Nonlaboratory-Based Risk Assessment Algorithm for Undiagnosed Type 2 Diabetes Developed on a Nation-Wide Diabetes Survey

نویسندگان

  • Xianghai Zhou
  • Qing Qiao
  • Linong Ji
  • Feng Ning
  • Wenying Yang
  • Jianping Weng
  • Zhongyan Shan
  • Haoming Tian
  • Qiuhe Ji
  • Lixiang Lin
  • Qiang Li
  • Jianzhong Xiao
  • Weiguo Gao
  • Zengchang Pang
  • Jianping Sun
چکیده

OBJECTIVE To develop a New Chinese Diabetes Risk Score for screening undiagnosed type 2 diabetes in China. RESEARCH DESIGN AND 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.

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عنوان ژورنال:

دوره 36  شماره 

صفحات  -

تاریخ انتشار 2013