Real-World Performance of the MiniMed 780G Advanced Hybrid Closed Loop Automated Insulin Delivery System in Greece

Abstract

Introduction

Real-world studies have demonstrated the effectiveness of automated insulin delivery systems in managing people with type 1 diabetes (T1D) worldwide. However, data on the effectiveness of such systems in the management of people with T1D in Greece have previously been lacking. This retrospective analysis sought to address this by providing real-world insights into the impact of the MiniMed 780G system on the glycemic control of people with T1D in Greece.

Methods

Data from MiniMed 780G systems, uploaded to the CareLink Personal Software program between January 2024 and January 2025 by 2284 users with T1D in Greece, were collected and analyzed. Outcomes considered included glycemic control measures such as time spent in target ranges, glucose management indicator (GMI), and international glycemic control targets. Data were analyzed across different age groups (≤ 15 years, 16–55 years, ≥ 56 years), with additional analyses of cohorts using recommended optimal settings (ROS), users with data from before and after the initiation of the advanced hybrid closed-loop system, and users with 12 months of data.

Results

Across the overall cohort, the mean (standard deviation [SD]) time in range (TIR) was 75.4% (9.5%), and 59.3% of users achieved a combined target of a GMI < 7%, TIR > 70%, time below 70 mg/dl < 4%, and time below 54 mg/dl < 1%. These findings were consistent across age groups and stable over a 12-month period, but were found to be better among users with ROS. Comparing data obtained before and after the initiation of the system, mean (SD) TIR was found to increase from 65.1% (13.9%) to 74.9% (9.6%), while the proportion of users achieving a TIR of > 70% increased from 36.1 to 72.8%.

Conclusion

These data demonstrate that most users of the MiniMed 780G system achieve the recommended glycemic control targets.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13300-026-01883-3.

Key Summary Points

Why carry out this study?

In Greece, type 1 diabetes is associated with large healthcare expenditures.

Automated insulin delivery devices can improve glycemic control and quality of life in people with type 1 diabetes and reduce the burden associated with its management.

This study sought to provide real-world insights into the impact of an automated insulin delivery system (MiniMed 780G) on the glycemic control of people with type 1 diabetes in Greece.

What was learned from the study?

Users of this automated insulin delivery system had a mean (standard deviation) time in range of 75.4% (9.5%), with a comparison from before and after initiation of the system showing an increase from 65.1% (13.9%) to 74.9% (9.6%) and longitudinal data showing this glycemic control to be stable over a 12-month period.

These data show that most users of the MiniMed 780G system achieve international glycemic control targets.

Introduction

Type 1 diabetes (T1D) is associated with a large clinical and economic burden across Europe, with an estimated 65.6 million adults living with T1D in 2024 [1] and incidence among children and adolescents currently trending upwards [2]. In Greece, in 2022, an evaluation of the Diabetes Mellitus Patients Registry database of the National Organization for Health Care Services Provision found that 424,118 people had diabetes mellitus [3]. Of these, 27,892 had T1D, representing a prevalence of 0.24%, with rates highest in the Greek islands [3]. In Greece, in 2024, the International Diabetes Federation estimated that diabetes was responsible for 8271 deaths and a total diabetes-related health expenditure of USD 1492 million [4].

Among people with T1D, achieving good glycemic control is important for minimizing the risk for long-term diabetes-related complications and all-cause and cardiovascular mortality [5–8]. However, treatment can also carry the risk of hypoglycemic events, which are associated with a considerable clinical and economic burden [9–14]. The management of diabetes, therefore, typically requires careful optimization of metabolic control involving both the patient and healthcare professionals.

The development of automated insulin delivery (AID) systems that incorporate the use of a continuous glucose monitor to provide information used to adjust insulin doses delivered by a pump has transformed the clinical management of T1D, resulting in better glycemic control, improved quality of life, and reductions in the overall burden of diabetes management for patients and their caregivers [15–19]. More recently, advanced hybrid closed-loop systems, providing automated-correction boluses up to every 5 min, different target setpoints, and a meal-detection module have been found to further improve glycemic control among people with T1D [20]. A growing volume of real-world data on such AID systems has been collected since the introduction of the first commercially available systems, providing further insight to inform practices and improve outcomes [21]. Large amounts of real-world evidence (RWE) have been generated, particularly for the more recent MiniMed 780G system, with a 2024 publication providing insights from > 100,000 users across Europe, the Middle East, and Africa, demonstrating consistent effectiveness and safety in achieving glycemic control [21]. These results are further supported by several smaller RWE analyses focusing on different countries and populations [21–28].

The aim of this study was to provide an analysis of such RWE generated by users with T1D in Greece, thus gathering information about the potential benefits of such AID systems in the management of T1D in this setting. Specifically, this analysis focused on users of the MiniMed 780G system who uploaded data through the CareLink Personal software program.

Methods

Design

Data from MiniMed 780G systems uploaded to the CareLink Personal software program from users with T1D in Greece between 29 January 2024 and 29 January 2025 were examined. Users included in the collected data were grouped into three cohorts for analyses. These included the overall cohort, a second cohort including users with data from pre- and post-initiation of the advanced hybrid closed loop (AHCL) system, and a third cohort of users with 12 months of data available for longitudinal analysis. Within these groups, users were further subdivided by age group where possible, providing comparisons of those aged ≤ 15 years, 16–55 years, and ≥ 56 years. Sub-analyses were also performed based on whether recommended optimal settings (ROS) were consistently used (glucose target of 100 mg/dl for ≥ 95% of the time in addition to an active insulin time of 2 h for ≥ 95% of the time) [23, 27]. This methodology has been used previously in related RWE studies in other countries [21–28] and will also be used in additional future country-specific studies published in this series.

Data Source

Data were collected using CareLink™ Personal, a software platform that collects information from MiniMed™ systems and can be used by users and healthcare professionals to monitor and report on progress [29]. The process of generating RWE from CareLink™ Personal has been validated and has previously been described by van den Heuvel et al. [29]. In short, CareLink™ Personal contains data from all users who have created a CareLink™ Personal account. Data were only used from MiniMed users who provided consent, which was > 91% of the overall user population (per the latest assessment in 2024). Users can upload either automatically (every night) or manually, based on the user’s preference. Ninety-nine percent of all uploads are done automatically; the MiniMed 780G pump has 3 months of data storage, so any gaps in (manual) uploads < 3 months will not result in missing data. In addition to device data, CareLink™ Personal includes self-reported data on age groups (≤ 15, 16–55, and ≥ 56 years) and gender. All users included in this analysis were using the MiniMed 780G as part of their routine care.

Eligibility

To be eligible for this analysis, users in Greece had to self-report having T1D on the CareLink™ Personal system and provide consent for their data to be used. Users were excluded from the cohort if they did not have ≥ 10 days of sensor glucose (SG) data in each relevant period, as previously validated [30]. Post-AHCL initiation data included all information from the relevant period regardless of whether the system was in AHCL or open-loop control, as described in previous papers. Similar criteria have been used in previous RWE studies in MiniMed 780G users [21–28].

Outcomes

Outcomes assessed in this analysis included measures of glycemic control, including time in range (TIR, 70–180 mg/dl, 3.9–10.0 mmol/l), time in tight range (TITR, 70–140 mg/dl, 3.9–7.8 mmol/l), time below 70 mg/dl (TB70), time below 54 mg/dl (TB54), time above 180 mg/dl (TA180), and time above 250 mg/dl (TA250). The proportions of users achieving various international targets for these parameters were also analyzed [30, 31]. These included TIR > 70%, TITR > 50%, TB70 < 4%, TB54 < 1%, TA180 < 25%, and TA250 < 5% [30, 31]. Other outcomes analyzed included mean SG, its coefficient of variation (CV), glucose management indicator (GMI), and composite glycemic control targets. For reasons of General Data Protection Regulation (GDPR) compliance, outcomes were only reported for subgroups consisting of > 100 users.

Ethics Approval

The study was conducted in accordance with the Declaration of Helsinki (1964) and its subsequent amendments. Ethics committee approval was not required for this study as it was a real-world analysis and therefore deemed to not qualify as "human subjects research." All CareLink™ Personal account holders are proactively asked to provide consent for the use of their de-identified data for research purposes by Medtronic. Users who did not provide consent were excluded from the analyses, and all data were analyzed only after de-identification.

Results

Cohort 1: Glycemic Control Across the Overall Cohort and in Different Age Groups

Data from 2284 users of MiniMed 780G systems living with T1D in Greece were collected and analyzed (Table 1). In the overall cohort, the mean (standard deviation [SD]) TIR was 75.4% (9.5%), and the TITR was 50.8% (10.3%) (Fig. 1). The mean (SD) SG over the study period was 147 mg/dl (15.3 mg/dl) with a CV of 33.3% (4.5%). Considering international targets, 73.2% of users achieved a GMI of < 7.0%, 74.5% achieved a TIR > 70%, 87.2% achieved a TB70 of < 4%, and 88.9% achieved a TB54 of < 1% (Fig. 2). Most users (59.3%) achieved the combined target of a GMI of < 7%, TIR of > 70%, TB70 of < 4%, and TB54 of < 1%.

Table 1.

Glycemic control of MiniMed 780G users divided by age group and whether ROS were used

	Overall cohort	≤ 15 years	16–55 years	≥ 56 years
All users
Users, n	2284	482	1579	182
SG, mg/dl	147 ± 15.3	148 ± 13.7	147 ± 16.1	146 ± 12.1
CV, %	33.3 ± 4.5	35.1 ± 4.5	33.0 ± 4.3	30.4 ± 4.3
GMI, %	6.83 ± 0.37	6.84 ± 0.33	6.83 ± 0.38	6.80 ± 0.29
TIR, %	75.4 ± 9.5	74.1 ± 8.6	75.4 ± 9.9	78.6 ± 8.2
TITR, %	50.8 ± 10.3	51.0 ± 8.8	50.7 ± 10.8	51.5 ± 10.2
TB70, %	2.2 ± 2.0	2.5 ± 1.8	2.2 ± 2.2	1.3 ± 1.2
TB54, %	0.4 ± 0.7	0.5 ± 0.6	0.5 ± 0.8	0.2 ± 0.3
% of users with GMI < 7%, TIR > 70%	69.9	68.0	69.8	76.9
% of users with GMI < 7%, TIR > 70%, TB70 < 4%	60	57.9	59.2	74.2
% of users with GMI < 7%, TIR > 70%, TB70 < 4%, TB54 < 1%	59.3	55.8	58.8	74.2
Median (IQR) sensor usage, days	266 (110–498)	377 (170–608)	245 (101–464)	230 (79–432)
Median (IQR) time on automatic mode, days	267 (108–503)	376 (175–626)	242 (96–469)	223 (73–426)
Basal %	42.3 ± 8.1	38.7 ± 5.9	43.2 ± 8.2	44.8 ± 9.4
Auto bolus %	16.9 ± 7.0	17.9 ± 6.0	16.7 ± 7.1	16.0 ± 7.7
TDD, U/day	48.0 ± 25.2	41.6 ± 21.7	50.2 ± 25.6	46.9 ± 27.8
ICR, g/U	10.8 ± 5.4	12.5 ± 7.6	10.2 ± 4.5	11.1 ± 4.5
Distinct ICRs, n/day	1.59 ± 0.80	2.46 ± 0.89	1.36 ± 0.58	1.25 ± 0.52
Users with optimized settings
Users, n	518	–	404	–
Mean SG, mg/dl	142 ± 12.2	–	142 ± 12.2	–
CV, %	32.6 ± 4.3	–	32.6 ± 4.1	–
GMI, %	6.71 ± 0.29	–	6.72 ± 0.29	–
TIR, %	78.8 ± 8.0		78.7 ± 8.0
TITR, %	54.8 ± 9.3	–	54.5 ± 9.2	–
TB70, %	2.1 ± 1.6	–	2.1 ± 1.6	–
TB54, %	0.4 ± 0.5	–	0.4 ± 0.5	–
% of users with GMI < 7%, TIR > 70%	84.6	–	84.7	–
% of users with GMI < 7%, TIR > 70%, TB70 < 4%	75.3	–	74.5	–
% of users with GMI < 7%, TIR > 70%, TB70 < 4%, TB54 < 1%	74.9	–	74	–
Median (IQR) sensor usage, days	231 (106–427)	–	207 (100–409)	–
Median (IQR) time on automatic mode, days	235 (111–438)	–	213 (102–419)	–
Basal %	42.3 ± 7.1	–	42.6 ± 6.7	–
Auto bolus %	17.3 ± 6.6	–	17.3 ± 6.6	–
TDD, U/day	51.0 ± 23.8	–	51.9 ± 24.3	–
ICR, g/U	10.2 ± 6.1	–	9.9 ± 4.5	–
Distinct ICRs, n/day	1.53 ± 0.69	–	1.42 ± 0.59	–

All values are mean ± standard deviation unless otherwise indicated. The total number of people with diabetes in the overall cohort is greater than the sum of people with diabetes in the individual age strata, owing to the self-reported nature of the age group data; where these data were missing, patients were included in the overall cohort, but omitted from the individual age strata

CV, coefficient of variation; GMI, glucose management indicator; ICR, insulin-to-carbohydrate ratio; IQR, interquartile range; ROS, recommended optimal settings; SG, sensor glucose; TB54, time below 54 mg/dl; TB70, time below 70 mg/dl; TDD, total daily dose; TIR, time in range; TITR, time in tight range

Fig. 1.

Mean percent of time spent in different glucose ranges across users of different age ranges. ROS, recommended optimal settings

Fig. 2.

Percentage of real-world MiniMed780G system users that reach international targets across age groups regardless of settings. GMI, glucose management indicator; TA180, time above 180 mg/dl; TA250, time above 250 mg/dl; TB54, time below 54 mg/dl; TB70, time below 70 mg/dl; TIR, time in range; TITR, time in tight range

These findings were broadly replicated across different age groups. In the largest group, adults aged 16 to 55 years (n = 1579), the mean (SD) TIR was 75.4% (9.9%), while the TITR was 50.7% (10.8%), and the TB70 was 2.2% (2.2%). Among pediatric users (≤ 15 years, n = 482), these values were 74.1% (8.6%), 51.0% (8.8%), and 2.5% (1.8%), respectively, while for older users (≥ 56 years, n = 182), they were 78.6% (8.2%), 51.5% (10.2%), and 1.3% (1.2%). Similarly, the GMI was broadly consistent across the age groups, with mean (SD) values of 6.83% (0.38%) for those aged 16–55 years, 6.80% (0.29%) for those ≥ 56 years, and 6.84% (0.33%) for those aged ≤ 15 years. A comparison of the rates of achievement of different international glycemic control targets between these age groups is presented in Fig. 2.

Data on users with ROS could only be reported for the overall and 16–55 years old age groups (Table 1). Only a minority of the overall cohort (22.7%) were included in this sub-analysis, largely reflecting the stringency of the criteria used to define users with ROS. The percentages of these users reaching international glycemic control targets are shown in Fig. 3. A comparison of time spent across different ranges is shown in Fig. 1. For all targets, the proportion of users reaching them was numerically higher among those who used ROS than in the overall cohort. For the overall subgroup of users with ROS regardless of age range, the mean (SD) TIR was 78.8% (8.0%), the TITR was 54.8% (9.3%), and the TBR70 was 2.1% (1.6%). Among those aged 16–55 years with ROS, the corresponding values were 78.7% (8.0%), 54.5% (9.2%), and 2.1% (1.6%).

Fig. 3.

Percentage of real-world MiniMed 780G system users that reach international targets with ROS. GMI, glucose management indicator; ROS, recommended optimal settings; TA180, time above 180 mg/dl; TA250, time above 250 mg/dl; TB54, time below 54 mg/dl; TB70, time below 70 mg/dl; TIR, time in range; TITR, time in tight range

Cohort 2: Changes in Glycemic Control Between Before and After AHCL Initiation

Data for at least 10 days prior to the initiation of AHCL were available for subsets of the entire cohort (n = 1234), 16–55 years (n = 801), and ≤ 15 years (n = 324) groups, allowing a comparison of glycemic control measures before and after initiation (Supplementary Table S1). In the overall cohort, mean (SD) TIR increased from 65.1% (13.9%) to 74.9% (9.6%; 15% increase), while TITR increased from 40.3% (13.5%) to 50.4% (10.3%; 25% increase), and TBR70 decreased from 2.86% (2.69%) to 2.12% (1.91%; 26% decrease). For those aged ≤ 15 years, there was a 12% increase in TIR and a 22% increase in TITR, while for those aged 16–55 years, the corresponding increases were 16% and 26%, respectively.

A comparison of times in ranges is shown in Supplementary Figure S1, and the percentages of users achieving international glycemic control targets before and after initiation are presented in Supplementary Figure S2. In all age groups, the percentage of users achieving glycemic control targets increased after initiation for all targets considered. In particular, the proportion of users achieving a TIR of > 70% increased from 36.1% to 72.8%.

Cohort 3: Sustained Glycemic Control over a 12-Month Period

A cohort of users (n = 702) had enough CGM data each month for the period of a year and was included in the longitudinal analysis. Data regarding their glycemic control are shown in Table 2, and times in range for each month across this period are presented in Fig. 4. Overall, glycemic control was stable over the period, with mean (SD) TIRs of 78.5% (8.4%) in Month 1 and 77.7% (9.4%) in Month 12. Similarly, mean TITR ranged between 53.2% and 54.4% across the 12 months. The mean TB70 was also stable, ranging from 2.2% to 2.3% over the period. The percentage of users with a TIR > 70% in this longitudinal cohort tended to be higher than that of the overall cohort but declined slightly from 86.0% in Month 1 to 80.6% in Month 12. The percentage of users with a TITR > 50% peaked at 68.9% in Month 3 and remained at 63.2% in Month 12.

Table 2.

Glycemic control of MiniMed 780G users over a 12-month period

	Month 1	Month 2	Month 3	Month 4	Month 5	Month 6	Month 7	Month 8	Month 9	Month 10	Month 11	Month 12
Users	702	702	702	702	702	702	702	702	702	702	702	702
SG, mg/dl	142 ± 13.2	142 ± 13.2	142 ± 13.8	142 ± 14.0	143 ± 14.3	143 ± 14.7	143 ± 15.2	143 ± 15.0	143 ± 15.1	143 ± 15.8	144 ± 15.5	143 ± 14.7
CV, %	32.3 ± 4.7	32.3 ± 4.8	32.2 ± 4.7	32.1 ± 4.6	32.3 ± 4.8	32.3 ± 4.6	32.2 ± 4.7	32.2 ± 4.8	32.4 ± 4.8	32.3 ± 4.8	32.4 ± 4.8	32.4 ± 4.9
GMI, %	6.71 ± 0.32	6.71 ± 0.31	6.71 ± 0.33	6.71 ± 0.33	6.72 ± 0.34	6.72 ± 0.35	6.72 ± 0.36	6.73 ± 0.36	6.73 ± 0.36	6.74 ± 0.38	6.75 ± 0.37	6.74 ± 0.35
TIR, %	78.5 ± 8.4	78.5 ± 8.5	78.5 ± 8.9	78.4 ± 8.9	78.2 ± 9.1	78.2 ± 9.2	78.2 ± 9.3	78.1 ± 9.3	77.9 ± 9.3	77.8 ± 9.9	77.6 ± 9.6	77.7 ± 9.4
TITR, %	54.1 ± 10.4	54.2 ± 10.4	54.4 ± 10.7	54.2 ± 10.7	54.0 ± 10.8	54.1 ± 11.2	53.9 ± 11.1	53.6 ± 10.9	53.8 ± 11.0	53.6 ± 11.4	53.2 ± 11.2	53.4 ± 10.9
TB70, %	2.3 ± 2.1	2.3 ± 2.0	2.3 ± 2.0	2.3 ± 2.0	2.3 ± 2.0	2.3 ± 2.0	2.3 ± 2.0	2.2 ± 2.0	2.2 ± 1.9	2.2 ± 2.0	2.3 ± 2.1	2.3 ± 1.9
TB54, %	0.5 ± 0.7	0.5 ± 0.6	0.5 ± 0.6	0.5 ± 0.6	0.5 ± 0.6	0.5 ± 0.7	0.5 ± 0.7	0.4 ± 0.6	0.4 ± 0.6	0.4 ± 0.6	0.5 ± 0.6	0.5 ± 0.6
% of users with GMI < 7%, TIR > 70%	82.1	81.8	82.1	82.1	81.1	79.6	80.5	79.1	77.9	78.6	77.4	78.3
% of users with GMI < 7%, TIR > 70%, TB70 < 4%	67.2	67.9	68.8	67.1	67.1	65.8	68.5	67.5	65.7	63.5	63.2	63.8
% of users with GMI < 7%, TIR > 70%, TB70 < 4%, TB54 < 1%	66.5	66.1	66.7	65.7	66	64.2	67.5	66.1	64.8	63.1	61.5	62.3
Median (IQR) sensor usage, days	28.4 (26.4–29.1)	28.2 (25.6–29.1)	28.0 (25.5–29.0)	28.2 (26.3–29.0)	28.3 (26.3–29.1)	28.2 (26.5–29.0)	28.4 (26.8–29.1)	28.4 (26.7–29.1)	28.4 (26.8–29.0)	28.3 (26.9–29.0)	28.4 (27.0–29.1)	28.2 (26.2–29.1)
Median (IQR) time on automatic mode, days	28.9 (26.4–29.8)	29.0 (26.1–29.8)	28.7 (26.0–29.7)	29.0 (26.9–29.8)	29.1 (27.0–29.9)	28.9 (27.3–29.8)	29.0 (27.1–29.8)	29.1 (27.2–29.9)	29.1 (27.4–29.8)	29.0 (27.5–29.8)	29.1 (27.5–29.9)	28.9 (26.8–29.8)
Basal %	41.5 ± 8.5	41.0 ± 8.3	40.9 ± 8.3	40.7 ± 8.1	40.8 ± 8.1	40.9 ± 8.2	40.8 ± 8.0	40.8 ± 8.0	41.0 ± 8.4	41.2 ± 8.2	41.1 ± 8.2	40.9 ± 7.8
Auto bolus %	15.0 ± 6.5	15.6 ± 6.8	15.4 ± 6.9	15.7 ± 7.0	15.8 ± 7.2	15.7 ± 7.1	15.8 ± 7.1	16.0 ± 7.3	16.0 ± 7.1	16.2 ± 7.4	16.4 ± 7.3	16.5 ± 7.5
TDD, U/day	42.5 ± 23.5	43.5 ± 23.2	43.9 ± 23.4	44.6 ± 24.3	45.0 ± 24.7	45.1 ± 24.2	45.0 ± 23.3	45.2 ± 23.4	45.6 ± 23.5	46.0 ± 24.0	46.1 ± 23.7	46.7 ± 24.1
ICR, g/U	11.8 ± 5.9	11.6 ± 5.9	11.5 ± 5.7	11.4 ± 5.5	11.3 ± 5.5	11.3 ± 5.5	11.2 ± 5.5	11.2 ± 5.4	11.1 ± 5.3	11.1 ± 5.4	11.1 ± 5.3	11.0 ± 5.3
Distinct ICRs, n/day	1.81 ± 0.94	1.81 ± 0.95	1.84 ± 0.98	1.84 ± 0.98	1.86 ± 1.00	1.85 ± 0.98	1.85 ± 0.98	1.86 ± 0.99	1.88 ± 1.01	1.86 ± 1.00	1.87 ± 0.99	1.87 ± 0.99

All values are mean ± standard deviation unless otherwise indicated

CV, coefficient of variation; GMI, glucose management indicator; ICR, insulin-to-carbohydrate ratio; IQR, interquartile range; ROS, recommended optimal settings; SG, sensor glucose; TB54, time below 54 mg/dl; TB70, time below 70 mg/dl; TDD, total daily dose; TIR, time in range; TITR, time in tight range

Fig. 4.

Mean percentages of times in range across a 12-month period

Discussion

Overall, the data presented here demonstrate that most users of the MiniMed 780G with T1D in Greece meet recommended glycemic targets. Users aged ≥ 56 years demonstrated particularly high rates of glycemic control, with higher TIRs and a larger proportion of users achieving international glycemic targets than in younger age ranges. Importantly, this analysis shows increases in TIR, TITR, and the proportion of users achieving glycemic control targets after initiation of AHCL compared with the period prior to initiation, with these parameters remaining stable for 1 year after initiation.

Improvements in glycemic control are of great importance in Greece, with data from the country suggesting that many people with T1D have not yet achieved therapeutic goals [32]. A study conducted at a Greek tertiary center in 2020 found that only 34% of 119 adults with T1D achieved a glycated hemoglobin (HbA1c) of < 7.0%, whereas only 69% of children achieved an HbA1c of < 7.5%. Of those included in that study, only a small proportion were using insulin pump therapy (26% of adults and 21% of children). However, those patients experienced a reduction in HbA1c from 8.0% to 7.2% after initiating pump therapy [32]. Similarly, low rates of pump use have been reported in a publication comparing glycemic control across countries, with the proportion of people with T1D in Greece using pumps ranging from 15.4% to 21.3% depending on age range [33]. However, AID systems have already been found to be effective in the management of T1D in Greece. An earlier study conducted in the country found that those using the MiniMed 780G system had lower mean glucose levels (138 vs 157 mg/dl), spent less time above range (15.5% vs 22.5%), and had better quality of life (Diabetes Quality of Life Brief Clinical Inventory scores: 27.7 vs 33.4, Type 1 Diabetes Distress Scale: 54 vs 43) than those using a sensor-augmented pump and predictive low glucose management system [34]. Additionally, a cost-effectiveness analysis found that the MiniMed 780G system is likely cost-saving (with a lower mean total lifetime cost of EUR 10,173) in the management of people with T1D in Greece compared with a sensor-augmented pump system with predictive low-glucose management [35]. This was due to reductions in costs associated with diabetes-related complications [35]. Additionally, MiniMed 780G was cost-effective (with an incremental cost-effectiveness ratio of EUR 29,869 per quality-adjusted life year [QALY]) and resulted in a gain of 2.708 QALYs compared with multiple daily insulin injections with intermittently scanned continuous glucose monitoring [35].

The results presented in the current study are broadly consistent with those found in other analyses of RWE from MiniMed 780G systems [21–28]. In particular, an analysis of data from over 100,000 users from Europe, the Middle East, and Africa found comparable results with a TIR of 72.3%, GMI of 7.0%, and TBR70 of 2.0% [21]. This study also highlights that optimizing settings will likely lead to better outcomes, as previously demonstrated [21, 22], suggesting that these settings could be adopted to achieve optimal outcomes. Promoting the use of such ROS, unless contraindicated, may therefore be important for achieving the full potential of AHCL systems in T1D management, allowing outcomes to be optimized while still achieving time-below-range goals.

The level of glycemic control achieved by users aged ≤ 15 years in this study is strikingly high, particularly compared with a previous analysis of > 100,000 users in which those aged ≤ 15 years had a TIR of 69.9%, with a higher percentage of time spent above normal glycemic range [21]. Similarly, much higher proportions of those aged ≤ 15 years in this study achieved glycemic targets than in that study, with particularly large differences in the percentages achieving a GMI < 7% (72.4% vs 55.7%), TIR > 70% (71.4% vs 52.1%), and a combined target of GMI < 7%, TIR > 70%, TB70 < 4%, and TB54 < 1% (55.8% vs 37.8%). However, comparable results have been reported in a study of 12,870 MiniMed 780G users across multiple countries. In that study, users aged ≤ 15 years achieved a GMI of 6.8% and TIR of 73.9%, compared with corresponding values of 6.8% and 76.5% for those aged > 15 years [22]. Similarly, a study of 111 children and adolescents in Italy found TIRs of 75.5–76.8% [27]. The TIR values reported here are also broadly in line with those previously reported in clinical studies of the MiniMed 780G conducted in children, adolescents, and young adults [36].

As an RWE study, this analysis had numerous strengths in providing insight into outcomes of the use of AID systems in real-world T1D management. The use of the CareLink Personal system provided a continuous, seamless, automated data upload, with interruptions only resulting in missing data if they spanned >3 months. Furthermore, the study benefited from a robust design aligned with RWE principles, with a rigorous methodology and outcomes that were both predefined and validated. In addition, selection bias was reduced by relying on automated uploads.

This study nevertheless had some limitations. First, as a real-world study focusing on the MiniMed 780G, no comparison against other AID systems was conducted. Furthermore, although CareLink Personal provides a wealth of data on glycemic control, some variables, including age range, gender, and diabetes type, are self-reported. Data on the dates of diagnosis were not available. Additionally, age information is grouped, and analyses were performed according to broad age categories. Furthermore, no analysis was performed according to sex, although a recent review noted no clinically relevant sex-based differences among users of the MiniMed 780G [37]. Sociodemographic data are also not provided by the system because of privacy concerns. Data were not available for other parameters such as lipid levels or dietary habits. There may be selection biases in which users upload their data to the platform, although the high (> 91%) upload rate would suggest that such biases are likely to be small. Similarly, data for small groups (< 100 patients) could not be provided because of policies relating to GDPR compliance. Furthermore, some comparisons with other studies may be limited by the lack of data on HbA1c. However, while HbA1c remains the primary measure used to guide glucose management, the use of GMI is becoming increasingly common as adoption of CGM increases and may contribute to the personalization of diabetes management [38]. A final limitation was that CareLink Personal do not collect data on adverse events such as severe hypoglycemia or diabetic ketoacidosis or provide data on prior diabetes therapies.

Conclusion

These data demonstrate that using the MiniMed 780G device in T1D management can result in increases in TIR. In particular, data from pre- and post-AHCL initiation highlight the effectiveness of the AHCL system in achieving glycemic control, even though that comparison is with users utilizing the same sensor for manual control. Importantly, this is the first analysis to provide large-scale effectiveness data for Greek patients, demonstrating high rates of users meeting international glycemic control targets in a real-world context, thereby highlighting the potential benefits of AHCL systems for people with T1D in the country.

Supplementary Information

Below is the link to the electronic supplementary material.

Author Contribution

Vaia Lambadiari, Nikolaos Tentolouris, Andriani Vazeou, Athanasios Christoforidis, Christina Kanaka-Gantenbein, Konstantinos Makrilakis, Triantafyllos Didangelos: Conceptualization, Writing. Maria-Joanna Juachon: Data Curation, Formal Analysis, Investigation. Vittorino Smanitto: Conceptualization, Funding Acquisition. Tim van den Heuvel, Goran Petrovski: Conceptualization, Methodology, Investigation, Validation, Writing, Supervision.

Funding

This study, medical writing support, and journal’s Rapid Service Fee were funded by Medtronic.

Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Conflict of Interest

Vaia Lambadiari has received honoraria in the context of Advisory Boards, research grants, clinical trials, speakers bureau, and consultancy by Novartis, Sanofi, Novo Nordisk, MSD, Eli Lilly, Boehringer, Vianex, AstraZeneca, Mylan, Amgen, Elpen, Amryt, Medtronic, Menarini, Bios, and Abbott. Andriani Vazeou has received funding for conference attendance from Novo Nordisk, Eli Lilly, Medtronic, and Abbott. Athanasios Christoforidis has received educational grants from Medtronic. Konstantinos Makrilakis has provided advisory board services and has received grant support and fees for consultancy from AstraZeneca, Boehringer Ingelheim, Lilly, Novo Nordisk, GSK, Sanofi, Medtronic, Hemoglobe, and Menarini. Maria-Joanna Juachon, Vittorino Smaniotto, Tim van den Heuvel, and Goran Petrovski are employees of Medtronic. Nikolaos Tentolouris, Christina Kanaka-Gantenbein, and Triantafyllos Didangelos have nothing to disclose.

Ethical Approval

The study was conducted in accordance with the Declaration of Helsinki (1964) and its subsequent amendments. Ethics committee approval was not required for this study as this study was a real-world analysis and therefore deemed to not qualify as “human subjects research." All CareLink™ Personal account holders are proactively asked to provide consent for the use of their de-identified data by Medtronic for research purposes. Users who did not provide consent were excluded from the analyses, and all data were analyzed only after de-identification.