Continuous Glucose Monitoring Measurements Research Articles

Glycemic and Psychosocial Outcomes of Advanced Hybrid Closed Loop Therapy in Youth With High HbA1c: A Randomized Clinical Trial.

To determine the efficacy of advanced hybrid closed loop (AHCL) therapy in a high-risk cohort of youth on continuous subcutaneous insulin infusion (CSII) ± continuous glucose monitoring (CGM) with suboptimal glycemia. In a 6-month multicenter clinical trial, youth with type 1 diabetes with mean and most recent HbA1c > 8.5% (65 mmol/mol) were randomly assigned 1:1 to AHCL or treatment as usual (CSII ± CGM). The primary outcome was the 24-week between-group difference in HbA1c. Secondary outcomes included CGM metrics from masked CGM and psychological measures (youth-reported problem areas in diabetes [PAID], quality of life, anxiety, depression, and hypoglycemia fear) assessed using validated questionnaires. A total of 42 participants were randomized (mean [SD] age 16.2 [2.5] years, HbA1c 9.8 [1.1]% or 84 [12] mmol/mol, PAID score 50.3 [19.8]). At study end, the mean (SD) HbA1c was 8.8 (1.1)% or 73 (12) mmol/mol with AHCL and 9.9 (1.2)% or 85 (13.1) mmol/mol with CSII ± CGM, with mean adjusted group difference of -0.77% (95% CI -1.45 to -0.09) or -8.4 mmol/mol (-15.8 to -1.0); P = 0.027. AHCL increased time in range 70-180 mg/dL (difference 19.1%; 95% CI 11.1 to 27.1), reduced time >180 mg/dL (difference -17.7%; 95% CI -26.6 to -8.8), with no increase in time spent <70 mg/dL (difference -0.8%; 95% CI -2.7 to 0.6). There was no evidence for difference in psychosocial outcomes between the two groups at study end. AHCL should be encouraged in youth with suboptimal glycemia, as AHCL improves glycemia. However, psychological support remains vital, as technology alone may not be able to reduce the burden of diabetes care in this subgroup.

Prediction of personalised postprandial glycaemic response in type 1 diabetes mellitus.

Patients with type 1 diabetes (T1D) face unique challenges in glycaemic control due to the complexity and uniqueness of the dietary structure in China, especially in terms of postprandial glycaemic response (PPGR). This study aimed to establish a personalized model for predicting PPGR in patients with T1D. Data provided by the First People's Hospital of Yunnan Province, 13 patients with T1D, were recruited and provided with an intervention for at least two weeks. All patients were asked to wear a continuous glucose monitoring (CGM) device under free-living conditions during the study period. To tackle the challenge of incomplete data from wearable devices for CGM measurements, the GAIN method was used in this paper to achieve a more rational interpolation process. In this study, patients' PPGRs were calculated, and a LightGBM prediction model was constructed based on a Bayesian hyperparameter optimisation algorithm and a random search algorithm, which integrated glucose measurement, insulin dose, dietary nutrient content, blood measurement and anthropometry as inputs. The experimental outcomes revealed that the PPGR prediction model presented in this paper demonstrated superior accuracy (R=0.63) compared to both the carbohydrate content only model (R=0.14) and the baseline model emulating the standard of care for insulin administration (R=0.43). In addition, the interpretation of the model using the SHAP method showed that blood glucose levels at meals and blood glucose trends 30 minutes before meals were the most important features of the model. The proposed model offers a heightened precision in predicting PPGR in patients with T1D, so it can better guide the diet plan and insulin intake dose of patients with T1D.

Romantic relationships and type 2 diabetes: The role of invisible social control.

This study aimed to determine whether invisible social control provided by a romantic partner is associated with improved objective glucose outcomes for patients with Type 2 diabetes. Social control reflects a partner's attempt to modify or influence a patient's health behaviors. We hypothesized that the best outcome for all continuous glucose monitoring measures would be captured by an interaction condition reflecting invisible social control. Patients with Type 2 diabetes and their partners (N = 63 couples) completed an 8-day daily diary period between 2016 and 2017. Self-report measures of social control receipt and provision were obtained each evening from patients and partners and patients wore a continuous glucose monitor throughout the diary period. Outcomes of daily glucose mean, standard deviation, time in range, and coefficient of variation were computed and two-way interactions between social control receipt and social control provision were probed and plotted. The two-way interaction significantly predicted daily glucose mean, standard deviation, and time in range, such that when patients reported no social control receipt, but partners reported social control provision, patients showed improvements in objective glucose measures. We found no significant effect for coefficient of variation. This study was the first to use an invisible social control framework to examine the daily dyadic associations between partner social control provision, patient social control receipt, and four objectively measured continuous glucose monitoring outcomes. Findings suggest that the visibility of social control provided by a romantic partner may be predictive of glycemic control in patients with Type 2 diabetes. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Assessing Patterns of CGM use and Metrics of Glycemic Control in T1D and T2D Patients in the Veterans Healthcare System: Integrating CGM Device Data with EHR Data.

To integrate long-term daily continuous glucose monitoring (CGM) device data with electronic health records (EHR) for type 1 and type 2 diabetes patients in the Veterans Affairs Healthcare System to assess real-world patterns of CGM use and the reliability of EHR-based CGM information. This observational study used Dexcom CGM device data linked with EHR (from 2015-2020) for a large national cohort of diabetes patients. We tracked the initiation and consistency of CGM use, assessed concordance of CGM use and measures of glucose control between CGM device data and EHR records, and examined results by age, ethnicity, and diabetes type. The time from pharmacy release of CGM to patients to initiation of uploading CGM data to Dexcom servers averaged three weeks but demonstrated wide variation among individuals; importantly, this delay decreased markedly over the later years. The average daily wear time of CGM exceeded 22 hours over nearly three years of follow-up. Patterns of CGM use were generally consistent across age, race/ethnicity groups and diabetes type. There was strong concordance between EHR-based estimates of CGM use and Dexcom CGM wear time and between estimates of glucose control from both sources. The study demonstrates our ability to reliably integrate CGM devices and EHR data to provide valuable insights into CGM use patterns. The results indicate in the real-world environment that CGM is worn consistently over many years for both type 1 and type 2 patients within the Veterans Affairs Healthcare System and is similar across major race/ethnicity and age groups.

Calibrating Glucose Sensors at the Edge: A Stress Generation Model for Tiny ML Drift Compensation

Background: Continuous glucose monitoring (CGM) systems offer the advantage of noninvasive monitoring and continuous data on glucose fluctuations. This study introduces a new model that enables the generation of synthetic but realistic databases that integrate physiological variables and sensor attributes into a dataset generation model and this, in turn, enables the design of improved CGM systems. Methods: The presented approach uses a combination of physiological data and sensor characteristics to construct a model that considers the impact of these variables on the accuracy of CGM measures. A dataset of 500 sensor responses over a 15-day period is generated and analyzed using machine learning algorithms (random forest regressor and support vector regressor). Results: The random forest and support vector regression models achieved Mean Absolute Errors (MAEs) of 16.13 mg/dL and 16.22 mg/dL, respectively. In contrast, models trained solely on single sensor outputs recorded an average MAE of 11.01±5.12 mg/dL. These findings demonstrate the variable impact of integrating multiple data sources on the predictive accuracy of CGM systems, as well as the complexity of the dataset. Conclusions: This approach provides a foundation for developing more precise algorithms and introduces its initial application of Tiny Machine Control Units (MCUs). More research is recommended to refine these models and validate their effectiveness in clinical settings.

Noninvasive Real-Time Glucose Monitoring Is in the Near Future.

Objective: Since the introduction of continuous glucose monitoring (CGM) technology, developers have rigorously researched the feasibility of creating a noninvasive glucose monitoring device. In a recent pilot study, investigators reported a strong correlation between glucose values obtained from novel noninvasive monitoring device (GWave) values to venous and capillary glucose measurements. Research Design and Methods: We investigated whether the level of accuracy observed in the pilot study could be reproduced in a larger cohort, using a smaller third-generation manufacturable device (Gen III GWave) containing a standardized sensor chip that can be mass produced for commercial use. The evaluation assessed concordance with capillary blood glucose, reproducibility between two Gen III devices, and accuracy during insulin-induced hypoglycemia. Results: Assessment of samples from 75 subjects (type 2 diabetes, n = 6; type 1 diabetes, n = 28; nondiabetic pregnant subjects, n = 10; and nondiabetic, n = 31) showed that 97% of values were in Zone A with 3% in Zone B of the Clarke Error Grid, with a mean absolute relative difference of 6.7% from reference blood glucose. Comparison between two independent Gen III GWave devices demonstrated reproducibility between the sensors (R2 = 0.95), with 100% of values within Zone A. In the hypoglycemia assessment, measurements from the Gen III sensor tightly followed the capillary glucose measurements down to 42 mg/dL (2.3 mmol/L), whereas the CGM measurements from two different CGM only converged with the GWave and capillary glucose readings after 90 min of decreasing glucose levels. Conclusion: Our results show promise as potentially the first noninvasive technology. Future studies will focus on larger number of people in all glucose ranges. Real-time noninvasive blood glucose monitoring is possible using GWave technology.

The Role of Continuous Glucose Monitoring in Detecting Early Dysglycemia and Clinical Outcomes in Patients with Cystic Fibrosis.

Cystic fibrosis-related diabetes (CFRD) is the most common comorbidity in patients with cystic fibrosis (CF). CFRD has been correlated with important clinical outcomes, including poor nutrition, reduced pulmonary function, and earlier mortality. However, clinical decline due to abnormalities of blood glucose (dysglycemia) begins early in CF, before the diagnosis of CFRD by the gold-standard oral glucose tolerance test (OGTT). Continuous glucose monitoring (CGM) has been validated in patients with CF and has been recognized as a valuable tool in detecting early glucose abnormalities in patients with CF. Several CGM parameters have been used to predict CFRD in some but not all studies, and there is no consensus regarding CGM use for diagnostic purposes. Thus, it remains a complementary test to OGTT in CFRD diagnosis. The aim of this review is to provide an update on the pathophysiological mechanisms of CFRD, recent advances in the use of CGM for CFRD screening, and the association between CGM measures and CF-related clinical outcomes.

Encapsulated stem cell-derived β cells exert glucose control in patients with type 1 diabetes.

Clinical studies on the treatment of type 1 diabetes with device-encapsulated pancreatic precursor cells derived from human embryonic stem cells found that insulin output was insufficient for clinical benefit. We are conducting a phase 1/2, open-label, multicenter trial aimed at optimizing cell engraftment (ClinicalTrials.gov identifier: NCT03163511 ). Here we report interim, 1-year outcomes in one study group that received 2-3-fold higher cell doses in devices with an optimized membrane perforation pattern. β cell function was measured by meal-stimulated plasma C-peptide levels at 3-month intervals, and the effect on glucose control was assessed by continuous glucose monitoring (CGM) and insulin dosing. Of 10 patients with undetectable baseline C-peptide, three achieved levels ≥0.1 nmol l-1 from month 6 onwards that correlated with improved CGM measures and reduced insulin dosing, indicating a glucose-controlling effect. The patient with the highest C-peptide (0.23 nmol l-1) increased CGM time-in-range from 55% to 85% at month 12; β cell mass in sentinel devices in this patient at month 6 was 4% of the initial cell mass, indicating directions for improving efficacy.

Bayesian denoising algorithm dealing with colored, non-stationary noise in continuous glucose monitoring timeseries

Introduction: The retrospective analysis of continuous glucose monitoring (CGM) timeseries can be hampered by colored and non-stationary measurement noise. Here, we introduce a Bayesian denoising (BD) algorithm to address both autocorrelation of measurement noise and temporal variability of its variance.Methods: BD utilizes adaptive, a-priori models of signal and noise, whose unknown variances are derived on partially-overlapped CGM windows, via smoothing approach based on linear mean square estimation. The CGM signal and noise variability profiles are then reconstructed using a kernel smoother. BD is first assessed on two simulated datasets, DS1 and DS2. On DS1, the effectiveness of accounting for colored noise is evaluated by comparison against a literature algorithm; on DS2, the effectiveness of accounting for the noise variance temporal variability is evaluated by comparison against a Butterworth filter. BD is then evaluated on 15 CGM timeseries measured by the Dexcom G6 (DR).Results: On DS1, BD allows reducing the root-mean-square-error (RMSE) from 8.10 [6.79–9.24] mg/dL to 6.28 [5.47–7.27] mg/dL (median [IQR]); on DS2, RMSE decreases from 6.85 [5.50–8.72] mg/dL to 5.35 [4.48–6.49] mg/dL. On DR, BD performs a reasonable tracking of noise variance variability and a satisfactory denoising.Discussion: The new algorithm effectively addresses the nature of CGM measurement error, outperforming existing denoising algorithms.

The Impact of Sex, Body Mass Index, Age, Exercise Type and Exercise Duration on Interstitial Glucose Levels during Exercise.

The impact of age, sex and body mass index on interstitial glucose levels as measured via continuous glucose monitoring (CGM) during exercise in the healthy population is largely unexplored. We conducted a multivariable generalized estimating equation (GEE) analysis on CGM data (Dexcom G6, 10 days) collected from 119 healthy exercising individuals using CGM with the following specified covariates: age; sex; BMI; exercise type and duration. Females had lower postexercise glycemia as compared with males (92 ± 18 vs. 100 ± 20 mg/dL, p = 0.04) and a greater change in glycemia during exercise from pre- to postexercise (p = 0.001) or from pre-exercise to glucose nadir during exercise (p = 0.009). Younger individuals (i.e., <20 yrs) had higher glucose during exercise as compared with all other age groups (all p < 0.05) and less CGM data in the hypoglycemic range (<70 mg/dL) as compared with those aged 20-39 yrs (p < 0.05). Those who were underweight, based on body mass index (BMI: <18.5 kg/m2), had higher pre-exercise glycemia than the healthy BMI group (104 ± 20 vs. 97 ± 17 mg/dL, p = 0.02) but similar glucose levels after exercise. Resistance exercise was associated with less of a drop in glycemia as compared with aerobic or mixed forms of exercise (p = 0.008) and resulted in a lower percent of time in the hypoglycemic (p = 0.04) or hyperglycemic (glucose > 140 mg/dL) (p = 0.03) ranges. In summary, various factors such as age, sex and exercise type appear to have subtle but potentially important influence on CGM measurements during exercise in healthy individuals.

Associations of predominant night-eating with plasma glycemic status and continuous glucose monitoring measures among pregnant women

To examine whether predominant night-eating, defined as more than 50% of total daily energy intake consumed between 1900 and 0659h, is associated with glycemic outcomes in pregnancy. This was a prospective cohort study of 277 healthy pregnant women with complete 4-day dietary intake records at 18-24 weeks gestation, recruited from KK Women's and Children's Hospital, Singapore. Primary outcomes were fasting, 1-h, and 2-h plasma glucose after a 75-g oral glucose tolerance test at 24-28 weeks gestation. Secondary outcomes were gestational diabetes mellitus (GDM), fasting insulin, homeostasis model assessment of insulin resistance (HOMA2-IR), β-cell function (HOMA2-%B), and continuous glucose monitoring (CGM) measures. Glucose variables in continuous form were loge-transformed before analyses. Predominant night-eating (11.6%) was associated with higher fasting glucose (geometric mean ratio (95% confidence interval) 1.05 (1.01, 1.08)) and 1-h glucose (1.11 (1.01, 1.21)), but not with 2-h glucose or GDM risk. Predominant night-eating women had lower fasting insulin (0.77 (0.63, 0.95)), lower HOMA2-IR (0.78 (0.64, 0.97)), and lower HOMA2-%B (0.77 (0.67, 0.89)) than their predominant day-eating counterparts. For CGM measures, predominant night-eating was associated with higher mean glucose (1.07 (1.00, 1.15)), higher glucose management indicator (1.05 (1.00, 1.10)), and higher overall glucose levels throughout 24h (1.10 (1.02, 1.19)). All these associations were adjusted for socio-demographic, lifestyle factors, and diet composition. Predominant night-eating was mainly associated with less desirable glycemic outcomes during pregnancy. Future studies should explore dietary interventions aimed at reducing consumption of relatively more calories at night than day during pregnancy.

Continuous glucose monitoring-derived time in range and CV are associated with altered tissue characteristics of the carotid artery wall in people with type 2 diabetes

Aims/hypothesisPrevious studies have suggested that glucose variability may accelerate atherosclerosis progression in people with type 2 diabetes. Current guidelines recommend assessing glycaemic control using continuous glucose monitoring (CGM), which provides a comprehensive glycaemic profile to supplement HbA1c measurement. However, the association between CGM-derived metrics and atherosclerosis progression is not entirely clear.MethodsThis exploratory study used baseline data and data obtained after 104 weeks from an ongoing prospective, multicentre, observational study. Six hundred study participants with type 2 diabetes and no apparent history of symptomatic cardiovascular disease underwent CGM and ultrasonographic atherosclerosis measurements of the carotid arteries, including the intima–media thickness (IMT) and grey-scale median (GSM), at baseline and 104 weeks. Non-invasive ultrasonic tissue characterisation of the carotid artery wall or plaque using the GSM reflects vascular composition. Multivariate regression models were used to analyse the association between CGM-derived indices, mainly time in range (TIR) and CV, and changes in carotid atherosclerosis index values.ResultsOver the 104-week study period, there were modest increases in mean IMT (from 0.759±0.153 to 0.773±0.152 mm, p<0.001) and thickened-lesion GSM (from 43.5±19.5 to 53.9±23.5 units, p<0.001), but no significant changes in common carotid artery maximum-IMT (from 1.109±0.442 to 1.116±0.469 mm, p=0.453) or mean GSM (from 48.7±19.3 to 49.8±20.8 units, p=0.092). In a linear regression model with adjustment for possible atherosclerotic risk factors, including HbA1c, TIR and CV at baseline were significantly associated with the annual change in mean GSM (regression coefficient per 10% increase in TIR 0.52; 95% CI 0.06, 0.98; Hochberg-adjusted p value 0.038; regression coefficient per 1% increase in CV −0.12; 95% CI −0.22, −0.02; Hochberg-adjusted p value 0.038). TIR and CV at baseline were also significantly associated with the annual change in thickened-lesion GSM (regression coefficient per 10% increase in TIR 0.95; 95% CI 0.12, 1.79; Hochberg-adjusted p value 0.038; regression coefficient per 1% increase in CV −0.19; 95% CI −0.36, −0.01; Hochberg-adjusted p value 0.038). Participants who achieved target CGM-derived metrics at baseline, as proposed by an international consensus, showed significant annual changes in mean GSM compared with those who did not (0.94±6.88 vs −0.21±6.19 units/year, p=0.007).Conclusions/interpretationTIR and CV were significantly associated with changes in the tissue characteristics of the carotid artery wall.Trial registrationUniversity Hospital Medical Information Network Clinical Trials Registry, number UMIN000032325Graphical

OGTT Metrics Surpass Continuous Glucose Monitoring Data for T1D Prediction in Multiple-Autoantibody-Positive Individuals.

The value of continuous glucose monitoring (CGM) for monitoring autoantibody (AAB)-positive individuals in clinical trials for progression of type 1 diabetes (T1D) is unknown. Compare CGM with oral glucose tolerance test (OGTT)-based metrics in prediction of T1D. At academic centers, OGTT and CGM data from multiple-AAB relatives were evaluated for associations with T1D diagnosis. Participants were multiple-AAB-positive individuals in a TrialNet Pathway to Prevention (TN01) CGM ancillary study (n = 93). The intervention was CGM for 1 week at baseline, 6 months, and 12 months. Receiver operating characteristic (ROC) curves of CGM and OGTT metrics for prediction of T1D were analyzed. Five of 7 OGTT metrics and 29/48 CGM metrics but not HbA1c differed between those who subsequently did or did not develop T1D. ROC area under the curve (AUC) of individual CGM values ranged from 50% to 69% and increased when adjusted for age and AABs. However, the highest-ranking metrics were derived from OGTT: 4/7 with AUC ∼80%. Compared with adjusted multivariable models using CGM data, OGTT-derived variables, Index60 and DPTRS (Diabetes Prevention Trial-Type 1 Risk Score), had higher discriminative ability (higher ROC AUC and positive predictive value with similar negative predictive value). Every 6-month CGM measures in multiple-AAB-positive individuals are predictive of subsequent T1D, but less so than OGTT-derived variables. CGM may have feasibility advantages and be useful in some settings. However, our data suggest there is insufficient evidence to replace OGTT measures with CGM in the context of clinical trials.

Defining a Continuous Glucose Baseline to assess the impact of nutritional interventions.

Accurate and robust estimation of individuals' basal glucose level is a crucial measure in nutrition research but is typically estimated from one or more morning fasting samples. The use of Continuous Glucose Monitoring (CGM) devices presents an opportunity to define more robust basal glucose levels, which estimates can be generalized to any time of the day. However, to date, no standardized method has been delineated. The current paper seeks to define a reliable algorithm to characterize the individual's basal glucose level over 24 h from CGM measurements. Data drawn from four nutritional intervention studies performed on adults free from chronic diseases were used to define that basal glucose levels were optimally estimated using the 40th percentile of the previous 24 h CGM data. This simple algorithm provides a Continuous Glucose Baseline over 24 h (24 h-CGB) that is an unbiased and highly correlated estimator (r = 0.86, p-value < 0.01) of standard fasting glucose. We conclude that 24-CGB can provide reliable basal glucose estimates across the day while being more robust to interference than standard fasting glucose, adaptable to evolving daily routines and providing useful reference values for free-living nutritional intervention research in non-diabetic individuals.

Artificial pancreas control using optimized fuzzy logic based genetic algorithm

The aim of this paper is to develop an artificial pancreas that can automate the process of monitoring blood glucose levels and administering insulin to diabetic patients. The device incorporates a fuzzy controller that is optimized through a genetic algorithm and designed using MATLAB. The system comprises three key components: a continuous glucose monitoring (CGM) system, an insulin pump, and the fuzzy controller. The CGM measures blood glucose levels in real-time, and the insulin pump administers insulin doses to maintain blood glucose levels within a specific range. The fuzzy controller adjusts the insulin delivery rate based on the patient's blood glucose levels and their target range. To enhance the system's performance, a genetic algorithm is used to fine-tune the parameters of the fuzzy controller, seeking the optimal set of parameters that minimize the difference between the patient's blood glucose levels and the desired target range. The system is implemented in MATLAB, and simulation results indicate its effectiveness in maintaining blood glucose levels within the desired target range, reducing the risk of hypoglycemia and hyperglycemia. In summary, the proposed artificial pancreas system provides an effective automated solution for monitoring blood glucose levels and administering insulin to diabetic patients, with the fuzzy controller and genetic algorithm optimization enhancing the system's performance.

952-P: The Accuracy and Feasibility of Continuous Glucose Monitoring in COVID-19 Non-Critically Ill Hospitalized Patients—A Pilot Study

Background: Optimal glycemic control is associated with favorable outcomes in COVID-19 hospitalized patients. Frequent capillary blood glucose (CBG) monitoring is difficult to perform. Using continuous glucose monitoring (CGM) system to assist glycemic care can reduce the exposure of healthcare personnel and PPE usage. However, data on the accuracy of CGM in this setting are limited. Objective: To evaluate the accuracy and feasibility of real-time CGM (rt-CGM) in non-ICU hospitalized adult COVID-19 patients. Methods: This is a single-center prospective study of non-ICU hospitalized adult patients with COVID-19 infection who had hyperglycemia requiring insulin therapy during admission. Medtronic Guardian Connect rt-CGM sensor and transmitter were placed on the patient’s abdomen. Paired CBG and sensor glucose values were analyzed for accuracy of CGM using mean absolute relative difference (MARD) and Clarke Error Grid Analysis (CEGA). Results: Fifteen patients were enrolled. Mean age was 48.6 ± 17.9 years. Thirteen patients (86.7%) had pre-existing diabetes. Mean HbA1c was 10.6± 3.6%. Mean duration of CGM use was 6 ± 1.2 days and mean calibration was 2.6 ± 0.7 times/day. There were 253 paired CBG and CGM measurements. The overall MARD was 9.9 ± 9.3%. The lowest MARD was observed in the CBG range of 70-180 mg/dl (9.6 ± 9.0%). The percentages of glucose readings within ±15%/15 mg/dL, ±20%/20 mg/dL, and ±30%/30 mg/dL were 80.2%, 89.7%, and 95.3%, respectively. A total of 99.2% of the data points were in zone A and B of CEGA, and none were in zone E. Percent time in range on day 1 was 57.9 ± 22.9 and improved to 64.9 ± 18.1 in the last 72 hours of sensor wear. No adverse events from the CGM were observed. CGM reduced POC testing by 30%. Conclusions: Rt-CGM use in hospitalized patients with COVID-19 infection demonstrates high accuracy and potentially improves glucose control, reduces the frequency of CBG testing, and preserves medical resources. Disclosure C.Sakjirapapong: None. S.Sirinvaravong: None. L.Preechasuk: None. N.Thongtang: None. Funding Specific League Funds (R016421005)

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)

Continuous glucose monitoring-derived glycemic metrics and adverse pregnancy outcomes among women with gestational diabetes: a prospective cohort study

Continuous glucose monitoring (CGM) has shown potential in improving maternal and neonatal outcomes in individuals with type 1/2 diabetes, but data in gestational diabetes mellitus (GDM) is limited. We aimed to explore the relationship between CGM-derived metrics during pregnancy and pregnancy outcomes among women with GDM. We recruited 1302 pregnant women with GDM at a mean gestational age of 26.0 weeks and followed them until delivery. Participants underwent a 14-day CGM measurement upon recruitment. The primary outcome was any adverse pregnancy outcome, defined as having at least one of the outcomes: preterm birth, large-for-gestational-age (LGA) birth, fetal distress, premature rupture of membranes, and neonatal intensive care unit (NICU) admission. The individual outcomes included in the primary outcome were considered as secondary outcomes. We conducted multivariable logistic regression to evaluate the association of CGM-derived metrics with these outcomes. Per 1-SD difference in time above range (TAR), glucose area under the curve (AUC), nighttime mean blood glucose (MBG), daytime MBG, and daily MBG was associated with higher risk of any adverse pregnancy outcome, with odds ratio: 1.22 (95% CI 1.08-1.36), 1.22 (95% CI 1.09-1.37), 1.18 (95% CI 1.05-1.32), 1.21 (95% CI 1.07-1.35), and 1.22 (95% CI 1.09-1.37), respectively. Time in range, TAR, AUC, nighttime MBG, daytime MBG, daily MBG, and mean amplitude of glucose excursions were positively associated, while time blow range was inversely associated with the risk of LGA. Additionally, higher value for TAR was associated with higher risk of NICU admission. We further summarized the potential thresholds of TAR (2.5%) and daily MBG (4.8mmol/L) to distinguish individuals with and without any adverse pregnancy outcome. The CGM-derived metrics may help identify individuals at higher risk of adverse pregnancy outcomes. These CGM biomarkers could serve as potential new intervention targets to maintain a healthy pregnancy status among women with GDM. National Key R&D Program of China, National Natural Science Foundation of China, and Westlake Laboratory of Life Sciences and Biomedicine.

Multicomponent provider–patient intervention to improve glycaemic control in Medicaid-insured pregnant individuals with type 2 diabetes: clinical trial protocol for the ACHIEVE study

IntroductionType 2 diabetes (T2D) is one of the most frequent comorbid medical conditions in pregnancy. Glycaemic control decreases the risk of adverse pregnancy outcomes for the pregnant individual and infant....

Integration of multiomics with precision nutrition for gestational diabetes: Study protocol for the Westlake Precision Birth Cohort

Integration of multiomics with precision nutrition for gestational diabetes: Study protocol for the Westlake Precision Birth Cohort