Background: Prediction rules for type 2 diabetes mellitus (T2DM) have been developed, but we lack consensus for the most effective approach.
Methods: We estimated the 7-year risk of T2DM in middle-aged participants who had an oral glucose tolerance test at baseline. There were 160 cases of new T2DM, and regression models were used to predict new T2DM, starting with characteristics known to the subject (personal model, ie, age, sex, parental history of diabetes, and body mass index [calculated as the weight in kilograms divided by height in meters squared]), adding simple clinical measurements that included metabolic syndrome traits (simple clinical model), and, finally, assessing complex clinical models that included (1) 2-hour post-oral glucose tolerance test glucose, fasting insulin, and C-reactive protein levels; (2) the Gutt insulin sensitivity index; or (3) the homeostasis model insulin resistance and the homeostasis model insulin resistance beta-cell sensitivity indexes. Discrimination was assessed with area under the receiver operating characteristic curves (AROCs).
Results: The personal model variables, except sex, were statistically significant predictors of T2DM (AROC, 0.72). In the simple clinical model, parental history of diabetes and obesity remained significant predictors, along with hypertension, low levels of high-density lipoprotein cholesterol, elevated triglyceride levels, and impaired fasting glucose findings but not a large waist circumference (AROC, 0.85). Complex clinical models showed no further improvement in model discriminations (AROC, 0.850-0.854) and were not superior to the simple clinical model.
Conclusion: Parental diabetes, obesity, and metabolic syndrome traits effectively predict T2DM risk in a middle-aged white population sample and were used to develop a simple T2DM prediction algorithm to estimate risk of new T2DM during a 7-year follow-up interval.