Background: While insulin pumps remain the most common form of therapy for youths with type 1 diabetes (T1DM), they differ in the extent to which they utilize data from continuous glucose monitoring (CGM) and automate insulin delivery. Methods: The aim of the study was to compare metabolic control in patients using different models of insulin pumps. This retrospective single-center study randomly sampled 30 patients for each of the following treatments: Medtronic 720G without PLGS (predictive low glucose suspend), Medtronic 640G or 740G with PLGS and Medtronic 780G. In the whole study group, we used CGM systems to assess patients’ metabolic control, and we collected lipid profiles. In three groups of patients, we utilized CGM sensors (Guardian 3, Guardian 4, Libre 2 and Dexcom G6) to measure the following glycemic variability proxy values: time in range (TIR), time below 70 mg/dL (TBR), time above 180 mg/dL (TAR), coefficient of variation (CV) and mean sensor glucose. Results: Medtronic 640G or 740G and 780G users were more likely to achieve a target time in the target range 70–180 mg/dL (≥80%) [Medtronic 720G = 4 users (13.3%) vs. Medtronic 640G/740G = 10 users (33.3%) vs. Medtronic 780G = 13 users (43.3%); p = 0.0357)] or low glucose variability [Medtronic 720G = 9 users (30%) vs. Medtronic 640G/740G = 18 users (60%) vs. Medtronic 780G = 19 users (63.3%); p = 0.0175)]. Conclusions: Any integration between the insulin pump and CGM was associated with better glycemic control. More advanced technologies and artificial intelligence in diabetes help patients maintain better glycemia by eliminating various factors affecting postprandial glycemia.

Continuous glucose monitoring (CGM) interpretation in people without diabetes is expanding beyond general glycemic control toward more dynamic metrics, such as Rate of Change (RoC). In this fixed-sequence pilot study conducted in 30 healthy adult females, we evaluated associations between two structured 14-day lifestyle interventions and free-living conditions using standard CGM-derived glycemic variability metrics and a novel framework based on the rate-of-change (RoC) metrics and data clarity using an unblinded Abbott FreeStyle Libre 2 monitoring. The dietary intervention (calorie-tailored low-glycemic index regular meals) were associated with lower time spent in rising (+1 to +2 mg/dL/min) and falling (−2 to −1 mg/dL/min) RoC bins by 4.76 ± 1.87% (complete-case delta, p ≤ 0.0071), whereas the 14-day physical activity intervention showed smaller and less consistent RoC changes (0.41 ± 2.20, p ≥ 0.3549). These findings, despite some major study design limitations, suggest that RoC may be more responsive and sensitive to short-term dietary pattern modifications in normoglycemic women.

Background: Type 1 diabetes mellitus (T1DM) is one of the most frequently occurring chronic metabolic conditions in the pediatric and adolescent population. That is why our aim in this study was to compare metabolic control, eating habits, activity, and mental health in patients using insulin pumps with predictive low glucose suspend (PLGS) and advanced hybrid closed loop (AHCL) systems. Methods: We selected 37 patients and collected clinical, continuous glucose monitoring (CGM), and question-naire data (food frequency questionnaire (FFQ-6), physical activity questionnaire for children (PAQ-C), pediatric quality of life inventory (PedsQL). Additionally, all pa-tients participated in culinary workshops, which included education on a low-glycemic-index diet. Results: We observed a significant difference between the PLGS and the AHCL groups for mean glucose, coefficient of variation, and Time in Range (≤54, 70–140, 70–180, ≥180, and ≥250 mg/dL). Patients with higher Time Below Range consumed juices or sugary drinks more frequently. All participants had incor-rect eating habits and engaged in irregular physical activity. Conclusions: We observed no significant differences in the diabetes-specific quality of life scores between the PLGS and AHCL groups.