|Year : 2019 | Volume
| Issue : 1 | Page : 172-176
Association between uric acid and coron ary collateral circulation in stable coronary artery disease patients
Ghada M Soltan1, Naglaa F Ahmed1, Ahmed G El-Sayed El-Raei2
1 Department of Cardiology, Faculty of Medicine, Menoufia University, Shibin Al Kawm, Egypt
2 Mahalla Cardiac Center Hospital, Tanta, Egypt
|Date of Submission||22-Jun-2017|
|Date of Acceptance||19-Sep-2017|
|Date of Web Publication||17-Apr-2019|
Ahmed G El-Sayed El-Raei
Mahalla Cardiac Center Hospital, Tanta, Garbia Governorate
Source of Support: None, Conflict of Interest: None
The aim of this study was to investigate the association between serum uric acid levels and the development of coronary collateral circulation (CCC) in patients with stable coronary artery disease.
Serum uric acid can cause atherosclerosis. High serum uric acid levels may be associated with poor CCC in patients with stable coronary artery disease.
Patients and methods
A cross-sectional study was carried out on 100 patients with stable CAD who underwent coronary angiography and documented total occlusion in one or more of the major coronary arteries in Cardiovascular Department Menoufia University Hospital during the period from 1 February 2016 to 30 January 2017. Development of CCC will be graded according to the Cohen–Rentrop method: grades 2–3 will be classified as good CCC (group A) and grades 0–1 as poor CCC (group B).
A significant relationship between high level of serum uric acid and poor coronary collaterals was observed. Mean value of uric acid in group A was 4.72 ± 1.69 mg/dl and mean value of uric acid in group B was 6.68 ± 2.36 mg/dl (P < 0.001). The receiver-operating characteristic analysis curve made between uric acid and two groups provided a cutoff point of 5.3 mg/dl, with 81.63% specificity and 64.71% sensitivity to detect poor CCC.
The measurement of uric acid level provides a good predictor of poor CCC in stable coronary artery disease patients.
Keywords: coronary artery disease, coronary collateral circulation, uric acid
|How to cite this article:|
Soltan GM, Ahmed NF, El-Sayed El-Raei AG. Association between uric acid and coron ary collateral circulation in stable coronary artery disease patients. Menoufia Med J 2019;32:172-6
|How to cite this URL:|
Soltan GM, Ahmed NF, El-Sayed El-Raei AG. Association between uric acid and coron ary collateral circulation in stable coronary artery disease patients. Menoufia Med J [serial online] 2019 [cited 2019 Jul 17];32:172-6. Available from: http://www.mmj.eg.net/text.asp?2019/32/1/172/256116
| Introduction|| |
Severe coronary artery stenosis or total occlusions are frequently observed in patients with stable and unstable coronary artery disease (CAD). Among these, some patients with a similar degree of angiographic coronary stenosis experienced more severe symptoms of coronary ischemia than others. This might lead to angina, shortness of breath, quality of life impairment, left ventricular (LV) dysfunction, and worsening of prognosis .
Coronary collateral (CC) vessels, the remnants of the embryonic arterial system, can develop in the heart as an adaptation to ischemia . True collateral vessels are not seen angiographically in normal hearts, and coronary arteries must be occluded 99 or 100% for CC to be visible . Collaterals are capable of blood supply to a myocardial area jeopardized by ischemia. Because they can help to preserve myocardial function by reducing infarct size , and may provide a survival benefit . It is important to know which factors contribute to their development. However, there is limited information on the factors affecting the development of CC. Although coronary lesion severity has been shown to be an independent pathogenetic variable related to collateral flow, there are interindividual differences in the number and extent of collateral vessels among patients with a similar degree of coronary atherosclerosis .
The heterogeneity in the degree of coronary collateral circulation (CCC) in patients with CAD is not clearly established. Growth factors, growth factor receptors, inflammatory cell mediators, endothelial chemokines, adhesion molecules, extracellular matrix, and oxidative stress affect the development of angiogenesis and arteriogenesis .
The etiology of atherosclerosis is unknown, but there are multiple factors that contribute to atherosclerotic plaque progression. These include genetic and acquired factors. Processes involved in atherosclerosis include coagulation, inflammation, lipid metabolism, intimal injury, and smooth muscle cell proliferation .
Uric acid (urate), an organic compound composed of carbon nitrogen, oxygen, and hydrogen, is the final oxidation product of purine metabolism. Elevated UA levels were also found to be independent risk factors for overall cardiovascular mortality .
Serum uric acid (SUA) levels have been proposed as a biomarker of CAD. It has been reported that SUA can cause endothelial dysfunction, inflammation, and vasoconstriction, lead to atherosclerosis, and affect the development of CCC ,. High SUA levels may be associated with poor CCC in patients with acute coronary syndrome (ACS) ,.
The aim of this study was to investigate the association between SUA levels and the development of CCC in patients with stable coronary artery disease.
| Patients and Methods|| |
A cross-sectional study was carried out on 100 patients with stable CAD who underwent coronary angiography and documented total occlusion in one or more of the major coronary arteries in Cardiovascular Department Menoufia University Hospital during the period from 1 February 2016 to 30 January 2017. Exclusion criteria were patients with acute/chronic infective or inflammatory disease, chronic kidney disease (serum creatinine >2.0 mg/dl), history of coronary artery bypass grafting, ACS within the past 3 months, sever valvular heart disease, history of gout, hepatic and hemolytic disorders, malignancy, and LV ejection fraction (EF) of less than 45%.
The ethical implications regarding the study were approved by the local Ethics Committee and informed consent was obtained from each patient.
Baseline demographic, clinical, and laboratory data were obtained from patients' charts and were recorded. For each patient, the following tests were done: fasting blood glucose level, SUA, serum urea, serum creatinine, lipid profile, and liver function tests. Standard 12-lead ECG, two-dimensional-echocardiography, and coronary angiography were performed. Coronary angiography was performed using the Judkins technique. Coronary angiograms and also collateral grading were examined by interventional cardiologists who were blinded to the clinical characteristics and laboratory results of the patients. Development of CCC was graded according to the Cohen–Rentrop method:  grade 0, no filling of any collateral vessels; grade 1, filling of side branches of the artery to be perfused by collateral vessels without visualization of epicardial segment; grade 2, partial filling of the epicardial artery by collateral vessels; and grade 3, complete filling of the epicardial artery by collateral vessels. Patients with grades 0–1 were classified as poor CCC and patients with grades 2–3 as good CCC. If the patient had more than one vessel with CCC, collateral grading was performed according to the vessel that had better antegrade or retrograde collateral.
The collected data were organized, tabulated, and statistically analyzed using SPSS, version 19, created by IBM (Illinois, Chicago, USA). For numerical values, the range mean and SDs were calculated. The differences between two mean values were calculated using Student's t-test . For categorical variable, the number and percentage were calculated and differences between subcategories were tested by χ2-test. Wilcoxon singed ranks test (Z) odds ratio was estimated to quantify the risk factors affecting CCC and 95% confidence interval was calculated for each affecting variable. Logistic regression analysis was performed to study the independent effect of each variable. The level of significance was adopted at P less than 0.05.
| Results|| |
The baseline characteristics of 100 patients in both groups show that the mean age for group A was 59.02 ± 6.55 years and the mean age for group B was 57.37 ± 6.91 years, and there was no statistical significant difference between the two groups as regards age (P = 0.225). A total of 82 patients were male, representing 82% of the study population, and the remainder 18 patients were female, representing 18% of the study population. Group A included 39 (79.6%) men and 10 (20.4%) women. Group B included 43 (84.3%) men and eight (15.7%) women, and thus there was no significant difference with regard to sex (P = 0.539); there was a significant difference between diabetes mellitus (DM) patients in group A and group B – 21 DM patients in group A and 33 DM patients in group B (P = 0.028). There is no significant difference between hypertension (HTN) patients in group A and group B, with 24 HTN patients in group A and 29 HTN patients in group B (P = 0.430). There is no significant difference between patients who smoke in group A and group B, as there were 22 smokers in group A and 35 smokers in group B (P = 0.017), as presented in [Table 1].
There is no significant difference between the two groups in the urea value in group A and group B, as in group A the mean ± SD was 34.45 ± 8.91 and in group B the mean ± SD was 33.61 ± 7.83 (P = 0.617). There is no significant difference between the two groups in the creatinine value in group A and group B, as in group A the mean ± SD was 1.10 ± 0.21 and in group B the mean ± SD was 1.06 ± 0.19 (P = 0.414). There is no significant difference between the two groups in the total cholesterol value in group A and group B, as in group A the mean ± SD was 174.67 ± 59.21 and in group B the mean ± SD was 185.08 ± 54.02 (P = 0.361). There is no significant difference between the two groups in the triglyceride value in group A and group B, as in group A the mean ± SD was 125.0 ± 44.59 and in group B the mean ± SD was 143.86 ± 62.52 (P = 0.262), as presented in [Table 2].
There is a highly significant difference between the two groups in the uric acid value in group A and group B, as in group A the mean ± SD was 4.72 ± 1.69 and in group B the mean ± SD was 6.68 ± 2.36 (P < 0.001), as presented in [Table 2] and [Figure 1].
The receiver-operating characteristic analysis provided a cutoff value of 5.3 mg/dl for SUA to detect poor CCC, and the sensitivity was 64.71% and specificity was 81.63%. Positive predictive value was 78.6% and negative predictive value was 69.0% of SUA to detect poor CCC between two groups, as presented in [Table 3] and [Figure 2].
There is no significant difference between patients with atrial fibrillation recorded by ECG in group A and group B, as there were four patients in group A and 10 patients in group B (P = 0.099). There is a significant difference between LV EF calculated by using modified Simpson's rule technique in group A and group B, as group A included 33 (67.3%) patients with normal EF more than 55%, 14 (28.5%) patients with fair LV function EF (50–54%), and two (4.0%) patients with impaired LV function EF less than 50%. Group B included 32 (62.7%) patients with normal EF, five (9.8%) patients with fair LV function EF (50–54%), and 14 (15.7%) patients with impaired LV function EF of less than 50%. (P = 0.0027), as presented in [Table 4].
|Table 4: Comparison of ECG rhythm and left ventriclar function ejection fraction among studied groups|
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| Discussion|| |
This study shows a significant relationship between diabetes mellitus and poor coronary collaterals. This is in agreement with the study of Abaci et al.  in evaluating the relationship between diabetes mellitus and the CCC. Abaci et al.  found that the mean number of diseased vessels in the DM group was significantly higher than that in the nondiabetic group. The mean collateral score was higher in the nondiabetic group than that in the diabetic group.
This study shows a significant relationship between smoking and poor coronary collaterals. This is in agreement with the study by Koerselman et al.  in evaluating the relationship between smoking and CCC. Koerselman et al.  found that the mean number of smokers is higher in the group with poor collateral (Rentrop 1 and 2) than that in the group with good collateral (Rentrop 3 and 4).
The study showed the negative effects of poor coronary collaterals on the cardiac muscle function according to EF assessed by echocardiography. This in disagreement with Tatli et al. , who reported that there are no negative effects of poor coronary collaterals on the cardiac muscle function. Tatli et al.  shows that there was no difference in mean left ventricle EF between group I with good collaterals and group II with bad collaterals.
This study shows a significant relationship between high level of SUA and poor coronary collaterals. This in agreement with the study by Kasapkara et al. , Uysal et al. , and Duran et al.  in evaluating the relationship between SUA level and the CCC. Kasapkara et al.  show that patients with poor CCC had significantly higher SUA levels compared with patients with well-developed CCC. Uysal et al.  reported that SUA was significantly higher in patients with poor CCC than in those with good CCC. Duran et al.  show that the mean value of SUA in the group with absence of coronary collateral vessels Rentrop grade 0 is higher than the group with the presence of coronary collateral vessels Rentrop grade 1 and more in patients with ACS.
In disagreement with the present study, Zorkun et al.  and Hsu et al.  reported that there is no significant relationship between SUA level and the development of coronary collaterals. Zorkun et al.  show that there is no difference in the mean value of SUA between the group with adequate collaterals and the group with inadequate collaterals. Hsu et al.  showed that mean value of SUA in the group with good collaterals was similar to the group with poor collaterals. There was no significant difference in SUA level between the two groups.
| Conclusion|| |
The measurement of uric acid level provides a good predictor of poor CCC in stable CAD patients.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3], [Table 4]