Abstract
Background
The effects of β-blockers in patients with unstable angina pectoris (UAP) are unclear. We tried to evaluate associations between β-blockers in UAP and long-term outcomes.
Methods
We enrolled 5591 UAP patients and divided them into 2 groups based on β-blockers at discharge: 3790 did β-blockers and 1801 did not used them. Propensity score matching at 1 : 1 was performed to select 1786 patients from each group. The primary endpoint was major adverse cardiac and cerebral events (MACCE) during the long-term follow-up period.
Results
67.8% of patients were on β-blockers at discharge; these patients were more likely to have CHD risk factors, lower ejection fraction, and severity of the coronary artery lesions. Over a median of 25.0 years, the incidence of MACCE was 25.5%. The risk was not significantly different between those on and those not on β-blocker treatment. The multivariate Cox regression analysis showed that no β-blocker use at discharge was not an independent risk factor for MACCE and sequence secondary endpoints. After propensity score matching, the results were similar.
Conclusions
β-blocker use was not associated with lower MACCE and other secondary composite endpoints in long-term outcomes. This result adds to the increasing body of evidence that the routine prescription of β-blockers might not be indicated in patients with UAP. Trial registration had retrospectively registered.
1. Introduction
Cardiovascular disease is the leading cause of death worldwide [1, 2]. β-Blockers have historically been integral to cardiovascular (CV) risk modification, and while the evidence for their use is most robust in patients with myocardial infarction, the evidence of effectiveness and safety mainly comes from patients with an acute myocardial fraction (AMI) and heart failure (HF), especially before the development of percutaneous coronary intervention (PCI) [3–6]. Current guidelines recommend the use of β-blockers in patients with coronary heart disease (CHD) early [7–9]. Whether all patients with CHD need β-blockers still lacks research evidence. Unstable angina pectoris (UAP), an important type of CHD, is a clinical syndrome caused by acute myocardial ischemia and accounted for a large proportion all over the world. Nowadays, the efficacy, safety, and long-term outcomes of β-blockers are lacking in large-scale evidence and long-term follow-up. The aim of this study, therefore, was to assess whether the use of β-blockers influences the incidence of major adverse cardiac and cerebral events (MACCE) in patients with UAP.
2. Methods
2.1. Study Population
Study subjects were identified from the database at the Cardiovascular Center of Beijing Friendship Hospital. From December 2012 to October 2020, a total of 10377 consecutive patients with UAP were enrolled in this study. UAP was diagnosed based on the diagnostic criteria recommended by the European Society of Cardiology (ESC) [10]. All of them underwent coronary angiography (CAG), and the coronary stenosis was more than 70%. Exclusion criteria were as follows: (1) lacking clinical or follow-up data; (2) in-hospital death or AMI (including acute ST-segment elevation myocardial infarction and acute non-ST-segment elevation myocardial infarction); (3) infectious diseases (tuberculosis, active infective endocarditis), rheumatic disease (systemic lupus erythematosus, rheumatoid arthritis, vasculitis), hematological diseases (leukemia, lymphoma, disseminated intravascular coagulation), and neoplastic disease; and (4) contraindication of β-blockers such as the systolic blood pressure (SBP) at admission <90 mmHg, heart rate < 50 bpm, second-degree type II or third-degree atrioventricular block or bronchial asthma, and sick sinus syndrome. Finally, 5591 patients were included in this analysis. Based on the β-blockers at discharge, patients were divided into 2 groups. 1801 were not treated with β-blockers at discharge (β-blockers (-)); 3790 were confirmed to receive β-blocker treatment at discharge (β-blockers (+)). All patients were followed up on January 31, 2021, with a median follow-up of 25.0 months (IQR: 12.3, 49.2 months). The study was also designed using propensity score matching to assemble a balanced cohort. The patient flow of the study is shown in Figure 1.
Figure 1.
Flow chart of patient inclusion. UAP, unstable angina pectoris; MACCE, major adverse cardiac and cerebral events.
The local institutional review board at our hospital approved the study protocol, and this study was in accordance with the Declaration of Helsinki.
2.2. Data Collection and Definitions
Patient demographic information, medical and medication history, and laboratory measurements were collected and confirmed through electronic medical records. The left atrium (LA), left ventricular end-diastolic dimension (LVEDD), left ventricular end-systolic dimension (LVESD), left ventricular ejection fraction (LVEF), and left ventricular fraction shortening (LVFS) were determined using 2-dimensional echocardiography during the index hospitalization.
The primary endpoint was major adverse cardiac and cerebral events (MACCE), which included all-cause death, heart failure (HF), nonfatal MI, nonfatal stroke, and cardiac rehospitalization at the clinical follow-up period. HF was defined as HF requiring hospital admission. Nonfatal MI was defined as chest pain with new ST-segment changes and elevation of myocardial necrosis markers to at least twice the upper limit of the normal range. Nonfatal stroke, including ischemic and hemorrhagic stroke, was defined as cerebral dysfunction caused by cerebral vascular obstruction or sudden rupture and was diagnosed based on signs of neurological dysfunction or evidence of brain imaging. Cardiac rehospitalization is referred to rehospitalization for angina pectoris or HF. In addition, to analyze mortality in more detail, cardiac death was also assessed. Cardiac death included death as a result of cardiogenic shock, MI, primary cardiac arrest, or HF. Secondary endpoints included the following: all-cause death; composite of all-cause death and HF; composite of all-cause death, HF, and nonfatal MI; and the composite of death, HF, nonfatal MI, and nonfatal stroke.
2.3. Statistical Analysis
Depending on the distribution of the data, continuous variables were expressed as mean value ± SD or median and interquartile range (IQR). Frequencies and percentages were used to describe categorical data. Differences between continuous and categorical variables were assessed using Student's t-test, analysis of variance, chi-square test, and Wilcoxon signed rank test as appropriate. In this observational study, we performed propensity score matching to reduce the effectiveness of treatment selection bias and potential confounding factors. The cumulative incidence of follow-up time MACCE was estimated by the Kaplan–Meier curves, and the groups were compared using the log-rank test.
The propensity score matching was used to reduce selection bias and confounding factors in this study. The matching process was conducted with a minimum distance scoring method and a 1-to-1 match between those on and those not on β-blocker treatment. The propensity score estimated the probability that patients would have been assigned to the use of β-blockers and was derived using a logistic regression model that included the use of β-blockers as the outcome variable and the following variables as predictors: age, sex, body mass index (BMI), fasting blood glucose (FBG), creatinine, alanine aminotransferase (ALT), glycosylated hemoglobin (HbA1C), platelet count, LA, history of CHD, old myocardial infarction (OMI), hypertension, diabetes mellitus (DM), PCI and coronary artery bypass grafting (CABG), and previous medication history of the antiplatelet agent. Ultimately, 1786 patients without β-blockers were individually 1 : 1 matched to 1786 patients with β-blocker treatment at discharge. The multivariate Cox proportional hazards regression analysis was used to assess the association between adverse clinical events and those on and those not on β-blocker treatment.
All analyses were two-tailed, and P value <0.05 was considered statistically significant. Data were analyzed using SPSS statistical package version 26.0 (SPSS Inc., Chicago, IL, USA).
3. Results
3.1. Baseline Characteristics
As shown in Table 1, of the 5591 eligible patients, 3790 patients (67.8%) used β-blockers at discharge and 1801 (32.2%) did not use them. Compared with the no β-blocker group, the β-blocker group showed significantly younger, higher BMI and diastolic blood pressure, higher heart rate, a higher percent of hypertension, DM, CHD, OMI, PCI, and CABG and was more likely to receive antiplatelet therapy or β-blockers before the hospital admission.
Table 1.
Clinical characteristics of patients in the β-blocker and no β-blocker groups.
| Before PS match | After PS match | |||||
|---|---|---|---|---|---|---|
| β-Blocker (-) N = 1801 | β-Blocker (+) N = 3790 | P value | β-Blocker (-) N = 1786 | β-Blocker (+) N = 1786 | P value | |
| Age (years) | 65.0 ± 9.4 | 64.4 ± 9.5 | 0.035 | 65.0 ± 9.4 | 65.0 ± 9.3 | 0.947 |
| Male (%) | 1156 (64.2) | 2379 (62.8) | 0.305 | 1142 (63.9) | 1145 (64.1) | 0.917 |
| BMI (kg/m2) | 25.8 ± 3.4 | 26.1 ± 3.5 | 0.004 | 25.8 ± 3.4 | 25.7 ± 3.4 | 0.298 |
| SBP (mmHg) | 132 ± 17.2 | 132.7 ± 17.3 | 0.144 | 132.1 ± 17.2 | 132.0 ± 16.7 | 0.790 |
| DBP (mmHg) | 75.6 ± 11.4 | 76.8 ± 11.0 | 0.001 | 75.8 ± 11.4 | 76.1 ± 10.9 | 0.387 |
| Heart rate (bpm) | 66 (60,73) | 71 (64,78) | <0.001 | 66 (60,73) | 70 (64,78) | <0.001 |
|
| ||||||
| Medical history | ||||||
| Current smoker (%) | 569 (31.6) | 1186 (31.3) | 0.821 | 562 (31.5) | 548 (30.7) | 0.613 |
| Hypertension (%) | 1216 (67.5) | 2842 (75.0) | <0.001 | 1216 (68.1) | 1226 (68.6) | 0.719 |
| DM (%) | 703 (39.0) | 1616 (42.6) | <0.05 | 700 (39.2) | 712 (39.9) | 0.681 |
| Dyslipidemia (%) | 878 (48.8) | 1909 (50.4) | 0.258 | 873 (48.9) | 862 (48.3) | 0.713 |
| Stroke (%) | 286 (15.9) | 625 (16.5) | 0.563 | 285 (16.0) | 297 (16.6) | 0.587 |
| CHD (%) | 816 (45.3) | 1994 (52.6) | <0.001 | 815 (45.6) | 821 (46.0) | 0.840 |
| OMI (%) | 104 (5.8) | 333 (8.8) | <0.001 | 104 (5.8) | 104 (5.8) | 1.000 |
| CABG (%) | 27 (1.5) | 97 (2.6) | 0.012 | 27 (1.5) | 27 (1.5) | 1.000 |
| PCI (%) | 235 (13) | 696 (18.4) | <0.001 | 235 (13.2) | 229 (12.8) | 0.765 |
|
| ||||||
| Medication used before admission | ||||||
| Antiplatelet agent (%) | 669 (37.1) | 1646 (43.4) | <0.001 | 669 (37.5) | 678 (38.0) | 0.756 |
| ACEI/ARB (%) | 697 (38.7) | 1483 (39.1) | 0.759 | 697 (39.0) | 720 (40.3) | 0.431 |
| β-Blockers (%) | 154 (8.6) | 1378 (36.4) | <0.001 | 154 (8.6) | 605 (33.8) | <0.001 |
| Statins (%) | 595 (33.0) | 1345 (35.5) | 0.072 | 594 (33.3) | 601 (33.7) | 0.804 |
Data are presented as mean ± SD, IQR, or n (%). BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; DM, diabetes mellitus; CHD, coronary heart disease; OMI, old myocardial infarction; CABG, coronary artery bypass grafting; PCI, percutaneous coronary intervention; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker.
As presented in Table 2, the β-blocker group had significantly higher white cell count, platelet count, and higher levels of sensitivity C-reactive protein (hsCRP), FBG, HbA1C%, ALT, and triglyceride at admission than the no β-blocker group. Echo evaluation showed that the β-blocker group had significantly lower LVEF and LVFS than the no β-blocker group. Angiographically, the β-blocker group had a significantly higher percentage of multivessels, chronic total occlusions (CTO), and PCI during hospitalization. In the medication at discharge, the no β-blocker group had significantly more likely to receive antiplatelet therapy, ACEI/ARB, or statins.
Table 2.
Laboratory test results and echocardiographic and angiographic characteristics.
| Before PS match | After PS match | |||||
|---|---|---|---|---|---|---|
| β-Blocker (-) N = 1801 | β-Blocker (+) N = 3790 | P value | β-Blocker (-) N = 1786 | β-Blocker (+) N = 1786 | P value | |
| Laboratory values | ||||||
| WBC (×109/L) | 6.1 (5.2, 7.3) | 6.4 (5.4, 7.5) | <0.001 | 6.2 (5.2, 7.3) | 6.3 (5.3, 7.4) | 0.011 |
| Hemoglobin (g/L) | 135.6 ± 15.9 | 135.4 ± 16.8 | 0.648 | 135.6 ± 15.9 | 135.0 ± 17.3 | 0.245 |
| PLT (×1012/L) | 211.0 (179.0, 248.0) | 215.0 (180.0, 254.0) | 0.021 | 211.0 (179.0, 249.0) | 211.0 (177.0, 250.0) | 0.988 |
| HsCRP (mg/L) | 1.3 (0.6, 2.5) | 1.6 (0.7, 3.3) | <0.001 | 1.3 (0.6, 2.5) | 1.5 (0.6, 3.3) | 0.001 |
| FBG (mmol/l) | 5.8 (5.0, 7.5) | 6.1 (5.1, 8.0) | <0.001 | 5.8 (5.0, 7.5) | 5.9 (5.0, 7.7) | 0.432 |
| HbA1C (%) | 6.0 (5.6, 6.8) | 6.2 (5.7, 7.2) | <0.001 | 6.0 (5.6, 6.8) | 6.1 (5.6, 6.9) | 0.294 |
| ALT (U/L) | 17.0 (12.0, 24.0) | 18.0 (13.0, 26.0) | <0.001 | 17.0 (12.8, 24.0) | 17.0 (13.0, 25.0) | 0.211 |
| Creatinine (μmol/L) | 75.3 (64.9, 86.1) | 75.9 (65.5, 87.9) | 0.354 | 75.2 (64.9, 86.1) | 75.4 (65.5, 86.9) | 0.318 |
| eGFR (mL/min/1.73 m2) | 156.6 (124.1, 191.9) | 153.3 (122.6, 190.9) | 0.084 | 156.6 (124.0,192.0) | 155.0 (124.2,190.5) | 0.594 |
| TC (mmol/L) | 4.1 (3.5, 4.8) | 4.0 (3.4, 4.8) | 0.165 | 4.1 (3.5, 4.8) | 4.1 (3.4, 4.8) | 0.287 |
| TG (mmol/L) | 1.3 (1.0, 1.9) | 1.4 (1.0, 2.0) | <0.001 | 1.3 (1.0, 1.9) | 1.3 (1.0, 1.9) | 0.734 |
| HDL (mmol/L) | 1.1 (0.9, 1.3) | 1.1 (0.9, 1.2) | 0.197 | 1.1 (0.9, 1.3) | 1.1 (0.9, 1.2) | 0.978 |
| LDL (mmol/L) | 2.3 (1.8, 2.8) | 2.2 (1.8, 2.7) | 0.074 | 2.3 (1.8, 2.8) | 2.2 (1.8, 2.7) | 0.127 |
|
| ||||||
| Echocardiographic values | ||||||
| LA (cm) | 3.7 ± 0.4 | 3.7 ± 0.5 | 0.091 | 3.7 ± 0.4 | 3.7 ± 0.5 | 0.406 |
| LVEDD (cm) | 5.0 (4.7, 5.3) | 5.0 (4.7, 5.3) | 0.912 | 5.0 (4.7, 5.3) | 5.0 (4.7, 5.3) | 0.330 |
| LVESD (cm) | 3.1 (2.9, 3.4) | 3.1 (2.9, 3.4) | 0.076 | 3.1 (2.9, 3.4) | 3.1 (2.9, 3.4) | 0.086 |
| LVEF (%) | 0.67 (0.64, 0.71) | 0.67 (0.63, 0.70) | 0.026 | 0.67 (0.64, 0.71) | 0.67 (0.63, 0.70) | 0.129 |
| LVEF (40–49) (%) | 48 (2.7) | 186 (4.9) | <0.001 | 48 (2.7) | 69 (3.9) | 0.048 |
| LVEF (<40) (%) | 10 (0.6) | 58 (1.5) | <0.001 | 10 (0.6) | 24 (1.3) | 0.039 |
| LVFS (%) | 0.38 (0.35, 0.40) | 0.37 (0.34, 0.40) | 0.025 | 0.38 (0.35, 0.40) | 0.37 (0.35,0.40) | 0.138 |
|
| ||||||
| Angiography values | ||||||
| LM (%) | 164 (9.1) | 402 (10.6) | 0.082 | 161 (9.0) | 183 (10.2) | 0.212 |
| Multivessels (%) | 1379 (76.6) | 3155 (83.2) | <0.001 | 1368 (76.6) | 1446 (81.0) | 0.001 |
| CTO (%) | 140 (7.8) | 404 (10.7) | 0.001 | 140 (7.8) | 181 (10.1) | 0.016 |
| PCI (%) | 957 (53.1) | 2277 (60.1) | <0.001 | 950 (53.2) | 1087 (60.9) | <0.001 |
|
| ||||||
| Medication used at discharge | ||||||
| Antiplatelet agent (%) | 1670 (92.7) | 3727 (98.3) | <0.001 | 1657 (92.8) | 1757 (98.4) | <0.001 |
| ACEI/ARB (%) | 798 (44.3) | 1984 (52.3) | <0.001 | 796 (44.6) | 920 (51.5) | <0.001 |
| Statins (%) | 1582 (87.8) | 3508 (92.6) | <0.001 | 1570 (87.9) | 1672 (93.6) | <0.001 |
Data are presented as mean ± SD, IQR, or n (%). WBC, white blood cells; PLT, platelet count; hsCRP, hypersensitivity C-reactive protein; eGFR, estimated glomerular filtration rate; FBG, fasting blood glucose; HbAIC, glycosylated hemoglobin; ALT, alanine aminotransferase; TC, total cholesterol; TG, triglyceride; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; LVEDD, left ventricular end-diastolic dimension; LVESD, left ventricular end-systolic dimension; LVEF, left ventricular ejection fraction; LVFS, left ventricular fraction shortening; LM, left main trunk; CTO, chronic total occlusions; PCI, percutaneous coronary intervention; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker.
Significant correlates of β-blocker therapy in the multivariable analysis are shown in Figure 2. Compared with no β-blockers treated patients at discharge, patients prescribed β-blockers were more likely to be women, aged < 65 years, and had worse baseline clinical: heart rate > 60 bpm, triglyceride > 1.7 mmol/l, and lower LVEF; what is more, the proportion of hypertension, previous PCI, multivessels, and CTO is higher. Also, the β-blockers treated patients were more likely to receive antiplatelet or statins therapy at discharge.
Figure 2.
Factors associated with β-blocker use in multivariable analysis. Variables associated with β-blocker use are shown along the vertical axis. The strength of effect is shown along the horizontal axis with the vertical line demarcating an odds ratio (OR) of 1 (i.e., no association); estimates to the right (i.e., >1) are associated with a greater likelihood of β-blocker use, whereas those to the left (i.e., <1) indicate a reduced likelihood of β-blocker use. Each dot represents the point estimate of the effect of that variable in the model, whereas the line shows the 95% confidence interval (CI). BMI, body mass index; DM, diabetes mellitus; OMI, old myocardial infarction; PCI, percutaneous coronary intervention; HR, heart rate; eGFR, estimated glomerular filtration rate; TG, triglyceride; LVEF, left ventricular ejection fraction; LM, left main trunk; CTO, chronic total occlusions; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker.
3.2. Propensity Score Matching
Propensity scores for β-blocker treatment were calculated for 3572 patients, and 1786 β-blocker users were 1 : 1 matched to 1786 patients without using β-blockers at discharge. As shown in Tables 1 and 2, compared with the no β-blocker group, the β-blocker group showed a significantly higher heart rate at admission, higher levels of white cell count, hsCRP, lower LVEF, and more likely to receive β-blockers before hospital admission. Meanwhile, angiographically, the β-blocker group had a significantly higher percentage of multivessels, CTO, and PCI during CAG. In the medication at discharge, the β-blocker group had significantly more likely to receive antiplatelet therapy, ACEI/ARB, or statins.
There were no significant differences in baseline clinical and past medical history between the β-blockers and no β-blockers used patients for the propensity score-matched subjects.
3.3. Primary and Secondary Outcomes
The median follow-up period was 25.0 months (IQR: 12.3, 49.2 months). Composite MACCE occurred in 1425 patients (25.5%) in the overall population. In the β-blocker group, the incidence rate of composite MACCE was higher than that in the no β-blocker group (26.5% vs. 23.3%, P=0.010, Table 3). We also analyzed the event rate in the subgroups. As shown in Table 3, all-cause death occurred in 2.7% of patients in the no β-blocker group and 3.7% in the β-blocker group (P=0.048), and HF occurred in 1.4% of patients in the no β-blocker group and 2.2% in the β-blocker group (P=0.048), respectively. There was no significant difference in cardiac death, nonfatal MI, nonfatal stroke, and cardiac rehospitalization.
Table 3.
Major adverse cardiac and cerebral events in patients with UAP in the β-blocker and no β-blocker groups.
| Before PS match | After PS match | |||||
|---|---|---|---|---|---|---|
| β-Blocker (-) N = 1801 | β-Blocker (+) N = 3790 | P value | β-Blocker (-) N = 1786 | β-Blocker (+) N = 1786 | P value | |
| Event | ||||||
| Composite MACCE | ||||||
| No. of patients | 420 | 1005 | 417 | 463 | ||
| Event rate (%) | 23.3 | 26.5 | 0.010 | 23.3 | 25.9 | 0.074 |
| Unadjusted HR (95% CI) | 1.00 | 1.08 (0.96, 1.21) | 0.212 | 1.00 | 1.04 (0.91, 1.19) | 0.545 |
| Adjusted HR (95% CI) | 1.00 | 0.98 (0.87, 1.10) | 0.725 | 1.00 | 0.98 (0.86, 1.13) | 0.812 |
|
| ||||||
| All-cause death | ||||||
| No. of patients | 49 | 142 | 49 | 70 | ||
| Event rate (%) | 2.7 | 3.7 | 0.048 | 2.7 | 3.9 | 0.050 |
| Unadjusted HR (95% CI) | 1.00 | 1.29 (0.93, 1.78) | 0.129 | 1.00 | 1.34 (0.93, 1.93) | 0.114 |
| Adjusted HR (95% CI) | 1.00 | 1.05 (0.75, 1.47) | 0.769 | 1.00 | 1.30 (0.90, 1.89) | 0.163 |
|
| ||||||
| Cardiac death | ||||||
| No. of patients | 17 | 53 | 17 | 26 | ||
| Event rate (%) | 0.9 | 1.4 | 0.153 | 1.0 | 1.5 | 0.167 |
| Unadjusted HR (95% CI) | 1.00 | 1.38 (0.80, 2.38) | 0.248 | 1.00 | 1.43 (0.78, 2.63) | 0.253 |
| Adjusted HR (95% CI) | 1.00 | 0.98 (0.56, 1.71) | 0.930 | 1.00 | 1.27 (0.68, 2.39) | 0.454 |
|
| ||||||
| Nonfatal MI | ||||||
| No. of patients | 32 | 72 | 32 | 35 | ||
| Event rate (%) | 1.8 | 1.9 | 0.751 | 1.8 | 2.0 | 0.711 |
| Unadjusted HR (95% CI) | 1.00 | 1.01 (0.67, 1.53) | 0.958 | 1.00 | 1.04 (0.65, 1.69) | 0.864 |
| Adjusted HR (95% CI) | 1.00 | 0.88 (0.57, 1.35) | 0.558 | 1.00 | 1.06 (0.65, 1.72) | 0.814 |
|
| ||||||
| Nonfatal stroke | ||||||
| No. of patients | 14 | 41 | 14 | 17 | ||
| Event rate (%) | 0.8 | 1.1 | 0.281 | 0.8 | 1.0 | 0.588 |
| Unadjusted HR (95% CI) | 1.00 | 1.28 (0.70, 2.35) | 0.423 | 1.00 | 1.12 (0.55, 2.27) | 0.757 |
| Adjusted HR (95% CI) | 1.00 | 1.04 (0.56, 1.94) | 0.891 | 1.00 | 1.13 (0.56, 2.31) | 0.732 |
|
| ||||||
| Heart failure | ||||||
| No. of patients | 25 | 82 | 18 | 29 | ||
| Event rate (%) | 1.4 | 2.2 | 0.048 | 1.0 | 1.6 | 0.106 |
| Unadjusted HR (95% CI) | 1.00 | 1.48 (0.95, 2.32) | 0.085 | 1.00 | 1.55 (0.56, 2.78) | 0.147 |
| Adjusted HR (95% CI) | 1.00 | 1.20 (0.78, 1.89) | 0.445 | 1.00 | 1.28 (0.71, 2.32) | 0.409 |
|
| ||||||
| Cardiac rehospitalization | ||||||
| No. of patients | 375 | 874 | 372 | 400 | ||
| Event rate (%) | 20.8 | 23.1 | 0.060 | 20.8 | 22.4 | 0.255 |
| Unadjusted HR (95% CI) | 1.00 | 1.05 (0.93, 1.18) | 0.456 | 1.00 | 1.02 (0.88, 1.17) | 0.803 |
| Adjusted HR (95% CI) | 1.00 | 0.96 (0.85, 1.09) | 0.554 | 1.00 | 0.99 (0.86, 1.14) | 0.889 |
Data are presented as number or HR (95% CI). MACCE, major adverse cardiac and cerebral events; MI, myocardial infarction.
The univariate Cox proportional hazards regression analysis showed that there was no difference significantly between those on and not on β-blockers in the all-cause death, HF, nonfatal MI, nonfatal stroke, and cardiac rehospitalization groups. In addition, the adjusted hazard ratios (HRs) for composite MACCE, all-cause mortality, HF, nonfatal MI, nonfatal stroke, and cardiac rehospitalization also had no significant difference in those on β-blockers and those not on β-blockers (P > 0.05). The multivariate Cox proportional hazards regression analysis showed that both the β-blocker patients and no β-blockers treated patients had a similar risk of composite MACCE, all-cause mortality, HF, nonfatal MI, nonfatal stroke, or cardiac rehospitalization.
To further verify these results, we performed a sensitivity analysis using propensity score matching. These results did not change before or after further adjustment; all-cause death and the incidence of HF, nonfatal MI, nonfatal stroke, and cardiac rehospitalization in patients were similar in those on β-blockers and those not on β-blockers (Table 3).
We also analyzed the secondary endpoints. The incidence of all-cause death/HF in the β-blocker group was higher than that in the no β-blocker group (P=0.003), and the univariate Cox proportional hazards regression analysis showed that using β-blockers was a risk factor for all-cause death/HF (P=0.003), but after multivariate Cox proportional hazards regression analysis, the role of β-blockers disappeared (P=0.296). Similarly, in the groups of all-cause death/HF/nonfatal MI and all-cause death/HF/nonfatal MI/nonfatal stroke, the event incidences were also higher in the β-blockers used patients than that in no β-blockers (7.2% vs. 5.2%, P=0.006; 8.0% vs. 6.0%, P=0.006). β-blockers were risk factors in univariate Cox proportional hazards regression analysis (all P < 0.05), whereas these disappeared after multivariate Cox proportional hazards regression analysis.
After propensity score matching, the incidence of secondary outcomes of matched patients in the β-blocker group was also higher, and β-blockers were risk factors in the group of all-cause death/HF and all-cause death/HF/nonfatal MI, but there were also no significant differences observed after multivariate Cox proportional hazards regression analysis between the two groups (Table 4).
Table 4.
Secondary endpoints in patients with UAP in the β-blocker and no β-blocker groups.
| Before PS match | After PS match | |||||
|---|---|---|---|---|---|---|
| β-Blocker (-) N = 1801 | β-Blocker (+) N = 3790 | P value | β-Blocker (-) N = 1786 | β-Blocker (+) N = 1786 | P value | |
| Event | ||||||
| All-cause death or heart failure | ||||||
| No. of patients | 68 | 214 | 67 | 99 | ||
| Event rate (%) | 3.8 | 5.6 | 0.003 | 3.8 | 5.5 | 0.011 |
| Unadjusted HR (95% CI) | 1.00 | 1.41 (1.07, 1.85) | 0.013 | 1.00 | 1.40 (1.03, 1.91) | 0.033 |
| Adjusted HR (95% CI) | 1.00 | 1.16 (0.88,1.53) | 0.296 | 1.00 | 1.20 (0.88, 1.65) | 0.256 |
|
| ||||||
| All-cause death, heart failure, or nonfatal MI | ||||||
| No. of patients | 94 | 272 | 93 | 128 | ||
| Event rate (%) | 5.2 | 7.2 | 0.006 | 5.2 | 7.2 | 0.015 |
| Unadjusted HR (95% CI) | 1.00 | 1.30 (1.03, 1.65) | 0.027 | 1.00 | 1.31 (1.01, 1.71) | 0.046 |
| Adjusted HR (95% CI) | 1.00 | 1.08 (0.85, 1.38) | 0.520 | 1.00 | 1.17 (0.89,1.53) | 0.267 |
|
| ||||||
| All-cause death, heart failure, nonfatal MI, or nonfatal stroke | ||||||
| No. of patients | 108 | 305 | 107 | 141 | ||
| Event rate (%) | 6.0 | 8.0 | 0.006 | 6.0 | 7.9 | 0.025 |
| Unadjusted HR (95% CI) | 1.00 | 1.27 (1.02, 1.58) | 0.035 | 1.00 | 1.25 (0.97, 1.61) | 0.082 |
| Adjusted HR (95% CI) | 1.00 | 1.04 (0.83, 1.31) | 0.712 | 1.00 | 1.11 (0.86, 1.44) | 0.410 |
Data are presented as number or HR (95% CI). MI, myocardial infarction.
3.4. Survival
In survival analysis, composite MACCE was no significant differences between the two groups. After adjusting for baseline clinical and propensity scores, there were also no significant differences (Figure 3).
Figure 3.
Kaplan–Meier curve for MACCE before (a) and after (b) propensity score matching patients in the β-blocker and no β-blocker groups. MACCE, major adverse cardiac and cerebral events.
Figure 4 showed the Kaplan–Meier curves for the secondary endpoints at 25.0 months (IQR: 12.3, 49.2 months) of median follow-up period. In the outcome of all-cause death/HF, the β-blocker group had a significantly higher incidence than the no β-blocker group (P=0.013). In terms of all-cause death/HF/nonfatal MI and all-cause death/HF/nonfatal MI/nonfatal stroke, the β-blocker group also had a significantly higher incidence than the no β-blocker group, respectively (P=0.027; P=0.034). After adjusting for baseline clinical and propensity scores, the incidence of all-cause death/HF and all-cause death/HF/nonfatal MI was also higher in the β-blocker patients than in no β-blocker patients (P=0.032; P=0.045). However, after propensity score matching, the incidence of all-cause death/HF/nonfatal MI/nonfatal stroke was not statistically different between the two groups (P=0.081).
Figure 4.
Kaplan-Meier curve for secondary endpoints before (a, b, c) and after (d, e, f) propensity score matching patients in the β-blocker and the no β-blocker group.
Before or after propensity score matching, the incidence of all-cause death was not statistically different between the two groups.
3.5. Independent Association between β-Blockers and Endpoints
In the multivariate analysis, we included variables that were identified to be significantly associated with secondary endpoints and composite MACCE in the univariate model. The multivariate analysis revealed that β-blocker therapy at discharge was not associated with primary and secondary endpoints (Tables 5 and 6); age, previous history of stroke, multivessels, left main trunk (LM), lower LVEF, higher HbA1C, hsCRP, and heart rate at admission were significantly and independently associated with the endpoints of all-cause death/HF and all-cause death/HF/nonfatal MI (Table 5). In terms of the secondary endpoint of all-cause death/HF/nonfatal MI/nonfatal stroke, age, previous history of stroke, multivessels, CTO, lower LVEF higher HbA1C, and hsCRP were the independent risk factors. With LVEF that decreased, the incidence rate of the three secondary endpoints increased correspondingly.
Table 5.
Multivariate Cox regression analysis of secondary endpoints.
| Univariate | Multivariate | |||||||
|---|---|---|---|---|---|---|---|---|
| HR (95% CI) | P value | A | B | C | ||||
| HR (95% CI) | P value | HR (95% CI) | P value | HR (95% CI) | P value | |||
| Age (years) | 1.07 (1.06, 1.08) | <0.001 | 1.07 (1.05, 1.08) | <0.001 | 1.05 (1.04, 1.07) | <0.001 | 1.05 (1.04, 1.06) | <0.001 |
| Male (%) | 0.89 (0.68, 1.09) | 0.205 | ||||||
| Stroke (%) | 1.86 (1.43, 2.43) | <0.001 | 1.44 (1.10, 1.88) | 0.008 | 1.38 (1.09, 1.76) | 0.008 | 1.3861.08, 1.70) | 0.009 |
| Heart rate (bpm) | 1.02 (1.01, 1.03) | <0.001 | 1.02 (1.01, 1.03) | <0.001 | 1.01 (1.00, 1.02) | 0.002 | 1.01 (1.00, 1.02) | <0.001 |
| hsCRP (mg/L) | 1.03 (1.02, 1.05) | <0.001 | 1.02 (1.00, 1.03) | 0.027 | 1.02 (1.00, 1.03) | 0.034 | 1.02 (1.00, 1.03) | 0.026 |
| HbA1C (%) | 1.14 (1.06, 1.23) | <0.001 | 1.09 (1.01, 1.19) | 0.032 | 1.15 (1.07, 1.23) | <0.001 | 1.15 (1.07, 1.22) | <0.001 |
| LVEF ≥ 50% | Ref | Ref | Ref | Ref | ||||
| 40% ≤ LVEF < 50% | 2.63 (1.67, 4.15) | <0.001 | 1.94 (1.21, 3.10) | 0.006 | 1.69 (1.10, 2.61) | 0.017 | 1.78 (1.19, 2.66) | 0.005 |
| LVEF < 40% | 6.41 (3.92, 10.49) | <0.001 | 6.24 (3.76, 10.35) | <0.001 | 4.42 (2.69, 7.29) | <0.001 | 4.25 (2.65, 6.81) | <0.001 |
| LM (%) | 2.06 (1.53, 2.78) | <0.001 | 1.47 (1.08, 2.00) | 0.016 | 1.39 (1.06, 1.84) | 0.019 | ||
| Multivessels (%) | 3.58 (2.19, 5.85) | <0.001 | 2.47 (1.50, 4.08) | <0.001 | 2.30 (1.51, 3.52) | <0.001 | 2.28 (1.56, 3.37) | <0.001 |
| CTO (%) | 2.03 (1.50, 2.76) | <0.001 | ||||||
| β-Blockers at discharge (%) | 1.41 (1.07, 1.85) | 0.013 | ||||||
A, present all-cause death/HF; B, present all-cause death/HF/nonfatal MI; C, present all-cause death/HF/nonfatal MI/nonfatal stroke. MI, myocardial infarction; HbAIC, glycosylated hemoglobin; hsCRP, hypersensitivity C-reactive protein; LVEF, left ventricular ejection fraction; LM, left main trunk; CTO, chronic total occlusions.
Table 6.
Multivariate Cox regression analysis of secondary endpoints in propensity score matching patients.
| Univariate | Multivariate | |||||
|---|---|---|---|---|---|---|
| HR (95% CI) | P value | A | B | |||
| HR (95% CI) | P value | HR (95% CI) | P value | |||
| Age (years) | 1.08 (1.06, 1.10) | <0.001 | 1.07 (1.05, 1.09) | <0.001 | 1.06 (1.04, 1.07) | <0.001 |
| Male (%) | 0.85 (0.63, 1.17) | 0.320 | ||||
| Stroke (%) | 1.90 (1.35, 2.68) | <0.001 | 1.47 (1.08, 1.98) | 0.014 | ||
| Heart rate (bpm) | 1.02 (1.01, 1.03) | <0.001 | 1.02 (1.01, 1.03) | 0.001 | 1.01 (1.00, 1.02) | 0.030 |
| hsCRP (mg/L) | 1.02 (1.00, 1.05) | 0.031 | ||||
| HbA1C (%) | 1.15 (1.04, 1.28) | 0.008 | 1.19 (1.08, 1.30) | <0.001 | ||
| LVEF ≥ 50% | Ref | Ref | Ref | |||
| 40% ≤ LVEF < 50% | 2.93 (1.59, 5.41) | 0.001 | 2.23 (1.19, 4.16) | 0.012 | 1.89 (1.07, 3.34) | 0.029 |
| LVEF < 40% | 1.30 (0.32, 5.24) | 0.714 | 1.19 (0.29, 4.87) | 0.814 | 0.87 (0.21, 3.54) | 0.845 |
| LM (%) | 2.47 (1.70, 3.61) | <0.001 | 1.62 (1.09, 2.39) | 0.016 | 1.52 (1.08, 2.16) | 0.017 |
| Multivessels (%) | 3.78 (2.05, 6.97) | <0.001 | 260 (1.39, 4.85) | 0.003 | 2.22 (1.34, 3.69) | 0.002 |
| CTO (%) | 2.42 (1.64, 3.58) | <0.001 | 1.71 (1.13, 2.57) | 0.010 | ||
| β-Blockers at discharge (%) | 1.40 (1.03, 1.91) | 0.033 | ||||
A, present all-cause death/HF; B, present all-cause death/HF/nonfatal MI. MI, myocardial infarction; HbAIC, glycosylated hemoglobin; hsCRP, hypersensitivity C-reactive protein; LVEF, left ventricular ejection fraction; LM, left main trunk; CTO, chronic total occlusions.
As shown in Table 6, after propensity score matching, age, multivessels, CTO, LM, lower LVEF, and higher heart rate were significantly and independently associated with the endpoints of all-cause death/HF. Age, previous history of stroke, multivessels, LM, lower LVEF, higher heart rate, and HbA1C were the risk factors for the secondary endpoints of all-cause death/HF/nonfatal MI.
4. Discussion
We found that patients with β-blockers had more CHD risk factors, lower LVEF, more severe coronary artery disease, and were more likely to use other secondary prevention of coronary heart disease at the discharge of our data. Compared with the no β-blockers used group, β-blocker therapy at discharge was associated with a similar risk of MACCE during 25 months of median follow-up period. After propensity score analysis, the result was also similar, which was consistent with other observational analyses.
As it is well known, the sympathetic adrenergic nervous system plays a fundamental role in the homeostatic regulation of cardiovascular function. Some diseases presenting decreased myocardial function can elicit activity from the sympathetic nervous system, which leads to the release of catecholamines through the G protein-coupled receptor (GPCR) system and leads to increased mechanical stress on the failing heart and causing harmful electrical and structural events [11, 12]. Moreover, augmented levels of catecholamines can cause myocardial damage via enhanced cardiac oxygen demand and by increasing peroxidative metabolism [12]. α1-Adrenergic receptors (ARs) and β-ARs are the two major categories of myocardial ARs. α1-AR stimulation can activate the enzyme phospholipase C (PLC). Activation of PLC generates the second messengers, inositol trisphosphate (IP3) and 2-diacylglycerol (DAG), ultimately ending with the release of intracellular calcium, producing positive inotropy (especially in failing myocardium), promotes adaptive hypertrophy, induces ischemic preconditioning, and prevents cardiac myocyte death [13]. Stimulation β1-ARs and β2-ARs can activate the stimulatory G protein/adenylyl-cyclase/cAMP/protein kinase A (PKA) signaling pathway, enhancing myocardial contractility and heart rate [11]. In addition, β1-ARs are associated with extracellular signal-regulated kinases (ERK) 1 and 2, and β-blockers can stimulate ERK1 and ERK2, which can produce cardioprotective during ischemia and heart failure [14]. G protein-coupled receptor kinase 2 (GRK2) is the most important isoform related to cardiac physiology. GRK2 appears to regulate cardiomyocyte function in part by controlling β1-AR in the regulation of cardiac contractility and chronotropic. GRK2 levels may reflect hemodynamic impairment and might have a meaningful prognostic value after myocardial infarction. Furthermore, GRK2 upregulation also affects cardiac metabolism and, in particular, myocardial glucose uptake [11]. Stimulation of β-ARs also can affect cytokines. β-Adrenoceptor-mediated activation of cAMP-responsive element and activating protein-1 directly contributes to interleukin-6 induction in the failing myocardium, which can prevent decompensation during cardiac overload and attenuates β-adrenergic inotropy [15]. Fibroblast growth factor 21 (FGF21) is induced by catecholamine via AMP-activated protein kinase activation in cardiomyocytes and FGF21 in turn activates FGF21 expression and AMPK pathway, having antioxidative and anti-hypertrophic effects [16]. In addition, β2-ARs also can activate the inhibitory G protein and β-arrestins, which have a major role in cardiomyocyte growth and cardiac hypertrophy. A previous study showed that antagonists cannot influence β2-ARs by combining β-arrestins [17]. GRK2 is implicated in the phosphorylation-dependent desensitization of β2-AR, which results in the dissociation of G protein from the receptor and its subsequent internalization mediated by β-arrestins, could be implicated in osteogenic differentiation of vascular smooth muscle cells or pericytes during artery calcification, and will be associated with an increased coronary artery calcification progression [18]. Recently, it has also been found that β3-ARs exist in the cardiomyocytes. β3-ARs are typically activated by high concentrations of catecholamines, stimulating nitric oxide synthase, thereby increasing cGMP levels and activating protein kinase G, which has potential negative inotropic effects [11]. Cardiac hypertrophy and heart failure are typically characterized by derangement of β-AR signaling and a reduction in the adrenergic reserve of the heart. This is primarily due to the selective downregulation of β1-AR density at the plasma membrane and by the uncoupling of the remaining β1-ARs and β2-ARs from G proteins. The previous study also showed that the elevated sympathetic activity in chronic heart failure cause enhanced GRK2-mediated cardiac β1-AR and β2-AR desensitization and β1-AR downregulation, the compensatory upregulation of β3-ARs, eventually leading to the progressive loss of the adrenergic, inotropic reserves of the heart, and the deterioration of cardiac function [11].
Therefore, counteracting adrenergic overdrive via β-AR antagonists reduces cardiac workload and increases O2 sparing in patients with failing hearts. Therefore, the consensus guidelines recommended early use in all UAP and non-ST-segment elevation myocardial fraction patients without contraindications within 24 hours. However, the usage of β-blockers can cause glucose and lipid metabolism disorders and is due to the blockade of β2-AR-dependent insulin release from the pancreatic islets of Langerhans [11, 19]. Selective β-blocker usage did not contribute to the glucose metabolism [19]. In addition, animal experiments show that β1-ARs can mediate vasodilator responses of rat cerebral arteries, implying that β-blockers may impair cerebral blood flow under some conditions, inducing ischemic stroke [20]. Thus, it has a good prospect for shifting the paradigm from purely adrenergic blockade to comprehensive adrenergic modulation.
Indeed, current research results about the application are inconsistent. Current advances mostly come from patients with myocardial infarction and heart failure, and it remains unclear whether other CHD patients benefit from β-blockers. β-blockers are beneficial in STEMI patients if given early PCI and hemodynamically stable, and this effect of β-blockers was largely driven by a reduction in ventricular arrhythmias and reinfarction [21, 22], and it may improve survival rate. Tetsuro et al. [23] showed that the use of β-blockers in patients with myocardial infarction or HF with reduced left ventricular ejection fraction and DM was associated with a decreased risk of all-cause mortality. However, some studies emphasized that β-blockers have no benefit, which has long been reflected in the clinical guidelines, which recommend early use [7]. Meta-analysis revealed that β-blockers do not provide any survival benefit in patients with angiographic CHD without a history of myocardial infarction or reduced ejection fraction [24]. Another study showed that β-blockers do not decrease the mortality of patients with post-myocardial infarction, especially more than 1 year after myocardial infarction[25]. The genetic variants and race differences are also associated with survival among ACS patients treated with β-blockers [26]. Therefore, the current use of β-blockers for risk reduction has increasingly come under question [27, 28]. These results are mostly confined to patients with AMI. Therefore, the evidence for using β-blockers in current UAP patients is lacking.
In our study, β-blockers appear to be frequently utilized (nearly 2 of every 3 patients) in patients with UAP at discharge. About the unadjusted analysis between those who did and did not receive β-blockers, the results revealed a large difference in all-cause death/HF, all-cause death/HF/non-fatal MI, and all-cause death/HF/non-fatal MI/non-fatal stroke. But after adjusted analysis, the difference was not observed. After propensity score analysis, β-blocker use was associated with a significant increase in the composite endpoint of all-cause death/HF and all-cause death/HF/nonfatal MI in this patient group. Similarly, the difference was not observed after the adjusted analysis. These conditions likely reflect the fact that unadjusted analysis in the observational studies might be influenced by selection bias and some confounding factors. β-blockers at discharge were associated with a nonsignificant difference in the risk of all-cause death, cardiac death, and HF, respectively, during the follow-up period.
We did not observe the benefit of β-blockers in UAP patients and the reason may be that first the proportion of PCI in all the selected patients was 57.8%, and successful PCI maybe reduces the mobility of recurrence of ischemic heart disease, and it will offset the benefit of β-blockers. Second, although β-blockers exert their effects by competitively inhibiting catecholamine binding to β-receptors [23], patients with UAP may have lower sympathetic excitability than AMI, which leads to hypofunction of α1-AR and β-ARs. The role may be rare. Third, β-blockers also had some side effects on both glucose and lipid metabolism that theoretically could increase the risk of cardiovascular disease [29–31]. Fourth, β-blockers had differences in hemodynamic effects, which can reduce brachial blood pressure, not central systolic blood pressure [32]. Fifth, the variability of β-blocker usage in acute coronary syndrome might be related in part to genetic heterogeneity [26]. Sixth, β-blockers may only block β1-ARs and β2-ARs, but not block β3-ARs, producing potential negative inotropic effects. These disadvantages of β-blockers may become more evident in normal clinical practice. These may be why the values were not revealed.
To date, several studies have shown that elevated heart rate was associated with an increased risk of long-term mortality after AMI [33, 34]. Our results were consistent with those studies. We observed that heart rate at admission was an independent risk factor for all-cause death/HF, all-cause death/HF/nonfatal MI, and all-cause death/HF/nonfatal MI/nonfatal stroke after multivariate regression analysis, not β-blockers. After propensity score matching, this phenomenon also appeared in the secondary composite endpoints of all-cause death/HF and all-cause death/HF/nonfatal MI. The results show that reducing heart rate at admission can benefit in the long-term follow-up period. In other words, using β-blockers in UAP patients is suboptimum, but controlling heart rate at admission can reduce MACCE and secondary endpoints.
5. Study Limitations
First, this is an observational study performed at a single national center. Second, only patients who survived the hospital stay were included, and the role of in-hospital β-blockers was not investigated. In addition, there was no information in this study about rates of discontinuation, duration or doses, and kinds of β-blockers after hospital discharge. Last but not least, the low ejection fraction was rarely in our data and therefore not included in our subgroup analysis, and we did not analyze the heart rate at discharge. The result may be partial.
6. Conclusion
Among patients who survived hospitalization with UAP, β-blocker use was not associated with lower MACCE and other secondary composite endpoints in long-term outcomes. This result adds to the increasing body of evidence that the routine prescription of β-blockers might not be indicated in patients with UAP.
Acknowledgments
The authors gratefully acknowledge the contributions of all staff who work at the Cardiovascular Center of Beijing Friendship Hospital Data Bank (CBD BANK). This study was supported by the National Key R&D Program of China (No. 2021ZD0111004), Natural Science Foundation of China (No. 82070357), and Beijing Municipal Administration of Hospital Incubating Program (No. PX2018002).
Abbreviations
- BMI:
Body mass index
- SBP:
Systolic blood pressure
- DBP:
Diastolic blood pressure
- MI:
Myocardial infarction
- CHD:
Coronary heart disease
- OMI:
Old myocardial infarction
- DM:
Diabetes mellitus
- WBC:
White blood cells
- hsCRP:
Hypersensitivity C-reactive protein
- eGFR:
Estimated glomerular filtration rate
- FBG:
Fasting blood glucose
- TC:
Total cholesterol
- TG:
Triglyceride
- LDL-C:
Low-density lipoprotein cholesterol
- HDL-C:
High-density lipoprotein cholesterol
- LVEDD:
Left ventricular end-diastolic dimension
- LVESD:
Left ventricular end-systolic dimension
- LVEF:
Left ventricular ejection fraction
- LVFS:
Left ventricular fraction shortening
- LM:
Left main trunk
- PCI:
Percutaneous coronary intervention
- CABG:
Coronary artery bypass grafting
- ACEI:
Angiotensin-converting enzyme inhibitor
- ARB:
Angiotensin II receptor blocker
- PLT:
Platelet count
- HbAIC:
Glycosylated hemoglobin
- ALT:
Alanine aminotransferase
- CTO:
Chronic total occlusions
- ACEI:
Angiotensin-converting enzyme inhibitor
- ARB:
Angiotensin II receptor blocker
- MACCE:
Major adverse cardiac and cerebral events.
Contributor Information
Weiping Li, Email: xueer09@163.com.
Hongwei Li, Email: lhw19656@sina.com.
Data Availability
The datasets used and/or analyzed during this study are available from the corresponding author on reasonable request.
Ethical Approval
The study data collections were approved by the Institutional Review Board of Beijing Friendship Hospital, Capital Medical University (2021-P2-107-02).
Consent
Written informed consent was obtained from all patients.
Conflicts of Interest
The authors declare that they have no conflicts of interest.
Authors' Contributions
HWL and WPL contributed to the conception or design of the work. LL, XSD, and WPL contributed to the acquisition, analysis, or interpretation of data for the work. HC discussed and edited the manuscript. LL drafted the manuscript. All authors critically revised the manuscript. All authors gave final approval and agreed to be accountable for all aspects of the work ensuring integrity and accuracy.
References
- 1.Zhang Y., Ding X., Hua B., et al. Real-world use of ACEI/ARB in diabetic hypertensive patients before the initial diagnosis of obstructive coronary artery disease: patient characteristics and long-term follow-up outcome. Journal of Translational Medicine . 2020;18(1):p. 150. doi: 10.1186/s12967-020-02314-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Roth G. A., Johnson C., Abajobir A., et al. Global, regional, and national burden of cardiovascular diseases for 10 causes, 1990 to 2015. Journal of the American College of Cardiology . 2017;70:1–25. doi: 10.1016/j.jacc.2017.04.052. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Ibanez B., James S., Agewall S., et al. 2017 ESC guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation: the task force for the management of acute myocardial infarction in patients presenting with ST-segment elevation of the European society of cardiology (ESC) European Heart Journal . 2018;39(2):119–177. doi: 10.1093/eurheartj/ehx393. [DOI] [PubMed] [Google Scholar]
- 4.Amsterdam E. A., Wenger N. K., Brindis R. G., et al. 2014 AHA/ACC guideline for the management of patients with non–ST-elevation acute coronary syndromes: executive summary. Circulation . 2014;130(25):2354–2394. doi: 10.1161/cir.0000000000000133. [DOI] [PubMed] [Google Scholar]
- 5.The Norwegian Multicenter Study Group. Timolol-induced reduction in mortality and reinfarction in patients surviving acute myocardial infarction. New England Journal of Medicine . 1981;304(14):801–807. doi: 10.1056/nejm198104023041401. [DOI] [PubMed] [Google Scholar]
- 6.Effect of metoprolol CR/XL in chronic heart failure: metoprolol CR/XL randomised intervention trial in congestive heart failure (MERIT-HF) Lancet . 1999;353:2001–2007. [PubMed] [Google Scholar]
- 7.Anderson J. L., Adams C. D., Antman E. M., et al. ACC/AHA 2007 guidelines for the management of patients with unstable angina/non-ST-elevation myocardial infarction: a report of the American college of cardiology/American heart association task force on practice guidelines (writing committee to revise the 2002 guidelines for the management of patients with unstable angina/non-ST-elevation myocardial infarction) developed in collaboration with the American college of emergency physicians, the society for cardiovascular angiography and interventions, and the society of thoracic surgeons endorsed by the American association of cardiovascular and pulmonary rehabilitation and the society for academic emergency medicine. Journal of the American College of Cardiology . 2007;50(7) doi: 10.1016/j.jacc.2007.02.013. [DOI] [PubMed] [Google Scholar]
- 8.Jneid H., Anderson J. L., Wright R. S., et al. ACCF/AHA focused update of the guideline for the management of patients with unstable angina/non-ST-elevation myocardial infarction (updating the 2007 guideline and replacing the 2011 focused update): a report of the American college of cardiology foundation/American heart association task force on practice guidelines. Journal of the American College of Cardiology . 2012;60(7):645–681. doi: 10.1016/j.jacc.2012.06.004. [DOI] [PubMed] [Google Scholar]
- 9.Fihn S. D., Gardin J. M., Abrams J., et al. 2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS guideline for the diagnosis and management of patients with stable ischemic heart disease: a report of the American college of cardiology foundation/American heart association task force on practice guidelines, and the American college of physicians, American association for thoracic surgery, preventive cardiovascular nurses association, society for cardiovascular angiography and interventions, and society of thoracic surgeons. Circulation . 2012;126(25):e354–471. doi: 10.1161/CIR.0b013e318277d6a0. [DOI] [PubMed] [Google Scholar]
- 10.Hamm C. W., Bassand J. P., Agewall S., et al. ESC guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation. the task force for the management of acute coronary syndromes (ACS) in patients presenting without persistent ST-segment elevation of the European society of cardiology (ESC) Glomale Italiano di Caradiologia . 2012;13(3):171–228. doi: 10.1714/1038.11322. [DOI] [PubMed] [Google Scholar]
- 11.Ciccarelli M. Endocrinology of the Heart in Health and Disease . Cambridge, MA, USA: Academic Press; 2017. [Google Scholar]
- 12.Santulli G., Iaccarino G. Adrenergic signaling in heart failure and cardiovascular aging. Maturitas . 2016;93:65–72. doi: 10.1016/j.maturitas.2016.03.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Zhang J., Simpson P. C., Jensen B. C. Cardiac α1A-adrenergic receptors: emerging protective roles in cardiovascular diseases. American Journal of Physiology - Heart and Circulatory Physiology . 2021;320(2):H725–H733. doi: 10.1152/ajpheart.00621.2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Nasrollahi-Shirazi S., Sucic S., Yang Q., Freissmuth M., Nanoff C. Comparison of the β-adrenergic receptor antagonists landiolol and esmolol: receptor selectivity, partial agonism, and pharmacochaperoning actions. Journal of Pharmacology and Experimental Therapeutics . 2016;359(1):73–81. doi: 10.1124/jpet.116.232884. [DOI] [PubMed] [Google Scholar]
- 15.Rohrbach S., Engelhardt S., Lohse M. J., Werdan K., Holtz J., Muller-Werdan U. Activation of AP-1 contributes to the beta-adrenoceptor-mediated myocardial induction of interleukin-6. Molecular Medicine . 2007;13(11-12):605–614. doi: 10.2119/2007-00071.rohrbach. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Sunaga H., Koitabashi N., Iso T., et al. Activation of cardiac AMPK-FGF21 feed-forward loop in acute myocardial infarction: role of adrenergic overdrive and lipolysis byproducts. Scientific Reports . 2019;9(1) doi: 10.1038/s41598-019-48356-1.11841 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Littmann T., Göttle M., Reinartz M. T., et al. Recruitment of -arrestin 1 and 2 to the 2-adrenoceptor: analysis of 65 ligands. Journal of Pharmacology and Experimental Therapeutics . 2015;355:183–190. doi: 10.1124/jpet.115.227959. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Gambardella J., Wang X., Mone P., Khondkar W., Santulli G. Genetics of adrenergic signaling drives coronary artery calcification. Atherosclerosis . 2020;310:88–90. doi: 10.1016/j.atherosclerosis.2020.07.025. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Wormgoor S. G., Dalleck L. C., Zinn C., Borotkanics R., Harris N. K. High-intensity interval training is equivalent to moderate-intensity continuous training for short- and medium-term outcomes of glucose control, cardiometabolic risk, and microvascular complication markers in men with type 2 diabetes. Frontiers in Endocrinology . 2018;9 doi: 10.3389/fendo.2018.00475. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Moore C. L., Henry D. S., McClenahan S. J., Ball K. K., Rusch N. J., Rhee S. W. Metoprolol impairs β1-adrenergic receptor-mediated vasodilation in rat cerebral arteries: implications for β-blocker therapy. Journal of Pharmacology and Experimental Therapeutics . 2021;376(1):127–135. doi: 10.1124/jpet.120.000176. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Puymirat E., Riant E., Aissaoui N., et al. β blockers and mortality after myocardial infarction in patients without heart failure: multicentre prospective cohort study. BMJ . 2016;354 doi: 10.1136/bmj.i4801.i4801 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Chen Z. M., Pan H. C., Chen Y. P., et al. Early intravenous then oral metoprolol in 45, 852 patients with acute myocardial infarction: randomised placebo-controlled trial. Lancet . 2005;366(9497):1622–1632. doi: 10.1016/S0140-6736(05)67661-1. [DOI] [PubMed] [Google Scholar]
- 23.Tsujimoto T., Sugiyama T., Kajio H. Effects of β-blockers on all-cause mortality in patients with type 2 diabetes and coronary heart disease. Diabetes, Obesity and Metabolism . 2017;19(6):800–808. doi: 10.1111/dom.12878. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Jaradat M., Shetty K., Hasan M., et al. Beta-blockers do not provide survival benefit in a population with angiographic coronary artery disease without myocardial infarction or reduced ejection fraction: a meta-analysis. International Journal of Cardiology . 2016;223:976–980. doi: 10.1016/j.ijcard.2016.08.239. [DOI] [PubMed] [Google Scholar]
- 25.Shang C. β-Blocker use for patients with or at risk for coronary artery disease. JAMA . 2013;309(5):p. 438. doi: 10.1001/jama.2012.128869. [DOI] [PubMed] [Google Scholar]
- 26.Cresci S., Dorn G. W., Jones P. G., et al. Adrenergic-pathway gene variants influence beta-blocker-related outcomes after acute coronary syndrome in a race-specific manner. Journal of the American College of Cardiology . 2012;60(10):898–907. doi: 10.1016/j.jacc.2012.02.051. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Danchin N., Laurent S. Coronary artery disease are β-blockers truly helpful in patients with CAD? Nature Reviews Cardiology . 2013;10(1):11–12. doi: 10.1038/nrcardio.2012.159. [DOI] [PubMed] [Google Scholar]
- 28.Ibáñez B., Raposeiras-Roubin S., García-Ruiz J. M. The swing of β-blockers: time for a system reboot. Journal of the American College of Cardiology . 2017;69(22):2721–2724. doi: 10.1016/j.jacc.2017.04.017. [DOI] [PubMed] [Google Scholar]
- 29.Tsujimoto T., Yamamoto-Honda R., Kajio H., et al. Effectiveness of prior use of beta-blockers for preventing adverse influences of severe hypoglycemia in patients with diabetes: an observational study. Medicine (Baltimore) . 2015;94(39) doi: 10.1097/md.0000000000001629.e1629 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Sarafidis P. A., Bakris G. L. Antihypertensive treatment with beta-blockers and the spectrum of glycaemic control. QJM: Monthly Journal of the Association of Physicians . 2006;99(7):431–436. doi: 10.1093/qjmed/hcl059. [DOI] [PubMed] [Google Scholar]
- 31.Aberg H., Mörlin C., Lithell H. Different long-term metabolic effects of enalapril and atenolol in patients with mild hypertension EGTA Group. Journal of Human Hypertension . 1995;9(2):149–153. [PubMed] [Google Scholar]
- 32.Lindholm L. H., Carlberg B., Samuelsson O. Should beta blockers remain first choice in the treatment of primary hypertension? a meta-analysis. The Lancet . 2005;366(9496):1545–1553. doi: 10.1016/s0140-6736(05)67573-3. [DOI] [PubMed] [Google Scholar]
- 33.Antoni M. L., Boden H., Delgado V., et al. Relationship between discharge heart rate and mortality in patients after acute myocardial infarction treated with primary percutaneous coronary intervention. European Heart Journal . 2012;33(1):96–102. doi: 10.1093/eurheartj/ehr293. [DOI] [PubMed] [Google Scholar]
- 34.Fosbøl E. L., Seibæk M., Brendorp B., et al. Long-term prognostic importance of resting heart rate in patients with left ventricular dysfunction in connection with either heart failure or myocardial infarction: the diamond study. International Journal of Cardiology . 2010;140(3):279–286. doi: 10.1016/j.ijcard.2008.11.084. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and/or analyzed during this study are available from the corresponding author on reasonable request.