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作 者:王锦纹[1] 胡大一[2] 孙艺红[2] 王家宏[2] 王桂莲[2] 谢江[3] 管绯[1]
机构地区:[1]首都医科大学附属北京安贞医院北京心肺血管疾病研究所,北京100029 [2]北京大学人民医院心脏中心 [3]首都医科大学公共卫生与家庭医学学院
出 处:《临床心血管病杂志》2009年第6期404-407,共4页Journal of Clinical Cardiology
摘 要:目的:在体质指数(BMI)<25kg/m2的人群中找到能有效评价代谢风险聚集的最佳指标。方法:2007年4~8月期间入选了北京社区18~95岁BMI<25kg/m2的居民(男性1881例,女性3101例),进行了身高、体重、腰围、臀围的测量及血压、空腹血糖、TC、TG的检测。代谢危险因素定义如下:①血压升高:收缩压≥130mmHg(1mmHg=0·133kPa)和(或)舒张压≥85mmHg,或已经接受相应治疗,或此前已经诊断为高血压;②空腹血糖升高:空腹血糖>5.6mmol/L,或已经接受相应治疗,或此前已经诊断为糖尿病;③TG水平升高:>1.7mmol/L,或已经接受相应治疗;④HDL-C水平降低:男性<0.9mmol/L,女性<1.0mmol/L,或已经接受相应治疗。使用相关分析、线性回归模型及ROC曲线分析对各种人体测量指数与代谢危险因素的关系进行分析。结果:所有人体测量指数均与代谢危险因素正相关。对于男性人群,要检出2个以上代谢危险因素的聚集,腰臀比、腰围身高比、腰围均较BMI有相对高的ROC曲线下面积(AUC),腰围、腰臀比和腰围身高比之间的AUC无显著性差异。而对于女性人群,4个体质指数的AUC之间均无显著性差异。结论:非肥胖人群中,中心性肥胖指标适宜作为评价男性代谢危险因素聚集的敏感指标,而对于女性人群,BMI和中心性肥胖指标均可用于检出代谢危险因素。Objective:To find anthropometrieal index that can value the metabolic risk factors among community individuals with body mass index (BMI)〈 25 kg/m2. Method:In this clinical cross-sectional study, 1881 male and 3101 female with BMI〈 25 kg/m2 (aged between 18 and 95 years )were recruited from communities in Beijing from April 2007 to August 2007. Medical examinations blood weight, height, waist circumference, hip circumference, fasting blood triglycerides, HDL, glucose concentrations, and blood pressure. BMI, waist to hip ratio (WHR), waist circumference (WC) and waist to stature ratio (WSR) were calculated. Four metabolic risk factors were defirred asfollow: (1) high blood pressure (BP) (diagnosed by systolic BP≥130 mmHg and/or diastolic blood pressure ≥85 mmHg and/or on antihypertensive therapy); (2) high triglycerides (≥1.7 mmol/L) ; (3)low highdensity lipoprotein cholesterol (〈0.9 mmol/L for male and 1.0 mmol/1 for female) ; (4) impaired glucose tolerance (plasma glucose concentration≥5. 6% or on antidiabetic therapy). The relationships between these indexs and metabolic risk factors were analyzed by using partial correlation analyses, linear regression, and Receiver Operator Characteristic (ROC) curves. Result:All anthropometric indices of obesity were positively correlated with metabolic risk factors. Among the various proposed indices for the evaluation of metabolic risk, ROC analysis revealed significant differences between AUCs for central obesity indexes (WC, WSR, WHR) and BMI. There were no significant differences between AUCs for WC, WSR and WHR. The analysis also revealed that there were no significant differences between AUCs for four indexes (WC, WSR, WHR, BMI) in non-obese women. Conclusion:Central obesity indexes are more sensitive than BMI to evaluate clustering of metabolic risk factors in Chinese nonobese male, while all obesity indexes are equally able to identify metabolic risks in non-obese women.
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