Department of Medicine, University of Valencia, Avenida Blasco Ibañez 15, Valencia 46010, Spain; Service of Endocrinology and Nutrition, Valencia University Clinical Hospital, Avenida Blasco Ibañez 17, Valencia 46010, Spain; INCLIVA Biomedical Research Institute, Menendez Pelayo 4acc, Valencia 46010, Spain; CIBER of Diabetes and Associated Metabolic Diseases CIBERDEM, Monforte de Lemos 3-5, Madrid 28029, Spain.
Department of Preventive Medicine, Unit of Public Health and Environmental Care, University of Valencia, Vicente Andres Estelles Avenue, Burjassot, Valencia 46100, Spain; CIBER of Epidemiology and Public Health (CIBERESP), Monforte de Lemos 3-5, Madrid 28029, Spain.
Eur J Intern Med. 2022 Aug;102:80-87. doi: 10.1016/j.ejim.2022.05.005. Epub 2022 May 13.
To develop a simple multivariate predictor model of incident type 2 diabetes in general population.
Participants were recruited from the Spanish Di@bet.es 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.
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.
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.
建立一个适用于一般人群的 2 型糖尿病事件的简单多变量预测模型。
参与者来自西班牙 Di@bet.es 队列研究，共有 2570 名符合纳入高危样本标准的受试者。使用访谈者管理的结构化问卷收集信息，随后进行身体和临床检查。使用 CHAID 算法收集有和无 2 型糖尿病个体的信息，建立基于 2 型糖尿病预测模型的决策树。
156 人被确定患有 2 型糖尿病（发病率为 6.5%）。研究开始时的空腹血糖（FPG）是 2 型糖尿病发生的主要预测变量：FPG≤92mg/dL（参考），92-106mg/dL（OR=3.76，95%CI=2.36-6.00），>106mg/dL（OR=13.21；8.26-21.12）。超过 25%的受试者在开始随访时 FPG 水平>106mg/dL 时发展为 2 型糖尿病。当 FPG<106mg/dL 时，需要其他变量（空腹甘油三酯（FTG）、BMI 或年龄）。对于 FPG≤92mg/dL，较高的 FTG 水平增加了 2 型糖尿病发病的风险（FTG>180mg/dL，OR=14.57；4.89-43.40），与 FTG≤97mg/dL 的组相比（FTG=97-180mg/dL，OR=3.12；1.05-9.24）。该模型正确分类了 93.5%的个体。
2 型糖尿病预测模型基于 FTG、FPG、年龄、性别和 BMI 值。利用常用的临床数据和简单的血液测试，一个简单的树形图有助于识别发生 2 型糖尿病的风险人群，即使是在 FPG 正常的低风险人群中也是如此。
