scholarly journals Optimization of Insulin Regimen and Glucose Outcomes with Short-Term Real-Time Continuous Glucose Monitoring in Adult Type 1 Diabetes Patients with Suboptimal Control on Multiple Daily Injections: The Adult DIACCOR Study

2018 ◽  
Vol 20 (6) ◽  
pp. 403-412 ◽  
Author(s):  
Sylvie Picard ◽  
Hélène Hanaire ◽  
Yves Reznik ◽  
Pierre-Yves Benhamou ◽  
Salha Fendri ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Real Time ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Suboptimal Control ◽  
Insulin Regimen ◽  
Short Term ◽  
Adult Type ◽  
Multiple Daily Injections

scholarly journals MANAGEMENT OF ENDOCRINE DISEASE: Beneficial effect of real-time continuous glucose monitoring system on glycemic control in type 1 diabetic patients: systematic review and meta-analysis of randomized trials

2012 ◽  
Vol 166 (4) ◽  
pp. 567-574 ◽  
Author(s):  
A Szypowska ◽  
A Ramotowska ◽  
K Dżygało ◽  
D Golicki
Keyword(s):  
Systematic Review ◽  
Type 1 Diabetes ◽  
Real Time ◽  
Continuous Glucose Monitoring ◽  
Insulin Pump ◽  
Meta Analysis ◽  
Glucose Monitoring ◽  
Cochrane Library ◽  
Insulin Regimen

ObjectiveReal-time continuous glucose monitoring (RT-CGM) provides detailed information on glucose patterns and trends, thus allowing the patients to manage their diabetes more effectively.DesignThe aim of this study was to explore the potential beneficial effects of the use of RT-CGM on diabetes management compared with self blood glucose measurement (SBGM) in patients with type 1 diabetes mellitus (T1DM), by means of a systematic review and meta-analysis of randomized controlled trials (RCTs).MethodsMEDLINE, EMBASE, and the Cochrane Library were searched through by two independent investigators for RCTs concerning the use of RT-CGM in patients with T1DM. Only studies with a similar insulin regimen in the experimental and control groups were included in the analysis.ResultsSeven RCTs (n=948) met the inclusion criteria. Combined data from all studies showed better HbA1c reduction in subjects using RT-CGM compared with those using SBGM (mean difference (MD) −0.25; 95% confidence interval (95% CI): from −0.34 to −0.17; P<0.001). Patients treated with insulin pump and RT-CGM had a lower HbA1c level compared with subjects managed with insulin pump and SBGM (four RCTs, n=497; MD −0.26; 95% CI: from −0.43 to −0.10; P=0.002). The benefits of applying RT-CGM were not associated with an increasing rate of major hypoglycemic episodes. The use of RT-CGM for over 60–70% of time was associated with a significant lowering of HbA1c.ConclusionsRT-CGM is more beneficial than SBGM in reducing HbA1c in patients with type 1 diabetes. Further studies are needed to evaluate the efficacy of this system in the pediatric population, especially in very young children.

scholarly journals Therapy adjustments in people with type 1 diabetes with impaired hypoglycemia awareness on multiple daily injections using real-time continuous glucose monitoring: a mechanistic analysis of the HypoDE study

2021 ◽  
Vol 9 (1) ◽  
pp. e001848
Author(s):  
Delia Waldenmaier ◽  
Guido Freckmann ◽  
Stefan Pleus ◽  
Norbert Hermanns ◽  
Dominic Ehrmann ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Real Time ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Multiple Daily Injections ◽  
Mechanistic Analysis ◽  
Multicenter Randomized Controlled Trial ◽  
Low Glucose

IntroductionStudies have shown beneficial effects of real-time continuous glucose monitoring (rtCGM) usage on clinical outcomes. The objective of this analysis was to identify which therapy adjustments were made by people with type 1 diabetes with impaired hypoglycemia awareness during rtCGM usage enabling reductions in the number of low glucose events observed in the HypoDE (Hypoglycemia in Deutschland) study.Research design and methodsIn the multicenter randomized controlled trial in people with type 1 diabetes on multiple daily injections with impaired hypoglycemia awareness, participants recorded their diabetes therapy in 7-day logbooks at baseline and at 6-month follow-up. They used rtCGM or self-monitoring of blood glucose for therapy adjustments. This mechanistic analysis looked at changes in various aspects of therapy.ResultsLogbooks were completed by 70 participants in the rtCGM group and 65 participants in the control group. Participants in the rtCGM group kept their total carbohydrate consumption, daily insulin doses and distribution constant during the study. However, they reported an increased intake of rescue carbohydrates (0.8±0.6 (mean±SD) vs 1.0±0.8 intake/day; baseline-adjusted between-group difference 0.3 intake (0.1–0.5), p=0.031). The glucose threshold at which rescue carbohydrate intake was initiated was elevated from 71±13 mg/dL (3.9±0.7 mmol/L) to 79±14 mg/dL (4.4±0.8 mmol/L) (adjusted between-group difference +7.6 mg/dL (2.4–12.8) (+0.4 mmol/L (0.1–0.7)); p=0.005) in the rtCGM group. Regression analysis showed that follow-up low glucose events were associated with group allocation (p<0.001), low glucose events at baseline (p=0.016) and rescue threshold (p=0.001).ConclusionsNo major adjustments in insulin therapy were made by study participants with impaired hypoglycemia awareness; however, they were more active in preventing hypoglycemia by taking rescue carbohydrates earlier and more often.Trial registration numberNCT02671968.

The impact of real-time continuous glucose monitoring on metabolic control in children with type 1 diabetes

2020 ◽  
Author(s):  
Ruxandra Calapod Ioana ◽  
Irina Bojoga ◽  
Duta Simona Gabriela ◽  
Ana-Maria Stancu ◽  
Amalia Arhire ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Real Time ◽  
Metabolic Control ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
The Impact

scholarly journals Functional data analysis and prediction tools for continuous glucose-monitoring studies

2020 ◽  
pp. 1-10
Author(s):  
Emrah Gecili ◽  
Rui Huang ◽  
Jane C. Khoury ◽  
Eileen King ◽  
Mekibib Altaye ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Real Time ◽  
Continuous Glucose Monitoring ◽  
Functional Data ◽  
Diabetes Management ◽  
Glucose Monitoring ◽  
Patient Specific ◽  
Prediction Errors ◽  
Medical Monitoring

Abstract Introduction: To identify phenotypes of type 1 diabetes based on glucose curves from continuous glucose-monitoring (CGM) using functional data (FD) analysis to account for longitudinal glucose patterns. We present a reliable prediction model that can accurately predict glycemic levels based on past data collected from the CGM sensor and real-time risk of hypo-/hyperglycemic for individuals with type 1 diabetes. Methods: A longitudinal cohort study of 443 type 1 diabetes patients with CGM data from a completed trial. The FD analysis approach, sparse functional principal components (FPCs) analysis was used to identify phenotypes of type 1 diabetes glycemic variation. We employed a nonstationary stochastic linear mixed-effects model (LME) that accommodates between-patient and within-patient heterogeneity to predict glycemic levels and real-time risk of hypo-/hyperglycemic by creating specific target functions for these excursions. Results: The majority of the variation (73%) in glucose trajectories was explained by the first two FPCs. Higher order variation in the CGM profiles occurred during weeknights, although variation was higher on weekends. The model has low prediction errors and yields accurate predictions for both glucose levels and real-time risk of glycemic excursions. Conclusions: By identifying these distinct longitudinal patterns as phenotypes, interventions can be targeted to optimize type 1 diabetes management for subgroups at the highest risk for compromised long-term outcomes such as cardiac disease or stroke. Further, the estimated change/variability in an individual’s glucose trajectory can be used to establish clinically meaningful and patient-specific thresholds that, when coupled with probabilistic predictive inference, provide a useful medical-monitoring tool.

Improvement of metabolic control after 3-month use of real-time continuous glucose monitoring in patients with type 1 diabetes: a multicenter study in Greece

HORMONES ◽  
2019 ◽  
Vol 18 (4) ◽  
pp. 443-450
Author(s):  
Ioanna Eleftheriadou ◽  
Triantafyllos Didangelos ◽  
Angelos C. Pappas ◽  
Eleni Anastasiou ◽  
Charalampos Vasilopoulos ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Real Time ◽  
Metabolic Control ◽  
Continuous Glucose Monitoring ◽  
Multicenter Study ◽  
Glucose Monitoring
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