Continuous Glucose Monitoring Sensor Research Articles

Diabetes Control and Clinical Outcomes among Children Attending a Regional Paediatric Diabetes Service in Australia.

Australian children with diabetes commonly struggle to achieve optimal glycaemic control, with minimal improvement observed over the past decade. The scarcity of research in the rural and regional Australian context is concerning, given high incidence rates and prominent barriers to healthcare access in these areas. We conducted a retrospective audit of 60 children attending a regional Australian paediatric diabetes service between January 2020 and December 2023. The majority of patients had type 1 diabetes (n = 57, 95.0%); approximately equal numbers were managed with continuous subcutaneous insulin infusion (CSII) pumps vs. multiple daily injections (MDIs), whilst 88.3% (n = 53) also utilised continuous glucose monitoring (CGM). The mean age at last visit was 14.0 years (SD, 3.4), mean diabetes duration 5.8 years (SD, 4.6), and mean HbA1c level 8.1% (65.3 mmol/mol); only 36.8% achieved the national target of 7.5% (58 mmol/mol). Mean BMI-SDS was 0.8 (SD, 1.0); almost half (n = 27, 45.0%) were overweight or obese. Many patients had mental health conditions (31.7%), which were associated with higher hospitalisation rates (p = 0.007). The attendance rate was 83.2%, with a mean of 3.3 clinic visits per year (SD, 0.7); higher attendance rates were associated with increased CGM sensor usage (r = 0.395, p = 0.007 Overall, the diabetes service performed similarly to other clinics with regards to glycaemic control. Whilst achieving treatment targets and addressing comorbidities remains a challenge, the decent attendance and the high uptake of healthcare technologies is commendable. Further efforts are needed to improve diabetes management for this regional community.

12395 Continuous Glucose Monitoring In Pseudohypoparathyroidism Type 1a

Abstract Disclosure: J. Tamaroff: None. A.E. Lee: None. A.H. Shoemaker: None. Background: Pseudohypoparathyroidism type 1A (PHP1A), due to heterozygous mutations in GNAS, is a rare disorder that leads to multi-hormone resistance, cognitive impairment, and early-onset obesity. Individuals with PHP1A have dysglycemia on oral glucose tolerance tests and increased rates of type 2 diabetes (T2DM). Individuals with rare diseases, like PHP1A, may travel significant distances for specialized clinical or research care. Wearable technology may alleviate barriers and provide useful information for clinical care or for use as potential biomarkers. The objective of this study is to evaluate glycemia in individuals with PHP1A and feasibility of utilizing continuous glucose monitors (CGMs) in this population. Methods: Observational study of individuals with PHP1A (3R01DK118407-03S1). CGMs (Dexcom Pro, Dexcom Inc, San Diego, CA) were placed on participants’ abdomens, typically by a family member, over secure-video call with study staff. CGMs collected data every 5 minutes for up to 10 days. Results: Seven participants (all female) with PHP1A without a diagnosis of diabetes had available data. Median age was 17 years (IQR: 14, 19; range: 8-23). Body mass index (BMI) categories (by percent of the 95th percentile for those under 18 years and BMI for adults) spanned from overweight to class 3 obesity. In terms of feasibility, 1 individual had to replace a CGM sensor due to an upcoming MRI and 1 individual had a “sensor failure” of unclear etiology. Both were able to place new CGMs. Individuals participated across 5 states in the United States. Median length of wear was 9.8 days (IQR 8.6, 9.9). Participants had a median average glucose of 104mg/dL (IQR 102, 111) and COV of 16% (15, 21). Percent of glucose > 140mg/dL was 5.0% (IQR: 1.8, 10.9; range:1.2-19.4%). One individual had 3.2% of glucose values ≥ 200mg/dL despite a normal hemoglobin A1c (HbA1c) level (5.5%). This level of hyperglycemia is qualitatively higher than published CGM data in healthy individuals. One study found individuals (6-24 years of age) had median percent of glucose values over 140mg/dL ranging from 1.2 to 2.4% with IQR values ranging from 0.3 to 4.4%, based on age categories.1Conclusions: In females with PHP1A, with overweight or obesity, CGM placement at home is a feasible way to collect real-world data. Participants with rare diseases often must travel for clinical and research care and CGMs may provide insight into glycemia without requiring an in-person visit. This “real-world” data mirrors research data reporting glycemia with normal HbA1c levels. The rate of hyperglycemia is higher than expected in some individuals with PHP1A though more data is needed. Ongoing studies will evaluate matched control participants and assess drivers of glycemia including inactivity and decreased sleep duration, as measured by actigraphy.

Defining continuous glucose monitor time in range in a large community-based cohort without diabetes.

Continuous glucose monitor (CGM) companies are beginning to market these sensors to populations without diabetes, but the range of CGM values clinicians should expect to see for this population is unclear because there have been no large studies reporting these ranges. To report the physiological range of continuous glucose monitor (CGM) time in range values observed across glycemic status, including individuals without diabetes, to serve as a reference for clinicians. The Framingham Heart Study, a prospective cohort study. Community-dwelling individuals. Adults with normoglycemia (n=560), prediabetes (n=463), and diabetes (n=152). We conducted a cross-sectional investigation in participants who wore a Dexcom G6 Pro CGM for ≥7 complete days. CGM metrics including mean glucose and time spent in glucose ranges. Normoglycemic participants (mean age 58.5y, 64.5% women, 93.3% non-Hispanic white) spent 87.0% time in the 70-140mg/dL CGM range, and, on average, >15min/day (1.2% time) >180mg/dL. Furthermore, normoglycemic participants spent ∼3 hours/day (12.1% time) with CGM glucose >140mg/dL. On average, participants with prediabetes and diabetes spent 77.1% and 46.2% time in 70-140mg/dL, respectively. Our results contribute to the understanding of the physiological CGM range in >1000 participants without diabetes. These results are also important for clinicians to reference as CGM sensors become more widely accessible to people without known diabetes.

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

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

Application of the outlier detection method for web-based blood glucose level monitoring system

Recent advancements in biosensors have empowered individuals with diabetes to autonomously monitor their blood glucose levels through continuous glucose monitoring (CGM) sensors. Nevertheless, the data collected from these sensors may occasionally include outliers due to the inherent imperfections of the sensor devices. Consequently, the identification of these outliers is critical to determine whether blood glucose levels deviate significantly from the norm, necessitating further action. This study employs an outlier detection approach based on the 3-sigma method and the interquartile range (IQR), along with the application of the Winsorizing technique to correct the identified outliers. Additionally, a web-based system for visualizing blood glucose levels is developed, utilizing both outlier detection methods. In order to assess the system's performance, two types of testing are conducted: black box testing and load testing. The results of black box testing indicate that all test scenarios operate as anticipated. As for the load testing response times, it is observed that the 3-sigma visualization page loads an average of 606.75 milliseconds faster compared to the IQR visualization page. This study's outcomes are expected to enhance data quality, enhance the precision of analyses, and facilitate more informed decision-making by identifying and addressing extreme data points.

Differences Between Glycated Hemoglobin and Glucose Management Indicator in Real-Time and Intermittent Scanning Continuous Glucose Monitoring in Adults With Type 1 Diabetes.

This study demonstrates the difference between glucose management indicator (GMI) and glycated hemoglobin (HbA1c) according to sensor mean glucose and HbA1c status using 2 continuous glucose monitoring (CGM) sensors in people with type 1 diabetes. A total of 275 subjects (117 Dexcom G6 [G6] and 158 FreeStyle Libre 1 [FL]) with type 1 diabetes was included. The G6 and FL sensors were used, respectively, over 90 days to analyze 682 and 515 glycemic profiles that coincide with HbA1c. The mean HbA1c was 6.6% in Dexcom G6 and 7.2% in FL profiles. In G6 profiles, GMI was significantly higher than HbA1c irrespective of mean glucose (all P < .001, mean difference: 0.58% ± 0.49%). The GMI was significantly higher than the given HbA1c when HbA1c was below 8.0% (all P < .001). The discordance was the highest at 0.9% for lower HbA1c values (5.0%-5.9%). The proportion of discordance >0.5% improved from 60.1% to 30.9% when using the revised GMI equation in G6 profiles. In FL profile, the overall mean difference between GMI and HbA1c was 0 (P = .966). The GMI was significantly lower by 0.9% than HbA1c of 9.0% to 9.9% and higher by 0.5% in HbA1c of 5.0% to 5.9% (all P < .001). The GMI is overestimated in G6 users, particularly those with well-controlled diabetes, but the GMI and HbA1c discordance improved with a revised equation from the observed data. The FL profile showed greater discordance for lower HbA1c levels or higher HbA1c levels.

Comparison of Metabolic Control in Children and Adolescents Treated with Insulin Pumps.

While insulin pumps remain the most common form of therapy for youths with type 1 diabetes (T1DM), they differ in the extent to which they utilize data from continuous glucose monitoring (CGM) and automate insulin delivery. The aim of the study was to compare metabolic control in patients using different models of insulin pumps. This retrospective single-center study randomly sampled 30 patients for each of the following treatments: Medtronic 720G without PLGS (predictive low glucose suspend), Medtronic 640G or 740G with PLGS and Medtronic 780G. In the whole study group, we used CGM systems to assess patients' metabolic control, and we collected lipid profiles. In three groups of patients, we utilized CGM sensors (Guardian 3, Guardian 4, Libre 2 and Dexcom G6) to measure the following glycemic variability proxy values: time in range (TIR), time below 70 mg/dL (TBR), time above 180 mg/dL (TAR), coefficient of variation (CV) and mean sensor glucose. Medtronic 640G or 740G and 780G users were more likely to achieve a target time in the target range 70-180 mg/dL (≥80%) [Medtronic 720G = 4 users (13.3%) vs. Medtronic 640G/740G = 10 users (33.3%) vs. Medtronic 780G = 13 users (43.3%); p = 0.0357)] or low glucose variability [Medtronic 720G = 9 users (30%) vs. Medtronic 640G/740G = 18 users (60%) vs. Medtronic 780G = 19 users (63.3%); p = 0.0175)]. Any integration between the insulin pump and CGM was associated with better glycemic control. More advanced technologies and artificial intelligence in diabetes help patients maintain better glycemia by eliminating various factors affecting postprandial glycemia.

A wearable microneedle patch incorporating reversible FRET-based hydrogel sensors for continuous glucose monitoring

Continuous glucose monitors are crucial for diabetes management, but invasive sampling, signal drift and frequent calibrations restrict their widespread usage. Microneedle sensors are emerging as a minimally-invasive platform for real-time monitoring of clinical parameters in interstitial fluid. Herein, a painless and flexible microneedle sensing patch is constructed by a mechanically-strong microneedle base and a thin layer of fluorescent hydrogel sensor for on-site, accurate, and continuous glucose monitoring. The Förster resonance energy transfer (FRET)-based hydrogel sensors are fabricated by facile photopolymerizations of acryloylated FRET pairs and glucose-specific phenylboronic acid. The optimized hydrogel sensor enables quantification of glucose with reversibility, high selectivity, and signal stability against photobleaching. Poly (ethylene glycol diacrylate)-co-polyacrylamide hydrogel is utilized as the microneedle base, facilitating effective skin piercing and biofluid extraction. The integrated microneedle sensor patch displays a sensitivity of 0.029 mM−1 in the (patho)physiological range, a low detection limit of 0.193 mM, and a response time of 7.7 min in human serum. Hypoglycemia, euglycemia and hyperglycemia are continuously monitored over 6 h simulated meal and rest activities in a porcine skin model. This microneedle sensor with high transdermal analytical performance offers a powerful tool for continuous diabetes monitoring at point-of-care settings.

Recent Advancements in Flexible Biosensors for Continuous Glucose Monitoring

Continuous glucose monitoring (CGM) systems have significantly improved diabetes management by continuously monitoring glucose levels in real-time. However, the existing CGM devices, which use inflexible and invasive sensors, pose difficulties in user comfort and can lead to skin irritation. This review explores recent advancements in flexible CGM technologies, highlighting their potential to overcome these limitations. Flexible CGM sensors, utilizing biofluids like sweat, tears, and interstitial fluid, offer a minimally invasive and more comfortable alternative. Recent developments and innovations in materials and fabrication techniques have brought these sensors closer to commercialization, showing noteworthy progress in their design and functionality. We examine flexible CGM prototypes, including sweat-based epidermal sensors, tear-based smart contact lenses, and interstitial fluid-based microneedle sensors. The review concludes by discussing prospects, emphasizing the need for continued innovation and improved manufacturing processes to achieve successful commercialization.

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 .

The Association of High and Low Glycation With Incident Diabetic Retinopathy in Adults With Type 1 Diabetes.

We investigated the risk of incident diabetic retinopathy (DR) among high glycator compared to low glycator patients based on the hemoglobin glycation index (HGI). Visit-to-visit variations in HGI also were assessed. Glycated hemoglobin (HbA1c) and continuous glucose monitoring data were collected up to 7 years prior to the date of eye examination defining incident DR or no retinopathy (control). Hemoglobin glycation index was calculated as difference in measured HbA1c and an estimated A1c from sensor glucose (eA1c) to define high (HbA1c - eA1c >0%) or low (HbA1c - eA1c <0%) glycator. Stable glycators were defined as ≥75% of visits with same HGI category. Logistic regression was used to assess the association between glycation category and incident DR. Of 119 adults with type 1 diabetes (T1D), 49 (41%) were stable low glycator (HbA1c - eA1c <0%), 36 (30%) were stable high glycator (HbA1c - eA1c >0%), and 34 (29%) were unstable glycator. Using alternate criteria to define high vs low glycator (consistent difference in HbA1c - eA1c of > 0.4% or <0.4%, respectively), 53% of the adults were characterized as unstable glycator. Compared to low glycators, high glycators did not have a significantly higher risk for incident DR over time when adjusted for age, T1D duration and continuous glucose monitoring (CGM) sensor type (odds ratio [OR] = 1.31, 95% confidence interval [CI] = 0.48-3.62, P = .15). The risk of diabetic retinopathy was not found to differ significantly comparing high glycators to low glycators in adults with T1D. Moreover, HbA1c - eA1c relationship was not stable in nearly 30% to 50% adults with T1D, suggesting that discordance in HbA1c and eA1c are mostly related either HbA1c measurements or estimation of A1c from sensor glucose rather than physiological reasons.

Budget impact analysis of continuous glucose monitoring in individuals with type 2 diabetes on insulin treatment in England

IntroductionIn 2022, updated guidance from NICE expanded the options for self-monitoring of blood glucose for patients with type 2 diabetes (T2DM), to include continuous glucose monitoring (CGM). In this budget impact analysis, the cost impact of CGM was compared with traditional self-monitoring of blood glucose (SMBG) in adults with T2DM over 1 year from the commissioner perspective in England.Research Design and methodsThe NICE-eligible T2DM cohort was split into 4 subgroups to enable nuanced costing by insulin administration frequency: basal human insulin, premixed insulin, basal-bolus insulin and bolus insulin. The model’s cost components comprised mild and severe hypoglycaemia (SH), diabetic ketoacidosis (DKA), consumables and healthcare resource utilisation in primary and secondary care.ResultsThe introduction of CGM is estimated to be cost additive by approximately £4.6 million in the basecase, driven by increased spending on the CGM device. Overall, healthcare activity was reduced by approximately 20,000 attendances, due to fewer SH and DKA episodes in the CGM arm. General Practitioner (GP) practice-based activity is expected to drop after the first year as patients requiring CGM training is reduced. The budget impact could be neutralised if the CGM sensor was discounted by 13.2% (£29.76 to £25.83).ConclusionsCGM may result in increased spending in the NICE-eligible T2DM cohort but is expected to reduce demand on secondary care services and GP time. These findings may be of interest to local decision-makers who wish to resolve the COVID-19 backlog with transformational investment in primary care to reduce secondary care activity.

Long-term assessment of the NHS hybrid closed-loop real-world study on glycaemic outcomes, time-in-range, and quality of life in children and young people with type 1 diabetes

Hybrid closed-loop (HCL) systems seamlessly interface continuous glucose monitoring (CGM) with insulin pumps, employing specialised algorithms and user-initiated automated insulin delivery. This study aimed to assess the efficacy of HCLs at 12 months post-initiation on glycated haemoglobin (HbA1c), time-in-range (TIR), hypoglycaemia frequency, and quality of life measures among children and young people (CYP) with type 1 diabetes mellitus (T1DM) and their caregivers in a real-world setting. Conducted between August 1, 2021, and December 10, 2022, the prospective recruitment took place in eight paediatric diabetes centres across England under the National Health Service England’s (NHSE) HCL pilot real-world study. A cohort of 251 CYP (58% males, mean age 12.3 years) with T1DM participated (89% white, 3% Asian, 4% black, 3% mixed ethnicity, and 1% other). The study utilised three HCL systems: (1) Tandem Control-IQ AP system, which uses the Tandem t:slim X2 insulin pump (Tandem Diabetes Care, San Diego, CA, USA) with the Dexcom G6® CGM (Dexcom, San Diego, CA, USA) sensor; (2) Medtronic MiniMed™ 780G with the Guardian 4 sensor (Medtronic, Northridge, CA, USA); and (3) the CamAPS FX (CamDiab, Cambridge, UK) with the Ypsomed insulin pump (Ypsomed Ltd, Escrick, UK) and Dexcom G6® CGM.All systems were fully funded by the NHS. Results demonstrated significant improvements in HbA1c (average reduction at 12 months 7 mmol/mol; P < 0.001), time-in-range (TIR) (average increase 13.4%; P < 0.001), hypoglycaemia frequency (50% reduction), hypoglycaemia fear, and quality of sleep (P < 0.001) among CYP over a 12-month period of HCL usage. Additionally, parents and carers experienced improvements in hypoglycaemia fear and quality of sleep after 6 and 12 months of use. In addition to the improvements in glycaemic management, these findings underscore the positive impact of HCL systems on both the well-being of CYP with T1DM and the individuals caring for them.

Differences in glycemic trends due to reconstruction methods after proximal gastrectomy from the perspective of continuous glucose-monitoring.

In recent years, clinicians have focused on the importance of preventing hypoglycemia. We evaluated the impact of different reconstruction procedures after proximal gastrectomy on glycemic variability in non-diabetic patients with gastric cancer. This prospective observational study was conducted between April 2020 and March 2023. Flash continuous glucose-monitoring, a novel method for assessing glycemic control, was used to evaluate the glycemic profiles after gastrectomy. A flash continuous glucose-monitoring sensor was placed subcutaneously at the time of discharge, and glucose trends were evaluated for 2weeks. The anastomotic methods for proximal gastrectomy were esophagogastrostomy in 10 patients and double-tract reconstruction in 10 patients. The time below this range (glucose levels < 70mg/dL) was significantly higher in the double-tract reconstruction group than in the esophagogastrostomy group (p = 0.049). A higher nocturnal time below this range was significantly correlated with an older age and double-tract reconstruction (p = 0.025 and p = 0.025, respectively). These findings provide new insights into reconstruction methods after proximal gastrectomy by assessing postoperative hypoglycemia in non-diabetic patients with gastric cancer.

Optimizing type 2 diabetes management: AI-enhanced time series analysis of continuous glucose monitoring data for personalized dietary intervention.

Despite advanced health facilities in many developed countries, diabetic patients face multifold health challenges. Type 2 diabetes mellitus (T2DM) go along with conspicuous symptoms due to frequent peaks, hypoglycemia <=70 mg/dL (while fasting), or hyperglycemia >=180 mg/dL two hours postprandial, according to the American Diabetes Association (ADA)). The worse effects of Type 2 diabetes mellitus are precisely associated with the poor lifestyle adopted by patients. In particular, a healthy diet and nutritious food are the key to success for such patients. This study was done to help T2DM patients improve their health by developing a favorable lifestyle under an AI-assisted Continuous glucose monitoring (CGM) digital system. This study aims to reduce the blood glucose level fluctuations of such patients by rectifying their daily diet and maintaining their exertion vs. food consumption records. In this study, a well-precise prediction is obtained by training the ML model on a dataset recorded from CGM sensor devices attached to T2DM patients under observation. As the data obtained from the CGM sensor is time series, to predict blood glucose levels, the time series analysis and forecasting are done with XGBoost, SARIMA, and Prophet. The results of different Models are then compared based on performance metrics. This helped in monitoring various trends, specifically irregular patterns of the patient's glucose data, collected by the CGM sensor. Later, keeping track of these trends and seasonality, the diet is adjusted accordingly by adding or removing particular food and keeping track of its nutrients with the intervention of a commercially available all-in-one AI solution for food recognition. This created an interactive assistive system, where the predicted results are compared to food contents to bring the blood glucose levels within the normal range for maintaining a healthy lifestyle and to alert about blood glucose fluctuations before the time that are going to occur sooner. This study will help T2DM patients get in managing diabetes and ultimately bring HbA1c within the normal range (<= 5.7%) for diabetic and pre-diabetic patients, three months after the intervention.

Glucose Color Index: Development and Validation of a Novel Measure of the Shape of Glycemic Variability.

Standard continuous glucose monitoring (CGM) metrics: mean glucose, standard deviation, coefficient of variation, and time in range, fail to capture the shape of variability in the CGM time series. This information could facilitate improved diabetes management. We analyzed CGM data from 141 adults with type 2 diabetes in the Hyperglycemic Profiles in Obstructive Sleep Apnea (HYPNOS) trial. Participants in HYPNOS wore CGM sensors for up to two weeks at two time points, three months apart. We calculated the log-periodogram for each time period, summarizing using disjoint linear models. These summaries were combined into a single value, termed the Glucose Color Index (GCI), using canonical correlation analysis. We compared the between-wear correlation of GCI with those of standard CGM metrics and assessed associations between GCI and diabetes comorbidities in 398 older adults with type 2 diabetes from the Atherosclerosis Risk in Communities (ARIC) study. The GCI achieved a test-retest correlation of R = .75. Adjusting for standard CGM metrics, the GCI test-retest correlation was R = .55. Glucose Color Index was significantly associated (p < .05) with impaired physical functioning, frailty/pre-frailty, cardiovascular disease, chronic kidney disease, and dementia/mild cognitive impairment after adjustment for confounders. We developed and validated the GCI, a novel CGM metric that captures the shape of glucose variability using the periodogram signal decomposition. Glucose Color Index was reliable within participants over a three-month period and associated with diabetes comorbidities. The GCI suggests a promising avenue toward the development of CGM metrics which more fully incorporate time series information.

Beyond HbA1c: Identifying Gaps in Glycemic Control Among Children and Young People with Type 1 Diabetes Using Continuous Glucose Monitoring.

To describe continuous glucose monitoring (CGM) derived glycemic variables, and study their association with HbA1c and socio-economic factors in young people with Type 1 diabetes mellitus (T1DM). Ninety-two participants [age 15.7 ± 5.0 y (mean ± SD), HbA1c 8.0 ± 1.5% (mean ± SD)] wore a professional CGM sensor for 14 d. Median (IQR) time in range (TIR) was 41 (18)%. Participants spent 41 ± 20% of their day in hyperglycemia (>180mg/dl), and 14 (13)% in hypoglycemia (<70mg/dl). High glycemic variability (percent CV >36%) was seen in 92% participants. Older age at diagnosis was associated with higher TIR (β = 0.267, p = 0.01), lower time above range (TAR) (β = -0.352, p <0.001), but higher time below range (TBR) (β = 0.274, p = 0.006). The use of NPH vs. glargine basal insulin was associated with higher TBR (β = -0.262, p = 0.009) but lower TAR (β = 0.202, p = 0.041). HbA1c showed negative correlation with TIR (r= -0.449, p <0.001) and TBR (r= -0.466, p <0.001) and positive correlation with TAR (r = 0.580, p <0.001) and mean glucose (r = 0.589, p <0.001). These data demonstrate wide gaps between the recommended vs. real world glycemic variables in patients with T1DM in thisregionon multiple daily insulin injections. CGM identifies glycemic variability and complements HbA1c in improving glycemic control.

Glycaemic Effects of a 156-km Ultra-trail Race in Athletes: An Observational Field Study.

Ultra-trail running races pose appreciable physiological challenges, particularly for glucose metabolism. Previous studies that yielded divergent results only measured glycaemia at isolated times. We aimed to explore the impact of an ultra-endurance race on continuously measured glycaemia and to understand potential physiological mechanisms, as well as the consequences for performance and behavioural alertness. Fifty-five athletes (78% men, 43.7 ± 9.6years) ran a 156-km ultra-trail race (six 26-km laps, total elevation 6000m). Participants wore a masked continuous glucose monitoring sensor from the day before the race until 10days post-race. Blood was taken at rest, during refuelling stops after each lap, and after 24-h recovery. Running intensity (% heart rate reserve), performance (lap times), psychological stress, and behavioural alertness were explored. Linear mixed models and logistic regressions were carried out. No higher risk of hypo- or hyperglycaemia was observed during the exercise phases of the race (i.e. excluding stops for scientific measurements and refuelling) compared with resting values. Laps comprising a greater proportion of time spent at maximal aerobic intensity were nevertheless associated with more time > 180mg/dL (P = 0.021). A major risk of hyperglycaemia appeared during the 48-h post-race period compared with pre-race (P < 0.05), with 31.9% of the participants spending time with values > 180mg/dL during recovery versus 5.5% during resting. Changes in circulating insulin, cortisol, and free fatty acids followed profiles comparable with those usually observed during traditional aerobic exercise. However, creatine phosphokinase, and to a lesser extent lactate dehydrogenase, increased exponentially during the race (P < 0.001) and remained high at 24-h post-race (P < 0.001; respectively 43.6 and 1.8 times higher vs. resting). Glycaemic metrics did not influence physical performance or behavioural alertness. Ultra-endurance athletes were exposed to hyperglycaemia during the 48-h post-race period, possibly linked to muscle damage and inflammation. Strategies to mitigate muscle damage or subsequent inflammation before or after ultra-trail races could limit recovery hyperglycaemia and hence its related adverse health consequences. NCT05538442 2022-09-21 retrospectively registered.

Abstract P461: Concordance of Hypoglycemia Symptoms Reported in Real-Time via Smartphone and Hypoglycemia Detected Using Continuous Glucose Monitoring (CGM) in Adults With Type 2 Diabetes

Introduction: Understanding the association between low glucose readings (hypoglycemia) detected by CGM and hypoglycemic symptoms experienced by people with diabetes is critically important for diabetes management. However, tools that capture hypoglycemic symptoms in real-time are lacking. Objective: Implement a real-time hypoglycemia symptom reporting tool and examine agreement between self-reported hypoglycemia symptoms and CGM-detected hypoglycemia. Methods: We developed a survey tool for real-time reporting of symptoms via smartphone ( Fig. 1A ) using REDCap, and implemented this tool in an ongoing, randomized clinical trial of adults with type 2 diabetes, the Dietary Approaches to Stop Hypertension for Diabetes (DASH4D) trial. DASH4D participants wore a CGM sensor (Abbott Libre Pro) for up to 5 periods (max 2-weeks per period), and we offered the option to report symptoms using the real-time survey tool during each CGM wear. We examined the concordance of real-time reported symptoms and CGM-detected hypoglycemia (glucose &lt;70 mg/dL for &gt; 15 minutes) ( Fig. 1B ). Concordance was defined as CGM-detected hypoglycemia occurring within 30 minutes before or after a reported symptom. Results: Of 159 participants who wore a CGM, 114 (72%; mean age 64 years; 86% Black; 64% female) used the real-time symptom reporting tool (the remainder used a paper-based booklet). Across all CGM wear-periods (N=280), participants reported a mean 1.94 symptoms per period. Fatigue was the most common (37 occurrences); unsteadiness and passing out were not reported ( Fig. 1C ). The overall concordance between self-reported symptoms and CGM-detected hypoglycemia was 3% ( Fig. 1D ). Conclusion: We developed a simple and secure smartphone-based tool allowing patients with diabetes to report hypoglycemic symptoms in real-time. Concordance of CGM-detected hypoglycemia with symptoms was low, indicating substantial disconnect between perceived symptoms and CGM-detected hypoglycemic events among adults with type 2 diabetes. [NCT04286555]

Abstract P144: 1,5-Anhydroglucitol as a Marker of Hyperglycemic Excursions Defined by Continuous Glucose Monitoring in Older Adults

Background: Low levels (≤6 μg/mL) of serum 1,5-anhydroglucitol (1,5-AG) are thought to reflect periods of hyperglycemia. During periods of hyperglycemia, 1,5-AG competes with glucose for reabsorption in the kidney and is excreted in the urine, resulting in low serum concentrations of 1,5-AG. However, the performance of 1,5-AG to identify hyperglycemic excursions detected by continuous glucose monitoring (CGM) is unknown. Methods: We conducted a cross-sectional analysis of participants with diabetes in the Atherosclerosis Risk in Communities Study who wore a CGM sensor in 2021-2022 for up to 2 weeks. We evaluated the performance of 1,5-AG, modeled continuously and dichotomously (≤6 μg/mL, &gt;6 μg/mL), to identify CGM derived metrics of hyperglycemia. Hyperglycemic excursions were defined as percent time spent with CGM glucose above 140 mg/dL, area under the curve (AUC) &gt;140 mg/dL*minute, peak (maximum) CGM glucose, and the coefficient of variation (CV). Results: Among 322 participants with diabetes (mean age 83, 55% female), 22% had 1,5-AG≤6 μg/mL. The median CGM wear time was 13.9 days. The mean CGM glucose was 125 mg/dL (SD, 28) among those with 1,5-AG&gt;6 μg/mL and 156 mg/dL (SD, 45) among those with 1,5-AG≤6 μg/mL. 1,5-AG was inversely associated with percent of time CGM glucose &gt;140 mg/dL ( Figure ), AUC &gt;140 mg/dL (Pearson correlation, r =-0.25), peak CGM glucose ( r =-0.41) and CV ( r =-0.25). Individuals with 1,5-AG≤6 μg/mL spent 5.5 hours more per day above 140 mg/dL compared to those with 1,5-AG&gt;6 μg/mL. Conclusion: 1,5-AG is correlated with CGM-defined hyperglycemia. Low levels of 1,5-AG identify older adults with diabetes experiencing glycemic excursions.