Researchers at the University of Virginia’s Center for Diabetes Technology have found that data from continuous glucose monitors (CGMs) can predict nerve, eye, and kidney damage in people with type 1 diabetes.
The findings suggest that CGMs could provide doctors with a powerful tool to help prevent serious complications such as blindness, diabetic neuropathy, and kidney disease.
The study found that the amount of time a patient’s blood sugar remained within a safe range—70 to 180 mg/dL—over 14 days was as effective at predicting complications as the traditional hemoglobin A1c test, which has been the standard method for assessing long-term blood sugar control for decades.
A Shift from Traditional Diabetes Monitoring: Glucose Monitors
The hemoglobin A1c test was established as the gold standard for evaluating diabetes control following the landmark Diabetes Control and Complications Trial (DCCT), a 10-year study of 1,440 people with type 1 diabetes published in 1993. That study demonstrated that A1c readings, taken every few months, could help predict a patient’s risk for developing diabetes-related complications.
However, continuous glucose monitoring has become increasingly common in diabetes care, offering real-time insights into blood sugar fluctuations. The researchers noted that, despite its growing use, CGM-based metrics have not yet been widely accepted as a primary outcome measure in diabetes drug studies or clinical guidelines.
“The lack of long-term, large-scale CGM data has several clinical and regulatory implications,” said Boris Kovatchev, PhD, director of the UVA Center for Diabetes Technology. “For example, CGM is still not accepted as a primary outcome from diabetes drug studies.”
Machine Learning Sheds New Light on CGM Data
To bridge the gap between traditional A1c readings and CGM-based data, the UVA research team used advanced machine learning techniques to analyze data from the original DCCT study. By reconstructing virtual CGM data for all trial participants, they could compare CGM metrics’ predictive power with hemoglobin A1c readings. Glucose Monitors
Their findings showed that a two-week snapshot of CGM data could predict diabetes complications as effectively as long-term A1c monitoring. Additionally, other CGM measurements—such as the time spent in a tighter blood sugar range (70-140 mg/dL) and the time spent above key thresholds (140 mg/dL, 180 mg/dL, and 250 mg/dL)—also proved to be strong indicators of future complications.
Implications for Diabetes Care
With CGMs now widely used among diabetes patients, these findings could lead to updates in treatment guidelines, allowing doctors to rely more on real-time glucose data to guide clinical decisions. Glucose Monitors
“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. “Virtualizing 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 suggest that CGMs could help patients manage their diabetes more effectively while also assisting researchers in advancing diabetes care. If widely adopted, CGM-based monitoring could lead to earlier interventions and a reduction in long-term complications for individuals with type 1 diabetes. Glucose Monitors
Reference: Boris P. Kovatchev, Benjamin Lobo, Chiara Fabris, Mohammadreza Ganji, Anas El Fathi, Marc D. Breton, Lauren Kanapka, Craig Kollman, Tadej Battelino, Roy W. Beck. The Virtual DCCT: Adding Continuous Glucose Monitoring to a Landmark Clinical Trial for Prediction of Microvascular Complications. Diabetes Technology & Therapeutics, 2025.
