Beyond HbA1c: Comparing Glycemic Variability and Glycemic Indices in Predicting Hypoglycemia in Type 1 and Type 2 Diabetes

Initiating or switching to insulin degludec/insulin aspart in a real-world population of adults with type 2 diabetes in Australia: results from a prospective, non-interventional study

BACKGROUND

Insulin degludec/insulin aspart (IDegAsp) is a fixed-ratio co-formulation of insulin degludec and insulin aspart for the treatment of people with diabetes and suboptimal glycaemic control. Few real-world studies of IDegAsp treatment have been conducted. Here, we report results from the Australian cohort of the global ARISE study of real-world IDegAsp use.

AIMS

To investigate glycaemic control and other clinical outcomes in people with type 2 diabetes (T2D) treated with IDegAsp in a real-world setting in Australia.

METHODS

A total of 183 adults with T2D initiating or switching to IDegAsp in the Australian cohort of the open-label, non-interventional ARISE study were followed for 26-36 weeks from August 2019 to December 2020.

RESULTS

IDegAsp was associated with significant reductions from baseline to end of study (EOS) in mean glycated haemoglobin (estimated change -0.8% (95% confidence interval (CI): -1.05 to -0.56; P < 0.0001)), fasting plasma glucose (-1.6 mmol/L (95% CI: -2.49 to -0.63; P = 0.0017)) and body weight (-2.6 kg (95% CI: -3.68 to -1.55; P < 0.0001)). In insulin-experienced patients, the mean total daily insulin dose did not change significantly (estimated change from baseline to EOS 3.8 (95% CI: -3.70 to 11.21; P = 0.3202)). The proportion of patients experiencing hypoglycaemia numerically decreased during the study (non-severe: 14.2-10.9%; nocturnal non-severe: 4.9-2.2%; and severe: 2.2-0%).

CONCLUSIONS

Initiating or switching to IDegAsp in a real-world population of people with T2D in Australia was associated with significant improvements in glycaemic control and body weight, and numerically lower levels of hypoglycaemia compared with baseline.

Current landscape and comprehensive management of glycemic variability in diabetic retinopathy

Diabetic retinopathy (DR), a well-known microvascular complication of diabetes mellitus, remains the main cause of vision loss in working-age adults worldwide. Up to now, there is a shortage of information in the study regarding the contributing factors of DR in diabetes. Accumulating evidence has identified glycemic variability (GV), referred to fluctuations of blood glucose levels, as a risk factor for diabetes-related complications. Recent reports demonstrate that GV plays an important role in accounting for the susceptibility to DR development. However, its exact role in the pathogenesis of DR is still not fully understood. In this review, we highlight the current landscape and relevant mechanisms of GV in DR, as well as address the mechanism-based therapeutic strategies, aiming at better improving the quality of DR management in clinical practice.

Glucose Targets Using Continuous Glucose Monitoring Metrics in Older Adults With Diabetes: Are We There Yet?

The older population is increasing worldwide and up to 30% of older adults have diabetes. Older adults with diabetes are at risk of glucose-related acute and chronic complications. Recently, mostly in type 1 diabetes (T1D), continuous glucose monitoring (CGM) devices have proven beneficial in improving time in range (TIR glucose, 70-180 mg/dL or glucose 3.9-10 mmol/L), glycated hemoglobin (HbA1c), and in lowering hypoglycemia (time below range [TBR] glucose <70 mg/dL or glucose <3.9 mmol/L). The international consensus group formulated CGM glycemic targets relating to older adults with diabetes based on very limited data. Their recommendations, based on expert opinion, were aimed at mitigating hypoglycemia in all older adults. However, older adults with diabetes are a heterogeneous group, ranging from healthy to very complex frail individuals based on chronological, biological, and functional aging. Recent clinical trial and real-world data, mostly from healthy older adults with T1D, demonstrated that older adults often achieve CGM targets, including TIR recommended for non-vulnerable groups, but less often meet the recommended TBR <1%. Existing data also support that hypoglycemia avoidance may be more strongly related to minimization of glucose variability (coefficient of variation [CV]) rather than lower TIR. Very limited data are available for glucose goals in older adults adjusted for the complexity of their health status. Herein, we review the bidirectional associations between glucose and health status in older adults with diabetes; use of diabetes technologies, and their impact on glucose control; discuss current guidelines; and propose a new set of CGM targets for older adults with insulin-treated diabetes that are individualized for health and living status.

Living with and managing type 1 diabetes in humanitarian settings: A qualitative synthesis of lived experience and stakeholder tacit knowledge

Humanitarian health actors are beginning to better consider and manage non-communicable diseases, such as diabetes, in emergency and protracted crisis settings. However, a focus on the more globally prevalent type 2 diabetes (T2D) dominates. Blind spots prevail in the unmet needs for type 1 diabetes (T1D), a chronic autoimmune condition where individuals are unable to produce insulin, thereby dependent on lifelong insulin therapy and blood glucose management. Although some T1D management requirements overlap with those of T2D, the immediate risk of fatal complications following insulin therapy disruption, the earlier age of onset during childhood, adolescence or young adulthood, and its lower prevalence compared to T2D within communities and local health systems mean that T1D requires nuanced consideration and targeted interventions. Intending to inform program and policy design for people with T1D (PWT1D), we synthesized themes of lived experience from PLWT1D and their caregivers, and the tacit working knowledge of health providers and policymakers in the context of local humanitarian operations. Through a strategic search of health databases (up to July 2023), we identified 11 articles that include interview excerpts from PWT1D, caregivers, healthcare providers and policymakers about T1D management in humanitarian settings. We used reflexive thematic analysis to guide data extraction, coding, and synthesis, resulting in the identification of four overarching themes: food and insulin security, family relations, knowledge translation, and response to diagnosis. The narratives highlight harsh trade-offs made by PWT1D and their families in the face of insulin and food insecurity, as well as the damaging impact of low T1D education in families, communities and health systems. Targeted family and community-based solutions are urgently required, alongside systemic reforms and international collaboration to enable better T1D coping and management in humanitarian settings.

Association between continuous glucose monitoring metrics and clinical outcomes in adults with type 1 diabetes in a real-world setting

AIMS

Continuous glucose monitoring (CGM) metrics can assist diabetes management. Consensus statements recommend > 70 % time in range (TIR) and ≤ 36 % glucose coefficient of variation (CV). However, how these targets perform in clinical practice is unknown. This retrospective, longitudinal cohort study analyzed relationships between TIR, CV, glycated hemoglobin (HbA1c), and hypoglycemia in a real-world setting.

METHODS

Data of 542 adults with type 1 diabetes who used CGM (January 2014-July 2020) were analyzed. Associations between TIR and HbA1c at the same and subsequent visits, incidence rate ratios (IRRs) for hypoglycemia at different CVs, and number of hypoglycemic events at cross-sections of HbA1c and CV were estimated by regression.

RESULTS

TIR was inversely related to HbA1c; for every 10 % increase in TIR, HbA1c was significantly reduced by 0.34 % (4 mmol/mol) and 0.20 % (2 mmol/mol) at the same and subsequent visits, respectively. Level 2 hypoglycemia was significantly reduced at CV < 30 %, 30-33 %, 33.1-36 %, and 36.1-40 %: adjusted IRRs vs CV ≥ 40.1 % of 0.14, 0.28, 0.32, and 0.50, respectively. Hypoglycemic events were reduced at lower CV across HbA1c levels and at higher HbA1c across CV levels.

CONCLUSION

This study quantifies HbA1c improvements with increased TIR and hypoglycemia reductions with improved CV in clinical practice.

The Cost-Effectiveness of an Advanced Hybrid Closed-Loop System Compared to Standard Management of Type 1 Diabetes in a Singapore Setting

Background: Despite advances in technology, glycemic outcomes in people with type 1 diabetes (T1D) remain suboptimal. The MiniMed 780G (MM780G) advanced hybrid closed-loop (AHCL) system is the latest technology for T1D management with established safety and efficacy. This study explores the cost-effectiveness of MM780G AHCL compared against multiple daily injections (MDI) plus intermittently scanned continuous glucose monitor (isCGM). Methods: A cost-utility analysis was conducted, simulating lifetime outcomes for 1000 T1D individuals, with baseline hemoglobin A1c of 8.4%, using the IQVIA Core Diabetes Model (CDM) v9.5. A Singapore health care payer perspective was taken with 2023 costs applied. Treatment effects were taken from the ADAPT study and treatment-related events from a combination of sources. T1D complication costs were derived from local literature, and health state utilities and disutilities from published literature. Scenario analyses and probabilistic sensitivity analyses (PSAs) explored uncertainty. Cost-effectiveness was assessed based on willingness-to-pay (WTP) thresholds set to Singapore Dollars (SGD) 45,000 (United States Dollars [USD] 33,087) per quality-adjusted life year (QALY) and Singapore's gross domestic product (GDP) per capita of SGD 114,165 (USD 83,941) per QALY. Results: A switch from MDI plus isCGM to MM780G resulted in expected gains in life-years (+0.78) and QALYs (+1.45). Cost savings through reduction in T1D complications (SGD 25,465; USD 18,723) partially offset the higher treatment costs in the AHCL arm (+SGD 74,538; +USD 54,805), resulting in an estimated incremental cost-effectiveness ratio of SGD 33,797 (USD 24,850) per QALY gained. Findings were robust, with PSA outputs indicating 81% and 99% probabilities of cost-effectiveness at the stated WTP thresholds. Conclusion: MM780G is a cost-effective option for people with T1D managed in a Singapore setting.

Hybrid Closed Loop in Adults With Type 1 Diabetes and Severely Impaired Hypoglycemia Awareness

BACKGROUND

Benefits of hybrid closed-loop (HCL) systems in a high-risk group with type 1 diabetes and impaired awareness of hypoglycemia (IAH) have not been well-explored.

METHODS

Adults with Edmonton HYPO scores ≥1047 were randomized to 26-weeks HCL (MiniMed™ 670G) vs standard therapy (multiple daily injections or insulin pump) without continuous glucose monitoring (CGM) (control). Primary outcome was percentage CGM time-in-range (TIR; 70-180 mg/dL) at 23 to 26 weeks post-randomization. Major secondary endpoints included magnitude of change in counter-regulatory hormones and autonomic symptom responses to hypoglycemia at 26-weeks post-randomization. A post hoc analysis evaluated glycemia risk index (GRI) comparing HCL with control groups at 26 weeks post-randomization.

RESULTS

Nine participants (median [interquartile range (IQR)] age 51 [41, 59] years; 44% male; enrolment HYPO score 1183 [1058, 1308]; Clarke score 6 [6, 6]; n = 5 [HCL]; n = 4 [control]) completed the study. Time-in-range was higher using HCL vs control (70% [68, 74%] vs 48% [44, 50%], P = .014). Time <70 mg/dL did not differ (HCL 3.8% [2.7, 3.9] vs control 6.5% [4.3, 8.6], P = .14) although hypoglycemia episode duration was shorter (30 vs 50 minutes, P < .001) with HCL. Glycemia risk index was lower with HCL vs control (38.1 [30.0, 39.2] vs 70.8 [58.5, 72.4], P = .014). Following 6 months of HCL use, greater dopamine (24.0 [12.3, 27.6] vs -18.5 [-36.5, -4.8], P = .014), and growth hormone (6.3 [4.6, 16.8] vs 0.5 [-0.8, 3.0], P = .050) responses to hypoglycemia were observed.

CONCLUSIONS

Six months of HCL use in high-risk adults with severe IAH increased glucose TIR and improved GRI without increased hypoglycemia, and partially restored counter-regulatory responses.

CLINICAL TRIAL REGISTRATION

ACTRN12617000520336.

Simultaneous assessment of stress hyperglycemia ratio and glycemic variability to predict mortality in patients with coronary artery disease: a retrospective cohort study from the MIMIC-IV database

BACKGROUND

Stress hyperglycemia and glycemic variability (GV) can reflect dramatic increases and acute fluctuations in blood glucose, which are associated with adverse cardiovascular events. This study aimed to explore whether the combined assessment of the stress hyperglycemia ratio (SHR) and GV provides additional information for prognostic prediction in patients with coronary artery disease (CAD) hospitalized in the intensive care unit (ICU).

METHODS

Patients diagnosed with CAD from the Medical Information Mart for Intensive Care-IV database (version 2.2) between 2008 and 2019 were retrospectively included in the analysis. The primary endpoint was 1-year mortality, and the secondary endpoint was in-hospital mortality. Levels of SHR and GV were stratified into tertiles, with the highest tertile classified as high and the lower two tertiles classified as low. The associations of SHR, GV, and their combination with mortality were determined by logistic and Cox regression analyses.

RESULTS

A total of 2789 patients were included, with a mean age of 69.6 years, and 30.1% were female. Overall, 138 (4.9%) patients died in the hospital, and 404 (14.5%) patients died at 1 year. The combination of SHR and GV was superior to SHR (in-hospital mortality: 0.710 vs. 0.689, p = 0.012; 1-year mortality: 0.644 vs. 0.615, p = 0.007) and GV (in-hospital mortality: 0.710 vs. 0.632, p = 0.004; 1-year mortality: 0.644 vs. 0.603, p < 0.001) alone for predicting mortality in the receiver operating characteristic analysis. In addition, nondiabetic patients with high SHR levels and high GV were associated with the greatest risk of both in-hospital mortality (odds ratio [OR] = 10.831, 95% confidence interval [CI] 4.494-26.105) and 1-year mortality (hazard ratio [HR] = 5.830, 95% CI 3.175-10.702). However, in the diabetic population, the highest risk of in-hospital mortality (OR = 4.221, 95% CI 1.542-11.558) and 1-year mortality (HR = 2.013, 95% CI 1.224-3.311) was observed in patients with high SHR levels but low GV.

CONCLUSIONS

The simultaneous evaluation of SHR and GV provides more information for risk stratification and prognostic prediction than SHR and GV alone, contributing to developing individualized strategies for glucose management in patients with CAD admitted to the ICU.

Association between brain imaging biomarkers and continuous glucose monitoring-derived glycemic control indices in Japanese patients with type 2 diabetes mellitus

INTRODUCTION

Although type 2 diabetes mellitus (T2DM) is associated with alterations in brain structure, the relationship between glycemic control indices and brain imaging markers remains unclear. This study aimed to investigate the association between continuous glucose monitoring (CGM)-derived glycemic control indices and brain imaging biomarkers assessed by MRI.

RESEARCH DESIGN AND METHODS

This cross-sectional study included 150 patients with T2DM. The severity of cerebral white matter lesions (WMLs) was assessed using MRI for deep and subcortical white matter and periventricular hyperintensities. The degree of medial temporal lobe atrophy (MTA) was assessed using voxel-based morphometry. Each participant wore a retrospective CGM for 14 consecutive days, and glycemic control indices, such as time in range (TIR) and glycemia risk index (GRI), were calculated.

RESULTS

The proportion of patients with severe WMLs showed a decreasing trend with increasing TIR (P for trend=0.006). The proportion of patients with severe WMLs showed an increasing trend with worsening GRI (P for trend=0.011). In contrast, no significant association was observed between the degree of MTA and CGM-derived glycemic control indices, including TIR (P for trend=0.325) and GRI (P for trend=0.447).

CONCLUSIONS

The findings of this study indicate that the severity of WMLs is associated with TIR and GRI, which are indices of the quality of glycemic control.

TRIAL REGISTRATION NUMBER

UMIN000032143.

Serum 25-hydroxyvitamin D level is associated with short-term glycemic variability metrics derived from continuous glucose monitoring in T2DM

This study aims to investigate the association between 25-hydroxyvitamin D (25OHD) and continuous glucose monitoring-assessed short-term glycemic variability (GV) and HbA1c among patients with type 2 diabetes mellitus (T2DM). We conducted a cross-sectional study recruiting 325 patients. The association between 25OHD and GV metrics (mean amplitude of glycemic excursions [MAGE], coefficient of variation [CV], standard deviation of sensor glucose [SD], and TIR) and HbA1c were analyzed using multivariable linear and logistic regression analyses. The 25OHD level and GV metrics showed significant differences among HbA1c groups (P < 0.01). CV, MAGE, SD and HbA1c decreased, and TIR increased with ascending 25OHD tertiles (P < 0.05). Serum 25OHD was inversely associated with CV (β = - 0.211 [- 0.350 to - 0.071], P < 0.01) and HbA1c (β = - 0.061 [- 0.114 to - 0.031], P < 0.01), and further multivariable analyses confirmed these results (P < 0.05). However, no association of HbA1c and 25OHD was found with the highest tertile of CV. These findings revealed that increased GV and HbA1c were both associated with lower 25OHD, and the relationship between HbA1c and 25OHD was attenuated with higher glucose CV in T2DM. Taken together, the analyses suggest that increasing vitamin D status has effects on improvements in long-term glycemic control and low glycemic variability.

Expert Recommendations for Using Time-in-Range and Other Continuous Glucose Monitoring Metrics to Achieve Patient-Centered Glycemic Control in People With Diabetes

New metrics for assessing glycemic control beyond HbA1c have recently emerged due to the increasing use of continuous glucose monitoring (CGM) in diabetes clinical practice. Among them, time in range (TIR) has appeared as a simple and intuitive metric that correlates inversely with HbA1c and has also been newly linked to the risk of long-term diabetes complications. The International Consensus on Time in Range established a series of target glucose ranges (TIR, time below range and time above range) and recommendations for time spent within these ranges for different diabetes populations. These parameters should be evaluated together with the ambulatory glucose profile (AGP). Using standardized visual reporting may help people with diabetes and healthcare professionals in the evaluation of glucose control in frequent clinical situations. The objective of the present review is to provide practical insights to quick interpretation of patient-centered metrics based on flash glucose monitoring data, as well as showing some visual examples of common clinical situations and giving practical recommendations for their management.

Reproducibility of continuous glucose monitoring results under real-life conditions in an adult population: a functional data analysis

Continuous glucose monitoring systems (CGM) are a very useful tool to understand the behaviour of glucose in different situations and populations. Despite the widespread use of CGM systems in both clinical practice and research, our understanding of the reproducibility of CGM data remains limited. The present work examines the reproducibility of the results provided by a CGM system in a random sample of a free-living adult population, from a functional data analysis approach. Functional intraclass correlation coefficients (ICCs) and their 95% confidence intervals (CI) were calculated to assess the reproducibility of CGM results in 581 individuals. 62% were females 581 participants (62% women) mean age 48 years (range 18-87) were included, 12% had previously been diagnosed with diabetes. The inter-day reproducibility of the CGM results was greater for subjects with diabetes (ICC 0.46 [CI 0.39-0.55]) than for normoglycaemic subjects (ICC 0.30 [CI 0.27-0.33]); the value for prediabetic subjects was intermediate (ICC 0.37 [CI 0.31-0.42]). For normoglycaemic subjects, inter-day reproducibility was poorer among the younger (ICC 0.26 [CI 0.21-0.30]) than the older subjects (ICC 0.39 [CI 0.32-0.45]). Inter-day reproducibility was poorest among normoglycaemic subjects, especially younger normoglycaemic subjects, suggesting the need to monitor some patient groups more often than others.

Low Blood Glucose Index Associated with Cardiovascular Events in Diabetic Hemodialysis Patients

INTRODUCTION

Blood glucose monitoring was vitally important in diabetic kidney disease (DKD) patients for preventing complications and improving survival rates. The associations between glycemic variability and blood biochemical indicators were underestimated in patients with DKD undergoing hemodialysis. Therefore, we primarily aimed to investigate the glycemic variability and 1-year risk of cardiovascular disease events in diabetic hemodialysis patients. And we secondarily aimed to explore the association between glycemic variability and blood biochemical indicators.

METHODS

In total, 27 patients were included in the final analysis. Continuous glucose monitoring (CGM) was used to evaluate glucose variability for 14 days. Patients were divided into two groups by the cutoff level of time in range (TIR; >70% or ≤70%). The three-point major adverse cardiovascular event (3P MACE) was recorded within 1 year.

RESULTS

After 1 year of follow-up, 4 patients in the high-TIR group and 3 patients in the low-TIR group had 3p MACE. Higher low blood glucose index (LBGI) level in diabetic hemodialysis patients increased the risk of 3p MACE outcomes (HR = 2.37, p = 0.018). And the level of albumin was positively associated with LBGI (β = 0.51, p = 0.036). The plasma levels of albumin, glycosylated hemoglobin, and hemoglobin were positively associated with other CGM parameters.

CONCLUSION

LBGI during 14 days was positively associated with the risk of cardiovascular events in diabetic hemodialysis patients.

Effectiveness of flash glucose monitoring in patients with type 1 diabetes and recurrent hypoglycemia between early and late stages after flash glucose monitoring initiation

AIMS

Evaluate the effectiveness of reimbursed flash glucose monitoring with optional alarms (FGM) in preventing severe hypoglycemia (SH) and reducing hypoglycemia exposure in T1D patients prone to hypoglycemia.

METHODS

Ambispective study in T1D patients treated with multiple daily injections (MDI) and prone to hypoglycemia, initiating reimbursed FGM (FreeStyle Libre 2). The primary outcome was the number of SH events (requiring third party assistance) and main secondary outcomes were time below range < 70 (TBR < 70) and < 54 mg/dL (TBR < 54), impaired awareness of hypoglycemia (IAH) and quality of life (QoL). Logistic regression models were constructed to explore variables associated with success of the intervention.

RESULTS

We included 110 patients (52.7 % women, mean age 47.8 ± 17.0 years). SH events at 1-year follow-up decreased from 0.3 ± 0.6 to 0.03 ± 0.2 (p < 0.001). Significant reductions in patients presenting an SH (26.4 % vs. 2.9 %, p < 0.001) and IAH (47.1 % vs. 25.9 %, p = 0.002) were observed, as well as improvements in QoL. TBR < 70 and TBR < 54 were not significantly reduced. Baseline GMI was inversely associated with a decrease in TBR < 70 [OR 0.37 (0.15-0.93)] and directly with an increase in time in range 70-180 mg/dL [OR 2.10 (1.03-4.28)].

CONCLUSIONS

FGM decreased SH and improved hypoglycemia awareness and QoL. Initial tight glycemic control was associated with a decrease in hypoglycemia, while patients with suboptimal control reduced hyperglycemia.

Association of Smartphone-Based Activity Tracking and Nocturnal Hypoglycemia in People With Type 1 Diabetes

BACKGROUND

Nocturnal hypoglycemia (NH) remains a major burden for people with type 1 diabetes (T1D). Daytime physical activity (PA) increases the risk of NH. This pilot study tested whether cumulative daytime PA measured using a smartphone-based step tracker was associated with NH.

METHODS

Adults with T1D for ≥ 5 years (y) on multiple daily insulin or continuous insulin infusion, not using continuous glucose monitoring and HbA1c 6 to 10% wore blinded Freestyle Libre Pro sensors and recorded total daily carbohydrate (TDC) and total daily dose (TDD) of insulin. During this time, daily step count (DSC) was tracked using the smartphone-based Fitbit MobileTrack application. Mixed effects logistic regression was used to estimate the effect of DSC on NH (sensor glucose <70, <54 mg/dl for ≥15 minutes), while adjusting for TDC and TDD of insulin, and treating participants as a random effect.

RESULTS

Twenty-six adults, with 65.4% females, median age 27 years (interquartile range: 26-32) mean body mass index 23.9 kg/m2, median HbA1c 7.6% (7.1-8.1) and mean Gold Score 2.1 (standard deviation 1.0) formed the study population. The median DSC for the whole group was 2867 (1820-4807). There was a significant effect of DSC on NH episodes <70 mg/dl. (odds ratio 1.11 [95% CI: 1.01-1.23, P = .04]. There was no significant effect on NH <54 mg/dl.

CONCLUSION

Daily PA measured by a smartphone-based step tracker was associated with the risk of NH in people with type 1 diabetes.

Telemonitoring With a Connected Glucose Meter Improves Glycemia Among People With Insulin-Treated Type 2 Diabetes

BACKGROUND

Delayed initiation and inadequate titration remain critical challenges to optimizing insulin therapy in type 2 diabetes (T2D). We aimed to study whether hemoglobin A1c (HbA1c) can be lowered in people with insulin-treated T2D using telemonitoring.

METHODS

This single-center study recruited adults with greater than or equal to six months of diabetes, greater than or equal to three months of insulin therapy, HbA1c ≥8.5% and ≤12.5%, and body mass index (BMI) ≤40 kg/m2. All participants received a connected glucose meter and the accompanying smartphone application. Participants sent weekly blood glucose (BG) diary to their primary endocrinologist via email. Adjustments in insulin doses were communicated to the participants. HbA1c, proportion of BG readings in range (70-180 mg/dL, PIR), below range (<70 mg/dL, PBR) and above range (>180 mg/dL, PAR), and glycemic variability as the coefficient of variation (% CV) were measured at baseline, week 12, and week 24 and compared using repeated-measures analysis of variance (ANOVA) or Friedman's ANOVA.

RESULTS

We recruited 40 people (55% women). Mean age was 57.9 years, BMI 27.8 kg/m2, and baseline HbA1c 9.8% (83.7 mmol/mol). Mean HbA1c improved by 1.7%, % CV reduced from 32.9% to 30.7%, PIR increased from 58.8% to 67.1% (all P <.01) by week 24, without any change in PBR. This was achieved with a 0.04 U/kg/d median increase in total daily dose of insulin and 0.9 kg weight gain over 24 weeks.

CONCLUSION

Telemonitoring and titration of insulin using a connected glucose meter resulted in significant improvements in glycemia, characterized by a reduction in HbA1c, increase in PIR, and reduction in glycemic variability without any increase in hypoglycemia.

Machine Learning-Based Time in Patterns for Blood Glucose Fluctuation Pattern Recognition in Type 1 Diabetes Management: Development and Validation Study

BACKGROUND

Continuous glucose monitoring (CGM) for diabetes combines noninvasive glucose biosensors, continuous monitoring, cloud computing, and analytics to connect and simulate a hospital setting in a person's home. CGM systems inspired analytics methods to measure glycemic variability (GV), but existing GV analytics methods disregard glucose trends and patterns; hence, they fail to capture entire temporal patterns and do not provide granular insights about glucose fluctuations.

OBJECTIVE

This study aimed to propose a machine learning-based framework for blood glucose fluctuation pattern recognition, which enables a more comprehensive representation of GV profiles that could present detailed fluctuation information, be easily understood by clinicians, and provide insights about patient groups based on time in blood fluctuation patterns.

METHODS

Overall, 1.5 million measurements from 126 patients in the United Kingdom with type 1 diabetes mellitus (T1DM) were collected, and prevalent blood fluctuation patterns were extracted using dynamic time warping. The patterns were further validated in 225 patients in the United States with T1DM. Hierarchical clustering was then applied on time in patterns to form 4 clusters of patients. Patient groups were compared using statistical analysis.

RESULTS

In total, 6 patterns depicting distinctive glucose levels and trends were identified and validated, based on which 4 GV profiles of patients with T1DM were found. They were significantly different in terms of glycemic statuses such as diabetes duration (P=.04), glycated hemoglobin level (P<.001), and time in range (P<.001) and thus had different management needs.

CONCLUSIONS

The proposed method can analytically extract existing blood fluctuation patterns from CGM data. Thus, time in patterns can capture a rich view of patients' GV profile. Its conceptual resemblance with time in range, along with rich blood fluctuation details, makes it more scalable, accessible, and informative to clinicians.

Relationship between Short- and Mid-Term Glucose Variability and Blood Pressure Profile Parameters: A Scoping Review

Background. Increased variability of glucose (GV) and blood pressure (BPV) is linked to a higher risk of macro- and microvascular complications and other hard endpoints. This scoping review aims to summarize the existing evidence regarding the association between the parameters of the blood pressure (BP) profile, especially BPV, with indices of short- and mid-term GV. Methods. A literature search was conducted in the MEDLINE/PubMed, Cochrane, Embase, Web of Science, and Wiley Online Library databases. Results. The main findings of this review are as follows: (i) 13 studies were included, mainly with small sample sizes; (ii) there was a considerable degree of heterogeneity in the characteristics of the study participants (age range, individuals with normoglycemia, type 1 or 2 diabetes, normal BP, or hypertension), as well as in the methodologies (mainly in terms of the duration of the data collection period) and variability indices examined (mean amplitude of glycemic excursions and coefficient of glucose variation most frequently reported); and (iii) the results were heterogeneous regarding the association between GV and the parameters of the BP profile. Conclusions. There is a significant lack of evidence on the association between GV and BPV. Future research implementing a standardized methodology should focus on the determinants, association, and clinical relevance of GV and BPV.

Glycaemia in low-premixed insulin analogue type 2 diabetes patients in a real-world setting: are the CGM targets met?

BACKGROUND

There are insufficient data on continuous glucose monitoring (CGM) in nonintensive insulin therapy patients. Using CGM and the recommended CGM targets, we wanted to evaluate low-premix insulin analogue therapy (biphasic aspart/NovoMix 30 and biphasic lispro 25/Humalog Mix 25) in real-world type 2 diabetes patients for glycaemic efficacy and especially hypoglycaemia.

METHODS

The prospective observational study was performed on 35 patients who were treated with a low-premixed insulin. We used the Dexcom G6 system for CGM (9.6 ± 1 days) to measure the clinically relevant CGM parameters: glycaemic variability (%CV), TBR (time below range) < 3.0 mmol/l = 54 mg/dl (level 2 hypoglycaemia), TBR 3.0-3.8 (= 54-69 mg/dl), TIR (time in range) 3.9-10-0 mmol/l (70-180 mg/dl), TAR (time above range) 10-13.9 mmol/l (180-250 mg/dl) and TAR > 13.9 mmol/l (250 mg/dl). We also assessed clinical and demographic characteristics, laboratory HbA1c, fasting blood glucose, peak postprandial glucose values, and the percentage of hypoglycaemia between 00:00 and 06:00.

RESULTS

In our patients, the average ± SD age was 70.4 ± 9.2 years, diabetes duration 17.4 ± 7.1 years, 51% were females, average daily insulin dose was 46.4 units (80% received biphasic aspart). The average ± SD TIR was 62.1 ± 12.2%, TBR < 3.0 mmol/l 0.8 ± 2.0%, TBR 3.0-3.8 mmol/l 1.5 ± 1.5%, TAR 10-13.9 mmol/l 29.2 ± 12.4%, TAR > 13.9 mmol/l 6.4 ± 7.2% and %CV 29.9 ± 7.1%. The average time in hypoglycemia was 33.1 min daily in our patients (11.5 min in the level 2 range). In the older/high-risk population, the TBR/TIR/TAR/level 2 TAR targets were met in 40/80/77/80%, respectively. For the general T2D people, level 2 TBR/TBR/TIR/TAR/level 2 TAR would be met in 74/83/34/77/49%. Average fasting blood glucose was 8.0 ± 2.5 mmol/l (144 ± 45 mg/dl), BMI 31.3 ± 5.1 kg/m², daily insulin dose 46.4 ± 12.1 units, HbA1c 57.4 ± 5.4 mmol/mol (7.4 ± 0.7%). The glycaemic variability goal was met in 80% (with 66% meeting the lower 33% CV goal). 17 ± 12% of hypoglycaemia was nocturnal. People with TBR > 4% were significantly older.

CONCLUSIONS

Most of our type 2 diabetes patients, treated with low-premixed insulin, did not meet the recommended TBR target for older/high-risk patients while meeting the TIR and TAR targets. Nevertheless, the time spent in (total and nocturnal) hypoglycemia was short. The study indicates that the general type 2 diabetes population targets would mostly be met for TBR and %CV in our patients but not the TIR and TAR targets. CGM appears to be a useful clinical tool in these patients.

A review of stress-induced hyperglycaemia in the context of acute ischaemic stroke: Definition, underlying mechanisms, and the status of insulin therapy

The transient elevation of blood glucose produced following acute ischaemic stroke (AIS) has been described as stress-induced hyperglycaemia (SIH). SIH is common even in patients with AIS who have no previous diagnosis of diabetes mellitus. Elevated blood glucose levels during admission and hospitalization are strongly associated with enlarged infarct size and adverse prognosis in AIS patients. However, insulin-intensive glucose control therapy defined by admission blood glucose for SIH has not achieved the desired results, and new treatment ideas are urgently required. First, we explore the various definitions of SIH in the context of AIS and their predictive value in adverse outcomes. Then, we briefly discuss the mechanisms by which SIH arises, describing the dual effects of elevated glucose levels on the central nervous system. Finally, although preclinical studies support lowering blood glucose levels using insulin, the clinical outcomes of intensive glucose control are not promising. We discuss the reasons for this phenomenon.

The continuous spectrum of glycaemic variability changes with pancreatic islet function: A multicentre cross-sectional study in China

AIMS

To investigate glycaemic variability (GV) patterns in patients with type 1 diabetes (T1D), type 2 diabetes (T2D), and latent autoimmune diabetes in adults (LADA).

MATERIALS AND METHODS

A total of 842 subjects (510 T1D, 105 LADA, 227 T2D) were enrolled and underwent 1 week of continuous glucose monitoring (CGM). Clinical characteristics and CGM parameters were compared among T1D, LADA, and T2D. LADA patients were divided into two subgroups based on glutamic acid decarboxylase autoantibody titres (≥180 U/mL [LADA-1], <180 U/mL [LADA-2]) and compared. The C-peptide cut-offs for predicting a coefficient of variation (CV) of glucose ≥36% and a time in range (TIR) > 70% were determined using receiver operating characteristic analysis.

RESULTS

Twenty-seven patients (9 T1D, 18 T2D) were excluded due to insufficient CGM data. Sex, diabetes duration and HbA1c were comparable among the three groups. Fasting and 2-h postprandial C-peptide (FCP, 2hCP) increased sequentially across T1D, LADA, and T2D. T1D and LADA patients had comparable TIR and GV, whereas those with T2D had much higher TIR and lower GV (p < 0.001). The GV of LADA-1 was close to that of T1D, while the GV of LADA-2 was close to that of T2D. CP exhibited the strongest negative correlation with GV. The cut-offs of FCP/2hCP for predicting a CV ≥ 36% and TIR >70% were 121.6/243.1 and 128.9/252.8 pmol/L, respectively.

CONCLUSIONS

GV presented a continuous spectrum across T1D, LADA-1, LADA-2, and T2D. More frequent glucose monitoring is suggested for patients with impaired insulin secretion.

CLINICAL TRAIL REGISTRATION

Chinese Clinical Trial Registration (ChiCTR) website approved by WHO; http://www.chictr.org.cn/ - ChiCTR2200065036.

One-Year Real-World Study on Comparison among Different Continuous Subcutaneous Insulin Infusion Devices for the Management of Pediatric Patients with Type 1 Diabetes: The Supremacy of Hybrid Closed-Loop Systems

Since their advent in daily clinical practice, continuous subcutaneous insulin infusion (CSII) systems have been increasingly improved, leading to a high percentage of both adult and pediatric patients with diabetes now using insulin pumps. Different types of CSII systems are currently available, which are characterized by different settings and technical features. This longitudinal observational study aims to evaluate real-word glycemic outcomes in children and adolescents with type 1 diabetes using three different CSII devices: hybrid closed-loop (HCL) systems, predictive low glucose (PLGS) systems, and non-automated insulin pumps. The secondary objective was to identify clinical variables that may significantly influence the achievement of therapeutic goals in our study cohort. One-hundred-and-one patients on CSII therapy attending our pediatric diabetes center were enrolled. When compared with the non-automated group, patients using HCL systems showed higher levels of time in target glucose range (p = 0.003) and lower glucose variability (p = 0.008). Similarly, we found significantly better glucose metrics in HCL users in comparison to PLGS patients (time in range p = 0.008; coefficient of variation p = 0.009; time above 250 mg/dL p = 0.007). Multiple linear regression models showed that HCL systems (time in range p < 0.001) and high daily percentage of glycemic sensor use (time in range p = 0.031) are predictors for good glycemic control. The introduction and increasing availability of novel technologies for diabetes represent a promising strategy to improve glycemic control and quality of life in pediatric patients with type 1 diabetes. Our real-world data confirm the superiority of HCL systems in terms of improvement of time spent in the target glucose range, prevention of hypoglycemia, and reduction of glycemic variability.

Glycemic Variability Assessment with a 14-Day Continuous Glucose Monitoring System: When and How Long to Measure MAGE (Mean Amplitude of Glucose Excursion) for Optimal Reliability?

Mean amplitude of glucose excursion (MAGE) is considered as the "gold standard" for assessing the short-term within-day glycemic variability (GV), which is an important component of overall glycemic control. A 14-day continuous glucose monitoring system is now widely used and allows easier assessment of GV. However, it is still unknown whether MAGE, usually calculated on a 48-hour period is identical whatever the time during the 14-day lifespan of the sensor and whether a longer time period might give additional information. We evaluated in 68 patients with type 1 diabetes, MAGE during three 2-day periods (day1-day3; day6-day8; day11-day13) and during periods of 3 days and 4 days. MAGE calculated at the three 2-day periods were identical and not different from MAGE of the 3-day or 4-day periods.

Efficacy of IDegLira Versus IDegAsp Therapy in Patients with Type 2 Diabetes: A Randomized Crossover Study by isCGM

Introduction

We aimed to compare the efficacy of insulin degludec/insulin aspart (IDegAsp) and insulin degludec/liraglutide (IDegLira) in controlling glucose fluctuation and suppressing postprandial glucose levels using intermittently scanned continuous glucose monitoring.

Methods

Twenty-four patients with type 2 diabetes mellitus were randomly allocated to receive either IDegLira or IDegAsp followed by IDegAsp or IDegLira, respectively. A crossover study was conducted with intermittently scanned continuous glucose monitoring. We compared the postprandial blood glucose level, time in range, and time below range from a 3-day intermittently scanned continuous glucose monitoring period for each treatment group.

Results

The time in range was significantly higher in IDegLira than in IDegAsp. Postprandial glucose levels 90 and 120 min after breakfast and 60, 90, and 120 min after lunch were significantly lower for IDegLira than for IDegAsp. However, postprandial glucose levels 90 and 120 min after supper were significantly lower for IDegAsp than for IDegLira. There was no significant difference in the time below range between IDegLira and IDegAsp.

Conclusion

IDegLira was more effective in treating type 2 diabetes mellitus than IDegAsp, as indicated by a higher time in range and lower postprandial glucose level at breakfast and lunch. This study was registered with the University Hospital Medical Information Network Clinical Trial Registry (UMIN 000039221).

Supplementary Information

The online version contains supplementary material available at 10.1007/s12325-022-02138-w.

Impact of anxiety, depression and disease-related distress on long-term glycaemic variability among subjects with Type 1 diabetes mellitus

BACKGROUND

Anxiety, depression, and disease-related distress are linked to worse overall glycaemic control, in terms of HbA1c. This study was aimed to evaluate whether traits of these emotional disorders are associated with long-term glycaemic variability in subjects with Type 1 diabetes.

METHODS

Longitudinal retrospective study. Six-year HbA1c data (2014-2019) from 411 subjects with Type 1 diabetes who had participated in a previous study to design a diabetes-specific quality of life questionnaire in the year 2014 were included. Scores for Spanish versions of the Hospital Anxiety and Depression Scale (HADS) and Problem Areas in Diabetes (PAID) scale were obtained at baseline, along with sociodemographic and clinical data. Long-term glycaemic variability was measured as the coefficient of variation of HbA1c (HbA1c-CV). The association between HADS and PAID scores and HbA1c-CV was analysed with Spearman correlations and multiple regression models, both linear and additive, including other covariates (age, sex, diabetes duration time, type of treatment, baseline HbA1c, use of anxiolytic or antidepressant drugs, education level and employment status).

RESULTS

Scores of depression, anxiety and distress were positively and significantly correlated to HbA1c-CV in univariate analyses. Multiple regression study demonstrated an independent association only for diabetes distress score (p < 0.001). Age, diabetes duration time, baseline HbA1c, education level and employment status were also significantly associated with HbA1c-CV. However, when subjects were analyzed separately in two age groups, distress scores were associated with HbA1c-CV only among those aged 25 years or older, while anxiety scores, but not distress, were associated with HbA1c-CV among those younger than 25 years.

CONCLUSIONS

Psychological factors, particularly disease-related distress and anxiety, are associated with long-term glycaemic variability in subjects with Type 1 diabetes.

Efficacy of Flash Glucose Monitoring in Type 1 and Type 2 Diabetes: A Systematic Review and Meta-Analysis of Randomised Controlled Trials

Objective

Flash glucose monitoring (FlashGM) is a sensor-based technology that displays glucose readings and trends to people with diabetes. In this meta-analysis, we assessed the effect of FlashGM on glycaemic outcomes including HbA1c, time in range, frequency of hypoglycaemic episodes and time in hypo/hyperglycaemia compared to self-monitoring of blood glucose, using data from randomised controlled trials.

Methods

A systematic search was conducted on MEDLINE, EMBASE and CENTRAL for articles published between 2014 and 2021. We selected randomised controlled trials comparing flash glucose monitoring to self-monitoring of blood glucose that reported change in HbA1c and at least one other glycaemic outcome in adults with type 1 or type 2 diabetes. Two independent reviewers extracted data from each study using a piloted form. Meta-analyses using a random-effects model was conducted to obtain a pooled estimate of the treatment effect. Heterogeneity was assessed using forest plots and the I2 statistic.

Results

We identified 5 randomised controlled trials lasting 10 – 24 weeks and involving 719 participants. Flash glucose monitoring did not lead to a significant reduction in HbA1c. However, it resulted in increased time in range (mean difference 1.16 hr, 95% CI 0.13 to 2.19, I2 = 71.7%) and decreased frequency of hypoglycaemic episodes (mean difference -0.28 episodes per 24 hours, 95% CI -0.53 to -0.04, I2 = 71.4%).

Conclusions

Flash glucose monitoring did not lead to a significant reduction in HbA1c compared to self-monitoring of blood glucose, however, it improved glycaemic management through increased time in range and decreased frequency of hypoglycaemic episodes.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/, identifier PROSPERO (CRD42020165688).

Significance of Glycemic Variability in Diabetes Mellitus

The goal of diabetes treatment is to maintain good glycemic control, prevent the development and progression of diabetic complications, and ensure the same quality of life and life expectancy as healthy people. Hemoglobin A1c (HbA1c) is used as an index of glycemic control, but strict glycemic control using HbA1c as an index may lead to severe hypoglycemia and cardiovascular death. Glycemic variability (GV), such as excessive hyperglycemia and hypoglycemia, is associated with diabetic vascular complications and has been recognized as an important index of glycemic control. Here, we reviewed the definition and evaluated the clinical usefulness of GV, and its relationship with diabetic complications and therapeutic strategies to reduce GV.

Largest Amplitude of Glycemic Excursion Calculating from Self-Monitoring Blood Glucose Predicted the Episodes of Nocturnal Asymptomatic Hypoglycemia Detecting by Continuous Glucose Monitoring in Outpatients with Type 2 Diabetes

AIMS

Nocturnal asymptomatic hypoglycemia (NAH) is a serious complication of diabetes, but it is difficult to be detected clinically. This study was conducted to determine the largest amplitude of glycemic excursion (LAGE) to predict the episodes of NAH in outpatients with type 2 diabetes.

METHODS

Data were obtained from 313 outpatients with type 2 diabetes. All subjects received continuous glucose monitoring (CGM) for consecutive 72 hours. The episodes of NAH and glycemic variability indices (glucose standard deviation [SD], mean amplitude of plasma glucose excursion [MAGE], mean blood glucose [MBG]) were accessed via CGM. LAGE was calculated from self-monitoring blood glucose (SMBG).

RESULTS

A total of 76 people (24.3%) had NAH. Compared to patients without NAH, patients with NAH showed higher levels of glucose SD (2.4 ± 0.9 mmol/L vs 1.7 ± 0.9 mmol/L, p <0.001), MAGE (5.2 ± 2.1 mmol/L vs 3.7 ± 2.0, p<0.001) and LAGE (4.6 ± 2.3 mmol/L vs 3.8 ± 1.9 mmol/L, p=0.007), and lower level of MBG (7.5 ± 1.5 mmol/L vs 8.4 ± 2.2 mmol/L, p=0.002). LAGE was significantly associated with the incidence of NAH and time below rang (TBR) in model 1 [NAH: 1.189 (1.027-1.378), p=0.021; TBR: 0.008 (0.002-0.014), p=0.013] with adjustment for age, BMI, sex, work, hyperlipidemia, complication and medication, and in model 2 [NAH: 1.177 (1.013-1.367), p=0.033; TBR: 0.008 (0.002-0.014), p=0.012] after adjusting for diabetes duration based on model 1, as well as in model 3 [NAH: 1.244 (1.057-1.464), p=0.009; TBR: 0.009 (0.002-0.016), p=0.007] with further adjustment for HbA1c based on model 2. In addition, no significant interactions were found between LAGE and sex, age, HbA1c, duration of diabetes, BMI and insulin therapy on the risk of NAH. The receiver operator characteristic (ROC) curve shows the ideal cutoff value of LAGE for the prediction of NAH was 3.48 mmol/L with 66.7% sensitivity, 50% specificity and 0.587 (95% CI: 0.509-0.665) of area under the ROC curve.

CONCLUSIONS

High glycemic variability is strongly associated with the risk of NAH. The LAGE based on SMBG could be an independent predictor of NAH for outpatients with type 2 diabetes, and LAGE greater than 3.48 mmol/L could act as a warning alarm for high risk of NAH in daily life.

The Effect of DPP4 Inhibitor on Glycemic Variability in Patients with Type 2 Diabetes treated with twice-daily Premixed Human Insulin

Objective

To evaluate the effect of adding DPP4 inhibitor (DPP4-i) on glycemic variability (GV) in patients with type 2 diabetes mellitus (T2DM) treated with premixed human insulin (MHI).

Methodology

We conducted a prospective study in patients with T2DM on twice-daily MHI with or without metformin therapy. Blinded continuous glucose monitoring was performed at baseline and following 6 weeks of Vildagliptin therapy.

Results

Twelve patients with mean (SD) age of 55.8 (13.1) years and duration of disease of 14.0 (6.6) years were recruited. The addition of Vildagliptin significantly reduced GV indices (mmol/L): SD from 2.73 (IQR 2.12-3.66) to 2.11 (1.76-2.55), p=0.015; mean amplitude of glycemic excursions (MAGE) 6.94(2.61) to 5.72 (1.87), p=0.018 and CV 34.05 (8.76) to 28.19 (5.36), p=0.010. In addition, % time in range (3.9-10 mmol/l) improved from 61.17 (20.50) to 79.67 (15.33)%, p=0.001; % time above range reduced from 32.92 (23.99) to 18.50 (15.62)%, p=0.016; with reduction in AUC for hyperglycemia from 1.24 (1.31) to 0.47 (0.71) mmol/day, p=0.015. Hypoglycemic events were infrequent and the reduction in time below range and AUC for hypoglycemia did not reach statistical significance.

Conclusion

The addition of DPP4-I to commonly prescribed twice-daily MHI in patients with T2DM improves GV and warrants further exploration.

Lost in Translation: A Disconnect Between the Science and Medicare Coverage Criteria for Continuous Subcutaneous Insulin Infusion

Numerous studies have demonstrated the clinical value and safety of insulin pump therapy in type 1 diabetes and type 2 diabetes populations. However, the eligibility criteria for insulin pump coverage required by the Centers for Medicare & Medicaid Services (CMS) discount conclusive evidence that supports insulin pump use in diabetes populations that are currently deemed ineligible. This article discusses the limitations and inconsistencies of the insulin pump eligibility criteria relative to current scientific evidence and proposes workable solutions to address this issue and improve the safety and care of all individuals with diabetes.

Quality of Glycemic Control: Assessment Using Relationships Between Metrics for Safety and Efficacy

Numerous methods have been proposed as measures of quality of glycemic control resulting in confusion regarding the best choice of metric to use by clinicians and researchers. Some methods use a single metric such as HbA1c, Mean Glucose, %Time In Range (%TIR), or Coefficient of Variation (%CV). Others use a combination of up to seven metrics, for example, Q-Score, Comprehensive Glucose Pentagon (CGP), and Personal Glycemic State (PGS). A recently proposed Composite continuous Glucose monitoring index utilizes three metrics: %TIR, Time Below Range (%TBR), and standard deviation (SD) of glucose. This review proposes that only two metrics can be sufficient when monitoring an individual patient or when comparing two or more forms of management interventions. These two metrics comprise (1) a measure of efficacy such as Mean Glucose, HbA1c, %TIR, or %Time Above Range (%TAR) and (2) a measure of safety based on risk of hypoglycemia such as %TBR, Low Blood Glucose Index (LBGI), or frequency of specified types of hypoglycemic events per patient year. By analysis of the two-dimensional graphical and statistical relationships between metrics for safety and efficacy and by testing identity versus nonidentity of these relationships, one can improve sensitivity for detection of the effects of medications and of other therapeutic interventions, avoid the need for arbitrary scoring systems for glucose values falling within versus outside the target range, and offer the advantage of conceptual and practical simplicity.

Glucose as a modifiable cause of atherosclerotic cardiovascular disease: Insights from type 1 diabetes and transplantation

Diabetes is a major risk factor for cardiovascular (CV) disease. In contrast to the clear benefits from treatments which reduce blood pressure and lipids, clinical trials targeting blood glucose have not shown clear CV benefits. Interventions to intensify glycemic control early in the course of diabetes may have benefits in long term observational studies (DCCT-EDIC/UKPDS), but may not be helpful if introduced late in the course of type 2 diabetes (ACCORD, ADVANCE, VA-DT). More recent CVOT in high risk subjects suggest that the benefits of SGLT2 and GLP1-RA are glucose-independent. Type 1 diabetes provides a "cleaner" model to study the links between glucose and cardiovascular disease. Abnormalities of glucose regulation in type 1 diabetes is not restricted to hyperglycemia, but includes glycemic variability and hypoglycemia. Increasingly the mechanisms linking glycemic variability and hypoglycemia as key mediators of cardiovascular complications are being understood. Furthermore, data from pancreas and islet transplantation showing reduced cardiovascular mortality and regression of intima-media thickness supports a causal role for glucose in the pathogenesis of atherosclerosis, but suggests that restoration of normal glucose regulation may be required to demonstrate substantial impact on CV risk accrued over decades of type 1 diabetes. Considering the limited organ supply and risks of immunosuppression, advances in biology (stem cell derived beta cells) or technology (automated insulin delivery systems) will be required to provide a scalable solution to deliver optimal glucose control and reduce CV risk for people with type 1 diabetes.

Insulin glargine 300 units/mL for the treatment of individuals with type 2 diabetes in the real world: A review of the DELIVER programme

Evidence from randomized controlled trials (RCTs) has shown that second-generation basal insulin (BI) analogues, insulin glargine 300 U/mL (Gla-300) and insulin degludec (IDeg), provide similar glycaemic control, with a lower risk of hypoglycaemia compared with the first-generation BI analogue insulin glargine 100 U/mL (Gla-100) in people with type 2 diabetes (T2D). However, the highly selected participants and frequent follow-up of RCTs may not be truly representative of real-life clinical practice. It is important to assess the safety and effectiveness of these second-generation BI analogues in real-life clinical practice settings. The DELIVER programme utilized electronic healthcare records from the United States to compare clinical outcomes in people with T2D who received either Gla-300 or other BI analogues in real-world clinical practice. This review provides a concise overview of the results of the DELIVER studies. Overall, Gla-300 provided similar antihyperglycaemic effectiveness and a lower risk of hypoglycaemia versus the first-generation BI analogues Gla-100 and insulin detemir in people with T2D who had switched BIs. In those who were insulin-naïve, initiation with Gla-300 versus Gla-100 was associated with significantly better antihyperglycaemic effectiveness and similar or lower hypoglycaemic risk. Both glycaemic control and hypoglycaemia risk were also shown to be similar with Gla-300 and IDeg, in people who had switched BIs and in those who were insulin-naïve. In addition, the DELIVER 2 study reported that people with T2D who switched to Gla-300 had reduced healthcare resource utilization, with an overall saving of US$1439 per person per year compared with those who switched to another BI analogue. Overall, the real-world DELIVER programme showed that the glycaemic control with a low risk of hypoglycaemia observed with Gla-300 in RCTs was also seen in standard clinical practice.

Intermittently Scanned Continuous Glucose Monitoring Data of Polish Patients from Real-Life Conditions: More Scanning and Better Glycemic Control Compared to Worldwide Data

Background: Randomized trials and observational studies have shown that the use of FreeStyle Libre^® intermittently scanned continuous glucose monitoring system (isCGMS) is associated with improved glycemic indices and quality of life. Materials and Methods: In this retrospective, real-world data analysis, we described country-specific glucometrics among isCGMS users from Poland and compared them with international data. The analyzed time period for the Polish data ranged between August 2016 and August 2020, and the analyzed time period for the international data ranged from September 2014 to August 2020. Results: Data from the Polish population were collected from 10,679 readers and 92,627 sensors with 113 million automatically recorded glucose readings. The worldwide database included information from 981,876 readers and 11,179,229 sensors with 13.1 billion glucose readings. On average, the users of isCGMS from Poland performed substantially more scans/day (21.2 ± 14.2 vs. 13.2 ± 10.7), achieved lower eHbA1c (7.0% ± 1.2% vs. 7.5% ± 1.5%), and spent more time-in-range (TIR) (64.2% ± 17.3% vs. 58.1% ± 20.3%) and less time-above-range (TAR) (29.7% ± 18.0% vs. 36.6% ± 21.3%) (P < 0.0001 for all comparisons). Moreover, they were more likely to achieve TIR >70% (36.3% vs. 28.8%), but spent more time-below-range (TBR) (4.7% vs. 3.6%). Our results confirmed that analyzed glucometrics improve as the scan rate frequency increases. However, at a similar scanning frequency to the comparative group, users from Poland achieved lower eHbA1c, higher TIR, and lower TAR, but higher TBR. Conclusions: We report more scanning and better glycemic control in isCGMS users in Poland than worldwide. The cause of this observation remains unknown. Our data also show that in real-life practice, a large number of patients may be willing to perform scanning more frequently than it is usually assumed.

Glucose Regulation Beyond HbA1c in Type 2 Diabetes Treated With Insulin: Real-World Evidence From the DIALECT-2 Cohort

OBJECTIVE

To investigate glucose variations associated with glycated hemoglobin (HbA1c) in insulin-treated patients with type 2 diabetes.

RESEARCH DESIGN AND METHODS

Patients included in Diabetes and Lifestyle Cohort Twente (DIALECT)-2 (n = 79) were grouped into three HbA1c categories: low, intermediate, and high (≤53, 54-62, and ≥63 mmol/mol or ≤7, 7.1-7.8, and ≥7.9%, respectively). Blood glucose time in range (TIR), time below range (TBR), time above range (TAR), glucose variability parameters, day and night duration, and frequency of TBR and TAR episodes were determined by continuous glucose monitoring (CGM) using the FreeStyle Libre sensor and compared between HbA1c categories.

RESULTS

CGM was performed for a median (interquartile range) of 10 (7-12) days/patient. TIR was not different for low and intermediate HbA1c categories (76.8% [68.3-88.2] vs. 76.0% [72.5.0-80.1]), whereas in the low category, TBR was higher and TAR lower (7.7% [2.4-19.1] vs. 0.7% [0.3-6.1] and 8.2% [5.7-17.6] vs. 20.4% [11.6-27.0], respectively, P < 0.05). Patients in the highest HbA1c category had lower TIR (52.7% [40.9-67.3]) and higher TAR (44.1% [27.8-57.0]) than the other HbA1c categories (P < 0.05), but did not have less TBR during the night. All patients had more (0.06 ± 0.06/h vs. 0.03 ± 0.03/h; P = 0.002) and longer (88.0 [45.0-195.5] vs. 53.4 [34.4-82.8] minutes; P < 0.001) TBR episodes during the night than during the day.

CONCLUSIONS

In this study, a high HbA1c did not reduce the occurrence of nocturnal hypoglycemia, and low HbA1c was not associated with the highest TIR. Optimal personalization of glycemic control requires the use of newer tools, including CGM-derived parameters.

Day-to-day variability of porcine lente, insulin glargine 300 U/mL and insulin degludec in diabetic dogs

Background

Day‐to‐day variability impacts safety of insulin therapy and the choice of monitoring strategies. Side‐by‐side comparisons of insulin formulations in diabetic dogs are scarce.

Hypothesis/Objectives

Insulin glargine 300 U/mL (IGla300) and insulin degludec (IDeg) are associated with less day‐to‐day glucose variability compared to porcine lente (PL) in diabetic dogs.

Animals

Seven intact male purpose‐bred beagles with toxin‐induced diabetes.

Methods

In this repeated measured study, PL, IGla300 and IDeg were compared in 2 phases: once‐daily (q24h) and twice‐daily (q12h) administration. Interstitial glucose concentrations (IG) were measured continuously throughout the study. For each formulation, maximal q24h dose was determined using the same algorithm (while avoiding hypoglycemia) and then maintained for 72 hours. In phase 2, 70% of the maximal q24h dose was administered q12h and maintained for 5 days regardless of hypoglycemia. Coefficient of variation (CV) and glycemic variability percentage (GVP) were calculated to determine day‐to‐day and intraday variability, respectively.

Results

There was no difference in day‐to‐day variability between PL, IGla300, and IDeg in the q24h phase. In the q12h phase, day‐to‐day variability was higher (P = .01) for PL (CV = 42.6 ± 6.8%) compared to IGla300 and IDeg (CV = 30.1 ± 7.7%, 25.2 ± 7.0%, respectively). The GVP of PL was lower (P = .02) compared to IGla300. There was no difference between PL, IGla300 and IDeg in %time IG < 70 mg/dL.

Conclusions and Clinical Importance

Insulin degludec and IGla300 administered q12h were associated with lower day‐to‐day variability, which might be advantageous in minimizing monitoring requirements without increasing the risk of hypoglycemia.

Afternoon aerobic and resistance exercise have limited impact on 24-h CGM outcomes in adults with type 1 diabetes: A secondary analysis

AIMS

This study examined post-exercise glycemic variability in individuals with type 1 diabetes after acute bouts of resistance (RE) and aerobic exercise (AE) compared to a no-exercise day (CON). We hypothesized that exercise days would have greater glucose variability (standard deviation - SD, coefficient of variation - CV), and less time in range (TIR), compared to CON.

METHODS

A secondary analysis was conducted on previously collected data. Twelve active participants with type 1 diabetes performed three testing sessions in random order with at least 48 h in between: AE (45-min treadmill run at 60%VO_2max), RE (three sets of eight repetitions, seven weight-lifting exercises), and CON (45-min no-exercise control). Interstitial glucose levels were monitored by blinded continuous glucose monitoring (CGM). Glycemic variability was evaluated for 0-6 h, overnight (00:00-06:00) and 24 h after exercise.

RESULTS

Mean CGM glucose, TIR, and time above/below range were similar among conditions (P > 0.05). Lower SD (0.8 [0.5-1.1], 1.4 [0.9-2.4]mmol/L, p = 0.009) and CV (11.4 [8.6-15.3], 23.4 [13.7-31.6]%, p = 0.007) were found overnight after AE versus CON. Otherwise, AE and RE had limited impact on post-exercise glycemia.

CONCLUSIONS

Acute RE and AE bouts may have limited impact on post-exercise glycemic variability compared to rest in habitually active individuals with type 1 diabetes.

Is the Consensual Threshold for Defining High Glucose Variability Implementable in Clinical Practice?

OBJECTIVE

Estimating glycemic variability (GV) through within-day coefficient of variation (%CV_w) is recommended for patients with type 1 Diabetes (T1D). High GV (hGV) is defined as %CV_w > 36%. However, continuous glucose monitoring (CGM) devices provide exclusively total CV (%CV_T). We aimed to assess consequences of this disparity.

RESEARCH DESIGN AND METHODS

We retrospectively calculated both %CV_T and %CV_w of consecutive T1D patients from their CGM raw data during 14 days. Patients with hGV with %CV_T >36% and %CV_w ≤36% were called the "inconsistent GV group".

RESULTS

A total of 104 patients were included. Mean ± SD %CV_T and %CV_w were 42.4 ± 8% and 37.0 ± 7.4% respectively (P < 0.0001). Using %CV_T, 81 patients (73.6%) were classified as having hGV, whereas 59 (53.6%) using %CV_w (P < 0.0001) corresponding to 22 patients (21%) in the inconsistent GV population.

CONCLUSIONS

Evaluation of GV through %CV in patients with T1D is highly dependent on the calculation method and then must be standardized.

Characteristics of Continuous Glucose Monitoring Metrics in Persons with Type 1 and Type 2 Diabetes Treated with Multiple Daily Insulin Injections

Background: Although guidelines advocate similar continuous glucose monitoring (CGM) targets for insulin-treated persons with type 1 diabetes (T1D) and type 2 diabetes (T2D), it is unclear how these persons differ with respect to hypoglycemia, glucose variability, and other CGM metrics in clinical practice. Methods: We used data from 2 multicenter randomized-controlled trials (GOLD and MDI-Liraglutide) where 161 persons with T1D and 124 persons with T2D treated with multiple daily injections were included and monitored with masked CGM. Results: Persons from both cohorts had similar mean glucose levels, 10.9 mmol/L (196 mg/dL) in persons with T1D and 10.8 mmol/L (194 mg/dL) in persons with T2D. Time in hypoglycemia (<3.9 mmol/L [70 mg/dL]) was 5.1% and 1.0% for persons with T1D and T2D, respectively (P < 0.001). Corresponding estimates for the standard deviations of mean glucose levels were 4.4 mmol/L (79 mg/dL) versus 3.0 (54 mg/dL) (P < 0.001), for coefficient of variation 41% versus 28% (P < 0.001), and for time in range 38.2% versus 45.3%, respectively (P = 0.004). Mean C-peptide levels were 0.05 nmol/L and 0.67 nmol/L (P < 0.001) for persons with T1D and T2D, respectively. Conclusions: Persons with T1D compared with persons with T2D treated with multiple daily insulin injections spend considerably more time in hypoglycemia, have higher glucose variability, and less "time in range." This needs to be taken into account in daily clinical care and in recommended targets for CGM metrics.

American Association of Clinical Endocrinology Clinical Practice Guideline: The Use of Advanced Technology in the Management of Persons With Diabetes Mellitus

OBJECTIVE

To provide evidence-based recommendations regarding the use of advanced technology in the management of persons with diabetes mellitus to clinicians, diabetes-care teams, health care professionals, and other stakeholders.

METHODS

The American Association of Clinical Endocrinology (AACE) conducted literature searches for relevant articles published from 2012 to 2021. A task force of medical experts developed evidence-based guideline recommendations based on a review of clinical evidence, expertise, and informal consensus, according to established AACE protocol for guideline development.

MAIN OUTCOME MEASURES

Primary outcomes of interest included hemoglobin A1C, rates and severity of hypoglycemia, time in range, time above range, and time below range.

RESULTS

This guideline includes 37 evidence-based clinical practice recommendations for advanced diabetes technology and contains 357 citations that inform the evidence base.

RECOMMENDATIONS

Evidence-based recommendations were developed regarding the efficacy and safety of devices for the management of persons with diabetes mellitus, metrics used to aide with the assessment of advanced diabetes technology, and standards for the implementation of this technology.

CONCLUSIONS

Advanced diabetes technology can assist persons with diabetes to safely and effectively achieve glycemic targets, improve quality of life, add greater convenience, potentially reduce burden of care, and offer a personalized approach to self-management. Furthermore, diabetes technology can improve the efficiency and effectiveness of clinical decision-making. Successful integration of these technologies into care requires knowledge about the functionality of devices in this rapidly changing field. This information will allow health care professionals to provide necessary education and training to persons accessing these treatments and have the required expertise to interpret data and make appropriate treatment adjustments.

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.

Glycemic and lipid variability for predicting complications and mortality in diabetes mellitus using machine learning

INTRODUCTION

Recent studies have reported that HbA1c and lipid variability is useful for risk stratification in diabetes mellitus. The present study evaluated the predictive value of the baseline, subsequent mean of at least three measurements and variability of HbA1c and lipids for adverse outcomes.

METHODS

This retrospective cohort study consists of type 1 and type 2 diabetic patients who were prescribed insulin at outpatient clinics of Hong Kong public hospitals, from 1st January to 31st December 2009. Standard deviation (SD) and coefficient of variation were used to measure the variability of HbA1c, total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and triglyceride. The primary outcome is all-cause mortality. Secondary outcomes were diabetes-related complications.

RESULT

The study consists of 25,186 patients (mean age = 63.0, interquartile range [IQR] of age = 15.1 years, male = 50%). HbA1c and lipid value and variability were significant predictors of all-cause mortality. Higher HbA1c and lipid variability measures were associated with increased risks of neurological, ophthalmological and renal complications, as well as incident dementia, osteoporosis, peripheral vascular disease, ischemic heart disease, atrial fibrillation and heart failure (p <  0.05). Significant association was found between hypoglycemic frequency (p <  0.0001), HbA1c (p <  0.0001) and lipid variability against baseline neutrophil-lymphocyte ratio (NLR).

CONCLUSION

Raised variability in HbA1c and lipid parameters are associated with an elevated risk in both diabetic complications and all-cause mortality. The association between hypoglycemic frequency, baseline NLR, and both HbA1c and lipid variability implicate a role for inflammation in mediating adverse outcomes in diabetes, but this should be explored further in future studies.

Associations of Time in Range and Other Continuous Glucose Monitoring-Derived Metrics With Well-Being and Patient-Reported Outcomes: Overview and Trends

Time in glucose ranges is increasingly relevant for research and clinical practice. Whereas the clinical validity of these metrics has been demonstrated with regard to long-term complications, their associations with patient-reported outcomes such as well-being, diabetes distress, and fear of hypoglycemia remain an open research question. This article reviews existing evidence on links between times in glycemic ranges and patient-reported outcomes. It also describes a novel research approach of using ecological momentary assessment to analyze on a more granular level in real time possible associations of these parameters of glycemic control and patient-reported outcomes. Such an approach could further our understanding of how glucose and patient-reported outcomes may be interconnected.

Improved Estimation of Glycated Hemoglobin from Continuous Glucose Monitoring and Past Glycated Hemoglobin Data

Background: Accurate estimation of glycated hemoglobin (HbA1c) from continuous glucose monitoring (CGM) remains challenging in clinic. We propose two statistical models and validate them in real-life conditions against the current standard, glucose management indicator (GMI). Materials and Methods: Modeling utilized routinely collected data from patients with type 1 diabetes from central Poland (eligibility criteria: age >1 year, diabetes duration >3 months, and CGM use between 01/01/2015 and 12/31/2019). CGM records were extracted from dedicated Medtronic/Abbott databases and cross-referenced with HbA1c values; 28-day periods preceding HbA1c measurement with >75% of the sensor-active time were analyzed. We developed a mixed linear regression, including glycemic variability indices and patient's ID (glucose variability-based patient specific model, GV-PS) intended for closed-group use and linear regression using patient-specific error of GMI (proportional error-based patient agnostic model, PE-PA) for general use. Models were validated with either new HbA1cs from closed-group patients or separate patient-HbA1c pool. External validation was performed with data from clinical trials. Performance metrics included bias, its 95% confidence interval (95% CI), coefficient of determination (R²), and root mean square error (RMSE). Results: We included 723 HbA1c-CGM pairs from 174 patients (mean age 9.9 ± 4.4 years and diabetes duration 3.7 ± 3.6 years). GMI yielded R² = 0.58, with different bias between Medtronic and Abbott devices [0.120% vs. -0.152%, P < 0.0001], and overall 95% CI = -0.9% to +1%, RMSE = 0.47%. GV-PS successfully captured patient-specific variance (closed-group validation: R² = 0.83, bias = 0.026%, 95% CI = -0.562% to 0.591%, RMSE = 0.31%). PE-PA performed similarly on new patients (R² = 0.76, bias = -0.069%, 95% CI = -0.790% to 0.653%, RMSE = 0.37%). In external validation GMI, GV-PS, and PE-PA produced 73.8%, 87.5%, and 91.0% predictions within 0.5% (5.5 mmol/mol) from the true value. Conclusion: Constructed models performed better than GMI. PE-PA provided an accurate estimate of HbA1c with fast and straightforward implementation.

Flash Continuous Glucose Monitoring: A Practical Guide and Call to Action for Pharmacists

Despite advances in diabetes technology, the proportion of patients with type 2 diabetes achieving recommended glycemic goals remains suboptimal. There is a growing interest in flash continuous glucose monitoring (CGM) among patients, pharmacists and providers. Pharmacists are well positioned to collaborate with patients and providers in ambulatory care or community-based settings to allow a greater number of patients with diabetes to harness the benefits of flash CGM. The purpose of this narrative review is to provide pharmacists with a background on flash CGM technology, review the data supporting pharmacist-driven flash CGM services, and address common questions that arise in pharmacy practice surrounding flash CGM.

Individualizing Time-in-Range Goals in Management of Diabetes Mellitus and Role of Insulin: Clinical Insights From a Multinational Panel

Diabetes mellitus is a global health concern associated with significant morbidity and mortality. Inadequate control of diabetes leads to chronic complications and higher mortality rates, which emphasizes the importance of achieving glycemic targets. Although glycated hemoglobin (HbA_1c) is the gold standard for measuring glycemic control, it has several limitations. Therefore, in recent years, along with the emergence of continuous glucose monitoring (CGM) technology, glycemic control modalities have moved beyond HbA_1c. They encompass modern glucometrics, such as glycemic variability (GV) and time-in-range (TIR). The key advantage of these newer metrics over HbA_1c is that they allow personalized diabetes management with person-centric glycemic control. Basal insulin analogues, especially second-generation basal insulins with properties such as longer duration of action and low risk of hypoglycemia, have demonstrated clinical benefits by reducing GV and improving TIR. Therefore, for more effective and accurate diabetes management, the development of an integrated approach with second-generation basal insulin and glucometrics involving GV and TIR is the need of the hour. With this objective, a multinational group of endocrinologists and diabetologists reviewed the existing recommendations on TIR, provided their clinical insights into the individualization of TIR targets, and elucidated on the role of the second-generation basal insulin analogues in addressing TIR.

Dietary intake and hospitalisation due to diabetic ketoacidosis and hypoglycaemia in individuals with type 1 diabetes

We investigated the association between diet and risk of hospitalisation for diabetic ketoacidosis (DKA) or hypoglycaemia in type 1 diabetes. Food records were used to assess dietary intake. Data on DKA and hypoglycaemia hospitalisations, within two years of dietary assessments, were obtained from registries. Analyses were conducted with and without macronutrient substitution. Data were available from 1391 participants, 28 (2.0%) and 55 (4.0%) of whom were hospitalised due to DKA or hypoglycaemia, respectively. In the adjusted model, self-reported alcohol intake was associated with increased (per 10 g: B = 1.463, 95% CI = 1.114-1.922, p = 0.006; per E%: B = 1.113, 95% CI = 1.027-1.206, p = 0.009), and fibre intake with reduced (per g/MJ: B = 0.934, 95% CI = 0.878-0.995, p = 0.034) risk of DKA hospitalisation. Substituting carbohydrates for fats was associated with increased risk for hypoglycaemia hospitalisation (B = 1.361, 95% CI = 1.031-1.795, p = 0.029), while substituting alcohol for carbohydrates (B = 1.644, 95% CI = 1.006-2.685, p = 0.047) or proteins (B = 2.278, 95% CI = 1.038-4.999, p = 0.040) increased the risk for DKA hospitalisation. In conclusion, refraining from alcohol intake is a preventable risk factor for DKA, while higher fibre intake seems rather protective. Increasing carbohydrate intake while decreasing that of fats, is associated with higher hypoglycaemia risk. Whether this is a cause or effect of hypoglycaemia remains to be established.