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 Characterizing Glycemic Control and Sleep in Adults with Long-Standing Type 1 Diabetes and Hypoglycemia Unawareness Initiating Hybrid Closed Loop Insulin Delivery

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Susan Kohl Malone ◽  
Amy J. Peleckis ◽  
Laura Grunin ◽  
Gary Yu ◽  
Sooyong Jang ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Closed Loop ◽  
Sleep Onset ◽  
Glucose Monitoring ◽  
Insulin Delivery ◽  
Effect Sizes ◽  
Onset Latency ◽  
Hypoglycemia Unawareness

Nocturnal hypoglycemia is life threatening for individuals with type 1 diabetes (T1D) due to loss of hypoglycemia symptom recognition (hypoglycemia unawareness) and impaired glucose counter regulation. 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. The purpose of this study was to describe long-term changes in glycemic control and objective sleep after initiating hybrid closed loop (HCL) insulin delivery in adults with type 1 diabetes and hypoglycemia unawareness. To accomplish this, six adults (median age = 58   y ) participated in an 18-month ongoing trial assessing HCL effectiveness. Glycemic control and sleep were measured using continuous glucose monitoring and wrist accelerometers every 3 months. Paired sample t -tests and Cohen’s d effect sizes modeled glycemic and sleep changes and the magnitude of these changes from baseline to 9 months. Reduced hypoglycemia ( d = 0.47 ‐ 0.79 ), reduced basal insulin requirements ( d = 0.48 ), and a smaller glucose coefficient of variation ( d = 0.47 ) occurred with medium-large effect sizes from baseline to 9 months. Hypoglycemia awareness improved from baseline to 6 months with medium-large effect sizes (Clarke score ( d = 0.60 ), lability index ( d = 0.50 ), HYPO score ( d = 1.06 )). Shorter sleep onset latency ( d = 1.53 ; p < 0.01 ), shorter sleep duration ( d = 0.79 ), fewer total activity counts ( d = 1.32 ), shorter average awakening length ( d = 0.46 ), and delays in sleep onset ( d = 1.06 ) and sleep midpoint ( d = 0.72 ) occurred with medium-large effect sizes from baseline to 9 months. HCL led to clinically significant reductions in hypoglycemia and improved hypoglycemia awareness. Sleep showed a delayed onset, reduced awakening length and onset latency, and maintenance of high sleep efficiency after initiating HCL. Our findings add to the limited evidence on the relationships between diabetes therapeutic technologies and sleep health. This trial is registered with ClinicalTrials.gov (NCT03215914).

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 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 Ketogenic Diet in a Patient With Type 1 Diabetes Mellitus With Hypoglycemia Unawareness

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. A460-A460
Author(s):  
Mohamad Anas Sukkari ◽  
Lucia Cotten ◽  
Murtaza Alam ◽  
Emily Temponi ◽  
Priya D John ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Ketogenic Diet ◽  
Severe Hypoglycemia ◽  
Glycemic Variability ◽  
Glucose Variability ◽  
Continuous Glucose Monitor ◽  
Hypoglycemia Unawareness ◽  
The Impact

Abstract Introduction: The high fat, low carbohydrate ketogenic diet has become increasingly popular in recent years for weight loss and glycemic control in patients with type 2 diabetes. Although prior studies have suggested this diet can improve glycemic control and decrease glucose variability, the impact of a ketogenic diet on rates of hypoglycemia in patients with hypoglycemia unawareness is not well described. Case Description: Our patient is a 37 year-old woman with Type 1 diabetes for 13 years complicated by hypoglycemia unawareness with HbA1c of 7.7%. Her insulin treatment regimen included insulin glargine 22 units daily, insulin aspart using a 1:15 carbohydrate ratio for prandial insulin dosing with a correction factor of 90. She had 5 episodes of severe hypoglycemia within the previous 3 months. The patient decided to resume a ketogenic diet given her previous improvement in glycemic control. Ketosis was confirmed using urine ketone strips performed by the patient. After 2 weeks on the ketogenic diet, a professional blinded continuous glucose monitor (CGM) was used for 4 weeks to monitor glycemic control. CGM data for weeks 1 and 2 showed overall stability of time in target glucose range [TIR, 60% and 69%, respectively], with a slight increase in time spent below range [TBR, 13% and 17%, respectively]. During week 3, the patient experienced a significant decline in TIR to 31%, and associated increase in hypoglycemia (TBR, 13% to 28%). In addition, glycemic variability increased during this time [CV (coefficient of variation), 40.6% during week 1 to 58.1% during week 3]. Patient did not experience symptoms concerning for DKA, and continued to have asymptomatic hypoglycemia despite reductions in her insulin doses during week 3. Following these dose adjustments, CGM data during week 4 were similar to week 1 (TIR 65%, TBR 10%, CV 35%). Patient stopped following the ketogenic diet after 6 weeks due to social factors. Conclusion: A ketogenic diet was associated with increased frequency of hypoglycemic events. In a patient with Type 1 diabetes and hypoglycemia unawareness, use of ketogenic diet may further increase the risk of severe hypoglycemia.

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.

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.

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&gt;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.

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