Glycemic Variability Bridges Time in Range and Time in Tight Range: A Unified Equation for Both Type 1 and Type 2 Diabetes Based on Large‐Scale Continuous Glucose Monitoring Data

ABSTRACT Aims This study aimed to establish a regression model for the relationship between time in range (TIR) and time in tight range (TITR) in individuals with type 1 diabetes (T1D) and type 2 diabetes (T2D) based on real‐world continuous glucose monitoring (CGM) data. Materials and Methods A cross‐sectional analysis was conducted on over 200 000 CGM users with diabetes. Participants self‐reported basic demographic and clinical details via in‐app fields. Exponential regression models were constructed to examine the TIR‐TITR association for individuals with T1D and T2D, respectively. After controlling for coefficient of variation (CV), the model was extended to provide more precise glycemic targets for clinical use. Model performance was evaluated using the coefficient of determination ( R 2 ), root mean square error (RMSE), and Akaike information criterion (AIC). Results The TIR‐TITR relationship exhibited a nonlinear relationship. Exponential models (TITR T1D = 8.54436 × exp[0.02414 × TIR]; TITR T2D = 5.52189 × exp[0.02839 × TIR]) provided the best fit compared to linear and quadratic models. A TIR of 70% corresponded to TITR values of 40.3%–46.3%, whereas achieving TITR of 50% required TIR of 73.2%–77.6%. For TIR below 60%, each 5% TIR increment boosted TITR by less than 5% points; above 60%, gains exceeded 5% points. Additionally, the inclusion of CV in the model was associated with reduced differences between the fitted T1D and T2D curves and improved the model's performance (TITR = 2.18448 × exp[0.03749 × TIR] +0.94018 × CV−14.99420). Conclusions This study established the exponential model for TIR‐TITR relationship in individuals with T1D and T2D, using a real‐world CGM dataset. The model may provide new insights into the setting of individualized treatment goals.