Chinese clinical guidelines for continuous glucose monitoring (2018 edition)

Continuous Glucose Monitoring (CGM) in Sports-A Comparison between a CGM Device and Lab-Based Glucose Analyser under Resting and Exercising Conditions in Athletes

The objective of this pilot study was to compare glucose concentrations in capillary blood (CB) samples analysed in a laboratory by a validated method and glucose concentrations measured in the interstitial fluid (ISF) by continuous glucose monitoring (CGM) under different physical activity levels in a postprandial state in healthy athletes without diabetes. As a physiological shift occurs between glucose concentration from the CB into the ISF, the applicability of CGM in sports, especially during exercise, as well as the comparability of CB and ISF data necessitate an in-depth assessment. Ten subjects (26 ± 4 years, 67 ± 11 kg bodyweight (BW), 11 ± 3 h) were included in the study. Within 14 days, they underwent six tests consisting of (a) two tests resting fasted (HC_Rest/Fast and LC_Rest/Fast), (b) two tests resting with intake of 1 g glucose/kg BW (HC_Rest/Glc and LC_Rest/Glc), (c) running for 60 min at moderate (ModExerc/Glc), and (d) high intensity after intake of 1 g glucose/kg BW (IntExerc/Glc). Data were collected in the morning, following a standardised dinner before test day. Sensor-based glucose concentrations were compared to those determined from capillary blood samples collected at the time of sensor-based analyses and subjected to laboratory glucose measurements. Pearson's r correlation coefficient was highest for Rest/Glc (0.92, p < 0.001) compared to Rest/Fast (0.45, p < 0.001), ModExerc/Glc (0.60, p < 0.001) and IntExerc/Glc (0.70, p < 0.001). Mean absolute relative deviation (MARD) and standard deviation (SD) was smallest for resting fasted and similar between all other conditions (Rest/Fast: 8 ± 6%, Rest/Glc: 17 ± 12%, ModExerc/Glc: 22 ± 24%, IntExerc/Glc: 18 ± 17%). However, Bland-Altman plot analysis showed a higher range between lower and upper limits of agreement (95% confidence interval) of paired data under exercising compared to resting conditions. Under resting fasted conditions, both methods produce similar outcomes. Under resting postprandial and exercising conditions, respectively, there are differences between both methods. Based on the results of this study, the application of CGM in healthy athletes is not recommended without concomitant nutritional or medical advice.

Asia-Pacific Perspectives on the Role of Continuous Glucose Monitoring in Optimizing Diabetes Management

Diabetes is prevalent, and it imposes a substantial public health burden globally and in the Asia-Pacific (APAC) region. The cornerstone for optimizing diabetes management and treatment outcomes is glucose monitoring, the techniques of which have evolved from self-monitoring of blood glucose (SMBG) to glycated hemoglobin (HbA1c), and to continuous glucose monitoring (CGM). Contextual differences with Western populations and limited regionally generated clinical evidence warrant regional standards of diabetes care, including glucose monitoring in APAC. Hence, the APAC Diabetes Care Advisory Board convened to gather insights into clinician-reported CGM utilization for optimized glucose monitoring and diabetes management in the region. We discuss the findings from a pre-meeting survey and an expert panel meeting regarding glucose monitoring patterns and influencing factors, patient profiles for CGM initiation and continuation, CGM benefits, and CGM optimization challenges and potential solutions in APAC. While CGM is becoming the new standard of care and a useful adjunct to HbA1c and SMBG globally, glucose monitoring type, timing, and frequency should be individualized according to local and patient-specific contexts. The results of this APAC survey guide methods for the formulation of future APAC-specific consensus guidelines for the application of CGM in people living with diabetes.

Chinese diabetes datasets for data-driven machine learning

Data of the diabetes mellitus patients is essential in the study of diabetes management, especially when employing the data-driven machine learning methods into the management. To promote and facilitate the research in diabetes management, we have developed the ShanghaiT1DM and ShanghaiT2DM Datasets and made them publicly available for research purposes. This paper describes the datasets, which was acquired on Type 1 (n = 12) and Type 2 (n = 100) diabetic patients in Shanghai, China. The acquisition has been made in real-life conditions. The datasets contain the clinical characteristics, laboratory measurements and medications of the patients. Moreover, the continuous glucose monitoring readings with 3 to 14 days as a period together with the daily dietary information are also provided. The datasets can contribute to the development of data-driven algorithms/models and diabetes monitoring/managing technologies.

Performance Evaluation of the Glunovo® Continuous Blood Glucose Monitoring System in Chinese Participants with Diabetes: A Multicenter, Self-Controlled Trial

INTRODUCTION

The present study was aimed to evaluate the performance and safety of the Glunovo® real-time continuous glucose monitoring system (CGMS) in monitoring interstitial fluid glucose in adult participants with diabetes (at least 18 years old) using venous blood glucose as control.

METHODS

This was a multicenter, self-controlled clinical trial, conducted in participants with diabetes from China, between March 2019 to October 2019. The CGMS was used by all the participants for a 14-day wear-in period. The real-time glucose values measured by Glunovo® CGMS were compared with venous blood glucose values measured by the Entwicklung, Konstruktion und Fertigung (EKF) blood glucose detector. The primary outcomes were the consistency rate of CGMS readings and venous blood glucose values (20/20% standard).

RESULTS

A total of 78 participants (41 men, 37 women) and 156 CGMS sensors were included in the study. Among the included participants, 25 and 53 participants had type 1 and type 2 diabetes, respectively, with median age of 52.50 years (range 32-62 years). The overall agreement rate (20/20%) was 89.71% (95% CI 89.18-90.24%). It was observed that 99.08% (95% CI 98.91-99.24%) and 99.82% (95% CI 99.74-99.89%) of the measuring points fell within the A + B zones of the Clarke error grid analysis and Parkes/consensus error grid analysis, respectively. The mean absolute relative difference was 10.30% ± 4.86%. The probability of a glucose measurement falling within a range, when stratified by venous glucose measurements, ranged from 7.14% for 19.44-22.22 mmol/L to 79.21% for 4.44-6.67 mmol/L. There were 73 (41.24%) and 27 (57.45%) successful CGMS alarms for hypoglycemic and hyperglycemic events, respectively.

CONCLUSION

From the results, Glunovo® CGMS had excellent accuracy and limited clinical risk compared with venous blood glucose in the range of 2.2-22.2 mmol/L over 14 days.

Emerging technologies for the management of type 2 diabetes mellitus

Diabetes mellitus is a global health problem affecting 422 million people worldwide, of which 34.2 million live in the United States alone. Complications due to diabetes can lead to considerable morbidity and mortality related to both microvascular and macrovascular disease. While glycosylated hemoglobin testing is the standard test utilized to evaluate glycemic control, emerging targets like "time in range" and "glycemic variability" often provide more accurate assessments of glycemic fluctuations and have implications for diabetes complications and quality of life. Patients with diabetes face considerable burdens of self-care including frequent glucose monitoring, multiple insulin injections, dietary management, and the need to track daily activities, all of which lead to reduced adherence and psychological burnout. From the provider perspective, limited patient data and access to self-management tools lead to treatment inertia and a reduced ability to help patients achieve and maintain their glycemic goals. In the past few decades, there have been considerable advances in treatment-based technology and technological applications designed to help reduce patient burden and provide tools for better self-management. These advances make real-time clinical data available for clinicians to make necessary changes in treatment regimens. In this review, we discuss the latest emerging technologies available for the management of people with type 2 diabetes mellitus.

Association of glycemic variability assessed by continuous glucose monitoring with subclinical diabetic polyneuropathy in type 2 diabetes patients

Aims/Introduction

Diabetic peripheral neuropathy is a common diabetes‐related microvascular complication. The relationship between peripheral nerve function and glucose variability is unclear. We investigated the association of glucose variability with subclinical diabetic polyneuropathy in a large‐scale sample of patients with type 2 diabetes.

Materials and Methods

We enrolled 509 individuals with type 2 diabetes who were screened for diabetic peripheral neuropathy and monitored using a continuous glucose monitoring system. Multiple glycemic variability parameters, including the mean amplitude of glycemic excursions, glucose standard deviation (SD_gluc) and glucose coefficient of variation were calculated from 3‐day glucose profiles obtained from continuous glucose monitoring. All participants underwent nerve conduction studies, and the composite Z‐scores for nerve conduction parameters were calculated.

Results

Multivariate logistic regression analyses showed that SD_gluc and the conventional risk factor hemoglobin A1c (HbA1c) were independently associated with abnormal nerve function, and the corresponding odds ratios (95% confidence interval) were 1.198 (1.027–1.397, SD_gluc) and 1.182 (1.061–1.316, HbA1c), respectively. The composite Z‐score of nerve conduction velocity and response amplitude obviously decreased with greater SD_gluc, and the composite Z‐score of distal latency significantly increased with increasing tertiles of SD_gluc (all P trend <0.05). After adjusting for age, sex, body mass index, diabetes duration and HbA1c, SD_gluc was independently associated with nerve conduction velocity (β = −0.124, P = 0.021).

Conclusions

The SD_gluc is a significant independent contributor to subclinical diabetic polyneuropathy, in addition to conventional risk factors including diabetes duration and HbA1c.

Thresholds of Glycemia and the Outcomes of COVID-19 Complicated With Diabetes: A Retrospective Exploratory Study Using Continuous Glucose Monitoring

OBJECTIVE

Although elevated glucose levels are reported to be associated with adverse outcomes of coronavirus disease 2019 (COVID-19), the optimal range of glucose in patients with COVID-19 and diabetes remains unknown. This study aimed to investigate the threshold of glycemia and its association with the outcomes of COVID-19.

RESEARCH DESIGN AND METHODS

Glucose levels were assessed through intermittently scanned continuous glucose monitoring in 35 patients for an average period of 10.2 days. The percentages of time above range (TAR), time below range (TBR), time in range (TIR), and coefficient of variation (CV) were calculated. Composite adverse outcomes were defined as either the need for admission to the intensive care unit, need for mechanical ventilation, or morbidity with critical illness.

RESULTS

TARs using thresholds from 160 to 200 mg/dL were significantly associated with composite adverse outcomes after adjustment of covariates. Both TBR (<70 mg/dL) and TIR (70-160 mg/dL), but not mean sensor glucose level, were significantly associated with composite adverse outcomes and prolonged hospitalization. The multivariate-adjusted odds ratios of the CV of sensor glucose across tertiles for composite adverse outcomes of COVID-19 were 1.00, 1.18, and 25.2, respectively.

CONCLUSIONS

Patients with diabetes and COVID-19 have an increased risk of adverse outcomes with glucose levels >160 mg/dL and <70 mg/dL and a high CV. Therapies that improve these metrics of glycemic control may result in better prognoses for these patients.

The Effect of Acarbose on Glycemic Variability in Patients with Type 2 Diabetes Mellitus Using Premixed Insulin Compared to Metformin (AIM): An Open-Label Randomized Trial

Background: Acarbose (ACA) can effectively reduce the postprandial blood glucose and has similar antidiabetic effects as metformin (MET). To our knowledge, few studies have compared the effect of ACA or MET on glucose fluctuations. In the present study, we explored the effect of ACA or MET combined with premixed insulin (INS) on glycemic control and glycemic variability (GV). Methods: This was an open-label randomized trial that was conducted in type 2 diabetic patients taking premixed insulin. The patients were assigned to 12 weeks of MET (n = 62) or ACA (n = 62) treatment combined with INS. The main outcomes were changes in GV and glycosylated hemoglobin A1c (HbA1c) compared with baseline. Results: Compared with baseline, several GV indices (standard deviation [SD], mean amplitude of glycemic excursions [MAGE]) and blood glucose control indices (mean glucose [MG], time in range [TIR] and HbA1c) were both significantly improved in INS+ACA and INS+MET after 12-week therapy. However, coefficient of variation (CV) was significantly reduced in INS+ACA but not in INS+MET. Moreover, compared with INS+MET, INS+ACA led to a more pronounced percentage change from baseline in CV (26.3% [1.7%-44.6%] vs. 11.9% [-7.0% to 29.9%], P = 0.022), MAGE (40.5% [20.1%-60.5%] vs. 25.2% [-2.1% to 43.4%], P = 0.007) and SD (38.6% [25.2%-57.9%] vs. 30.1% [10.8%-46.5%], P = 0.041). Conclusion: Both MET and ACE combined with INS effectively reduced blood glucose. Compared with MET, ACA combined with INS reduced GV.