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Background:Achieving optimal glycemic control for persons with diabetes remains difficult. Real-world continuous glucose monitoring (CGM) data can illuminate previously underrecognized glycemic fluctuations. We aimed to characterize glucose trajectories in individuals with Type 1 and Type 2 diabetes, and to examine how baseline glycemic control, CGM usage frequency, and regional differences shape these patterns. Methods:We linked Dexcom CGM data (2015–2020) with Veterans Health Administration electronic health records, identifying 892 Type 1 and 1716 Type 2 diabetes patients. Analyses focused on the first three years of CGM use, encompassing over 2.1 million glucose readings. We explored temporal trends in average daily glucose and time-in-range values. Results:Both Type 1 and Type 2 cohorts exhibited a gradual rise in mean daily glucose over time, although higher CGM usage frequency was associated with lower overall glucose or attenuated increases. Notable weekly patterns emerged: Sundays consistently showed the highest glucose values, whereas Wednesdays tended to have the lowest. Seasonally, glycemic control deteriorated from October to February and rebounded from April to August, with more pronounced fluctuations in the Northeast compared to the Southwest U.S. Conclusions:Our findings underscore the importance of recognizing day-of-week and seasonal glycemic variations in diabetes management. Tailoring interventions to account for these real-world fluctuations may enhance patient engagement, optimize glycemic control, and ultimately improve health outcomes. 

Both long- and short-term glycemic variability have been associated with incident diabetes complications. We evaluated their relative and potential additive effects on incident renal complications in the Action to Control Cardiovascular Risk in Diabetes trial. A marker of short-term glycemic variability, 1,5-anhydroglucitol (1,5-AG), was measured in 4,000 random 12-month postrandomization plasma samples (when hemoglobin A1c [HbA1c] was stable). Visit-to-visit fasting plasma glucose coefficient of variation (CV-FPG) was determined from 4 months postrandomization until the end point of microalbuminuria or macroalbuminuria. Using Cox proportional hazards models, high CV-FPG and low 1,5-AG were independently associated with microalbuminuria after adjusting for clinical risk factors. However, only the CV-FPG association remained after additional adjustment for average HbA1c. Only CV-FPG was a significant risk factor for macroalbuminuria. This post hoc analysis indicates that long-term rather than short-term glycemic variability better predicts the risk of renal disease in type 2 diabetes. Article HighlightsThe relative and potential additive effects of long- and short-term glycemic variability on the development of diabetic complications are unknown. We aimed to assess the individual and combined relationships of long-term visit-to-visit glycemic variability, measured as the coefficient of variation of fasting plasma glucose, and short-term glucose fluctuation, estimated by the biomarker 1,5-anhydroglucitol, with the development of proteinuria. Both estimates of glycemic variability were independently associated with microalbuminuria, but only long-term glycemic variability remained significant after adjusting for average hemoglobin A1c. Our findings suggest that longer-term visit-to-visit glucose variability improves renal disease prediction in type 2 diabetes.