International Consensus on Use of Continuous Glucose Monitoring

Value of Glycemic Dispersion Index in Predicting Major Adverse Cardiovascular Events in Diabetic Patients with Concomitant Acute Coronary Syndrome

Aim

This investigation aims to assess the predictive value of the glycemic dispersion index (GDI), calculated by incorporating glycated hemoglobin, fasting plasma glucose, and 2-hour postprandial plasma glucose, in predicting major adverse cardiovascular events (MACE) within a 12-month timeframe for diabetic patients with concomitant acute coronary syndrome (ACS).

Methods

A retrospective study was conducted on 3261 diabetic patients with ACS who were hospitalized in the Department of Cardiology, the Sixth Affiliated Hospital of Kunming Medical University, from January 2016 to July 2022. Based on the inclusion and exclusion criteria, 512 patients were ultimately enrolled in the study. Their general information and laboratory test indicators were collected, and the occurrence of MACE within 12 months after admission was followed up and recorded for the enrolled patients, With the last follow-up having been concluded on July 31, 2023. The enrolled patients were stratified into four groups (Q1, Q2, Q3, Q4) based on their GDI values, from the lowest to the highest. Cox proportional hazards regression analysis and Kaplan-Meier survival analysis were employed to investigate the risk factors associated with MACE occurrence across these groups and to assess the cumulative risk of MACE over time within each group.

Results

The percentages of enrolled patients experiencing MACE in groups Q1 to Q4 were 10.16%, 12.50%, 15.63%, and 16.41%, respectively. GDI independently predicted the hazards for MACE in enrolled patients. The cumulative risk of MACE over time was considerably more significant in those with a GDI>4.21 than those with a GDI≤4.21.

Conclusion

The elevated GDI is correlated with an augmented risk of MACE in diabetic patients with concomitant ACS, thereby serving as an early indicator for assessing the unfavorable clinical prognosis of patients. This study offers novel insights into glycemic variability monitoring, enhancing prevention and treatment strategies for cardiovascular disease in people with diabetes.

Analytical Performance of the FreeStyle Libre 2 Glucose Sensor in Healthy Male Adults

Continuous Glucose Monitoring (CGM) not only can be used for glycemic control in chronic diseases (e.g., diabetes), but is increasingly being utilized by individuals and athletes to monitor fluctuations in training and everyday life. However, it is not clear how accurately CGM reflects plasma glucose concentration in a healthy population in the absence of chronic diseases. In an oral glucose tolerance test (OGTT) with forty-four healthy male subjects (25.5 ± 4.5 years), the interstitial fluid glucose (ISFG) concentration obtained by a CGM sensor was compared against finger-prick capillary plasma glucose (CPG) concentration at fasting baseline (T0) and 30 (T30), 60 (T60), 90 (T90), and 120 (T120) min post OGTT to investigate differences in measurement accuracy. The overall mean absolute relative difference (MARD) was 12.9% (95%-CI: 11.8-14.0%). Approximately 100% of the ISFG values were within zones A and B in the Consensus Error Grid, indicating clinical accuracy. A paired t-test revealed statistically significant differences between CPG and ISFG at all time points (T0: 97.3 mg/dL vs. 89.7 mg/dL, T30: 159.9 mg/dL vs. 144.3 mg/dL, T60: 134.8 mg/dL vs. 126.2 mg/dL, T90: 113.7 mg/dL vs. 99.3 mg/dL, and T120: 91.8 mg/dL vs. 82.6 mg/dL; p < 0.001) with medium to large effect sizes (d = 0.57-1.02) and with ISFG systematically under-reporting the reference system CPG. CGM sensors provide a convenient and reliable method for monitoring blood glucose in the everyday lives of healthy adults. Nonetheless, their use in clinical settings wherein implications are drawn from CGM readings should be handled carefully.

Glucose Variability in People with Type 1 Diabetes: Associations with Body Weight, Body Composition, and Insulin Sensitivity

The prevalence of overweight and obesity increases in people with type 1 diabetes (T1D). However, the impact of fat accumulation on glucose dynamics in T1D is poorly understood. We assessed continuous glucose monitoring (CGM) parameters in patients with T1D depending on their body weight, body composition, and insulin sensitivity. In 547 patients, including 238 overweight/obese individuals, CGM-derived time in range (TIR) and glucose variability (GV) were estimated. Body composition was assessed by DXA. Estimated glucose disposal rate (eGDR) was used as an indicator of insulin sensitivity. Overweight/obese patients, when compared to normal-weight ones, have a lower time below range (TBR) (<3 mmol/L), GV, and experienced fewer episodes of low glucose. In men, lower TIR, higher time above range (TAR), and GV reduction were associated with central adiposity assessed by total, trunk, and android fat mass. In women, gynoid fat mass only was associated with a lower TIR and higher TAR. The eGDR was a positive predictor of TIR and a negative predictor of TAR, TBR, and GV in men and women. In conclusion, adiposity in people with T1D is associated with a lower risk of CGM-confirmed hypoglycemia, higher TAR, and reduced GV. These features of daily glucose dynamics may be mediated by insulin resistance.

Enhancing the Capabilities of Continuous Glucose Monitoring With a Predictive App

BACKGROUND

Despite abundant evidence demonstrating the benefits of continuous glucose monitoring (CGM) in diabetes management, a significant proportion of people using this technology still struggle to achieve glycemic targets. To address this challenge, we propose the Accu-Chek® SmartGuide Predict app, an innovative CGM digital companion that incorporates a suite of advanced glucose predictive functionalities aiming to inform users earlier about acute glycemic situations.

METHODS

The app's functionalities, powered by three machine learning models, include a two-hour glucose forecast, a 30-minute low glucose detection, and a nighttime low glucose prediction for bedtime interventions. Evaluation of the models' performance included three data sets, comprising subjects with T1D on MDI (n = 21), subjects with type 2 diabetes (T2D) on MDI (n = 59), and subjects with T1D on insulin pump therapy (n = 226).

RESULTS

On an aggregated data set, the two-hour glucose prediction model, at a forecasting horizon of 30, 45, 60, and 120 minutes, achieved a percentage of data points in zones A and B of Consensus Error Grid of: 99.8%, 99.3%, 98.7%, and 96.3%, respectively. The 30-minute low glucose prediction model achieved an accuracy, sensitivity, specificity, mean lead time, and area under the receiver operating characteristic curve (ROC AUC) of: 98.9%, 95.2%, 98.9%, 16.2 minutes, and 0.958, respectively. The nighttime low glucose prediction model achieved an accuracy, sensitivity, specificity, and ROC AUC of: 86.5%, 55.3%, 91.6%, and 0.859, respectively.

CONCLUSIONS

The consistency of the performance of the three predictive models when evaluated on different cohorts of subjects with T1D and T2D on different insulin therapies, including real-world data, offers reassurance for real-world efficacy.

Characteristics of Nocturnal Hypoglycaemic Events and Their Impact on Glycaemia

BACKGROUND

Nocturnal hypoglycaemia is a burden for people with diabetes, particularly when treated with multiple daily injections (MDI) therapy. However, the characteristics of nocturnal hypoglycaemic events in this patient group are only poorly described in the literature.

METHOD

Continuous glucose monitoring (CGM) data from 185 study participants with type 1 diabetes using MDI therapy were collected under everyday conditions for up to 13 weeks. Hypoglycaemic events were identified as episodes of consecutive CGM readings <70 mg/dl or <54 mg/dl for at least 15 minutes. Subsequently, the time <54 mg/dl (TB54), time below range (TBR), time in range (TIR), time above range (TAR), glucose coefficient of variation (CV), and incidence of hypoglycaemic events were calculated for diurnal and nocturnal periods. Furthermore, the effect of nocturnal hypoglycaemic events on glucose levels the following day was assessed.

RESULTS

The incidence of hypoglycaemic events <70 mg/dl was significantly lower during the night compared to the day, with 0.8 and 3.8 events per week, respectively, while the TBR, TB54, and incidence of events with CGM readings <54 mg/dl was not significantly different. Nocturnal hypoglycaemic events <70 mg/dl were significantly longer (60 vs 35 minutes) and enveloped by less rapidly changing glucose levels. On days following nights containing hypoglycaemic events, there was a decrease in TAR, mean CGM glucose level and morning glucose levels and an increase in TB54, TBR, and CV.

CONCLUSIONS

The results showed that nocturnal hypoglycaemic events are a common occurrence in persons with type 1 diabetes using MDI with significant differences between the characteristics of nocturnal and diurnal events.

Glycemia Risk Index Assessment in a Pediatric and Adult Patient Cohort With Type 1 Diabetes Mellitus

BACKGROUND

To evaluate the glycemia risk index (GRI) as a new glucometry in pediatric and adult populations with type 1 diabetes (T1D) in clinical practice.

METHODS

A cross-sectional study of 202 patients with T1D receiving intensive treatment with insulin (25.2% continuous subcutaneous insulin infusion [CSII]) and intermittent scanning (flash) glucose monitoring (isCGM). Clinical and glucometric isCGM data were collected, as well as the component of hypoglycemia (CHypo) and component of hyperglycemia (CHyper) of the GRI.

RESULTS

A total of 202 patients (53% males and 67.8% adults) with a mean age of 28.6 ± 15.7 years and 12.5 ± 10.9 years of T1D evolution were evaluated.Adult patients (>19 years) presented higher glycated hemoglobin (HbA1c) (7.4 ± 1.1 vs 6.7 ± 0.6%; P < .01) and lower time in range (TIR) (55.4 ± 17.5 vs 66.5 ± 13.1%; P < .01) values than the pediatric population, with lower coefficient of variation (CV) (38.6 ± 7.2 vs 42.4 ± 8.9%; P < .05). The GRI was significantly lower in pediatric patients (48.0 ± 22.2 vs 56.8 ± 23.4; P < .05) associated with higher CHypo (7.1 ± 5.1 vs 5.0 ± 4.5; P < .01) and lower CHyper (16.8 ± 9.8 vs 26.5 ± 15.1; P < .01) than in adults.When analyzing treatment with CSII compared with multiple doses of insulin (MDI), a nonsignificant trend to a lower GRI was observed in CSII (51.0 ± 15.3 vs 55.0 ± 25.4; P= .162), with higher levels of CHypo (6.5 ± 4.1 vs 5.4 ± 5.0; P < .01) and lower CHyper (19.6 ± 10.6 vs 24.6 ± 15.2; P < .05) compared with MDI.

CONCLUSIONS

In pediatric patients and in those with CSII treatment, despite a better control by classical and GRI parameters, higher overall CHypo was observed than in adults and MDI, respectively. The present study supports the usefulness of the GRI as a new glucometric parameter to evaluate the global risk of hypoglycemia-hyperglycemia in both pediatric and adult patients with T1D.

Performance of Continuous Glucose Monitoring in Patients With Acute Respiratory Failure: A Prospective, Single-Center Observational Study

OBJECTIVE

Continuous glucose monitoring (CGM) may have benefits in achieving glycemic control in critically ill patients. The aim of this study was to assess the accuracy of the Freestyle Libre H (professional version similar to the Libre Pro) in patients with acute respiratory failure (ARF) in the intensive care unit (ICU).

METHODS

Fifty-two adult patients with ARF were selected. The performance of CGM was evaluated using the arterial blood glucose (aBG) and point-of-care (POC) glucose levels as the reference values. Numerical accuracy was evaluated by the mean absolute relative difference, Bland-Altman analysis, %15/15 (the percentage of CGM values within 15 mg/dL or 15% of reference values <100 or >100 mg/dL, respectively), %20/20, and %30/30. Clinical accuracy was assessed using the Clarke error grid analysis.

RESULTS

A total of 519 and 1504 pairs of aBG/CGM and POC/CGM glucose values were analyzed. The mean absolute relative difference values were 13.8% and 14.7%, respectively. The mean deviations of the Bland-Altman analysis were 0.82 mmol/L and 0.81 mmol/L. The proportions of CGM values within %15/15, %20/20, and %30/30 of the aBG values were 62.6%, 75.5%, and 92.4%, respectively; those within %15/15, %20/20, and %30/30 of the POC values were 57.1%, 72.9%, and 88.7%, respectively. The Clarke error grid analysis showed that 97.8% and 99.3% of the values were located in zone A + B. Additionally, the accuracy of CGM was not affected by general patient factors.

CONCLUSION

This study demonstrated that the accuracy of CGM in patients with ARF is lower than that in most outpatients and it is not affected by general patient factors. Whether CGM is beneficial to glucose management in the intensive care unit needs further evaluation.

Continuous Glucose Monitoring and the Effect of Liraglutide in Cardiac Surgery Patients: A Substudy of the Randomized Controlled GLOBE Trial

OBJECTIVES

The objectives of the current research were to evaluate the accuracy and reliability of continuous glucose monitoring (CGM) in patients undergoing cardiac surgery and assess the impact of preoperative liraglutide administration on perioperative glucose control as captured by CGM.

DESIGN

This was a prospective, single-center, prespecified analysis of the GLOBE trial, a randomized controlled trial comparing preoperative liraglutide treatment to placebo in patients undergoing cardiac surgery.

SETTING

The work took place at a single-center academic hospital in the Netherlands.

PARTICIPANTS

Twenty-five patients undergoing cardiac surgery were recruited from the hospital's cardiac surgery department.

INTERVENTIONS

Participants received the Dexcom G5 CGM system from the day before surgery until discharge from the intensive care unit after surgery. Additionally, participants were randomized to receive either preoperative liraglutide or placebo.

MEASUREMENTS AND MAIN RESULTS

Arterial blood gas (ABG) glucose measurements were collected as a reference and matched to CGM readings to assess accuracy and reliability. In 240 paired CGM-ABG glucose measurements, the mean absolute relative difference was 14.4 ± 12.5%. Temporary sensor interruption occurred mainly intraoperatively (92% of patients). The median duration of intraoperative sensor interruption was 65 (48-95) minutes. Liraglutide increased glycemic time in range 72% versus 47% in the control group (absolute difference 25%, 95% confidence interval -41.4 to -8.9, p = .004).

CONCLUSIONS

Despite intraoperative sensor interruption, CGM seems an accurate method for semi-invasive, real-time assessment of blood glucose levels. CGM can provide a detailed observation of the pre- and postoperative glycemic trajectory, demonstrating increased time in range following perioperative liraglutide treatment compared with placebo.

Evaluation of folliculogenesis and oxidative stress parameters in type 1 diabetes mellitus women with different glycemic profiles

BACKGROUND

Despite enormous advances in diabetes treatment, women with type 1 diabetes mellitus (DM) still experience delayed menarche, menstrual irregularities, fewer pregnancies, and a higher rate of stillbirths compared to women without the disease. Due to the fact that type 1 DM occurs at a young age, the preservation of reproductive health is one of the most important goals of treatment.

AIMS

The aim of this study was to evaluate the relationship between different glycemic profiles and changes in the pro-oxidant-antioxidant balance and ovarian follicular apparatus in reproductive-age patients with type 1 DM.

METHODS

We examined 50 reproductive-age (19-38 years) women with type 1 DM with a disease duration of at least ten years. Carbohydrate metabolism was assessed with the continuous glucose monitoring (CGM) system and glycated hemoglobin (HbA1c) concentration measurement. CGM was performed using the FreeStyle Libre flash glucose monitoring system (Abbott Diabetes Care, Witney, UK). In each patient, malondialdehyde level, catalase activity and 3-nitrotyrosine level in the blood serum were determined. To assess the ovarian function, we measured the ovarian volume, the antral follicle count, and the serum levels of anti-Müllerian hormone and follicle-stimulating hormone. All patients were divided into four groups (glucotypes) based on the CGM results. Group 1 included type 1 DM patients with satisfactory compensation of carbohydrate metabolism; group 2 consisted of patients with frequent hypoglycemic conditions and pathological glucose variability; group 3 included individuals with prolonged hyperglycemic conditions and maximum HbA1c levels; and group 4 comprised patients with the glycemic profile characterized by all the presented types of dysglycemia (intermittent glycemia).

RESULTS

We revealed a negative correlation between serum catalase activity and time of hypoglycemic conditions in patients with type 1 DM based on the CGM results (rs = -0.47, p < 0.01). In group 4 (intermittent glycemia), patients demonstrated the lowest serum catalase activity and increased serum 3-nitrotyrosine level, while in group 3, women with chronic hyperglycemia (HbA1c 8.4 [8.1; 9.9]%; 68 [65; 85] mmol/mol) had a moderate change in antioxidant defense and oxidative stress parameters. Correlation analysis of ovarian volume, the antral follicle count, and the serum anti-Müllerian hormone level in type 1 DM women with different glycemic profiles established a negative relationship (rs = -0.82, p < 0.05) between the antral follicle count and glucose variability in group 1, a positive relationship (rs = 0.68, p < 0.05) between ovarian volume and glucose variability in group 2, and a positive relationship (rs = 0.88, p < 0.05) between ovarian volume and time of hypoglycemic conditions, which, according to the CGM results, amounted to a critical value of 57.5 [40.0; 82.0]%.

CONCLUSIONS

The data obtained indicate the relationship between the ovarian volume, serum anti-Müllerian hormone level, the antral follicle count and oxidative stress parameters not only in patients with hyperglycemia, but also in those with hypoglycemic conditions, as well as with pathological glucose variability.

Estimation of a Machine Learning-Based Decision Rule to Reduce Hypoglycemia Among Older Adults With Type 1 Diabetes: A Post Hoc Analysis of Continuous Glucose Monitoring in the WISDM Study

BACKGROUND

The Wireless Innovation for Seniors with Diabetes Mellitus (WISDM) study demonstrated continuous glucose monitoring (CGM) reduced hypoglycemia over 6 months among older adults with type 1 diabetes (T1D) compared with blood glucose monitoring (BGM). We explored heterogeneous treatment effects of CGM on hypoglycemia by formulating a data-driven decision rule that selects an intervention (ie, CGM vs BGM) to minimize percentage of time <70 mg/dL for each individual WISDM participant.

METHOD

The precision medicine analyses used data from participants with complete data (n = 194 older adults, including those who received CGM [n = 100] and BGM [n = 94] in the trial). Policy tree and decision list algorithms were fit with 14 baseline demographic, clinical, and laboratory measures. The primary outcome was CGM-measured percentage of time spent in hypoglycemic range (<70 mg/dL), and the decision rule assigned participants to a subgroup reflecting the treatment estimated to minimize this outcome across all follow-up visits.

RESULTS

The optimal decision rule was found to be a decision list with 3 steps. The first step moved WISDM participants with baseline time-below range >1.35% and no detectable C-peptide levels to the CGM subgroup (n = 139), and the second step moved WISDM participants with a baseline time-below range of >6.45% to the CGM subgroup (n = 18). The remaining participants (n = 37) were left in the BGM subgroup. Compared with the BGM subgroup (n = 37; 19%), the group for whom CGM minimized hypoglycemia (n = 157; 81%) had more baseline hypoglycemia, a lower proportion of detectable C-peptide, higher glycemic variability, longer disease duration, and higher proportion of insulin pump use.

CONCLUSIONS

The decision rule underscores the benefits of CGM for older adults to reduce hypoglycemia. Diagnostic CGM and laboratory markers may inform decision-making surrounding therapeutic CGM and identify older adults for whom CGM may be a critical intervention to reduce hypoglycemia.

Effects of Dexamethasone on Postoperative Glycemic Control in Patients After Primary Total Joint Arthroplasty: A Randomized Double-Blind Controlled Trial

BACKGROUND

In patients undergoing total joint arthroplasty, the use of dexamethasone (DEX) may cause perioperative blood glucose (BG) disorders, leading to complications even in patients who do not have diabetes. We aimed to evaluate the effects of different DEX doses on perioperative BG levels.

METHODS

A total of 135 patients who do not have diabetes were randomized into three groups: preoperative intravenous (IV) injection of normal saline (Group A, the placebo group), preoperative IV injection of 10 mg DEX (Group B), and preoperative IV injection of 20 mg DEX (Group C). Postoperative fasting BG (FBG) levels were designated as the primary outcome, while postoperative postprandial BG (PBG) levels were assigned as the secondary outcome. The incidence of complications was recorded. We also investigated the risk factors for FBG ≥ 140 mg/dL and PBG ≥ 180 mg/dL.

RESULTS

The FBG levels were higher in Groups B and C than in Group A on postoperative days (PODs) 0 and 1. The PBG levels were lower for Groups A and B compared to Group C on POD 1. No differences in FBG or PBG were detected beyond POD 1. Elevated preoperative glycosylated hemoglobin A1c levels increased the risk of FBG ≥ 140 mg/dL and PBG ≥ 180 mg/dL, respectively. However, preoperative IV injection of DEX was not associated with FBG ≥ 140 mg/dL or PBG ≥ 180 mg/dL. No differences were found in postoperative complications among the three groups.

CONCLUSIONS

The preoperative IV administration of 10 or 20 mg DEX in patients who do not have diabetes showed transient effects on postoperative BG after total joint arthroplasty. The preoperative glycosylated hemoglobin A1c level threshold (regardless of the administration or dosage of DEX) that increased the risk for the occurrence of FBG ≥ 140 mg/dL and PBG ≥ 180 mg/dL was 5.75 and 5.85%, respectively.

Impact of Continuous Glucose Monitoring and its Glucometrics in Clinical Practice in Spain and Future Perspectives: A Narrative Review

INTRODUCTION

Continuous glucose monitoring (CGM) devices allow for 24-h real-time measurement of interstitial glucose levels and have changed the interaction between people with diabetes and their health care providers. The large amount of data generated by CGM can be analyzed and evaluated using a set of standardized parameters, collectively named glucometrics. This review aims to provide a summary of the existing evidence on the use of glucometrics data and its impact on clinical practice based on published studies involving adults and children with type 1 diabetes (T1D) in Spain.

METHODS

The PubMed and MEDES (Spanish Medical literature) databases were searched covering the years 2018-2022 and including clinical and observational studies, consensus guidelines, and meta-analyses on CGM and glucometrics conducted in Spain.

RESULTS

A total of 16 observational studies were found on the use of CGM in Spain, which have shown that cases of severe hypoglycemia in children with T1D were greatly reduced after the introduction of CGM, resulting in a significant reduction in costs. Real-world data from Spain shows that CGM is associated with improved glycemic markers (increased time in range, reduced time below and above range, and glycemic variability), and that there is a relationship between glycemic variability and hypoglycemia. Also, CGM and analysis of glucometrics proved highly useful during the COVID-19 pandemic. New glucometrics, such as the glycemic risk index, or new mathematical approaches to the analysis of CGM-derived glucose data, such as "glucodensities," could help patients to achieve better glycemic control in the future.

CONCLUSION

By using glucometrics in clinical practice, clinicians can better assess glycemic control and a patient's individual response to treatment.

Glycemic Risk Index Profiles and Predictors Among Diverse Adults With Type 1 Diabetes

BACKGROUND

The Glycemia Risk Index (GRI) was introduced as a single value derived from the ambulatory glucose profile that identifies patients who need attention. This study describes participants in each of the five GRI zones and examines the percentage of variation in GRI scores that is explained by sociodemographic and clinical variables among diverse adults with type 1 diabetes.

METHODS

A total of 159 participants provided blinded continuous glucose monitoring (CGM) data over 14 days (mean age [SD] = 41.4 [14.5] years; female = 54.1%, Hispanic = 41.5%). Glycemia Risk Index zones were compared on CGM, sociodemographic, and clinical variables. Shapley value analysis examined the percentage of variation in GRI scores explained by different variables. Receiver operating characteristic curves examined GRI cutoffs for those more likely to have experienced ketoacidosis or severe hypoglycemia.

RESULTS

Mean glucose and variability, time in range, and percentage of time in high, and very high, glucose ranges differed across the five GRI zones (P values < .001). Multiple sociodemographic indices also differed across zones, including education level, race/ethnicity, age, and insurance status. Sociodemographic and clinical variables collectively explained 62.2% of variance in GRI scores. A GRI score ≥84.5 reflected greater likelihood of ketoacidosis (area under the curve [AUC] = 0.848), and scores ≥58.2 reflected greater likelihood of severe hypoglycemia (AUC = 0.729) over the previous six months.

CONCLUSIONS

Results support the use of the GRI, with GRI zones identifying those in need of clinical attention. Findings highlight the need to address health inequities. Treatment differences associated with the GRI also suggest behavioral and clinical interventions including starting individuals on CGM or automated insulin delivery systems.

Glycaemic outcomes in people living with diabetes under 65 and over 65 years old using an intermittently scanned continuous glucose monitoring system

Objective

Intermittently scanned continuous glucose monitoring (isCGM) has revolutionised the care of people with diabetes but its uptake and benefits in older adults are not well known. We examined the impact of isCGM (Freestyle Libre, FSL) on glycaemic outcomes in younger (⩽65 years) and older adults (>65 years) with diabetes.

Design and methods

In total, 2260 adult patients registered on the Libreview account at University Hospitals Birmingham NHS Foundation Trust, UK, were included. Inclusion criteria: all patients with type 1 and type 2 diabetes aged >18 years, use of isCGM >6 months, scanning at least 6 times/day. Demographics, diabetes history and glycaemic outcomes (time in range (TIR), time above range and time below range (TBR), estimated HbA1c, HbA1c at start and at end of study) were collected by accessing electronic patient records and Libreview. Outcomes were compared between age groups ⩽65 or >65 years old.

Results

Most patients were of Caucasian ethnicity (⩽65 years 68%, >65 years 73%) and had type 1 diabetes. Mean duration of diabetes was 19.5 years (range 0-65 years) and 34.5 years (range 0-79 years) for ⩽65 and >65 years, respectively. Only a quarter of those ⩽65 years achieved (219/943; 23.2%) their age specific TIR target compared to 69% (78/113) of those >65 years cohort, while 70.1% (663/946) of ⩽65 years and 40.7% (46/113) of >65 years achieved their age-specific TBR target. When the less strict ⩽65 years TBR target was applied, 75% (85/113) of >65 years cohort achieved this.

Conclusion

FSL use was associated with improved glycaemic outcomes across all age groups. Individualised targets may be needed to improve TBR in those aged >65 years.

Ketogenic diet but not free-sugar restriction alters glucose tolerance, lipid metabolism, peripheral tissue phenotype, and gut microbiome: RCT

Restricted sugar and ketogenic diets can alter energy balance/metabolism, but decreased energy intake may be compensated by reduced expenditure. In healthy adults, randomization to restricting free sugars or overall carbohydrates (ketogenic diet) for 12 weeks reduces fat mass without changing energy expenditure versus control. Free-sugar restriction minimally affects metabolism or gut microbiome but decreases low-density lipoprotein cholesterol (LDL-C). In contrast, a ketogenic diet decreases glucose tolerance, increases skeletal muscle PDK4, and reduces AMPK and GLUT4 levels. By week 4, the ketogenic diet reduces fasting glucose and increases apolipoprotein B, C-reactive protein, and postprandial glycerol concentrations. However, despite sustained ketosis, these effects are no longer apparent by week 12, when gut microbial beta diversity is altered, possibly reflective of longer-term adjustments to the ketogenic diet and/or energy balance. These data demonstrate that restricting free sugars or overall carbohydrates reduces energy intake without altering physical activity, but with divergent effects on glucose tolerance, lipoprotein profiles, and gut microbiome.

Oral Anaerobutyricum soehngenii augments glycemic control in type 2 diabetes

This randomized, double-blind, placebo-controlled trial investigated the impact of 14-day Anaerobutyricum soehngenii L2-7 supplementation on postprandial glucose levels in 25 White Dutch males with type 2 diabetes (T2D) on stable metformin therapy. The primary endpoint was the effect of A. soehngenii versus placebo on glucose excursions and variability as determined by continuous glucose monitoring. Secondary endpoints were changes in ambulatory 24-h blood pressure, incretins, circulating metabolites and excursions of plasma short-chain fatty acids (SCFAs) and bile acids upon a standardized meal. Results showed that A. soehngenii supplementation for 14 days significantly improved glycemic variability and mean arterial blood pressure, without notable changes in SCFAs, bile acids, incretin levels, or anthropometric parameters as compared to placebo-treated controls. Although well-tolerated and effective in improving glycemic control in the intervention group, further research in larger and more diverse populations is needed to generalize these findings.

How to Use Continuous Glucose Monitoring Efficiently in Diabetes Management: Opinions and Recommendations by German Experts on the Status and Open Questions

Today, continuous glucose monitoring (CGM) is a standard diagnostic option for patients with diabetes, at least for those with type 1 diabetes and those with type 2 diabetes on insulin therapy, according to international guidelines. The switch from spot capillary blood glucose measurement to CGM was driven by the extensive and immediate support and facilitation of diabetes management CGM offers. In patients not using insulin, the benefits of CGM are not so well studied/obvious. In such patients, factors like well-being and biofeedback are driving CGM uptake and outcome. Apps can combine CGM data with data about physical activity and meal consumption for therapy adjustments. Personalized data management and coaching is also more feasible with CGM data. The same holds true for digitalization and telemedicine intervention ("virtual diabetes clinic"). Combining CGM data with Smart Pens ("patient decision support") helps to avoid missing insulin boluses or insulin miscalculation. Continuous glucose monitoring is a major pillar of all automated insulin delivery systems, which helps substantially to avoid acute complications and achieve more time in the glycemic target range. These options were discussed by a group of German experts to identify concrete gaps in the care structure, with a view to the necessary structural adjustments of the health care system.

Isolated short stature as the only presenting symptom of glycogen storage disease type 0a in a Chinese child: A case report

RATIONALE

Glycogen storage disease type 0a (GSD0a) is a rare autosomal recessive disorder caused by glycogen synthase deficiency. Short stature is a characteristic feature in 29% of GSD0a patients, but isolated short stature as the only presenting symptom is exceedingly rare, with only 2 cases reported worldwide.

PATIENT CONCERNS

A 4-year-old girl presented with persistent growth retardation despite previous treatment for renal tubular acidosis.

DIAGNOSES

Based on clinical presentation and whole exome sequencing results, the patient was diagnosed with GSD0a.

INTERVENTIONS

Uncooked cornstarch therapy was initiated at 2 g/kg every 6 hours.

OUTCOMES

After 3 years of treatment, the patient's height SDS improved from -2.24 to -1.06, with enhanced glycemic control and no complications.

LESSONS

This case emphasizes considering GSD0a in unexplained short stature and the value of continuous glucose monitoring. Early diagnosis and treatment can optimize growth in GSD0a patients.

End-to-end offline reinforcement learning for glycemia control

The development of closed-loop systems for glycemia control in type I diabetes relies heavily on simulated patients. Improving the performances and adaptability of these close-loops raises the risk of over-fitting the simulator. This may have dire consequences, especially in unusual cases which were not faithfully - if at all - captured by the simulator. To address this, we propose to use model-free offline RL agents, trained on real patient data, to perform the glycemia control. To further improve the performances, we propose an end-to-end personalization pipeline, which leverages offline-policy evaluation methods to remove altogether the need of a simulator, while still enabling an estimation of clinically relevant metrics for diabetes.

Clinical application of real-time continuous glucose monitoring system during postoperative enteral nutrition therapy in esophageal cancer patients

BACKGROUND

Enteral nutrition (EN) support therapy increases the risk of abnormal blood glucose (BG). The aim of this study is to evaluate the clinical value of a real-time continuous glucose monitoring (rt-CGM) system in BG monitoring during postoperative EN support therapy in patients with esophageal cancer.

METHODS

Patients without diabetes mellitus (DM) with esophageal cancer who planned to receive postoperative EN were enrolled. With the self-monitoring of BG value as the reference BG, the accuracy of rt-CGM was evaluated by the mean absolute relative difference (MARD) value, correlation efficient, agreement analysis, and Parkes and Clarke error grid plot. Finally, paired t tests were used to compare the differences in glucose fluctuations between EN and non-EN days and slow and fast days.

RESULTS

The total MARD value of the rt-CGM system was 13.53%. There was a high correlation between interstitial glucose and fingertip capillary BG (consistency correlation efficient = 0.884 [95% confidence interval, 0.874-0.894]). Results of 15/15%, 20/20%, 30/30% agreement analysis were 58.51%, 84.71%, and 99.65%, respectively. The Parkes and Clarke error grid showed that the proportion of the A and B regions were 100% and 99.94%, respectively. The glucose fluctuations on EN days vs non-EN days and on fast days vs slow days were large, and the difference was statistically significant (P < 0.001).

CONCLUSION

The rt-CGM system achieved clinical accuracy and can be used as a new option for glucose monitoring during postoperative EN therapy. The magnitude of glucose fluctuation during EN therapy remains large, even in the postoperative population without DM.

An Aerobic Cooldown After Morning, Fasted Resistance Exercise Has Limited Impact on Post-exercise Hyperglycemia in Adults With Type 1 Diabetes: A Randomized Crossover Study

OBJECTIVES

Expert guidelines recommend an aerobic cooldown to lower blood glucose for the management of post-exercise hyperglycemia. This strategy has never been empirically tested. Our aim in this study was to compare the glycemic effects of performing an aerobic cooldown vs not performing a cooldown after a fasted resistance exercise session. We hypothesized that the cooldown would lower blood glucose in the 30 minutes after exercise and would result in less time in hyperglycemia in the 6 hours after exercise.

METHODS

Participants completed 2 identical resistance exercise sessions. One was followed by a low-intensity (30% of peak oxygen consumption) 10-minute cycle ergometer cooldown, and the other was followed by 10 minutes of sitting. We compared the changes in capillary glucose concentration during these sessions and continuous glucose monitoring (CGM) outcomes over 24 hours post-exercise.

RESULTS

Sixteen participants completed the trial. Capillary glucose was similar between conditions at the start of exercise (p=0.07). Capillary glucose concentration decreased by 0.6±1.0 mmol/L during the 10-minute cooldown, but it increased by 0.7±1.3 mmol/L during the same time in the no-cooldown condition. The resulting difference in glucose trajectory led to a significant interaction (p=0.02), with no effect from treatment (p=0.7). Capillary glucose values at the end of recovery were similar between conditions (p>0.05). There were no significant differences in CGM outcomes.

CONCLUSIONS

An aerobic cooldown reduces glucose concentration in the post-exercise period, but the small and brief nature of this reduction makes this strategy unlikely to be an effective treatment for hyperglycemia occurring after fasted exercise.

The association of chronic complications with time in tight range and time in range in people with type 1 diabetes: a retrospective cross-sectional real-world study

AIMS/HYPOTHESIS

The aim of this study was to evaluate the association of chronic complications with time in tight range (TITR: 3.9-7.8 mmol/l) and time in range (TIR: 3.9-10.0 mmol/l) in people with type 1 diabetes.

METHODS

The prevalence of microvascular complications (diabetic retinopathy, diabetic nephropathy and diabetic peripheral neuropathy [DPN]) and macrovascular complications according to sensor-measured TITR/TIR was analysed cross-sectionally in 808 adults with type 1 diabetes. Binary logistic regression was used to evaluate the association between TITR/TIR and the presence of complications without adjustment, with adjustment for HbA1c, and with adjustment for HbA1c and other confounding factors (sex, age, diabetes duration, BMI, BP, lipid profile, smoking, and use of statins and renin-angiotensin-aldosterone system inhibitors).

RESULTS

The mean TITR and TIR were 33.9 ± 12.8% and 52.5 ± 15.0%, respectively. Overall, 46.0% had any microvascular complication (34.5% diabetic retinopathy, 23.8% diabetic nephropathy, 16.0% DPN) and 16.3% suffered from any macrovascular complication. The prevalence of any microvascular complication, diabetic retinopathy, diabetic nephropathy and a cerebrovascular accident (CVA) decreased with increasing TITR/TIR quartiles (all ptrend<0.05). Each 10% increase in TITR was associated with a lower incidence of any microvascular complication (OR 0.762; 95% CI 0.679, 0.855; p<0.001), diabetic retinopathy (OR 0.757; 95% CI 0.670, 0.856; p<0.001), background diabetic retinopathy (OR 0.760; 95% CI 0.655, 0.882; p<0.001), severe diabetic retinopathy (OR 0.854; 95% CI 0.731, 0.998; p=0.048), diabetic nephropathy (OR 0.799; 95% CI 0.699, 0.915; p<0.001), DPN (OR 0.837; 95% CI 0.717, 0.977; p=0.026) and CVA (OR 0.651; 95% CI 0.470, 0.902; p=0.010). The independent association of TITR with any microvascular complication (OR 0.867; 95% CI 0.762, 0.988; p=0.032), diabetic retinopathy (OR 0.837; 95% CI 0.731, 0.959; p=0.010), background diabetic retinopathy (OR 0.831; 95% CI 0.705, 0.979; p=0.027) and CVA (OR 0.619; 95% CI 0.426, 0.899; p=0.012) persisted after adjustment for HbA1c. Similar results were obtained when controlling for HbA1c and other confounding factors.

CONCLUSIONS/INTERPRETATION

TITR and TIR are inversely associated with the presence of microvascular complications and CVA in people with type 1 diabetes. Although this study was not designed to establish a causal relationship, this analysis adds validity to the use of TITR and TIR as key measures in glycaemic management.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02601729 and NCT02898714.

Glycemic Variability as a Predictor of Graft Failure Following Infrainguinal Bypass for Peripheral Arterial Disease: A Retrospective Cohort Study

BACKGROUND

Glycemic variability (GV), measured as the change in visit-to-visit glycated hemoglobin (HbA1c), increases the risk of multiple adverse outcomes. However, the impact of GV on graft patency following infrainguinal bypass (IIB) is unknown. A retrospective cohort study was undertaken to assess the impact of GV on graft patency.

METHODS

A 3-year single-center retrospective case notes analysis of all people undergoing IIB between 2017 and 2019. Rutherford stage, graft conduit, level of bypass, procedure details, baseline demographics, comorbidities, and GV were assessed. Time to reintervention, ipsilateral amputation, or death was recorded to determine primary patency (PP).

RESULTS

One hundred six IIB outcomes were analyzed: mean (± standard deviation) age 68.0 (9.2) years; 69 (65.1%) male, 37 (33.9%), 75 (70.8%) had diabetes mellitus; and 46 (43.4%) underwent elective procedures. GV > 9.1% was associated with significantly lower median PP than GV < 9.1%, 198 (97-753.5) vs. 713 (166.5-1,044.5) days (P = 0.045). On univariate analysis, GV > 9.1% vs. < 9.1% was significantly associated with PP (hazard ratio [HR] 1.85 [confidence interval {CI} 1.091-3.136], P = 0.022). Bypass level was also a univariate predictor, with below knee bypasses (HR 2.31 [CI 1.164-4.564], P = 0.017), and tibial (HR 2.00 [CI 1.022-3.090], P < 0.043) having lower PP than above knee bypasses. On multivariate adjustment, GV > 9.1% and level of bypass remained independent predictors of PP, HR 1.96 (95% CI: 1.12-3.42, P = 0.018) and HR 2.54 (95% CI: 1.24-5.22, P = 0.011), respectively.

CONCLUSIONS

GV is an independent predictor of PP following infrainguinal bypass, thus optimizing GV should be a therapeutic target.

Review Article - Diabetes Technology in the Hospital: An Update

PURPOSE OF REVIEW

There have been many developments in diabetes technology in recent years, with continuous glucose monitoring (CGM), insulin pump therapy (CSII) and automated insulin delivery (AID) becoming progressively accepted in outpatient diabetes care. However, the use of such advanced diabetes technology in the inpatient setting is still limited for several reasons, including logistical challenges and staff training needs. On the other hand, hospital settings with altered diet and stress-induced hyperglycemia often pose challenges to tight glycemic control using conventional treatment tools. Integrating smarter glucose monitoring and insulin delivery devices into the increasingly technical hospital environment could reduce diabetes-related morbidity and mortality. This narrative review describes the most recent literature on the use of diabetes technology in the hospital and suggests avenues for further research.

RECENT FINDINGS

Advanced diabetes technology has the potential to improve glycemic control in hospitalized people with and without diabetes, and could add particular value in certain conditions, such as nutrition therapy or perioperative management. Taken together, CGM allows for more accurate and patient-friendly follow-up and ad hoc titration of therapy. AID may also provide benefits, including improved glycemic control and reduced nursing workload. Before advanced diabetes technology can be used on a large scale in the hospital, further research is needed on efficacy, accuracy and safety, while implementation factors such as cost and staff training must also be overcome.

Discordance between mean glucose and time in range in relation to HbA1c in individuals with type 1 diabetes: results from the GOLD and SILVER trials

AIMS/HYPOTHESIS

Previous studies have shown that individuals with similar mean glucose levels (MG) or percentage of time in range (TIR) may have different HbA1c values. The aim of this study was to further elucidate how MG and TIR are associated with HbA1c.

METHODS

Data from the randomised clinical GOLD trial (n=144) and the follow-up SILVER trial (n=98) of adults with type 1 diabetes followed for 2.5 years were analysed. A total of 596 paired HbA1c/continuous glucose monitoring measurements were included. Linear mixed-effects models were used to account for intra-individual correlations in repeated-measures data.

RESULTS

In the GOLD trial, the mean age of the participants (± SD) was 44±13 years, 63 (44%) were female, and the mean HbA1c (± SD) was 72±9.8 mmol/mol (8.7±0.9%). When correlating MG with HbA1c, MG explained 63% of the variation in HbA1c (r=0.79, p<0.001). The variation in HbA1c explained by MG increased to 88% (r=0.94, p value for improvement of fit <0.001) when accounting for person-to-person variation in the MG-HbA1c relationship. Time below range (TBR; <3.9 mmol/l), time above range (TAR) level 2 (>13.9 mmol/l) and glycaemic variability had little or no effect on the association. For a given MG and TIR, the HbA1c of 10% of individuals deviated by >8 mmol/mol (0.8%) from their estimated HbA1c based on the overall association between MG and TIR with HbA1c. TBR and TAR level 2 significantly influenced the association between TIR and HbA1c. At a given TIR, each 1% increase in TBR was related to a 0.6 mmol/mol lower HbA1c (95% CI 0.4, 0.9; p<0.001), and each 2% increase in TAR level 2 was related to a 0.4 mmol/mol higher HbA1c (95% CI 0.1, 0.6; p=0.003). However, neither TIR, TBR nor TAR level 2 were significantly associated with HbA1c when accounting for MG.

CONCLUSIONS/INTERPRETATION

Inter-individual variations exist between MG and HbA1c, as well as between TIR and HbA1c, with clinically important deviations in relatively large groups of individuals with type 1 diabetes. These results may provide important information to both healthcare providers and individuals with diabetes in terms of prognosis and when making diabetes management decisions.

Impact in real-world of intermittent-scanned continuous glucose monitoring with alarms on hypoglycemia and its recognition in type 1 diabetes

BACKGROUND

Recent studies have demonstrated that real-time CGM use reduce the incidence severe hypoglycemic events and impaired awareness of hypoglycemia (IAH) However, there are few real-world studies evaluating the effect of intermittently scanned continuous glucose monitoring (isCGM) on hypoglycemic episodes and hypoglycemia unawareness (IAH). The present study was designed to cover this research-practice gap.

METHODS

This is a real-world, observational, prospective cohort study with 2 years of follow-up in which 60 subjects with T1D who experienced frequent hypoglycemic events were included. All the patients were invited to use isCGM type Abbott FreeStyle Libre 2® on a continuous basis for 2 years. Glucometric parameters were obtained during the initial 2 weeks using isCGM and compared with data collected for the same period at 1 year and at the end of follow-up. The IAH was evaluated using the Clarke questionnaire, and to assess psychological aspects related to hypoglycemia the Hypoglycemia Fear Survey (HFS) was used.

RESULTS

After 2-years of follow-up using isCGM, we observed a decrease in glucose variability (40.3 ± 0.8 % vs. 37.1 ± 0.9 %, p = 0.003), time in low glucose range (54-69 mg/dL) (5.2 ± 0.4 % vs. 3.6 ± 0.3 %, p = 0.001), time in very low glucose range (<54 mg/dL) (3.2 ± 0.5 % vs. 0.8 ± 0.2 %, p < 0.001), less events related to low glucose levels (10.6 ± 1.1 vs 8.0 ± 1.0, p = 0.042) and a short duration of hypoglycemia episodes (106.1 ± 5.9 min vs. 85.7 ± 5.7 min, p = 0.008). In addition, participants presented a reduction of 47 % in the frequency of IAH, assessed by the Clarke questionnaire scores (24.6 % vs. 11.6 %, p = 0.034), as well as hypoglycemia fear (77.8 ± 2.4 vs 68.2 ± 2.1, p < 0.001). Furthermore, a reduction in total insulin dose was also observed (0.64 ± 0.30 UI/Kg/day vs 0.56 ± 0.11 UI/Kg/day, p = 0.018).

CONCLUSIONS

In the real-world, long-term use of isCGM could reduce both hypoglycemic episodes and IAH in people with T1D.

What difference does sleep make? Continuous glucose monitoring metrics during fixed-overnight time versus sleep periods among older adults with type 1 diabetes

Hypoglycaemia during sleep is a common and clinically important issue for people living with insulin-treated diabetes. Continuous glucose monitoring devices can help to identify nocturnal hypoglycaemia and inform treatment strategies. However, sleep is generally inferred, with diabetes researchers and physicians using a fixed-overnight period as a proxy for sleep-wake status when analysing and interpretating continuous glucose monitoring data. No study to date has validated such an approach with established sleep measures. Continuous glucose monitoring and research-grade actigraphy devices were worn and sleep diaries completed for 2 weeks by 28 older adults (mean age 67 years [SD 5]; 17 (59%) women) with type 1 diabetes. Using continuous glucose monitoring data from a total of 356 nights, fixed-overnight (using the recommended period of 00:00 hours-06:00 hours) and objectively-measured sleep periods were compared. The fixed-overnight period approach missed a median 57 min per night (interquartile range: 49-64) of sleep for each participant, including five continuous glucose monitoring-detected hypoglycaemia episodes during objectively-measured sleep. Twenty-seven participants (96%) had at least 1 night with continuous glucose monitoring time-in-range and time-above-range discrepancies both ≥ 10 percentage points, a clinically significant discrepancy. The utility of fixed-overnight time continuous glucose monitoring as a proxy for sleep-awake continuous glucose monitoring is inadequate as it consistently excludes actual sleep time, obscures glycaemic patterns, and misses sensor hypoglycaemia episodes during sleep. The use of validated measures of sleep to aid interpretation of continuous glucose monitoring data is encouraged.

Assessing the feasibility of time in tight range (TITR) targets with advanced hybrid closed loop (AHCL) use in children and adolescents: A single-centre real-world study

AIMS

Time in Tight Range (TITR) is a novel glycaemic metric in monitoring type 1 diabetes (T1D) management. The aim of this study was to assess the attainability of the TITR target in children and adolescents using the advanced hybrid closed loop (AHCL).

METHODS

The 2128-day CGM data from 56 children and adolescents with T1D using AHCL (Minimed-780G) were analysed. Time in Range (TIR) (3.9-10 mmol/L), TITR (3.9-7.7 mmol/L), and other glycaemic parameters were separately analysed in terms of whole day, daytime (06.00-23:59), and nighttime (00.00-05.59) results. The participants were divided into two groups by autocorrection rate where Group 1 had a rate of <30% and Group 2 had a rate of ≥30.

RESULTS

All glycaemic parameters indicated a better glycaemic outcome in the nighttime with higher TIR and TITR values compared with daytime (for TIR 87.5 ± 9.5% vs. 78.8 ± 8%, p < 0.001, and TITR 68.2 ± 13.5% vs. 57.5 ± 8.8%, p < 0.001). The rates of TITR >50% and >60% were 87% and 52%, respectively. When those with TITR >60% (n: 29) and those without (n: 27) were evaluated in terms of hypoglycaemia, no statistically significant difference was found in time below range (TBR) 3-3.9 mmol/L (0.3% vs. 2.1%, p: 0.084) and TBR < 3 mmol/L (0.47% vs. 0.3%, p: 0.298). Group 1 had a significantly higher TIR and TITR compared to Group 2 (82.6 ± 6.1% vs. 75.6 ± 8.6%, p: 0.008 and 62.1 ± 7.5% vs. 53.8 ± 7.5%, p: 0.002, respectively).

CONCLUSIONS

Most children and adolescents on AHCL achieved the 50% target for TITR whereas more than half achieved the >60% target. A target of >50% for TITR seems realistic in children with T1D using AHCL.

Perspectives and Behaviors of People with Diabetes toward Time in Range and Glucose Control in Diabetes Management: An Online Survey

INTRODUCTION

This study assessed experiences, attitudes, and behaviors of people with diabetes (PwD) regarding diabetes self-management and glucose control, and their level of awareness, knowledge, and attitudes toward time in range (TIR).

METHODS

This quantitative survey was conducted using an online questionnaire across seven countries. Respondents were PwD classified into three subgroups: type 1 (T1), type 2 insulin (T2 insulin), and type 2 not on insulin (T2 N/insulin).

RESULTS

Respondents included 621 people in the T1, 780 people in the T2 insulin, and 735 people in the T2 N/insulin subgroups. Awareness of TIR was low, particularly in the T2 N/insulin subgroup (T1 53%, T2 insulin 29%, T2 N/insulin 9%). Despite a lower current use of continuous glucose monitoring (CGM) among the T2 insulin and T2 N/insulin participants (38% and 9%, respectively), versus T1 participants (64%), most (> 70%) were positive toward utilizing new tools and measures to self-manage blood glucose. Recommendations from their healthcare professionals (HCPs) were cited as a strong motivator to try new measures for analyzing glucose levels. The main barriers cited were limited access to CGM and lack of understanding of TIR benefits. Cost was the main reason given by ≥ 40% of respondents for stopping CGM use.

CONCLUSIONS

There is an unmet need in diabetes management, and TIR and CGM offer a potential solution. PwD are motivated to manage their blood glucose levels and are positive toward utilizing new tools and measures to achieve this goal. HCPs play a pivotal role in informing and guiding PwD on new measures for analyzing glucose.

Consequences of Data Loss on Clinical Decision-Making in Continuous Glucose Monitoring: Retrospective Cohort Study

BACKGROUND

The impact of missing data on individual continuous glucose monitoring (CGM) data is unknown but can influence clinical decision-making for patients.

OBJECTIVE

We aimed to investigate the consequences of data loss on glucose metrics in individual patient recordings from continuous glucose monitors and assess its implications on clinical decision-making.

METHODS

The CGM data were collected from patients with type 1 and 2 diabetes using the FreeStyle Libre sensor (Abbott Diabetes Care). We selected 7-28 days of 24 hours of continuous data without any missing values from each individual patient. To mimic real-world data loss, missing data ranging from 5% to 50% were introduced into the data set. From this modified data set, clinical metrics including time below range (TBR), TBR level 2 (TBR2), and other common glucose metrics were calculated in the data sets with and that without data loss. Recordings in which glucose metrics deviated relevantly due to data loss, as determined by clinical experts, were defined as expert panel boundary error (εEPB). These errors were expressed as a percentage of the total number of recordings. The errors for the recordings with glucose management indicator <53 mmol/mol were investigated.

RESULTS

A total of 84 patients contributed to 798 recordings over 28 days. With 5%-50% data loss for 7-28 days recordings, the εEPB varied from 0 out of 798 (0.0%) to 147 out of 736 (20.0%) for TBR and 0 out of 612 (0.0%) to 22 out of 408 (5.4%) recordings for TBR2. In the case of 14-day recordings, TBR and TBR2 episodes completely disappeared due to 30% data loss in 2 out of 786 (0.3%) and 32 out of 522 (6.1%) of the cases, respectively. However, the initial values of the disappeared TBR and TBR2 were relatively small (<0.1%). In the recordings with glucose management indicator <53 mmol/mol the εEPB was 9.6% for 14 days with 30% data loss.

CONCLUSIONS

With a maximum of 30% data loss in 14-day CGM recordings, there is minimal impact of missing data on the clinical interpretation of various glucose metrics.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05584293; https://clinicaltrials.gov/study/NCT05584293.

Current landscape and comprehensive management of glycemic variability in diabetic retinopathy

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

Adherence to the Mediterranean diet and sleep quality are inter-correlated with flash glucose monitoring (FGM)-measured glycemia among children with type 1 diabetes

BACKGROUND

We examined the inter-correlation between diet quality, objectively measured sleep duration, and subjectively measured sleep quality with flash glucose monitoring (FGM)-measured glycemia among young patients with type 1 diabetes (T1D).

MATERIALS AND METHODS

Following cross-sectional design, Fitbit® accelerometers were used to objectively assess sleep duration, while the validated questionnaires Pittsburgh sleep quality index and Mediterranean diet (MD) adherence were used to subjectively assess sleep quality and diet quality, respectively. Glycated hemoglobin (HbA1c) and FGM-reported glycemia components among children with T1D were assessed as well.

RESULTS

Of the 47 participants surveyed (25 boys, 22 girls, 9.31 ± 2.88 years), the majority reported high HbA1c, good sleep quality, and high adherence to the MD. However, only one-third of the participants reported a healthy sleep duration. Only the sleep latency was significantly (P < 0.05) associated with the time above range level 2 and time below range level 2 (P = 0.048) components of the FGM. A positive correlation (r = 0.309, P = 0.035) was reported between adherence to MD and time in range of the FGM.

CONCLUSIONS

Diet quality and sleep quality are variably inter-correlated with FGM-measured glycemia among young patients with T1D and are suggested to be considered influential factors in FGM-monitored diabetes research on this age group.

Exploring the Continuous Glucose Monitoring in Pediatric Diabetes: Current Practices, Innovative Metrics, and Future Implications

Continuous glucose monitoring (CGM) systems, including real-time CGM and intermittently scanned CGM, have revolutionized diabetes management, particularly in children and adolescents with type 1 diabetes (T1D). These systems provide detailed insights into glucose variability and detect asymptomatic and nocturnal hypoglycemia, addressing limitations of traditional self-monitoring blood glucose methods. CGM devices measure interstitial glucose concentrations constantly, enabling proactive therapeutic decisions and optimization of glycemic control through stored data analysis. CGM metrics such as time in range, time below range, and coefficient of variation are crucial for managing T1D, with emerging metrics like time in tight range and glycemia risk index showing potential for enhanced glycemic assessment. Recent advancements suggest the utility of CGM systems in monitoring the early stages of T1D and individuals with obesity complicated by pre-diabetes, highlighting its therapeutic versatility. This review discusses the current CGM systems for T1D during the pediatric age, established and emerging metrics, and future applications, emphasizing the critical role of CGM devices in improving glycemic control and clinical outcomes in children and adolescents with diabetes.

Stenopool: A Comprehensive Platform for Consolidating Diabetes Device Data

BACKGROUND

The growing adoption of diabetes devices has highlighted the need for integrated platforms to consolidate data from various vendors and device types, enhancing the patient experience and treatment. This shift could pave the way for a transition from conventional outpatient diabetes clinics to advanced home monitoring and virtual care methods. Overall, we wished to empower individuals with diabetes and healthcare providers to interpret and utilize information from diabetes devices more effectively.

METHODS

Stenopool integrates most diabetes devices for glucose monitoring and insulin administration in our clinic. The platform was initially developed with inspiration from open-source software, and the current version is a unique digital platform for managing and analyzing diabetes device data. The development process, outcomes, and status are described.

RESULTS

Since November 2021, Stenopool has been used in our outpatient clinic to integrate over 30 different diabetes devices from around 7000 individuals. Data are primarily uploaded via wired connections, but also using semi-automated and automated cloud-to-cloud data transfers. The platform offers a streamlined workflow for healthcare providers and displays data from various glucose meter, insulin pump, and continuous glucose monitor (CGM) vendors on a single screen in a manner that healthcare providers can modify. A data warehouse with data from Stenopool and electronical health records is nearing completion, preparing the development of tools for population health management, quality assessment, and risk stratification of patients.

CONCLUSION

Using Stenopool, we aimed to enhance diabetes device data management, facilitate the future for virtual patient care pathways, and improve outcomes. This article outlines the platform's development process and challenges.

Semen quality and metabolic profile in people with type 1 diabetes with and without erectile dysfunction: a cross-sectional study

PURPOSE

The aim of the present study is to evaluate the association of metabolic and glycemic variables with semen parameters in patients with type 1 diabetes (T1D) with and without erectile dysfunction (ED).

METHODS

The study population included 88 adults with T1D using a continuous glucose monitoring, of whom 28 with ED (ED group) and 60 without it (NO ED group). All men completed the International Index of Erectile Function (IIEF-5) and underwent body composition analysis (BIA) and semen analysis.

RESULTS

ED group showed worse HbA1c levels [median (IQR), 8.4 (7.7, 9.9) vs 7.4 (7, 8.2) %, P < 0.001)], higher insulin dose [60 (51, 65) vs 45 (38, 56) UI/die, P = 0.004)] and a higher total body water and intracellular water as compared with ED group. Men in the ED group presented higher semen volume [2.8 (2.6, 4.2) vs 2.5 (2.2, 2.7) mL, P < 0.001] and sperm concentration [24 (19, 29) vs 20 (12, 23) mil/mL, P = 0.010], but reduced sperm progressive motility [28 (25, 35) vs 35 (25, 36) %, P = 0.011], higher rate of non-progressive motility [15 (10, 15) vs 10 (5, 10) %, P < 0.001] and higher rate of typical morphology [7(5, 8) vs 5 (4, 5) %, P = 0.001]. Based on multivariate logistic regression analysis performed to assess the association between clinical variables and ED, intracellular water (OR 3.829, 95% CI 1.205, 12.163, P = 0.023) resulted as the only independent predictor of ED.

CONCLUSION

Men with T1D and ED showed worse metabolic profile which is associated with poor semen quality, as compared with those without ED.

Development and Validation of a Machine Learning Model to Predict Weekly Risk of Hypoglycemia in Patients with Type 1 Diabetes Based on Continuous Glucose Monitoring

Aim: The aim of this study was to develop and validate a prediction model based on continuous glucose monitoring (CGM) data to identify a week-to-week risk profile of excessive hypoglycemia. Methods: We analyzed, trained, and internally tested two prediction models using CGM data from 205 type 1 diabetes patients with long-term CGM monitoring. A binary classification approach (XGBoost) combined with feature engineering deployed on the CGM signals was utilized to predict excessive hypoglycemia risk defined by two targets (time below range [TBR] >4% and the upper TBR 90th percentile limit) of TBR the following week. The models were validated in two independent cohorts with a total of 253 additional patients. Results: A total of 61,470 weeks of CGM data were included in the analysis. The XGBoost models had an area under the receiver operating characteristic curve (ROC-AUC) of 0.83-0.87 (95% confidence interval; 0.83-0.88) in the test dataset. The external validation showed ROC-AUCs of 0.81-0.90. The most discriminative features included the low blood glucose index, the glycemic risk assessment diabetes equation (GRADE), hypoglycemia, the TBR, waveform length, the coefficient of variation and mean glucose during the previous week. This highlights that the pattern of hypoglycemia combined with glucose variability during the past week contains information on the risk of future hypoglycemia. Conclusion: Prediction models based on real-world CGM data can be used to predict the risk of hypoglycemia in the forthcoming week. The models showed good performance in both the internal and external validation cohorts.

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.

A Comparison of the Rates of Clock-Based Nocturnal Hypoglycemia and Hypoglycemia While Asleep Among People Living with Diabetes: Findings from the Hypo-METRICS Study

Introduction: Nocturnal hypoglycemia is generally calculated between 00:00 and 06:00. However, those hours may not accurately reflect sleeping patterns and it is unknown whether this leads to bias. We therefore compared hypoglycemia rates while asleep with those of clock-based nocturnal hypoglycemia in adults with type 1 diabetes (T1D) or insulin-treated type 2 diabetes (T2D). Methods: Participants from the Hypo-METRICS study wore a blinded continuous glucose monitor and a Fitbit Charge 4 activity monitor for 10 weeks. They recorded details of episodes of hypoglycemia using a smartphone app. Sensor-detected hypoglycemia (SDH) and person-reported hypoglycemia (PRH) were categorized as nocturnal (00:00-06:00 h) versus diurnal and while asleep versus awake defined by Fitbit sleeping intervals. Paired-sample Wilcoxon tests were used to examine the differences in hypoglycemia rates. Results: A total of 574 participants [47% T1D, 45% women, 89% white, median (interquartile range) age 56 (45-66) years, and hemoglobin A1c 7.3% (6.8-8.0)] were included. Median sleep duration was 6.1 h (5.2-6.8), bedtime and waking time ∼23:30 and 07:30, respectively. There were higher median weekly rates of SDH and PRH while asleep than clock-based nocturnal SDH and PRH among people with T1D, especially for SDH <70 mg/dL (1.7 vs. 1.4, P < 0.001). Higher weekly rates of SDH while asleep than nocturnal SDH were found among people with T2D, especially for SDH <70 mg/dL (0.8 vs. 0.7, P < 0.001). Conclusion: Using 00:00 to 06:00 as a proxy for sleeping hours may underestimate hypoglycemia while asleep. Future hypoglycemia research should consider the use of sleep trackers to record sleep and reflect hypoglycemia while asleep more accurately. The trial registration number is NCT04304963.

Equitable implementation of a precision digital health program for glucose management in individuals with newly diagnosed type 1 diabetes

Few young people with type 1 diabetes (T1D) meet glucose targets. Continuous glucose monitoring improves glycemia, but access is not equitable. We prospectively assessed the impact of a systematic and equitable digital-health-team-based care program implementing tighter glucose targets (HbA1c < 7%), early technology use (continuous glucose monitoring starts <1 month after diagnosis) and remote patient monitoring on glycemia in young people with newly diagnosed T1D enrolled in the Teamwork, Targets, Technology, and Tight Control (4T Study 1). Primary outcome was HbA1c change from 4 to 12 months after diagnosis; the secondary outcome was achieving the HbA1c targets. The 4T Study 1 cohort (36.8% Hispanic and 35.3% publicly insured) had a mean HbA1c of 6.58%, 64% with HbA1c < 7% and mean time in the range (70-180 mg dl-1) of 68% at 1 year after diagnosis. Clinical implementation of the 4T Study 1 met the prespecified primary outcome and improved glycemia without unexpected serious adverse events. The strategies in the 4T Study 1 can be used to implement systematic and equitable care for individuals with T1D and translate to care for other chronic diseases. ClinicalTrials.gov registration: NCT04336969 .

Limitations of 14-Day Continuous Glucose Monitoring Sampling for Assessment of Hypoglycemia and Glycemic Variability in Type 1 Diabetes

Continuous glucose monitoring (CGM) has become the standard of care in diabetes management with the recent advances in technology and accessibility in the last decade. An International Consensus was established to define CGM metrics and its goals in diabetes care. The 2019 International Consensus suggested 14 days of CGM sampling for the assessment of CGM metrics stating the limitations that may occur for hypoglycemia and glycemic variability metrics. Since then, several studies assessed the correlation between CGM metrics and duration of the sampling period. This review summarized the studies that investigated the relationship between 14-day CGM sampling to 90-day CGM data in >70% CGM users for all CGM metrics and highlighted possible solutions for more accurate CGM sampling durations in type 1 diabetes (T1D). Accumulating evidence showed that 14-day CGM sampling correlates well with 90-day CGM data for mean glucose, time in 70-180 mg/dL, and hyperglycemia metrics; however, it correlates weakly for hypoglycemia and glycemic variability metrics. In the studies included in this review, in adults with T1D, minimum sampling duration was 14 days for mean glucose, time in 70-180 mg/dL, and time in hyperglycemia (>180 and >250 mg/dL); however, minimum sampling duration varied between 21 to 30 days for time <70 mg/dL, 30 to 35 days for time <54 mg/dL, and 28 to 35 days for coefficient of variation. Longer than 14 days of CGM, sampling was required to properly assess hypoglycemia and glycemic variability in T1D.

Diabetic peripheral neuropathy and glycemic variability assessed by continuous glucose monitoring: A systematic review and meta-analysis

Continuous glucose monitoring (CGM)-derived metrics have been used to accurately assess glycemic variability (GV) to facilitate management of diabetes mellitus, yet their relationship with diabetic peripheral neuropathy (DPN) is not fully understood. We performed a systematic review and meta-analysis to evaluate the association between GV metrics and the risk of developing DPN. Nine studies totaling 3,649 patients with type 1 and type 2 diabetes mellitus were included. A significant association was found between increased GV, as indicated by metrics including standard deviation (SD) with OR and 95% CI of 2.58 (1.45-4.57), mean amplitude of glycemic excursions (MAGE) with OR and 95% CI of 1.90 (1.01-3.58), mean of daily difference (MODD) with OR and 95% CI of 2.88 (2.17-3.81) and the incidence of DPN. Our findings support a link between higher GV and an increased risk of DPN in patients with diabetes. These findings highlight the potential of GV metrics as indicators for the development of DPN, advocating for their inclusion in diabetes management strategies to potentially mitigate neuropathy risk. Longitudinal studies with longer observation periods and larger sample sizes are necessary to validate these associations across diverse populations.

Continuous glucose monitoring for the routine care of type 2 diabetes mellitus

Although continuous glucose monitoring (CGM) devices are now considered the standard of care for people with type 1 diabetes mellitus, the uptake among people with type 2 diabetes mellitus (T2DM) has been slower and is focused on those receiving intensive insulin therapy. However, increasing evidence now supports the inclusion of CGM in the routine care of people with T2DM who are on basal insulin-only regimens or are managed with other medications. Expanding CGM to these groups could minimize hypoglycaemia while allowing efficient adaptation and escalation of therapies. Increasing evidence from randomized controlled trials and observational studies indicates that CGM is of clinical value in people with T2DM on non-intensive treatment regimens. If further studies confirm this finding, CGM could soon become a part of routine care for T2DM. In this Perspective we explore the potential benefits of widening the application of CGM in T2DM, along with the challenges that must be overcome for the evidence-based benefits of this technology to be delivered for all people with T2DM.

Real-Time Continuous Glucose Monitoring in Adolescents and Young Adults With Type 2 Diabetes Can Improve Quality of Life

OBJECTIVE

Real-time continuous glucose monitoring (CGM) is effective for diabetes management in cases of type 1 diabetes and adults with type 2 diabetes (T2D) but has not been assessed in adolescents and young adults (AYAs) with T2D. The objective of this pilot interventional study was to assess the feasibility and acceptability of real-time CGM use in AYAs with T2D.

METHODS

Adolescents and young adults (13-21 years old) with T2D for six months or more and hemoglobin A1c (A1c) greater than 7%, on any Food and Drug Administration-approved treatment regimen, were included. After a blinded run-in period, participants were given access to a real-time CGM system for 12 weeks. The use and acceptability of the real-time CGM were evaluated by sensor usage, surveys, and focus group qualitative data.

RESULTS

Participants' (n = 9) median age was 19.1 (interquartile range [IQR] 16.8-20.5) years, 78% were female, 100% were people of color, and 67% were publicly insured. Baseline A1c was 11.9% (standard deviation ±2.8%), with median diabetes duration of 2.5 (IQR 1.4-6) years, and 67% were using insulin. Seven participants completed the study and demonstrated statistically significant improvement in diabetes-related quality of life, with the mean Pediatric Quality of Life inventory (PedsQL) diabetes score increasing from 70 to 75 after using CGM (P = .026). Focus group results supported survey results that CGM use among AYAs with T2D is feasible, can improve quality of life, and has the potential to modify behavior.

CONCLUSION

Real-time CGM is feasible and acceptable for AYAs with T2D and may improve the quality of life of patients with diabetes. Larger randomized controlled trials are needed to assess the effects on glycemic control and healthy lifestyle changes.

Japanese candlestick charts for diabetes

Continuous glucose monitoring (CGM) is a popular technology among the diabetic population, especially in patients with type 1 diabetes and those with type 2 diabetes treated with insulin. The American Diabetes Association recommends standardization of CGM reports with visual cues, such as the ambulatory glucose profile. Nevertheless, interpreting this report requires training and time for CGM to be cost-efficient. In this work it has been proposed to incorporate Japanese candlestick charts in glucose monitoring. These graphs are used in price analysis in financial markets and are easier to view. Each candle provides extra information to make prudent decisions since it reports the opening, maximum, minimum and closing glucose levels of the chosen time frame, usually the daily one. The Japanese candlestick chart is an interesting tool to be considered in glucose control. This graphic representation allows identification of glucose trends easily through the colors of the candles and maximum and minimum glucose values.

Association of perioperative glucose profiles assessed by continuous glucose monitoring (CGM) with prognosis in Chinese patients with non-ST-elevation acute coronary syndrome: a cohort study protocol

INTRODUCTION

Non-ST-elevation acute coronary syndrome (NSTE-ACS) remains a significant clinical concern, accounting for over 70% of acute coronary syndrome cases. One well-established risk factor for NSTE-ACS is abnormal glucose metabolism, which is associated with a poor prognosis postpercutaneous coronary intervention. Effective monitoring of blood glucose is crucial in diabetes care, as it helps identify glucose metabolic imbalances, thereby guiding therapeutic strategies and assessing treatment efficacy. Continuous glucose monitoring (CGM) provides comprehensive glucose profiles. Therefore, the study aims to use CGM to track perioperative glucose variations in NSTE-ACS patients and to determine its prognostic implications.

METHODS AND ANALYSIS

This is a multicentre, prospective observational study in a sample of patients (aged >18 years) with NSTE-ACS. A total of 1200 eligible patients will be recruited within 1 year at 6 sites in China. The primary composite endpoint will be determined as major adverse cardiovascular events (MACE) at 3 years. MACE includes all-cause mortality, non-fatal myocardial infarction, non-fatal stroke and target vessel revascularisation. Employing the CGM system, glucose levels will be continuously monitored throughout the perioperative phase. Prespecified cardiovascular analyses included analyses of the components of this composite and outcomes according to CGM-derived glucometrics at baseline.

ETHICS AND DISSEMINATION

This study has received approval from the Medical Research Ethics Committee of The First Affiliated Hospital of the University of Science and Technology of China (No. 2022KY357) and will adhere to the moral, ethical and scientific principles outlined in the Declaration of Helsinki. All participants will provide written informed consent prior to any study-related procedures. Findings from the study will be shared at conferences and published in peer-reviewed scientific journals.

TRIAL REGISTRATION NUMBER

ChiCT2300069663.

Design, rationale and protocol for Glycemic Observation and Metabolic Outcomes in Mothers and Offspring (GO MOMs): an observational cohort study

INTRODUCTION

Given the increasing prevalence of both obesity and pre-diabetes in pregnant adults, there is growing interest in identifying hyperglycaemia in early pregnancy to optimise maternal and perinatal outcomes. Multiple organisations recommend first-trimester diabetes screening for individuals with risk factors; however, the benefits and drawbacks of detecting glucose abnormalities more mild than overt diabetes in early gestation and the best screening method to detect such abnormalities remain unclear.

METHODS AND ANALYSIS

The goal of the Glycemic Observation and Metabolic Outcomes in Mothers and Offspring study (GO MOMs) is to evaluate how early pregnancy glycaemia, measured using continuous glucose monitoring and oral glucose tolerance testing, relates to the diagnosis of gestational diabetes (GDM) at 24-28 weeks' gestation (maternal primary outcome) and large-for-gestational-age birth weight (newborn primary outcome). Secondary objectives include relating early pregnancy glycaemia to other adverse pregnancy outcomes and comprehensively detailing longitudinal changes in glucose over the course of pregnancy. GO MOMs enrolment began in April 2021 and will continue for 3.5 years with a target sample size of 2150 participants.

ETHICS AND DISSEMINATION

GO MOMs is centrally overseen by Vanderbilt University's Institutional Review Board and an Observational Study Monitoring Board appointed by National Institute of Diabetes and Digestive and Kidney Diseases. GO MOMs has potential to yield data that will improve understanding of hyperglycaemia in pregnancy, elucidate better approaches for early pregnancy GDM screening, and inform future clinical trials of early GDM treatment.

TRIAL REGISTRATION NUMBER

NCT04860336.

Postprandial Glucose Control With Different Hybrid Closed-Loop Systems According to Type of Meal in Adults With Type 1 Diabetes

BACKGROUND

Hybrid Closed-Loop Systems (HCLs) may not perform optimally on postprandial glucose control. We evaluated how first-generation and advanced HCLs manage meals varying in carbohydrates, fat, and protein.

METHOD

According to a cross-sectional design, seven-day food records and HCLs reports from 120 adults with type 1 diabetes (MiniMed670G: n = 40, MiniMed780G: n = 49, Control-IQ [C-IQ]: n = 31) were analyzed. Breakfasts (n = 570), lunches (n = 658), and dinners (n = 619) were divided according to the median of their carbohydrate (g)/fat (g) plus protein (g) ratio (C/FP). After breakfast (4-hour), lunch (6-hour), and dinner (6-hour), continuous glucose monitoring (CGM) metrics and early and late glucose incremental area under the curves (iAUCs) and delivered insulin doses were evaluated. The association of C/FP and HCLs with postprandial glucose and insulin patterns was analyzed by univariate analysis of variance (ANOVA) with a two-factor design.

RESULTS

Postprandial glucose time-in-range 70 to 180 mg/dL was optimal after breakfast (78.3 ± 26.9%), lunch (72.7 ± 26.1%), and dinner (70.8 ± 27.3%), with no significant differences between HCLs. Independent of C/FP, late glucose-iAUC after lunch was significantly lower in C-IQ users than 670G and 780G (P < .05), with no significant differences at breakfast and dinner. Postprandial insulin pattern (Ins3-6h minus Ins0-3h) differed by type of HCLs at lunch (P = .026) and dinner (P < .001), being the early insulin dose (Ins0-3h) higher than the late dose (Ins3-6h) in 670G and 780G users with an opposite pattern in C-IQ users.

CONCLUSIONS

Independent of different proportions of dietary carbohydrates, fat, and protein, postprandial glucose response was similar in users of different HCLs, although obtained through different automatic insulin delivery patterns.

Research progress on the association between glycemic variability index derived from CGM and cardiovascular disease complications

Currently, glycated hemoglobin A1c (HbA1c) has been widely used to assess the glycemic control of patients with diabetes. However, HbA1c has certain limitations in describing both short-term and long-term glycemic control. To more accurately evaluate the glycemic control of diabetes patients, the continuous glucose monitoring (CGM) technology has emerged. CGM technology can provide robust data on short-term glycemic control and introduce new monitoring parameters such as time in range, time above range, and time below range as indicators of glycemic fluctuation. These indicators are used to describe the changes in glycemic control after interventions in clinical research or treatment modifications in diabetes patient care. Recent studies both domestically and internationally have shown that these indicators are not only associated with microvascular complications of diabetes mellitus but also closely related to cardiovascular disease complications and prognosis. Therefore, this article aims to comprehensively review the association between CGM-based glycemic parameters and cardiovascular disease complications by analyzing a large number of domestic and international literature. The purpose is to provide scientific evidence and guidance for the standardized application of these indicators in clinical practice, in order to better evaluate the glycemic control of diabetes patients and prevent the occurrence of cardiovascular disease complications. This research will contribute to improving the quality of life for diabetes patients and provide important references for clinical decision-making.

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

AIMS

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

METHODS

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

RESULTS

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

CONCLUSION

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