Disparities in prevalence and treatment of diabetes, cardiovascular and chronic kidney diseases - Recommendations from the taskforce of the guideline workshop

There is a mounting clinical, psychosocial, and socioeconomic burden worldwide as the prevalence of diabetes, cardiovascular disease (CVD), and chronic kidney disease (CKD) continues to rise. Despite the introduction of therapeutic interventions with demonstrated efficacy to prevent the development or progression of these common chronic diseases, many individuals have limited access to these innovations due to their race/ethnicity, and/or socioeconomic status (SES). However, practical guidance to providers and healthcare systems for addressing these disparities is often lacking. In this article, we review the prevalence and impact of healthcare disparities derived from the above-mentioned chronic conditions and present broad-based recommendations for improving access to quality care and health outcomes within the most vulnerable populations.

Telemonitoring, Telemedicine and Time in Range During the Pandemic: Paradigm Change for Diabetes Risk Management in the Post-COVID Future

People with diabetes are at greater risk for negative outcomes from COVID-19. Though this risk is multifactorial, poor glycaemic control before and during admission to hospital for COVID-19 is likely to contribute to the increased risk. The COVID-19 pandemic and restrictions on mobility and interaction can also be expected to impact on daily glucose management of people with diabetes. Telemonitoring of glucose metrics has been widely used during the pandemic in people with diabetes, including adults and children with T1D, allowing an exploration of the impact of COVID-19 inside and outside the hospital setting on glycaemic control. To date, 27 studies including 69,294 individuals with T1D have reported the effect of glycaemic control during the COVID-19 pandemic. Despite restricted access to diabetes clinics, glycaemic control has not deteriorated for 25/27 cohorts and improved in 23/27 study groups. Significantly, time in range (TIR) 70-180 mg/dL (3.9-10 mmol/L) increased across 19/27 cohorts with a median 3.3% (- 6.0% to 11.2%) change. Thirty per cent of the cohorts with TIR data reported an average clinically significant TIR improvement of 5% or more, possibly as a consequence of more accurate glucose monitoring and improved connectivity through telemedicine. Periodic consultations using telemedicine enables care of people with diabetes while limiting the need for in-person attendance at diabetes clinics. Reports that sustained hyperglycaemia and early-stage diabetic ketoacidosis may go untreated because of the lockdown and concerns about potential exposure to the risk of infection argue for wider access to glucose telemonitoring. Therefore, in this paper we have critically reviewed reports concerning use of telemonitoring in the acute hospitalized setting as well as during daily diabetes management. Furthermore, we discuss the indications and implications of adopting telemonitoring and telemedicine in the present challenging time, as well as their potential for the future.

Has COVID-19 lockdown improved glycaemic control in pediatric patients with type 1 diabetes? An analysis of continuous glucose monitoring metrics

AIMS

Our observational study aimed to evaluate the impact of the lockdown period due to 2019 Coronavirus disease pandemic on glycaemic control in a cohort of paediatric patients with type 1 diabetes (T1D).

METHODS

Eighty-five patients with T1D aged 5-18 years using continuous glucose monitoring (CGM) systems were enrolled. Demographic and clinical data, including glucose metrics generated by CGM-specific web-based cloud platforms, were collected in three different periods (pre-lockdown phase, lockdown phase, and post-lockdown phase) of 90 days each and were statistically analysed.

RESULTS

During the lockdown period, a clear improvement in almost all CGM metrics (time in range, time above range, coefficient of variation, and glucose management indicator) was observed in our study population, regardless of age and insulin type treatment. In the months following lockdown, maintaining satisfactory diabetes outcomes was confirmed only in younger patients (aged 5-9 years) and in those individuals on hybrid closed loop therapy.

CONCLUSIONS

The increasing use of innovative technological devices together with data sharing systems and interaction with multidisciplinary diabetes team through telemedicine allowed paediatric patients with T1D to improve glucose metrics during the lockdown period. However, our findings showed that the achievement of better glycaemic control was transient for most patients.

Profiles of Intraday Glucose in Type 2 Diabetes and Their Association with Complications: An Analysis of Continuous Glucose Monitoring Data

Aims: To identify profiles of type 2 diabetes from continuous glucose monitoring (CGM) data using ambulatory glucose profile (AGP) indicators and examine the association with prevalent complications. Methods: Two weeks of CGM data, collected between 2015 and 2019, from 5901 adult type 2 diabetes patients were retrieved from a clinical database in Chennai, India. Non-negative matrix factorization was used to identify profiles as per AGP indicators. The association of profiles with existing complications was examined using multinomial and logistic regressions adjusted for glycated hemoglobin (HbA1c; %), sex, age at onset, and duration of diabetes. Results: Three profiles of glycemic variability (GV) were identified based on CGM data-Profile 1 ["TIR Profile"] (n = 2271), Profile 2 ["Hypo"] (n = 1471), and Profile 3 ["Hyper"] (n = 2159). Compared with time in range (TIR) profile, those belonging to Hyper had higher mean fasting plasma glucose (202.9 vs. 167.1, mg/dL), 2-h postprandial plasma glucose (302.1 vs. 255.6, mg/dL), and HbA1c (9.7 vs. 8.6; %). Both "Hypo profile" and "Hyper profile" had higher odds of nonproliferative diabetic retinopathy ("Hypo": 1.44, 1.20-1.73; "Hyper": 1.33, 1.11-1.58), macroalbuminuria ("Hypo": 1.58, 1.25-1.98; "Hyper": 1.37, 1.10-1.71), and diabetic kidney disease (DKD; "Hypo": 1.65, 1.18-2.31; "Hyper": 1.88, 1.37-2.58), compared with "TIR profile." Those in "Hypo profile" (vs. "TIR profile") had higher odds of proliferative diabetic retinopathy (PDR; 2.84, 1.65-2.88). Conclusions: We have identified three profiles of GV from CGM data. While both "Hypo profile" and "Hyper profile" had higher odds of prevalent DKD compared with "TIR profile," "Hypo profile" had higher odds of PDR. Our study emphasizes the clinical importance of recognizing and treating hypoglycemia (which is often unrecognized without CGM) in patients with type 2 Diabetes Mellitus.

Glycemic Variability in Type 1 Diabetes Mellitus Saudis Using Ambulatory Glucose Profile

Background

Glucose variability (GV) is a common and challenging clinical entity in the management of people with type 1 diabetes (T1DM). The magnitude of GV in Saudi people with T1DM was not addressed before. Therefore, we aimed to study GV in a consecutive cohort of Saudis with T1DM.

Methods

We prospectively assessed interstitial glucose using FreeStyle^® Libre flash glucose monitoring in people with TIDM who attended follow-up in the diabetes clinics at King Fahad Medical City between March and June 2017. Glycemia profile, standard deviation (SD), coefficient of variation (CV), mean of daily differences (MODD), and mean amplitude of glucose excursion (MAGE) were measured using the standard equations over a period of 2 weeks.

Results

Fifty T1DM subjects (20 males) with mean age 20.2 ± 6.1 years and mean fortnight glucose 192 ± 42.3 mg/dl were included. The mean SD of 2-week glucose readings was 100.4 ± 36.3 mg/dl and CV was 52.1% ± 13%. Higher levels of glucose excursions were also observed. MODD and MAGE were recorded as 104.5 ± 51.7 and 189 ± 54.9 mg/dl, respectively which is 2 to 4 times higher than the international standards. Higher MODD and MAGE were observed on weekends compared to weekdays (111.3 ± 62.1 vs 98.6 ± 56.2 mg/dl and 196.4 ± 64.6 vs 181.7 ± 52.4 mg/dl, respectively; P ⩽ .001).

Conclusion

Higher degree of glycemic variability was observed in this cohort of TIDM Saudis. Weekends were associated with higher glucose swings than weekdays. More studies are needed to explore these findings further.

The Ambulatory Glucose Profile: Opportunities for Enhancement

The ambulatory glucose profile (AGP) and the frequency distribution for glucose by ranges are well established as standard methods for display, analysis, and interpretation of glucose data arising from self-monitoring, continuous glucose monitoring, and automated insulin delivery systems. In this review, we consider several refinements that may further improve the utility of the AGP. These include (1) display of the AGP together with information regarding dietary intake, medication administration (e.g., insulin), glucose lowering (pharmacodynamic) activity of medications, and physical activity measured by accelerometers or heart rate; (2) display of average time below range (%TBR), time above range (%TAR), and time in range (%TIR) by time of day to indicate timing of hypoglycemic and hyperglycemic episodes; (3) detailed analysis of postprandial excursions for each of the major meals after synchronizing by onset of meals and adjusting for the premeal glucose levels, enabling comparisons of magnitude, shape, and patterns; (4) methods to characterize distinct patterns on different days of the week; (5) display of glucose on a nonlinear scale to improve the balance between deviations associated with hypoglycemia versus hyperglycemia; (6) use of time scales other than midnight-to-midnight to facilitate analysis of time segments of particular interest (e.g., overnight); (7) options to display individual glucose values to assist in the identification of dates and times of outliers and episodes of hypoglycemia and hyperglycemia; and (8) methods to compare AGPs obtained from different individuals or groups receiving alternative interventions in terms of therapy or technology. These refinements, individually or collectively, can potentially further enhance the effectiveness of the AGP for assessment of glucose levels, patterns, and variability. We discuss several questions regarding implementation and optimization of these methods.

Standardized Hybrid Closed-Loop System Reporting

The hybrid closed-loop (HCL) system has been shown to improve glycemic control and reduce hypoglycemia. Optimization of HCL settings requires interpretation of the glucose, insulin, and factors affecting glucose such as food intake and exercise. To the best of our knowledge, there is no published guidance on the standardized reporting of HCL systems. Standardization of HCL reporting would make interpretation of data easy across different systems. We reviewed the literature on patient and provider perspectives on downloading and reporting glucose metric preferences. We also incorporated international consensus on standardized reporting for glucose metrics. We describe a single-page HCL data reporting, referred to here as "artificial pancreas (AP) Dashboard." We propose seven components in the AP Dashboard that can provide detailed information and visualization of glucose, insulin, and HCL-specific metrics. The seven components include (A) glucose metrics, (B) hypoglycemia, (C) insulin, (D) user experience, (E) hyperglycemia, (F) glucose modal-day profile, and (G) insight. A single-page report similar to an electrocardiogram can help providers and patients interpret HCL data easily and take the necessary steps to improve glycemic outcomes. We also describe the optimal sampling duration for HCL data download and color coding for visualization ease. We believe that this is a first step in creating a standardized HCL reporting, which may result in better uptake of the systems. For increased adoption, standardized reporting will require input from providers, patients, diabetes device manufacturers, and regulators.

AGP and Nutrition - Analysing postprandial glucose courses with CGM

Nutritional therapies are one of the fundamentals of effective management of diabetes type 1 and type 2. Lifestyle interventions, including nutritional recommendations, are also part of the basic therapy for people with prediabetes or obesity. It is recommended that the diet should be individually adapted to personal circumstances, preferences and metabolic goals. In the age of digitalisation, mHealth interventions, like continuous glucose monitoring systems (CGM), are increasingly finding their way into nutrition therapy. The ambulatory glucose profile (AGP), a structured and graphical compilation of the obtained CGM data, can also be used as a support for dietary adjustment. After assessment of the glycaemic situation (hypoglycaemia, variability and stability of glucose levels). This publication aims to provide a general overview of nutritional recommendations, especially in Germany, and to describe the benefits of CGM measurements with regard to nutrition.

Utilizing continuous glucose monitoring in primary care practice: What the numbers mean

Use of continuous glucose monitoring (CGM) has been shown to improve glycemia control, reduce hypoglycemia, lower glycemic variability and enhance quality of life for individuals with type 1 diabetes and type 2 diabetes. However, many primary care physicians may be unfamiliar with the how CGM data can interpreted and acted upon. As adoption of this technology continues to grow, primary care physicians will be challenged to integrate CGM into their clinical practices. This article is intended to provide clinicians with practical guidance in interpreting and utilizing CGM data with their patients.

Standardizing Reporting of Glucose and Insulin Data for Patients on Multiple Daily Injections Using Connected Insulin Pens and Continuous Glucose Monitoring

Background: Recent development and availability of several connected insulin pens with digital memory are likely to expand the availability of glucose and insulin metrics that previously had been available only for the much smaller number of people using insulin pumps. It would be highly desirable to standardize data presentations to avoid the chaotic emergence of multiple formats that might reduce the clinical utility of connected pens. Methods: We reviewed the literature and analyzed data displays from multiple blood glucose monitoring, continuous glucose monitoring (CGM), insulin pump, and automated insulin delivery systems, and methods for combination of glucose and insulin data. We examined multiple forms of presentation and now propose a prototype for a standardized method for data analysis and display, focusing on the content and format of a one-page dashboard summary for patients on multiple daily injection (MDI) insulin regimens. Results: We propose the following metrics to be included in the one-page report: (A) glucose metrics: simplified glucose distribution in the form of a stacked bar chart showing percentages of time below-, above-, or within-target ranges overall and (optionally) by date, by time of day, or day of the week; (B) insulin metrics: types and doses, and timing of basal and bolus insulin; (C) an enhanced ambulatory glucose profile or "AGP+" showing glucose data points and/or distributions (10th to 90th percentiles), dosages and timing of basal and bolus insulins and (optionally) graphical display of risk of hypoglycemia and hyperglycemia; and (D) user experience regarding technology usage, frequency of alerts for hypo- and hyperglycemia, and information regarding lifestyle, meals, exercise, and sleep, if available; and (E) clinical insights and interpretation. Conclusion: We propose a prototype for a dashboard summary report of glucose, insulin, meals, and activity data intended for providers and patients on MDI using connected pens and CGM. Our goal is to stimulate development of a standardized approach.

Lessons learned from the continuous glucose monitoring metrics in pediatric patients with type 1 diabetes under COVID-19 lockdown

AIMS

Billions of people have been under lockdown in an attempt to prevent COVID-19 spread. Lifestyle changes during lockdown could lead to deterioration of glycemic control in type 1 diabetes (T1D). We aimed to assess the impact of COVID-19 lockdown on the glycemic control of pediatric patients with T1D.

METHODS

This observational real-life study from the AWeSoMe Group assessed continuous glucose monitoring (CGM) metrics of 102 T1D patients (52.9% males, mean age 11.2 ± 3.8 years, mean diabetes duration 4.2 ± 3.8 years) who used  Dexcom G5. The data were accessed without any interface between patients, caregivers, and the diabetes team. Study variables from CGM metrics were: mean glucose level, time-in-range (TIR, 70-180 mg/dL; 3.9-10 mmol/L), hypoglycemia (< 54 mg/dL; < 3 mmol/L), hyperglycemia (> 250 mg/dL; > 13.3 mmol/L), coefficient of variation (CV), and time CGM active before and during lockdown. Delta-variable = lockdown variable minus before-lockdown variable.

RESULTS

The mean TIR was 60.9 ± 14.3% before lockdown, with no significant change during lockdown (delta-TIR was 0.9 ± 7.9%). TIR during lockdown was significantly correlated with TIR before lockdown (r = 0.855, P < 0.001). Patients with improved TIR (delta-TIR > 3%) were significantly older than patients with stable or worse TIR (P = 0.028). Children aged < 10 years had a significantly higher CV before lockdown and during lockdown than children aged ≥ 10 years (P = 0.02 and P = 0.005, respectively). Among children aged < 10 years, a multiple linear regression model revealed associations of age and lower socioeconomic cluster with delta-TIR (F = 4.416, P = 0.019) and with delta-mean glucose (F = 4.459, P = 0.018).

CONCLUSIONS

CGM metrics in pediatric patients with T1D were relatively stable during a nationwide lockdown. Intervention plans should focus on younger patients with lower socioeconomic position.

HbA1c: The Glucose Management Indicator, Time in Range, and Standardization of Continuous Glucose Monitoring Reports in Clinical Practice

Continuous glucose monitoring (CGM) use is growing rapidly among people with diabetes and beginning to be standard of care for managing glucose levels in insulin therapy. With this increased use, there is a need to standardize CGM data. CGM standardization has been set forth by expert panels. The Glucose Management Indicator is a concept using the CGM-derived mean glucose to provide a value that can be understood similarly to hemoglobin A_1c. The times an individual spends in various glucose ranges is emerging as an important set of metrics. Metrics derived from patient CGM data are changing the way diabetes is managed.

Utilizing the Ambulatory Glucose Profile to Standardize and Implement Continuous Glucose Monitoring in Clinical Practice

Use of continuous glucose monitoring (CGM) is recognized as a valuable component of diabetes self-management and is increasingly considered a standard of care for individuals with diabetes who are treated with intensive insulin therapy. As the clinical use of CGM technology expands, consistent and standardized glycemic metrics and glucose profile visualization have become increasingly important. A common set of CGM metrics has been proposed by an international expert panel in 2017, including standard definitions of time in ranges, glucose variability, and adequacy of data collection. We describe the core CGM metrics, as well as the standardized glucose profile format consolidating 2 weeks of CGM measurements, referred to as the ambulatory glucose profile (AGP), which was also recommended by the CGM expert panel. We present an updated AGP report featuring the core CGM metrics and a visualization of glucose patterns that need clinical attention. New tools for use by clinicians and patients to interpret AGP data are reviewed. Strategies based on the authors' experience in implementing CGM technology across the clinical care spectrum are highlighted.

Flash Glucose Monitoring in Subjects with Diabetes on Hemodialysis: A Pilot Study

BACKGROUND

In patients with diabetes related end-stage renal disease (ESRD) on hemodialysis, blood glucose management can be challenging due to the kinetics of glucose and insulin in addition to other factors. The glucose monitoring systems which measure glucose levels continuously may be useful to study the glucose profile of patients with diabetes undergoing hemodialysis. Our study is designed to use ambulatory glucose profile to study the glucose pattern - during, before, and after a session of hemodialysis.

MATERIALS AND METHODS

Ten patients with type 2 diabetes with ESRD undergoing hemodialysis were recruited. Forty-eight glucose readings were recorded in a 12-h period which included 4 h each prior, during, and after the dialysis session with a flash glucose monitor (FreeStyle Libre-pro). The same 12 h time frame was also monitored on a non-dialysis day.

RESULTS

On the day of dialysis, the mean glucose level was significantly lower (P = 0.013) compared to the day without dialysis (95 ± 12.7 mg/dl vs 194 ± 76.8 mg/dl). As compared to the pre-dialysis period, the mean blood glucose levels during dialysis were lower (P = 0.004). As compared to the dialysis period, the mean blood glucose levels in the post-dialysis period were higher but did not reach statistical significance.

CONCLUSION

In our study, subjects with type 2 diabetes on hemodialysis had lower glucose levels on the day of dialysis compared to non-dialysis day. Glucose levels showed a fall during hemodialysis and then a rise to higher levels after dialysis.

The Effectiveness of Continuous Glucose Monitoring in Patients with Type 2 Diabetes: A Systematic Review of Literature and Meta-analysis

BACKGROUND

Continuous glucose monitoring (CGM) provides glucose trend information that can be used to guide treatment and motivate patients with diabetes. Currently, the evidence on effectiveness of CGM in patients with type 2 diabetes is debatable. We aim to provide a systematic review and meta-analysis to synthesize current evidence of effectiveness of CGM in adults with type 2 diabetes.

MATERIALS AND METHODS

Cochrane, Embase, PubMed, and Web of Science were searched to include all studies that reported effectiveness of CGM in terms of HbA1c in adults aged 18 and older, with type 2 diabetes, on any treatment for diabetes. Heterogeneity (I²) was used to determine the variability between studies. All data were analyzed using Review Manager 5.3 software.

RESULTS

Seven randomized controlled trials and three cohort studies, involving 1384 patients for real-time CGM (RT-CGM) and professional CGM (P-CGM) and 4902 patients for flash glucose monitoring (FGM), were included. RT-CGM and P-CGM were associated with a modest but statistically significant reduction in HbA1c of 0.20% (95% confidence interval [CI] -0.31 to -0.09) compared with control. Patients who received FGM had a nonsignificant reduction of 0.02% (95% CI -0.07 to 0.04) compared with control. The meta-analysis indicated a low heterogeneity between studies.

CONCLUSION

Our analysis of current evidence suggests that RT-CGM and P-CGM are effective in improving HbA1c in adults with type 2 diabetes. Due to insufficient evidence, it is premature to form conclusions on the effectiveness of FGM. Future multicenter trials accessing the effectiveness of CGM as well as safety and cost-effectiveness may be necessary.

Glucose Variability: A Review of Clinical Applications and Research Developments

Glycemic variability (GV) is a major consideration when evaluating quality of glycemic control. GV increases progressively from prediabetes through advanced T2D and is still higher in T1D. GV is correlated with risk of hypoglycemia. The most popular metrics for GV are the %Coefficient of Variation (%CV) and standard deviation (SD). The %CV is correlated with risk of hypoglycemia. Graphical display of glucose by date, time of day, and day of the week, and display of simplified glucose distributions showing % of time in several ranges, provide clinically useful indicators of GV. SD is highly correlated with most other measures of GV, including interquartile range, mean amplitude of glycemic excursion, mean of daily differences, and average daily risk range. Some metrics are sensitive to the frequency, periodicity, and complexity of glycemic fluctuations, including Fourier analysis, periodograms, frequency spectrum, multiscale entropy (MSE), and Glucose Variability Percentage (GVP). Fourier analysis indicates progressive changes from normal subjects to children and adults with T1D, and from prediabetes to T2D. The GVP identifies novel characteristics for children, adolescents, and adults with type 1 diabetes and for adults with type 2. GVP also demonstrated small rapid glycemic fluctuations in people with T1D when using a dual-hormone closed-loop control. MSE demonstrated systematic changes from normal subjects to people with T2D at various stages of duration, intensity of therapy, and quality of glycemic control. We describe new metrics to characterize postprandial excursions, day-to-day stability of glucose patterns, and systematic changes of patterns by day of the week. Metrics for GV should be interpreted in terms of percentiles and z-scores relative to identified reference populations. There is a need for large accessible databases for reference populations to provide a basis for automated interpretation of GV and other features of continuous glucose monitoring records.

Improving the clinical value and utility of CGM systems: issues and recommendations : A joint statement of the European Association for the Study of Diabetes and the American Diabetes Association Diabetes Technology Working Group

The first systems for continuous glucose monitoring (CGM) became available over 15 years ago. Many then believed CGM would revolutionise the use of intensive insulin therapy in diabetes; however, progress towards that vision has been gradual. Although increasing, the proportion of individuals using CGM rather than conventional systems for self-monitoring of blood glucose on a daily basis is still low in most parts of the world. Barriers to uptake include cost, measurement reliability (particularly with earlier-generation systems), human factors issues, lack of a standardised format for displaying results and uncertainty on how best to use CGM data to make therapeutic decisions. This scientific statement makes recommendations for systemic improvements in clinical use and regulatory (pre- and postmarketing) handling of CGM devices. The aim is to improve safety and efficacy in order to support the advancement of the technology in achieving its potential to improve quality of life and health outcomes for more people with diabetes.

A Multicenter Real-Life Study on the Effect of Flash Glucose Monitoring on Glycemic Control in Patients with Type 1 and Type 2 Diabetes

AIM

To assess the efficacy of ambulatory glucose profiling (AGP) generated by FreeStyle LibrePro^™ flash glucose monitoring (FCGM) on glycemic control in patients with uncontrolled type 1 diabetes (T1D) and type 2 diabetes (T2D).

METHODS

Clinical and biochemical data were obtained from 5072 patients with diabetes who had an A1c ≥7% (2536 who had been initiated on FCGM-based AGP between March 2015 and October 2016 [cases] and 2536 age-, gender-, A1c-, site- and time-matched controls who were not initiated on AGP) across seven diabetes clinics in India. Anthropometric and clinical measurements were obtained through standardized techniques. Fasting and postprandial plasma glucose and glycated hemoglobin(A1c) were estimated before and after initiation of AGP.

RESULTS

Overall, there was a significant decrease in A1c both in cases and controls; however, the magnitude of reduction was higher among cases (1% vs.0.7%; P < 0.001).The overall reduction in A1c among cases was higher in T2D (9.2% to 8.3%) compared with T1D (9.6% to 9.4%); however, the absolute difference in A1c reduction between cases and controls was higher among T1D (0.5% vs. 0.2%) patients. The reduction in glycemic parameters was irrespective of age or gender (P for trend <0.001) across all study sites. The greatest reductions in A1c were noted within 6 months of AGP initiation. Multiple logistic regression showed that those who did not use AGP had a 1.42 higher risk (95% CI: 1.24-1.64) of not achieving even 0.1% reduction in A1c compared with those who were initiated on AGP even after adjusting for age, gender, body-mass index, systolic blood pressure, time to follow-up A1c, and medication use.

CONCLUSIONS

This study shows that FCGM-based AGP with FreeStyle LibrePro is associated with significant reductions in A1c levels in both T1D and T2D. In addition, improvement in A1c levels was maintained across all age groups and in patients enrolled at different diabetes clinics in India.

Sensor and software use for the glycaemic management of insulin-treated type 1 and type 2 diabetes patients

Lowering glucose levels, while avoiding hypoglycaemia, can be challenging in insulin-treated patients with diabetes. We evaluated the role of ambulatory glucose profile in optimising glycaemic control in this population. Insulin-treated patients with type 1 and type 2 diabetes were recruited into a prospective, multicentre, 100-day study and randomised to control (n = 28) or intervention (n = 59) groups. The intervention group used ambulatory glucose profile, generated by continuous glucose monitoring, to assess daily glucose levels, whereas the controls relied on capillary glucose testing. Patients were reviewed at days 30 and 45 by the health care professional to adjust insulin therapy. Comparing first and last 2 weeks of the study, ambulatory glucose profile-monitored type 2 diabetes patients (n = 28) showed increased time in euglycaemia (mean ± standard deviation) by 1.4 ± 3.5 h/day (p = 0.0427) associated with reduction in HbA1c from 77 ± 15 to 67 ± 13 mmol/mol (p = 0.0002) without increased hypoglycaemia. Type 1 diabetes patients (n = 25) showed reduction in hypoglycaemia from 1.4 ± 1.7 to 0.8 ± 0.8 h/day (p = 0.0472) associated with a marginal HbA1c decrease from 75 ± 10 to 72 ± 8 mmol/mol (p = 0.0508). Largely similar findings were observed comparing intervention and control groups at end of study. In conclusion, ambulatory glucose profile helps glycaemic management in insulin-treated diabetes patients by increasing time spent in euglycaemia and decreasing HbA1c in type 2 diabetes patients, while reducing hypoglycaemia in type 1 diabetes patients.