New study shows CGM data can predict diabetes complications

Researchers at the University of Virginia Center for Diabetes Technology have found that data from continuous glucose monitors (CGMs) can predict nerve, eye, and kidney damage caused by type 1 diabetes. This breakthrough suggests that doctors could potentially use CGM data to prevent patients from suffering from blindness, diabetic neuropathy, and other severe diabetes complications.

The study reveals that the amount of time patients spend within a safe blood-sugar range of between 70 and 180 mg/DL over 14 days is as effective a predictor of diabetes-related complications. This finding challenges the long-standing reliance on hemoglobin A1c, a blood test commonly used to assess diabetes control and predict complications.

“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. 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,” said Dr Boris Kovatchev, director of the UVA Center for Diabetes Technology. “The lack of long-term large-scale CGM data has a number of clinical and regulatory implications; for example, CGM is still not accepted as a primary outcome from diabetes drug studies.”

The study found that just 14 days of data from these virtual CGMs was as predictive of diabetes complications as traditional hemoglobin A1c readings. Additionally, other CGM metrics such as the time spent in a ‘tight range’ (between 70 and 140 mg/DL) and the time spent above certain thresholds (140 mg/DL, 180 mg/DL, and 250 mg/DL) also provided accurate predictions of complications.

With CGMs becoming increasingly popular among people with diabetes, these findings could significantly improve diabetes management and further advance research in the field. The ability to monitor blood-sugar levels in real-time provides an advantage over traditional methods and may help researchers develop better treatment protocols and preventative measures for complications associated with diabetes.

“A study of the magnitude of the DCCT done with continuous glucose monitoring in addition to hemoglobin A1c would be prohibitively time-consuming and expensive,” Kovatchev said. “Virtualising a clinical trial to fill in the gaps in old, sparse data using advanced data science methods is the next best thing we can do today.”

The study’s findings have been published in Diabetes Technology & Therapeutics.