GlyCulator: a glycemic variability calculation tool for continuous glucose monitoring data

Perioperative Glycemic Variability Influences Infection Rates Differently Following Revision Hip and Knee Arthroplasty

INTRODUCTION

Recent investigations have determined that abnormal postoperative glycemia following primary total joint arthroplasty is associated with adverse events. Our study aimed to determine if hyperglycemia and glycemic variability following aseptic revision total joint arthroplasty (rTJA) were associated with periprosthetic joint infection (PJI) within two years postoperatively.

METHODS

A retrospective review was performed of 2,208 patients within a single institution undergoing aseptic rTJA from 2012 to 2019. Postoperative glucose values were recorded. Glycemic variability was measured via three parameters: coefficient of variation (%CV), mean amplitude of glycemic excursions (MAGE), and J-index. Logistic regression analyses were performed to examine associations with PJI at 90-day, 1-, and 2-year follow-up.

RESULTS

In revision hips, all glycemic measures were not associated with PJI at any timepoint in logistic regression analyses, except for MAGE, which predicted PJI at one year (P = 0.045); body mass index (BMI) was the only factor associated with PJI at all timepoints in all models. In revision knees, all glycemic measures were not associated with PJI at any timepoint in logistic regression analyses; however, PJI rates differed between diabetics and non-diabetics at all time-points (P < 0.05).

CONCLUSIONS

Our findings illustrate that decreasing preoperative BMI and postoperative glycemic variability may be critical in reducing PJI rates in revision hips. Furthermore, patients who have diabetes should be counseled that they remain at higher risk of PJI regardless of perioperative glucose control after revision knee surgery.

Time-Restricted Eating in Adults With Metabolic Syndrome : A Randomized Controlled Trial

BACKGROUND

Time-restricted eating (TRE), limiting daily dietary intake to a consistent 8 to 10 hours without mandating calorie reduction, may provide cardiometabolic benefits.

OBJECTIVE

To determine the effects of TRE as a lifestyle intervention combined with current standard-of-care treatments on cardiometabolic health in adults with metabolic syndrome.

DESIGN

Randomized controlled trial. (ClinicalTrials.gov: NCT04057339).

SETTING

Clinical research institute.

PARTICIPANTS

Adults with metabolic syndrome including elevated fasting glucose or hemoglobin A1c (HbA1c; pharmacotherapy allowed).

INTERVENTION

Participants were randomly assigned to standard-of-care (SOC) nutritional counseling alone (SOC group) or combined with a personalized 8- to 10-hour TRE intervention (≥4-hour reduction in eating window) (TRE group) for 3 months. Timing of dietary intake was tracked in real time using the myCircadianClock smartphone application.

MEASUREMENTS

Primary outcomes were HbA1c, fasting glucose, fasting insulin, homeostasis model assessment of insulin resistance, and glycemic assessments from continuous glucose monitors.

RESULTS

108 participants from the TIMET study completed the intervention (89% of those randomly assigned; 56 women, mean baseline age, 59 years; body mass index of 31.22 kg/m2; eating window of 14.19 hours). Compared with SOC, TRE improved HbA1c by -0.10% (95% CI, -0.19% to -0.003%). Statistical outcomes were adjusted for age. There were no major adverse events.

LIMITATION

Short duration, self-reported diet, potential for multiple elements affecting outcomes.

CONCLUSION

Personalized 8- to 10-hour TRE is an effective practical lifestyle intervention that modestly improves glycemic regulation and may have broader benefits for cardiometabolic health in adults with metabolic syndrome on top of SOC pharmacotherapy and nutritional counseling.

PRIMARY FUNDING SOURCE

National Institutes of Health.

Short-term Glycemic Variability and Its Association With Macrovascular and Microvascular Complications in Patients With Diabetes

The introduction of continuous glucose monitoring inaugurated a new era in clinical practice by shifting the characterization of glycemic control from HbA1c to novel metrics. The one that gained widespread attention over the past decades was glycemic variability (GV), which typically refers to peaks and nadirs of blood glucose measured over a given time interval. GV can be dichotomized into two main categories: short-term and long-term. Short-term GV reflects within-day and between-day glycemic oscillations, and its contribution to diabetic complications remains an enigma. In this review, we summarize the available data about short-term GV and its possible association with both microvascular and macrovascular complications, evaluating different pathogenic mechanisms and demonstrating nonpharmaceutical, as well as pharmaceutical, therapeutic interventions.

Enhancement of Ambulatory Glucose Profile for Decision Assistance and Treatment Adjustments

The ambulatory glucose profile (AGP) lacks sufficient statistical metrics and insightful graphs; indeed, it is missing important information on the temporal patterns of glucose variations. The AGP graph is difficult to interpret due to the overlapping metrics and fluctuations in glucose levels over 14 days. The objective of this proposed work is to overcome these challenges, specifically the lack of insightful information and difficulty in interpreting AGP graphs, to create a platform for decision assistance. The present work proposes 20 findings built from decision rules that were developed from a combination of AGP metrics and additional statistical metrics, which have the potential to identify patterns and insightful information on hyperglycemia and hypoglycemia. The "CGM Trace" webpage was developed, in which insightful metrics and graphical representations can be used to make inferences regarding the glucose data of any user. However, doctors (endocrinologists) can access the "Findings" tab for a summarized presentation of their patients' glycemic control. The findings were implemented for 67 patients' data, in which the data of 15 patients were collected from a clinical study and the data of 52 patients were gathered from a public dataset. The findings were validated by means of MANOVA (multivariate analysis of variance), wherein a p value of < 0.05 was obtained, depicting a strong significant correlation between the findings and the metrics. The proposed work from "CGM Trace" offers a deeper understanding of the CGM data, enhancing AGP reports for doctors to make treatment adjustments based on insightful information and hidden patterns for better diabetic management.

Deep Learning-Based Glucose Prediction Models: A Guide for Practitioners and a Curated Dataset for Improved Diabetes Management

Accurate short- and mid-term blood glucose predictions are crucial for patients with diabetes struggling to maintain healthy glucose levels, as well as for individuals at risk of developing the disease. Consequently, numerous efforts from the scientific community have focused on developing predictive models for glucose levels. This study harnesses physiological data collected from wearable sensors to construct a series of data-driven models based on deep learning approaches. We systematically compare these models to offer insights for practitioners and researchers venturing into glucose prediction using deep learning techniques. Key questions addressed in this work encompass the comparison of various deep learning architectures for this task, determining the optimal set of input variables for accurate glucose prediction, comparing population-wide, fine-tuned, and personalized models, and assessing the impact of an individual's data volume on model performance. Additionally, as part of our outcomes, we introduce a meticulously curated dataset inclusive of data from both healthy individuals and those with diabetes, recorded in free-living conditions. This dataset aims to foster research in this domain and facilitate equitable comparisons among researchers.

To What Extent Is HbA1c Associated with Glycemic Variability in Patients with Type 1 Diabetes? A Retrospective, Noninterventional Study

BACKGROUND

Glycemic variability (GV) is a novel parameter used in evaluating the quality of diabetes management. Current guidelines recommend the use of GV indexes alongside the traditional parameter to evaluate glycemic control: hemoglobin A1c (HbA1c). This study aims to evaluate the extent to which HbA1c explains the GV phenomena in patients with Type 1 diabetes (T1DM).

METHODS

In 147 patients with T1DM, associations between HbA1c and several GV indexes were analyzed.

RESULTS

Patients with an HbA1c < 7% had a lower median standard deviation of glycemia (60 vs. 48; p < 0.001), a lower coefficient of variation (34.1 vs. 38.0; p < 0.001), and a significantly increased median time in range (78 vs. 58; p < 0.001). HbA1c was positively correlated with the coefficient of variation (r = 0.349; p < 0.001) and the standard deviation (r = 0.656; p < 0.001) but reversely correlated with a lower time in range (r = -0.637; p < 0.001).

CONCLUSIONS

HbA1c only partially explains the GV phenomena in patients with T1DM. The HbA1c value is associated more strongly with the time in range and standard deviation than with the coefficient of variation.

Statistical Packages and Algorithms for the Analysis of Continuous Glucose Monitoring Data: A Systematic Review

BACKGROUND

Continuous glucose monitoring (CGM) measures glucose levels every 1 to 15 minutes and is widely used in clinical and research contexts. Statistical packages and algorithms reduce the time-consuming and error-prone process of manually calculating CGM metrics and contribute to standardizing CGM metrics defined by international consensus. The aim of this systematic review is to summarize existing data on (1) statistical packages for retrospective CGM data analysis and (2) statistical algorithms for retrospective CGM analysis not available in these statistical packages.

METHODS

A systematic literature search in PubMed and EMBASE was conducted on September 19, 2023. We also searched Google Scholar and Google Search until October 12, 2023 as sources of gray literature and performed reference checks of the included literature. Articles in English and Danish were included. This systematic review is registered with PROSPERO (CRD42022378163).

RESULTS

A total of 8731 references were screened and 46 references were included. We identified 23 statistical packages for the analysis of CGM data. The statistical packages could calculate many metrics of the 2022 CGM consensus and non-consensus CGM metrics, and 22/23 (96%) statistical packages were freely available. Also, 23 statistical algorithms were identified. The statistical algorithms could be divided into three groups based on content: (1) CGM data reduction (eg, clustering of CGM data), (2) composite CGM outcomes, and (3) other CGM metrics.

CONCLUSION

This systematic review provides detailed tabular and textual up-to-date descriptions of the contents of statistical packages and statistical algorithms for retrospective analysis of CGM data.

How to Measure Glycemic Variability? A Literature Review

Optimal glycemic control without the presence of diabetes-related complications is the primary goal for adequate diabetes management. Recent studies have shown that hemoglobin A1c level cannot fully evaluate diabetes management as glycemic fluctuations are demonstrated to have a major impact on the occurrence of diabetes-related micro- and macroangiopathic comorbidities. The use of continuous glycemic monitoring systems allowed the quantification of glycemic fluctuations, providing valuable information about the patients' glycemic control through various indicators that evaluate the magnitude of glycemic fluctuations in different time intervals. This review highlights the significance of glycemic variability by describing and providing a better understanding of common and alternative indicators available for use in clinical practice.

A Probabilistic Approach to Blood Glucose Prediction in Type 1 Diabetes Under Meal Uncertainties

Currently, most reliable and commercialized artificial pancreas systems for type 1 diabetes are hybrid closed-loop systems, which require the user to announce every meal and its size. However, estimating the amount of carbohydrates in a meal and announcing each and every meal is an error-prone process that introduces important uncertainties to the problem, which when not considered, lead to sub-optimal outcomes of the controller. To address this problem, we propose a novel deep-learning-based model for probabilistic glucose prediction, called the Input and State Recurrent Kalman Network (ISRKN), which consists in the incorporation of an input and state Kalman filter in the latent space of a deep neural network so that the posterior distributions can be computed in closed form and the uncertainty can be propagated using the Kalman equations. In addition, the proposed architecture allows explicit estimation of the meal uncertainty distribution, whose parameters are encoded in the filter parameters. Results using the UVA/Padova simulator and data from a clinical trial show that the proposed model outperforms other probabilistic models using several probabilistic metrics across different degrees of distributional shifts.

Associations between sleep characteristics and glycemic variability in youth with type 1 diabetes

OBJECTIVE

This study aimed to determine sleep characteristics and their associations with glycemic variability in youth with type 1 diabetes (T1D).

MATERIAL AND METHODS

This cross-sectional study conducted at two pediatric diabetes centers in Istanbul, Turkey, included 84 children with T1D (mean age 10.5 years). Sleep characteristics and glycemic variability were determined by actigraphy, DSM-5 Level 2-Sleep Disturbance Scale Short Form and continuous glucose monitoring. Circadian preference was evaluated by the Children's Chronotype Questionnaire. Sleep disturbances were assessed by the. The sleep quality was determined by actigraphy-derived sleep measures.

RESULTS

Eighty-eight percent of participants had insufficient age-appropriate total sleep time (TST) (<9 h for 6-13-year-olds and <8 h for 14-17-year-olds). Chronotype was classified as intermediate in 50%, evening in 45.2%, and morning in 4.8%. A higher chronotype score indicating a stronger eveningness preference was associated with more time spent in hypoglycemia (β = 0.433, p = 0.002). On nights when participants had lower sleep efficiency and longer sleep onset latency, they had significantly higher overnight glycemic variability (β = -0.343, p = 0.016, β = 0.129, p = 0.017, respectively). Prolonged nocturnal wake duration was significantly associated with more time spent in daytime hypoglycemia (β = 0.037, p = 0.046) and higher overnight glycemic variability (J index, β = 0.300, p = 0.015). The associations between TST and glycemic variability indices were not significant.

CONCLUSIONS

Sleep quality rather than TST was significantly associated with glycemic variability in children with T1D. Eveningness preference might contribute to an increased risk of hypoglycemia. Addressing sleep patterns and chronotypes can be crucial in management plans for youth with T1D.

A composite measure of sleep health is associated with glycaemic target achievement in young adults with type 1 diabetes

We investigated whether sleep health (each individual dimension and a composite measure) was associated with better glycaemia among a cohort of young adults with type 1 diabetes (mean age 21.5 years, mean body mass index 24.55 kg m-2 ). Multiple validated self-report questionnaires were completed, and raw continuous glucose monitor data were shared. One self-reported sleep characteristic for each of the five sleep health dimensions was selected. A composite score was calculated by summing the number of "good" sleep health dimensions. We evaluated the associations between sleep health and glycaemia, and whether covariates, including age, type 1 diabetes duration and sleep apnea risk, influenced the relationships among the study variables using multivariable linear regression. Individual dimensions of sleep satisfaction (β = 0.380, p = 0.019; β = -0.414, p = 0.010), timing (β = 0.392, p = 0.015; β = -0.393, p = 0.015) and sleep efficiency (β = 0.428, p = 0.007) were associated with higher achievement of glycaemic targets (J-index and time in range); however, these associations did not persist after considering covariates. A better Sleep Health Composite score was associated with higher achievement of glycaemic targets even after considering covariates. Using a multidimensional framework can guide future research on causal pathways between sleep and diabetes health, interventions to target sleep health profiles, and may improve sleep screening in routine diabetes care.

Faster-acting insulin aspart reduces glycaemic variability in sensor-augmented pump treated type 1 diabetes patients

AIM

To evaluate the effect of faster aspart over glycaemic variability in type 1 diabetes (T1D) patients treated with sensor-augmented pump (SAP) in a real-world scenario.

METHODS

Observational study with SAP-treated adult T1D patients treated with faster aspart for three months. The primary endpoint was the mean amplitude of glucose excursions (MAGE).

RESULTS

Fifty patients were treated with faster aspart. Eleven patients (23%) withdrew during the follow-up mainly due to worsening of diabetes control (9 patients). Mean age was 41.2 yrs. (range 21-59) and T1D duration 22.4±10.0 yrs. Mean SAP treatment duration was 3.6±3.1 yrs. We detected a reduction of -7.0 (95% CI -1.1, -12.9; p=0.021) in MAGE at the end of the study. Other glycemic variability indices were also improved: standard deviation of mean interstitial glucose (-3mg/dl; 95% CI, -1, -5; p=0.01), CONGA4 (-2.2; 95% CI -0.3, -4.2; p=0.029), CONGA6 (-2.6; 95% CI -0.6, -4.6; p=0.011), GRADE (-0.5; 95% CI -0.1, -0.9; p=0.022), HBGI (-0.7; 95% CI -0.2, -1.3; p=0.013), J-index (-2.9; 95% CI -0.7, -5.0; p=0.011) and MODD (-5.7; 95% CI -1.7, -9.7; p=0.006). A slight reduction in mean glucose management indicator was also detected (-0.14%; 95% CI, -0.02, -0.27; -1.4mmol/mol; 95% CI -0.1, -3.3; p=0.03).

CONCLUSIONS

In SAP-treated T1D patients, faster aspart insulin was associated with reduced glycaemic variability, but also a high percentage of dropouts due to worsened glycaemic control. NCT04233203.

Beyond hyperglycemia: glycaemic variability as a prognostic factor after acute ischemic stroke

INTRODUCTION

Glycaemic variability (GV) refers to variations in blood glucose levels, and may affect stroke outcomes. This study aims to assess the effect of GV on acute ischaemic stroke progression.

METHODS

We performed an exploratory analysis of the multicentre, prospective, observational GLIAS-II study. Capillary glucose levels were measured every 4 hours during the first 48 hours after stroke, and GV was defined as the standard deviation of the mean glucose values. The primary outcomes were mortality and death or dependency at 3 months. Secondary outcomes were in-hospital complications, stroke recurrence, and the impact of the route of insulin administration on GV.

RESULTS

A total of 213 patients were included. Higher GV values were observed in patients who died (n = 16; 7.8%; 30.9 mg/dL vs 23.3 mg/dL; p = 0.05). In a logistic regression analysis adjusted for age and comorbidity, both GV (OR = 1.03; 95% CI, 1.003-1.06; p = 0.03) and stroke severity (OR = 1.12; 95% CI, 1.04-1.2; p = 0.004) were independently associated with mortality at 3 months. No association was found between GV and the other outcomes. Patients receiving subcutaneous insulin showed higher GV than those treated with intravenous insulin (38.95 mg/dL vs 21.34 mg/dL; p < 0.001).

CONCLUSIONS

High GV values during the first 48 hours after ischaemic stroke were independently associated with mortality. Subcutaneous insulin may be associated with higher VG levels than intravenous administration.

Long-Term Glucose Forecasting for Open-Source Automated Insulin Delivery Systems: A Machine Learning Study with Real-World Variability Analysis

Glucose forecasting serves as a backbone for several healthcare applications, including real-time insulin dosing in people with diabetes and physical activity optimization. This paper presents a study on the use of machine learning (ML) and deep learning (DL) methods for predicting glucose variability (GV) in individuals with open-source automated insulin delivery systems (AID). A three-stage experimental framework is employed in this work to systematically implement and evaluate ML/DL methods on a large-scale diabetes dataset collected from individuals with open-source AID. The first stage involves data collection, the second stage involves data preparation and exploratory analysis, and the third stage involves developing, fine-tuning, and evaluating ML/DL models. The performance and resource costs of the models are evaluated alongside relative and proportional errors for 17 GV metrics. Evaluation of fine-tuned ML/DL models shows considerable accuracy in glucose forecasting and variability analysis up to 48 h in advance. The average MAE ranges from 2.50 mg/dL for long short-term memory models (LSTM) to 4.94 mg/dL for autoregressive integrated moving average (ARIMA) models, and the RMSE ranges from 3.7 mg/dL for LSTM to 7.67 mg/dL for ARIMA. Model execution time is proportional to the amount of data used for training, with long short-term memory models having the lowest execution time but the highest memory consumption compared to other models. This work successfully incorporates the use of appropriate programming frameworks, concurrency-enhancing tools, and resource and storage cost estimators to encourage the sustainable use of ML/DL in real-world AID systems.

Continuous Glucose Monitoring in Enterally Fed Children with Severe Central Nervous System Impairment

Children with severe central nervous system (CNS) impairment are at risk of developing various degrees of nutritional deficit that require long-term nutritional intervention. Interventions are most often implemented through enteral nutrition (EN) using commercially manufactured feeds administered via gastro/jejunostomy or nasogastric or nasojejunal tubes. The modality of feeding-continuous feeding or bolus feeding-is dependent on the function of the gastrointestinal tract, particularly the efficiency of gastric emptying. In the literature, the relationship between this type of nutrition and the occurrence of hyperglycaemia is often discussed. In addition, children with chronic neurological diseases are vulnerable to disorders of many mechanisms of neurohormonal counter-regulation related to carbohydrate management, and due to limited verbal and logical contact, it is difficult to recognise the symptoms of hypoglycaemia in such patients. We aimed to assess the carbohydrate metabolism in children with severe CNS impairment, with enteral nutrition delivered via nasogastric, nasoenteral, or percutaneous tubes, based on continuous glycaemic monitoring (CGM) and the measurement of glycated haemoglobin (HbA1c) levels.

MATERIALS AND METHODS

This prospective, observational study included nineteen patients (median (25-75 pc) age: 12.75 (6.17-15.55) years) with permanent CNS damage (Gross Motor Function Classification System V) receiving long-term tube enteral feeding, recruited from two paediatric university nutritional treatment centres. Patients with acute conditions and diagnosed diabetes were excluded. The nutritional status and nutritional support were analysed in all the inpatients in accordance with a uniform protocol. Using the CGM system (Medtronic iPro2), glycaemic curves were analysed, and in addition, HbA1C levels were determined in fourteen patients. CGM results were analysed using GlyCulator2.0. Statistical analysis was performed using the Statistica version 11 software (StatSoft Inc. Tulsa, OK, USA).

RESULTS

More than half (11/19; 58%) of the patients were undernourished (BMI < 3 pc for age and gender), with the stature age being significantly lower than calendar age (5 (4.5-9) vs. 12.75 (6.17-15.55) years; p = 0.0010). The actual caloric intake was 50 (37.7-68.8) kcal/kg (median; 25-75 pc). In patients fed using the bolus method, the number of calories consumed per day was statistically significantly higher than in children subjected to a continuous feeding supply (56.00 (41.00-75.00) vs. 33.40 (26.70-50.00) kcal/kg BW (body weight; p = 0.0159). Decreases in blood glucose levels below the alarm level (<70 mg/dL) were recorded in fifteen patients (78.9%), including two patients with episodes of clinically significant hypoglycaemia (<54 mg/dL). The minimum and maximum glycaemic values recorded in any individual CGM records were 67 mg/dL (median) (minimum: 41 mg/dL; maximum: 77 mg/dL) and 146 (minimum: 114 mg/dL; maximum: 180 g/dL), respectively, for the entire recording. The maximum percentage of glycaemic concentrations > 140 mg/dL (TAR 140) recorded overnight in children with BMI ≥ 3 amounted to 1.6% vs. 0% in undernourished patients (TAR 140: 0.0 (0.00-1.6%) vs. 0% (0.00-0.0%; p = 0.0375); the percentage of glycaemic concentrations <70 mg/dL in the entire recording was comparable (0.77% (0.13-2.2%) vs. 1.8% (0.5-14.4%) vs. p = 0.2629). There was a positive correlation between the mean daily glucose recorded using the CGM method and patients' BMI z-scores (R = 0.48, p = 0.0397). No statistically significant relationship was demonstrated between the occurrence of alarm hypoglycaemia events in the CGM records and undernutrition expressed by BMI z-scores (OR = 1.50 (95%CI: 0.16-13.75), the type of diet (for commercially manufactured OR = 0.36 (95%CI: 0.04-3.52), and the modality of diet delivery (for bolus feeding OR = 2.75 (95%CI: 0.28-26.61).

CONCLUSIONS

In children with chronic OU damage, enteral feeding is associated with a risk of hypoglycaemia, but further studies involving a larger number of patients are needed, and CGM might be a useful tool to estimate the metabolic adequacy of enteral nutritional support in terms of glucose control.

Glycemic indices at night measured by CGM are predictive for a lower pulmonary function in adults but not in children with cystic fibrosis

INTRODUCTION

In patients with cystic fibrosis (CF), it is still unclear to which extent glucose abnormalities - preceding the diagnosis of cystic fibrosis related diabetes (CFRD) - are associated with pulmonary and nutritional outcome parameters. This study related circadian glycemic patterns to clinical outcomes in a group of CF patients not previously diagnosed with diabetes.

METHODS

Continuous glucose monitoring (CGM) readings (7 days) of 47 CF patients (26 children, 21 adults) with an impaired oral glucose tolerance test (OGTT) (n = 25) and/or increased Hb1Ac (> 5.5%) were analyzed. Biometric, pulmonary function and clinical parameters were retrospectively collected over a period of 1 year before (T-1) and 1 year after (T + 1) CGM (T0).

RESULTS

96% (45/47) of CGM readings showed glucose values > 140 mg/dL ≥ 4.5% of the time and at least one ≥ 200 mg/dL. In the pediatric cohort, no significant associations were found between CGM parameters and pulmonary and nutritional outcome parameters. In the adult cohort, an area under the curve (AUC) > 140 mg/dL and%-time > 140 mg/dL during the night were associated with a lower forced expiratory volume in 1 s (FEV1)% predicted (pp) at time of evaluation but not with change in FEV1pp.

CONCLUSION

This is the first study reporting the circadian glycemic pattern in children and adults at risk for CFRD. In the adult cohort an association between detection of abnormal glucose exposure and a lower FEV1pp was found. Our results support continued screening for glucose intolerance in patients with CF.

Software Packages and Tools for the Analysis of Continuous Glucose Monitoring Data

The advancement of technology in the field of glycemic control has led to the widespread use of continuous glucose monitoring (CGM), which can be nowadays obtained from wearable devices equipped with a minimally invasive sensor, that is, transcutaneous needle type or implantable, and a transmitter that sends information to a receiver or smart device for data storage and display. This work aims to review the currently available software packages and tools for the analysis of CGM data. Based on the purposes of this work, 12 software packages have been identified from the literature, published until December 2021, namely: GlyCulator, EasyGV (Easy Glycemic Variability), CGM-GUIDE^© (Continuous Glucose Monitoring Graphical User Interface for Diabetes Evaluation), GVAP (Glycemic Variability Analyzer Program), Tidepool, CGManalyzer, cgmanalysis, GLU, CGMStatsAnalyser, iglu, rGV, and cgmquantify. Comparison of available software packages and tools has been done in terms of main characteristics (i.e., publication year, presence of a graphical user interface, availability, open-source code, number of citations, programming language, supported devices, supported data format and organization of the data structure, documentation, presence of a toy example, video tutorial, data upload and download, measurement-units conversion), preprocessing procedures, data display options, and computed metrics; also, each of the computed metrics has been analyzed in terms of its adherence to the American Diabetes Association (ADA) 2017 international consensus on CGM data analysis and the ADA 2019 international consensus on time in range. Eventually, the agreement between metrics computed by different software and tools has been investigated. Based on such comparison, usability and complexity of data management, as well as the possibility to perform customized or patients-group analyses, have been discussed by highlighting limitations and strengths, also in relation to possible different user categories (i.e., patients, clinicians, researchers). The information provided could be useful to researchers interested in working in the diabetic research field as to clinicians and endocrinologists who need tools capable of handling CGM data effectively.

Effects of DPP4 Inhibitor in Platelet Reactivity and Other Cardiac Risk Markers in Patients with Type 2 Diabetes and Acute Myocardial Infarction

Background: The management of acute myocardial infarction (AMI) presents several challenges in patients with diabetes, among them the higher rate of recurrent thrombotic events, hyperglycemia and risk of subsequent heart failure (HF). The objective of our study was to evaluate effects of DPP-4 inhibitors (DPP-4i) on platelet reactivity (main objective) and cardiac risk markers. Methods: We performed a single-center double-blind randomized trial. A total of 70 patients with type 2 diabetes (T2DM) with AMI Killip ≤2 on dual-antiplatelet therapy (aspirin plus clopidogrel) were randomized to receive sitagliptin 100 mg or saxagliptin 5 mg daily or matching placebo. Platelet reactivity was assessed at baseline, 4 days (primary endpoint) and 30 days (secondary endpoint) after randomization, using VerifyNow Aspirin™ assay, expressed as aspirin reaction units (ARUs); B-type natriuretic peptide (BNP) in pg/mL was assessed at baseline and 30 days after (secondary endpoint). Results: Mean age was 62.6 ± 8.8 years, 45 (64.3%) male, and 52 (74.3%) of patients presented with ST-segment elevation MI. For primary endpoint, there were no differences in mean platelet reactivity (p = 0.51) between the DPP-4i (8.00 {−65.00; 63.00}) and placebo (−14.00 {−77.00; 52.00}) groups, as well in mean BNP levels (p = 0.14) between DPP-4i (−36.00 {−110.00; 15.00}) and placebo (−13.00 {−50.00; 27.00}). There was no difference between groups in cardiac adverse events. Conclusions: DPP4 inhibitor did not reduce platelet aggregation among patients with type 2 diabetes hospitalized with AMI. Moreover, the use of DPP-4i did not show an increase in BNP levels or in the incidence of cardiac adverse events. These findings suggests that DPP-4i could be an option for management of T2DM patients with acute MI.

Glycemic Variability in Type 1 Diabetes Mellitus Pregnancies—Novel Parameters in Predicting Large-for-Gestational-Age Neonates: A Prospective Cohort Study

Pregnancies with type 1 diabetes mellitus (T1DM) have a high incidence of large-for-gestational-age neonates (LGA) despite optimal glycemic control. In recent years, glycemic variability (GV) has emerged as a possible risk factor for LGA, but the results of the conducted studies are unclear. This study analyzed the association between GV and LGA development in pregnancies with T1DM. This was a prospective cohort study of patients with T1DM who used continuous glucose monitoring (CGM) during pregnancy. Patients were followed from the first trimester to birth. GV parameters were calculated for every trimester using the EasyGV calculator. The main outcomes were LGA or no-LGA. Logistic regression analysis was used to assess the association between GV parameters and LGA. In total, 66 patients were included. The incidence of LGA was 36%. The analysis extracted several GV parameters that were significantly associated with the risk of LGA. The J-index was the only significant parameter in every trimester of pregnancy (odds ratios with confidence intervals were 1.33 (1.02, 1.73), 3.18 (1.12, 9.07), and 1.37 (1.03, 1.82), respectively. Increased GV is a risk factor for development of LGA. The J-index is a possible novel GV parameter that may be assessed in all three trimesters of pregnancy together with glycated hemoglobin and time-in-range.

Discrepancies in glycemic metrics derived from different continuous glucose monitoring systems in adult patients with type 1 diabetes mellitus

BACKGROUND

Continuous glucose monitoring systems have been widely used but discrepancies among various brands of devices are rarely discussed. This study aimed to explore differences in glycemic metrics between FreeStyle Libre (FSL) and iPro2 among adults with type 1 diabetes mellitus (T1DM).

METHODS

Participants with T1DM and glycosylated hemoglobin of 7%-10% were included and wore FSL and iPro2 for 2 weeks simultaneously. Datasets collected on the insertion and detachment day, and those with insufficient quantity (<90%) were excluded. Agreements of measurement accuracy and glycemic metrics were evaluated.

RESULTS

A total of 40 498 paired data were included. Compared with the values from FSL, significantly higher median value was observed in iPro2 (147.6 [106.2, 192.6] vs. 144.0 [100.8, 192.6] mg/dl, p < 0.001) and the largest discordance was observed in hypoglycemic range (median absolute relative difference with iPro2 as reference value: 25.8% [10.8%, 42.1%]). Furthermore, significant differences in glycemic metrics between iPro2 and FSL were also observed in time in range (TIR) 70-180 mg/dl (TIR, 62.8 ± 12.4% vs. 58.8 ± 12.3%, p = 0.004), time spent below 70 mg/dl (4.4 [1.8, 10.9]% vs. 7.2 [5.4, 13.3]%, p < 0.001), time spent below 54 mg/dl (0.9 [0.3, 4.0]% vs. 2.6 [1.3, 5.6]%, p = 0.011), and coefficient of variation (CV, 38.7 ± 8.5% vs. 40.9 ± 9.3%, p = 0.017).

CONCLUSIONS

During 14 days of use, FSL and iPro2 provided different estimations on TIR, CV, and hypoglycemia-related parameters, which needs to be considered when making clinical decisions and clinical trial designs.

Objective Sleep-Wake Characteristics Are Associated With Diabetes Symptoms in Young Adults With Type 1 Diabetes

PURPOSE

The primary purpose of this descriptive cross-sectional study was to examine the associations between sleep-wake characteristics (total sleep time, sleep variability, sleep onset latency, and sleep efficiency), distress symptoms (general and diabetes), and diabetes physical symptoms in young adults ages 18 to 30 years with type 1 diabetes (T1D). The secondary purpose was to determine whether biological sex, body mass index (BMI), and T1D duration (covariates) influence the relationships among the study variables.

METHODS

Forty-six young adults with T1D, recruited from diabetes clinics from December 2018 to February 2020, wore a wrist actigraph and continuous glucose monitor concurrently for 6 to 14 days and completed the PROMIS Emotional Distress Scale, Diabetes Distress Scale, and Diabetes Symptom Checklist-Revised.

RESULTS

Shorter total sleep time and poorer sleep efficiency were associated with higher diabetes emotional distress symptoms. Higher sleep variability was associated with higher neurological pain symptoms. A longer sleep onset latency was associated with higher symptoms of diabetes distress, including psychological, cognitive, hyperglycemia, and a higher total symptom burden. Associations remained statistically significant after adjusting for biological sex and BMI, with the exception of sleep onset latency and total symptom burden.

CONCLUSIONS

Poorer objective sleep-wake characteristics were associated with higher diabetes symptoms even after considering biological sex and BMI among young adults with T1D.

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.

Real-world outcomes of two different sensor-augmented insulin pumps with predictive low glucose suspend function in type 1 diabetes patients

AIM

To analyse the real-life outcomes of two sensor-augmented pumps (SAP) with predictive low glucose suspend (PLGS) function, Medtronic Minimed 640G™ with SmartGuard (MM640G) and Tandem T Slim X2™ with Basal-IQ™ (TTSX2), in Type 1 Diabetes Mellitus (T1DM) patients.

METHODS

Observational cross-sectional study using data obtained from computerized clinical records. All T1DM patients on TTSX2 therapy were compared (1:1) with MM640G treated patients selected through stratified sampling. Primary efficacy outcome was to describe time in rage (TIR, 70-180 mg/dL, 3.9-10 mmol/L) interstitial glucose differences according to a non-inferiority hypothesis with TTSX2 compared to MM640G.

RESULTS

Forty-four patients were analyzed (female 66%). Mean age was 38.9 yrs. (range 23-59 yrs.) and mean diabetes duration was 23.4 ± 9.2 yrs. Patients treated with TTSX2 showed a numerically slightly lower, but non-statistically significantly different, TIR from the MM640G pump group (64.9 ± 16.4% vs. 72.4 ± 17.0%, P = 0.108). Similarly, we did no find differences in HbA1c between T1D patients treated with TTSX2 and MM640G (6.8 ± 1.0% vs. 7.0 ± 0.9%, 51 ± 11 mmol/mol vs. 53 ± 10 mmol/mol, P = 0.312). Moreover, rest of evaluated glycemic outcomes were similar between both treatment groups.

CONCLUSIONS

Patients using two different SAP with PLGS automatic function showed similar glycaemic control in a real-world scenario. NCT04741685.

Circadian characteristics of the rest-activity rhythm, executive function, and glucose fluctuations in young adults with type 1 diabetes

Circadian alignment is an important element in individual health, and one behavioral marker, rest-activity rhythm, could influence self-management in young adults with type 1 diabetes (T1D). Little is known about the rest-activity rhythms, executive function, and glycemia among young adults with type 1 diabetes (T1D). The purpose of this study was to evaluate parametric and nonparametric circadian characteristics of the rest-activity rhythm and the associations between these variables, sleep-wake behavior, executive function, and glycemia among young adults with T1D. Young adults with T1D, recruited from diabetes clinics, wore wrist actigraphs and a continuous glucose monitor (CGM) concurrently for 6-14 days. Participants completed a 3-minute Trail Making Test on paper and electronic questionnaires - 8-item PROMIS v1.0 Emotional Distress Scale, 17-item Diabetes Distress Scale, including twice-daily Pittsburgh sleep diaries. Cosinor and nonparametric analyses were used to compute the rest-activity rhythm parameters, and linear regression modeling procedures were performed to determine the associations among the study variables. The sample included 46 young adults (mean age 22.3 ± 3.2; 32.6% male; 84.8% non-Hispanic White, HbA1c mean 7.2 ± 1.1%, BMI mean 27.0 ± 4.4 kg/m²). A number of parametric associations were observed between a stronger rhythm, better objective sleep-wake characteristics, and less daytime sleepiness. Nonparametric circadian parameters were significantly associated with several outcomes: a stronger rhythm adherence (higher inter-daily stability) with better objective sleep-wake characteristics, better executive function, lower diabetes distress, less hyperglycemia risk, and more time spent in hypoglycemia/hypoglycemia risk; and a more robust rhythm (higher relative amplitude) with better objective sleep-wake characteristics and more time spent in hypoglycemia/higher hypoglycemia risk. Future work should be directed at designs that test causality, such as interventions directed at the strength and stability of rest-activity rhythms, for the potential to improve glucoregulation and other diabetes outcomes.

Sleep-wake characteristics, daytime sleepiness, and glycemia in young adults with type 1 diabetes

STUDY OBJECTIVES

The purpose of this study was to describe objective sleep-wake characteristics and glycemia over 7 - 14 days in young adults with type 1 diabetes (T1D). Additionally, person level associations among objective sleep-wake characteristics (total sleep time, sleep variability, and sleep fragmentation index), daytime sleepiness, and glycemia (glycemic control and glucose variability) were examined.

METHODS

In this cross-sectional study, objective sleep-wake characteristics were measured via actigraphy and glucose variability via continuous glucose monitoring (CGM) over 6-14 days. At baseline, participants completed a psychomotor vigilance test (PVT), Trail Making Test, and questionnaires on daytime sleepiness, sleep quality, and sleep disturbance including sleep diaries.

RESULTS

Forty-six participants (mean age 22.3 ± 3.2 years) wore a wrist actigraph and CGM concurrently for 6-14 days. Greater sleep variability was directly associated with greater glucose variability (mean of daily differences) (r = 0.33, p = .036). Higher daytime sleepiness was directly associated with greater glucose variability (mean of daily differences) (r = 0.50, p = .001). The association between sleep variability and glucose variability (mean of daily differences) was no longer significant when accounting for daytime sleepiness and controlling for T1D duration (p > .05). A higher sleep fragmentation index was associated with greater glucose variability (B = 1.27, p = .010, pr² = .40) after controlling for T1D duration and accounting for higher daytime sleepiness.

CONCLUSIONS

Sleep-wake variability, sleep fragmentation, daytime sleepiness, and the associations with glycemia are new dimensions to consider in young adults with T1D. Sleep habits in this population may explain higher glucose variability and optimizing sleep may improve overall diabetes management.

Glucose Variability is Independently Correlated with Serum Level of Pigment Epithelium-Derived Factor in Type 2 Diabetes

INTRODUCTION

Pigment epithelium-derived factor (PEDF) may play a role in cardiometabolic disorders. The aim of this study was to investigate which biochemical and clinical parameters are independently associated with serum PEDF levels in patients with type 2 diabetes mellitus (T2DM).

METHODS

We performed a cross-sectional analysis of 124 patients with T2DM who underwent continuous glucose monitoring (CGM) and blood chemistry analysis, including the diacron-reactive oxygen metabolites (d-ROMs) test and serum PEDF measurement (study 1). Then we investigated whether the changes in the studied biochemical and clinical parameters after 24 weeks of treatment (Δparameters) with anti-hyperglycemic agents, including sodium-glucose cotransporter 2 inhibitors, glucagon-like peptide 1 receptor agonists, and/or insulin and anti-hypertensive drugs and statins, were independently correlated with change in PEDF (ΔPEDF) in 52 of the patients with T2DM for whom there was sufficient serum samples to perform the post-treatment analysis (study 2). Serum levels of PEDF were measured with an enzyme-linked immunosorbent assay. CGM metrics were calculated on days 2 and 3. Oxidative stress was evaluated using the d-ROMs test.

RESULTS

Body mass index (BMI), triglycerides, fasting C-peptide, mean amplitude of glycemic excursions (MAGE), urinary albumin-to-creatinine ratio (UACR), and d-ROMs were positively associated with serum PEDF level, and high-density lipoprotein cholesterol (HDL-C) and estimated glomerular filtration rate (eGFR) were inversely associated with serum PEDF level. Because these parameters were correlated with each other, multivariate stepwise analysis was performed: eGFR, HDL-C, BMI, MAGE, and UACR remained significant (R² = 0.452). Furthermore, ΔMAGE and Δd-ROMs were positively correlated with ΔPEDF in study 2.

CONCLUSIONS

The results of this study suggest that MAGE may be independently correlated with elevations in serum PEDF level in patients with T2DM.

Starch digestibility modulation significantly improves glycemic variability in type 2 diabetic subjects: A pilot study

BACKGROUND AND AIMS

In type 2 diabetes (T2D) patients, the reduction of glycemic variability and postprandial glucose excursions is essential to limit diabetes complications, beyond HbA1c level. This study aimed at determining whether increasing the content of Slowly Digestible Starch (SDS) in T2D patients' diet could reduce postprandial hyperglycemia and glycemic variability compared with a conventional low-SDS diet.

METHODS AND RESULTS

For this randomized cross-over pilot study, 8 subjects with T2D consumed a controlled diet for one week, containing starchy products high or low in SDS. Glycemic variability parameters were evaluated using a Continuous Glucose Monitoring System. Glycemic variability was significantly lower during High-SDS diet compared to Low-SDS diet for MAGE (Mean Amplitude of Glycemic Excursions, p < 0.01), SD (Standard Deviation, p < 0.05), and CV (Coefficient of Variation, p < 0.01). The TIR (Time In Range) [140-180 mg/dL[ was significantly higher during High-SDS diet (p < 0.0001) whereas TIRs ≥180 mg/dL were significantly lower during High-SDS diet. Post-meals tAUC (total Area Under the Curve) were significantly lower during High-SDS diet.

CONCLUSION

One week of High-SDS Diet in T2D patients significantly improves glycemic variability and reduces postprandial glycemic excursions. Modulation of starch digestibility in the diet could be used as a simple nutritional tool in T2D patients to improve daily glycemic control. REGISTRATION NUMBER: in clinicaltrials.gov: NCT03289494.

CGMS and Glycemic Variability, Relevance in Clinical Research to Evaluate Interventions in T2D, a Literature Review

Glycemic variability (GV) appears today as an integral component of glucose homeostasis for the management of type 2 diabetes (T2D). This review aims at investigating the use and relevance of GV parameters in interventional and observational studies for glucose control management in T2D. It will first focus on the relationships between GV parameters measured by continuous glucose monitoring system (CGMS) and glycemic control and T2D-associated complications markers. The second part will be dedicated to the analysis of GV parameters from CGMS as outcomes in interventional studies (pharmacological, nutritional, physical activity) aimed at improving glycemic control in patients with T2D. From 243 articles first identified, 63 articles were included (27 for the first part and 38 for the second part). For both analyses, the majority of the identified studies were pharmacological. Lifestyle studies (including nutritional and physical activity-based studies, N-AP) were poorly represented. Concerning the relationships of GV parameters with those for glycemic control and T2D related-complications, the standard deviation (SD), the coefficient of variation (CV), the mean blood glucose (MBG), and the mean amplitude of the glycemic excursions (MAGEs) were the most studied, showing strong relationships, in particular with HbA1c. Regarding the use and relevance of GV as an outcome in interventional studies, in pharmacological ones, SD, MAGE, MBG, and time in range (TIR) were the GV parameters used as main criteria in most studies, showing significant improvement after intervention, in parallel or not with glycemic control parameters' (HbA1c, FBG, and PPBG) improvement. In N-AP studies, the same results were observed for SD, MAGE, and TIR. Despite the small number of N-AP studies addressing both GV and glycemic control parameters compared to pharmacological ones, N-AP studies have shown promising results on GV parameters and would require more in-depth work. Evaluating CGMS-GV parameters as outcomes in interventional studies may provide a more integrative dimension of glucose control than the standard postprandial follow-up. GV appears to be a key component of T2D dysglycemia, and some parameters such as MAGE, SD, or TIR could be used routinely in addition to classical markers of glycemic control such as HbA1c, fasting, or postprandial glycemia.

Comparison of CGM-Derived Measures of Glycemic Variability Between Pancreatogenic Diabetes and Type 2 Diabetes Mellitus

BACKGROUND

To compare glycemic variability (GV) indices between patients with fibrocalculous pancreatic diabetes (FCPD) and type 2 diabetes mellitus (T2D) using continuous glucose monitoring (CGM).

METHODS

We measured GV indices using CGM (iPro™2 Professional CGM, Medtronic, USA) data in 61 patients each with FCPD and T2D who were matched for glycated hemoglobin A1c (HbA1c) and duration of diabetes. GlyCulator2 software was used to estimate the CGM-derived measures of GV (SD, mean amplitude of glycemic excursion [MAGE], continuous overall net glycemic action [CONGA], absolute means of daily differences [MODD], M value, and coefficient of variance [%CV]), hypoglycemia (time spent below 70 mg/dL, AUC below 70 mg/dL, glycemic risk assessment diabetes equation hypoglycemia, Low Blood Glucose Index), and hyperglycemia (time spent above 180 mg/dL at night [TSA > 180], AUC above 180 mg/dL [AUC > 180], glycemic risk assessment diabetes equation hyperglycemia, High Blood Glucose Index [HBGI], and J index). The correlation of GV indices with HbA1c, duration of diabetes, and demographic and biochemical parameters were also assessed.

RESULTS

All the CGM-derived measures of GV (SD, MAGE, CONGA, MODD, and %CV), except M value, were significantly higher in the FCPD group than in the T2D group (P < 0.05). Measures of hyperglycemia (TSA >180, AUC >180, HBGI, and J index) were significantly higher in the FCPD group than in the T2D group (P < 0.05). The measures of hypoglycemia were not significantly different between the two groups. All the hyperglycemia indices showed a positive correlation with HbA1c in both groups.

CONCLUSIONS

FCPD is associated with higher GV than is T2D. The findings of higher postprandial glycemic excursions in patients with FCPD could have potential therapeutic implications.

Daily variations in sleep and glucose in adolescents with type 1 diabetes

OBJECTIVE

We used multilevel models (MLMs) to determine person (between-persons) and day level (within-person) associations between glucose variability indices and sleep characteristics in adolescents with type 1 diabetes (T1D).

METHODS

Adolescents with T1D (Mean age 13.4 ± 1.8 years; 37.8% male; mean HbA1c 8.2 ± 1.2%, 66 mmol/mol) monitored their sleep and glucose patterns concurrently for 3-7 days with a wrist actigraph on their non-dominant wrist and a continuous glucose monitor (CGM) (their own or a provided, blinded CGM). Glucose variability indices included J index, coefficient of variation, low and high blood glucose risk indices (LBGI and HBGI), time in range, and sleep characteristics, including bedtime, wake time, total sleep time, sleep efficiency, wake after sleep onset, awakenings, and sleep fragmentation index.

RESULTS

More overall glucose variability was associated within person, more sleep disruptions (more awakenings and more fragmentation) or poorer sleep in our study (earlier wake time or longer wake after sleep onset). Also, more time spent in hypoglycemia <70 mg/dL and a higher LBGI was associated within person with earlier wake time indicating poorer sleep. However, a lower LBGI was associated with a later between-persons wake time.

CONCLUSIONS

Monitoring over a longer period of time in subsequent studies would allow researchers to determine the within person association between habitual short sleep duration and glucose variability. Providers should regularly assess sleep habits in adolescents as a way to improve glycemic control. Targeting a euglycemic range overnight is also important to promote better sleep and to decrease sleep disruptions.

Beyond hyperglycemia: glycaemic variability as a prognostic factor after acute ischemic stroke

INTRODUCTION

Glycaemic variability (GV) refers to variations in blood glucose levels, and may affect stroke outcomes. This study aims to assess the effect of GV on acute ischaemic stroke progression.

METHODS

We performed an exploratory analysis of the multicentre, prospective, observational GLIAS-II study. Capillary glucose levels were measured every 4 hours during the first 48 hours after stroke, and GV was defined as the standard deviation of the mean glucose values. The primary outcomes were mortality and death or dependency at 3 months. Secondary outcomes were in-hospital complications, stroke recurrence, and the impact of the route of insulin administration on GV.

RESULTS

A total of 213 patients were included. Higher GV values were observed in patients who died (n = 16; 7.8%; 30.9 mg/dL vs 23.3 mg/dL; p = 0.05). In a logistic regression analysis adjusted for age and comorbidity, both GV (OR = 1.03; 95% CI, 1.003-1.06; p = 0.03) and stroke severity (OR = 1.12; 95% CI, 1.04-1.2; p = 0.004) were independently associated with mortality at 3 months. No association was found between GV and the other outcomes. Patients receiving subcutaneous insulin showed higher GV than those treated with intravenous insulin (38.95 mg/dL vs 21.34 mg/dL; p < 0.001).

CONCLUSIONS

High GV values during the first 48 hours after ischaemic stroke were independently associated with mortality. Subcutaneous insulin may be associated with higher VG levels than intravenous administration.

White coat adherence effect on glucose control in adult individuals with diabetes

BACKGROUND

White coat adherence (WCA) is defined as an increased adherence to treatment regimens directly before a visit with a healthcare provider. Little is known on the effect of WCA on glucose control in adult patients with diabetes mellitus.

METHODS

The present study is based on 618 CGM-observations of 276 patients with diabetes treated between January 2013 and July 2018. The analysis compares data from the 3 days prior to a visit (p1) with the preceding 25 days (p2).

RESULTS

Sensor use was higher during p1 than p2 (92.8 ± 7.3% vs 88.8 ± 7.5%; p < 0.001). Mean glucose [MG] and coefficient of variation [CV] were lower in p1 compared to p2 (MG 163.9 ± 39.2 mg/dL vs 166.9 ± 35.7 mg/dL, p = 0.001; CV 33.5 ± 8.4% vs 36.0 ± 7.0%, p < 0.001; respectively). Time in range (70-180 mg/dL) was higher in p1 than p2 (61.4 ± 21.2% vs 60.0 ± 18.4%, p = 0.002). Sensitivity-analysis showed that WCA effect was mainly detected in patients with HbA_1c > 7% [53 mmol/mol].

CONCLUSION

This study reveals a WCA effect on pre-visit glucose control in adult patients with diabetes. The effect was most pronounced in patients with moderate to poor glycemic control. In these patients, analysis of CGM data should encompass a minimum of 1 to 2 weeks prior to a consultation.

Updated Software for Automated Assessment of Glucose Variability and Quality of Glycemic Control in Diabetes

Background: Glycemic variability is an important factor to consider in diabetes management. It can be assessed with multiple glycemic variability metrics and quality of control indices based on continuous glucose monitoring (CGM) recordings. For this, a robust repeatable calculation is important. A widely used tool for automated assessment is the EasyGV software. The aim of this work is to implement new methods of glycemic variability assessment in EasyGV and to validate implementation of each glucose metric in EasyGV against a reference implementation of the calculations. Methods: Validation data used came from the JDRF CGM study. Validation of the implementation of metrics that are available in EasyGV software v9 was carried out and the following new methods were added and validated: personal glycemic state, index of glycemic control, times in ranges, and glycemic variability percentage. Reference values considered gold standard calculations were derived from MATLAB implementation of each metric. Results: The Pearson correlation coefficient was above 0.98 for all metrics, except for mean amplitude of glycemic excursion (r = 0.87) as EasyGV implements a fuzzy logic approach to assessment of variability. Bland-Altman plots demonstrated validation of the new software. Conclusions: The new freely available EasyGV software v10 (www.phc.ox.ac.uk/research/technology-outputs/easygv) is a validated robust tool for analyzing different glycemic variabilities and control metrics.

Small changes in glucose variability induced by low and high glycemic index diets are not associated with changes in β-cell function in adults with pre-diabetes

Oscillating glucose levels can increase oxidative stress and may contribute to β-cell dysfunction. We tested the hypothesis that increased glycemic variability contributes to β-cell dysfunction by experimentally altering glucose variability with controlled diets varying in glycemic index (GI). Fifty-two adults with prediabetes received a 2-week moderate GI (GI = 55-58) control diet followed by randomization to a four-week low GI (LGI: GI < 35) or high GI (HGI HI > 70) diet. Those on the HGI diet were randomized to placebo or the antioxidant N-acetylcysteine (NAC). Participants underwent blinded CGMS, fasting oxidative stress markers and an intravenous glucose tolerance test to estimate β-cell function (disposition index: DI). On the control diet, DI was inversely correlated with SD glucose (r = -0.314, p = 0.03), but neither DI nor glucose variability were associated with oxidative stress markers. The LGI diet decreased SD glucose (Control 0.96 ± 0.08 vs. LGI 0.79 ± 0.06, p = 0.02) while the HGI diet increased it (Control 0.88 ± 0.06 vs. HGI 1.06 ± 0.07, p = 0.03). Neither DI nor oxidative stress markers changed after the LGI or HGI diets. NAC had no effect on DI, glucose variability or oxidative stress markers. We conclude small changes in glucose variability induced by dietary GI in adults with pre-diabetes are unlikely to contribute to β-cell dysfunction.

Improved glycaemic variability and time in range with dapagliflozin versus gliclazide modified release among adults with type 2 diabetes, evaluated by continuous glucose monitoring: A 12-week randomized controlled trial

AIMS

To evaluate whether there is a difference between the effects of dapagliflozin and gliclazide modified release (MR) on glycaemic variability (GV) and glycaemic control, as assessed by continuous glucose monitoring (CGM), in individuals with uncontrolled type 2 diabetes.

MATERIALS AND METHODS

This randomized, open-label, active-controlled study was conducted in individuals with uncontrolled type 2 diabetes who were drug-naïve or on steady-dose metformin monotherapy. Participants were treated once daily with 10 mg dapagliflozin or 120 mg gliclazide MR. CGM and GV index calculations were performed at baseline and after 12 weeks.

RESULTS

In total, 97 participants (age 57.9 ± 8.7 years, 50.5% men, baseline glycated haemoglobin 63 ± 9.8 mmol/mol [7.9 ± 0.9%]) were randomized, and 94 completed the 12-week protocol. Intention-to-treat (ITT) and per-protocol (PP) analyses showed that the reduction in GV, as measured by the mean amplitude of glycaemic excursions, was superior in the dapagliflozin group versus the gliclazide MR group (-0.9 mmol/L [95% CI -1.5, -0.4] vs -0.2 mmol/L [95% CI -0.6, 0.3]; P = 0.030 [ITT]). The reductions in GV estimated by the coefficient of variation and SD were greater in the dapagliflozin group. Moreover, dapagliflozin increased the glucose time in range (TIR; 3.9-10 mmol/L) by 24.9% (95% CI 18.6, 31.2) vs. 17.4% (95% CI 11.6, 23.3) in the gliclazide MR group (P = 0.089 [ITT]; P = 0.041 [PP]).

CONCLUSIONS

Dapagliflozin improved GV and increased TIR more efficiently than gliclazide MR in individuals with type 2 diabetes over 12 weeks, as demonstrated by CGM.

A low-carbohydrate protein-rich bedtime snack to control fasting and nocturnal glucose in type 2 diabetes: A randomized trial

In type 2 diabetes, liver insulin resistance and excess hepatic glucose production results in elevated fasting glucose. A bedtime snack has been recommended to improve fasting glucose, yet there is little evidence supporting this recommendation. Moreover, the optimal composition of a bedtime snack is unknown.

PURPOSE

To determine whether a low-carbohydrate protein-rich bedtime snack (Egg) could reduce fasting plasma glucose levels in people with type 2 diabetes when compared to a high-carbohydrate protein-rich bedtime snack (Yogurt) or a No Bedtime Snack condition. Secondary outcomes included glucose control assessed by continuous glucose monitoring (CGM) and fasting insulin sensitivity markers.

METHODS

Using a randomized crossover design, participants with type 2 diabetes (N = 15) completed three separate isocaloric conditions: i) Egg, ii) Yogurt, and iii) No Bedtime Snack, each lasting three days. CGM was collected throughout and duplicate fasting blood samples were obtained on the morning of day 4 in each condition.

RESULTS

Fasting plasma glucose (P = 0.04, d = 0.68), insulin (P = 0.04, d = 0.45), and nocturnal glucose (P = 0.02, d = 0.94) were significantly lower, and quantitative insulin sensitivity check index (QUICKI; P = 0.003) was improved, in the Egg compared to the Yogurt bedtime snack. There were no significant differences between either bedtime snack and No Bedtime Snack.

CONCLUSION

In the short-term, a low-carbohydrate bedtime snack (Egg) lowered fasting glucose and improved markers of insulin sensitivity when compared to a high-carbohydrate protein-matched bedtime snack (Yogurt). However, consuming a low- or high-carbohydrate bedtime snack did not appear to lower fasting glucose compared to consuming an isocaloric diet with no bedtime snack. CLINICAL TRIAL REGISTRY: clinicaltrials.gov (NCT03207269).

Type 2 Diabetes and Glycemic Variability: Various Parameters in Clinical Practice

It has become possible to measure blood glucose levels continuously from 24 h to approximately 2 weeks due to the recent development of relevant devices such as continuous glucose monitoring and flash glucose monitoring systems. This has enabled not only medical professionals but also patients to monitor details of glycemic variability (GV) which was not possible in the past. Details of GV for both short and intermediate periods can now be obtained, and it is important in patient care to appropriately evaluate the data obtained. Types of GV in terms of time frame vary from short-term to long-term. Several studies reported that long-term GV was related to micro- and macro-angiopathies in patients with type 2 diabetes mellitus (T2DM). However, there are still unknown aspects regarding the relationships of various durations of GV with prognosis. Further clinical trials are required to examine the mechanism of GV and to evaluate whether GV can be a valuable therapeutic target in treatment of patients with T2DM.

Effect of short-term treatment with sitagliptin or glibenclamide on daily glucose fluctuation in drug-naïve Japanese patients with type 2 diabetes mellitus

AIMS

To compare the effect of a dipeptidyl peptidase-4 inhibitor (DPP4-i) and a sulfonylurea (SU) on daily glucose fluctuation in drug-naïve Japanese patients with type 2 diabetes mellitus (T2DM).

MATERIALS AND METHODS

A total of 53 drug-naïve Japanese patients with T2DM (HbA1c, 7.0%-9.0%; fasting plasma glucose, 6.1 mmol/L or higher) were randomly assigned to either sitagliptin 50 mg qd or glibenclamide 2.5 mg per day (given in divided doses) in a 1:1 ratio. A continuous glucose monitoring (CGM) device was used to obtain 24-hour glucose profiles for each patient at baseline and at Week 2. The primary study endpoint was change from baseline in mean amplitude of glucose excursion (MAGE) during a 24-hour period. A key secondary endpoint was change from baseline in the standard deviation (SD) of 24-hour glucose levels.

RESULTS

After 2 weeks of treatment, a numerically greater reduction in MAGE from baseline was observed in the sitagliptin group compared with the glibenclamide group, but the between-treatment difference was not statistically significant (LS mean difference [95% CI]: -0.48 mmol/L [-1.31, 0.34]; P = .245). However, a significantly greater reduction in the change from baseline in SD was observed in the sitagliptin group compared with the glibenclamide group (LS mean difference [95% CI]: -0.33 mmol/L [-0.62, -0.03]; P = .029).

CONCLUSIONS

This study suggests that the DPP4 inhibitor sitagliptin has a greater ability to reduce daily glucose fluctuation than the SU glibenclamide in drug-naïve Japanese patients with T2DM. ClinicalTrials.gov: NCT02318693.

Increased glycemic variability in type 2 diabetes patients treated with insulin - a real-life clinical practice, continuous glucose monitoring (CGM) study

Chronic hyperglycemia is an important cause for the development of chronic complications of diabetes, but glycemic variability has emerged in recent years as an independent contributor to diabetes-related complications. Our objective was to evaluate glycemic variability in patients with T2DM treated with insulin compared with other antidiabetic drugs. In this retrospective study, we collected 24-hour continuous glucose monitoring (CGM) recording data from 95 patients with T2DM, of which 27 treated with insulin and 68 with non-insulin treatment. We calculated and compared 16 glucose variability parameters in the insulin-treated and non-insulin treated groups. Insulin treated patients had significantly higher values of parameters describing the amplitude of glucose value fluctuations (standard deviation of glucose values, percentage coefficient of variation [%CV], and mean amplitude of glycemic excursion [MAGE], p <0.05) and time-dependent glucose variability (percentage of time with glycemic values below 70 mg/dl and continuous overall net glycemic action [CONGA] at 2, 4 and 6 hours, p <0.05). In conclusion, insulin therapy in T2DM is correlated with significantly higher glycemic variability.

Correlation Between Third Trimester Glycemic Variability in Non-Insulin-Dependent Gestational Diabetes Mellitus and Adverse Pregnancy and Fetal Outcomes

BACKGROUND

Gestational diabetes mellitus (GDM) is a pregnancy-related metabolic complication. Despite optimal glycemic control from self-monitoring blood glucose (SMBG) in non-insulin-dependent GDM, variations in pregnancy outcomes persist. Glycemic variability is believed to be a factor that causes adverse pregnancy outcomes. Continuous glucose monitoring system (CGMS) detects interstitial glucose values every 5 minutes, and glycemic variability data from CGMS during the third trimester may be a predictor of fetal birth weight and pregnancy outcomes. The aim of this study was to investigate correlation between third trimester glycemic variability in non-insulin-dependent GDM and fetal birth weight.

METHOD

This prospective study was conducted in 55 pregnant volunteers with non-insulin-dependent GDM that were recruited at 28 to 32 weeks' gestation from the outpatient clinic of the Department of Obstetrics and Gynecology, Faculty of Medicine Siriraj Hospital during the study period of August 1 to December 31, 2016. Patients had CGMS installed for at least 72 hours and glycemic variability data were analyzed.

RESULTS

Of 55 enrolled volunteers, the data from 47 women were included in the analysis. Mean CGMS duration was 85.5 ± 12.83 hours. No statistically significant correlation was identified between glycemic variability in third trimester and birth weight percentiles, or between third trimester CGMS parameters and pregnancy outcomes in the study.

CONCLUSION

Based on these findings, third trimester glycemic variability data from CGMS are not a predictor of fetal birth weight percentile, and no significant association was found between CGMS parameters and adverse pregnancy outcomes; thus, CGMS is not necessary in non-insulin-dependent GDM.

A randomized controlled trial to compare the effects of sulphonylurea gliclazide MR (modified release) and the DPP-4 inhibitor vildagliptin on glycemic variability and control measured by continuous glucose monitoring (CGM) in Brazilian women with type 2 diabetes

AIMS

This study aims to evaluate whether there is a difference between the effects of vildagliptin and gliclazide MR (modified release) on glycemic variability (GV) in women with type 2 diabetes (T2DM) as evaluated by continuous glucose monitoring (CGM).

METHODS

An open-label, randomized study was conducted in T2DM women on steady-dose metformin monotherapy which were treated with 50 mg vildagliptin twice daily or 60-120 mg of gliclazide MR once daily. CGM and GV indices calculation were performed at baseline and after 24 weeks.

RESULTS

In total, 42 patients (age: 61.9 ± 5.9 years, baseline glycated hemoglobin (HbA1c): 7.3 ± 0.56) were selected and 37 completed the 24-week protocol. Vildagliptin and gliclazide MR reduced GV, as measured by the mean amplitude of glycemic excursions (MAGE, p = 0.007 and 0.034, respectively). The difference between the groups did not reach statistical significance. Vildagliptin also significantly decreased the standard deviation of the mean glucose (SD) and the mean of the daily differences (MODD) (p = 0.007 and 0.030).

CONCLUSIONS

Vildagliptin and gliclazide MR similarly reduced the MAGE in women with T2DM after 24 weeks of treatment. Further studies are required to attest differences between vildagliptin and gliclazide MR regarding glycemic variability.

Disordered Eating Behaviors Are Not Increased by an Intervention to Improve Diet Quality but Are Associated With Poorer Glycemic Control Among Youth With Type 1 Diabetes

OBJECTIVE

This study examines whether participation in an 18-month behavioral intervention shown previously to improve overall diet quality inadvertently increases disordered eating behaviors (DEBs) in youth with type 1 diabetes and investigates the association of DEB with multiple measures of glycemic control and variability.

RESEARCH DESIGN AND METHODS

Participants reported DEB and diabetes management at baseline and 6, 12, and 18 months; masked continuous glucose monitoring, HbA_1c, and 1,5-anhydroglucitol (1,5-AG) were obtained concurrently. Linear mixed models estimated the intervention effect on DEB, the association of DEB with diabetes adherence and measures of glycemic control and variability, and whether DEB modified glycemic trajectories.

RESULTS

There was no intervention effect on DEB (P = 0.84). DEB was associated with higher HbA_1c (P = 0.001), mean sensor glucose (P = 0.001), and percent sensor glucose values >180 mg/dL (P = <0.001); with lower 1,5-AG (P = 0.01); and with worse diabetes adherence (P = 0.03). DEB was not associated with percent sensor glucose values <70 mg/dL or any measures of glycemic variability. There was a significant DEB × time interaction effect for mean sensor glucose (P = 0.05) and percent sensor glucose values >180 mg/dL (P = 0.04). Participants reporting less DEB had a developmentally expected deterioration in glycemic control throughout the study. Participants reporting more DEB had poor glycemic control at baseline that remained poor throughout the study.

CONCLUSIONS

Findings show a potential to improve diet quality without increasing DEB and indicate an association of DEB with persistent hyperglycemia but not hypoglycemia or glycemic variability.

Calculating the Mean Amplitude of Glycemic Excursions from Continuous Glucose Data Using an Open-Code Programmable Algorithm Based on the Integer Nonlinear Method

The mean amplitude of glycemic excursions (MAGE) is an essential index for glycemic variability assessment, which is treated as a key reference for blood glucose controlling at clinic. However, the traditional “ruler and pencil” manual method for the calculation of MAGE is time-consuming and prone to error due to the huge data size, making the development of robust computer-aided program an urgent requirement. Although several software products are available instead of manual calculation, poor agreement among them is reported. Therefore, more studies are required in this field. In this paper, we developed a mathematical algorithm based on integer nonlinear programming. Following the proposed mathematical method, an open-code computer program named MAGECAA v1.0 was developed and validated. The results of the statistical analysis indicated that the developed program was robust compared to the manual method. The agreement among the developed program and currently available popular software is satisfied, indicating that the worry about the disagreement among different software products is not necessary. The open-code programmable algorithm is an extra resource for those peers who are interested in the related study on methodology in the future.

Relationship of Glucose Variability With Glycated Hemoglobin and Daily Mean Glucose: A Post Hoc Analysis of Data From 5 Phase 3 Studies

BACKGROUND

The association of glucose variability (GV) with other glycemic measures is emerging as a topic of interest. The aim of this analysis is to study the correlation between GV and measures of glycemic control, such as glycated hemoglobin (HbA1c) and daily mean glucose (DMG).

METHODS

Data from 5 phase 3 trials were pooled into 3 analysis groups: type 2 diabetes (T2D) treated with basal insulin only, T2D treated with basal-bolus therapy, and type 1 diabetes (T1D). A generalized boosted model was used post hoc to assess the relationship of the following variables with glycemic control parameters (HbA1c and DMG): within-day GV, between-day GV (calculated using self-monitored blood glucose and fasting blood glucose [FBG]), hypoglycemia rate, and certain baseline characteristics.

RESULTS

Within-day GV (calculated using standard deviation [SD]) was found to have a significant influence on endpoints HbA1c and DMG in all 3 patient groups. Between-day GV from FBG (calculated using SD), within-day GV (calculated using coefficient of variation), and hypoglycemia rate were found to significantly influence the endpoint HbA1c in the T2D basal-only group.

CONCLUSIONS

Lower within-day GV was significantly associated with improvement in DMG and HbA1c. This finding suggests that GV could be a marker in the early phases of new antihyperglycemic therapy development for predicting clinical outcomes in terms of HbA1c and DMG.

The relationship between fasting blood glucose variability and coronary artery collateral formation in type 2 diabetes patients with coronary artery disease

BACKGROUND

Coronary collaterals are an alternative source of blood supply to ischemic myocardium. Well-developed coronary collateral arteries in patients with coronary artery disease (CAD) limit the size of acute myocardial infarction and improves survival. The aim of this study was to investigate the relationship between glycemic variability and coronary collateral formation in patients with type 2 diabetes mellitus and CAD.

METHODS

Consecutive patients undergoing percutaneous coronary intervention or coronary artery bypass grafting procedures were studied. Multivariate logistic regression models were used to examine the association between coronary artery collateral formation graded by Rentrope classification and glycemic variability, measured by coefficient variation of fasting blood glucose.

RESULTS

In our study, we retrospectively enrolled 300 patients, of whom 239 were diabetic (age: 70.1±11.9, 56% men) and 61 were nondiabetic (age: 71.5±11.5, 72% men). Diabetic patients were further stratified as follows: those with poor coronary collateral artery development (n=171, age: 69.7±12.4, 55% men) and those with good coronary collateral artery development (n=68, age 71.1±10.8, 59% men) according to the Rentrope classification. Our findings did not show association between glycemic variability and coronary collateral vessels development after controlling for potential confounders (odds ratio: 2.51; 95% confidence interval: 0.57-11.03; P=0.22). The culprit lesion (≥75% stenosis) in the left anterior descending artery and the right coronary artery was more frequent in the good collateral group compared with the poor collateral group (66 vs. 50%, P=0.02; 63 vs. 45%, P=0.01 respectively).

CONCLUSION

Glycemic variability is not associated with coronary collateral artery formation in patients with type 2 diabetes mellitus and CAD.

Poststroke glycemic variability increased recurrent cardiovascular events in diabetic patients

BACKGROUND AND PURPOSE

The association between blood glucose fluctuation and poststroke cardiovascular outcome has been largely unknown. This study attempted to evaluate whether initial glycemic variability increases cardiovascular events and mortality in diabetic patients with acute ischemic stroke.

METHODS

We recruited consecutive patients with acute ischemic stroke or transient ischemic attack from March 2005 to December 2014. A total of 674 patients with diabetes within 72 hours from stroke onset were included. The serum glucose levels were checked 4 times per day during the initial 3 hospital days. J-index, coefficients of variation and standard deviation were calculated for glycemic variability. Composite outcome (nonfatal stroke, nonfatal myocardial infarction, cardiovascular death) and all-cause mortality at 3 months were prospectively captured. Multivariable logistic regression analyses were done adjusting for covariates which can influence on cardiovascular outcomes.

RESULTS

Cardiovascular composite outcomes at 3 months were identified in 71 (10.5%): 11 (6.5%), 15 (8.9%), 18 (10.7%) and 27 (16.0%) in each J-index quartiles (P = .035). The highest quartile of J-index had significantly higher cardiovascular death (4.2%, 3.6%, 6.5% and 11.8%; P = .008). In multivariable logistic regression, age (odds ratio [OR] 1.045; 95% confidence interval [CI] 1.006-1.084), P = .021), NIH stroke scale (OR 1.078; 95% CI 1.024-1.134, P = .004), and the highest J-index (OR 12.058; 95% 1.890-76.912, P = .008) were significantly associated with 3-month cardiovascular composite outcome. Increased cardiovascular outcomes in highest J-index quartile were similar in both euglycemic and hyperglycemic groups.

CONCLUSION

The initial glycemic variability might increase cardiovascular events in acute ischemic stroke patients with diabetes.

Acute Low-Volume High-Intensity Interval Exercise and Continuous Moderate-Intensity Exercise Elicit a Similar Improvement in 24-h Glycemic Control in Overweight and Obese Adults

Background: Acute exercise reduces postprandial oxidative stress and glycemia; however, the effects of exercise intensity are unclear. We investigated the effect of acute low-volume high-intensity interval-exercise (LV-HIIE) and continuous moderate-intensity exercise (CMIE) on glycemic control and oxidative stress in overweight and obese, inactive adults.

Methods: Twenty-seven adults were randomly allocated to perform a single session of LV-HIIE (9 females, 5 males; age: 30 ± 1 years; BMI: 29 ± 1 kg·m⁻²; mean ± SEM) or CMIE (8 females, 5 males; age: 30 ± 2.0; BMI: 30 ± 2.0) 1 h after consumption of a standard breakfast. Plasma redox status, glucose and insulin were measured. Continuous glucose monitoring (CGM) was conducted during the 24-h period before (rest day) and after exercise (exercise day).

Results: Plasma thiobarbituric acid reactive substances (TBARS; 29 ±13%, p < 0.01; mean percent change ±90% confidence limit), hydrogen peroxide (44 ± 16%, p < 0.01), catalase activity (50 ± 16%, p < 0.01), and superoxide dismutase activity (21 ± 6%, p < 0.01) significantly increased 1 h after breakfast (prior to exercise) compared to baseline. Exercise significantly decreased postprandial glycaemia in whole blood (−6 ± 5%, p < 0.01), irrespective of the exercise protocol. Only CMIE significantly decreased postprandial TBARS (CMIE: −33 ± 8%, p < 0.01; LV-HIIE: 11 ± 22%, p = 0.34) and hydrogen peroxide (CMIE: −25 ± 15%, p = 0.04; LV-HIIE: 7 ± 26%; p = 0.37). Acute exercise provided a similar significant improvement in 24-h average glucose levels (−5 ± 2%, p < 0.01), hyperglycemic excursions (−37 ± 60%, p < 0.01), peak glucose concentrations (−8 ± 4%, p < 0.01), and the 2-h postprandial glucose response to dinner (−9 ± 4%, p < 0.01), irrespective of the exercise protocol.

Conclusion: Despite elevated postprandial oxidative stress compared to CMIE, LV-HIIE is an equally effective exercise mode for improving 24-h glycemic control in overweight and obese adults.