Letter: Response to: "Lack of Association Between Hemoglobin A1c and Continuous Glucose Monitor Metrics Among Individuals with Prediabetes and Normoglycemia".

Continuous glucose monitoring versus blood glucose monitoring for risk of severe hypoglycaemia and diabetic ketoacidosis in children, adolescents, and young adults with type 1 diabetes: a population-based study

Economic models in type 1 diabetes have relied on a change in haemoglobin A1c as the link between the blood glucose trajectory and long-term clinical outcomes, including microvascular and macrovascular disease. The landscape has changed in the past decade with the availability of regulatory approved, accurate and convenient continuous glucose monitoring devices and their ability to track patients' glucose levels over time. The data emerging from continuous glucose monitoring have enriched the clinical understanding of the disease and indirectly of patients' behaviour. This has triggered the development of new measures proposed to better define the quality of glycaemic control, beyond haemoglobin A1c. The objective of this paper is to review recent developments in clinical knowledge brought into focus with the application of continuous glucose monitoring devices, and to discuss potential approaches to incorporate the concepts into economic models in type 1 diabetes. Based on a targeted review and a series of multidisciplinary workshops, an influence diagram was developed that captures newer concepts (e.g. continuous glucose monitoring metrics) that can be integrated into economic models and illustrates their association with more established concepts. How the additional continuous glucose monitoring-based indicators of glycaemic control may contribute to economic modelling beyond haemoglobin A1c, and more accurately reflect the economic value of novel type 1 diabetes treatments, is discussed.

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

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.

Objective: Determine whether continuous glucose monitor (CGM) metrics can provide actionable advance warning of an emergency department (ED) visit or hospitalization for hypoglycemic or hyperglycemic (dysglycemic) events. Research Design and Methods: Two nested case-control studies were conducted among insulin-treated diabetes patients at Kaiser Permanente, who shared their CGM data with their providers. Cases included dysglycemic events identified from ED and hospital records (2016-2021). Controls were selected using incidence density sampling. Multiple CGM metrics were calculated among patients using CGM >70% of the time, using CGM data from two lookback periods (0-7 and 8-14 days) before each event. Generalized estimating equations were specified to estimate odds ratios and C-statistics. Results: Among 3626 CGM users, 108 patients had 154 hypoglycemic events and 165 patients had 335 hyperglycemic events. Approximately 25% of patients had no CGM data during either lookback; these patients had >2 × the odds of a hypoglycemic event and 3-4 × the odds of a hyperglycemic event. While several metrics were strongly associated with a dysglycemic event, none had good discrimination. Conclusion: Several CGM metrics were strongly associated with risk of dysglycemic events, and these can be used to identify higher risk patients. Also, patients who are not using their CGM device may be at elevated risk of adverse outcomes. However, no CGM metric or absence of CGM data had adequate discrimination to reliably provide actionable advance warning of an event and thus justify a rapid intervention.

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.

Continuous glucose monitoring (CGM) is a new technology that allows patients to measure glucose levels continuously over several days. It has several advantages over traditional glucose meters in that it does not involve repeated finger sticks and can measure trends and track changes in glucose levels over time. The Clinical and Laboratory Standards Institute, working with the Diabetes Technology Society, published Performance Metrics for Continuous Interstitial Glucose Monitoring; Approved Guideline, which provides recommendations for methods for determining analytical and clinical metrics of CGMs. The document provides guidance on how CGM data should be presented, compared between devices, and compared between measurement technologies. The document serves as a roadmap for the testing of CGM devices and will ultimately advance the potential of this exciting technology. Performance Metrics for Continuous Interstitial Glucose Monitoring; Approved Guideline represents the consensus view on preparing and presenting CGM data.

We aimed to assess whether continuous glucose monitor (CGM) metrics can accurately predict stage 3 type 1 diabetes diagnosis in those with islet autoantibodies (AAb). Baseline CGM data were collected from participants with ≥1 positive AAb type from five studies: ASK (n=79), BDR (n=22), DAISY (n=18), DIPP (n=8) and TrialNet Pathway to Prevention (n=91). Median follow-up time was 2.6 years (quartiles: 1.5 to 3.6 years). A participant characteristics-only model, a CGM metrics-only model and a full model combining characteristics and CGM metrics were compared. The full model achieved a numerically higher performance predictor estimate (C statistic=0.74; 95% CI 0.66, 0.81) for predicting stage 3 type 1 diabetes diagnosis compared with the characteristics-only model (C statistic=0.69; 95% CI 0.60, 0.77) and the CGM-only model (C statistic=0.68; 95% CI 0.61, 0.75). Greater percentage of time >7.8 mmol/l (p<0.001), HbA1c (p=0.02), having a first-degree relative with type 1 diabetes (p=0.02) and testing positive for IA-2 AAb (p<0.001) were associated with greater risk of type 1 diabetes diagnosis. Additionally, being male (p=0.06) and having a negative GAD AAb (p=0.09) were selected but not found to be significant. Participants classified as having low (n=79), medium (n=98) or high (n=41) risk of stage 3 type 1 diabetes diagnosis using the full model had a probability of developing symptomatic disease by 2 years of 5%, 13% and 48%, respectively. CGM metrics can help predict disease progression and classify an individual's risk of type 1 diabetes diagnosis in conjunction with other factors. CGM can also be used to better assess the risk of type 1 diabetes progression and define eligibility for potential prevention trials.

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.

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.

Multicentre, randomised, controlled study of the impact of continuous sub-cutaneous glucose monitoring (GlucoDay ®) on glycaemic control in type 1 and type 2 diabetes patients

In Brief OBJECTIVE To compare the daily glycemic profile reflected by continuous and intermittent blood glucose monitoring in pregnant women with type 1 diabetes and to compare the treatment protocols based on the two monitoring methods. METHODS The study sample consisted of 34 gravid patients at gestational weeks 16–32, with type 1 diabetes being treated by multiple insulin injections. Data derived from the continuous glucose monitoring system for 72 hours were compared with fingerstick glucose measurements performed 6–8 times per day. During the study period, patients documented the time of food intake, insulin injections, and hypoglycemic events. Data on demographics, gravidity, parity, body mass index, hemoglobin A1c, and fructosamine levels were collected for each patient. RESULTS An average (± standard deviation) of 780 ± 54 glucose measurements was recorded for each patient with continuous glucose monitoring. The mean total time of hyperglycemia (glucose level greater than 140 mg/dL) undetected by the fingerstick method was 192 ± 28 minutes per day. Nocturnal hypoglycemic events (glucose level less than 50 mg/dL) were recorded in 26 patients; in all cases, there was an interval of 1–4 hours before clinical manifestations appeared or the event was revealed by random blood glucose examination. Based on the additional information obtained by continuous monitoring, the insulin therapeutic regimen was adjusted in 24 patients (70%). CONCLUSION Continuous glucose monitoring can diagnose high postprandial blood glucose levels and nocturnal hypoglycemic events that are unrecognized by intermittent blood glucose monitoring and may serve as a basis for determining treatment regimens. A large, prospective study on maternal and neonatal outcome is needed to evaluate the clinical implications of this new monitoring technique. Continuous glucose monitoring may be a method for adjusting treatment in gravid women with type 1 diabetes mellitus.

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%.

The Iron Swords war created stressful circumstances that could negatively impact glycemic control in individuals with type 1 diabetes (T1D). To evaluate changes in continuous glucose monitoring (CGM) metrics in pediatric T1D patients during the war. This retrospective study included T1D patients monitored by CGM. Metrics from three selected 2-week periods were compared (before the war, after the war outbreak, and 4 months later). Study variables included time-in-range (70-180 mg/dl; 3.9-10 mmol/L), time-in-tight-range (70-140 mg/dl; 3.9-7.8 mmol/L), time-in-marked-hypoglycemia (< 54 mg/dl; < 3 mmol/liter), and time-in-severe-hyperglycemia (> 250 mg/dl; >13.3 mmol/liter). Patients were treated with either a multiple daily insulin (MDI) regimen or insulin pump, with or without an open-source automated insulin delivery (OS-AID) system. Data of 99 patients were analyzed (mean age 12.2 ± 4.0 years, mean diabetes duration 4.6 ± 3.9 years, 52.5% males). No significant changes in CGM metrics were observed across the entire cohort at any time point. Patients with higher socioeconomic position (SEP; cluster > 7) had better CGM metrics, with an increase in time-in-tight-range in the lower SEP group and in time-in-severe-hyperglycemia in the higher SEP group (P = 0.003). OS-AID users (n=20) had superior pre-war CGM metrics and maintained stable glycemia during the war, MDI users showed increased time-in-severe-hyperglycemia post-outbreak (P = 0.05). Throughout the war, children and adolescents with T1D treated with insulin pumps maintained relatively stable glycemic control. Susceptibility to change following the onset of war was influenced by SEP and mode of insulin therapy.