Plain language summary
One in 10 individuals is affected by diabetes, a condition involving abnormal regulation of blood glucose. Currently, diabetes is assessed using single-time or average measurements of blood glucose, without consideration for how blood glucose fluctuates over time. This study used continuous glucose monitoring (CGM) technology to evaluate how blood glucose fluctuates in individuals over time. The authors found that many individuals considered nondiabetic by standard measures experienced frequent elevations in blood glucose levels into the pre-diabetic or diabetic range (15% and 2% of the time, respectively). The authors developed a model for determining the “glucotype” (low, moderate or severe variability) of an individual, a more comprehensive measure of glucose patterns than the standard tests currently used. The authors argue that CGM should become an important tool in early identification of those at risk for type 2 diabetes.
Abstract
Diabetes is an increasing problem worldwide; almost 30 million people, nearly 10% of the population, in the United States are diagnosed with diabetes. Another 84 million are prediabetic, and without intervention, up to 70% of these individuals may progress to type 2 diabetes. Current methods for quantifying blood glucose dysregulation in diabetes and prediabetes are limited by reliance on single-time-point measurements or on average measures of overall glycemia and neglect glucose dynamics. We have used continuous glucose monitoring (CGM) to evaluate the frequency with which individuals demonstrate elevations in postprandial glucose, the types of patterns, and how patterns vary between individuals given an identical nutrient challenge. Measurement of insulin resistance and secretion highlights the fact that the physiology underlying dysglycemia is highly variable between individuals. We developed an analytical framework that can group individuals according to specific patterns of glycemic responses called "glucotypes" that reveal heterogeneity, or subphenotypes, within traditional diagnostic categories of glucose regulation. Importantly, we found that even individuals considered normoglycemic by standard measures exhibit high glucose variability using CGM, with glucose levels reaching prediabetic and diabetic ranges 15% and 2% of the time, respectively. We thus show that glucose dysregulation, as characterized by CGM, is more prevalent and heterogeneous than previously thought and can affect individuals considered normoglycemic by standard measures, and specific patterns of glycemic responses reflect variable underlying physiology. The interindividual variability in glycemic responses to standardized meals also highlights the personal nature of glucose regulation. Through extensive phenotyping, we developed a model for identifying potential mechanisms of personal glucose dysregulation and built a webtool for visualizing a user-uploaded CGM profile and classifying individualized glucose patterns into glucotypes.
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