Identifying the deficiencies of currently available CGM to improve uptake and benefit

BACKGROUND AND AIMS

The use of diabetes technologies is increasing worldwide, with health systems facilitating improved access to devices. Continuous glucose monitoring is a complex intervention that provides information on glucose concentration, rate and direction of change, historical data and alerts and alarms for extremes of glucose. These data do not themselves change glycaemia and require translation to a meaningful action for impact. It is, therefore, crucial that such systems advance to better meet the needs of individuals using them.

METHODS

Narrative review of the use of, engagement with, limitations and unmet needs of continuous glucose monitoring systems.

RESULTS

CGM devices have made a significant contribution to the self-management of diabetes; however, challenges with access and user experience persist, with multiple limitations to uptake and benefit. These limitations include physical size and implementation, with associated stigma, alarm fatigue, sleep disturbance and the challenge of addressing large volumes of real-time data. Greater personalisation throughout the continuous glucose monitoring journey, with a focus on usability, may improve the benefits derived from the device and reduce the burden of self-management. Healthcare professionals may have unconscious biases that affect the provision of continuous glucose monitors due to deprivation, education, age, ethnicity and other characteristics.

CONCLUSIONS

Continuous glucose monitoring exerts a dose-dependent response; the more it is used, the more effective it is. For optimal use, continuous glucose monitors must not just reduce the burden of management in one dimension but facilitate net improvement in all domains of self-management for all users.

Intermittently scanned continuous glucose monitoring in adults with type 1 diabetes: A subgroup analysis from the FLASH-UK study

AIMS

The FLASH-UK trial showed lower HbA1c with intermittently scanned continuous glucose monitoring (isCGM), as compared with self monitoring of blood glucose (SMBG), in adults with type 1 diabetes and HbA1c ≥58 mmol/mol (≥7.5%). Here, we present results from the pre-specified subgroup analysis for the 24-week HbA1c (primary outcome) and selected sensor-based secondary outcomes.

METHODS

This was a multi-centre, parallel-design, randomised controlled trial. The difference in treatment effect between subgroups (baseline HbA1c [≤75 vs. >75 mmol/mol] [≤9.0 vs >9.0%], treatment modality [pump vs injections], prior participation in structured education, age, educational level, impaired awareness of hypoglycaemia, deprivation index quintile sex, ethnic group and Patient Health Questionnaire-9 [PHQ-9] detected depression category) were evaluated.

RESULTS

One hundred fifty-six participants (females 44%, mean [SD] baseline HbA1c 71 [9] mmol/mol 8.6 [0.8%], age 44 [15]) were randomly assigned, in a 1:1 ratio to isCGM (n = 78) or SMBG (n = 78). The mean (SD) baseline HbA1c (%) was 8.7 (0.9) in the isCGM group and 8.5 (0.8) in the SMBG group, lowering to 7.9 (0.8) versus 8.3 (0.9), respectively, at 24 weeks (adjusted mean difference -0.5, 95% confidence interval [CI] -0.7 to -0.3; p < 0.001]. For HbA1c, there was no impact of treatment modality, prior participation in structured education, deprivation index quintile, sex or baseline depression category. The between-group difference in HbA1c was larger for younger people (a reduction of 2.7 [95% CI 0.3-5.0; p = 0.028] mmol/mol for every additional 15 years of age). Those with HbA1c 76-97 mmol/mol (>9.0%-11.0%) had a marginally non-significant higher reduction in HbA1c of 8.4 mmol/mol (3.3-13.5) compared to 3.1 (0.3-6.0) in those with HbA1c 58-75 mmol/mol (p = 0.08). For 'Time in range' (% 3.9-10 mmol/L), the difference was larger for those with at least a bachelor's degree. For 'Time below range' (% <3.9 mmol/L), the difference was larger for those using injections, older people and those with less than bachelor's degree.

CONCLUSIONS

Intermittently scanned continuous glucose monitoring is generally effective across a range of baseline characteristics.

Real-world glycaemic outcomes in adult persons with type 1 diabetes using a real-time continuous glucose monitor compared to an intermittently scanned glucose monitor: A retrospective observational study from the Canadian LMC diabetes registry (REAL-CGM-T1D)

Real-time continuous glucose monitoring (rtCGM) and intermittently scanned CGM (isCGM) have both been shown to improve glycaemic outcomes in people with T1D. The aim of this study was to compare real-world glycaemic outcomes at 6-12 months in a propensity score matched cohort of CGM naïve adults with T1D who initiated a rtCGM or an isCGM. Among the matched rtCGM and isCGM cohorts (n = 143/cohort), rtCGM users had a significantly greater HbA_1c benefit compared to isCGM users (adjusted difference, -3 mmol/mol [95% CI, -5 to -1]; -0.3% [95% CI, -0.5 to -0.1]; p = 0.01). There was a significantly greater lowering of HbA_1c for rtCGM compared to isCGM when baseline HbA_1c was <69 mmol/mol (8.5%) (adjusted difference, -4 mmol/mol [95% CI, -7 mmol/mol to -2 mmol/mol]; -0.4% [95% CI, -0.6% to -0.2%]; p < 0.001), and in MDI users (adjusted difference, -3 mmol/mol [95% CI, -6 mmol/mol to -0 mmol/mol]; -0.3% [95% CI -0.5% to 0.0%], p = 0.04). The rtCGM cohort had significantly greater time in range (58.3 ± 16.1% vs. 54.5 ± 17.1%, p = 0.03), lower time below range (2.1 ± 2.7% vs. 6.1 ± 5.0%, p < 0.001) and lower glycaemic variability compared to the isCGM cohort. In this real-world analysis of adults with T1D, rtCGM users had a significantly greater reduction in HbA_1c at 6-12 months compared to isCGM, and significantly greater time in range, lower time below range and lower glycaemic variability, compared to a matched cohort of isCGM users.

Current provision and HCP experiences of remote care delivery and diabetes technology training for people with type 1 diabetes in the UK during the COVID-19 pandemic

BACKGROUND

The COVID-19 pandemic has led to the rapid implementation of remote care delivery in type 1 diabetes. We studied current modes of care delivery, healthcare professional experiences and impact on insulin pump training in type 1 diabetes care in the United Kingdom (UK).

METHODS

The UK Diabetes Technology Network designed a 48-question survey aimed at healthcare professionals providing care in type 1 diabetes.

RESULTS

One hundred and forty-three healthcare professionals (48% diabetes physicians, 52% diabetes educators and 88% working in adult services) from approximately 75 UK centres (52% university hospitals, 46% general and community hospitals), responded to the survey. Telephone consultations were the main modality of care delivery. There was a higher reported time taken for video consultations versus telephone (p < 0.001). Common barriers to remote consultations were patient familiarity with technology (72%) and access to patient device data (67%). We assessed the impact on insulin pump training. A reduction in total new pump starts (73%) and renewals (61%) was highlighted. Common barriers included patient digital literacy (61%), limited healthcare professional experience (46%) and time required per patient (44%). When grouped according to size of insulin pump service, pump starts and renewals in larger services were less impacted by the pandemic compared to smaller services.

CONCLUSION

This survey highlights UK healthcare professional experiences of remote care delivery. While supportive of virtual care models, a number of factors highlighted, especially patient digital literacy, need to be addressed to improve virtual care delivery and device training.

Glucose Concentrations from Continuous Glucose Monitoring Devices Compared to Those from Blood Plasma during an Oral Glucose Tolerance Test in Healthy Young Adults

Continuous glucose monitoring devices measure glucose in interstitial fluid. The devices are effective when used by patients with type 1 and 2 diabetes but are increasingly being used by researchers who are interested in the effects of various behaviours of glucose concentrations in healthy participants. Despite their more frequent application in this setting, the devices have not yet been validated for use under such conditions. A total of 124 healthy participants were recruited to a ten-day laboratory study. Each participant underwent four oral glucose tolerance tests, and a total of 3315 out of a possible 4960 paired samples were included in the final analysis. Bland-Altman plots and mean absolute relative differences were used to determine the agreement between the two methods. Bland-Altman analyses revealed that the continuous glucose monitoring devices had proportional bias (R = 0.028, p < 0.001) and a mean bias of -0.048 mmol/L, and device measurements were more variable as glucose concentrations increased. Ninety-nine per cent of paired values were in Zones A and B of the Parkes Error Grid plot, and there was an overall mean absolute relative difference of 16.2% (±15.8%). There was variability in the continuous glucose monitoring devices, and this variability was higher when glucose concentrations were higher. If researchers were to use continuous glucose monitoring devices to measure glucose concentrations during an oral glucose tolerance test in healthy participants, this variability would need to be considered.

Continuous Blood Glucose Monitoring Increases Vigorous Physical Activity Levels and Is Associated With Reduced Hypoglycemia Avoidance Behavior In Youth With Type 1 Diabetes

The primary goal of this study was to explore physical activity (PA) levels, hypoglycemia fear scores and hypoglycemia episodes according to insulin administration and blood glucose monitoring methods in youth with type 1 diabetes (T1D). A self-administered questionnaire was completed by 28 children and 33 adolescents with T1D, and their PA was assessed. Hypoglycemia episodes, fear of hypoglycemia scores, insulin therapy (pump vs. injection) and blood glucose monitoring (continuous blood glucose monitors [CGMs] vs. blood glucose meters) methods are reported in the present work. There were no significant differences in the number of hypoglycemic episodes, child hypoglycemia fear survey behavior or total scores, or any components of the PA profile between youth using injections and those using a pump. However, these variables differed significantly when compared according to blood glucose monitoring method (CGMs vs. blood glucose meters): 41.2 vs. 81.8, p<0.01; 1.03 ± 0.05 vs. 2.6 ± 0.63, p<0.01; 1.09 ± 0.43 vs. 2.94 ± 0.22, p<0.01; and 222 ± 18 vs. 49 ± 11, p<0.01 (for total time in vigorous PA in minutes per week), respectively. CGM use correlated significantly with VPA levels (β=0.6; p=0.04). Higher VPA levels were associated with higher child hypoglycemia fear survey behavior scores (β=0.52; p=0.04). The latter correlates negatively with the number of episodes of hypoglycemia in the past 12 months in all category groups. The type of insulin injection was not associated with more activity in youth with T1D. In contrast, CGM use may be associated with increased vigorous PA among T1D youth. Those with higher hypoglycemia fear survey behavior scores engaged in more VPA and had fewer hypoglycemia episodes. Although CGM use ensures continuous monitoring of glycemia during exercise, increasing hypoglycemia avoidance behavior is still a necessary part of exercise management strategies in active youth with T1D.

Blood glucose fluctuations detected by continuous glucose monitoring system in gout patients with normal glucose tolerance and the effect of urate-lowering therapy

AIM

The aim of this study was to investigate whether there are blood glucose fluctuations in gout patients with hyperuricemia and normal glucose tolerance, and the effect of urate-lowering therapy on blood glucose fluctuations.

METHODS

Thirty patients with newly diagnosed gout, hyperuricemia and normal glucose tolerance were enrolled in our study. Continuous glucose monitoring system (CGMS) was used to detect the blood glucose fluctuations of these gout patients. Changes in blood glucose fluctuations after allopurinol therapy were also evaluated.

RESULTS

Compared with the reference values of blood glucose fluctuation parameters in China, gout patients had greater glycemic fluctuations including higher mean amplitude of glucose excursions (MAGE) (4.65 vs 1.94 mmol/L, P < .001), higher largest amplitude of blood glucose excursions (LAGE) (4.99 vs 3.72 mmol/L, P < .001) and higher standard deviations of blood glucose (SDBG) (1.36 vs 0.79 mmol/L, P < .001). MAGE was significantly correlated with uric acid (β = .007, P = .024) and HOMA-insulin resistance (IR) (β = .508, P = .03). Allopurinol treatment significantly reduced MAGE (4.16 vs 4.65 mmol/L, P < .001), SDBG (0.99 vs 1.36 mmol/L, P < .001) and HOMA-IR (2.26 vs 3.01, P < .001) in gout patients.

CONCLUSION

Blood glucose fluctuation increased even in the stage of normal glucose tolerance among gout patients. Blood glucose fluctuations in gout patients were associated with the level of serum uric acid and allopurinol could decrease blood glucose fluctuation as well as IR.

The effect of rising vs. falling glucose level on amperometric glucose sensor lag and accuracy in Type 1 diabetes

BACKGROUND

Because declining glucose levels should be detected quickly in persons with Type 1 diabetes, a lag between blood glucose and subcutaneous sensor glucose can be problematic. It is unclear whether the magnitude of sensor lag is lower during falling glucose than during rising glucose.

METHODS

Initially, we analysed 95 data segments during which glucose changed and during which very frequent reference blood glucose monitoring was performed. However, to minimize confounding effects of noise and calibration error, we excluded data segments in which there was substantial sensor error. After these exclusions, and combination of data from duplicate sensors, there were 72 analysable data segments (36 for rising glucose, 36 for falling). We measured lag in two ways: (1) the time delay at the vertical mid-point of the glucose change (regression delay); and (2) determination of the optimal time shift required to minimize the difference between glucose sensor signals and blood glucose values drawn concurrently.

RESULTS

Using the regression delay method, the mean sensor lag for rising vs. falling glucose segments was 8.9 min (95%CI 6.1-11.6) vs. 1.5 min (95%CI -2.6 to 5.5, P<0.005). Using the time shift optimization method, results were similar, with a lag that was higher for rising than for falling segments [8.3 (95%CI 5.8-10.7) vs. 1.5 min (95% CI -2.2 to 5.2), P<0.001]. Commensurate with the lag results, sensor accuracy was greater during falling than during rising glucose segments.

CONCLUSIONS

In Type 1 diabetes, when noise and calibration error are minimized to reduce effects that confound delay measurement, subcutaneous glucose sensors demonstrate a shorter lag duration and greater accuracy when glucose is falling than when rising.

The business of self-monitoring of blood glucose: a market profile

The market for self-monitoring of blood glucose (SMBG) approached $8.8 billion worldwide in 2008. Yet despite dramatic double-digit growth in sales of SMBG products since 1980, the business is now facing declining prices and slower dollar growth. Given that SMBG meters and test strips are viewed by consumers and insurers as essentially generic products, it will be extremely challenging for new market entrants to displace well-entrenched existing competitors without a truly innovative technology. Also, in the face of declining glucose test strip prices, market expansion can only occur through identification of more of the undiagnosed diabetes population and convincing existing diabetes patients to adopt glucose testing or to test more frequently. Ultimately, a combination of technology innovations, patient education, and economic incentives may be needed to significantly expand the SMBG market and build sustainable long-term dollar growth for SMBG vendors.