The Relationship between Hypoglycemia and Glucose Variability in Type 1 Diabetes

2021 ◽  
Author(s):  
Jordan E. Perlman ◽  
Theodore A. Gooley ◽  
Jedidiah Meyers ◽  
Irl B. Hirsch
Keyword(s):  
Type 1 Diabetes ◽  
Glucose Variability ◽  
The Relationship

733-P: Postprandial Glucose Variability in People with Type 1 Diabetes Is Individual and Impacted by Physiological and Clinical Parameters

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 733-P
Author(s):  
CASSY F.B. DINGENA ◽  
AILSA MARSH ◽  
RAMZI AJJAN ◽  
MATTHEW CAMPBELL
Keyword(s):  
Type 1 Diabetes ◽  
Glucose Variability ◽  
Postprandial Glucose ◽  
Clinical Parameters

A Decade of Disparities in Diabetes Technology Use and HbA1c in Pediatric Type 1 Diabetes: A Trans-Atlantic Comparison

2020 ◽  
Author(s):  
Ananta Addala ◽  
Marie Auzanneau ◽  
Kellee Miller ◽  
Werner Maier ◽  
Nicole Foster ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Technology Use ◽  
Hemoglobin A1c ◽  
Diabetes Technology ◽  
Design And Methods ◽  
Relationship Of ◽  
The Relationship ◽  
Pediatric Type 1 Diabetes

<b>Objective:</b> As diabetes technology use in youth increases worldwide, inequalities in access may exacerbate disparities in hemoglobin A1c (HbA1c). We hypothesized an increasing gap in diabetes technology use by socioeconomic status (SES) would be associated with increased HbA1c disparities. <p> </p> <p><b>Research Design and Methods: </b>Participants aged <18 years with diabetes duration ≥1 year in the Type 1 Diabetes Exchange (T1DX, US, n=16,457) and Diabetes Prospective Follow-up (DPV, Germany, n=39,836) registries were categorized into lowest (Q1) to highest (Q5) SES quintiles. Multiple regression analyses compared the relationship of SES quintiles with diabetes technology use and HbA1c from 2010-2012 and 2016-2018. </p> <p> </p> <p><b>Results: </b>HbA1c was higher in participants with lower SES (in 2010-2012 & 2016-2018, respectively: 8.0% & 7.8% in Q1 and 7.6% & 7.5% in Q5 for DPV; and 9.0% & 9.3% in Q1 and 7.8% & 8.0% in Q5 for T1DX). For DPV, the association between SES and HbA1c did not change between the two time periods, whereas for T1DX, disparities in HbA1c by SES increased significantly (p<0.001). After adjusting for technology use, results for DPV did not change whereas the increase in T1DX was no longer significant.</p> <p> </p> <p><b>Conclusions: </b>Although causal conclusions cannot be drawn, diabetes technology use is lowest and HbA1c is highest in those of the lowest SES quintile in the T1DX and this difference for HbA1c broadened in the last decade. Associations of SES with technology use and HbA1c were weaker in the DPV registry. </p>

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.

scholarly journals The UVA/Padova Type 1 Diabetes Simulator Goes From Single Meal to Single Day

2018 ◽  
Vol 12 (2) ◽  
pp. 273-281 ◽  
Author(s):  
Roberto Visentin ◽  
Enrique Campos-Náñez ◽  
Michele Schiavon ◽  
Dayu Lv ◽  
Martina Vettoretti ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
In Silico ◽  
Artificial Pancreas ◽  
Real Life ◽  
Glucose Monitoring ◽  
Glucose Variability ◽  
Self Monitoring ◽  
Inhaled Insulin ◽  
Single Meal

Background: A new version of the UVA/Padova Type 1 Diabetes (T1D) Simulator is presented which provides a more realistic testing scenario. The upgrades to the previous simulator, which was accepted by the Food and Drug Administration in 2013, are described. Method: Intraday variability of insulin sensitivity (SI) has been modeled, based on clinical T1D data, accounting for both intra- and intersubject variability of daily SI. Thus, time-varying distributions of both subject’s basal insulin infusion and insulin-to-carbohydrate ratio were calculated and made available to the user. A model of “dawn” phenomenon based on clinical T1D data has been also included. Moreover, the model of subcutaneous insulin delivery has been updated with a recently developed model of commercially available fast-acting insulin analogs. Models of both intradermal and inhaled insulin pharmacokinetics have been included. Finally, new models of error affecting continuous glucose monitoring and self-monitoring of blood glucose devices have been added. Results: One hundred in silico adults, adolescent, and children have been generated according to the above modifications. The new simulator reproduces the intraday glucose variability observed in clinical data, also describing the nocturnal glucose increase, and the simulated insulin profiles reflect real life data. Conclusions: The new modifications introduced in the T1D simulator allow to extend its domain of validity from “single-meal” to “single-day” scenarios, thus enabling a more realistic framework for in silico testing of advanced diabetes technologies including glucose sensors, new insulin molecules and artificial pancreas.

Sign in / Sign up

Export Citation Format

Share Document