Continuous glucose monitoring use and glucose variability in very young children with type 1 diabetes (VibRate): A multinational prospective observational real-world cohort study.
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SUBMITTER: Dovc K
PROVIDER: S-EPMC9306649 | biostudies-literature | 2022 Mar
REPOSITORIES: biostudies-literature
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Continuous glucose monitoring use and glucose variability in very young children with type 1 diabetes (VibRate): A multinational prospective observational real-world cohort study.
Dovc Klemen K Van Name Michelle M Jenko Bizjan Barbara B Rusak Ewa E Piona Claudia C Yesiltepe-Mutlu Gul G Mentink Rosaline R Frontino Giulio G Macedoni Maddalena M Ferreira Sofia Helena SH Serra-Caetano Joana J Galhardo Júlia J Pelicand Julie J Silvestri Francesca F Sherr Jennifer J Chobot Agata A Biester Torben T
Diabetes, obesity & metabolism 20211227 3
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Project description:BackgroundThis real-world data study analyzed glucose metrics from FreeStyle Libre® flash glucose monitoring in relation to scanning frequency, time in range (TIR) and estimated A1c (eA1c) in Saudi Arabia.MethodsAnonymized reader data were analyzed according to scanning frequency quartiles, eA1c categories (<7%,≥7%‒≤9% or>9%) and TIR categories (<50%,≥50%‒≤70% or>70%). Sensors, grouped by reader, were required to have≥120 h of operation. Differences in scanning frequency, eA1c, TIR, time in hypoglycemia and hyperglycemia, and glucose variability (standard deviation [SD] and coefficient of variation [CV]) were analyzed between groups.Results6097 readers, 35,747 sensors, and 40 million automatic glucose measurements were analyzed. Patients in the highest scanning frequency quartile (Q4, mean 32.0 scans/day) had lower eA1c (8.47%), greater TIR (46.4%) and lower glucose variation (SD 75.0 mg/dL, CV 38.2%) compared to the lowest quartile (Q1, mean 5.2 scans/day; eA1c 9.77%, TIR 32.8%, SD 94.9 mg/dL, CV 41.3%). Lower eA1c and higher TIR were associated with greater scanning frequency, lower glucose variability and less time in hyperglycemia.ConclusionsHigher scanning frequency in flash glucose users from Saudi Arabia is associated with lower eA1c, higher TIR, lower glucose variability and less time in hypoglycemia or hyperglycemia.
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Project description:BACKGROUND:Self-monitoring of blood glucose using capillary glucose testing (C) has a number of shortcomings compared to continuous glucose monitoring (CGM). We aimed to compare these two methods and used blood glucose measurements in venous blood (IV) as a reference. Postprandial blood glucose levels were measured after 50 g oral glucose load and after the consumption of a portion of different foods containing 50 g of carbohydrates. We also evaluated the associations between postprandial glucose responses and the clinical characteristics of the participants at the beginning of the study. METHODS:12 healthy volunteers (age: 36 ± 17 years, BMI: 24.9 ± 3.5 kg/m²) ate white bread (WB) and whole grain (WG) bread and drank a 50 g glucose drink as reference. Postprandial glucose responses were evaluated by CGM, IV and C blood glucose measurements. Incremental area under the curve (AUCi) of postprandial blood glucose was calculated for 1 h (AUCi 0-60) and 2 h (AUCi 0-120). RESULTS:After the consumption of white bread and whole grain bread, the AUCi 0-60 min did not differ between CGM and IV or C. AUCi 0-120 min of CGM showed no difference compared to C. Correlation analyses revealed a positive association of age with glucose AUCi 0-120 (r = 0.768; P = 0.004) and WG AUCi 0-120 (r = 0.758; P = 0.004); fasting blood glucose correlated with WG AUCi 0-120 (r = 0.838; P < 0.001). CONCLUSION:Despite considerable inter-individual variability of postprandial glycemic responses, CGM evaluated postprandial glycemic excursions which had comparable results compared to standard blood glucose measurements under real-life conditions. Associations of AUCi 0-60 and AUCi 0-120 postprandial glucose response with age or fasting blood glucose could be shown.
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Project description:ObjectivesHypoglycemia is frequent in very low birth weight (VLBW) neonates and compromises their neurological outcome. The aim of this study was to compare real-time continuous glucose monitoring system (RT-CGMS) to standard methods by intermittent capillary blood glucose testing in detecting and managing hypoglycemia.Study designForty-eight VLBW neonates were enrolled in this prospective study. During their 3 first days of life, their glucose level was monitored either by RT-CGMS (CGM-group), or by intermittent capillary glucose testing (IGM-group) associated with a blind-CGMS to detect retrospectively missed hypoglycemia. Outcomes were the number and duration of hypoglycemic (≤ 50 mg/dl) episodes per patient detected by CGMS.ResultsForty-three monitorings were analyzed (IGM n = 21, CGM n = 22), with a median recording time of 72 hours. In the IGM group, blind-CGMS revealed a significantly higher number of hypoglycemia episodes than capillary blood glucose testing (1.2 ± 0.4 vs 0.4 ± 0.2 episode/patient, p<0.01). In the CGM-group, the use of RT-CGMS made it possible (i) to detect the same number of hypoglycemia episodes as blind-CGMS (1.2 ± 0.4 episode/patient), (ii) to adapt the glucose supply in neonates with hypoglycemia (increased supply during days 1 and 2), and (iii) to significantly reduce the duration of hypoglycemia episodes per patient (CGM 44[10-140] min versus IGM 95[15-520] min, p<0.05). Furthermore, it reduced the number of blood samples (CGM 16.9 ± 1.0 vs IGM 21.9 ± 1.0 blood sample/patient, p<0.001).ConclusionRT-CGMS played a beneficial role in managing hypoglycemia in VLBW neonates by adjusting the carbohydrate supply to the individual needs and by reducing the duration of hypoglycemia episodes. The clinical significance of the biological differences observed in our study need to be explored.
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Project description:BackgroundInformation about factors related to better adherence to continuous glucose monitoring (CGM) sensor adherence is quite limited.Materials and methodsForty-six participants with type 1 diabetes using continuous subcutaneous insulin infusion (CSII) without CGM were recruited. The participants' characteristics and diabetes-related quality of life (QOL) were evaluated at baseline and one year after starting to use CGM. Participants wearing the sensor for ≥60% of the time were considered as adherent.ResultsThe mean age of the 46 participants was 44.1 ± 15.0 years old and the mean glycohemoglobin (HbA1c) was 7.7 ± 1.0%; 60.9% of the participants were classified as adherent. The duration of using CSII was longer in the adherent group, and the degree of diabetic retinopathy was significantly different. There were no significant differences in age, frequency of self-monitoring of blood glucose, or Hypoglycemia Fear Survey (HFS-B for behavior, HFS-W for worry) score at baseline between the adherent and nonadherent groups. The Problem Areas in Diabetes (PAID) score at baseline was significantly higher and the total CSII-QOL score at baseline was significantly lower in the adherent group. The usage of dual-wave bolus was significantly increased in the adherent group (34.6%-61.5%, P = .016), but not in the nonadherent group (33.3%-33.3%, P > .999). The HbA1c level showed a significant improvement in the adherent group (7.8%-7.3%, P < .001), but not in the nonadherent group (7.5%-7.2%, P = .102).ConclusionsHigher adherence to CGM sensors may be associated with a heavier emotional burden of diabetes and a worse QOL in relation to CSII at baseline.
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Project description:IntroductionDay-long fasting creates considerable metabolic stress that poses challenges in people with diabetes and those who have undergone bariatric surgery. Clinical knowledge of glucose fluctuations and the risks for such patients during fasting is limited.ObjectivesThis study examined the effect of intermittent fasting on glucose excursions, hypoglycemia, and hyperglycemia in people with or without diabetes who had sleeve gastrectomy compared with healthy individuals.MethodsThis open-label, prospective study compared interstitial glucose profiles measured with continuous glucose monitoring system for 72 h during fasting and non-fasting periods between four groups comprising 15 participants each: people with obesity and medicine-treated type 2 diabetes (T2D) only, obesity and T2D treated with sleeve gastrectomy, obesity without T2D treated with sleeve gastrectomy, and healthy, normal-weight non-diabetic controls.ResultsThe mean 72-h glucose concentration was significantly lower during the fasting period for all groups (p ≤ 0.041), with the highest glucose concentrations in the medicine-treated T2D-only group and the lowest concentrations in the sleeve gastrectomy in non-T2D group. The mean glucose profiles of all the groups showed a marked increase in interstitial glucose on breaking the fast, which was exaggerated in the two diabetes groups. The mean amplitude of glycemic excursions did not differ significantly within each group between fasting and non-fasting. No significant difference was noted in the fraction of time in the hypoglycemic range between the fasting and non-fasting periods in any group.ConclusionIntermittent fasting had no adverse effect on glycemic control in people with or without diabetes who had undergone sleeve gastrectomy.
| S-EPMC7467959 | biostudies-literature
Project description:BackgroundThe within-person and between-sensor variability of metrics from different interstitial continuous glucose monitoring (CGM) sensors in adults with type 2 diabetes not taking insulin is unclear.MethodsSecondary analysis of data from 172 participants from the Hyperglycemic Profiles in Obstructive Sleep Apnea randomized clinical trial. Participants simultaneously wore Dexcom G4 and Abbott Libre Pro CGM sensors for up to 2 weeks at baseline and again at the 3-month follow-up visit.ResultsAt baseline (up to 2 weeks of CGM), mean glucose for both the Abbott and Dexcom sensors was approximately 150 mg/dL (8.3 mmol/L) and time in range (70180 mg/dL [3.910.0 mmol/L]) was just below 80. When comparing the same sensor at 2 different time points (two 2-week periods, 3 months apart), the within-person coefficient of variation (CVw) in mean glucose was 17.4 (Abbott) and 14.2 (Dexcom). CVw for percent time in range: 20.1 (Abbott) and 18.6 (Dexcom). At baseline, the Pearson correlation of mean glucose from the 2 sensors worn simultaneously was r 0.86, root mean squared error (RMSE), 13 mg/dL (0.7 mmol/L); for time in range, r 0.88, RMSE, 8 percentage points.ConclusionsSubstantial variation was observed within sensors over time and across 2 different sensors worn simultaneously on the same individuals. Clinicians should be aware of this variability when using CGM technology to make clinical decisions.ClinicalTrials.gov Identifier: NCT02454153.
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Project description: Not available
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Project description:ObjectiveReal-time continuous glucose monitoring (CGM) is effective for diabetes management in cases of type 1 diabetes and adults with type 2 diabetes (T2D) but has not been assessed in adolescents and young adults (AYAs) with T2D. The objective of this pilot interventional study was to assess the feasibility and acceptability of real-time CGM use in AYAs with T2D.MethodsAdolescents and young adults (13-21 years old) with T2D for six months or more and hemoglobin A1c (A1c) greater than 7%, on any Food and Drug Administration-approved treatment regimen, were included. After a blinded run-in period, participants were given access to a real-time CGM system for 12 weeks. The use and acceptability of the real-time CGM were evaluated by sensor usage, surveys, and focus group qualitative data.ResultsParticipants' (n = 9) median age was 19.1 (interquartile range [IQR] 16.8-20.5) years, 78% were female, 100% were people of color, and 67% were publicly insured. Baseline A1c was 11.9% (standard deviation ±2.8%), with median diabetes duration of 2.5 (IQR 1.4-6) years, and 67% were using insulin. Seven participants completed the study and demonstrated statistically significant improvement in diabetes-related quality of life, with the mean Pediatric Quality of Life inventory (PedsQL) diabetes score increasing from 70 to 75 after using CGM (P = .026). Focus group results supported survey results that CGM use among AYAs with T2D is feasible, can improve quality of life, and has the potential to modify behavior.ConclusionReal-time CGM is feasible and acceptable for AYAs with T2D and may improve the quality of life of patients with diabetes. Larger randomized controlled trials are needed to assess the effects on glycemic control and healthy lifestyle changes.
| S-EPMC11307231 | biostudies-literature
Project description:BACKGROUND:The I-HART CGM study has shown that real-time continuous glucose monitoring (rtCGM) has greater beneficial impact on hypoglycemia than intermittently scanned continuous glucose monitoring (iscCGM) in adults with type 1 diabetes at high risk (Gold score ?4 or recent severe hypoglycemia using insulin injections). In this subanalysis, we present the impact of rtCGM and iscCGM on glycemic variability (GV). METHODS:Forty participants were recruited to this parallel group study. Following two weeks of blinded rtCGM (DexcomG4), participants were randomized to rtCGM (Dexcom G5; n = 20) or iscCGM (Freestyle Libre; n = 20) for eight weeks. An open-extension phase enabled participants on rtCGM to continue for a further eight weeks and those on iscCGM to switch to rtCGM over this period. Glycemic variability measures at baseline, 8- and 16-week endpoints were compared between groups. RESULTS:At the eight-week endpoint, between-group differences demonstrated significant reduction in several GV measures with rtCGM compared to iscCGM (GRADE%hypoglycemia, index of glycemic control [IGC], and average daily risk range [ADRR]; P < .05). Intermittently scanned continuous glucose monitoring reduced mean average glucose and glycemic variability percentage and GRADE%hyperglycemia compared with rtCGM (P < .05). At 16 weeks, the iscCGM group switching to rtCGM showed significant improvement in GRADE%hypoglycemia, personal glycemic status, IGC, and ADRR. CONCLUSION:Our data suggest most, but not all, GV measures improve with rtCGM compared with iscCGM, particularly those measures associated with the risk of hypoglycemia. Selecting appropriate glucose monitoring technology to address GV in this high-risk cohort is important to minimize the risk of glucose extremes and severe hypoglycemia. CLINICAL TRIAL REGISTRATION:ClinicalTrials.gov NCT03028220.
| S-EPMC7576953 | biostudies-literature
Project description:IntroductionStress-induced hyperglycaemia, which has been shown to be associated with an unfavourable prognosis, is common among critically ill patients. Additionally, it has been reported that hypoglycaemia and high glucose variabilities are also associated with adverse outcomes. Thus, continuous glucose monitoring (CGM) may be the optimal method to detect severe hypoglycaemia, hyperglycaemia and decrease glucose excursion. However, the overall accuracy and reliability of CGM systems and the effects of CGM systems on glucose control and prognosis in critically ill patients remain inconclusive. Therefore, we will conduct a systematic review and meta-analysis to clarify the associations between CGM systems and clinical outcome.Methods and analysisWe will search PubMed, EMBASE and the Cochrane Library from inception to October 2014. Studies comparing CGM systems with any other glucose monitoring methods in critically ill patients will be eligible for our meta-analysis. The primary endpoints include the incidence of hypoglycaemia and hyperglycaemia, mean glucose level, and percentage of time within the target range. The second endpoints include intensive care unit (ICU) mortality, hospital mortality, duration of mechanical ventilation, length of ICU and hospital stay, and the Pearson correlation coefficient and the results of error grid analysis. In addition, we will record all complications (eg, acquired infections) in control and intervention groups and local adverse events in intervention groups (eg, bleeding or infections).Ethics and disseminationEthics approval is not required as this is a protocol for a systematic review. The findings will be disseminated in a peer-reviewed journal and presented at a relevant conference.Trial registration numberPROSPERO registration number: CRD42014013488.
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