scholarly journals Liraglutide as additional treatment for type 1 diabetes

2011 ◽  
Vol 165 (1) ◽  
pp. 77-84 ◽  
Author(s):  
Ajay Varanasi ◽  
Natalie Bellini ◽  
Deepti Rawal ◽  
Mehul Vora ◽  
Antoine Makdissi ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Insulin Dose ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
Additional Treatment ◽  
The Body ◽  
The Mean ◽  
Bolus Insulin

ObjectiveTo determine whether the addition of liraglutide to insulin to treat patients with type 1 diabetes leads to an improvement in glycemic control and diminish glycemic variability.Subjects and methodsIn this study, 14 patients with well-controlled type 1 diabetes on continuous glucose monitoring and intensive insulin therapy were treated with liraglutide for 1 week. Of the 14 patients, eight continued therapy for 24 weeks.ResultsIn all the 14 patients, mean fasting and mean weekly glucose concentrations significantly decreased after 1 week from 130±10 to 110±8 mg/dl (P<0.01) and from 137.5±20 to 115±12 mg/dl (P<0.01) respectively. Glycemic excursions significantly improved at 1 week. The mean s.d. of glucose concentrations decreased from 56±10 to 26±6 mg/dl (P<0.01) and the coefficient of variation decreased from 39.6±10 to 22.6±7 (P<0.01). There was a concomitant fall in the basal insulin from 24.5±6 to 16.5±6 units (P<0.01) and bolus insulin from 22.5±4 to 15.5±4 units (P<0.01).In patients who continued therapy with liraglutide for 24 weeks, mean fasting, mean weekly glucose concentrations, glycemic excursions, and basal and bolus insulin dose also significantly decreased (P<0.01). HbA1c decreased significantly at 24 weeks from 6.5 to 6.1% (P=0.02), as did the body weight by 4.5±1.5 kg (P=0.02).ConclusionLiraglutide treatment provides an additional strategy for improving glycemic control in type 1 diabetes. It also leads to weight loss.

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

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; <i>P</i>&lt;.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 (<i>P</i>=.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%; <i>P</i>=.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%; <i>P</i>&lt;.001) than in those with a GMI of &lt;7.5% (n=120; baseline TIR=68%, SD 15% and follow-up TIR=69%, SD 15%; <i>P</i>=.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) (<i>P</i>=.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.

scholarly journals Smoking is Associated With Increased Risk of Not Achieving Glycemic Target, Increased Glycemic Variability, and Increased Risk of Hypoglycemia for People With Type 1 Diabetes

2020 ◽  
pp. 193229682092225
Author(s):  
Morten Hasselstrøm Jensen ◽  
Simon Lebech Cichosz ◽  
Irl B. Hirsch ◽  
Peter Vestergaard ◽  
Ole Hejlesen ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Linear Models ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
Treatment Intensification ◽  
Increased Risk ◽  
Glycemic Target ◽  
Inadequate Glycemic Control

Background: The prevalence of smoking and diabetes is increasing in many developing countries. The aim of this study was to investigate the association of smoking with inadequate glycemic control and glycemic variability with continuous glucose monitoring (CGM) data in people with type 1 diabetes. Methods: Forty-nine smokers and 320 nonsmokers were obtained from the Novo Nordisk Onset 5 trial. After 16 weeks of treatment with continuous subcutaneous insulin infusion, risk of not achieving glycemic target and glycemic variability from six CGM measures was investigated. Analyzes were carried out with logistic regression models (glycemic target) and general linear models (glycemic variability). Finally, CGM median profiles were examined for the identification of daily glucose excursions. Results: A 4.7-fold (95% confidence interval: 1.5-15.4) increased risk of not achieving glycemic target was observed for smokers compared with nonsmokers. Increased time in hyperglycemia, decreased time in range, increased time in hypoglycemia (very low interstitial glucose), and increased fluctuation were observed for smokers compared with nonsmokers from CGM measures. CGM measures of coefficient of variation and time in hypoglycemia were not statistically significantly different. Examination of CGM median profiles revealed that risk of morning hypoglycemia is increased for smokers. Conclusions: In conclusion, smoking is associated with inadequate glycemic control and increased glycemic variability for people with type 1 diabetes with especially risk of morning hypoglycemia. It is important for clinicians to know that if the patient has type 1 diabetes and is smoking, a preemptive action to treat high glycated hemoglobin levels should not necessarily be treatment intensification due to the risk of hypoglycemia.

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 &gt;180 mg/dL) or hypoglycemia (glucose &lt;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 Measurement Of Peak C-Peptide At Diagnosis Informs Glycemic Control But Not Hypoglycemia In Adults With Type 1 Diabetes

2021 ◽  
Author(s):  
Alice L J Carr ◽  
Richard A Oram ◽  
Shannon M Marren ◽  
Timothy J McDonald ◽  
Parth Narendran ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Insulin Dose ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
Newly Diagnosed ◽  
C Peptide ◽  
Glucose Levels ◽  
Normal Glucose ◽  
The Impact

Abstract Context High residual C-peptide in longer duration type 1 diabetes associates with fewer hypoglycemic events and reduced glycemic variability. Little is known about the impact of C-peptide close-to-diagnosis. Objective Using continuous glucose monitoring (CGM) data from a study of newly diagnosed adults with type 1 diabetes, we aimed to explore if variation in C-peptide close-to-diagnosis influenced glycemic variability and risk of hypoglycemia. Design We studied newly diagnosed adults with type 1 diabetes who wore a Dexcom G4 CGM for 7 days as part the EXTOD study. We examined the relationship between peak stimulated C-peptide and glycemic metrics of variability and hypoglycemia for 36 CGM traces from 23 participants. Results For every 100 pmol/l increase in peak C-peptide, percentage time spent range 3.9-10 mmol/l was increased by 2.4% [95% CI: 0.5,4.3], p=0.01) with a reduction in time spent in level 1 hyperglycemia (&gt; 10 mmol/l) and level 2 hyperglycemia (&gt; 13.9 mmol/l) by 2.6% [95% CI: -4.9, -0.4, p=0.02) and 1.3% [95% CI: -2.7, -0.006], p= 0.04) respectively. Glucose levels were on average lower by 0.19 mmol/l ([95 % CI: -0.4,0.02], p=0.06) and standard deviation reduced by 0.14 [95% CI: -0.3, -0.02], p=0.02). Hypoglycemia was not common in this group and no association was observed between time spent in hypoglycemia (p=0.97) or hypoglycemic risk (p=0.72). There was no association between peak C-peptide and insulin dose adjusted HbA1c (IDAA1c, p=0.45). Conclusions C-peptide associates with time spent in normal glucose range and with less hyperglycemia, but not risk of hypoglycemia in newly diagnosed people with type 1 diabetes.

scholarly journals Impact of COVID-19 Lockdown on Glycemic Control in Adults with Type 1 Diabetes Mellitus

2020 ◽  
Vol 4 (12) ◽  
Author(s):  
Begoña Pla ◽  
Alfonso Arranz ◽  
Carolina Knott ◽  
Miguel Sampedro ◽  
Sara Jiménez ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Type 1 Diabetes Mellitus ◽  
Glycosylated Hemoglobin ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
Glucose Management ◽  
The Impact

Abstract Aim To examine the impact of the lockdown caused by the COVID-19 pandemic on both the glycemic control and the daily habits of a group of patients with type 1 diabetes mellitus (T1DM) using flash continuous glucose monitoring devices (flash CGMs). Methods Retrospective analysis based on all the information gathered in virtual consultations from a cohort of 50 adult patients with T1DM with follow-up at our site. We compared their CGM metrics during lockdown with their own previous data before the pandemic occurred, as well as the potential psychological and therapeutic changes. Results We observed a reduction of average glucose values: 160.26 ± 22.55 mg/dL vs 150 ± 20.96 mg/dL, P = .0009; estimated glycosylated hemoglobin: 7.21 ± 0.78% vs 6.83 ± 0.71%, P = .0005; glucose management indicator 7.15 ± 0.57% vs 6.88 ± 0.49%; P = .0003, and glycemic variability: 40.74 ± 6.66 vs 36.43 ± 6.09 P &lt; .0001. Time in range showed an improvement: 57.46 ± 11.85% vs a 65.76 ± 12.09%, P &lt; .0001, without an increase in percentage of time in hypoglycemia. Conclusions COVID-19 lockdown was associated with an improvement in glycemic control in patients with T1DM using CGMs.

Assessment of Safety and Glycemic Control During Football Tournament in Children and Adolescents With Type 1 Diabetes—Results of GoalDiab Study

2019 ◽  
Vol 31 (4) ◽  
pp. 401-407 ◽  
Author(s):  
Andrzej Gawrecki ◽  
Aleksandra Araszkiewicz ◽  
Agnieszka Szadkowska ◽  
Grzegorz Biegański ◽  
Jan Konarski ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Glycemic Control ◽  
Children And Adolescents ◽  
Insulin Dose ◽  
Glucose Monitoring ◽  
Severe Hypoglycemia ◽  
Low Glucose ◽  
Lactate Levels

Purpose: To assess glycemic control and safety of children and adolescents with type 1 diabetes participating in a 2-day football tournament. Methods: In total, 189 children with type 1 diabetes from 11 diabetes care centers, in Poland, participated in a football tournament in 3 age categories: 7–9 (21.2%), 10–13 (42.9%), and 14–17 (36%) years. Participants were qualified and organized in 23 football teams, played 4 to 6 matches of 30 minutes, and were supervised by a medical team. Data on insulin dose and glycemia were downloaded from personal pumps, glucose meters, continuous glucose monitoring, and flash glucose monitoring systems. Results: The median level of blood glucose before the matches was 6.78 (4.89–9.39) mmol/L, and after the matches, it was 7.39 (5.5–9.87) mmol/L (P = .001). There were no episodes of severe hypoglycemia or ketoacidosis. The number of episodes of low glucose value (blood glucose ≤3.9 mmol/L) was higher during the tournament versus 30 days before: 1.2 (0–1.5) versus 0.7 (0.3–1.1) event/person/day, P < .001. Lactate levels increased during the matches (2.2 [1.6–4.0] mmol/L to 4.4 [2.6–8.5] mmol/L after the matches, P < .001). Conclusions: Large football tournaments can be organized safely for children with type 1 diabetes. For the majority of children, moderate mixed aerobic–anaerobic effort did not adversely affect glycemic results and metabolic safety.

scholarly journals Differences of FreeStyle Libre Flash Glucose Monitoring System and Finger Pricks on Clinical Characteristics and Glucose Monitoring Satisfactions in Type 1 Diabetes Using Insulin Pump

2019 ◽  
Vol 12 ◽  
pp. 117955141986110 ◽  
Author(s):  
Ayman A Al Hayek ◽  
Asirvatham A Robert ◽  
Mohamed A Al Dawish
Keyword(s):  
Insulin Pump ◽  
Insulin Dose ◽  
Glucose Monitoring ◽  
Flash Glucose Monitoring ◽  
Daily Insulin Dose ◽  
Freestyle Libre ◽  
Capillary Glucose ◽  
The Mean ◽  
Bolus Insulin

Background: To evaluate the different experience of freestyle libre and finger pricks on clinical characteristics and glucose monitoring satisfaction (GMS) in patients with type 1 diabetes (T1D) using insulin pump (IP). Methods: A prospective study was carried out on 47 (aged 17-21 years) T1D, who used conventional finger-pricking method for self-testing the glucose. The experiments were conducted between March 2018 and September 2018. For carrying out the study, the flash glucose monitoring (FGM) sensors were placed on each participant, at the baseline visit, by a trained diabetes educator. Furthermore, to determine the total number of scans conducted during the study period, the respective ambulatory glucose profiles were generated by computing the data collected from the sensors. In addition, a trained interviewer handed over the GMS questionnaire to each patient, at the baseline and at 12 weeks of the study. Results: In comparison to the baseline (finger pricks), various parameters such as: HbA1c ( P = .042), hypoglycemia ( P = .001), mean capillary glucose ( P = .004), total daily insulin dose ( P = .0001), percentage of bolus insulin ( P = .0001), daily bolus frequency ( P = .0001), and daily carbohydrates intake ( P = .0001) showed a significant improvement at 12 weeks. Similarly, substantial augmentation was noticed, in the sub domains of GMS, that is, openness ( P = .0001), emotional burden ( P = .0001), behavioral burden ( P = .0001), and trust ( P = .0001) at 12 weeks as compared to baseline. Overall, total GMS score at baseline was 1.72 ± 0.37, which increased up to 3.41 ± 0.49 ( P = .0001) in the time period of 12 weeks. The HbA1c (r2 = 0.45), hypoglycemia (r2 = 0.58), and the mean number of FGM scans, exhibited a negative correlation, while GMS (r2 = 0.52) and the mean number of FGM scans, exhibited a positive correlation. Conclusion: The frequency of hypoglycemia, HbA1c level, capillary glucose, daily carbohydrates intake decreased, while the total daily insulin dose, daily bolus insulin and total GMS score increased with the use of FGM scanning for 12 weeks.

scholarly journals Tackling hypoglycemia in children: teams, targets and technology

2021 ◽  
Vol 29 (1) ◽  
pp. 11-19
Author(s):  
Thomas Danne
Keyword(s):  
Type 1 Diabetes ◽  
Diabetes Education ◽  
Glucose Monitoring ◽  
Severe Hypoglycemia ◽  
Glycemic Variability ◽  
Multidisciplinary Teams ◽  
Team Approach ◽  
Glycemic Targets ◽  
Bolus Insulin

On the occasion of the Somogyi Award lecture this review focusses on the current advances in tackling hypoglycemia in pediatric patients with type 1 diabetes providing evidence for the importance of multidisciplinary teams, ambitious glycemic targets and implementation of diabetes technology. Meal-related intensified insulin replacement with differential substitution of basal- and bolus insulin is the therapy of choice in the care of children and adolescents with type 1 diabetes. The use of insulin pumps and continuous glucose monitoring devices is increasing rapidly, with the type of insulin therapy (insulin pen or pump) depending on the age of the patients and family preference. Education appropriate to the age and current challenges is essential for the children's participation in everyday life as undisturbed as possible. New parameters like time in range and time below range suitable for identifying high glycemic variability as risk factor for severe hypoglycemia complement the HbA1c targets and the ambulatory glucose profile (AGP) in a shared decision making on therapeutic adjustments between the diabetes team and people with diabetes. Automated insulin delivery as a hybrid closed loop or dosing advice using artificial intelligence are becoming a clinical reality. However, diabetes education as a team approach, defining clear targets with outcomes evaluated in multinational registries like SWEET remain important for shaping the future of pediatric diabetology.

scholarly journals Evaluating the Infuence of Mood and Stress on Glycemic Variability in T1DM Patients using Glucose Monitoring Sensors and Pools

2020 ◽  
Author(s):  
Sergio Contador Pachón ◽  
Marta Botella Serrano ◽  
Aranzazu Aramendi Zurimendi ◽  
Remedios Rodríguez Martínez ◽  
Esther Maqueda Villaizán ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Glycemic Control ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
P Value ◽  
Glucose Levels ◽  
Blood Glucose Levels

Objective: Assess in a sample of patients with type 1 diabetes mellitus whether mood and stress influence blood glucose levels and variability. Material and Methods: Continuous glucose monitoring was performed on 10 patients with type 1 diabetes, where interstitial glucose values were recorded every 15 minutes. A daily survey was conducted through Google Forms, collecting information on mood and stress. The day was divided into 6 slots of 4-hour each, asking the patient to assess each slot in relation to mood (sad, normal or happy) and stress (calm, normal or nervous). Different measures of glycemic control (arithmetic mean and percentage of time below/above the target range) and variability (standard deviation, percentage coefficient of variation, mean amplitude of glycemic excursions and mean of daily differences) were calculated to relate the mood and stress perceived by patients with blood glucose levels and glycemic variability. A hypothesis test was carried out to quantitatively compare the data groups of the different measures using the Student's t-test. Results: Statistically significant differences (p-value < 0.05) were found between different levels of stress. In general, average glucose and variability decrease when the patient is calm. There are statistically significant differences (p-value < 0.05) between different levels of mood. Variability increases when the mood changes from sad to happy. However, the patient's average glucose decreases as the mood improves. Conclusions: Variations in mood and stress significantly influence blood glucose levels, and glycemic variability in the patients analyzed with type 1 diabetes mellitus. Therefore, they are factors to consider for improving glycemic control. The mean of daily differences does not seem to be a good indicator for variability. Keywords: Diabetes mellitus, continuous glucose monitoring, glycemic variability, average glycemia, glycemic control, stress, mood.

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