Narrowing the A1c gap: Personalized modeling of HbA1c- continuous glucose monitor discordance in type 1 diabetes
Narrowing the A1c gap: Personalized modeling of HbA1c- continuous glucose monitor discordance in type 1 diabetes is drawing significant interest across the industry.
Author summary The management of type 1 diabetes relies on two key tools: continuous glucose monitoring (CGM) and laboratory-measured HbA1c. While both measure sugar levels, they often disagree, leading to a “discordance” where a patient’s CGM-calculated average does not match their clinical blood test. We found that clinically significant discordance is common, affecting 31% of cases. Importantly, while this discrepancy tends to persist in the short term, it is not permanent and can vary over longer periods, suggesting it is influenced by transient factors like behavior or biology rather than genetics alone. To address this, we developed a personalized statistical model that uses an individual’s historical data to “adjust” the CGM estimate. This adjusted GMI significantly improved the alignment with laboratory results. These findings provide a practical method for clinicians to better interpret glucose data, ensuring more precise and personalized care for people living with diabetes.
Experts suggest this could influence future trends and innovation in the sector.
More updates are expected as the story develops.
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