Normal Reference Range for Glucose Rates of Change in Nondiabetic Individuals Using Continuous Glucose Monitoring.

The Official Journal of ATTD Advanced Technologies & Treatments for Diabetes Conference 22‐25 February 2023 I Berlin & Online

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.

Closed-loop insulin delivery systems, which integrate continuous glucose monitoring (CGM) and algorithms that continuously guide insulin dosing, have been shown to improve glycaemic control. The ability to predict future glucose values can further optimize such devices. In this study, we used machine learning to train models in predicting future glucose levels based on prior CGM and accelerometry data. We used data from The Maastricht Study, an observational population-based cohort that comprises individuals with normal glucose metabolism, prediabetes, or type 2 diabetes. We included individuals who underwent >48h of CGM (n = 851), most of whom (n = 540) simultaneously wore an accelerometer to assess physical activity. A random subset of individuals was used to train models in predicting glucose levels at 15- and 60-minute intervals based on either CGM data or both CGM and accelerometer data. In the remaining individuals, model performance was evaluated with root-mean-square error (RMSE), Spearman's correlation coefficient (rho) and surveillance error grid. For a proof-of-concept translation, CGM-based prediction models were optimized and validated with the use of data from individuals with type 1 diabetes (OhioT1DM Dataset, n = 6). Models trained with CGM data were able to accurately predict glucose values at 15 (RMSE: 0.19mmol/L; rho: 0.96) and 60 minutes (RMSE: 0.59mmol/L, rho: 0.72). Model performance was comparable in individuals with type 2 diabetes. Incorporation of accelerometer data only slightly improved prediction. The error grid results indicated that model predictions were clinically safe (15 min: >99%, 60 min >98%). Our prediction models translated well to individuals with type 1 diabetes, which is reflected by high accuracy (RMSEs for 15 and 60 minutes of 0.43 and 1.73 mmol/L, respectively) and clinical safety (15 min: >99%, 60 min: >91%). Machine learning-based models are able to accurately and safely predict glucose values at 15- and 60-minute intervals based on CGM data only. Future research should further optimize the models for implementation in closed-loop insulin delivery systems.

BackgroundClosed-loop insulin delivery systems, which integrate continuous glucose monitoring (CGM) and algorithms that continuously guide insulin dosing, have been shown to improve glycaemic control. The ability to predict future glucose values can further optimize such devices. In this study, we used machine learning to train models in predicting future glucose levels based on prior CGM and accelerometry data.MethodsWe used data from The Maastricht Study, an observational population‐based cohort that comprises individuals with normal glucose metabolism, prediabetes, or type 2 diabetes. We included individuals who underwent >48h of CGM (n = 851), most of whom (n = 540) simultaneously wore an accelerometer to assess physical activity. A random subset of individuals was used to train models in predicting glucose levels at 15- and 60-minute intervals based on either CGM data or both CGM and accelerometer data. In the remaining individuals, model performance was evaluated with root-mean-square error (RMSE), Spearman’s correlation coefficient (rho) and surveillance error grid. For a proof-of-concept translation, CGM-based prediction models were optimized and validated with the use of data from individuals with type 1 diabetes (OhioT1DM Dataset, n = 6).ResultsModels trained with CGM data were able to accurately predict glucose values at 15 (RMSE: 0.19mmol/L; rho: 0.96) and 60 minutes (RMSE: 0.59mmol/L, rho: 0.72). Model performance was comparable in individuals with type 2 diabetes. Incorporation of accelerometer data only slightly improved prediction. The error grid results indicated that model predictions were clinically safe (15 min: >99%, 60 min >98%). Our prediction models translated well to individuals with type 1 diabetes, which is reflected by high accuracy (RMSEs for 15 and 60 minutes of 0.43 and 1.73 mmol/L, respectively) and clinical safety (15 min: >99%, 60 min: >91%).ConclusionsMachine learning-based models are able to accurately and safely predict glucose values at 15- and 60-minute intervals based on CGM data only. Future research should further optimize the models for implementation in closed-loop insulin delivery systems.

Objectives: There are limited data on time spent in hypoglycemia range among older adults (≥ 60 yrs of age) with type 1 diabetes (T1D). We analyzed blinded continuous glucose monitoring (CGM) data collected at baseline in a randomized trial assessing the effect of CGM on hypoglycemia in older adults with T1D. Methods: Data from 203 older adults with T1D enrolled in the Wireless Innovations for Seniors with Diabetes Mellitus (WISDM) study at 22 sites in the United States were analyzed. Eligibility criteria for the trial included age ≥60 yrs, no use of real-time CGM in the 3 months prior to enrollment and HbA1c <10.0%. All participants wore a blinded Dexcom G4 CGM at baseline for up to 21 days to collect at least 240 hours of CGM data. Associations of demographic and clinical characteristics with CGM-measured glucose levels and variability were assessed using linear regression models. Results: The analysis cohort included 203 participants; 52% female, median age of 68 years (IQR 65, 71), 93% non-Hispanic white and 53% used insulin pumps. Mean HbA1c was 7.5% (SD = 0.9%). Older adults spent a median of 5.0% of time <70 mg/dL (72 minutes per day) and 1.6% of time < 54 mg/dL (24 minutes per day). Impaired hypoglycemia awareness was associated with greater amounts of time spent with glucose levels < 70 and < 54 mg/dL, with a median % time <70 mg/dL of 7% vs. 5% (101 vs. 72 minutes per day, p=0.01) and median % time <54 mg/dL of 3% vs. 1% (43 vs. 14 minutes per day, p=0.008) in those with reduced awareness vs. those who were aware or uncertain. Participants spent a mean 56% of time in target glucose range of 70 to 180 mg/dL (13.4 hours per day), a median 35% of time above 180 mg/dL (8.4 hours per day), and a median 12% of time above 250 mg/dL (2.8 hours per day). Compared with participants who reported a status of “Employed” or “Unemployed”, participants reporting an employment status of “Retired” spent more time in target glucose range (p=0.003) and less time above 180 mg/dL (p=0.02) and above 250 mg/dL (p<0.001). Lower total daily insulin per kg was associated with having a higher percentage of glucose levels in range 70-180 mg/dL (p=0.02), a lower coefficient of variation (CV) and a lower percentage of glucose levels above 180 mg/dL (p=0.04) and above 250 mg/dL (p=0.008). Overall participants had a median CV of 42% with a higher CV observed among participants diagnosed at age <18 vs. adult onset (p=0.02). Conclusions: On review of blinded CGM data, older adults (≥60 yrs of age) with T1D spend over an hour a day in the hypoglycemic range and >100 minutes per day among those with impaired hypoglycemia awareness. Interventions to improve time in range and reduce biochemical hypoglycemia are needed to reduce the risk of severe hypoglycemia in this age group.

Abstracts from ATTD 20158th International Conference on Advanced Technologies &amp; Treatments for DiabetesParis, France—February 18–21, 2015

Introduction: Consensus guidelines recommend reviewing 14 days of continuous glucose monitor (CGM) data when assessing glycemia in people with type 1 diabetes (T1D). Adult studies have shown that 7 days of CGM data provide a reliable assessment of glycemia. Objectives: To understand the minimum amount of CGM data required to assess glycemia in the pediatric T1D population. Methods: Real-world Dexcom G6 CGM data were extracted from cloud-based CGM software for 8 time windows (3, 5, 7, 10, 14, 30, 60, and 90 days), all starting on March 1, 2023. Youth <21 years with T1D and ≥70% CGM active time in each window were included. Pearson correlation and interclass correlation coefficients (ICCs) between 14-day data and other windows were calculated. Differences in the percentage of youth within predetermined thresholds of 14-day CGM metrics (±0.3% glucose management indicator [GMI]; ±5% time in range [TIR]/time in tight range; ±1% time below range <70 and <54 mg/dL) were assessed using chi-squared analyses. Sub-analyses were conducted according to categorical groupings of 14-day TIR, coefficient of variation (CV), and age. Results: A total of 1316 youth were included (45.0% female, 76.9% non-Hispanic White, median age 14.6 years). Median 14-day CGM active time was 97.2% and GMI and TIR were 7.4% (7.0, 7.9) and 60.5% (48.6, 70.6), respectively. Pearson correlation coefficients and ICCs between 14-day and GMI and TIR for all 8 windows were >0.9; however, categorical agreement as defined by the percentage of subjects acceptable thresholds for GMI and TIR only exceeded 90% at 10 days. Although there was no difference in agreement for CGM metrics according to categorical groupings of age, agreement was stronger for youth with TIR ≥70% and CV <36%. Conclusions: Although 14 days of CGM data are considered the gold standard, assessing ∼9.6 days of data in youth with T1D provides a reliable assessment of glycemia. For youth with higher TIR (≥70%) and lower CV (<36%), 7-day CGM data may prove sufficient.

Dear Editor: We are thankful to Mitre et al.1 for addressing the important issue of how to best measure outcomes during closed-loop studies. Mitre et al.1 contrast four approaches: reference glucose, unmodified continuous glucose monitoring (CGM) data, stochastic transformation of CGM data, and their newly proposed CGM transformation to assess time in, time above, and time below target. We previously proposed the stochastic CGM transformation to correct for the bias introduced by the Navigator® CGM device (Abbott Diabetes Care, Alameda, CA) when using a single CGM device to direct closed-loop insulin delivery and simultaneously to assess outcomes.2 Like us, Mitre et al.1 found that the use of unmodified CGM data, in their case obtained using the Sof-Sensor® probe (Medtronic Diabetes, Northridge, CA), leads to an overestimation of time in target during closed-loop but not during conventional pump therapy. Additionally, they document an underestimation of time above target during closed-loop. Thus Mitre et al.1 concur with us in their generic findings but differ in the transformation needed to eliminate the bias. We are grateful to learn that the 15% measurement error we proposed for the Navigator is not applicable to the Sof-Sensor, which tends to underestimate glucose in hyperglycemia, and a simple mapping correction (4–8 mmol/L mapped to 4–7 mmol/L) is applicable. The corrections proposed by Mitre et al.1 and ourselves2 are relatively simple to implement and are advisable for home or transitional studies of closed-loop systems when reference glucose measurements are not available. We are aware that these relatively simple transformations do not fully reflect the complex relationship between CGM and underlying plasma glucose across the physiological glucose range. Furthermore, it is possible that the CGM–plasma glucose relationship (in mathematical terms the conditional probability of plasma glucose given CGM level) may be altered by closed-loop glucose control. Further research may be needed to clarify these issues and to increase confidence in outcomes derived from CGM levels. Finally, we acknowledge that according to Mitre et al.1 the treatment effect defined as the difference between time in target during closed loop and conventional therapy is unbiased when using unmodified Sof-Sensor data. This is reassuring in case researchers decide to analyze outcomes using unmodified Sof-Sensor data but is not generalizable to other sensors and may prohibit comparison with studies using a different sensor.

Young children with type 1 diabetes are vulnerable to glycemic excursion. Continuous glucose monitoring (CGM), combined with variability statistics, can offer a richer and more complete picture of glycemic variability in young children. In particular, we present data for the Average Daily Risk Range (ADRR) and compare ADRR scores calculated using CGM versus self-monitoring of blood glucose (SMBG) data for young children. CGM and SMBG data from 48 young children with type 1 diabetes (mean age, 5.1 years) were used to calculate two separate ADRR scores, using SMBG data (ADRRs) and CGM data (ADRRc), for each child. Additionally, we calculated mean amplitude of glycemic excursion (MAGE) scores for children to examine the concurrent validity of the ADRRs and ADRRc. Young children's mean ADRRc score was significantly greater than their ADRRs score (55±12 and 46±11, respectively; P<0.001). In addition, 74% of the time the children's ADRRc score reflected greater variability risk than their ADRRs score. Examining the concurrent validity, children's ADRRc scores correlated positively with MAGE scores calculated using their CGM and SMBG data, whereas their ADRRs scores only correlated with MAGE scores calculated using SMBG. ADRR scores generated for young children with type 1 diabetes demonstrate a high risk for glucose variability, but ADRR scores generated from CGM data may provide a more sensitive measure of variability than ADRR scores generated from SMBG. In young children with type 1 diabetes, ADRR scores calculated from CGM data may be superior to scores calculated from SMBG for measuring risk of excursion.

Glycemic variability has previously been associated with risk of hypoglycemia. Cornerstone4Care (C4C), a digital patient management program for diabetes mellitus, supports diabetes self-managing and captures patient data inclusive real-world real-time continuous glucose monitoring (CGM) data. The objective was to explore the association between glucose variability and time spent in hypoglycemia (TIH; percent time with interstitial glucose (IG) &amp;lt;54 mg/dL [3.0mmol/L]) and the association between mean IG and TIH in real-world CGM data. Glucose variability was determined based on CGM data from 112 type 1 diabetes (T1D) patients uploaded via the C4C app and calculated as the coefficient of variation (CV) based on the last 14 days’ available CGM data. For each patient a CV-index, mean IG, and proportion of TIH was determined. The variability CV-index ranged from 11% to 56%. Increase in CV appeared to increase TIH. Mean IG did not seem to influence the time spent in hypoglycemia as much. In addition, when CV exceeded the newly recommended cut-off of 36%, the TIH seemed to increase. In conclusion, higher glucose variability (CV) based on CGM data from T1D was observed to increase the time spent in hypoglycemia. The increase seemed to occur with a CV around 30-36% and seemed to be more independent of mean IG. Data source: https://www.cornerstones4care.com/. Disclosure E. Hachmann-Nielsen: Employee; Self; Novo Nordisk A/S. T. Bartholdy: Employee; Self; Novo Nordisk A/S. Stock/Shareholder; Self; Novo Nordisk A/S. C. Djurhuus: Employee; Self; Novo Nordisk A/S. Stock/Shareholder; Self; Novo Nordisk A/S. K. Kvist: Employee; Self; Novo Nordisk A/S.

Introduction: In recent years the use of Continuous Glucose Monitoring (CGM) has significantly increased. The Dexcom G6 CGM sends real-time glucose readings automatically to a smart device. The Dexcom Clarity app allows users to share up-to-date glucose data with providers, enabling remote monitoring of patients' glycemic control. Although studies have shown better glucose control in patients who use CGM, it is unexplored if glucose metrics are further improved in those who share CGM data with providers. In this study, we compared glycemic outcomes in patients who share their data with their providers via the Dexcom G6 sensor to those who do not. Method: We conducted a single-center cross-sectional study in a community hospital with patients who use Dexcom G6 CGM. Data was obtained from Dexcom Clarity for Healthcare Professionals, an online platform for clinicians to review patient trends. The following data was collected for each patient: date of last upload, data sharing status, average glucose, standard deviation, glucose management indicator, glucose range, sensor usage, and coefficient of variation. Two-tailed unpaired Student's t-tests were used to analyze CGM metrics based on data sharing status. Significance was defined as a p-value &amp;lt; 0.05. Results: 123 (61.19%) out of 201 participants shared data with their provider. Participants with sharing data ‘on’ were significantly younger (49±15 years,) and had increased sensor usage (90±21%) compared to those with sharing off. Participants who shared data also spent more time in range (57±24 %), indicating a higher percentage of readings with glucose levels in a healthy range (70-180 mg/dL). Lastly, average glucose was lower in participants who shared data, although not statistically significant. Conclusion: The data-sharing feature on Dexcom G6 was utilized more by younger people and was associated with a higher percentage of time in range. Using the data-sharing feature of the Dexcom G6 could lead to clinically meaningful improvement in glycemic control. Disclosure B. Sadek: None. M. Hashem: None. M. Seetha: None. R. Raj: None.

The accuracy of the GlucoWatch G2 Biographer (GW2B; Cygnus, Inc., Redwood City, CA) was assessed in children and adolescents with type 1 diabetes mellitus (T1DM). During a 24-h clinical research center stay, 89 children and adolescents with T1DM (3.5-17.7 years old) wore 174 GW2Bs and had frequent serum glucose determinations during the day and night and during insulin-induced hypoglycemia and meal-induced hyperglycemia, resulting in 3672 GW2B-reference glucose pairs. The median relative absolute difference between the GW2B and reference glucose values was 16% (25th, 75th percentiles = 7%, 29%). The proposed International Organisation for Standardisation criteria were met for 60% of sensor values. Accuracy was better at higher serum glucose levels than low glucose levels. Accuracy degraded slightly as the sensor aged. Time of day, subject age, gender, or body mass index did not impact GW2B accuracy. There were no cases of serious skin reactions. Although the accuracy of this generation of sensor does not approach that of current home glucose meters, the majority of sensor glucose values are within 20% of the serum glucose. This level of accuracy may be sufficient for detecting trends and modifying diabetes management. Further longitudinal outpatient studies are needed to assess the utility of the GW2B as a management tool to improve glycemic control and decrease the incidence of severe hypoglycemia in children with diabetes.

Dear Editor: Closed-loop systems have been assessed in many inpatient studies under standardized and controlled conditions but are rarely evaluated in outpatient settings. In most outpatient settings, frequent reference glucose measurements to assess the performance of closed-loop systems are impractical, and continuous glucose monitoring (CGM) could be used alternatively to estimate study outcomes. However, the ability of CGM to provide unbiased outcomes might be hindered by its suboptimal accuracy, and certain mathematical transformations of CGM data might be needed before calculating the end points. Hovorka et al.1 proposed a stochastic modification for CGM readings to provide an unbiased assessment for outpatient closed-loop studies. The authors based their analysis on data from the Navigator® CGM system (Abbott Diabetes Care, Alameda, CA). Their work was an important first step tackling the methodological issues in assessing outpatient studies but is limited by the usage of only one CGM system. We assessed whether their findings generalize to the Medtronic Sof-Sensor® glucose sensor (Medtronic Diabetes, Northridge, CA). We performed secondary analysis on data from two randomized trials (one published2 and one still being conducted) comparing conventional pump therapy with dual-hormone closed-loop delivery driven by the Sof-Sensor. We analyzed data from 23 subjects and included only nighttime data (10 p.m.–7 a.m.) to allow comparison with the work of Hovorka et al.1 Similar to Hovorka et al.,1 the unmodified CGM resulted in overestimation of the time spent in target range with closed-loop therapy (P=0.005) but not with the conventional pump therapy (P=0.22) (Table 1). This is due to, as discussed by Hovorka et al.,1 using the sensor both for driving the algorithm and for calculating the end points. In addition, our unmodified CGM resulted in significant underestimation (P=0.001) of the time spent in hyperglycemia during closed-loop therapy, reflecting that this specific sensor under-reads in the hyperglycemia range.2,3 Table 1. Comparison Between Outcome Measures of Closed-Loop Delivery and Conventional Pump Therapy Calculated Using Reference Glucose and Unmodified, Stochastic, and Corrected Continuous Glucose Monitoring We assessed whether the stochastic CGM would eliminate bias, similar to what was reported with the Navigator CGM system.1 The overestimation of time spent in target range and the underestimation of time spent in hyperglycemia were unfortunately still present (Table 1). Moreover, the stochastic CGM resulted in an additional bias in time spent in the hypoglycemia range (overestimation values, Table 1). Our studies indicate that the Sof-Sensor under-reads in the hyperglycemic range but not in the nonhyperglycemic ranges,2 comparable to observations by others.3 Reflecting these unique characteristics, we proposed a modified CGM that maps the target range from 4.0–8.0 mmol/L in the reference glucose domain to 4.0–7.0 mmol/L in the CGM domain. We adopted this 1 mmol/L difference from analysis of CGM–YSI pairs.2 This corrected CGM would alter the estimation of times spent in target range and hyperglycemia but not hypoglycemia. This corrected CGM resulted in unbiased estimates for all the three end points (Table 1). We also compared the paired difference (closed-loop–conventional pump therapy) when measured using plasma glucose data with that when measured using the unmodified CGM data, stochastic CGM data, and the corrected CGM data (Table 1). It is interesting that all CGM methods resulted in similar conclusions compared with the plasma glucose data (P>0.05). This suggests that if the paired difference is considered, then no transformation for CGM data is necessary, and raw data may provide unbiased estimates. CGM can be used as an outcome measure in closed-loop trials,4 but methodological hurdles remain to be tackled. Hovorka et al.1 proposed a stochastic CGM transformation that led to unbiased estimates with the Navigator system but not, in our data, with the Medtronic Sof-Sensor. Based on the unique characteristics of the Sof-Sensor, we proposed an alternative CGM transformation that led to unbiased estimates. Caution needs to be taken when applying these methods, and considerations for different CGM systems need to be taken.

IntroductionMining knowledge from continuous glucose monitoring (CGM) data to classify highly heterogeneous patients with type 2 diabetes according to their characteristics remains unaddressed. A refined clustering method that retrieves hidden...

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