Longitudinal study assesses glycemic control in youth with type 1 diabetes, indicating the need for proactive management strategies.
Introduction: Evidence on the long-term durability of glycemic control beyond the first year of automated insulin delivery therapy remains limited, particularly in large multicenter pediatric cohorts. The present study aimed to evaluate long-term effectiveness and identify determinants of sustained optimal outcomes over 3 years of MiniMed™ 780G use in youth with type 1 diabetes (T1D). Materials and Methods: In this longitudinal, multicenter, real-world study, 359 youth with T1D (median age 12.2 years, 50.9% female) from 20 Italian pediatric diabetes centers were followed for 3 years after MiniMed 780G initiation. Glucose metrics, insulin delivery parameters, device settings, and engagement indicators were analyzed at 1, 2, and 3 years. Longitudinal changes were assessed using linear mixed-effects models. Multivariable logistic regression identified predictors of achieving time in tight range target (TITR ≥ 50%) at 3 years. Results: Median time in range (TIR) remained within international targets but declined modestly from 75% at 1 year to 74% at 3 years ( P = 0.002). The percentage of automatic correction boluses increased over time, whereas user-initiated boluses and carbohydrate entries declined ( P < 0.001). Youth with higher TIR at 1 year showed a progressive reduction in TIR and greater increase in automated corrections (time × group interaction P < 0.001). At 3 years, higher SmartGuard use (odds ratio [OR]: 1.13, 95% confidence interval [CI]: 1.03–1.23; P = 0.007), optimal system settings (OR: 3.21, 95% CI: 1.33–7.77; P = 0.010), and lower automatic correction boluses (OR: 0.84, 95% CI: 0.80–0.89; P < 0.001) were independently associated with achieving TITR ≥50%. Conclusions: MiniMed 780G provides sustained glycemic control over 3 years. However, progressive reliance on automated corrections and reduced user engagement may attenuate tight glycemic control, highlighting the need for ongoing education and proactive device optimization.
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Bombaci et al. (2026) studied this question.
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