage | <0.01 | ||||
| I | 196 | 15.5 | 356 | 21.9 | |
| II | 292 | 23.0 | 417 | 25.7 | |
| III | 197 | 15.6 | 257 | 15.8 | |
| IV | 262 | 20.7 | 222 | 13.7 | |
| Unknown | 320 | 25.2 | 372 | 22.9 | |
| Surgery | <0.01 | ||||
| No | 387 | 30.5 | 380 | 23.4 | |
| Yes | 880 | 69.5 | 1,244 | 76.6 | |
| Chemotherapy | <0.01 | ||||
| No | 333 | 26.3 | 509 | 31.3 | |
| Yes | 934 | 73.7 | 1,115 | 68.7 | |
| Radiotherapy | <0.01 | ||||
| No | 1,082 | 85.4 | 1,453 | 89.5 | |
| Yes | 185 | 14.6 | 171 | 10.5 |
Abbreviation: TNM, tumor size, number of lymph nodes, and whether metastasized.
a 1 Chinese yuan = approximately US$0.15.
b Weight (kg)/height (m)2.
Table 2.
Demographic, Lifestyle, and Clinical Characteristics of Cancer Cases for 5-Year Survival According to Cancer Type, From the Shanghai Women’s Health Study (1996–2000) and Shanghai Men’s Health Study (2002–2006), Shanghai, China
| Characteristic | Breast Cancer | Colorectal Cancer | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| No. of Participants | No. of Deaths | 3-Year OS, %a | 5-Year OS, %a | P Value | No. of Participants | No. of Deaths | 3-Year OS, %a | 5-Year OS, %a | P Value | |
| Overall survival | 633 | 107 | 92.7 | 87.6 | N/A | 890 | 383 | 65.9 | 58.4 | N/A |
| Sex | N/A | 0.27 | ||||||||
| Female | 633 | 107 | 92.7 | 87.6 | 477 | 197 | 66.4 | 59.9 | ||
| Male | N/A | N/A | N/A | N/A | 413 | 186 | 65.2 | 56.6 | ||
| Educational level, % | <0.01 | 0.05 | ||||||||
| Up to elementary school | 97 | 31 | 90.7 | 77.7 | 232 | 116 | 58.1 | 50.4 | ||
| Middle school | 243 | 44 | 91.0 | 86.4 | 281 | 119 | 67.2 | 59.7 | ||
| High school | 198 | 123 | 95.0 | 90.7 | 237 | 92 | 70.4 | 61.7 | ||
| College or beyond | 95 | 9 | 94.7 | 93.6 | 140 | 56 | 68.4 | 63.4 | ||
| Annual family income, Chinese yuanb | 0.32 | <0.01 | ||||||||
| <10,000 | 85 | 14 | 95.3 | 88.5 | 128 | 64 | 56.0 | 49.3 | ||
| 10,000–19,999 | 269 | 52 | 91.8 | 85.5 | 392 | 169 | 66.3 | 58.8 | ||
| 20,000–29,999 | 163 | 27 | 91.4 | 86.1 | 257 | 116 | 66.0 | 56.7 | ||
| ≥30,000 | 116 | 14 | 94.8 | 93.8 | 113 | 34 | 75.2 | 71.0 | ||
| Body mass indexc | <0.01 | 0.03 | ||||||||
| <25 | 365 | 51 | 93.2 | 90.1 | 508 | 224 | 65.3 | 58.3 | ||
| 25–29 | 228 | 44 | 93.0 | 85.7 | 333 | 131 | 69.0 | 60.7 | ||
| ≥30 | 40 | 12 | 87.5 | 75.0 | 49 | 28 | 50.6 | 43.1 | ||
| Smoking status | 0.03 | 0.55 | ||||||||
| Never | 621 | 102 | 93.1 | 87.8 | 607 | 256 | 65.8 | 58.9 | ||
| Ever | 12 | 5 | 75.0 | 75.0 | 283 | 127 | 65.9 | 57.3 | ||
| Alcohol consumption | 0.51 | 0.34 | ||||||||
| Never | 620 | 104 | 92.9 | 87.7 | 736 | 313 | 66.6 | 59.0 | ||
| Ever | 13 | 3 | 84.6 | 84.6 | 154 | 70 | 62.6 | 55.4 | ||
| Hypertension diagnosis | 0.45 | <0.01 | ||||||||
| No | 270 | 49 | 90.4 | 84.7 | 586 | 283 | 61.7 | 54.3 | ||
| Yes | 363 | 58 | 94.5 | 89.8 | 304 | 100 | 73.9 | 66.3 | ||
| Age at cancer diagnosis, years | <0.01 | <0.01 | ||||||||
| 40–49 | 52 | 5 | 98.1 | 96.2 | 24 | 10 | 70.8 | 66.7 | ||
| 50–59 | 280 | 42 | 93.6 | 89.5 | 189 | 58 | 75.6 | 71.7 | ||
| ≥60 | 301 | 60 | 91.0 | 84.3 | 677 | 316 | 63.0 | 54.3 | ||
| TNM stage | <0.01 | <0.01 | ||||||||
| I | 205 | 13 | 98.1 | 95.3 | 189 | 30 | 93.6 | 85.6 | ||
| II | 292 | 41 | 95.2 | 91.1 | 201 | 59 | 78.9 | 72.2 | ||
| III | 43 | 20 | 69.8 | 67.4 | 248 | 123 | 61.6 | 52.0 | ||
| IV | 12 | 8 | 41.7 | 31.3 | 94 | 88 | 10.3 | 6.2 | ||
| Unknown | 81 | 25 | 90.1 | 75.0 | 158 | 83 | 55.6 | 49.1 | ||
| Chemotherapy | 0.08 | <0.01 | ||||||||
| No | 81 | 19 | 90.1 | 81.7 | 236 | 125 | 54.6 | 47.4 | ||
| Yes | 552 | 88 | 93.1 | 88.5 | 654 | 258 | 69.9 | 62.4 | ||
| Radiotherapy | 0.55 | <0.01 | ||||||||
| No | 497 | 81 | 93.8 | 87.7 | 837 | 349 | 67.4 | 59.6 | ||
| Yes | 136 | 26 | 89.0 | 87.3 | 53 | 34 | 41.1 | 39.0 | ||
| Surgery | <0.01 | <0.01 | ||||||||
| No | 17 | 11 | 58.8 | 32.7 | 80 | 66 | 18.8 | 17.4 | ||
| Yes | 616 | 96 | 93.7 | 89.1 | 810 | 317 | 70.5 | 62.4 | ||
| Lung Cancer | Stomach Cancer | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| No. of Participants | No. of Deaths | 3-Year OS, %a | 5-Year OS, %a | P Value | No. of Participants | No. of Deaths | 3-Year OS, %a | 5-Year OS, %a | P Value | |
| Overall survival | 824 | 681 | 26.2 | 18.4 | N/A | 544 | 358 | 42.3 | 36.6 | N/A |
| Sex | <0.01 | 0.60 | ||||||||
| Female | 340 | 271 | 32.1 | 23.0 | 203 | 135 | 40.4 | 35.5 | ||
| Male | 484 | 410 | 22.1 | 15.2 | 341 | 223 | 43.4 | 37.3 | ||
| Educational level, % | <0.01 | <0.01 | ||||||||
| Up to elementary school | 2,258 | 239 | 16.3 | 9.7 | 129 | 99 | 29.5 | 24.5 | ||
| Middle school | 276 | 227 | 25.7 | 17.9 | 200 | 130 | 42.5 | 37.1 | ||
| High school | 173 | 127 | 37.0 | 27.7 | 131 | 81 | 46.5 | 40.4 | ||
| College or beyond | 117 | 88 | 33.3 | 25.8 | 84 | 48 | 54.8 | 48.1 | ||
| Annual family income, Chinese yuanb | <0.01 | 0.08 | ||||||||
| <10,000 | 120 | 100 | 25.0 | 20.5 | 73 | 51 | 32.9 | 30.1 | ||
| 10,000–19,999 | 391 | 339 | 20.7 | 14.1 | 266 | 183 | 40.6 | 34.5 | ||
| 20,000–29,999 | 230 | 181 | 32.6 | 20.7 | 166 | 97 | 46.1 | 39.6 | ||
| ≥30,000 | 83 | 61 | 36.1 | 29.0 | 49 | 27 | 52.8 | 48.0 | ||
| Body mass indexc | 0.60 | 0.56 | ||||||||
| <25 | 563 | 464 | 26.6 | 18.9 | 322 | 209 | 44.7 | 38.0 | ||
| 25–29 | 221 | 179 | 25.8 | 19.0 | 196 | 131 | 38.8 | 35.2 | ||
| ≥30 | 40 | 38 | 22.5 | 10.0 | 26 | 18 | 38.5 | 29.9 | ||
| Smoking status | <0.01 | 0.91 | ||||||||
| Never | 374 | 295 | 32.6 | 24.1 | 296 | 192 | 41.6 | 36.5 | ||
| Ever | 450 | 386 | 20.9 | 13.8 | 248 | 166 | 43.1 | 36.8 | ||
| Alcohol consumption | 0.29 | 0.43 | ||||||||
| Never | 605 | 499 | 27.3 | 19.4 | 404 | 271 | 41.8 | 35.9 | ||
| Ever | 219 | 182 | 23.3 | 15.6 | 140 | 87 | 43.5 | 38.8 | ||
| Hypertension diagnosis | <0.01 | 0.20 | ||||||||
| No | 644 | 549 | 21.4 | 15.0 | 437 | 293 | 40.9 | 35.2 | ||
| Yes | 180 | 132 | 43.3 | 30.7 | 107 | 65 | 47.7 | 42.3 | ||
| Age at cancer diagnosis, years | <0.01 | 0.03 | ||||||||
| 40–49 | 31 | 22 | 41.9 | 35.0 | 28 | 17 | 57.1 | 45.9 | ||
| 50–59 | 173 | 125 | 37.6 | 29.2 | 120 | 70 | 47.4 | 43.8 | ||
| ≥60 | 620 | 534 | 22.3 | 14.5 | 396 | 271 | 39.6 | 35.1 | ||
| TNM stage | <0.01 | <0.01 | ||||||||
| I | 59 | 17 | 83.1 | 72.2 | 99 | 23 | 90.9 | 84.4 | ||
| II | 87 | 41 | 59.8 | 52.4 | 129 | 73 | 51.9 | 44.5 | ||
| III | 96 | 79 | 34.4 | 18.3 | 67 | 56 | 28.4 | 16.9 | ||
| IV | 290 | 281 | 11.4 | 3.7 | 88 | 84 | 5.2 | 3.5 | ||
| Unknown | 292 | 263 | 16.8 | 12.2 | 161 | 122 | 30.4 | 26.4 | ||
| Chemotherapy | <0.01 | <0.01 | ||||||||
| No | 326 | 280 | 20.3 | 15.6 | 199 | 141 | 36.7 | 31.3 | ||
| Yes | 498 | 401 | 30.1 | 20.4 | 345 | 217 | 45.5 | 39.7 | ||
| Radiotherapy | 0.91 | 0.80 | ||||||||
| No | 668 | 545 | 27.0 | 19.9 | 533 | 351 | 42.4 | 36.6 | ||
| Yes | 156 | 136 | 23.1 | 12.3 | 11 | 7 | 36.4 | 36.4 | ||
| Surgery | <0.01 | <0.01 | ||||||||
| No | 539 | 518 | 10.8 | 4.4 | 131 | 119 | 12.2 | 9.4 | ||
| Yes | 285 | 163 | 55.4 | 44.6 | 413 | 239 | 51.8 | 45.3 | ||
Abbreviations: N/A, not applicable; OS, overall survival; TNM, tumor size, number of lymph nodes, and whether metastasized.
a Survival rates calculated using the Kaplan-Meier Method.
b 1 Chinese yuan = approximately US$0.15.
c Weight (kg)/height (m)2.
Table 3 shows associations between antihypertensive medication use and OS according to cancer site. In model 1 with no adjustment, worse OS was observed among users of CCBs in breast cancer and users of diuretics in all 4 cancers, while better OS was seen among users of ARBs in lung cancer and users of CCBs in lung and stomach cancers. After fully adjusting for multiple potential confounding factors, we found better OS only among users of β-blockers (adjusted hazard ratio (HR) = 0.69, 95% confidence interval (CI): 0.49, 0.96) or ARBs in colorectal cancer (adjusted HR = 0.71, 95% CI: 0.51, 0.98), while inverse associations between diuretic use and OS persisted for all 4 cancers. In model 2 (with inclusion of a 6-month lag time for medication use), we found that ARB use was associated with better OS in patients with colorectal cancer (adjusted HR = 0.62, 95% CI: 0.44, 0.86) or stomach cancer (adjusted HR = 0.62, 95% CI: 0.41, 0.94). We also observed a better OS for use of β-blockers in colorectal cancer (adjusted HR = 0.50, 95% CI: 0.35, 0.72) and for use of diuretics in stomach cancer (adjusted HR = 0.66, 95% CI: 0.45, 0.96). However, diuretic use was no longer associated with OS for breast, colorectal, and lung cancers. No significant associations between OS and ACEIs or CCBs were observed for cancers after applying model 2.
Table 3.
Associations Between Use of Antihypertensive Medications With Overall Survival According to Cancer Site, From the Shanghai Women’s Health Study (1996–2000) and Shanghai Men’s Health Study (2002–2006), Shanghai, China
| Medication and Modela | Breast Cancer | Colorectal Cancer | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| No. of Deaths | Total No. | Person-Years | HR | 95% CI | No. of Deaths | Total No. | Person-Years | HR | 95% CI | |
| Overall | 107 | 633 | 4,041 | 383 | 890 | 3,839 | ||||
| Use of β-blockers | ||||||||||
| Model 1 | ||||||||||
| Never use | 76 | 449 | 2,878 | 1.00 | Referent | 293 | 628 | 2,591 | 1.00 | Referent |
| Ever use (unadjusted) | 31 | 184 | 1,163 | 1.51 | 0.99, 2.34 | 90 | 262 | 1,248 | 0.91 | 0.71, 1.16 |
| Ever use (adjusted) | 31 | 184 | 1,163 | 1.54 | 0.92, 2.58 | 90 | 262 | 1,248 | 0.69 | 0.49, 0.96 |
| Model 2 | ||||||||||
| Nonusers | 81 | 457 | 2,917 | 1.00 | Referent | 308 | 643 | 2,620 | 1.00 | Referent |
| Users (adjusted) | 26 | 176 | 1,124 | 1.10 | 0.65, 1.88 | 75 | 247 | 1,219 | 0.50 | 0.35, 0.72 |
| Use of ARBs | ||||||||||
| Model 1 | ||||||||||
| Never use | 76 | 413 | 2,645 | 1.00 | Referent | 277 | 550 | 2,148 | 1.00 | Referent |
| Ever use (unadjusted) | 31 | 220 | 1,396 | 1.27 | 0.83, 1.94 | 106 | 340 | 1,691 | 0.82 | 0.65, 1.03 |
| Ever use (adjusted) | 31 | 220 | 1,396 | 1.00 | 0.55, 1.80 | 106 | 340 | 1,691 | 0.71 | 0.51, 0.98 |
| Model 2 | ||||||||||
| Nonusers | 78 | 417 | 2,669 | 1.00 | Referent | 285 | 558 | 2,164 | 1.00 | Referent |
| Users (adjusted) | 29 | 216 | 1,372 | 0.83 | 0.45, 1.52 | 98 | 332 | 1,675 | 0.62 | 0.44, 0.86 |
| Use of ACEIs | ||||||||||
| Model 1 | ||||||||||
| Never use | 82 | 492 | 3,176 | 1.00 | Referent | 287 | 653 | 2,779 | 1.00 | Referent |
| Ever use (unadjusted) | 25 | 141 | 865 | 1.23 | 0.78, 1.95 | 96 | 237 | 1,060 | 1.13 | 0.89, 1.42 |
| Ever use (adjusted) | 25 | 141 | 865 | 0.82 | 0.45, 1.50 | 96 | 237 | 1,060 | 0.99 | 0.71, 1.39 |
| Model 2 | ||||||||||
| Nonusers | 82 | 492 | 3,176 | 1.00 | Referent | 291 | 657 | 2,787 | 1.00 | Referent |
| Users (adjusted) | 25 | 141 | 865 | 0.82 | 0.45, 1.50 | 92 | 233 | 1,052 | 0.91 | 0.65, 1.28 |
| Use of CCBs | ||||||||||
| Model 1 | ||||||||||
| Never use | 54 | 343 | 2,251 | 1.00 | Referent | 202 | 417 | 1,712 | 1.00 | Referent |
| Ever use (unadjusted) | 53 | 290 | 1,790 | 1.67 | 1.12, 2.48 | 181 | 473 | 2,126 | 1.06 | 0.86, 1.31 |
| Ever use (adjusted) | 53 | 290 | 1,790 | 1.65 | 0.98, 2.76 | 181 | 473 | 2,126 | 0.96 | 0.70, 1.32 |
| Model 2 | ||||||||||
| Nonusers | 57 | 347 | 2,268 | 1.00 | Referent | 212 | 427 | 1,729 | 1.00 | Referent |
| Users (adjusted) | 50 | 286 | 1,773 | 1.42 | 0.84, 2.40 | 171 | 463 | 2,110 | 0.82 | 0.59, 1.12 |
| Use of diuretics | ||||||||||
| Model 1 | ||||||||||
| Never use | 78 | 479 | 3,106 | 1.00 | Referent | 206 | 554 | 2,476 | 1.00 | Referent |
| Ever use (unadjusted) | 29 | 154 | 935 | 1.94 | 1.26, 3.00 | 177 | 336 | 1,363 | 2.76 | 2.25, 3.40 |
| Ever use (adjusted) | 29 | 154 | 935 | 2.03 | 1.10, 3.74 | 177 | 336 | 1,363 | 3.06 | 2.27, 4.12 |
| Model 2 | ||||||||||
| Nonusers | 87 | 490 | 3,157 | 1.00 | Referent | 280 | 628 | 2,611 | 1.00 | Referent |
| Users (adjusted) | 20 | 143 | 884 | 0.92 | 0.47, 1.78 | 103 | 262 | 1,228 | 1.13 | 0.81, 1.57 |
| Lung Cancer | Stomach Cancer | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| No. of Deaths | Total No. | Person-Years | HR | 95% CI | No. of Deaths | Total No. | Person-Years | HR | 95% CI | |
| Overall | 681 | 824 | 1,684 | 358 | 544 | 1,657 | ||||
| Use of β-blockers | ||||||||||
| Model 1 | ||||||||||
| Never use | 541 | 640 | 1,222 | 1.00 | Referent | 292 | 425 | 1,255 | 1.00 | Referent |
| Ever use (unadjusted) | 140 | 184 | 462 | 0.87 | 0.72, 1.06 | 66 | 119 | 402 | 0.87 | 0.66, 1.15 |
| Ever use (adjusted) | 140 | 184 | 462 | 1.15 | 0.86, 1.54 | 66 | 119 | 402 | 0.90 | 0.61, 1.31 |
| Model 2 | ||||||||||
| Nonusers | 567 | 667 | 1,254 | 1.00 | Referent | 303 | 436 | 1,261 | 1.00 | Referent |
| Users (adjusted) | 114 | 157 | 430 | 0.92 | 0.68, 1.24 | 55 | 108 | 396 | 0.72 | 0.48, 1.08 |
| Use of ARBs | ||||||||||
| Model 1 | ||||||||||
| Never use | 537 | 613 | 1,049 | 1.00 | Referent | 283 | 405 | 1,122 | 1.00 | Referent |
| Ever use (unadjusted) | 144 | 211 | 635 | 0.68 | 0.56, 0.83 | 75 | 139 | 535 | 0.81 | 0.63, 1.06 |
| Ever use (adjusted) | 144 | 211 | 635 | 0.91 | 0.67, 1.24 | 75 | 139 | 535 | 0.73 | 0.49, 1.10 |
| Model 2 | ||||||||||
| Nonusers | 553 | 632 | 1,072 | 1.00 | Referent | 289 | 411 | 1,127 | 1.00 | Referent |
| Users (adjusted) | 128 | 192 | 612 | 0.74 | 0.54, 1.03 | 69 | 133 | 530 | 0.62 | 0.41, 0.94 |
| Use of ACEIs | ||||||||||
| Model 1 | ||||||||||
| Never use | 558 | 665 | 1,325 | 1.00 | Referent | 282 | 421 | 1,246 | 1.00 | Referent |
| Ever use (unadjusted) | 123 | 159 | 359 | 0.90 | 0.73, 1.09 | 76 | 123 | 411 | 0.98 | 0.75, 1.26 |
| Ever use (adjusted) | 123 | 159 | 359 | 0.97 | 0.71, 1.31 | 76 | 123 | 411 | 0.92 | 0.61, 1.37 |
| Model 2 | ||||||||||
| Nonusers | 572 | 679 | 1,338 | 1.00 | Referent | 287 | 426 | 1,246 | 1.00 | Referent |
| Users (adjusted) | 109 | 145 | 346 | 0.84 | 0.61, 1.16 | 71 | 118 | 411 | 0.80 | 0.53, 1.21 |
| Use of CCBs | ||||||||||
| Model 1 | ||||||||||
| Never use | 418 | 479 | 814 | 1.00 | Referent | 215 | 299 | 783 | 1.00 | Referent |
| Ever use (unadjusted) | 263 | 346 | 870 | 0.76 | 0.65, 0.90 | 143 | 245 | 874 | 0.75 | 0.59, 0.94 |
| Ever use (adjusted) | 263 | 346 | 870 | 1.03 | 0.71, 1.33 | 143 | 245 | 874 | 0.82 | 0.58, 1.15 |
| Model 2 | ||||||||||
| Nonusers | 449 | 510 | 836 | 1.00 | Referent | 224 | 308 | 785 | 1.00 | Referent |
| Users (adjusted) | 232 | 314 | 848 | 0.78 | 0.60, 1.03 | 134 | 236 | 872 | 0.74 | 0.52, 1.04 |
| Use of diuretics | ||||||||||
| Model 1 | ||||||||||
| Never use | 469 | 565 | 1,097 | 1.00 | Referent | 219 | 342 | 1,089 | 1.00 | Referent |
| Ever use (unadjusted) | 212 | 259 | 587 | 1.40 | 1.19, 1.65 | 139 | 202 | 568 | 1.83 | 1.47, 2.28 |
| Ever use (adjusted) | 212 | 259 | 587 | 1.79 | 1.36, 2.37 | 139 | 202 | 568 | 2.15 | 1.54, 3.01 |
| Model 2 | ||||||||||
| Nonusers | 550 | 646 | 1,196 | 1.00 | Referent | 289 | 412 | 1,172 | 1.00 | Referent |
| Users (adjusted) | 131 | 178 | 488 | 0.78 | 0.57, 1.08 | 69 | 132 | 485 | 0.66 | 0.45, 0.96 |
Abbreviations: β-blocker, β-adrenergic receptor blocker; ACEI, angiotensin I-converting enzyme inhibitors; ARB, angiotensin II receptor blocker; CCB, calcium channel blocker; CI, confidence interval; HR, hazard ratio.
a Model 1’s adjusted analyses included mutual adjustment for ARBs, ACEIs, β-blockers, CCBs, diuretics, and potential confounding from coprescriptions, as well as other covariates: 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.
Table 4 presents associations between antihypertensive medications and DSS. Similar to the associations of OS, fully adjusted analysis using model 1 showed better DSS among users of ARBs or β-blockers with colorectal cancer and worse DSS among users of diuretics in all 4 cancers. In model 2, we found a better DSS among users of ARBs (adjusted HR = 0.61, 95% CI: 0.43, 0.87) or β-blockers (adjusted HR = 0.50, 95% CI: 0.34, 0.73) in colorectal cancer patients, and among users of ARBs in stomach cancer patients (adjusted HR = 0.63, 95% CI: 0.41, 0.98). We also found that better DSS was associated with use of CCBs or diuretics in patients with stomach cancer (adjusted HR = 0.67, 95% CI: 0.47, 0.97; and adjusted HR = 0.57, 95% CI: 0.38, 0.85, respectively). DSS was not associated with ACEI use in all 4 cancers or diuretic use in breast, colorectal, and lung cancers.
Table 4.
Associations Between Use of Antihypertensive Medications With Disease-Specific Survival According to Cancer Site, From the Shanghai Women’s Health Study (1996–2000) and Shanghai Men’s Health Study (2002–2006), Shanghai, China
| Medication and Modela | Breast Cancer | Colorectal Cancer | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Cancer Deaths | Total No. | Person-Years | HR | 95% CI | Cancer Deaths | Total No. | Person-Years | HR | 95% CI | |
| Overall | 84 | 633 | 4,041 | 342 | 890 | 3,839 | ||||
| Use of β-blockers | ||||||||||
| Model 1 | ||||||||||
| Never use | 64 | 449 | 2,878 | 1.00 | Referent | 267 | 628 | 2,591 | 1.00 | Referent |
| Ever use (unadjusted) | 20 | 184 | 1,163 | 1.16 | 0.70, 1.93 | 75 | 262 | 1,248 | 0.85 | 0.66, 1.11 |
| Ever use (adjusted) | 20 | 184 | 1,163 | 1.26 | 0.68, 2.34 | 75 | 262 | 1,248 | 0.67 | 0.47, 0.97 |
| Model 2 | ||||||||||
| Nonusers | 66 | 457 | 2,917 | 1.00 | Referent | 279 | 643 | 2,620 | 1.00 | Referent |
| Users (adjusted) | 18 | 176 | 1,124 | 1.05 | 0.56, 1.97 | 63 | 247 | 1,219 | 0.50 | 0.34, 0.73 |
| Use of ARBs | ||||||||||
| Model 1 | ||||||||||
| Never use | 64 | 413 | 2,645 | 1.00 | Referent | 252 | 550 | 2,148 | 1.00 | Referent |
| Ever use (unadjusted) | 20 | 220 | 1,396 | 0.99 | 0.60, 1.67 | 90 | 340 | 1,691 | 0.78 | 0.61, 0.99 |
| Ever use (adjusted) | 20 | 220 | 1,396 | 0.76 | 0.37, 1.56 | 90 | 340 | 1,691 | 0.69 | 0.49, 0.97 |
| Model 2 | ||||||||||
| Nonusers | 65 | 417 | 2,669 | 1.00 | Referent | 258 | 558 | 2,164 | 1.00 | Referent |
| Users (adjusted) | 19 | 216 | 1,372 | 0.66 | 0.32, 1.38 | 84 | 332 | 1,675 | 0.61 | 0.43, 0.87 |
| Use of ACEIs | ||||||||||
| Model 1 | ||||||||||
| Never use | 64 | 492 | 3,176 | 1.00 | Referent | 259 | 653 | 2,779 | 1.00 | Referent |
| Ever use (unadjusted) | 20 | 141 | 865 | 1.32 | 0.79, 2.20 | 83 | 237 | 1,060 | 1.09 | 0.85, 1.40 |
| Ever use (adjusted) | 20 | 141 | 865 | 1.26 | 0.65, 2.45 | 83 | 237 | 1,060 | 0.96 | 0.67, 1.38 |
| Model 2 | ||||||||||
| Nonusers | 64 | 292 | 3,176 | 1.00 | Referent | 261 | 657 | 2,787 | 1.00 | Referent |
| Users (adjusted) | 20 | 141 | 865 | 1.26 | 0.65, 2.45 | 81 | 233 | 1,052 | 0.91 | 0.63, 1.31 |
| Use of CCBs | ||||||||||
| Model 1 | ||||||||||
| Never use | 48 | 343 | 2,251 | 1.00 | Referent | 148 | 417 | 1,712 | 1.00 | Referent |
| Ever use (unadjusted) | 36 | 290 | 1,790 | 1.33 | 0.85, 2.09 | 158 | 473 | 2,126 | 1.04 | 0.83, 1.30 |
| Ever use (adjusted) | 36 | 290 | 1,790 | 1.44 | 0.80, 2.60 | 158 | 473 | 2,126 | 1.00 | 0.72, 1.40 |
| Model 2 | ||||||||||
| Nonusers | 50 | 347 | 2,268 | 1.00 | Referent | 193 | 427 | 1,729 | 1.00 | Referent |
| Users (adjusted) | 34 | 286 | 1,773 | 1.30 | 0.71, 2.37 | 149 | 463 | 2,110 | 0.87 | 0.62, 1.21 |
| Use of diuretics | ||||||||||
| Model 1 | ||||||||||
| Never use | 64 | 479 | 3,106 | 1.00 | Referent | 185 | 557 | 2,476 | 1.00 | Referent |
| Ever use (unadjusted) | 20 | 154 | 935 | 1.72 | 1.03, 2.88 | 157 | 333 | 1,363 | 2.88 | 2.32, 3.59 |
| Ever use (adjusted) | 20 | 154 | 935 | 2.77 | 1.37, 5.60 | 157 | 333 | 1,363 | 3.40 | 2.48, 4.66 |
| Model 2 | ||||||||||
| Nonusers | 72 | 490 | 3,157 | 1.00 | Referent | 253 | 628 | 2,611 | 1.00 | Referent |
| Users (adjusted) | 12 | 143 | 884 | 1.06 | 0.48, 2.35 | 89 | 262 | 1,228 | 1.19 | 0.84, 1.69 |
| Lung Cancer | Stomach Cancer | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Cancer Deaths | Total No. | Person-Years | HR | 95% CI | Cancer Deaths | Total No. | Person-Years | HR | 95% CI | |
| Overall | 648 | 824 | 1,684 | 334 | 544 | 1,657 | ||||
| Use of β-blockers | ||||||||||
| Model 1 | ||||||||||
| Never use | 520 | 640 | 1,222 | 1.00 | Referent | 274 | 425 | 1,255 | 1.00 | Referent |
| Ever use (unadjusted) | 128 | 184 | 462 | 0.85 | 0.69, 1.03 | 60 | 119 | 402 | 0.87 | 0.65, 1.16 |
| Ever use (adjusted) | 128 | 184 | 462 | 1.10 | 0.82, 1.44 | 60 | 119 | 402 | 0.96 | 0.65, 1.44 |
| Model 2 | ||||||||||
| Nonusers | 544 | 667 | 1,254 | 1.00 | Referent | 284 | 436 | 1,261 | 1.00 | Referent |
| Users (adjusted) | 104 | 157 | 430 | 0.89 | 0.65, 1.22 | 50 | 108 | 396 | 0.79 | 0.50, 1.18 |
| Use of ARBs | ||||||||||
| Model 1 | ||||||||||
| Never use | 512 | 613 | 1,049 | 1.00 | Referent | 269 | 405 | 1,122 | 1.00 | Referent |
| Ever use (unadjusted) | 136 | 211 | 635 | 0.69 | 0.57, 0.84 | 65 | 139 | 535 | 0.75 | 0.57, 0.99 |
| Ever use (adjusted) | 136 | 211 | 635 | 0.99 | 0.72, 1.36 | 65 | 139 | 535 | 0.76 | 0.49, 1.16 |
| Model 2 | ||||||||||
| Nonusers | 528 | 632 | 1,072 | 1.00 | Referent | 274 | 411 | 1,127 | 1.00 | Referent |
| Users (adjusted) | 120 | 192 | 612 | 0.80 | 0.58, 1.12 | 59 | 133 | 530 | 0.63 | 0.41, 0.98 |
| Use of ACEIs | ||||||||||
| Model 1 | ||||||||||
| Never use | 532 | 665 | 1,325 | 1.00 | Referent | 268 | 421 | 1,246 | 1.00 | Referent |
| Ever use (unadjusted) | 116 | 159 | 359 | 0.89 | 0.73, 1.09 | 66 | 123 | 411 | 0.91 | 0.69, 1.20 |
| Ever use (adjusted) | 116 | 159 | 359 | 0.92 | 0.67, 1.26 | 66 | 123 | 411 | 0.90 | 0.58, 1.37 |
| Model 2 | ||||||||||
| Nonusers | 545 | 679 | 1,334 | 1.00 | Referent | 273 | 426 | 1,246 | 1.00 | Referent |
| Users (adjusted) | 103 | 145 | 346 | 0.80 | 0.58, 1.11 | 61 | 118 | 411 | 0.77 | 0.50, 1.19 |
| Use of CCBs | ||||||||||
| Model 1 | ||||||||||
| Never use | 401 | 479 | 814 | 1.00 | Referent | 207 | 299 | 783 | 1.00 | Referent |
| Ever use (unadjusted) | 247 | 345 | 870 | 0.76 | 0.64, 0.90 | 127 | 245 | 874 | 0.69 | 0.55, 0.88 |
| Ever use (adjusted) | 247 | 345 | 870 | 1.02 | 0.78, 1.33 | 127 | 245 | 874 | 0.75 | 0.52, 1.08 |
| Model 2 | ||||||||||
| Nonusers | 432 | 510 | 836 | 1.00 | Referent | 216 | 308 | 785 | 1.00 | Referent |
| Users (adjusted) | 216 | 314 | 848 | 0.77 | 0.58, 1.01 | 118 | 236 | 872 | 0.67 | 0.47, 0.97 |
| Use of diuretics | ||||||||||
| Model 1 | ||||||||||
| Never use | 448 | 565 | 1,097 | 1.00 | Referent | 206 | 342 | 1,089 | 1.00 | Referent |
| Ever use (unadjusted) | 200 | 259 | 587 | 1.40 | 1.18, 1.66 | 128 | 202 | 568 | 1.80 | 1.44, 2.26 |
| Ever use (adjusted) | 200 | 259 | 587 | 1.80 | 1.35, 2.41 | 128 | 202 | 568 | 1.97 | 1.39, 2.76 |
| Model 2 | ||||||||||
| Nonusers | 525 | 646 | 1,196 | 1.00 | Referent | 274 | 412 | 1,172 | 1.00 | Referent |
| Users (adjusted) | 123 | 178 | 488 | 0.80 | 0.58, 1.11 | 60 | 132 | 485 | 0.57 | 0.38, 0.85 |
Abbreviations: β-blocker, β-adrenergic receptor blocker; ACEI, angiotensin I-converting enzyme inhibitor; ARB, angiotensin II receptor blocker; CCB, calcium channel blocker; CI, confidence interval; HR, hazard ratio.
a Model 1’s adjusted analyses included mutual adjustment for ARBs, ACEIs, β-blockers, CCBs, diuretics, and potential confounding from coprescriptions, as well as other covariates: 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.
Competing risk analyses for DSS yielded similar results. For example, the competing risk analysis with model 2 showed that use of ARBs or β-blockers was associated with better DSS in colorectal cancer patients (adjusted HR = 0.71, 95% CI: 0.51, 0.98; adjusted HR = 0.52, 95% CI: 0.36, 0.74, respectively) (data not shown).
We conducted a sensitivity analysis restricted to patients who had TNM stage data and found similar results. For example, analysis with model 2 showed that use of ARBs or β-blockers was associated with better OS (adjusted HR = 0.66, 95% CI: 0.46, 0.94; adjusted HR = 0.55, 95% CI: 0.38, 0.81, respectively) and DSS (adjusted HR = 0.64, 95% CI: 0.44, 0.94; adjusted HR = 0.55, 95% CI: 0.36, 0.84, respectively) in colorectal cancer patients (data not shown).
Because 92% of β-blocker users in our study used β1-blockers alone, we further conducted an analysis excluding users of nonselective β-blockers (8%). Results showed similar association patterns of β-blockers with better OS and DSS in colorectal cancer (HR = 0.58, 95% CI: 0.41, 0.83 for OS; HR = 0.63, 95% CI: 0.43, 0.92 for DSS) in model 2, suggesting that β1-blocker use is associated with improved survival (data not shown).
Additionally, we conducted a subanalysis with never-use of common antihypertensive medications as a referent group, and results were consistent with our present findings (data not shown). We also carried out additional analyses using time from cancer diagnosis to event/censoring as the time scale for the significant associations and found similar results (data not shown).
DISCUSSION
In this study, we examined associations of antihypertensive medication use with OS and DSS in breast, colorectal, lung, and stomach cancers. Our study has several noteworthy strengths. First, data come from 2 large population-based prospective studies with comprehensive information on sociodemographic factors and disease/mortality-related risk factors. Second, use of antihypertensive and other medications was assessed from an EMR database, allowing an adjustment for a wide range of clinical confounding factors during analyses, including coprescriptions—such as aspirin, statins, and diabetes medications that have been previously linked to cancer survival (20, 31, 32). Third, our study evaluated survival after cancer diagnosis, rather than cancer mortality in a general population that could reflect the influence of medicines on cancer risk, prognosis, or both. More important, we treated use of medications after cancer diagnosis as time-dependent variables in the survival analysis, alleviating the concern of potential immortal time bias. We further built a lag period into the definition of exposure to prevent potential reverse causation (27, 28). Additionally, we conducted competing risk analysis, and results showed that DSS in our study population was influenced little by noncancer cause of death.
Existing evidence has linked the RAS to cancer progression (5, 6). Epidemiologic studies specifically examining associations between use of RAS inhibitors (ARBs, ACEIs) and cancer outcomes, however, have generated mixed results (33–37). For example, in colorectal cancer, ARB use was associated with improved survival in 2 studies (33, 34), while a null result has also been reported (35). A recent meta-analysis of 55 studies, which included mainly cases of lung cancer, colorectal carcinoma, breast cancer, renal cell carcinoma, and pancreatic cancer, found that RAS inhibitors are significantly associated with improved survival outcomes in cancer patients as a whole; however, such an association also depended on cancer type and types of RAS inhibitors (18). Subgroup analysis according to specific types of RAS inhibitors in this meta-analysis, including 11 studies on ARBs and 12 studies on ACEIs, found a significant improvement in OS only in ARB users and not in ACEI users (18). In line with these findings, our study found that the use of ARBs, but not ACEIs, was associated with improved survival among patients with colorectal or stomach cancers even after adjusting for a wide range of confounding factors and removing influence of potential immortal time bias.
Studies on the association between use of ARBs and/or ACEIs and stomach cancer outcomes have been lacking. Most recently, using data from the English National Cancer Data Repository, investigators examined ARB use and survival of 5,124 gastroesophageal cancer patients (29). This study used time-dependent Cox regression models with a 6-month lag period for definition of exposure during analyses and found that, after a median follow-up of 1.4 years, ARB users had a moderately reduced cancer-specific mortality (HR = 0.83, 95% CI: 0.71, 0.98). In our study, with a median follow-up of 3.4 years among 544 patients with stomach cancer, better OS and DSS were observed among ARB users compared with nonusers (HR = 0.62 and 0.63, respectively). The consistency of the associations, observed among 2 populations with vast differences in cancer treatment and patient care, suggests strongly that ARBs use might improve outcomes in stomach cancer patients.
A number of studies have examined the associations of β-blocker use with cancer outcomes with conflicting results (12–16, 20, 33, 38–44). Four meta-analyses of the association between β-blocker use and breast cancer survival have been published, with 2 reporting improved survival (12, 14) and 2 showing a null association (13, 15). It has been suggested that immortal time bias can lead to spurious beneficial associations of β-blocker use among cancer patients (43). A large study containing 18,733 breast cancer patients from the Danish Breast Cancer Cooperative Group registry reported no evidence that β-blockers attenuated breast cancer recurrence risk (45). Additionally, an analysis of the Nurses’ Health Study found that when they were used concurrently, only aspirin, but not β-blocker use, was associated with significantly improved survival (20). In our study, all medications, including β-blockers, were treated as time-dependent variables in the survival analysis to avoid the potential for immortal time bias. We also adjusted for several potentially confounding coprescriptions, including aspirin, in our analyses. We found that β-blocker use was unrelated to outcomes for breast, lung, and stomach cancer patients but was associated with improved OS/DSS in colorectal cancer patients.
Research has shown that β-blockers can inhibit multiple molecular and cellular processes in cancer progression and metastasis (7, 8). It has also been suggested that nonselective blockers might be preferred over selective β1-blockers because immunocytes predominantly express β2-adrenergic receptors over β1-adrenergic receptors (11). A previous study reported that use of the nonselective β-blocker propranolol, but not use of the β1-selective blocker atenolol, significantly reduced breast cancer progression and mortality (40). In our study, 92% of β-blocker users used β1-blockers alone. Thus, this result likely reflects primarily the association between β1-blocker use and survival. A further subgroup analysis excluding those nonselective β-blocker users showed that the observed associations persisted. To our knowledge, our study is the first to suggest that use of β1-blockers is associated with improved OS and DSS from colorectal cancer.
Information on CCB use and cancer survival is very limited. A meta-analysis of 11 studies (including 9 studies with cancer cases numbering less than 220) reported no associations between CCB use and cancer mortality (46). However, a large study conducted in Canada, which was included in that meta-analysis, found that CCB use was associated with increased mortality in breast cancer patients but improved survival in lung cancer patients (19). Most recently, a large UK study of breast cancer (47) showed a null association between CCB use and breast cancer survival. In our study, CCB use was significantly associated with improved DSS in patients with stomach cancers but not in patients with breast, colorectal, or lung cancers. To our knowledge, this is the first study to report that CCB use is associated with improved stomach cancer survival. Further studies are warranted to verify this finding.
Only a few studies have evaluated the relationship between use of diuretics and survival outcomes in cancer patients; however, results are mixed. An analysis from the Surveillance, Epidemiology and End Results (SEER)–Medicare database suggested that use of diuretics might be associated with poor outcomes among older breast cancer patients (44), while in a consortium study, use of diuretics was associated with a nearly 30% reduction in ovarian cancer mortality (48). To date, there is no biological evidence linking diuretics to cancer prognosis. In our study, without including a lag period into the definition of exposure, diuretic use was associated with increased mortality risk in all 4 cancers examined. However, after adding a 6-month lag period to minimize the potential for reverse causation, diuretic use was no longer associated with OS or DSS in patients with breast, colorectal, or lung cancers but with an improved OS and DSS in stomach cancer patients. Diuretics, particularly nonthiazide diuretics, are often used for managing heart failure and other serious medical conditions, including malignant ascites (49), and these medical conditions are highly associated with mortality. Thus, the increase in mortality with diuretic use, observed without a lag period, might be a result of reverse causation.
Our study has several limitations. First, many antihypertensive medication users in our study take multiple medications; thus, we were unable to evaluate the association in monotherapy patients due to low statistical power. Second, we were unable to accurately account for length of medication use, due mainly to the inability to capture use of medications prior to establishment of the EMR. Thus, we were unable to reliably examine dose-response relationships. Third, compared with several national registry-based large-scale studies (19, 29, 45), our sample size was relatively small, which might have limited our statistical power to conduct some analyses, such as medication-survival associations exclusively among hypertensive cancer patients, as well as medication use and outcomes of subtypes of cancer. Additionally, we were not able to exclude the possibility of overattributing death to cancer, which is likely when death occurred among cancer patients close to cancer diagnosis. Finally, we were missing TNM stage information for about 24% of patients included in the analyses, which might have introduced a residual confounder. However, our sensitivity analyses that excluded patients without information on TNM stage yielded similar results, suggesting this missing information had little influence on our findings.
In summary, applying statistical approaches that are not susceptible to immortal time bias, and comprehensively adjusting for potential confounding factors, our study offers evidence that use of ARBs or β-blockers improves OS and/or DSS in patients with colorectal or stomach cancer and that use of CCBs improves DSS in patients with stomach cancer. In addition, we found that β1-blocker use in colorectal cancer and CCB and diuretic use in stomach cancer are associated with better survival outcomes. Our findings, in general, support the notion that suppressing the RAS and sympathetic nervous system might improve survival of patients with gastrointestinal cancers.
ACKNOWLEDGMENTS
Author affiliations: Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee (Yong Cui, Wanqing Wen, Hui Cai, Mingrong You, Gong Yang, Wei Zheng, Xiao-Ou Shu); Tongren Hospital, Shanghai Jiao Tong University, Shanghai, China (Tao Zheng); State Key Laboratory of Oncogene and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China (Honglan Li, Yu-Tang Gao, Jing Gao, Yong-Bing Xiang); and Department of Epidemiology, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China (Honglan Li, Yu-Tang Gao, Jing Gao, Yong-Bing Xiang).
This work was funded by US National Institutes of Health (grants UM1 CA182910 to Shanghai Women’s Health Study (W.Z.) and UM1 CA173640 to Shanghai Men’s Health Study (X.-O.S.)).
We thank the participants and the research staff members of the Shanghai Women’s Health Study and Shanghai Men’s Health Study, without whom this study would not have been possible.
Conflict of interest: none declared.
Abbreviations
- β-blocker
β-adrenergic receptor blocker
- ACEI
angiotensin I-converting enzyme inhibitor
- ARB
angiotensin II receptor blocker
- CCB
calcium channel blocker
- CI
confidence interval
- DSS
disease-specific survival
- EMR
electronic medical record
- HR
hazard ratio
- IQR
interquartile range
- OS
overall survival
- RAS
renin-angiotensin system
- SMHS
Shanghai Men’s Health Study
- SWHS
Shanghai Women’s Health Study
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