Metrics for glycaemic control - from HbA1c to continuous glucose monitoring

Real-world prospective observational single-centre study: Hybrid closed loop improves HbA1c, time-in-range and quality of life for children, young people and their carers

Hybrid closed-loop (HCL) systems are characterised by integrating continuous glucose monitoring (CGM) with insulin pumps which automate insulin delivery via specific algorithms and user-initiated insulin delivery. The aim of the study was to evaluate the effectiveness of HCLs on Hba1c, time-in-range (TIR), time in hypoglycaemia, fear of hypoglycaemia, sleep and quality of life measure in children and young people (CYP) with T1D and their carers. Data on HbA1c, TIR and hypoglycaemia frequency were reviewed at baseline prior to starting HCL and 3 months after commencement. As part of clinical care, all patients and carers were provided with key education on the use of the HCL system by trained diabetes healthcare professionals. CYP aged 12 years and above independently completed the validated Hypoglycaemia Fear Survey (HFS). Parents of patients <12 were asked to complete a modified version of the HFS-Parent (HFS-P) survey. There were 39 CYP (22 men) with T1D included with a mean age of 11.8 ± 4.4 at commencement of HCL. Median duration of diabetes was 3.8 years (interquartile range 1.3-6.0). There were 55% of patients who were prepubertal at the time of HCL commencement. 91% were on the Control-IQ system and 9% on the CamAPS FX system. HCL use demonstrated significant improvements at 3 months in the following: HbA1c in mmol/mol (63.0 vs. 56.6, p = 0.03), TIR (50.5 vs. 67.0, p = 0.001) and time in hypoglycaemia (4.3% vs. 2.8%, p = 0.004). HFS scores showed improved behaviour (34.0 vs. 27.5.9, p = 0.02) and worry (40.2 vs. 31.6, p = 0.03), and HFS-P scores also showed improved behaviour (p < 0.001) and worry (p = 0.01). Our study shows that HCL at 3 months improves glucose control, diabetes management and quality of life measures such as fear and worry of hypoglycaemia for CYP and carers.

Endothelial Dysfunction and Platelet Hyperactivation in Diabetic Complications Induced by Glycemic Variability

The development and progression of the complications of chronic diabetes mellitus are attributed not only to increased blood glucose levels but also to glycemic variability. Therefore, a deeper understanding of the role of glycemic variability in the development of diabetic complications may provide more insight into targeted clinical treatment strategies in the future. Previously, the mechanisms implicated in glycemic variability-induced diabetic complications have been comprehensively discussed. However, endothelial dysfunction and platelet hyperactivation, which are two newly recognized critical pathogenic factors, have not been fully elucidated yet. In this review, we first evaluate the assessment of glycemic variability and then summarise the roles of endothelial dysfunction and platelet hyperactivation in glycemic variability-induced complications of diabetes, highlighting the molecular mechanisms involved and their interconnections.

Diabetes Technology Meeting 2021

Diabetes Technology Society hosted its annual Diabetes Technology Meeting on November 4 to November 6, 2021. This meeting brought together speakers to discuss various developments within the field of diabetes technology. Meeting topics included blood glucose monitoring, continuous glucose monitoring, novel sensors, direct-to-consumer telehealth, metrics for glycemia, software for diabetes, regulation of diabetes technology, diabetes data science, artificial pancreas, novel insulins, insulin delivery, skin trauma, metabesity, precision diabetes, diversity in diabetes technology, use of diabetes technology in pregnancy, and green diabetes. A live demonstration on a mobile app to monitor diabetic foot wounds was presented.

Potential misclassification of diabetes and prediabetes in the U.S.: Mismatched HbA1c and glucose in NHANES 2005-2016

AIMS

To assess the prevalence and clinical implications of "mismatches" between HbA1c and glucose levels in the United States across the life course.

METHODS

Participants ages 12-79 years from U.S. National Health and Nutrition Examination Survey (NHANES) 2005-2016 without known diagnosis of diabetes and who had a 75 g oral glucose tolerance test were included. Previously undiagnosed diabetes (DM), prediabetes, and normal glucose metabolism (NGM) were defined using American Diabetes Association cut-points. Mismatches were defined by the hemoglobin glycation index (HGI).

RESULTS

In 10,361 participants, 5% and 41% had diabetes and prediabetes, respectively, by fasting or 2-hour glucose criteria. By HbA1c criteria, the high HGI tertile consisted of mostly abnormal classification (3% DM, 52% prediabetes) and the low HGI tertile contained mostly normal classification (78% NGM). Across all ages, 15% (weighted: 30 million individuals) had clinically significant mismatches of HGI magnitude ≥+0.5% (i.e., high mismatch) or ≤-0.5% (low mismatch). Mismatch was most common in older adults and non-Hispanic Black participants.

CONCLUSIONS

Mismatches of clinically significant magnitude could lead to HbA1c-related misdiagnosis or inappropriate management in up to 30 million Americans. Older adults, non-Hispanic Black individuals, and others with high mismatches may benefit from complementing HbA1c with additional diagnostic and management strategies.

Comparison of two advanced hybrid closed loop in a pediatric population with type 1 diabetes: a real-life observational study

OBJECTIVE

The Advanced Hybrid Closed Loop (AHCL) systems have provided the potential to ameliorate glucose control in children with Type 1 Diabetes. The aim of the present work was to compare metabolic control obtained with 2 AHCL systems (Medtronic 780G system and Tandem Control IQ system) in a pediatric real-life clinical context.

RESEARCH DESIGN AND METHODS

It is an observational, real-life, monocentric study; thirty one children and adolescents (M:F = 15:16, age range 7.6-18 years, mean age 13.05 ± 2.4 years, Diabetes duration > 1 year) with T1D, previously treated with Predictive Low Glucose Suspend (PLGS) systems and then upgraded to AHCL have been enrolled. CGM data of the last four weeks of "PLGS system" (PRE period) with the first four weeks of AHCL system (POST period) have been compared.

RESULTS

For both AHCL systems, Medtronic 780G and Tandem Control IQ, respectively TIR at 4 weeks significantly increased, from 65.7 to 70.5% (p < 0.01) and from 64.8 to 70.1% (p < 0.01). (p < 0.01). The comparison between CGM metrics of the 2 evaluated systems doesn't show difference at baseline (last four weeks of PLGS system) and after four weeks of AHCL use.

CONCLUSIONS

To our knowledge, this study is the first real-life one comparing 2 AHCL systems in a pediatric population with T1D. It shows an improvement in glucose control when upgrading to AHCL. The comparison between the two AHCL systems did not show significant differences in the analyzed CGM metrics, meaning that the algorithms currently available are equally effective in promoting glucose control.

Semi-Implantable Bioelectronics

Developing techniques to effectively and real-time monitor and regulate the interior environment of biological objects is significantly important for many biomedical engineering and scientific applications, including drug delivery, electrophysiological recording and regulation of intracellular activities. Semi-implantable bioelectronics is currently a hot spot in biomedical engineering research area, because it not only meets the increasing technical demands for precise detection or regulation of biological activities, but also provides a desirable platform for externally incorporating complex functionalities and electronic integration. Although there is less definition and summary to distinguish it from the well-reviewed non-invasive bioelectronics and fully implantable bioelectronics, semi-implantable bioelectronics have emerged as highly unique technology to boost the development of biochips and smart wearable device. Here, we reviewed the recent progress in this field and raised the concept of "Semi-implantable bioelectronics", summarizing the principle and strategies of semi-implantable device for cell applications and in vivo applications, discussing the typical methodologies to access to intracellular environment or in vivo environment, biosafety aspects and typical applications. This review is meaningful for understanding in-depth the design principles, materials fabrication techniques, device integration processes, cell/tissue penetration methodologies, biosafety aspects, and applications strategies that are essential to the development of future minimally invasive bioelectronics.

A New Case of Hb Headington (HBB: c.217A>C) Due to a New DNA Transversion, Found in a Patient with Type 2 Diabetes Mellitus

We here report a novel case of Hb Headington [β72(E16)Ser→Arg, HBB: c.217A>C, p.Ser73Arg], in a 68-year-old woman with type 2 diabetes mellitus (T2DM). Glycosylated hemoglobin (Hb) was measured by capillary electrophoresis (CE). The spectrum showed abnormal peaks between the A₀ and A₂ peaks. DNA sequencing demonstrated a mutation on the HBB gene, which predicted a substitution of serine to arginine at position 73 in the β-globin chain. Moreover, this amino acid substitution occurs at the same position as Hb Headington [β72(E16)Ser→Arg, HBB: c.219T>A, p.Ser73Arg], which showed increased oxygen affinity.

Large-Scale Data Analysis for Glucose Variability Outcomes with Open-Source Automated Insulin Delivery Systems

Open-source automated insulin delivery (AID) technologies use the latest continuous glucose monitors (CGM), insulin pumps, and algorithms to automate insulin delivery for effective diabetes management. Early community-wide adoption of open-source AID, such as OpenAPS, has motivated clinical and research communities to understand and evaluate glucose-related outcomes of such user-driven innovation. Initial OpenAPS studies include retrospective studies assessing high-level outcomes of average glucose levels and HbA1c, without in-depth analysis of glucose variability (GV). The OpenAPS Data Commons dataset, donated to by open-source AID users with insulin-requiring diabetes, is the largest freely available diabetes-related dataset with over 46,070 days’ worth of data and over 10 million CGM data points, alongside insulin dosing and algorithmic decision data. This paper first reviews the development toward the latest open-source AID and the performance of clinically approved GV metrics. We evaluate the GV outcomes using large-scale data analytics for the n = 122 version of the OpenAPS Data Commons. We describe the data cleaning processes, methods for measuring GV, and the results of data analysis based on individual self-reported demographics. Furthermore, we highlight the lessons learned from the GV outcomes and the analysis of a rich and complex diabetes dataset and additional research questions that emerged from this work to guide future research. This paper affirms previous studies’ findings of the efficacy of open-source AID.

External validation of a classifier of daily continuous glucose monitoring (CGM) profiles

As continuous glucose monitoring (CGM) sensors generate ever increasing amounts of CGM data, the need for methods to simplify the storage and analysis of this data becomes increasingly important. Lobo et al. developed a classifier of daily CGM profiles as an initial step in addressing this need. The classifier has several important applications including, but not limited to, data compression, data encryption, and indexing of databases. While the classifier has already successfully classified 99.0% of the 42,595 daily CGM profiles in a Test Set, this work presents an external validation using an external validation set (EVal Set) derived from 8 publicly available data sets. The Test Set and the EVal Set differ in terms of (but not limited to) demographics, data collection time periods, and data collection geographies. The classifier successfully classified 98.2% of the 137,030 daily CGM profiles in the EVal Set. Furthermore, each of the 483 distinct groups of classified daily CGM profiles from the EVal Set retains the same clinical characteristics as the corresponding group from the Test Set, as desired. Finally, the set of unclassified daily CGM profiles from the EVal Set retains the same statistical characteristics as the set of unclassified daily CGM profiles from the Test Set, as desired. These results establish the robustness and generalizability of the classifier: the performance of the classifier is unchanged despite the marked differences between the Test Set and the EVal Set.

Oxidative Stress in Neutrophils: Implications for Diabetic Cardiovascular Complications

Significance: Neutrophil behavior and function are altered by hyperglycemia associated with diabetes. Aberrant activation by hyperglycemia causes neutrophils to respond with increased production of reactive oxidative species (ROS). Excess ROS, a signature of primed neutrophils, can intracellularly induce neutrophils to undergo NETosis, flooding surrounding tissues with ROS and damage-associated molecular patterns such as S100 calcium binding proteins (S100A8/A9). The cargo associated with NETosis also attracts more immune cells to the site and signals for increased immune cell production. This inflammatory response to diabetes can accelerate other associated conditions such as atherosclerosis and thrombosis, increasing the risk of cardiovascular disease. Recent Advances: As the prevalence of diabetes continues to grow, more attention has been focused on developing effective treatment options. Currently, glucose-lowering medications and insulin injections are the most widely utilized treatments. As the disease progresses, medications are usually stacked to maintain glucose at desired target levels, but this approach often fails and does not effectively reduce cardiovascular risk, even with the latest drugs. Critical Issues: Despite advances in treatment options, diabetes remains a progressive disease as glucose lowering alone has failed to abolish the associated cardiovascular complications. Future Directions: Significant interest is being generated in developing treatments that do not solely focus on glucose control but rather mitigate glucotoxicity. Several therapies have been proposed that target cellular dysfunction downstream of hyperglycemia, such as using antioxidants to scavenge ROS, inhibiting ROS production from NOX, and suppressing neutrophil release of S100A8/A9 proteins. Antioxid. Redox Signal. 36, 652-666.

Glycaemia dynamics in gestational diabetes mellitus

Pregnant women may develop gestational diabetes mellitus (GDM), a disease of pregnancy characterised by maternal and fetal hyperglycaemia with hazardous consequences to the mother, the fetus, and the newborn. Maternal hyperglycaemia in GDM results in fetoplacental endothelial dysfunction. GDM-harmful effects result from chronic and short periods of hyperglycaemia. Thus, it is determinant to keep glycaemia within physiological ranges avoiding short but repetitive periods of hyper or hypoglycaemia. The variation of glycaemia over time is defined as 'glycaemia dynamics'. The latter concept regards with a variety of mechanisms and environmental conditions leading to blood glucose handling. In this review we summarized the different metrics for glycaemia dynamics derived from quantitative, plane distribution, amplitude, score values, variability estimation, and time series analysis. The potential application of the derived metrics from self-monitoring of blood glucose (SMBG) and continuous glucose monitoring (CGM) in the potential alterations of pregnancy outcome in GDM are discussed.

Disparities in HbA1c testing between aging US Latino and non-Latino white primary care patients

US Latinos disproportionately face diabetes-related disparities compared to non-Latino Whites. A number of barriers, including linguistic and cultural discordance, have been consistently linked to these disparities. Glycated hemoglobin (HbA1c) testing is used to assess glycemic control among individuals living with diabetes. This study aimed to compare HbA1c levels and corresponding testing rates among non-Latino Whites and Latinos with both English and Spanish preference from a national cohort of primary care patients within community health centers. We analyzed electronic health records from patients who turned 50 years of age (n = 66,921) and were diagnosed with diabetes during or prior to the study period. They also must have been under observation for at least one year from January 1, 2013 to December 31, 2017. We calculated the rates of HbA1c tests each person received over the number of years observed and used covariate-adjusted negative binomial regression to estimate incidence rate ratios for Spanish preferring Latinos and English preferring Latinos compared to non-Latino Whites. Spanish preferring Latinos (rate ratio = 1.23, 95% CI = 1.16-1.30), regardless of HbA1c level, had higher testing rates than non-Latino Whites and English preferring Latinos. English preferring Latinos with controlled HbA1c levels had higher rates of HbA1c testing compared to non-Latino whites. Overall, the Latinos with Spanish preference maintained higher HbA1c testing rates and had disproportionately higher rates of uncontrolled HbA1c levels compared to non-Latino whites. Future efforts should focus on understanding effective approaches to increasing engagement among Spanish preferring Latinos and addressing organizational-level barriers, given HbA1c disparities.

Insulin Degludec Versus Insulin Glargine on Glycemic Variability in Diabetic Patients: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

BACKGROUND/AIMS

Currently, glycemic variability has more deleterious effects than sustained hyperglycemia and is closely associated with acute and chronic complications of diabetes. Reducing glycemic excursion is becoming another vital goal of glycemic control in clinical practice. This study aimed to determine whether insulin degludec (IDeg) or insulin glargine (IGla) was more beneficial for reducing glycemic fluctuations.

MATERIALS AND METHODS

This research was constructed according to the PRISMA guidelines. We searched eight databases and ClinicalTrials.gov from their inception to 30 November 2021. All randomized controlled trials comparing the efficacy of glucose variability between IDeg and IGla in diabetic patients were included.

RESULTS

Fourteen trials with 8,683 participants were included. In patients with T1DM, IDeg was associated with a lower mean (MD: -16.25, 95% CI -29.02 to -3.07, P = 0.01) and standard deviation (P = 0.03) compared to IGla in fasting blood glucose (FBG); in people with T2DM, IDeg was related to a lower mean of FBG versus insulin glargine 100 U/ml (IGla100) (P <0.001) and had a more extended time in the range (TIR) than IGla100 (SMD: 0.15, 95% CI 0.02 to 0.27, P = 0.02) but not longer than insulin glargine 300 U/ml (IGla300). Moreover, IDeg had a lower coefficient of variation of FBG than IGla (P = 0.0254). For other indicators of glycemic variability, namely, standard deviation of blood glucose for 24 h, the mean of 24-h blood glucose, mean amplitude of glycemic excursion, the coefficient of variation for 24 h, the mean of daily differences, area under the glucose curve, and M-value, no significant differences were identified between IDeg and IGla, regardless of T1DM or T2DM.

CONCLUSIONS

Based on the current studies, there was comparable efficacy between IDeg and IGla from multiple aspects of glycemic variability, regardless of T1DM or T2DM. However, IDeg may be superior to IGla in reducing FBG variability in T1DM and T2DM. Nonetheless, due to the limitations of the original studies, it is still unclear whether IDeg is superior to both IGla100 and IGla300. In T2DM, IDeg had more extended TIR than IGla100 but not longer than IGla300. Additionally, more well-designed randomized controlled trials comparing IDeg with IGla300 for different indicators of glycemic variability are still warranted.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO, CRD42021283203.

Changing Meal Sequence Affects Glucose Excursions in Gestational Diabetes Mellitus

Studies on nutrient sequences during meals suggest that consuming carbohydrates last lowers postprandial glucose excursions more than consuming carbohydrates first. However, this phenomenon has not been studied in gestational diabetes mellitus (GDM). Ten women with GDM consumed the same caloric foods in different sequences over five successive days: (A) dish first, followed by carbohydrate and soup last; (B) carbohydrate first, followed by dish and soup last; (C) soup first, followed by dish and carbohydrate last; (D) three meals a day ad libitum; and (E) six meals a day as ad libitum. Continuous glucose monitoring (CGM) was used to assess diurnal glycemia. Decreases in mean glucose levels and the largest glucose levels in A were similar to group C. The peak glucose of breakfast and lunch in group B was more significant than in groups A and C. The B meal pattern showed more marked glycemic excursions than groups A and C. Increasing the number of meals reduced the peak glucose level and the glycemic excursions with the same total calories. Changing meal sequences or increasing the number of meals may reduce glycemic excursions in GDM. Our trial was registered retrospectively and the trial registration number is ChiCTR2200057044.

Effect of chiglitazar and sitagliptin on glucose variations, insulin resistance and inflammatory-related biomarkers in untreated patients with type 2 diabetes

AIMS

To evaluate glycemic variations, changes in insulin resistance and oxidative stress after chiglitazar or sitagliptin treatment in untreated patients with type 2 diabetes mellitus (T2DM).

METHODS

Based on the study inclusion and exclusion criteria, 81 patients with T2DM were randomly divided to receive chiglitazar or sitagliptin treatment for 24 weeks. Continuous glucose monitoring (CGM) systems were conducted for 72 h in eligible patients. We analyzed the following glycemic variation parameters derived from the CGM data and measured the serum levels of hemoglobin A1c (HbA1c), fasting blood glucose (FBG), 2-h postprandial blood glucose (2-h PBG), fasting insulin (Fins) and inflammatory-related indicators at baseline and the end of the study.

RESULTS

After treatment for 24 weeks, our data showed a similar reduction in HbA1c between chiglitazar and sitagliptin. The 24-h mean blood glucose (MBG), standard deviation (SD) and mean amplitude of glycemic excursion (MAGE) were significantly decreased, and the time in range (TIR) was increased after chiglitazar and sitagliptin therapy. Chiglitazar administration led to significant improvement in insulin resistance/insulin secretion (HOMA-IR, HOMA-IS), interleukin-6 (IL-6), prostaglandin F2α (PGF-2α), 17-hydroxyprogesterone (17-OHP) and adiponectin (ADP) score values compared with sitagliptin administration.

CONCLUSIONS

Chiglitazar therapy effectively reduced glucose variation and showed a larger improvement in insulin resistance and inflammatory parameters than sitagliptin.

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.

Perspectives of glycemic variability in diabetic neuropathy: a comprehensive review

Diabetic neuropathy is one of the most prevalent chronic complications of diabetes, and up to half of diabetic patients will develop diabetic neuropathy during their disease course. Notably, emerging evidence suggests that glycemic variability is associated with the pathogenesis of diabetic complications and has emerged as a possible independent risk factor for diabetic neuropathy. In this review, we describe the commonly used metrics for evaluating glycemic variability in clinical practice and summarize the role and related mechanisms of glycemic variability in diabetic neuropathy, including cardiovascular autonomic neuropathy, diabetic peripheral neuropathy and cognitive impairment. In addition, we also address the potential pharmacological and non-pharmacological treatment methods for diabetic neuropathy, aiming to provide ideas for the treatment of diabetic neuropathy.

Zhang et al. describe metrics for evaluating glycaemic variability (GV) in clinical practice and summarize the role and related mechanisms of GV in diabetic neuropathy, including cardiovascular autonomic neuropathy, diabetic peripheral neuropathy and cognitive impairment. They aim to stimulate ideas for the treatment of diabetic neuropathy.

Advanced hybrid artificial pancreas system improves on unannounced meal response - In silico comparison to currently available system

BACKGROUND AND OBJECTIVE

Glycemic control, especially meal-related disturbance rejection, has proven to be a major challenge for people with type 1 diabetes. In this manuscript, we introduce a novel, personalized, advanced hybrid insulin infusion system (a.k.a. artificial pancreas) based on the Model Predictive Control (MPC) methodology to adjust insulin infusion while automatically rejecting uninformed meals.

METHODS

The proposed advanced hybrid closed-loop system relies on the integration of three key elements: (i) an adaptive personalized MPC control law that modulates the control strength depending on recent past control actions, glucose measurements, and its derivative, (ii) an automatic Bolus Priming System (BPS) that commands additional insulin injections safely upon the detection of enabling metabolic conditions (e.g., an unacknowledged meal), and (iii) a new hyperglycemia mitigation system to avoid prevailing hyperglycemia. The benefits of the proposed system are demonstrated through simulations and tests using the most up-to-date Type 1 UVA/Padova simulator as preclinical stage prior to in vivo clinical tests. We used a legacy algorithm (USS Virginia), currently used in clinical care, as a benchmark controller.

RESULTS

Overall, the proposed control strategy enhanced by an automatic BPS improves glycemic control when compared with an available system. When a large meal is not announced (80g CHO), the proposed controller outperformed the legacy controller in time-in-target-range TIR (postprandial and overnight) and time-in-tight-range TTR (overall, postprandial, and overnight).

CONCLUSION

The integration of a novel BPS into an advanced control system allowed to automatically reject unannounced meals. Exhaustive simulation studies indicated the safety and feasibility of the proposed controller to be deployed in human clinical trials.

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 variability and periodontal disease in type 1 diabetes: a cross-sectional study-The "PAROdontopatia e DIAbete" (PARODIA) project

AIMS

Periodontal disease (PD) is a chronic inflammation of periodontal tissue associated with infection from specific anaerobic pathogens contained in dental plaque. Both type 1 and type 2 diabetes are associated with an increased prevalence of PDs. A two-way relationship between diabetes and periodontitis has been proposed, with diabetes increasing the risk for periodontitis, and periodontal inflammation negatively affecting glycaemic control. To date, the relationship between PD and glucose variability in type 1 diabetes has not been evaluated. To investigate the prevalence of PD in patients with type 1 diabetes and its association with glycemic control and glucose variability.

METHODS

In this cross-sectional study, all enrolled patients were scheduled to attend both a diabetologic and a periodontal visit. HbA1c, glucose coefficient of variation (CV), loss of clinical attachment (CAL), and periodontal probing depth (PPD) were collected.

RESULTS

136 patients were included in the analysis. The prevalence of PD was 63%. A significant correlation was found between mean CAL and glucose CV (r = 0.31, p = 0.002), but not with HbA1c. Mean PPD was also associated with glucose CV (r = 0.27 and 0.044), but not with HbA1c. In a multiple linear regression model, with mean CAL as dependent variable, age, glucose CV, and smoking habit resulted significantly associated (r = 0.23, p = 0.013; r = 0.33, p = 0.001; r = 0.34, p < 0.001, respectively). Assuming mean PPD as dependent variable, multiple linear regression analysis showed a significant association with glucose CV and smoking habits only.

CONCLUSIONS

PD is associated with glucose variability in patients with type 1 diabetes also after adjusting for the main confounders.

Glycaemic variabilities: Key questions in pursuit of clarity

After years of intensive investigation, the definition of glycaemic variability remains unclear and the term variability in glucose homoeostasis might be more appropriate covering both short and long-term glycaemic variability. For the latter, we remain in the search of an accurate definition and related targets. Recent work leads us to consider that the within-subject variability of HbA1c calculated from consecutive determinations of HbA1c at regular time-intervals could be the most relevant index for assessing the long-term variability with a threshold value of 5% (%CV = SD of HbA1c/mean HbA1c) to separate stability from lability of HbA1c. Presently, no one can deny that short- and long-term glucose variability should be maintained within their lower ranges to limit the incidence of hypoglycaemia. Usually, therapeutic strategies aimed at reducing post-meal glucose excursions, i.e. the major contributor to daily glucose fluctuations, exert a beneficial effect on the short-term glucose variability. This explains the effectiveness of adjunct therapies with either GLP- receptor agonists or SGLT inhibitors in type 2 diabetes. In type 1 diabetes, the application of a CGM device alone reduces the short-term glycaemic variability. In contrast, sophisticated insulin delivery does not necessarily lead to such reductions despite marked downward shifts of 24-hour glycaemic profiles. Such contrasting observations raise the question as to whether the prolonged wear of CGM devices is or not the major causative factor for improvement in glucose variability among intensively insulin-treated persons with type 1 diabetes.

One Year Real-World Use of the Control-IQ Advanced Hybrid Closed-Loop Technology

Background: The t:slim X2™ insulin pump with Control-IQ^® technology from Tandem Diabetes Care is an advanced hybrid closed-loop system that was first commercialized in the United States in January 2020. Longitudinal glycemic outcomes associated with real-world use of this system have yet to be reported. Methods: A retrospective analysis of Control-IQ technology users who uploaded data to Tandem's t:connect^® web application as of February 11, 2021 was performed. Users age ≥6 years, with >2 weeks of continuous glucose monitoring (CGM) data pre- and >12 months post-Control-IQ technology initiation were included in the analysis. Results: In total 9451 users met the inclusion criteria, 83% had type 1 diabetes, and the rest had type 2 or other forms of diabetes. The mean age was 42.6 ± 20.8 years, and 52% were female. Median percent time in automation was 94.2% [interquartile range, IQR: 90.1%-96.4%] for the entire 12-month duration of observation, with no significant changes over time. Of these users, 9010 (96.8%) had ≥75% of their CGM data available, that is, sufficient data for reliable computation of CGM-based glycemic outcomes. At baseline, median percent time in range (70-180 mg/dL) was 63.6 (IQR: 49.9%-75.6%) and increased to 73.6% (IQR: 64.4%-81.8%) for the 12 months of Control-IQ technology use with no significant changes over time. Median percent time <70 mg/dL remained consistent at ∼1% (IQR: 0.5%-1.9%). Conclusion: In this real-world use analysis, Control-IQ technology retained, and to some extent exceeded, the results obtained in randomized controlled trials, showing glycemic improvements in a broad age range of people with different types of diabetes.

Integrating an Islet-Based Biosensor in the Artificial Pancreas: In Silico Proof-of-Concept

OBJECTIVE

Current treatment of type 1 diabetes by closed-loop approaches depends on continuous glucose monitoring. However, glucose readings alone are insufficient for an artificial pancreas to truthfully restore glucose homeostasis where additional physiological regulators of insulin secretion play a considerable role. Previously, we have developed an electrophysiological biosensor of pancreatic islet activity, which integrates these additional regulators through electrical measurement. This work aims at investigating the performance of the biosensor in a blood glucose control loop, to establish an in silico proof-of-concept.

METHODS

Two islet algorithm models were identified on experimental data recorded with the biosensor. First, we validated electrical measurement as a means to exploit the inner regulation capabilities of islets for intravenous glucose measurement and insulin infusion. Then, an artificial pancreas integrating the islet-based biosensor was compared to standard treatment approaches using subcutaneous routes. The closed-loop simulations were performed in the UVA/Padova T1DM Simulator where a series of realistic meal scenarios were applied to virtual diabetic patients.

RESULTS

With intravenous routes, the endogenous islet algorithms successfully restored glucose homeostasis for all patient categories (mean time in range exceeds 90%) while mitigating the risk of adverse glycaemic events (mean BGI < 2). Using subcutaneous routes, the biosensor-based artificial pancreas was as performing as standard treatments, and outperformed them under challenging conditions.

CONCLUSION

This work validates the concept of using pancreatic islets algorithms in an artificial pancreas in silico.

SIGNIFICANCE

Pancreatic islet endogenous algorithms obtained via an electrophysiological biosensor successfully regulate blood glucose levels of virtual type 1 diabetic patients.

A data-driven approach to classifying daily continuous glucose monitoring (CGM) time series

According to the World Health Organization, about 422 million people worldwide have type 1 or type 2 diabetes (T1D, T2D), with the latter accounting for 90-95% of cases. Safe and effective treatment of patients with diabetes requires accurate and frequent monitoring of their blood sugar levels. Continuous glucose monitoring (CGM) is a monitoring technology developed to address this need, and its use among U.S. T1D patients has increased from 6% in 2011 to 38% in 2018 and continues to increase worldwide in both T1D and T2D. This paper presents a data-driven approach to determine Ω, a finite set of representative daily profiles (motifs) such that almost any daily CGM profile generated by a patient can be matched to one of the motifs in Ω. The training data set (9,741 profiles) was used to identify 8 candidate sets of motifs, while the validation data set (14,175 profiles) was used to select the final set Ω. The robustness of Ω was established by using it to successfully classify (match against a representative daily profile in Ω) 99.0% of 42,595 daily CGM profiles in the testing data set. All data sets contained daily CGM profiles from six studies involving T1D and T2D patients using a variety of treatment modes, including daily insulin injections, insulin pumps, or artificial pancreas (AP). The classified profiles can be used in predictive modeling, decision support, and automated control systems (e.g., AP).

Non-invasive wearables for remote monitoring of HbA1c and glucose variability: proof of concept

INTRODUCTION

Diabetes prevalence continues to grow and there remains a significant diagnostic gap in one-third of the US population that has pre-diabetes. Innovative, practical strategies to improve monitoring of glycemic health are desperately needed. In this proof-of-concept study, we explore the relationship between non-invasive wearables and glycemic metrics and demonstrate the feasibility of using non-invasive wearables to estimate glycemic metrics, including hemoglobin A1c (HbA1c) and glucose variability metrics.

RESEARCH DESIGN AND METHODS

We recorded over 25 000 measurements from a continuous glucose monitor (CGM) with simultaneous wrist-worn wearable (skin temperature, electrodermal activity, heart rate, and accelerometry sensors) data over 8-10 days in 16 participants with normal glycemic state and pre-diabetes (HbA1c 5.2-6.4). We used data from the wearable to develop machine learning models to predict HbA1c recorded on day 0 and glucose variability calculated from the CGM. We tested the accuracy of the HbA1c model on a retrospective, external validation cohort of 10 additional participants and compared results against CGM-based HbA1c estimation models.

RESULTS

A total of 250 days of data from 26 participants were collected. Out of the 27 models of glucose variability metrics that we developed using non-invasive wearables, 11 of the models achieved high accuracy (<10% mean average per cent error, MAPE). Our HbA1c estimation model using non-invasive wearables data achieved MAPE of 5.1% on an external validation cohort. The ranking of wearable sensor's importance in estimating HbA1c was skin temperature (33%), electrodermal activity (28%), accelerometry (25%), and heart rate (14%).

CONCLUSIONS

This study demonstrates the feasibility of using non-invasive wearables to estimate glucose variability metrics and HbA1c for glycemic monitoring and investigates the relationship between non-invasive wearables and the glycemic metrics of glucose variability and HbA1c. The methods used in this study can be used to inform future studies confirming the results of this proof-of-concept study.

DNA damage in leukocytes and serum nitrite concentration are negatively associated in type 1 diabetes

Chronic hyperglycaemia leads to DNA damage in diabetes and might be associated with nitrosative stress. In this study, we aimed at assessing the level of DNA strand breaks in leukocytes, serum nitrite and nitrate in patients with type 1 diabetes and healthy controls and associations of these parameters with diabetes-related outcomes in a prospective study. The level of DNA damage was determined in 71 patients with type 1 diabetes and 57 healthy controls by comet assay and scored with arbitrary units (AU). The chemiluminescence method was used to measure nitrite and nitrate. Clinical information and data on consumption of alcohol, physical activity and smoking were collected. Progression of complications in patients with diabetes was assessed after a follow-up time of 4-5 years. We observed a higher level of DNA damage in leukocytes of patients with type 1 diabetes compared with healthy subjects [type 1 diabetes AU 50 (36-74.5); control AU 30 (24.1-43), P < 0.001]. According to regression, type 1 diabetes leads to a 2-fold increase in DNA damage. In the group of type 1 diabetes, DNA damage correlated positively with total cholesterol (R = 0.262, P = 0.028) and negatively with serum glucose level (R = -0.284; P = 0.018) and serum nitrite (R = -0.335; P = 0.008). DNA damage was not significantly associated with HbA1c, diabetes duration, complications and lifestyle factors. However, DNA damage > 57 AU was associated with statistically significantly lower serum nitrite and 1.52 higher risk of progression of complications of diabetes over the follow-up period. The latter result was not statistically significant due to insufficient study power [relative risk 1.52 (95% confidence interval = 0.68, 3.42, P = 0.31)]. Our results confirm that type 1 diabetes is associated with a higher level of DNA strand breaks in leukocytes when compared with the reference group and demonstrate the negative association between DNA damage and serum nitrite concentration.

Impaired insulin secretion predicting unstable glycemic variability and time below range in type 2 diabetes patients regardless of glycated hemoglobin or diabetes treatment

AIMS/INTRODUCTION

To identify the coefficient of variation (CV) threshold for unstable glucose variability (GV) and hypoglycemia, and to characterize a patient population with unstable GV and hypoglycemia.

MATERIALS AND METHODS

This was an observational study that enrolled 284 Japanese outpatients with type 2 diabetes who underwent continuous glucose monitoring. The C-peptide index (CPI = [(fasting serum C-peptide) / (plasma glucose)] × 100) was used as a marker of endogenous insulin secretion. The CV threshold between stable and unstable GV was defined as the upper limit of the CV distribution in the subgroup of patients who did not receive insulin nor insulin secretagogues (relatively stable GV subgroup, n = 104). The optimal CV range corresponding to time below target range ≥4% was determined for all patients using receiver operating characteristic curve analysis. Various characteristics of patients with unstable GV and hypoglycemia were extracted using multivariate logistic regression analysis.

RESULTS

The upper limit of the CV in the relatively stable GV subgroup was 40. The optimal CV range corresponding to time below target range ≥4% was also defined as CV ≥40 (area under the curve 0.85) for all patients. The CPI was an independent risk for CV ≥40 (odds ratio 0.17, 95% confidence interval 0.04-0.50, P < 0.01). The optimal cut-off point for CPI to predict a CV cut-off value of 40 was equivalent to 0.81 (area under the curve 0.80).

CONCLUSIONS

A CV of 40 discriminates unstable GV and hypoglycemia from stable GV in Japanese outpatients with type 2 diabetes. Impaired insulin secretion might affect the stability of GV.

Log-linear relationship between endogenous insulin secretion and glycemic variability in patients with type 2 diabetes on continuous glucose monitoring

The contribution of endogenous insulin secretion to glycemic variability (GV) may differ between patients with impaired insulin secretion and those with preserved secretion. Our objective was to determine the linearity of the relationship between fasting C-peptide (CPR) as a marker of endogenous insulin secretion and GV in type 2 diabetes (T2DM), regardless of the type of antidiabetic treatment. We conducted a prospective observational study using continuous glucose monitoring obtained from 284 Japanese outpatients with T2DM with various HbA1c values and antidiabetic treatment. We constructed a prediction curve of base-line CPR versus coefficient of variation (CV) and identified the clinical factors associated with CV using multiple regression analysis. Fasting CPR showed a significant negative log-linear relationship with CV (P < 0.0001), and the latter being strikingly high in the low-CPR group. The multiple regression analysis showed that low CPR was an independent predictor of high CV (P < 0.0001). The significant correlations were sustained in both patients with/without insulin treatment. The contribution of endogenous insulin secretion to GV depends on the extent of insulin secretion impairment. Fasting CPR may represent a useful indicator of GV instability in T2DM.

Antihyperglycemic and Antilipidemic Properties of a Tea Infusion of the Leaves from Annona cherimola Miller on Streptozocin-Induced Type 2 Diabetic Mice

The antihyperglycemic and antilipidemic effects of the tea infusion extracts of leaves from Annona cherimola Miller (IELAc-0.5, IELAc-1.5, and IELAc-3.0) were evaluated on normoglycemic (NG) and streptozocin-induced diabetic (STID) mice. In the acute test, IELAc-1.5 at 300 mg/kg bodyweight (bw) exhibited antihyperglycemic activity on STID mice since the first hour of treatment. Then, its antidiabetic potential was analyzed in a subchronic evaluation. IELAc-1.5 was able to reduce the blood glucose level, glycated hemoglobin (HbA1c), cholesterol (CHO), and triglycerides (TG); high-density lipoprotein (HDL) showed an increase at the end of treatment. IELAc-1.5 did not modify the urine profile at the end of the evaluation, and neither toxicity nor macroscopic organ damage were observed in acute and subchronic assays. In addition, a major flavonol glycoside present in the tea infusion extracts was identified using high-performance liquid chromatography with diode array detection (HPLC-DAD). The analysis of the tea infusion extracts by HPLC revealed that rutin was the major component. This study supports the use of tea infusions from Annona cherimola for the treatment of diabetes and suggests that rutin could be responsible, at least in part, for their antidiabetic properties.

[Impact of physical activity on glycemic variability in people with diabetes mellitus type 2]

One of the main roles of the prescription of physical activity for people with type 2 diabetes is to reduce hyperglycemia. The beneficial effect of physical training on glycemic levels is considered as the sum of the effects of each exercise session. A better understanding of acute responses to exercise, through short-term glycemic variability, could explain the differences in the results of distinct training protocols. The objective of this study was to analyze the scientific information on different exercise protocols and their association with short-term glycemic variability in patients with type 2 diabetes. A systematic review of studies published in English and Spanish was carried out. The databases used were PubMed, Cochrane, ScienceDirect, and Medline. Only studies conducted in adults (older than 18 years) were included. A total of 36 studies were identified, which were analyzed and completed using the Covidence® platform. The final analysis included 10 articles with 296 patients. The 10 included articles were divided according to the type of intervention protocol used: group 1, acute exercise, and group 2, training. Significant differences were found in glycemic variability in 71.4% of the articles in group 1 and in 100% of the articles included in group 2. Positive effects of acute exercise and physical training on short-term glycemic variability were demonstrated. The findings were more pronounced in the intervention protocols than in physical training.

Glycemic variability in type 2 diabetes mellitus and acute coronary syndrome: liraglutide compared with insulin glargine: a pilot study

Objective

To explore the glucagon-like peptide-1 analogue liraglutide in the hospital setting in patients with type 2 diabetes mellitus (T2DM) and acute coronary syndrome and to evaluate the safety and efficacy and its impact on hospitalization and short-term glycemic variability (GV).

Methods

A 12-week, open-label, prospective, randomized pilot clinical study with parallel groups that compared liraglutide (group 1) with glargine (group 2) and its impact on glycemic control and GV.

Results

Thirteen patients were included. During hospitalization, mean glucose was 164.75 mg/dL (standard deviation [SD] 19.94) in group 1 and 166.69 mg/dL (38.22) in group 2. GV determined by CV and SD was 20.98 (7.68) vs. 25.48 (7.19) and 34.37 (13.05) vs. 43.56 (19.53) in groups 1 and 2, respectively. Group 1 prandial insulin requirements during hospitalization were lower compared with group 2. Follow-up A1c in group 1 was 6.9% (−1.51%) and 6.5% in group 2 (−1.27). GV after discharge and hypoglycemia were lower in group 1 compared with group 2.

Conclusions

Liraglutide seems to reduce GV in the acute phase of acute coronary syndrome, and patients achieved optimal control with a low incidence of hypoglycemia. These results support the need to explore liraglutide in a larger multicenter trial.

Trial registration: The study was approved by the National Medical Ethics Committee of Spain. The study was registered at European Clinical Trials Database (EudraCT): 2014003298-40.

The Association of Diabetes and Hyperglycemia on Inpatient Readmissions

OBJECTIVE

To evaluate the association between inpatient glycemic control and readmission in individuals with diabetes and hyperglycemia (DM/HG).

METHODS

Two data sets were analyzed from fiscal years 2011 to 2013: hospital data using the International Classification of Diseases, Ninth Revision (ICD-9) codes for DM/HG and point of care (POC) glucose monitoring. The variables analyzed included gender, age, mean, minimum and maximum glucose, along with 4 measures of glycemic variability (GV), standard deviation, coefficient of variation, mean amplitude of glucose excursions, and average daily risk range.

RESULTS

Of 66 518 discharges in FY 2011-2013, 28.4% had DM/HG based on ICD-9 codes and 53% received POC monitoring. The overall readmission rate was 13.9%, although the rates for individuals with DM/HG were higher at 18.9% and 20.6% using ICD-9 codes and POC data, respectively. The readmitted group had higher mean glucose (169 ± 47 mg/dL vs 158 ± 46 mg/dL, P < .001). Individuals with severe hypoglycemia and hyperglycemia had the highest readmission rates. All 4 GV measures were consistent and higher in the readmitted group.

CONCLUSION

Individuals with DM/HG have higher 30-day readmission rates than those without. Those readmitted had higher mean glucose, more extreme glucose values, and higher GV. To our knowledge, this is the first report of multiple metrics of inpatient glycemic control, including GV, and their associations with readmission.

Anticipation of Historical Exercise Patterns by a Novel Artificial Pancreas System Reduces Hypoglycemia During and After Moderate-Intensity Physical Activity in People with Type 1 Diabetes

Objective: Physical activity is a major challenge to glycemic control for people with type 1 diabetes. Moderate-intensity exercise often leads to steep decreases in blood glucose and hypoglycemia that closed-loop control systems have so far failed to protect against, despite improving glycemic control overall. Research Design and Methods: Fifteen adults with type 1 diabetes (42 ± 13.5 years old; hemoglobin A_1c 6.6% ± 1.0%; 10F/5M) participated in a randomized crossover clinical trial comparing two hybrid closed-loop (HCL) systems, a state-of-the-art hybrid model predictive controller and a modified system designed to anticipate and detect unannounced exercise (APEX), during two 32-h supervised admissions with 45 min of planned moderate activity, following 4 weeks of data collection. Primary outcome was the number of hypoglycemic episodes during exercise. Continuous glucose monitor (CGM)-based metrics and hypoglycemia are also reported across the entire admissions. Results: The APEX system reduced hypoglycemic episodes overall (9 vs. 33; P = 0.02), during exercise (5 vs. 13; P = 0.04), and in the 4 h following (2 vs. 11; P = 0.02). Overall CGM median percent time <70 mg/dL decreased as well (0.3% vs. 1.6%; P = 0.004). This protection was obtained with no significant increase in time >180 mg/dL (18.5% vs. 16.6%, P = 0.15). Overnight control was notable for both systems with no hypoglycemia, median percent in time 70-180 mg/dL at 100% and median percent time 70-140 mg/dL at ∼96% for both. Conclusions: A new closed-loop system capable of anticipating and detecting exercise was proven to be safe and feasible and outperformed a state-of-the-art HCL, reducing participants' exposure to hypoglycemia during and after moderate-intensity physical activity. ClinicalTrials.gov NCT03859401.

The association between hypoglycemia and glycemic variability in elderly patients with type 2 diabetes: a prospective observational study

BACKGROUND

This study aimed to explore predictive factors of time below target glucose range (TBR) ≥ 1% among patients' characteristics and glycemic variability (GV) indices using continuous glucose monitoring data in elderly patients with type 2 diabetes.

METHODS

We conducted a prospective observational study on 179 (71 female) Japanese outpatients with type 2 diabetes aged ≥ 65 years. The characteristics of the participants with TBR ≥ 1% were evaluated by multivariate logistic regression analysis. Receiver-operating characteristic (ROC) curve analyses of GV indices, comprising coefficient of variation (CV), standard deviation, and mean amplitude of glycemic excursions, were performed to identify the optimal index for the identification of patients with TBR ≥ 1%.

RESULTS

In the multivariate logistic regression analysis, none of the clinical characteristics, including HbA1c and C-peptide index, were independent markers for TBR ≥ 1%, while all three GV indices showed significant associations with TBR ≥ 1%. Among the three GV indices, CV showed the best performance based on the area under the curve in the ROC curve analyses.

CONCLUSIONS

Among elderly patients with type 2 diabetes, CV reflected TBR ≥ 1% most appropriately among the GV indices examined. Trial registration UMIN-CTR: UMIN000029993. Registered 16 November 2017.

GLU: a software package for analysing continuously measured glucose levels in epidemiology

Continuous glucose monitors (CGM) record interstitial glucose levels 'continuously', producing a sequence of measurements for each participant (e.g. the average glucose level every 5 min over several days, both day and night). To analyse these data, researchers tend to derive summary variables such as the area under the curve (AUC), to then use in subsequent analyses. To date, a lack of consistency and transparency of precise definitions used for these summary variables has hindered interpretation, replication and comparison of results across studies. We present GLU, an open-source software package for deriving a consistent set of summary variables from CGM data. GLU performs quality control of each CGM sample (e.g. addressing missing data), derives a diverse set of summary variables (e.g. AUC and proportion of time spent in hypo-, normo- and hyper- glycaemic levels) covering six broad domains, and outputs these (with quality control information) to the user. GLU is implemented in R and is available on GitHub at https://github.com/MRCIEU/GLU. Git tag v0.2 corresponds to the version presented here.

Analysis of Glycemic Improvement in Hemodialysis Patients Based on Time in Range, Assessed by Flash Glucose Monitoring

INTRODUCTION

This study aimed to investigate the application value of "time in ranges (TIRs)" in dialysis patients with diabetes and summarize the experience of optimizing glycemic control by flash glucose monitoring (FGM) system.

METHODS

In this monocentric 2-week pilot study, FGM was applied for 14 days in 57 type 2 diabetes mellitus medically stable patients under maintenance hemodialysis to determine their glycemic improvement. The diagnostic value of TIR versus HbA1c in detecting glucose fluctuations and levels was evaluated using receiver operating characteristic analysis.

RESULTS

Average glucose exhibited stronger association with TIR (r = -0.785, p < 0.001) than HbA1c (r = 0.644, p < 0.001), and mean amplitude of glycemic excursion (MAGE) had the same conclusion (r = -0.568, p < 0.001 for TIR vs. r = 0.423, p = 0.016 for HbA1c). TIR exhibited a higher area under curve than HbA1c in detecting significant derangements in glucose fluctuation, using a 14-day average FGM-derived coefficient of variation >36% as the reference standard (difference between areas: 0.237; 95% CI 0.092-0.383, p = 0.001). We found a significant improvement in TIR (58.38 ± 19.42 vs. 46.45 ± 24.42 mmol/L, p < 0.001) and a significant decline in MAGE (median 5.64 vs.7.42 mmol/L, p < 0.001) compared to the baseline without deterioration of time spent in hypoglycemia.

CONCLUSION

TIR seems to be feasible and clinically useful for AGP analysis in dialysis patients with diabetes, and FGM can be used to improve glycemic control.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Spousal Influence on Diabetes Self-care: Moderating Effects of Distress and Relationship Quality on Glycemic Control

BACKGROUND

Spouses often attempt to influence patients' diabetes self-care. Spousal influence has been linked to beneficial health outcomes in some studies, but to negative outcomes in others.

PURPOSE

We aimed to clarify the conditions under which spousal influence impedes glycemic control in patients with type 2 diabetes. Spousal influence was hypothesized to associate with poorer glycemic control among patients with high diabetes distress and low relationship quality.

METHODS

Patients with type 2 diabetes and their spouses (N = 63 couples) completed self-report measures before patients initiated a 7-day period of continuous glucose monitoring. Mean glucose level and coefficient of variation (CV) were regressed on spousal influence, diabetes distress, relationship quality, and their two- and three-way interactions.

RESULTS

The three-way interaction significantly predicted glucose variability, but not mean level. Results revealed a cross-over interaction between spousal influence and diabetes distress at high (but not low) levels of relationship quality, such that spousal influence was associated with less variability among patients with low distress, but more among those with high distress. Among patients with high distress and low relationship quality, a 1 SD increase in spousal influence predicted a difference roughly equivalent to the difference between the sample mean CV and a CV in the unstable glycemia range.

CONCLUSIONS

This was the first study to examine moderators of the link between spousal influence and glycemic control in diabetes. A large effect was found for glucose variability, but not mean levels. These novel results highlight the importance of intimate relationships in diabetes management.

Oscillatory pattern of glycemic control in patients with diabetes mellitus

Daily glucose variability is higher in diabetic mellitus (DM) patients which has been related to the severity of the disease. However, it is unclear whether glycemic variability displays a specific pattern oscillation or if it is completely random. Thus, to determine glycemic variability pattern, we measured and analyzed continuous glucose monitoring (CGM) data, in control subjects and patients with DM type-1 (T1D). CGM data was assessed for 6 days (day: 08:00-20:00-h; and night: 20:00-08:00-h). Participants (n = 172; age = 18-80 years) were assigned to T1D (n = 144, females = 65) and Control (i.e., healthy; n = 28, females = 22) groups. Anthropometry, pharmacologic treatments, glycosylated hemoglobin (HbA1c) and years of evolution were determined. T1D females displayed a higher glycemia at 10:00-14:00-h vs. T1D males and Control females. DM patients displays mainly stationary oscillations (deterministic), with circadian rhythm characteristics. The glycemia oscillated between 2 and 6 days. The predictive model of glycemia showed that it is possible to predict hyper and hypoglycemia (R² = 0.94 and 0.98, respectively) in DM patients independent of their etiology. Our data showed that glycemic variability had a specific oscillation pattern with circadian characteristics, with episodes of hypoglycemia and hyperglycemia at day phases, which could help therapeutic action for this population.

The Differential and Combined Action of Insulin Glargine and Lixisenatide on the Fasting and Postprandial Components of Glucose Control

BACKGROUND

iGlarLixi is an injectable combination of long acting insulin glargine (iGlar) and glucagon-like peptide 1 receptor agonist lixisenatide in a fixed ratio, which was proven safe and effective for the treatment of type 2 diabetes. Lixisenatide and iGlar act differently on fasting and postprandial plasma glucose (fasting plasma glucose [FPG] and postprandial glucose [PPG]). Here, we deconstruct quantitatively their respective FPG and PPG effects.

METHOD

This post hoc study analyzes data from the Lixilan-O trial, where 1170 subjects with type 2 diabetes were randomly assigned to 30 weeks of once daily injections of lixisenatide, iGlar, and iGlarLixi (1:2:2). The FPG and PPG components of glucose control were assessed in terms of mean glucose (fasting mean plasma glucose [FMPG] and prandial mean plasma glucose [PMPG], respectively). The MPGP was computed across all meals as a delta between post- and premeal glucose; glucose variability was measured by the high blood glucose index (HBGI) (fasting HBGI and prandial HBGI [PHBGI], respectively), and glycemic exposure measured by area under the curve (AUC) computed overall. All metrics were derived from seven-point self-monitoring glucose profiles.

RESULTS

Insulin glargine lowered significantly FMPG by 15.3 mg/dL (P < .01) without any significant change in PMPG. Lixisenatide, when added to iGlar, reduced PMPG by 9.7 mg/dL (P < .01), AUC by 96.3 mg∙h/dL (P < .01), and PHBGI by 2.4 (P < .01), primarily due to attenuation of PPG and without significant change in mean FPG.

CONCLUSION

Insulin glargine and lixisenatide act selectively on FPG and PPG. Their combination iGlarLixi offers more effective glucose control than its components due to the cumulative effect on FPG and PPG, which is evidenced by reduced average glycemia, glycemic exposure, and glucose variability.

Glycemic variability and cardiovascular disease in patients with type 2 diabetes

Glycated hemoglobin is currently the gold standard for assessment of long-term glycemic control and response to medical treatment in patients with diabetes. Glycated hemoglobin, however, does not address fluctuations in blood glucose. Glycemic variability (GV) refers to fluctuations in blood glucose levels. Recent clinical data indicate that GV is associated with increased risk of hypoglycemia, microvascular and macrovascular complications, and mortality in patients with diabetes, independently of glycated hemoglobin level. The use of continuous glucose monitoring devices has markedly improved the assessment of GV in clinical practice and facilitated the assessment of GV as well as hypoglycemia and hyperglycemia events in patients with diabetes. We review current concepts on the definition and assessment of GV and its association with cardiovascular complications in patients with type 2 diabetes.

Time in Range in Relation to All-Cause and Cardiovascular Mortality in Patients With Type 2 Diabetes: A Prospective Cohort Study

OBJECTIVE

There is growing evidence linking time in range (TIR), an emerging metric for assessing glycemic control, to diabetes-related outcomes. We aimed to investigate the association between TIR and mortality in patients with type 2 diabetes.

RESEARCH DESIGN AND METHODS

A total of 6,225 adult patients with type 2 diabetes were included from January 2005 to December 2015 from a single center in Shanghai, China. TIR was measured with continuous glucose monitoring at baseline, and the participants were stratified into four groups by TIR: >85%, 71-85%, 51-70%, and ≤50%. Cox proportional hazards regression models were used to estimate the association between different levels of TIR and the risks of all-cause and cardiovascular disease (CVD) mortality.

RESULTS

The mean age of the participants was 61.7 years at baseline. During a median follow-up of 6.9 years, 838 deaths were identified, 287 of which were due to CVD. The multivariable-adjusted hazard ratios associated with different levels of TIR (>85% [reference group], 71-85%, 51-70%, and ≤50%) were 1.00, 1.23 (95% CI 0.98-1.55), 1.30 (95% CI 1.04-1.63), and 1.83 (95% CI 1.48-2.28) for all-cause mortality (P for trend <0.001) and 1.00, 1.35 (95% CI 0.90-2.04), 1.47 (95% CI 0.99-2.19), and 1.85 (95% CI 1.25-2.72) for CVD mortality (P for trend = 0.015), respectively.

CONCLUSIONS

The current study indicated an association of lower TIR with an increased risk of all-cause and CVD mortality among patients with type 2 diabetes, supporting the validity of TIR as a surrogate marker of long-term adverse clinical outcomes.

Choice of Continuous Glucose Monitoring Systems May Affect Metrics: Clinically Relevant Differences in Times in Ranges

Background Continuous glucose monitoring-derived parameters are becoming increasingly important in the treatment of people with diabetes. The aim of this study was to assess whether these parameters, as calculated from different continuous glucose monitoring systems worn in parallel, are comparable. In addition, clinical relevance of differences was investigated.

Methods A total of 24 subjects wore a FreeStyle Libre (A) and a Dexcom G5 (B) sensor in parallel for 7 days. Mean glucose, coefficient of variation, glucose management indicator and time spent in different glucose ranges were calculated for each system. Pairwise differences between the two different continuous glucose monitoring systems were computed for these metrics.

Results On average, the two CGM systems indicated an identical time in range (67.9±10.2 vs. 67.9±11.5%) and a similar coefficient of variation; both categorized as unstable (38.1±5.9 vs. 36.0±4.8%). In contrast, the mean time spent below and above range, as well as the individual times spent below, in and above range differed substantially. System A indicated about twice the time spent below range than system B (7.7±7.2 vs. 3.8±2.7%, p=0.003). This could have led to different therapy recommendations in approximately half of the subjects.

Discussion The differences in metrics found between the two continuous glucose monitoring systems may result in different therapy recommendations. In order to make adequate clinical decisions, measurement performance of CGM systems should be standardized and all available information, including the HbA1c, should be utilized.

Impact of endogenous insulin secretion on the improvement of glucose variability in Japanese patients with type 2 diabetes treated with canagliflozin plus teneligliptin

Aims/Introduction

To identify the effect of combination therapy with a dipeptidyl peptidase‐4 inhibitor and a sodium–glucose cotransporter 2 inhibitor compared with switching from a dipeptidyl peptidase‐4 inhibitor to a sodium–glucose cotransporter 2 inhibitor on improving the glucose variability in patients with or without impaired endogenous insulin secretion.

Materials and Methods

A secondary analysis regarding the relationship between endogenous insulin secretion and the change in mean amplitude of glycemic excursions (ΔMAGE) was carried out in a multicenter, prospective, randomized, parallel‐group comparison trial that enrolled patients with type 2 diabetes who had been taking teneligliptin and were treated by switching to canagliflozin (SWITCH) or adding canagliflozin (COMB). Participants were categorized into the following four subgroups: SWITCH or COMB and high or low fasting C‐peptide (CPR) divided at baseline by the median.

Results

ΔMAGE in the COMB group was greatly improved independent of a high or low CPR (−29.2 ± 28.3 vs −20.0 ± 24.6, respectively; P = 0.60). However, ΔMAGE was not ameliorated in the low CPR SWITCH group, and the ΔMAGE was significantly smaller than that in the high CPR COMB group (P < 0.01).

Conclusions

COMB would be a better protocol rather than switching teneligliptin to canagliflozin to improve daily glucose variability in patients with impaired endogenous insulin secretion.

Glucose variability and diabetes complications: Risk factor or biomarker? Can we disentangle the "Gordian Knot"?

« Variability in glucose homoeostasis » is a better description than « glycaemic variability » as it encompasses two categories of dysglycaemic disorders: i) the short-term daily glucose fluctuations and ii) long-term weekly, monthly or quarterly changes in either HbA1c, fasting or postprandial plasma glucose. Presently, the relationship between the "variability in glucose homoeostasis" and diabetes complications has never been fully clarified because studies are either observational or limited to retrospective analysis of trials not primarily designed to address this issue. Despite the absence of definitive evidence from randomized controlled trials (RCTs), it is most likely that acute and long-term glucose homoeostasis "cycling", akin to weight and blood pressure "cycling" in obese and hypertensive individuals, are additional risk factors for diabetes complications in the presence of sustained ambient hyperglycaemia. As hypoglycaemic events are strongly associated with short- and long-term glucose variability, two relevant messages can be formulated. Firstly, due consideration should be given to avoid within-day glucose fluctuations in excess of 36% (coefficient of variation) at least for minimizing the inconvenience and dangers associated with hypoglycaemia. Secondly, it seems appropriate to consider that variability in glucose homoeostasis is not only associated with cardiovascular events but is also a causative risk factor via hypoglycaemic episodes as intermediary step. Untangling the" Gordian Knot", to provide confirmation about the impact of variability in glucose homoeostasis and diabetes complications remains a daunting prospect.

Comprehensive elaboration of glycemic variability in diabetic macrovascular and microvascular complications

Diabetes mellitus is the major risk factor for the development of macrovascular and microvascular complications. It is increasingly recognized that glycemic variability (GV), referring to oscillations in blood glucose levels and representing either short-term or long-term GV, is involved in the pathogenesis of diabetic complications and has emerged as a possible independent risk factor for them. In this review, we summarize the metrics and measurement of GV in clinical practice, as well as comprehensively elaborate the role and related mechanisms of GV in diabetic macrovascular and microvascular complications, aiming to provide the mechanism-based therapeutic strategies for clinicians to manage diabetes mellitus.