健康管理人群2型糖尿病发病风险预测模型

doi:10.6040/j.issn.1671-7554.0.2017.347

山东大学学报（医学版） ›› 2017, Vol. 55 ›› Issue (6): 82-86.doi: 10.6040/j.issn.1671-7554.0.2017.347

健康管理人群2型糖尿病发病风险预测模型

苏萍^1,2,杨亚超³,杨洋^1,2,季加东^1,2,阿力木·达依木^1,2,李敏^1,2,薛付忠^1,2,刘言训^1,2

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:2017-04-20 出版日期:2017-06-10 发布日期:2017-06-10
通讯作者: 刘言训. E-mail:liu-yx@sdu.edu.cn薛付忠. E-mail:xuefzh@sdu.edu.cn E-mail:liu-yx@sdu.edu.cn
基金资助:
国家自然科学基金(81273177)

Prediction models on the onset risks of type 2 diabetes among the health management population

SU Ping^1,2, YANG Yachao³, YANG Yang^1,2, JI Jiadong^1,2, DAYIMU Alimu^1,2, LI Min^1,2, XUE Fuzhong^1,2, LIU Yanxun^1,2

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:2017-04-20 Online:2017-06-10 Published:2017-06-10

摘要/Abstract

摘要： 目的构建健康管理人群2型糖尿病3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大数据库,选择20~75岁的基线未患2型糖尿病者构建队列。采用Cox比例风险回归构建2型糖尿病预测模型,以受试者工作特征曲线下面积(AUC)评价模型的预测效能,以十折交叉验证法检验模型的稳定性。结果随访期间共新发糖尿病1 624例,男性和女性的发病密度分别为15.00‰、10.83‰。男性预测模型最终纳入的变量包括年龄、体质量指数、空腹血糖、甘油三酯、谷丙转氨酶、白细胞计数。纳入女性预测模型的变量包括年龄、空腹血糖、甘油三酯、高密度脂蛋白、谷丙转氨酶。男性和女性预测模型的AUC分别为0.795(95%CI:0.764~0.827)和0.707(95%CI:0.654~0.759)。结论分性别建立的2型糖尿病发病风险预测模型在健康管理人群中均具有较好预测能力。

关键词: 2型糖尿病, Cox比例风险回归, 风险预测模型, 队列

Abstract: Objective To construct prediction models to estimate the risks of developing type 2 diabetes mellitus(T2DM)in 3 years among the health management population in mainland China. Methods Non-diabetic people aged 20 to 75 years at the baseline were chosen from Shandong Multi-center Longitudinal Cohort for Health Management to compose our cohort. Coxs proportional hazards regression model was adopted to build T2DM prediction model. The area under the receiver operating characteristic(ROC)curve(AUC)was used to evaluate the predictability of the model. Ten-fold cross-validation was adopted to test the stability of the model. Results During the follow-up of 3.68±2.8 years, 1,624 cases of new-onset diabetes occurred. The incidence density of male and female was 15.00‰ and 10.83‰, respectively. The risk factors for the male model included age, body mass index(BMI), fasting plasma glucose(FPG), triglyceride, alanine aminotransferase(ALT), and white blood cell(WBC)count. The risk factors 山东大学学报 (医学版)55卷6期 -苏萍,等.健康管理人群2型糖尿病发病风险预测模型 \=-for the female model included age, FPG, triglyceride, high density lipoprotein cholesterol(HDL-C), and ALT. The AUC of the male model and female model was 0.795(95% CI: 0.764-0.827)and 0.707(95%CI: 0.654-0.759), respectively. Conclusion The male and female prediction models we constructed have high predictability and reliability among the health management population.

Key words: Cohort study, Coxs proportional hazards regression, Type 2 diabetes, Risk prediction model

中图分类号:

R587.1

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.

SU Ping, YANG Yachao, YANG Yang, JI Jiadong, DAYIMU Alimu, LI Min, XUE Fuzhong, LIU Yanxun. Prediction models on the onset risks of type 2 diabetes among the health management population[J]. JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES), 2017, 55(6): 82-86.

参考文献

[1] Lu C, Sun W. Prevalence of diabetes in Chinese adults[J]. JAMA, 2014, 311(2): 199-200.
[2] Yang W, Lu J, Weng J, et al. Prevalence of diabetes among men and women in China[J]. N Engl J Med, 2010, 362(12): 1090-1101.
[3] Bhushan R, Elkindhirsch KE, Bhushan M, et al. Improved glycemic control and reduction of cardiometabolic risk factors in subjects with type 2 diabetes and metabolic syndrome treated with exenatide in a clinical practice setting[J]. Diabetes Technol Ther, 2009, 11(6): 353-359.
[4] Tuomilehto J, Lindstro MJ, Eriksson J, et al. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance[J]. N Engl J Med, 2001, 344: 1343-1350.
[5] Park PJ, Griffin SJ, Sargeant L, et al. The performance of a risk score in predicting undiagnosed hyperglycemia[J]. Diabetes Care, 2002, 25: 984-988.
[6] Lindström J, Tuomilehto J. The Diabetes risk score: a practical tool to predict type 2 diabetes risk[J]. Diabetes Care, 2003, 26(3): 725-731.
[7] 江慧, 徐慧兰, 肖水源, 等. 3种糖尿病筛查问卷在农村居民中应用的效果评价[J]. 中南大学学报(医学版), 2012, 37(11): 1108-1111. JIANG Hui, XU Huilan, XIAO Shuiyuan, et al. Performance of 3 diabetes screening questionnaires for a rural sample in China[J]. Journal of Central South University(Medical Sciences), 2012, 37(11): 1108-1111.
[8] Glümer C, Carstensen B, Sandbæk A, et al. A danish diabetes risk score for targeted screening the inter99 study[J]. Diabetes Care, 2004, 27(3): 727-733.
[9] 董建军, 娄能俊, 辛颖, 等. 不同糖尿病筛查问卷在中国人群糖尿病普查中的价值评估[J]. 中华内分泌代谢杂志, 2009, 25(1): 64-65.
[10] Aekplakorn W, Bunnag P, Woodward M, et al. A risk score for predicting incident diabetes in the Thai population[J]. Diabetes Care, 2006, 29(29): 1872-1877.
[11] Gao WG, Dong YH, Pang ZC, et al. A simple Chinese risk score for undiagnosed diabetes[J]. Diabetic Medicine, 2010, 27(3): 274-281.
[12] 师正坤, 郭佳, Monica Parry, 等. 中国糖尿病风险评估工具的研究现状与进展[J]. 中国全科医学, 2015, 20: 2368-2372.
[13] 中国高血压防治指南修订委员会. 中国高血压防治指南(2010年修订版)[J]. 中国实用乡村医生杂志, 2012, 19(10): 1-15.
[14] 黎衍云, 李锐, 张胜年. 无症状糖尿病不同筛查方法效果评价[J]. 中国公共卫生, 2006, 22(6): 687-689. LI Yanyun, LI Rui, ZHANG Shengnian, et al. Evaluation on effect of screening method for undiagnosed diabetes[J]. Chinese Journal of Public Health, 2006, 22(6): 687-689.
[15] 王孝勇. 无症状2型糖尿病及糖尿病前期人群社区筛查策略研究[D]. 济南: 山东大学, 2011.
[16] 沈洪兵, 俞顺章, 徐耀初, 等. 危险因素记分法筛检无症状糖尿病及其评价[J]. 中华流行病学杂志, 1999, 20(2): 114-117.
[17] 雷先阳. 农村社区2型糖尿病筛查及初步干预研究[D]. 长沙: 中南大学, 2013.
[18] Lim NK, Park SH, Choi SJ, et al. A risk score for predicting the incidence of type 2 diabetes in a middle-aged Korean cohort: the Korean genome and epidemiology study[J]. Circ J, 2012, 76(8): 1904-1910.
[19] Demmer RT, Jr JD, Desvarieux M. Periodontal disease and incident type 2 diabetes: results from the First National Health and Nutrition Examination Survey and its epidemiologic follow-up study[J]. Diabetes Care, 2008, 31(7): 1373-1379.
[20] Yoriko H, Yasuji A, Kazumi S, et al. Development of a screening score for undiagnosed diabetes and its application in estimating absolute risk of future type 2 Diabetes in Japan: Toranomon Hospital Health Management Center Study 10(TOPICS 10)[J]. J Clin Endocrinol Metab, 2013, 98(3): 1051-1060.
[21] 王忠东. 谷丙转氨酶和尿酸等指标检测在2型糖尿病中的意义分析[J]. 糖尿病新世界, 2016, 19(12): 67-68.
[22] 米生权. 中国成人个体糖尿病发病风险预测模型的建立及验证[D]. 北京: 中国疾病预防控制中心, 2011.
[23] Xie J, Hu D, Yu D, et al. A quick self-assessment tool to identify individuals at high risk of type 2 diabetes in the Chinese general population[J]. J Epidemiol Community Health, 2010, 64(3): 236-242.
[24] Xin Z, Yuan J, Hua L, et al. A simple tool detected diabetes and prediabetes in rural Chinese[J]. J Clin Epidemiol, 2010, 63(9): 1030-1035.

相关文章 15

[1]	王建民,李晓峰,由志涛,董圣杰,赵宇驰,李占菊,邹德鑫,张剑锋,孙涛,杜伟. 基于可解释机器学习的后路腰椎椎体间融合术后慢性疼痛风险预测模型构建[J]. 山东大学学报 (医学版), 2026, 64(2): 78-88.
[2]	逄锦宏,苏萍,乔俊鹏,陈巧巧,陈学禹,赵颖颖,施婕,孙晓茹,李秋春,何蕊言,范轶欧,迟蔚蔚. 老年人群可改变心血管危险因素聚集模式与脑卒中的关联[J]. 山东大学学报 (医学版), 2025, 63(9): 11-19.
[3]	王乐,罗清馨,吴思佳,吴雨桐,葛祎蕾,俞一凡,韦云,吉寒冰,刘铁梅,张紫妍,修佳伟,薛付忠,李洪凯. 虚弱和癫痫关联研究:前瞻性队列和孟德尔随机化分析[J]. 山东大学学报 (医学版), 2025, 63(9): 20-30.
[4]	孟晓梅,郝亚平,王亮,于晓,唐与晓. 血清Isthmin1、Gremlin2水平与2型糖尿病患者视网膜病变的相关性[J]. 山东大学学报 (医学版), 2025, 63(9): 102-107.
[5]	薛付忠. AI语言表征的多模态大数据队列设计理论方法体系[J]. 山东大学学报 (医学版), 2025, 63(8): 1-16.
[6]	申路佳,逯天威,巩伟明,赵岩松,王淑康,袁中尚. 代谢风险评分在2型糖尿病人群心血管结局预测中的应用[J]. 山东大学学报 (医学版), 2025, 63(8): 69-78.
[7]	陈莹莹,王鲁,胡锡峰,朱高培,薛付忠. 基于贝叶斯网络的2型糖尿病患者并发脑卒中风险预测[J]. 山东大学学报 (医学版), 2025, 63(8): 94-102.
[8]	于晴晴,常开锋,李吉庆,张凯,付苹,刘晓雯,赵英淇,薛付忠. 中老年人群高血压在非甾体抗炎药暴露与心脑血管疾病风险关联中的中介分析[J]. 山东大学学报 (医学版), 2023, 61(8): 79-85.
[9]	卢伟,刘欢,贾贤杰,苏萍,施婕,王雅倩,迟蔚蔚. 植被覆盖与呼吸系统恶性肿瘤死亡风险关联分析[J]. 山东大学学报 (医学版), 2023, 61(8): 94-100.
[10]	李金泉,高美芳,闫飞,董明. 136例2型糖尿病患者肌肉痉挛的发生频率及危险因素[J]. 山东大学学报 (医学版), 2023, 61(5): 20-24.
[11]	张天鑫,张婷,黄鑫,韩佳沂,王淑康. 氨基酸与2型糖尿病因果关系的孟德尔随机化分析[J]. 山东大学学报 (医学版), 2023, 61(5): 102-107.
[12]	钟璐,薛付忠. 基于贝叶斯网络不确定性推理的肺癌风险预测模型[J]. 山东大学学报 (医学版), 2023, 61(4): 86-94.
[13]	韩梅,孟维静,陶子琨,杨希,徐雅琪,穆华夏,卜伟晓,王素珍,石福艳. 基于G-计算的高血压、抑郁在2型糖尿病与认知功能之间的因果多中介分析[J]. 山东大学学报 (医学版), 2023, 61(10): 101-108.
[14]	赵美茹,朱迪,刘淋,管庆波,张栩. 简易胰岛素抵抗指标与698例2型糖尿病患者发生高尿酸血症风险的关联[J]. 山东大学学报 (医学版), 2022, 60(12): 44-51.
[15]	于书卷,王美娟,陈丽,曹英娟,吕晓燕,刘雪燕,林鹏,颜景政. 老年2型糖尿病患者轻度认知功能障碍的影响因素[J]. 山东大学学报 (医学版), 2022, 60(11): 108-112.

多维度评价

Viewed

Full text

Abstract

Cited

Shared

Discussed

健康管理人群2型糖尿病发病风险预测模型

Prediction models on the onset risks of type 2 diabetes among the health management population

PDF (PC)

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

多维度评价

本文评价

推荐阅读 0