Çelikkanat Şirin, Güngörmüş Zeynep, Akay Özlem
Gaziantep Islam Science and Technology, Department of Nursing, University of Health Sciences, Gaziantep, Turkey.
Gaziantep Islam Science and Technology University Medical School Department, Gaziantep, Turkey.
J Diabetes Metab Disord. 2023 Oct 11;23(1):563-571. doi: 10.1007/s40200-023-01315-0. eCollection 2024 Jun.
The study was conducted to develop a risk assessment tool to determine the Turkish population's risk of undiagnosed type 2 diabetes.
The study was carried out in a methodological design. A total of 610 individuals, including those diagnosed with diabetes (321) and not diagnosed with diabetes (289), who applied to the internal medicine and diabetes outpatient clinics of a public hospital, were included in the study. The sample of patients with diabetes was created with the individuals who applied to diabetes outpatient clinics, were 40 years of age and older, and had the values of FPG ≥ 126 mg/dl and HbA1C ≥ 6.5%. The sample of healthy individuals consisted of people over the age of 40 who were not diagnosed with diabetes or prediabetes. Logistic regression and random forest algorithms were used to evaluate the diabetes risk of individuals. The performance of the models was evaluated with sensitivity, specificity, accuracy, and area under the ROC (AUC).
In the study, the variables of exercise in daily routines, presence of prediabetes, getting angry, feeling hungry frequently, and excessive thirst formed the diabetes risk assessment model with Sensitivity 0.983 and Specificity 0.984 according to the logistic regression model obtained. Body mass index, physical activity, age, gender, and family history of diabetes were not found to be significant in differentiating cases with diabetes (0.05 < p).
This diabetes risk assessment tool is a reliable tool for Turkish society to identify individuals at high risk for diabetes and to raise awareness of the importance of modifiable risk factors.
开展本研究以开发一种风险评估工具,用于确定土耳其人群未确诊2型糖尿病的风险。
本研究采用方法学设计。共有610名个体纳入研究,包括那些在一家公立医院的内科和糖尿病门诊就诊的已确诊糖尿病患者(321名)和未确诊糖尿病患者(289名)。糖尿病患者样本由年龄在40岁及以上、空腹血糖(FPG)≥126mg/dl且糖化血红蛋白(HbA1C)≥6.5%、前往糖尿病门诊就诊的个体组成。健康个体样本由40岁以上未被诊断为糖尿病或糖尿病前期的人群组成。采用逻辑回归和随机森林算法评估个体的糖尿病风险。通过敏感性、特异性、准确性和ROC曲线下面积(AUC)评估模型的性能。
在本研究中,根据所获得的逻辑回归模型,日常锻炼、糖尿病前期的存在、生气、频繁饥饿和极度口渴等变量构成了糖尿病风险评估模型,其敏感性为0.983,特异性为0.984。未发现体重指数、身体活动、年龄、性别和糖尿病家族史在区分糖尿病患者方面具有显著性(0.05<p)。
这种糖尿病风险评估工具是土耳其社会识别糖尿病高风险个体并提高对可改变风险因素重要性认识的可靠工具。