CAJ | 학술논문

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分另为0.813(0.789～0.837)、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.
목적 구건태만지구35 ～ 74세건강체검인군대사종합정5년발병풍험(개체화)예측모형.방법 재1997-2006년초차삼가태만미조자동화건강체검궤구(미조건검)적35～74세인군중,장수방만5년기선시무대사종합정13 973인작위수방대렬,병분위건모대렬(용우건립5년발병예측모형)화험증대렬(용우평고모형외부효도),채용logistic회귀구건예측모형.이ROC곡선하면적(AUC)평개의합우도,병장인군적예측풍험개솔진행풍험등급화분.결과 거제기선환자후연구인군5년대사종합정환병솔위11.7％.납입발병풍험예측모형변량유년령、당뇨병가족사、수축압、공복혈당、감유삼지、고밀도지단백담고순、저밀도지단백담고순、총담고순、체중지수화혈뇨산,건모대렬건립예측모형적AUC위0.827(95％CI:0.814～0.839),험증대렬적AUC분령위0.813(0.789～0.837)、0.826(0.800～ 0.852)、0.794(0.768 ～ 0.820).장건모대렬화분위4개풍험등급후,제시개체발병개솔≥17.6％자위중위인군,발병개솔≥59.0％자위고위인군.결론 유미조건검종향수거고건립적5년대사종합정개체풍험예측모형유교고적험증효도,대우체검인군5년대사종합정발병예측구유실용、가행적특점,예측모형대평고대사종합정개체발병화군체감측균유교고응용개치.
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 subjects 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 all the variables.The predictability of the model was evaluated by areas under curve (AUC) the receiver-operating characteristic (ROC) followed by the testification of its diagnostic property on the testing sample.Once the final model was defined,the next step was to establish rules to characterize 4 different 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 of diabetes,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 0.813 (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 17.6％ of the incidence probability was in the population under mediate risk while over 59.0％ of them was in the high risk group,respectively.Conclusion 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 the 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.