Early identification of diabetes complications remains challenging because conventional monitoring often fails to reveal high-risk patients. This study, presented at the European Association for the Study of Diabetes Conference 2025, evaluated whether continuous glucose monitoring (CGM) could detect predictive patterns of glycemic variability.

A total of 134 adults with diabetes underwent 12,048 hours of CGM monitoring. Advanced time-series algorithms identified six glycemic variability fingerprints (GVFs). Each of these GVFs was strongly associated with a specific future complication. Patterns predicted autonomic neuropathy, nephropathy progression, retinopathy advancement, recurrent diabetic ketoacidosis or hyperglycemic hyperosmolar state, treatment resistance, and hypoglycemia unawareness. GVFs preceded clinical manifestations by 11 to 32 months and were detectable even when HbA1c, time-in-range, and other standard CGM metrics were within target.

Patients receiving GVF-guided therapy experienced a 62% reduction in complications and a 41% reduction in hospitalizations compared to observation. These findings highlight GVFs as a powerful tool for proactive, personalized diabetes care.