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Researchers use CGM-based metrics for evaluating diabetes control 

Researchers use CGM-based metrics for evaluating diabetes control 

CHARLOTTESVILLE, Va. – Data from continuous glucose monitors can predict nerve, eye and kidney damage caused by Type 1 diabetes, researchers at the University of Virginia Center for Diabetes Technology have found. This suggests doctors may be able to use data from CGMs to help save patients from blindness, diabetic neuropathy and other conditions, they say. “The landmark 10-year, 1,440-person Diabetes Control and Complications Trial (DCCT), published in 1993, established hemoglobin A1c as the gold standard for evaluating the risk for complications from Type 1 diabetes,” said Boris Kovatchev, PhD, director of the UVA Center for Diabetes Technology. “However, the use of continuous glucose monitoring is on the rise and there is no study of the magnitude of the DCCT to affirm CGM-based metrics as standard for evaluating diabetes control.” Using advanced machine learning techniques to process the DCCT data sets, the researchers were able to create virtual CGM traces for all participants and for the duration of their participation in the trial. They found that 14 days of data from the virtual continuous glucose monitors had a similar ability to predict diabetes complications as hemoglobin A1c readings. In addition to the time spent in a safe blood-sugar range of 70 to 180 mg/DL, the researchers found that other CGM readings also accurately predicted diabetes complications. These readings included the time spent in “tight range” (between 70 and 140 mg/DL) as well as the time spent above 140 mg/DL, above 180 mg/DL and above 250 mg/DL. The study results have been published in the journal Diabetes Technology & Therapeutics. The article’s authors are Benjamin Lobo, Chiara Fabris, Mohammadreza Ganji, Anas El Fathi, Marc D. Breton, Lauren Kanapka, Craig Kollman, Tadej Battelino, Roy W. Beck and Kovatchev. Disclosures from the researchers can be found in the paper. 

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