Effectiveness of Continuous Glucose Monitoring on Metrics of Glycemic Control in Type 2 Diabetes Mellitus: A Systematic Review and Meta-analysis of Randomized Controlled Trials

Intermittent Use of Flash Glucose Monitoring Improves Glycemic Control in Chinese Older Patients with Type 2 Diabetes Mellitus

Objective

To explore the efficacy and safety of intermittent use of flash glucose monitoring (FGM) for improving glycemic control in Chinese elderly patients with type 2 diabetes mellitus (T2DM).

Methods

This is a prospective observational study involving patients with T2DM aged ≥60 years. The study period spans 12 weeks, with participants wearing FGM at weeks 0, 5, and 10. Participants were divided into two subgroups based on HbA1c at enrollment: < 7.0% and ≥7.0%. The primary outcome of the study was HbA1c level. Secondary outcomes included time in range (3.9-10mmol/L) (TIR), time below range (<3.9mmol/L) (TBR), time above range (>10.0mmol/L) (TAR), and glycemic variability (GV).

Results

A total of 68 patients completed the 12-week FGM follow-up (age 67.9 ± 5.2 years; BMI 25.4 ± 3.3kg/m²). Overall findings revealed that compared to baseline, HbA1c decreased from 7.81 ± 1.25% to 7.44±1.10% after 12 weeks of intermittent wearing of FGM (p <0.001). In the subgroup analysis with HbA1c ≥7.0%, the results showed a significant reduction in HbA1c of 0.51mmol/L after 12 weeks (8.36 ± 0.95% vs 7.75 ± 0.97%, p < 0.001). And there was a significant reduction in TBR in the subgroup with HbA1c < 7% (p = 0.028). Multiple linear regression analysis showed that the baseline HbA1c (β = -0.529, P<0.001), duration of T2DM (β = 0.341, P = 0.001), and the frequency of sensor use (β = -0.269, P = 0.043) were associated with the reduction in HbA1c level.

Conclusion

Intermittent use of FGM is associated with an improvement in glycemic outcomes and reduces the risk of hypoglycemia in Chinese elderly patients with T2DM.

Efficacy of flash glucose monitoring on HbA1c in type 2 diabetes: An individual patient data meta-analysis of real-world evidence

AIMS

There is a growing body of evidence demonstrating the benefit of flash glucose monitoring in type 2 diabetes mellitus (T2DM). This individual patient data meta-analysis aimed to investigate the impact of commencing flash glucose monitoring on HbA1c in people living with T2DM treated with insulin in a real-world setting.

METHODS

A meta-analysis of eight observational studies which assessed change in HbA1c at 3-6 months following initiating flash glucose monitoring for which Abbott Diabetes Care could provide individual patient data was performed. Studies included adults with T2DM managed with insulin and baseline HbA1c between 8.0 %-12.0 % (64-108 mmol/mol). A one-stage model was created to explore heterogeneity.

RESULTS

A total of 803 patients were included in the analysis (mean(SD) age: 62.8(11.4) years, BMI: 32.2(6.8) kg/m2, baseline HbA1c 9.0(0.9) % [75 (10) mmol/mol]). Commencement of flash glucose monitoring was associated with an HbA1c reduction of 0.89 % (95 % CI 0.71 to 1.08) (9.8 mmol/mol (95 % CI 7.8 to 11.8)) at 3-6 months. In the one stage model, age, BMI and baseline HbA1c accounted for the substantial heterogeneity observed between studies.

CONCLUSIONS

Commencement of flash glucose monitoring was associated with a significant reduction in HbA1c at 3-6 months in a real-world setting in T2DM managed with insulin.

The efficacy of using continuous glucose monitoring as a behaviour change tool in populations with and without diabetes: a systematic review and meta-analysis of randomised controlled trials

BACKGROUND

Continuous glucose monitoring (CGM) holds potential as a precision public health intervention, offering personalised insights into how diet and physical activity affect glucose levels. Nevertheless, the efficacy of using CGM in populations with and without diabetes to support behaviour change and behaviour-driven outcomes remains unclear. This systematic review and meta-analysis examines whether using CGM-based feedback to support behaviour change affects glycaemic, anthropometric, and behavioural outcomes in adults with and without diabetes.

METHODS

Ovid MEDLINE, Cochrane Central Register of Controlled Trials, Elsevier Embase, EBSCOhost PsycINFO, and ProQuest Dissertations & Theses Global were searched through January 2024. Eligible studies were randomised controlled trials in adults that implemented CGM-based feedback in at least one study arm compared to a control without CGM feedback. Dual screening, data extraction, and bias assessment were conducted independently. Mean differences in outcomes between intervention and comparison groups were analysed using generic inverse variance models and random effects. Robustness of pooled estimates from random-effects models was considered with sensitivity and subgroup analyses.

RESULTS

Twenty-five clinical trials with 2996 participants were included. Most studies were conducted in adults with type 2 diabetes (n = 17/25; 68%), followed by type 1 diabetes (n = 3/25, 12%), gestational diabetes (n = 3/25, 12%), and obesity (n = 3/25, 12%). Eleven (44%) studies reported CGM-affiliated conflicts of interest. Interventions incorporating CGM-based feedback reduced HbA1c by 0.28% (95% CI 0.15, 0.42, p < 0.001; I2 = 88%), and increased time in range by 7.4% (95% CI 2.0, 12.8, p < 0.008; I2 = 80.5%) compared to arms without CGM, with non-significant effects on time above range, BMI, and weight. Sensitivity analyses showed consistent mean differences in HbA1c across different conditions, and differences between subgroups were non-significant. Only 4/25 studies evaluated the effect of CGM on dietary changes; 5/25 evaluated physical activity.

CONCLUSIONS

This evidence synthesis found favourable, though modest, effects of CGM-based feedback on glycaemic control in adults with and without diabetes. Further research is needed to establish the behaviours and behavioural mechanisms driving the observed effects across diverse populations.

TRIAL REGISTRATION

CRD42024514135.

Beyond the Pain Management Clinic: The Role of AI-Integrated Remote Patient Monitoring in Chronic Disease Management - A Narrative Review

Remote Patient Monitoring (RPM) stands as a pivotal advancement in patient-centered care, offering substantial improvements in the diagnosis, management, and outcomes of chronic conditions. Through the utilization of advanced digital technologies, RPM facilitates the real-time collection and transmission of critical health data, enabling clinicians to make prompt, informed decisions that enhance patient safety and care, particularly within home environments. This narrative review synthesizes evidence from peer-reviewed studies to evaluate the transformative role of RPM, particularly its integration with Artificial Intelligence (AI), in managing chronic conditions such as heart failure, diabetes, and chronic pain. By highlighting advancements in disease-specific RPM applications, the review underscores RPM's versatility and its ability to empower patients through education, shared decision-making, and adherence to therapeutic regimens. The COVID-19 pandemic further emphasized the importance of RPM in ensuring healthcare continuity during systemic disruptions. The integration of AI with RPM has refined these capabilities, enabling personalized, real-time data collection and analysis. While chronic pain management serves as a focal area, the review also examines AI-enhanced RPM applications in cardiology and diabetes. AI-driven systems, such as the NXTSTIM EcoAI™, are highlighted for their potential to revolutionize treatment approaches through continuous monitoring, timely interventions, and improved patient outcomes. This progression from basic wearable devices to sophisticated, AI-driven systems underscores RPM's ability to redefine healthcare delivery, reduce system burdens, and enhance quality of life across multiple chronic conditions. Looking forward, AI-integrated RPM is expected to further refine disease management strategies by offering more personalized and effective treatments. The broader implications, including its applicability to cardiology, diabetes, and pain management, showcase RPM's capacity to deliver automated, data-driven care, thereby reducing healthcare burdens while enhancing patient outcomes and quality of life.

Metadata information and fundus image fusion neural network for hyperuricemia classification in diabetes

OBJECTIVE

In diabetes mellitus patients, hyperuricemia may lead to the development of diabetic complications, including macrovascular and microvascular dysfunction. However, the level of blood uric acid in diabetic patients is obtained by sampling peripheral blood from the patient, which is an invasive procedure and not conducive to routine monitoring. Therefore, we developed deep learning algorithm to detect noninvasively hyperuricemia from retina photographs and metadata of patients with diabetes and evaluated performance in multiethnic populations and different subgroups.

MATERIALS AND METHODS

To achieve the task of non-invasive detection of hyperuricemia in diabetic patients, given that blood uric acid metabolism is directly related to estimated glomerular filtration rate(eGFR), we first performed a regression task for eGFR value before the classification task for hyperuricemia and reintroduced the eGFR regression values into the baseline information. We trained 3 deep learning models: (1) metadata model adjusted for sex, age, body mass index, duration of diabetes, HbA1c, systolic blood pressure, diastolic blood pressure; (2) image model based on fundus photographs; (3)hybrid model combining image and metadata model. Data from the Shanghai General Hospital Diabetes Management Center (ShDMC) were used to develop (6091 participants with diabetes) and internally validated (using 5-fold cross-validation) the models. External testing was performed on an independent dataset (UK Biobank dataset) consisting of 9327 participants with diabetes.

RESULTS

For the regression task of eGFR, in ShDMC dataset, the coefficient of determination (R2) was 0.684±0.07 (95 % CI) for image model, 0.501±0.04 for metadata model, and 0.727±0.002 for hybrid model. In external UK Biobank dataset, a coefficient of determination (R2) was 0.647±0.06 for image model, 0.627±0.03 for metadata model, and 0.697±0.07 for hybrid model. Our method was demonstrably superior to previous methods. For the classification of hyperuricemia, in ShDMC validation, the area, under the curve (AUC) was 0.86±0.013for image model, 0.86±0.013 for metadata model, and 0.92±0.026 for hybrid model. Estimates with UK biobank were 0.82±0.017 for image model, 0.79±0.024 for metadata model, and 0.89±0.032 for hybrid model.

CONCLUSION

There is a potential deep learning algorithm using fundus photographs as a noninvasively screening adjunct for hyperuricemia among individuals with diabetes. Meanwhile, combining patient's metadata enables higher screening accuracy. After applying the visualization tool, it found that the deep learning network for the identification of hyperuricemia mainly focuses on the fundus optic disc region.

Closed-Loop Insulin Therapy for People With Type 2 Diabetes Treated With an Insulin Pump: A 12-Week Multicenter, Open-Label Randomized, Controlled, Crossover Trial

OBJECTIVE

Continuous glucose monitoring (CGM) combined with continuous subcutaneous insulin infusion (CSII) achieves better glycemic control than multi-injection therapy in people with type 2 diabetes. The effectiveness of closed-loop therapy needs to be further evaluated in this population.

RESEARCH DESIGN AND METHODS

The study objective was to measure the impact of a hybrid closed-loop device (DBLG1) compared with CSII + CGM on glycemic control in people with type 2 diabetes previously treated with CSII. The randomized, controlled, crossover, two-period, open-label, and multicenter study was conducted from August 2022 to July 2023 in 17 individuals (9 to receive 6 weeks of CSII + CGM first and 8 to receive 6 weeks of closed-loop therapy first). The primary end point was the percentage time in range (TIR: 70-180 mg/dL). Secondary outcomes were other CGM-glucose metrics, physical activity, and sleep objectively measured using 1-week actimetry.

RESULTS

Data were analyzed using a modified intention-to-treat approach. Mean age was 63 (SD 9) years and 35% were women. Mean HbA1c at inclusion was 7.9% (SD 0.9). TIR increased to 76.0% (interquartile range 69.0-84.0) during the closed-loop condition vs. 61.0% (interquartile range 55.0-70.0) during the CSII + CGM condition; mean difference was 15.0 percentage points (interquartile range 8.0-22.0; P < 0.001). Analyses of secondary end points showed a decrease in time above range, in glucose management indicator, in glucose variability, and an increase in daily insulin dose. Actimetric sleep analysis showed an improvement in sleep fragmentation during closed-loop treatment.

CONCLUSIONS

Closed-loop therapy improved glycemic control more than did CSII + CGM in people with type 2 diabetes.

Efficacy and Safety of Different Hybrid Closed Loop Systems for Automated Insulin Delivery in People With Type 1 Diabetes: A Systematic Review and Network Meta-Analysis

AIMS

To compare the efficacy and safety of different hybrid closed loop (HCL) systems in people with diabetes through a network meta-analysis.

METHODS

We searched MEDLINE, EMBASE, CENTRAL and PubMed for randomised clinical trials (RCTs) enrolling children, adolescents and/or adults with type 1 or type 2 diabetes, evaluating Minimed 670G, Minimed 780G, Control-IQ, CamAPS Fx, DBLG-1, DBLHU, and Omnipod 5 HCL systems against other types of insulin therapy, and reporting time in target range (TIR) as outcome.

RESULTS

A total of 28 RCTs, all enrolling people with type 1 diabetes, were included. HCL systems significantly increased TIR compared with subcutaneous insulin therapy without continuous glucose monitoring (SIT). Minimed 780G achieved the highest TIR ahead of Control IQ (mean difference (MD) 5.1%, 95% confidence interval (95% CI) [0.68; 9.52], low certainty), Minimed 670G (MD 7.48%, 95% CI [4.27; 10.7], moderate certainty), CamAPS Fx (MD 8.94%, 95% CI [4.35; 13.54], low certainty), and DBLG1 (MD 10.69%, 95% CI [5.73; 15.65], low certainty). All HCL systems decreased time below target range, with DBLG1 (MD -3.69%, 95% CI [-5.2; -2.19], high certainty), Minimed 670G (MD -2.9%, 95% CI [-3.77; -2.04], moderate certainty) and Minimed 780G (MD -2.79%, 95% CI [-3.94; -1.64], high certainty) exhibiting the largest reductions compared to SIT. The risk of severe hypoglycaemia and diabetic ketoacidosis was similar to other types of insulin therapy.

CONCLUSIONS

We show a hierarchy of efficacy among the different HCL systems in people with type 1 diabetes, thus providing support to clinical decision-making.

TRIAL REGISTRATION

PROSPERO CRD42023453717.

Continuous glucose monitoring in adults with type 2 diabetes: a systematic review and meta-analysis

AIMS/HYPOTHESIS

Continuous glucose monitoring (CGM) is increasingly used in the treatment of type 2 diabetes, but the effects on glycaemic control are unclear. The aim of this systematic review and meta-analysis is to provide a comprehensive overview of the effect of CGM on glycaemic control in adults with type 2 diabetes.

METHODS

We performed a systematic review using Embase, MEDLINE, Web of Science, Scopus and ClinicalTrials.gov from inception until 2 May 2023. We included RCTs investigating real-time CGM (rtCGM) or intermittently scanned CGM (isCGM) compared with self-monitoring of blood glucose (SMBG) in adults with type 2 diabetes. Studies with an intervention duration <6 weeks or investigating professional CGM, a combination of CGM and additional glucose-lowering treatment strategies or GlucoWatch were not eligible. Change in HbA1c and the CGM metrics time in range (TIR), time below range (TBR), time above range (TAR) and glycaemic variability were extracted. We evaluated the risk of bias using the Cochrane risk-of-bias tool version 2. Data were synthesised by performing a meta-analysis. We also explored the effects of CGM on severe hypoglycaemia and micro- and macrovascular complications.

RESULTS

We found 12 RCTs comprising 1248 participants, with eight investigating rtCGM and four isCGM. Compared with SMBG, CGM use (rtCGM or isCGM) led to a mean difference (MD) in HbA1c of -3.43 mmol/mol (-0.31%; 95% CI -4.75, -2.11, p<0.00001, I2=15%; moderate certainty). This effect was comparable in studies that included individuals using insulin with or without oral agents (MD -3.27 mmol/mol [-0.30%]; 95% CI -6.22, -0.31, p=0.03, I2=55%), and individuals using oral agents only (MD -3.22 mmol/mol [-0.29%]; 95% CI -5.39, -1.05, p=0.004, I2=0%). Use of rtCGM showed a trend towards a larger effect (MD -3.95 mmol/mol [-0.36%]; 95% CI -5.46 to -2.44, p<0.00001, I2=0%) than use of isCGM (MD -1.79 mmol/mol [-0.16%]; 95% CI -5.28, 1.69, p=0.31, I2=64%). CGM was also associated with an increase in TIR (+6.36%; 95% CI +2.48, +10.24, p=0.001, I2=9%) and a decrease in TBR (-0.66%; 95% CI -1.21, -0.12, p=0.02, I2=45%), TAR (-5.86%; 95% CI -10.88, -0.84, p=0.02, I2=37%) and glycaemic variability (-1.47%; 95% CI -2.94, -0.01, p=0.05, I2=0%). Three studies reported one or more events of severe hypoglycaemia and macrovascular complications. In comparison with SMBG, CGM use led to a non-statistically significant difference in the incidence of severe hypoglycaemia (RR 0.66, 95% CI 0.15, 3.00, p=0.57, I2=0%) and macrovascular complications (RR 1.54, 95% CI 0.42, 5.72, p=0.52, I2=29%). No trials reported data on microvascular complications.

CONCLUSIONS/INTERPRETATION

CGM use compared with SMBG is associated with improvements in glycaemic control in adults with type 2 diabetes. However, all studies were open label. In addition, outcome data on incident severe hypoglycaemia and incident microvascular and macrovascular complications were scarce.

REGISTRATION

This systematic review was registered on PROSPERO (ID CRD42023418005).