Temporal Glycemic Patterns in Type 1 and Type 2 Diabetes: Insights From Extended Continuous Glucose Monitoring
Year: 2025
Type: article
Study design: Observational Study
Abstract: 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.
Authors: Tomoki Okuno, Lucas Sort, Bowen Zhang, Kerry Zhou, Matthew Kitchen, Victor Li, Donald R. Miller, Gregory J. Norman, Peter Reaven, Jin J. Zhou
Institutions: University of California, Los Angeles, Phoenix VA Health Care System, Supélec, Centre National de la Recherche Scientifique, Université Paris-Saclay, Laboratoire des signaux et systèmes, CentraleSupélec, Bronx High School of Science, University of Massachusetts Lowell, Veterans Health Administration, Dexcom (United States)
Language: English
Subfield: Endocrinology, Diabetes and Metabolism
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: SCH: Statistical Foundation and Predictive Modeling for Personalized Diabetes Management: Continuous Glucose Monitoring (CGM), Electronic Health Records (EHR), and Biobanks, DMS/NIGMS 2: Statistical Methods and Computational Algorithms for Biobank Data, Genomics, EHRs, GPUs, and Next Generation Computational Statistics
OpenAlex ID: W4410717330
