台湾地区35～74岁健康体检人群代谢综合征发病风险预测模型的建立

杨兴华; 陶秋山; 孙凤; 曹纯铿; 詹思延

Abstract

杨兴华,陶秋山,孙凤,曹纯铿,詹思延.台湾地区35～74岁健康体检人群代谢综合征发病风险预测模型的建立[J].Chinese journal of Epidemiology,2013,34(9):874-878

台湾地区35～74岁健康体检人群代谢综合征发病风险预测模型的建立

Setting up a risk prediction model on metabolic syndrome among 35-74 year-olds based on the Taiwan MJ Health-checkup Database

Received:March 22, 2013

DOI：

KeyWord: 代谢综合征|风险预测模型|纵向数据

English Key Word: metabolic syndrome|Risk predictive model|Longitudinal data

FundProject:

Author Name	Affiliation	E-mail
YANG Xing-hua	Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing 100069, China
TA0 Qiu-shan	Department of Epidemiology and Biostatistics, School of Public Health ,Health Science Center of Peking University
SUN Feng	Department of Epidemiology and Biostatistics, School of Public Health ,Health Science Center of Peking University
CAO Chun-keng	MJ, Health Management Organization, Taiwan
ZHAN Si-yan	Department of Epidemiology and Biostatistics, School of Public Health ,Health Science Center of Peking University	siyan-zhan@bjmu．edu．Cn

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Abstract:

目的构建台湾地区35～74岁健康体检人群代谢综合征5年发病风险(个体化)预测模型。方法在1997-2006年初次参加台湾美兆自动化健康体检机构(美兆健检)的35～74岁人群中，将随访满5年基线时无代谢综合征13 973人作为随访队列，并分为建模队列(用于建立5年发病预测模型)和验证队列(用于评估模型外部效度)，采用logistic回归构建预测模型。以ROC曲线下面积(AUC)评价拟合优度，并将人群的预测风险概率进行风险等级划分。结果去除基线患者后研究人群5年代谢综合征患病率为11．7％。纳入发病风险预测模型变量有年龄、糖尿病家族史、收缩压、空腹血糖、甘油三酯、高密度脂蛋白胆固醇、低密度脂蛋白胆固醇、总胆固醇、体重指数和血尿酸，建模队列建立预测模型的AUC为0．827(95％CI：0．814～0．839)，验证队列的AUC分别为0．813(O．789～0．837)、0．826(0．800～0．852)、0．794(0．768～0．820)。将建模队列划分为4个风险等级后，提示个体发病概率≥17．6％者为中危人群，发病概率≥59．0％者为高危人群。结论由美兆健检纵向数据库建立的5年代谢综合征个体风险预测模型有较高的验证效度，对于体检人群5年代谢综合征发病预测具有实用、可行的特点，预测模型对评估代谢综合征个体发病和群体监测均有较高应用价值。

English Abstract:

Objective This study aimed to provide an epidemiological modeling method to evaluate the risk of metabolic syndrome(MS) development in the coming 5 years among 35-74 year-olds from Taiwan．Methods A cohort of 13 973 subiects aged 35-74 years who did not have metabolic syndrome but took the initial testing during 1997-2006 was formed to derive a risk score which tended to predict the incidence of MS．Multivariate logistic regression was used to derive the risk functions and using the‘check-up center’(Taipei training cohort) as the overall cohort．Rules based on these risk functions were evaluated in the remaining three centers(as testing cohort)．Risk functions were produced to detect the MS on a training sample using the multivariate logistic regression models．Started with those variables that could predict the MS through univariate models， we then constructed multivariable logistic regression models in a stepwise manner which eventually could include a11 the variables．The predictability of the model was evaluated by areas under calve (AUC)the receiver-operating characteristic (ROC) followed by the testification of its diagnostic property on the testing sample．Once the fmal model was defined．the next step was to establish rules to characterize 4 difierent degrees of risks based on the cut points of these probabilities，after being transformed into normal distribution by log-transformation．Results At baseline．the range of the proportion of MS was 23．9％ and the incidence of MS in 5-years was 11．7％ in the non-MS cohort． The final multivariable logistic regression model would include ten risk factors as：age，history ofdiabetes，contractive pressure，fasting blood-glucose，triglyceride，high density lipoprotein cholesterol， low density lipoprotein cholesterol。body mass index and blood uric acid．AUC Was 0．827(95％CI： 0．814-0．839)that could predict the development of MS within the next 5 years．The curve also showed adequate performance in the three tested samples．with the AUC and 95％CI as 0．8l3(0．789-0．837)，0．826(0．800-0．852)and 0．794(0．768-0．820)，respectively．After labeling the degrees of the four risks．it was showed that over l7．6％of the incidence probability was in the population under mediate risk while over 59．0％ of them was in the high risk group．respectively．Conciusion Both predictability and reliability of Our Metabolic Syndrome Risk Score Model，derived based on Taiwan MJ Longitudinal Health-checkup-based Population Database．were relatively satisfactory in the testing cohort．This model was simple，with practicable predictive variables and feasible form on degrees of risk．This model not only could help individuals to assess me situation of their own risk on MS but could also provide guidance on the group surveillance programs in the community regarding the development of MS．

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