Glycemic Variability in Type 1 Diabetes Mellitus Saudis Using Ambulatory Glucose Profile

Background: Glucose variability (GV) is a common and challenging clinical entity in the management of people with type 1 diabetes (T1DM). The magnitude of GV in Saudi people with T1DM was not addressed before. Therefore, we aimed to study GV in a consecutive cohort of Saudis with T1DM. Methods: We prospectively assessed interstitial glucose using FreeStyle® Libre flash glucose monitoring in people with TIDM who attended follow-up in the diabetes clinics at King Fahad Medical City between March and June 2017. Glycemia profile, standard deviation (SD), coefficient of variation (CV), mean of daily differences (MODD), and mean amplitude of glucose excursion (MAGE) were measured using the standard equations over a period of 2 weeks. Results: Fifty T1DM subjects (20 males) with mean age 20.2 ± 6.1 years and mean fortnight glucose 192 ± 42.3 mg/dl were included. The mean SD of 2-week glucose readings was 100.4 ± 36.3 mg/dl and CV was 52.1% ± 13%. Higher levels of glucose excursions were also observed. MODD and MAGE were recorded as 104.5 ± 51.7 and 189 ± 54.9 mg/dl, respectively which is 2 to 4 times higher than the international standards. Higher MODD and MAGE were observed on weekends compared to weekdays (111.3 ± 62.1 vs 98.6 ± 56.2 mg/dl and 196.4 ± 64.6 vs 181.7 ± 52.4 mg/dl, respectively; P ⩽ .001). Conclusion: Higher degree of glycemic variability was observed in this cohort of TIDM Saudis. Weekends were associated with higher glucose swings than weekdays. More studies are needed to explore these findings further.

Download Full-text

Teleassistance for Patients With Type 1 Diabetes During the COVID-19 Pandemic: Results of a Pilot Study (Preprint)

10.2196/preprints.24552 ◽

2020 ◽

Author(s):

Martina Parise ◽

Linda Tartaglione ◽

Antonio Cutruzzolà ◽

Maria Ida Maiorino ◽

Katherine Esposito ◽

...

Keyword(s):

Type 1 Diabetes ◽

Glycemic Control ◽

Disease Management ◽

Health Care Providers ◽

Glucose Monitoring ◽

Glycemic Variability ◽

Care Providers ◽

Virtual Visits

BACKGROUND Telemedicine use in chronic disease management has markedly increased during health emergencies due to COVID-19. Diabetes and technologies supporting diabetes care, including glucose monitoring devices, software analyzing glucose data, and insulin delivering systems, would facilitate remote and structured disease management. Indeed, most of the currently available technologies to store and transfer web-based data to be shared with health care providers. OBJECTIVE During the COVID-19 pandemic, we provided our patients the opportunity to manage their diabetes remotely by implementing technology. Therefore, this study aimed to evaluate the effectiveness of 2 virtual visits on glycemic control parameters among patients with type 1 diabetes (T1D) during the lockdown period. METHODS This prospective observational study included T1D patients who completed 2 virtual visits during the lockdown period. The glucose outcomes that reflected the benefits of the virtual consultation were time in range (TIR), time above range, time below range, mean daily glucose, glucose management indicator (GMI), and glycemic variability. This metric was generated using specific computer programs that automatically upload data from the devices used to monitor blood or interstitial glucose levels. If needed, we changed the ongoing treatment at the first virtual visit. RESULTS Among 209 eligible patients with T1D, 166 completed 2 virtual visits, 35 failed to download glucose data, and 8 declined the visit. Among the patients not included in the study, we observed a significantly lower proportion of continuous glucose monitoring (CGM) and continuous subcutaneous insulin infusion (CSII) users (n=7/43, 16% vs n=155/166, 93.4% and n=9/43, 21% vs n=128/166, 77.1%, respectively; P<.001) compared to patients who completed the study. TIR significantly increased from the first (62%, SD 18%) to the second (65%, SD 16%) virtual visit (P=.02); this increase was more marked among patients using the traditional meter (n=11; baseline TIR=55%, SD 17% and follow-up TIR=66%, SD 13%; P=.01) than among those using CGM, and in those with a baseline GMI of ≥7.5% (n=46; baseline TIR=45%, SD 15% and follow-up TIR=53%, SD 18%; P<.001) than in those with a GMI of <7.5% (n=120; baseline TIR=68%, SD 15% and follow-up TIR=69%, SD 15%; P=.98). The only variable independently associated with TIR was the change of ongoing therapy. The unstandardized beta coefficient (B) and 95% CI were 5 (95% CI 0.7-8.0) (P=.02). The type of glucose monitoring device and insulin delivery systems did not influence glucometric parameters. CONCLUSIONS These findings indicate that the structured virtual visits help maintain and improve glycemic control in situations where in-person visits are not feasible.

Download Full-text

Morning (Fasting) vs Afternoon Resistance Exercise in Individuals With Type 1 Diabetes: A Randomized Crossover Study

The Journal of Clinical Endocrinology & Metabolism ◽

10.1210/jc.2018-02384 ◽

2019 ◽

Vol 104 (11) ◽

pp. 5217-5224 ◽

Cited By ~ 6

Author(s):

Saeed Reza Toghi-Eshghi ◽

Jane E Yardley

Keyword(s):

Type 1 Diabetes ◽

Blood Glucose ◽

Resistance Exercise ◽

Blood Glucose Concentration ◽

Venous Blood ◽

Glucose Monitoring ◽

Glucose Variability ◽

Interstitial Glucose ◽

Design And Methods

Abstract Objective To determine the effect of morning exercise in the fasting condition vs afternoon exercise on blood glucose responses to resistance exercise (RE). Research Design and Methods For this randomized crossover design, 12 participants with type 1 diabetes mellitus [nine females; aged 31 ± 8.9 years; diabetes duration, 19.1 ± 8.3 years; HbA1c, 7.4% ± 0.8% (57.4 ± 8.5 mmol/mol)] performed ∼40 minutes of RE (three sets of eight repetitions, seven exercises, at the individual’s predetermined eight repetition maximum) at either 7 am (fasting) or 5 pm. Sessions were performed at least 48 hours apart. Venous blood samples were collected immediately preexercise, immediately postexercise, and 60 minutes postexercise. Interstitial glucose was monitored overnight postexercise by continuous glucose monitoring (CGM). Results Data are presented as mean ± SD. Blood glucose rose during fasting morning exercise (9.5 ± 3.0 to 10.4 ± 3.0 mmol/L), whereas it declined with afternoon exercise (8.2 ± 2.5 to 7.4 ± 2.6 mmol/L; P = 0.031 for time-by-treatment interaction). Sixty minutes postexercise, blood glucose concentration was significantly higher after fasting morning exercise than after afternoon exercise (10.9 ± 3.2 vs 7.9 ± 2.9 mmol/L; P = 0.019). CGM data indicated more glucose variability (2.7 ± 1.1 vs 2.0 ± 0.7 mmol/L; P = 0.019) and more frequent hyperglycemia (12 events vs five events; P = 0.025) after morning RE than after afternoon RE. Conclusions Compared with afternoon RE, morning (fasting) RE was associated with distinctly different blood glucose responses and postexercise profiles.

Download Full-text

Glycemic variability predicts inflammation in adolescents with type 1 diabetes

Journal of Pediatric Endocrinology and Metabolism ◽

10.1515/jpem-2016-0139 ◽

2016 ◽

Vol 29 (10) ◽

Cited By ~ 11

Author(s):

Robert P. Hoffman ◽

Amanda S. Dye ◽

Hong Huang ◽

John A. Bauer

Keyword(s):

Type 1 Diabetes ◽

Glucose Monitoring ◽

Glycemic Variability ◽

Glucose Variability ◽

Oxidative Capacity ◽

Endothelial Injury ◽

Control Measures ◽

Intracellular Adhesion Molecule ◽

Increased Risk

AbstractBackground:Adolescents with type 1 diabetes (T1D) have increased risk of cardiovascular disease as well as elevations in biomarkers of systemic inflammation, plasma protein oxidation and vascular endothelial injury. It is unclear whether hyperglycemia itself, or variations in blood glucose are predictors of these abnormalities.Methods:This study was designed to determine the relationship of inflammatory (C-reactive protein, CRP), oxidative (total anti-oxidative capacity, TAOC) and endothelial injury (soluble intracellular adhesion molecule 1, sICAM1) markers to glycemic control measures from 3 days of continuous glucose monitoring (CGM) and to hemoglobin AResults:Seventeen adolescents (8 F/9M; age, 13.1±1.6 years (mean±SD); duration, 4.8±3.8 years, BMI, 20.3±3.1 kg/mConclusions:Increased glucose variability is associated with increased inflammation in adolescents withT1D. Increased TAOC with increasing variability may be an effort to compensate for the ongoing oxidative stress.

Download Full-text

Glucose Fluctuations during Gestation: An Additional Tool for Monitoring Pregnancy Complicated by Diabetes

International Journal of Endocrinology ◽

10.1155/2013/279021 ◽

2013 ◽

Vol 2013 ◽

pp. 1-8 ◽

Cited By ~ 19

Author(s):

M. G. Dalfrà ◽

N. C. Chilelli ◽

G. Di Cianni ◽

G. Mello ◽

C. Lencioni ◽

...

Keyword(s):

Type 1 Diabetes ◽

Pregnant Women ◽

Glycemic Index ◽

Glucose Monitoring ◽

Mean Amplitude ◽

Glucose Variability ◽

Healthy Controls ◽

Additional Tool ◽

Glucose Fluctuations

Continuous glucose monitoring (CGM) gives a unique insight into magnitude and duration of daily glucose fluctuations. Limited data are available on glucose variability (GV) in pregnancy. We aimed to assess GV in healthy pregnant women and cases of type 1 diabetes mellitus or gestational diabetes (GDM) and its possible association with HbA1c. CGM was performed in 50 pregnant women (20 type 1, 20 GDM, and 10 healthy controls) in all three trimesters of pregnancy. We calculated mean amplitude of glycemic excursions (MAGE), standard deviation (SD), interquartile range (IQR), and continuous overlapping net glycemic action (CONGA), as parameters of GV. The high blood glycemic index (HBGI) and low blood glycemic index (LBGI) were also measured as indicators of hyperhypoglycemic risk. Women with type 1 diabetes showed higher GV, with a 2-fold higher risk of hyperglycemic spikes during the day, than healthy pregnant women or GDM ones. GDM women had only slightly higher GV parameters than healthy controls. HbA1c did not correlate with GV indicators in type 1 diabetes or GDM pregnancies. We provided new evidence of the importance of certain GV indicators in pregnant women with GDM or type 1 diabetes and recommended the use of CGM specifically in these populations.

Download Full-text

Beneficial Effect of Flash Glucose Monitoring Persists in a Two-Year Perspective—A Clinical Follow-Up Study of 334 Individuals with Type 1 Diabetes

Diabetes ◽

10.2337/db18-958-p ◽

2018 ◽

Vol 67 (Supplement 1) ◽

pp. 958-P ◽

Cited By ~ 1

Author(s):

MAGNUS LONDAHL ◽

KATARINA FAGHER ◽

PER KATZMAN ◽

KARIN FILIPSSON

Keyword(s):

Type 1 Diabetes ◽

Beneficial Effect ◽

Glucose Monitoring ◽

Flash Glucose Monitoring ◽

Follow Up Study

Download Full-text

Glycemic Variability and Insulin Needs in Patients with Type 1 Diabetes Mellitus Supplemented with Vitamin D: A Pilot Study Using Continuous Glucose Monitoring System

Current Diabetes Reviews ◽

10.2174/1573399813666170616075013 ◽

2018 ◽

Vol 14 (4) ◽

pp. 395-403 ◽

Cited By ~ 7

Author(s):

Karem Mileo Felício ◽

Ana Carolina Contente Braga de Souza ◽

Joao Felicio Abrahao Neto ◽

Franciane Trindade Cunha de Melo ◽

Carolina Tavares Carvalho ◽

...

Keyword(s):

Diabetes Mellitus ◽

Vitamin D ◽

Type 1 Diabetes ◽

Pilot Study ◽

Type 1 Diabetes Mellitus ◽

Monitoring System ◽

Continuous Glucose Monitoring ◽

Glucose Monitoring ◽

Glycemic Variability

Download Full-text

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

Journal of Pediatric Endocrinology and Metabolism ◽

10.1515/jpem-2021-0153 ◽

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.

Download Full-text

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

Journal of Diabetes Science and Technology ◽

10.1177/1932296818757747 ◽

2018 ◽

Vol 12 (2) ◽

pp. 273-281 ◽

Cited By ~ 41

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.

Download Full-text