Evaluation of HbA1c from CGM traces in an Indian population - Majumdar, Sayantan; Kalamkar, Saurabh D.; Dudhgaonkar, Shashikant; Shelgikar, Kishor M.; Ghaskadbi, Saroj; Goel, Pranay (Frontiers in Endocrinology, 2023)
Introduction Development of continuous glucose monitoring (CGM) in the last decade has given us access to a large number of consecutive glucose concentration measurements from patients. A standard method of estimating glycated hemoglobin (HbA1c), already established in the literature, is based on its relationship to average blood glucose concentration (aBG). We show that the estimates obtained using the standard method were not sufficiently reliable for an Indian population and suggest two new methods for estimating HbA1c. Methods Two datasets providing a total of 128 CGM and their corresponding HbA1c was received from two centers; Health Centre, Savitribai Phule Pune University, Pune and Joshi Hospital, Pune from patients already diagnosed as diabetic, non-diabetic, and pre-diabetic. We filtered out 112 data-sufficient CGM traces of which 80 traces were used to construct two models using linear regression. The first model estimates HbA1c directly from the average interstitial fluid glucose concentration (aISF) of the CGM trace and the second model proceeds in two steps: First aISF is scaled to aBG, then aBG is converted to HbA1c via the Nathan model. Our models were tested on the remaining 32 data-sufficient traces. We also provide 95 % confidence and prediction intervals for the HbA1c estimates. Results The direct model (first model) for estimating HbA1c is HbA1c mmol/mol = 0.319 × aISF_mg/dL + 16.73 and the adapted Nathan model (second model) for estimating HbA1c is HbA1c_mmol/dL = 0.38 × 1.17 × aISF_mg/dL − 5.60. Discussion Our results show that the new equations are likely to provide better estimates of HbA1c than the standard model at a population level, which is especially suited for clinical epidemiology with Indian populations.