Using Latent Class Trajectory Analysis to Determine Glucose Response Curve Patterns

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Investigators identified 4 glucose curve patterns that that differed in regard to insulin sensitivity, insulin secretion, obesity, plasma lipids, and low-grade inflammatory markers.
Investigators identified 4 glucose curve patterns that that differed in regard to insulin sensitivity, insulin secretion, obesity, plasma lipids, and low-grade inflammatory markers.

Results of an oral glucose tolerance test can be utilized to classify individuals without diabetes into different glucose response curve patterns, according to a study published in Diabetes Care

Investigators from the European Group for the Study of Insulin Resistance (EGIR) analyzed the pathophysiological characteristics and metabolic function of the Relationship between Insulin Sensitivity and Cardiovascular Disease (RISC) cohort to create different glucose response curve patterns. The researchers assessed measurements of plasma glucose, serum insulin, and C-peptide concentrations at 5 time points during an oral glucose tolerance test (0, 30, 60 90, and 120 minutes), which was given at baseline and at a 3-year follow up. Glucose response curve patterns were identified using latent class trajectory analysis at all time points.

Researchers identified 4 different glucose response curve patterns, or classes, based on the area under the curve. The key indicators were insulin sensitivity and acute insulin response, obesity, plasma lipid levels, and inflammatory markers. Class 1 had an early and low glucose peak, class 2 had low insulin sensitivity and normal secretion, class 3 had an early and high glucose peak, and class 4 had a later and high glucose peak. Class 1 had the most favorable risk profile, and class 4 had the least favorable risk profile. Changes in obesity, triglycerides, and inflammatory markers were modifiable lifestyle risk factors and, if altered, could reclassify an individual. This finding suggests that the latent class trajectory and the resulting glucose response curve patterns could be used to stratify cardiometabolic risk factors for individuals without diabetes.

The resulting glucose response curve patterns have been formatted into an online application to facilitate classifications outside this study. Further research is needed to determine long-term effects of different glucose response curve patterns and the associated risk for diabetes and cardiovascular disease. 

Reference

Hulman A, Witte DR, Vistisen D, et al. Pathophysiological characteristics underlying different glucose response curves: a latent class trajectory analysis from the prospective EGIR-RISC study [published online May 31, 2018]. Diabetes Care. doi: 10.2337/dc18-0279

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