目的 探讨稳定型心绞痛(stable angina pectoris,SAP)患者血尿酸(serum uric acid, SUA)水平与冠状动脉钙化(coronary artery calcification, CAC)程度的相关性,为CAC的诊断和评估提供依据。方法 选择2021年9月—2024年8月在北京市仁和医院诊断为SAP的患者125例,并于北京市仁和医院行冠状动脉CTA扫描,采用Agatston修正法计算冠状动脉钙化积分(coronary artery calcification score, CACS)后将患者分为5组:无钙化组(CACS=0,n=11)、少量钙化组(1≤CACS≤10,n=34)、轻度钙化组(11≤CACS≤100,n=26)、中度钙化组(101≤CACS≤399,n=36 )、重度钙化组(CACS≥400,n=18)。比较各组临床资料,采用logistic回归分析筛选CAC的影响因素,采用相关性分析探讨SUA水平与其他指标的相关性,采用受试者工作特征曲线分析评估SUA水平预测CAC严重程度的价值。结果 5组性别、年龄、饮酒史、甘油三酯(triglycerides, TG)、低密度脂蛋白胆固醇(low-density lipoprotein cholesterol, LDL-C)、脂蛋白α[lipoprotein(α), Lp(α)]水平差异均无统计学意义(P>0.05);高血压病史占比、糖尿病史占比、吸烟史占比、总胆固醇(total cholesterol, TC)水平、SUA水平差异均有统计学意义(P<0.05)。行有序logistic回归分析,结果表明,高血压病史、糖尿病史、吸烟史、饮酒史、TG、TC、LDL-C、LP(α)、SUA均为CAC的危险因素(P<0.05)。相关性分析表明,SUA水平与TC、LDL-C、LP(α)和CAC程度均呈正相关(P<0.05)。绘制受试者工作特征曲线显示,SUA水平预测CAC的曲线下面积为0.884(95%CI: 0.816~0.951),截断值为367.5 μmol/L,敏感度为81.6%,特异度为90.9%。结论 SAP患者SUA水平与CAC程度呈正相关,可作为独立危险因素,且对CAC有一定的预测价值。

Objective To investigate the correlation between serum uric acid (SUA) levels and coronary artery calcification (CAC) severity in patients with stable angina pectoris (SAP), thus providing a basis for the diagnosis and assessment of CAC. Methods A total of 125 patients diagnosed with SAP in Beijing Renhe Hospital from September 2021 to August 2024 were recruited, and all patients were given CTA scan in the hospital. After calculating the Agatston coronary artery calcification score (CACS), the patients were divided into non-calcification group (CACS=0, n=11), less calcification group (1≤CACS≤10, n=34), mild calcification group (11≤CACS≤100, n=26), moderate calcification group (101≤CACS≤399, n=36), severe calcification group (CACS≥400, n=18). The clinical data of each group were compared. Logistic regression analysis was employed to screen the influencing factors of CAC. Correlation analysis was used to explore the correlation between SUA level and other indicators. Receiver operating characteristic (ROC) curve analysis was applied to evaluate the value of SUA level in predicting CAC severity. Results There were no significant differences in gender, age, drinking history, triglycerides (TG), low-density lipoprotein cholesterol (LDL-C) and lipoprotein (α) (Lp[α]) levels among the five groups (P>0.05). There were statistically significant differences in the proportions of hypertension, diabetes and smoking, and levels of total cholesterol (TC) and SUA (P<0.05). The results of ordinal logistic regression analysis showed that hypertension history, diabetes history, smoking history, drinking history, TG, TC, LDL-C, LP (α) and SUA were all risk factors for CAC (P<0.05). Correlation analysis showed that SUA level was positively correlated with TC, LDL-C, LP (α) and CAC severity (P<0.05). The area under ROC curve (AUC) of SUA level in predicting CAC severity was 0.884(95%CI: 0.816-0.951), with the cutoff value of 367.5 μmol/L, the sensitivity of 81.6%, and the specificity of 90.9%. Conclusion SUA levels in SAP patients are positively correlated with CAC severity, making SUA an independent risk factor with certain diagnostic value for CAC.