Impact of pre- and post-meal exercise on 24-H glucose profiles in young adults who are overweight and obese

BackgroundCohort data on continuous glucose monitoring (CGM) metrics are scarce for liver glycogen storage diseases (GSDs) and idiopathic ketotic hypoglycemia (IKH). The aim of this study was to retrospectively describe CGM metrics for people with liver GSDs and IKH. Patients and methodsCGM metrics (descriptive, glycemic variation and glycemic control parameters) were calculated for 47 liver GSD and 14 IKH patients, categorized in cohorts by disease subtype, age and treatment status, and compared to published age-matched CGM metrics from healthy individuals. Glycemic control was assessed as time-in-range (TIR; ≥3.9 - ≤7.8 and ≥3.9 - ≤10.0 mmol/L), time-below-range (TBR; <3.0 mmol/L and ≥3.0 - ≤3.9 mmol/L), and time-above-range (TAR; >7.8 and >10.0 mmol/L). ResultsDespite all patients receiving dietary treatment, GSD cohorts displayed significantly different CGM metrics compared to healthy individuals. Decreased TIR together with increased TAR were noted in GSD I, GSD III, and GSD XI (Fanconi-Bickel syndrome) cohorts (all p < 0.05). In addition, all GSD I cohorts showed increased TBR (all p < 0.05). In GSD IV an increased TBR (p < 0.05) and decreased TAR were noted (p < 0.05). In GSD IX only increased TAR was observed (p < 0.05). IKH patient cohorts, both with and without treatment, presented CGM metrics similar to healthy individuals. ConclusionDespite dietary treatment, most liver GSD cohorts do not achieve CGM metrics comparable to healthy individuals. International recommendations on the use of CGM and clinical targets for CGM metrics in liver GSD patients are warranted, both for patient care and clinical trials.

Relationships between HbA1c and continuous glucose monitoring metrics of glycaemic control and glucose variability in a large cohort of children and adolescents with type 1 diabetes

To investigate the association between continuous glucose monitoring (CGM) metrics and perinatal outcomes in insulin-treated diabetes mellitus in pregnancy. In a post-hoc analysis of the GlucoMOMS randomized controlled trial, we investigated the association between the metrics of an offline, intermittent CGM, glycated haemoglobin (HbA1c) and perinatal outcomes per trimester in different types of diabetes (type 1, 2 or insulin-treated gestational diabetes mellitus [GDM]). Data were analysed using multivariable binary logistic regression. Outcomes of interest were neonatal hypoglycaemia, pre-eclampsia, preterm birth, large for gestational age (LGA) and Neonatal Intensive Care Unit (NICU) admission. The glucose target range was defined as 3.5-7.8 mmol/L (63-140 mg/dL). Of the 147 participants (N = 50 type 1 diabetes, N = 94 type 2 diabetes/insulin-treated GDM) randomized to the CGM group of the GlucoMOMS trial, 115 participants had CGM metrics available and were included in the current study. We found that, in pregnancies with type 1 diabetes, a higher second trimester mean glucose was associated with LGA (odds ratio 2.6 [95% confidence interval 1.1-6.2]). In type 2 and insulin-treated gestational diabetes, an increased area under the curve above limit was associated with LGA (odds ratio 10.0 [95% confidence interval 1.4-72.8]). None of the CGM metrics were associated with neonatal hypoglycaemia, pre-eclampsia, shoulder dystocia, preterm birth and NICU admission rates for pregnancies complicated by any type of diabetes. In this study, in type 2 diabetes or insulin-treated GDM, the glucose increased area under the curve above limit was associated with increased LGA. In type 1 diabetes, the mean glucose was the major determinant of LGA. Our study found no evidence that other CGM metrics determined adverse pregnancy outcomes.

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.

To determine gestational weekly changes in continuous glucose monitoring (CGM) metrics and 24-h glucose profiles and their relationship to infant birth weight in pregnant women with type 1 diabetes. An analysis of >10.5 million CGM glucose measures from 386 pregnant women with type 1 diabetes from two international multicenter studies was performed. CGM glucose metrics and 24-h glucose profiles were calculated for each gestational week, and the relationship to normal (10-90th percentile) and large (>90th percentile) for gestational age (LGA) birth weight infants was determined. Mean CGM glucose concentration fell and percentage of time spent in the pregnancy target range of 3.5-7.8 mmol/L (63-140 mg/dL) increased in the first 10 weeks of pregnancy and plateaued until 28 weeks of gestation, before further improvement in mean glucose and percentage of time in range until delivery. Maternal CGM glucose metrics diverged at 10 weeks of gestation, with significantly lower mean CGM glucose concentration (7.1 mmol/L; 95% CI 7.05-7.15 [127.8 mg/dL; 95% CI 126.9-128.7] vs. 7.5 mmol/L; 95% CI 7.45-7.55 [135 mg/dL; 95% CI 134.1-135.9]) and higher percentage of time in range (55%; 95% CI 54-56 vs. 50%; 95% CI 49-51) in women who had normal versus LGA. The 24-h glucose profiles were significantly higher across the day from 10 weeks of gestation in LGA. Normal birth weight is associated with achieving significantly lower mean CGM glucose concentration across the 24-h day and higher CGM time in range from before the end of the first trimester, emphasizing the need for a shift in clinical management, with increased focus on using weekly CGM glucose targets for optimizing maternal glycemia from early pregnancy.

Given the limitations of glycated hemoglobin (HbA1c), continuous glucose monitoring (CGM) metrics have been proposed as complementary indicators of glycemic control. This study evaluated the association between CGM metrics and HbA1c and developed HbA1c prediction models in Korean pediatric patients with type 1 diabetes (T1D). We retrospectively analyzed CGM data from 85 patients aged 2-18 years using real-time CGM systems (G6 or G7, Dexcom, USA). CGM records over 12 weeks were segmented into five intervals (0-2, 0-4, 4-8, 8-12, and 0-12 weeks) prior to HbA1c measurement. Metrics included time-in-range (TIR), time-above-range (TAR), time-below-range (TBR), time-in-normoglycemia (TING), coefficient of variation (CV), and average glucose. HbA1c prediction models were constructed using ridge regression and validated in a separate test dataset. TIR consistently showed the strongest negative association with HbA1c, while TAR and average glucose showed the strongest positive associations. Among all intervals, 0-4 week CGM data demonstrated the strongest relationship with HbA1c (all P<0.05). Average glucose achieved the best explanatory power among all metrics (R²=0.83, AIC=84.34), and prediction models incorporating average glucose and TAR yielded the lowest mean squared error (MSE=0.15) and highest R² (0.83), with robust results in the test dataset. Short-term CGM metrics, particularly average glucose during the 0-4 week preceding HbA1c testing, are strong predictors of HbA1c. These findings support the clinical utility of recent CGM data in optimizing the individualized glycemic management in pediatric patients with T1D.

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.

Glycated albumin (GA), a biomarker reflecting short-term glycaemia, may be useful to assess glycaemic control in pregnancy. We examined the association between GA and continuous glucose monitoring (CGM) metrics across gestation. In this prospective cohort study including 40 women with pre-gestational diabetes, blood samples for analysis of GA and glycated haemoglobin A1c (HbA1c) were collected at pregnancy week 12, 20, 24, 28, 32 and 36. In the CGM-group (n=19), CGM data were collected from first trimester until pregnancy week 36. Receiver operating characteristic (ROC) curves were used to assess the accuracy of GA and HbA1c to detect poor glycaemic control, using CGM metrics as the reference standard. This study was conducted at Stavanger University Hospital, Norway, in 2016-2018. Glycaemic control improved across gestation with more time spent in target range, coinciding with decreased glycaemic variability and lower mean GA level. There was statistically significant correlation between GA and most CGM metrics. The area under the ROC curves (AUC) for detecting time in range <70% and time above range >25% for the pregnancy glucose target 63-140 mg/dl (3.5-7.8 mmol/L) were 0.78 and 0.82 for GA, whereas AUCs of 0.60 and 0.72 were found for HbA1c, respectively. Higher GA levels were associated with less time spent in target range, more time spent in the above range area and increased glycaemic variability. GA was more accurate than HbA1c to detect time above range >25% and time in range <70%.

Dietary intakes and physical activity of women with gestational diabetes are not associated with continuous glucose monitoring metrics; secondary analysis of the DiGest trial.

AimsThis study aimed to assess the impact of the Omnipod 5 automated insulin delivery (AID) system on continuous glucose monitoring (CGM) metrics, HbA1c, and weight in a real‐world setting. Additionally, independent predictors of glycaemic response were assessed.MethodsObservational analysis of adults with type 1 diabetes using Omnipod 5 (n = 353). Paired data on CGM metrics (n = 268), HbA1c (n = 193), and weight (n = 173) were collected at baseline and compared after median of 191, 120, and 221 days, respectively. Independent predictors of TIR response (≥5%) and HbA1c (≥5 mmol/mol) were assessed.ResultsOmnipod 5 use was associated with improved TIR (+16%, p < 0.001) and a reduction in HbA1c (−3 mmol/mol, p < 0.001). The greatest improvements (−7 mmol/mol, p < 0.001) were observed in individuals with elevated baseline HbA1c (≥58 mmol/mol). Sensor choice (Dexcom G6 vs. Freestyle Libre 2 Plus) influenced time in full auto mode (94% vs. 96%, p < 0.001) but did not affect the likelihood of improved TIR or HbA1c. Logistic regression identified baseline HbA1c (OR 1.24 per mmol/mol, p < 0.001) as the main association with improved HbA1c. Similarly, baseline TIR was associated with improvement in TIR (OR 0.83 per %, p < 0.001). Greater time in automation and using the lowest glucose target were also associated with improved outcomes.ConclusionsOmnipod 5 is associated with significant and sustained improvements in CGM metrics and HbA1c, particularly in individuals with higher baseline HbA1c. The results suggest the potential benefits of prioritizing AID for individuals at greatest risk of complications.

Extended hypoglycemia (Ehypo) and extended hyperglycemia (Ehyper) are recently defined continuous glucose monitoring (CGM) metrics by the International Consensus for clinical trials as secondary endpoints for continuous outcomes. This study aims to evaluate the changes in Ehypo and Ehyper before and after automated insulin delivery (AID) initiation in adults with type 1 diabetes (T1D). This is a retrospective single-center study that evaluated Ehypo and Ehyper in addition to other CGM metrics in 154 adults that initiated an AID system. Metrics were compared before and after AID initiation by Wilcoxon signed-rank test. Median (interquartile range) Ehypo (<70 mg/dL) events/week decreased from 0.1 (0-0.4) to 0 (0-0.1) and Ehyper (>250 mg/dL) events/week decreased from 2.2 (0.9-4.5) to 0.8 (0.3-1.7) (both P < .001) after AID initiation compared with before AID initiation. All other CGM metrics improved after AID initiation. There was a strong positive correlation between Ehyper (>250 mg/dL) and mean glucose (before AID: r = 0.947, after AID: r = 0.894), glucose management indicator (before AID: r = 0.947, after AID: r = 0.887), and time above range (TAR; >180 mg/dL) (before AID: r = 0.957, after AID: r = 0.917) and a strong positive correlation between Ehypo (<70 mg/dL) and time below range (TBR; <70 mg/dL) (before AID: r = 0.823, after AID: r = 0.608) before and after AID initiation, respectively. Automated insulin delivery initiation significantly improved Ehypo and Ehyper metrics. Ehypo and Ehyper had a strong positive correlation with TBR and TAR, respectively. Ehypo and Ehyper events can be used in addition to TBR and TAR metrics in clinical studies as secondary outcomes.

Introduction and Objective: To examine whether continuous glucose monitoring (CGM) metrics predict 5-year all-cause mortality in adults with type 1 or type 2 diabetes (T1D/T2D). Methods: 2,752 Veterans (age ≥21 years; 70% T2D) who initiated Dexcom CGM (2015-2020) had all CGM data merged with electronic health records data. Cox proportional hazard models assessed associations between mortality and CGM metrics, including estimated blood glucose (eBG), time in range (TIR), time above range (TAR), coefficient of variation (CV), and glycemic risk index (GRI). Results: Mean age was 64 years, with a median CGM use of 3 years, and 407 total deaths. After adjusting for mortality related variables, higher eBG, TAR, GRI, and CV, and lower TIR from 6 months of LM CGM were significantly linked with mortality (all p ≤ 0.01). After adjusting for average LM HbA1c, these associations remained, and shorter CGM windows (14 days or 3 months) showed similar but slightly weaker effects (Table). CV’s association was independent of other metrics and strongest among those with lower HbA1c. Conclusion: CGM-derived metrics predict all-cause mortality in patients with diabetes, independent of HbA1c, underscoring their importance for risk stratification. Disclosure T. Okuno: None. S. Macwan: None. G.J. Norman: Employee; Dexcom, Inc. D.R. Miller: None. P. Reaven: Research Support; Dexcom, Inc. J. Zhou: None.

Aims: We investigated the association between continuous glucose monitoring (CGM) metrics and clinical outcomes in the nonintensive care unit (non-ICU) setting. Methods: In this observational cohort study, patients on non-ICU floors wore blinded Dexcom G6 Pro CGM. CGM metrics and occurrence of CGM-detected severe hypoglycemia were measured. Clinical data, including infection, diabetic ketoacidosis, renal replacement therapy, thrombosis, and 30-day post-discharge readmissions and emergency department (ED) visits were identified from the medical record and participant phone interview. Multivariate regression assessed predictors of CGM-detected severe hypoglycemia and the associations between CGM metrics and clinical outcomes. Regression models using CGM data or reference glucose data were compared with receiver operating characteristic (ROC) curves. Results: A total of 326 hospitalized adults were enrolled with median % time in range 70-180 mg/dL 44.5% (17.1, 70.2%), % time above range >180 mg/dL 54.8% (28.8, 82.3%), and % time below range 0.6% (0, 0.2%). Predictors of severe hypoglycemia included type 1 diabetes, female gender, lower admission hemoglobin, lower A1c, and longer hospital stay. Regression analyses demonstrated an association of 30-day ED visits with increased %TAR (P = 0.01). ROC curves showed models using CGM data or reference data predicted clinical outcomes similarly. Conclusions: CGM can be useful in identifying patients at risk of inpatient hypoglycemia and 30-day ED visits.

It is not clear whether there are differences in glycemic control between the Equil patch and the MMT-712 insulin pump. Our objective was to compare two types of insulin pumps in the treatment of type 2 diabetes mellitus (T2DM), using continuous glucose monitoring (CGM) metrics and profiles. This was a randomized case-crossover clinical trial. Participants were hospitalized and randomly allocated to two groups and underwent two types of insulin pump treatments (group A: Equil patch-Medtronic MMT-712 insulin pump; group B: Medtronic MMT-712-Equil patch insulin pump) separated by a 1-day washout period. Glycemic control was achieved after 7-8 days of insulin pump therapy. Each patient received CGM for 5 consecutive days (from day 1 to day 5). On day 3 of CGM performance, the Equil patch insulin pump treatment was switched to Medtronic MMT-712 insulin pump treatment at the same basal and bolus insulin doses or vice versa. CGM metrics and profiles including glycemic variability (GV), time in range (TIR, 3.9-10.0 mmol/L), time below range (TBR, <3.9 mmol/L), time above range (TAR, >10.0 mmol/L), and postprandial glucose excursions, as well as incidence of hypoglycemia. Forty-six T2DM patients completed the study. There was no significant difference in parameters of daily GV and postprandial glucose excursions between the Equil patch insulin pump treatment and the Medtronic insulin pump treatment. Similarly, there was no between-treatment difference in TIR, TBR, and TAR, as well as the incidence of hypoglycemia. The Equil patch insulin pump was similar to the traditional MMT-712 insulin pump in terms of glycemic control. Equil patch insulin pump is a reliable tool for glycemic management of diabetes mellitus.

Objective: There was limited evidence to evaluate the association between lifestyle habits and continuous glucose monitoring (CGM) metrics. Thus, we aimed to depict the behavioral and metabolic determinants of CGM metrics in insulin-treated patients with type 2 diabetes (T2DM). Methods: This is a prospective observational study. We analyzed data from 122 insulin-treated patients with T2DM. Participants wore Dexcom G6 and Fitbit, and diet information was identified for 10 days. Multivariate-adjusted logistic regression analysis was performed for the simultaneous achievement of CGM-based targets, defined by the percentage of time in terms of hyper, hypoglycemia and glycemic variability (GV). Intake of macronutrients and fiber, step counts, sleep, postprandial C-peptide to glucose ratio (PCGR), information about glucose-lowering medications and metabolic factors were added to the analyses. Additionally, we evaluated the impact of the distribution of energy and macronutrient during a day, and snack consumption on CGM metrics. Results: Logistic regression analysis revealed that female, participants with high PCGR, low HbA1c and daytime step count had a higher probability of achieving all targets based on CGM (odds ratios [95% confidence intervals] which were 0.24 [0.09–0.65], 0.95 [0.9–0.99], 1.34 [1.03–1.25], and 1.15 [1.03–1.29], respectively). And participants who ate snacks showed a shorter period of hyperglycemia and less GV compared to those without. Conclusions: We confirmed that residual insulin secretion, daytime step count, HbA1c, and women were the most relevant determinants of adequate glycemic control in insulin-treated patients with T2DM. In addition, individuals with snack consumption were exposed to lower times of hyperglycemia and GV. Disclosure D.Lee: None. N.Kim: None. I.Jung: None. S.Park: None. J.Yu: None. J.Seo: None. S.Park: None. N.Kim: None. Funding National Research Foundation of Korea (2019M3E5D3073102, 2019R1H1A2039682, 2020R1I1A1A01071665); National IT Industry Promotion Agency (S0252-21-1001)