International Consensus on Use of Continuous Glucose Monitoring

Glycemic Variability and Control by CGM in Healthy Older and Young Adults and Their Relationship With Diet

Continuous glucose monitoring (CGM) might be beneficial for investigating healthy aging since high glycemic variability may increase protein glycation, oxidative stress, and inflammation, resulting in vascular damage. Additionally, CGM data on the risks for hypoglycemia and hyperglycemia are scarce, have not been analyzed by individual day and night blocks, and have not been related to diet. Therefore, this study aimed to compare glucose parameters of healthy older and young adults and the relationship with diet. Participants were 34 young (age 20-35 years) and 27 older volunteers (age 60-75 years) with a normal glycated hemoglobin A1c less than 39 mmol/mol hemoglobin, free of disorders and medication. Twenty-four CGM-derived glucose parameters measured over 5 consecutive days were analyzed for whole days and for individual daytime and nighttime blocks. Dietary intake was determined by 3-day dietary record. Neither intraday nor interday glycemic variability differed between the healthy age groups. Glycemic control was good in both age groups, but somewhat poorer in older adults. The risk of hyperglycemia was higher and of hypoglycemia lower in older adults. During the daytime, mean and minimum glucose were higher in older adults. During the nighttime, age group differences were small. The carbohydrate intake correlated positively with glycemic variability in both age groups. The protein intake correlated positively with the hypoglycemic risk in young adults, but negatively in older adults. Results suggest that healthy aging does not increase glycemic variability and the risk of hypoglycemia. The effect of diet on hypoglycemic and hyperglycemic risk might change with aging.

Continuous Glucose Monitoring in the Intensive Care Unit: A Multicenter, Retrospective Hospital-Based Analysis

BACKGROUND

There is limited experience with continuous glucose monitoring (CGM) in intensive care units (ICUs). This study examined CGM accuracy and changes during hemodynamic instability in ICU patients with COVID-19.

METHODS

We pooled data from three ICUs using CGM within a hybrid protocol combining point-of-care (POC) blood glucose testing with intermittent nonadjunctive CGM use. We compared sensor-meter agreement during lowest oxygen saturation, arterial partial pressure of oxygen (PaO2), pH, or mean arterial pressure (MAP). Linear mixed models (LMM) were used to estimate the effects of clinical condition on estimates of sensor accuracy.

RESULT

Of 169 patients, >80% had a history of diabetes, mean age was 61 ± 12 years, and 82%, 93%, and 62% received corticosteroids, mechanical ventilation and vasopressors respectively. The median percent CGM time in range (TIR, 70-180 mg/dL) was 72% (64.9-81.4), 70% (54.0-76.9), and 46% (26.6-68.5) for hospitals A, B, and C. Median time below 70 mg/dL was <0.1% for all hospitals. The absolute relative difference between CGM and POC pairs did not correlate with the lowest PaO2, oxygen saturation, pH, or mean arterial pressure. In LMM adjusting for within subject and between subject variability, patients on dialysis had higher mean absolute relative difference (MARD, [coefficient = 2.39, P = .05]), while patients on mechanical ventilation had lower MARD ventilation (coefficient = -4.33, P = .05). Of the 6783 pairs 97.3% fell within Clare zones A and B.

CONCLUSION

These preliminary findings suggest CGM use does not appear to be significantly affected during critical illness. Confirmatory accuracy studies are needed.

Narrative Review: Continuous Glucose Monitoring (CGM) in Older Adults with Diabetes

INTRODUCTION

Continuous glucose monitoring (CGM) has revolutionised diabetes care, with proven effect on glycaemic control, adverse diabetic events (such as hypoglycaemia and diabetic ketoacidosis) and hospitalisations in the general population. However, the evidence for CGM in older people is less robust.

METHOD

We conducted a narrative review of trials reporting data comparing standard blood glucose monitoring (SBGM) and CGM in adults over 65 with type 1 or type 2 diabetes who were treated with insulin published between 1999 and 2024.

RESULTS

Seventeen studies were identified, including eight retrospective cohort studies and five randomised controlled trials (RCTs). Sixteen of the 17 papers were based in Europe or North America. The studies were highly heterogeneous; however, they provided clear evidence supporting the use of CGM in reducing hypoglycemia in older adults, with potential benefits for overall wellbeing and quality of life..

CONCLUSIONS

Current approaches to diabetes care in older adults may over-rely on HbA1c (haemoglobin A1c) as a measurement of control given accuracy may be reduced in older adults and propensity for hypoglycaemia. Although goals should be personalised, avoidance of hypoglycaemia is a key goal for many older people with diabetes. There is good evidence that CGM can improve time-in-range and reduce hypoglycaemia and glucose variability in older adults. CGM should be considered for older adults as a means of reducing hypoglycaemia and associated potential harm.

Deep reinforcement learning for Type 1 Diabetes: Dual PPO controller for personalized insulin management

BACKGROUND

Managing blood glucose levels in Type 1 Diabetes Mellitus (T1DM) is essential to prevent complications. Traditional insulin delivery methods often require significant patient involvement, limiting automation. Reinforcement Learning (RL)-based controllers offer a promising approach for improving automated insulin administration.

METHODS

We propose a Dual Proximal Policy Optimization (Dual PPO) controller for personalized insulin delivery in a hybrid closed-loop system. The controller optimizes patient-specific insulin bounds through a grid search on pre-trained models to manage both hyperglycemia and hypoglycemia. A safe-control mechanism prevents insulin administration when glucose levels drop below a predefined threshold. The system was evaluated on 10 in silico adult patients using the UVA/Padova simulator, with five-day randomized meal scenarios.

RESULTS

The Dual PPO controller significantly improved Time in Range (TIR) (69.30% ±1.61) compared to a single PPO model (61.69% ±1.54). The system effectively reduced severe hyperglycemia while maintaining a low incidence of severe hypoglycemia. Unlike conventional open-loop methods such as Basal-Bolus (BBC) and Proportional-Integral-Derivative (PIDC) controllers, our system requires minimal patient interaction, eliminating the need for carbohydrate estimation.

CONCLUSIONS

The Dual PPO controller enhances personalized insulin delivery in T1DM, improving glycemic control while reducing patient burden. This approach advances precision medicine in diabetes management, with potential for future real-world applications.

Prebiotic supplementation in patients with type 1 diabetes: study protocol for a randomised controlled trial in Canada

INTRODUCTION

Type 1 diabetes (T1D) mellitus is caused by autoimmune destruction of insulin-producing beta-cells, requiring exogenous insulin to sustain life. Achieving near normal blood glucose levels with insulin, a primary goal of diabetes management, carries a significant risk of hypoglycaemia. There is compelling evidence that an abnormal gut microbiota or dysbiosis can increase intestinal permeability (IP) and contribute to dysglycaemia seen in T1D. Given that prebiotic fibre can mitigate dysbiosis, reduce IP and improve glycaemic control, we hypothesise that microbial changes induced by prebiotics contribute to gut and endocrine adaptations that reduce glucose fluctuations, including less hypoglycaemia. In a pilot study, we showed that in children who had T1D for at least 1 year, a 3-month course of prebiotic fibre significantly reduced the frequency of hypoglycaemia. The prebiotic group had an increase in Bifidobacterium with a moderate improvement in IP. Importantly, the prebiotic group maintained their serum C peptide level (marker of residual beta cell function) while the placebo group saw a drop. Given that preserving endogenous beta cell function in patients with T1D, particularly in the first year of diagnosis, reduces hypoglycaemia and glycaemic variability, we propose to examine the effect of prebiotic supplementation in patients with T1D.

METHODS AND ANALYSIS

This is a multicentre, randomised, double-blind, placebo-controlled study. Individuals (n=144) with T1D will be randomised 1:1 for 6 months to prebiotic (oligofructose-enriched inulin) or placebo (isocaloric maltodextrin). Participants will have three in-person study visits at baseline, 3 months and 6 months. The primary outcome, frequency of hypoglycaemia, will be determined from continuous glucose monitor (CGM) reports and patient blood glucose logs. Secondary outcomes will include glycaemic variability, time-in-range, glycated haemoglobin, stimulated C peptide, IP, serum inflammatory markers, quality of life and fear of hypoglycaemia ratings, as well as gut microbiome and metabolomics analysis. At 9 months, participant CGM data will be used to assess frequency of hypoglycaemia and glycaemic variability at 3 months postintervention.

ETHICS AND DISSEMINATION

The study received ethical approval from the University of Calgary Conjoint Health Research Ethics Board (REB21-0852). The University of Alberta subsite was granted ethical approval under the province of Alberta's research ethics reciprocity agreement as a participating site (REB21-0852; pSite00000066). The University of Saskatchewan subsite was granted ethical approval by the Biomedical Research Ethics Board (#4149). Trial findings will be disseminated through peer-reviewed publications and conference presentations.

TRIAL REGISTRATION NUMBER

clinicaltrials.gov NCT04963777.

Study protocol: The effect of a low-carbohydrate enteral nutrition formula on postoperative hyperglycemia in non-diabetic patients with esophageal cancer: A randomized exploratory phase II trial (ENLICHE study)

BACKGROUND

Postoperative hyperglycemia in diabetic patients is a widely known risk factor for postoperative infectious complications (PICs) after esophagectomy; however, the significance of glycemic control in non-diabetic patients is less clear. In diabetic patients, early postoperative management of esophagectomy favors low-carbohydrate enteral nutrition (EN) over standard EN to suppress the risk of glycemic spike. Our single-center, randomized phase II trial seeks to test the hypothesis that low-carbohydrate EN can suppress hyperglycemia in non-diabetic patients who undergo esophagectomy. Herewith we present the study protocol.

METHODS

A total of 50 patients will be enrolled and randomly assigned (1:1 ratio) to standard or low-carbohydrate EN. Randomization will be stratified by operation time (≥560 vs. < 560 min) and HbA1c (6.0-6.4% vs. < 6.0%). Both EN formula will be fed according to the following protocol: 400 mL/24 h on postoperative day (POD)-1; 800 mL on POD-2; 1200 mL on POD-3 and 1600 mL from POD-4 to POD-8. On POD-9, oral food intake will be initiated. A continuous glucose monitoring (CGM) device will be used to monitor blood glucose levels from POD-1 to -8. The primary outcome is the mean time-in-range (TIR) across the 48 h from POD-1 to -2. TIR is defined as the percentage-time that blood glucose remains within the targeted range of 70-180 mg/dL. The primary analysis will calculate the least squares mean difference in TIR over the 48 h (POD-1 to -2) between the two groups, with p-values calculated to test the null hypothesis that the mean difference between the groups is zero. The secondary outcomes will be as follows: 1) the incidence of PICs and/or other adverse events within 30 days after esophagectomy or during the hospital stay; 2) the number of cases requiring any dose alteration in EN formula during monitoring; 3) the number of cases requiring interventions for hyperglycemia or hypoglycemia; 4) the rates in change of nutritional indicators, such as serum albumin, prealbumin, and total protein levels, during the post-surgical hospital stay (vs. those values on the day of admission); and 5) the following CGM indices in relation to the incidence rate of PICs within 30 days after esophagectomy: the mean values for time-above-range (TAR), area under the curve (AUC), and TIR for each POD or from POD-1 to -8. TAR is defined as the percentage of time of a patient is recorded as having hyperglycemia (>blood glucose level of 180 mg/dL), and is indicative of the frequency and duration of hyperglycemia. AUC, which identifies periods of hyperglycemia and provides a comprehensive picture of glucose variability and control in diabetes management, is defined as the area under the curve over blood glucose level of 180 mg/dL on CGM monitoring.

DISCUSSION

This study is the first to investigate the impact of a low-carbohydrate EN formula on hyperglycemic control during perioperative nutritional management of esophageal cancer. These results will help to outline whether glycemic control should be also considered for non-diabetic patients during hospital care.

TRIAL REGISTRATION

This trial has been registered in the Japanese Registry of Clinical Trials (jRCTs031240081).

Artificial pancreas: the past and the future

In glucose management using continuous insulin infusion, artificial pancreas systems prevent blood glucose fluctuations and severe hypoglycemia using insulin pumps and continuous glucose monitoring. Advances in both insulin pumps and continuous glucose monitoring have enabled the transition from sensor augmented pump therapy, where insulin delivery is manually adjusted, to hybrid closed-loop insulin pump therapy, which automatically adjusts basal insulin infusion. Furthermore, fully automated insulin delivery systems that adjust insulin based on variations due to meals and exercise are now possible. These systems have been primarily applied to patients with type 1 diabetes but are now expanding to all insulin-dependent patients. Wearable artificial pancreas systems measure glucose levels in subcutaneous tissue fluid, while bedside artificial pancreas systems measure glucose levels in venous blood, making them suitable for managing the highly variable blood glucose levels of perioperative and critically ill patients. Future developments are anticipated to integrate the benefits of both wearable and bedside systems.

Breaking up prolonged sedentary behavior to improve cardiometabolic health (BREAK2): protocol for a dose-finding adaptive randomization trial

BACKGROUND

Excessive sedentary behavior is highly prevalent in developed nations and is a risk factor for cardiovascular disease (CVD) morbidity and mortality. As such, health agencies have provided general recommendations to "sit less, move more" by interspersing brief periods of activity. However, a lack of empirical evidence describing how often (e.g. every 30 min, every 60 min) and for how long (e.g., 1 min activity bouts, 5 min activity bouts) sedentary time should be interrupted (a "sedentary break") to yield health benefit has precluded more quantitative, actionable guidelines. To date, rigorous and methodical dose escalation experiments have not been conducted to elucidate effective and tolerated sedentary break doses. The objective of the proposed study is to determine the minimally effective dose (e.g., the lowest dose) for two elements of a sedentary break (frequency and duration) that yields improvements in established CVD risk factors. The maximally tolerated dose (e.g. the highest dose that does not cause undue physical/psychological distress) for both frequency and duration of sedentary breaks will also be determined.

METHODS

This study is a randomized crossover trial conducted under laboratory conditions among 324 adults without chronic medical conditions. Participants complete two trial conditions (8 h each), a sedentary break (intervention) condition and an uninterrupted sitting (control) condition, in a randomized order. The sedentary break condition consists of 1 of 25 possible frequency/duration combinations (e.g. walk every 30 min for 5 min), selected according to a Bayesian adaptive randomization method. Primary outcomes used to inform the adaptive randomization are glucose and blood pressure serially assessed over each trial condition. Constructs of dose toxicity (tolerability, safety, physical exhaustion/fatigue, and affect) are also serially assessed.

DISCUSSION

This study will elucidate the minimally effective and maximally tolerated frequency and duration of a sedentary break that yields improvements in established CVD risk factors, information important for the development of quantitative sedentary behavior guidelines.

TRIAL REGISTRATION

This trial has been registered with the Clinical Trials Registry maintained by the National Library of Medicine at the National Institutes of Health on April 29, 2022. The registration ID is NCT05353322.

Accuracy and Safety of a Novel Intravenous Continuous Glucose Monitoring System in Patients Admitted to a Cardiothoracic ICU: A Pilot Trial

BACKGROUND

In critically ill patients, deviations in glucose levels may lead to significant harm to individuals with and without diabetes. Although subcutaneous continuous glucose monitoring (scCGM) has proven beneficial for patients in standard wards, its implementation in critical care settings has been limited due to multiple factors, potentially resulting in inadequate glycemic control and consequent complications; here, intravascular systems (ivCGM) have the potential to overcome these limitations.

METHOD

This single-center, open-label study, aimed to assess accuracy and safety of a novel intravenous glucose monitoring system in patients with and without diabetes, admitted to a cardiothoracic surgery intensive care unit. Glucose levels were continuously monitored for up to 72 hours in the predefined glucose range of 20 to 400 mg/dL and compared with arterial glucose measurements (blood gas analyses [BGAs]).

RESULTS

Twenty-eight participants successfully completed the study, allowing the collection of 1224 ivCGM/BGA data pairs. Due to the exploratory nature of the trial in this vulnerable patient population, no data pairs <70 mg/dL and limited data pairs in level 2 hyperglycemia (>250 mg/dL) were observed. A mean absolute relative difference (MARD) of 8.7 ± 7.8% was found, whereas the mean absolute difference (MAD) for values <100 mg/dL was 3.3 ± 2.7 mg/dL. In participants with diabetes (N = 8332 ivCGM/BGA data pairs), MARD was 9.6 ± 8.1%. Diabetes Technology Society Error Grid (DTSEG) analysis revealed that all data pairs fell within clinically acceptable zones A and B. Notably, no serious adverse events associated with the device were observed during the study.

CONCLUSION

The present findings indicate that the investigated intravenous glucose monitoring system provides accurate glucose monitoring and demonstrates its safety in critical care settings. This technology offers promise for improved glycemic management in critically ill patients, particularly those with diabetes, potentially mitigating the associated risks and complications.

The effect of sleep quality on glucose variability among adolescents with type 1 diabetes in China: A multi-central temporal longitudinal association analysis

STUDY OBJECTIVES

High glucose variability is common among adolescents with type 1 diabetes mellitus (T1DM). On person level, poor sleep quality has been reported to be an important factor associated with high glucose variability among adolescents with T1DM. However, on day level, the effect of sleep quality on glucose variability remains unclear, limiting temporal adjustment of treatment regimens. This study aimed to concurrently explore associations between sleep quality and glucose variability at both day and person levels among Chinese adolescents with T1DM based on the 24-h Recursive Cycle model.

METHODS

A multi-center, seven-day, temporal longitudinal study was conducted among Chinese adolescents with T1DM. Glucose variability measures were calculated by fingertip blood glucose level at least seven times a day. Subjective sleep quality was measured by total sleep time, wake after sleep onset, number of awakenings, a score of sleep quality, sleep time, and wake time using a sleep diary. Objective sleep quality was assessed by Fitbit Inspire HR and included total sleep time, wake after sleep onset, and number of awakenings, rapid eye movement, light sleep time, deep sleep time, sleep time, wake time, sleep midpoint, and sleep efficiency. A multilevel linear regression model was performed to examine the associations between objective and subjective sleep quality and glucose variability at day and person levels. Gender, age, diabetes duration, complications documented within the preceding 6 months, HbA1c, and insulin pump therapy were controlled at person-level model.

RESULTS

A total of 51 adolescents with T1DM participated in this study, which included 357 records of data. Only 21.57 % (N = 12) of adolescents met the recommended sleep time of 480 min per night measured by Fitbit. About a quarter (N = 11) of adolescents had a coefficient of variation of blood glucose >36 %. At person level, there was no significant association between sleep quality and glucose variability (p > 0.05). Multilevel models found significant associations between sleep quality and glucose variability at day level. Lower score of subjective sleep quality was significantly associated with higher standard deviation of blood glucose (p < 0.05) in the next day. Less Fitbit-measured light sleep time was significantly associated with higher standard deviation of blood glucose, and postprandial of glycemic excursions in the next day (p < 0.05). More Fitbit-measured awakenings and less rapid eye movement were associated with higher postprandial glycemic excursions in the next day (p < 0.05).

CONCLUSION

Nearly 80 % of Chinese adolescents with T1DM did not meet the recommended amount of sleep for their age group. They experienced more wakes after sleep onset at night and poorer sleep quality than their subjective experience. According to the findings on temporal relationships, intervention components targeting reducing wakes after sleep onset and improving subjective sleep quality would reduce glucose variability over time among Chinese adolescents with T1DM.

Glycemic Control in Patients with Diabetes on Peritoneal Dialysis: From Glucose Sparing Approach to Glucose Monitoring

Optimized glycemic management is crucial for controlling atherosclerosis and consequent cardiovascular morbidity in patients with diabetes. Due to the continuous glucose burden from glucose-containing peritoneal dialysis (PD) solutions, PD patients with diabetes experience difficulties in glucose level regulation with glucose hypervariability and worsening dyslipidemia. Even in non-diabetic PD patients, glucose-containing PD solutions aggravate insulin resistance and cause overweight. Additionally, glucose degradation products (GDP) from glucose-based PD solutions provoke oxidative stress and complex inflammatory processes, leading to chronic deleterious and fibrotic peritoneal membrane changes. In this narrative review, we searched the literature using PubMed, MEDLINE, and Google Scholar over the last three decades to summarize the most important facts relevant to the presented issues, aiming to inform both endocrinologists and nephrologists in providing the best currently available care for people with diabetes on PD. We not only focus on adequate tailoring of insulin therapy adapted at the time of PD exchange with hypertonic glucose solution., but also emphasize the use of continuous glucose monitoring (CGM) that allows assessment of mean glucose values and time spent in normal, hypo, and hyperglycemia. However, the routine use of CGM in PD patients is limited due to high cost, and hemoglobin A1c (HbA1c) analysis is still recommended as a basic clinical tool for the assessment of glycemic control. Possible choices of antidiabetic drugs were considered given the narrowed choice due to contraindications for metformin and sulfonylurea. The other important therapeutic approach in PD patients with diabetes is using glucose-sparing PD regimens based on icodextrin and amino acid PD solutions with the addition of just one or two bags of low glucose concentration PD solution daily. This glucose-sparing approach not only reduces the glucose load and improves glycoregulation with correction of the lipid profile but also maintains the viability of the peritoneal membrane by reducing the harmful effects of GDPs.

Assessing the Accuracy of Continuous Glucose Monitoring Metrics: The Role of Missing Data and Imputation Strategies

Aim: This study aims to evaluate the accuracy of continuous glucose monitoring (CGM)-derived metrics, particularly those related to glycemic variability, in the presence of missing data. It systematically examines the effects of different missing data patterns and imputation strategies on both standard glycemic metrics and complex variability metrics. Methods: The analysis modeled and compared the effects of three types of missing data patterns-missing completely at random, segmental, and block-wise gaps-with proportions ranging from 5% to 50% on CGM metrics derived from 14-day profiles of individuals with type 1 and type 2 diabetes. Six imputation strategies were assessed: data removal, linear interpolation, mean imputation, piecewise cubic Hermite interpolation, temporal alignment imputation, and random forest-based imputation. Results: A total of 933 14-day CGM profiles from 468 individuals with diabetes were analyzed. Across all metrics, the coefficient of determination (R2) improved as the proportion of missing data decreased, regardless of the missing data pattern. The impact of missing data on the agreement between imputed and reference metrics varied depending on the missing data pattern. To achieve high accuracy (R2 > 0.95) in representing true metrics, at least 70% of the CGM data were required. While no imputation strategy fully compensated for high levels of missing data, simple removal outperformed others in most scenarios. Conclusion: This study examines the impact of missing data and imputation strategies on CGM-derived metrics. The findings suggest that while missing data may have varying effects depending on the metric and imputation method, removing periods without data is a general acceptable approach.

Time-divided hypoglycemia management derived by continuous glucose monitoring may reduce microvascular diseases in type 2 diabetes patients

PURPOSE

To assess the association between hypoglycemia exposure and the risk of developing microvascular diseases (MVDs) in type 2 diabetes patients via continuous glucose monitoring (CGM), aiming to identify optimal hypoglycemia thresholds for improved glucose management.

METHODS

A total of 174 type 2 diabetes patients underwent 2 weeks of CGM. Binary multivariate logistic regression assessed the association between hypoglycemia exposure and MVDs prevalence at thresholds of 3.0-3.9 mmol/L.

RESULTS

After 14 days of monitoring, 79.9% of patients experienced hypoglycemia. The MVDs group had a significantly greater percentage of participants with hypoglycemia (glucose < 3.4 mmol/L: 57.6 vs. 39.8%, P = 0.023) and nocturnal hypoglycemia (glucose < 3.9 mmol/L: 62.1 vs. 46.3%, P = 0.043), with no significant difference in daytime hypoglycemia. After adjustment for confounders, only nocturnal hypoglycemia was independently associated with the risk of developing MVDs, and the prevalence of MVDs was positively associated with hypoglycemia severity. When the hypoglycemia threshold was reduced from 3.4-3.0 mmol/L, the odds ratio increased from 1.342 (1.026, 1.798) to 1.855 (1.141, 3.457). Further subgroup analysis revealed that nocturnal hypoglycemia below 3.4 mmol/L was a risk factor for the development of retinopathy and neuropathy (P < 0.05) but not for nephropathy.

CONCLUSION

Nighttime glucose levels below 3.4 mmol/L are strongly associated with a greater risk of developing retinopathy and neuropathy in type 2 diabetes patients. Thus, blood glucose management should be time-specific, with daytime levels maintained above 3.9 mmol/L and nighttime levels maintained above 3.4 mmol/L.

Time in range-A new gold standard in type 2 diabetes research?

Glycated haemoglobin (HbA1c) is currently the gold standard outcome measure for type 2 diabetes trials. Time in range is a continuous glucose monitoring (CGM) metric defined as the proportion of time in euglycemia (3.9-10.0 mmol/L) and may be valuable not only in type 1 diabetes clinical trials but also as an endpoint in type 2 diabetes trials. This narrative review aimed to assess the relative merits of time in range versus HbA1c as outcome measures for type 2 diabetes studies. It reviews the strengths and limitations of time in range as an outcome measure and evaluates studies in type 2 diabetes that have used time in range as a primary or secondary outcome measure. A literature search was conducted on PubMed and MEDLINE databases using key terms "time in range" AND "diabetes" OR "type 2 diabetes mellitus". Further evidence was obtained from relevant references of retrieved articles. Literature search identified 247 papers, of which 110 were included in this review. These included a broad range of articles, including 45 randomized trials using time in range as an outcome measure in patients with type 2 diabetes, as well as papers validating time in range. Time in range provides valuable and clinically relevant information and should be used as an important endpoint in type 2 diabetes in clinical trial settings, in conjunction with HbA1c.

Gaming the System: A Fun Continuous Glucose Monitor Interface Improves Glycemic Outcomes for Children

Achieving optimal glycemic control in children with type 1 diabetes (T1D) is challenging even when wearing a continuous glucose monitor (CGM). We measured the impact of eddii, a gamified real-time app connected to a CGM, on glycemic control. An open label, eight-week randomized controlled trial (RCT) compared glycemic control utilizing the gamified CGM app connected to Dexcom G6 with only Dexcom G6 usage. Children with T1D using Dexcom G6 were enrolled (N=92, ages 5-12 years). Time-in-range (TIR) data were collected four weeks prior to and during the study period. The gamified CGM app utilization effect was measured by difference-in-difference (D-I-D) models. The TIR and time-above-range (TAR) improved among users of the gamified CGM app; 5.38% higher and 5.80% lower than controls (P = .001 and P = .019, respectively).

Treatment satisfaction and time in range after 16 weeks of treatment with iGlarLixi in insulin-naive adults with suboptimally controlled type 2 diabetes

AIMS

In Soli-CGM, treatment with iGlarLixi (insulin glargine 100 U/mL and lixisenatide 33 μg/mL) in insulin-naive adults with suboptimally controlled type 2 diabetes (T2D; haemoglobin A1c 9%-13% on ≥2 oral antihyperglycaemic agents (OADs) ± glucagon-like peptide-1 receptor agonist (GLP-1 RA) therapy) increased time in range (TIR; primary endpoint) from 26.4% at baseline to 52.7% at Week 16. This exploratory analysis examined the impact of treatment with iGlarLixi on patient-reported treatment satisfaction.

MATERIALS AND METHODS

Soli-CGM was a single-arm, 16-week, multicentre, interventional, open-label, phase 4 study using blinded continuous glucose monitoring (CGM; FreeStyle Libre Pro) to assess glycaemic metrics (N = 124). CGM data were collected for a 2-week period before initiation of iGlarLixi, and after treatment with iGlarLixi (Weeks 14-16). Treatment satisfaction was assessed using the Diabetes Medication Treatment Satisfaction Tool (DM-SAT, which comprises four domains: well-being, medical control, lifestyle and convenience), at baseline and end-of-treatment. Association of TIR and overall satisfaction (sum of all items) was also assessed.

RESULTS

Overall, 118 (95.9%) and 107 (87.0%) participants completed the DM-SAT at baseline and Week 16, respectively. Mean overall score increased by 0.18, from 0.59 (baseline) to 0.78 (Week 16). A trend in improvement in score was observed in all domains. Improvement in TIR had a positive, but weak, trend of association with improvement in overall treatment satisfaction (mean r = 0.14).

CONCLUSIONS

In people with T2D suboptimally controlled on ≥2 OADs ± GLP-1 RA, 16 weeks' treatment with iGlarLixi resulted in a trend of improvement in treatment satisfaction.

The Development and Potential Applications of an Automated Method for Detecting and Classifying Continuous Glucose Monitoring Patterns

INTRODUCTION

Continuous glucose monitoring (CGM) is emerging as a transformative tool for helping people with diabetes self-manage their glucose and supporting clinicians in effective treatment. Unfortunately, many CGM users, and clinicians, find interpreting the large volume of CGM data to be overwhelming and complex. To address this challenge, an efficient, intelligent method for detecting and classifying discernable patterns in CGM data was desired.

METHODS

We developed an automated artificial intelligence (AI)-driven method to detect and classify different discernable CGM patterns which called "CGM events." We trained different models using 60 days of CGM data from 27 individuals with diabetes from a publicly available data set and then evaluated model performance using separate test data from the same group. Each event is classified according to clinical significance based on three parameters: (1) glucose category at or near the beginning of the CGM event; (2) a calculated severity score that encompasses both signal shape and temporal characteristics (e.g., how high the CGM curve goes (measured in mg/dL) and how long it stays above target (as established by published consensus guidelines); and (3) the glucose category at or near the end of the event.

RESULTS

The system accurately detected and classified events from actual CGM data. This was also validated with expert diabetes clinicians.

CONCLUSIONS

Advanced pattern recognition methods can be used to detect and classify CGM events of interest and may be used to provide actionable insights and self-management support to CGM users and decision support to the clinicians caring for them.

Dynamic Relationships Among Continuous Glucose Metrics and Momentary Cognitive Performance in Diverse Adults With Type 1 Diabetes

OBJECTIVE

Despite established relationships between glycemia and cognition, few studies have evaluated within-person changes over time. We paired continuous glucose monitoring (CGM) with ambulatory cognitive testing to examine bidirectional associations among adults with type 1 diabetes (T1D).

RESEARCH DESIGN AND METHODS

Participants wore blinded CGM and completed ambulatory tests of perceptual speed and sustained attention five or six times daily for 14 days. CGM metrics were calculated over 3-h periods (mean glucose, %time in range [70-180 mg/dL], %time in low [<70 mg/dL], %time in high [181-250 mg/dL], %time in very high [>250 mg/dL], and coefficient of variation). Immediate glucose values within 15 min of cognitive assessments were also examined. Dynamic structural equation models evaluated bidirectional relationships over sequential 3-h periods.

RESULTS

Among 182 diverse adults with T1D (age 40 ± 14 years, 46% male, 41% Latino, 29% White, 15% Black), more time in low glucose over 3 h was associated with slower perceptual speed at the end of that interval (P < 0.05) but not 3 h later. More time in high glucose (>250 mg/dL) was associated with faster perceptual speed initially but slower speed 3 h later (P < 0.05). Physical activity partially mediated the effect of high glucose on slower perceptual speed. Glycemia did not predict attention scores within persons. Lower attention and higher perceptual speed predicted higher mean glucose and more time in very high glucose over the following 3 h (P < 0.05).

CONCLUSIONS

These novel observations of significant bidirectional association between glycemia and cognitive performance over the course of the day among adults with T1D emphasize the importance of examining within-person longitudinal effects over different time frames.

Characterizing insulin dosing behaviour and glycaemic excursions: Development of metrics using connected insulin pen and continuous glucose monitoring

AIMS

Connected insulin pens (CIPs) provide insulin dosing data that can be leveraged to drive improvements in glycaemic control. To realize this potential in routine care, insulin data need to be distilled into actionable insights for clinicians. We describe two algorithms for detecting glucose excursions using continuous glucose monitoring (CGM) data and then link excursions to CIP data to derive "insulin metrics" characterizing suboptimal bolus dosing practices.

MATERIALS AND METHODS

This post hoc analysis used CGM and CIP data from a 12-week observational study (clinicaltrials.gov: NCT03368807) of 64 adults with type 1 or type 2 diabetes receiving multiple daily injection insulin therapy and glycated haemoglobin ≥8%. Two updated algorithms were applied to analyse glucose excursions associated with pre-meal boluses, missed bolus doses (MBDs), delayed boluses and correction boluses.

RESULTS

Glycaemic metrics obtained using both algorithms were similar. Time in range (%TIR) was lower, and time above range (%TAR) and glycaemic variability (%GV) were higher, on days with MBDs. Compared with pre-meal boluses, delayed and correction boluses were followed by glucose excursions with larger change in glucose, longer duration with glucose >180 mg/dL, lower post-excursion %TIR and higher post-excursion %TAR; excursions following MBDs showed lower %TIR, higher %TAR and lower percent recovery. Glucose excursions were larger and longer when CGM was masked than when unmasked.

CONCLUSIONS

This analysis demonstrates "insulin metrics"-characterizations of insulin dosing behaviour derived from integrated CGM and CIP data-and provides a foundation for future work that will expand the understanding of an individual's insulin self-administration practices and improve clinical decision-making.

Perioperative Fully Closed-loop Versus Usual Care Glucose Management in Adults Undergoing Major Abdominal Surgery: A Two-centre Randomized Controlled Trial

OBJECTIVE

To assess the efficacy and safety of fully closed-loop (FCL) compared with usual care (UC) glucose control in patients experiencing major abdominal surgery-related stress hyperglycemia.

BACKGROUND

Major abdominal surgery-related stress and periprocedural interventions predispose to perioperative hyperglycemia, both in diabetes and non-diabetes patients. Insulin corrects hyperglycemia effectively, but its safe use remains challenging.

METHODS

In this two-centre randomized controlled trial, we contrasted subcutaneous FCL with UC glucose management in patients undergoing major abdominal surgery anticipated to experience prolonged hyperglycemia. FCL (CamAPS HX, Dexcom G6, mylife YpsoPump 1.5x) or UC treatment was used from hospital admission to discharge (max 20 d). Glucose control was assessed using continuous glucose monitoring (masked in the UC group). The primary outcome was the proportion of time with sensor glucose values in a target range of 5.6 to 10.0 mmol/L.

RESULTS

Thirty-seven surgical patients (54% pancreas, 22% liver, 19% upper gastrointestinal, 5% lower gastrointestinal), of whom 18 received FCL and 19 UC glucose management, were included in the analysis. The mean ± SD percentage time with sensor glucose in the target range was 80.1% ± 10.0% in the FCL and 53.7% ± 19.7% in the UC group ( P < 0.001). Mean glucose was 7.5 ± 0.5 mmol/L in the FCL and 9.1 ± 2.4 mmol/L in the UC group ( P = 0.015). Time in hypoglycemia (<3.0 mmol/L) was low in either group. No study-related serious adverse events occurred.

CONCLUSIONS

The FCL approach resulted in significantly better glycemic control compared with UC management, without increasing the risk of hypoglycemia. Automated glucose-responsive insulin delivery is a safe and effective strategy to minimize hyperglycemia in complex surgical populations.

Long-Term Performance of Two Systems for Automated Insulin Delivery in Adults With Type 1 Diabetes: An Observational Study

AIMS

To compare glycaemic outcomes for two automated insulin delivery (AID) systems, the Tandem Control IQ (CIQ) and the MiniMed 780G (MM780G).

MATERIAL AND METHODS

In this observational study, we evaluated 60 days of glycaemic data from 139 persons with type 1 diabetes (CIQ: 79 persons, MM780G: 60 persons), who had an active glucose sensor time ≥ 85%.

RESULTS

The time with AID was median 620 (IQR, 439-755) days for CIQ users and 509 (429-744) days for MM780G users (p = 0.26). The last HbA1c before initiation of AID was 59.7 mmol/mol in CIQ and 60.1 mmol/mol in MM780G (p = 0.88). The time with an active glucose sensor was higher for CIQ than MM780G (median 98.5 (97.4-98.0)% vs. 96.5 (94.9-97.0)%, p < 0.001). Time in range (TIR, glucose 3.9-10.0 mmol/L) was lower in CIQ than MM780G (mean 68.9% ± 11.4% vs. 73.7% ± 12.0%, p = 0.02) as was time in tight range (TITR) (glucose 3.9-7.8 mmol/L) (43.0% ± 12.2% vs. 48.4% ± 12.7%, p = 0.01). The difference in TIR (4.2 (95% CI 1.0-7.5)%, p = 0.01) and TITR (5.0 (1.4-8.6)%, p < 0.01) remained statistically significant in a multiple regression model controlling for various baseline variables. Time with an absolute rate of glucose change > 1.5 mmol/L/15 min was higher in CIQ than MM780G (9.4 (IQR, 7.2-13.3)% vs. 7.4 (5.2-10.4)%, p < 0.001).

CONCLUSIONS

The CIQ system had a higher active glucose sensor time but a lower TIR, TITR, and a higher time with a rapid glucose rate of change than the MM780G system.

A pretrained transformer model for decoding individual glucose dynamics from continuous glucose monitoring data

Continuous glucose monitoring (CGM) technology has grown rapidly to track real-time blood glucose levels and trends with improved sensor accuracy. The ease of use and wide availability of CGM will facilitate safe and effective decision making for diabetes management. Here, we developed an attention-based deep learning model, CGMformer, pretrained on a well-controlled and diverse corpus of CGM data to represent individual's intrinsic metabolic state and enable clinical applications. During pretraining, CGMformer encodes glucose dynamics including glucose level, fluctuation, hyperglycemia, and hypoglycemia into latent space with self-supervised learning. It shows generalizability in imputing glucose value across five external datasets with different populations and metabolic states (MAE = 3.7 mg/dL). We then fine-tuned CGMformer towards a diverse panel of downstream tasks in the screening of diabetes and its complications using task-specific data, which demonstrated a consistently boosted predictive accuracy over direct fine-tuning on a single task (AUROC = 0.914 for type 2 diabetes (T2D) screening and 0.741 for complication screening). By learning an intrinsic representation of an individual's glucose dynamics, CGMformer classifies non-diabetic individuals into six clusters with elevated T2D risks, and identifies a specific cluster with lean body-shape but high risk of glucose metabolism disorders, which is overlooked by traditional glucose measurements. Furthermore, CGMformer achieves high accuracy in predicting an individual's postprandial glucose response with dietary modelling (Pearson correlation coefficient = 0.763) and helps personalized dietary recommendations. Overall, CGMformer pretrains a transformer neural network architecture to learn an intrinsic representation by borrowing information from a large amount of daily glucose profiles, and demonstrates predictive capabilities fine-tuned towards a broad range of downstream applications, holding promise for the early warning of T2D and recommendations for lifestyle modification in diabetes management.

Perioperative use of flash glucose monitoring system in dogs undergoing general surgery: a clinical feasibility study

IMPORTANCE

Flash Glucose Monitoring System (FGMS; FreeStyle Libre) enables non-invasive, continuous glucose monitoring. However, its perioperative utility in canine surgeries has not been evaluated. This study highlights the feasibility of FGMS in perioperative glucose management for veterinary patients.

OBJECTIVE

This study aimed to evaluate the clinical applicability and accuracy of FGMS in perioperative glucose monitoring in dogs.

METHODS

Thirty-two dogs that underwent surgery were monitored during the preoperative, intraoperative, and postoperative phases using FGMS and portable blood glucose meter (PBGM).

RESULTS

A total of 224 paired glucose measurements were analyzed for correlation and accuracy using ISO 15197:2013 standards. Spearman's correlation analysis revealed positive correlation between FGMS and PBGM (ρ = 0.894, ρ = 0.823, and ρ = 0.795, respectively) during the preoperative, intraoperative, and postoperative phases, respectively (p < 0.001). The Bland-Altman plot of the difference between FGMS and PBGM showed a positive bias during the intraoperative phase (23.3 mg/dL), despite a negative bias in the preoperative (-16.0 mg/dL) and postoperative (-4.58 mg/dL) phases. Parkes consensus error grid analysis demonstrated that 100% of the data pairs fell within clinically acceptable zones (zones A and B) throughout the operation.

CONCLUSIONS AND RELEVANCE

FGMS provides continuous and noninvasive glucose monitoring in dogs with clinical feasibility throughout the perioperative period, although its accuracy decreases during the intraoperative phase. For patients with relatively stable blood glucose levels, the Libre device is clinically effective for perioperative glucose monitoring and provides a simpler and more accessible alternative to traditional methods.

Extended use of real-time continuous glucose monitoring in adults with insulin-requiring type 2 diabetes: Results from the first 26 weeks of the 2GO-CGM trial

AIMS

The first 26 weeks of the 2GO-CGM trial assessed the efficacy and safety of real-time continuous glucose monitoring (rtCGM) use within a supported specialist model of care in a cohort of community-based adults with insulin-requiring type 2 diabetes in New Zealand.

METHODS

A 26-week randomised one-way crossover 'waitlist-controlled' trial comparing rtCGM (Dexcom G6) with self-monitoring of blood glucose (SMBG). All participants completed 2 weeks of SMBG before being randomised to 12 weeks (phase 1) use of SMBG followed by 12 weeks (phase 2) use of rtCGM (Group A) or 24 weeks of rtCGM (Group B). A time-adjusted within-subject analysis was conducted to estimate the overall treatment effect of rtCGM versus SMBG.

RESULTS

Sixty-seven participants were randomised to Group A or B, and all were included in the analysis (53% indigenous Māori, 57% female, median age 53 [range 16-69] years). Baseline-adjusted mean time in range (3.9-10.0 mmol/L) was 15% (95% CI 10-20; p = <0.001) higher with rtCGM use versus SMBG use. There was no evidence of a difference in Hba1c between rtCGM and SMBG use (-3.4 mmol/mol [0.31%], 95% CI -9.4 to 2.7 mmol/mol [-0.86 to 0.24%], p = 0.27). One participant withdrew in phase 2 due to unmanageable skin reactions to the CGM device. There were no severe hypoglycaemia or ketoacidosis events in either group during the study.

CONCLUSIONS

Use of rtCGM demonstrates safe and sustained glycaemic improvement in rtCGM use with insulin-requiring type 2 diabetes during the first 26 weeks of the 2GO-CGM study.

Realigning diabetes regimens in older adults: a 4S Pathway to guide simplification and deprescribing strategies

Treating older people with diabetes is challenging due to multiple medical comorbidities that might interfere with patients' ability to perform self-care. Most diabetes guidelines focus on improving glycaemia through addition of medications, but few address strategies to reduce medication burden for older adults-a concept known as deprescribing. Strategies for deprescribing might include stopping high-risk medications, decreasing the dose, or substituting for less harmful agents. Accordingly, glycaemic management strategies for older adults with type 1 and type 2 diabetes not responding to their current regimen require an understanding of how and when to realign therapy to meet patient's current needs, which represents a major clinical practice gap. With the gap in guidance on how to deprescribe or otherwise adjust therapy in older adults with diabetes in mind, the International Geriatric Diabetes Society, an organisation dedicated to improving care of older individuals with diabetes, convened a Deprescribing Consensus Initiative in May, 2023, to discuss Optimization of diabetes treatment regimens in older adults: the role of de-prescribing, de-intensification and simplification of regimens. The recommendations from this group initiative are discussed and described in this Review.

Seasonal, weekly, and individual variations in long-term use of wearable medical devices for diabetes management

Wearable medical-grade devices are transforming the standard of care for prevalent chronic conditions like diabetes. Yet, adoption and long-term use remain a challenge for many people. In this study, we investigate patterns of consistent versus disrupted use of continuous glucose monitors (CGMs) through analysis of more than 118,000 days of data, with over 22 million blood glucose samples, from 108 young adults with type 1 diabetes (average: 3 years of CGM data per person). In this population, we found more consistent CGM use at the start and end of the year (e.g., January, December), and more disrupted CGM use in the middle of the year/warmer months (i.e., May to July). We also found more consistent CGM use on weekdays (Monday to Thursday) and during waking hours (6AM - 6PM), but more disrupted CGM use on weekends (Friday to Sunday) and during evening/night hours (7PM - 5AM). Only 52.7% of participants (57 out of 108) had consistent and sustained CGM use over the years (i.e., over 70% daily wear time for more than 70% of their data duration). From semi-structured interviews, we unpack factors contributing to sustained CGM use (e.g., easier and better blood glucose management) and factors contributing to disrupted CGM use (e.g., changes in insurance coverage, issues with sensor adhesiveness/lifespan, and college/life transitions). We leverage insights from this study to elicit implications for next-generation technology and interventions that can circumvent seasonal and other factors that disrupt sustained use of wearable medical devices for the goal of improving health outcomes.

Assessing the predictive value of time-in-range level for the risk of postoperative infection in patients with type 2 diabetes: a cohort study

Aim

To analyze the correlation between preoperative time-in-range (TIR) levels and postoperative infection in patients with type 2 diabetes mellitus (T2DM) and to evaluate the value of the TIR as a predictor of postoperative infection in patients with T2DM.

Methods

A total of 656 patients with T2DM during the perioperative period were divided into a TIR standard group (TIR≥70%) and a TIR nonstandard group (TIR<70%) according to the TIR value. Modified Poisson regression was used to analyze postoperative risk factors in patients with T2DM. All patients were subsequently divided into a training set and a validation set at a ratio of 7:3. LASSO regression and the Boruta algorithm were used to screen out the predictive factors related to postoperative infection in T2DM patients in the training set. The discrimination and calibration of the model were evaluated by the area under the receiver operating characteristic curve (ROC) and calibration curve, and the clinical net benefit of the model was evaluated and verified through the decision analysis (DCA) curve. Finally, a forest plot was used for relevant subgroup analysis.

Results

Modified Poisson regression analysis revealed that the TIR was a risk factor for postoperative infection in T2DM patients, and when the TIR was <70%, the risk of postoperative infection increased by 52.2% (P <0.05). LASSO regression and Boruta algorithm screening variables revealed that the TIR, lymphocytes, neutrophils, total serum cholesterol, superoxide dismutase and type of incision were predictive factors for postoperative infection in patients with T2DM (P<0.05). The calibration curve confirmed that the model predictions were consistent with reality, and the decision curve confirmed that the model had better clinical benefits. Finally, the results of the subgroup analysis revealed that in each subgroup, the risk of postoperative infection was greater when the TIR was <70% than when the TIR was ≥70%, and there was no interaction between subgroups.

Conclusion

The TIR is related to postoperative infection and can be used as a new indicator to predict the risk of postoperative infection in patients with type 2 diabetes mellitus.

Predicting High Glycemia Risk Index Trajectory in Individuals With Type 1 Diabetes and Long-term Continuously Glucose Monitoring

The glycemia risk index (GRI) is an emerging metric designed to quantify the risk of both hypo- and hyperglycemia, providing a combined assessment of glycemic control quality. A high GRI is associated with an increased risk of diabetic complications. In this study, we leverage long-term continuous glucose monitoring (CGM) data to develop and validate predictive models for a high GRI (>60) in individuals with T1D. We assessed over 250 000 days of measurements collected over four years from 736 patients with type 1 diabetes. Our modeling approach shows promise for predicting glycemic control quality (area under the receiver operating characteristic curve [ROC-AUC] of 0.87) six to nine months from baseline. However, additional analysis and validation are imperative to determine its full clinical utility.

Time at High Risk of Hypoglycemia: Validation of a Consensus-Based Continuous Glucose Monitoring-Metric for Hospitalized Patients

BACKGROUND

Time at high risk of hypoglycemia (THRH), 3.9 to 5.6 mmol/L, is a continuous glucose monitoring (CGM)-based metric recommended for reporting in hospitalized patients. This study aims to validate THRH as a predictor of hypoglycemia.

METHODS

The CGM data from 166 non-intensive care unit (non-ICU) inpatients with type 2 diabetes from the DIATEC trial were analyzed. All participants received basal-bolus insulin therapy. Of these, 82 were monitored with point-of-care glucose testing and blinded CGM, while 84 had open CGM. Linear and negative binomial regression analyses assessed the relationship between THRH and time below range (TBR) (<3.0 mmol/L, 3.0-3.9 mmol/L, and <3.9 mmol/L) and hypoglycemic events. Analyses were conducted for day (07:00-23:00), night (23:01-06:59), and 24-hour periods.

RESULTS

For CGM-monitored patients, every 10%-point increase in THRH was associated with a 0.13%-point increase in TBR (<3.0 mmol/L) (95% confidence interval [CI] = 0.06-0.21), 0.66%-point increase in TBR (3.0-3.9 mmol/L) (95% CI = 0.47-0.86), and 0.74%-point increase in TBR (<3.9 mmol/L) (95% CI = 0.51-0.97), all P < .001. A THRH threshold below 50% was linked to a TBR <3.9 mmol/L of less than 4%, as recommended. Similar results were observed during both day and night analyses and for point-of-care monitored patients, also for hypoglycemic events.

CONCLUSIONS

The THRH is strongly associated with hypoglycemia in non-ICU hospitalized patients with type 2 diabetes on basal-bolus insulin. Aiming for THRH below 50% aligns with the recommended TBR target of <3.9 mmol/L below 4%, supporting THRH's role in guiding hypoglycemia prevention strategies.

A Randomized Clinical Trial of a Culturally Tailored Diabetes Education Curriculum With and Without Real-Time Continuous Glucose Monitoring in a Latino Population With Type 2 Diabetes: The CUT-DM With Continuous Glucose Monitoring Study

BACKGROUND

Data on culturally tailored diabetes education with and without real-time continuous glucose monitoring (RT-CGM) in Latinos with type 2 diabetes, who are not on intensive insulin management, is lacking.

RESEARCH DESIGN AND METHODS

This is an open-label randomized control trial of Latinos with uncontrolled (HbA1c > 8.0%) type 2 diabetes conducted in a Federally Qualified Health Center (FQHC). All participants received 12 one-hour culturally tailored education sessions. Patients were randomized (1:1) to education sessions only (blinded CGM) or cyclic (50 days wear: 10 days on, 7 days off) RT-CGM. The primary outcome was a change in HbA1c from baseline to 12 weeks in those with or without CGM. Secondary outcomes included 24-week HbA1c, CGM, and metabolic parameters.

RESULTS

Participants (n = 120) were 46 years old on average, 44% female, 98% preferred Spanish language, 30% with income <$25,000, 68% uninsured and 26% using basal insulin only. Mean 1-hour session attendance and RT-CGM wear was 7.0 (±4.4) and 27.9 (±20.5) days, respectively. Mean baseline HbA1c was 10.5% (±1.8). HbA1c reduced by 1.9% (95% confidence interval [CI]: 1.5-2.3) overall (P < .001). Participants in the RT-CGM group reduced HbA1c at 12 weeks by 2.3% (95% CI: 1.5-3.2) compared to 1.5% (95% CI: 0.6-2.3) in the blinded CGM group (P =.04). At 24 weeks, overall HbA1c reduction was maintained but between-group differences attenuated.

CONCLUSIONS

In a Latino type 2 diabetes population that was primarily noninsulin-requiring, virtually delivered, culturally tailored education improved HbA1c, with RT-CGM conferring greater improvement. RT-CGM should be an adjunctive therapy to diabetes education, irrespective of insulin use but continued cyclic CGM use may be needed for sustained effect.

U-shaped association between the glycemic variability and prognosis in hemorrhagic stroke patients: a retrospective cohort study from the MIMIC-IV database

Background

Elevated glycemic variability (GV) is commonly observed in intensive care unit (ICU) patients and has been associated with clinical outcomes. However, the relationship between GV and prognosis in ICU patients with hemorrhagic stroke (HS) remains unclear. This study aims to investigate the association between GV and short- and long-term all-cause mortality.

Methods

Clinical data for hemorrhagic stroke (HS) patients were obtained from the MIMIC-IV 3.1 database. GV was quantified using the coefficient of variation (CV), calculated as the ratio of the standard deviation to the mean blood glucose level. The association between GV and clinical outcomes was analyzed using Cox proportional hazards regression models. Additionally, restricted cubic spline (RCS) curves were employed to examine the nonlinear relationship between GV and short- and long-term all-cause mortality.

Results

A total of 2,240 ICU patients with HS were included in this study. In fully adjusted models, RCS analyses revealed a U-shaped association between the CV and both short- and long-term all-cause mortality (P for nonlinearity < 0.001 for all outcomes). Two-piecewise Cox regression models were subsequently applied to identify CV thresholds. The thresholds for all-cause mortality in ICU, during hospitalization, and at 30, 90, and 180 days were determined to be 0.14, 0.16, 0.155, 0.14, and 0.14, respectively. These findings were consistent in sensitivity and subgroup analyses.

Conclusions

In HS patients, higher GV is associated with an increased risk of both short- and long-term all-cause mortality. Our findings suggest that stabilizing GV may improve the prognosis of HS patients.

Impact of time in tight range on all-cause and cardiovascular mortality in type 2 diabetes: A prospective cohort study

AIMS

Currently, there is a lack of evidence regarding time in tight range (TITR) and long-term adverse outcomes. We aimed to investigate the association between TITR and the risk of all-cause and cardiovascular mortality among patients with type 2 diabetes.

MATERIALS AND METHODS

A total of 6061 patients with type 2 diabetes were prospectively recruited in a single centre. TITR was measured with continuous glucose monitoring (CGM) at baseline and was defined as the percentage of time in the target glucose range of 3.9-7.8 mmol/L (70-140 mg/dL) during a 24-h period. Cox proportion hazard regression models were used to examine the association between TITR and the risk of all-cause and cardiovascular mortality.

RESULTS

During a median follow-up period of 10.9 years, 1898 (31.3%) death events were confirmed, with 689 (11.4%) due to cardiovascular mortality. The restricted cubic spline revealed significant linear relationships between lower TITR and higher risks of all-cause and cardiovascular mortality (p for linearity <0.01). In the fully adjusted model including glycated haemoglobin A1c, each 10% decrease in TITR was associated with 4% (95% confidence interval, 1.01-1.06) increased risk of all-cause mortality and 4% (95% confidence interval, 1.00-1.08) increased risk of cardiovascular mortality. Subgroup analyses showed that the linear relationship between TITR and all-cause mortality risk was sustained in patients with haemoglobin A1c <7.0% and patients with fasting plasma glucose <7.0 mmol/L.

CONCLUSIONS

Lower TITR is associated with an increased risk of all-cause and cardiovascular mortality in patients with type 2 diabetes, indicating that tight glycaemic control within the physiological range may be crucial for reducing long-term mortality risk, especially in those with seemingly well-controlled diabetes.

The association between glycaemic variability and sleep quality and quantity in adults with type 1 and type 2 diabetes: A systematic review

AIMS

Individuals with diabetes frequently encounter sleep disturbances, which can detrimentally impact glycaemic management. We reviewed the relationship between sleep outcomes and glycaemic variability in adults with diabetes.

METHODS

We systematically searched Medline, EMBASE and Cochrane Library (2002-March 2023) for studies evaluating sleep and glycaemic variability in adults with type 1 and type 2 diabetes. Among the 3049 records, 27 met the inclusion criteria (type 1 diabetes studies = 22). Due to methodological heterogeneity, a qualitative analysis was conducted.

RESULTS

Most studies measuring sleep quality (5 out 7; 71%) reported a significant association with glycaemic variability in type 1 and type 2 diabetes. Sleep duration was not significantly associated with glycaemic variability in type 1 diabetes, whereas other sleep metrics yielded inconclusive results. Hybrid closed-loop pump interventions (n = 12) demonstrated varying sleep outcomes with improved glycaemic variability. Similarly, sleep interventions (n = 3) consistently enhanced sleep but not glycaemic variability. Limitations included moderate to high risk of study bias, confounders, methodological heterogeneity and limited type 2 diabetes data.

CONCLUSIONS

A potential association between sleep quality and glycaemic variability exists. However, associations with other sleep metrics remain elusive, with no discernible association between sleep duration and glycaemic variability in type 1 diabetes. Despite advancements in continuous glucose monitoring and ambulatory sleep monitoring, standardised sleep assessment methodologies are lacking in real-world studies. Establishing standard protocols for sleep assessment and defining optimal sleep targets are crucial for meaningful comparisons between studies. Understanding the complex interplay between sleep and glycaemic variability holds promise in improving diabetes management and sleep health.

Using continuous glucose monitoring to prescribe an exercise time: a randomised controlled trial in adults with type 2 diabetes

PURPOSE

Growing evidence suggests the exercise timing, time-of-day it is performed, is important for maximizing glycemic benefits in type 2 diabetes (T2D). This randomized controlled trial investigated the impact of utilizing continuous glucose monitoring to personalise exercise timing on peak hyperglycaemia and cardiometabolic health in people with T2D.

METHODS

Adults with T2D (HbA1c: 7.2 ± 0.8 %; Age: 63 ± 12 y; BMI: 29 ± 5 kg/m2) were randomized to eight weeks: i) waitlist control (CTL, eight week CTL then re-randomized to interventions), ii) 22-min daily exercise beginning ∼ 30 min before peak hyperglycemia (ExPeak) or iii) 22-min daily exercise ∼ 90 min after peak hyperglycemia (NonPeak). Time of peak hyperglycemia was pre-determined for each participant using the median of a 14-d habitual continuous glucose monitoring (CGM) period. Glycemic control (HbA1c [primary outcome], CGM), vascular function (flow-mediated dilation [FMD]), arterial stiffness, blood pressure) and body composition were assessed. Linear mixed models compared changes across time between groups.

RESULTS

There was no intervention effect for HbA1c, however there was a significant interaction for changes in 24-h peak glucose and %FMD between groups. Compared to CTL, both intervention groups significantly lowered peak glucose (ExPeak: 95 %CI: -2.0 to -0.3 mmol/L, NonPeak: CI: -2.3 to -0.6 mmol/L) and %FMD increased (ExPeak: 95 %CI: 0.6 to 1.5 %, NonPeak: 95 %CI: 0.0 to 1.1 %). Adherence to interventions was high for both intervention groups (>90 %).

CONCLUSION

Prescribing exercise to target peak hyperglycemia did not improve HbA1c; however cardiometabolic health outcomes improved in both groups prescribed an exercise time compared to control. Personalizing exercise prescription by prescribing a time to exercise may be a novel approach to improve health outcomes and physical activity participation.

Perception of hyper-/hypoglycemia and its related factors in type 2 diabetes: a continuous glucose monitoring-based prospective observational study

Aims

Underestimating hyper-/hypoglycemia or failure to perceive hyperglycemia hinders optimal glucose management in diabetes care. Our study investigated individuals who, while aware of their hyper-/hypoglycemia, may not perceive them as problematic. Also, we clarified the factors contributing to discrepancies between these individuals' perceptions and the objective measurements.

Materials and methods

This study was a prospective observational study comprising 284 Japanese individuals with type 2 diabetes who underwent ambulatory blinded professional continuous glucose monitoring (CGM) and self-administered the Diabetes Treatment Satisfaction Questionnaire (DTSQ). Individuals with a time above range (TAR; > 180 mg/dL) ≥ 25% and those who answered 0 ("never") or + 1 ("almost never") for the frequency of hyperglycemia in the DTSQ were defined as having no-perception of hyperglycemia. Individuals with a time below range (TBR; < 70 mg/dL) ≥ 4% with an answer of 0 or + 1 for the frequency of hypoglycemia were labeled as having no-perception of hypoglycemia. Multivariate logistic regression analysis was performed to analyze clinical characteristics associated with the discrepancies between failure to perceive hyper-/hypoglycemia and TAR ≥ 25% or TBR ≥ 4%.

Results

Insulin-use (odds ratio [OR] = 0.29, p < 0.05) and older age (OR = 1.05, p < 0.05) were independent determinants of no-perception of hyperglycemia. Low eGFR was an independent determinant of no-perception of hypoglycemia (OR = 0.94, p < 0.05).

Conclusions

No-insulin-use, being an older adult, and renal dysfunction are linked to the discrepancy between the perception of hyper-/hypoglycemia and actual blood glucose. These results will help create personalized diabetes care.

Psychosocial predictors of short-term glucose among people with diabetes: A narrative review

Type 1 and type 2 diabetes are metabolic disorders that require one to manage one's blood glucose levels on a daily basis through a series of behaviorally complex tasks. Research shows that psychosocial factors, including mood, stress, and social relationships, have a significant influence on one's ability to maintain these disease management routines and achieve healthy blood glucose levels. However, researchers have typically approached these questions from a between-person perspective. Here, we argue for greater consideration of short-term, within-person links of psychosocial factors-including mood, stress, and social interactions-to glucose outcomes. Drawing from existing social and health psychology theories, we put forth an organizing theoretical framework describing how psychosocial experiences may operate on glucose outcomes over subsequent hours. We then review the small but burgeoning literature of intensive longitudinal studies that have examined the short-term effects of negative affect, positive affect, stress, and social interactions on glucose outcomes. Findings showed somewhat stronger links for negative affect and stress compared to positive affect and social interactions, but studies varied greatly in their methodologies, making direct comparisons challenging. A number of findings, particularly in the social interaction literature, depended on dispositional or contextual factors, further complicating interpretation. There was little investigation of the mechanistic pathways that may connect psychosocial factors to glucose outcomes, and few studies conducted lagged analyses to probe the directionality of these links. We conclude by proposing best practices for future research that will address the key weaknesses in the extant literature.

A Pilot Study Using Continuous Glucose Monitoring among Patients with a Low 1-Hour Glucose Challenge Test Result versus Controls to Detect Maternal Hypoglycemia

A low 1-hour glucose challenge test (GCT) result (<10th percentile for population) has been associated with neonatal morbidity, including small-for-gestational-age birth weight, and it is hypothesized that underlying maternal hypoglycemia may contribute to this neonatal morbidity. We sought to assess whether eligible patients would undergo continuous glucose monitoring to allow comparison of maternal hypoglycemia between those with a low GCT result versus controls.This exploratory study enrolled patients who completed a GCT between 24 and 30 weeks' gestation from June to September 2022. English- or Spanish-speaking participants aged ≥18 years wore a blinded continuous glucose monitor (CGM) for 10 days. There were 10 participants each in the low GCT (<82 mg/dL) and normal GCT group. Proportions were calculated to determine recruitment rates and describe the low versus normal glycemic groups across clinical and sociodemographic characteristics. Maternal hypoglycemia, defined using various proposed thresholds, was analyzed as continuous data (time duration) with Student's t-tests and categorical data (number of episodes) with chi-square tests and bivariate analyses were performed comparing participants with a low versus normal GCT. Primary outcome measures were recruitment, enrollment, and adherence rates, and overall glycemic values for each group.Of 64 eligible patients, 58 (91%) were approached, and of them, 20 (35%) were enrolled. All 20 participants had CGM data to review with 100% adherence. Average glucose values were similar between participants in the low GCT and normal GCT groups (111.7 ± 18.0 vs. 111.6 ± 11.7 mg/dL, p = 0.99), and participants with a low GCT value did not demonstrate more hypoglycemia than those with a normal GCT value across five proposed thresholds on CGM analysis.In this pilot study, participants wore blinded CGMs to collect glycemic data, and those with a low GCT result did not experience more hypoglycemia than those with a normal GCT on CGM analysis. · Study participants wore continuous glucose monitors in blinded mode to gather glycemic data with 100% adherence.. · Participants with a low GCT result (<82 mg/dL) as compared with those with a normal GCT result were not more likely to demonstrate maternal hypoglycemia using several thresholds on CGM analysis.. · In our cohort, there were few participants in either glycemic group who reported food insecurity or lived in a food desert..

Effect of iGlarLixi on continuous glucose monitoring-measured time in range in insulin-naive adults with suboptimally controlled type 2 diabetes

AIMS

People with type 2 diabetes (T2D) and glycated haemoglobin (HbA1c) ≥9% may benefit from fixed-ratio combination therapies such as iGlarLixi (insulin glargine 100 U/mL and lixisenatide 33 μg/mL). Use of continuous glucose monitoring (CGM) is recommended, but data are lacking to assess the impact of iGlarLixi in individuals with HbA1c ≥9%.

MATERIALS AND METHODS

Soli-CGM (NCT05114590) was a 16-week, multicentre, open-label study evaluating the efficacy of once-daily iGlarLixi using blinded CGM-based metrics in insulin-naive adults with HbA1c ≥9%-13% who were receiving ≥2 oral antihyperglycaemic agents (OADs) ± glucagon-like peptide-1 receptor agonists (GLP-1 RAs). The primary outcome was the change from baseline to week 16 in percent time in range (TIR; 70-180 mg/dL). Secondary outcomes included change in mean daily blood glucose (BG), maximum postprandial glucose 4 h post-breakfast (PPG-4 h), and time above range (TAR; >180 mg/dL). On-treatment hypoglycaemia was assessed.

RESULTS

The study enrolled 124 participants (mean age, 55.6 years; HbA1c, 10.2%). Sixteen weeks of treatment with iGlarLixi improved TIR (+26.2%), mean BG (-52.5 mg/dL), maximum PPG-4 h (-73.7 mg/dL), and TAR (-28.7%); all p < 0.001. Rates of American Diabetes Association level 1 (BG <70 but ≥54 mg/dL) and level 2 (BG <54 mg/dL) hypoglycaemia were reported as 1.4 and 0.6 events per person-year, respectively. No level 3 events (requiring assistance) were reported.

CONCLUSIONS

In people with T2D suboptimally controlled on ≥2 OADs ± GLP-1 RAs, 16 weeks of treatment with iGlarLixi significantly improved TIR and reduced TAR without severe hypoglycaemia.

A Comparison of Glucose and Additional Signals for Three Different Exercise Types in Adolescents with Type 1 Diabetes Using a Hybrid Closed-Loop System

Objective: To compare glycemic outcomes during and following moderate-intensity exercise (MIE), high-intensity interval exercise (HIE), and resistance exercise (RE) in adolescents with type 1 diabetes (T1D) using a hybrid closed-loop (HCL) insulin pump while measuring additional physiological signals associated with activity. Methods: Twenty-eight adolescents (average age 16.3 ± 2.1 years, 50% females, average duration of T1D 9.4 ± 4 years) using HCL (Medtronic MiniMed 670G) undertook 40 min of MIE, HIE, and RE. A temporary glucose target (8.3 mmol/L, 150 mg/dL) was set for 2 h prior and during exercise. Heart rate, accelerometer, venous glucose, lactate, ketones, and counter-regulatory hormones were measured for 280 min postexercise commencement. The primary outcome was glucose percentage time in range (TIR): 3.9-10 mmol/L (70-180 mg/dL) for 14 h from exercise onset. Results: Median (interquartile range) TIR for HIE was 88 (78, 96)%, MIE 79 (63, 88)%, and RE 86 (72, 95)% for 14 h from exercise onset. For MIE compared with HIE, TIR was lower (P = 0.012) and time above range (TAR) was greater (18 [2.4, 28] vs. 6.9 [0.0, 14]%, P = 0.041). Hypoglycemia occurred in 13 (46%), 11 (39%), and 14 (50%) of participants for HIE, MIE, and RE, respectively, the majority following the meal after exercise. There were higher levels of lactate (P = 0.001), growth hormone (P = 0.001), noradrenaline (P = 0.001), and heart rate (P = 0.01) during HIE and RE compared with MIE. Conclusions: HCL use in adolescents with T1D results in excellent TIR during different forms of exercise when a temporary target is set 2 h before. Extending the temporary target after exercise may also be needed to help minimize postexercise hypoglycemia.

Associations between physical activity, glucose variability, and cognitive function in older adults with type 2 diabetes

Higher glucose variability is linked to cognitive impairment in older adults with type 2 diabetes. While physical activity can reduce glucose variability and improve cognitive function, these relationships remain unexplored using continuous glucose monitoring. This study examined associations between physical activity, glucose variability, and cognitive function through secondary data analysis of 87 older adults with type 2 diabetes using self-reported questionnaires, computerized cognitive assessments, and continuous glucose monitoring data. Subgroup analysis showed that physical activity was associated with better cognitive function in individuals with lower cognitive function but not in those with higher cognitive function. This suggests that the effects of physical activity may vary depending on cognitive status. Future research should incorporate objective physical activity measures and longer-duration continuous glucose monitoring to explore how activity intensity, type, and timing influence glucose variability and cognitive outcomes, informing targeted interventions for this population.

Education Practices of Dietitians Across Australia and New Zealand Around the Glycaemic Management of Dietary Fat and Protein in Type 1 Diabetes and the Use of Continuous Glucose Monitoring: A Survey Evaluation

Background/Objectives: International guidelines recommend that all children and adolescents with type 1 diabetes (T1D) receive education on the glycaemic impact of fat and protein from diagnosis. In addition, the insulin strategy should be adjusted to compensate for fat and protein excursions. Data from continuous glucose monitoring (CGM) can guide insulin adjustment. This study sought to determine whether the current practices of dietitians in Australia and New Zealand align with guidelines. Methods: An anonymous, online survey of paediatric T1D dietitians working in tertiary centres (n = 20; Australia, n = 14, New Zealand, n = 6) was undertaken from February to March 2023. The Australian and New Zealand Society for Paediatric Endocrinology and Diabetes (ANZSPED) disseminated the survey link. The questionnaire covered three content domains: demographic information about the clinic and practitioner, the health professionals' education practices regarding fat and protein, and the use of CGM. Results: This pilot study had a 100% response rate, with a dietitian representative from all eligible centres responding on behalf of the diabetes team. Only 10% (n = 2) of respondents both (i) provided education on the glycaemic impact of fat and protein to all families at diagnosis and (ii) always provided insulin strategies to manage fat and protein where it impacted glycemia, as per guidelines. Barriers to education included a lack of procedure (47%, n = 7), consumer resources (40%, n = 6), and time (33%, n = 5). Reasons for not recommending strategies to manage fat and protein were perceptions that the family was overwhelmed (100%, n = 10) or not interested (60%, n = 6), and uncertainty of the best strategy (40%, n = 4). CGM was used by "almost all" respondents to educate and adjust the insulin strategy (90%, n = 18). Conclusions: Most dietitians surveyed were not consistently providing fat and protein education and management strategies to children with T1D in line with guidelines. CGM is a key tool routinely used by dietitians in nutrition education to help guide insulin adjustment. Dietitians need greater support through educational resources for families and training in evidence-based strategies to manage deglycation from dietary fat and protein to align with guidelines.

Personalized Hemoglobin A1c Shows Better Correlation with Mean Glucose than Laboratory Hemoglobin A1c in Ugandan Youth with Type 1 Diabetes, but Mean Glucose Is Not Clinically Useful in This Population Due to Extreme Glucose Variability

Introduction: Continuous glucose monitoring (CGM) is unaffordable in sub-Saharan Africa, and providers rely heavily on hemoglobin A1c (A1c) to guide insulin adjustment. The relationship between A1c and mean glucose (MG) varies between individuals and populations. We assessed this relationship in Ugandan youth of age 4-26 years with type 1 diabetes, and evaluated whether calculation of the personalized A1c (pA1c), which only requires a brief initial sensor wear, is clinically useful. Materials and Methods: CGM data were averaged across three blinded sensor wears (31-41 days). We calculated individual apparent glycation ratios using A1c after the second sensor, and applied these to A1cs collected after the third sensor to determine pA1c. Participants were evaluated for clinical factors that influence red blood cell (RBC) lifespan (malaria, G6PD deficiency, sickle-cell trait, hemolysis, iron deficiency). Results: Patients across the A1c spectrum experienced substantial time in both hyper- and hypoglycemia; average coefficient of variation was 44%. MG was >250 mg/dL (13.9 mmol/L) in 50% of participants, and 55% of participants spent ≥4% time with glucose <70 mg/dL (3.9 mmol/L). There was considerable variability in the A1c-MG relationship. The pA1c more accurately represented MG by significantly reducing variation in this relationship (R2 = 0.84 vs. 0.40; r = 0.92 vs. 0.63), but MG is not useful in individuals with the wide glucose fluctuations seen in this population. Clinical factors did not impact the A1c-MG relationship. Conclusions: Neither the measured A1c nor the calculated pA1c provided reliable guidance for insulin adjustment in this population. No matter how accurately MG is measured or estimated, it is just an average, with limited clinical application in individuals with wide glycemic variation. These measures cannot replace the information available from CGM about glycemic excursion, daily glucose patterns, or percent time in various glucose ranges. Our data suggest that it is essential to find a way to make CGM at least periodically affordable in low-resource settings.

Evaluating the Adequacy of Coefficient of Variation and Standard Deviation as Metrics of Glucose Variability in Type 1 Diabetes Based on Data from the GOLD and SILVER Trials

Objective: Evaluate the adequacy of the coefficient of variation (CV) and standard deviation (SD) as metrics of glucose variability (GV) across mean glucose (MG) levels in individuals with type 1 diabetes. Methods: Data from the GOLD and SILVER trials were analyzed. Glucose metrics were derived from continuous glucose monitoring (CGM). Generalized estimating equations were used to assess the relationship between SD and MG, considering intraindividual correlations. Nonlinear associations were evaluated using restricted cubic splines, and glucose values outside the CGM detection range (<2.22 mmol/L and >22.2 mmol/L) were handled using a censored Gamma model. Results: In total, 158 individuals with an MG of 10.6 (SD 1.7) mmol/L were included. The SD of glucose values exhibited a nonlinear relationship with the MG during CGM and self-monitoring of blood glucose (SMBG) (both P < 0.001 vs. linear model). The lack of fit of the constant CV model was most distinct at high glucose levels >12 mmol/L. During SMBG, a 25% reduction in MG from 12 to 9 mmol/L was associated with a 16% (95% confidence interval [CI] 10%-21%) reduction in the SD of glucose values. Similar associations were observed during CGM. This deviation was attributed to the censoring of glucose values outside the detection range. After adjusting for censoring, the lack of fit was resolved. When transitioning from SMBG to CGM, the ordinary CV and SD underestimated the treatment effect on GV by 30% and 27%, respectively, compared to estimates adjusted for censoring. Similarly, ordinary CV underestimated the treatment effect by 11% compared with CV adjusted for the nonlinear SD-MG relationship in the GOLD study. Conclusion: The SD of glucose values does not increase linearly with the MG during glucose-lowering therapy, suggesting that CV is not an optimal measure of GV. After adjusting for censored glucose values, CV remains reliable. Alternatively, nonlinear SD adjustments relative to MG effectively evaluate glucose-lowering therapies' impact on GV.

The Usefulness of the Glucose Management Indicator in Evaluating the Quality of Glycemic Control in Patients with Type 1 Diabetes Using Continuous Glucose Monitoring Sensors: A Cross-Sectional, Multicenter Study

The Glucose Management Indicator (GMI) is a biomarker of glycemic control which estimates hemoglobin A1c (HbA1c) based on the average glycemia recorded by continuous glucose monitoring sensors (CGMS). The GMI provides an immediate overview of the patient's glycemic control, but it might be biased by the patient's sensor wear adherence or by the sensor's reading errors. This study aims to evaluate the GMI's performance in the assessment of glycemic control and to identify the factors leading to erroneous estimates. In this study, 147 patients with type 1 diabetes, users of CGMS, were enrolled. Their GMI was extracted from the sensor's report and HbA1c measured at certified laboratories. The median GMI value overestimated the HbA1c by 0.1 percentage points (p = 0.007). The measurements had good reliability, demonstrated by a Cronbach's alpha index of 0.74, an inter-item correlation coefficient of 0.683 and an inter-item covariance between HbA1c and GMI of 0.813. The HbA1c and the difference between GMI and HbA1c were reversely associated (Spearman's r = -0.707; p < 0.001). The GMI is a reliable tool in evaluating glycemic control in patients with diabetes. It tends to underestimate the HbA1c in patients with high HbA1c values, while it tends to overestimate the HbA1c in patients with low HbA1c.

Correlation between key indicators of continuous glucose monitoring and the risk of diabetic foot

BACKGROUND

Continuous glucose monitoring (CGM) metrics, such as time in range (TIR) and glycemic risk index (GRI), have been linked to various diabetes-related complications, including diabetic foot (DF).

AIM

To investigate the association between CGM-derived indicators and the risk of DF in individuals with type 2 diabetes mellitus (T2DM).

METHODS

A total of 591 individuals with T2DM (297 with DF and 294 without DF) were enrolled. Relevant clinical data, complications, comorbidities, hematological parameters, and 72-hour CGM data were collected. Logistic regression analysis was employed to examine the relationship between these measurements and the risk of DF.

RESULTS

Individuals with DF exhibited higher mean blood glucose (MBG) levels and increased proportions of time above range (TAR), TAR level 1, and TAR level 2, but lower TIR (all P < 0.001). Patients with DF had significantly lower rates of achieving target ranges for TIR, TAR, and TAR level 2 than those without DF (all P < 0.05). Logistic regression analysis revealed that GRI, MBG, and TAR level 1 were positively associated with DF risk, while TIR was inversely correlated (all P < 0.05). Achieving TIR and TAR was inversely correlated with white blood cell count and glycated hemoglobin A1c levels (P < 0.05). Additionally, achieving TAR was influenced by fasting plasma glucose, body mass index, diabetes duration, and antidiabetic medication use.

CONCLUSION

CGM metrics, particularly TIR and GRI, are significantly associated with the risk of DF in T2DM, emphasizing the importance of improved glucose control.

Classifying Continuous Glucose Monitoring Documents From Electronic Health Records

BACKGROUND

Clinical use of continuous glucose monitoring (CGM) is increasing storage of CGM-related documents in electronic health records (EHR); however, the standardization of CGM storage is lacking. We aimed to evaluate the sensitivity and specificity of CGM Ambulatory Glucose Profile (AGP) classification criteria.

METHODS

We randomly chose 2244 (18.1%) documents from NYU Langone Health. Our document classification algorithm: (1) separated multiple-page documents into a single-page image; (2) rotated all pages into an upright orientation; (3) determined types of devices using optical character recognition; and (4) tested for the presence of particular keywords in the text. Two experts in using CGM for research and clinical practice conducted an independent manual review of 62 (2.8%) reports. We calculated sensitivity (correct classification of CGM AGP report) and specificity (correct classification of non-CGM report) by comparing the classification algorithm against manual review.

RESULTS

Among 2244 documents, 1040 (46.5%) were classified as CGM AGP reports (43.3% FreeStyle Libre and 56.7% Dexcom), 1170 (52.1%) non-CGM reports (eg, progress notes, CGM request forms, or physician letters), and 34 (1.5%) uncertain documents. The agreement for the evaluation of the documents between the two experts was 100% for sensitivity and 98.4% for specificity. When comparing the classification result between the algorithm and manual review, the sensitivity and specificity were 95.0% and 91.7%.

CONCLUSION

Nearly half of CGM-related documents were AGP reports, which are useful for clinical practice and diabetes research; however, the remaining half are other clinical documents. Future work needs to standardize the storage of CGM-related documents in the EHR.

The Effect of Insulin Degludec Versus Insulin Glargine U100 on Glucose Metrics Recorded During Continuous Glucose Monitoring in People With Type 1 Diabetes and Recurrent Nocturnal Severe Hypoglycemia

AIM

Comparing continuous glucose monitoring (CGM)-recorded metrics during treatment with insulin degludec (IDeg) versus insulin glargine U100 (IGlar-100) in people with type 1 diabetes (T1D) and recurrent nocturnal severe hypoglycemia.

MATERIALS AND METHODS

This is a multicenter, two-year, randomized, crossover trial, including 149 adults with T1D and minimum one episode of nocturnal severe hypoglycemia within the last two years. Participants were randomized 1:1 to treatment with IDeg or IGlar-100 and given the option of six days of blinded CGM twice during each treatment. CGM traces were reviewed for the percentage of time-within-target glucose range (TIR), time-below-range (TBR), time-above-range (TAR), and coefficient of variation (CV).

RESULTS

Seventy-four participants were included in the analysis. Differences between treatments were greatest during the night (23:00-06:59). Treatment with IGlar-100 resulted in 54.0% vs 49.0% with IDeg TIR (70-180 mg/dL) (estimated treatment difference [ETD]: -4.6%, 95% confidence interval [CI]: -9.1, -0.0, P = .049). TBR was lower with IDeg at level 1 (54-69 mg/dL) (ETD: -1.7% [95% CI: -2.9, -0.5], P < .05) and level 2 (<54 mg/dL) (ETD: -1.3% [95% CI: -2.1, -0.5], P = .001). TAR was higher with IDeg compared with IGlar-100 at level 1 (181-250 mg/dL) (ETD: 4.0% [95% CI: 0.8, 7.3], P < .05) and level 2 (> 250 mg/dL) (ETD: 4.0% [95% CI: 0.8, 7.2], P < .05). The mean CV was lower with IDeg than that with IGlar-100 (ETD: -3.4% [95% CI: -5.6, -1.2], P < .05).

CONCLUSION

For people with T1D suffering from recurrent nocturnal severe hypoglycemia, treatment with IDeg, compared with IGlar-100, results in a lower TBR and CV during the night at the expense of more TAR.

Acute effects of a single bout structured resistance and combined exercise on blood glucose profile during exercise in patients with type 2 diabetes and healthy adults: A randomized crossover study

AIMS

The effects of postprandial resistance and combined exercise on blood glucose profiles, particularly in patients with type 2 diabetes, remain unclear. Comprehending these responses may aid in diabetes management.

METHODS

Three trials were conducted: trial A examined aerobic, resistance, and combined exercise; trials B and C focused on three intensities of resistance and combined exercise. Participants including patients with type 2 diabetes and healthy adults completed a randomized crossover experiment with two arrangements of three interventions and continuous glucose monitoring. Blood glucose iAUC and slope were analyzed via repeated measures two-way ANOVA.

RESULTS

A total of 21 patients with type 2 diabetes (47.81±11.88 years) and 26 healthy adults (31.77±6.66 years) were assigned. In trials A-C, the main effect of subject group on iAUC/min was significant (p＜0.001, p = 0.003, and p＜0.001). The exercise in trial A (p = 0.006) and subject group in trial C (p = 0.005) significantly impacted the blood glucose slope.

CONCLUSIONS

Resistance and combined exercise reduce postprandial hyperglycemia in type 2 diabetes patients. Monitoring glucose before exercise may help prevent extreme events.