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.

Racial-Ethnic Inequity in Young Adults With Type 1 Diabetes

CONTEXT

Minority young adults (YA) currently represent the largest growing population with type 1 diabetes (T1D) and experience very poor outcomes. Modifiable drivers of disparities need to be identified, but are not well-studied.

OBJECTIVE

To describe racial-ethnic disparities among YA with T1D and identify drivers of glycemic disparity other than socioeconomic status (SES).

DESIGN

Cross-sectional multicenter collection of patient and chart-reported variables, including SES, social determinants of health, and diabetes-specific factors, with comparison between non-Hispanic White, non-Hispanic Black, and Hispanic YA and multilevel modeling to identify variables that account for glycemic disparity apart from SES.

SETTING

Six diabetes centers across the United States.

PARTICIPANTS

A total of 300 YA with T1D (18-28 years: 33% non-Hispanic White, 32% non-Hispanic Black, and 34% Hispanic).

MAIN OUTCOME

Racial-ethnic disparity in HbA1c levels.

RESULTS

Non-Hispanic Black and Hispanic YA had lower SES, higher HbA1c levels, and much lower diabetes technology use than non-Hispanic White YA (P < 0.001). Non-Hispanic Black YA differed from Hispanic, reporting higher diabetes distress and lower self-management (P < 0.001). After accounting for SES, differences in HbA1c levels disappeared between non-Hispanic White and Hispanic YA, whereas they remained for non-Hispanic Black YA (+ 2.26% [24 mmol/mol], P < 0.001). Diabetes technology use, diabetes distress, and disease self-management accounted for a significant portion of the remaining non-Hispanic Black-White glycemic disparity.

CONCLUSION

This study demonstrated large racial-ethnic inequity in YA with T1D, especially among non-Hispanic Black participants. Our findings reveal key opportunities for clinicians to potentially mitigate glycemic disparity in minority YA by promoting diabetes technology use, connecting with social programs, and tailoring support for disease self-management and diabetes distress to account for social contextual factors.

Acceptability and Utilization of Newer Technologies and Effects on Glycemic Control in Type 2 Diabetes: Lessons Learned from Lockdown

Aim: To evaluate the effects of a prolonged lockdown due to Coronavirus (COVID-19) on the adoption of newer technologies and changes in glycemic control on patients with type 2 diabetes (T2D) in India. Methods: The study population included a random list of 3000 individuals with T2D derived from 30,748 individuals who had visited a large tertiary diabetes center during the past year. The survey was carried out through a telephonic interview. A structured questionnaire was used to collect information on changes in lifestyle, access and challenges to diabetes care and use of technologies such as telemedicine facilities and use of self-monitoring of blood glucose (SMBG), etc. Results: Of the 2510 individuals successfully interviewed (83.7% response rate), 382 (15.2%) reported having attempted to consult their health care providers during the lockdown, of whom only 30.6% utilized the telemedicine facility. However, 96 (82%) of those who utilized the telemedicine facility (n = 117) were happy with their experience and 68 (58.1%) were willing to continue to use the facility in the future. Only 11.4% of participants utilized online support for management of diabetes. Use of SMBG increased significantly from 15.5% to 51.3% during the lockdown. There was an improvement in glycemic control during the lockdown (HbA1c:before vs. during lockdown: 8.2% ± 1.9% vs. 7.7% ± 1.7%, P < 0.001) in a nonrandomly selected subset of subjects (n = 205). Conclusions: Acceptance of telemedicine facilities remains suboptimal in this Asian Indian population, in spite of high levels of satisfaction among those who utilized it. The COVID-19 pandemic and the subsequent lockdown have not adversely affected metabolic control in our patients, and indeed there appears to be an improvement in HbA1c levels. Greater accessibility and acceptance of technology could help individuals with diabetes to maintain better contact with their physicians and ensure better metabolic control in the future.

Coping with diabetes during the COVID - 19 lockdown in India: Results of an online pilot survey

AIM

The management of diabetes has become a challenge due to the COVID 19 lockdown. An online-based pilot survey was conducted to study how people with diabetes were coping with their Diabetes during the COVID - 19 lockdown.

METHOD

The questions were designed in an online survey, Survey Monkey, to conduct this cross-sectional study. The link was generated and sent to 100 registered patients of the MV Hospital for Diabetes Royapuram who had not contacted the hospital after the lockdown announcement. The survey was done between April 1 and April 15, 2020.Oral consent was obtained through telephone before the link was sent by Whatsapp to them.The questionnaire consisted of questions on home blood glucose monitoring, regularity in doing their physical activity and dietary compliance and anxiety about the viral infection.

RESULTS

92% of the participants had Type 2 diabetes. Only 28% of the participants were checking their blood glucose levels regularly. 80% of the participants mentioned that they were following regular exercise and diet control during the lockdown period. 40% of the participants were anxious about the Covid infection.

CONCLUSION

SMBG needs to be practiced on regular basis, especially among the patients with diabetes on insulin therapy. Most of the people surveyed were coping well with their Diabetes. Patients have reported that they were able to maintain proper dietary compliance and be more physically active at home during this lockdown. These findings need to be ascertained in larger sample of patients.

Simple Linear Support Vector Machine Classifier Can Distinguish Impaired Glucose Tolerance Versus Type 2 Diabetes Using a Reduced Set of CGM-Based Glycemic Variability Indices

BACKGROUND

Many glycemic variability (GV) indices exist in the literature. In previous works, we demonstrated that a set of GV indices, extracted from continuous glucose monitoring (CGM) data, can distinguish between stages of diabetes progression. We showed that 25 indices driving a logistic regression classifier can differentiate between healthy and nonhealthy individuals; whereas 37 GV indices and four individual parameters, feeding a polynomial-kernel support vector machine (SVM), can further distinguish between impaired glucose tolerance (IGT) and type 2 diabetes (T2D). The latter approach has some limitations to interpretability (complex model, extensive index pool). In this article, we try to obtain the same performance with a simpler classifier and a parsimonious subset of indices.

METHODS

We analyzed the data of 62 subjects with IGT or T2D. We selected 17 interpretable GV indices and four parameters (age, sex, BMI, waist circumference). We trained a SVM on the data of a baseline visit and tested it on the follow-up visit, comparing the results with the state-of-art methods.

RESULTS

The linear SVM fed by a reduced subset of 17 GV indices and four basic parameters achieved 82.3% accuracy, only marginally worse than the reference 87.1% (41-features polynomial-kernel SVM). Cross-validation accuracies were comparable (69.6% vs 72.5%).

CONCLUSION

The proposed SVM fed by 17 GV indices and four parameters can differentiate between IGT and T2D. Using a simpler model and a parsimonious set of indices caused only a slight accuracy deterioration, with significant advantages in terms of interpretability.

Self-Monitoring of Blood Glucose Requirements with the Use of Intermittently Scanned Continuous Glucose Monitoring

Current intermittently scanned continuous glucose monitoring (isCGM) devices do not allow calibration and require confirmatory self-monitored blood glucose (SMBG) under specific defined conditions. The overall time spent in an SMBG-indicated state while using isCGM has not been reported in the existing literature. We present the time spent in an SMBG-indicated state and the daily number of transitions to an SMBG-indicated state during isCGM use in a high-risk cohort of adults with type 1 diabetes. This is a post hoc analysis of participants randomized to isCGM as part of a clinical study. The percentage of values meeting one or more of the criteria for SMBG testing (hours per day) and the number of transitions to an SMBG-indicated state per day were calculated according to the manufacturer's label in the United Kingdom. Complete data sets for 20 participants on isCGM for 8 weeks were included in the analysis. Participants spent a mean (SD) of 5.61 (1.81) h/day in an SMBG-indicated state and there were 5.43 (1.42) transitions to an SMBG-indicated state per day while using isCGM. These findings have important implications for cost-effectiveness and for decision-making around insulin dose calculations and driving. Our findings emphasize the need for continued SMBG testing among people at high risk of hypoglycemia who use isCGM. Cost-effectiveness analyses for isCGM may require revision.

Differences for Percentage Times in Glycemic Range Between Continuous Glucose Monitoring and Capillary Blood Glucose Monitoring in Adults with Type 1 Diabetes: Analysis of the REPLACE-BG Dataset

Background: Self-monitored blood glucose (SMBG) and real-time continuous glucose monitoring (rtCGM) are used by people living with type 1 diabetes (T1D) to assess glucose and inform decision-making. Percentage time in range (%TIR) between 3.9 and 10 mmol/L has been associated with incident microvascular complications using historical SMBG data. However, the association between %TIR calculated from rtCGM data has not been identified. This study investigates whether %TIR values generated from rtCGM and SMBG data significantly differ from each other in adults with T1D. Materials and Methods: rtCGM and SMBG data from the REPLACE-BG study were obtained and analyzed. The dataset contained rtCGM (Dexcom G4 Platinum) and SMBG (Contour Next) values for 226 participants during a run-in phase lasting up to 10 weeks, followed by the 26-week trial. Percentages times in hypoglycemic, euglycemia and hyperglycemic ranges were generated from rtCGM and SMBG data using last observation carry forward method (zero-order hold) and linear interpolation (first-order hold). Results: Participants had a median (interquartile range [IQR]) age of 43.0 (31.0-55.0) years, and hemoglobin A1C of 53 (49-57) mmol/mol [7.0 (6.6-7.4)%]. The median (IQR) %TIR was significantly higher with rtCGM than with SMBG; 63.0 (55.9-71.0)% versus 54.6 (45.6-63.0)%, respectively, P < 0.001. Median %times in hypoglycemia and hyperglycemia were significantly different with SMBG than rtCGM (P < 0.001). SMBG-derived data using linear interpolation significantly differed from the carry forward method (P < 0.001 for all glycemic ranges). Differences reported were greater at night than during the day (P < 0.001 for all glycemic ranges). Conclusion: The %time in all glycemic ranges reported by SMBG and rtCGM differ significantly, suggesting relationships between times in ranges, and complication status may be different between monitoring modalities. In addition, varying methods of calculating %TIR from SMBG-derived data provide significantly differing results. %TIR targets may therefore vary by monitoring choice and methods of calculation and harmonization of TIR standards may be challenging.

Diabetes Healthcare Professionals Use Multiple Continuous Glucose Monitoring Data Indicators to Assess Glucose Management

BACKGROUND

Continuous glucose monitoring (CGM) offers multiple data features that can be leveraged to assess glucose management. However, how diabetes healthcare professionals (HCPs) actually assess CGM data and the extent to which they agree in assessing glycemic management are not well understood.

METHODS

We asked HCPs to assess ten de-identified CGM datasets (each spanning seven days) and rank order each day by relative glycemic management (from "best" to "worst"). We also asked HCPs to endorse features of CGM data that were important in making such assessments.

RESULTS

In the study, 57 HCPs (29 endocrinologists; 28 diabetes educators) participated. Hypoglycemia and glycemic variance were endorsed by nearly all HCPs to be important (91% and 88%, respectively). Time in range and daily lows and highs were endorsed more frequently by educators (all Ps < .05). On average, HCPs endorsed 3.7 of eight data features. Overall, HCPs demonstrated agreement in ranking days by relative glycemic control (Kendall's W = .52, P < .001). Rankings were similar between endocrinologists and educators (R² = .90, Cohen's kappa = .95, mean absolute error = .4 [all Ps < .05]; Mann-Whitney U = 41, P = .53).

CONCLUSIONS

Consensus in the endorsement of certain data features and agreement in assessing glycemic management were observed. While some practice-specific differences in feature endorsement were found, no differences between educators and endocrinologists were observed in assessing glycemic management. Overall, HCPs tended to consider CGM data holistically, in alignment with published recommendations, and made converging assessments regardless of practice.

Effect of frequency of sensor use on glycaemic control in individuals on sensor-augmented pump therapy with and without Predictive Low Glucose Management System

Improved frequency of sensor use improves glycaemic control. Furthermore, there is no deterioration of glycaemic control with increased sensor use in individuals on Predictive Low Glucose Management (PLGM) system. Younger children are more likely to have better sensor uptake than older children.

Association between mandatory health examination attendance and diabetes treatment initiation among employees being treated for hypertension

OBJECTIVES

It is unclear whether mandatory health examination is effective for employees who are already being treated for chronic diseases. We focused on patients being treated for hypertension and evaluated the association between employer-based health examination attendance and diabetes treatment initiation.

METHODS

Using a database that stores health insurance claims and health examination results of subscribers enrolled in society-managed health insurance plans in Japan, we identified employees aged 40-59 years who were being treated for hypertension when starting diabetes treatment from April 2012 to December 2016. A case-crossover analysis was conducted using 90, 180, and 270 days prior to diabetes treatment initiation as reference points and 90 days after the mandatory health examination as the exposure period. We conducted a subgroup analysis by hemoglobin A1c (HbA1c) level and frequency of outpatient blood glucose testing before the mandatory health examination.

RESULTS

We identified 1464 individuals starting treatment for diabetes while on antihypertensive drugs. The overall odds ratio for starting diabetes treatment within 90 days of the health examination was 1.89 (95% confidence interval: 1.70-2.10). The subgroup analysis showed that this odds ratio increased as HbA1c level increased and as blood glucose testing frequency decreased.

CONCLUSIONS

Among employees starting treatment for diabetes while being treated for hypertension, employer-based mandatory health examination attendance was associated with initiation of diabetes treatment. The health examinations may be functioning as a complement to screening in outpatient settings.

Improved well-being and decreased disease burden after 1-year use of flash glucose monitoring (FLARE-NL4)

Introduction

The FreeStyle Libre is a flash glucose monitoring (FSL-FGM) system. Compared with finger-prick based self-monitoring of blood glucose, FSL-FGM may provide benefits in terms of improved glycemic control and decreased disease burden.

Methods

Prospective nationwide registry. Participants with diabetes mellitus (DM) used the FSL-FGM system for a period of 12 months. End points included changes in HbA1c, hypoglycemia, health-related quality of life (12-Item Short Form Health Surveyv2 (SF-12v2) and 3-level version of EuroQol 5D (EQ-5D-3L)), a specifically developed patient-reported outcome measures (PROMs) questionnaire, diabetes-related hospital admission rate and work absenteeism. Measurements were performed at baseline, and after 6 months and 12 months.

Results

1365 persons (55% male) were included. Mean age was 46 (16) years, 77% of participants had type 1 DM, 16% type 2 DM and 7% other forms. HbA1c decreased from 64 (95%CI 63 to 65) mmol/mol to 60 (95%CI 60 to 61) mmol/mol with a difference of -4 (95% CI -6 to 3) mmol/mol. Persons with a baseline HbA1c >70 mmol/mol had the most profound HbA1c decrease: -9 (95% CI -12 to to 5) mmol/mol. EQ-5D tariff (0.03 (95%CI 0.01 to 0.05)), EQ-VAS (4.4 (95%CI 2.1 to 6.7)) and SF-12v2 mental component score (3.3 (95%CI 2.1 to 4.4)) improved. For most, PROMs improved. Work absenteeism rate (/6 months) and diabetes-related hospital admission rate (/year) decreased significantly, from 18.5% to 7.7% and 13.7% to 2.3%, respectively.

Conclusions

Real world data demonstrate that use of FSL-FGM results in improved well-being and decreased disease burden, as well as improvement of glycemic control.

Diabetes-specific family conflict and responsibility among emerging adults with type 1 diabetes

Emerging adulthood is a transitional period for type 1 diabetes management, and aspects of family functioning such as family conflict and responsibility for diabetes management likely change following high school graduation. This study examined changes in diabetes-specific family conflict, family responsibility for diabetes management tasks, and associations with glycemic control up to 1 year after high school. Seventy-nine emerging adults with type 1 diabetes (M age = 18.09 ± .43 years; 51% female; 71% Caucasian) and their parents (73% female) completed self-report measures on diabetes-specific family conflict and family responsibility at 3 consecutive clinic visits, beginning in the spring of their senior year of high school. Hemoglobin A1c (HbA1c) was obtained from medical records. Diabetes-specific family conflict was relatively low; scores did not significantly change from baseline to Time 3. Parent responsibility for diabetes care decreased from baseline to Time 3. Higher parent- and emerging adult-reported family conflict and higher parent responsibility for diabetes care were associated with worse glycemic control (ps < .05). Parent-reported family conflict and the interaction between parent-reported family conflict and responsibility predicted HbA1c 1 year after high school. Conversely, HbA1c did not predict diabetes-specific family conflict or responsibility 1 year after high school. Findings indicate that diabetes-specific family conflict is associated with glycemic control after high school, even when emerging adults assume greater responsibility for diabetes self-care. Diabetes-specific family conflict levels were generally low and did not change over time despite this transitional period. If diabetes-specific conflict is present, it should be an important avenue for potential intervention for emerging adults with type 1 diabetes. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Autism spectrum disorder in children with Type 1 diabetes

AIM

Links between autism spectrum disorder (ASD) and autoimmune diseases, including Type 1 diabetes have been proposed. This study assessed the frequency of ASD in children with Type 1 diabetes in the T1D Exchange (T1DX) registry and the impact of ASD on characteristics of children with Type 1 diabetes.

METHODS

Analysis included 10 032 participants aged < 18 years (median Type 1 diabetes duration 6.5 years, 48% female, 77% non-Hispanic White). Diagnosis of ASD was defined as autism, Asperger's or pervasive developmental disorder.

RESULTS

A diagnosis of ASD was recorded for 159 (1.58%) participants. Those with ASD were predominantly male (88% vs. 51% of those without ASD, P < 0.001) and slightly older (median 14 vs. 13 years, P = 0.022). Occurrence of diabetic ketoacidosis at Type 1 diabetes diagnosis was similar (35% vs. 41%, P = 0.161). Pump use was lower in those with ASD (51% vs. 63%, P = 0.005) but continuous glucose monitor use was similar (24% vs. 27%, P = 0.351). Median HbA_1c was slightly lower in those with ASD [68 vs. 69 mmol/mol (8.4% vs. 8.5%), P = 0.006]. This difference was more pronounced after adjusting for confounders.

CONCLUSIONS

The frequency of ASD in the T1DX registry was similar to that in the general population. These data show that despite deficits in communication, occurrence of diabetic ketoacidosis was similar in youth with and without ASD. Pump use was less frequent in those with ASD, possibly due to sensory issues, although CGM use did not differ. The lower HbA_1c may be due to a more regimented routine with ASD. Because comorbidities such as ASD complicate care of patients with Type 1 diabetes, further research is needed to support these children.

Use of flash glucose monitoring system in assessing safety of the SGLT2 inhibitors during Ramadan fasting in high risk insulin treated patients with type 2 diabetes

BACKGROUND

The risks of hypoglycemia, dehydration and kidney injury may theoretically be aggravated by people with type 2 diabetes treated with Insulin and SGLT2 inhibitors during Ramadan. Data on safety and efficacy of SGLT2-I in people with type 2 diabetes treated with insulin is scanty. We aimed to assess the impact of SGLT2 inhibitors during Ramadan in high-risk patients with type 2 diabetes treated with insulin, on hypoglycemia, glycemic control and kidney function.

METHODS

This is a prospective interventional study on high-risk diabetes patients who insisted on fasting. All patients were treated with insulin ± SGLT2I. All patients received a FGMS and Ramadan focused education. All patients attended clinic before and post Ramadan where they were advised on treatment modification as well as biometric and biochemical measurements.

RESULTS

95 patients enrolled in the study and 49 of them were on SGLT2i. There was a no significant change in creatinine in both groups. FGMS showed an improvement in the sensor-calculated HbA1c from 7.3 ± 1.5 to 6.8 ± 1.1 and from 8 ± 1.6 to 7.7 ± 1.5 in the SGLT2 group and the non-SGT2i groups, respectively. The hypoglycemia was predominantly reported during Ramadan between 12:00 to 18:00 h, while in pre-Ramadan readings was during 2400-0600 and 1200-1800 slots.

CONCLUSIONS

This is the first study that assesses the use of SGLT2i along with insulin during Ramadan, using FGMS in high-risk patients with type 2 diabetes under optimal care. There was minimal interruption of fasting, significant improvement in glycemic control, and no significant change in the kidney function after Ramadan.

Effects of mobile phone application combined with or without self-monitoring of blood glucose on glycemic control in patients with diabetes: A randomized controlled trial

AIMS/INTRODUCTION

There is potential for mobile applications to deliver new self-management interventions for chronic disease, especially in diabetes. The aim of the present study was to evaluate the effects of a mobile phone application (MPA) combined with or without self-monitoring of blood glucose (SMBG) on glycemic control in patients with diabetes.

MATERIALS AND METHODS

The study was a 24-week period, four-arm parallel group, non-blinded, randomized trial. A total of 185 patients with mean age of 52 years were randomized to group A (no MPA and no SMBG), group B (SMBG only), group C (MPA only) and group D (both MPA and SMBG were used). Changes in glycated hemoglobin (HbA1c), fasting plasma glucose and 1,5-anhydroglucitol from baseline to week 24 were analyzed.

RESULTS

At 24 weeks, the HbA1c levels in patients of all groups decreased significantly from baseline. There were significant differences in the proportions of patients that achieved HbA1c <7% between groups, especially in group C and group D, compared with group A at week 24 (60.4%, 62.2% vs 25.5%, all P < 0.05). 1,5-Anhydroglucitol changes were obvious in group A and group C at week 24 from baseline (all P < 0.05 within groups). Factorial analysis of anova showed that MPA intervention was the main effective factor for HbA1c change (F = 4.59, P = 0.034), and there was no effect on HbA1c change for SMBG intervention (P = 0.975).

CONCLUSIONS

Implementation of the MPA, Diabetes-Carer, is effective in improving the proportion of HbA1c <7% in patients with type 2 diabetes.

Defining High Glycemic Variability in Type 1 Diabetes: Comparison of Multiple Indexes to Identify Patients at Risk of Hypoglycemia

Background: International consensus on the use of continuous glucose monitoring (CGM) recommends coefficient of variation (CV) as the metric of choice to express glycemic variability (GV) with a cutoff of 36% to define unstable diabetes. Even though, CV is associated with hypoglycemia in type 2 diabetes patients, the evidence on the use of one particular measure of GV in type 1 diabetes (T1DM) patients as a predictor of hypoglycemia is limited. Methods: A cohort of T1DM ambulatory patients was evaluated using CGM. Number and incidence rate of events <54 and <70 mg/dL were calculated. Bivariate and multivariate analysis of different glycemic indexes and clinical variables were performed to identify those associated with hypoglycemia. Receiver operating characteristic (ROC) curve analysis for each of the glycemic indexes was performed to define the best index and its optimal cutoff threshold to discriminate patients with events of hypoglycemia. Results: Seventy-three patients were included. A total of 128 events <54 mg/dL were recorded in 34 patients, and 350 events <70 mg/dL were registered in 51 patients. CV was the only variable significantly associated with hypoglycemia <54 mg/dL in the multivariate analysis (adjusted relative risk [aRR] 1.44, 95% confidence interval [CI]: 1.10-1.88, P = 0.008). CV, HbA1c (glycated hemoglobin), and mean glucose were associated with events <70 mg/dL. ROC curve analysis showed that, among GV metrics, CV had the best performance to discriminate patients with events <54 mg/dL (area under the curve [AUC] 0.87, 95% CI: 0.79-0.95) and events <70 mg/dL (AUC 0.79, 95% CI: 0.68-0.90) with optimal cutoff thresholds values of 34% and 31%, respectively. Among glycemic risk (GR) indexes, low blood glucose index (LBGI) showed the best performance. Conclusions: This analysis shows that CV is the best GV index, and LBGI the best GR index, to identify patients at risk of clinically significant hypoglycemia and hypoglycemia alert events in T1DM patients.

Continuous Subcutaneous Insulin Infusion in Adult Type 1 Diabetes Mellitus Patients: Results from a Public Health System

Aims: To analyze prevalence and clinical effect of continuous subcutaneous insulin infusion (CSII) in adult type 1 diabetes mellitus (T1DM) patients in a public health system real-world scenario. Materials and Methods: All adult T1DM patients on CSII being followed at Castilla-La Mancha Health Public Service were included. Primary efficacy outcome was the change in HbA_1c during the follow-up. Secondary efficacy outcomes included evaluation of the following variables: insulin pump indications, diabetes complication rates, insulin and pump use, continuous glucose monitoring use, patients achieving an HbA_1c decrease ≥6 mmol/mol (0.5%) with or without severe hypoglycemia, and discontinuations. Direct patient data were typed through the web-based Spanish national registry on CSII therapy by nine diabetologists from eight different health care areas. Results: A total of 7% of T1DM adult patients were treated with insulin pumps in our region, with a regional prevalence of 18.7 CSII patients/100,000 inhabitants. Three hundred thirteen patients were analyzed with a mean age of 34.1 ± 11.0 years and T1DM duration of 16.6 ± 9.7 years. Mean duration of CSII therapy was 6.2 ± 4.0 years. Data completion was 91.2%. Main indications for treatment were high glucose variability (36%) and suboptimal glycemic control (32%). Mean duration of CSII therapy was 6.2 ± 4.0 years. Sensor-augment pump therapy was used by 26% of the patients. Glycated hemoglobin decreased to -5 mmol/mol (95% CI -6 to -3 mmol/mol; P < 0.001) during the follow-up (Mean difference in change -0.4%, 95% CI -0.5 to -0.2; P < 0.001). Percentage of patients with severe hypoglycemia decreased from 32% to 13% (P < 0.001). Frequent nonsevere hypoglycemia, severe hypoglycemia, and diabetic ketoacidosis were less frequent among patients using higher number of daily basal rates at the study end. The rate of CSII interruption was 3.8%. Conclusions: Prevalence of CSII therapy in our region remains under 10% of adult T1DM patients, although CSII treatment was associated with a sustained improvement in glycemic control.

Continuous Glucose Monitoring Predicts Progression to Diabetes in Autoantibody Positive Children

CONTEXT

Accurate measures are needed for the prediction and diagnosis of type 1 diabetes (T1D) in at-risk persons.

OBJECTIVE

The purpose of this study was to explore the value of continuous glucose monitoring (CGM) in predicting T1D onset.

DESIGN AND SETTING

The Diabetes Autoimmunity Study in the Young (DAISY) prospectively follows children at increased risk for development of islet autoantibodies (islet autoantibody positive; Ab+) and T1D.

PARTICIPANTS

We analyzed 23 Ab+ participants with available longitudinal CGM data.

MAIN OUTCOME MEASURE

CGM metrics as glycemic predictors of progression to T1D.

RESULTS

Of 23 Ab+ participants with a baseline CGM, 8 progressed to diabetes at a median age of 13.8 years during a median follow-up of 17.7 years (interquartile range, 14.6 to 22.0 years). Compared with nonprogressors, participants who progressed to diabetes had significantly increased baseline glycemic variability (SD, 29 vs 21 mg/dL; P = 0.047), daytime sensor average (122 vs 106 mg/dL; P = 0.02), and daytime sensor area under the curve (AUC, 470,370 vs 415,465; P = 0.047). They spent 24% of time at >140 mg/dL and 12% at >160 mg/dL compared with, respectively, 8% and 3% for nonprogressors (both P = 0.005). A receiver-operating characteristic curve analysis showed an AUC of 0.85 for percentage of time spent at >140 or 160 mg/dL. The cutoff of 18% time spent at >140 mg/dL had 75% sensitivity, 100% specificity, and a 100% positive predictive value for diabetes prediction, although these values could change because some nonprogressors may develop diabetes with longer follow-up.

CONCLUSIONS

Eighteen percent or greater CGM time spent at >140 mg/dL predicts progression to diabetes in Ab+ children.

Factors associated with glycemic control in community-dwelling elderly individuals with type 2 diabetes mellitus in Zhejiang, China: a cross-sectional study

BACKGROUND

Although exercise seems to be beneficial for type 2 diabetes mellitus (T2DM) patients, there is limited research elucidating the optimal accessible indices of adiposity and insulin resistance for identifying elderly T2DM patients with poor glycemic control, which could be improved by performing regular exercise.

METHODS

A community-based, cross-sectional study was conducted with 918 Chinese elderly individuals with T2DM in Zhejiang. Relevant risk factors for poor glycemic control, as determined using glycated haemoglobin A1c (HbA1c) > 7%, were explored using logistic regression analyses and included body mass index (BMI), waist circumference (WC), waist to height ratio (WHtR), fasting blood glucose (FBG), triglycerides (TGs), total cholesterol (TC), the product of fasting triglycerides and glucose (TyG), visceral adiposity index (VAI), lipid accumulation product (LAP), TyG-BMI, and TyG-WC. Comparisons of the risk factors' ability to discriminate poor glycemic control as well as their optimal cutoff values were determined using receiver operating characteristic (ROC) analyses, and then the extent of poor glycemic control risk reduction through regular exercise was examined using multivariate logistic regression analyses.

RESULTS

The overall poor glycemic control rate was 49.3%. The factors associated with poor glycemic control included FBG > 3.869, TyG > 8.73, TyG-BMI > 222.45, and TyG-WC > 713.48 in logistic regression analyses. The optimal cutoff points of FBG, TyG, TyG-WC, and TyG-BMI in discriminating poor glycemic control were 7.38, 9.22, 813.33, and 227.77, and their corresponding areas under the ROC curves were 0.864(0.840-0.886), 0.684(0.653-0.714), 0.604(0.571-0.635), and 0.574(0.541-0.606), respectively. Occasional and regular exercise reduced the odds ratios (95% confidence interval) of poor glycemic control to 0.187 (0.063-0.557) and 0.183 (0.059-0.571) for subjects with TyG-WC > 813.33 (p = 0.008), to 0.349 (0.156-0.782) and 0.284 (0.123-0.652) for subjects with TyG > 9.22 (p = 0.011), and to 0.390 (0.175-0.869) and 0.300(0.130-0.688) for subjects with TyG-BMI > 227.77 (p = 0.017), respectively, after adjusting for multiple confounding factors.

CONCLUSION

Among elderly individuals with T2DM, poor glycemic control risk might be identified using indices calculated from FBG, TG, BMI, and WC measurements, which are indicative of adiposity and insulin resistance. TyG-WC seems to be an accessible and useful indicator to identify which elderly T2DM patients would benefit from performing regular exercise to achieve good glycemic control.

Flash Glucose Monitoring Accepted in Daily Life of Children and Adolescents with Type 1 Diabetes and Reduction of Severe Hypoglycemia in Real-Life Use

Background: Flash glucose monitoring (FGM) is covered by the Belgian public health insurance for type 1 diabetes since 2016. The objective of this study was to describe the use of FGM and diabetes outcomes in type 1 diabetic children and adolescents 1 year after reimbursement. Methods: All patients had the choice to convert to FGM or to continue with self-monitoring of blood glucose (SMBG). Clinical data were collected at baseline, at the next visit, and after 12 months; glucose profiles at next visit and after 12 months. Regression analyses were performed to identify predictors of FGM acceptance and changes in metabolic control. Results: A total of 334 subjects were included, of whom 278 (83.2%) switched to FGM. They were younger (13.6 vs. 15.2 years; P = 0.012) and performed more SMBG testing at baseline than patients who did not switch (4.3 vs. 4.1 tests daily; P = 0.008). At the end of follow-up, the rate of severe hypoglycemia decreased by 53% in the group of FGM users (P = 0.012) while it remained stable in SMBG users. Median glycated hemoglobin did not change significantly in both groups. Among subjects who switched to FGM, 15.8% reverted to SMBG after a median use of 5.3 months. Adverse events, diabetes duration, and FGM utilization were independent predictors of the risk for reverting. FGM-related adverse events were associated with a fivefold increased risk to revert to SMBG (hazard ratio = 5.12; P < 0.0001). Conclusions: FGM is relatively well accepted and decreases the risk of severe hypoglycemic events in our pediatric population. FGM is more often discontinued in patients experiencing adverse events and with longer diabetes duration.

The impact of using a closed-loop system on food choices and eating practices among people with Type 1 diabetes: a qualitative study involving adults, teenagers and parents

AIMS

We explored whether, how and why moving onto and using a hybrid day-and-night closed-loop system affected people's food choices and dietary practices to better understand the impact of this technology on everyday life and inform recommendations for training and support given to future users.

METHODS

Twenty-four adults, adolescents and parents were interviewed before commencing use of the closed-loop system and following its 3-month use. Data were analysed thematically and longitudinally.

RESULTS

While participants described preparing and/or eating similar meals to those consumed prior to using a closed-loop, many described feeling more normal and less burdened by diabetes in dietary situations. Individuals also noted how the use of this technology could lead to deskilling (less precise carbohydrate counting) and less healthy eating (increased snacking and portion sizes and consumption of fatty, energy-dense foods) because of the perceived ability of the system to deal with errors in carbohydrate counting and address small rises in blood glucose without a corrective dose needing to be administered.

CONCLUSIONS

While there may be quality-of-life benefits to using a closed-loop, individuals might benefit from additional nutritional and behavioural education to help promote healthy eating. Refresher training in carbohydrate counting may also be necessary to help ensure that users are able to undertake diabetes management in situations where the technology might fail or that they take a break from using it.

Assessment of a Precision Medicine Analysis of a Behavioral Counseling Strategy to Improve Adherence to Diabetes Self-management Among Youth: A Post Hoc Analysis of the FLEX Trial

IMPORTANCE

The Flexible Lifestyles Empowering Change (FLEX) trial, an 18-month randomized clinical trial testing an adaptive behavioral intervention in adolescents with type 1 diabetes, showed no overall treatment effect for its primary outcome, change in hemoglobin A1c (HbA1c) percentage of total hemoglobin, but demonstrated benefit for quality of life (QoL) as a prespecified secondary outcome.

OBJECTIVE

To apply a novel statistical method for post hoc analysis that derives an individualized treatment rule (ITR) to identify FLEX participants who may benefit from intervention based on changes in HbA1c percentage (primary outcome), QoL, and body mass index z score (BMIz) (secondary outcomes) during 18 months.

DESIGN, SETTING, AND PARTICIPANTS

This multisite clinical trial enrolled 258 adolescents aged 13 to 16 years with type 1 diabetes for 1 or more years, who had literacy in English, HbA1c percentage of total hemoglobin from 8.0% to 13.0%, a participating caregiver, and no other serious medical conditions. From January 5, 2014, to April 4, 2016, 258 adolescents were recruited. The post hoc analysis excluded adolescents missing outcome measures at 18 months (2 participants [0.8%]) or continuous glucose monitoring data at baseline (40 participants [15.5%]). Data were analyzed from April to December 2018.

INTERVENTIONS

The FLEX intervention included a behavioral counseling strategy that integrated motivational interviewing and problem-solving skills training to increase adherence to diabetes self-management. The control condition entailed usual diabetes care.

MAIN OUTCOMES AND MEASURES

Subgroups of FLEX participants were derived from an ITR estimating which participants would benefit from intervention, which would benefit from control conditions, and which would be indifferent. Multiple imputation by chained equations and reinforcement learning trees were used to estimate the ITR. Subgroups based on ITR pertaining to changes during 18 months in 3 univariate outcomes (ie, HbA1c percentage, QoL, and BMIz) and a composite outcome were compared by baseline demographic, clinical, and psychosocial characteristics.

RESULTS

Data from 216 adolescents in the FLEX trial were reanalyzed (166 [76.9%] non-Hispanic white; 108 teenaged girls [50.0%]; mean [SD] age, 14.9 [1.1] years; mean [SD] diabetes duration, 6.3 [3.7] years). For the univariate outcomes, a large proportion of FLEX participants had equivalent predicted outcomes under intervention vs usual care settings, regardless of randomization, and were assigned to the muted group (HbA1c: 105 participants [48.6%]; QoL: 63 participants [29.2%]; BMIz: 136 participants [63.0%]). Regarding the BMIz univariate outcome, mean baseline BMIz of participants assigned to the muted group was lower than that of those assigned to the intervention and control groups (muted vs intervention: mean difference, 0.48; 95% CI, 0.21 to 0.75; P = .002; muted vs control: mean difference, 0.86; 95% CI, 0.61 to 1.11; P < .001); this group also had a higher proportion of individuals with underweight or normal weight using weight status cutoffs (95 [69.9%] in muted group vs 24 [54.6%] in intervention group and 11 [30.6%] in control group; χ24 = 24.67; P < .001). The approach identified subgroups estimated to benefit based on HbA1c percentage (54 participants [25.0%]), QoL (89 participants [41.2%]), and BMIz (44 participants [20.4%]). Regarding the HbA1c percentage outcome, participants expected to benefit from the intervention did not have significantly higher baseline HbA1c percentages than those expected to benefit from usual care (9.4% vs 9.2%; difference, 0.2%; 95% CI, -0.16% to 0.56%; P = .44). However, participants in the muted group had higher mean HbA1c percentages at baseline than those assigned to the intervention or control groups (muted vs intervention: 9.9% vs 9.4%; difference, 0.5%; 95% CI, 0.13% to 0.89%; P = .02; muted vs control; 9.9% vs 9.2%; difference, 0.7%; 95% CI, 0.34% to 1.08%; P = .001). No significant differences were found between subgroups estimated to benefit in terms of the composite outcome from the FLEX intervention (91 participants [42.1%]) vs usual care (125 participants [57.9%]).

CONCLUSIONS AND RELEVANCE

The precision medicine approach represents a conceptually and analytically novel approach to post hoc subgroup identification. More work is needed to understand markers of positive response to the FLEX intervention.

TRIAL REGISTRATION

ClinicalTrial.gov identifier: NCT01286350.

Text-message responsiveness to blood glucose monitoring reminders is associated with HbA1c benefit in teenagers with Type 1 diabetes

AIMS

To evaluate an 18-month text-messaging intervention in teenagers with Type 1 diabetes and to assess factors associated with text responsiveness and glycaemic benefit.

METHODS

Teenagers with diabetes (N = 147), aged 13-17 years, received two-way text reminders at self-selected times to check blood glucose levels and reply with blood glucose results.

RESULTS

At baseline, the participants (48% boys, 78% white, 63% pump-treated) had a mean ± sd age of 14.9 ± 1.3 years, diabetes duration of 7.1 ± 3.9 years and HbA_1c concentration of 69 ± 12 mmol/mol (8.5 ± 1.1%). The mean proportion of days with ≥1 blood glucose response declined over time (0-6 months, 60 ± 26% of days, 7-12 months, 53 ± 31% of days, 13-18 months, 43 ± 33% of days). Over 18 months, 49% responded with ≥1 blood glucose result on ≥50% of days (high responders). Regression analysis controlling for baseline HbA_1c revealed no significant change in HbA_1c from baseline to 18 months in high responders (P = 0.54) compared with a significant HbA_1c increase in low responders (+0.3%, P = 0.01). In participants with baseline HbA_1c ≥64 mmol/mol (≥8%), high responders were 2.5 times more likely than low responders to have a clinically significant [≥5.5 mmol/mol (≥0.5%)] HbA_1c decrease over 18 months (P < 0.05). In participants with baseline HbA_1c <64 mmol/mol(<8%), high responders were 5.7 times more likely than low responders to have an 18-month HbA_1c <58 mmol/mol (<7.5%; P < 0.05).

CONCLUSIONS

Teenagers with Type 1 diabetes who responded to text reminders on ≥50% of days over 18 months experienced clinically significant glycaemic benefit. There remains a need to tailor interventions to maintain teenager engagement and optimize improvements.

Socioeconomic status moderates the association between patient satisfaction with community health service and self-management behaviors in patients with type 2 diabetes: A cross-sectional survey in China

The objective of this study was to examine the association between patient satisfaction with community health service (CHS) and self-management behaviors in patients with type 2 diabetes mellitus (T2DM).In all, 1691 patients with T2DM from 8 community health centers in 5 provinces in China participated in the present study. The dependent variables included 4 measures of self-management behaviors: regular self-monitoring of blood glucose (SMBG), prescribed medication adherence, recommended dietary changes, and regular exercise. The independent variable was patient satisfaction with CHS. Multivariable logistic regression models were performed to examine the association between patient satisfaction with CHS and self-management behaviors.The mean satisfaction score in the participants was 3.14 (out of a maximum of 5). After adjusting for covariates including demographic factors, health status, health knowledge, and socioeconomic status (SES), diabetic patients with high CHS satisfaction had better medication adherence (odds ratio [OR] 1.25, 95% confidence interval [CI] 1.02-1.55), increased exercise management (OR 1.19, 95% CI 1.06-1.35), and more SMBG (OR 1.16, 95% CI 1.03-1.32); all these associations varied across SES groups. The association between satisfaction and medication adherence was significant among participants younger than 65 years with lower education (OR 2.15, 95% CI 1.37-3.37), income (OR 1.62, 95% CI 1.13-2.32), and lower-status occupations (OR 1.69, 95% CI 1.16-2.47). Among participants younger than 65 years and had lower education attainment, the association between satisfaction and diet management was observed. There were positive associations between satisfaction and regular exercise among subgroups of participants younger than 65 years, except for lower education group. A significant association between satisfaction and SMBG among participants ≥65 years old, who also had lower SES and higher-status occupations, was also observed.The study findings suggested that T2DM patient satisfaction with CHS was moderate. High satisfaction with CHS indicated better medication adherence, exercise management, and SMBG, and these associations varied by SES.

Evaluation of health utility values for diabetic complications, treatment regimens, glycemic control and other subjective symptoms in diabetic patients using the EQ-5D-5L

AIMS

This study aimed to reveal health utility values for diabetic complications and treatment regimens with adjustment for glycemic control and other clinical manifestations in a diabetic population.

METHODS

The EuroQol 5-Dimension 5-Level (EQ-5D-5L) health utility values for 4963 Japanese diabetic patients were analyzed using a multivariate regression model including major complications and treatment regiments (minimally adjusted model), and that additionally included glycemic control and other subjective symptoms (musculoskeletal, dental, respiratory, gastrointestinal, urinary, and cutaneous symptoms, and hearing impairment) (further adjusted model).

RESULTS

The mean utility value was 0.901 ± 0.137. In the minimally adjusted model, blindness, overt nephropathy, regular dialysis, cardiac symptom, sequelae of stroke, symptomatic peripheral neuropathy, decreased sensation, claudication, foot ulcer/gangrene, major amputation, and complex treatment regimens were significantly associated with lower utility values, whereas proliferative retinopathy without blindness, coronary artery disease without cardiac symptom, sequela-free cerebrovascular disease, asymptomatic peripheral artery disease, and minor amputation were not. Major complications and treatment regimens that showed significant association in the minimally adjusted model still presented significant impact on the utility decrement in the further adjusted model. However, most of their regression coefficients were lower in absolute value compared to those in the minimally adjusted model.

CONCLUSIONS

The utility decrement related to each diabetic complication varied with its severity and accompanying symptoms. Complex treatment regimens were independently associated with lower utility values. The utility decrement associated with diabetic complication and complex treatment regimens would be overestimated in the analysis without adjustment for glycemic control or other subjective symptoms.

Continuous Glucose Monitoring Linked to an Artificial Intelligence Risk Index: Early Footprints of Intraventricular Hemorrhage in Preterm Neonates

OBJECTIVE

To develop and validate a new risk score for intraventricular hemorrhage (IVH) in preterm neonates based on continuous glucose monitoring (CGM).

STUDY DESIGN

We retrospectively analyzed CGM traces obtained from 50 very preterm neonates, grouped into two sub-cohorts started on CGM within 12 and 48 h of birth, respectively. A CGM linked to an Artificial Intelligence Risk (CLAIR) index was developed to quantify glucose variability during the first 72 h of life in neonates with and without IVH. Brain-US was performed at least twice a day for the first 5 days of birth. An integrated remote monitoring platform was developed to capture major clinical events in real time and gather data for the risk index. The new score performance was further compared with other measures of glucose variability (coefficient of variation [CV] and standard deviation [SD]) and with a clinical risk index for babies II (CRIB-II) as a predictor of IVH event. The two cohorts were analyzed separately for internal validation of the method.

RESULTS

The primary cohort consisted of 26 neonates (gestational age 30 [28, 31] weeks; BW1275 g[1090, 1750]). Controls (n = 23) exhibited higher CLAIR index than cases (P = 0.004). A cut-off of 0.69 for the new CLAIR index allowed a 100% sensitivity and an 83% specificity for IVH prediction. The CLAIR index was the sole significant predictor for IVH (P = 0.003) when compared with clinical variables, CV, SD, and CRIB-II. In a subgroup analysis in very low-birth-weight infants, the CLAIR index was the sole variable significantly associated with IVH (P = 0.009). Analysis on the secondary cohort (five cases and 16 controls) confirmed a higher CLAIR index in the controls (P = 0.008), in the absence of a difference for CV, SD, and CRIB-II between the two groups.

CONCLUSION

CGM, combined with the AI-algorithm, provides a high-sensitivity index for risk detection of IVH that reflects the glycemic impairment preceding IVH.

Frequency of self-monitoring of blood glucose in relation to weight loss and A1C during intensive multidisciplinary weight management in patients with type 2 diabetes and obesity

OBJECTIVE

We evaluated the relationship between frequency of self-monitoring of blood glucose (SMBG) and body weight, A1C, and cardiovascular risk factors in patients with type 2 diabetes (T2D) and obesity enrolled in a 12-week intensive multidisciplinary weight management (IMWM) program.

RESEARCH DESIGN AND METHODS

We conducted a retrospective analysis of 42 patients who electronically uploaded their SMBG data over 12 weeks of an IMWM program and divided them into tertiles based on their average frequency of SMBG per day. Mean (range) SMBG frequencies were 2.3 (1.1-2.9) times/day, 3.4 (3-3.9) times/day, and 5 (4-7.7) times/day in the lowest, middle, and highest tertiles, respectively. Anthropometric and metabolic parameters were measured at baseline and after 12 weeks of intervention.

RESULTS

Participants in the highest tertile achieved a median change (IQR) in body weight of -10.4 kg (-7.6 to -14.4 kg) compared with -8.3 kg (-5.2 to -12.2 kg), and -6.9 kg (-4.2 to -8.9 kg) in the middle and lowest tertiles, respectively (p=0.018 for trend). Participants in the highest tertile had a median change (IQR) in A1C of -1.25% (-0.6 to -3.1%) compared with -0.8% (-0.3% to -2%) and -0.5% (-0.2% to -1.2%) in the middle and lowest tertiles, respectively (p=0.048 for trend). The association between change in body weight and SMBG frequency remained significant after adjusting for age, sex, baseline body mass index, diabetes duration, and use of insulin therapy.

CONCLUSIONS

Increased frequency of SMBG during IMWM is associated with significantly better weight loss and improvement of A1C in patients with T2D and obesity. These findings may suggest future clinical recommendations aimed at increasing SMBG frequency to achieve the most favorable outcomes.

Asymptomatic hypoglycaemia in Type 1 diabetes: incidence and risk factors

AIM

The epidemiology of asymptomatic (silent) hypoglycaemia is not well-described. We investigated incidence and risk factors for asymptomatic hypoglycaemia in Type 1 diabetes.

METHODS

A cohort of 153 people with Type 1 diabetes participated in 6 days of blinded continuous glucose monitoring (CGM) and recording of hypoglycaemia symptoms. At entry, hypoglycaemia awareness was classified (by three different methods) and HbA_1c and C-peptide were measured. Hypoglycaemic episodes were defined as interstitial glucose ≤ 3.9 mmol/l (IG_3.9 ) or ≤ 3.0 mmol/l (IG_3.0 ) for ≥ 15 min, and were considered asymptomatic if no hypoglycaemic symptoms were reported.

RESULTS

At thresholds IG_3.9 and IG_3.0 , the incidence rates of hypoglycaemic episodes were 5.0 (7.9) [median (IQR)] and 1.3 (3.4) episodes/person-week, respectively. Three-quarters of episodes were asymptomatic. In total, 77% and 52% of participants experienced one or more episode of asymptomatic hypoglycaemia at IG_3.9 and IG_3.0 [3.0 (6.2) and 1.0 (2.3) asymptomatic episodes/person-week]. At multivariate analysis, reduced awareness was positively associated with asymptomatic hypoglycaemia, particularly nocturnal events, and negatively with symptomatic hypoglycaemia. High insulin dose was associated with increased risk of both asymptomatic and symptomatic hypoglycaemia, whereas low HbA_1c and long diabetes duration were risk factors only for symptomatic hypoglycaemia.

CONCLUSIONS

Asymptomatic hypoglycaemia constitutes the majority of hypoglycaemic events in Type 1 diabetes. Reduced hypoglycaemia awareness and high insulin dose are risk factors for asymptomatic hypoglycaemia but other conventional risk factors for severe hypoglycaemia do not correlate with risk of asymptomatic episodes.

Regular performance feedback may be key to maintain good quality DKA management: results from a five-year study

OBJECTIVES

We postulate that performance feedback is a prerequisite to ensure sustained improvement in diabetic ketoacidosis (DKA) management.

DESIGN

The study was based on 'theory of change' concept that suggests changes of primary drivers determine the main outcome. A set of secondary drivers can be implemented to achieve improvements in these primary drivers and thus the main outcome.

SETTING

This study was conducted at a large tertiary care center in the West Midlands, UK. The region has above average prevalence of diabetes and DKA admissions in the country.

PARTICIPANTS

All participants diagnosed with DKA as per national guidelines, except those managed in intensive care unit from April 2014 to March 2018, were included in this study.

INTERVENTIONS

Monthly feedback of performance was the main intervention. Development of a real-time live DKA audit tool, automatic referral system of DKA to the specialist team, electronic monitoring of blood gas measurements and education and redesigning of local (trust) guidelines were the other interventions in this study.

MAIN OUTCOME MEASURES

Total DKA duration, appropriateness of fixed rate intravenous insulin infusion, fluid prescription, glucose monitoring, ketone monitoring and referral to specialists.

RESULTS

There was a significant reduction in the duration of DKA postintervention compared with baseline results. However, in the absence of regular feedback, the duration of DKA showed an upward trend nearing baseline values. Similar trends were noted in secondary drivers influencing DKA duration.

CONCLUSION

Based on these results, we recommend regular audit and feedback is required to sustain improvements in DKA management.

Real-world practice level data analysis confirms link between variability within Blood Glucose Monitoring Strip (BGMS) and glycosylated haemoglobin (HbA1c) in Type 1 Diabetes

AIMS/HYPOTHESIS

Our aim was to quantify the impact of Blood Glucose Monitoring Strips variability (BGMSV) at GP practice level on the variability of reported glycated haemoglobin (HbA1cV) levels.

METHODS

Overall GP Practice BGMSV and HbA1cV were calculated from the quantity of main types of BGMS being prescribed combined with the published accuracy, as % results within ±% bands from reference value for the selected strip type. The regression coefficient between the BGMSV and HbA1cV was calculated. To allow for the aggregation of estimated three tests/day over 13 weeks (ie, 300 samples) of actual Blood Glucose (BG) values up to the HbA1c, we multiplied HbA1cV coefficient by √300 to estimate an empirical value for impact of BGMSV on BGV.

RESULTS

Four thousand five hundred and twenty-four practice years with 159 700 T1DM patient years where accuracy data were available for more than 80% of strips prescribed were included, with overall BGMSV 6.5% and HbA1c mean of 66.9 mmol/mol (8.3%) with variability of 13 mmol/mol equal to 19% of the mean. At a GP practice level, BGMSV and HbA1cV as % of mean HbA1c (in other words, the spread of HbA1c) were closely related with a regression coefficient of 0.176, P < 0.001. Thus, greater variability in the BGMS at a GP practice level resulted in a greater spread of HbA1C readings in T1DM patients. Applying this factor for BGMS to the national ISO accepted standard where 95% results must be ≤±15% from reference, revealed that for BG, 95% results would be ≤±45% from the reference value. Thus, the variation in BG is three times that of the BGMS. For a patient with BG target @10 mmol/L using the worst performing ISO standard strips, on 1/20 occasions (average 1/week) actual blood glucose value could be >±4.5 mmol/L from target, compared with the best performing BGMS with BG >±2.2 mmol/L from reference on 1/20 occasions.

CONCLUSION

Use of more variable/less accurate BGMS is associated both theoretically and in practice with a larger variability in measured BG and HbA1c, with implications for patient confidence in their day-to-day monitoring experience.