A Processing Algorithm to Address Real-World Data Quality Issues With Continuous Glucose Monitoring Data.

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

The aim of this work was to define a unique remission status using glycaemia risk index (GRI) and other continuous glucose monitoring (CGM) metrics in individuals with type 1 diabetes for improved phenotyping. A group of 140 individuals with type 1 diabetes were recruited for a cross-sectional study. The participants were categorised into four groups based on their remission status, which was defined as insulin-dose-adjusted A1c (IDAA1c) <9 or C-peptide ≥300 pmol/l: new-onset (n=24); mid-remission (n=44); post-remission (n=44); and non-remission (individuals who did not experience remission, n=28). Participants in the remission phase were referred to as 'remitters', while those who were not in the remission phase were referred to as 'non-remitters', the latter group including new-onset, post-remission and non-remission participants. Clinical variables such as HbA1c, C-peptide and insulin daily dose, as well as IDAA1C and CGM data, were collected. The patterns of CGM metrics were analysed for each group using generalised estimating equations to investigate the glycaemic variability patterns associated with remission status. Then, unsupervised hierarchical clustering was used to place the participants into subgroups based on GRI and other CGM core metrics. The glycaemic variability patterns associated with remission status were found to be distinct based on the circadian CGM metrics. Remitters showed improved control of blood glucose levels over 14 days within the range of 3.9-10 mmol/l, and lower GRI compared with non-remitters (p<0.001). Moreover, GRI strongly correlated with IDAA1C (r=0.62; p<0.001) and was sufficient to distinguish remitters from non-remitters. Further, four subgroups demonstrating distinct patterns of glycaemic variability associated with different remission status were identified by clustering on CGM metrics: remitters with low risk of dysglycaemia; non-remitters with high risk of hypoglycaemia; non-remitters with high risk of hyperglycaemia; and non-remitters with moderate risk of dysglycaemia. GRI, an integrative index, together with other traditional CGM metrics, helps to identify different glycaemic variability patterns; this might provide specifically tailored monitoring and management strategies for individuals in the various subclusters.

Objective: To assess the performance of continuous glucose monitoring (CGM) metrics in predicting development of gestational diabetes mellitus (GDM) and adverse pregnancy outcomes. Methods: In a prospective observational study, CGM data were collected from 760 pregnant participants. Predictive CGM-based models were built using logistic and elastic net regression. Predictive performance was assessed by the area under the receiver operating characteristic curve (AUROC). Results: The AUROCs of using 2nd trimester % time &amp;gt;140 mg/dL (TA140) and week 13-14 TA140 in predicting GDM were 0.81 and 0.74, respectively. The AUROCs for predicting large for gestational age births (LGA) and hypertensive disorders of pregnancy (HDP) using 2nd trimester TA140 were both 0.58. Adding multiple predictors to the models did not meaningfully improve AUROC. When matching the specificity of OGTT, a model using TA140 in weeks 13-14 achieved similar sensitivity to OGTT in predicting HDP (13% vs. 10%, respectively) and LGA (6% for both) (Figure). Conclusion: CGM metrics such as TA140 predicted GDM with high AUROCs as early as 13-14 weeks’ gestation. CGM metrics at 13-14 weeks were statistically similar to OGTT at 24-28 weeks in predicting APOs although sensitivity was low for both. CGM could potentially be used as an alternative to OGTT and/or an early screening tool for elevated hyperglycemia during gestation. Disclosure Z. Li: None. R. Beck: Consultant; Insulet Corporation. Research Support; Insulet Corporation. Consultant; Tandem Diabetes Care, Inc. Research Support; Tandem Diabetes Care, Inc. Consultant; Beta Bionics, Inc. Research Support; Beta Bionics, Inc., Dexcom, Inc., Bigfoot Biomedical, Inc. Consultant; Novo Nordisk. Research Support; Novo Nordisk, Eli Lilly and Company. Consultant; embecta, Vertex Pharmaceuticals Incorporated, Hagar, Ypsomed AG, Sanofi, Zucara Therapeutics, Sequel. C. Durnwald: Consultant; Dexcom, Inc. Research Support; Dexcom, Inc. A.L. Carlson: Research Support; Medtronic, Insulet Corporation, Tandem Diabetes Care, Inc., Eli Lilly and Company, Sanofi. Other Relationship; Novo Nordisk. Advisory Panel; MannKind Corporation. Research Support; Dexcom, Inc. E. Norton: None. R.M. Bergenstal: Other Relationship; Abbott. Research Support; Arkray Marketing. Consultant; Ascensia Diabetes Care, Bigfoot Biomedical, Inc., CeQur. Other Relationship; Dexcom, Inc., Eli Lilly and Company. Consultant; embecta, Hygieia. Research Support; Insulet Corporation. Consultant; MannKind Corporation. Other Relationship; Medtronic, Novo Nordisk. Consultant; Onduo LLC, Roche Diabetes Care. Other Relationship; Sanofi. Research Support; Tandem Diabetes Care, Inc. Other Relationship; UnitedHealth Group. Consultant; Vertex Pharmaceuticals Incorporated, Zealand Pharma A/S. M.L. Johnson: Research Support; Abbott, Lilly Diabetes, National Institute of Diabetes and Digestive and Kidney Diseases, Novo Nordisk, Medtronic, Dexcom, Inc., Leona M. and Harry B. Helmsley Charitable Trust. Consultant; Jaeb Center for Health Research. Research Support; Patient-Centered Outcomes Research Institute, Jaeb Center for Health Research. S.M. Dunnigan: Research Support; Jaeb Center for Health Research, Patient-Centered Outcomes Research Institute, National Institute of Diabetes and Digestive and Kidney Diseases. M. Banfield: None. K.J. Krumwiede: None. J. Sibayan: None. P. Calhoun: None. Funding Study funding was provided by The Leona M. and Harry B. Helmsley and UnitedHealth Group. Dexcom provided the devices used in the study.

Continuous glucose monitoring (CGM) metrics are increasingly used to study the relationship between glycemic control and diabetic complications; however, the correlated glucose measurements and unequal follow-up times in real-world CGM data necessitate more advanced analytical approaches to yield unbiased estimates. This retrospective case-cohort study aimed to estimate longitudinal changes in time in range (TIR) on progression to diabetic retinopathy (DR) among patients with type 1 diabetes (T1D). This was a retrospective case-cohort study among patients with T1D, using CGM devices. We analyzed linked CGM and electronic health record data from 161 patients with T1D, with long-term follow-up for incident DR diagnoses. TIR was defined as time spent in sensor glucose between 70 and 180 mg/dL. Multilevel mixed-effects parametric survival models and Markov models were constructed to obtain effects of TIR (eg, hazard ratios [HRs]) and lifetime trajectories of developing retinopathy and blindness, respectively. A retrospective case cohort of 161 patients with T1D (mean duration 13.7 years) included 71 cases (baseline glycated hemoglobin A1c [HbA1c] of 8.2%) and 90 controls (baseline HbA1c of 7.3%). A 10% increase in TIR was associated with a modestly lower risk of retinopathy progression (HR 0.88; 95% CI, 0.78-0.98) and an estimated prevention of 39 cases of blindness per 1000 individuals over time (TIR 70% vs 40%). Economic simulation modeling suggested $2581 lower costs per person and a gain of 0.13 quality-adjusted life years (QALYs). These findings can guide real-world CGM studies and support diabetes simulation models to predict future treatment outcomes from CGM metrics.

Introduction: Continuous glucose monitoring (CGM) devices record blood glucose every few minutes over a maximum 2-week wear period. Despite the richness of these data, existing first and second moment statistics (such as mean glucose) fail to capture the “shape” of variation in the CGM time-series. Objective: We developed novel, reproducible CGM shape metrics using functionals of the periodogram, a decomposition of CGM signal ( Figure 1A ) variance, to comprehensively characterize glucose patterns. Methods: We analyzed CGM data from N = 160 HYPNOS clinical trial participants with type 2 diabetes who wore CGM sensors twice, 3 months apart. We first calculated log-transformed periodograms for each person-period. To these decompositions, we fit piece-wise linear models over 3 frequency ranges: less than 1/24 hrs -1 (long term patterns), 1/24 to 2/5 hrs -1 (daily diet patterns), and greater than 2/5 hrs -1 (immediate fluctuations due to food). Slopes of fit segments, value at first frequency, and values at segment midpoints were extracted as our initial metrics ( Figure 1B ). We estimated and compared the within-person test-retest correlation for our shape measures against existing CGM and glucose metrics: mean glucose, time-in-range, and HhbA1c. Results: The highest raw correlation among the new shape metrics (r = 0.737) was comparable to that of the existing metrics (r = 0.798) ( Figure 1C ). Even after adjusting the shape metrics for existing metrics, there was a maximum test-retest correlation of r = 0.778. The shape metrics also carried information distinct from existing metrics. According to mixed effect models fit to data from both wear periods, the shape metrics explain less than 16% of variance in any existing metric when controlled for the other existing metrics. Conclusion: These new shape metrics carry reproducible information distinct from that of standard CGM metrics and HbA1c. The next step is to evaluate whether these novel shape metrics, which leverage the granularity of CGM data, can be linked to clinical endpoints.

Aims: Standardized continuous glucose monitoring (CGM) metrics for clinical care were announced in 2019. There have been no reports, however, on the relationship between standardized CGM metrics and oxidative stress. We therefore decided to investigate the relationships between standardized CGM metrics, classical glycemic variability, and oxidative stress. Methods: This study was a cross-sectional analysis of 117 patients with type 2 diabetes mellitus (T2DM). Oxidative stress was estimated using the diacron-reactive oxygen metabolites (d-ROMs) test. The following parameters were used as CGM metrics: mean glucose level (MGL), percentage coefficient of variation for glucose (%CV), time above range (TAR), time in range (TIR), time below range (TBR), standard deviation (SD), and mean amplitude of glycemic excursions (MAGE), a classic index. Results: A total of 117 patients (mean age of 64.1 ± 12.6 years, mean disease duration of 13.1 ± 11.5 years, and HbA1c of 8.3 ± 1.5%) who met the study inclusion criteria were finally analyzed. The univariate analysis showed that age, triglyceride, HbA1c, MGL, %CV, SD, MAGE, and TAR were significantly correlated with d-ROMs. Further, a stepwise multiple regression analysis identified SD, MAGE, and sex as independent contributors to d-ROMs. Conclusions: Oxidative stress was associated with the SD and MAGE, two parameters affected by the mean glucose level, as CGM metrics in patients with T2DM. Disclosure Y. Kohata: None. M. Ohara: None. T. Fujikawa: None. H. Nagaike: None. H. Kushima: None. M. Hiromura: None. Y. Mori: Research Support; Self; Taisho Pharmaceutical Co., Ltd. T. Fukui: None. T. Hirano: None. S. Yamagishi: None.

Influence of obesity on blood glucose control using continuous glucose monitoring data among patients with type 1 diabetes

To evaluate the glycaemia risk index (GRI) and its association with other continuous glucose monitoring (CGM) metrics after initiation of an automated insulin delivery (AID) system in patients with type 1 diabetes (T1D). Up to 90 days of CGM data before and after initiation of an AID system from 185 CGM users with T1D were collected. GRI and other CGM metrics were calculated using cgmanalysis R software and were analysed for 24 hours, for both night-time and daytime. GRI values were assigned to five GRI zones: zone A (0-20), B (21-40), C (41-60), D (61-80) and E (81-100). Compared with baseline, GRI and its components decreased significantly after AID initiation (GRI: 48.7 ± 21.8 vs. 29 ± 13; hypoglycaemia component: 2.7 ± 2.8 vs. 1.6 ± 1.7; hyperglycaemia component: 25.3 ± 14.5 vs. 15 ± 8.5; P < .001 for all). The GRI was inversely correlated with time in range before (r = -0.962) and after (r = -0.961) AID initiation (P < .001 for both). GRI was correlated with time above range (before: r = 0.906; after = 0.910; P < .001 for both), but not with time below range (P > .05). All CGM metrics improved after AID initiation during 24 hours, for both daytime and night-time (P < .001 for all). Metrics improved significantly more during night-time than daytime (P < .01). GRI was highly correlated with various CGM metrics above, but not below target range, both before and after AID initiation.

Prior studies have not identified if continuous glucose monitoring (CGM) metrics at a critical gestational age window can discriminate risk of adverse pregnancy outcomes. We evaluated late second- and third-trimester CGM metrics by gestational age associated with pregnancy outcomes in gravidas with type 1 diabetes (T1DM). Dexcom G6 CGM data from a retrospective cohort of singleton gestations with T1DM (2018-2022) at an academic medical center were analyzed. Time in, above, and below range 63 to 140 mg/dL (TIR, TAR, TBR), glycemic variability, and mean glucose concentration were computed in two-week CGM intervals from 240 to 396 weeksdays. Adverse pregnancy outcomes were hypertensive disorders of pregnancy (HDP), large-for-gestational age (LGA), and neonatal hypoglycemia. Linear mixed-effects models were fitted on CGM metrics computed from two-week CGM intervals, with gestational age, adverse pregnancy outcomes (i.e. presence/absence of HDP, LGA, and/or neonatal hypoglycemia), and their interaction as fixed effects. In 87 gravidas with preconception median hemoglobin A1c 6.5% (IQR 6.0, 7.1) and maternal body mass index 24.8 kg/m2 (IQR 21.9, 27.1), 71% had at least one adverse pregnancy outcome. Between weeks 240 and 376, gravidas with HDP had higher TAR and mean glucose and lower TIR (P < .05). Gravidas with LGA had lower TBR between weeks 240 and 356. TIR, TAR, and mean glucose evolution differed by HDP status, with greatest divergence between groups at 280 to 296 weeks' gestation (P ≤ .001). CGM metrics in the late second to early third trimester, a period of peak insulin resistance, may help to distinguish risk of HDP and LGA in gravidas with T1DM.

Disclosure: K.L. Flint: None. T. Ting: None. K. Rivera: None. P. Tamang: None. C.A. Colling: None. J.H. Li: None. M.S. Putman: Consulting Fee; Self; Synspira Therapeutics. Research Investigator; Self; Dexcom. Other; Self; Vertex Pharmaceuticals Incorporated. Introduction: Continuous glucose monitors (CGM) are FDA-approved for the management of diabetes and have been shown to improve glycemic control. For optimal utilization, CGM can be connected to cloud-based clinic portals for real-time sharing of glycemic data with clinicians to guide clinical care. Although studies have previously identified disparities in access to CGM for patients who receive Medicare, limited data are available examining disparities in CGM utilization and data sharing. Hypothesis: We hypothesized that racial and socioeconomic disparities in CGM access exist both in utilization and in real-time remote sharing of CGM data. Methods: This was a retrospective cohort study examining patients with type 2 diabetes on insulin using Medicare as primary or secondary insurance in a single diabetes clinic affiliated with a tertiary care medical center. Clinical data extracted from the electronic health record (EHR) included age, self-reported race and ethnicity, sex, preferred language, education level, ZIP code-based median household income, and enrollment in the EHR-patient portal. The most recent diabetes clinic note for each patient as of October 2023 was reviewed to assess whether each patient was using CGM at the time of the visit. The clinic’s CGM portal accounts were reviewed to assess whether the patient was connected to and actively sharing CGM data with the clinic. Two sample t-tests and chi-square tests were used to evaluate continuous and categorical predictors of CGM use and real-time remote sharing, respectively. Results: Of the 847 patients who qualified for inclusion, 420 (49.6%) were using CGM and 213 (25.1%) were sharing CGM data in real-time. Compared to patients not using CGM (n=427), patients using CGM were younger (70.8 years vs 73.5 years, p&amp;lt;0.001) and more often enrolled in the EHR-based patient portal (91.9% vs 84.1%, p&amp;lt;0.001); however, there were no differences in racial, ethnic, or socioeconomic factors between the groups (p&amp;gt;0.05 for all). Of the patients using CGM, remote sharing of data was associated with younger age (69.5 years vs 72.3 years, p&amp;lt;0.01), identifying as White (74.2% vs 65.0%, p=0.04), using English as preferred language (92.5% vs 84.2%, p=0.02), higher levels of education (p=0.01), and using the EHR-based patient portal (97.2% vs 86.7%, p&amp;lt;0.001). Conclusions: In this cohort of patients with type 2 diabetes on insulin receiving Medicare, there were no significant socioeconomic disparities in CGM utilization. However, racial and socioeconomic disparities were pronounced in real-time remote sharing of CGM data, suggesting that patients from minoritized racial backgrounds, patients who do not use English as their preferred language, and patients with less education may benefit from additional support and training to connect and share their CGM with their providers. Presentation: 6/2/2024

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

Aims: Prediabetes is defined by HbA1c, fasting (FPG) and 2-hour plasma glucose (2h-PG) during 75 gram oral glucose test (OGTT) although these values often show poor concordance. Most diabetes prevention trials involved people with impaired glucose tolerance (IGT) diagnosed as 2h-PG 7.8-11.0 mmol/L. Continuous glucose monitoring (CGM) may detect early dysglycemia especially postprandial glucose excursions. We examined correlations amongst HbA1c, FPG, 1-h and 2h-PG during OGTT and CGM metrics in individuals with IGT. Methods: 85 Chinese with IGT (mean±SD age 57±8 years, 51 (60%) female, BMI 26.6±4.0 kg/m2) participating in a lifestyle modification study had measurement of HbA1c, PG during 75g OGTT and 14-day CGM metrics (Freestyle Libre, Abbott) during a 3-week period at baseline. We examined their correlations using Spearman coefficients. Results: The mean HbA1c was 5.8±0.3 %, FPG 5.3±0.4 mmol/L, 1h-PG 11.0±1.6 mmol/L and 2h-PG 8.5±1.3 mmol/L. For CGM, the mean glucose management index (GMI) was 5.9±0.2 % and time in tight glycemic range (TITR 3.9-7.8 mmol/L) was 86.7±6.6 %. There was a tendency for HbA1c (r=0.587, p=0.097) to correlate with GMI and negatively with TITR (r=-0.644, p=0.061). The respective correlates between FPG and GMI was r=0.623 (p=0.073) and that between FPG and TITR was r=-0.559 (p=0.118). There was weak correlation between 1h-PG and CGM time above range &amp;gt;7.8 mmol/L (r=0.301, p=0.43). There was no correlation amongst 1h-PG, 2h-PG with HbA1c and other conventional CGM metrics. Conclusions: In individuals with IGT, HbA1c and FPG showed better correlations with CGM metrics of average glycemia than PG values during OGTT. Future studies are needed to define the use of CGM and appropriate metrics in detection of IGT. Disclosure E.Chow: Research Support; Medtronic, Merck KGaA, Speaker's Bureau; Novartis, Bayer Inc., Sanofi. J.He: None. N.Chu: None. E.W.M.Poon: None. J.C.Chan: Board Member; Asia Diabetes Foundation, Consultant; Bayer Inc., Celltrion, Boehringer Ingelheim and Eli Lilly Alliance, Sanofi, Research Support; AstraZeneca, Servier Laboratories, Viatris Inc., Hua Medicine, Merck KGaA, Applied Therapeutics Inc., Lee Powder, Pfizer Inc., Speaker's Bureau; Novartis, Stock/Shareholder; GemVCare Ltd. Funding Health and Medical Research Fund (17180431)

Glycemic variability contributes to oxidative stress, which has been linked to the pathogenesis of the long-term complications of diabetes. Currently, the best metric for assessing glycemic variability is mean amplitude of glycemic excursion (MAGE); however, MAGE is not in routine clinical use. A glycemic variability metric in routine clinical use could potentially be an important measure of overall glucose control and a predictor of diabetes complication risk not detected by glycosylated hemoglobin (A1C) levels. This study aimed to develop and evaluate new automated metrics of glycemic variability that could be routinely applied to continuous glucose monitoring (CGM) data to assess and enhance glucose control. Individual 24 h CGM tracings from our clinical diabetes research database were scored for MAGE and two additional metrics designed to compensate for aspects of variability not captured by MAGE: (1) number of daily glucose fluctuations >75 mg/dl that leave the normal range (70-175 mg/dl), or excursion frequency, and (2) total daily fluctuation, or distance traveled. These scores were used to train machine learning algorithms to recognize excessive variability based on physician ratings of daily CGM charts, producing a third metric of glycemic variability: perceived variability. Finger stick A1C (average) and serum 1,5-anhydroglucitol (postprandial) levels were used as clinical markers of overall glucose control for comparison. Mean amplitude of glycemic excursion, excursion frequency, and distance traveled did not adequately quantify the glycemic variability visualized by physicians who evaluated the daily CGM plots. A naive Bayes classifier was developed that characterizes CGM tracings based on physician interpretations of tracings. Preliminary results suggest that the number of excessively variable days, as determined by this naive Bayes classifier, may be an effective way to automatically assess glycemic variability of CGM data. This metric more closely reflects 90-day changes in serum 1,5-anhydroglucitol levels than does MAGE. We have developed a new automated metric to assess overall glycemic variability in people with diabetes using CGM, which could easily be incorporated into commercially available CGM software. Additional work to validate and refine this metric is underway. Future studies are planned to correlate the metric with both urinary 8-iso-prostaglandin F2 alpha excretion and serum 1,5-anhydroglucitol levels to see how well it identifies patients with high glycemic variability and increased markers of oxidative stress to assess risk for long-term complications of diabetes.

Introduction and Objective: The Automated Insulin Delivery as an Adaptive NETwork FCL system may improve glycemic outcomes while reducing burden by eliminating meal announcement. Continuous glucose monitoring (CGM) metrics were compared in users with T1D using the FCL system who had high or low baseline A1c. Methods: Youth and adults with T1D were enrolled in a randomized crossover study with a supervised hotel stay followed by 7 days of home use in FCL compared to usual care (UC). Half were selected with baseline A1c &amp;lt;8% (low A1c) and half with baseline A1c 8-12% (high A1c). CGM metrics were analyzed by A1c subgroups. Results: Thirty-four participants (25.4±12.6 years, 62% female) completed the study. Those with high A1c showed non-inferiority of FCL vs UC on all CGM metrics and lower mean glucose with FCL (Table). Time in 70-180 mg/dL, in 70-140 mg/dL, &amp;gt;180 mg/dL, and &amp;gt;250 mg/dL showed significant improvement with FCL in the high A1c group. Those with low A1c had statistically equivalent mean glucose and time &amp;lt;54 mg/dL with FCL vs UC; other CGM metrics were inconclusive. Conclusion: Use of a FCL system results in significant improvement in CGM metrics in those with less optimal glycemic management, while not deteriorating control in those with more optimal A1c. FCL systems have the potential to make the most impact in those with challenges in meeting glycemic goals. Disclosure J.C. Wong: Research Support; Abbott, Dexcom, Inc., Tandem Diabetes Care, Inc. M. Moscoso-Vasquez: Other Relationship; Dexcom, Inc. Research Support; Tandem Diabetes Care, Inc, National Institute of Diabetes and Digestive and Kidney Diseases. L. Ekhlaspour: Other Relationship; Medtronic. Advisory Panel; Abbott, Medtronic. Consultant; Jaeb Center for Health Research. Research Support; MannKind Corporation. Speaker's Bureau; Insulet Corporation. Advisory Panel; Sequel Med Tech. Other Relationship; Tandem Diabetes Care, Inc. Research Support; Abbott. Other Relationship; Sanofi. S.A. Brown: Research Support; Dexcom, Inc., Insulet Corporation, Tandem Diabetes Care, Inc, Tolerion, Roche Diabetes Care. Other Relationship; MannKind Corporation. M.D. Breton: Speaker's Bureau; Sinocare Inc, Tandem Diabetes Care, Inc. Consultant; Roche Diabetes Care, Boydsense. G.P. Forlenza: Advisory Panel; Medtronic. Research Support; Medtronic, Dexcom, Inc. Consultant; Dexcom, Inc. Research Support; Insulet Corporation. Consultant; Insulet Corporation. Research Support; Tandem Diabetes Care, Inc. Advisory Panel; Tandem Diabetes Care, Inc. Research Support; Abbott. Advisory Panel; Sequel Med Tech. Funding Breakthrough T1D

Introduction and Objective: This study compares glycemic metrics across different types of FreeStyle Libre Abbott CGM devices in Indians with Type 2 Diabetes. Furthermore, correlations between CGM-derived metrics and metabolic dysfunction-associated steatotic liver disease (MASLD) parameters—Fibrosis-4 (FIB-4) score, Controlled Attenuation Parameter (CAP) and Liver Stiffness Measurement (LSM) were explored Methods: Data from 83 participants (19 - Pro, 22 -1st Gen, 42 - 2nd Gen) were analyzed. Metrics included Time in Range (TIR), Glucose Management Indicator (GMI), Coefficient of Variation (CV) and Interquartile Range (IQR). Advanced 2nd Gen CGM metrics such as low glucose events, sensor activity and hypoglycemia risk stratification were evaluated. Statistical analyses, including correlations between CGM metrics and MASLD markers were performed Results: The 2nd Gen CGM demonstrated superior glycemic outcomes, with a mean TIR of 75% and significantly reduced hyperglycemia (% Above Threshold: 5% vs. 60% in Pro CGM, p &amp;lt; 0.01). Glycemic stability, reflected by an IQR of 25 mg/dL, was highest in the 2nd Gen group. A predictive model for hypoglycemia risk, incorporating IQR and sensor duration data achieved 92% accuracy. The 2nd Gen CGM demonstrated enhanced detection of post-meal glucose excursions and nocturnal hypoglycemia. Significant negative correlations were observed between % In Target and MASLD markers, including FIB-4 scores (r = -0.65, p &amp;lt; 0.01), CAP (r = -0.58, p &amp;lt; 0.05) and LSM (r = -0.54, p &amp;lt; 0.05). Conclusion: Advancements in CGM technology have revolutionized diabetes management enabling more precise personalized care. The integration of advanced metrics and predictive modeling for hypoglycemia risk enhances clinical decision-making. Correlations with MASLD markers, including FIB-4, CAP and LSM underscore CGM's potential to address co-morbidities such as MASLD and positions it as an indispensable tool in optimizing diabetes outcomes Disclosure K. Ravi Teja: None.