A systematic review on clinical implication of continuous glucose monitoring in diabetes management

Demographic variation in continuous glucose monitoring utilisation among patients with type 1 diabetes from a US regional academic medical centre: a retrospective cohort study, 2018-2021

OBJECTIVE

While continuous glucose monitoring (CGM) utilisation has been increasing among patients with type 1 diabetes (T1D), few studies have examined patterns of use across age, race/ethnicity and insurance status together. In this study, we examine CGM utilisation among patients with T1D from a regional academic medical centre across all insurance types.

DESIGN AND SETTING

This is a retrospective cohort study including both paediatric and adult patients with T1D who visited a regional academic medical centre between 1 January 2018 and 31 December 2021.

METHODS

Patients were followed from the date of their first T1D encounter during the study period until the first of the following: CGM use was documented, ≥730 days with no encounters at this centre or the end of the study period. We compared CGM use across demographic and clinical characteristics and used logistic regression models to assess the association between demographic variables and CGM utilisation.

RESULTS

Among 3311 eligible patients with T1D, CGM utilisation was 51.22%. The highest utilisation rates were among patients <18 years old while the lowest rates were among those in the 65+ years age group. Patients with private insurance and those who attended diabetes self-management education and support (DSMES) programmes had significantly higher CGM utilisation than those with public insurance and those who did not attend DSMES, respectively. In models stratified by age, we examined patterns of CGM use across insurance categories and found that CGM rates were persistently low among those with public versus private insurance.

CONCLUSIONS

In this retrospective review of patients with T1D receiving care at a regional academic medical centre from 2018 to 2021, nearly half of our sample used CGM. However, we found substantial variation in CGM utilisation with lower rates among older versus younger adults and individuals covered by public versus private insurance. Enhancing CGM access is important to mitigate diabetes-related complications for all patients with T1D.

The Impact of Self-Regulation Education Combined with Continuous Glucose Monitoring (CGM) on Diabetes Outcomes: A Randomized Controlled Study

In previous studies exploring continuous glucose monitoring (CGM), there has been a limited focus on how CGM influences key behavioral outcomes such as self-efficacy, health behaviors, and medication adherence. Background/Objectives: The aim of this study was to assess the impact of combining self-regulation health education with CGM on medication adherence, diabetes self-efficacy, diabetes health behaviors, and glucose control in individuals with diabetes. Methods: A randomized controlled study, reported following the CONSORT 2010 reporting guidelines. Individuals with diabetes volunteered to participate and were randomly allocated into two groups: the CGM group (n = 34) and the control group (n = 34). The CGM group received real-time CGM devices and education on self-regulation theory to enable them to self-adjust health promotion strategies and behaviors, while the control group received routine diabetes health education focusing on self-monitoring of blood glucose. Outcome measures included medication adherence, diabetes self-efficacy, diabetes health behaviors, and glucose control. Results: The CGM group demonstrated consistent diabetes self-efficacy, significant improvements in diabetes health behaviors, and a reduction in HbA1c levels over time. However, no significant differences in outcomes were observed between the CGM group and the control group. Conclusions: The use of continuous glucose monitoring (CGM) provides continuous, real-time glucose data. When combined with self-regulation education, it may help provide personalized insights into how specific foods, activities, medications, and stress levels affect blood glucose levels. This allows individuals with diabetes to make personalized adjustments to their lifestyle and treatment plans to optimize their blood sugar control.

Real-Time Continuous Glucose Monitoring in Adolescents and Young Adults With Type 2 Diabetes Can Improve Quality of Life

OBJECTIVE

Real-time continuous glucose monitoring (CGM) is effective for diabetes management in cases of type 1 diabetes and adults with type 2 diabetes (T2D) but has not been assessed in adolescents and young adults (AYAs) with T2D. The objective of this pilot interventional study was to assess the feasibility and acceptability of real-time CGM use in AYAs with T2D.

METHODS

Adolescents and young adults (13-21 years old) with T2D for six months or more and hemoglobin A1c (A1c) greater than 7%, on any Food and Drug Administration-approved treatment regimen, were included. After a blinded run-in period, participants were given access to a real-time CGM system for 12 weeks. The use and acceptability of the real-time CGM were evaluated by sensor usage, surveys, and focus group qualitative data.

RESULTS

Participants' (n = 9) median age was 19.1 (interquartile range [IQR] 16.8-20.5) years, 78% were female, 100% were people of color, and 67% were publicly insured. Baseline A1c was 11.9% (standard deviation ±2.8%), with median diabetes duration of 2.5 (IQR 1.4-6) years, and 67% were using insulin. Seven participants completed the study and demonstrated statistically significant improvement in diabetes-related quality of life, with the mean Pediatric Quality of Life inventory (PedsQL) diabetes score increasing from 70 to 75 after using CGM (P = .026). Focus group results supported survey results that CGM use among AYAs with T2D is feasible, can improve quality of life, and has the potential to modify behavior.

CONCLUSION

Real-time CGM is feasible and acceptable for AYAs with T2D and may improve the quality of life of patients with diabetes. Larger randomized controlled trials are needed to assess the effects on glycemic control and healthy lifestyle changes.

Development of Machine Learning Models for the Identification of Elevated Ketone Bodies During Hyperglycemia in Patients with Type 1 Diabetes

Aims: Diabetic ketoacidosis (DKA) is a serious life-threatening condition caused by a lack of insulin, which leads to elevated plasma glucose and metabolic acidosis. Early identification of developing DKA is important to start treatment and minimize complications and risk of death. The aim of the present study is to develop and test prediction model(s) that gives an alarm about their risk of developing elevated ketone bodies during hyperglycemia. Methods: We analyzed data from 138 type 1 diabetes patients with measurements of ketone bodies and continuous glucose monitoring (CGM) data from over 30,000 days of wear time. We utilized a supervised binary classification machine learning approach to identify elevated levels of ketone bodies (≥0.6 mmol/L). Data material was randomly divided at patient level in 70%/30% (training/test) dataset. Logistic regression (LR) and random forest (RF) classifier were compared. Results: Among included patients, 913 ketone samples were eligible for modeling, including 273 event samples with ketone levels ≥0.6 mmol/L. An area under the receiver operating characteristic curve from the RF classifier was 0.836 (confidence interval [CI] 90%, 0.783-0.886) and 0.710 (CI 90%, 0.646-0.77) for the LR classifier. Conclusions: The novel approach for identifying elevated ketone levels in patients with type 1 diabetes utilized in this study indicates that CGM could be a valuable resource for the early prediction of patients at risk of developing DKA. Future studies are needed to validate the results.

Wearable Devices Beyond Activity Trackers in Youth With Obesity: Summary of Options

Background: Current treatment protocols to prevent and treat pediatric obesity focus on prescriptive lifestyle interventions. However, treatment outcomes are modest due to poor adherence and heterogeneity in responses. Wearable technologies offer a unique solution as they provide real-time biofeedback that could improve adherence to and sustainability of lifestyle interventions. To date, all reviews on wearable devices in pediatric obesity cohorts have only explored biofeedback from physical activity trackers. Hence, we conducted a scoping review to (1) catalog other biofeedback wearable devices available in this cohort, (2) document various metrics collected from these devices, and (3) assess safety and adherence to these devices. Methods: This scoping review was conducted adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist. Fifteen eligible studies examined the use of biofeedback wearable devices beyond activity trackers in pediatric cohorts, with an emphasis on feasibility of these devices. Results: Included studies varied in sample sizes (15-203) and in ages 6-21 years. Wearable devices are being used to capture various metrics of multicomponent weight loss interventions to provide more insights about glycemic variability, cardiometabolic function, sleep, nutrition, and body fat percentage. High safety and adherence rates were reported among these devices. Conclusions: Available evidence suggests that wearable devices have several applications aside from activity tracking, which could modify health behaviors through real-time biofeedback. Overall, these devices appear to be safe and feasible so as to be employed in various settings in the pediatric age group to prevent and treat obesity.

Neuromuscular electrical stimulation changes glucose, but not its variability in type 2 diabetes: a randomized clinical trial

Neuromuscular electrical stimulation (NMES) can be an alternative to conventional exercising. This randomized clinical trial evaluated the effect of NMES in type 2 diabetes patients. Twenty-eight individuals with type 2 diabetes were assigned to NMES (n=14) or NMES-placebo (n=14) applied to knee extensor muscles for 60 minutes. Glucose variability, microvascular function and endothelial function were evaluated through continuous glucose monitoring system, near infrared spectroscopy and flow-mediated dilatation, respectively. Glucose levels (mg/dl) decreased 2h (184 ± 11 vs 223 ±15), 3h (179 ± 12 vs 219 ±14) and 4h (177 ± 12 vs 212 ±12) after NMES, in comparison to NMES-placebo. No differences in glucose variability were found: coefficient of variation (%) at 0-6h (11.4±1.3 vs 11.4±1.2), 6-12h (9.8±1.0 vs 11.6±1.6), 12-18h (15.5±2.0 vs 11.4±2.1), 18-24h (12.8±2.3 vs 10.0±1.6); standard deviation (mg/dl) at 0-6h (21.6±2 vs 24.6±3.5), 6-12h (19.5±1.8 vs 20.3±2.8), 12-18h (29.9±3.5 vs 21.3±2.8),18-24h (22.8±4.1 vs 16.6±2.0) and mean amplitude of glycemic excursions (mg/dl) 54.9±25.0 vs 70.3±35.7. Endothelial and microvascular functions did not change. In conclusion, one acute NMES session was strong enough to trigger glucose reduction in individuals with type 2 DM, but it failed to induce any significant change in glucose variability, endothelial and microvascular functions.

Evaluating Factors Associated With Continuous Glucose Monitoring Utilization With the Type 1 Diabetes Exchange Registry

BACKGROUND

The 2022 American Diabetes Association (ADA) Standards of Care recommends considering use of continuous glucose monitoring (CGM) for insulin-managed diabetes mellitus (DM), but equitable access remains challenging. This study evaluates socioeconomic and demographic metrics associated with CGM use.

METHODS

RStudio 2021.09.1+372 was utilized to perform uni- and bivariable analysis, as well as binomial logistic regression modeling for categorical CGM use (yes/no) on the most recent cross-section from the Type 1 Diabetes Exchange (T1DX) Registry 2016-2018 cohort (n = 22 418).

RESULTS

Compared with White Non-Hispanic participants, Black Non-Hispanic (OR = 0.45, CI = 0.36-0.57, P < 0.001) and American Indian/Alaskan Native individuals (OR = 0.33, CI = 0.14-0.70, P = 0.008) had lower odds of CGM use. Compared with private insurance, government insurance had reduced odds of CGM use (OR = 0.59, CI = 0.52-0.66, P < 0.001). Individuals earning $100,000 or more were twice as likely to use CGMs (OR = 2.06, CI = 1.75-2.45, P < 0.001) compared with those earning <$25,000 annually. Subgroup analysis based on income bracket demonstrated that government insured individuals earning <$25,000 annually were the least likely to use CGMs (OR = 0.44, CI = 0.32-0.61, P < 0.001), as compared with private insurance.

CONCLUSIONS

T1DX Registry data demonstrate that CGM use follows the inverse care law, with health technology utilization inversely related to disease burden. Federal policies promoting CGM use in Medicare and Medicaid populations can facilitate the ADA's recommendation for patients with insulin-managed diabetes mellitus.

Coordinated human sleeping brainwaves map peripheral body glucose homeostasis

Insufficient sleep impairs glucose regulation, increasing the risk of diabetes. However, what it is about the human sleeping brain that regulates blood sugar remains unknown. In an examination of over 600 humans, we demonstrate that the coupling of non-rapid eye movement (NREM) sleep spindles and slow oscillations the night before is associated with improved next-day peripheral glucose control. We further show that this sleep-associated glucose pathway may influence glycemic status through altered insulin sensitivity, rather than through altered pancreatic beta cell function. Moreover, we replicate these associations in an independent dataset of over 1,900 adults. Of therapeutic significance, the coupling between slow oscillations and spindles was the most significant sleep predictor of next-day fasting glucose, even more so than traditional sleep markers, relevant to the possibility of an electroencephalogram (EEG) index of hyperglycemia. Taken together, these findings describe a sleeping-brain-body framework of optimal human glucose homeostasis, offering a potential prognostic sleep signature of glycemic control.

Salivary ATR-FTIR Spectroscopy Coupled with Support Vector Machine Classification for Screening of Type 2 Diabetes Mellitus

The blood diagnosis of diabetes mellitus (DM) is highly accurate; however, it is an invasive, high-cost, and painful procedure. In this context, the combination of ATR-FTIR spectroscopy and machine learning techniques in other biological samples has been used as an alternative tool to develop a non-invasive, fast, inexpensive, and label-free diagnostic or screening platform for several diseases, including DM. In this study, we used the ATR-FTIR tool associated with linear discriminant analysis (LDA) and a support vector machine (SVM) classifier in order to identify changes in salivary components to be used as alternative biomarkers for the diagnosis of type 2 DM. The band area values of 2962 cm^-1, 1641 cm^-1, and 1073 cm^-1 were higher in type 2 diabetic patients than in non-diabetic subjects. The best classification of salivary infrared spectra was by SVM, showing a sensitivity of 93.3% (42/45), specificity of 74% (17/23), and accuracy of 87% between non-diabetic subjects and uncontrolled type 2 DM patients. The SHAP features of infrared spectra indicate the main salivary vibrational modes of lipids and proteins that are responsible for discriminating DM patients. In summary, these data highlight the potential of ATR-FTIR platforms coupled with machine learning as a reagent-free, non-invasive, and highly sensitive tool for screening and monitoring diabetic patients.

Effectiveness of Non-Contact Dietary Coaching in Adults with Diabetes or Prediabetes Using a Continuous Glucose Monitoring Device: A Randomized Controlled Trial

We aimed to evaluate the effectiveness of dietary coaching and continuous glucose monitoring (CGM) in patients with diabetes or prediabetes to improve their behavioral skills and health outcomes. A randomized controlled study with pre- and post-testing was conducted. Data were collected between November 2020 and April 2021. Forty-five patients with diabetes or prediabetes who used a CGM device were enrolled and analyzed. Dietary education, individual coaching and group coaching were provided to participants in the experimental group for 4 weeks. After the intervention, the thigh circumference in men significantly differed between the two groups (z = -2.02, p = 0.044). For women, participants in the experimental group showed greater improvement in eating self-efficacy compared with those in the control group (z = -2.66, p = 0.008). Insomnia was negatively related to the change in eating self-efficacy (r = -0.35, p = 0.018) and increase in thigh circumference (r = -0.35, p = 0.017). Even if used within a short intervention period, non-contact dietary coaching programs can help enhance behavioral skills, such as eating self-efficacy and health outcomes, such as thigh circumference. Moreover, the changed variables can indirectly improve other health outcomes in patients with diabetes or prediabetes.

Maintenance of plasma glucose variability after an acute session of aerobic exercise despite changes in insulin and glucagon-like peptide-1 levels in type 2 diabetes

Objective

The present study aimed to evaluate glucose variability and hormonal responses during and after an aerobic exercise session performed after breakfast in type 2 diabetes patients treated with metformin.

Methods

In this quasi-experimental study individuals underwent clinical and laboratory evaluations and maximal exercise test. After two weeks an aerobic exercise session (30 minutes at 60%-70% of the peak heart rate) was performed. At rest, during and after the exercise session, glucose variability (mean amplitude glucose excursions, glucose coefficient of variation, and glucose standard deviation) and levels of plasma glucose, insulin, glucagon, and glucagon-like-peptide-1 were evaluated.

Results

Thirteen patients were enrolled in the study. Plasma glucose increased at 15 minutes during the exercise session (244.6 ± 61.9 mg/dL), and decreased at 60 min after exercise (195.6 ± 50.0 mg/dL). Glucose variability did not show any difference before and after exercise. Insulin levels at 15 min [27.1 μU/mL (14.2-42.1)] and 30 min [26.3 μU/mL (14.6-37.4)] during the exercise were higher than those at fasting [11.2 μU/mL (6.7-14.9)] but decreased 60 minutes after exercise (90 minutes) [16.6 μU/mL (8.7-31.7)]. Glucagon levels did not show any difference. GLP-1 levels increased at 30 min [7.9 pmol/L (7.1-9.2)] during exercise and decreased 60 min after exercise (90 minutes) [7.7 pmol/L (6.8-8.5)].

Conclusion

Subjects with type 2 diabetes presented expected changes in insulin, glucagon and GLP-1 levels after breakfast and a single aerobic exercise session, not accompanied by glycemic variability changes.