Continuous Glucose Monitoring Data Research Articles

Cross-sectional association of continuous glucose monitoring-derived metrics with cerebral small vessel disease in older adults with type 2 diabetes.

To examine cross-sectional associations between continuous glucose monitoring (CGM)-derived metrics and cerebral small vessel disease (SVD) in older adults with type 2 diabetes. In total, 80 patients with type 2 diabetes aged ≥70 years were analysed. Participants underwent CGM for 14 days. From the CGM data, we derived mean sensor glucose, percentage glucose coefficient of variation, mean amplitude of glucose excursion, time in range (TIR, 70-180 mg/dl), time above range (TAR) and time below range metrics, glycaemia risk index and high/low blood glucose index. The presence of cerebral SVD, including lacunes, microbleeds, enlarged perivascular spaces and white matter hyperintensities, was assessed, and the total number of these findings comprised the total cerebral SVD score (0-4). Ordinal logistic regression analyses were performed to examine the association of CGM-derived metrics with the total SVD score. The median SVD score was 1 (interquartile range 0-2). Higher hyperglycaemic metrics, including mean sensor glucose, TAR >180 mg/dl, TAR >250 mg/dl, and high blood glucose index and glycaemia risk index, were associated with a higher total SVD score. In contrast, a higher TIR (per 10% increase) was associated with a lower total SVD score (odds ratio 0.73, 95% confidence interval 0.56-0.95). Glycated haemoglobin, percentage glucose coefficient of variation, mean amplitude of glucose excursions, time below range and low blood glucose index were not associated with total cerebral SVD scores. The hyperglycaemia metrics and TIR, derived from CGM, were associated with cerebral SVD in older adults with type 2 diabetes.

Glycemic control assessed by continuous glucose monitoring during pregnancy in women with type 1 diabetes and its association with preeclampsia, an observational Swedish cohort study.

Women with type 1 diabetes have an increased risk of preeclampsia (PE), but it is not fully understood if degree of glycemic control is associated with this risk. The aim of this study was to assess glycemic control during pregnancy analyzed by continuous glucose monitoring (CGM) in women with and without PE and to investigate if glycemic control is associated with increased risk of PE. A total of 120 pregnant Swedish women with type 1 diabetes using CGM were included. Background factors and pregnancy outcomes were collected from medical records. CGM data were collected via the internet-based platform Diasend. Mean glucose, standard deviation of mean glucose, percentage of time in target, time below target, and time above target were presented for each trimester in women who did or did not develop PE. Associations between CGM-derived metrics and PE were analyzed with logistic regression and adjusted for confounders. Twenty-two women (18.3%) developed PE. There were no significant differences in maternal characteristics between women with and without PE. Glycemic control improved in each trimester but was suboptimal in both groups. Time in target increased from 59% in the non-PE group and 54% in the PE group in the first trimester to 65% in both groups in the third trimester. There were no significant associations between glycemic control and PE after adjustment for confounders. Degree of glycemic control during pregnancy assessed by CGM was not associated with development of PE in women with type 1 diabetes. However, more research is needed to understand the role of glycemic control in relation to development of PE.

Ultra rapid lispro improves postprandial glucose control versus lispro in combination with basal insulin: a study based on CGM in type 2 diabetes in China

AimTo evaluate the efficacy and safety of URLi (ultra rapid lispro insulin) compared to insulin lispro as bolus insulin with basal insulin using CGM in the individuals with type 2 diabetes(T2D) in China.MethodsThis was a double-blind, randomized, parallel, prospective, phase 3 study. Subjects with uncontrolled T2D were recruited and randomized 1:2 into the insulin lispro and URLi groups. Subjects received a consistent basal insulin regimen during the study and self-administered insulin lispro or URLi before each meal throughout the treatment period. Subjects underwent a 3-day continuous glucose monitoring (CGM) at the baseline and endpoint respectively, and then CGM data were analyzed. The primary endpoint was to compare the difference in postprandial glucose (PPG) control using CGM between the two groups.ResultsA total of 57 subjects with T2D completed the study. Our CGM data showed that postprandial glucose excursions after breakfast (BPPGE) in the URLi group was lower than that in the insulin lispro group (1.59 ± 1.57 mmol/L vs 2.51 ± 1.73 mmol/L, p = 0.046). 1-hour PPG was observed to decrease more in the URLi group than that in the insulin lispro group (-1.37 ± 3.28 mmol/L vs 0.24 ± 2.58 mmol/L, p = 0.047). 2-hour PPG was observed to decrease more in the URLi group than that in the insulin lispro group (-1.12 ± 4.00 mmol/L vs 1.22 ± 2.90 mmol/L, p = 0.021). The mean HbA1c level decreased by 1.1% in the URLi group and 0.99% in the insulin lispro group, with no treatment difference (p = 0.642). In the CGM profile, TBR was not significantly different between the two groups (p = 0.743). The weight gain also did not differ between the two groups (p = 0.303).ConclusionURLi can control breakfast PPG better than insulin lispro in adults with T2D in China, while it is non-inferior in improving HbA1c. The incidence of hypoglycemic and weight gain were similar between the two groups.

Correlation Between the Glycemia Risk Index and Longitudinal Hemoglobin A1c in Children and Young Adults With Type 1 Diabetes.

The glycemia risk index (GRI) is a composite metric developed and used to estimate quality of glycemia in adults with diabetes who use continuous glucose monitor (CGM) devices. In a cohort of youth with type 1 diabetes (T1D), we examined the utility of the GRI for evaluating quality of glycemia between clinic visits by analyzing correlations between the GRI and longitudinal glycated hemoglobin A1c (HbA1c) measures. Using electronic health records and CGM data, we conducted a retrospective cohort study to analyze the relationship between the GRI and longitudinal HbA1c measures in youth (T1D duration ≥1 year; ≥50% CGM wear time) receiving care from a Midwest pediatric diabetes clinic network (March 2016 to May 2022). Furthermore, we analyzed correlations between HbA1c and the GRI high and low components, which reflect time spent with high/very high and low/very low glucose, respectively. In this cohort of 719 youth (aged = 2.5-18.0 years [median = 13.4; interquartile range [IQR] = 5.2]; 50.5% male; 83.7% non-Hispanic White; 68.0% commercial insurance), baseline GRI scores positively correlated with HbA1c measures at baseline and 3, 6, 9, and 12 months later (r = 0.68, 0.65, 0.60, 0.57, and 0.52, respectively). At all time points, strong positive correlations existed between HbA1c and time spent in hyperglycemia. Substantially weaker, negative correlations existed between HbA1c and time spent in hypoglycemia. In youth with T1D, the GRI may be useful for evaluating quality of glycemia between scheduled clinic visits. Additional CGM-derived metrics are needed to quantify risk for hypoglycemia in this population.

Summary of clinical investigation plan for The DIATEC trial: in-hospital diabetes management by a diabetes team and continuous glucose monitoring or point of care glucose testing – a randomised controlled trial

BackgroundWorldwide, up to 20 % of hospitalised patients have diabetes mellitus. In-hospital dysglycaemia increases patient mortality, morbidity, and length of hospital stay. Improved in-hospital diabetes management strategies are needed. The DIATEC trial investigates the effects of an in-hospital diabetes team and operational insulin titration algorithms based on either continuous glucose monitoring (CGM) data or standard point-of-care (POC) glucose testing.MethodsThis is a two-armed, two-site, prospective randomised open-label blinded endpoint (PROBE) trial. We recruit non-critically ill hospitalised general medical and orthopaedic patients with type 2 diabetes treated with basal, prandial, and correctional insulin (N = 166).In both arms, patients are monitored by POC glucose testing and diabetes management is done by ward nurses guided by in-hospital diabetes teams. In one of the arms, patients are monitored in addition to POC glucose testing by telemetric CGM viewed by the in-hospital diabetes teams only. The in-hospital diabetes teams have operational algorithms to titrate insulin in both arms. Outcomes are in-hospital glycaemic and clinical outcomes.DiscussionThe DIATEC trial will show the glycaemic and clinical effects of in-hospital CGM handled by in-hospital diabetes teams with access to operational insulin titration algorithms in non-critically ill patients with type 2 diabetes. The DIATEC trial seeks to identify which hospitalised patients will benefit from CGM and in-hospital diabetes teams compared to POC glucose testing. This is essential information to optimise the use of healthcare resources before broadly implementing in-hospital CGM and diabetes teams.Trial registrationProspectively registered at ClinicalTrials.gov with identification number NCT05803473 on March 27th 2023.

The Glycemia Risk Index Predicts Performance of Diabetes Self-Management Habits in Youth With Type 1 Diabetes Mellitus.

The Glycemia Risk Index (GRI) was developed in adults with diabetes and is a validated metric of quality of glycemia. Little is known about the relationship between GRI and type 1 diabetes (T1D) self-management habits, a validated assessment of youths' engagement in habits associated with glycemic outcomes. We retrospectively examined the relationship between GRI and T1D self-management habits in youth with T1D who received care from a Midwest pediatric diabetes clinic network. The GRI was calculated using seven days of continuous glucose monitor (CGM) data, and T1D self-management habits were assessed ±seven days from the GRI score. A mixed-effects Poisson regression model was used to evaluate the total number of habits youth engaged in with GRI, glycated hemoglobin A1c (HbA1c), age, race, ethnicity, and insurance type as fixed effects and participant ID as a random effect to account for multiple clinic visits per individual. The cohort included 1182 youth aged 2.5 to 18.0 years (mean = 13.8, SD = 3.5) comprising 50.8% male, 84.6% non-Hispanic White, and 64.8% commercial insurance users across a total of 6029 clinic visits. Glycemia Risk Index scores decreased as total number of habits performed increased, suggesting youth who performed more self-management habits achieved a higher quality of glycemia. In youth using CGMs, GRI may serve as an easily obtainable metric to help identify youth with above target glycemia, and engagement/disengagement in the T1D self-management habits may inform clinicians with suitable interventions for improving glycemic outcomes.

Continuous glucose monitoring for glycaemic control and cardiovascular risk reduction in patients with type 2 diabetes not on insulin therapy: A clinical trial.

To evaluate the impact of the Dexcom G6 continuous glucose monitoring (CGM) device on glycaemic control and cardiometabolic risk in patients with type 2 diabetes mellitus (T2DM) at high cardiovascular risk who are not on insulin therapy. Adults with T2DM with glycated haemoglobin (HbA1c) >7% and body mass index (BMI) ≥30 kg/m2 not using insulin were enrolled in a two-phase cross-over study. In phase 1, CGM data were blinded, and participants performed standard glucose self-monitoring. In phase 2, the CGM data were unblinded, and CGM, demographic and cardiovascular risk factor data were collected through 90 days of follow-up and compared using paired tests. Forty-seven participants were included (44% women; 34% Black; mean age 63 years; BMI 37 kg/m2; HbA1c 8.4%; 10-year predicted atherosclerotic cardiovascular disease risk 24.0%). CGM use was associated with a reduction in average glucose (184.0 to 147.2 mg/dl, p < .001), an increase in time in range (57.8 to 82.8%, p < .001) and a trend towards lower glucose variability (26.2 to 23.8%). There were significant reductions in HbA1c, BMI, triglycerides, blood pressure, total cholesterol, diabetes distress and 10-year predicted risk for atherosclerotic cardiovascular disease (p < .05 for all) and an increase in prescriptions for sodium-glucose cotransporter 2 inhibitors (36.2 to 83.0%) and glucagon-like peptide-1 receptor agonists (42.5 to 87.2%, p < .001 for both). Dexcom G6 CGM was associated with improved glycaemic control and cardiometabolic risk in patients with T2DM who were not on insulin. CGM can be a safe and effective tool to improve diabetes management in patients at high risk for adverse cardiovascular outcomes.

Real-World Continuous Glucose Monitoring Data from a Population with Type 1 Diabetes in South Korea: Nationwide Single-System Analysis.

Background: We used continuous glucose monitoring (CGM) data to investigate glycemic outcomes in a real-world population with type 1 diabetes (T1D) from South Korea, where the widespread use of CGM and the nationwide education program began almost simultaneously. Methods: Data from Dexcom G6 users with T1D in South Korea were collected between January 2019 and January 2023. Users were included if they provided at least 90 days of glucose data and used CGM at least 70% of the days in the investigational period. The relationship between CGM utilization and glycemic metrics, including the percentage of time in range (TIR), time below range (TBR), and time above range (TAR), was assessed. The study was approved by the Institutional Review Board of Samsung Medical Center (SMC 2023-05-030). Results: A total of 2288 users were included. Mean age was 41.5 years (57% female), with average uploads of 428 days. Mean TIR was 62.4% ± 18.5%, mean TBR <70 mg/dL was 2.6% ± 2.8%, mean TAR >180 mg/dL was 35.0% ± 19.3%, mean glucose was 168.1 ± 35.8 mg/dL, mean glucose management indicator was 7.2% ± 0.9%, and mean coefficient of variation was 36.7% ± 6.0%. Users with higher CGM utilization had higher TIR (67.8% vs. 52.7%), and lower TBR <70 mg/dL (2.3% vs. 4.7%) and TAR >180 mg/dL (30.0% vs. 42.6%) than those with low CGM utilization (P < 0.001 for all). Users whose data were shared with others had higher TIR than those who did not (63.3% vs. 60.8%, P = 0.001). Conclusions: In this South Korean population, higher CGM utilization was associated with a favorably higher mean TIR, which was close to the internationally recommended target. Using its remote data-sharing feature showed beneficial impact on TIR.

A Nutrition-Focused Approach During Continuous Glucose Monitoring Initiation in People With Type 2 Diabetes: Using a Theoretical Framework to Unite Continuous Glucose Monitoring and Food Choices.

Continuous glucose monitoring (CGM) has transformed diabetes care, yet opportunities for further innovations still exist. Some research suggests CGM could be an ideal tool to guide food choices and other healthy lifestyle behaviors, especially for people with type 2 diabetes (T2D). Behavior change theories can be used to understand and describe how CGM users make food-related decisions, which could ultimately lead to the design of more tailored and effective interventions. In this commentary, we describe what it looks like to use the behavior change wheel-a theory-based intervention development framework-to design an intervention for people with T2D who will use CGM data to guide food choices aligned with evidence-based nutrition recommendations. Such frameworks may be beneficial when designing or evaluating future technology-focused behavior change interventions.

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.

Beneficial Recognition of Glucose Variability for Adequate Lifestyle by Continuous Glucose Monitoring (CGM)

Recently, actual changes in blood glucose can be measured by continuous glucose monitoring (CGM) using FreeStyle Libre. The case involves a 67-year-old male patient with type 2 diabetes (T2D) treated with Multiple Daily Insulin (MDI) therapy, who underwent CGM. Analysis of the CGM data revealed that hyperglycemia resulted from increased carbohydrate intake and irregular meal timings and quantities. The estimated HbA1c from CGM was 6.6%, whereas the HbA1c value recorded at the outpatient clinic during the same period was 7.3%. The use of CGM applications encourages diabetic patients to be mindful of their carbohydrate intake in daily life, leading to an increased ratio of time spent in the target range (TIR).

A low-glucose eating pattern is associated with improvements in glycemic variability among women at risk for postmenopausal breast cancer: an exploratory analysis.

High glycemic variability (GV) is a biomarker of cancer risk, even in the absence of diabetes. The emerging concept of chrononutrition suggests that modifying meal timing can favorably impact metabolic risk factors linked to diet-related chronic disease, including breast cancer. Here, we examined the potential of eating when glucose levels are near personalized fasting thresholds (low-glucose eating, LGE), a novel form of timed-eating, to reduce GV in women without diabetes, who are at risk for postmenopausal breast cancer. In this exploratory analysis of our 16-week weight loss randomized controlled trial, we included 17 non-Hispanic, white, postmenopausal women (average age = 60.7 ± 5.8 years, BMI = 34.5 ± 6.1 kg/m2, HbA1c = 5.7 ± 0.3%). Participants were those who, as part of the parent study, provided 3-7 days of blinded, continuous glucose monitoring data and image-assisted, timestamped food records at weeks 0 and 16. Pearson's correlation and multivariate regression were used to assess associations between LGE and GV, controlling for concurrent weight changes. Increases in LGE were associated with multiple unfavorable measures of GV including reductions in CGM glucose mean, CONGA, LI, J-Index, HBGI, ADDR, and time spent in a severe GV pattern (r = -0.81 to -0.49; ps < 0.044) and with increases in favorable measures of GV including M-value and LBGI (r = 0.59, 0.62; ps < 0.013). These associations remained significant after adjusting for weight changes. Low-glucose eating is associated with improvements in glycemic variability, independent of concurrent weight reductions, suggesting it may be beneficial for GV-related disease prevention. Further research in a larger, more diverse sample with poor metabolic health is warranted.Clinical trial registration: ClinicalTrials.gov, NCT03546972.

Continuous Glucose Monitoring-Derived Glucometrics in Adults with Type 1 Diabetes When Switching Basal Insulins.

Context: Limited evidence is available on the real-world effect of insulin degludec (IDeg) in type 1 diabetes (T1D), using continuous glucose monitoring (CGM)-derived metrics. Objective: To assess the real-world effect of switching to IDeg from other long-acting insulins on time in ranges (TIRs) measured by CGM, metabolic control, and insulin dose for people with T1D. Design: This retrospective multicenter study encompassed five time points during a 12-month pre-switch of IDeg and a 12-month follow-up period. For each visit, clinical and CGM data were collected to evaluate temporal trends in glycemic outcomes. Participants: Of 753 persons with T1D who were assessed for eligibility, 486 persons were included, mostly men (61.5%), 47.4 (16.9) years old and diabetes duration of 23.8 (14.2) years at IDeg-initiation. Main Outcome Measure: Primary outcome was the evolution of percent TIR (70-180 mg/dL or 3.9-10.0 mmol/L, TIR) before versus after switch to IDeg. Results: TIR over 24 h increased at 12 months versus baseline (56.7% vs. 52.3%, P < 0.001), mostly during daytime. Time <54 mg/dL (<3.0 mmol/L) over 24 h decreased at 12 months versus baseline (2.02% vs. 2.86%, P < 0.001), mostly during nighttime. Glycated hemoglobin (7.9% vs. 8.1%, P < 0.001) and coefficient of variation (40.0% vs. 41.5%, P < 0.001) improved at 12 months versus baseline. Mean daily basal, bolus and total insulin doses decreased at 12 months (P < 0.001 for all vs. baseline). Conclusions: This retrospective real-world study reports that switching basal insulin significantly improved time spent in glucometric ranges and glycemic variability in the studied population of people with T1D. Clinical Trial Registration number: NCT05434559.

Continuous glucose monitoring and intrapersonal variability in fasting glucose.

Plasma fasting glucose (FG) levels play a pivotal role in the diagnosis of prediabetes and diabetes worldwide. Here we investigated FG values using continuous glucose monitoring (CGM) devices in nondiabetic adults aged 40-70 years. FG was measured during 59,565 morning windows of 8,315 individuals (7.16 ± 3.17 days per participant). Mean FG was 96.2 ± 12.87 mg dl-1, rising by 0.234 mg dl-1 per year with age. Intraperson, day-to-day variability expressed as FG standard deviation was 7.52 ± 4.31 mg dl-1. As there are currently no CGM-based criteria for diabetes diagnosis, we analyzed the potential implications of this variability on the classification of glycemic status based on current plasma FG-based diagnostic guidelines. Among 5,328 individuals who would have been considered to have normal FG based on the first FG measurement, 40% and 3% would have been reclassified as having glucose in the prediabetes and diabetes ranges, respectively, based on sequential measurements throughout the study. Finally, we revealed associations between mean FG and various clinical measures. Our findings suggest that careful consideration is necessary when interpreting FG as substantial intraperson variability exists and highlight the potential impact of using CGM data to refine glycemic status assessment.

Association Between Treatment Adherence and Continuous Glucose Monitoring Outcomes in People With Diabetes Using Smart Insulin Pens in a Real-World Setting.

To evaluate the association of insulin injection adherence, smart insulin pen engagement, and glycemic control using real-world data from 16 countries from adults self-administering basal insulin degludec and bolus insulin with a smart insulin pen (NovoPen 6 or NovoPen Echo Plus) alongside continuous glucose monitoring (CGM). Data were aggregated over 14-day periods. Treatment adherence was defined according to the number of missed basal and missed bolus insulin doses and smart pen engagement according to the number of days with data uploads. Data from 3,945 adults, including 25,157 14-day periods with ≥70% CGM coverage, were analyzed. On average, 0.2 basal and 6.0 bolus insulin doses were missed over 14 days. The estimated probability of missing at least one basal insulin dose over a 14-day period was 17.6% (95% CI 16.5, 18.7). Missing one basal or bolus insulin dose per 14 days was associated with a significant decrease in percentage of time with glucose levels in range (TIR) (3.9-10.0 mmol/L), of -2.8% (95% CI -3.7, -1.8) and -1.7% (-1.8, -1.6), respectively; therefore, missing two basal or four bolus doses would decrease TIR by >5%. Smart pen engagement was associated positively with glycemic outcomes. This combined analysis of real-world smart pen and CGM data showed that missing two basal or four bolus insulin doses over a 14-day period would be associated with a clinically relevant decrease in TIR. Smart insulin pens provide valuable insights into treatment injection behaviors.

Association of Fructosamine Levels With Glycemic Control in Children With Type 1 Diabetes as Determined by Continuous Glucose Monitoring: Results From the CGM TIME Trial

ObjectiveOur aim in this study was to determine the correlation between serum fructosamine and average blood glucose, as measured by continuous glucose monitoring (CGM) in children with type 1 diabetes. MethodsNinety-seven blood samples were collected from 70 participants in the Timing of Initiation of continuous glucose Monitoring in Established pediatric diabetes (CGM TIME) Trial. Each eligible participant had 3 weeks of CGM data with at least 60% CGM adherence before blood collection. Ordinary least-squares linear regression incorporating restricted cubic splines was used to determine the association between fructosamine levels and mean blood glucose. ResultsAn association was found between fructosamine and mean blood glucose, with an F statistic of 9.543 (p<0.001). Data were used to create a formula and conversion chart for calculating mean blood glucose from fructosamine levels for clinical use. ConclusionsThere is a complex relationship between average blood glucose, as determined by CGM and fructosamine. Fructosamine levels may be clinically useful for assessing short-term glycemic control when CGM is not available.

Evaluation of a community-based pharmacy resident-led continuous glucose monitoring program within a family medicine clinic

BackgroundPharmacist-driven continuous glucose monitoring (CGM) is associated with reduced hemoglobin A1c (HbA1c) and achievement of daily glycemic goals. Community-based pharmacists are well-positioned to improve CGM uptake among patients with diabetes due to their accessibility and expertise. However, little data exists evaluating the outcomes of CGM services led by a community-based pharmacist. ObjectiveTo evaluate the impact of a community-based pharmacy resident-driven CGM service on HbA1c, revenue, and patient satisfaction. Practice DescriptionIndependent community pharmacy sharing a clinical services agreement with a primary care clinic for Postgraduate Year One (PGY1) Community-based Pharmacy Residents to provide patient care under general supervision of the physician. Practice innovationPatients were offered CGM services if they were 18+ years with an HbA1c > 7.0% and had insurance coverage for CGM. Enrolled patients engaged in three months of pharmacist-led appointments for CGM application, data interpretation, diabetes education, and lifestyle management. Current Procedural Terminology (CPT) codes 99211, 95250, or 95251 were billed based on each encounter. HbA1c values were collected at program enrollment and conclusion. Patients completed a satisfaction survey at program conclusion. Evaluation methodsDemographics and billed CPT codes were collected from the electronic health record. Descriptive statistics were used to analyze data. ResultsEighteen patients were included. A mean reduction of 1.2% occurred in HbA1c (n = 12; 9.7%−8.5%). Forty CPT codes were billed, generating $3671.40 of revenue. Satisfaction surveys were collected for 50% of participants (n = 9). Most were satisfied with the CGM service and its individual components (n = 8, 89%). Most were willing to continue using CGM devices and receive diabetes education from a pharmacist (n = 8, 89%). ConclusionA community-based pharmacist-led CGM service demonstrated a reduction in HbA1c and generated revenue for the clinic. Patients reported satisfaction and willingness to continue the service.

Machine Learning and Deep Learning Models for Nocturnal High- and Low-Glucose Prediction in Adults with Type 1 Diabetes.

Glucose management at night is a major challenge for people with type 1 diabetes (T1D), especially for those managed with multiple daily injections (MDIs). In this study, we developed machine learning (ML) and deep learning (DL) models to predict nocturnal glucose within the target range (3.9-10 mmol/L), above the target range, and below the target range in subjects with T1D managed with MDIs. The models were trained and tested on continuous glucose monitoring data obtained from 380 subjects with T1D. Two DL algorithms-multi-layer perceptron (MLP) and a convolutional neural network (CNN)-as well as two classic ML algorithms, random forest (RF) and gradient boosting trees (GBTs), were applied. The resulting models based on the DL and ML algorithms demonstrated high and similar accuracy in predicting target glucose (F1 metric: 96-98%) and above-target glucose (F1: 93-97%) within a 30 min prediction horizon. Model performance was poorer when predicting low glucose (F1: 80-86%). MLP provided the highest accuracy in low-glucose prediction. The results indicate that both DL (MLP, CNN) and ML (RF, GBTs) algorithms operating CGM data can be used for the simultaneous prediction of nocturnal glucose values within the target, above-target, and below-target ranges in people with T1D managed with MDIs.

Metabolic and Clinical Outcomes in Type 1 Diabetes in the COVID-19 Pre- and Post-Vaccination Periods in Spain: The COVID-SED1 Study.

Aims: To evaluate the metabolic and clinical outcomes in the Spanish type 1 diabetes mellitus (T1D) population before and after COVID-19 vaccination. Methods: A retrospective observational study was carried out in Spanish public hospitals previously enrolled in the SED1 study. Adults and children with T1D were included and their clinical electronic records were reviewed. Clinical, laboratory, and glucometric parameters from continuous glucose monitoring (CGM) data corresponding to the periods before and after administering the first COVID-19 vaccination were analyzed. Results: A total of 26 centers and 228 patients participated in this new phase of the SED1 study and 187 were finally evaluable (mean age 37.5 ± 15.6 years, 56.7% women). Overall, 94.6% of the sample was vaccinated, and this percentage increased with higher levels of education (p-value = 0.027). In the pre- and post-vaccination periods, respectively, the number of patients with acute hyperglycemic decompensation was 6/161 (3.7%) and 7/161 (4.3%) (p = 1) and with acute hypoglycemic decompensation was 6/161 (3.7%) and 6/161 (3.7%) (p = 1). The HbA1c level was lower in the post-vaccination period(mean ± SD, mg/dL): pre-vaccination 7.4 ± 0.9; post-vaccination 7.2 ± 1.0, (-0.19; p-value = 0.0006). A total of 31.9% of patients (95% CI: 24.7-39.7) in the pre-vaccination period and 45.0% (IC95%: 37.1-53.1) in the post-vaccine period had HbA1c < 7% (p-value < 0.001). Glucometrics from CGM data also showed numerical improvements post-vaccination. Conclusions: The COVID-19 vaccination was highly accepted in the Spanish T1D population, with hesitancy about the COVID-19 vaccine being higher in those with lower educational levels. A mildly better glycemic control was observed in the post-vaccination period.