Efficacy of Flash Glucose Monitoring in Type 1 and Type 2 Diabetes: A Systematic Review and Meta-Analysis of Randomised Controlled Trials

Efficacy and Safety of Continuous Glucose Monitoring and Intermittently Scanned Continuous Glucose Monitoring in Patients With Type 2 Diabetes: A Systematic Review and Meta-analysis of Interventional Evidence

BACKGROUND

Traditional diabetes self-monitoring of blood glucose (SMBG) involves inconvenient finger pricks. Continuous glucose monitoring (CGM) and intermittently scanned CGM (isCGM) systems offer CGM, enhancing type 2 diabetes (T2D) management with convenient, comprehensive data.

PURPOSE

To assess the benefits and potential harms of CGM and isCGM compared with usual care or SMBG in individuals with T2D.

DATA SOURCES

We conducted a comprehensive search of MEDLINE, Embase, the Cochrane Library, Web of Science, and bibliographies up to August 2023.

STUDY SELECTION

We analyzed studies meeting these criteria: randomized controlled trials (RCT) with comparison of at least two interventions for ≥8 weeks in T2D patients, including CGM in real-time/retrospective mode, short-/long-term CGM, isCGM, and SMBG, reporting glycemic and relevant data.

DATA EXTRACTION

We used a standardized data collection form, extracting details including author, year, study design, baseline characteristics, intervention, and outcomes.

DATA SYNTHESIS

We included 26 RCTs (17 CGM and 9 isCGM) involving 2,783 patients with T2D (CGM 632 vs. usual care/SMBG 514 and isCGM 871 vs. usual care/SMBG 766). CGM reduced HbA1c (mean difference -0.19% [95% CI -0.34, -0.04]) and glycemic medication effect score (-0.67 [-1.20 to -0.13]), reduced user satisfaction (-0.54 [-0.98, -0.11]), and increased the risk of adverse events (relative risk [RR] 1.22 [95% CI 1.01, 1.47]). isCGM reduced HbA1c by -0.31% (-0.46, -0.17), increased user satisfaction (0.44 [0.29, 0.59]), improved CGM metrics, and increased the risk of adverse events (RR 1.30 [0.05, 1.62]). Neither CGM nor isCGM had a significant impact on body composition, blood pressure, or lipid levels.

LIMITATIONS

Limitations include small samples, single-study outcomes, population variations, and uncertainty for younger adults. Additionally, inclusion of <10 studies for most end points restricted comprehensive analysis, and technological advancements over time need to be considered.

CONCLUSIONS

Both CGM and isCGM demonstrated a reduction in HbA1c levels in individuals with T2D, and unlike CGM, isCGM use was associated with improved user satisfaction. The impact of these devices on body composition, blood pressure, and lipid levels remains unclear, while both CGM and isCGM use were associated with increased risk of adverse events.

Sustained Improvements in Readings in-Range Using an Advanced Bluetooth® Connected Blood Glucose Meter and a Mobile Diabetes App: Real-World Evidence from more than 55,000 People with Diabetes

INTRODUCTION

The OneTouch Verio Reflect^® (OTVR) Blood Glucose Meter features a color range indicator and provides on-meter guidance, insights, and encouragement. Diabetes management is enhanced by the OneTouch Reveal^® (OTR) Mobile App. We sought to provide real-world evidence (RWE) that combining devices improves glycemia.

METHODS

Anonymized glucose and app analytics from more than 55,000 people with diabetes (PWDs) were extracted from a server. Data from their first 14 days using OTVR Meter and OTR App was compared with 14 days prior to 90- and 180-day timepoints using paired within-subject differences.

RESULTS

In people with type 1 (PwT1D) or type 2 diabetes (PwT2D), readings in-range (RIR 70-180 mg/dL) improved by 7.8 percentage points (57.9-65.7%) and 12.0 percentage points (72.8-84.8%), respectively, over 180 days and hyperglycemia (> 180 mg/dL) was reduced by - 8.4 percentage points (37.9-29.5%) and - 12.2 percentage points (26.2-14.1%). RIR improved by > 10 percentage points in 38% of PwT1D and 39% of PwT2D. PwT1D spending > 2 to 4 sessions or > 10 to 20 min per week on the app improved RIR by 7.0 and 8.2 percentage points, respectively. PwT2D spending > 2 to 4 sessions or > 10 to 20 min per week on the app improved RIR by 12.6 and 12.1 percentage points, respectively. In PwT1D or T2D, mean blood glucose reduced by - 14.3 and - 19.8 mg/dL, respectively, from baseline to 180 days, with no clinically meaningful changes in percentage of hypoglycemic readings (< 70 mg/dL). PwT1D 65 years and older performed the most app sessions (10 per week) and improved RIR by 7.9 percentage points. PwT2D 65 years and older spent more time on the app (45 min per week) than PwT2D of any other age and improved RIR by 7.6 percentage points. All glycemic changes were statistically significant (p < 0.0005).

CONCLUSION

Real-world data from more than 55,000 PWDs demonstrates sustained improvements in readings in-range in PWDs using the OneTouch Verio Reflect Blood Glucose Meter and OneTouch Reveal App.

Automatic inference of hypoglycemia causes in type 1 diabetes: a feasibility study

Background

Hypoglycemia is the most common adverse consequence of treating diabetes, and is often due to suboptimal patient self-care. Behavioral interventions by health professionals and self-care education helps avoid recurrent hypoglycemic episodes by targeting problematic patient behaviors. This relies on time-consuming investigation of reasons behind the observed episodes, which involves manual interpretation of personal diabetes diaries and communication with patients. Therefore, there is a clear motivation to automate this process using a supervised machine learning paradigm. This manuscript presents a feasibility study of automatic identification of hypoglycemia causes.

Methods

Reasons for 1885 hypoglycemia events were labeled by 54 participants with type 1 diabetes over a 21 months period. A broad range of possible predictors were extracted describing a hypoglycemic episode and the subject's general self-care from participants' routinely collected data on the Glucollector, their diabetes management platform. Thereafter, the possible hypoglycemia reasons were categorized for two major analysis sections - statistical analysis of relationships between the data features of self-care and hypoglycemia reasons, and classification analysis investigating the design of an automated system to determine the reason for hypoglycemia.

Results

Physical activity contributed to 45% of hypoglycemia reasons on the real world collected data. The statistical analysis provided a number of interpretable predictors of different hypoglycemia reasons based on self-care behaviors. The classification analysis showed the performance of a reasoning system in practical settings with different objectives under F1-score, recall and precision metrics.

Conclusion

The data acquisition characterized the incidence distribution of the various hypoglycemia reasons. The analyses highlighted many interpretable predictors of the various hypoglycemia types. Also, the feasibility study presented a number of concerns valuable in the design of the decision support system for automatic hypoglycemia reason classification. Therefore, automating the identification of the causes of hypoglycemia may help objectively to target behavioral and therapeutic changes in patients' care.