Continuous GlucoSe Monitor And SmaRT Insulin Pen SysteM: A Real WorlD LIfe Analysis of Cohort Study

1. Background

Diabetes is one of the most significant public health challenges worldwide [1,2,3]. The World Health Organization (WHO) estimates that over 460 million people are affected by this condition, with predictions of exponential growth in the coming decades, largely driven by changes in dietary habits and lifestyle [4,5,6,7]. Although Type 1 Diabetes (T1D) is less prevalent than Type 2 Diabetes (T2D), it remains a significant concern, particularly among younger populations, due to its chronic nature and the therapeutic challenges it entails [8,9,10,11]. The epidemiology of diabetes, both nationally and globally, highlights an increasing burden on healthcare systems, exacerbated by factors such as aging populations, rising obesity rates, and the growing prevalence of sedentary lifestyles [12,13,14]. While T2D dominates the global diabetes landscape, T1D poses unique challenges that demand specific and innovative therapeutic approaches, especially given its increasingly complex and technologically advanced management [15,16,17].

The management of diabetes, particularly T2D, has made significant progress in recent decades, with a decisive shift toward personalized therapeutic approaches supported by increasingly sophisticated devices [18,19]. Among these innovations, the introduction of continuous glucose monitoring (CGM) systems and insulin pumps (IP) has marked a major step forward toward more precise glycemic control and improved quality of life for patients [20,21]. For individuals with T1D, who must manage daily insulin doses to maintain stable blood sugar levels, the introduction of technologies such as CGM, IP, and "smart pens" has been a crucial innovation [22,23,24]. These devices not only enhance glycemic control but also help reduce the emotional and psychological burden associated with diabetes management—a key aspect that should never be underestimated in the overall and holistic management of T1D patients [25,26].

Indeed, living with T1D involves daily challenges that extend far beyond the clinical aspects: the continuous management of blood sugar levels and self-monitoring can lead to anxiety, stress, and frustration for all component of community care [27,28]. Difficulties in managing diabetes, particularly among younger patients, are often associated with a diminished quality of life, negatively impacting emotional, social, and psychological well-being, which, in turn, can reduce treatment adherence [29,30].

The ability to alleviate this psychological burden through the adoption of technologies that automate parts of the management process, such as CGM, IP, and "smart" IP, represents one of the most promising advancements [31]. Furthermore, these technologies have demonstrated improvements in treatment compliance, both qualitatively and quantitatively, in clinical practice [32,33]. They have also shown potential benefits in management and economic terms, contributing to a notable reduction in healthcare costs [34,35]. Given the significant challenges in managing T1D, particularly in younger populations, and the promising advancements in diabetes technology, it is crucial to better understand the real-world impact of these innovations. Specifically, exploring the effectiveness of systems like the Medtronic Smart MDI in improving glycemic outcomes and quality of life is essential to inform clinical practice and guide future research.

1.1. Aim

The aim of this study was to evaluate the impact of transitioning to the Medtronic Smart MDI system in adult patients with T1D.

1.1.1. Primary Objective

To describe the baseline characteristics of patients switching from MDI therapy (with or without CGM) to the Medtronic Smart MDI system [composed by a smart insulin pen (InPenTM) and a connected CGM Medtronic Simplera TM sensor].

1.1.2. Secondary Objectives

To compare glycemic outcomes between the last 14 days of prior therapy and the first 14 days following one month of Medtronic Smart MDI system use. To assess glycemic outcomes over the first 30 and 90 days of treatment with the Medtronic Smart MDI system.

2. Methods

2.1. Study Design

A single-center retrospective observational clinical study was conducted at the Diabetes and Nutrition Clinic of the tertiary hospital in Ast Fermo (Italy). The STROBE checklist was adopted for study reporting (Supplementary File 1) [36,37].

2.2. Ethical Considerations

The study was conducted in full compliance with the ethical principles outlined in the Declaration of Helsinki and in accordance with institutional and national research ethics guidelines. Ethical approval was granted by the Institutional Review Board of Ast Fermo (approval code: INF04/2024). All participants provided informed consent for the collection and use of their data.

2.3. Sample and Criteria

All patients on Medtronic Smart MDI therapy who authorized the use of their data were included.

The inclusion criteria were: T1D diagnosis, male or female, age ≥ 18 years, therapy requirement determined by the physician’s judgment, and provision of signed informed consent.

The exclusion criteria were: refusal to consent to data use, presence of concomitant or suspected malignant diseases, pregnancy or breastfeeding, recent (within 3 months of enrollment) acute illnesses (excluding viral illnesses), renal impairment (eGFR < 60 mL/min), severe liver failure, congestive heart failure (NYHA class IV), proliferative diabetic retinopathy, presence of cholelithiasis, chronic pancreatitis or ongoing acute pancreatitis, and adherence to a ketogenic diet.

2.4. Endpoints

Description of the patients in terms of age, gender, Body Mass Index (BMI), duration of diabetes, smoking status, and type of T1D therapy used before switching to Medtronic Smart MDI. Statistical comparison between the last 14 days of the previous therapy and the first 14 days after completing one month on Medtronic Smart MDI in terms of: mean Sensor Glucose (SG), Standard Deviation (SD) and Coefficient of Variation (CV); Time Below Range level 2 (TBR2): time in < 54 mg/dL; Time Below Range level 1 (TBR1): time in 54-69 mg/dl; Time Below Range (TBR): time in < 70 mg/dL; Time In Range (TIR): time in 70-180 mg/dL; Time Above Range level 1 (TAR1): time in 181-250 mg/dL; Time Above Range level 2 (TAR2): time in > 250 mg/dL; Time Above Range (TAR): time in > 180 mg/dL. Description of the glycemic outcomes listed above during the first 30 and 90 days of treatment with Medtronic Smart MDI.

2.5. Statistical Analysis

2.5.1. General Methodology

Descriptive statistics were employed to summarize the results. These included the mean and standard deviation (SD), minimum, maximum, and median with interquartile range (IQR) for continuous variables, as well as counts and percentages for categorical variables. Summary statistics were reported with a maximum of two decimal places, as appropriate. Boxplots were created to visually represent the distribution of glycemic outcomes. Comparisons of glycemic outcomes across periods were performed using the Wilcoxon signed-rank test. All statistical tests were two-sided, with p-values < 0.05 considered statistically significant. All analyses were conducted using SAS software, version 9.4 (SAS Institute Inc., Cary, NC, USA).

2.5.2. Data Analysis

All data were collected by researchers from device data files, Medtronic CareLink™ reports [38], clinical records, and other relevant sources. Raw CGM data from Medtronic devices were used to calculate glycemic outcomes, as outlined in the subsequent section. For the last 14 days of the previous T1D therapy, if a subject had used a non-Medtronic CGM system, glycemic outcomes were derived directly from the available CGM data sources using validated methodologies.

2.5.3. Derived Variables

The criteria used to derive glycemic outcomes from CGM data are summarized in Table 1.

2.5.4. Handling of Missing Data and Outliers and Validation Requirements

Potential outliers were retained in the analysis, and no imputation methods were applied to handle missing data. To ensure accuracy and reliability, all analyses were independently reviewed and validated by a second statistician.

3. Results

3.1. Baseline Characteristics

The study included 21 subjects, with an average age of 51.5 years. Females represented 38.1% of the cohort. The mean Body Mass Index (BMI) was 24.7 ± 4.1, ranging from 14.6 to 31.1, while the mean duration of T1D was 21.9 ± 12.2 years, ranging from 4 to 52 years. Among the 20 participants with available smoking data, 55% were current smokers (11/20). Regarding the type of standard MDI therapy used prior to transitioning to the Medtronic Smart MDI system, 4 patients (20.0%) utilized the Medtronic Guardian™ Connect (GC) CGM system, 13 (65.0%) used other CGM systems, and 3 (15.0%) relied solely on a Blood Glucose Meter (BGM) (Table 2).

3.2. Time Study

A total of 21 T1D patients were included in this analysis. Prior to switching to the Medtronic Smart MDI, three patients did not use any CGM systems, and for one patient information on CGM usage was unavailable. Consequently, glycemic outcomes were available only for the remaining 17 patients when considering the last 14 days of the old therapy. In addition, one patient (ID = 46) was excluded from the analysis of the first 90 days of the new therapy due to a lack of device data beyond what was already included in the other periods ().

Figure 1. Chronological Study Flow Chart. T1D: type 1 diabetes.

Figure 1. Chronological Study Flow Chart. T1D: type 1 diabetes.

3.3. Sensor Use

The sensor usage (when applicable) during the four time periods considered in the analyses was reported anonymously for each subject included in the study (Table 3).

3.4. Glycemic Outcomes with Medtronic Smart MDI

Table 4 summarizes the glycemic outcomes of patients following the switch to Medtronic Smart MDI. Data are presented for three time periods: the first 14 days after one month of the new therapy, the first 30 days of the new therapy, and the first 90 days of the new therapy. The median TIR remained consistent across all three periods, ranging between 64% and 65%.

3.5. Comparison Between Standard MDI and Medtronic Smart MDI

Statistical comparison between the glycemic outcomes observed during the last 14 days with standard MDI and those observed during the first 14 days after one month of Medtronic Smart MDI is reported in Error! Reference source not found. for the 17 subjects with available CGM data in both periods. P-values lower than 0.05 are highlighted in bold. In period observed, the results demonstrate a significant improvement in glycemic control, with a reduction in SG mean from 171.0 to 153.5 mg/dL (p = 0.035). While no significant changes were observed in SG SD, the SG CV (calculated as (SG SD / SG mean) * 100) increased from 28.1% to 33.3% (p = 0.009). Furthermore, TIR increased from 58.0% to 64.4% (p = 0.005), TAR decreased from 39.0% to 34.2% (p = 0.015), indicating an overall enhancement in glycemic management. No significant differences were detected in terms of TBR.

3.6. Boxplot Analysis

A visual representation of glycemic outcomes for the two periods is illustrated in Figure 2 and Figure 3 using boxplots. These graphs highlight the positive impact of transitioning from standard MDI to Medtronic Smart MDI therapy. Specifically, Figure 2 demonstrates a reduction in SG mean, while Figure 3 shows an increase in TIR, both indicating improved glycemic management. The dotted lines in the plots connect the mean values for each period, emphasizing the changes achieved with the new therapy.

4. Discussion

The findings of this study underscore the significant potential of the Medtronic Smart MDI system in enhancing glycemic control among T1D patients transitioning from standard MDI therapy. The observed improvements in glycemic parameters, particularly the reduction in mean SG levels and the increase in TIR, highlight the system’s effectiveness in achieving tighter glycemic control. Specifically, the significant reduction in SG mean from 171.0 mg/dL to 153.5 mg/dL (p = 0.035) provides robust evidence of the Smart MDI system’s capability to address hyperglycemia effectively. These findings align with earlier studies that have demonstrated the advantages of advanced insulin delivery technologies, such as CGM-integrated systems, in optimizing glucose management by enhancing precision and personalization of therapy [39,40].

The increase in TIR from 58.0% to 64.4% (p = 0.005) and the corresponding reduction in Time Above Range (TAR) from 39.0% to 34.2% (p = 0.015) further validate the Smart MDI system’s ability to reduce hyperglycemia while maintaining a stable hypoglycemia risk, as suggested by the non-significant changes in TBR. These changes are particularly meaningful as TIR is increasingly recognized as a critical indicator of glycemic control, with direct correlations to better patient outcomes and reduced risks of long-term diabetes-related complications [41,42]. By shifting the glycemic profile toward greater time spent in the optimal range, the Smart MDI system may also help mitigate patient anxiety associated with glucose variability and frequent hyperglycemic episodes.

Moreover, the enhancements in glycemic control observed in this study are likely to contribute to improved long-term health outcomes for T1D patients. An increase in TIR has been shown to correlate strongly with a reduction in the risk of microvascular and macrovascular complications, including diabetic retinopathy, nephropathy, and cardiovascular disease [43]. In addition, better glycemic control could lower the likelihood of acute complications, such as severe hypoglycemia and diabetic ketoacidosis, thereby improving the overall quality of life for patients.

Interestingly, as recent studies demonstrated [44,45,46,47] the study also revealed an increase in the coefficient of CV of SG from 28.1% to 33.3% (p = 0.009), suggesting slightly higher glucose variability. While this finding may initially appear counterintuitive, it is essential to consider it alongside the improvements in SG mean and TIR. A potential explanation for this increase in CV could be the initial adjustment period to the new therapy or more frequent corrections of hyperglycemia, which might result in temporary fluctuations. Nonetheless, the overall reduction in hyperglycemia and improvement in TIR suggest that the benefits of the Smart MDI system outweigh any potential concerns related to glucose variability [48,49]. Further investigations are necessary to assess the clinical implications of increased CV and its long-term impact on glycemic stability and patient well-being.

From a practical perspective, these results reinforce the growing role of smart insulin delivery systems as a cornerstone of modern diabetes management. The Smart MDI system’s integration of advanced technologies, including CGM data and insulin dose optimization algorithms, simplifies the complexity of diabetes care while empowering patients to achieve better control with fewer manual adjustments. This innovation is particularly valuable for T1D patients who face the dual challenge of maintaining optimal glycemic levels and managing the psychological burden of their condition [50,51,52].

The findings also have broader implications for public health and healthcare systems [53,54,55]. Improved glycemic control and reduced hyperglycemia may not only enhance patient outcomes but also lead to significant cost savings by reducing the need for emergency interventions, hospitalizations, and long-term management of diabetes-related complications. These economic benefits further support the adoption of advanced insulin delivery systems like the Medtronic Smart MDI in routine clinical practice [56,57,58].

Limitations

This analysis has several limitations that should be taken into account when interpreting the results. First, the study was conducted on a selected cohort of patients, and the retrospective observational nature of the data introduces a potential risk of selection bias. Additionally, the relatively small sample size limits the statistical power of the analysis and restricts the generalizability of the findings to broader and more diverse populations. Consequently, the results should be considered preliminary, providing valuable insights that require confirmation through larger, multicenter studies. Further research involving more diverse and larger patient cohorts is crucial to validate these findings and to evaluate the long-term efficacy and safety of the Medtronic Smart MDI system in routine clinical practice. Moreover, the absence of a control group and the potential influence of confounding factors, such as differences in patient adherence and the presence of comorbid conditions, should be addressed in future studies to offer a more comprehensive assessment of the therapy’s impact.

5. Conclusion

This study demonstrates that transitioning to the Medtronic Smart MDI system significantly improves glycemic control in T1D patients, as evidenced by reductions in SG mean, increases in TIR, and decreases in TAR. These improvements highlight the potential of Smart MDI therapy to enhance overall diabetes management, reduce hyperglycemia, and support better long-term outcomes. The findings underscore the importance of integrating advanced technologies, such as CGM and Smart MDI systems, into routine diabetes care. These systems not only improve clinical outcomes but also have the potential to alleviate the psychological and logistical burden of diabetes management, thereby enhancing treatment adherence and patient quality of life. However, while the results of this study are promising, it is important to acknowledge the need for further research. Longitudinal studies with larger sample sizes and diverse patient populations are necessary to confirm these findings and evaluate the Smart MDI system’s impact on long-term clinical outcomes. Additionally, exploring the psychosocial benefits of this technology, including its effects on patient adherence, satisfaction, and quality of life, could provide a more comprehensive understanding of its role in diabetes care.