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Charlottesville, VA — Researchers at the University of Virginia Center for Diabetes Technology have uncovered groundbreaking evidence that continuous glucose monitors (CGMs) are as effective as hemoglobin A1c tests in predicting complications from type 1 diabetes. The findings could transform diabetes management, potentially sparing patients from severe conditions such as blindness, diabetic neuropathy, and kidney failure.
The study, recently published in the journal Diabetes Technology & Therapeutics, reveals that data from CGMs—devices that provide real-time blood sugar readings—can predict nerve, eye, and kidney damage as reliably as hemoglobin A1c, a long-standing benchmark for assessing diabetes control.
Key Findings
Researchers found that the percentage of time patients maintained blood sugar levels within the safe range of 70 to 180 mg/dL over a 14-day period was as effective at predicting complications like neuropathy, retinopathy, and nephropathy as A1c levels. Additional CGM metrics, such as time spent in tighter ranges (70–140 mg/dL) or above thresholds like 140, 180, or 250 mg/dL, also proved predictive.
“This study shows that CGM metrics can rival A1c in predicting complications,” said Boris Kovatchev, Ph.D., director of the UVA Center for Diabetes Technology. “It represents a significant step forward in how we understand and manage diabetes.”
A New Era for Diabetes Monitoring
The findings could have profound implications for diabetes care. CGMs, which are increasingly used by patients with diabetes, provide continuous data that enables real-time adjustments in care. Until now, A1c levels—measured every few months—were considered the gold standard, following the landmark Diabetes Control and Complications Trial (DCCT) conducted in the 1990s.
“The DCCT established A1c as the primary measure of diabetes control, but long-term CGM data to affirm its predictive power has been lacking,” Kovatchev explained. “This study bridges that gap using advanced machine learning techniques to virtualize CGM data from the DCCT.”
Leveraging Machine Learning
Using archived data from the DCCT, the researchers applied machine learning to simulate CGM readings for participants over the 10-year trial period. This innovative approach allowed the team to compare the predictive power of CGM metrics with A1c readings, without the need for a new, time-intensive clinical trial.
“A study of DCCT’s scale using CGMs would be prohibitively expensive and time-consuming,” Kovatchev said. “Our virtual trial approach represents the next best thing, enabling us to glean insights from past data.”
Implications for Patients and Research
With CGMs becoming a mainstay in diabetes care, these findings could improve patient outcomes and drive innovation in diabetes research. By incorporating CGM data into risk assessments, doctors may better prevent complications and tailor treatments to individual needs.
The study’s authors include Benjamin Lobo, Chiara Fabris, Mohammadreza Ganji, Anas El Fathi, Marc D. Breton, Lauren Kanapka, Craig Kollman, Tadej Battelino, Roy W. Beck, and Boris P. Kovatchev. Full disclosures and details can be found in the published paper.
Reference
Boris P. Kovatchev et al, “The Virtual DCCT: Adding Continuous Glucose Monitoring to a Landmark Clinical Trial for Prediction of Microvascular Complications,” Diabetes Technology & Therapeutics (2025). DOI: 10.1089/dia.2024.0404
This breakthrough reaffirms the potential of advanced data science to reshape healthcare, offering new hope to millions living with type 1 diabetes.
