# Narrowing the A1c gap: Personalized modeling of HbA1c– continuous glucose monitor discordance in type 1 diabetes

## TL;DR

This study shows that CGM and HbA1c measurements often disagree in type 1 diabetes, and introduces a personalized model to improve their alignment for better diabetes management.

## Contribution

A novel statistical model that adjusts CGM estimates using historical data to better align with HbA1c measurements.

## Key findings

- 31% of cases showed clinically significant discordance between CGM and HbA1c.
- The proposed model improved alignment between GMI and HbA1c, explaining 82% of the variance.
- Discordance tends to persist in the short term but varies over time, influenced by transient factors.

## Abstract

This study aims to characterize the temporal discordance between CGM-derived glucose exposure and HbA1c over time in individuals with type 1 diabetes, and to explore the development of a statistical model to adjust the relationship between these measures based on previously observed individual discrepancies. We paired CGM-data in a 60-day window prior to each HbA1c measurement and included individuals with type 1 diabetes with multiple pairs to assess and model discordance over time. Discordance was defined as difference between HbA1c and Glucose Management Indicator at each pair. At baseline (first pair), participants were categorized into three groups based on the degree of discordance: positive (≥0.5%), negative (≤–0.5%), and neutral (within ±0.5%). A multiple linear regression model incorporating historical discordance values, HbA1c levels, and the current GMI was utilized for an adjustment. 477 individuals were included and 1,523 instances of paired HbA1c and CGM-data were analyzed. Absolute discordance of ≥0.5% was observed in 31% of cases. In 51% of instances, the direction of discordance in each pair was maintained. In the modeling analysis, GMI accounted for 69% of the variance in HbA1c levels (r = 0.83, p < 0.001, MAE = 0.42%). Adjusting improved variance explainability to 82% (r = 0.90, p < 0.001, MAE = 0.33%). HbA1c-CGM discordance is highly prevalent, and while inter-individual discordance shows some degree of persistence, it also appears to vary over time for a substantial proportion of individuals. Adjusting for individual discordance in the short term can improve the alignment between adjusted GMI and laboratory-measured HbA1c.

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.