scholarly journals MON-086 Various Subcutaneous Continuous Glucose Monitors Comparably Lower HbA1c in Children

2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Steven Gold ◽  
Liam McGuirk ◽  
James Haigney ◽  
Jane Torres ◽  
Tara Patale ◽  
...  
Keyword(s):  
Glycemic Control ◽  
Metabolic Control ◽  
Glucose Monitoring ◽  
Initial Visit ◽  
High Hba1c ◽  
The Difference ◽  
Continuous Glucose Monitors ◽  
Type 1 Dm ◽  
Hba1c Levels

Abstract Background: Preliminary studies have demonstrated improvement in metabolic control of patients (PTs) using subcutaneous Continuous Glucose Monitoring systems (CGMs). In this study, we investigated the effect of CGMs on PTs’ glycemic control and compared the change in patient HbA1c levels between sensors. Objective: To determine how CGMs affect metabolic control in PTs and the effect of different sensors on glycemic control. Patients and Methods: 33 PTs with Type 1 diabetes mellitus (DM) who began using a CGM between 2017 and 2019 were selected for inclusion. CGM systems used included DexcomG6™, DexcomG5™, DexcomG4™, Enlite™, Guardian 3™, or Medtronic Sure-T™ sensors. Results: The mean (MN) age of PTs at initial visit was 15.3 ± 5.1 yrs and the MN age at second visit was 15.8 ± 5.1 yrs. The MN time between visits was 5.0 ± 2.4 months (mos). 6 PTs had follow up (F/U) times less than 3 mos, 18 PTs had F/U times between 3 and 6 mos, 6 PTs had F/U times between 6 and 9 mos, and 3 PTs had F/U times greater than 9 mos. The MN and median (MD) HbA1c at the initial visit for all PTs was 8.28% ± 1.48 and 8.10%, respectively. The MN and MD HbA1c at final F/U for all PTs was 7.57% ± 1.11 and 7.50%, respectively. The difference in MN HbA1c was significant (p<0.001). The MN and MD HbA1c at the initial visit for PTs with a F/U time less than 3 mos was 7.55% ± 0.77 and 7.75%, respectively. The MN and MD HbA1c at F/U for these PTs was 7.20% ± 0.79 and 7.20%, respectively. The difference in MN HbA1c was significant (p<0.05). The MN and MD HbA1c at the initial visit for all PTs with a F/U time greater than 3 mos was 8.44% ± 1.53 and 8.10%, respectively. The MN and MD HbA1c at F/U for these PTs was 7.66% ± 1.15 and 7.50%, respectively. The difference in MN HbA1c was significant (p<0.001). The MN change of HbA1c between visits was not significant between PTs who had 3–6 mo, 6–9 mo, and 9+ mo F/U times (p=0.96) 15 PTs had HbA1c levels less than or equal to 8.0%. The MN and MD HbA1c at initial visit for these PTs was 7.20% ± 0.41 and 7.30%, respectively. The MN and MD HbA1c at F/U for these PTs was 6.75% ± 0.47 and 6.80%, respectively. The difference in MN HbA1c was significant (p<0.001). 20 PTs had HbA1c levels greater than 8.0% at initial visit. The MN and MD HbA1c at the initial visit for these PTs was 9.18% ± 1.47 and 8.80%, respectively. The MN and MD HbA1c at F/U for these PTs was 8.26% ± 1.03 and 8.00%, respectively. The difference in MN HbA1c was significant (p<0.001). The MN change in HbA1c between the high HbA1c group (-.92% ± 1.02) and low HbA1c group (-0.45% ± 0.32) was not significant (p>0.05). 25 PTs used a Dexcom™ sensor while 8 PTs used a Medtronic™ sensor. The MN change in HbA1c was not significant between these brands (p>0.05). Conclusion: CGMs improve metabolic control in pediatric PTs with Type 1 DM regardless of initial HbA1c. Further, this improved control is sustained over time. Sensor brands appear to be equally effective at achieving this goal.

scholarly journals Different insulin treatment regimens in patient with diabetes mellitus type 1: Effects on quality of life

2012 ◽  
Vol 69 (7) ◽  
pp. 569-575 ◽  
Author(s):  
Jelena Stojanovic ◽  
Dragoslav Milosevic ◽  
Ilija Antovic ◽  
Goran Sekulic ◽  
Teodora Beljic-Zivkovic
Keyword(s):  
Glycemic Control ◽  
Metabolic Control ◽  
Insulin Treatment ◽  
Vascular Complications ◽  
Well Being ◽  
Insulin Analogues ◽  
Treatment Regimens ◽  
Type 1 Dm

Background/Aim. Despite of contemporary diabetes mellitus (DM) treatment, one half of patients do not achieve an optimal metabolic control. Considering great psychological burden of diabetic patients, the purpose of this study was to assess the effect of different insulin treatment regimens, glycemic control and the presence of vascular complications on self-reported well-being and quality of life (QoL) of subjects with type 1 DM. Methods. The patients with type 1 DM (n = 122) recruited from the outpatient Diabetes Endocrinology Clinic of Zvezdara University Medical Center were divided into 4 groups according to the specific treatment regimen: 26 were on continuous subcutaneous insulin infusion (CSII), 30 on conventional insulin therapy, 33 on multiple daily injections (MDI) with human insulins, and 33 on MDI with insulin analogues. QoL was assessed by self-reported well-being with the following questionnaires: WHO-5 item Well Being Index (WHO- 5), 36 item Short Form (SF-36) survey, and Insulin Treatment Appraisal Scale (ITAS). Objective metabolic control was assessed by glycosylated hemoglobin (HbA1c), lipid levels and the presence of vascular complications. Statistical analyses used in this crosssectional study included: descriptive statistics, Student?s t-test, Chisqare test, contingency tables, ANOVA and correlation methods. Results. The patients on CSII had significantly better metabolic control than all other treatment groups, especially when compared to the one on conventional therapy (CSII HbA1c 7.07 ? 1.48% vs conventional therapy, HbA1c 10.04 ? 1.44; p = 0.000). No significant difference in glycemic control was observed between patients on MDI with human insulins and insulin analogues. Good glycemic control significantly influenced the reported QoL. The patients with retinopathy and nephropathy reported significantly lower physical well-being, and the patients with polyneuropathy and cardiovascular complications reported also lower psychological well being. Conclusions. Insulin treatment regiment selection affects not only objective metabolic control, but also QoL.

scholarly journals 1020 Sleep and Glycemic Control in Adults With Long-Standing Type 1 Diabetes and Hypoglycemia Unawareness

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A387-A388
Author(s):  
S K Malone ◽  
A J Peleckis ◽  
A I Pack ◽  
N Perez ◽  
G Yu ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Sleep Onset ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
Sleep Efficiency ◽  
Hypoglycemia Unawareness ◽  
Time In Bed ◽  
Hba1c Levels

Abstract Introduction Nocturnal hypoglycemia is life threatening for individuals with type 1 diabetes (T1D) due to loss of hypoglycemia symptom recognition (hypoglycemia unawareness) and impaired glucose counterregulation. These individuals also show disturbed sleep, which may result from glycemic dysregulation. Whether use of a hybrid closed loop (HCL) insulin delivery system with integrated continuous glucose monitoring (CGM) designed for improving glycemic control, relates to better sleep across time in this population remains unknown. Methods Six adults (median age=58y,T1D duration=41y) participated in an 18-month ongoing clinical trial assessing the effectiveness of an HCL system. Sleep and glycemic control were measured concurrently using wrist actigraphs and CGM at baseline (1 week) and months 3 and 6 (3 weeks) following HCL initiation. BMI and hemoglobin A1c (HbA1c) were collected at all timepoints. Spearman’s correlations modeled associations between sleep, BMI, and glycemic control at each time point. Repeated ANOVAs modeled sleep and glycemic control changes from baseline to 3 months and to 6 months. Results Sleep and glycemic control indices showed significant associations at baseline and 3 months. More time-in-bed and later sleep offset related to higher HbA1c levels at baseline. Later sleep onset, midpoint and offset, and greater sleep efficiency associated with greater %time with hyperglycemia (glucose >180 mg/dL) or hypoglycemia (glucose <70 mg/dL) at baseline and 3 months. Longer sleep duration and greater sleep efficiency related to greater %time with hyperglycemia at 3 months. At 3 months, more wake after sleep onset associated with lower HbA1c levels and longer nocturnal awakenings and more sleep fragmentation associated with less glycemic variability. While both sleep and glycemic control improved from baseline to 3 and 6 months, these were not statistically significant. Conclusion Various dimensions of actigraphic sleep related to concurrently estimated glycemic indices indicative of poorer glycemic control and HbA1c across time in adults with long-standing T1D and hypoglycemia unawareness. Support This work was supported by NIH R01DK117488 (NG), R01DK091331 (MRR), and K99NR017416 (SKM).

scholarly journals The Role of Continuous Glucose Monitoring, Diabetes Smartphone Applications, and Self-Care Behavior in Glycemic Control: Results of a Multi-National Online Survey

2019 ◽  
Vol 8 (1) ◽  
pp. 109 ◽  
Author(s):  
Mihiretu Kebede ◽  
Cora Schuett ◽  
Claudia Pischke
Keyword(s):  
Glycemic Control ◽  
Glucose Monitoring ◽  
Self Care ◽  
Self Management ◽  
Type 2 Dm ◽  
Type 1 Dm ◽  
Care Behavior

Background: This study investigated the determinants (with a special emphasis on the role of diabetes app use, use of continuous glucose monitoring (CGM) device, and self-care behavior) of glycemic control of type 1 and type 2 diabetes mellitus (DM). Methods: A web-based survey was conducted using diabetes Facebook groups, online patient-forums, and targeted Facebook advertisements (ads). Demographic, CGM, diabetes app use, and self-care behavior data were collected. Glycemic level data were categorized into hyperglycemia, hypoglycemia, and good control. Multinomial logistic regression stratified by diabetes type was performed. Results: The survey URL was posted in 78 Facebook groups and eight online forums, and ten targeted Facebook ads were conducted yielding 1854 responses. Of those owning smartphones (n = 1753, 95%), 1052 (62.6%) had type 1 and 630 (37.4%) had type 2 DM. More than half of the type 1 respondents (n = 549, 52.2%) and one third the respondents with type 2 DM (n = 210, 33.3%) reported using diabetes apps. Increased odds of experiencing hyperglycemia were noted in persons with type 1 DM with lower educational status (Adjusted Odds Ratio (AOR) = 1.7; 95% Confidence Interval (CI): 1.21–2.39); smokers (1.63, 95% CI: 1.15–2.32), and high diabetes self-management concern (AOR = 2.09, 95% CI: 1.15–2.32). CGM use (AOR = 0.66, 95% CI: 0.44–1.00); “general diet” (AOR = 0.86, 95% CI: 0.79–0.94); and “blood glucose monitoring” (AOR = 0.88, 95%CI: 0.80–0.97) self-care behavior reduced the odds of experiencing hyperglycemia. Hypoglycemia in type 1 DM was reduced by using CGM (AOR = 0.24, 95% CI: 0.09–0.60), while it was increased by experiencing a high diabetes self-management concern (AOR = 1.94, 95% CI: 1.04–3.61). Hyperglycemia in type 2 DM was increased by age (OR = 1.02, 95% CI: 1.00–1.04); high self-management concern (AOR = 2.59, 95% CI: 1.74–3.84); and poor confidence in self-management capacity (AOR = 3.22, 2.07–5.00). Conversely, diabetes app use (AOR = 0.63, 95% CI: 0.41–0.96) and “general diet” self-care (AOR = 0.84, 95% CI: 0.75–0.94), were significantly associated with the reduced odds of hyperglycemia. Conclusion: Diabetes apps, CGM, and educational interventions aimed at reducing self-management concerns and enhancing dietary self-care behavior and self-management confidence may help patients with diabetes to improve glycemic control.

scholarly journals MON-625 Utility of Continuous Glucose Monitoring in Children with Type 1 Diabetes: Is HbA1c Enough?

2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Lindsey L Owens ◽  
Sweta Chalise ◽  
Neha Vyas ◽  
Shilpa Gurnurkar
Keyword(s):  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Glycemic Control ◽  
Continuous Glucose Monitoring ◽  
Pediatric Patients ◽  
Glucose Monitoring ◽  
Self Monitoring ◽  
Average Change ◽  
Hba1c Levels

Abstract Introduction: Type 1 diabetes is an autoimmune condition resulting in insulin deficiency that requires daily insulin therapy and self-monitoring of blood glucose. Continuous glucose monitoring (CGM) systems allow for measurement of interstitial fluid glucose levels in a continuous fashion to identify variations and trends that are not feasible with conventional self-monitoring. Hemoglobin A1C (HbA1C) is the method used to assess adequate glycemic control and relates to future risk of developing complications. Current evidence has shown improvement in HbA1C with concomitant use of CGM in adults over 25 years of age with Type 1 diabetes, whereas studies in children and adolescents have failed to show this. However, it is important to note the limitations in HbA1C use as it is a marker of average blood glucose over 3 months but does not reflect glycemic variability. More recent data has suggested that factors such as time in range (TIR), which can be determined with CGM use, are also associated with decrease risk of diabetes complications. Methods: The goal of our study was to analyze the change in HbA1C levels after using a CGM (DEXCOM G4, G5, G6) over a 6-month period in pediatric patients with Type I diabetes. Two HBA1c levels 3 months apart from 92 patients were collected before using a CGM and two while using a CGM. Results were compared by using a dependent samples t-test. IBM SPSS 25.0 was used for data analysis. Results: Preliminary analysis indicates the average change in HBA1C among the patients (N=92) before (-0.08 ± 1.16) and while using the CGM (0.12 ± 1.00) was not significantly different (t (79) = -1.27, p = 0.21). The average change in HBA1C was also not significantly different (p>0.05) among the patients before and while using the CGM for gender (males and females), age groups (0-7 years, 8-14 years, and 15-24 years), and generations of DEXCOM used (G4, G5, and G6). Conclusion: As has been shown in other studies, we did not find a significant change in HbA1c after CGM use for 6 months in our patients. While HbA1C is a reflection of blood sugars over a 3-month period, it does not provide information about glycemic excursions. Metrics derived from CGM use, such as TIR, can provide actionable information which we did not address in our study. There have been reports of the association between TIR and long-term complications of diabetes. Most data comes from studies in adults and pediatric data is lacking. We propose that future studies must look into CGM metrics such as TIR to better define glycemic control in pediatric patients with diabetes mellitus.

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 Anxiety, depression, and glycemic control during Covid-19 pandemic in youths with type 1 diabetes

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Maria Cusinato ◽  
Mariangela Martino ◽  
Alex Sartori ◽  
Claudia Gabrielli ◽  
Laura Tassara ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Psychological Impact ◽  
Glucose Monitoring ◽  
Severe Hypoglycemia ◽  
Well Being ◽  
Glucose Variability ◽  
Threshold Score ◽  
The Impact

Abstract Objectives Our study aims to assess the impact of lockdown during the coronavirus disease 2019 pandemic on glycemic control and psychological well-being in youths with type 1 diabetes. Methods We compared glycemic metrics during lockdown with the same period of 2019. The psychological impact was evaluated with the Test of Anxiety and Depression. Results We analyzed metrics of 117 adolescents (87% on Multiple Daily Injections and 100% were flash glucose monitoring/continuous glucose monitoring users). During the lockdown, we observed an increase of the percentage of time in range (TIR) (p<0.001), with a significant reduction of time in moderate (p=0.002), and severe hypoglycemia (p=0.001), as well as the percentage of time in hyperglycemia (p<0.001). Glucose variability did not differ (p=0.863). The glucose management indicator was lower (p=0.001). 7% of youths reached the threshold-score (≥115) for anxiety and 16% for depression. A higher score was associated with lower TIR [p=0.028, p=0.012]. Conclusions Glycemic control improved during the first lockdown period with respect to the previous year. Symptoms of depression and anxiety were associated with worse glycemic control; future researches are necessary to establish if this improvement is transient and if psychological difficulties will increase during the prolonged pandemic situation.

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