Lophaven Søren, Bruun-Rasmussen Neda Esmailzadeh, Holmager Therese, Jepsen Randi, Kofoed-Enevoldsen Allan, Lynge Elsebeth
Omicron Aps, Roskilde, Denmark.
Center for Epidemiological Research, Nykøbing Falster Hospital, Denmark.
Prev Med Rep. 2023 Apr 20;33:102215. doi: 10.1016/j.pmedr.2023.102215. eCollection 2023 Jun.
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.
在丹麦人口中，约十分之一的成年人患有糖尿病前期、未确诊、控制不佳或潜在调控不足的糖尿病（简称DMRC）。为这些公民提供相关的医疗保健干预措施非常重要。因此，我们构建了一个预测DMRC患病率的模型。数据来自于在丹麦一个健康状况较差的农村地区进行的洛兰-法尔斯特健康研究。我们纳入了来自公共登记处的变量（年龄、性别、国籍、婚姻状况、社会经济地位、居住状况）；来自自我管理问卷的变量（吸烟状况、饮酒情况、教育程度、自我健康评分、饮食习惯、身体活动）；以及来自临床检查的变量（体重指数（BMI）、脉搏率、血压、腰臀比）。数据被分为训练/测试数据集，用于预测模型的开发和测试。该研究包括15,801名成年人，其中1,575人患有DMRC。最终模型中具有统计学意义的变量包括年龄、自我健康评分、吸烟状况、BMI、腰臀比和脉搏率。在测试数据集中，该模型的曲线下面积（AUC）=0.77，敏感性为50%，特异性为84%。在健康状况较差的丹麦人群中，可以根据年龄、自我健康评分、吸烟状况、BMI、腰臀比和脉搏率预测糖尿病前期、未确诊或控制不佳或潜在调控不足的糖尿病的存在。年龄可从丹麦个人身份证号码得知，自我健康评分和吸烟状况可通过简单问题获得，BMI、腰臀比和脉搏率可由任何医疗保健人员测量，甚至可能由个人自行测量。因此，我们的模型可能作为一种筛查工具很有用。
