Pathophysiological Characteristics Underlying Different Glucose Response Curves: A Latent Class Trajectory Analysis From the Prospective EGIR-RISC Study.

OBJECTIVE

Glucose measurements during an oral glucose tolerance test (OGTT) are useful in predicting diabetes and its complications. However, knowledge of the pathophysiology underlying differences in glucose curve shapes is sparse. We examined the pathophysiological characteristics that create different glucose curve patterns and studied their stability and reproducibility over 3 years of follow-up.

RESEARCH DESIGN AND METHODS

We analyzed data from participants without diabetes from the observational cohort from the European Group for the Study of Insulin Resistance: Relationship between Insulin Sensitivity and Cardiovascular Disease study; participants had a five-time point OGTT at baseline ( = 1,443) and after 3 years ( = 1,045). Measures of insulin sensitivity and secretion were assessed at baseline with a euglycemic-hyperinsulinemic clamp and intravenous glucose tolerance test. Heterogeneous glucose response patterns during the OGTT were identified using latent class trajectory analysis at baseline and at follow-up. Transitions between classes were analyzed with multinomial logistic regression models.

RESULTS

We identified four different glucose response patterns, which differed with regard to insulin sensitivity and acute insulin response, obesity, and plasma levels of lipids and inflammatory markers. Some of these associations were confirmed prospectively. Time to glucose peak was driven mainly by insulin sensitivity, whereas glucose peak size was related to both insulin sensitivity and secretion. The glucose patterns identified at follow-up were similar to those at baseline, suggesting that the latent class method is robust. We integrated our classification model into an easy-to-use online application that facilitates the assessment of glucose curve patterns for other studies.

CONCLUSIONS

The latent class analysis approach is a pathophysiologically insightful way to classify individuals without diabetes based on their response to glucose during an OGTT.