Blinded Continuous Glucose Monitoring Research Articles

Associations of clinical, psychological, and sociodemographic characteristics and ecological momentary assessment completion in the 10-week Hypo-METRICS study: Hypoglycaemia MEasurements ThResholds and ImpaCtS.

Reporting of hypoglycaemia and its impact in clinical studies is often retrospective and subject to recall bias. We developed the Hypo-METRICS app to measure the daily physical, psychological, and social impact of hypoglycaemia in adults with type 1 and insulin-treated type 2 diabetes in real-time using ecological momentary assessment (EMA). To help assess its utility, we aimed to determine Hypo-METRICS app completion rates and factors associated with completion. Adults with diabetes recruited into the Hypo-METRICS study were given validated patient-reported outcome measures (PROMs) at baseline. Over 10 weeks, they wore a blinded continuous glucose monitor (CGM), and were asked to complete three daily EMAs about hypoglycaemia and aspects of daily functioning, and two weekly sleep and productivity PROMs on the bespoke Hypo-METRICS app. We conducted linear regression to determine factors associated with app engagement, assessed by EMA and PROM completion rates and CGM metrics. In 602 participants (55% men; 54% type 2 diabetes; median(IQR) age 56 (45-66) years; diabetes duration 19 (11-27) years; HbA1c 57 (51-65) mmol/mol), median(IQR) overall app completion rate was 91 (84-96)%, ranging from 90 (81-96)%, 89 (80-94)% and 94(87-97)% for morning, afternoon and evening check-ins, respectively. Older age, routine CGM use, greater time below 3.0 mmol/L, and active sensor time were positively associated with app completion. High app completion across all app domains and participant characteristics indicates the Hypo-METRICS app is an acceptable research tool for collecting detailed data on hypoglycaemia frequency and impact in real-time.

Continuous Glucose Monitoring Profiles in Pregnancies With and Without Gestational Diabetes Mellitus.

To determine whether continuous glucose monitoring (CGM)-derived glycemic patterns can characterize pregnancies with gestational diabetes mellitus (GDM) as diagnosed by standard oral glucose tolerance test at 24-28 weeks' gestation compared with those without GDM. The analysis includes 768 individuals enrolled from two sites prior to 17 weeks' gestation between June 2020 and December 2021 in a prospective observational study. Participants wore blinded Dexcom G6 CGMs throughout gestation. Main outcome of interest was a diagnosis of GDM by oral glucose tolerance test (OGTT). Glycemic levels in participants with GDM versus without GDM were characterized using CGM-measured glycemic metrics. Participants with GDM (n = 58 [8%]) had higher mean glucose (109 ± 13 vs. 100 ± 8 mg/dL [6.0 ± 0.7 vs. 5.6 ± 0.4 mmol/L], P < 0.001), greater glucose SD (23 ± 4 vs. 19 ± 3 mg/dL [1.3 ± 0.2 vs. 1.1 ± 0.2 mmol/L], P < 0.001), less time in range 63-120 mg/dL (3.5-6.7 mmol/L) (70% ± 17% vs. 84% ± 8%, P < 0.001), greater percent time >120 mg/dL (>6.7 mmol/L) (median 23% vs. 12%, P < 0.001), and greater percent time >140 mg/dL (>7.8 mmol/L) (median 7.4% vs. 2.7%, P < 0.001) than those without GDM throughout gestation prior to OGTT. Median percent time >120 mg/dL (>6.7 mmol/L) and time >140 mg/dL (>7.8 mmol/L) were higher as early as 13-14 weeks of gestation (32% vs. 14%, P < 0.001, and 5.2% vs. 2.0%, P < 0.001, respectively) and persisted during the entire study period prior to OGTT. Prior to OGTT at 24-34 weeks' gestation, pregnant individuals who develop GDM have higher CGM-measured glucose levels and more hyperglycemia compared with those who do not develop GDM.

Daily glucose pattern and physical activity levels in cancer survivors: preliminary results from remote monitoring

Background: Cancer survivors have elevated risk for type 2 diabetes. Increasing physical activity improves glucose control. However, this evidence is majorly based on blood glucose metrics (e.g., glucose or hemoglobin A1c) at a single point in time. Little is known about the effect of physical activity on blood glucose changes throughout a day in cancer survivors. Thus, we tested the hypothesis that higher physical activity levels is associated with a more favorable daily glucose pattern in cancer survivors in free-living settings. Methods: This is a preliminary analysis of an ongoing randomized controlled trial (Project KNOWN) to examine the effect of physical activity on daily glucose pattern via continuous remote monitoring. A total of 11 cancer survivors were included in this analysis (90% female, 90% white, average age 62.5 years [range 44-76 yr], 67% obese, and 75% hypertension). Participants wore a wrist-worn accelerometer (for daily steps) and a blinded continuous glucose monitor (CGM; for daily glucose averages [interstitial glucose was measured at every 15 minutes] and daily frequency of glucose spikes [defined as glucose level &gt;140 mg/dL]) for two weeks. Multilevel regression model was used to account for multiple observations within each individual and to separate the within-person (WP) and between-person (BP) effects. The WP effect represents deviation from one’s own mean on any given day, and the BP effect represents the individual mean deviation from the study sample mean. To explore moderating effect on the relationship between physical activity and daily glucose, inflammatory biomarkers, blood lipids, blood pressure and body fatness (DEXA scan) were measured. Results: At the WP level, participants with elevated (&gt;4 mg/L) systemic C-reactive protein (marker of inflammatory status), on the day when they had higher-than-usual step count, their glucose average was also lower than their usual level ( p=0.04). At the BP level, daily steps were negatively associated with daily frequency of glucose spikes in participants with less severe hypertension, lower % gynoid fat, and higher android/gynoid ratio than others in the study sample (all p&lt;0.05). Further, daily steps were negatively associated with daily glucose average in participants with higher trunk-to-total fat ratio than others (p&lt;0.05). Discussion: This preliminary study demonstrated the use of data from wearable sensors to analyze intra- and inter-person variability in daily glucose patterns in cancer survivors. Results suggested the impact of physical activity on daily glucose patterns might differ by a person’s physiological status. Future research can further characterize meaningful daily glucose dynamics from CGM data in cancer survivors to better understand how physical activity may have downstream effect on their health outcomes. American Institute for Cancer Research. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Abstract P269: Glycemic Dysregulation Detected by Continuous Glucose Monitoring in Children With Metabolic Dysfunction-Associated Steatotic Liver Disease

MASLD, also known as NAFLD, is the leading cause of chronic liver disease in youth, especially among children who are obese and Hispanic/Latino. Insulin resistance and dysglycemia are strongly linked with MASLD. Current methods for evaluating dysglycemia, including fasting glucose, HbA1c, and oral glucose tolerance tests, have limitations. We utilized continuous glucose monitors (CGMs), wearable devices that detect 24 hour, real-time glucose alterations, to test our hypothesis that children with MASLD exhibit greater glycemic dysregulation compared to non-MASLD controls. This study included 19 Hispanic children (63% (12/19) male; mean age 7.8 years ± 0.9; mean BMI percentile 92% ± 17; 5% (10/19)) with MASLD who were otherwise healthy. Hepatic steatosis was measured by magnetic resonance spectroscopy (MRS). MASLD was defined as hepatic steatosis ≥5%, with at least one of five cardiometabolic risk factors. All participants underwent clinical evaluation and wore a blinded Dexcom G6 Pro CGM for 10 days or until the device became detached. Data was analyzed using RStudio v. 4.2.3. In children with MASLD, the mean time in range (TIR, 70-140 mg/dL) of 84% was lower than the mean TIR of 90% observed in non-MASLD controls ( p =0.02). The mean high TIR (140-250 mg/dL) of 16 % in MASLD subjects was significantly higher than the 9% observed in non-MASLD controls (p= 0.01). Mean average glucose and the coefficient of variation (CV) were also higher in children with MASLD, although differences were non-significant ( p &gt;0.05). Fasting insulin and ALT levels were significantly elevated in children with MASLD compared to controls (p&lt; 0.05).Findings demonstrate greater glycemic dysregulation in pre-pubertal children with MASLD compared to non-MASLD controls, as detected by CGM. Importantly, the 24 hour monitoring detected glycemic variability not apparent by fasting glucose or HbA1c. This study suggests early onset MASLD is associated with early glucose dysregulation, and treatments will need to address both conditions.

Clinically relevant stratification of patients with type 2 diabetes by using continuous glucose monitoring data.

The wealth of data generated by continuous glucose monitoring (CGM) provides new opportunities for revealing heterogeneities in patients with type 2 diabetes mellitus (T2DM). We aimed to develop a method using CGM data to discover T2DM subtypes and investigate their relationship with clinical phenotypes and microvascular complications. The data from 3119 patients with T2DM who wore blinded CGM at an academic medical centre was collected, and a glucose symbolic pattern (GSP) metric was created that combined knowledge-based temporal abstraction with numerical vectorization. The k-means clustering was applied to GSP to obtain subgroups of patients with T2DM. Clinical characteristics and the presence of diabetic retinopathy and albuminuria were compared among the subgroups. The findings were validated in an independent population comprising 773 patients with T2DM. By using GSP, four subgroups were identified with distinct features in CGM profiles and parameters. Moreover, the clustered subgroups differed significantly in clinical phenotypes, including indices of pancreatic β-cell function and insulin resistance (all p < .001). After adjusting for confounders, group C (the most insulin resistant) had a significantly higher risk of albuminuria (odds ratio = 1.24, 95% confidence interval: 1.03-1.39) relative to group D, which had the best glucose control. These findings were confirmed in the validation set. Subtyping patients with T2DM using CGM data may help identify high-risk patients for microvascular complications and provide insights into the underlying pathophysiology. This method may help refine clinically meaningful stratification of patients with T2DM and inform personalized diabetes care.

A Comparison of the Rates of Clock-Based Nocturnal Hypoglycemia and Hypoglycemia While Asleep Among People Living with Diabetes: Findings from the Hypo-METRICS Study.

Introduction: Nocturnal hypoglycemia is generally calculated between 00:00 and 06:00. However, those hours may not accurately reflect sleeping patterns and it is unknown whether this leads to bias. We therefore compared hypoglycemia rates while asleep with those of clock-based nocturnal hypoglycemia in adults with type 1 diabetes (T1D) or insulin-treated type 2 diabetes (T2D). Methods: Participants from the Hypo-METRICS study wore a blinded continuous glucose monitor and a Fitbit Charge 4 activity monitor for 10 weeks. They recorded details of episodes of hypoglycemia using a smartphone app. Sensor-detected hypoglycemia (SDH) and person-reported hypoglycemia (PRH) were categorized as nocturnal (00:00-06:00 h) versus diurnal and while asleep versus awake defined by Fitbit sleeping intervals. Paired-sample Wilcoxon tests were used to examine the differences in hypoglycemia rates. Results: A total of 574 participants [47% T1D, 45% women, 89% white, median (interquartile range) age 56 (45-66) years, and hemoglobin A1c 7.3% (6.8-8.0)] were included. Median sleep duration was 6.1 h (5.2-6.8), bedtime and waking time ∼23:30 and 07:30, respectively. There were higher median weekly rates of SDH and PRH while asleep than clock-based nocturnal SDH and PRH among people with T1D, especially for SDH <70 mg/dL (1.7 vs. 1.4, P < 0.001). Higher weekly rates of SDH while asleep than nocturnal SDH were found among people with T2D, especially for SDH <70 mg/dL (0.8 vs. 0.7, P < 0.001). Conclusion: Using 00:00 to 06:00 as a proxy for sleeping hours may underestimate hypoglycemia while asleep. Future hypoglycemia research should consider the use of sleep trackers to record sleep and reflect hypoglycemia while asleep more accurately. The trial registration number is NCT04304963.

Analysis of Nutritional Composition and Glycemic Control in Patients with Gestational Diabetes Mellitus

Background:The association between meal content and glycemic control is not well-understood in pregnancy, limits our ability to counsel patients regarding the optimal diet. We therefore sought to evaluate the relationship between maternal dietary content and glycemic control.&#x0D; Methods:This is a secondary analysis of the GDM-MOMS study, which was a randomized controlled pilot trial that compared glycemic targets in 60 pregnant individuals with GDM and either overweight or obesity. During the pilot trial, participants wore a blinded continuous glucose monitor (CGM) for two five-day periods, with the first data collection between 12-32 weeks and the second data collection between 32-36 weeks. During the time that participants wore their CGM, they also collected 3-day food diaries with detailed information regarding intake and cooking technique. These food diaries are being entered into the Nutrition Data System for Research (NDSR) software, which analyzes nutritional composition for each meal. Glycemic control as assessed by CGM will then be assessed based on maternal nutritional intake.&#x0D; Results:The American Diabetes Association recommends a 2,000-calorie daily diet with a minimum of 175g of carbohydrates (with 35% of the total calories coming from carbohydrates), 71g of protein, and 28g of fat. Preliminary data extracted from NDSR includes nutritional analysis of 38 daily food diaries from 14 patients. 12/38 (31.6%) food logs show consumption of less than 35% of their total calories from carbohydrates, with the other 26 consuming 36-62%. Glycemic load can be used to assess how a patient’s diet affects their glycemic levels. 25/38 (65.8%) of food logs demonstrate a daily glycemic load (GL) of 100, with the other 13 showing daily GLs rangingfrom 108-252.&#x0D; Conclusions and Further Directions:Further analyses will assess post-meal glycemic response using both patient-monitored glucose values and reports from their CGM to determine which types of diets allow for optimal glycemic control.

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.

Characterizing Movement Patterns of Older Individuals with T2D in Free-Living Environments Using Wearable Accelerometers

(1) Background: Type 2 Diabetes (T2D) is associated with reduced muscle mass, strength, and function, leading to frailty. This study aims to analyze the movement patterns (MPs) of older individuals with T2D across varying levels of physical capacity (PC). (2) Methods: A cross-sectional study was conducted among individuals aged 60 or older with T2D. Participants (n = 103) were equipped with a blinded continuous glucose monitoring (CGM) system and an activity monitoring device for one week. PC tests were performed at the beginning and end of the week, and participants were categorized into three groups: low PC (LPC), medium PC (MPC), and normal PC (NPC). Group differences in MPs and physical activity were analyzed using non-parametric Kruskal–Wallis tests for both categorical and continuous variables. Dunn post-hoc statistical tests were subsequently carried out for pairwise comparisons. For data analysis, we utilized pandas, a Python-based data analysis tool, and conducted the statistical analyses using the scipy.stats package in Python. The significance level was set at p &lt; 0.05. (3) Results: Participants in the LPC group showed lower medio-lateral acceleration and higher vertical and antero-posterior acceleration compared to the NPC group. LPC participants also had higher root mean square values (1.017 m/s2). Moreover, the LPC group spent less time performing in moderate to vigorous physical activity (MVPA) and had fewer daily steps than the MPC and NPC groups. (4) Conclusions: The LPC group exhibited distinct movement patterns and lower activity levels compared to the NPC group. This study is the first to characterize the MPs of older individuals with T2D in their free-living environment. Several accelerometer-derived features were identified that could differentiate between PC groups. This novel approach offers a manpower-free alternative to identify physical deterioration and detect low PC in individuals with T2D based on real free-living physical behavior.

4 Associations Between Glycemia and Cognitive Performance in Adults with Type 1 Diabetes (T1D) using Continuous Glucose Monitoring (CGM) and Ecological Momentary Assessment (EMA)

Objective:Despite associations between hypoglycemia and cognitive performance using cross-sectional and experimental methods (e.g., Insulin clamp studies), few studies have evaluated this relationship in a naturalistic setting. This pilot study utilizes an EMA study design in adults with T1D to examine the impact of hypoglycemia and hyperglycemia, measured using CGM, on cognitive performance, measured via ambulatory assessment.Participants and Methods:Twenty adults with T1D (mean age 38.9 years, range 26-67; 55% female; 55% bachelor’s degree or higher; mean HbA1c = 8.3%, range 5.4% - 12.5%), were recruited from the Joslin Diabetes Center at SUNY Upstate Medical University. A blinded Dexcom G6 CGM was worn during everyday activities while completing 3-6 daily EMAs using personal smartphones. EMAs were delivered between 9 am and 9 pm, for 15 days. EMAs included 3 brief cognitive tests developed by testmybrain.org and validated for brief mobile administration (Gradual Onset CPT d-prime, Digit Symbol Matching median reaction time, Multiple Object Tracking percent accuracy) and self-reported momentary negative affect. Day-level average scores were calculated for the cognitive and negative affect measures. Hypoglycemia and hyperglycemia were defined as the percentage of time spent with a sensor glucose value &lt;70 mg/dL or &gt; 180 mg/dL, respectively. Daytime (8 am to 9 pm) and nighttime (9 pm to 8 am) glycemic excursions were calculated separately. Multilevel models estimated the between- and within-person association between the night prior to, or the same day, time spent in hypoglycemia or hyperglycemia and cognitive performance (each cognitive test was modeled separately). To evaluate the effect of between-person differences, person-level variables were calculated as the mean across the study and grand-mean centered. To evaluate the effect of within-person fluctuations, day-level variables were calculated as deviations from these person-level means.Results:Within-person fluctuations in nighttime hypoglycemia were associated with daytime processing speed. Specifically, participants who spent a higher percentage of time in hypoglycemia than their average percentage the night prior to assessment performed slower than their average performance on the processing speed test (Digit Symbol Matching median reaction time, b = 94.16, p = 0.042), while same day variation in hypoglycemia was not associated with variation in Digit Symbol Matching performance. This association remained significant (b = 97.46, p = 0.037) after controlling for within-person and between-person effects of negative affect. There were no significant within-person associations between time spent in hyperglycemia and Digit Symbol Matching, nor day/night hypoglycemia or hyperglycemia and Gradual Onset CPT or Multiple Object Tracking.Conclusions:Our findings from this EMA study suggest that when individuals with T1D experience more time in hypoglycemia at night (compared to their average), they have slower processing speed the following day, while same day hypoglycemia and hyperglycemia does not similarly impact processing speed performance. These results showcase the power of intensive longitudinal designs using ambulatory cognitive assessment to uncover novel determinants of cognitive variation in real world settings that have direct clinical applications for optimizing cognitive performance. Future research with larger samples is needed to replicate these findings.

SAT172 Unmasking A Pediatric Disease In Adult, A Belated Diagnosis

Abstract Disclosure: H. Syed: None. P. Au: None. M.J. Gardner: None. Background: Glycogen storage diseases (GSD) are a rare group of diseases that change the way the body uses and stores glycogen. They are usually diagnosed in the pediatric population. Hypoglycemia can be a sign. Clinical Case: A 32 year old female presented with worsening and more frequent episodes of hypoglycemia. She had no history of diabetes and was not on any hypoglycemic agents. She had been having episodes of spontaneous hypoglycemia since childhood and would get symptomatic as well. No comprehensive workup had been done prior. The episodes were becoming more frequent. A blinded continuous glucose monitor was placed on the patient to monitor her glucose levels. Results indeed showed few episodes of hypoglycemia with glucose levels in the 40s. Interestingly, she also had postprandial hyperglycemia with glucose level above 180 following an episode of hypoglycemia. She underwent a 72 hour fast in the hospital and became hypoglycemic within 24 hours with a low blood glucose of 55, consistent with hypoglycemia, and she was also symptomatic as well. At the same time, her beta hydroxybutyrate increased to 3.91 mmol/L, consistent with ketosis. Her insulin level and C-peptide level at the time of hypoglycemia were 4.35 mcunit/mL and 0.239 nmol/L, respectively, and thus insulinoma was ruled out. She next underwent a mixed meal study during which her postprandial blood glucose increased to at least 198 indicating postprandial hyperglycemia. At the same time, patient's postprandial lactate also elevated to 4 mmol/L, consistent with lactic acidosis. Her symptoms of hypoglycemia resolved once her blood sugar (BG) improved. She met criteria for Whipple’s triad. She also had had an 8 am cortisol of 13.06 mcg/dL and had an appropriate response to the ACTH stimulation test with a 60 minute post cortisol above 20 mcg/dL, thus ruling out adrenal insufficiency as a cause of hypoglycemia. These findings of the 72 hour fast that resulted in hypoglycemia, followed by postprandial hyperglycemia with lactic acidosis are consistent with Glycogen Storage Disorder Type 0. Treatment involves intake of uncooked cornstarch with protein. Patient incorporated both uncooked cornstarch along with increasing amount of protein in her diet and started using a continuous glucose monitor with alarm to monitor her BGs. As a result her episodes of frequent hypoglycemia improved. Conclusion: This case illustrates a rare case of hypoglycemia in an adult that was not thoroughly investigated when she was in her childhood and adolescence. This case teaches the importance of being familiar with pediatric conditions since it is possible that pediatric conditions, especially if they are missed by providers and not appropriately diagnosed. Presentation: Saturday, June 17, 2023

Assessing the Content Validity, Acceptability, and Feasibility of the Hypo-METRICS App: Survey and Interview Study.

The Hypoglycaemia - MEasurement, ThResholds and ImpaCtS (Hypo-METRICS) smartphone app was developed to investigate the impact of hypoglycemia on daily functioning in adults with type 1 diabetes mellitus or insulin-treated type 2 diabetes mellitus. The app uses ecological momentary assessments, thereby minimizing recall bias and maximizing ecological validity. It was used in the Hypo-METRICS study, a European multicenter observational study wherein participants wore a blinded continuous glucose monitoring device and completed the app assessments 3 times daily for 70 days. The 3 aims of the study were to explore the content validity of the app, the acceptability and feasibility of using the app for the duration of the Hypo-METRICS study, and suggestions for future versions of the app. Participants who had completed the 70-day Hypo-METRICS study in the United Kingdom were invited to participate in a brief web-based survey and an interview (approximately 1h) to explore their experiences with the app during the Hypo-METRICS study. Thematic analysis of the qualitative data was conducted using both deductive and inductive methods. A total of 18 adults with diabetes (type 1 diabetes: n=10, 56%; 5/10, 50% female; mean age 47, SD 16 years; type 2 diabetes: n=8, 44%; 2/8, 25% female; mean age 61, SD 9 years) filled out the survey and were interviewed. In exploring content validity, participants overall described the Hypo-METRICS app as relevant, understandable, and comprehensive. In total, 3 themes were derived: hypoglycemia symptoms and experiences are idiosyncratic; it was easy to select ratings on the app, but day-to-day changes were perceived as minimal; and instructions could be improved. Participants offered suggestions for changes or additional questions and functions that could increase engagement and improve content (such as providing more examples with the questions). In exploring acceptability and feasibility, 5 themes were derived: helping science and people with diabetes; easy to fit in, but more flexibility wanted; hypoglycemia delaying responses and increasing completion time; design, functionality, and customizability of the app; and limited change in awareness of symptoms and impact. Participants described using the app as a positive experience overall and as having a possible, although limited, intervention effect in terms of both hypoglycemia awareness and personal impact. The Hypo-METRICS app shows promise as a new research tool to assess the impact of hypoglycemia on an individual's daily functioning. Despite suggested improvements, participants' responses indicated that the app has satisfactory content validity, overall fits in with everyday life, and is suitable for a 10-week research study. Although developed for research purposes, real-time assessments may have clinical value for monitoring and reviewing hypoglycemia symptom awareness and personal impact.

Association between sleep variability and time in range of glucose levels in patients with type 1 diabetes: Cross-sectional study

ObjectiveSleep and circadian disturbances emerge as novel factors influencing glycemic control in type 1 diabetes (T1D). We aimed to explore the associations among sleep, behavioral circadian parameters, self-care, and glycemic parameters in T1D. MethodsSeventy-six non–shift-working adult T1D patients participated. Blinded 7-day continuous glucose monitoring (CGM) and hemoglobin A1C (A1C) were collected. Percentages of time-in-range (glucose levels 70-180 mg/dL) and glycemic variability (measured by the coefficient of variation [%CV]) were calculated from CGM. Sleep (duration and efficiency) was recorded using 7-day actigraphy. Variability (standard deviation) of midsleep time was used to represent sleep variability. Nonparametric behavioral circadian variables were derived from actigraphy activity recordings. Self-care was measured by diabetes self-management questionnaire-revised. Multiple regression analyses were performed to identify independent predictors of glycemic parameters. ResultsMedian (interquartile range) age was 34.0 (27.2, 43.1) years, 48 (63.2%) were female, and median (interquartile range) A1C was 6.8% (6.2, 7.4). Sleep duration, efficiency, and nonparametric behavioral circadian variables were not associated with glycemic parameters. After adjusting for age, sex, insulin delivery mode/CGM use, and ethnicity, each hour increase in sleep variability was associated with 9.64% less time-in-range (B = −9.64, 95% confidence interval [−16.29, −2.99], p ≤ .001). A higher diabetes self-management questionnaire score was an independent predictor of lower A1C (B = −0.18, 95% confidence interval [−0.32, −0.04]). ConclusionGreater sleep timing variability is independently associated with less time spent in the desirable glucose range in this T1D cohort. Reducing sleep timing variability could potentially lead to improved metabolic control and should be explored in future research. Data availability statementData are available upon a reasonable request to the corresponding author.

Interrupting prolonged sitting with frequent short bouts of light-intensity activity in people with type 1 diabetes improves glycaemic control without increasing hypoglycaemia: The SIT-LESS randomised controlled trial.

To examine the impact of interrupting prolonged sitting with frequent short bouts of light-intensity activity on glycaemic control in people with type 1 diabetes (T1D). In total, 32 inactive adults with T1D [aged 27.9 ± 4.7 years, 15 men, diabetes duration 16.0 ± 6.9 years and glycated haemoglobin 8.4 ± 1.4% (68 ± 2.3 mmol/mol)] underwent two 7-h experimental conditions in a randomised crossover fashion with >7-day washout consisting of: uninterrupted sitting (SIT), or, interrupted sitting with 3-min bouts of self-paced walking at 30-min intervals (SIT-LESS). Standardised mixed-macronutrient meals were administered 3.5 h apart during each condition. Blinded continuous glucose monitoring captured interstitial glucose responses during the 7-h experimental period and for a further 48-h under free-living conditions. SIT-LESS reduced total mean glucose (SIT 8.2 ± 2.6 vs. SIT-LESS 6.9 ± 1.7 mmol/L, p = .001) and increased time in range (3.9-10.0 mmol/L) by 13.7% (SIT 71.5 ± 9.5 vs. SIT-LESS 85.1 ± 7.1%, p = .002). Hyperglycaemia (>10.0 mmol/L) was reduced by 15.0% under SIT-LESS (SIT 24.2 ± 10.8 vs. SIT-LESS 9.2 ± 6.4%, p = .002), whereas hypoglycaemia exposure (<3.9 mmol/L) (SIT 4.6 ± 3.0 vs. SIT-LESS 6.0 ± 6.0%, p = .583) was comparable across conditions. SIT-LESS reduced glycaemic variability (coefficient of variation %) by 7.8% across the observation window (p = .021). These findings were consistent when assessing discrete time periods, with SIT-LESS improving experimental and free-living postprandial, whole-day and night-time glycaemic outcomes (p < .05). Interrupting prolonged sitting with frequent short bouts of light-intensity activity improves acute postprandial and 48-h glycaemia in adults with T1D. This pragmatic strategy is an efficacious approach to reducing sedentariness and increasing physical activity levels without increasing risk of hypoglycaemia in T1D.

Dynamics of glucose concentration during the initiation of ketogenic diet treatment in children with refractory epilepsy: Results of continuous glucose monitoring.

The ketogenic diet (KD) is a diet low in carbohydrates and rich in fats which has long been used to treat refractory epilepsy. The metabolic changes related to the KD may increase the risk of hypoglycemia, especially during the first days. The study focused on the impact of KD initiation on glycemia in non-diabetic patients with refractory epilepsy. The subjects were 10 pediatric patients (6 boys, mean age 6.1 ± 2.4 years), treated for intractable epilepsy. Blinded continuous glucose monitoring system (CGM) Dexcom G4 was used. Patients started on their regular diet in the first 36 hours of monitoring, followed by an increase in lipids intake and a gradual reduction of carbohydrates (relations 1:1; 2:1; 3:1; 3.5:1). We analyzed changes in glycemia during fat: nonfat ratio changes using a generalized linear model. The mean monitored time per person was 6 days, 10 hours and 44 minutes. The mean ± SD glycemia for the regular diet was 4.84 ± 0.20 mmol/L, for the carbohydrates/fat ratio of 1:1 it was 4.03 ± 0.16, for the ratio of 2:1 it was 3.57 ± 0.10, for the ratio 3:1 it was 3.39 ± 0.13 and for the final ratio of 3.5:1 it was 2.79 ± 0.06 mmol/L (P < 0.001). The portions of time spent in glycemia ≤3.5 mmol/L (≤2.5 mmol/L respectively) were: on the normal diet 0.88% (0.31%) of the monitored period, during 1:1 KD ratio 1.92% (0.95%), during 2:1 ratio 3.18% (1.02%), and during 3:1 and 3.5:1 ratios 13.64% (2.36%) of the monitored time (P < 0.05). Continuous glucose monitoring system shows the dynamic of glucose concentration in ketogenic diet treatment initiation. It may be a useful tool to control the effects of this diet on glucose metabolism, especially in hypoglycemia detection.

617-P: Time in Range following Mealtime Protein Supplementation Is Predicted by Liver Health and ß-Cell Function in Adults with Type 2 Diabetes

Background: Consuming mealtime whey protein supplementation increases daily time in euglycemic range [TIR] in adults with type 2 diabetes [T2D] with inter-individual responses. The present post-hoc analysis aimed to identify metabolic predictors to the effectiveness of pre-meal whey protein. Methods: In a randomized-crossover design, 16 individuals with T2D [HbA1c 57±9 mmol/mol) underwent two 7d free-living periods where they consumed a whey protein (15g) or placebo beverage before each main meal. Glucose control was captured by blinded continuous glucose monitoring. Patient anthropometric and fasting markers of liver health and glucose metabolism were obtained at baseline. β-cell function was determined by the ratio of the incremental changes in insulin secretion to glucose during a feeding test. The relationship between TIR and clinical markers were analyzed by correlational analysis, and predictors of TIR were determined by multiple regression. Results: β-cell function was a strong determinant to TIR during both the whey (rs=0.615; p=0.011) and placebo (rs=0.598; p=0.019) weeks. Around 76% of the variance in TIR during the whey week was accounted by β-cell function, and fasting insulin and alanine aminotransferase concentrations (adj R2=0.760; p&amp;lt;0.001). During the whey intervention, TIR was inversely associated with aspartate aminotransferase (rs=−0.506; p=0.046) and tended to be related to fasting glucagon (rs=−0.482; p=0.058), whereas there were no associations between liver health or fasting hormonal markers with TIR during placebo (p&amp;gt;0.05). Conclusions: In a small cohort of people with T2D, our data indicates that those with compromised liver health and reduced β-cell function may experience a reduction in TIR when consuming mealtime protein supplementation. This is possibly due to disruptions in the liver-α-cell axis, which may be further aggravated by increasing plasma amino acid availability from protein supplementation. Disclosure K.Smith: None. G.Taylor: None. M.Walker: None. K.A.Bowden davies: None. E.J.Stevenson: None. D.J.West: None. Funding Francis James Bell Endowment Fund; County Durham Community Foundation

161-OR: Validation of the Short Type 1 Diabetes Stigma Assessment Scale (DSAS-1) and Its Association with Affective States, Diabetes Distress, Diabetes Self-Management, and Glycemic Control

Introduction: We validated an 8-item version of the DSAS-1 and examined its relationship with momentary ratings of affective states, diabetes distress, diabetes self-care, and glycemic control over time. Methods: We recruited 184 adults with T1D from Los Angeles, California, and the Bronx, New York. Participants completed a short version of the DSAS-1, other validated questionnaires, and 10-14 days of intensive longitudinal data collection using blinded continuous glucose monitoring and Ecological Momentary Assessment (EMA) via smartphone. Psychometric validation included exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and internal consistency reliability assessment. We used Spearman's rho correlations to explore the convergent validity using EMA data. Results: EFA indicated an acceptable fit of a 3-factor model (Bartlett's = 0.000; KMO = 0.774). This was corroborated with CFA, which demonstrated a reasonable three-factor fit (chi-squared = 36.44; RMSEA = 0.079; SRMR = 0.036; TLI = 0.985; CFI = 0.991); less so for a single-factor model. Satisfactory convergent and discriminant validity was demonstrated. The DSAS-1 was significantly correlated with momentary ratings of negative emotions (0.30, p = &amp;lt;0.001), diabetes distress (0.32, p = &amp;lt;0.001), insulin regimen adherence (-0.20, p = 0.007), diabetes self-care (-0.24, p = 0.002), mean blood glucose (0.20, p = 0.008) and time in range (-0.21, p = 0.006), but was not significantly associated with positive emotions. Conclusions: The 8-item DSAS-1 is a valid and reliable measure of T1D stigma. Convergent validity confirms the importance of diabetes-related stigma in relation to psychosocial and blood glucose outcomes, and underscores the need for further examination of stigma in diabetes, which may be facilitated by this shorter scale. Disclosure G.Crespo-ramos: None. H.Jin: None. E.Pyatak: Research Support; Abbott Diabetes. D.Spruijt-metz: None. J.S.Gonzalez: Consultant; Virta Health Corp. C.J.Hoogendoorn: None. S.Schneider: None. R.Hernandez: None. L.T.Pham: None. A.L.Peters: Advisory Panel; Abbott Diabetes, Medscape, Novo Nordisk, Vertex Pharmaceuticals Incorporated, Zealand Pharma A/S, Research Support; Abbott Diabetes, Dexcom, Inc., Insulet Corporation, Stock/Shareholder; Omada Health, Inc., Livongo. V.F.Ruelas: None. J.P.Crandall: Research Support; Abbott. P.Lee: None. Funding National Institutes of Health (R01DK121298)

697-P: How Does Daily Experience of Social Determinants of Health (SDOH) Impact Diabetes Self-Management and Glycemic Control in Underserved Young Adults (YA) with Type 1 Diabetes (T1D)?

Background: While it is recognized that SDOH can impact diabetes self-management and glycemic control in underserved YA with T1D, the real-time dynamic interplay among these variables within a given individual is poorly understood, which limits intervention potential. Methods: We recruited 61 underserved YA with T1D (ages 18-30 yo) from the Bronx, New York. We administered 14 days of 3 times daily Ecological Momentary Assessment (EMA) surveys via smartphone, paired with blinded continuous glucose monitoring (CGM). Young adults reported twice daily on their experience of SDOH and once daily on their diabetes self-management. Using dynamic structural equation modeling, we evaluated within-person direct and indirect effects of SDOH on glycemic control, mediated through self-management. Results: We included 51 YA with complete data (mean age 23 yo, 36% Black, 54% Hispanic, 73% Medicaid-insured, mean A1c 8.6%). Within an individual, on days when reported, presence of SDOH predicted lower time in range (TIR, 70-180 mg/dl) by 0.8% and increased mean blood glucose by 1.7 mg/dl that same day, but only when diabetes self-management was affected (indirect pathway). Separately, on days when daily self-management rating was higher, regardless of whether SDOH were experienced, TIR (+6.3%) and mean blood glucose (-14 mg/dl) improved that same day. Conclusions: We examined the daily dynamic interplay of changes in SDOH, self-management, and glycemia in a novel way in underserved YA with T1D. While experiencing daily SDOH negatively affected glycemic control, it was only when self-management was also impacted. Just-in-time self-management support delivered in the context of daily SDOH experiences offers a new approach to timely targeted intervention. Interrupting the chronic cycle of cumulative effects of daily SDOH on glycemia could affect long-term outcomes. Disclosure G.Crespo-ramos: None. C.Schechter: None. M.Finnan: None. C.J.Hoogendoorn: None. J.S.Gonzalez: Consultant; Virta Health Corp. S.Agarwal: Advisory Panel; Medtronic, Consultant; Beta Bionics, Inc., Research Support; Abbott Diabetes, Dexcom, Inc. Funding NY-Regional Center for Diabetes Translational Research (5P30DK111022-08); National Institute for Diabetes and Digestive and Kidney Diseases (K23DK115896-05)

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)

159-OR: Greater Sleep Variability Is Associated with Less Time in Range of Glucose Levels in Patients with Type 1 Diabetes

Background: Sleep and circadian disturbances emerge as novel factors influencing glycemic control in type 1 diabetes (T1D). We aimed to explore the associations between sleep, behavioral circadian parameters, self-care, and glycemic parameters in T1D. Methods: Baseline data of 76 non-shift working adult T1D patients participating in a randomized study to optimize sleep health (NCT04506151) were analyzed. Glycemic assessments were derived from blinded 7-day continuous glucose monitoring (CGM) and A1C. Percentages of time-in-range (TIR of glucose levels between 70-180 mg/dL) and %CV were calculated from CGM. Sleep was recorded using 7-day actigraphy, yielding sleep duration and efficiency. Variability (SD) of mid-sleep time (MST) was used to represent sleep variability. Non-parametric behavioral circadian variables were derived from actigraphy activity recordings. Self-care was measured by the Diabetes Self-Management Questionnaire (DSMQ-R). Multiple regression analyses were performed to identify independent predictors of glycemic parameters. Results: Mean (SD) age was 36.3 (11) years, 48 (63.2%) female, and median (IQR) A1C 6.8% (6.2, 7.4). Sleep duration, efficiency, and non-parametric behavioral circadian variables were not associated with glycemic parameters. After adjusting for age, sex, insulin delivery mode/CGM use, and ethnicity, each hour increase in SD of MST was associated with 9.64 % less TIR (B= -9.64, 95% (CI) [-16.29, -2.99], p=&amp;lt;0.001). Higher DSMQ score was an independent predictor of lower A1C (B = -0.18, 95% CI -0.32, -0.04). Conclusion: Greater sleep variability is independently associated with less time spent in desirable glucose range in this modern T1D cohort, with clinically important effect sizes. Sleep optimization to reduce variable sleep schedule could potentially lead to improved metabolic control and should be explored in future research. Disclosure P.Martyn-nemeth: None. L.T.Quinn: None. M.Clark withington: None. A.H.Saleh: None. B.Loiacono: None. D.Mihailescu: None. S.Reutrakul: Speaker's Bureau; Eli Lilly and Company. G.Abu irsheed: None. M.Park: None. A.D.Steffen: None. L.Burke: None. S.Pratuangtham: None. K.Baron: Other Relationship; National Sleep Foundation, Research Support; Google. J.Duffecy: Consultant; Chorus Health, K Health, Stock/Shareholder; Healent Health, My Alloy. R.Perez: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (R01DK121726)