Salim Agus, Tai E Shyong, Tan Vincent Y, Welsh Alan H, Liew Reginald, Naidoo Nasheen, Wu Yi, Yuan Jian-Min, Koh Woon P, van Dam Rob M
Saw Swee Hock School of Public Health, National University of Singapore, Singapore Department of Mathematics and Statistics, La Trobe University, Melbourne, Australia
Saw Swee Hock School of Public Health, National University of Singapore, Singapore Department of Medicine, National University Health System, Singapore.
Eur J Prev Cardiol. 2016 Aug;23(12):1339-49. doi: 10.1177/2047487315626547. Epub 2016 Jan 18.
In western populations, high-sensitivity C-reactive protein (hsCRP), and to a lesser degree serum creatinine and haemoglobin A1c, predict risk of coronary heart disease (CHD). However, data on Asian populations that are increasingly affected by CHD are sparse and it is not clear whether these biomarkers can be used to improve CHD risk classification.
We conducted a nested case-control study within the Singapore Chinese Health Study cohort, with incident 'hard' CHD (myocardial infarction or CHD death) as an outcome. We used data from 965 men (298 cases, 667 controls) and 528 women (143 cases, 385 controls) to examine the utility of hsCRP, serum creatinine and haemoglobin A1c in improving the prediction of CHD risk over and above traditional risk factors for CHD included in the ATP III model. For each sex, the performance of models with only traditional risk factors used in the ATP III model was compared with models with the biomarkers added using weighted Cox proportional hazards analysis. The impact of adding these biomarkers was assessed using the net reclassification improvement index.
For men, loge hsCRP (hazard ratio 1.25, 95% confidence interval: 1.05; 1.49) and loge serum creatinine (hazard ratio 4.82, 95% confidence interval: 2.10; 11.04) showed statistically significantly associations with CHD risk when added to the ATP III model. We did not observe a significant association between loge haemoglobin A1c and CHD risk (hazard ratio 1.83, 95% confidence interval: 0.21; 16.06). Adding hsCRP and serum creatinine to the ATP III model improved risk classification in men with a net gain of 6.3% of cases (p-value = 0.001) being reclassified to a higher risk category, while it did not significantly reduce the accuracy of classification for non-cases. For women, squared hsCRP was borderline significantly (hazard ratio 1.01, 95% confidence interval: 1.00; 1.03) and squared serum creatinine was significantly (hazard ratio 1.81, 95% confidence interval: 1.49; 2.21) associated with CHD risk. However, the association between squared haemoglobin A1c and CHD risk was not significant (hazard ratio 1.05, 95% confidence interval: 0.99; 1.12). The addition of hsCRP and serum creatinine to the ATP III model resulted in 3.7% of future cases being reclassified to a higher risk category (p-value = 0.025), while it did not significantly reduce the accuracy of classification for non-cases.
Adding hsCRP and serum creatinine, but not haemoglobin A1c, to traditional risk factors improved CHD risk prediction among non-diabetic Singaporean Chinese. The improved risk estimates will allow better identification of individuals at high risk of CHD than existing risk calculators such as the ATP III model.
在西方人群中,高敏C反应蛋白(hsCRP)以及程度稍轻的血清肌酐和糖化血红蛋白可预测冠心病(CHD)风险。然而,受冠心病影响日益增加的亚洲人群的数据却很稀少,尚不清楚这些生物标志物是否可用于改善冠心病风险分类。
我们在新加坡华人健康研究队列中开展了一项巢式病例对照研究,以新发“严重”冠心病(心肌梗死或冠心病死亡)为研究结果。我们使用了965名男性(298例病例,667例对照)和528名女性(143例病例,385例对照)的数据,来检验hsCRP、血清肌酐和糖化血红蛋白在改善冠心病风险预测方面的作用,这种预测是在ATP III模型中所包含的冠心病传统风险因素之上进行的。对于每一种性别,将仅使用ATP III模型中的传统风险因素的模型的表现,与加入生物标志物后的模型的表现进行比较,采用加权Cox比例风险分析。使用净重新分类改善指数评估加入这些生物标志物的影响。
对于男性,当加入ATP III模型时,loge hsCRP(风险比1.25,95%置信区间:1.05;1.49)和loge血清肌酐(风险比4.82,95%置信区间:2.10;11.04)与冠心病风险存在统计学显著关联。我们未观察到loge糖化血红蛋白与冠心病风险之间存在显著关联(风险比1.83,95%置信区间:0.21;16.06)。将hsCRP和血清肌酐加入ATP III模型可改善男性的风险分类,6.3%的病例(p值 = 0.001)被重新分类到更高风险类别,而这并未显著降低非病例分类的准确性。对于女性,hsCRP的平方处于临界显著水平(风险比1.01,95%置信区间:1.00;1.03),血清肌酐的平方与冠心病风险显著相关(风险比1.81,95%置信区间:1.49;2.21)。然而,糖化血红蛋白平方与冠心病风险之间的关联并不显著(风险比1.05,95%置信区间:0.99;1.12)。将hsCRP和血清肌酐加入ATP III模型导致3.7%的未来病例被重新分类到更高风险类别(p值 = 0.025),而这并未显著降低非病例分类的准确性。
在传统风险因素中加入hsCRP和血清肌酐,而非糖化血红蛋白,可改善非糖尿病新加坡华人的冠心病风险预测。与现有的风险计算器(如ATP III模型)相比,改进后的风险估计将能更好地识别冠心病高危个体。