您的位置：山东大学 -> 科技期刊社 -> 《山东大学学报（医学版）》

山东大学学报（医学版） ›› 2017, Vol. 55 ›› Issue (6): 87-92.doi: 10.6040/j.issn.1671-7554.0.2017.350

• • 上一篇    下一篇

健康管理人群代谢综合征发病风险预测模型

孙苑潆1,2,杨亚超3,曲明苓3,陈雁敏3,李敏1,2,王淑康1,2,薛付忠1,2,刘云霞1,2   

  1. 1.山东大学公共卫生学院生物统计学系, 山东 济南 250012;2.山东大学齐鲁生物医学大数据研究中心, 山东 济南 250012;3.威海市立医院健康体检科, 山东 威海 264200
  • 收稿日期:2017-04-24 出版日期:2017-06-10 发布日期:2017-06-10
  • 通讯作者: 杨亚超. E-mail:790587538@qq.com E-mail:790587538@qq.com
  • 基金资助:
    国家自然科学基金(81273177)

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

PDF (PC)

473

摘要： 目的 基于健康管理人群队列,构建代谢综合征的5年发病风险预测模型。 方法 依托山东多中心健康管理纵向观察队列,选取20~80岁且基线未患代谢综合征者构建队列,采用Cox比例风险回归构建预测模型,并利用十折交叉验证法检验模型的稳定性,通过受试者工作特征曲线(ROC)下面积(AUC)和观测/期望(OE比)评价模型的预测效果。 结果 随访期间共发生代谢综合征1 591例(男1 273例,女318例),发病密度为38.57/1 000人年。男性代谢综合征预测模型纳入的变量包括年龄、BMI、空腹血糖、甘油三酯、高密度脂蛋白、血尿酸、总胆固醇和是否高血压,女性模型纳入变量包括年龄、BMI、空腹血糖、甘油三酯、血尿酸和是否高血压;模型ROC曲线下面积分别为0.751(95%CI:0.742~0.759)和0.745(95%CI:0.734~0.756);OE比分别为1.03和1.00;十折交叉验证ROC曲线下面积平均值分别为0.749和0.746。 结论 本研究利用健康管理纵向队列数据,建立了代谢综合征5年发病风险预测模型,经十折交叉验证结果表明,其在健康管理人群中有较好的预测效果,有助于识别高发病风险人群,进而减少和预防代谢综合征的发生。

关键词: 代谢综合征, 健康管理人群, Cox比例风险回归, 纵向队列, 风险预测模型

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

中图分类号: 

  • R589
[1] 中华医学会糖尿病学分会. 中国2型糖尿病防治指南(2013年版)[J]. 中华内分泌代谢杂志, 2014, 30(10): 26-89. Chinese Diabetes Society. China guideline for type 2 diabetes[J]. Chin J Endocrinol Metab, 2014, 30(10): 26-89.
[2] Ford ES, Giles WH, Dietz WH. Prevalence of the metabolic syndrome among US adults: findings from the third National Health and Nutrition Examination Survey[J]. JAMA, 2002, 287(3): 356-359.
[3] Beltrán-Sánchez H, Harhay MO, Harhay MM, et al. Prevalence and trends of metabolic syndrome in the adult U.S. population, 1999-2010[J]. J Am Coll Cardiol, 2013, 62(8): 697-703.
[4] Gu D, Reynolds K, Wu X, et al. Prevalence of the metabolic syndrome and overweight among adults in China[J]. Lancet, 2005, 365(9468): 1398-1405.
[5] Villegas R, Xiang YB, Yang G, et al. Prevalence and determinants of metabolic syndrome according to three definitions in middle-aged Chinese men[J]. Metab Syndr Relat Disord, 2009, 7(1): 37-45.
[6] Li G, de Courten M, Jiao S, et al. Prevalence and characteristics of the metabolic syndrome among adults in Beijing, China[J]. Asia Pac J Clin Nutr, 2010, 19(1): 98-102.
[7] Xiao J, Wu CL, Gao YX, et al. Prevalence of metabolic syndrome and its risk factors among rural adults in Nantong, China[J]. Sci Rep, 2016, 6: 38089. doi: 10.1038/srep38089.
[8] Kassi E, Pervanidou P, Kaltsas G, et al. Metabolic syndrome: definitions and controversies[J]. BMC Med, 2011, 9: 48. doi: 10.1186/1741-7015-9-48.
[9] Ford ES. The metabolic syndrome and mortality from cardiovascular disease and all-causes: findings from the National Health and Nutrition Examination Survey II Mortality Study[J]. Atherosclerosis, 2004, 173(2): 309-314.
[10] Grundy SM, Cleeman JI, Daniels SR, et al. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement[J]. Circulation, 2005, 112(17): 2735-2752.
[11] Wilson PW, D'Agostino RB, Parise H, et al. Metabolic syndrome as a precursor of cardiovascular disease and type 2 diabetes mellitus[J]. Circulation, 2005, 112(20): 3066-3072.
[12] Yang XH, Tao QS, Sun F, et al. Setting up a risk prediction model on metabolic syndrome among 35-74 year-olds based on the Taiwan MJ Health-checkup Database[J]. Chin J Epidemiol, 2013, 34(9): 874-878.
[13] Hsiao FC, Wu CZ, Hsieh CH, et al. Chinese metabolic syndrome risk score[J]. South Med J, 2009, 102(2): 159-164.
[14] 中华医学会糖尿病学分会代谢综合征研究协作组. 中华医学会糖尿病学分会关于代谢综合征的建议[J]. 中华糖尿病杂志, 2004, 12(3): 156-161. Metabolic syndrome research collaboration group from Chinese Diabetes Society. Suggestions on metabolic syndrome from Chinese Diabetes Society[J]. Chin J Diabetes, 2004, 12(3): 156-161.
[15] Kaur J. A comprehensive review on metabolic syndrome[J]. Cardiol Res Pract, 2014, 2014:943162. doi:10.1155/2014/943162.
[16] Jiang B, Li B, Wang Y, et al. The nine-year changes of the incidence and characteristics of metabolic syndrome in China: longitudinal comparisons of the two cross-sectional surveys in a newly formed urban community[J]. Cardiovasc Diabetol, 2016, 15:84. doi: 10.1186/s12933-016-0402-9.
[17] Lu J, Wang L, Li M, et al. Metabolic syndrome among adults in China - the 2010 China noncommunicable disease surveillance[J]. J Clin Endocrinol Metab, 2017, 102(2): 507-515.
[18] Sheu WH, Chuang SY, Lee WJ, et al. Predictors of incident diabetes, metabolic syndrome in middle-aged adults: a 10-year follow-up study from Kinmen, Taiwan[J]. Diabetes Res Clin Pract, 2006, 74(2): 162-168.
[19] Yu S, Guo X, Yang H, et al. An update on the prevalence of metabolic syndrome and its associated factors in rural northeast China[J]. BMC Public Health, 2014, 14: 877. doi: 10.1186/1471-2458-14-877.
[20] Tao LX, Li X, Zhu HP, et al. Association of hematological parameters with metabolic syndrome in Beijing adult population: a longitudinal study[J]. Endocrine, 2014, 46(3): 485-495.
[21] Regitz-Zagrosek V, Lehmkuhl E, Weickert MO. Gender differences in the metabolic syndrome and their role for cardiovascular disease[J]. Clin Res Cardiol, 2006, 95(3): 136-147.
[22] Zhou P, Meng Z, Liu M, et al. The associations between leukocyte, erythrocyte or platelet, and metabolic syndrome in different genders of Chinese[J]. Medicine, 2016, 95(44): e5189. doi: 10.1097/MD.0000000000005189
[23] Hwang LC, Bai CH, Chen CJ, et al. Gender difference on the development of metabolic syndrome: a population-based study in Taiwan[J]. Eur J Epidemiol, 2007, 22(12): 899-906.
[24] Park YW, Zhu S, Palaniappan L, et al. The metabolic syndrome: Prevalence and associated risk factor findings in the US population from the Third National Health and Nutrition Examination Survey, 1988-1994[J]. Arch Intern Med, 2003, 163(4): 427-436.
[25] Li JB, Wang X, Zhang JX, et al. Metabolic Syndrome: prevalence and risk factors in southern China[J]. J Int Med Res, 2010, 38(3): 1142-1148.
[26] Wang F, Wu S, Song Y, et al. Waist circumference, body mass index and waist to hip ratio for prediction of the metabolic syndrome in Chinese[J]. Nutr Metab Cardiovasc Dis, 2009, 19(8): 542-547.
[27] Zhou XH, Song XX, Ji LN. The components of metabolic syndrome analyzed by factor analysis[J]. J Diabetes, 2005, 13(6): 434-436.
[28] Dai X, Yuan J, Yao P, et al. Association between serum uric acid and the metabolic syndrome among a middle- and old-age Chinese population[J]. Eur J Epidemiol, 2013, 28(8): 669-676.
[1] 扈艳雯,王志媛,郁万江,赵蕙琛,韩合理,徐志鹏,马红,张玉超,刘元涛. 52例肥胖患者脂肪分布与代谢综合征及糖代谢指标的相关性[J]. 山东大学学报 (医学版), 2020, 1(8): 101-106.
[2] 李吉庆,赵焕宗,宋炳红,张理纯,李向一,陈亚飞,王萍,薛付忠. 基于健康管理队列的心血管事件风险预测模型[J]. 山东大学学报（医学版）, 2017, 55(6): 56-60.
[3] 王春霞,许艺博,杨宁,夏冰,王萍,薛付忠. 基于健康管理队列的冠心病风险预测模型[J]. 山东大学学报（医学版）, 2017, 55(6): 66-71.
[4] 张光,王广银,吴红彦, 张红玉,王停停,李吉庆,李敏,康凤玲,刘言训,薛付忠. 健康管理人群高脂血症风险预测模型[J]. 山东大学学报（医学版）, 2017, 55(6): 72-76.
[5] 李江冰,宋心红,林海燕,张冬芝,李向一,许艺博,王丽,薛付忠. 健康管理人群缺血性异常心电图的影响因素[J]. 山东大学学报（医学版）, 2017, 55(6): 77-81.
[6] 苏萍,杨亚超,杨洋,季加东,阿力木·达依木,李敏,薛付忠,刘言训. 健康管理人群2型糖尿病发病风险预测模型[J]. 山东大学学报（医学版）, 2017, 55(6): 82-86.
[7] 周苗,夏同耀,孙爱玲,李明,申振伟,卞伟玮,蒋正,康凤玲,柳晓涓,薛付忠,刘静. 健康管理人群慢性肾脏病风险预测模型[J]. 山东大学学报（医学版）, 2017, 55(6): 98-103.
[8] 李敏,王春霞,夏冰,朱茜,孙苑潆,王淑康,薛付忠,贾红英. 健康管理人群脑卒中风险预测模型[J]. 山东大学学报（医学版）, 2017, 55(6): 93-97.
[9] 于媛媛,王春霞,苏萍,孙苑潆,薛付忠,刘言训. 健康管理队列白内障发病风险预测模型[J]. 山东大学学报（医学版）, 2017, 55(6): 104-107.
[10] 柳晓涓,蒋正,康凤玲,周苗,林伟强,薛付忠. 中性粒细胞计数与非酒精性脂肪肝关联性的前瞻性队列研究[J]. 山东大学学报（医学版）, 2017, 55(6): 119-123.
[11] 于珍珍,陈慧,杨晓云,吕明. 高尿酸血症、幽门螺杆菌感染与代谢综合征的相关性[J]. 山东大学学报（医学版）, 2017, 55(5): 76-80.
[12] 杨洋,张光,张成琪,宋心红,薛付忠,王萍,王丽,刘言训. 基于体检队列的2型糖尿病风险预测模型[J]. 山东大学学报（医学版）, 2016, 54(9): 69-72.
[13] 许艺博,季晓康,李向一,申振伟,薛付忠. 尿液pH与代谢综合征的相关性[J]. 山东大学学报（医学版）, 2016, 54(12): 82-85.
[14] 肖娟, 陈启才,张鹏鹏,陈莉莉,陈潇潇,王束玫. 东营市某三甲医院纵向体检资料的血清谷丙转氨酶水平与代谢综合征的相关性[J]. 山东大学学报（医学版）, 2016, 54(1): 86-91.
[15] 张风雷, 郑曼, 张琦, 顾磊, 徐新生. 代谢综合征与P波离散度关系的探讨[J]. 山东大学学报（医学版）, 2015, 53(2): 52-55.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 2018 《山东大学学报(医学版)》编辑部　
地址:山东大学科技期刊社（济南市历城区山大南路27号山东大学中心校区明德楼B座721室） 电话: 0531-88366918 E-mail: xbyxb@sdu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发