INTRODUCTION
Cardiovascular diseases remain a major cause of health loss for all regions[1] and more than half of cardiovascular death occurred in Asian countries[2]. Coronary artery disease (CAD) is considered the majority of cardiovascular diseases and the leading cause of morality[2]. Atherosclerosis plays a central role in the pathogenesis of CAD[3] and its progression causes diverse cardiovascular events, including myocardial infarction (MI), heart failure (HF), and sudden cardiac death[4]. In the past two decades, several treatment modalities for CAD have been developed, including antiplatelet, lipid-lowering, anti-inflammatory agents, and coronary revascularization[5,6]. Despite adherence to guideline-recommended prevention measures, adverse cardiovascular events still presented in some patients with CAD[7–9]; this could relate to the presence of comorbidities, including diabetes mellitus (DM), chronic kidney disease (CKD), and peripheral artery disease (PAD). The diverse outcomes may be explained by the heterogeneous pathogenesis of CAD and its associated comorbidities[5,10].
There were several biomarkers proposed for the use of diagnosing or predicting future cardiovascular events (Table 1)[10–12]. Despite different cardiovascular diseases sharing many biomarkers, there were still many heterogeneous associations[10]. Hence, understanding the applicability and limitations of predictive biomarkers of adverse cardiovascular events in patients with heterogeneous CAD courses may support the need for more aggressive treatment.
Table 1. -
Circulating biomarkers of cardiovascular diseases
| Cardiovascular diseases |
Related biomarkers |
| Coronary artery disease |
Troponin T and I, creatinine phosphokinase-MB, lipid measures (low-density lipoprotein cholesterol, apolipoprotein B/apolipoprotein A-1 ratio and apolipoprotein B, very-low-density lipoprotein, low-density lipoprotein, and high-density lipoprotein), monounsaturated fatty acids, amino acids (alanine, branched-chain amino acid), glycoprotein acetyls |
| Peripheral artery disease |
Fibrinogen, C-reactive protein, erythrocyte sedimentation rate, Beta 2-macroglobulin , and cystatin C, inflammatory protein markers (glycoprotein acetyls), glycolysis-related measures (glucose, lactate, pyruvate, and glycerol), lipid measures (very-low-density lipoprotein, low-density lipoprotein, and high-density lipoprotein), monounsaturated fatty acids, amino acid (alanine, glutamine) |
| Heart failure |
N-terminal pro B-type natriuretic peptide, midregional pro-atrial natriuretic peptide, Troponin I, T, cardiac myosin binding protein-C, heart-type fatty acid binding protein, galectin-3, growth differentiation factor 15, soluble suppression of tumorigenesis-2 |
Circulating endothelial progenitor cells (EPCs) can differentiate into endothelial cells. Several studies have shown the role of EPCs in angiogenesis and reendothelialization after endothelial damage[13–15]. The numbers and functional properties of circulating EPCs are considered to represent the vascular repair capability[13–15]. Impaired EPC function had been demonstrated in patients with various metabolic and cardiovascular diseases, including type 1 and type 2 DM[16,17], hypertension[18], and CAD[19]. In patients with autoimmune diseases, impaired EPC function has been proposed to explain the excess cardiovascular mortality[20]. Nevertheless, the circulating EPC level may be a more clinically applicable biomarker compared with EPC function, which needs a time-consuming step of cell culture.
Current studies have shown the connection between circulating EPC levels and a variety of atherosclerosis-related diseases. However, the conclusions vary between studies. Some studies demonstrate that patients with CAD[19,21], CKD, PAD[22,23], and systemic lupus erythematosus[20] had reduced EPC levels. On the other hand, other studies showed that patients with CAD[24,25], PAD[26], and HF[27] had increased EPC levels. Therefore, the use of circulating EPC levels to predict cardiovascular outcomes requires further examination and interpretation[28].
Circulating EPCs may be a promising biomarker to predict the clinical outcomes of patients with CAD. Due to the complexity and heterogenous spectrum of atherosclerosis-related diseases, the associations between circulating EPC levels and adverse cardiovascular events may be different. Our study aimed to conduct a prospective cohort study to clarify whether circulating EPC levels can predict adverse cardiovascular events in patients with suspected CAD and also individual atherosclerosis-related diseases.
METHODS
Study design and subjects
This prospective cohort study was conducted at Taipei Veterans General Hospital from December 2009 to March 2015. Patients with suspected CAD who were hospitalized and received elective coronary angiography (CAG) procedures were enrolled in this study. We excluded patients who underwent emergent CAG procedures or aged less than 20 years old. Baseline characteristics, including anthropometric measurements, laboratory data, and comorbidities, were collected before receiving CAG procedures during hospitalization. The implementation of the percutaneous coronary intervention was based on the clinical judgments of the cardiologists and the patient’s autonomy. The enrolled participants then received regular follow up in the cardiology outpatient clinics after the CAG examination. This study was approved by the Research Ethics Committee of Taipei Veterans General Hospital in September 2009 (2009-09-005A). The approval documentation from the institutional review board was regularly renewed. The study was conducted in accordance with the principles of the 1975 Declaration of Helsinki and its revision in 2013. All participants were well informed and signed informed consent.
Serum biochemistry and covariates
Blood samples were acquired for the laboratory tests prior to the CAG procedure and after 8 hours of fasting. The serum biochemistry tests were performed using a Hitachi 7600 Autoanalyzer (Hitachi Ltd, Tokyo, Japan). The estimated glomerular filtration rates (eGFRs) were calculated using the Chronic Kidney Disease Epidemiology Collaboration equation. CKD was defined as an eGFR < 60 mL/min/1.73 m2. Information regarding patients’ age, gender, body mass indexes, and smoking histories was collected from detailed chart reviews. Related atherosclerosis-related diseases, including DM, HF, atrial fibrillation (AFib), and PAD were determined according to the medical record and based on the diagnostic codes by the International Classification of Diseases, 10th Revision (ICD-10). Medication use was defined by the latest prescription at the outpatient department before the indexed hospital admission. Obstructive CAD was defined as the presence of significant stenosis that involved >50% of a vessel’s diameter in ≥1 coronary vessel according to CAG.
Circulating EPC levels
In the present study, EPCs were identified by the co-expression of hematopoietic markers (CD34+ and CD133+) and endothelial marker [human kinase insert domain receptor (KDR+)][29]. Peripheral blood was collected in ethylene diamine tetraacetic acid (EDTA)-coated tubes on the day before the patient received CAG examination. The circulating EPCs were quantified using flow cytometry as described previously[30]. In brief, peripheral whole blood was incubated with a KDR antibody (R&D Systems, Inc., Minneapolis, MN, USA) for 30 minutes, followed by incubation with an allophycocyanin-conjugated secondary antibody, a phycoerythrin-conjugated human CD133 antibody (Miltenyi Biotec, Bergisch Gladbach, Germany), and fluorescein isothiocyanate-conjugated human CD34 antibody (Becton Dickinson PharMingen, Franklin Lakes, NJ, USA). After incubating the blood cells for 30 minutes, they were washed with phosphate-buffered saline before they were analyzed. The CD34+KDR+CD133+ cells were defined as EPCs. The numbers of EPCs were expressed as percentages of the number of events measured by flow cytometry. Patients were assigned to tertiles according to the proportions of circulating EPCs.
Outcome variable definition
This study’s outcome variable was major adverse cardiovascular events (MACE), which was defined as a composite of nonfatal MI, nonfatal stroke, hospital admission because of HF or ischemic cardiovascular events, and cardiovascular death, during the follow-up period. Nonfatal MI is defined as the elevation of cardiac troponin I (>1 ng/mL) with ischemic symptoms. Nonfatal Stroke is defined as the presence of new neurological defeat with evidence of cerebral infarction or hemorrhage as verified by either computed tomography or magnetic resonance imaging. HF hospitalization was defined as admission for ≥24 hours with a primary diagnosis of HF, with ≥1 symptom and ≥2 physical examination, laboratory, or invasive findings of HF, and receiving a HF-specific treatment. Admission for ischemic cardiovascular events was denied as an imbalance between myocardial blood supply and demand, which presented with angina or acute MI and the need for hospitalization.
Statistical analysis
Continuous variables are expressed as mean ± standard deviation (SD) or as medians with interquartile ranges based on the distribution of variables. Categorical variables are expressed as numbers and percentages. The continuous variables were compared by using t tests for independent samples or non-parametric Wilcoxon test. The categorical variables were compared by using the chi-squared test.
Univariate Cox proportional hazards analyses were used to evaluate the ability of each parameter to predict the occurrence of MACE. Covariates with values of P < 0.01 in the univariate models were included in the multivariate regression model. The predictive values of EPC tertiles on MACE among different pre-existing comorbidities were also evaluated by similar methods. The associations between the covariates and the outcome were expressed as hazard ratios (HRs) and 95% confidence intervals (CIs). Survival curves were generated using the Kaplan-Meier method and log-rank test to examine the difference. P values <0.05 were considered to indicate significance. The statistical analyses were performed using SAS software, version 9.4 (SAS Institute Inc., Cary, NC, USA).
RESULTS
Patients’ demographic and clinical characteristics
This study included 1099 patients with suspected CAD that underwent elective CAG procedures. Among all patients, 736 (67%) were men, and the mean age was 66.7 ± 12.5 years. There were 637 (58%) patients diagnosed with obstructive CAD according to CAG. According to chart review, 92 (8.4%) patients had HF, 66 (6%) patients had AFib, 394 (35.9%) patients had DM, 111 (10.1%) patients had CKD, and 100 (10.1%) patients had PAD. The mean eGFR was 69.7 mL/min/1.73 m2, serum total cholesterol level was 161.5 mg/dL, and glycosylated hemoglobin (Hb) level was 7%. The mean left ventricular ejection fraction (LVEF) determined by left ventriculography was 56.3%. There were 268 (24.4%) patients who encountered MACE during the follow-up period.
Subjects were assigned to tertiles according to the proportions of circulating EPCs measured by flow cytometry, where tertile 1 was <0.4%, tertile 2 was 0.4% to 0.9%, and tertile 3 was >0.9%, accordingly. The patients in tertile 3 had significantly higher CKD and lower PAD rates, higher Hb, and lower serum total cholesterol levels. The numbers of patients with obstructive CAD, AFib, HF, and DM, and the MACE rates showed no difference among groups (Table 2). The patients in tertile 2 had the highest LVEF. Patients in tertile 3 also received significantly more prescriptions of angiotensin-converting enzyme inhibitors (ACEi)/angiotensin II receptor blockers (ARB), oral antidiabetic drugs, and statins, but fewer prescriptions of nitroglycerins (NTGs).
Table 2. -
Baseline characteristics of patient grouped by three levels of circulating endothelial progenitor cell percentages
|
Total |
Tertile 1 |
Tertile 2 |
Tertile 3 |
P value |
|
n = 1099 |
n = 399 |
n = 374 |
n = 326 |
| Men, n (%) |
686 (67.2) |
263 (65.9) |
241 (64.4) |
232 (71.2) |
0.144 |
| Age, y |
66.7 ± 12.5 |
67.3 ± 13.4 |
66.0 ± 12.3 |
66.9 ± 11.6 |
0.317 |
| BMI, kg/m2
|
25.8 ± 4.1 |
25.4 ± 4.2 |
26.0 ± 4.1 |
25.9 ± 4.0 |
0.119 |
| Smoking, n (%) |
375 (34.1) |
150 (37.6) |
116 (31.0) |
109 (33.4) |
0.206 |
| Obstructive CAD, n (%) |
637 (58.0) |
233 (58.4) |
216 (57.8) |
188 (57.7) |
0.839 |
| AFib, n (%) |
66 (6.0) |
27 (6.8) |
20 (5.4) |
19 (5.8) |
0.571 |
| HF, n (%) |
92 (8.4) |
38 (9.5) |
24 (6.4) |
30 (9.2) |
0.799 |
| CKD, n (%) |
111 (10.1) |
32 (8.0) |
37 (9.9) |
42 (12.9) |
0.032 |
| DM, n (%) |
394 (35.9) |
131 (32.8) |
148 (39.6) |
115 (35.3) |
0.429 |
| PAD, n (%) |
100 (10.1) |
47 (13.0) |
35 (10.5) |
18 (6.0) |
0.003 |
| Hb, g/dL |
12.9 ± 1.8 |
12.7 ± 1.8 |
12.8 ± 1.9 |
13.2 ± 1.8 |
0.003 |
| Platelet, 103/μL |
217.0 ± 84.3 |
218.7 ± 72.2 |
219.2 ± 69.3 |
212.4 ± 110.0 |
0.500 |
| Cr, mg/dL |
1.1 (0.9, 1.3) |
1.1 (0.9, 1.3) |
1.0 (0.9, 1.3) |
1.1 (0.9, 1.3) |
0.047 |
| eGFR, mL/(min·1.73 m2) |
69.7 ± 27.9 |
72.9 ± 25.0 |
72.8 ± 27.8 |
70.6 ± 28.0 |
0.243 |
| T Chol, mg/dL |
161.5 ± 34.0 |
164.1 ± 36.5 |
163.5 ± 32.2 |
157.2 ± 32.7 |
0.012 |
| TG, mg/dL |
128.0 ± 86.6 |
125.2 ± 86.9 |
128.6 ± 79.8 |
128.1 ± 90.3 |
0.865 |
| LDL-C, mg/dL |
93 (74, 114) |
96 (75, 119) |
93 (75, 112) |
92 (72, 111) |
0.471 |
| HDL-C, mg/dL |
42.9 ± 14.9 |
42.7 ± 15.5 |
43.6 ± 15.9 |
42.4 ± 13.0 |
0.534 |
| Uric acid, mg/dL |
6.1 ± 2.1 |
6.2 ± 2.7 |
5.9 ± 1.6 |
6.0 ± 1.8 |
0.282 |
| CRP, mg/dL |
0.1 (0.1, 0.3) |
1.0 (0.2, 4.4) |
0.5 (0.2, 2.6) |
1.1 (0.2, 3.3) |
0.622 |
| Glucose, mg/dL |
113.7 ± 52.6 |
118.4 ± 73.4 |
114.8 ± 44.4 |
109.4 ± 39.6 |
0.098 |
| HbA1c, % |
7.0 ± 1.5 |
6.9 ± 1.6 |
7.3 ± 1.6 |
7.0 ± 1.5 |
0.065 |
| LVEF, % |
56.3 ± 10.5 |
55.9 ± 10.3 |
57.7 ± 9.6 |
54.9 ± 11.8 |
0.046 |
| Antiplatelets, n (%) |
601 (54.7) |
219 (54.9) |
201 (53.7) |
181 (55.5) |
0.890 |
| ACEi/ARB, n (%) |
322 (29.3) |
109 (27.3) |
91 (24.3) |
122 (37.4) |
<0.001 |
| β-blocker, n (%) |
285 (25.9) |
97 (24.3) |
88 (23.5) |
100 (30.7) |
0.064 |
| CCB, n (%) |
274 (24.9) |
93 (23.3) |
85 (22.7) |
96 (29.5) |
0.079 |
| NTG, n (%) |
381 (34.7) |
162 (40.6) |
124 (33.2) |
95 (29.1) |
0.004 |
| Diuretic, n (%) |
131 (11.9) |
49 (12.3) |
43 (11.5) |
39 (12.0) |
0.945 |
| Statin, n (%) |
351 (31.9) |
118 (29.6) |
104 (27.8) |
129 (39.6) |
0.002 |
| MACE, n (%) |
268 (24.4) |
105 (26.3) |
86 (23.0) |
77 (23.6) |
0.376 |
The data with normal distribution are expressed as mean ± SD; the data without normal distribution are expressed as median (25th percentile, 75th percentile). The EPC levels are expressed as percentages of the events measured by flow cytometry, and the tertiles were defined as follows: tertile 1: ≤0.4% EPCs; tertile 2: <0.4%–≤0.9% EPCs; and tertile 3: >0.9% EPCs.
ACEi: Angiotensin-converting enzyme inhibitor; AFib: Atrial fibrillation; ARB: Angiotensin II receptor blocker; BMI: Body mass index; CAD: Coronary artery disease; CCB: Calcium channel blocker; CKD: Chronic kidney disease; Cr: Creatinine; CRP: C-reactive protein; DM: Diabetes mellitus; eGFR: Estimated glomerular filtration rate; EPC: Endothelial progenitor cell; Hb: Hemoglobin; HbA1c: Glycosylated hemoglobin; HDL-C: High-density lipoprotein cholesterol; HF: Heart failure; LDL-C: Low-density lipoprotein cholesterol; LVEF: Left ventricular ejection fraction; MACE: Major adverse cardiovascular events; NTG: Nitroglycerin; PAD: Peripheral artery disease; T Chol: Total cholesterol; TG: Triglyceride.
Circulating EPC levels and the risk of MACE
In the initial univariate Cox analysis proportional hazards regression model (Table 3), patients in tertile 3 had an increased risk of developing MACE (HR: 1.7, 95% CI: 1.2–2.3). Other associated risk factors included older age, patients with obstructive CAD, congestive heart failure (CHF), CKD, DM, PAD, lower Hb and high-density lipoprotein cholesterol (HDL-C) levels, higher C-reactive protein (CRP) levels, or patients under the prescription of antiplatelet, ACEi/ARB, β-blockers, calcium channel blockers (CCBs), NTGs, diuretics, and statin. The Kaplan-Meier curves were used to demonstrate associations between the circulating EPC levels and MACE over time (Figure 1). The freedom from MACE event probability was significantly lower among the patients in tertile 3 (log-rank P = 0.019).
Table 3. -
Cox proportional hazards analysis of the risks of major adverse cardiovascular events
|
Univariate analysis |
Multivariate analysis |
| HR (95% CI) |
P Value |
HR (95% CI) |
P value |
| Circulating EPC level |
| Tertile 1 |
Reference |
|
|
|
| Tertile 2 |
1.1 (0.8-1.5) |
0.474 |
1.2 (0.9-1.7) |
0.275 |
| Tertile 3 |
1.7 (1.2-2.3) |
0.001 |
1.9 (1.4-2.8) |
<0.001 |
| Male gender |
1.1 (0.8-1.4) |
0.475 |
|
|
| Age (per 10-y increment) |
1.0 (0.9-1.0) |
0.086 |
1.0 (0.9-1.1) |
0.907 |
| BMI (per 1 increment) |
1.0 (0.9-1.0) |
0.167 |
|
|
| Smoking |
1.1 (0.9-1.4) |
0.489 |
|
|
| Obstructive CAD |
2.3 (1.7-3.1) |
<0.001 |
1.9 (1.3-2.8) |
<0.001 |
| CHF |
2.4 (1.8-3.3) |
<0.001 |
1.9 (1.3-2.9) |
0.002 |
| CKD |
2.4 (1.8-3.3) |
<0.001 |
1.0 (0.6-1.5) |
0.888 |
| DM |
1.5 (1.2-1.9) |
0.001 |
1.1 (0.8-1.6) |
0.424 |
| PAD |
2.0 (1.5-2.8) |
<0.001 |
1.9 (1.3-2.7) |
<0.001 |
| Hb (per 1 g/dL increment) |
0.9 (0.8-0.9) |
<0.001 |
0.9 (0.8-1.0) |
0.263 |
| HDL-C (per 10 mg/dL increment) |
0.9 (0.8-0.9) |
0.021 |
0.9 (0.8-0.9) |
0.007 |
| LDL-C (per 10 mg/dL increment) |
1.0 (0.9-1.0) |
0.238 |
|
|
| TG (per 10 mg/dL increment) |
1.0 (0.9-1.0) |
0.754 |
|
|
| CRP (per 1 mg/dL increment) |
1.1 (1.0-1.2) |
<0.001 |
1.1 (0.9-1.1) |
0.102 |
| Uric acid (per 1 mg/dL increment) |
1.0 (0.9-1.1) |
0.182 |
|
|
| Antiplatelet |
1.4 (1.1-1.8) |
0.011 |
0.8 (0.7-1.1) |
0.160 |
| ACEi/ARB |
2.0 (1.5-2.5) |
<0.001 |
1.6 (1.2-2.2) |
0.005 |
| β-blockers |
1.7 (1.3-2.2) |
<0.001 |
1.4 (0.9-1.9) |
0.069 |
| CCBs |
1.4 (1.1-1.8) |
0.015 |
1.1 (0.8-1.5) |
0.628 |
| NTGs |
1.3 (1.1-1.6) |
0.035 |
1.1 (0.8-1.5) |
0.673 |
| Diuretics |
1.7 (1.3-2.3) |
<0.001 |
1.0 (0.6-1.5) |
0.938 |
| Statin |
1.7 (1.3-2.2) |
<0.001 |
1.3 (0.9-1.7) |
0.143 |
The values are expressed as HR (95% CI).
ACEi: Angiotensin-converting enzyme inhibitor; ARB: Angiotensin II receptor blocker; BMI: Body mass index; CAD: Coronary artery disease; CCB: Calcium channel blocker; CHF: Congestive heart failure; CI: Confidence interval; CKD: Chronic kidney disease; CRP: C-reactive protein; DM: Diabetes mellitus; EPC: Endothelial progenitor cell; Hb: Hemoglobin; HDL-C: High-density lipoprotein cholesterol; HR: Hazard ratio; LDL-C: Low-density lipoprotein cholesterol; MACE: Major adverse cardiovascular events; NTG: Nitroglycerin; PAD: Peripheral artery disease; TG: Triglyceride.
;)
Figure 1.: Circulating EPC levels and the probability of major cardiovascular adverse events expressed by Kaplan-Meier curve. The tertiles of circulating EPC were defined as follows: tertile 1: ≤0.4% EPCs; tertile 2: <0.4%–≤0.9% EPCs; and tertile 3: >0.9% EPCs.EPC: Endothelial progenitor cell.
In our final Cox multivariate proportional hazards regression models, patients in tertile 3, with obstructive CAD, CHF, and PAD, lower HDL-C, or under the prescription of ACEi/ARB remained with a significantly higher risk of encountering MACE. There was no significant association between the presence of CKD and DM and MACE after multivariate analysis.
Effects modification of comorbidities on the predictive value of the EPC levels for MACE
Our study examined the ability of the circulating EPC levels to predict the development of MACE events among different comorbidities. The circulating EPC levels showed no significant difference among patients with or without obstructive CAD, AFib, CKD, HF, and DM. Instead, patients with PAD had significantly lower circulating EPC levels, indicating that the presence of PAD may be a confounding factor (Figure 2). Multivariate Cox proportional hazards analyses on the predictive values of different circulating EPC levels on MACE events based on these subgroups (Table 4). Patients with the highest EPC levels (tertile 3) had a higher risk of MACE, regardless of the presence or absence of obstructive CAD or CKD. However, EPC levels were not significantly associated with the risk of MACE among patients with PAD.
Table 4. -
Cox proportional hazards analysis of the risks of major adverse cardiovascular events in the subgroups of
atherosclerosis-related diseases
| Circulating EPC levels |
Non-obstructive CAD |
Obstructive CAD |
| HR (95% CI) |
P value |
HR (95% CI) |
P value |
| Tertile 1 |
Reference |
|
Reference |
|
| Tertile 2 |
1.3 (0.6–3.1) |
0.533 |
1.3 (0.9–1.8) |
0.311 |
| Tertile 3 |
2.8 (1.2–6.4) |
0.016 |
1.7 (1.1–2.6) |
0.018 |
|
Circulating EPC levels
|
Non CKD
|
CKD
|
|
HR (95% CI)
|
P value
|
HR (95% CI)
|
P value
|
| Tertile 1 |
Reference |
|
Reference |
|
| Tertile 2 |
1.2 (0.8–1.7) |
0.361 |
2.4 (1.0–5.9) |
0.058 |
| Tertile 3 |
1.7 (1.1–2.5) |
0.012 |
3.7 (1.4–10.3) |
0.011 |
|
Circulating EPC levels
|
Non PAD
|
PAD
|
|
HR (95% CI)
|
P value
|
HR (95% CI)
|
P value
|
| Tertile 1 |
Reference |
|
Reference |
|
| Tertile 2 |
1.3 (0.9–1.9) |
0.128 |
1.1 (0.6–1.9) |
0.798 |
| Tertile 3 |
1.9 (1.4–2.8) |
<0.001 |
1.1 (0.7–1.9) |
0.611 |
The values are expressed as hazard ratio [95% confidence interval (CI)]. The CAD models were adjusted for age, CHF, CKD, PAD, antiplatelets, ACEi/ARB, β-blockers, CCB, NTG, diuretics, statins, Hb, and glucose. The CKD models were adjusted for age, CHF, PAD, antiplatelets, ACEi/ARB, β-blockers, CCB, NTG, diuretics, statins, Hb, and glucose. The PAD models were adjusted for age, CHF, antiplatelets, ACEi/ARB, β-blockers, CCB, NTG, diuretics, statins, Hb, and glucose.
HR: Hazard ratio; CI: Confidence interval; Ref: Reference; EPC: Endothelial progenitor cell; CAD: Coronary artery disease; CHF: Congestive heart failure; CKD: Chronic kidney disease; PAD: Peripheral artery disease; ACEi: Angiotensin-converting enzyme inhibitor; ARB: Angiotensin II receptor blocker; CCB: Calcium channel blocker; NTG: Nitroglycerin.
Figure 2.: Circulating EPC levels according to different atherosclerosis-related diseases. A, Obstructive coronary artery disease. B, Heart failure. C, Atrial fibrillation. D, Diabetes mellitus. E, Chronic kidney disease. F, Peripheral artery disease.AFib: Atrial fibrillation; CAD: Coronary artery disease; CKD: Chronic kidney disease; DM: Diabetes mellitus; EPC: Endothelial progenitor cell; HF: Heart failure; PAD: Peripheral artery disease.
DISCUSSION
Our study indicated that higher EPC levels were associated with a significantly higher risk of MACE among patients with suspected CAD. Studies have shown that the local endothelial migration[31–33] and circulating EPCs[13–15] contribute to endothelial repair. Following stimulation by proinflammatory cytokines, EPCs mobilize to augment the endothelial repair[34]. Two possible mechanisms that alleviate denudation injuries to the endothelium. If a small area of endothelium is denuded, the major mechanism underlying reendothelialization is the growth of the adjacent endothelium[32,35], and circulating EPCs play vital roles in the reendothelialization of larger denuded areas[36,37].
Atherosclerosis is considered a disease involving systemic vascular inflammation[38,39]. Our study enrolled patients with stable cardiovascular conditions. Persisted elevated circulating EPC could plausibly be considered to represent the endothelial repair capability recruited via the inflammation caused by atherosclerosis. While a certain level of circulating EPC reflects an integral ability to repair the endothelium, a higher circulating EPC level may indicate a higher degree of atherosclerosis and an associated higher risk of MACE as this study’s results demonstrated.
The inconsistencies regarding associations between the circulating EPC levels and MACE may be explained by the differences in the follow-up durations among different studies. The findings from a study involving 2,028 patients with CAD of different severities and a 5-year follow-up period showed that higher circulating EPC levels were associated with a higher risk of cardiovascular death[29]. Another study involving 155 patients with stable CAD and a 5-year follow-up period showed that higher circulating EPC levels were associated with a higher risk of MACE[40]. However, two studies that included 519 patients with CAD and a 12-month follow-up period[41] and 100 patients with acute MI and acute ischemic stroke and a 6-month follow-up period[42] showed that the circulating EPC levels were not associated with the cardiovascular outcomes. This study’s follow-up period spanned 6 years, and its findings showed that the circulating EPC level was positively associated with the risk of MACE, and that the association may not be significant before the end of the first year of follow-up. Therefore, higher circulating EPC levels may be associated with long-term risk rather than a greater short-term risk of MACE.
The different outcome event rates among the studies may also distort associations between the circulating EPC levels and the cardiovascular outcomes. The overall MACE rate of our study was 24.4%. The incidence was much higher than a similar study, which reported event rate was less than 15%[28]. Lower event rates may attenuate the statistical power of the prediction variable. The difference in event rates may also indicate different disease severity.
The predictive effects of EPC levels on MACE vary on different comorbidities. There was no difference in circulating EPC levels in the presence or absence of obstructive CAD, HF, AFib, or DM. The circulating EPC levels were significantly lower in the patients with PAD. However, there was no association between the EPC level and the risk of MACE in PAD patients.
Our finding implies that EPCs are more critical for repairing the larger endothelial defects that are present in patients with PAD. In contrast, EPCs may be less important for repairing smaller endothelial injuries, which are present in patients with CAD. Thus, patients with lower circulating EPC levels may be more likely to have PAD based on our study. However, only a small portion (10.1%) of enrolled patients have been diagnosed PAD. Whether an impaired ability to recruit bone marrow EPCs contributes to the pathogenesis of PAD remains to be clarified.
Moreover, the circulating EPC levels were not associated with the presence of DM or the blood glucose level in our study. Previous studies have reported the impaired EPC function, specifically, proliferation and adhesion, and their incorporation into vascular structures, in patients with type 1 and type 2 DM[16,17]. These results indicated that endothelial repair requires sufficient EPC function and numbers. Thus, differences in EPC function may be another confounding factor affecting EPC’s predictive ability for MACE.
Compared to previous investigations, our study had the advantage of a larger study sample size, regular and long-term follow-up of the outcomes, comprehensive data collection, and adjustments for comorbidities. In addition, despite the EPCs being identified by co-expression of hematopoietic and endothelial markers on peripheral blood cells, part of these cells could be mononuclear precursors rather than EPCs.
There were several limitations of our study. First, our study enrolled patients with suspected CAD and the heterogeneity of the CAD severity was presented. To address this potential confounding effect, we performed subgroup analyses on patients with obstructive and non-obstructive CAD. Second, our study did not limit the coronary angioplasty procedures and the impact of coronary revascularization on MACE may be inestimable.
CONCLUSION
Our prospective study showed that higher circulating EPC levels were associated with a higher long-term risk for MACE for patients with stable CAD, regardless of the presence or absence of obstructive CAD or CKD. The association did not present among the patients with PAD. We look forward to further study on the effect of circulating EPC on different atherosclerosis-related diseases.
FUNDING
This study was supported, in part, by research grants from the Ministry of Science and Technology of Taiwan (MOST 104-2314-B-075-047), Novel Bioengineering and Technological Approaches to Solve Two Major Health Problems in Taiwan sponsored by the Taiwan Ministry of Science and Technology Academic Excellence Program (MOST 108-2633-B-009-001), the Ministry of Health and Welfare (MOHW106-TDU-B-211-113001), Taipei Veterans General Hospital (V105C-0207, V106C-045), and Wanfang Hospital (109-wf-swf-05). These funding agencies had no influence on the study design, data collection or analysis, decision to publish, or preparation of the manuscript.
AUTHOR CONTRIBUTIONS
Conception and designation: RHC, PHH. Data acquisition: RHC, YWL, YLT. Statistical analysis: CTL, RHC. Data interpretation: CTL, RHC, PHH. Manuscript writing: CTL, JYG. Approval of the article: JYG, RHC, CSK, CCC, PHH, JWC, SJL.
CONFLICTS OF INTEREST STATEMENT
Jawwen Chen is the Editorial Board member of Cardiology Plus. The article was subject to the journal’s standard procedures, with peer review handled independently of the Editorial Board members and their research groups.
DATA SHARING STATEMENT
The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.
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