Interstitial Glucose Levels Research Articles

Comparison of feature learning methods for non-invasive interstitial glucose prediction using wearable sensors in healthy cohorts: a pilot study

BackgroundAlterations in glucose metabolism, especially the postprandial glucose response (PPGR), are crucial contributors to metabolic dysfunction, which underlies the pathogenesis of metabolic syndrome (MetSyn). Personalized low-glycemic diets (PLGD) have shown promise in reducing postprandial glucose spikes. However, current methods such as invasive continuous glucose monitoring (CGM) or multi-omics data integration to assess PPGR come with limitations, including cost and invasiveness, hindering widespread adoption in primary disease prevention. Our aim is to assess machine-learning algorithms for predicting individual PPGR using non-invasive wearable data, thereby circumventing the limitations associated with existing approaches. By identifying the most accurate model, we seek to provide a more accessible and efficient method for managing glucose metabolism. MethodsThis data-driven analysis utilized the experimental dataset from the SENSE (”Systemische Ernährungsmedizin”) study. Healthy participants used an Empatica E4 wristband and Abbott Freestyle Libre 3 CGM for ten days. Blood volume pulse (BVP), electrodermal activity (EDA), heart rate (HR), skin temperature (TEMP), and the corresponding CGM values were measured. Subsequently, four data-driven deep learning (DL) model-convolutional neural network (CNN), lightweight transformer (LTF), long short-term memory with attention (LSTM-A), and Bi-directional LSTM (BiLSTM)-were implemented and compared to determine the potential of DL in predicting interstitial glucose levels without involving food and activity logs. ResultsThe proposed BiLSTM achieved the best interstitial glucose prediction performance, with an average root mean squared error (RMSE) of 13.42 mg/dL, an average mean absolute percentage error (MAPE) of 0.12, and only 3.01% values falling within area D in Clarke error grid analysis (CEGA), incorporating the leave-one-out cross-validation (LOOCV) strategy for a five-minute prediction horizon. ConclusionThe findings of this study may demonstrate the feasibility of transferring knowledge gained from invasive glucose monitoring devices to non-invasive approaches. Furthermore, it emphasizes the promising prospects of combining deep learning with wearable technologies to predict glucose levels in healthy individuals.

A randomized feasibility trial of time-restricted eating during pregnancy in people with increased risk of gestational diabetes

Time-restricted eating (TRE) is a nutritional intervention that confines the daily time-window for energy intake. TRE reduces fasting glucose concentrations in non-pregnant individuals, but whether this eating protocol is feasible and effective for glycemic control in pregnancy is unknown. The aim of this randomized controlled trial was to investigate the adherence to and effect of a 5-week TRE intervention (maximum 10 h daily eating window) among pregnant individuals at risk of gestational diabetes mellitus (GDM), compared with a usual-care control group. Participants underwent 2-h oral glucose tolerance tests and estimation of body composition, before and after the intervention. Interstitial glucose levels were continuously measured, and adherence rates and ratings of hunger were recorded daily. Thirty of 32 participants completed the trial. Participants allocated to TRE reduced their daily eating window from 12.3 (SD 1.3) to 9.9 (SD 1.0) h, but TRE did not affect glycemic measures, blood pressure, or body composition, compared with the control group. TRE increased hunger levels in the evening, but not in the morning, and induced only small changes in dietary intake. Adhering to a 5-week TRE intervention was feasible for pregnant individuals with increased risk of GDM but had no effect on cardiometabolic outcomes.

Personalized sleep and nutritional strategies to combat adverse effects of night shift work: a controlled intervention protocol

Abstract Background Working during the night interferes with the timing of normal daily activities and is associated with an increased risk of chronic diseases. Under controlled experimental conditions, interventions focusing on sleep and nutrition can mitigate the short-term adverse effects of shift work. However, it is unclear how these results translate to real-life, how they can be targeted to individual conditions, and how they relate to long-term health. Therefore, the current study aims to implement a personalized sleep and nutritional intervention among night shift workers in the field. Methods A non-blinded controlled intervention study is used, consisting of a run-in period, an intervention of 3 months, post-intervention measurements, and a follow-up after 12 months. Three study arms are included: sleep intervention, nutritional intervention, and control group (n = 25 each). Participants are healthy 18–60-year male night shift workers, with at least one year of experience in night shift work. Information from the run-in period will be used to personalize the interventions. The main outcomes are sleep measurements and continuous interstitial glucose levels. Furthermore, general health biomarkers and parameters will be determined to further evaluate effects on long-term health. Discussion This study aims to mitigate negative health consequences associated with night shift work by introducing two personalized preventive interventions. If proven effective, the personalized interventions may serve as practical solutions that can have a meaningful impact on the sustainable health and employability of night shift workers. This study will thereby contribute to the current need for high–quality data on preventative strategies for night shift work in a real-life context. Trial registration This trial has been registered under ClinicalTrials.gov Identifier NCT06147089. Registered 27 November 2023.

Hot Water Eliminates the Bitter Taste of Oral Semaglutide: A Report of Four Cases.

Semaglutide is a well-designed drug with a special coating that allows for oral administration to patients with type 2 diabetes mellitus. However, patients taking oral semaglutide complain of its bitter taste. We therefore considered suggesting that patients take oral semaglutide with hot water. When the hot water temperature was increased to above 46.0°C but below 52.0°C, no bitter taste was perceived, with the daily mean interstitial glucose level remaining at the target range. Taking oral semaglutide with hot water helps reduce its bitter taste.

Comparison of Machine Learning Models for Predicting Interstitial Glucose Using Smart Watch and Food Log

This study examines the performance of various machine learning (ML) models in predicting Interstitial Glucose (IG) levels using data from wrist-worn wearable sensors. The insights from these predictions can aid in understanding metabolic syndromes and disease states. A public dataset comprising information from the Empatica E4 smart watch, the Dexcom Continuous Glucose Monitor (CGM) measuring IG, and a food log was utilized. The raw data were processed into features, which were then used to train different ML models. This study evaluates the performance of decision tree (DT), support vector machine (SVM), Random Forest (RF), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Gaussian Naïve Bayes (GNB), lasso cross-validation (LassoCV), Ridge, Elastic Net, and XGBoost models. For classification, IG labels were categorized into high, standard, and low, and the performance of the ML models was assessed using accuracy (40–78%), precision (41–78%), recall (39–77%), F1-score (0.31–0.77), and receiver operating characteristic (ROC) curves. Regression models predicting IG values were evaluated based on R-squared values (−7.84–0.84), mean absolute error (5.54–60.84 mg/dL), root mean square error (9.04–68.07 mg/dL), and visual methods like residual and QQ plots. To assess whether the differences between models were statistically significant, the Friedman test was carried out and was interpreted using the Nemenyi post hoc test. Tree-based models, particularly RF and DT, demonstrated superior accuracy for classification tasks in comparison to other models. For regression, the RF model achieved the lowest RMSE of 9.04 mg/dL with an R-squared value of 0.84, while the GNB model performed the worst, with an RMSE of 68.07 mg/dL. A SHAP analysis identified time from midnight as the most significant predictor. Partial dependence plots revealed complex feature interactions in the RF model, contrasting with the simpler interactions captured by LDA.

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

Continuous glucose monitoring (CGM) devices allow for 24-h real-time measurement of interstitial glucose levels and have changed the interaction between people with diabetes and their health care providers. The large amount of data generated by CGM can be analyzed and evaluated using a set of standardized parameters, collectively named glucometrics. This review aims to provide a summary of the existing evidence on the use of glucometrics data and its impact on clinical practice based on published studies involving adults and children with type 1 diabetes (T1D) in Spain. The PubMed and MEDES (Spanish Medical literature) databases were searched covering the years 2018-2022 and including clinical and observational studies, consensus guidelines, and meta-analyses on CGM and glucometrics conducted in Spain. A total of 16 observational studies were found on the use of CGM in Spain, which have shown that cases of severe hypoglycemia in children with T1D were greatly reduced after the introduction of CGM, resulting in a significant reduction in costs. Real-world data from Spain shows that CGM is associated with improved glycemic markers (increased time in range, reduced time below and above range, and glycemic variability), and that there is a relationship between glycemic variability and hypoglycemia. Also, CGM and analysis of glucometrics proved highly useful during the COVID-19 pandemic. New glucometrics, such as the glycemic risk index, or new mathematical approaches to the analysis of CGM-derived glucose data, such as "glucodensities," could help patients to achieve better glycemic control in the future. By using glucometrics in clinical practice, clinicians can better assess glycemic control and a patient's individual response to treatment.

Glycemic metrics in Japanese isCGM users - Analysis by diabetes type and therapy.

The FreeStyle Libre (FSL) intermittently scanned continuous glucose monitoring (isCGM) system continually measures interstitial glucose levels and provides the data to users in numerical and graphical formats that guide users in their daily diabetes self-management. Although numerous studies have demonstrated the glycemic benefits of FSL in pediatric and adult populations, few studies have characterized FSL use specifically by Japanese adults with type 1 or 2 diabetes. We utilized established CGM metrics to assess glycemic control in a large cohort of Japanese adults with type 1 and 2 diabetes. A total of 3,463 anonymized FSL users provided categorization into one of four therapy groups of interest: type 1 diabetes (n = 1,768), type 2 diabetes-multiple daily injections (MDI) (n = 612), type 2 diabetes-basal (BOI) (n = 343), and type 2 diabetes-non-insulin (NIT) (n = 740). Established CGM metrics were used to assess glycemic control. All study groups showed relatively good glycemic control. Type 1 diabetes users showed the highest glucose variability (SD, 61 mg/dL; and %CV, 40%), above the established target level (%CV ≤ 36%). type 2 diabetes-MDI and type 2 diabetes-BOI users had similar levels of glucose variability (both within target). Type 2 diabetes-NIT users had the highest mean % time in range (TIR) (84.3%) and largest percentage of users that met the target of %TIR > 70% (87.4%). In contrast, type 1 diabetes users had the lowest mean %TIR (62.6%) and the lowest percentage meeting the established %TIR target (30.5%). By utilizing CGM devices in daily diabetes care, both healthcare professionals and patients can monitor glycemic excursions and gain insights into their historical glucose control patterns.

The Ecology of Human Sleep (EcoSleep) Cohort Study: Protocol for a longitudinal repeated measurement burst design study to assess the relationship between sleep determinants and outcomes under real-world conditions across time of year.

The interplay of daily life factors, including mood, physical activity, or light exposure, influences sleep architecture and quality. Laboratory-based studies often isolate these determinants to establish causality, thereby sacrificing ecological validity. Furthermore, little is known about time-of-year changes in sleep and circadian-related variables at high resolution, including the magnitude of individual change across time of year under real-world conditions. The Ecology of Human Sleep (EcoSleep) cohort study will investigate the combined impact of sleep determinants on individuals' daily sleep episodes to elucidate which waking events modify sleep patterns. A second goal is to describe high-resolution individual sleep and circadian-related changes across the year to understand intra- and inter-individual variability. This study is a prospective cohort study with a measurement-burst design. Healthy adults aged 18-35 years (N = 12) will be enrolled for 12 months. Participants will continuously wear actimeters and pendant-attached light loggers. A subgroup will also measure interstitial fluid glucose levels (six paticipants). Every 4 weeks, all participants will undergo three consecutive measurement days of four ecological momentary assessments each day ('bursts') to sample sleep determinants during wake. Participants will also continuously wear temperature loggers (iButtons) during the bursts. Body weight will be captured before and after the bursts in the laboratory. The bursts will be separated by two at-home electroencephalogram recordings each night. Circadian phase and amplitude will be estimated during the bursts from hair follicles, and habitual melatonin onset will be derived through saliva sampling. Environmental parameters (bedroom temperature, humidity, and air pressure) will be recorded continuously.

Continuous Glucose Monitoring Captures Glycemic Variability After Roux-en-Y Gastric Bypass in Patients with and Without Type 2 Diabetes Mellitus: A Prospective Cohort Study.

To evaluate glycemic variability (GV) using continuous glucose monitoring (CGM) in individuals with and without type 2 diabetes mellitus (T2DM) undergoing Roux-en-Y gastric bypass (RYGB). This prospective cohort study compared the CGM data of fourteen patients with T2DM (n = 7) and without T2DM (n = 7) undergoing RYGB. After 6months, these patients were compared to a non-operative control group (n = 7) matched by BMI, sex, and age to the T2DM group. Fourteen patients underwent RYGB, with a mean BMI of 46.9 ± 5.3kg/m2 and an average age of 47.9 ± 8.9years; 85% were female. After 6months post-surgery, the total weight loss (TWL) was 27.1 ± 6.3%, with no significant differences between the groups. Patients without diabetes had lower mean interstitial glucose levels (81 vs. 94 and 98mg/dl, p < 0.01) and lower glucose management indicator (GMI) (5.2 vs. 5.6 and 5.65%, p = 0.01) compared to the control and T2DM groups, respectively. The coefficient of variation (CV) significantly increased only in patients with diabetes (17% vs. 26.7%, p < 0.01). Both groups with (0% vs. 2%, p = 0.03) and without (3% vs. 22%, p = 0.03) T2DM experienced an increased time below range with low glucose (54-69mg/dL). However, patients without T2DM had significantly less time in rage (70-180mg/dL) (97% vs. 78%, p = 0.04). Significant differences in CGM metrics among RYGB patients suggest an increase in glycemic variability after surgery, with a longer duration of hypoglycemia, especially in patients without T2DM.

Recent Progress in Glucose-Responsive Insulin.

Insulin replacement therapy is indispensable in the treatment of type 1 and advanced type 2 diabetes. However, insulin’s clinical application is challenging due to its narrow therapeutic index. To mitigate acute and chronic risks of glucose excursions, glucose-responsive insulin (GRI) has long been pursued for clinical application. By integrating GRI with glucose-sensitive elements, GRI is capable of releasing or activating insulin in response to plasma or interstitial glucose levels without external monitoring, thereby improving glycemic control and reducing hypoglycemic risk. In this Perspective, we first introduce the history of GRI development and then review major glucose-responsive components that can be leveraged to control insulin delivery. Subsequently, we highlight the recent advances in GRI delivery carriers and insulin analogs. Finally, we provide a look to the future and the challenges of clinical application of GRI. Article Highlights

Continuous glucose monitoring and rates of hyperglycemia during chemotherapy for early-stage breast cancer.

e24134 Background: Diabetes (DM) and hyperglycemia (HG) during chemotherapy increase the risk of treatment-related toxicities. The prevalence of HG and daily patterns of HG over the course of chemotherapy in patients with early-stage breast cancer (ESBC) are currently unknown. Methods: We conducted a prospective single-arm pilot study of continuous glucose monitoring in patients with ESBC receiving chemotherapy from 12/2020 – 2/2022. Eligibility criteria included: diagnosis of Stage I-III BC, age ≥18, and planned chemotherapy with concurrent corticosteroid supportive care use. Patients were excluded if they were receiving other systemic corticosteroids or insulin. Patients wore FreeStyle Libre Pro continuous glucose monitoring sensors from the time of chemotherapy initiation (± 7 days) through completion. The sensors detected interstitial glucose levels every 15 minutes and were reapplied by the study team every 2-3 weeks. Primary endpoints were: (1) prevalence of HG, defined as number of patients with ≥1 glucose reading ≥140 mg/dL and (2) among those who developed HG, the proportion of time spent hyperglycemic, defined as number of readings ≥140 mg/dL divided by total number of readings. Key secondary endpoints included prevalence of prediabetes (pre-DM) and diabetes (DM) at baseline and changes in A1c from baseline to weeks 12 and 24. The analysis was stratified by baseline A1c (euglycemia [A1c &lt; 5.7%], pre-DM [A1c 5.7%-6.4%], or DM [A1c ≥6.5% and clinical diagnosis of DM prior to study enrollment]). Results: Among 20 evaluable patients, the median age at baseline was 59 (range 34-78) and median body mass index (BMI) was 29.5 kg/m2 (range 19.5-41.6). The most common chemotherapy regimens included docetaxel/cyclophosphamide (25%), weekly paclitaxel ± trastuzumab (20%), paclitaxel followed by doxorubicin/cyclophosphamide (15%), docetaxel/carboplatin/trastuzumab/pertuzumab (15%), and cyclophosphamide/methotrexate/fluorouracil (15%). All 20 patients developed HG. Of 124,165 total readings, 17% were ≥140 mg/dL and mean glucose was 112.7 mg/dL. At baseline, n = 10 were euglycemic, n = 7 had pre-DM, and n = 3 had DM. By cohort, the proportion of time spent hyperglycemic was 3.9% (euglycemia group), 10% (pre-DM group), and 73.3% (DM group) (p &lt; .0001). Mean glucose values were 95.5 mg/dL (euglycemia group), 104.5 mg/dL (pre-DM group), and 183.0 mg/dL (DM group) (p &lt; .0001). Change in mean A1c from baseline to weeks 12 and 24 varied significantly across groups. Conclusions: Among patients receiving chemotherapy for ESBC, we found evidence of HG in all patients, and the proportion of time spent hyperglycemic during chemotherapy varied significantly by baseline A1c. Further interventions should evaluate the benefits of improved glucose control among patients with baseline A1c &gt; 5.7%. Clinical trial information: NCT04473378 .

GLSTM: On Using LSTM for Glucose Level Prediction.

The Prediabetes impacts one in every three individuals, with a 10% annual probability of transitioning to type 2 diabetes without lifestyle changes or medical interventions. It's crucial to manage glycemic health to deter the progression to type 2 diabetes. In the United States, 13% of individuals (18 years of age and older) have diabetes, while 34.5% meet the criteria for prediabetes. Diabetes mellitus and prediabetes are more common in older persons. Currently, nevertheless, there aren't many noninvasive, commercially accessible methods for tracking glycemic status to help with prediabetes self-management. This study tackles the task of forecasting glucose levels using personalized prediabetes data through the utilization of the Long Short-Term Memory (LSTM) model. Continuous monitoring of interstitial glucose levels, heart rate measurements, and dietary records spanning a week were collected for analysis. The efficacy of the proposed model has been assessed using evaluation metrics including Root Mean Square Error (RMSE), Mean Squared Error (MSE), Mean Absolute Error (MAE), and the coefficient of determination (R2).

#2103 Accuracy of a continuous glucose monitor in people with diabetes on peritoneal dialysis

Abstract Background and Aims Globally 40% of people receiving peritoneal dialysis (PD) have a diagnosis of diabetes. Good glycaemic control can by complicated by the obligate glucose load associated with PD. Monitoring and treatment of people with diabetes (PwD) on PD has been limited by reduced reliability of traditional markers of glycaemia such as HbA1c in end-stage kidney disease. Continuous glucose monitors (CGMs) have been increasingly used in the care of PwD over the last 20 years. Monitors worn on the upper arm or abdomen measure glucose in interstitial fluid at regular intervals over the 24 hour period. CGMs are currently unlicensed in PD populations as they have not been tested in large scale studies. Concerns remain around accuracy in the presence of potentially interfering substances generated by the breakdown of osmotic agents used in PD fluid (e.g. Icodextrin). We aimed to assess the analytical and clinical accuracy of the FreeStyle Libre(FSL) CGM system in PwD on maintenance PD. Method We conducted a single centre observational study of 10 PwD who had been on PD for at least 3 months. For the 10 day study period participants wore a blinded research model FSL monitor which measured interstitial glucose levels every 15 minutes. Results from the FSL were compared with; Analytical accuracy was assessed using three methods; Clinical accuracy was assessed using the Parkes consensus error grid to assign each pair of readings to one of five risk zones and quantify the clinical implications of any difference between the two measurements. The International Organisation for Standardisation guidelines stipulates 99% of pairs should be in zones A and B, denoting little or no effect on clinical actions. Results Of 11 recruited participants, 10 completed the 10 day study period resulting in 76 paired CGM-YSI measurements and 375 paired CGM-CBG measurements. 7 participants were on APD and 3 on CAPD. All participants were prescribed a PD regime that included one daily dwell with Icodextrin. The aggregate MARD was 9.4% (95% CI 7.9, 10.4) for CGM-YSI matched pairs and 17.6% (95% CI 17, 19.3) for CGM-CBG matched pairs. Spearman's r was 0.94 (p &amp;lt; 0.0001) for CGM-YSI and 0.89 (p &amp;lt; 0.0001) for CGM-CBG. From Bland-Altman analysis, the systematic error of the CGM compared with YSI and CBG was −0.81 mmol/l (SD 0.81) and −1.6 mmol/l (SD 1.9) respectively. Parkes consensus error grid analysis showed that compared with YSI and CBG respectively, 100% and 99.9% of sensor values were in the clinically acceptable zones A and B (Fig. 1). Conclusion We have demonstrated satisfactory performance of the FSL monitoring system by both analytical and clinical metrics in this cohort of PD patients using treatment regimens including Icodextrin based fluids. CGMs provide much more information for the PwD and their clinical care team compared with random CBGs (which in themselves have been shown to exhibit significant between user variability) and time averaged markers such as HbA1c. They allow detection of asymptomatic hypoglycaemia and direct monitoring of the systemic effects of any change to the PD prescription resulting in a larger or smaller glucose load. These results should be reassuring for clinicians wishing to utilise the greater depth of information provided by CGMs to improve clinical care for their patients with diabetes on PD.

Accuracy of a novel real-time continuous glucose monitoring system: a prospective self-controlled study in thirty hospitalized patients with type 2 diabetes.

The present study aimed to investigate the accuracy of the Glunovo® real-time continuous glucose monitoring system (rtCGMS). We conducted a 14-day interstitial glucose level monitoring using Glunovo® rtCGMS on thirty hospitalized patients with type 2 diabetes. The flash glucose monitoring (FGM) was used as a self-control. Consistency tests, error grid analysis, and calculation of the mean absolute relative difference (MARD) were performed using R software to assess the accuracy of Glunovo® rtCGMS. Glunovo® exhibited an overall MARD value of 8.89% during hospitalization, compared to 10.42% for FGM. The overall percentages of glucose values within ±10%/10, ± 15%/15, ± 20%/20, ± 30%/30, and ±40%/40 of the venous blood glucose reference value were 63.34%, 81.31%, 90.50%, 97.29%, and 99.36% for Glunovo®, respectively, compared with 61.58%, 79.63%, 88.31%, 96.22% and 99.23% for FGM. The Clarke Error Grid Analysis showed that 99.61% of Glunovo® glucose pairs and 100.00% of FGM glucose pairs within zones A and B. Our study confirms the superior accuracy of Glunovo® in monitoring blood glucose levels among hospitalized patients with type 2 diabetes.

Carbohydrate Ingestion Increases Interstitial Glucose and Mitigates Neuromuscular Fatigue during Single-Leg Knee Extensions.

We aimed to investigate the neuromuscular contributions to enhanced fatigue resistance with carbohydrate (CHO) ingestion and to identify whether fatigue is associated with changes in interstitial glucose levels assessed using a continuous glucose monitor (CGM). Twelve healthy participants (six males, six females) performed isokinetic single-leg knee extensions (90°·s -1 ) at 20% of the maximal voluntary contraction (MVC) torque until MVC torque reached 60% of its initial value (i.e., task failure). Central and peripheral fatigue were evaluated every 15 min during the fatigue task using the interpolated twitch technique and electrically evoked torque. Using a single-blinded crossover design, participants ingested CHO (85 g sucrose per hour), or a placebo (PLA), at regular intervals during the fatigue task. Minute-by-minute interstitial glucose levels measured via CGM and whole blood glucose readings were obtained intermittently during the fatiguing task. CHO ingestion increased time to task failure over PLA (113 ± 69 vs 81 ± 49 min, mean ± SD; P < 0.001) and was associated with higher glycemia as measured by CGM (106 ± 18 vs 88 ± 10 mg·dL -1 , P < 0.001) and whole blood glucose sampling (104 ± 17 vs 89 ± 10 mg·dL -1 , P < 0.001). When assessing the values in the CHO condition at a similar time point to those at task failure in the PLA condition (i.e., ~81 min), MVC torque, percentage voluntary activation, and 10 Hz torque were all better preserved in the CHO versus PLA condition ( P < 0.05). Exogenous CHO intake mitigates neuromuscular fatigue at both the central and peripheral levels by raising glucose concentrations rather than by preventing hypoglycemia.

Improved glycaemic control induced by evening activity breaks does not persist overnight amongst healthy adults: A randomized crossover trial.

To compare the effects of 4 hours of laboratory-based regular activity breaks (RABs) and prolonged sitting (SIT) on subsequent 48-h free-living interstitial glucose levels in a group of healthy adults. In this randomized crossover trial, participants completed two 4-h laboratory-based interventions commencing at ~5:00 pm: (1) SIT and (2) SIT interrupted with 3 min of body weight resistance exercise activity breaks every 30 min (RABs). Continuous glucose monitoring was performed during the intervention and for 48-h after, during which time participants returned to a free-living setting. Twenty-eight adults (female n = 20, mean ± SD age 25.5 ± 5.6 years, body mass index 29.2 ± 6.9 kg/m2) provided data for this analysis. During the intervention period, RABs lowered mean interstitial glucose by 8.3% (-0.47 mmol/L/4 h, 95% confidence interval [CI] -0.74 to -0.20; p = 0.001) and area under the curve (AUC) by 8.9% (-2.01 mmol/L/4 h, 95% CI -3.05 to -0.97; p < 0.001) compared to SIT. Measures of glycaemic variability were not significantly different during the intervention. There were no significant differences in mean glucose and AUC between conditions during the first nocturnal period and 24-h post intervention. When compared to SIT, RABs increased continuous overall net action of glucose at 1 h and SD glucose by 22% (0.18 mmol/L, 95% CI 0.03 to 0.29; p = 0.018) and 26% (95% CI 4.9 to 42.7; p = 0.019) in the first nocturnal period and by 10% (0.09 mmol/L, 95% CI 0.01, 0.17; p = 0.025) and 15% (95% CI 6.6 to 22.4; p = 0.001) in the 24-h post intervention period, respectively. Performing activity breaks in the evening results in acute reductions in interstitial glucose concentrations; however, the magnitude of these changes is not maintained overnight or into the following 48 hours.

Does continuous glucose monitoring influence adherence to time-restricted eating?

Obesity is a significant health issue in Aotearoa; effective and pragmatic strategies to facilitate weight loss are urgently required. Growing recognition of the circadian rhythm’s impact on metabolism has popularised diets like time-restricted eating (TRE)(1). The 16:8 TRE method involves limiting food intake to an 8-hour daily eating window and can lead to weight loss without other substantial changes to diet(2). Nonetheless, TRE requires accountability and tolerating hunger for short periods. Continuous glucose monitors (CGM) are small wearable biofeedback devices that measure interstitial glucose levels scanned via smartphones. By providing immediate feedback on the physiological effects of eating and fasting, CGM use may promote adherence to TRE(3). This pilot study aimed to 1) investigate how CGM affects adherence to TRE and 2) assess the feasibility of CGM use while undertaking TRE. This two-arm randomised controlled trial enrolled healthy adults from Dunedin, assigning them to TRE-only or TRE+CGM groups for 14 days. Successful adherence to TRE was defined a priori as maintaining an 8-hour eating window on 80% of days. CGM feasibility was defined a priori as scanning the glucose monitor thrice daily on 80% of days. Secondary outcomes included well-being, anthropometry, glucose levels, and overall TRE and CGM experiences via semi-structured interviews. Twenty-two participants were randomised into two groups: TRE-only (n = 11) and TRE+CGM (n = 11, with n = 2 excluded from analysis post-randomisation for medical reasons). Participants had a diverse range of ethnicities, the mean age was 32 (+/-14.9) years, and 55% were female. The TRE+CGM group adhered to the 8-hour eating window for an average of 10.0 days (range 2-14) compared with 8.6 days (range 2-14) in the TRE-only group. Both groups had similar mean eating window durations of 8.1 hours. Five (56%) participants in the TRE+CGM group achieved the a priori criteria for TRE adherence, compared to 3 (27%) in the TRE-only group. Participants in the TRE+CGM group performed an average of 8.2 (+/-5.6) daily scans, with n = 7 (78%) of participants meeting the a priori CGM feasibility criteria. Neither group reported consistent adverse psychological impacts in DASS-21 and WHO-5 scores. Interviews highlighted that CGM increased hunger tolerance during fasting as participants felt reassured by their normal glucose levels. CGM aided TRE accountability by acting as a biological tracker of food intake. Participants reported that TRE led to improved energy and self-efficacy, a more productive daily routine, and healthier food choices. Promisingly, 72% of participants would use CGM and undertake TRE in future. This study demonstrates that using CGM while undertaking TRE is feasible and can improve adherence by enhancing hunger tolerance and accountability. Overall, participants experienced increased awareness of eating habits and physiological mechanisms. Over the longer term, this simple and synergistic approach may be a helpful weight loss strategy.

Accuracy of Two Continuous Glucose Monitoring Devices During Aerobic and High-Intensity Interval Training in Individuals with Type 1 Diabetes.

Background: This study aimed to evaluate the accuracy of Dexcom G6 (DG6) and FreeStyle Libre-2 (FSL2) during aerobic training and high-intensity interval training (HIIT) in individuals with type 1 diabetes. Methods: Twenty-six males (mean age 29.3 ± 6.3 years and mean duration of diabetes 14.9 ± 6.1 years) participated in this study. Interstitial glucose levels were measured using DG6 and FSL2, while plasma glucose levels were measured every 10 min using YSI 2500 as the reference for glucose measurements in this study. The measurements began 20 min before the start of exercise and continued for 20 min after exercise. Seven measurements were taken for each subject and exercise. Results: Both DG6 and FSL2 devices showed significant differences compared to YSI glucose data for both aerobic and HIIT exercises. Continuous glucose monitoring (CGM) devices exhibited superior performance during HIIT than aerobic training, with DG6 showing a mean absolute relative difference of 14.03% versus 31.98%, respectively. In the comparison between the two devices, FSL2 demonstrated significantly higher effectiveness in aerobic training, yet its performance was inferior to DG6 during HIIT. According to the 40/40 criteria, both sensors performed similarly, with marks over 93% for all ranges and both exercises, and above 99% for HIIT and in the >180 mg/dL range, which is in accordance with FDA guidelines. Conclusions: The findings suggest that the accuracy of DG6 and FSL2 deteriorates during and immediately after exercise but remains acceptable for both devices during HIIT. However, accuracy is compromised with DG6 during aerobic exercise. This study is the first to compare the accuracy of two CGMs, DG6, and FSL2, during two exercise modalities, using plasma glucose YSI measurements as the gold standard for comparisons. It was registered at clinicaltrials.gov (NCT06080542).

Continuous glucose monitoring (CGM)

Continuous glucose monitoring (CGM) is a sensorbased glucose-measuring technology, initially developed for diabetes management that gives insight into interstitial glucose levels. Healthy persons and athletes have increasingly used it, but in the mentioned instances it lacks data on how CGM-measured glucose values affect health outcomes and what are the limits of desired glucose levels. The main CGM use by healthy populations is to modify dietary choices according to personal metabolic responses although it should be used with caution, while its utility for understanding and modifying individualized dietary responses is limited by the numerous non-dietary factors influencing glycemic control. In the case of its use by athletes, one must additionally have in mind the duration and intensity of training together with its timing to meal.

Subdermal Progestin Implant and an Oral Combined Hormonal Contraceptive in Youth with Type 1 Diabetes

Study ObjectiveTo determine the metabolic effects of the subcutaneous etonogestrel implant compared to an oral contraceptive in adolescents and young adults (AYA) with T1D on body weight, body composition, glucose, lipids, and C-reactive protein levels (CRP). MethodsNon-randomized, interventional, prospective study. Thirty-nine AYA with T1D participated; twenty used the implant (Implant-T1D), and nineteen used an oral combined contraceptive (OC-T1D). Body composition, HbA1c, intermittent continuous glucose monitoring (CGM), lipids, and high sensitivity C-reactive protein (hsCRP) levels were evaluated. ResultsAll participants were followed for at least 12 months, and twenty-six completed the 24-month follow-up. No women discontinued the intervention due to side effects. Body weight increased by 0.8 ± 3.5 kg and 1 ± 2.9 kg in the OC-T1D and the Implant-T1D group at 12 months and by 2.6 ± 3.9 kg and 3.3 ± 3.6 kg at 24 months, respectively. OC-T1D and Implant-T1D had similar HbA1c, mean interstitial glucose levels, and time in range throughout the study; no significant difference over time was observed. hsCRP levels increased in both groups and were associated with BMI and HbA1c (p<0.001 for both variables). Women in the OC-T1D group had higher total cholesterol, HDL-C, and triglyceride levels compared with the Implant-T1D. ConclusionGlucose levels were similar in youth using the subdermal progestin implant and an OC. However, both AYA groups showed increased BMI, fat mass, and subclinical inflammation. Changes in lipid levels were associated with the OC method. These data highlight the importance of weight gain prevention in young women with T1D using hormonal contraception.