Klisic Aleksandra, Kotur-Stevuljevic Jelena, Kavaric Nebojsa, Matic Marija
Primary Health Care Center, Podgorica, Montenegro.
Department for Medical Biochemistry, Faculty of Pharmacy, University of Belgrade, Belgrade, Serbia.
Arch Iran Med. 2016 Dec;19(12):845-851.
We aimed to examine the relationship between high levels of cystatin C, retinol-binding protein 4 (RBP4) and cardiovascular risk score [determined by Framingham Risk Score (FRS)] in postmenopausal women.
A total of apparently healthy 129 postmenopausal women (mean age 57.1 ± 4.6 years) were included. Serum cystatin C, RBP4, glucose, lipid parameters, creatinine, uric acid and high sensitivity C-reactive protein (hsCRP) were determined. Anthropometric parameters and blood pressure were also obtained. FRS was calculated. Multiple linear regression analysis (MLR) was performed to identify independent factors affecting FRS and to estimate the final predictors of its variability. Receiver Operating Characteristic (ROC) curve analysis was used with the purpose of testing discriminatory potential of a group of parameters selected in MLR analysis, with FRS level as dependent variable.
We found significantly higher levels of both proteins, cystatin C (P = 0.001) and RBP4 (P = 0.006), in the FRS higher (medium and high) risk groups (FRS ≥ 10%) compared to low risk FRS group (FRS < 10%). MLR revealed the best model consisting of 4 parameters (e.g., body mass index (BMI) (P < 0.001), triglycerides (TG) (P = 0.004), RBP4 (P = 0.021), and cystatin C (P = 0.046), R2-adjusted = 0.347) for FRS prediction. Construction of a model consisted of those 4 FRS formula independent parameters (BMI, TG, cystatin C and RBP4) using logistic regression analysis showed that new ROC curve had excellent discriminatory capability (area under the curve = 0.820).
High cystatin C and retinol-binding protein 4 may contribute significantly to cardiovascular risk burden in addition to traditional cardiovascular markers.
我们旨在研究绝经后女性中高水平胱抑素C、视黄醇结合蛋白4(RBP4)与心血管风险评分[由弗明汉姆风险评分(FRS)确定]之间的关系。
共纳入129名明显健康的绝经后女性(平均年龄57.1±4.6岁)。测定血清胱抑素C、RBP4、血糖、血脂参数、肌酐、尿酸和高敏C反应蛋白(hsCRP)。还获取了人体测量参数和血压。计算FRS。进行多元线性回归分析(MLR)以确定影响FRS的独立因素,并估计其变异性 的最终预测因子。以FRS水平为因变量,采用受试者工作特征(ROC)曲线分析来测试MLR分析中选择的一组参数的鉴别潜力。
我们发现,与低风险FRS组(FRS<10%)相比,FRS较高(中高)风险组(FRS≥10%)中胱抑素C(P = 0.001)和RBP4(P = 0.006)这两种蛋白的水平显著更高。MLR显示,由4个参数(即体重指数(BMI)(P<0.001)、甘油三酯(TG)(P = 0.004)、RBP4(P = 0.021)和胱抑素C(P = 0.046),调整后R2 = 0.347)组成的最佳模型可用于FRS预测。使用逻辑回归分析构建由这4个FRS公式独立参数(BMI、TG、胱抑素C和RBP4)组成的模型,结果显示新的ROC曲线具有出色的鉴别能力(曲线下面积 = 0.820)。
除传统心血管标志物外,高胱抑素C和视黄醇结合蛋白4可能对心血管风险负担有显著影响。