医学
2型糖尿病
糖尿病
内科学
查德
入射(几何)
人口
队列
多元分析
决策树
内分泌学
环境卫生
数据挖掘
计算机科学
物理
光学
作者
Sergio Martínez‐Hervás,María Morales-Suárez-Varela,Irene Andrés‐Blasco,Francisco Lara-Hernández,Isabel Peraita‐Costa,José T. Real,Ana-Bárbara García-García,Felipe Javier Chaves
标识
DOI:10.1016/j.ejim.2022.05.005
摘要
Aims To develop a simple multivariate predictor model of incident type 2 diabetes in general population. Methods Participants were recruited from the Spanish [email protected] cohort study with 2570 subjects meeting all criteria to be included in the at-risk sample studied here. Information was collected using an interviewer-administered structured questionnaire, followed by physical and clinical examination. CHAID algorithm, which collects the information of individuals with and without type 2 diabetes, was used to develop a decision tree based type 2 diabetes prediction model. Results 156 individuals were identified as having developed type 2 diabetes (6.5% incidence). Fasting plasma glucose (FPG) at the beginning of the study was the main predictive variable for incident type 2 diabetes: FPG ≤ 92 mg/dL (ref.), 92–106 mg/dL (OR = 3.76, 95%CI = 2.36–6.00), > 106 mg/dL (OR = 13.21; 8.26–21.12). More than 25% of subjects starting follow-up with FPG levels > 106 mg/dL developed type 2 diabetes. When FPG <106 mg/dL, other variables (fasting triglycerides (FTGs), BMI or age) were needed. For levels ≤ 92 mg/dL, higher FTGs levels increased risk of incident type 2 diabetes (FTGs > 180 mg/dL, OR = 14.57; 4.89–43.40) compared with the group of FTGs ≤ 97 mg/dL (FTGs = 97–180 mg/dL, OR = 3.12; 1.05–9.24). This model correctly classified 93.5% of individuals. Conclusions The type 2 diabetes prediction model is based on FTGs, FPG, age, gender, and BMI values. Utilizing commonly available clinical data and a simple blood test, a simple tree diagram helps identify subjects at risk of developing type 2 diabetes, even in apparently low risk subjects with normal FPG.
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