Does the metabolic syndrome improve identification of individuals at risk of type 2 diabetes and/or cardiovascular disease?
Does the metabolic syndrome improve identification of individuals at risk of type 2 diabetes and/or cardiovascular disease?
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DOI：
10.2337/diacare.27.11.2676
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发表时间：
2004-11-01
期刊：
影响因子：
16.2
通讯作者：
Haffner, SM
中科院分区：
文献类型：
--
作者：
Stern, MP;Williams, K;Haffner, SM
目的-代谢综合征已被推广作为一种方法，以确定高风险的个人为2型糖尿病和心血管疾病（CVD）。因此，我们试图比较这种综合征，定义的国家胆固醇教育计划，糖尿病预测模型和Fragrance风险评分作为2型糖尿病和CVD的预测，研究设计和方法-一个基于人口的样本1,709最初非糖尿病圣安东尼奥心脏研究（SAHS）的参与者进行了为期7.5年，其中195人发展为2型糖尿病。在相同的时间间隔内，2,570名SANS参与者中有156名经历了心血管事件。对1,353名最初非糖尿病的墨西哥城糖尿病研究（MCDS）参与者进行了为期6.5年的随访，其中125人发展为2型糖尿病。基线测量包括病史，年龄，性别，种族，吸烟状况，BMI，血压，空腹和2小时血糖水平，空腹血清总胆固醇和高密度脂蛋白胆固醇和甘油三酯。结果-预测糖尿病与代谢综合征的敏感性分别为66.2%和62.4%，在SAHS和MCDS，分别为27.8%和38.7%，假阳性率分别为27.8%和38.7%。分别SAHS患者代谢综合征预测CVD的敏感性和假阳性率分别为67.3%和34.2%。在相应的假阳性率，两个预测模型有显着更高的灵敏度，并在相应的灵敏度，显着较低的假阳性率比代谢综合征的两个终点。将代谢综合征与任一预测模型相结合并不能改善任一终点的预测结果。结论-代谢综合征劣于2型糖尿病或心血管疾病的预测模型。
OBJECTIVE- The metabolic syndrome has been promoted as a method for identifying high-risk individuals for type 2 diabetes and cardiovascular disease (CVD). We therefore sought to compare this syndrome, as defined by the National Cholesterol Education Program, to the Diabetes Predicting Model and the Framingham Risk Score as predictors of type 2 diabetes and CVD, respectively.RESEARCH DESIGN AND METHODS- A population-based sample of 1,709 initially nondiabetic San Antonio Heart Study (SAHS) participants were followed for 7.5 years, 195 of whom developed type 2 diabetes. Over the same time interval, 156 of 2,570 SANS participants experienced a cardiovascular event. A population-based sample of 1,353 initially nondiabetic Mexico City Diabetes Study (MCDS) participants were followed for 6.5 years, 125 of whom developed type 2 diabetes. Baseline measurements included medical history, age, sex, ethnicity, smoking status, BMI, blood pressure, fasting and 2-h plasma glucose levels, and fasting serum total and HDL cholesterol and triglycerides.RESULTS- The sensitivities for predicting diabetes with the metabolic syndrome were 66.2 and 62.4% in the SAHS and the MCDS, respectively, and the false-positive rates were 27.8 and 38.7%, respectively. The sensitivity and false-positive rates for predicting CVD with the metabolic syndrome in the SAHS were 67.3 and 34.2%, respectively. At corresponding false-positive rates, the two predicting models had significantly higher sensitivities and, at corresponding sensitivities, significantly lower false-positive rates than the metabolic syndrome for both end points. Combining the metabolic syndrome with either predicting model did not improve the of either end point.CONCLUSIONS- The metabolic syndrome is inferior to established predicting models for either type 2 diabetes or CVD.