The interdependence of targets for continuous glucose monitoring outcomes in type 1 diabetes with automated insulin delivery.

AIM: The aim was to determine the interdependence of targets for glucose management indicator (GMI), time within the ranges of 70-180 mg/dL (TIR) and 70-140 mg/dL (time in tight glucose range [TITR]), time above 180 mg/dL (TA180) and 250 mg/dL (TA250) and time below 70 mg/dL (TB70) and 54 mg/dL (TB54) and its implications for setting targets in automated insulin delivery (AID). MATERIALS AND METHODS: Real-world data from individuals with type 1 diabetes using the 780G system were used to calculate the receiver operating characteristic curves and establish interdependent targets for time in ranges based on several GMI benchmarks. Correlation, regression and principal component analysis were used to determine their association and dimensionality. RESULTS: In individuals aged >15 years (n = 41 692), a GMI <6.5% required targets of >81%, >58%, <15% and <1.9% for TIR, TITR, TA180 and TA250, respectively, with high sensitivity, specificity and accuracy (>90%), whereas these values were poor for time in hypoglycaemia and GMI, which had a modest correlation (-0.21 to -0.43). Two dimensions emerged: (1) GMI, TIR, TITR, TA180 and TA250, and (2) TB70 and TB54, explaining 95% of total variability. GMI (or TIR) and TB70 explained >81% of the variability in the remaining continuous glucose monitoring (CGM) metrics, providing accurate predictions. Individuals aged ≤15 years (n = 14 459) showed similar results. CONCLUSION: We developed a methodology to establish interdependent CGM targets for therapies with CGM data outputs. In AID with the 780G system, a GMI <7% requires time in ranges close to consensus targets. Targets for GMI, TIR, TITR, TA180 and TA250 could be reduced to targets for GMI or TIR, whereas targets for time in hypoglycaemia are not inherently tied to GMI/TIR targets.

Impact of Probiotics on the Glycemic Control of Pediatric and Adolescent Individuals with Type 1 Diabetes: A Systematic Review and Meta-Analysis.

AIMS: Human recombinant insulin is currently the only therapy for children and adolescents with type 1 diabetes (T1D), although not always efficient for the glycemic control of these individuals. The interrelation between the gut microbiome and the glycemic control of apparently healthy populations, as well as various populations with diabetes, is undeniable. Probiotics are biotherapeutics that deliver active components to various targets, primarily the gastrointestinal tract. This systematic review and meta-analysis examined the effect of the administration of probiotics on the glycemic control of pediatric and adolescent individuals with T1D. MATERIALS AND METHODS: Randomized controlled trials employing the administration of probiotics in children and adolescents with T1D (with ≥10 individuals per treatment arm), written in English, providing parameters of glycemic control, such as mean glucose concentrations and glycosylated hemoglobin (HbA1c), were deemed eligible. RESULTS: The search strategy resulted in six papers with contradictory findings. Ultimately, five studies of acceptable quality, comprising 388 children and adolescents with T1D, were included in the meta-analysis. Employing a random and fixed effects model revealed statistically significant negative effect sizes of probiotics on the glycemic control of those individuals, i.e., higher concentrations of glucose and HbA1c than controls. CONCLUSIONS: Children and adolescents with T1D who received probiotics demonstrated worse glycemic control than controls after the intervention. Adequately powered studies, with extended follow-up periods, along with monitoring of compliance and employing the proper strains, are required to unravel the mechanisms of action and the relative effects of probiotics, particularly concerning diabetes-related complications and metabolic outcomes.

Two-week continuous glucose monitoring-derived metrics and degree of hepatic steatosis: a cross-sectional study among Chinese middle-aged and elderly participants.

BACKGROUND: Continuous glucose monitoring (CGM) devices provide detailed information on daily glucose control and glycemic variability. Yet limited population-based studies have explored the association between CGM metrics and fatty liver. We aimed to investigate the associations of CGM metrics with the degree of hepatic steatosis. METHODS: This cross-sectional study included 1180 participants from the Guangzhou Nutrition and Health Study. CGM metrics, covering mean glucose level, glycemic variability, and in-range measures, were separately processed for all-day, nighttime, and daytime periods. Hepatic steatosis degree (healthy: n = 698; mild steatosis: n = 242; moderate/severe steatosis: n = 240) was determined by magnetic resonance imaging proton density fat fraction. Multivariate ordinal logistic regression models were conducted to estimate the associations between CGM metrics and steatosis degree. Machine learning models were employed to evaluate the predictive performance of CGM metrics for steatosis degree. RESULTS: Mean blood glucose, coefficient of variation (CV) of glucose, mean amplitude of glucose excursions (MAGE), and mean of daily differences (MODD) were positively associated with steatosis degree, with corresponding odds ratios (ORs) and 95% confidence intervals (CIs) of 1.35 (1.17, 1.56), 1.21 (1.06, 1.39), 1.37 (1.19, 1.57), and 1.35 (1.17, 1.56) during all-day period. Notably, lower daytime time in range (TIR) and higher nighttime TIR were associated with higher steatosis degree, with ORs (95% CIs) of 0.83 (0.73, 0.95) and 1.16 (1.00, 1.33), respectively. For moderate/severe steatosis (vs. healthy) prediction, the average area under the receiver operating characteristic curves were higher for the nighttime (0.69) and daytime (0.66) metrics than that of all-day metrics (0.63, P < 0.001 for all comparisons). The model combining both nighttime and daytime metrics achieved the highest predictive capacity (0.73), with nighttime MODD emerging as the most important predictor. CONCLUSIONS: Higher CGM-derived mean glucose and glycemic variability were linked with higher steatosis degree. CGM-derived metrics during nighttime and daytime provided distinct and complementary insights into hepatic steatosis.

Differences Between Glycated Hemoglobin and Glucose Management Indicator in Real-Time and Intermittent Scanning Continuous Glucose Monitoring in Adults With Type 1 Diabetes.

BACKGROUND: This study demonstrates the difference between glucose management indicator (GMI) and glycated hemoglobin (HbA1c) according to sensor mean glucose and HbA1c status using 2 continuous glucose monitoring (CGM) sensors in people with type 1 diabetes. METHODS: A total of 275 subjects (117 Dexcom G6 [G6] and 158 FreeStyle Libre 1 [FL]) with type 1 diabetes was included. The G6 and FL sensors were used, respectively, over 90 days to analyze 682 and 515 glycemic profiles that coincide with HbA1c. RESULTS: The mean HbA1c was 6.6% in Dexcom G6 and 7.2% in FL profiles. In G6 profiles, GMI was significantly higher than HbA1c irrespective of mean glucose (all P < .001, mean difference: 0.58% ± 0.49%). The GMI was significantly higher than the given HbA1c when HbA1c was below 8.0% (all P < .001). The discordance was the highest at 0.9% for lower HbA1c values (5.0%-5.9%). The proportion of discordance >0.5% improved from 60.1% to 30.9% when using the revised GMI equation in G6 profiles. In FL profile, the overall mean difference between GMI and HbA1c was 0 (P = .966). The GMI was significantly lower by 0.9% than HbA1c of 9.0% to 9.9% and higher by 0.5% in HbA1c of 5.0% to 5.9% (all P < .001). CONCLUSIONS: The GMI is overestimated in G6 users, particularly those with well-controlled diabetes, but the GMI and HbA1c discordance improved with a revised equation from the observed data. The FL profile showed greater discordance for lower HbA1c levels or higher HbA1c levels.

Discordance between mean glucose and time in range in relation to HbA1c in individuals with type 1 diabetes: results from the GOLD and SILVER trials.

AIMS/HYPOTHESIS: Previous studies have shown that individuals with similar mean glucose levels (MG) or percentage of time in range (TIR) may have different HbA1c values. The aim of this study was to further elucidate how MG and TIR are associated with HbA1c. METHODS: Data from the randomised clinical GOLD trial (n=144) and the follow-up SILVER trial (n=98) of adults with type 1 diabetes followed for 2.5 years were analysed. A total of 596 paired HbA1c/continuous glucose monitoring measurements were included. Linear mixed-effects models were used to account for intra-individual correlations in repeated-measures data. RESULTS: In the GOLD trial, the mean age of the participants (± SD) was 44±13 years, 63 (44%) were female, and the mean HbA1c (± SD) was 72±9.8 mmol/mol (8.7±0.9%). When correlating MG with HbA1c, MG explained 63% of the variation in HbA1c (r=0.79, p<0.001). The variation in HbA1c explained by MG increased to 88% (r=0.94, p value for improvement of fit <0.001) when accounting for person-to-person variation in the MG-HbA1c relationship. Time below range (TBR; <3.9 mmol/l), time above range (TAR) level 2 (>13.9 mmol/l) and glycaemic variability had little or no effect on the association. For a given MG and TIR, the HbA1c of 10% of individuals deviated by >8 mmol/mol (0.8%) from their estimated HbA1c based on the overall association between MG and TIR with HbA1c. TBR and TAR level 2 significantly influenced the association between TIR and HbA1c. At a given TIR, each 1% increase in TBR was related to a 0.6 mmol/mol lower HbA1c (95% CI 0.4, 0.9; p<0.001), and each 2% increase in TAR level 2 was related to a 0.4 mmol/mol higher HbA1c (95% CI 0.1, 0.6; p=0.003). However, neither TIR, TBR nor TAR level 2 were significantly associated with HbA1c when accounting for MG. CONCLUSIONS/INTERPRETATION: Inter-individual variations exist between MG and HbA1c, as well as between TIR and HbA1c, with clinically important deviations in relatively large groups of individuals with type 1 diabetes. These results may provide important information to both healthcare providers and individuals with diabetes in terms of prognosis and when making diabetes management decisions.

The Associations of Mean Glucose and Time in Range from Continuous Glucose Monitoring with HbA1c in Adults with Type 2 Diabetes.

Associations of mean glucose and time in range (70-180 mg/dL) from continuous glucose monitoring (CGM) with HbA1c in adults with type 2 diabetes are not well characterized. We conducted a secondary analysis of 186 participants from the Hyperglycemic Profiles in Obstructive Sleep Apnea (HYPNOS) trial. Participants simultaneously wore Dexcom G4 and Abbott Libre Pro CGM sensors up to 4 weeks. Mean HbA1c was 7.7% (SD, 1.3). There were strong negative Pearson's correlations of HbA1c with CGM time in range (-0.79, Abbott; -0.81, Dexcom) and strong positive correlations with CGM mean glucose (Dexcom, 0.84; Abbott, 0.82). However, there were large differences in CGM mean glucose (±20 mg/dL) and time in range (±14%) at any given HbA1c value. Mean glucose and HbA1c are strongly correlated in type 2 diabetes patients not taking insulin but discordance is evident at the individual level. Clinicians should expect discordance and use HbA1c and CGM in a complementary manner. ClinicalTrials.gov Identifier: NCT02454153.

Continuous glucose monitoring metrics (Mean Glucose, time above range and time in range) are superior to glycated haemoglobin for assessment of therapeutic efficacy.

AIM: To evaluate continuous glucose monitoring (CGM) metrics for use as alternatives to glycated haemoglobin (HbA1c) to evaluate therapeutic efficacy. METHODS: We re-analysed correlations among CGM metrics from studies involving 545 people with type 1 diabetes (T1D), 5910 people with type 2 diabetes (T2D) and 98 people with T1D during pregnancy and the postpartum period. RESULTS: Three CGM metrics, interstitial fluid Mean Glucose level, proportion of time above range (%TAR) and proportion of time in range (%TIR), were correlated with HbA1c and provided metrics that can be used to evaluate therapeutic efficacy. Mean Glucose showed the highest correlation with %TAR (r = 0.98 in T1D, 0.97 in T2D) but weaker correlations with %TIR (r = -0.92 in T1D, -0.83 in T2D) or with HbA1c (r = 0.78 in T1D). %TAR and %TIR were highly correlated (r = -0.96 in T1D, -0.91 in T2D). After 6 months of use of real-time CGM by people with T1D, changes in Mean Glucose level were more highly correlated with changes in %TAR (r = 0.95) than with changes in %TIR (r = -0.85) or with changes in HbA1c level (r = 0.52). These metrics can be combined with metrics of hypoglycaemia and/or glycaemic variability to provide a more comprehensive assessment of overall quality of glycaemic control. CONCLUSION: The CGM metrics %TAR and %TIR show much higher correlations with Mean Glucose than with HbA1c and provide sensitive indicators of efficacy. Mean glucose may be the best metric and shows consistently higher correlations with %TAR than with %TIR.

Association study between hypothalamic functional connectivity, early nutrition, and glucose levels in healthy children aged 6 years: The COGNIS study follow-up.

Breastfeeding (BF) is the gold standard in infant nutrition; knowing how it influences brain connectivity would help understand the mechanisms involved, which would help close the nutritional gap between infant formulas and breast milk. We analyzed potential long-term differences depending on the diet with an experimental infant formula (EF), compared to a standard infant formula (SF) or breastfeeding (BF) during the first 18 months of life on children's hypothalamic functional connectivity (FC) assessed at 6 years old. A total of 62 children participating in the COGNIS randomized clinical trial (Clinical Trial Registration: www.ClinicalTrials.gov, identifier: NCT02094547) were included in this study. They were randomized to receive an SF (n = 22) or a bioactive nutrient-enriched EF (n = 20). BF children were also included as a control study group (BF: n = 20). Brain function was evaluated using functional magnetic resonance imaging (fMRI) and mean glucose levels were collected through a 24-h continuous glucose monitoring (CGM) device at 6 years old. Furthermore, nutrient intake was also analyzed during the first 18 months of life and at 6 years old through 3-day dietary intake records. Groups fed with EF and BF showed lower FC between the medial hypothalamus (MH) and the anterior cingulate cortex (ACC) in comparison with SF-fed children. Moreover, the BF children group showed lower FC between the MH and the left putamen extending to the middle insula, and higher FC between the MH and the inferior frontal gyrus (IFG) compared to the EF-fed children group. These areas are key regions within the salience network, which is involved in processing salience stimuli, eating motivation, and hedonic-driven desire to consume food. Indeed, current higher connectivity found on the MH-IFG network in the BF group was associated with lower simple sugars acceptable macronutrient distribution ranges (AMDRs) at 6 months of age. Regarding linoleic acid intake at 12 months old, a negative association with this network (MH-IFG) only in the BF group was found. In addition, BF children showed lower mean glucose levels compared to SF-fed children at 6 years old. Our results may point out a possible relationship between diet during the first 18 months of life and inclined proclivity for hedonic eating later in life. Clinical trial registration: https://www.clinicaltrials.gov/, identifier NCT02094547.

Continuous Glucose Monitoring Profiles in Healthy, Nondiabetic Young Children.

Context: Continuous glucose monitoring (CGM) is increasingly being used both for day-to-day management in patients with diabetes and in clinical research. While data on glycemic profiles of healthy, nondiabetic individuals exist, data on nondiabetic very young children are lacking. Objective: This work aimed to establish reference sensor glucose ranges in healthy, nondiabetic young children, using a current-generation CGM sensor. Methods: This prospective observational study took place in an institutional practice with healthy, nondiabetic children aged 1 to 6 years with normal body mass index. A blinded Dexcom G6 Pro CGM was worn for approximately 10 days by each participant. Main outcome measures included CGM metrics of mean glucose, hyperglycemia, hypoglycemia, and glycemic variability. Results: Thirty-nine participants were included in the analyses. Mean average glucose was 103 mg/dL (5.7 mmol/L). Median percentage time between 70 and 140 mg/dL (3.9-7.8 mmol/L) was 96% (interquartile range, 92%-97%), mean within-individual coefficient of variation was 17 ±â€…3%, median time spent with glucose levels greater than 140 mg/dL was 3.4% (49 min/day), and median time less than 70 mg/dL (3.9 mmol/L) was 0.4% (6 min/day). Conclusion: Collecting normative sensor glucose data and describing glycemic measures for young children fill an important informational gap and will be useful as a benchmark for future clinical studies.

Association of Multiple Glycemic Parameters at Hospital Admission with Mortality and Short-Term Outcomes in Acutely Poisoned Patients.

The prognostic value of multiple glycemic parameters in poisoned patients was never assessed. We aim to explore the effects of glucose variability on short-term outcomes in nondiabetic and diabetic patients acutely poisoned with undifferentiated xenobiotics. We performed a prospective observational study in a tertiary center for toxicology in northeastern Romania. Over the course of 3 years, we included 1076 adults, older than 18 years, admitted for acute poisoning with a xenobiotic. The mortality rate was 4.1%. The admission blood glucose level (BGL) predicted mortality (OR 1.015, 95% CI 1.011-1.019, p < 0.001) and complications (OR 1.005, 95% CI 1.001-1.009, p 0.02). The mean glucose level (MGL) after admission (OR 1.007, 95% CI 1.000-1.013, p 0.034) and coefficient of glucose variability (CV) were predictive for complications (OR 40.58, 95% CI 1.35-1220.52, p 0.033), using the same multivariable model. The receiver operating characteristic curve (ROC) analysis revealed that BGL had good predictive value for in-hospital mortality (area under the curve (AUC) = 0.744, 95% CI = 0.648-0.841, p < 0.001), and complications (AUC = 0.618, 95% CI = 0.584-0.653, p < 0.001). In patients acutely poisoned with xenobiotics, the BGL, MGL and CV can be useful as mortality and short-outcome predictors.

Relationship of Glucose Variability With Glycated Hemoglobin and Daily Mean Glucose: A Post Hoc Analysis of Data From 5 Phase 3 Studies.

BACKGROUND: The association of glucose variability (GV) with other glycemic measures is emerging as a topic of interest. The aim of this analysis is to study the correlation between GV and measures of glycemic control, such as glycated hemoglobin (HbA1c) and daily mean glucose (DMG). METHODS: Data from 5 phase 3 trials were pooled into 3 analysis groups: type 2 diabetes (T2D) treated with basal insulin only, T2D treated with basal-bolus therapy, and type 1 diabetes (T1D). A generalized boosted model was used post hoc to assess the relationship of the following variables with glycemic control parameters (HbA1c and DMG): within-day GV, between-day GV (calculated using self-monitored blood glucose and fasting blood glucose [FBG]), hypoglycemia rate, and certain baseline characteristics. RESULTS: Within-day GV (calculated using standard deviation [SD]) was found to have a significant influence on endpoints HbA1c and DMG in all 3 patient groups. Between-day GV from FBG (calculated using SD), within-day GV (calculated using coefficient of variation), and hypoglycemia rate were found to significantly influence the endpoint HbA1c in the T2D basal-only group. CONCLUSIONS: Lower within-day GV was significantly associated with improvement in DMG and HbA1c. This finding suggests that GV could be a marker in the early phases of new antihyperglycemic therapy development for predicting clinical outcomes in terms of HbA1c and DMG.