Glycemia Risk Index as a novel metric to evaluate the safety of glycemic control in children and adolescents with Type 1 Diabetes: an observational, multicenter, real-life cohort study

Evaluation of time in tight range and the glycaemia risk index in adults with type 1 diabetes using an advanced hybrid closed loop system: A 1-year real-world assessment

AIM

To assess the long-term glycaemic outcomes, with additional metrics, in adults with type 1 diabetes (T1D) using the Tandem t:slim X2 with Control-IQ technology advanced hybrid closed-loop (AHCL) system.

METHODS

This was a single-centre, retrospective study involving 56 T1D patients who transitioned to the Tandem t:slim X2 with Control-IQ system. The primary and secondary endpoints consisted of variations in time in tight range (TiTR; 70-140 mg/dL) and the glycaemia risk index (GRI), respectively. Additional standardized continuous glucose monitoring (CGM) metrics, mean sensor glucose, coefficient of variation, the glucose management indicator (GMI), HbA1c and insulin daily dose, were also evaluated. Variables were measured at baseline and at 15 days, 3 months, 6 months and 1 year after Tandem t:slim X2 Control-IQ initiation. Glucose outcomes are expressed as mean (standard deviation).

RESULTS

Use of Tandem t:slim X2 with Control-IQ over 1 year was associated with an increase in mean TiTR, from 38.11% (17.05%) to 43.10% (13.20%) (P = .059), and with a decline in the GRI, from 41.03 (25.48) to 28.55 (16.27) (P = .008). CGM metrics, including time in range and time above range, showed consistent improvements. Mean sensor glucose, the GMI and HbA1c decreased significantly over time. After an initial increase, insulin daily dose remained stable throughout the 12 months.

CONCLUSIONS

The results highlight the sustained effectiveness of Tandem t:slim X2 with Control-IQ in improving glycaemic outcomes over 1 year and support the use of this technology for the management of T1D.

Impact on Glycemia Risk Index and other metrics in type 1 adult patients switching to Advanced Hybrid Closed-Loop systems: a one-year real-life experience

BACKGROUND

Advanced Hybrid Closed-Loop system (AHCL) has profoundly changed type 1 diabetes therapy. This study primarily aimed to assess the impact on Glycemia Risk Index (GRI) and other continuous glucose monitoring (CGM) metrics when switching from one of four insulin strategies to AHCL in type 1 adult patients.

METHODS

A single-center, retrospective pre/post observational study; 198 patients (age 44.4 ± 12.7 years, 115 females/83 males, diabetes duration 24.7 ± 11.6 years, HbA1c 7.4 ± 1%), treated with different insulin therapies (MDI, CSII, SAP with PLGS, HCL) were assessed before and after switching to an AHCL (MiniMed 780G, Diabeloop Roche, Tandem Control-IQ) at 1, 3, 6, and 12 months. Mixed-effects multivariable regression models were used to estimate the mean pre/post variations at different time points, adjusted for potential confounders.

RESULTS

A month after the switch, there was an improvement in CGM metrics and HbA1c for all patients: GRI -10.7, GMI -0.27%, CV -2.1%, TAR>250 -3.7%, TAR180-250 -5.6%, TIR + 9.7%, HbA1c -0.54% (all p < 0.001). This improvement was maintained throughout the observational period (at 3, 6, and 12 months, with all p-values < 0.001). When improvements across the 780, Diabeloop, and Tandem CIQ devices were compared: Diabeloop demonstrated significantly better performance in terms of GRI, GMI, CV, TAR>250 at T1 (for all p < 0.01); 780 recorded highest average decrease in TAR180-250 (p = 0.020), while Tandem achieved the most significant reduction in TBR54-69 (p = 0.004).

CONCLUSIONS

Adopting an AHCL leads to a rapid and sustained improvement in GRI and other parameters of metabolic control for up to a year, regardless of prior insulin therapies, baseline conditions or brands.

Correlation Between the Glycemia Risk Index and Longitudinal Hemoglobin A1c in Children and Young Adults With Type 1 Diabetes

BACKGROUND

The glycemia risk index (GRI) is a composite metric developed and used to estimate quality of glycemia in adults with diabetes who use continuous glucose monitor (CGM) devices. In a cohort of youth with type 1 diabetes (T1D), we examined the utility of the GRI for evaluating quality of glycemia between clinic visits by analyzing correlations between the GRI and longitudinal glycated hemoglobin A1c (HbA1c) measures.

METHOD

Using electronic health records and CGM data, we conducted a retrospective cohort study to analyze the relationship between the GRI and longitudinal HbA1c measures in youth (T1D duration ≥1 year; ≥50% CGM wear time) receiving care from a Midwest pediatric diabetes clinic network (March 2016 to May 2022). Furthermore, we analyzed correlations between HbA1c and the GRI high and low components, which reflect time spent with high/very high and low/very low glucose, respectively.

RESULTS

In this cohort of 719 youth (aged = 2.5-18.0 years [median = 13.4; interquartile range [IQR] = 5.2]; 50.5% male; 83.7% non-Hispanic White; 68.0% commercial insurance), baseline GRI scores positively correlated with HbA1c measures at baseline and 3, 6, 9, and 12 months later (r = 0.68, 0.65, 0.60, 0.57, and 0.52, respectively). At all time points, strong positive correlations existed between HbA1c and time spent in hyperglycemia. Substantially weaker, negative correlations existed between HbA1c and time spent in hypoglycemia.

CONCLUSIONS

In youth with T1D, the GRI may be useful for evaluating quality of glycemia between scheduled clinic visits. Additional CGM-derived metrics are needed to quantify risk for hypoglycemia in this population.

Assessment of Glycemia Risk Index and Standard Continuous Glucose Monitoring Metrics in a Real-World Setting of Exercise in Adults With Type 1 Diabetes: A Post-Hoc Analysis of the Type 1 Diabetes and Exercise Initiative

BACKGROUND

Standardized reporting of continuous glucose monitoring (CGM) metrics does not provide extra weighting for very high or very low glucose, despite their distinct clinical significance, and thus may underestimate glycemic risk in people with type 1 diabetes (T1D) during exercise. Glycemia Risk Index (GRI) is a novel composite metric incorporating clinician-validated extra weighting for glycemic extremes, which may provide a novel summary index of glycemia risk around exercise.

METHODS

Adults (≥18 years) in the T1D EXercise Initiative study wore CGM and activity trackers for four weeks. For this analysis, exercise days were defined as 24 hours following ≥20 minutes of exercise, with no other exercise in the 24-hour period. Sedentary days were defined as any 24 hours with no recorded exercise within that period or the preceding 24 hours. Linear mixed-effects regression was used to evaluate exercise effects on GRI and CGM metrics within 24 hours postexercise.

RESULTS

In 408 adults with T1D with >70% CGM and activity data, GRI on exercise (N = 3790) versus sedentary days (N = 1865) was significantly lower (mean [SD]: 29.9 [24.0] vs 34.0 [26.1], respectively, absolute mean difference -1.70 [-2.73, -0.67], P < .001), a ~5% reduction in glycemic risk. Percent time in range (TIR; 70-180 mg/dL) increased on exercise days (absolute mean difference 2.67 [1.83, 3.50], P < .001), as did time below range (TBR; relative mean difference 1.17 [1.12, 1.22], P < .001), while time above range (TAR) decreased (relative mean difference 0.84 [0.79, 0.88], P < .001).

CONCLUSIONS

Glycemia Risk Index improved on exercise versus sedentary days, despite increased TBR, which is weighted most heavily in the GRI calculation, due to a robust reduction in TAR.

The Glycemia Risk Index Predicts Performance of Diabetes Self-Management Habits in Youth With Type 1 Diabetes Mellitus

BACKGROUND

The Glycemia Risk Index (GRI) was developed in adults with diabetes and is a validated metric of quality of glycemia. Little is known about the relationship between GRI and type 1 diabetes (T1D) self-management habits, a validated assessment of youths' engagement in habits associated with glycemic outcomes.

METHOD

We retrospectively examined the relationship between GRI and T1D self-management habits in youth with T1D who received care from a Midwest pediatric diabetes clinic network. The GRI was calculated using seven days of continuous glucose monitor (CGM) data, and T1D self-management habits were assessed ±seven days from the GRI score. A mixed-effects Poisson regression model was used to evaluate the total number of habits youth engaged in with GRI, glycated hemoglobin A1c (HbA1c), age, race, ethnicity, and insurance type as fixed effects and participant ID as a random effect to account for multiple clinic visits per individual.

RESULTS

The cohort included 1182 youth aged 2.5 to 18.0 years (mean = 13.8, SD = 3.5) comprising 50.8% male, 84.6% non-Hispanic White, and 64.8% commercial insurance users across a total of 6029 clinic visits. Glycemia Risk Index scores decreased as total number of habits performed increased, suggesting youth who performed more self-management habits achieved a higher quality of glycemia.

CONCLUSIONS

In youth using CGMs, GRI may serve as an easily obtainable metric to help identify youth with above target glycemia, and engagement/disengagement in the T1D self-management habits may inform clinicians with suitable interventions for improving glycemic outcomes.

Hybrid Closed-Loop Versus Manual Insulin Delivery in Adults With Type 1 Diabetes: A Post Hoc Analysis Using the Glycemia Risk Index

BACKGROUND

Glycemia risk index (GRI) is a novel composite metric assessing overall glycemic risk, accounting for both hypoglycemia and hyperglycemia and weighted toward extremes. Data assessing GRI as an outcome measure in closed-loop studies and its relation with conventional key continuous glucose monitoring (CGM) metrics are limited.

METHODS

A post hoc analysis was performed to evaluate the sensitivity of GRI in assessing glycemic quality in adults with type 1 diabetes randomized to 26 weeks hybrid closed-loop (HCL) or manual insulin delivery (control). The primary outcome was GRI comparing HCL with control. Comparisons were made with changes in other CGM metrics including time in range (TIR), time above range (TAR), time below range (TBR), and glycemic variability (standard deviation [SD] and coefficient of variation [CV]).

RESULTS

GRI with HCL (N = 61) compared with control (N = 59) was significantly lower (mean [SD] 33.5 [11.7] vs 56.1 [14.4], respectively; mean difference -22.8 [-27.2, -18.3], P = .001). The mean increase in TIR was +14.8 (11.0, 18.5)%. GRI negatively correlated with TIR for combined arms (r = -.954; P = .001), and positively with TAR >250 mg/dL (r = .901; P = .001), TBR < 54 mg/dL (r = .416; P = .001), and glycemic variability (SD [r = .916] and CV [r = .732]; P = .001 for both).

CONCLUSIONS

Twenty-six weeks of HCL improved GRI, in addition to other CGM metrics, compared with standard insulin therapy. The improvement in GRI was proportionally greater than the change in TIR, and GRI correlated with all CGM metrics. We suggest that GRI may be an appropriate primary outcome for closed-loop trials.

Sustained Effectiveness of an Advanced Hybrid Closed-Loop System in a Cohort of Children and Adolescents With Type 1 Diabetes: A 1-Year Real-World Study

OBJECTIVE

To investigate glucose metrics and identify potential predictors of the achievement of glycemic outcomes in children and adolescents during their first 12 months of MiniMed 780G use.

RESEARCH DESIGN AND METHODS

This multicenter, longitudinal, real-world study recruited 368 children and adolescents with type 1 diabetes (T1D) starting SmartGuard technology between June 2020 and June 2022. Ambulatory glucose profile data were collected during a 15-day run-in period (baseline), 2 weeks after automatic mode activation, and every 3 months. The influence of covariates on glycemic outcomes after 1 year of MiniMed 780G use was assessed.

RESULTS

After 15 days of automatic mode use, all glucose metrics improved compared with baseline (P < 0.001), except for time below range (P = 0.113) and coefficient of variation (P = 0.330). After 1 year, time in range (TIR) remained significantly higher than at baseline (75.3% vs. 62.8%, P < 0.001). The mean glycated hemoglobin (HbA1c) over the study duration was lower than the previous year (6.9 ± 0.6% vs. 7.4 ± 0.9%, P < 0.001). Time spent in tight range (70-140 mg/dL) was 51.1%, and the glycemia risk index was 27.6. Higher TIR levels were associated with a reduced number of automatic correction boluses (P < 0.001), fewer SmartGuard exits (P = 0.021), and longer time in automatic mode (P = 0.030). Individuals with baseline HbA1c >8% showed more relevant improvement in TIR levels (from 54.3% to 72.3%).

CONCLUSIONS

Our study highlights the sustained effectiveness of MiniMed 780G among youth with T1D. Findings suggest that even children and adolescents with low therapeutic engagement may benefit from SmartGuard technology.

Heterogeneity of glycaemic phenotypes in type 1 diabetes

AIMS/HYPOTHESIS

Our study aims to uncover glycaemic phenotype heterogeneity in type 1 diabetes.

METHODS

In the Study of the French-speaking Society of Type 1 Diabetes (SFDT1), we characterised glycaemic heterogeneity thanks to a set of complementary metrics: HbA1c, time in range (TIR), time below range (TBR), CV, Gold score and glycaemia risk index (GRI). Applying the Discriminative Dimensionality Reduction with Trees (DDRTree) algorithm, we created a phenotypic tree, i.e. a 2D visual mapping. We also carried out a clustering analysis for comparison.

RESULTS

We included 618 participants with type 1 diabetes (52.9% men, mean age 40.6 years [SD 14.1]). Our phenotypic tree identified seven glycaemic phenotypes. The 2D phenotypic tree comprised a main branch in the proximal region and glycaemic phenotypes in the distal areas. Dimension 1, the horizontal dimension, was positively associated with GRI (coefficient [95% CI]) (0.54 [0.52, 0.57]), HbA1c (0.39 [0.35, 0.42]), CV (0.24 [0.19, 0.28]) and TBR (0.11 [0.06, 0.15]), and negatively with TIR (-0.52 [-0.54, -0.49]). The vertical dimension was positively associated with TBR (0.41 [0.38, 0.44]), CV (0.40 [0.37, 0.43]), TIR (0.16 [0.12, 0.20]), Gold score (0.10 [0.06, 0.15]) and GRI (0.06 [0.02, 0.11]), and negatively with HbA1c (-0.21 [-0.25, -0.17]). Notably, socioeconomic factors, cardiovascular risk indicators, retinopathy and treatment strategy were significant determinants of glycaemic phenotype diversity. The phenotypic tree enabled more granularity than traditional clustering in revealing clinically relevant subgroups of people with type 1 diabetes.

CONCLUSIONS/INTERPRETATION

Our study advances the current understanding of the complex glycaemic profile in people with type 1 diabetes and suggests that strategies based on isolated glycaemic metrics might not capture the complexity of the glycaemic phenotypes in real life. Relying on these phenotypes could improve patient stratification in type 1 diabetes care and personalise disease management.

Association between glycemia risk index and arterial stiffness in type 2 diabetes

AIM

This study aims to investigate the association of glycemia risk index (GRI), a novel composite metric derived from continuous glucose monitoring (CGM), with arterial stiffness in patients with type 2 diabetes.

MATERIALS AND METHODS

A total of 342 adults with type 2 diabetes were enrolled between April and June 2023 from 11 communities in Shanghai, China. Medical examinations, including measurements of anthropometric parameters, blood pressure, and venous blood samples were conducted. Brachial-ankle pulse wave velocity (baPWV) was examined to evaluate arterial stiffness. All the participants underwent a 14 day CGM recording and GRI was calculated from the CGM data.

RESULTS

The mean age was 70.3 ± 6.8 years, and 162 (47.4%) were male. Participants with a higher baPWV had significantly higher levels of GRI and hyperglycemia component (both P for trend < 0.05). Linear regression revealed the significant positive linear associations of the GRI with baPWV in unadjusted or adjusted models (All P < 0.05). In the multivariable logistic analysis, each increase in the GRI quartile was associated with a 1.30-fold (95% CI 1.01-1.68, P for trend < 0.05) higher prevalence of increased arterial stiffness after adjustment for age, sex, BMI, diabetes duration, current smoking status, blood pressure, and lipid profile. Subgroup analyses showed that the association between the GRI quartiles and increased arterial stiffness was stronger among participants with a diabetes duration ≥15 years (P for interaction = 0.014).

CONCLUSION

Glycemia risk index assessed by continuous glucose monitoring is associated with increased arterial stiffness in type 2 diabetes.

Aiming for the Best Glycemic Control Beyond Time in Range: Time in Tight Range as a New Continuous Glucose Monitoring Metric in Children and Adolescents with Type 1 Diabetes Using Different Treatment Modalities

Introduction: To evaluate time in tight range (TITR) 70-140 mg/dL (3.9-7.8 mmol/L), its correlation with standard continuous glucose monitoring (CGM) metrics and the clinical variables that possibly have a substantial impact on its value, in a large cohort of pediatric subjects using different treatment strategies. Materials and Methods: A total of 854 children and adolescents with type 1 diabetes were consecutively recruited in this real world, dual center, cross-sectional study. Participants were categorized into four treatment groups (multiple daily injections [MDI] + real-time CGM, MDI + intermittently scanned CGM, sensor augmented pump, and hybrid closed loop [HCL]). Demographical and clinical data, including CGM data, were collected and analyzed. Results: The overall study population exhibited an average TITR of 36.4% ± 12.8%. HCL users showed higher TITR levels compared to the other treatment groups (P < 0.001). A time in range (TIR) cut-off value of 71.9% identified subjects achieving a TITR ≥50% (area under curve [AUC] 0.98; 95% confidence interval 0.97-0.99, P < 0.001), and a strong positive correlation between these two metrics was observed (r = 0.95, P < 0.001). An increase in TIR of 1% was associated with 1.84 (R2 Nagelkerke = 0.35, P < 0.001) increased likelihood of achieving TITR ≥50%. Use of HCL systems (B = 7.78; P < 0.001), disease duration (B = -0.26, P = 0.006), coefficient of variation (B = -0.30, P = 0.004), and glycated hemoglobin (B = -8.82; P < 0.001) emerged as significant predictors of TITR levels. Conclusions: Our study highlights that most children and adolescents with type 1 diabetes present TITR levels below 50%, except those using HCL. Tailored interventions and strategies should be implemented to increase TITR.

Safety, Metabolic and Psychological Outcomes of Medtronic MiniMed 780G™ in Children, Adolescents and Young Adults: A Systematic Review

The MiniMed™ 780G is a second-generation automated insulin delivery system that implements a modified proportional-integral-derivative algorithm with some features of an MD-Logic artificial pancreas algorithm. The system may deliver automatic correction boluses up to every 5 min, and it allows the user to choose between three glucose target setpoints (100, 110 and 120 mg/dL). We aimed to review the current evidence on this device in children, adolescents, and young adults living with type 1 diabetes. We screened 783 papers, but only 31 manuscripts were included in this review. Data on metabolic outcomes show that this system is safe as regards severe hypoglycaemia and diabetic ketoacidosis. The glycated haemoglobin may drop to levels about 7%, with CGM reports showing a time in range of 75-80%. The time above range and the time below range are within the recommended target in most of the subjects. Few studies evaluated the psychological outcomes. This system seems to be more effective than the first-generation automated insulin delivery systems. The MiniMed™ 780G has been associated with an improvement in sleep quality in subjects living with diabetes and their caregivers, along with an improvement in treatment satisfaction. Psychological distress is as reduced as the glucose control is improved. We also discuss some case reports describing particular situations in clinical practice. Finally, we think that data show that this system is a further step towards the improvement of the treatment of diabetes as concerns both metabolic and psychological outcomes.

Advanced Hybrid Closed-Loop System Achieves and Maintains Recommended Time in Range Levels for Up To 2 Years: Predictors of Best Efficacy

Aim: To evaluate the long-term efficacy, up to 2 years, of an advanced hybrid closed-loop (AHCL) system and to assess predictors of best results of the therapy. Methods: We retrospectively evaluated 296 adults with type 1 diabetes mellitus [mean age 42.8 ± 16.5 years, men 42.9%, duration of diabetes 22.5 ± 12.8 years, body mass index 24.9 ± 4.7 kg/m2, baseline glycated hemoglobin (HbA1c) 63.4 ± 12.2 mmol/mol (8.0 ± 1.1%) ] who used the MiniMed™ 780G system. Demographic and clinical data were recorded. Continuous glucose monitoring (CGM)-derived metrics and insulin requirement were analyzed from the 4 weeks before and from every quarter after the switch to the AHCL system. Results: In the first quarter of AHCL treatment, all CGM metrics improved. Time in range (TIR) increased from 58.1 ± 17.5% to 70.3 ± 9.5% (P < 0.0001). The improvement lasted for up to 2 years of observation regardless of previous insulin therapies. Throughout the period of observation, 53.4% of participants achieved mean TIR >70%, 92.6% mean time below range <4%, and 46% mean glucose management indicator <53 mmol/mol (7.0%). At univariable logistic regression older age, lower baseline HbA1c and insulin requirement were associated with mean TIR >70%. At multivariable analysis, lower HbA1c remained independently associated with a better glycemic control. However, mean TIR increased more in participants with a higher baseline HbA1c. Conclusions: Switching to an AHCL leads to a rapid improvement in glycemic control lasting for up to 24 months along with a low risk for hypoglycemia, confirming the safety of the system. Lower baseline HbA1c was the main predictor of better efficacy of therapy, although higher baseline HbA1c was associated with the greatest improvement in mean TIR.