Continuous Glucose Monitoring Glucose Research Articles

Device-Related Skin Reactions Increase Emotional Burden in Youths With Type 1 Diabetes and Their Parents.

Skin reactions due to technological devices pose a significant concern in the management of type 1 diabetes (T1D). This multicentric, comparative cross-sectional study aimed to assess the psychological impact of device-related skin issues on youths with T1D and their parents. Participants with skin reactions were matched in a 1:1 ratio with a control group. Diabetes-related emotional distress was evaluated using the Problem Areas in Diabetes-Teen version (PAID-T) for participants aged 11 to 19 years and the Problem Areas in Diabetes-Parent Revised version (PAID-PR) completed by parents. In addition, glucose control was assessed through glycated hemoglobin (HbA1c) values and continuous glucose monitoring (CGM) metrics. A total of 102 children and adolescents were consecutively recruited. Adolescents with skin issues had higher PAID-T scores compared to those without (79.6 ± 21.1 vs 62 ± 16.8; P = .004). Parents of youths with skin reactions also reported higher PAID-PR scores than the control group (34.0 ± 11.0 vs 26.9 ± 12.3; P = .015). No differences were observed in HbA1c levels (6.9 ± 0.8% vs 6.8 ± 0.8%, P = .555) or CGM glucose metrics between the two groups. Remarkably, 25.5% were forced to discontinue insulin pumps and/or glucose sensors (21.5% and 5.9%, respectively). Our study highlighted the increased emotional burden experienced by youths with T1D and their parents due to device-related skin reactions, emphasizing the need for further research and interventions in this crucial aspect of diabetes management.

What Is the Influence of HIV Serostatus on HbA1c? A Prospective Analysis Using Continuous Glucose Monitoring.

Type 2 diabetes (T2D) is reported to be more common in people living with HIV (PLWH). Clinical guidelines recommend screening for diabetes in PLWH, but there is no agreed method due to studies reporting HbA1c is falsely low in PLWH. These studies were performed in the early HIV era when participants were taking older preparations of antiretroviral therapy that are rarely used today. We aimed to investigate whether HIV serostatus influences HbA1c. We conducted a prospective cohort study of PLWH and age- and sex-matched HIV-negative participants who were purposely recruited from clinics in Brighton, U.K. Each participant wore a Dexcom G6 continuous glucose monitor (CGM) for up to 10 days, had glucose measured during an oral glucose tolerance test, and fructosamine and paired HbA1c were measured. We performed regression analysis to assess the influence of HIV on HbA1c and used a separate model for CGM glucose, venous glucose, and fructosamine. In addition, we included predictor variables used in previous studies that explored HbA1c discrepancy. We recruited 60 PLWH (90% men, 50% with T2D, mean ± SD age 57 ± 10.7 years, 100% undetectable viral load) and 48 people without HIV (92% men, 30% with T2D, mean age 57.7 ± 8.9 years). We found that HIV serostatus did not have a significant influence on HbA1c within the regression models. We performed a comprehensive assessment of glycemia to assess whether HIV serostatus influences HbA1c. We did not find any strong evidence that HIV serostatus influenced HbA1c. The results of our study support incorporating HbA1c into routine clinical blood work in PLWH.

114 Effects of greater levels of soy protein and methionine on periparturient health of sows

Abstract Sow prolificacy in the U.S. continues to trend greater, as does sow mortality. Replacement of soy protein (SBM) in sow diets with corn and purified amino acids (AA), may impact the health of highly prolific sows. In 2015, Denmark increased recommendations for SID methionine (MET) to SID lysine (LYS), MET:LYS to 32%, NRC (2012) recommend 27%. The primary objective of this research was to evaluate SBM and MET at greater levels for their effects on markers of health in the peripartum and lactation periods. A total of 38 parity 1 to 3 females (PIC 1050) were utilized with a starting body weight (BW) of 223.4kg ± 13.0 SD. On d 85 of gestation, sows were allocated by BW and parity to one of four treatments: 1 - 30% SBM (30% of diet), 36% MET:LYS; 2 - 15% SBM, 36% MET:LYS; 3 - 30% SBM, 27% MET:LYS; 4 - 15% SBM, 27% MET:LYS. Sows were fed dietary treatments 28 d pre-farrow, according to BW, and ad libitum throughout lactation. Body condition by caliper (BCS), BW, and backfat (BF) were measured on d 0, 21, 2 d post-farrow, and weaning. Sows were assigned a farrowing ease score of 1 to 4 (1-easy to 4-difficult) based on duration, restless activity, savaging, and assistance required. Continuous glucose monitors (CGM) were placed on a subset of sows (n = 12) on d 113 of gestation through 3 d post-farrow. Data were analyzed using the GLM procedure in SAS with sow serving as the experimental unit. A planned t-test was performed on CGM glucose data. Two sows were removed during lactation. The combined effect of 30% SBM and 36% MET:LYS produced an average lactation BW loss of 0.3 kg, while the 15% SBM and 27% MET:LYS treatment, diets similar to NRC, lost an average of 18.1kg. Sows fed the 30% SBM diets had less lactation BW loss than 15% SBM (Table 1; P < 0.10). Sows fed 36% MET:LYS lost less BW than those fed 27% MET:LYS (P < 0.05). The 36% MET:LYS group also lost less backfat during lactation and had lower pre- and post-farrow body temperatures than 27% MET:LYS (P < 0.01 and P < 0.05, respectively). While no differences in blood glucose were observed, in sows with CGM, greater levels of SBM decreased interstitial glucose concentrations (P = 0.055). In conclusion, greater inclusion of SBM and increasing the MET:LYS ratio in sow diets may improve periparturient changes in body condition and health outcomes.

Practical considerations for continuous glucose monitoring in elite athletes with type 1 diabetes mellitus: A narrative review.

Type 1 diabetes mellitus (T1DM) refers to a metabolic condition where a lack of insulin impairs the usual homeostatic mechanisms to control blood glucose levels. Historically, participation in competitive sport has posed a challenge for those with T1DM, where the dynamic changes in blood glucose during exercise can result in dangerously high (hyperglycaemia) or low blood glucoses (hypoglycaemia) levels. Over the last decade, research and technological development has enhanced the methods of monitoring and managing blood glucose levels, thus reducing the chances of experiencing hyper- or hypoglycaemia during exercise. The introduction of continuous glucose monitoring (CGM) systems means that glucose can be monitored conveniently, without the need for frequent fingerpick glucose checks. CGM devices include a fine sensor inserted under the skin, measuring levels of glucose in the interstitial fluid. Readings can be synchronized to a reader or mobile phone app as often as every 1-5min. Use of CGM devices is associated with lower HbA1c and a reduction in hypoglycaemic events, promoting overall health and athletic performance. However, there are limitations to CGM, which must be considered when being used by an athlete with T1DM. These limitations can be addressed by individualized education plans, using protective equipment to prevent sensor dislodgement, as well as further research aiming to: (i) account for disparities between CGM and true blood glucose levels during vigorous exercise; (ii) investigate the effects of temperature and altitude on CGM accuracy, and (iii) explore of the sociological impact of CGM use amongst sportspeople without diabetes on those with T1DM.

Carbohydrate Ingestion Increases Interstitial Glucose and Mitigates Neuromuscular Fatigue During Single-Leg Knee Extensions.

We aimed to investigate the neuromuscular contributions to enhanced fatigue resistance with carbohydrate ingestion, and to identify whether fatigue is associated with changes in interstitial glucose levels assessed using a continuous glucose monitor (CGM). Twelve healthy participants (6 males, 6 females) performed isokinetic single-leg knee extensions (90°/s) at 20% of the maximal voluntary contraction (MVC) torque until MVC torque reached 60% of its initial value (i.e, task failure). Central and peripheral fatigue were evaluated every 15 min during the fatigue task using the interpolated twitch technique (ITT), and electrically evoked torque. Using a single-blinded cross-over design, participants ingested carbohydrates (CHO) (85 g sucrose/h), or a placebo (PLA), at regular intervals during the fatigue task. Minute-by-minute interstitial glucose levels measured via CGM, and whole blood glucose readings were obtained intermittently during the fatiguing task. CHO ingestion increased time to task failure over PLA (113 ± 69 vs. 81 ± 49 min; mean ± SD; p < 0.001) and was associated with higher glycemia as measured by CGM (106 ± 18 vs 88 ± 10 mg/dL, p < 0.001) and whole blood glucose sampling (104 ± 17 vs 89 ± 10 mg/dL, p < 0.001). When assessing the values in the CHO condition at a similar timepoint to those at task failure in the PLA condition (i.e., ~81 min), MVC torque, % voluntary activation, and 10 Hz torque were all better preserved in the CHO vs. PLA condition (p < 0.05). Exogenous CHO intake mitigates neuromuscular fatigue at both the central and peripheral levels by raising glucose concentrations rather than by preventing hypoglycemia.

Leveraging continuous glucose monitoring for personalized modeling of insulin-regulated glucose metabolism

Continuous glucose monitoring (CGM) is a promising, minimally invasive alternative to plasma glucose measurements for calibrating physiology-based mathematical models of insulin-regulated glucose metabolism, reducing the reliance on in-clinic measurements. However, the use of CGM glucose, particularly in combination with insulin measurements, to develop personalized models of glucose regulation remains unexplored. Here, we simultaneously measured interstitial glucose concentrations using CGM as well as plasma glucose and insulin concentrations during an oral glucose tolerance test (OGTT) in individuals with overweight or obesity to calibrate personalized models of glucose-insulin dynamics. We compared the use of interstitial glucose with plasma glucose in model calibration, and evaluated the effects on model fit, identifiability, and model parameters’ association with clinically relevant metabolic indicators. Models calibrated on both plasma and interstitial glucose resulted in good model fit, and the parameter estimates associated with metabolic indicators such as insulin sensitivity measures in both cases. Moreover, practical identifiability of model parameters was improved in models estimated on CGM glucose compared to plasma glucose. Together these results suggest that CGM glucose may be considered as a minimally invasive alternative to plasma glucose measurements in model calibration to quantify the dynamics of glucose regulation.

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

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

Efficacy of Semaglutide in Overweight and Obese Patients with Type 1 Diabetes.

Introduction: More than two-thirds of patients with type 1 diabetes (T1D) are overweight (OW) and/or obese (OB) in the USA and Western Europe, resulting in insulin resistance as in type 2 diabetes. None of the currently available glucagon like polypeptide 1 (GLP-1) analogs are approved for patients with T1D. A higher dose of semaglutide has been approved by the Food and Drug Administration (FDA) for subjects with body mass index (BMI) >27 kg/m2. We evaluated the real-world use of semaglutide in patients with T1D. Methods: This was a retrospective chart review study of 50 OW or OB patients with T1D who were initiated on semaglutide and followed for 1 year. The control group comprised of 50 computer-matched patients (for sex, race, weight, BMI, and diabetes duration) during a similar time period and were not on any weight loss medications. Results: Most patients (92%) were non-Hispanic white in both arms. Mean ± standard deviation (SD) age and duration of diabetes were 42 ± 11 and 27 ± 12 years, respectively. The continuous glucose monitors (CGM), insulin pump use, baseline BMI and body weight were also similar in the two groups. Baseline glycosylated hemoglobin (HbA1c) was insignificantly lower in the semaglutide group (7.6% vs. 8.2%, respectively; P = non-significant [NS]). Total daily insulin dose (TDD) and insulin dose per kg body weight were higher in the semaglutide group at baseline with no difference in basal or prandial insulin dose. There were significantly greater declines in mean (±SD), BMI (7.9% ± 2.6%), body weight (15.9 lbs ± 5.4 lbs), HbA1c, CGM glucose SD and coefficient of variation (CV), and increase in CGM time in range (TIR) in the semaglutide group compared to the control group with no difference in insulin dose changes, time above range (TAR), or time below range (TBR). Conclusions: We conclude that use of semaglutide in patients who are OW and/or OB with T1D was effective in lowering body weight and BMI, and improving glycemic metrics in this pilot real-world study. We strongly recommend performing prospective, large-randomized clinical trials with newer GLP-1 analogs like semaglutide and tirzepatide (twin-cretin) for subjects with T1D associated with OW and/or OB.

The Glucose Management Indicator: Time to Change Course?

Laboratory measurement of hemoglobin A1c (HbA1c) has, for decades, been the standard approach to monitoring glucose control in people with diabetes. Continuous glucose monitoring (CGM) is a revolutionary technology that can also aid in the monitoring of glucose control. However, there is uncertainty in how best to use CGM technology and its resulting data to improve control of glucose and prevent complications of diabetes. The glucose management indicator, or GMI, is an equation used to estimate HbA1c based on CGM mean glucose. GMI was originally proposed to simplify and aid in the interpretation of CGM data and is now provided on all standard summary reports (i.e., average glucose profiles) produced by different CGM manufacturers. This Perspective demonstrates that GMI performs poorly as an estimate of HbA1c and suggests that GMI is a concept that has outlived its usefulness, and it argues that it is preferable to use CGM mean glucose rather than converting glucose to GMI or an estimate of HbA1c. Leaving mean glucose in its raw form is simple and reinforces that glucose and HbA1c are distinct. To reduce patient and provider confusion and optimize glycemic management, mean CGM glucose, not GMI, should be used as a complement to laboratory HbA1c testing in patients using CGM systems.

Feasibility and performance of continuous glucose monitoring in hyperglycemia after lung transplantation

BackgroundPost-Transplant Diabetes Mellitus (PTDM) affects 20%–40% of lung transplant recipients within five years, impacting rejection, infection, cardiovascular events, and mortality. Continuous glucose monitoring (CGM) is used in diabetes but not well-studied in PTDM.ObjectiveThis study assessed CGM performance in detecting hypoglycemia and hyperglycemia post-lung transplantation, compared to self-monitoring blood glucose.MethodsA prospective pilot study included 15 lung transplant patients (mean age 58.6 years; 53.3% men; 73.3% with pre-transplantation diabetes) managing hyperglycemia with insulin. Patients used a blinded CGM and self-monitored glucose for ten days. Data were categorized (% time in range, % high, % very high, % low, % very low) and compared using paired t-tests.ResultsCGM showed superior hyperglycemia detection. Mean differences for “% very high”, “% high”, and “% high and % very high” were 7.12 (95% CI, 1.8–12.4), 11.1 (95% CI, 3.5–18.8), and 18.3 (95% CI: 7.37–29.24), respectively. No significant difference was found for “% low and % very low”. All patients reported a positive CGM experience.ConclusionCGM use post-lung transplantation seems feasible and offers advantages in detecting hyperglycemia and in optimizing glucose management. Study limitations include a small sample size, requiring larger studies to assess glycemic control, hypoglycemia detection, and transplant outcomes.

Altered carbohydrate oxidation during exercise in overreached endurance athletes is applicable to training monitoring with continuous glucose monitors.

The purpose of the study was to investigate whether carbohydrate utilization is altered during exercise in overreached endurance athletes and examine the utility of continuous glucose monitors (CGM) to detect overreaching status. Eleven endurance athletes (M:8, F:3) completed a 5-week training block consisting of 1 week of reduced training (PRE), 3 weeks of high-intensity overload training (POST), and 1 week of recovery training (REC). Participants completed a Lamberts and Lambert Submaximal Cycling Test (LSCT) and 5 km time-trial at PRE, POST, and REC time points, 15 min following the ingestion of a 50 g glucose beverage with glucose recorded each minute via CGM. Performance in the 5 km time-trial was reduced at POST (∆-7 ± 10 W, p = 0.04, = 0.35) and improved at REC (∆12 ± 9 W from PRE, p = 0.01, = 0.66), with reductions in peak lactate (∆-3.0 ± 2.0 mmol/L, p = 0.001, = 0.71), peak HR (∆-6 ± 3 bpm, p < 0.001, = 0.86), and Hooper-Mackinnon well-being scores (∆10 ± 5 a.u., p < 0.001, = 0.79), indicating athletes were functionally overreached. The respiratory exchange ratio was suppressed at POST relative to REC during the 60% (POST: 0.80 ± 0.05, REC: 0.87 ± 0.05, p < 0.001, = 0.74), and 80% (POST: 0.93 ± 0.05, REC: 1.00 ± 0.05, p = 0.003, = 0.68) of HR-matched submaximal stages of the LSCT. CGM glucose was reduced during HR-matched submaximal exercise in the LSCT at POST (p = 0.047, = 0.36), but not the 5 km time-trial (p = 0.07, = 0.28) in overreached athletes. This preliminary investigation demonstrates a reduction in CGM-derived glucose and carbohydrate oxidation during submaximal exercise in overreached athletes. The use of CGM during submaximal exercise following standardized nutrition could be employed as a monitoring tool to detect overreaching in endurance athletes.

Decline in HbA1c during the first year of elexacaftor/tezacaftor/ivacaftor treatment in the Danish cystic fibrosis cohort: Short title: Decline in HbA1c after elexacaftor/tezacaftor/ivacaftor treatment

BackgroundElexacaftor/tezacaftor/ivacaftor (ETI) has improved the clinical status of individuals with cystic fibrosis (CF), however, whether ETI impacts glucose tolerance remains unknown. We aimed to study the change in glycated hemoglobin (HbA1c) and CF related diabetes (CFRD) status after initiation of ETI. MethodsWe included individuals ≥12 years treated with ETI in Denmark in a longitudinal observational study. HbA1c was measured at baseline, 3, 6, 9 and 12 months after treatment initiation. Change in HbA1c was assessed in mixed models adjusted for age, sex, glucose tolerance and prior CFTR modulator treatment. In a sub-population with CFRD, we assessed the change in insulin usage, hypoglycemic events and the 30-day continuous glucose monitoring (CGM) parameters (i.e., average blood glucose, time below (≤3.9 mM) and above (>10.0 mM) normal range, and the variation in glucose) after 12 months of treatment. ResultsAmong 321 individuals with CF, HbA1c declined by 2.1 mmol/mol [95 % confidence interval (CI): -2.6; -1.5 mmol/mol] after 3 months and by 2.3 mmol/mol [95 %CI: -2.8; -1.9 mmol/mol] after 12 months of ETI treatment. The decline was independent of glucose tolerance status at baseline. In 26 individuals with CFRD at baseline, the mean decline in HbA1c was 3.6 mmol/mol [95 %CI: -6.9; -0.4 mmol/mol] after 12 months, but we did not observe any change in insulin usage, weekly number of hypoglycemic events or CGM parameters. ConclusionIn the Danish CF cohort, HbA1c declined over 12 months of ETI treatment, however, among a subset with CFRD, we observed no change in insulin usage and CGM glucose levels.

SAT107 Assessing Continuous Glucose Monitor Alarm Use By Families Of Children With Diabetes

Abstract Disclosure: V.S. Ochs: None. S.E. Woerner: Employee; Self; Lilly USA, LLC. A. Ustyol: None. L.A. DiMeglio: Advisory Board Member; Self; Merck, Abata. Consulting Fee; Self; Vertex. Background: Continuous glucose monitors (CGM) offer customizable alarms which alert persons with diabetes and their caregivers of current or pending glycemic changes. However, there has been little work studying real-world CGM alert setting use in large clinical populations. We sought to understand CGM alarm use through report analyses. Methods: After IRB approval we analyzed data from two-week CGM reports obtained clinically to determine common settings and compared those to target guidelines. Results: CGM download data from 150 children using a Dexcom® G6 were analyzed (median age 14y, (range: 1-19), 89% white, 9.5% black, and 1.5% Asian, 47% female, 51% pump users). Median A1c was 7.8% (range: 5.4-15). Median CGM glucose was 190 (range: 56-374). Average time in range was 47.7% (range: 3.4-99.1).A Low Alarm alert was set for 131 participants (87%) with a median sensor glucose alert threshold of 74 mg/dL (range: 60-100). The High Alarm was used by 109 children (73%) with a median threshold of 272 mg/dL (range: 120-400). The Signal Loss alarm was used by 103 (69%) participants with a median time until notification of 20 min (range: 20-240). Low Repeat and High Repeat alarms were used by only 50 (33%) and 36 (24%) of children, respectively. Rise Rate and Fall Rate alarms were used by 18 (12%) and 34 (23%). There was a significant difference between the median High alarm cutoff of 272 mg/dL and a recommended standard (e.g. PantherProgram.org) of 250 mg/dL (p=0.0016). The same was true with the median Low alarm cutoff of 74 mg/dL and a recommended 70 mg/dL (p&amp;lt;0.0001). There was significant difference between the median High Repeat notification time of 1 hour and the recommended 2 hours (p&amp;lt;0.0001). Pump users were 2.2x more likely to use High Repeat alarms compared to injection users (95% CI: 1.18-4.15, p&amp;lt;0.01). The same was true with the Low Repeat alarm being 1.8x more likely to be used by pumpers compared to injection users (95% CI: 1.12-2.99, p=0.01). There were no significant differences in Low, High, Rise Rate, Fall Rate, Urgent Low Soon, Urgent Low Soon Repeat, or Signal Loss alarms.When the group was divided based on age, above 12 and &amp;lt;=12, younger CGM users were 1.2 times more likely to use the Low alarm compared to older users (95% CI: 1.03-1.30). Also, younger CGM users were more likely to use the Rise Rate (RR=3.6, 95% CI:1.37-9.67), Low Repeat (RR=1.7, 95% CI:1.09-2.71), Signal Loss (RR=1.3, 95% CI:1.04-1.60), Urgent Low Soon (RR=1.2, 95% CI:1.07-1.41), and Urgent Low Soon Repeat (RR=1.2, 95% CI:1.05-1.38) alarms. There were no significant differences in High, High Repeat, or Fall Rate alarm settings. Conclusions: The wide variability of alarm settings used by patients indicates likely educational gaps in CGM onboarding and use. Ensuring CGM alarm best practices will help children with diabetes and their caregivers get needed real-time glycemic data while minimizing alarm fatigue. Presentation: Saturday, June 17, 2023

Real-World Glycemic Improvements with Hybrid Closed Loop Pumps in Youth with Type 1 Diabetes

Objective. The use of hybrid closed-loop insulin delivery systems, specifically the t:slim X2 insulin pump with Control IQ (CIQ), has demonstrated improvement in glycemic control in clinical trials and real-world settings. We sought to describe changes in glycemic control with use of CIQ in minority and nonminority youth. Research Design and Methods. This was a retrospective study of youth with type 1 diabetes (T1D) using CIQ over a 12-month period. Medical record data, pump data, and hemoglobin A1c (HbA1c) were collected from the visit prior to starting CIQ and at each clinic visit up to 12 months after starting CIQ. Continuous glucose monitor (CGM) data and HbA1c trajectory over time were compared to baseline and between minority and nonminority youth. Results. The study included 136 patients of whom 21 were minority youth (non-Hispanic Black and Hispanic), 50% were male, with median age of 13.3y, and median diabetes duration of 4.9y. After starting CIQ, baseline median HbA1c for the nonminority group decreased from 7.8% to 7.1% ( p &lt; 0.001 ), baseline median HbA1c for minority youth decreased from 9.8% to 7.8% ( p = 0.03 ), and the percentage of patients meeting target HbA1c &lt;7% increased from 26% to 45%. Both nonminority and minority youth had a significant increase in time in range and decrease of average CGM glucose ( p &lt; 0.05 ). Conclusions. HbA1c levels decreased in both minority and nonminority youth within 12 months of starting CIQ, and more patients reached the HbA1c target of less than 7%. Disparities in HbA1c between minority and nonminority youth remained and additional studies are warranted to improve this.

SAT099 Should We Use HbA1c Or Continuous Glucose Monitoring (CGM) For Treatment Adjustment In Type 2 Diabetes Non-insulin Treated Patients?

Abstract Disclosure: T. Totomirova: None. M. Arnaudova: None. Current recommendations include treatment changes in type 2 non-insulin patients to be based preferably on HbA1c. HbA1c shows insufficiency as glucose control assessment tool omitting glucose fluctuations and hypoglycemic episodes. Continuous glucose monitoring (CGM) was mostly used for insulin dose adjustment in type 1 and type 2 diabetes patients on insulin. In our study we compared the retrospective CGM data with HbA1c to find out their usefulness for treatment decision in patients with type 2 diabetes. We studied 62 patients with type 2 diabetes non-treated with insulin (28 women, 34 men; age 59.35±11.0г years, disease duration 8.06±5.86г years). All of them performed retrospective Continuous Glucose Monitoring by using iProTM for seven days and HbA1c was measured at the end of this period. We found HbA1c level to show good significant correlation with percentage of time in range be similar in three groups (7.13±0.85 vs 55.65±25.99%, r=-0.48), with maximum CGM glucose level (r=0.47, p&amp;lt;0.01) and minimum CGM glucose level (r=0.44, p&amp;lt;0.01) as well as with the mean CGM glucose level (7.15±1.80mmol/l, r=0.74, p&amp;lt;0.01). Patients estimated to be well controlled are the same percentage based on HbA1c as well based on TIR. HbA1c does not show any correlation with the number of excursions, with AUC below the limit and percentage of time below the limit. Our conclusion is that CGM gives more precise data about glycemic control and thus could be much more useful for treatment adjustment in non-insulin treated patients. Presentation: Saturday, June 17, 2023

An mHealth Text Messaging Program Providing Symptom Detection Training and Psychoeducation to Improve Hypoglycemia Self-Management: Intervention Development Study.

Hypoglycemia remains a challenge for roughly 25% of people with type 1 diabetes (T1D) despite using advanced technologies such as continuous glucose monitors (CGMs) or automated insulin delivery systems. Factors impacting hypoglycemia self-management behaviors (including reduced ability to detect hypoglycemia symptoms and unhelpful hypoglycemia beliefs) can lead to hypoglycemia development in people with T1D who use advanced diabetes technology. This study aims to develop a scalable, personalized mobile health (mHealth) behavioral intervention program to improve hypoglycemia self-management and ultimately reduce hypoglycemia in people with T1D who use advanced diabetes technology. We (a multidisciplinary team, including clinical and health psychologists, diabetes care and education specialists, endocrinologists, mHealth interventionists and computer engineers, qualitative researchers, and patient partners) jointly developed an mHealth text messaging hypoglycemia behavioral intervention program based on user-centered design principles. The following five iterative steps were taken: (1) conceptualization of hypoglycemia self-management processes and relevant interventions; (2) identification of text message themes and message content development; (3) message revision; (4) patient partner assessments for message readability, language acceptability, and trustworthiness; and (5) message finalization and integration with a CGM data-connected mHealth SMS text message delivery platform. An mHealth web-based SMS text message delivery platform that communicates with a CGM glucose information-sharing platform was also developed. The mHealth SMS text messaging hypoglycemia behavioral intervention program HypoPals, directed by patients' own CGM data, delivers personalized intervention messages to (1) improve hypoglycemia symptom detection and (2) elicit self-reflection, provide fact-based education, and suggest practical health behaviors to address unhelpful hypoglycemia beliefs and promote hypoglycemia self-management. The program is designed to message patients up to 4 times per day over a 10-week period. A rigorous conceptual framework, a multidisciplinary team (including patient partners), and behavior change techniques were incorporated to create a scalable, personalized mHealth SMS text messaging behavioral intervention. This program was systematically developed to improve hypoglycemia self-management in advanced diabetes technology users with T1D. A clinical trial is needed to evaluate the program's efficacy for future clinical implementation.

Adolescent- and Young Adult-Reported Outcomes and Use of Continuous Glucose Monitoring Features: A Report from the CITY Trial

Objective. To evaluate patterns of continuous glucose monitor (CGM) use and perceptions of quality of life in adolescents/young adults with type 1 diabetes (T1D) after using CGM for up to 52 weeks in the CGM Intervention in Teens and Young (CITY) Adults randomized clinical trial (RCT). Subjects and Methods. Participants with T1D were initially randomized 1 : 1 to use of CGM or blood glucose meter (BGM) for 26 weeks. Following the RCT, participants in the BGM group initiated CGM (BGM–CGM cohort) and participants in the CGM group continued CGM (CGM–CGM cohort) for another 26 weeks. Problem Areas in Diabetes Survey-Pediatric Version (PAID-peds), Glucose Monitoring Satisfaction Survey (GMSS), Hypoglycemia Confidence Scale (HCS), Diabetes Technology Attitudes (DTA), Pittsburgh Sleep Quality Index (PSQI), Benefits of CGM, and Burdens of CGM were completed at baseline, 26 and 52 weeks. Results. In both cohorts, &gt;70% of participants were wearing CGM &gt; 5 days/week at 52 weeks; 5% discontinued CGM. The majority used the mobile app to receive glucose data. Adolescents (14 to &lt;19 years) were more likely to use SHARE features than young adults (80% versus 41%). CGM–CGM participants had significantly higher scores on GMSS, DTA, and HCS at 52 weeks compared with baseline, and reported higher benefit and lower burden perceptions than at baseline. Similar results were observed for the BGM–CGM cohort. Conclusions. Improvements in self-reported measures were observed in adolescents and young adults using CGM. As CGM use is also associated with better glycemic control, utilizing CGM may contribute to improving both medical outcomes and emotional health.

Treatment Satisfaction and Well-Being With CGM in People With T1D: An Analysis Based on the GOLD Randomized Trial.

The GOLD trial demonstrated that continuous glucose monitoring (CGM) in people with type 1 diabetes (T1D) managed with multiple daily insulin injections (MDI) improved not only glucose control but also overall well-being and treatment satisfaction. This analysis investigated which factors contributed to improved well-being and treatment satisfaction with CGM. The GOLD trial was a randomized crossover trial comparing CGM versus self-monitored blood glucose (SMBG) over 16 months. Endpoints included well-being measured by the World Health Organization-Five Well-Being Index (WHO-5) and treatment satisfaction by the Diabetes Treatment Satisfaction Questionnaire (DTSQ) as well as glucose metrics. Multivariable R2-decomposition was used to understand which variables contributed most to treatment satisfaction. A total of 139 participants were included. Multivariable analyses revealed that increased convenience and flexibility contributed to 60% (95% confidence interval [CI] = 50%-69%) of the improvement in treatment satisfaction (Diabetes Treatment Satisfaction Questionnaire change version [DTSQc]) observed with CGM, whereas perceived effects on hypoglycemia and hyperglycemia only contributed to 6% (95% CI = 2%-11%) of improvements. Significant improvements in well-being (WHO-5) by CGM were observed for the following: feeling cheerful (P = .025), calm and relaxed (P = .024), being active (P = .046), and waking up fresh and rested (P = .044). HbA1c reductions and increased time in range (TIR) were associated with increased treatment satisfaction, whereas glycemic variability was not. HbA1c reduction showed also an association with increased well-being and increased TIR with less diabetes-related distress. While CGM improves glucose control in people with T1D on MDI, increased convenience and flexibility through CGM is of even greater importance for treatment satisfaction and patient well-being. These CGM-mediated effects should be taken into account when considering CGM initiation.

Development and Validation of Binary Classifiers to Predict Nocturnal Hypoglycemia in Adults With Type 1 Diabetes.

One of the biggest challenges for people with type 1 diabetes (T1D) using multiple daily injections (MDIs) is nocturnal hypoglycemia (NH). Recurrent NH can lead to serious complications; hence, prevention is of high importance. In this work, we develop and externally validate, device-agnostic Machine Learning (ML) models to provide bedtime decision support to people with T1D and minimize the risk of NH. We present the design and development of binary classifiers to predict NH (blood glucose levels occurring below 70 mg/dL). Using data collected from a 6-month study of 37 adult participants with T1D under free-living conditions, we extract daytime features from continuous glucose monitor (CGM) sensors, administered insulin, meal, and physical activity information. We use these features to train and test the performance of two ML algorithms: Random Forests (RF) and Support Vector Machines (SVMs). We further evaluate our model in an external population of 20 adults with T1D using MDI insulin therapy and wearing CGM and flash glucose monitoring sensors for two periods of eight weeks each. At population-level, SVM outperforms RF algorithm with a receiver operating characteristic-area under curve (ROC-AUC) of 79.36% (95% CI: 76.86%, 81.86%). The proposed SVM model generalizes well in an unseen population (ROC-AUC = 77.06%), as well as between the two different glucose sensors (ROC-AUC = 77.74%). Our model shows state-of-the-art performance, generalizability, and robustness in sensor devices from different manufacturers. We believe it is a potential viable approach to inform people with T1D about their risk of NH before it occurs.

19-OR: Monitoring Stage 2 to Stage 3 Transition in Type 1 Diabetes Using Continuous Glucose Monitoring (CGM)

Introduction: CGM can be used to monitor evolution of dysglycemia in presymptomatic, stage 2 type 1 diabetes (T1D) and to target early initiation of insulin upon reaching stage 3 T1D. Methods: Participants (N=31) with dysglycemia by A1c, OGTT, CGM and/or home glucose testing (HGT) measures (Table 1) were randomized 2:1 into 6 months unblinded CGM-guided education intervention vs blinded CGM and monitoring only. Surveys included PHQ8, PedsQLFamilyImpact, Tolerance of Uncertainty and Diabetes Knowledge (30 point scale). Results: Intervention and control group were similar in baseline characteristics (Table), except younger age and significantly higher 2-hour OGTT glucose level in the intervention group. Diabetes knowledge was not significantly different between intervention and control at baseline (22±6 vs 20±7, p=0.61) or 3-month follow-up (23±6 vs 20±4, p=0.28). There were no differences between groups for PHQ8, PedsQLFamilyImpact or Tolerance of Uncertainty at baseline or follow-up to date. Of those diagnosed with stage 3 T1D, CGM average sensor glucose and time &amp;gt;140 mg/dL were higher than at study entry. All participants met ADA stage 3 diagnostic criteria; however, none had A1c≥6.5%. Conclusion: CGM facilitates early recognition of stage 3 T1D before elevation in HbA1c and metabolic decompensation. Unblinded CGM does not appear to negatively impact quality of life. Disclosure B.I.Frohnert: Advisory Panel; Provention Bio, Inc. F.Sepulveda: None. J.E.Stoughton: None. A.Steck: None. K.M.Simmons: Advisory Panel; Provention Bio, Inc., Consultant; Dexcom, Inc., Provention Bio, Inc., Research Support; Novartis. Funding The Leona M. and Harry B. Helmsley Charitable Trust (G-1911-03463)