ESTIMATION OF HEMOGLOBIN A1C FROM CONTINUOUS GLUCOSE MONITORING DATA IN INDIVIDUALS WITH TYPE 1 DIABETES: IS TIME IN RANGE ALL WE NEED?
- Chiara Fabris, United States of America
- Chiara Fabris, United States of America
- Lutz Heinemann, Germany
- Roy W. Beck, United States of America
- Claudio Cobelli, Italy
- Boris Kovatchev, United States of America
Abstract
Background and Aims
Glycated hemoglobin (A1c) has long been considered the gold-standard metric for quality of glycemic control in diabetes and key predictor of long-term complications. At last-year ATTD, an international panel of experts convened to provide guidance on interpreting and reporting continuous glucose monitoring (CGM) data, recognized time in target range (TIR) as an important metric to complement A1c. In this work, we bridge the gap between the two metrics and present a method to derive estimates of A1c from TIR.
Methods
Data from the International Diabetes Closed-Loop Trial included CGM measurements from N=168 adults with type 1 diabetes over six months, and A1c samples at three and six months. Hemoglobin glycation and clearance were modeled through a first-order differential equation driven by a linear function of daily TIR. A population model was trained on half of the subjects and tested without modifications on the remaining half. The model was then personalized by identifying an individual glycation-rate coefficient using the first A1c sample. Measured and estimated A1c at six months were compared in terms of mean absolute difference (MAD), mean absolute relative difference (MARD), and correlation coefficient (R); 100 replicates of training/testing were performed to assess robustness.
Results
Compared to the population-based model, personalization with individual glycation rates decreased MAD (0.43% to 0.25%) and MARD (6.1% to 3.56%), and increased R (0.77 to 0.93 [figure]).
Conclusions
The TIR-A1c relationship is mediated by identifiable individual glycation coefficients. Following personalization, TIR provides high-precision A1c estimates, therefore representing a viable alternative to A1c in research and clinical-practice settings.