PMID- 28608942 OWN - NLM STAT- MEDLINE DCOM- 20180601 LR - 20180618 IS - 1520-7560 (Electronic) IS - 1520-7552 (Linking) VI - 33 IP - 7 DP - 2017 Oct TI - A risk-score model for predicting risk of type 2 diabetes mellitus in a rural Chinese adult population: A cohort study with a 6-year follow-up. LID - 10.1002/dmrr.2911 [doi] AB - BACKGROUND: Several prediction tools have been developed to identify people with type 2 diabetes mellitus (T2DM) and to quantify the probability of developing T2DM. However, most of the risk models were constructed based on cross-sectional studies and tea-drinking was not included. METHODS: A total of 15 768 participants without known T2DM were followed up from 2007-2008 to 2013-2014; 12 654 were randomly assigned to the derivation dataset and 3114 to the validation dataset. We constructed a risk-score model for T2DM by using a Cox proportional-hazards model. Risk scores were calculated by multiplying beta by 10 in the derivation cohort and were verified in the validation dataset. The model's accuracy was assessed by the area under the receiver operating characteristic curve (AUC). RESULTS: Predictors for T2DM risk in the derivation dataset were drinking tea frequently, body mass index >/=28.0 kg/m(2) , waist to height ratio >/= 0.5, triglycerides level 1.70 to 2.25 and >/=2.26 mmol/L, and fasting plasma glucose 5.6 to 6.0 and >/=6.1 mmol/L. The corresponding scores were -2, 7, 7, 4, 6, 11, and 25, respectively. The sensitivity, specificity, and AUC (95% confidence interval) for this full model were 69.63%, 75.56%, and 0.791 (0.783-0.799), respectively. The ability of the non-invasive models to predict T2DM was not superior to that of the full model. With the validation dataset, the predictive performance was better for our full model than the Framingham risk-score model (AUC 0.731 vs 0.525, P < .001). CONCLUSIONS: Our risk-score model has fair efficacy for predicting 6-year risk of T2DM in a rural adult Chinese population. CI - Copyright (c) 2017 John Wiley & Sons, Ltd. FAU - Zhang, Hongyan AU - Zhang H AD - Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China. AD - The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Guangdong, People's Republic of China. FAU - Wang, Chongjian AU - Wang C AD - Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China. FAU - Ren, Yongcheng AU - Ren Y AD - Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China. FAU - Wang, Bingyuan AU - Wang B AD - Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China. AD - The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Guangdong, People's Republic of China. FAU - Yang, Xiangyu AU - Yang X AD - Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China. AD - The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Guangdong, People's Republic of China. FAU - Zhao, Yang AU - Zhao Y AD - Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China. AD - The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Guangdong, People's Republic of China. FAU - Han, Chengyi AU - Han C AD - Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China. AD - The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Guangdong, People's Republic of China. AD - Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China. FAU - Zhou, Junmei AU - Zhou J AD - Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China. FAU - Zhang, Lu AU - Zhang L AD - Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China. FAU - Qi, Minjie AU - Qi M AD - Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China. FAU - Zhai, Yujia AU - Zhai Y AD - Department of Public Health Surveillance, Zhejiang Provincial Centre for Disease Control and Prevention, Hangzhou, Zhejiang, People's Republic of China. FAU - Pang, Chao AU - Pang C AD - Department of Prevention and Health Care, Military Hospital of Henan Province, Zhengzhou, Henan, People's Republic of China. FAU - Yin, Lei AU - Yin L AD - Department of Prevention and Health Care, Military Hospital of Henan Province, Zhengzhou, Henan, People's Republic of China. FAU - Zhao, Jingzhi AU - Zhao J AD - Department of Prevention and Health Care, Military Hospital of Henan Province, Zhengzhou, Henan, People's Republic of China. FAU - Hu, Dongsheng AU - Hu D AUID- ORCID: 0000-0001-9537-2138 AD - Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China. AD - The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Guangdong, People's Republic of China. FAU - Zhang, Ming AU - Zhang M AD - Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China. LA - eng PT - Journal Article PT - Research Support, Non-U.S. Gov't DEP - 20170713 PL - England TA - Diabetes Metab Res Rev JT - Diabetes/metabolism research and reviews JID - 100883450 RN - 0 (Blood Glucose) SB - IM MH - Adult MH - Blood Glucose/analysis MH - Body Mass Index MH - China MH - Cross-Sectional Studies MH - Diabetes Mellitus, Type 2/blood/*diagnosis/*epidemiology MH - Female MH - Humans MH - Male MH - Middle Aged MH - *Models, Theoretical MH - Risk Assessment MH - Risk Factors MH - Rural Population MH - Sensitivity and Specificity OTO - NOTNLM OT - Chinese rural population OT - cohort study OT - non-invasive OT - risk-score model OT - type 2 diabetes mellitus EDAT- 2017/06/14 06:00 MHDA- 2018/06/02 06:00 CRDT- 2017/06/14 06:00 PHST- 2016/03/13 00:00 [received] PHST- 2017/04/20 00:00 [revised] PHST- 2017/05/22 00:00 [accepted] PHST- 2017/06/14 06:00 [pubmed] PHST- 2018/06/02 06:00 [medline] PHST- 2017/06/14 06:00 [entrez] AID - 10.1002/dmrr.2911 [doi] PST - ppublish SO - Diabetes Metab Res Rev. 2017 Oct;33(7). doi: 10.1002/dmrr.2911. Epub 2017 Jul 13.