AIM: To develop tools for predicting diabetes development in middle-aged Chinese adults living in Taiwan. METHODS: This study made use of data from 24,899 non-diabetic adults aged ≥35 years who received health examination service from a private health check-up clinic during the period of 1994-1996 and had one or more examinations before December 31, 2006. The proportional hazard model and the receiver operating characteristic (ROC) curve method were used respectively to construct the prediction equation and assess the model's performance. A point system is developed for the ease to calculate diabetes risk. RESULTS: Increased risk of diabetes development was associated with older age, lower education level, alcohol abstinence, abdominal obesity, elevated body mass index (BMI), blood pressure (BP), triglycerides, and impaired fasting glucose. Model 1, incorporating personal socio-demographic and lifestyle characteristics, BMI, and waist circumference (WC), had an area-under-curve (AUC) of 0.717. The AUC increased to 0.726 (model 2) when BP was introduced and to 0.823 (model 3) when both BP and clinical chemistry measures were added. The AUCs in the testing set for models 1, 2, and 3 were 0.688, 0.694, and 0.799 respectively. CONCLUSIONS: These predictive equations of diabetic risk were easy to use by clinical professions and general subjects.
AIM: To develop tools for predicting diabetes development in middle-aged Chinese adults living in Taiwan. METHODS: This study made use of data from 24,899 non-diabetic adults aged ≥35 years who received health examination service from a private health check-up clinic during the period of 1994-1996 and had one or more examinations before December 31, 2006. The proportional hazard model and the receiver operating characteristic (ROC) curve method were used respectively to construct the prediction equation and assess the model's performance. A point system is developed for the ease to calculate diabetes risk. RESULTS: Increased risk of diabetes development was associated with older age, lower education level, alcohol abstinence, abdominal obesity, elevated body mass index (BMI), blood pressure (BP), triglycerides, and impaired fasting glucose. Model 1, incorporating personal socio-demographic and lifestyle characteristics, BMI, and waist circumference (WC), had an area-under-curve (AUC) of 0.717. The AUC increased to 0.726 (model 2) when BP was introduced and to 0.823 (model 3) when both BP and clinical chemistry measures were added. The AUCs in the testing set for models 1, 2, and 3 were 0.688, 0.694, and 0.799 respectively. CONCLUSIONS: These predictive equations of diabetic risk were easy to use by clinical professions and general subjects.
Authors: Sara Sokooti; Jose L Flores-Guerrero; Hiddo J L Heerspink; Margery A Connelly; Stephan J L Bakker; Robin P F Dullaart Journal: Cardiovasc Diabetol Date: 2021-07-28 Impact factor: 9.951