Developing a simple and practical decision model to predict the risk of incident type 2 diabetes among the general population: The Di@bet.es Study

Abstract

Aims: To develop a simple multivariate predictor model of incident type 2 diabetes in general population.

Methods: 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.

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.

Keywords: Algorithm; Chi-square automatic interaction detection (CHAID); Fasting plasma glucose; Incident diabetes; Triglycerides; Type 2 diabetes.