title: Nonlaboratory-based risk assessment algorithm for undiagnosed type 2 diabetes developed on a nation-wide diabetes survey.
abstract: OBJECTIVE
  To develop a New Chinese Diabetes Risk Score for screening undiagnosed type 2 diabetes in China.
  
  
  METHODS
  Data from the China National Diabetes and Metabolic Disorders Study conducted from June 2007 to May 2008 comprising 16,525 men and 25,284 women aged 20-74 years were analyzed. Undiagnosed type 2 diabetes was detected based on fasting plasma glucose ≥7.0 mmol/L or 2-h plasma glucose ≥11.1 mmol/L in people without a prior history of diabetes. β-Coefficients derived from a multiple logistic regression model predicting the presence of undiagnosed type 2 diabetes were used to calculate the New Chinese Diabetes Risk Score. The performance of the New Chinese Diabetes Risk Score was externally validated in two studies in Qingdao: one is prospective with follow-up from 2006 to 2009 (validation 1) and another cross-sectional conducted in 2009 (validation 2).
  
  
  RESULTS
  The New Chinese Diabetes Risk Score includes age, sex, waist circumference, BMI, systolic blood pressure, and family history of diabetes. The score ranges from 0 to 51. The area under the receiver operating curve of the score for undiagnosed type 2 diabetes was 0.748 (0.739-0.756) in the exploratory population, 0.725 (0.683-0.767) in validation 1, and 0.702 (0.680-0.724) in validation 2. At the optimal cutoff value of 25, the sensitivity and specificity of the score for predicting undiagnosed type 2 diabetes were 92.3 and 35.5%, respectively, in validation 1 and 86.8 and 38.8% in validation 2.
  
  
  CONCLUSIONS
  The New Chinese Diabetes Risk Score based on nonlaboratory data appears to be a reliable screening tool to detect undiagnosed type 2 diabetes in Chinese population.
authors.affiliation: Corresponding author: Linong Ji, jiln@bjmu.edu.cn.
authors.forename: Xianghai
authors.initials: X
authors.lastname: Zhou
authors.forename: Qing
authors.initials: Q
authors.lastname: Qiao
authors.forename: Linong
authors.initials: L
authors.lastname: Ji
authors.forename: Feng
authors.initials: F
authors.lastname: Ning
authors.forename: Wenying
authors.initials: W
authors.lastname: Yang
authors.forename: Jianping
authors.initials: J
authors.lastname: Weng
authors.forename: Zhongyan
authors.initials: Z
authors.lastname: Shan
authors.forename: Haoming
authors.initials: H
authors.lastname: Tian
authors.forename: Qiuhe
authors.initials: Q
authors.lastname: Ji
authors.forename: Lixiang
authors.initials: L
authors.lastname: Lin
authors.forename: Qiang
authors.initials: Q
authors.lastname: Li
authors.forename: Jianzhong
authors.initials: J
authors.lastname: Xiao
authors.forename: Weiguo
authors.initials: W
authors.lastname: Gao
authors.forename: Zengchang
authors.initials: Z
authors.lastname: Pang
authors.forename: Jianping
authors.initials: J
authors.lastname: Sun
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D002681:China / Q000453:epidemiology; D003430:Cross-Sectional Studies; D003924:Diabetes Mellitus, Type 2 / Q000175:diagnosis* / Q000453:epidemiology*; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D015994:Incidence; D008297:Male; D008875:Middle Aged; D011159:Population Surveillance; D011446:Prospective Studies; D015203:Reproducibility of Results; D012189:Retrospective Studies; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D055815:Young Adult