成年人2型糖尿病风险预测模型的建立

Establishment of the new risk model for prediction of type 2 diabetes

目的在我国北方农村地区人群中构建2型糖尿病（T2D）发生风险预测模型。方法队列研究。以参加2006至2013年邯郸眼病调查30岁以上农村普通人群为研究对象，2006至2007年基线调查时经病史或血液化验确定无糖尿病，同时具有完整的2012至2013年随访糖尿病相关资料，共4 132例，其中男性1 793例，女性2 339例。在前期研究基础上，随机选择2/3人群为训练集，余下1/3人群为测试集，应用非条件Logistic回归方法筛选与T2D发生风险相关的实验室标志物，包括空腹血糖（FPG）、甘油三酯（TG）、高敏C反应蛋白等，校正因素包括年龄、糖尿病家族史、体质指数（BMI）和腰围，然后建立T2D风险最佳预测模型，最后参照Framingham评分模式建立相应评分工具并验证。结果T2D风险最佳预测模型纳入指标：年龄（8分）、BMI（6分）、腰围（8分）、糖尿病家族史（9分）、FPG（23分）、TG（4分），总计分范围0～58分。在验证人群中AUC值0.802（0.780～0.822），在最佳切点27分时敏感度70.27%（58.50%～80.30%），特异度80.83%（78.60%～82.90%）。结论成功建立了基于中国北方农村地区普通人群的T2D发生风险预测模型，经人群验证，适于在基层临床实践中使用。

ObjectiveTo develop a new risk model for predicting type 2 diabetes （T2D） in a rural Chinese population in north China.MethodsA village-based cohort study was performed. Data from Handan Eye Study conducted from 2006-2013 comprising 4 132 participants aged 30 years old （1 793 male and 2 339 female） with complete diabetes data at baseline and follow-up were analyzed. The blood biomarkers of T2D incident risk were screened and a new risk model was derived by using unconditional stepwise logistic regression after adjustment of age, body mass index （BMI）, waist circumference, and family history of diabetes in random two-thirds of the sample cohort （selected randomly）. In addition, a simple point system for T2D risk was built according to the procedures as described in Framingham Study, and the new risk score was subsequently validated in the final one-third of the sample cohort.ResultsThe new risk score included age （8 points）, BMI （6 points）, waist circumference （8 points）, family history of diabetes （9 points）, fasting plasma glucose （23 points）, and triglycerides （4 points）. The score ranged from 0 to 58. The AUC was 0.802 （0.780-0.822） in the validation sample. At the optimal cutoff value of 27, the sensitivity and specificity were 70.27% （58.50%-80.30%） and 80.83% （78.60%-82.90%） respectively.ConclusionsA new risk model for predicting T2D have been developed in a rural Chinese population in north China, and the risk score can be used in rural basic health care settings after validation.