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The role of glucose-stimulated release of GLP-1 in the development of obesity and type 2 diabetes is unclear.We assessed GLP-1 response to oral glucose in a large study population of lean and obese men and women with normal and impaired glucose regulation. Circulating concentrations of glucose, insulin, and GLP-1 during an oral glucose tolerance test (OGTT) were analyzed in individuals with normal glucose tolerance (NGT) (n = 774), prediabetes (n = 525), or screen-detected type 2 diabetes (n = 163) who attended the Danish ADDITION-PRO study (n = 1,462). Compared with individuals with NGT, women with prediabetes or type 2 diabetes had 25% lower GLP-1 response to an OGTT, and both men and women with prediabetes or type 2 diabetes had 16-21% lower 120-min GLP-1 concentrations independent of age and obesity. Obese and overweight individuals had up to 20% reduced GLP-1 response to oral glucose compared with normal weight individuals independent of glucose tolerance status. Higher GLP-1 responses were associated with better insulin sensitivity and b-cell function, older age, and lesser degree of obesity. Our findings indicate that a reduction in GLP-1 response to oral glucose occurs prior to the development of type 2 diabetes and obesity, which can have consequences for early prevention strategies for diabetes.

Background— Patients with type 1 diabetes mellitus are at increased risk of developing cardiovascular disease (CVD), but they are currently undertreated. There are no risk scores used on a regular basis in clinical practice for assessing the risk of CVD in type 1 diabetes mellitus. Methods and Results— From 4306 clinically diagnosed adult patients with type 1 diabetes mellitus, we developed a prediction model for estimating the risk of first fatal or nonfatal CVD event (ischemic heart disease, ischemic stroke, heart failure, and peripheral artery disease). Detailed clinical data including lifestyle factors were linked to event data from validated national registers. The risk prediction model was developed by using a 2-stage approach. First, a nonparametric, data-driven approach was used to identify potentially informative risk factors and interactions (random forest and survival tree analysis). Second, based on results from the first step, Poisson regression analysis was used to derive the final model. The final CVD prediction model was externally validated in a different population of 2119 patients with type 1 diabetes mellitus. During a median follow-up of 6.8 years (interquartile range, 2.9–10.9) a total of 793 (18.4%) patients developed CVD. The final prediction model included age, sex, diabetes duration, systolic blood pressure, low-density lipoprotein cholesterol, hemoglobin A 1c , albuminuria, glomerular filtration rate, smoking, and exercise. Discrimination was excellent for a 5-year CVD event with a C-statistic of 0.826 (95% confidence interval, 0.807–0.845) in the derivation data and a C-statistic of 0.803 (95% confidence interval, 0.767–0.839) in the validation data. The Hosmer-Lemeshow test showed good calibration ( P >0.05) in both cohorts. Conclusions— This high-performing CVD risk model allows for the implementation of decision rules in a clinical setting.