Continuous Glucose Monitoring Data Research Articles

Validating the Use of Continuous Glucose Monitors With Nondiabetic Recreational Runners.

Continuous glucose monitors (CGMs) are becoming increasingly popular among endurance athletes despite unconfirmed accuracy. We assessed the concurrent validity of the FreeStyle Libre 2 worn on 2 different sites at rest, during steady-state running, and postprandial. Thirteen nondiabetic, well-trained recreational runners (age = 40 [8]y, maximal aerobic oxygen consumption = 46.1 [6.4]mL·kg-1·min-1) wore a CGM on the upper arm and chest while treadmill running for 30, 60, and 90minutes at intensities corresponding to 50%, 60%, and 70% of maximal aerobic oxygen consumption, respectively. Glucose was measured by manually scanning CGMs and obtaining a finger-prick capillary blood glucose sample. Mean absolute relative difference, time in range, and continuous glucose Clarke error grid analysis were used to compare paired CGM and blood glucose readings. Across all intensities of steady-state running, we found a mean absolute relative difference of 13.8 (10.9) for the arm and 11.4 (9.0) for the chest. The coefficient of variation exceeded 70%. Approximately 47% of arm and 50% of chest paired glucose measurements had an absolute difference ≤10%. Continuous glucose Clarke error grid analysis indicated 99.8% (arm) and 99.6% (chest) CGM data fell in clinically acceptable zones A and B. Time-in-range analysis showed reduced accuracy at lower glucose levels. However, CGMs accurately detected trends in mean glucose readings over time. CGMs are not valid for point glucose monitoring but appear to be valid for monitoring glucose trends during steady-state exercise. Accuracy is similar for arm and chest. Further research is needed to determine whether CGMs can detect important events such as hypoglycemia during exercise.

The emotional burden of type 1 diabetes: A cross-sectional study to understand associations between diabetes distress and glucose metrics in adulthood.

Advancements in type 1 diabetes (T1D) management, such as continuous glucose monitoring (CGM), have helped people achieve narrower glucose ranges, but associations between CGM and diabetes distress are unclear. Although higher HbA1c is associated with higher distress, associations with other glucose metrics are unknown. To better understand this relationship, we characterized diabetes distress in a sample of CGM users and compared differences in glucose metrics (measured via CGM) between those with higher versus lower distress. CGM users with T1D from the T1D Exchange Registry completed an online survey including diabetes distress (DDS-2) and shared CGM data (N = 199). CGM metrics were computed from all available data within 3 months prior to survey completion. Participants were grouped by distress level: lower (DDS-2 < 3, n = 120) or higher (DDS-2 ≥ 3, n = 79). Welch's t-tests were used to compare mean differences in CGM metrics between groups and MANCOVA was used to further probe mean differences. Approximately 39.7% participants reported higher diabetes distress. Welch's t-tests revealed participants with higher distress spent significantly more time in higher glucose ranges (above 180 mg/dL and above 250 mg/dL), less time in target glucose ranges (between 70 and 180 mg/dL and between 70 and 140 mg/dL) and had higher glucose management index values compared to those with lower distress (p < 0.01). MANCOVA models showed similar results. CGM users continue to experience diabetes distress. Moreover, higher distress appears to be associated with hyperglycaemia. These findings provide support for broader screening efforts for diabetes distress.

Prediction of Incident Diabetic Retinopathy in Adults With Type 1 Diabetes Using Machine Learning Approach: An Exploratory Study.

Early detection and intervention are crucial for preventing vision-threatening diabetic retinopathy (DR) in adults with type 1 diabetes (T1D). This exploratory study uses machine learning on continuous glucose monitoring (CGM) data to identify factors influencing DR and predict high-risk individuals for timely intervention. Between June 2018 and March 2022, adults with T1D with incident DR or no retinopathy (control) were identified. The CGM data were collected retrospectively for up to seven years before the date of defining incident DR or no retinopathy. A mixture of three machine learning algorithms was trained and evaluated in two different scenarios, using different glycemic features extracted from CGM traces (scenario 1), and the two principal components (two PCs; exposure to hyperglycemia and hypoglycemia risk) of those features (scenario 2). Classifiers were evaluated through 10-fold cross-validation using the receiver operating characteristic area under the curve (AUC-ROC) to select the best classification model. The CGM data of 30 adults with incident DR (mean±SD age of 21.2±9.4 years, glycated hemoglobin [HbA1c] of 8.6%±1.0%, and body mass index [BMI] of 24.5±4.8 kg/m2) and 30 adults without DR (age of 41.8±14.7 years, HbA1c of 7.0%±0.9%, and BMI of 26.2±3.6 kg/m2) were included in this analysis. In scenario 2, classifiers outperformed scenario 1, resulting in an average AUC-ROC increase to 0.92 for two of three models, indicating that the two PCs captured vital classification data, representing the most discriminative aspects and enhancing model performance. Machine learning approaches using CGM data may have potential to aid in identifying adults with T1D at risk of DR.

Impact of Hypoglycemia on Glucose Variability over Time for Individuals with Open-Source Automated Insulin Delivery Systems

This study investigates glucose conditions preceding and following various hypoglycemia levels in individuals with type 1 diabetes using open-source automated insulin delivery (AID) systems. It also seeks to evaluate relationships between hypoglycemia and subsequent glycemic variability. Methods: Analysis of continuous glucose monitor (CGM) data from 122 adults with type 1 diabetes using open-source AID from the OpenAPS Data Commons was conducted. This study comprehensively analyzed the effects of hypoglycemia on glycemic variability, covering time periods before and after hypoglycemia. Results: Glucose variability normalization post-hypoglycemia can take up to 48 h, with severe hypoglycemia (41–50 mg/dL) linked to prolonged normalization. A cyclical pattern was observed where hypoglycemia predisposes individuals to further hypoglycemia, even with AID system use. A rise in glucose levels often precedes hypoglycemia, followed by an elevated mean time above range (TAR) post-hypoglycemia, indicating a ‘rebound’ effect. The experimental results are further validated on T1DEXI data (n = 222), originating from commercial AID systems. Different hypoglycemia categorization approaches did not show significant differences in glycemic variability outcomes. The level of hypoglycemia does influence the pattern of subsequent glucose fluctuations. Conclusion: Hypoglycemia, especially at lower levels, significantly impacts subsequent glycemic variability, even with the use of open-source AID systems. This should be studied further with a broader set of commercial AID systems to understand if these patterns are true of all types of AID systems. If these patterns occur in all types of AID systems, it underscores potential opportunities for enhancements in AID algorithms and highlights the importance of educating healthcare providers and people with diabetes about post-hypoglycemia glucose variability.

The Contribution of Postprandial Glucose Levels to Hyperglycemia in Type 2 Diabetes Calculated from Continuous Glucose Monitoring Data: Real World Evidence from the DIALECT-2 Cohort.

Traditional glycemic monitoring in type 2 diabetes is limited, whereas continuous glucose monitoring (CGM) offers better insights into glucose fluctuations. This study aimed to determine the correlations and relative contributions of fasting plasma glucose (FPG) and postprandial plasma glucose (PPG) levels to hyperglycemia. We utilized CGM and recorded carbohydrate intake data from lifestyle diaries of 59 patients enrolled in the Diabetes and Lifestyle Cohort Twente (DIALECT-2). Correlations between FPG and the glucose management indicator (GMI), FPG and Time Above Range (TAR), PPG and GMI, and PPG and TAR were conducted. Daily and mealtime relative contributions of PPG and FPG to glycated hemoglobin (HbA1c) and GMI were determined, considering two ranges: on target (<7.0%, 53 mmol/mol) and not on target (≥7.0%, 53 mmol/mol). Correlations between mealtime PPG and carbohydrate consumption were examined. FPG and PPG correlated with GMI (r = 0.82 and 0.41, respectively, p < 0.05). The relative contribution of PPG in patients with HbA1c, GMI, and TAR values not on target was lower than in patients with HbA1c, GMI, and TAR values on target. When analyzing different mealtimes, patients with target GMI values had a higher PPG (73 ± 21%) than FPG after breakfast (27 ± 21%, p < 0.001). Individuals with elevated GMI levels had lower PPG after lunch (30 ± 20%), dinner (36 ± 23%), and snacks (34 ± 23%) than FPG. PPG after breakfast positively correlated (r = 0.41, p < 0.01) with breakfast carbohydrate intake. Both PPG and FPG contribute to hyperglycemia, with PPG playing a larger role in patients with better glycemic control, especially after breakfast. Targeting PPG may be crucial for optimizing glucose management.

Predicting the Risk of Adverse Neonatal Outcomes in Women With Insulin-Treated Diabetes: Is It Time for a Pregnancy-Specific Glycemic Risk Index (GRI)?

The Glycemia Risk Index (GRI) describes the quality of glycemic control, emphasizing extreme hypoglycemia and hyperglycemia more than less extreme values. However, a pregnancy-specific GRI (pGRI), tailored to the tighter target glucose range required during pregnancy, has not been established. We retrospectively evaluated clinical, metabolic, and Continuous Glucose Monitoring (CGM) data across pregnancy in women with insulin-treated diabetes, managed between September 2021 and March 2024 at the University Hospital of Pisa. First and second levels of hyperglycemia (TAR1: 140-180 mg/dL, TAR2: >180 mg/dL) and hypoglycemia (TBR1: 63-54 mg/dL, TBR2: <54 mg/dL) were used to calculate the pGRI at each trimester. Logistic regression analysis investigated the association between pGRI and risk of at least one adverse neonatal outcome (among preterm delivery, macrosomia, large for gestational age, small for gestational age, neonatal hypoglycemia, neonatal jaundice, and neonatal intensive care unit admission). Of 45 pregnant women, 25 (56%) experienced at least one adverse neonatal outcome. In the third trimester, women with adverse outcomes had significantly higher total TAR (26 [12-32]% vs 10 [4-23]%, P = .018) and lower TIR (71 [64-83]% vs 88 [75-92]%, P = .007). Specifically, the difference was notable in TAR2 (6 [2-15]% vs 1 [0-4]%, P = .004), whereas TAR1 was comparable between the 2 groups. Accordingly, third trimester pGRI was higher in women with adverse neonatal outcomes (38 [18-49]% vs 18 [10-31]%, P = .013) and, at logistic regression, slightly but significantly increased the risk of adverse neonatal outcomes (1.044 [1.004-1.086], P = .024). Pregnant women with insulin-treated diabetes reporting adverse neonatal outcomes spent more time in hyperglycemia, particularly in extreme hyperglycemia. Therefore, the level of hyperglycemia should always be assessed during pregnancy. The pGRI, emphasizing extreme hyperglycemia, may be a novel comprehensive tool for assessing the risk of adverse neonatal outcomes.

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

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

Technology and Continuous Glucose Monitoring Access, Literacy, and Use Among Patients at the Diabetes Center of an Inner-City Safety-Net Hospital: Mixed Methods Study.

Despite the existence of an increasing array of digital technologies and tools for diabetes management, there are disparities in access to and uptake and use of continuous glucose monitoring (CGM) devices, particularly for those most at risk of poor diabetes outcomes. This study aims to assess communication technology and CGM access, literacy, and use among patients receiving treatment for diabetes at an inner-city safety-net hospital. A survey on digital technology ownership and use was self-administered by 75 adults with type 1 and type 2 diabetes at the diabetes clinic of Grady Memorial Hospital in Atlanta, Georgia. In-depth interviews were conducted with 16% (12/75) of these patient participants and 6 health care providers (HCPs) to obtain additional insights into the use of communication technology and CGM to support diabetes self-management. Most participants were African American (66/75, 88%), over half (39/75, 52%) were unemployed or working part time, and 29% (22/75) had no health insurance coverage, while 61% (46/75) had federal coverage. Smartphone ownership and use were near universal; texting and email use were common (63/75, 84% in both cases). Ownership and use of tablets and computers and use and daily use of various forms of media were more prevalent among younger participants and those with type 1 diabetes, who also rated them as easier to use. Technology use specifically for diabetes and health management was low. Participants were supportive of a potential smartphone app for diabetes management, with a high interest in such an app helping them track blood sugar levels and communicate with their care teams. Younger participants showed higher levels of interest, perceived value, and self-efficacy for using an app with these capabilities. History of CGM use was reported by 56% (42/75) of the participants, although half (20/42, 48%) had discontinued use, above all due to the cost of the device and issues with its adhesive. Nonuse was primarily due to not being offered CGM by their HCP. Reasons given for continued use included convenience, improved blood glucose control, and better tracking of blood glucose. The in-depth interviews (n=18) revealed high levels of satisfaction with CGM by users and supported the survey findings regarding reasons for continued use. They also highlighted the value of CGM data to enhance communication between patients and HCPs. Smartphone ownership was near universal among patients receiving care at an inner-city hospital. Alongside the need to address barriers to CGM access and continued use, there is an opportunity to leverage increased access to communication technology in combination with CGM to improve diabetes outcomes among underresourced populations.

Explainable Machine-Learning Models to Predict Weekly Risk of Hyperglycemia, Hypoglycemia, and Glycemic Variability in Patients With Type 1 Diabetes Based on Continuous Glucose Monitoring.

The aim of this study was to develop and validate explainable prediction models based on continuous glucose monitoring (CGM) and baseline data to identify a week-to-week risk of CGM key metrics (hyperglycemia, hypoglycemia, glycemic variability). By having a weekly prediction of CGM key metrics, it is possible for the patient or health care personnel to take immediate preemptive action. We analyzed, trained, and internally tested three prediction models (Logistic regression, XGBoost, and TabNet) using CGM data from 187 type 1 diabetes patients with long-term CGM monitoring. A binary classification approach combined with feature engineering deployed on the CGM signals was used to predict hyperglycemia, hypoglycemia, and glycemic variability based on consensus targets (time above range ≥5%, time below range ≥4%, coefficient of variation ≥36%). The models were validated in two independent cohorts with a total of 223 additional patients of varying ages. A total of 46 593 weeks of CGM data were included in the analysis. For the best model (XGBoost), the area under the receiver operating characteristic curve (ROC-AUC) was 0.9 [95% confidence interval (CI) = 0.89-0.91], 0.89 [95% CI = 0.88-0.9], and 0.8 [95% CI = 0.79-0.81] for predicting hyperglycemia, hypoglycemia, and glycemic variability in the interval validation, respectively. The validation test showed good generalizability of the models with ROC-AUC of 0.88 to 0.95, 0.84 to 0.89, and 0.80 to 0.82 for predicting the glycemic outcomes. Prediction models based on real-world CGM data can be used to predict the risk of unstable glycemic control in the forthcoming week. The models showed good performance in both internal and external validation cohorts.

6894 Disparities in Continuous Glucose Monitor Utilization and Real-Time Remote Sharing in Patients with Type 2 Diabetes Treated with Insulin on Medicare

Abstract Disclosure: K.L. Flint: None. T. Ting: None. K. Rivera: None. P. Tamang: None. C.A. Colling: None. J.H. Li: None. M.S. Putman: Consulting Fee; Self; Synspira Therapeutics. Research Investigator; Self; Dexcom. Other; Self; Vertex Pharmaceuticals Incorporated. Introduction: Continuous glucose monitors (CGM) are FDA-approved for the management of diabetes and have been shown to improve glycemic control. For optimal utilization, CGM can be connected to cloud-based clinic portals for real-time sharing of glycemic data with clinicians to guide clinical care. Although studies have previously identified disparities in access to CGM for patients who receive Medicare, limited data are available examining disparities in CGM utilization and data sharing. Hypothesis: We hypothesized that racial and socioeconomic disparities in CGM access exist both in utilization and in real-time remote sharing of CGM data. Methods: This was a retrospective cohort study examining patients with type 2 diabetes on insulin using Medicare as primary or secondary insurance in a single diabetes clinic affiliated with a tertiary care medical center. Clinical data extracted from the electronic health record (EHR) included age, self-reported race and ethnicity, sex, preferred language, education level, ZIP code-based median household income, and enrollment in the EHR-patient portal. The most recent diabetes clinic note for each patient as of October 2023 was reviewed to assess whether each patient was using CGM at the time of the visit. The clinic’s CGM portal accounts were reviewed to assess whether the patient was connected to and actively sharing CGM data with the clinic. Two sample t-tests and chi-square tests were used to evaluate continuous and categorical predictors of CGM use and real-time remote sharing, respectively. Results: Of the 847 patients who qualified for inclusion, 420 (49.6%) were using CGM and 213 (25.1%) were sharing CGM data in real-time. Compared to patients not using CGM (n=427), patients using CGM were younger (70.8 years vs 73.5 years, p&amp;lt;0.001) and more often enrolled in the EHR-based patient portal (91.9% vs 84.1%, p&amp;lt;0.001); however, there were no differences in racial, ethnic, or socioeconomic factors between the groups (p&amp;gt;0.05 for all). Of the patients using CGM, remote sharing of data was associated with younger age (69.5 years vs 72.3 years, p&amp;lt;0.01), identifying as White (74.2% vs 65.0%, p=0.04), using English as preferred language (92.5% vs 84.2%, p=0.02), higher levels of education (p=0.01), and using the EHR-based patient portal (97.2% vs 86.7%, p&amp;lt;0.001). Conclusions: In this cohort of patients with type 2 diabetes on insulin receiving Medicare, there were no significant socioeconomic disparities in CGM utilization. However, racial and socioeconomic disparities were pronounced in real-time remote sharing of CGM data, suggesting that patients from minoritized racial backgrounds, patients who do not use English as their preferred language, and patients with less education may benefit from additional support and training to connect and share their CGM with their providers. Presentation: 6/2/2024

12596 Characterizing Glycemic Variability In Novel Subphenotypes Of Individuals At Elevated Risk For Type 2 Diabetes Using Continuous Glucose Monitoring

Abstract Disclosure: A.J. Kretowski: None. P. Konopka: None. A. Janucik: None. A. Citko: None. A. Paszko: None. M. Klak: None. A. Golonko: None. A. Szklaruk: None. L. Szczerbinski: None. Background: The heterogeneity among individuals at elevated risk for Type 2 Diabetes (T2D) recently has led to the identification of six subphenotypes, each with distinct risks for developing T2D, its complications, and mortality. Our study leveraged Continuous Glucose Monitoring (CGM) to offer a detailed characterization of these subphenotypes, aiming to uncover differences in glycemic variability and control. Methods: CGM data from 616 individuals without diabetes, from the Polish Registry of Diabetes (PolReD) study, classified into six clusters, were analyzed. The PolReD used Freestyle Libre 2 (Abbott) as the CGM device. CGM-derived measures of glucose control and glucose variability were calculated using the iglu R package. We utilized Analysis of Variance (ANOVA) and Analysis of Covariance (ANCOVA), adjusting for age and sex, to identify significant differences in CGM parameters across the clusters. Results: Our analysis identified significant differences in several CGM-derived parameters among the clusters, notably in the Glycemic Risk Assessment Diabetes Equation (GRADE), Standard Deviation of weekly glucose values (SDw), Mean Amplitude of Glycemic Excursions (MAGE), Maximum glucose value, Glucose range, and Mean Absolute Glucose (MAG). Clusters 3 and 5, characterized by beta-cell dysfunction and high insulin resistance respectively, and associated with the highest risk of T2D, exhibited the greatest glucose variability, with elevated risk for glycemic excursions. In contrast, Cluster 4, indicative of a metabolically healthy obesity profile with a low risk of T2D, demonstrated the most stable glycemic control and the lowest variability among the clusters. Conclusions: This study shows that subtypes of patients at elevated risk for T2D exhibit distinct patterns in CGM-derived parameters, indicating variations in glucose control and stability that extend beyond traditional fasting and post-challenge glucose assessments. Notably, we found that subtypes facing the highest T2D risk display increased glycemic variability, despite their differing underlying mechanisms of dysglycemia. These findings highlight the utility of CGM as a tool for identifying individuals at an increased risk of T2D, reducing the need for extensive phenotyping typically required for cluster assignment. Presentation: 6/1/2024

6971 Effects of the Hybrid Closed-loop System in Pediatric-Onset Type 1 Diabetes

Abstract Disclosure: H. Woo: None. J. Yoo: None. S. Shin: None. Y. Lee: None. H. Kim: None. M. Gu: None. J. Kim: None. J. Kim: None. C. Shin: None. Y. Lee: None. Purpose: We evaluated the effectiveness and limitations of the MiniMedTM 770G, a hybrid closed-loop (HCL) system which adjusts basal insulin every five minutes using continuous glucose monitoring (CGM) data, for glycemic control in pediatric-onset type 1 diabetes (T1D) in South Korea. Methods: We enrolled 20 patients (10 boys and 10 girls) who had been using the 770G pump for over three months, initiating its use between February 2022 and May 2023. Prior to this, they were CGM users and were followed for over a year post T1D diagnosis. Clinical and biochemical data, along with CGM metrics such as time in range (TIR), time below range (TBR), time above range (TAR), and coefficient of variation (CV), were analyzed. Additionally, the percentage of SmartGuard Auto Mode system usage was collected. Results: The median age at the start of 770G was 13.97 years (IQR 11.24–17.86), with a median of 6.5 years of duration of type 1 diabetes. After utilizing the 770G pump (n=20), TIR, TAR, and CV improved over the year (P&amp;lt;0.05). Patients utilizing Auto Mode over 85% of the time exhibited significant improvements in HbA1c (P=0.02), TIR (P=0.04), CV (P&amp;lt;0.01), and TAR 250 (P=0.02), in contrast to those who used Auto Mode 85% or less. Hyperglycemia and lack of calibration contributed to over 50% of the exits from Auto Mode. Conclusions: The implementation of HCL in adolescents and young adults with T1D enhanced glycemic control over one year. Educating patients on sustained Auto Mode use without interruptions is crucial. Presentation: 6/2/2024

12503 An Observational Study Of The Glycemic Variability In Type 1 Diabetes Mellitus Women Preconception And During Pregnancy And Its Associations With Adverse Maternal And Neonatal Outcomes

Abstract Disclosure: S. Avula: None. A.R. Ankireddypalli: None. K. Tessier: None. E.R. Seaquist: None. Background: Pregnancy in T1 diabetes mellitus (T1DM) women is associated with increased risk of maternal/fetal complications. Despite glycemic optimization during pregnancy in T1DM, offspring are at increased risk for neonatal complications. Aim: To examine glycemic variability by continuous glucose monitor (CGM) preconception and during pregnancy by trimester and assess the association with adverse maternal/fetal outcomes. Methods: This was a retrospective cohort study of pregnant patients with T1DM who used real-time CGM and delivered at a U.S. tertiary center (2012-2023). Multiple gestations, Cystic fibrosis-related diabetes (CFRD), twin or multiple births, and no valid CGM data for at least one trimester were excluded. 63 met the inclusion criteria. The primary outcome was composite neonatal morbidity (CNM), including large or small for gestational age (LGA/SGA), NICU admission, neonatal hypoglycemia, shoulder dystocia, respiratory distress syndrome, APGAR 1 or 5 minutes &amp;lt; 7, prematurity (&amp;lt;37 weeks), intrauterine growth restriction (IUGR), CPAP, and ventilator. We also collected data about maternal outcomes. Results:54 patients (86%) met at least one outcome included in the CNM. 20 patients (32%) were LGA, 3 (5%) were SGA, 32 (51%) premature, 29 (46%) required NICU admission, 44 (70%) had neonatal hypoglycemia, 3 (5%) had shoulder dystocia, 23 (37%) had respiratory distress, 22 (35%) needed CPAP, 3 (5%) were on ventilator, 11 (18%) had APGAR 1 minute &amp;lt;7. Among maternal outcomes, 18 (29%) developed pre-eclampsia, and 46 (73%) had a cesarean delivery. Overall, individuals with neonatal morbidity had a lower target in range (TIR) and higher target above range(TAR), average glucose, standard deviation(SD), and coefficient of variation (CV) compared with those without the primary composite, but statistical significance was seen for average glucose and TAR. For every 5 mg/dl increase in average glucose (mean of the 3-trimester values), there was a 25% increase in odds of neonatal morbidity (OR 1.25; 95% CI 1.04-1.56; p=0.028). There was a significant association between 1st trimester SD and neonatal morbidity (OR 1.11; 95% CI 1.02-1.24; p=0.027). There was a trend toward significance between the 3rd-trimester average blood glucose and neonatal morbidity (OR 1.26; 95% CI 1.02-1.63; p=0.051). Conclusion: In our single-center cohort, we found maternal average glucose and TAR, as well as first trimester SD, were significantly associated with CNM. This confirms that maternal hyperglycemia and demonstrates that first trimester SD are associated with poorer neonatal outcomes. Presentation: 6/1/2024

8122 CGM use and its Impact on Diabetes Self-Management Behaviors: Insights from a Pilot Study Paving the Way for Targeted Interventions in Specialty Clinic

Abstract Disclosure: K.M. St. Jean: None. P. Underwood: None. R. Longo: None. J. Keels: None. J. Upadhyay: None. Introduction: Continuous glucose monitor (CGM) use is increasing. While the relationship between CGM use and improved diabetes (DM) clinical outcomes is well known (lower hemoglobin A1c (A1c), reduced hypoglycemia events), the mechanisms underlying this association are unknown. Further, the factors that contribute to successful CGM use by patients and whether CGM use influences diabetes self-management behavior (DMB) change are still undetermined. Objective: To understand the extent to which personal CGM use influences DMB versus user-blind CGM. In addition, patient satisfaction and provider perspective were evaluated during the study. Methodology: A pilot-randomized control trial (RCT) was conducted on 30 patients (15 libre-professional CGM and 15 libre 2 CGM). Adults (18-80y) with an ICD-10 diagnosis of DM within the past 12 months, A1c &amp;gt;8% measured within the past 6 months, stable weight within the past 3 months (BMI ± 2 units), able to speak, read, and write English, and a negative pregnancy test for women of childbearing age seen in the Lahey Endocrine Clinic were included.Exclusion criteria included evidence of personal CGM device use prior to the trial, addition of new oral or injectable hypoglycemic agents within 3 months of trial start, current or anticipated acute use of steroids, pregnancy, medical conditions that, per investigator determination, make it unsafe to target A1c &amp;lt;8%, severe psychiatric illness, homeless, current substance abuse, vision or hearing impairment that may affect the use of CGM. All participants were given information on how to incorporate the CGM into daily DM management and general diabetes self-management education (DSME). DMB was measured using the Diabetes Self Care Inventory-Revised scale, which was provided at visit 1 (baseline), visit 2 (2 weeks), and visit 3 (3-6 months). Additional outcome variables, including Diabetes Distress, sleep quality, exercise habits, and nutrition, were obtained. A1c was measured at baseline and at the end of visit 3. Provider satisfaction with patient CGM use was also evaluated using a newly developed provider satisfaction survey. Results: This study remains active for enrollment. Thus far, 14 patients have been recruited (7 in Libre 2 arm and 7 in Libre Pro User blind arm). Based on initial results, personal-CGM use supports improved diabetes self-care. Further, improvements in A1c, glucose time in target range, and lower rates of hypoglycemia are evident in all CGM users. Further, 100% of the providers felt that CGM use by their patients was very effective and helped their clinical decision-making during the visit. Providers did not feel that reviewing CGM data prolonged their visit. Conclusion: This pilot suggests that personal CGM use improves diabetes self-management behavior and reaffirms that CGM use in patients with poorly controlled diabetes is associated with improved DM clinical outcomes. Presentation: 6/3/2024

12314 Effectiveness Of Continuous Glucose Monitors In Patients With Type 2 Diabetes On Washington Medicaid Or Financial Assistance

Abstract Disclosure: A. Deng: None. A. Thirumalai: None. J. Lin: None. M. Weber: None. S. Zuo: None. Continuous glucose monitors (CGMs) have become more accessible for patients with type 2 diabetes mellitus (T2DM) on one insulin injection per day who have either commercial or Medicare plans. However, there are still strict requirements for patients with Washington State Medicaid, creating disparities in diabetes care for vulnerable populations. The aim of this study is to assess the effectiveness and adherence of CGMs in patients with T2DM on Washington State Medicaid plans or institutional financial assistance who are prescribed at least one insulin injection daily, similar to Medicare requirements. This ongoing prospective cohort study enrolled 40 adult patients with T2DM, baseline A1C greater than 7%, and prescribed at least one insulin injection per day from three Harborview Medical Center clinics. Through grant funding, patients were provided with Freestyle Libre 2 CGMs for 6 months. Since this is an ongoing study, we present preliminary results of patients who completed 3 months of CGM use. Effectiveness was measured by the change in A1C from baseline to 3 months after initiation of the CGM (primary outcome) and change in CGM glycemic metrics on ambulatory glucose profile (AGP) from baseline to 3 months after initiation of CGM (secondary outcomes). Assessment of adherence to the CGMs involved tracking the percentage of CGM data captured from the AGP reports recorded at each visit and monitoring dispense history. Twenty patients reached the 3-month follow-up with a median age of 59 years; 60% female, 70% non-English speaking; 45% with Medicaid, and 55% with Harborview Financial Assistance as insurance coverage. Median baseline A1C was 9.2%, duration of diabetes was 10 years, number of diabetes medications was 3, daily insulin injections of 2, and number of blood glucose checks per day was 1. At first CGM download (2 weeks), the median time in range (TIR, glucose 70-180 mg/dL) was 53.5%, coefficient of variation was 35.3% and percent of data captured was 80%. At 3-month follow-up, the median reduction in A1c was –1.2% (IQR -1.8, -0.5). On CGM, the median TIR had increased to 60.5%, coefficient of variation had improved to 31.5% and percent of data captured was 79%. In this ongoing study, preliminary findings show encouraging early evidence of improved glycemic control after CGMs were initiated and patients sustained greater than 70% CGM data captured at 3 months. These results should be interpreted with caution given the small number of participants with results at 3 months. Nonetheless, once the study is complete, the outcomes may be used to inform eligibility requirements for continuous glucose monitors in patients with Washington Medicaid or Harborview Financial Assistance. Presentation: 6/3/2024

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 &amp;gt; 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.

9391 Clinical Accuracy Of Continuous Glucose Monitoring In Post-kidney Transplant Patients With Type 2 Diabetes

Abstract Disclosure: Q. Aziz: None. K. Batra: None. S. Fatima: None. J. Splinter: None. A.L. Champion: None. A.M. Kumar: None. K.E. Izuora: Research Investigator; Self; Novo Nordisk. Background: Continuous glucose monitoring (CGM) devices provide real time blood glucose data to guide therapy in patients with diabetes mellitus. Diabetic nephropathy is a major complication of diabetes which can eventually lead to End Stage Renal Disease (ESRD). Kidney transplant is a treatment option for ESRD and patients who undergo kidney transplant are exposed to anti-rejection therapy, including high-dose steroids, resulting in significant fluctuations in blood glucose. Having a reliable system for monitoring blood glucose continuously, such as a CGM, can provide valuable information to guide optimal diabetes management. Objective This study aims to compare the clinical accuracy of the CGM data with the Point of Care Testing (POCT) glucose values as a reference among post kidney transplant patients with Type 2 diabetes mellitus (T2DM). Methodology This study was conducted at an academic medical center from September 2023 to January 2024, enrolling subjects ≥ 18 years, diagnosed with T2DM , admitted for kidney transplant. Following informed consent, a blinded CGM (Freestyle Libre Pro®) was applied. We collected all POCT and serum glucose values and downloaded corresponding CGM glucose data. Using matched pairs between CGM and POCT glucose values, bias and absolute relative differences (ARD) were calculated. Based on the ARD, the Clarke error grid (CEG) analysis was conducted to quantify the clinical accuracy of CGM and POCT by assigning glucose data points into 5 zones A, B, C, D, and E. Summary statistics for numeric variables included mean and standard deviation, whereas categorical variables were described as counts and proportions. All analyses were performed using SAS, 9.4 version. Results: A total of 22 subjects were analyzed after excluding one patient due to CGM malfunction. Subjects were 58±9.69 years old, and 82% were males. The mean body mass index was 30±6.41 kg/m2 and mean HbA1c was 6.7 ± 1.07%, at baseline. The mean duration of diabetes and ESRD were 19±10.6 years and 3±2.27 years respectively. Average CGM wear time was 76±24.5 hours. The CEG comparing CGM values with POCT values showed 83.79% values in zone A, 15.29% in zone B (combined =99.08%) with the Mean Absolute Relative Difference (MARD) of 13.24%. The CEG comparing CGM values with serum glucose showed 83.1% values in zone A, 16.9% in zone B (combined =100%) with the MARD of 13.10%. Conclusion: Our results indicate that CGM values are comparable to the POCT and serum glucose values in post kidney transplant patients with T2DM receiving high dose steroids. Therefore, use of CGMs should be considered as a useful tool, providing timely information to guide prompt interventions and improve outcomes in this patient population. Presentation: 6/3/2024

5040 Leveraging Continuous Glucose Monitoring Data as an Additional Source for Glucagon Prescription Behavior

Abstract Disclosure: J. Ambalavanan: None. J. Rusticelli: None. D. Isaacs: Consulting Fee; Self; Medtronic Diabetes, Lilly USA, LLC, Novo Nordisk, Insulet Corporation, Abbott Laboratories, Dexcom. H. Xiao: None. J. Bena: None. Z. Jin: None. C. Babiuch: None. M. Lansang: Consulting Fee; Self; Glooko. Research Investigator; Self; Abbott Laboratories, Dexcom, Xeris. Background: Hypoglycemia is a major source of anxiety in, and can be life-threatening for, patients with diabetes (DM). Even though the American Diabetes Association (ADA) recommends glucagon be made available to all individuals taking insulin or at high risk for hypoglycemia (level E evidence), few patients have access to this life-saving medication. Given the rapid adoption of continuous glucose monitoring (CGM) in the management of DM, we aimed to 1) evaluate glucagon prescription by identifying patients with hypoglycemia on CGM reports, and 2) deliver an educational letter to the providers. Methods: Our study had a retrospective chart review and a quality improvement (QI) component. From Mar-Oct 2023, we identified adult patients in the Cleveland Clinic Ohio health system with type 1 or type 2 DM on insulin (including insulin pumps), sulfonylurea, or meglitinide, and hypoglycemia defined as time below range (TBR) ≥4% on CGM reports. We evaluated the percentages of pre-existing and incident glucagon prescription among those patients. For the QI component, we contacted providers individually via letter if the patients did not have a glucagon prescription and shared with them the ADA Standards of Care on hypoglycemia along with information about various forms of glucagon. Whether to prescribe glucagon was left to the clinical judgement of the providers. Information was collected 4 weeks later to see whether there was any change in the prescription practice. Results: Patients numbered 1543, with170 patients having TBR ≥4%. Among patients with hypoglycemia, 37% had pre-existing prescription and 14% incident glucagon prescription, compared with patients without hypoglycemia (p&amp;lt;0.001). Significant (p&amp;lt;0.001) risk factors for hypoglycemia included type 1 DM, insulin-only therapy, use of conventional insulin pumps, and lower A1c. Among patients with TBR ≥4%, 66 (39%) had level 1 hypoglycemia (54 to &amp;lt;70 mg/dl), 88 (52%) had level 2, and 16 (9%) had level 3 (hospitalization or ER admission for hypoglycemia). Pre-existing or incident glucagon prescription was seen in 28% without hypoglycemia, 38% with level 1 hypoglycemia, 49% with level 2, and 63% with level 3 (p&amp;lt;0.001 level 1 vs level 3; level 2 vs no hypoglycemia; level 3 vs no hypoglycemia). Among 70 patients whose providers received education letters, 27 (39%) prescribed glucagon after education. Glucagon emergency kit, glucagon autoinjector, and inhaled glucagon were the top prescription choices. A greater proportion of advanced practice providers responded to our education than physicians (p=0.021). Conclusion: Glucagon prescription remains suboptimal though better than in other studies even among patients with documented hypoglycemia on CGM reports. Effective provider education and communication can increase glucagon prescription. Provider and patient factors should be studied next to determine steps for further improvement. Presentation: 6/1/2024

Continuous glucose monitoring-based metrics and the duration of hypoglycaemia events with once-weekly insulin icodec versus once-daily insulin glargine U100 in insulin-naive type 2 diabetes: an exploratory analysis of ONWARDS 1

Continuous glucose monitoring (CGM) can provide a comprehensive assessment of glycaemic control. This exploratory analysis of the ONWARDS 1 trial assessed CGM-based metrics and CGM-derived hypoglycaemia duration in insulin-naive individuals with type 2 diabetes treated with subcutaneous once-weekly insulin icodec (icodec) versus once-daily insulin glargine U100 (glargine U100). ONWARDS 1 was a 78-week (52-week main treatment phase and a 26-week treatment extension phase plus a 5-week follow-up), randomised, open-label, treat-to-target, phase 3a trial done at 143 sites (outpatient clinics and hospital departments) across 12 countries. Adults (aged ≥18 years) with type 2 diabetes (HbA1c: 7·0-11·0%) who had not previously received insulin were randomly assigned (1:1) via an interactive web-response system to once-weekly icodec or once-daily glargine U100. Double-masked CGM data were collected during treatment initiation (weeks 0-4), midtrial (weeks 22-26), end of main phase (weeks 48-52), end of extension phase (weeks 74-78), and follow-up (weeks 78-83). Secondary and exploratory outcomes were CGM-based metrics, including the mean percentages of time in glycaemic range (TIR; sensor glucose 3·9-10·0 mmol/L [70-180 mg/dL]), time in tight range (TITR; 3·9-7·8 mmol/L [70-140 mg/dL]), time above range (TAR; >10·0 mmol/L [>180 mg/dL]), and time below range (TBR; <3·9 mmol/L [<70 mg/dL] and <3·0 mmol/L [<54 mg/dL]), and CGM-derived hypoglycaemic episode durations (episodes defined by sensor glucose <3·9 mmol/L [<70 mg/dL for ≥15 consecutive minutes]). Analyses were done in the full analysis set (all randomly assigned participants). The ONWARDS 1 trial is registered with ClinicalTrials.gov, NCT04460885, and is complete. Participants were enrolled and randomly assigned in ONWARDS 1 between Nov 25, 2020, and Dec 1, 2022 (n=492 in each treatment group). During treatment initiation, we observed no statistically significant differences in the mean percentages of TIR, TITR, TAR, and TBR with icodec versus glargine U100. During the midtrial, end of main phase, and end of extension phase periods, the mean percentages of TIR and TITR were statistically significantly greater and the mean percentages of TAR statistically significantly lower with icodec versus glargine U100. The mean percentages of TIR met the internationally recommended CGM target (>70%) with icodec but not with glargine U100 during the three periods. TBR (<3·9 mmol/L [<70 mg/dL] and <3·0 mmol/L [<54 mg/dL]) was low and below recommended targets (<4% and <1%, respectively) across all study periods in both treatment groups, with no statistically significant differences between treatment groups for the lower threshold (<3·0 mmol/L [<54 mg/dL]). During the follow-up period, mean percentages of TIR, TITR, TAR, and TBR did not statistically significantly differ with icodec versus glargine U100. The duration of overall hypoglycaemic episodes was similar between treatment groups throughout the trial (median duration ≤35 min). These CGM data support the long-term efficacy and safety of icodec versus glargine U100 during treatment and indicated no increase in the duration of individual hypoglycaemic episodes with icodec versus glargine U100 in insulin-naive individuals with type 2 diabetes. Novo Nordisk.

Using continuous glucose monitoring data in daily clinical practice.

Access to and use of glycemic data are central to optimal management of diabetes. Use of continuous glucose monitoring (CGM) data to guide the management of diabetes has increased dramatically thanks to improved ease of use, accuracy, and availability. Retrospective CGM data collected throughout the day and night allow clinicians to visualize glycemic patterns, and single-page summary views like the Ambulatory Glucose Profile (AGP) Report make rapid interpretation both feasible and intuitive. A systematic approach that integrates retrospective CGM-generated data at clinic visits and other clinical interactions with personal use of CGM data can optimize glycemic management.