JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES) ›› 2017, Vol. 55 ›› Issue (6): 87-92.doi: 10.6040/j.issn.1671-7554.0.2017.350

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A prediction model for metabolic syndrome risk: a study based on the health management cohort

SUN Yuanying1,2, YANG Yachao3, QU Mingling3, CHEN Yanmin3, LI Min1,2, WANG Shukang1,2, XUE Fuzhong1,2, LIU Yunxia1,2   

  1. 1. Department of Biostatistics, School of Public Health, Shandong University, Jinan 250012, Shandong, China;
    2. Cheeloo Research Center for Biomedical Big Data, Shandong University, Jinan 250012, Shandong, China;
    3. Physical Examination Department, Weihai Municipal Hospital, Weihai 264200, Shandong, China
  • Received:2017-04-24 Online:2017-06-10 Published:2017-06-10

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Abstract: Objective To construct a model to evaluate the risk of developing metabolic syndrome(MetS)within 5 years in Chinese mainland Han population based on a health management population. Methods A total of 15 872 people without MetS at baseline were included based on the Shandong Multi-center Longitudinal Cohort for Health Management. Cox proportional hazards regression model was used to build the prediction model and the discriminatory ability was evaluated with area under the receiver operating characteristic(ROC)curve(AUC)and observed/expected counts(OE ratio). Results A total of 1 591 new MetS cases(1 273 males and 318 females)were observed during the follow-up of 5 years, accounting for an incidence density of 38.57/1000 person-year. In the male model, the risk factors included age, body mass index(BMI), fasting blood-glucose(FBG), triglyceride(TG), high density lipoprotein cholesterol(HDL-C), uric acid(UA), total cholesterol and hypertension. In the female model, the risk factors included age, BMI, FBG, TC, UA and hypertension. The AUC was 0.751(95%CI: 0.742-0.759)and 0.745(95%CI: 山 东 大 学 学 报 (医 学 版)55卷6期 -孙苑潆,等.健康管理人群代谢综合征发病风险预测模型 \=- 0.734-0.756)in the male and female model, respectively. The OE ratio was 1.03 and 1.00 in the male and female model, respectively. Conclusion This study has constructed a 5-year risk model that could be informative for identifying individuals at a high risk of developing MetS in a health management population.

Key words: Health management population, Metabolic syndrome, Longitudinal cohort, Prediction model, Coxs proportional hazards regression

CLC Number: 

  • R589
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