A comprehensive analysis of diabetic patient data before and during the COVID-19 pandemic - Lessons from the MÉRY diabetes database (MDD)

AIMS

Our study examined changes in average blood glucose levels (ABG), measurement frequency (MF), and data uploading (DU) before and during the COVID-19 pandemic in 882-day spans, which were divided into further 20-week intervals to highlight the pandemic's impact.

METHODS

T-Tests assessed the statistical significance of blood glucose data from 26,655/20,936 patients and 19.5/16.6 million records during pre-COVID/COVID.

RESULTS

During COVID, patients had significantly lower ABG levels (9.1/8.9 mmol/L, p < 0.001). Weekly DU decreased (155,945/128,445, p < 0.05), while daily MF increased (0.83/0.87, p < 0.001). Comparing the last 20 weeks pre-COVID to the first 20 weeks during COVID, ABG levels were lower (9.0 /8.9, p < 0.01), MF increased (0.83 /0.99, p < 0.001), and DU decreased (153,133/145,381, p < 0.05). In the initial 20 weeks of COVID compared to the second 20 weeks of COVID, ABG increased (8.9/9.1, p < 0.01), MF decreased (0.99/0.95, p < 0.001), and DU decreased (145,381/140,166, p < 0.05). Our most striking observation was the temporary dramatic fall in glucose uploads during the first few weeks of COVID. The changes of ABG and MF values were statistically significant, but were not deemed clinically relevant.

CONCLUSIONS

Despite COVID's prolonged impact, diabetic patients showed improved attitudes. A significant drop in data uploads occurred during the first 20 weeks of COVID; home office and lockdowns apparently disrupted patient routines.

Self-care practice and associated factors among patients with diabetes on follow-up at Yirgalem General Hospital, Sidama, Ethiopia: a cross-sectional study

BACKGROUND

Self-care practice is an integral and efficient part of comprehensive diabetes management, which could be influenced by various socio-demographic, clinical, and lifestyle factors.

OBJECTIVE

The study aimed to assess the level of diabetes self-care practice and its associated factors among patients with diabetes on follow-up at Yirgalem General Hospital, Yirgalem, Sidama, Ethiopia.

METHODOLOGY

An Institution-based cross-sectional study was conducted from February 15 to May 10, 2022, involving 298 patients with diabetes on follow-up at Yirgalem General Hospital. A pre-tested interviewer-administered questionnaire was utilized to collect data from patients. A descriptive analysis was conducted to determine the level of good self-care practice. Bivariate and multivariable binary logistics regression were performed to determine factors associated with good diabetic self-care practice. Associations with a p-value < 0.05 were considered statistically significant.

RESULT

The overall good diabetic self-care practice among patients was 59.4%. Regarding the specific domains of care, 15 (5%) participants had good self-glucose monitoring care, 228 (76.5%) had good exercise self-care, 268 (89.9%) had good dietary self-care, 228 (76.5%) had good foot self-care, and 260 (87.2%) had good diabetic medication adherence. Single marital status (AOR = 5.7, 95% CI: (1.418, 22.915), urban residence (AOR = 2.992, 95% CI: (1.251, 7.153)), and having a glucometer (AOR = 2.273, 95% CI: (1.083, 4.772)) were factors that were significantly associated with good diabetic self-care practice.

CONCLUSION

Good diabetic self-care practices among participants was low. Marital status, place of residence, and having a glucometer were statistically significant predictors of good diabetic self-care practices. Targeted intervention addressing those patients from rural areas to increase awareness and practice of self-care, as well as the promotion of having a glucometer at home for self-glucose monitoring is recommended.

Emergent inequity of glycaemic metrics for Māori children with type 1 diabetes is negated by early use of continuous glucose monitoring

AIM

We investigated if continuous glucose monitoring (CGM) in children with type 1 diabetes (T1D) within 12 months of being diagnosed modifies the development of glycaemic outcome inequity on the basis of either ethnicity or socio-economic status (SES).

METHOD

De-identified clinical and SES data from the KIWIDIAB data network were collected 12 months after diagnosis in children under 15 years diagnosed with T1D between 1 October 2020 and 1 October 2021.

RESULTS

There were 206 children with new onset T1D: CGM use was 56.7% for Māori and 77.2% for Europeans. Mean (SD) HbA1c was 62.4 (14.2) mmol/mol at 12 months post diagnosis, but Māori were 9.4mmol/mol higher compared to Europeans (p<0.001). For those without CGM, Māori had an HbA1c 10.8 (95% CI 2.3 to 19.4, p=0.013) mmol/mol higher than Europeans, whereas there was no evidence of a difference between Māori and Europeans using CGM (62.1 [9.3] mmol/mol vs 58.5 [12.4] mmol/mol p=0.53 respectively). Comparing quintiles of SES, HbA1c was 10.8 (95% CI 4.7 to 16.9, p<0.001) mmol/mol higher in the lowest quintile of SES compared to the highest.

CONCLUSION

These observational data suggest CGM use ameliorates the ethnic disparity in HbA1c at 12 months in new onset T1D.

Real-World Continuous Glucose Monitoring Data from a Population with Type 1 Diabetes in South Korea: Nationwide Single-System Analysis

Background: We used continuous glucose monitoring (CGM) data to investigate glycemic outcomes in a real-world population with type 1 diabetes (T1D) from South Korea, where the widespread use of CGM and the nationwide education program began almost simultaneously. Methods: Data from Dexcom G6 users with T1D in South Korea were collected between January 2019 and January 2023. Users were included if they provided at least 90 days of glucose data and used CGM at least 70% of the days in the investigational period. The relationship between CGM utilization and glycemic metrics, including the percentage of time in range (TIR), time below range (TBR), and time above range (TAR), was assessed. The study was approved by the Institutional Review Board of Samsung Medical Center (SMC 2023-05-030). Results: A total of 2288 users were included. Mean age was 41.5 years (57% female), with average uploads of 428 days. Mean TIR was 62.4% ± 18.5%, mean TBR <70 mg/dL was 2.6% ± 2.8%, mean TAR >180 mg/dL was 35.0% ± 19.3%, mean glucose was 168.1 ± 35.8 mg/dL, mean glucose management indicator was 7.2% ± 0.9%, and mean coefficient of variation was 36.7% ± 6.0%. Users with higher CGM utilization had higher TIR (67.8% vs. 52.7%), and lower TBR <70 mg/dL (2.3% vs. 4.7%) and TAR >180 mg/dL (30.0% vs. 42.6%) than those with low CGM utilization (P < 0.001 for all). Users whose data were shared with others had higher TIR than those who did not (63.3% vs. 60.8%, P = 0.001). Conclusions: In this South Korean population, higher CGM utilization was associated with a favorably higher mean TIR, which was close to the internationally recommended target. Using its remote data-sharing feature showed beneficial impact on TIR.

Continuous Glucose Monitoring Systems in Noninsulin-Treated People with Type 2 Diabetes: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Introduction: Continuous glucose monitoring (CGM) has shown favorable outcomes in patients with type 2 diabetes (T2D) who are on insulin therapy. However, the efficacy of CGM in managing glucose levels in noninsulin-treated people with T2D remains controversial. Methods: PubMed, Cochrane, and Embase were searched for randomized controlled trials (RCTs) comparing CGM to self-monitoring of blood glucose (SMBG) in people with T2D not using insulin. We computed weighted mean differences (WMDs) and standard mean differences (SMD) for continuous outcomes, with 95% confidence intervals (CIs). Heterogeneity was assessed using I2 statistics. Statistical analyses were performed using R version 4.2.3. Results: We included six RCTs comprising 407 noninsulin-treated people with T2D of whom 228 were randomized to CGM. Diabetes duration ranged from 5.4 to 13.9 years. The mean age was 57.9 years and the mean body mass index was 30.8 kg/m2. Four trials used real-time CGM (rt-CGM) and two intermittent scanning CGM (is-CGM). Compared with SMBG, CGM significantly reduced the glycated hemoglobin level (WMD -0.31%; 95% CI -0.42 to -0.21; I2 = 0%), glucose level (WMD -11.16 mg/dL; 95% CI -19.94 to -2.39; I2 = 0%), time in hypoglycemia level 2 (WMD -0.28%; 95% CI -0.52 to -0.03; I2 = 91%), glucose time >180 mg/dL (WMD -7.75%; 95% CI -12.04 to -3.45; I2 = 0%), and the standard deviation of glucose variation (WMD -4.00 mg/dL; 95% CI -6.86 to -1.14; I2 = 0%). CGM also increased time in range (WMD 8.63%; 95% CI 4.54-12.71; I2 = 0%) and treatment satisfaction (SMD 0.79; 95% CI 0.54-1.05; I2 = 0%). Conclusion: In this meta-analysis, rt-CGM and is-CGM were associated with improvement in glycemic control in people with T2D not using insulin when compared to SMBG.

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

PURPOSE

To provide a systematic review and meta-analysis synthesizing the findings of randomized controlled trials (RCTs) of continuous glucose monitors (CGMs) in the management of adults with type 2 diabetes mellitus (T2DM) on glucose control and clinical outcomes.

METHODS

MEDLINE, Embase, and Cochrane were searched for RCTs that assessed the effectiveness of real-time CGM (rt-CGM) or flash CGM (FGM) in adults (≥18 years) with T2DM that reported on at least 1 of the following outcomes: hemoglobin A1c (HbA1c), time in range, time in hyperglycemia, or time in hypoglycemia. The GRADE approach was used to assess certainty of evidence for primary outcomes.

RESULTS

Fourteen RCTs assessing CGM were included, with 825 patients in 9 RCTs using rt-CGM and 822 in 5 RCTs using FGM. Moderate certainty of evidence indicated that use of CGM had a modest but statistically significant reduction in HbA1c levels of about 0.32%. Our analyses of each device type separately showed similar reductions in HbA1c (0.34% and 0.33%, respectively, for rt-CGM and FGM), with trends for improvement in other glucose metrics favoring rt-CGM over self-monitored blood glucose.

CONCLUSION

Both rt-CGM and flash CGM led to modest but statistically significant declines in HbA1c among individuals with T2DM, with little heterogeneity in the results. However, the duration of the included RCTs was relatively short and few studies reported on important clinical outcomes, such as adverse events, emergency department use, or hospitalization. Longer term studies are needed to determine if the short-term improvements in glucose control leads to improvements in clinically important outcomes.

Role of Caregivers in Remote Management of Patients With Type 2 Diabetes Mellitus: Systematic Review of Literature

BACKGROUND

With the growing use of remote monitoring technologies in the management of patients with type 2 diabetes mellitus (T2DM), caregivers are becoming important resources that can be tapped into to improve patient care.

OBJECTIVE

This review aims to summarize the role of caregivers in the remote monitoring of patients with T2DM.

METHODS

We performed a systematic review in MEDLINE, Embase, Scopus, PsycINFO, and Web of Science up to 2022. Studies that evaluated the role of caregivers in remote management of adult patients with T2DM were included. Outcomes such as diabetes control, adherence to medication, quality of life, frequency of home glucose monitoring, and health care use were evaluated.

RESULTS

Of the 1198 identified citations, 11 articles were included. The majority of studies were conducted in North America (7/11, 64%) and South America (2/11, 18%). The main types of caregivers studied were family or friends (10/11, 91%), while the most common remote monitoring modalities evaluated were interactive voice response (5/11, 45%) and phone consultations (4/11, 36%). With regard to diabetes control, 3 of 6 studies showed improvement in diabetes-related laboratory parameters. A total of 2 studies showed improvements in patients' medication adherence rates and frequency of home glucose monitoring. Studies that evaluated patients' quality of life showed mixed evidence. In 1 study, increased hospitalization rates were noted in the intervention group.

CONCLUSIONS

Caregivers may play a role in improving clinical outcomes among patients with T2DM under remote monitoring. Studies on mobile health technologies are lacking to understand their impact on Asian populations and long-term patient outcomes.

The utility of continuous glucose monitoring systems in the management of children with persistent hypoglycaemia

OBJECTIVES

Glucose monitoring is vital in children with persistent hypoglycaemia to reduce the risk of adverse neuro-behavioural outcomes; especially in children with hyperinsulinism. The role of continuous glucose monitoring (CGM) systems in monitoring glucose levels in this cohort is limited. The objective of this study was to ascertain the effectiveness of CGM and to evaluate parents' experience of using CGM for monitoring glucose levels in children with hypoglycaemia.

METHODS

Retrospective analysis of sensor glucose (SG) values from Dexcom G4 CGM with paired finger-prick blood glucose (BG) values was performed to determine the accuracy of CGM. The parent experience of CGM was assessed using a questionnaire administered to families of children with congenital hyperinsulinism currently attending the clinic.

RESULTS

SG data from 40 children (median age 6 months) with persistent hypoglycaemia (60% Hyperinsulinism) were analysed. The mean difference between 5,650 paired BG and SG values was 0.28 mmol/L. The sensitivity and specificity of CGM to identify severe hypoglycaemia (BG < 3.0 mmol/L) were 54.3% (95% CI: 39.0%, 69.1%) and 97.4% (95% CI: 96.9%, 97.8%) respectively. Parents (n=11) reported less anxiety (n=9), better sleep at night (n=7) and preferred to use CGM for monitoring (n=9).

CONCLUSIONS

Although the high number of false-positive readings precludes the routine use of CGM in the evaluation of hypoglycaemia, it avoids unnecessary BG testing during normoglycaemia. It is an acceptable tool for parents for monitoring their children who are at risk of hypoglycaemia. Newer CGM systems with improved accuracy at lower glucose levels have the potential to further improve monitoring.

Telemedicine and urban diabetes during COVID-19 pandemic in Milano, Italy during lock-down: epidemiological and sociodemographic picture

BACKGROUND

Since 2010, more than half of World population lives in Urban Environments. Urban Diabetes has arisen as a novel nosological entity in Medicine. Urbanization leads to the accrual of a number of factors increasing the vulnerability to diabetes mellitus and related diseases. Herein we report clinical-epidemiological data of the Milano Metropolitan Area in the contest of the Cities Changing Diabetes Program. Since the epidemiological picture was taken in January 2020, on the edge of COVID-19 outbreak in the Milano Metropolitan Area, a perspective addressing potential interactions between diabetes and obesity prevalence and COVID-19 outbreak, morbidity and mortality will be presented. To counteract lock-down isolation and, in general, social distancing a pilot study was conducted to assess the feasibility and efficacy of tele-monitoring via Flash Glucose control in a cohort of diabetic patients in ASST North Milano.

METHODS

Data presented derive from 1. ISTAT (National Institute of Statistics of Italy), 2. Milano ATS web site (Health Agency of Metropolitan Milano Area), which entails five ASST (Health Agencies in the Territories). A pilot study was conducted in 65 screened diabetic patients (only 40 were enrolled in the study of those 36 were affected by type 2 diabetes and 4 were affected by type 1 diabetes) of ASST North Milano utilizing Flash Glucose Monitoring for 3 months (mean age 65 years, HbA1c 7,9%. Patients were subdivided in 3 groups using glycemic Variability Coefficient (VC): a. High risk, VC > 36, n. 8 patients; Intermediate risk 20 < VC < 36, n. 26 patients; Low risk VC < 20, n. 4 patients. The control group was constituted by 26 diabetic patients non utilizing Flash Glucose monitoring.

RESULTS

In a total population of 3.227.264 (23% is over 65 y) there is an overall prevalence of 5.65% with a significant difference between Downtown ASST (5.31%) and peripheral ASST (ASST North Milano, 6.8%). Obesity and overweight account for a prevalence of 7.8% and 27.7%, respectively, in Milano Metropolitan Area. We found a linear relationship (R = 0.36) between prevalence of diabetes and aging index. Similarly, correlations between diabetes prevalence and both older people depending index and structural dependence index (R = 0.75 and R = 0.93, respectively), were found. A positive correlation (R = 0.46) with percent of unoccupied people and diabetes prevalence was also found. A reverse relationship between diabetes prevalence and University level instruction rate was finally identified (R = - 0.82). Our preliminary study demonstrated a reduction of Glycated Hemoglobin (p = 0.047) at 3 months follow-up during the lock-down period, indicating Flash Glucose Monitoring and remote control as a potential methodology for diabetes management during COVID-19 lock-down.

HYPOTHESIS AND DISCUSSION

The increase in diabetes and obesity prevalence in Milano Metropolitan Area, which took place over 30 years, is related to several environmental factors. We hypothesize that some of those factors may have also determined the high incidence and virulence of COVID-19 in the Milano area. Health Agencies of Milano Metropolitan Area are presently taking care of diabetic patients facing the new challenge of maintaining sustainable diabetes care costs in light of an increase in urban population and of the new life-style. The COVID-19 pandemic will modify the management of diabetic and obese patients permanently, via the implementation of approaches that entail telemedicine technology. The pilot study conducted during the lock-down period indicates an improvement of glucose control utilizing a remote glucose control system in the Milano Metropolitan Area, suggesting a wider utilization of similar methodologies during the present "second wave" lock-down.

Association of Real-time Continuous Glucose Monitoring With Glycemic Control and Acute Metabolic Events Among Patients With Insulin-Treated Diabetes

IMPORTANCE

Continuous glucose monitoring (CGM) is recommended for patients with type 1 diabetes; observational evidence for CGM in patients with insulin-treated type 2 diabetes is lacking.

OBJECTIVE

To estimate clinical outcomes of real-time CGM initiation.

DESIGN, SETTING, AND PARTICIPANTS

Exploratory retrospective cohort study of changes in outcomes associated with real-time CGM initiation, estimated using a difference-in-differences analysis. A total of 41 753 participants with insulin-treated diabetes (5673 type 1; 36 080 type 2) receiving care from a Northern California integrated health care delivery system (2014-2019), being treated with insulin, self-monitoring their blood glucose levels, and having no prior CGM use were included.

EXPOSURES

Initiation vs noninitiation of real-time CGM (reference group).

MAIN OUTCOMES AND MEASURES

Ten end points measured during the 12 months before and 12 months after baseline: hemoglobin A1c (HbA1c); hypoglycemia (emergency department or hospital utilization); hyperglycemia (emergency department or hospital utilization); HbA1c levels lower than 7%, lower than 8%, and higher than 9%; 1 emergency department encounter or more for any reason; 1 hospitalization or more for any reason; and number of outpatient visits and telephone visits.

RESULTS

The real-time CGM initiators included 3806 patients (mean age, 42.4 years [SD, 19.9 years]; 51% female; 91% type 1, 9% type 2); the noninitiators included 37 947 patients (mean age, 63.4 years [SD, 13.4 years]; 49% female; 6% type 1, 94% type 2). The prebaseline mean HbA1c was lower among real-time CGM initiators than among noninitiators, but real-time CGM initiators had higher prebaseline rates of hypoglycemia and hyperglycemia. Mean HbA1c declined among real-time CGM initiators from 8.17% to 7.76% and from 8.28% to 8.19% among noninitiators (adjusted difference-in-differences estimate, -0.40%; 95% CI, -0.48% to -0.32%; P < .001). Hypoglycemia rates declined among real-time CGM initiators from 5.1% to 3.0% and increased among noninitiators from 1.9% to 2.3% (difference-in-differences estimate, -2.7%; 95% CI, -4.4% to -1.1%; P = .001). There were also statistically significant differences in the adjusted net changes in the proportion of patients with HbA1c lower than 7% (adjusted difference-in-differences estimate, 9.6%; 95% CI, 7.1% to 12.2%; P < .001), lower than 8% (adjusted difference-in-differences estimate, 13.1%; 95% CI, 10.2% to 16.1%; P < .001), and higher than 9% (adjusted difference-in-differences estimate, -7.1%; 95% CI, -9.5% to -4.6%; P < .001) and in the number of outpatient visits (adjusted difference-in-differences estimate, -0.4; 95% CI, -0.6 to -0.2; P < .001) and telephone visits (adjusted difference-in-differences estimate, 1.1; 95% CI, 0.8 to 1.4; P < .001). Initiation of real-time CGM was not associated with statistically significant changes in rates of hyperglycemia, emergency department visits for any reason, or hospitalizations for any reason.

CONCLUSIONS AND RELEVANCE

In this retrospective cohort study, insulin-treated patients with diabetes selected by physicians for real-time continuous glucose monitoring compared with noninitiators had significant improvements in hemoglobin A1c and reductions in emergency department visits and hospitalizations for hypoglycemia, but no significant change in emergency department visits or hospitalizations for hyperglycemia or for any reason. Because of the observational study design, findings may have been susceptible to selection bias.

Association of time in range with hemoglobin A1c, glycated albumin and 1,5-anhydro-d-glucitol

AIMS/INTRODUCTION

Hemoglobin A1c (HbA1c), glycated albumin (GA) and 1,5-anhydro-d-glucitol (1,5-AG) are used as indicators of glycemic control, whereas continuous glucose monitoring (CGM) is used to assess daily glucose profiles. The aim of this study was to investigate the relationships between CGM metrics, such as time in range (TIR), and glycemic control indicators.

MATERIALS AND METHODS

We carried out retrospective CGM and blood tests on 189 outpatients with impaired glucose tolerance (n = 22), type 1 diabetes mellitus (n = 67) or type 2 diabetes mellitus (n = 100).

RESULTS

In type 1 diabetes mellitus and type 2 diabetes mellitus patients, HbA1c and GA were negatively correlated with TIR, whereas 1,5-AG was positively correlated with TIR. In type 1 diabetes mellitus patients, a TIR of 70% corresponded to HbA1c, GA and 1,5-AG of 6.9% (95% confidence interval [CI] 6.5-7.2%), 20.3% (95% CI 19.0-21.7%) and 6.0 µg/mL (95% CI 5.1-6.9 µg/mL), respectively. In type 2 diabetes mellitus patients, a TIR of 70% corresponded to HbA1c, GA and 1,5-AG of 7.1% (95% CI 7.0-7.3%), 19.3% (95% CI 18.7-19.9%) and 10.0 µg/mL (95% CI 9.0-11.0 µg/mL), respectively. TIR values corresponding to HbA1c levels of 7.0% were 56.1% (95% CI 52.3-59.8%) and 74.2% (95% CI 71.3-77.2%) in type 1 diabetes mellitus and type 2 diabetes mellitus patients, respectively.

CONCLUSIONS

The results of this study showed that the estimated HbA1c corresponding to a TIR of 70% was approximately 7.0% for both type 1 diabetes mellitus and type 2 diabetes mellitus patients, and that the estimated 1,5-AG calculated from the TIR of 70% might be different between type 1 diabetes mellitus and type 2 diabetes mellitus patients.

Defining the target value of the coefficient of variation by continuous glucose monitoring in Chinese people with diabetes

AIMS/INTRODUCTION

To define the target value for the percentage coefficient of variation for glucose (%CV) as a measure of glycemic variability (GV) in Chinese diabetes patients.

MATERIALS AND METHODS

This retrospective study included 3,007 diabetes patients who underwent continuous glucose monitoring for 3 days. Type 2 diabetes was divided into groups according to the received treatment: group 1, non-insulinotropic agent (n = 138); group 2, insulinotropic agent (n = 761); group 3, basal insulin therapy (n = 100); group 4, premixed insulin (n = 784); and group 5, intensive insulin therapy (n = 612). Type 1 diabetes patients were included as group 6 (n = 612). %CV and percentage of time per day within, below (3.9mmol/L; TBR3.9 ) and above (10.0 mmol/L) the target glucose range (3.9-10.0 mmol/L) were computed. TBR3.9 ≥4% was defined as excessive hypoglycemia.

RESULTS

Type 2 diabetes with a premixed or intensive insulin regimen had an increased %CV compared with those receiving oral therapy or basal insulin. The upper limit of %CV in group 1 was 33%, which was adopted as the threshold to define excessive GV. For each treatment group, the percentage of people with TBR3.9 ≥4% was significantly greater in the subgroup with %CV >33% than ≤33% (P < 0.001). In participants who achieved TBR3.9 <4%, the time per day spent within the target glucose range of 3.9-10.0 mmol/L > 70% and time per day above 10.0 mmol/L <25%, the 95th percentile of %CV was 32.70%. Further receiver operating characteristic curve analysis showed that the cut-off values of %CV for predicting TBR3.9 ≥4% varied by the type of diabetes and glycated hemoglobin categories.

CONCLUSIONS

A %CV of 33% was set as the threshold for excess glucose variability in Chinese diabetes patients. Meanwhile, glycated hemoglobin and the type of diabetes should be considered for the goal-setting of %CV.

Effects of real-time continuous glucose monitoring in type 1 diabetes: a meta-analysis of randomized controlled trials

AIMS

Self-monitoring of blood glucose (SMBG) represented a major breakthrough in the treatment of type 1 diabetes. The aim of the present meta-analysis is to assess the effect of continues glucose monitoring (CGM) and flash glucose monitoring (FGM), on glycemic control in type 1 diabetes.

MATERIALS AND METHODS

The present analysis includes randomized clinical trials comparing CGM or FGM with SMBG, with a duration of at least 12 weeks, identified in Medline or clinicaltrials.gov. The principal endpoint was HbA1c at the end of the trial. A secondary endpoint was severe hypoglycemia. Mean and 95% confidence intervals for HbA1c and Mantel-Haenzel odds ratio [MH-OR] for severe hypoglycemia were calculated, using random effect models. A sensitivity analysis was performed using fixed effect models. In addition, the following secondary endpoints were explored, using the same methods: time in range, health-related quality of life, and treatment satisfaction. Separate analyses were performed for trials comparing CGM with SMBG, and those comparing CGM + CSII and SMBG + MDI and CGM-regulated insulin infusion system (CRIS) and CSII + SMBG.

RESULTS

CGM was associated with a significantly lower HbA1c at endpoint in comparison with SMBG (- 0.24 [- 0.34, - 0.13]%); CGM was associated with a significantly lower risk of severe hypoglycemia than SMBG. Treatment satisfaction and quality of life were not measured, or not reported, in the majority of studies. FGM showed a significant reduction in the incidence of mild hypoglycemia and an increased treatment satisfaction, but no significant results are shown in HbA1c. CGM + CSII in comparison with SMBG + MDI was associated with a significant reduction in HbA1c. Only two trials with a duration of at least 12 weeks compared a CRIS with SMBG + CSII; HbA1c between the two treatment arms was not statistically significant (difference in means: - 0.23 [- 0.91; 0.46]%; p = 0.52).

CONCLUSION

GCM compared to SMBG has showed a reduction in HbA1c and severe hypoglycemia in patient with type 1 diabetes. The comparison between CGM + CSII and SMBG + MDI showed a large reduction in HbA1c; it is conceivable that the effects of CSII + CGM on glycemic control additives. The only comparison available between FGM and SMBG was conducted in patients in good control.

Glycemic deviation index: a novel method of integrating glycemic numerical value and variability

BACKGROUND

There are many continuous blood glucose monitoring (CGM) data-based indicators, and most of these focus on a single characteristic of abnormal blood glucose. An ideal index that integrates and evaluates multiple characteristics of blood glucose has not yet been established.

METHODS

In this study, we proposed the glycemic deviation index (GDI) as a novel integrating characteristic, which mainly incorporates the assessment of the glycemic numerical value and variability. To verify its effectiveness, GDI was applied to the simulated 24 h glycemic profiles and the CGM data of type 2 diabetes (T2D) patients (n = 30).

RESULTS

Evaluation of the GDI of the 24 h simulated glycemic profiles showed that the occurrence of hypoglycemia was numerically the same as hyperglycemia in increasing GDI. Meanwhile, glycemic variability was added as an independent factor. One-way ANOVA results showed that the application of GDI showed statistically significant differences in clinical glycemic parameters, average glycemic parameters, and glycemic variability parameters among the T2D groups with different glycemic levels.

CONCLUSIONS

In conclusion, GDI integrates the characteristics of the numerical value and the variability in blood glucose levels and may be beneficial for the glycemic management of diabetic patients undergoing CGM treatment.

Impact of COVID-19 on Health Economics and Technology of Diabetes Care: Use Cases of Real-Time Continuous Glucose Monitoring to Transform Health Care During a Global Pandemic

Background: The coronavirus disease 2019 (COVID-19) pandemic has exposed vulnerabilities and placed tremendous financial pressure on nearly all aspects of the U.S. health care system. Diabetes care is an example of the confluence of the pandemic and heightened importance of technology in changing care delivery. It has been estimated the added total direct U.S. medical cost burden due to COVID-19 to range between $160B (20% of the population infected) and $650B (80% of the population infected) over the course of the pandemic. The corresponding range for the population with diabetes is between $16B and $65B, representing between 5% and 20% of overall diabetes expenditure in the United States. We examine the evidence to support allocating part of this added spend to infrastructure capabilities to accelerate remote monitoring and management of diabetes. Methods and Results: We reviewed recent topical literature and COVID-19-related analyses in the public health, health technology, and health economics fields in addition to databases and surveys from government sources and the private sector. We summarized findings on use cases for real-time continuous glucose monitoring in the community, for telehealth, and in the hospital setting to highlight the successes and challenges of accelerating the adoption of a digital technology out of necessity during the pandemic and beyond. Conclusions: One critical and lasting consequence of the pandemic will be the accelerated adoption of digital technology in health care delivery. We conclude by discussing ways in which the changes wrought by COVID-19 from a health care, policy, and economics perspective can add value and are likely to endure postpandemic.

Real-Time Continuous Glucose Monitoring During the Coronavirus Disease 2019 Pandemic and Its Impact on Time in Range

Background: The coronavirus disease 2019 (COVID-19) pandemic disrupted the lives of people with diabetes. Use of real-time continuous glucose monitoring (rtCGM) helped manage diabetes effectively. Some of these disruptions may be reflected in population-scale changes to metrics of glycemic control, such as time-in-range (TIR). Methods: We examined data from 65,067 U.S.-based users of the G6 rtCGM System (Dexcom, Inc., San Diego, CA) who had uploaded data before and during the COVID-19 pandemic. Users associated with three counties that included the cities of Los Angeles, Chicago, and New York or with five regions designated by the Centers for Disease Control and Prevention (CDC) were compared. Public data were used to associate regions with prepandemic and intrapandemic glycemic parameters, COVID-19 mortality, and median household income. Results: Compared with an 8-week prepandemic interval before stay-at-home orders (January 6, 2020, to March 1, 2020), overall mean (standard deviation) TIR improved from 59.0 (20.1)% to 61.0 (20.4)% during the early pandemic period (April 20, 2020 to June 14, 2020, P < 0.001). TIR improvements were noted in all three counties and in all five CDC-designated regions. Higher COVID-19 mortality was associated with higher proportions of individuals experiencing TIR improvements of ≥5 percentage points. Users in economically wealthier zip codes had higher pre- and intrapandemic TIR values and greater relative improvements in TIR. TIR and pandemic-related improvements in TIR varied across CDC-designated regions. Conclusions: Population-level rtCGM data may be used to monitor changes in glycemic control with temporal and geographic specificity. The COVID-19 pandemic is associated with improvements in TIR, which were not evenly distributed across the United States.

Magnitude and predictors of poor glycemic control among patients with diabetes attending public hospitals of Western Ethiopia

BACKGROUND

Diabetes is one of the most prevalent non-communicable diseases globally, which rapidly is increasing in developing countries. Ethiopia is also facing growing morbidity and mortality related to diabetes complications. Thus, dealing with glycemic control is essential for controlling the development of devastating acute and chronic complications related to diabetes. Therefore, this study aims to assess the magnitude and predictors of poor glycemic control among diabetic patients in western Ethiopia.

METHODS

The cross-sectional study design was employed on a sample of 423 diabetic patients. A systematic random sampling method was employed. An interviewer-administered structured questionnaire was used. The data entered into Epi data version 3.1 and exported into Statistical Package for the Social Sciences window version 24 for analysis. All variables significant at p-<0.25 in bivariate were entered into multivariate analysis. The multivariable logistic regressions were used to determine predictors' poor glycemic control by considering the Adjusted Odds Ratio at CI 95% and the significance level was set at p <0.05.

RESULTS

The magnitude of poor glycemic control was 64.1%. Being females (AOR = 1.684,95%CI = 1.066,2.662), duration of diabetes >8years (AOR = 2.552,95%CI = 1.397, 4.665), presence of diabetes complication (AOR = 2.806,95%CI = 1.594,4.941), negligence of blood glucose test at home (AOR = 1.720, 95%CI = 1.078, 2.743), poor self-care behavior (AOR = 1.787, 95%CI = 1.083,2.959) and poor self-efficacy (AOR = 1.934, 95%CI = 1.078,3.469) were significant predictors of poor glycemic control.

CONCLUSION

The proportion of poor glycemic control was high which was nearly comparable to that reported from many countries. This could be due to factors that were significantly associated with poor glycemic control like lack of home blood glucose test, increased duration of diabetes, presence of diabetes complications, poor self-efficacy, and poor self-care behaviors. Each were significant independent predictors of poor glycemic control. Thus, we recommend patients with diabetes and health care providers enhancing self-monitoring practices, and preventing potential complications should be a priority concern to improve blood glucose levels. Further studies are also recommended to explore important factors which were not identified by the current study.

Type 1 diabetes outcomes of children born in Israel of Eritrean asylum seekers

AIMS

Disparities in health outcomes in pediatric type 1 diabetes (T1D) based on race/ethnicity and socioeconomic position (SEP) have been reported. We compared T1D characteristics between Eritrean status-less children living in Israel and native-born Israeli children.

METHODS

This observational study compared 7 Eritrean and 28 Israeli children (< 8 years old at T1D diagnosis) who were diagnosed in a single diabetes center during 2015-2019. Sociodemographic and diabetes-related data from diagnosis until the last clinic visit were retrieved from their medical files.

RESULTS

At diagnosis, the mean age was 4.8 ± 2.2 years, 17 (48.6%) had diabetic ketoacidosis with a mean HbA1c level of 10.5 ± 2.1% (91.3 mmol/mol) and 29 (82.9%) had ≥ 2 pancreatic autoantibodies. The mean T1D duration of follow-up was 2.7 ± 1.4 years. Overall glycemic control during follow-up (> 6 months from diagnosis, mean number of samples 10.6 ± 5.2) was good, with mean, best, and peak HbA1c levels of 7.4 ± 0.8% (57.4 mmol/mol), 6.7 ± 0.7% (49.7 mmol/mol), and 8.1 ± 1.1% (65 mmol/mol), respectively. Thirty-two children (91.4%) used continuous glucose monitoring devices (CGMs), and the mean time from diagnosis to CGM initiation was 10.8 ± 14.1 months. CGM metrics: time CGM active: 95.4 ± 3.8%, mean glucose level: 170.0 ± 27.0 mg/dl (9.4 mmol/L), time-in-range: 56.4 ± 14.7%, time-below-range: 5.5 ± 5.7%, and time-above-range: 38.6 ± 16.1%. Diabetes-related parameters at diagnosis and during follow-up were similar between groups. Eritrean children had significantly lower SEPs (P < 0.001) and parental education levels (P < 0.001). Correlations between SEP and diabetes parameters and SEP and growth parameters were not significant.

CONCLUSIONS

Eritrean status-less children in Israel achieved glycemic targets similar to those of Israeli children, perhaps reflecting uniformity in the standard of care and CGM usage.

Bacterial strains colonizing the sensor electrodes of a continuous glucose monitoring system in children with diabetes

INTRODUCTION

The higher frequency of infections in diabetic patients is caused by a hyperglycemic environment, which promotes immune dysfunction. People with diabetes are more prone to skin infections. A continuous glucose monitoring (CGM) system provides information on changes in blood glucose (BG) levels throughout the day. Its use facilitates optimal therapeutic decisions for a diabetic patient. One of the factors limiting the use of CGM is inflammation at the insertion site.

AIM OF THE STUDY

The aim of the study was the microbiological identification of the bacterial strains which are found on CGM sensor electrodes.

MATERIAL AND METHODS

We performed microbiological tests on patients' CGM Enlite Medtronic electrodes, which were removed after 6 days of usage according to the manufacturer's instructions. 31 sensors were examined from 31 children (14 girls) aged from 0.5 to 14.6 years. The microbiological analysis was routinely performed at the Department of Children's Diabetology Medical University of Silesia in Katowice, Poland.

RESULTS

12 (39%) of the electrodes were colonized. In 11 (92%) cases the electrodes were colonized by one bacteria strain. 7 times methicillin-sensitive coagulase negative staphylococcus (MSCNS) was detected. We also found one case of Klebsiella pneumoniae, Ochrobactrum tritici, Bacillus sonorensis and methicillin-resistant coagulase-negative Staphylococci (MRCNS) colonization. One electrode was colonized by the mixed flora Enterococcus faecalis, methicillin-susceptible coagulase-negative Staphylococci (MSCNS), Pseudomonas stutzeri, methicillin-susceptible Staphylococcus aureus (MSSA). The median HbA1c in the group with colonization of electrodes was 6, 85% (6, 3-7, 6%) versus 6, 3% (5, 8-7, 5%) in the group without colonization. The median BMI in the group with colonization of the electrodes was 17.10 kg/m² (16.28-18.62 kg/m²) versus 15.98 kg/m² (15.14-17.96 kg/m²) in the group without colonization. Statistically, significantly more frequently electrodes are colonized in older children (median age in the group with colonization of electrodes 11.43 years (6.52-12.27 years), without colonization 8.42 years. (3.098-9.375 years); (p = 0.033).

CONCLUSIONS

It seems that older children are more likely to have their sensor electrode colonized by bacterial strains.

Glycemic qualification rate and frequency of self-monitoring blood glucose glycemic qualification rate and frequency of self-monitoring blood glucose (SMBG) in women with gestational diabetes mellitus (GDM)

AIMS

To explore the relationship between blood glucose (BG) control rate and self-monitoring blood glucose (SMBG) compliance of women with gestational diabetes mellitus (GDM).

METHODS

Women with GDM (n = 309) were randomized to receive routine clinical prenatal care or additional online management. Follow-up visits were conducted every two weeks (noted here as T) from enrollment to delivery. SMBG records were used for the analysis.

RESULTS

Both the intervention group and the control group had an increasing BG control rate and decreasing SMBG compliance during the whole follow-up period. Detailed data analysis on separate follow-up periods showed that the SMBG frequency was negatively correlated with the BG control rate in most Ts and that the BG control rate of T_n-1 was negatively correlated with the SMBG frequency of T_n in the adjacent T. Only in the intervention group was T₂ SMBG compliance not under the influence of the T₁ BG control rate.

CONCLUSIONS

Our data suggested that regardless of management approach, the BG control rate increased, and the SMBG frequency decreased as gestational weeks increased in women with GDM. Even in separate follow-up periods, the SMBG frequency was negatively correlated with the BG control rate both within one follow-up period and between two adjacent follow-up periods.

Uninterrupted continuous glucose monitoring access is associated with a decrease in HbA1c in youth with type 1 diabetes and public insurance

OBJECTIVE

Continuous glucose monitor (CGM) use is associated with improved glucose control. We describe the effect of continued and interrupted CGM use on hemoglobin A1c (HbA1c) in youth with public insurance.

METHODS

We reviewed 956 visits from 264 youth with type 1 diabetes (T1D) and public insurance. Demographic data, HbA1c and two-week CGM data were collected. Youth were classified as never user, consistent user, insurance discontinuer, and self-discontinuer. Visits were categorized as never-user visit, visit before CGM start, visit after CGM start, visit with continued CGM use, visit with initial loss of CGM, visit with continued loss of CGM, and visit where CGM is regained after loss. Multivariate regression adjusting for age, sex, race, diabetes duration, initial HbA1c, and body mass index were used to calculate adjusted mean and delta HbA1c.

RESULTS

Adjusted mean HbA1c was lowest for the consistent user group (HbA1c 8.6%;[95%CI 7.9,9.3]). Delta HbA1c (calculated from visit before CGM start) was lower for visit after CGM start (-0.39%;[95%CI -0.78,-0.02]) and visit with continued CGM use (-0.29%;[95%CI -0.61,0.02]), whereas it was higher for visit with initial loss of CGM (0.40%;[95%CI -0.06,0.86]), visit with continued loss of CGM (0.46%;[95%CI 0.06,0.85]), and visit where CGM is regained after loss (0.57%;[95%CI 0.06,1.10]).

CONCLUSIONS

Youth with public insurance using CGM have improved HbA1c, but only when CGM use is uninterrupted. Interruptions in use, primarily due to gaps in insurance coverage of CGM, were associated with increased HbA1c. These data support both initial and ongoing coverage of CGM for youth with T1D and public insurance.

Comparison of oral glucose tolerance test and ambulatory glycaemic profiles in pregnant women in Uganda with gestational diabetes using the FreeStyle Libre flash glucose monitoring system

BACKGROUND

The diagnosis of hyperglycaemia in sub-Saharan Africa (SSA) is challenging. Blood glucose levels obtained during oral glucose tolerance test (OGTT) may not reflect home glycaemic profiles. We compare OGTT results with home glycaemic profiles obtained using the FreeStyle Libre continuous glucose monitoring device (FSL-CGM).

METHODS

Twenty-eight women (20 with gestational diabetes [GDM], 8 controls) were recruited following OGTT between 24 and 28 weeks of gestation. All women wore the FSL-CGM device for 48-96 h at home in early third trimester, and recorded a meal diary. OGTT was repeated on the final day of FSL-CGM recording. OGTT results were compared with ambulatory glycaemic variables, and repeat OGTT was undertaken whilst wearing FSL-CGM to determine accuracy of the device.

RESULTS

FSL-CGM results were available for 27/28 women with mean data capture 92.8%. There were significant differences in the ambulatory fasting, post-prandial peaks, and mean glucose between controls in whom both primary and secondary OGTT was normal (n = 6) and those with two abnormal OGTTs or "true" GDM (n = 7). There was no difference in ambulatory mean glucose between these controls and the 13 women who had an abnormal primary OGTT and normal repeat OGTT. These participants had significantly lower body mass index (BMI) than the true GDM group (29.0 Vs 36.3 kg/m2, p-value 0.014). Paired OGTT/FSL-CGM readings revealed a Mean Absolute difference (MAD) -0.58 mmol/L and Mean Absolute Relative Difference (MARD) -11.9%. Bland-Altman plot suggests FSL-CGM underestimated blood glucose by approximately 0.78 mmol/L.

CONCLUSION

Diagnosis of GDM on a single OGTT identifies a proportion of women who do not have a significantly higher home glucose levels than controls. This raises questions about factors which may affect the reproducibility of OGTT in this population, including food insecurity and atypical phenotypes of diabetes. More investigation is needed to understand the suitability of the OGTT as a diagnostic test in sub-Saharan Africa.

Improved glycaemic variability and basal insulin dose reduction during a running competition in recreationally active adults with type 1 diabetes-A single-centre, prospective, controlled observational study

INTRODUCTION

To investigate the glycaemic response, macronutrient intake and insulin management in people with type 1 diabetes (T1D) compared to healthy individuals around a running competition.

MATERIAL AND METHODS

This was a single-centre, prospective, controlled observational study performed in individuals with T1D and healthy people. 24 people (12 T1D) were included in this study (age: T1D 41±12 vs. healthy 38±6 years, females: 3 vs. 6, BMI: 25.53.0 vs. 22.9±2.8 kg/m2). Both groups received an intermittently scanned continuous glucose monitoring (isCGM; FreeStyle Libre 1, Abbott, USA) system to assess glycaemia 24 hours before, during and 24 hours after a running competition. During this period, participants recorded their food intake and insulin administration. Data were analysed via ANOVA and mixed model analyses with post-hoc testing (p≤0.05).

RESULTS

For overall glycaemic ranges in comparison of groups, significant differences were found for time in range (T1D 63±21% vs. healthy 89±13%, p = 0.001), time above range (TAR) 1 (T1D 21±15% vs. healthy 0±0%, p<0.001) and TAR 2 (T1D 8 [0-16%] vs. healthy 0±0%, p<0.001). When glycaemic variability was assessed, people with T1D had a higher glycaemic variability compared to healthy individuals (p<0.0001). Basal insulin dose was significantly reduced when compared against the regular pre-study basal insulin dose (pre-study 22±6 vs. pre-competition day 11±9 (-50±41%), p = 0.02; competition day 15±5 (-32± 1%)).

CONCLUSION

People with T1D have impaired glucose responses around a running competition compared to healthy individuals. However, basal insulin dose reductions were sufficient to prevent further dysglycaemia.

CLINICAL TRIAL ID

drks.de; DRKS00019886.

Effects of nationwide lockdown during COVID-19 epidemic on lifestyle and other medical issues of patients with type 2 diabetes in north India

BACKGROUND AND AIMS

During the current pandemic of COVID-19, India is under lockdown which could cause disruption in diet and lifestyle in patients with type 2 diabetes (T2DM). We aimed to study lifestyle changes and other common issues related to treatment in our previously seen and treated patients with T2DM.

METHODS

Patients (n, 150) who were regularly following up before lockdown were interviewed telephonically (after 45 days of start of lockdown) regarding lifestyle changes, stress and other diabetes-related questions.

RESULT

Carbohydrate consumption and frequency of snacking increased in 21% and 23% patients, respectively. Interestingly, 27% patients reported an increase in consumption of fruits. Exercise duration was reduced in 42% and weight gain occurred in 19% patients. Frequency of doing self-monitoring of blood glucose (SMBG) was decreased in 23% patients. 'Mental stress' of any kind was reported in 87% patients. Availability of medicines and insulin was uninterrupted in 91% patients. Knowledge about telemedicine was present in 69% and majority (92%) of these patients preferred video consultation.

CONCLUSION

During 45 days of lockdown increase in carbohydrate intake, decrease in exercise, decreased SMBG and widespread mental stress in patients with T2DM was recorded, factors which may destabilize or exacerbate hyperglycemia and hypertension. Some positive changes (e.g. increased intake of fruits) were also observed.