Automated insulin delivery (AID) is widely available to people with type 1 diabetes (T1D), providing superior glycemic control versus traditional methods. The next generation of AID devices focus on minimizing user/device interactions, especially around meals ("full closed loop," [FCL]). Our goal was to assess the postprandial glycemic impact of the bolus priming system (BPS), an algorithm delivering fixed insulin doses based on the likelihood of a meal having occurred, in conjunction with UVA's latest AID. Eleven adults with T1D participated in a supervised randomized-crossover trial assessing glycemic control during two 24-h sessions with identical meals and activity-with and without BPS. On the day in-between study sessions, participants underwent food and activity challenges to test BPS safety and robustness. Continuous glucose monitor (CGM) outcomes and total insulin doses were assessed overall and following meals with potential for BPS to dose additional insulin (CGM >90 mg/dL for 1 h prior). Daytime CGM outcomes were similar with and without BPS: time-in-range (TIR) 70-180 mg/dL 70.6% [62.2-76.5] versus 65.7% [58.6%-80.6%]; time-below-range <70 mg/dL 0% [0-2.1] versus 0% [0-1.3]; respectively. Insulin delivery during 3 h postprandial was indistinguishable 33.5 U [26.4-47.0] versus 35.7 U [28.7-44.9]. Among 43 out of 66 meals with potential to trigger BPS (24/19 BPS/no-BPS), postprandial incremental area-under-the-curve (iAUC) was lower for BPS versus no-BPS (2530 ± 1934 versus 3228 ± 2029, = 0.047), but CGM outcomes were inconclusive: 4-h-TIR 51.2% [19.8-83.3] versus 40.2% [20.8-56.3] ( = 0.24). There were no severe adverse events. While there was no difference in TIR, when BPS was active an improved postprandial AUC in FCL was obtained via earlier insulin injection.

This study examined acute effects of interrupting prolonged sitting with short activity breaks on postprandial glucose/insulin responses and estimations of insulin sensitivity in adults with type 1 diabetes (T1D). In a randomized crossover trial, eight adults (age = 46 ± 14 years [mean ± SD], body mass index [BMI] = 27.2 ± 3.8 kg/m) receiving continuous subcutaneous insulin infusion (CSII) therapy completed two 6-h conditions as follows: uninterrupted sitting (SIT) and sitting interrupted with 3-min bouts of simple resistance activities (SRAs) every 30 min. Basal and bolus insulin were standardized across conditions except in cases of hypoglycemia. Postprandial responses were assessed using incremental area-under-the-curve (iAUC) and total AUC (tAUC) from half-hourly venous sampling. Meal-based insulin sensitivity determined from glucose sensor and insulin pump (S) was assessed from flash continuous glucose monitor and insulin pump data. Outcomes were analyzed using mixed models adjusted for sex, BMI, treatment order, and preprandial values. Glucose iAUC did not differ by condition (SIT: 19.8 ± 3.0 [estimated marginal means ± standard error] vs. SRA: 14.4 ± 3.0 mmol.6 h.L; = 0.086). Despite CSII being standardized between conditions, insulin iAUC was higher in SRA compared to SIT (137.1 ± 22.7 vs. 170.9 ± 22.7 mU.6 h.L; < 0.001). This resulted in a lower glucose response relative to the change in plasma insulin in SRA (tAUCglu/tAUCins: 0.32 ± 0.02 vs. 0.40 ± 0.02 mmol.mU; = 0.03). Si was also higher at dinner following the SRA condition, with no between-condition differences at breakfast or lunch. Regularly interrupting prolonged sitting in T1D may increase plasma insulin and improve insulin sensitivity when meals and CSII are standardized. Future studies should explore underlying mechanistic determinants and the applicability of findings to those on multiple daily injections. Australian and New Zealand Clinical Trial Registry Identifier-ACTRN12618000126213 (www.anzctr.org.au).

To compare the accuracy of commonly used continuous glucose monitoring (CGM) analysis programs with ambulatory glucose profile (AGP) and Dexcom Clarity (DC) in analyzing CGM metrics in patients with type 1 diabetes (T1D). CGM data up to 90 days from 152 adults using the same CGM and automated insulin delivery system with T1D were collected. Six of the 19 CGM analysis programs (CDGA, cgmanalysis, Glyculator, iglu, EasyGV, and GLU) were selected to compare with AGP and DC. Metrics were compared etween all tools with two one-sided -tests equivalence testing. For the equivalence test, the acceptable range of deviation was set as ±2 mg/dL for mean glucose, ±2% for time in range (TIR), ±1% for time above range (TAR), time above range level 1 (TAR1), time above range level 2 (TAR2), and coefficient of variation (CV). All packages were compared with each other for all CGM metrics, and most of them had statistically significant differences for at least some metrics. All tools were equivalent to AGP for mean glucose, TIR, TAR, TAR1, and TAR2 within ±2 mg/dL, ±2%, ±1%, ±1% and 1%, respectively. CDGA, Glyculator, cgmanalysis, and iglu were not equivalent to AGP for CV within ±1%. All tools were equivalent to DC for mean glucose, TIR, and TAR2 within ±2 mg/dL, ±2%, and ±1%, respectively. Glyculator was not equivalent for TAR1, TAR, and CV. CGDA, cgmanalysis, and iglu were not equivalent to DC for TAR1 and TAR. EasyGV and GLU were not equivalent for TAR within ±1%. CGM analysis programs reported CGM metrics statistically differently, but these differences may not be applicable in clinical practice. The equivalence test also confirmed that the differences are negligible for TIR and mean glucose, while they can be important for hyperglycemic ranges and CV. A standardization for CGM data handling and analysis is necessary for clinical studies reporting CGM-generated outcomes.

Low- and very-low-carbohydrate eating patterns, including ketogenic eating, can reduce glycated hemoglobin (HbA1c) in people with type 2 diabetes (T2D). Continuous glucose monitoring (CGM) has also been shown to improve glycemic outcomes, such as time in range (TIR; % time with glucose 70-180 mg/dL), more than blood glucose monitoring (BGM). CGM-guided nutrition interventions are sparse. The primary objective of this study was to compare differences in change in TIR when people with T2D used either CGM or BGM to guide dietary intake and medication management during a medically supervised ketogenic diet program (MSKDP) delivered via continuous remote care. IGNITE (Impact of Glucose moNitoring and nutrItion on Time in rangE) study participants were randomized to use CGM ( = 81) or BGM ( = 82) as part of a MSKDP. Participants and their care team used CGM and BGM data to support dietary choices and medication management. Glycemia, medication use, ketones, dietary intake, and weight were assessed at baseline (Base), month 1 (M1), and month 3 (M3); differences between arms and timepoints were evaluated. Adults ( = 163) with a mean (standard deviation) T2D duration of 9.7 (7.7) years and HbA1c of 8.1% (1.2%) participated. TIR improved from Base to M3, 61-89% for CGM and 63%-85% for BGM ( < 0.001), with no difference in change between arms ( = 0.26). Additional CGM metrics also improved by M1, and improvements were sustained through M3. HbA1c decreased by ≥1.5% from Base to M3 for both CGM and BGM arms ( < 0.001). Diabetes medications were de-intensified based on change in medication effect scores from Base to M3 ( < 0.001). Total energy and carbohydrate intake decreased ( < 0.001), and participants in both arms lost clinically significant weight ( < 0.001). Both the CGM and BGM arms saw similar and significant improvements in glycemia and other diabetes-related outcomes during this MSKDP. Additional CGM-guided nutrition intervention research is needed.

To assess the impact and feasibility of a telehealth program using continuous glucose monitoring (CGM) and a connected insulin pen cap (CIPC) in nursing homes for older adults with insulin-treated diabetes. This multicenter, prospective, sequential, single-arm study consisted of three phases: (1) baseline, blind CGM (); (2) intervention 1, CGM () without alarms; and (3) intervention 2, CGM with alarms for hypo and hyperglycemia. Two telehealth visits from reference diabetes units were conducted to adjust antidiabetic treatments. Insulin treatment was tracked using the CIPC. The study's primary objective was to evaluate the reduction of hypoglycemia rate. Of 82 eligible patients at seven nursing homes, 54 completed the study (age: 87.7 ± 7.1, 68-102 years, 56% women, duration of diabetes: 18.7 years, baseline glycated hemoglobin: 6.9% [52 mmol/mol]). The mean number of hypoglycemic events was significantly reduced from baseline (4.4) to intervention 1 (2.8; = 0.060) and intervention 2 (2.1; = 0.023). The time below range 70 mg/dL (3.9 mmol/L) significantly decreased from 3.7% at baseline to 1.4% at intervention 2 ( = 0.036). The number of insulin injections significantly decreased from baseline to intervention 1 (1.2 to 0.99; = 0.027). Nursing home staff expressed a positive view of the program, greater convenience, and potential to reduce hypoglycemia with the CGM versus the glucometer. A telehealth program using CGM and a CIPC was associated with improved glycemic profiles among institutionalized older individuals with diabetes receiving insulin and was well perceived by professionals.

To compare the safety in terms of hypoglycemic events and continuous glucose monitoring (CGM) metrics during aerobic exercise (AE) of using temporary target (TT) versus suspension of insulin infusion (SII) in adults with type 1 diabetes (T1D) using advanced hybrid closed-loop systems. This was a randomized crossover clinical trial. Two moderate-intensity AE sessions were performed, one with TT and one with SII. Hypoglycemic events and CGM metrics were analyzed during the immediate (baseline to 59 min), early (60 min to 6 h), and late (6 to 36 h) post-exercise phases. In total, 33 patients were analyzed (44.6 ± 13.8 years), basal time in range (%TIR 70-180 mg/dL) was 79.4 ± 12%, and time below range (%TBR) <70 mg/dL was 1.8 ± 1.7% and %TBR <54 mg/dL was 0.5 ± 0.9%. No difference was found in the number of hypoglycemic events, %TBR <70 mg/dL and %TBR <54 mg/dL between TT and SII. Differences were found in the early phase, with better values when using TT for %TIR 70-180 mg/dL (83.0 vs. 65.3, = 0.005), time in tight range (%TITR 70-140 mg/dL) (56.3 vs. 41.5, = 0.04), and time above range (%TAR >180 mg/dL) (15.3 vs. 31.8, = 0.01). In the diurnal period, again %TIR was better for TT use (82.1 vs. 73.1, = 0.02) and %TAR (15.0 vs. 22.96, = 0.04). No significant differences were found in the CGM metrics during the different phases of AE. Our data appear to show that the use of TT compared with SII is equally safe in all phases of AE. However, the use of TT allows for a better glycemic profile in the early phase of exercise.

Structural inequities impede technology uptake in marginalized populations living with type 1 diabetes (T1D). Our objective was to describe hemoglobin A1c (HbA), time in range (TIR), and pump use to evaluate the impact of a universal funding policy for continuous glucose monitoring (CGM) across levels of deprivation in children with T1D in the Canadian province of British Columbia (BC). Patients with T1D and at least one outpatient visit after June 10, 2020 (1-year before universal CGM funding) who were enrolled in the BC Pediatric Diabetes Registry were included ( = 477). The Canadian Index of Multiple Deprivation (quintile 1 = least deprived; quintile 5 = most deprived) was determined using postal code. Mixed effects models were used to describe HbA, TIR, and pump use, and an interrupted time series generalized additive model estimated the change in CGM use pre- and postintroduction of universal coverage. No differences were observed among the five levels of deprivation for HbA and TIR; however, for residential instability, those with the highest level of deprivation had a lower probability of pump use (-18.9%, 95% confidence interval [CI] = -26.1% to -11.7% for quintile 5 vs. 1). There was an increase in CGM uptake across all levels of deprivation 1-year after introduction of universal CGM funding. For example, the difference in sensor use from the most to least deprived situational group was -21.0% (-35.4%, -6.6%) at the time of universal coverage and shrank to -4.6% (-21.6%, 12.4%) after 12 months of coverage. However, an equity gap in CGM use persisted between the least and most deprived groups (-21.9, 95% CI = -34.5 to -9.4 for quintile 5 vs. 1 in economic dependency). Universal coverage of CGM improved uptake; however, equity gaps persisted. More research is needed to explore nonfinancial barriers to diabetes technology use in marginalized populations.

Integrating mobile health (mHealth) apps into daily diabetes management allows users to monitor and track their health data, creating a comprehensive system for managing daily diabetes activities and generating valuable real-world data. This analysis investigates the impact of transitioning from traditional self-monitoring of blood glucose (SMBG) to real-time continuous glucose monitoring (rtCGM), alongside the use of a mHealth app, on users' glycemic control. Data were collected from 1271 diabetes type 1 and type 2 users of the mySugr app who made a minimum of 50 SMBG logs 1 month before transitioning to rtCGM and then used rtCGM for at least 6 months. The mean and coefficient of variation of glucose, along with the proportions of glycemic measurements in and out of range, were compared between baseline and 1, 2, 3, and 6 months of rtCGM use. A mixed-effects linear regression model was built to quantify the specific effects of transitioning to a rtCGM sensor in different subsamples. A novel validation analysis ensured that the aggregated metrics from SMBG and rtCGM were comparable. Transitioning to a rtCGM sensor significantly improved glycemic control in the entire cohort, particularly among new users of the mySugr app. Additionally, the sustainability of the change in glucose in the entire cohort was confirmed throughout the observation period. People with type 1 and type 2 diabetes exhibited distinct variations, with type 1 experiencing a greater reduction in glycemic variance, while type 2 displayed a relatively larger decrease in monthly averages.

Hybrid closed-loop (HCL) systems remain underexplored within aviation, and as atmospheric pressure changes can independently affect insulin pumps and continuous glucose monitoring readings, this preliminary study assessed the feasibility of HCL safety evaluation, in both fasting and post-prandial states, by using hypobaric chamber to simulate flights. Participants with type 1 diabetes and on HCL were studied: Medtronic Guardian 4-Medtronic 780G-SmartGuard ( = 4), Dexcom G6-Omnipod DASH-Android APS ( = 1), and Dexcom G6-Ypsomed Pump-CamAPS ( = 1). Flight cabin pressures of 550 mmHg and 750 mmHg were simulated in a hypobaric chamber. Seven-hundred-50 glucose measurements were taken, with glucose levels demonstrating a stable decline to 4 mmol/L during fasting. To maintain a tight fasting and post-prandial glucose range across the different pressure settings, the HCL administered insulin as expected. While not demonstrating any apparent issues, repeating flight simulation protocol with other systems, examining longer flights, and undertaking larger, powered randomised controlled trials can confirm their safety in aviation.

Autonomic neuropathy is associated with dysglycemia that is difficult to control. We investigated if transcutaneous vagus nerve stimulation (tVNS) could improve glycemic levels. We randomized 145 individuals with type 1 diabetes (T1D) ( = 70) or type 2 diabetes (T2D) ( = 75) and diabetic autonomic neuropathy (DAN) to self-administered treatment with active cervical tVNS ( = 68) or sham ( = 77) for 1 week (4 daily stimulations) and 8 weeks (2 daily stimulations), separated by a wash-out period of at least 2 weeks. Continuous glucose monitoring (CGM) indices were measured for 104 participants starting 5 days prior to intervention periods, during the 1-week period, and at end of the 8-week period. Primary outcomes were between-group differences in changes in coefficient of variation (CV) and in time in range (TIR 3.9-10 mmol/L). Secondary outcomes were other metrics of CGM and HbA1c. For the 1-week period, median [interquartile range] changes of CV from baseline to follow-up were -1.1 [-4.3;2.0] % in active and -1.5 [-4.4;2.5] % in sham, with no significance between groups ( = 0.54). For TIR, the corresponding changes were 2.4 [-2.1;7.4] % in active and 5.1 [-2.6;8.8] in sham group ( = 0.84). For the 8-week treatment period, changes in CV and TIR between groups were also nonsignificant. However, in the subgroup analysis, persons with T1D receiving active tVNS for 8 weeks had a significant reduction in CV compared with the T1D group receiving sham stimulation (estimated treatment effect: -11.6 [95% confidence interval -20.2;-2.0] %, = 0.009). None of the changes in secondary outcomes between treatment groups were significantly different. Overall, no significant changes were observed in CGM metrics between treatment arms, while individuals with T1D and DAN decreased their CV after 8 weeks of tVNS treatment.

Continuous subcutaneous insulin infusion (CSII) in type 1 diabetes has been regarded as a major diabetic ketoacidosis (DKA) risk factor. We aimed to determine secular trends in risk since CSII implementation in the 1980s. We assessed the relationship between time-varying CSII use and DKA events from 1983 to 2017 and by each decade in the 1441 Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Study participants using crude and adjusted Cox proportional hazards models. Time-varying CSII exposure was associated with significantly higher DKA risk in the 1980s (adjusted hazard ratio [HR] 5.81; 95% confidence interval [CI] 3.28-10.29; < 0.001), but in the 2010s, this risk was not significantly elevated (adjusted HR 1.24; 95% CI 0.73-2.12; = 0.43). DKA risk associated with CSII in type 1 diabetes has declined substantially since the 1980s such that the remaining risk in the past decade appears to be of low magnitude.

The usage and safety of the Boost and Ease-off features in the CamAPS FX hybrid closed-loop system were analyzed in a retrospective analysis of real-world data from 7,464 users over a 12-month period. Boost was used more frequently than Ease-off, but for a shorter duration per use. Mean starting glucose was above range for Boost (229 ± 51 mg/dL), and within range for Ease-off (114 ± 29 mg/dL). Time spent below 70 mg/dL was low during Boost periods [median (interquartile range; IQR) 0.0% (0.0, 0.5%)], and lower than during no Boost periods [2.1% (1.2, 3.4%)], while time spent above 180 mg/dL was lower during Ease-off periods (15 ± 14%) compared with no Ease-off periods (25 ± 12%). There were no episodes of severe hypoglycemia or diabetic ketoacidosis attributed to Boost or Ease-off use. Boost and Ease-off allow users to engage safely with CamAPS FX to manage their glucose levels during periods of more-than-usual and less-than-usual insulin needs.

Serum C-peptide concentration is often utilized for diagnostic, prognostic, or therapeutic assessment in diabetes mellitus. However, there are limited clinical data regarding diagnostic and predictive value of C-peptide measured during hospitalizations for hyperglycemia. Adults admitted to Mayo Clinic inpatient facilities due to an acute hyperglycemic emergency between January 2017 and November 2022 were included in our study. Predictive capacity of C-peptide for discontinuation of therapeutic insulin was examined in the entire cohort and the subgroup of non-autoimmune non-pancreatitis diabetes (NANP-DM). We included 187 patients (63 women) in our study. During hospitalization, patients with type 1 diabetes antibodies displayed diminished serum C-peptide concentration ( = 0.014), correlating inversely with subsequent hemoglobin A1c% [ = (-0.22), = 0.005]. Initial C-peptide concentrations did not differ between patients requiring insulin therapy during follow-up and those who did not ( = 0.16). C-peptide concentrations showed limited predictive capacity for achieving glycemic control. Subgroup analyses in NANP-DM exhibited similar limited capacity for anticipating therapeutic insulin needs and achieving glycemic controls. C-peptide concentration did not exhibit a robust predictive capability for future need of insulin therapy and achieving glycemic control, limiting its utility in clinical practice within inpatient settings.

Disordered eating behaviors (DEB) are common among individuals with type 1 diabetes (T1D). Glycemic variability, potentially harmful in T1D, may reveal distinct characteristics between those with higher versus lower variability, particularly concerning DEB. Our aim was to evaluate the prevalence of DEB and associated risk factors among adolescents and young adults with T1D and to investigate unique factors associated with DEB across different levels of glycemic variability. An observational, cross-sectional study was conducted with 147 individuals with T1D, aged 13-21 years. Data were collected from medical charts, personal technological devices for assessing glycemic variability, and self-reported questionnaires, including assessments of DEB. DEB were found in 62 (42.1%) individuals, and 41.5% achieved the glycemic variability (% coefficient of variation) target ≤36%. Among individuals with low glycemic variability, DEB were positively associated with diabetes distress (odds ratio [OR]: 1.14 [95% confidence interval or CI: 1.05-1.22], < 0.001), longer diabetes duration (OR: 1.34 [95% CI: 1.05-1.70],  = 0.016) and lower socioeconomic-status (OR: 0.53 [95% CI: 0.31-0.90],  = 0.019). Among those with high glycemic variability, body mass index Z score (OR: 3.82 [95% CI: 1.48-9.85], = 0.005), HbA1c (OR: 4.12 [95% CI: 1.33-12.80],  = 0.014), disinhibited eating (OR: 1.57 [95% CI: 1.14-2.15], = 0.005), and tendency to lower socioeconomic status (OR: 0.75 [95% CI: 0.56-1.01],  = 0.065). DEB are prevalent among adolescents and young adults with T1D and are associated with various risk factors. Factors associated with DEB vary across different levels of glycemic variability. Both low and high glycemic variability are associated with specific risk factors for DEB. One notable risk factor is diabetes-specific disinhibited eating among individuals with high glycemic variability, in contrast to those with low glycemic variability. Given these different risk factors, it may be prudent to adjust intervention programs to reduce DEB among T1D adolescents according to their glycemic variability levels.

This study aims to investigate the continuum of glucose control from normoglycemia to dysglycemia (HbA1c ≥ 5.7%/39 mmol/mol) using metrics derived from continuous glucose monitoring (CGM). In addition, we aim to develop a machine learning-based classification model to classify dysglycemia based on observed patterns. Data from five distinct studies, each featuring at least two days of CGM, were pooled. Participants included individuals classified as healthy, with prediabetes, or with type 2 diabetes mellitus (T2DM). Various CGM indices were extracted and compared across groups. The data set was split 70/30 for training and testing two classification models (XGBoost/Logistic Regression) to differentiate between prediabetes or dysglycemia and the healthy group. The analysis included 836 participants (healthy: = 282; prediabetes: = 133; T2DM: = 432). Across all CGM indices, a progressive shift was observed from the healthy group to those with diabetes ( < 0.001). Statistically significant differences ( < 0.01) were noted in mean glucose, time below range, time above 140 mg/dl, mobility, multiscale complexity index, and glycemic risk index when transitioning from health to prediabetes. The XGBoost models achieved the highest receiver operating characteristic area under the curve values on the test data set ranging from 0.91 [confidence interval (CI): 0.87-0.95] (prediabetes identification) to 0.97 [CI: 0.95-0.98] (dysglycemia identification). Our findings demonstrate a gradual deterioration of glucose homeostasis and increased glycemic variability across the spectrum from normo- to dysglycemia, as evidenced by CGM metrics. The performance of CGM-based indices in classifying healthy individuals and those with prediabetes and diabetes is promising.

The advanced hybrid closed-loop (AHCL) algorithm combines automated basal rates and corrections yet requires meal announcement for optimal performance, which poses a challenge for some. We aimed to compare glucose control in adults with type 1 diabetes (T1D) using the MiniMed 780G AHCL system, utilizing simplified meal announcement versus precise carbohydrate (CHO) counting. In a study involving 14 adults with T1D, we evaluated glycemic control during a 13-week "precise phase," followed by two 3- to 4-week simplified meal announcement phases: "fixed one-step" (preset of one personalized fixed CHO amount) and "multistep" (entry of multiples of one, two, or three of these presets depending on meal size estimate). The mean age was 45.7 ± 12.4, and 10 participants were male (71%). Mean baseline HbA1c was 6.8% ± 1.2% and time in range (TIR) was 67.5% ± 16.7%. Comparing the fixed one-step to the precise study phase, TIR was similar (75.4 ± 13% vs. 77.7 ± 9%, = 0.12), and glucose management indicator (GMI) was slightly higher (6.8 ± 0.4 vs. 6.6 ± 0, = 0.01). Furthermore, there was less level 1 and 2 hypoglycemia (1.6 ± 1% vs. 2.8 ± 2%, = 0.03 and 0.3 ± 5% vs. 0.65 ± 1%, = 0.08) but slightly more level 1 and 2 hyperglycemia (17.1 ± 8% vs. 15.0 ± 7%, = 0.05 and 5.5 ± 5% vs. 3.6 ± 3%, = 0.04). When comparing the multistep with the precise phase, GMI was identical (6.6%) and TIR superior (80.5 ± 10% vs. 77.7 ± 9%, = 0.02). Additionally, there was less level 1 hypoglycemia (1.9 ± 1% vs. 2.8 ± 2%, = 0.01) and a trend for less level 2 hypoglycemia (0.4 ± 0.7% vs. 0.65 ± 1%, = 0.08). A simplified meal announcement strategy for adults using the MiniMed 780G system, relying on three increments of a fixed one-step CHO amount, may offer a way to improve glycemic control and ease self-care. For patients with more limitations, using one fixed one-step CHO amount could be a safe alternative to meeting most consensus glycemic targets.

Continuous glucose monitoring (CGM) can improve glycemic control in people with diabetes on insulin therapy. We assessed rates of prescriptions for CGM in a national sample of Veterans across subgroups defined by race and ethnicity. This cross-sectional analysis of data from the U.S. Veterans Health Administration included adults with type 1 or type 2 diabetes on insulin therapy. Main exposures included self-reported race and ethnicity, and primary outcome was the percentage of patients with at least one CGM prescription between January 1, 2020, and December 31, 2021. Association of race and ethnicity categories with CGM prescription was examined using multilevel, multivariable mixed-effects models. Among 368,794 patients on insulin (mean age, 68.5 years; 96% male; 96.8% type 2 diabetes; 0.8% American Indian or Alaska Native, 0.7% Asian, 18.9% Black or African American, 0.9% Native Hawaiian or other Pacific Islander, 70.2% White, 2.8% multiracial, 5.7% with unknown race, and 7.0% Hispanic or Latino ethnicity), 11.2% were prescribed CGM. CGM was prescribed for 10.4% American Indian or Alaska Native, 9.7% Asian, 9.2% Black or African American, 9.3% Native Hawaiian or other Pacific Islander, 11.8% White, 11.8% multiracial, and 10.1% patients with unknown race. CGM was prescribed for 8.3% Hispanic or Latino, 11.4% non-Hispanic, and 11.5% of patients with unknown ethnicity. After accounting for patient-, clinical-, and system-level factors, Black or African American patients had significantly lower odds of CGM prescription compared with White patients (adjusted odds ratio [aOR] 0.62, 95% confidence interval [CI] 0.59-0.64), whereas Hispanic or Latino patients had significantly lower odds compared with non-Hispanic patients (aOR 0.79, 95% CI 0.74-0.84). Findings were consistent across subgroups with clinical indications for CGM use. Among Veterans with diabetes on insulin therapy, there were significant disparities in prescribing of CGM technology by race and ethnicity, which require further study and intervention.