Predictors of Clinical SYNTAX Score in Coronary Artery Disease: Serum Uric Acid, Smoking, and Framingham Risk Stratification

Abstract: Background. High serum uric acid (SUA) has been well demonstrated to be associated with morbidity and mortality in the general population as well as in patients with coronary artery disease (CAD). Recent studies show that the clinical SYNTAX score (CSS) is a new tool for the risk stratification of patients with complex CAD. In this study, we aimed to evaluate whether SUA was associated with the complexity of CAD as evaluated by the CSS. Methods. The study population consisted of 451 patients (69% male) who underwent coronary angiography for the assessment of CAD. A lesion was defined as significant if it caused a 50% reduction of the luminal diameter by visual estimation in vessels ≥1.5 mm. CSS was calculated by multiplying the SYNTAX score by a modified value of age, creatinine, and ejection fraction (ACEF) score (age/ejection fraction +1 for each 10 mL the creatinine clearance <60 mL/min per 1.73 m²). Results. All subjects were divided into three groups according to CSS tertiles: CSS_LOW (CSS 2-11; n = 147), CSS_MID (CSS 12-21; n = 152), and CSS_HIGH (CSS 22-68; n = 152). The SUA level was prominently related with CSS (5.29 ± 1.23 mg/dL, 6.92 ± 1.23 mg/dL, and 8.31 ± 1.46 mg/dL; P<.001). SUA was a significant predictor of CSS after adjustment for other risk factors (OR, 2.68; P<.001). Conclusion. SUA level was significantly associated with the severity and complexity of CAD evaluated by CSS. Further prospective clinical studies are needed to clarify the exact physiopathologic role of SUA in CAD.

J INVASIVE CARDIOL 2011;23(12):501-504

Key words: serum uric acid, coronary angiography, clinical SYNTAX score

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The relationship between serum uric acid (SUA) levels and the risk and prognosis of atherosclerotic cardiovascular diseases has been well demonstrated.¹ Hyperuricemia is an independent predictor of mortality in patients with cardiovascular disease.² Higher SUA has been correlated with several cardiovascular risk factors, endothelial dysfunction, subclinical atherosclerosis and the severity of coronary atherosclerotic plaques.^3,4 A recent meta-analysis about hyperuricemia and coronary heart disease showed that hyperuricemia may marginally increase the risk of coronary artery disease (CAD) events, independently of traditional CAD risk factors.⁵

Coronary arteriography is the gold standard for diagnosing coronary heart disease. Elevated SUA has been associated with increased mortality in percutaneous coronary intervention (PCI) patients.⁶

The SYNergy between percutaneous coronary intervention with TAXus and cardiac surgery (SYNTAX) score is a semiquantitative angiographic tool used to determine the extent and severity of CAD on the basis of coronary anatomic risk factors, including the number of lesions, total occlusion, bifurcation, trifurcation, aorta-ostial stenosis, tortuosity, calcification, thrombus, diffuse lesion, and small vessel/diffuse disease. The score system is derived entirely from the coronary anatomy and lesion characteristics.^7,8 The score aims to create an angiographic tool grading the complexity of CAD and promote evidence-based guidelines for selecting the optimal technique of revascularization coronary artery bypass grafting (CABG) or PCI. This scoring system has recently been assessed in numerous cohorts in which it was able to predict major adverse cardiac events (MACE) after percutaneous revascularization in patients with left main disease⁹ and/or multivessel CAD.¹⁰ An improvement in the ability of the SYNTAX score to predict MACE and mortality can be achieved by combining the SYNTAX score with a simple clinical risk score incorporating age, ejection fraction, and creatinine clearance to produce the CSS.¹¹

The aim of this study was to investigate whether SUA was associated with the complexity of CAD as evaluated by CSS.

Methods

Subjects. The study population consisted of 451 consecutive patients (mean age, 63.44 ± 11.2 years; 69% men) who underwent coronary angiography at our institute between January 2007 and March 2011. This study was cross-sectional.

Exclusion criteria were: previous PCI or stent implantation and/or previous CABG; being in the first 4 weeks of acute coronary syndrome; single-vessel disease; heart failure; renal dysfunction (GFR <30 mL/min); chronic liver disease; hemolytic disorders; active infectious or autoimmune diseases; neoplastic diseases or any other systemic disorders; alcohol consumption; vitamin use (including vitamin C, folate, and niacin); and known drug history including anti-gout agents.

Baseline definitions and cardiovascular risk assessment. Diabetes was defined as fasting plasma glucose ≥126 mg/dL at study entrance or treatment with a hypoglycemic agent or any extended-release insulin. Hypertension was defined as systolic pressure ≥140 mm Hg and/or diastolic pressure ≥90 mm Hg or if the individual was taking antihypertensive medications. Subjects were classified as smokers if they had smoked at least one cigarette per day in the year before the study. Family history of CAD was defined as CAD in a parent or sibling before the age of 55 for men and 65 for women. Glomerular filtration rate (GFR) was estimated using the modified formula of Levey et al,¹² and severely impaired renal function was categorized as GFR <30 mL/min. Body mass index (BMI) was calculated as weight (kilograms) divided by height (meters squared). Metabolic syndrome (MS) was defined by the presence of ≥3 of the following criteria: obesity, defined as BMI ≥25 kg/m²; systolic blood pressure ≥130 mm Hg and/or diastolic blood pressure (DBP) ≥85 mm Hg and/or history of hypertension; serum triglyceride (TG) ≥150 mg/dL; high-density lipoprotein (HDL) cholesterol <40 mg/dL in men and <50 mg/dL in women; and fasting glucose ≥100 mg/dL. Demographic data, including age, gender, comorbidities, actual treatment, family history of CAD, smoking status, weight, and height were collected before the angiographic procedure from the individual charts in the electronic hospital database. The study was approved by our local ethics committee, and written informed consent was obtained from all patients.

CSS calculation. All patients underwent standard coronary angiography assessment performed by the same cardiologist using a common technique. Two experienced physicians blinded to the study analyzed angiograms with a validated quantitative coronary angiographic system (Allura Xper FD10; Philips Healthcare). A lesion was defined as significant if it caused a 50% reduction of the luminal diameter by visual estimation in vessels ≥1.5 mm. Each coronary lesion was separately scored and added for each coronary vessel to provide the vessel SYNTAX score, which were then summed to provide the overall patient SYNTAX score, as previously described,^7,8 using the SYNTAX score website (www.syntaxscore.com). CSS was calculated by multiplying the SYNTAX score by a modified value of age, creatinine, and ejection fraction (ACEF) score (age/ejection fraction +1 for each 10 mL the creatinine clearance <60 mL/min per 1.73 m²) (up to a maximum of 6 points).¹¹ As in a study by Garg et al,11 the patients in our work were divided according to their CSS into tertiles defined as CSS_LOW, CSS_MID, and CSS_HIGH.

Blood sample collection and measurement of biochemical markers. Blood samples were obtained by venipuncture to perform routine blood chemistry after fasting for at least 8 hours. Fasting serum UA, blood urea nitrogen (BUN), serum creatinine, triglycerides (TG), total cholesterol (TC), low-density lipoprotein (LDL) cholesterol, and high-density lipoprotein (HDL) cholesterol levels were determined using the Sysmex Chemix-180 automatic biochemical analysis device (Sysmex Infosystems).

Statistical methods. All variables were stratified according to CSS tertiles. Descriptive characteristics for all variables are expressed as mean ± standard deviation for continuous variables and percentages for categorical variables. Continuous variables were compared using 2-tailed student’s t-test (age). Categorical data were analyzed by the Chi-square test. To further analyze the independent contribution of uric acid to the variance of CSS, we performed multinomial logistic regression models based on traditional and nontraditional risk factors impacting this variable. All tests of significance were two-tailed. A P-value <.05 was considered statistically significant. SPSS 19.0 for Windows statistical software (SPSS, Inc.) was used for all statistical calculations.

Results

A total of 451 consecutive patients were included in this study (mean age, 63.44 ± 11.2 years; 69% men). Characteristics of subjects, stratified according to CSS tertiles, are shown in Table 1. In this analysis, the 451 patients were classified as CSS_LOW (CSS ≤11; n = 147), CSS_MID (12≤ CSS <21; n = 152), and CSS_HIGH (CSS ≥22; n = 152 ). Among the patients who were analyzed, 63.3% had hypertension, 26.8% had diabetes mellitus, 29% were smokers, and 31.3% had higher Framingham Risk Stratification.

When we divided the patients into three groups according to the tertiles of CSS, we found that the SUA level was prominently related with CSS (5.29 ± 1.23 mg/dL, 6.92 ± 1.23 mg/dL, and 8.31± 1.46 mg/dL; P<.001) (Table 1). Other than SUA, age (61.25 ± 10.89 years, 65.64 ± 9.42 years, and 67.19 ± 11.09 years; P<.001), the prevalence of hypertension (53.7%, 65.8%, and 70.2%) and smoking prevalence (15.6%, 32.2%, and 45.7%; P<.001) were significantly related with CSS. Serum creatinine level (0.83 ± 0.18 mg/dL, 0.96 ± 0.47 mg/dL, and 0.96 ± 0.24 mg/dL; P<.001), fasting blood glucose level (98.45 ± 39.96 mg/dL, 103.21 ± 35.02 mg/dL, and 118.89 ± 54.84 mg/dL; P<.001), total cholesterol level (182.51 ± 33.14 mg/dL, 189.87 ± 36.38 mg/dL, and 208.81 ± 38.76 mg/dL; P<.001) and higher LDL cholesterol level (98.97 ± 33.14 mg/dL, 104.24 ± 35.73 mg/dL, and 123 ± 37.1 mg/dL; P<.001) were also prominently related with CSS.

In our study group, metabolic syndrome (MS) was detected in 133 patients (29.5%). Patients in the CSS_HIGH tertile had a higher prevalence of MS (P=.001) (Table 1).

To further analyze the independent contribution of uric acid to the variance of CSS, we performed ordinal regression models based on cardiovascular risk factors impacting this variable. In the ordinal regression analysis, we found that SUA was an independent predictor of the complexity of CAD as evaluated by the CSS (OR, 2.68; 95% CI, 2.23-3.22; P<.001). Other than SUA, smoking prevalence (OR, 3.53; 95% CI, 2.02-6.16; P<.001) and Framingham Risk Stratification (OR, 4.16; 95% CI, 2.48-6.97; P<.001) were also independent predictors of the complexity of CAD as evaluated by the CSS (Table 2).

Discussion

In this cross-sectional study, we assessed whether SUA and other cardiovascular risk factors were associated with the complexity of CAD as evaluated by CSS in a cohort of patients admitted to our department who underwent CAG. To the best of our knowledge, this is the first description of the relationship between CSS and SUA. Patients in the CSS_HIGH tertile had higher SUA than patients in the lower 2 tertiles.

Endothelial dysfunction is an early process in the development of atherosclerosis.¹³ Increased SUA has been correlated with a decrease in flow-mediated dilatation of the brachial artery, which is a surrogate measure of endothelial dysfunction.¹⁴ Several in vitro studies suggest that SUA has proinflammatory effects. SUA stimulates the production of monocyte chemoattractant protein-1 by vascular smooth muscle cells and interleukin-1β, interleukin-6, and tumor necrosis factor-α (TNF-α) by human mononuclear cells.¹⁵ Kono et al also confirmed the role of uric acid as a proinflammatory molecule released from dying cells.¹⁶ Infusion of UA into mice leads to a marked increase in circulating TNF-α.¹⁷

Elevated SUA is correlated with age, male gender, obesity, hyperinsulinemia, glucose intolerance, diabetes mellitus, and hyperlipidemia.¹⁸ Moreover, elevated SUA is correlated with hypertension.¹⁹ In nondiabetic patients with CAD, elevated SUA is associated with an increased risk of cardiac events, independent of renal function.²⁰ SUA has been associated with risk factors of CAD in a large population-based sample of older persons²¹ and in young people.²² A great quantity of experimental, clinical, and epidemiological data show that SUA is an independent risk factor for cardiovascular disease.^23,24 SUA has also been associated with the presence and severity of angiographically proven CAD.²⁵ It has also been associated with the severity and the calcified morphology of coronary atherosclerotic plaques detected by multidetector computed tomography after adjustment for other risk factors.²⁶ However, some contrasting results that showed SUA was not an independent predictor of coronary heart disease^27,28 also exist. The reasons for the contrary results need further exploration. In our study, SUA level was significantly associated with the severity and complexity of CAD evaluated by CSS. Further prospective clinical studies are needed to clarify the exact physiopathologic role of SUA in CAD. Other than SUA, age, the prevalence of hypertension, and smoking prevalence were significantly related with CSS. Serum creatinine level, fasting blood glucose level, total cholesterol level, and higher LDL cholesterol level were also prominently related with CSS.

The SYNTAX score system has recently been assessed in numerous cohorts of patients undergoing coronary revascularization by PCI or bypass surgery. It is also able to predict MACE after percutaneous revascularization in patients with left main disease and/or multivessel CAD.⁹ SYNTAX score as the dependent variable was also positively correlated with the level of SUA (data not shown). However, the SYNTAX score does not incorporate clinical characteristics. CSS is the score that represents a risk score combining both clinical and angiographic variables. Ranucci et al²⁹ illustrated the utility of value of age, creatinine, and ejection fraction serum creatinine (ACEFSCr) by demonstrating that a simple scoring method using only age, left ventricular ejection fraction, and ACEFSCr score is as good as complex scores such as the EuroSCORE (17 clinical variables) and Parsonnet score in predicting mortality in patients undergoing elective CABG. CSS is superior to either the SYNTAX score or ACEFSCr score alone in the prediction of MACE and mortality at 5-year follow-up in patients with complex CAD undergoing PCI, and logCSS is an independent predictor of long-term MACE in these patients.¹⁶ According to the ordinal regression models in our study, SUA, smoking, and Framingham Risk Stratification were significant predictors of the complexity of CAD as evaluated by CSS after adjustment for other risk factors.

Study limitations. First, the subjects undergoing CAG who visited a single center do not represent the entire Chinese population. Second, this was a cross-sectional study; therefore, we were unable to examine the impact of hyperuricemia over time. Third, the present study excluded lifestyle information from statistical analysis due to a lack of complete records of dietary purine intake and the volumes and types of alcoholic beverages consumed; this prevented us from controlling for confounding factors that may also impact SUA elevation.

Conclusion

Our study shows that high serum uric acid is associated with higher clinical SYNTAX score in CAD, and SUA level was significantly associated with the severity and complexity of CAD evaluated by CSS. These data show a trend for uric acid in the pathophysiology of atherosclerotic CAD and highlight the need for further prospective clinical research.

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From the ¹Department of Cardiovascular Diseases, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China, and ²Department of Neurology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
^*Zhao-Jun Xiong and Can-Sheng Zhu contributed equally to this work.
Disclosure: The authors have completed and returned the ICMJE Form for Disclosure of Potential Conflicts of Interest. The authors report no conflicts of interest regarding the content herein.
Funding: This study was supported by grants from the Science and Technology Planning Project of Guangdong Province, China (2009B030801146) and the Medical Scientific Research Foundation of Guangdong Province, China (A2009200).
Manuscript submitted July 12, 2011, provisional acceptance given August 16, 2011, final version accepted September 27, 2011.
Address correspondence to: Dr. Lin Chen, Department of Cardiovascular Diseases, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510630, China. Email: linchenzssy@yahoo.com.cn