PDF(Pubmed)
Abstract：
In the Danish population, about one-in-ten adults have prediabetes, undiagnosed, poorly or potentially sub-regulated diabetes, for short DMRC. It is important to offer these citizens relevant healthcare intervention. We therefore built a model for prediction of prevalent DMRC. Data were derived from the Lolland-Falster Health Study undertaken in a rural-provincial area of Denmark with disadvantaged health. We included variables from public registers (age, sex, age, citizenship, marital status, socioeconomic status, residency status); from self-administered questionnaires (smoking status, alcohol use, education, self-rated health, dietary habits, physical activity); and from clinical examinations (body mass index (BMI), pulse rate, blood pressure, waist-to-hip ratio). Data were divided into training/testing datasets for development and testing of the prediction model. The study included 15,801 adults; of whom 1,575 with DMRC. Statistically significant variables in the final model included age, self-rated health, smoking status, BMI, waist-to-hip ratio, and pulse rate. In the testing dataset this model had an area under the curve (AUC) = 0.77 and a sensitivity of 50% corresponding to a specificity of 84%. In a health disadvantaged Danish population, presence of prediabetes, undiagnosed, or poorly or potentially sub-regulated diabetes could be predicted from age, self-rated health, smoking status, BMI, waist-to-hip ratio, and pulse rate. Age is known from the Danish personal identification number, self-rated health and smoking status can be obtained from simple questions, and BMI, waist-to-hip ratio, and pulse rate can be measured by any person in health care and potentially by the person him/her-self. Our model might therefore be useful as a screening tool.
摘要：
在丹麦人口中，大约十分之一的成年人患有糖尿病前期，未确诊,不良或潜在的亚调节糖尿病，简称DMRC。重要的是为这些公民提供相关的医疗干预。因此，我们建立了一个预测流行DMRC的模型。数据来自在丹麦处于不利健康状态的农村省地区进行的Lolland-Falster健康研究。我们包括来自公共登记册的变量(年龄，性别,年龄,公民身份,婚姻状况,社会经济地位,居住状况)；来自自我管理问卷(吸烟状况，酒精使用,教育,自我评估的健康，饮食习惯，身体活动)；以及从临床检查(体重指数(BMI)，脉搏率,血压,腰臀比)。将数据分为训练/测试数据集，以开发和测试预测模型。该研究包括15,801名成年人；其中1,575名患有DMRC。最终模型中具有统计意义的变量包括年龄，自我评估的健康，吸烟状况,BMI,腰臀比,和脉搏率。在测试数据集中,该模型具有曲线下面积(AUC)=0.77和对应于84%特异性的50%的灵敏度。在健康不利的丹麦人口中，糖尿病前期的存在，未确诊,或者不良或潜在的亚调节糖尿病可以从年龄预测，自我评估的健康，吸烟状况,BMI,腰臀比,和脉搏率。年龄可以从丹麦的个人身份号码中得知，可以从简单的问题中获得自我评估的健康状况和吸烟状况，BMI,腰臀比,脉搏率可以由医疗保健中的任何人测量，也可以由他/她自己的人测量。因此，我们的模型可能是有用的筛选工具。
